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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 0 5 e4 1 6
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Effect of conventional chemical treatment on the microbial population in a biofouling layer of reverse osmosis systems L.A. Bereschenko a,b,c, H. Prummel d, G.J.W. Euverink a,*, A.J.M. Stams b, M.C.M. van Loosdrecht c a
Wetsus, Centre of Excellence for Sustainable Water Technology, PO Box 1113, 8900 CC Leeuwarden, The Netherlands Laboratory of Microbiology, Wageningen University, Dreijenplein 10, 6703 HB Wageningen, The Netherlands c Department of Biotechnology, Delft University of Technology, Julianalaan 67, 2628 BC Delft, The Netherlands d Waterlaboratorium Noord, Rijksstraatweg 85, 9756 AD Glimmen, The Netherlands b
article info
abstract
Article history:
The impact of conventional chemical treatment on initiation and spatiotemporal devel-
Received 10 February 2010
opment of biofilms on reverse osmosis (RO) membranes was investigated in situ using flow
Received in revised form
cells placed in parallel with the RO system of a full-scale water treatment plant. The flow
24 June 2010
cells got the same feed (extensively pre-treated fresh surface water) and operational
Accepted 18 July 2010
conditions (temperature, pressure and membrane flux) as the full-scale installation. With
Available online 27 July 2010
regular intervals both the full-scale RO membrane modules and the flow cells were cleaned using conventional chemical treatment. For comparison some flow cells were not cleaned.
Keywords:
Sampling was done at different time periods of flow cell operation (i.e., 1, 5, 10 and 17 days
Biofilm
and 1, 3, 6 and 12 months). The combination of molecular (FISH, DGGE, clone libraries and
Membrane
sequencing) and microscopic (field emission scanning electron, epifluorescence and
Sphingomonas
confocal laser scanning microscopy) techniques made it possible to thoroughly analyze the
Clone library
abundance, composition and 3D architecture of the emerged microbial layers. The results
CSLM
suggest that chemical treatment facilitates initiation and subsequent maturation of biofilm
DGGE
structures on the RO membrane and feed-side spacer surfaces. Biofouling control might be possible only if the cleaning procedures are adapted to effectively remove the (dead) biomass from the RO modules after chemical treatment. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
In current full-scale reverse osmosis (RO) water treatment plants drastic changes in system performance (i.e., significant increase in the feed pressure of RO membrane units and/or long-term membrane flux decline) indicate fouling of membrane surfaces within RO membrane units (Wiesner and Aptel, 1996; Vrouwenvelder and van der Kooij, 2001; Bishop, 2007). Fouling by precipitation and abundance of
membrane-rejected feed water dissolved solids and organic compounds (i.e., organic and/or inorganic fouling) are usually manageable by application of conventional cleaning agents. Prevention and control of attachment and proliferation of feed water bacteria on the membrane, feed-side spacer and other internals within the RO units are still difficult (Ridgway and Safarik, 1991; Flemming et al., 1997; Baker and Dudley, 1998; Al-Ahmad et al., 2000). The common techniques to reduce membrane fouling comprise
* Corresponding author. Tel.: þ31 (0)58283000; fax: þ31 (0)582843001. E-mail addresses:
[email protected] (L.A. Bereschenko),
[email protected] (H. Prummel),
[email protected] (G.J.W. Euverink),
[email protected] (A.J.M. Stams),
[email protected] (M.C.M. van Loosdrecht). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.07.058
406
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dosing of chemical agents and pre-treatment of the feed water. These treatments generally only have a temporary effect. Microorganisms may survive pre-treatment processes like coagulation, flocculation, sand filtration, ultra filtration and cartridge filtration. With time they will colonize a variety of surfaces within the plant (Bereschenko et al., 2008). On the surface of new and clean RO membranes, fed with extensively pre-treated water, early biofilm structures occur within the first 4 days of the system operation (Bereschenko et al., 2010). Within the following 12 days, the biofilm spreads over the entire surface area and forms a mature heterogeneous layer (Bereschenko et al., 2010). When living within the complex, three-dimensional structures of a self-produced organic polymer matrix (Davey and O’Toole, 2000; Tolker-Nielsen and Molin, 2000; Watnick and Kolter, 2000), the microbial communities are less sensitive to chemical cleaning (Nichols, 1989; Anwar et al., 1992; Davies et al., 1998; LeChevalier et al., 1988; Branda et al., 2005). As a result, chemical treatment of biofouled RO membrane units is generally not effective in removing and/ or completely destroying the complex multicellular structures (Flemming, 2002). Re-growth of the membrane surfaceattached microbial layer quickly results in a repetition of the biofouling-related system failure. The cleaning-related improvement of the RO system performance is commonly associated with a decline of the pressure drop and increase of water flux, but is of temporary nature. Periodic and more frequent chemical cleanings are, therefore, unavoidable for membrane filtration installations but lead to an increased usage of cleaning chemicals and increased production of waste water. Frequent cleaning procedures also result in a shortened membrane life and ultimately in a loss of capacity of the water supply plant (Baker and Dudley, 1998; Flemming, 2002). The effect of chemical cleaning on the microorganisms in fouling layers is hardly investigated. Often, only the change in pressure drop and membrane flux is measured to determine the effect of cleaning procedures. The development of more effective strategies for biofouling control requires research directed to determine the effect on the microorganisms and the structure of the biofouling layer on the RO membranes. Insight into processes that are important for membrane biofilm formation and development may help to find ways to prevent biofouling. Nevertheless, a proper assessment of the in situ biofilm formation and development is rarely done in RO biofouling research (Bereschenko et al., 2010). In addition, biofilm monitoring studies that were done previously may not provide a true representation of the RO biofilm problem in situ. These experiments were performed using simplified laboratory systems with one or a few bacterial strains (Pang et al., 2005; Eshed et al., 2008; Herzberg and Elimelech, 2007, 2008) or ignored the impact of prevailing environmental conditions (Pang and Liu, 2006). In this study, we monitored in situ initiation and spatiotemporal development of microbial biofilm layers on the surfaces of fresh and chemically cleaned reverse osmosis membranes and feed-side spacers. This was done by using stainless steel flow cells connected in parallel to the reverse osmosis system of a full-scale water treatment plant. Members of a feed water microbial community, responsible
for initial colonization of the membrane and feed-side spacer surfaces were identified by molecular biological techniques. Their abundance and spatial organization during the temporal development of the biofilm was studied by microscopic techniques. The development of membrane-attached biofilms to a level of “biofouling” e recognized by the pressure drop increase e and the impact of chemical cleaning was assessed over a 1-year period.
2.
Materials and methods
2.1.
Sampling
Four high-pressure (12 bar) test flow cells of stainless steel were operated from March 2007 to March 2008 (experimental phase I) and from 11 April to 11 May 2008 (experimental phase II) parallel to a full-scale RO installation (Fig. S1, for more details see Bereschenko et al. [2010]). Chemical cleaning of RO membranes and feed-side spacers e excised from a commercial spiral-wound ESPA membrane element (Hydranautics ESPA 2, CA, USA) and placed in the flow chambers of the flow cells e occurred during a routine chemical treatment of the full-scale RO membrane units, used to maintain a reasonable flux in the system. The treatment consisted of sequentially applied washing steps: RO permeate (20e25 C), biocide (30% sodium bisulfite solution, 30e40 C, pH 10e11, for 2e3 h) and mixed acid detergent descaler (Divos 2 [JohnsonDiversey, UK], 20e25 C, pH 2.6, for 2 h). After each step, the chemical compounds were washed away with RO permeate of ambient temperature. The development of pressure drop (i.e., pressure drop is defined as the difference between the feed pressure and the concentrate pressure) over the flow cell feed channels during each particular experiment was monitored using a differential pressure transmitter (Deltabar S PMD70 [Endress & Hauser Inc., CA], range: 0.05e500 mbar), with accuracy of 0.1 mbar. The measurements were recorded automatically every 30 min by a data logger device and the acquired data were read out with the READWIN 2000 software (Endress & Hauser Inc.). At the end of each experiment, the membranes and spacers were removed from the sacrificed flow cells. Small sections from randomly selected positions on their surfaces along the length of the feed channel were carefully cut out and processed for total DNA extraction and microscopical analysis (fluorescence in situ hybridization [FISH] and epifluorescence [EPIM], confocal laser scanning [CLSM] and field emission scanning electron [FESEM] microscopy) as previously described (Bereschenko et al., 2010). The simultaneously collected water samples (i.e., fresh surface water fed to the plant and permeate from the flow cells and ultra filtration and RO systems) were kept on ice and transferred to a laboratory for further processing.
2.2.
Processing of water samples
Each water sample (100 ml) was mixed with 3 volumes of freshly prepared 4% formaldehyde, incubated for 1 h and filtered through a black polycarbonate filter (pore size, 0.2 mm; type GTTP 4700, Millipore, Germany). The filters were processed further using FISH of bacteria. The determination of
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 0 5 e4 1 6
the total number of bacteria was done by incubating the preserved filters with DAPI (40 ,6-diamidino-2-phenylindole) solution (2 mg/ml, SigmaeAldrich) in the dark at 4 C. After 10 min the membranes were gently rinsed with MilliQ water, air-dried and mounted in a Vectashield medium (Vector Laboratories, UK). The stained cells were counted (in triplicate) in 20 randomly chosen EPIM viewing fields. For FESEM, microbial biomass from 1 L of each water sample was concentrated by filtration on the 0.2-mm filter. The cells on the filter were fixed by submerging the filter in a 2.5% (v/v) glutaraldehyde solution and processed further as described previously (Bereschenko et al., 2010). For total DNA extractions, 10 ml of each water sample was centrifuged at 10,000g for 10 min and the pellet was resuspended in 0.5 ml of 1 phosphate-buffered saline (PBS) solution (pH 7.0).
2.3.
Microbial community analysis
The samples from the biofilms and the water were analyzed using denaturation gradient gel electrophoresis (DGGE) and clone library analysis of 16S rRNA genes. The procedures to extract the total community DNA, PCR amplifications of bacterial 16S rRNA gene fragments, DGGE separations of the generated amplicons, construction and analysis of the 16S rRNA gene clone libraries were done as previously described (Bereschenko et al., 2010). The nucleotide sequence data reported in this study were submitted to the GenBank under the accession numbers GQ385250, GQ385251, GQ385256, GQ385260, GQ385262, GQ385264eGQ385269, GQ385276, GQ385277, GQ385280, GQ385282, GQ385286, GQ385287, GQ385290eGQ385292, GQ385294, GQ385295 and GU585911eGU585936.
3.
Results
Four reverse osmosis test flow cells were operated for 3e12 months (experimental phase I) and 1e32 days (experimental phase II) parallel to a full-scale RO installation (Fig. S1). Chemical cleaning of RO membranes and feed-side spacers within the flow cells occurred during the routine cleaning of the full-scale system with sodium bisulfite and Divos 2 (mixed acid detergent descaler). In phase I, the cleaning was applied weekly and in the phase II e after 11 days of the start of the flow cell operation (Fig. 1). For comparison, some RO membranes and their feed-side spacers were not cleaned. At the end of each experiment, the chemically cleaned and noncleaned flow cells were opened and their membrane and spacer surfaces were examined visually (Figs. 2 and 3) and microscopically (Figs. 2, 6, 7 and S4) on the presence, intensity, distribution and nature of fouling. Diversity, abundance and distribution of bacterial species during different stages of biofilm community development at these surfaces were evaluated by clone libraries and sequencing (Table 1), DGGE fingerprinting (Fig. S3) and FISH microscopy (Fig. 4) analyses. Three-dimensional (3-D) distribution of microbial organisms e with respect to each other and to exopolysaccharides e was examined using CLSM and image analysis (Figs. 2, 5e7 and S2). Presence, abundance and diversity of planktonic bacterial communities in the collected water samples were investigated
407
by the FESEM (Fig. S4), DGGE (Fig. S5) and FISH microscopy (Fig. 4). Below we describe the effect of cleaning on the occurrence and proliferation of microbial population in the surfaceattached fouling layer.
3.1.
Development of fouling in membrane systems
Fouling in RO systems is in practice often recognized as a longterm membrane flux decline of the RO plant and/or significant increase in the feed pressure of the RO module to maintain constant permeate production (Bishop, 2007; Vrouwenvelder and van der Kooij, 2001; Wiesner and Aptel, 1996). This is in the case of biofouling the result of the formation of a “critical level biofilm” in the spiral-wound RO filtration units that lead to the arbitrary threshold of interference of the pressure drop (Flemming, 2002). In the present study, establishment of the “critical level biofilm” was indeed associated with significant changes in pressure drop over the feed channels of the test RO flow cells, operated parallel to a full-scale RO installation. The pressure drop measurements indicated that overall the development of a “critical level biofilm” was not very different for cleaned and non-cleaned surfaces in the short term (1 month) experiment (Fig. 1-A and B). Cleaning leads to a temporary decline in pressure drop, but very rapidly the fouling layer grew again leading to a quick increase in pressure drop after the cleaning event. When the flow cells operation time was prolonged for 3e12 months and the cleaning occurred weekly, the chemical treatment was effective in decreasing the pressure drop over the system. A quite abrupt and significant (9e13 mbar) decrease in the pressure drop value was observed after each of the cleaning steps (Fig. 1-C), indicating that the weekly treatment could be used to control the pressure drop during long-term operation. The long-term (12 months) system operation without chemical treatment resulted in a slow but sure pressure drop increase (data not shown) to a value of 47 mbar, indeed much higher then for the cleaned system, being 21 mbar.
3.2.
Biofilm structure after cleaning
The direct impact of the weekly applied chemical cleaning procedures on the established biofilm structures at the RO membrane and feed-side spacer surfaces was evaluated using samples collected the day after the treatment. Visual inspection of the membranes revealed the presence of moist, slimy, yellow and light to dark-brown coloured deposits, distributed irregularly (1e10 days old samples) or uniformly (samples from long-term operated membranes) over the surface of the cleaned membranes and spacers. Compared to the fouling layers in the samples collected the day before the cleaning they were slightly less intense in colour and density (Figs. 2-A and 3). In addition, they could be much easier scraped from the membrane and/or spacer surfaces. Microscopic examinations showed the presence of damaged protozoa (e.g., the Trinema, Fig. 2-B), deformed bacterial microcolonies (Fig. 2-C) and squashed (to 1e2 mm of the overall thickness) EPS biofilm matrix (Figs. 2-D and 5) on membrane and/or spacer surface the day after the treatment. The observations were similar for membranes examined after short-term and long-term operation. No intact protozoa were present on the top of the
408
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Fig. 1 e Pressure drop development in the RO flow cells as function of time. The graphs show the development of pressure drop (in mbar) over the feed channels of the non-cleaned (A) and cleaned (after 11 days [B] and weekly [C]) flow cells, operated in parallel with RO systems of a full-scale RO water purification plant. Feed water (UF permeate) was supplied to the flow cells at a pressure of 8e11 bar. “Cleaning” indicates application of chemical treatment to the membranes and spacers within the flow cells. “Shutting” point to the occurrence of an unexpected (two days) shut-down of the full-scale RO installation.
collapsed biofilm structures, while a variety of growing and dividing bacteria (Fig 2-C and D) of the a, b and g-Proteobacteria, Cytophaga-Flavobacter-Bacteroidetes (CFB), Verrucomicrobia and Planctomycetes were abundantly present as detected by FISH analysis (Fig. 5 [a and b-Proteobacteria], other bacteria: data not shown). In both CLSM and SEM images no EPS layers were
visible around their cells (Figs. 2-D and 5). In contrast, many of the intact bacterial cells (9e3700 cells/cm2 membrane surface) within the collapsed biofilm matrix were EPS-embedded (Figs. 2-D and 5). These cells hybridized with the SPH120 probe, indicating the presence of the Sphingomonas species (Neef et al., 1999). The diffused fluorescence from the FITC-labeled
Fig. 2 e Effect of conventional chemical treatment on biofilm occurrence. The photograph A shows the appearance of the fouling deposits at the RO membrane surface, operated for one year with the weekly applied cleaning procedures and removed from the test flow cells the day after the cleaning. The SEM images BeD represent the associated with the treated fouling layer damaged protozoa (i.e., Trinema, B), bacterial microcolonies (C) and EPS network (D). Note: the presence of freshly deposited feed water bacteria on the collapsed biofilm structures in B-D and the presence of intact bacterial cells (2) within and/or under the collapsed biofilm matrix (1) in D. SEM (E) and CLSM (F) images represent surface of the re-grown [within 6 days after the chemical treatment application] biofilm. Green fluorescence is from the ConA-positive bacterial EPSs, red e from the (SPH120-Cy3-positive) Sphingomonas cells and blue e from the DAPI-stained remainder of the biofilm community members.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 0 5 e4 1 6
409
Fig. 3 e Photographs depicting the failure of the weekly applied chemical treatment to prevent accumulation of fouling deposits on the surfaces of RO membranes and their feed-side spacers. The photographs were taken during autopsy of the RO test flow cells, operated for 10 days, 6 months and 1 year with or without a routine (once a week) cleaning application and are representatives of a series of observations. The direction of the feed water flow along the length of each flow cell was from left to right.
Concanavalin A (ConA) around their cells indicated the presence of b-1,4-linked sugar polymers (Johnson et al., 2000). However, the specificity of these probes for polysaccharides is not 100%. It cannot be excluded that the matrix around the cells consisted of other molecules that also interacted with the fluorescent probes. Irrespectively of the cleaning frequency (weekly or after 11 days of the flow cell operation), within 6e7 days after the treatment the collapsed biofilm structures appeared to be completely covered by a fresh layer of EPS-embedded bacterial cells and (single or clustered) microcolonies (Fig. 2 [E and F], Figs. 5 and 7 [cleaned: 3e6 months]). In all the examined microscopic images, the re-grown biofilms appeared to be, in general, more uniformly stretched at the membranes than at the associated feed-side spacer surfaces. The overall thickness of this re-grown layer was also different (e.g., in the 17 days old samples: 3e6 mm [membrane] versus 1e3 mm [spacer]; in the 3e6 months old samples: 4e9 mm [membrane] versus 1e7 mm [spacer]). This observation correlated with the visual inspection of the routinely treated membranes and spacers, where all the examined membrane surfaces appeared to be more severely fouled than their feed-side spacers (e.g., see the noncleaned 6 months and cleaned 1-year old samples in Fig. 3). The phylogenetic analysis of the sequences obtained from the clone libraries (Table 1), constructed for the biofilms from the cleaned membranes, revealed dominance of the Actinobacteria in the clone library from the weekly cleaned 6 months old membrane sample (50% of the total clones). In the younger samples (17 days e cleaned once; 3 months e cleaned weekly) there was prevalence of the Proteobacteria division in the clone libraries. In the cleaned 17 days old membrane sample, the largest bacterial group within the Proteobacteria was
represented by the b-Proteobacteria subdivision (39% of the total clones). This group was also dominating the planktonic community in the fresh surface water fed to the RO plant and in the plant cartridge-treated ultrafiltration permeate fed to the flow cells and RO systems (Fig. 4). The a-Proteobacteria subdivision members were numerically the most frequently encountered in the weekly-cleaned 3e6 months old samples (50% and 37% of the total clones, respectively). Within the a-Proteobacteria, the family Sphingomonadaceae dominated all the three clone libraries. Within the family, Sphingopyxis was numerically most abundant in the weekly-cleaned 6 months old membrane sample, while Sphingomonas was most abundant in the other two biofilms. The same phylogenetic groups within the cleaned membrane samples were identified by the FISH approach (Fig. 4). Compared to the associated feed-side spacers, the membranes showed larger a-Proteobacteria (e.g., cleaned 3 months old biofilm sample: 50% [membrane] versus 38% [spacer]) and smaller b-Proteobacteria (17% [membrane] versus 29% [spacer]) fractions in the biofilm-forming communities at their surfaces. Nevertheless, the 3-D structural organization of the re-grown biofilms (Figs. 5 and S2) was similar at both surfaces. In all the examined CLSM sections, the cleaned 17 days and 3e6 months old membrane and spacer samples possessed a layer of the Sphingomonas cells at the dark areas of 1e2 mm (17 days) or 2e3 mm (3e6 months). The areas showed no fluorescence signal with the applied probes or staining dyes (Bereschenko et al., 2010) and filled the space between the Sphingomonas cell monolayer (at the biofilm bottom) and the membrane or spacer surface. In the Sphingomonas layer, individual cells were sporadically distributed near the top of a uniformly spread EPS matrix of 1 mm (17 days) or 2e3 mm (3e6 months) thick. On top of the Sphingomonas layer, a second film
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Table 1 e Phylogenetic affiliations and frequencies of cloned bacterial 16S rRNA gene ampliconsa retrieved from RO membrane samples. Closest relative in GenBank
Clone library Cleaned
Accession no., taxon EF140635.1 Endosymbiont of Acanthamoeba sp. AY118225.1 Azospirillum sp. FJ711209.1 Hyphomicrobium sp. EF012357.1 Devosia insulae AY689051.1 Mycoplana sp. DQ303329.1 Uncultured Bradyrhizobium sp. DQ303345.1 Uncultured Bradyrhizobium sp. AM403722.1 Microbacterium sp. AY162029.1 Mycobacterium sp. AM921641.1 Nocardiaceae bacterium EU440981.1 Novosphingobium sp. FJ193529.1 Uncultured Sphingobium sp. Z23157.1 Sphingomonas sp. AB365794.1 Sphingomonas oligophenolica AY521009.2 Sphingomonas suberifaciens CP000699.1 Sphingomonas wittichii EU591707.1 Sphingomonas sp. GQ161989.1 Sphingomonas sp. AB362260.1 Sphingomonas sp. AF410927.1 Sphingomonas sp. AY162145.1 Sphingomonas sp. DQ789172.1 Sphingomonas sanxanigenens AY599670.1 Uncultured Sphingomonas sp. DQ177493.1 Sphingopyxis sp. EU703439.1 Uncultured Sphingopyxis sp. EF540479.1 Sphingopyxis sp. EU304287.1 Acidovorax sp. AB120965.1 Aquamonas fontana AB074524.1 Aquaspirillum autotrophicum EU817490.1 Hydrogenophaga sp. AJ556799.1 Comamonadaceae bacterium EF127651.1 Polaromonas rhizosphaerae AB504747.1 Xylophilus sp. EF667920.1 Uncultured Burkholderiales AF236004.1 Beta proteobacterium AB452986.1 Beta proteobacterium AJ621027.1 Nitrosomonas sp. AY123811.1 Nitrosomonas sp. AY123797.1 Nitrosomonas sp. AY123798.1 Nitrosomonas sp. DQ839562.1 Candidatus Nitrotoga arctica EF540467.1 Pseudomonas sp. AM689949.1 Pseudomonas sp. EU275166.1 Pseudomonas sp. EU034540.1 Stenotrophomonas maltophilia AM230485.1 Flavobacterium aquatile AB252939.1 Uncultured Nitrospirae AF239693.1 Gemmata-like str. Total clones in the library
b
(%) 93 91 96 99 99 98 99 99 96 99 96 93 98 96 96 97 92 97 95 95 94 94 97 98 98 99 99 92 96 92 99 98 97 91 95 95 96 94 99 95 98 96 98 98 99 97 99 95
17 days
32 days
3 months
1.1 1.1
2.2 2.2 1.1
2.2 2.2 2.2 3.3
Non-cleaned 6 months
5 days
2.3 2.3 4.5 6.8
1.1
6.7
2.2 1.1 3.2 2.2 2.2
1.1 4.3 4.3 2.2 2.2
1.1
3.3 3.3
9.1 9.1 32.0
6.7
2.3
6.7
6.7 10.0
3 months 1.6 4.7 6.3 3.1 6.3 3.1
3.1 3.1 3.2 2.1 1.6 4.7 3.1 4.7
2.3
2.2
3.2
2.1
11.8
9.7
8.5
3.1
3.2 1.1 2.2 2.2 1.1 4.3 2.2 2.2 3.2 3.2 2.2 1.1 2.2 3.2 3.2 4.3 4.3
4.3 2.2 5.4 2.2 2.2 2.2 5.4 4.3 2.2 1.1 1.1
5.4 5.4 9.7
2.2 7.5 13.0
1.1 3.2 1.1
2.2 2.2 2.2
93
3.3
2.1 16 2.1 2.3
6.7 3.3
1.1 1.1
3.3 3.3
2.2 3.2
93
90
3.1 3.1 1.6 1.6 6.3
1.2 3.7 3.7 7.4 1.2
3.7 2.5 1.2 1.2 2.5 3.7 1.2 6.2 3.7 2.5 6.2 2.5 4.9 11.1 4.9
1.2
1.6 6.4 3.2 9.6 6.4 27.7 5.3 4.2 7.4
3.3 3.3 6.7 13.3
4.7 1.6 6.3 3.1 1.6
6 months
4.5 2.3 2.3 2.3 88
2.1 4.2
96
1.2 1.2 2.5 3.7 1.6 1.6
1.2
3.1 3.1 1.6 6.3
2.5 2.5 6.2 2.5
128
81
a Amplicons were approximately 1.45 kb in size. b Percentage of similarity between the cloned 16S rRNA gene and its closest relative in the NCBI database.
with heterogeneous EPS and cellular biomass was present. The majority (>80%) of the EPS network appeared within the first 4e8 mm of this layer and was detectable with ConA and Calcofluore white, indicating the presence of the b-1,4-linked and a-D-glucose and a-D-mannose polymers (Johnson et al., 2000).
The b-1,4-linked polymers were quite uniformly spread, while the a-D-glucose and a-D-mannose polymers were scattered irregularly. Most of the detected bacteria were dispersed as individual cells and/or microcolonies within the basal 4 mm (17 days) or 2e6 mm (3e6 months) thick fraction. The Sphingomonas
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 0 5 e4 1 6
411
Fig. 4 e Composition of microbial populations in two water samples and membranes obtained from the flow cells after different operating times determined by FISH analyses. The membranes were removed from the flow cells after 5 (5d), 10 (10d), 17 (17d), 32 (32d) days, 3 (3 m) and 6 (6 m) months of operation with or without the chemical treatment application. The F and UF represent patterns of the planktonic bacterial communities in the RO plant feed water (surface water) and UF permeate (RO system feed water). The biovolume obtained for each taxonomic group was expressed as a percentage of the total biovolume obtained by DAPI staining.
cells were uniformly spread over the entire EPS-matrix of this fraction, while the other a-Proteobacteria, CFB, b-Proteobacteria and Actinobacteria colonized its upper and the g-Proteobacteria the middle part. The Planctomycetales were mostly present in the basis and the Verrucomicrobia on top of the biofilm. The cylindrical and/or mushroom shaped microcolonies were associated with the a-Proteobacteria, while the microcolonies of b- and/or g-Proteobacteria were round shaped. Irregularly shaped microcolonies consisted of members of the Burkholderiales, CFB and/or Verrucomicrobia. Most of the b-Proteobacteria microcolonies stuck together in the EPS-associated stacks and extended at irregular intervals from the surface of the basal
fraction into the bulk aqueous phase. In all three samples, the stacks were up to 6 mm high and showed the presence of irregularly scattered single Sphingomonas and/or Verrucomicrobia cells and/or microcolonies of the g-Proteobacteria and/or CFB origin. In some SEM images of biofilms eukaryotes were also visible (Fig. 6). Overall, up to 2.0 106 bacterial cells/cm2 were recovered from the membrane surface. No significant changes in the structure of RO membrane and spacer-associated biofilm layers were observed within the next 15 days of the flow cell operation without cleaning (see the 32 days old sample in Table 1 and Figs. 4, 5 and S3), however the layers increased in thickness (6e9 mm [membrane] and 2e5 mm
Fig. 5 e Representative sagittal (xez) sections of biofilms on cleaned RO membranes. The sections were taken at 1 mm intervals across the z axis of biofilm samples and show the form and spatial arrangement of EPSs, cells and microcolonies in vertical cross sections. The flow cells were operated during 32 days. After 11 days the membranes were cleaned and samples were taken at day 12, 17 and 32 days of operation. Red e Sphingomonas (SPH120-Cy3 probe), blue e b-Proteobacteria (BET42-Cy5 probe) and green e FITC-ConA-positive EPSs. In the 3e6 months operation the membranes were cleaned once a week. Red e a-Proteobacteria (ALF968-Cy3 probe), green e b-Proteobacteria (BET42-FITC probe) and blue e Calcofluor white-positive EPSs.
412
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 0 5 e4 1 6
Fig. 6 e Scanning electron and epifluorescence micrographs showing presence of unicellular eukaryotes in biofilms from chemically cleaned and non-cleaned RO membranes. (AeC) e SEM images of unknown unicellular eukaryotes on top of the biofilms from the non-cleaned 3 (AeB) and 6 (C) months old membranes. (D) e SEM image of the Euglypha in the biofilm from the weekly cleaned 6 months old membrane. (E) e SEM image of the Trinema on the biofilm from the weekly cleaned 3 months old membrane. Various single and EPS-embedded bacterial cells on the surface of the eukaryotes and within the biofilms can be observed. (F) e EPIM image of two trophozoites of the Acanthamoeba sp. on the surface of the non-cleaned 3 months old membrane biofilm. Note cell wall (FITC-ConA-stained, green fluorescence) and nucleus (DAPI-stained, blue fluorescence) of the eukaryotes and microcolonies of the b-Proteobacteria (red fluorescence from positive hybridization with the Cy-3-labeled BET42a probe). Bars: 1 mm (AeC) and 10 mm (DeF).
[spacer]), cell density (1.2 109 cells/cm2 membrane) and diversity (e.g., occurrence of the Actinobacteria, Euglypha and trophozoites of Acanthamoeba sp.).
3.3. Biofouling on cleaned versus non-cleaned membranes Compared to the biofouling rate of the weekly cleaned RO membrane and/or feed-side spacer surfaces, the biofilm initiation at the new membrane and/or spacer surfaces occurred slower, but its spatiotemporal development resulted in an evidently higher severity of the fouling (Fig. 3). Without cleaning, the appearance of single and EPS-embedded bacterial cells was observed within the first 5 days of the flow cell operation (Fig. S4, panel E and F). Their accumulation was associated with the presence of pieces of floating biofilms (flocks) and single bacterial cells in the RO feed water (i.e., a cartridge-treated ultrafiltration permeate), as detected by the FESEM (Fig. S4, panel AeD), FISH (Fig. 4) and DGGE analyses (Fig. S5). Based on total bacterial cell number (DAPI) determinations, from 11 April to 11 May 2008 approximately 2.3 106e1.5 107 cells/L were present in the fresh surface water that was fed into the fullscale RO plant. About 1.5 103 to 7.0 104 cells/L were present in the ultrafiltration permeate that was fed into the RO
membrane modules and test flow cell units. Surprisingly, 6.1 102 to 2.0 104 cells/L were detected in the RO permeate from the full-scale RO. In contrast, no bacterial cells were detected in permeate from the test flow cells. SEM and CLSM examinations of the emerging biofilms on the non-cleaned 5 and 10 days old membrane and feed-side spacer surfaces revealed differences in their spatial organization. In the flocks, cells of the b or g-Proteobacteria were uniformly distributed within a common (<0.5 mm thick) EPS matrix. The b and g-Proteobacteria also emerged in the close proximity to each other. The uniform species clusters were small (w1 3 mm) and occurred at irregular intervals over the entire feed side of the membrane and in the corners of the associated spacer. The mixed species aggregates (Fig. S4-E) were large (w10 20 mm) and appeared primarily at the flow cell entrance. Their accumulation was also visible by the naked eye (Fig. 3). At the surfaces of these aggregates cells of the a-Proteobacteria, CFB, Verrucomicrobia and/or Planctomycetes were randomly distributed. In the Sphingomonas monolayers, individual cells were embedded in a 1 mm thick EPS matrix that filled the 2e10 mm spaces between the cells (Fig. S4-F). In the 10 days old samples, these layers were stretched up to 60 mm wide and covered (at irregular intervals) up to 50% (membrane) and 20% (spacer) of the total surface
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 0 5 e4 1 6
413
Fig. 7 e Scanning electron and CLSM micrographs demonstrating the effect of weekly chemical cleaning procedures on the structure and complexity of RO membrane and feed-side spacer fouling layers. Vertical columns represent images from the not-cleaned 3 months old and cleaned 3e6 months old samples. Horizontal panels represent SEM and CLSM images of biofilms at the RO membrane and feed-side spacer surfaces. Note presence of water channels in the images. Red fluorescence in the CLSM images was acquired from the Cy3-labeled BET42a probe, while green e from the FAM-labeled SPH120 probe and blue e from the Calcofluor white-stained a-D-glucose and a-D-mannose of the biofilm EPS matrix. Bars: 10 and 100 mm.
area. According to the clone libraries analysis (Table 1), the b-Proteobacteria subdivision was the largest bacterial group in the libraries from the non-cleaned 5 days old membrane sample (62% on the total clones). Within the group, the genera Candidatus Nitrotoga arctica and Nitrosomonas dominated (36% and 24% of total clones) the non-cleaned 5 days old membrane library. In the longer (17 dayse6 months) operated systems, the arrangement of biomass and biogenic extracellular material at the non-cleaned membranes and/or spacers was similar to the 3-D biofilm organization on the weekly cleaned and longterm (3e12 months) operated surfaces. However, the presence of a dark (no fluorescent) area between the biofilm bottom and membrane or spacer surface was not observed. The second
fraction of the biofilm (on the top of the basal, Sphingomonas biofilm) was 4e5 mm thicker and the g-Proteobacteria emerged in the upper part of this fraction. The b-Proteobacteria stacks were 6 mm higher and the majority (>80%) of the bacterial EPS appeared within the first 10e13 mm (from the biofilm bottom). The Actinobacteria were not detected in the biofilms that were present on the non-cleaned membrane and spacer surfaces. Observed from the top, the biofilms appeared as lumpy establishments on the non-cleaned surfaces and as relatively flat carpets on the cleaned surfaces (Fig. 7). Separated microcolonies were more abundant and larger in size (10e15 mm) on the non-cleaned surfaces compared to the size (<5 mm) of the microcolonies on the cleaned surfaces (Fig. 7). Voids larger than 5 mm occurred only within the biofilm matrix on the
414
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 0 5 e4 1 6
cleaned surfaces (Fig. 7). The number of total bacteria was higher and increased with the operating time at the noncleaned membrane surface: 6.3 104 (5 days), 9.7 105 (10 days), 6.1 108 (3 months) and 2.1 109 (6 months) cells/cm2. On the cleaned membrane surface lower numbers of bacteria were detected after 3 (8.2 107 cells/cm2) and 6 months (3.7 107 cells/cm2).
4.
Discussion
During a period of one year we have studied the effect of conventional chemical treatment on occurrence and development of biofouling in reverse osmosis (RO) membrane units. A comprehensive evaluation of the cleaning impact was achieved by monitoring microbial populations on the surfaces of cleaned and non-cleaned RO membranes and feed-side spacers and correlating the outcomes with pressure drop measurements over the feed channel of the test flow cells during one year. The test flow cells were connected in parallel to an RO system of a full-scale water treatment plant that produced process water from extensively pre-treated surface water (Bereschenko et al., 2010). The result of this study describes the dynamics of biofouling under real field conditions and may be important for the development of new anti-fouling strategies in membrane separation processes.
4.1.
Chemical treatment is not cleaning
This research confirms previous (Baker and Dudley, 1998; Flemming, 2002) suggestions that the failure in removing established biofilms from RO membrane unit surfaces is the main reason for the limited effect of conventional chemical treatment on prevention and/or elimination of biofouling in full-scale RO water purification plants. The biofilm layers are often still present on the RO membrane and feed-side spacer surfaces within the RO test flow cells after the weekly applied chemical cleaning procedures (Bereschenko et al., 2010, 2007, 2008, this study). However, their structures were drastically affected (Figs. 2 and 5) and became more loosely attached (i.e., could be more easily scraped than the biofilms on the non-cleaned surfaces). This indeed results in a lower pressure drop over the feed channel (Fig. 1). The loosely attached biofilm is not completely removed, most likely because the flow inside the membrane module cannot exert sufficient friction to flush the biomass away due to the presence of the feed spacer. Similar phenomena were observed in our previous studies (Bereschenko et al., 2007, 2008), on the surfaces of the industrially used (for 1 and 5.5 years) bi-weekly cleaned (by a similar cleaning procedure) RO membrane from the same RO system. It appears that factors as surface texture (rough: membrane or smooth: spacer), system configuration (flat-sheet: test flow cell or spiral wound: commercial RO module), operation time (days, months or years) and frequency of conventional cleaning do not have a significant influence on the stability of microbial biofilms. Apparently, the inherent properties of the biofilmassociated bacterial cells and extracellular polymeric substances play a role. From the microscopic examinations, it
is evident that the network of biofilm-associated EPSs appeared to be remarkably stable to the chemical cleaning procedures, whereas the majority (67%e79% of the total clones, Table 1) of the associated bacterial population disappeared due to toxic effect of the chemicals. Consequently, each single chemical treatment resulted in the collapse of the established three-dimensional biofilm structure and not in biofilm removal from the different surfaces as was expected. In the CLSM and SEM images, only the upper RO biofilm layer was usually affected (i.e., collapsed or disappeared), while the structural integrity of the base layer was hardly changed (Figs. 2 and 5). Only Sphingomonas species e typically localized at the biofilm base, according to this and previous study (Bereschenko et al., 2010) e were able to survive the chemical cleaning procedures (Fig. 5). There are two options that might lead to their resistance to cleaning. Firstly, by being present in the base of the biofilm they might be protected from the biocide (sodium bisulphite). The biocide will react with the organic matter in the top-layer of the biofilm and most likely will not reach the lower localized Sphingomonas cells. Increase in applied concentration would be an option to circumvent this problem, but there is a delicate balance between disinfection efficiency and protection of the membrane (certainly on places without biofouling) from the adverse effects of the biocide. It might also be that the specific properties of sphingomonads EPS offer additional protection against chemical attack. The sphingomonads are producers of various extracellular biopolymers (sphingans), including gellan-like polysaccharides (Pollock, 1993; Lobas et al., 1994; Pollock and Armentrout, 1999), which are known for their relative stability to many environmental conditions (i.e., extremes of pH, temperature, salinity and autoclaving [Ashtaputre and Shah, 1995]). Microorganisms that are present in these EPS layers are much more resistant to many antibiotics (Smalley et al., 1983). There is however no literature data on the effect of bisulphite on these gellans and microorganisms that are embedded in these polysaccharides. A large amount of EPS structures was visible in the CSLM images compared to the amount of cells (Fig. 5). Newly produced EPS will require a lot of space and push newly divided cells wide apart preventing the formation of microcolonies in the biofilm (Picioreanu et al., 2004). The sphingans are localized at the base (Fig. 5 and S2) and take up a major part (up to 80% of the volume) of the biofilm matrix in the chemically treated samples. It can, therefore, be assumed that the sphingans are the most important contributors to the cohesive strength of the fouling layer on the membrane surface. Furthermore, the presence of glycosphingolipids in the cell envelopes of the sphingomonads, which is unique and clearly distinguish them from other bacteria (Kawasaki et al., 1994; Balkwill et al., 2006), may give them a more substantial protection to chemically active agents than the lipopolysaccharides that are present in the cell envelopes of other bacteria (Smalley et al., 1983). Additional experiments with Spinghomonas spp. will be necessary to prove this hypothesis. Current cleaning procedures with surfactants and chelators are often tested on non-sphingomonads biofilms. Apparently, they are not effective on Sphingomonas sp. and their EPSs as might be expected from the physicochemical properties of the components involved (Balkwill
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 0 5 e4 1 6
et al., 2006; Denner et al., 2001; Pollock, 2002). The study of the unique EPSs and glycosphingolipids of sphingomonads species might result in the development of more effective and directed cleaning methods to control biofouling.
4.2.
Rapid re-growth of biofouling layers
The results indicate that microbial colonization of the collapsed biofilm layers starts directly after chemical cleaning. Two clearly different features were hereby observed: attachment and growth of primary colonizers (single cells and cells in clumps, Figs. 2, 5 and S4) transported by the RO feed water to the surfaces and proliferation of organisms that survived the chemical cleaning within the collapsed biofilm layer (Fig. 2). The colonization process consists of similar events as described previously for clean surfaces (Bereschenko et al., 2010): the initiation of early biofilm structures and a spatiotemporal development into a multispecies slime layer with a complex three-dimensional architecture (Figs. 5 and S2). The re-growth of the bacterial biofilms attached to the membrane and feed-side spacer surface results in the same biofoulingrelated system failure as before the cleaning and occurs within a relatively short operational time (approx. 1 week). In contrast, the development of a “critical level biofilm” on fresh (noncleaned) RO membrane and feed-side spacer surfaces take approximately 16e17 days (Fig. 1-A). Factors that facilitates this rapid biofilm re-growth on the treated surfaces may be: (i) presence of attractive attachment surfaces (i.e., clearly rough surface with, possibly, adhesive EPSs), (ii) abundance of nutrients (i.e., damaged EPSs, proteins and other macromolecules from lysed cells) trapped in the EPS matrix and (iii) presence of viable cells under the collapsed top of treated biofilm layer. The microbial communities within the re-grown biofilm layers are usually more complex in structure and composition (Table 1 and Figs. 5e7 and S3), compared to the communities on the fresh RO surfaces. However, the general biofilm architecture was the same in both cases (i.e., the mixed species layer on top of the Sphingomonas monolayer at the basis, Figs. 5 and S2). The observed biofilm removal failure and subsequent rapid biofilm layer re-growth were observed after each scheduled treatment. From a microbiological point of view, the regrowth process remains the same, with some small shifts in the structure and composition of the involved microbial community, more related to seasonal changes (Fig. S3) than to the operating and cleaning procedures. Remarkably is, however, that within 6e7 days after cleaning the biofilm reached already a structure similar to a five years old fouling layer as observed in a previous study (Bereschenko et al., 2008) on a membrane module from the same water production plant. This emphasizes the need for radical new biofouling control methods, potentially based on the properties of the sphingomonads and their EPSs.
5.
Conclusions
This microbial molecular ecology study clearly demonstrates that conventional cleaning with toxic chemicals has an effect on the occurrence of biofouling in RO systems, but is not effective in really cleaning the RO system. For development of
415
new approaches to control biofouling in membrane-based water treatment systems special attention has to be paid to the sphingomonads. These versatile bacteria are widely spread in natural water environments and man-made water systems (Chen et al., 2004; Koskinen et al., 2000; Pang and Liu, 2006). They are strong competitors in scavenging a variety of nutrient sources under oligotrophic conditions. They contribute a lot to the cleaning-associated stability of bacterial biofilms, even if they are number wise not the dominant group in the surface-attached biofilm communities.
Acknowledgements This work was performed at Waterlaboratorium Noord (Kisuma, Veendam) in the TTIW-cooperation framework of Wetsus, Centre of Excellence for Sustainable Water Technology (www. wetsus.nl). Wetsus is funded by the Dutch Ministry of Economic Affairs, the European Union Regional Development Fund, the Province of Fryslaˆn, the City of Leeuwarden and the EZ/Kompas program of the “Samenwerkingsverband NoordNederland”. The authors like to thank the participants of the research theme “Biofouling” for the discussions and their financial support. We gratefully appreciate Wiebe Kunst, Eran Amar, Kisuma and Veendam, for flow cell operation and Harrie Bos, Wetsus, for technical support by pressure drop analyses. In addition, we thank Tiny Franssen-Verheijen, Laboratory of Plant Cell Biology, Wageningen, and Dr. Arie Zwijnenburg, Wetsus, for SEM imaging and Dr. N.C.A. de Ruijter, Laboratory of Plant Cell Biology, Wageningen University, for CLSM imaging and assistance with CLSM image analysis.
Appendix. Supplementary material Supplementary data associated with this article can be found in the on-line version, at doi:10.1016/j.watres.2010.07.058.
references
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Adsorption and desorption of trace organic contaminants from granular activated carbon adsorbers after intermittent loading and throughout backwash cycles Christopher J. Corwin*, R. Scott Summers Dept. of Civil, Environmental and Architectural Engineering, University of Colorado, 1111 Engineering Drive, Boulder, CO 80309-0428, USA
article info
abstract
Article history:
A granular activated carbon (GAC) adsorption simulation methodology using the observed
Received 4 June 2010
trace organic contaminant mid-point breakthrough and the pore diffusion model is pre-
Received in revised form
sented, validated, and used to model adsorption and concentration gradient driven
18 August 2010
desorption. Trace organic contaminant adsorption was well-simulated by this approach;
Accepted 22 August 2010
however, desorption from GAC adsorbers was found to occur at lower concentrations than
Available online 27 August 2010
predicted by either pore or surface diffusion model calculations. The observed concentration profiles during desorption yielded a lower peak concentration and more elongated
Keywords:
attenuation of contaminants after intermittent loading conditions than predicted by the
GAC
models. Hindered back diffusion caused by irreversibly adsorbed dissolved organic matter
Pore diffusion
on the GAC surface is hypothesized to be responsible for slowing the desorption kinetics. In
PSDM
addition, laboratory test results indicate a negligible impact of simulated backwashing the
Backwashing
GAC media on trace organic contaminant breakthrough. ª 2010 Elsevier Ltd. All rights reserved.
RSSCT
1.
Introduction
The presence of background dissolved organic matter (DOM) impacts the adsorption of organic contaminants by granular activated carbon (GAC) reducing the effectiveness of the adsorption process. A few recent methods to predict the impact of fouling by DOM show promise where single-solute isotherm data is available (Jarvie et al., 2005; Magnuson and Speth, 2005), but have not gained acceptance to replace RSSCT and pilot testing. The impact of DOM on the desorption phase has not been reported. Desorption occurs when 1) adsorbed compounds are displaced by more strongly adsorbing compounds, or 2) when the concentration gradient in the adsorber reverses and adsorbed compounds are driven into the water phase by back diffusion. Both desorption types maybe impacted by the presence of
DOM. Displaced desorption is a result of competitive adsorption, has been studied and reported in the literature (Snoeyink and Summers, 1999). Desorption caused by a concentration gradient reversal may be affected by interactions between the desorbing compound and other adsorbed organic species. A few studies have reported experimental concentration gradient reversal desorption results (Yuasa, 1982; Summers et al., 1990; Hubel et al., 1992; To et al., 2008a, 2008b), while other studies have just modeled this form of desorption (Thacker et al., 1983; Hong and Summers, 2006). Reversal of the concentration gradient can occur in two common scenarios, 1) when the end of an intermittent loading of contaminant passes through the adsorber, and 2) after longitudinal mixing of the adsorbent media during backwashing. Intermittent loading of organic contaminants can be caused by runoff events, irregular wastewater discharges, and
* Corresponding author. Tel.: þ1 303 735 4147; fax: þ1 303 492 7317. E-mail address:
[email protected] (C.J. Corwin). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.08.039
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spills whether accidental or intentional. When adsorption is reversible and the influent concentration of an adsorbate falls below the equilibrium point of the solid phase concentration of the adsorbate, the adsorbate desorbs due to the concentration gradient reversal. Given enough time, the entire adsorbed mass of reversibly adsorbed compounds should theoretically desorb. In recent work, To et al. (2008a, 2008b) studied desorption kinetics of target compounds at concentrations greater than 0.1 mg/L in the presence of a strongly competing component and a pore blocking component. Results showed that the strongly competing component enhanced desorption through displacement while the pore blocking component retarded desorption, likely via hindered diffusion where back diffusion was slowed by the accumulated adsorbed organic matter on the GAC. Thacker et al. (1983) used homogeneous surface diffusion model simulations to show that the rate of adsorbate desorption in response to a decrease in influent concentration increased as i) the diffusion coefficient of the adsorbate increased, ii) the amount of compound adsorbed increased, iii) the strength of adsorption decreased, and iv) the GAC particle size decreased. Research by Sander and Pignatello (2007) found that Freundlich equilibrium parameters did not change from adsorption to desorption on low porosity (3 w0.15) black carbon for benzene, toluene, and nitrobenzene over six orders of magnitude in concentration range down to the parts-per-billion level. In practice, GAC filter-adsorbers are periodically backwashed, daily to weekly, to remove accumulated particles and restore hydraulic capacity. Post-filter GAC adsorbers may be occasionally backwashed to prevent channeling. Filter adsorber bed replacement frequencies of two to five years are typical, while post-filter adsorber media are typically replaced every two months to two years. Backwashing results in some degree of longitudinal mixing of the GAC media leading to the rearrangement of the mass transfer zone that can cause desorption. Theoretically, this can cause earlier breakthrough of trace organic contaminants relative to a non-backwashed system. After a short operation period, GAC media at the top of the adsorber becomes loaded with the target adsorbate and the GAC media at the bottom of the bed has a lower or no adsorbate loading. Backwashing can result in the transfer of highly loaded GAC media from the top of the bed to deeper into the bed, and consequently transfer of fresher GAC media from deep in the adsorber to the top of the bed. The fresh adsorbent now located at the top of the bed will have sufficient capacity to adsorb the incoming target compound. At the same time, the loaded media now deeper in the bed where the liquid phase concentration of target compound is near zero, experiences a reversal of concentration gradient leading to desorption. Using the pore and surface diffusion model (PSDM), Hong and Summers (2006) showed that the repetition of adsorption and desorption through backwash cycles led to earlier breakthrough of the target compound in the adsorber effluent. During each backwash cycle, the model predicted an immediate increase in effluent concentration after each backwashing caused by desorption from loaded particles deep in the bed. The effluent concentration quickly tapered off as the
mass transfer zone re-developed, but then began to rise steeply as the mass transfer zone migrated through the bed. More strongly adsorbing compounds were modeled to be more affected. Hong and Summers (2006) used the assumption of complete mixing of the GAC media during backwashing. Complete mixing is unlikely to occur because the uniformity coefficient of the GAC in the bed is typically specified to promote stratification during backwashing (Snoeyink and Summers, 1999). The objective of this study was to experimentally assess and model the adsorption and desorption of organic contaminants at environmentally relevant concentrations after intermittent loadings and throughout backwash cycles. The rapid small-scale column test (RSSCT) and the PSDM were employed to study desorption caused by a reversal of the concentration gradient.
2.
Materials and methods
2.1.
Materials
2.1.1.
Waters
Surface water samples were collected from five sources and batch coagulated in the laboratory. The water quality values of the GAC influent are shown in Table 1; the pH ranged from 7.2 to 7.6, the dissolved organic carbon (DOC) ranged from 1.4 to 2.7 mg/L, and the specific ultraviolent absorbance (SUVA) ranged from 1.1 to 2.9 L/mg/m.
2.1.2.
Adsorbents
Fresh 10 40 mesh lignite (Norit HD4000) and 8 30 mesh bituminous (Calgon F300) GACs with a log mean particle diameter of 1.16 and 1.29 mm, particle densities of 0.60 and 0.76 g/cm3, and bed densities of 0.38 and 0.48 g/cm3, respectively were crushed with a mortar and pestle and separated with US Standard sieves on a sieve shaker. The fraction passing the #100 sieve and retained on the #200 sieve was collected (log mean particle diameter of 0.11 mm) resulting in a 30e50% yield and rinsed in laboratory reagent water then dried in an oven at 105 C to a constant weight. The GAC was stored in a capped bottle in a desiccator until use.
2.1.3.
Adsorbates
Seven probe compounds (Table 1) were selected to be representative of trace organic contaminants based on physical properties that are known to affect adsorption; molecular weight, ionic state, and hydrophobicity. The probe compounds were also selected from different contaminant classes; bisphenol A (BPA) is a plasticizer, 2-methylisoborneol (MIB) is a taste and odor causing compound, methyl tert-butyl ether (MtBE) is a fuel oxygenate, chloroform is a regulated disinfection byproduct, 2,4-dichlorophenoxyacetic acid (2,4-D) is a regulated pesticide, erythromycin (ERY) is an antibiotic, and diclofenac (DCF) is an analgesic. Molecular weights ranged from 88.2 to 733.9 Da, logKow values ranged from 0.70 to 3.32, and three compounds were nonionic, with the remaining four compounds with pKa values ranging between 2.73 and 10.5.
Table 1 e RSSCT run conditions. RSSCT Run
2
3
4
5
6
7
GAC Type GAC Bed Porosity, 3 GAC Particle Porosity, 3p
Bituminous 0.37 0.5
Bituminous 0.37 0.5
Bituminous 0.37 0.5
Bituminous 0.37 0.5
Lignite 0.37 0.5
Lignite 0.37 0.5
Lignite 0.37 0.5
Full-Scale Mesh Size Full-Scale EBCT RSSCT column diameter RSSCT Flow Rate RSSCT EBCT RSSCT Bed Volume, V RSSCT Bed Length, l RSSCT Mass GAC, MGAC
8 30 8.0 4.76 2.0 0.67 1.35 7.56 0.647
8 30 8.0 4.76 2.0 0.67 1.35 7.56 0.647
8 30 8.0 4.76 2.0 0.67 1.35 7.56 0.647
8 30 8.0 4.76 2.0 0.67 1.35 7.56 0.647
10 30 7.0 4.76 2.0 0.65 1.30 7.31 0.495
10 30 4.0 4.76 2.0 0.37 0.74 4.18 0.283
Compound Influent Concentration, C0 Background DOC SUVA pH
BPA 100 1.9 1.2 7.5
ERY 100 1.9 1.2 7.5
DCF 100 1.9 1.2 7.5
2,4-D 100 1.9 1.2 7.5
MtBE 7600 1.4 2.9 7.2
Diffusivity in Water, DL Tortuosity,s Schmidt number, SC Film M.T. Coefficient, kf Biot number, Bi Stanton number, St
5.56E-06 3 1676 8.04E-03 558 37.8
2.89E-06 3 3230 4.94E-03 660 23.2
5.36E-06 3 1739 7.82E-03 563 36.8
7.78E-06 3 1198 1.04E-02 513 48.7
8.61E-06 3 1083 1.12E-02 453 50.9
8
9
10
11
Lignite 0.37 0.5
Bituminous 0.37 0.5
Bituminous 0.37 0.5
Bituminous 0.37 0.5
10 30 6.0 4.76 2.0 0.56 1.12 6.27 0.424
10 30 5.7 4.76 2.0 0.53 1.06 5.95 0.403
8 30 7.0 4.76 2.0 0.59 1.18 6.62 0.566
8 30 5.0 4.76 2.0 0.42 0.84 4.73 0.404
8 30 8.0 4.76 2.0 0.67 1.35 7.56 0.647
2,4-D 100 2.4 1.2 7.6
DCF 100 2.4 1.2 7.6
BPA 70 2.4 1.2 7.6
Chloroform 70,000 2.7 2.0 7.3
MIB 50 2.7 1.1 7.6
DCF 100 1.5 1.6 7.2
7.78E-06 3 1198 1.04E-02 464 26.9
5.36E-06 3 1739 7.82E-03 509 30.5
5.56E-06 3 1676 8.04E-03 505 29.8
1.09E-05 3 855 1.34E-02 474 55.1
6.20E-06 3 1504 8.72E-03 543 25.7
5.36E-06 3 1739 7.82E-03 563 36.8
units
min mm mL/min min mL cm g
ng/L mg/L L/mg/m cm2/s
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1
cm/s
419
420
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2.2.
Methods
2.2.1.
Analytical methods
2.2.1.1. Probe compound analysis. Radiolabeled probe compounds were adjusted to a specific activity to yield detection limits around 10 ng/L by adding a measured mass of unlabeled carrier stock. 14C bisphenol A, 14C diclofenac, 14C MIB, 3H 2,4-D, and 3H erythromycin were obtained from American Radiolabeled Chemical, St. Louis, MO. Samples were prepared by placing 4 mL of sample and 16 mL of Ultima Gold scintillation cocktail into a 20 mL polyethylene vial. Samples were analyzed on a Packard Tri Carb 2300 liquid scintillation analyzer for 40 min of counting time. A series of dual-labeled quenched standards was prepared and analyzed to determine scintillation counting efficiencies. Chloroform samples were analyzed with an Agilent 6890 GC-ECD using U.S. EPA Method 551.1. MtBE analysis was performed by GC-FID at the North Carolina State University. 2.2.1.2. Organic carbon, UV absorbance, and pH. DOC samples were analyzed with a Sievers 800 TOC Analyzer with an inorganic carbon removal unit using the ultraviolet irradiation/persulfate oxidation method in accordance with Standard Method 5310 C (APHA, 2005). Ultraviolet absorbance (UVA) was analyzed at a wavelength of 253.7 nm using a HACH DR4000 Spectrophotometer in accordance with Standard Method 5910 (APHA, 2005). pH was measured with a Denver Instruments Model 220 m.
2.2.2.
Experimental methods
2.2.2.1. Rapid small-scale column tests. RSSCTs were designed according to the proportional diffusivity approach (Crittenden et al., 1991). Proportional diffusivity was used in order to match the loading of the background DOM and capture fouling interactions as accurately as possible; however, the dependence of adsorption capacity on GAC particle size are not included in this work (Corwin and Summers, 2010). The RSSCT was designed for a large column system with a hydraulic loading rate of 10 m/h, bed density of 0.5 g/cm3 and a Reynolds number of 9.3 and 10.3 for the lignite and bituminous GACs, respectively. The large column empty bed contact times are listed in Table 1. The 100 200 mesh GAC was packed into a 4.76 mm inside diameter Teflon column yielding an aspect ratio of 44, which is well above the 8e10 needed to avoid wall effects Knappe et al. (1999). The smaller GAC particle size used in the RSSCT should accentuate the impact of desorption (Thacker et al., 1983). A ColeeParmer Masterflex 7521e40 pump driver and 7090-62 Teflon diaphragm pump head delivered the influent water to the column at a hydraulic loading rate of 6.74 m/h and a Reynolds number of 0.6. The system was protected from high pressures with a Swagelok pressure relieve valve set to discharge to waste at around 35 psi. A glass wool pre-filter removed particulates to avoid high head losses that can lead to early termination of a run. All materials in contact with the water were glass, Teflon, or stainless steel. Influent was stored in a 20 L glass carboy and collected from the column in
a similar effluent reservoir. The system flow rate was calibrated to 2.0 mL/min and checked daily. Effluent samples were collected directly after the column. The volume of water in the discharge tank was measured and recorded to determine the throughput at each sample. Influent samples were periodically taken from immediately above the column. In addition to target compound concentration, samples were analyzed for UVA, pH, and DOC concentration. Conditions of the RSSCTs are summarized in Table 1. Runs 1 through 8 are intermittent loading adsorption-desorption tests, runs 5 through 9 backwash simulations, and runs 10 and 11 are adsorption breakthrough tests. In runs 5 to 8, the lignite GAC was first used in a run with only DOM to a DOC breakthrough between 70 and 75% to simulate an adsorber that has been in operation prior to exposure to the target compound and to minimize desorption caused by displacement. The preloaded lignite GAC was then used in a subsequent run with a trace contaminant. The backwash columns, runs 5 to 9, were divided into three segments in series that were individually completely mixed. The segmented columns may be more representative of fullscale adsorbers because complete mixing of the entire column would likely overestimate the effect of backwashing because the uniformity coefficient of the media in the adsorber is specified to prevent complete mixing. Unmixed control RSSCTs and segmented mixed backwash RSSCTs were run for all backwash simulations. For runs 6 and 7, an additional column was completely mixed every backwash cycle to attempt to compare the results with the previous PSDM predictions of Hong and Summers (2006). The simulated backwash columns were completely mixed twice each day, which represents a scaled operating time of approximately 5 days between backwashes. For the MtBE run, mixing was accomplished by removing the GAC media from the column, stirring it in a beaker, then repacking it in the column. Removal of the media assured complete mixing but resulted in attrition of media in the backwash column that was not present in the control column. GAC attrition was not a significant problem for MtBE because breakthrough occurred quickly before much media was lost. For runs 6 to 9 mixing was accomplished by inverting each column several times (>10) with light agitation to break up clumps. Column inversion avoids the loss of media over time, but complete mixing cannot be guaranteed. However, good mixing was visually observed during column inversion.
3.
Modeling approach
Quantitative prediction of the reduction of adsorption capacity of trace organic contaminants by competing DOM has been elusive. In this study, a mathematical simulation procedure was developed that utilizes the apparent adsorption capacity from the observed breakthrough curve to determine adsorption equilibrium parameters, and utilizes pore diffusion to describe adsorption kinetics as input to the PSDM. The PSDM is a mechanistic model of fixed bed adsorption that has been shown to successfully model multi-solute adsorption systems at a relatively high concentration level (Crittenden et al., 1986; Hutzler et al., 1986). The PSDM requires input of the system design and operating parameters such as,
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 1 7 e4 2 6
421
particle diameter, bed porosity, bed density, EBCT, filter approach velocity, and initial concentration of the target compound. The Freundlich isotherm parameters K, and 1/n, film mass transfer coefficient, tortuosity, and the surface and pore diffusion coefficients are also required. Surface diffusion of trace organic contaminants has been shown to be negligible for systems containing DOM (Hand et al., 1989; Carter and Weber, 1994; Matsui et al., 2001; Jarvie et al., 2005). The pore diffusion coefficient, Dp, was calculated by dividing the liquid diffusion coefficient in water, DL, of the target compound by the tortuosity. The liquid diffusion coefficient in water of each target compound was estimated via the Hayduk and Laudie (1974) correlation. The Gnielinski correlation was used to estimate the film mass transfer coefficient (Gnielinski, 1978). A tortuosity, s, value of three was found to result in a good match between the PSDM to the experimental adsorption data and is in the range considered typical for GAC in water treatment (Sontheimer et al., 1988; Cussler, 2009). The Freundlich equilibrium parameters were calculated by assuming that the apparent capacity, the volume of water treated at 50 percent breakthrough, of a non-ideal adsorber is near the quantity of water treated at exhaustion of an ideal plug flow adsorber. The assumption is good if the mass transfer zone is symmetrical. Sontheimer et al. (1988) showed the point at which ideal capacity intersected actual capacity was variable, and averaged about 60 percent; indicating that this is a good, but not perfect assumption. In some cases, mass transfer resistance can also cause a non-symmetric breakthrough curve, in which case the methodology will have to be checked for an acceptable fit. A mass balance is written around the ideal adsorber for the mass adsorbate removed per mass of the GAC in the adsorber, MGAC, and equated to the solid phase concentration, qe, in the Freundlich equilibrium:
compound, respectively. For the assumed ideal plug flow adsorber, Ce is equal to zero. The product of Q and t is the volume of water treated at breakthrough and is equal to the number of bed volumes, BV, treated multiplied by the volume of the bed, Vbed. These substitutions are made and the equation solved for K, which is now replaced with K* to denote that this is the apparent capacity of a specific GAC for the target compound in specific water:
4.
Results and discussion
ðC0 Ce Þ$Q$t ¼ qe ¼ K$C01=n MGAC
4.1.
Adsorption
K ¼
BV50 Vbed C0 MGAC C01=n
(2)
where BV50 is the number of bed volumes treated at 50 percent breakthrough. When the concentration of the target compound is sufficiently low and DOM is present in the system, the value of 1/n has been shown to be effectively equal to 1 (Knappe et al., 1998; Graham et al., 2000). The threshold concentration where linearization occurs is not well defined, but is typically in the low microgram-per-liter range. The threshold becomes higher as adsorbability decreases because weakly adsorbing compounds cannot compete against the background matrix as effectively. The substitutions 1/n ¼ 1 and the bed density, rbed; ¼ MGAC =Vbed are made in Eq. (2) to the form shown in Eq. (3), which is identical to the specific throughput at 50 percent breakthrough, which equals the inverse of the GAC use rate. K ¼
BV50 rbed
(3)
Eq. (2) may be used to calculate K* for higher concentration ranges by assuming the 1/n value stays constant as shown in Sontheimer et al. (1988).
(1)
where Q is the adsorber flowrate, t is the operation time, and C0 and Ce are the influent and effluent concentrations of the target
Results of the apparent capacity simulation approach using the PSDM (part of the AdDesignS package from Michigan Technological University) with no surface diffusivity and s ¼ 3
Fig. 1 e The apparent capacity modeling approach matches the RSSCT breakthrough curves for a range of probe compounds. Units of K* are in mmol/g$L/mmol1/n. The numbers in parenthesis indicate the run with conditions shown in Table 1.
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are shown in Fig. 1 for seven different probe compounds with fresh and DOC preloaded GAC. The simulation provides a good match of the breakthrough curves for the seven probe compounds over a wide range of adsorb abilities and diffusivities. Data in Fig.1 shows the widely varying slopes of the breakthrough curves are well described by using a pore diffusion model with Dp based on the calculated liquid diffusivity and s ¼ 3. Chloroform was run at a relatively high influent concentration of 70 mg/L so Eq. (2) was used in the calculation of K* with 1/n ¼ 0.669 as reported by Speth and Miltner (1990) for a concentration range of 13.2e226 mg/L. The model resulted in a good match to the chloroform data. Runs 5 and 6, which used GAC preloaded with DOC, appear to be equally well modeled by the methodology relative to the fresh GAC modeling; runs 2, 9, 10, and 11. The good simulation with the pore diffusion approach is a result of the effects of DOC preloading being implicit in the observed breakthrough curve.
However, the calculated K* of a preloaded column is specific to that level of DOC preloading.
4.2.
Desorption after intermittent loading
Probe compounds were spiked into the influent water of the fresh or DOC preloaded GAC columns until compound breakthrough of 40e100% had occurred. The GAC columns were then continued on unspiked influent water to simulate the end of an intermittent loading period. Fig. 2 shows results of the desorption testing for the eight runs, which indicate a very fast initial decline in effluent concentration of all compounds after the target compound feed was stopped. After the fast initial desorption phase, an extended slow desorption phase occurred. For example, results for erythromycin indicate about seven percent of the influent loading concentration was still desorbing after 100,000 bed volumes, or two years at a full-scale EBCT of 10 min, of desorption.
A
B
Fig. 2 e Desorption of probe compounds from a GAC column (split into two panels for clarity). Vertical dashed lines indicate the point that the probe compound feed was stopped. The numbers in parenthesis indicate the run with conditions shown in Table 1.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 1 7 e4 2 6
These findings are consistent with the batch test results on atrazine by To et al. (2008b) who reasoned that desorption occurred from unblock pores initially, then desorbs more slowly from blocked pores. Results shown in Fig. 2 indicate that desorption after an intermittent loading will occur at low levels for a long period, consistent with the findings of Summers et al. (1990) for cis 1,2-dicloroethene. Even though some of the effluent points are below the calculated method detection limit, influent samples without target organics added analyzed during this period were consistently close to zero, suggesting desorption was still occurring. Desorption may last longer than the GAC bedlife or until the next intermittent loading begins, which means complete desorption of a contaminant is unlikely. Replacing the GAC media before complete desorption results in some overall removal of the target compound and not just attenuation of the compound. Even if the entire adsorbed compound does desorb, the attenuation may provide an effective treatment strategy where treatment objectives are based on the maximum concentration in the effluent. Extending the apparent capacity simulation to model desorption predicts an increase in effluent concentration after an intermittent loading (Fig. 3). The increase in concentration occurs because the mass transfer zone continues to migrate through the bed for compounds that have not reached complete breakthrough. Results in Fig. 2 show no indication that an increase in concentration occurs after the intermittent loading has ended. The PSDM prediction results in a higher predicted desorption concentration and mass than observed in the RSSCT columns. Results in Fig. 3 indicate that desorption occurred at lower concentrations than anticipated by the PSDM consistent with the results reported by Hubel et al. (1992). Because the desorption concentrations were lower than the PSDM predicts, methods involving competitive adsorption were not explored. Competitive adsorption described by the ideal adsorbed solution theory only include displaced desorption as a mechanism, which can only increase the desorption concentration profile. Although these experiments were not designed to determine the mechanism causing the discrepancy between the observed
423
desorption behavior and model, it is hypothesized that hindered diffusion during desorption is responsible. To et al. (2008a; 2008b) determined that pore blocking components hindered back diffusion during desorption. Hong (1995) also reported hindered diffusion during desorption of DOC. Fig. 3 also shows a desorption sensitivity analysis using the PSDM at desorption tortuosities values ranging from 3 to 100, with constant Freundlich equilibrium parameters. Tortuosity is defined as the diffusion path length of an adsorbate relative to the path length parallel to the concentration gradient. Thus, increasing tortuosity decreases desorption kinetics, i.e. a s value of 60 means desorption diffusivity is 20 times slower than adsorption diffusivity with a s value of 3. To model the desorption phase, the tortuosity of adsorption had to be set equal to the desorption tortuosity, as a version of the PSDM with user-controlled variable tortuosities is not available. The slow adsorption kinetics resulted in a very long mass transfer zone and immediate breakthrough, which is why the models begin with a plateau near the influent level and not nearer to the observed breakthrough. The length of this plateau increases with increasing Freundlich K. The model adsorption phases were run until the solid phase concentration matched the observed solid phase concentration. Matching the solid phase concentration creates the concentration gradient towards the liquid phase which is the mechanism being investigated. After the plateau, the model at a tortuosity of 50 results in a good match of the observed desorption behavior for erythromycin. However, the plateau results in a poor match of the mass desorbed from the column. Desorption modeling results of runs 1 through 4 showed that the tortuosity required for a good fit was approximately 50 for bisphenol A, erythromycin, and diclofenac and approximately 150 for 2,4-D. Tortuosities above 10 indicate that diffusion is hindered by interactions with pore walls or other solutes (Cussler, 2009). The long persistent desorption profile that is around 10% of the feed concentration makes irreversible adsorption of the target compounds an unlikely cause. Irreversible adsorption would result only in a lower solid phase concentration at the
Fig. 3 e Comparison of the PSDM prediction and simulation of desorption compared to RSSCT results for erythromycin from run 2.
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Table 2 e Percent of mass desorbed at one-quarter and one times the throughput of the adsorption phase versus PSDM predictions. Mass desorbed/total tal mass adsorbed (%) compound (run) MtBE (5) diclofenac (3) bisphenol A (1) erythromycin (2) 2,4-D (4)
K* (mmol/g$L/mmol1/n) 3.9 64 39 142 333
BVdes ¼ 0.25BVads observed 27% 13% 8% 7% 4%
predicted 100% 59% 100% 23% 18%
BVdes ¼ BVads observed 38% 30% n/a 18% n/a
predicted 100% 100% 100% 78% 82%
BVdes ¼ bed volumes of desorption, BVads ¼ bed volumes of adsorption.
beginning of desorption and could not explain the long desorption phase at close to 10% of the feed concentration. Similarly, attempts to fit the data with the PSDM by changing the Freundlich parameters could also not explain the slow desorption phase. Combinations of changes in solid phase concentration, tortuosity, and Freundlich parameters are too complex to fully investigate with the available versions of the PSDM. Therefore, other mechanisms such as hysteresis in the Freundlich parameters and irreversible adsorption may also be occurring in addition to the hypothesized the hindered diffusion. The desorption mass relative to adsorption mass at a given time was compared between compounds using both desorption data and the apparent capacity simulations. Table 2 presents measured and PSDM predicted mass desorbed relative to the total mass adsorbed at two throughputs relative to the throughput of the adsorption phase. The desorption model runs were carried out to times at which the model predicted 80e100% of adsorbed mass would be desorbed. However, the highest amount desorbed was measured to be 38%, while the lowest was 8%. Overall, the data indicate that the PSDM overestimates the desorption mass by about three to four times, except for bisphenol A for which the model over-predicts by a factor of 12. Generally, the compounds that are most weakly adsorbed are the most easily desorbed, consistent with the findings of Thacker et al. (1983). Summers and Roberts (1988) found no measurable desorption of four humic substances, measured by UVA254, from the GAC surface under a concentration gradient reversal.
They proposed that the DOM was not reversibly adsorbed, due to multiple site adsorption of the large molecular weight humic substances. Hong (1995) showed 7 to 14 percent of DOM, measured by TOC, from five whole waters was desorbable by a concentration gradient reversal. The nondesorbable nature of DOM indicates that no significant DOM desorption would be expected after DOM concentration peaks.
4.3.
Impact of media mixing (simulated backwashing)
Longitudinal mixing of the GAC media may occur during backwashing, which could result in desorption from the GAC causing early breakthrough. Desorption during actual backwashing phase is considered negligible because filteradsorbers are typically backwashed for less than one percent of the production cycle; therefore, only the effects of bed mixing were studied. Results of mixed column breakthrough compared to unmixed control column breakthrough, presented in Fig. 4, showed little to no measurable impact of mixing. The segmented mixed columns, as well as the completely mixed columns of runs 6 and 7, did not indicate a significant difference in breakthrough compared to the control columns. The weakly adsorbed volatile organic at the highest concentration, chloroform, should be highly susceptible to desorption; however, almost no difference because of mixing was observed. Additionally, the most strongly adsorbing
Fig. 4 e Mixing of GAC media is shown to have a negligible impact on overall breakthrough. The numbers in parenthesis indicate the RSSCT run with conditions shown in Table 1.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 1 7 e4 2 6
compound, 2,4-D, showed no impact of mixing which contrasts the model predictions by Hong and Summers (2006). Slow desorption kinetics may also explain the discrepancy. The immediate rise in effluent concentration observed in Hong and Summers (2006) modeling work does not occur because desorption kinetics are slow. The progression of the mass transfer zone through the column is faster than desorption from deep in the adsorber, leading to a negligible overall impact on the breakthrough. Mixing of the GAC media is expected to have little effect on DOC breakthrough due to the nondesorbable nature of DOM. Hong (1995) found that DOC breakthrough curves for three of five waters were slightly impacted by backwashing, while the remaining two waters were not impacted.
5.
Conclusions
Adsorption and desorption was investigated using RSSCT runs and the PSDM. A procedure was developed and tested in which apparent adsorption capacity was calculated directly from the mid-point of breakthrough and the pore diffusivity calculated from the liquid bulk diffusivity and a tortuosity of 3. The values were then used in the PSDM to simulate the adsorption and desorption data. The adsorption leg of the breakthrough was well simulated, while the desorption kinetics of trace organic contaminants appeared to be slower by a factor of 20e50, when compared to PSDM results based on adsorption kinetic parameters. Mixing of GAC media in a column to simulate backwashing did not appear to lead to early breakthrough as theoretically expected. The practical implications of the desorption results are that performance concerns over desorption may not be a significant problem a) in that a rise in effluent concentration after an intermittent loading was not observed to occur, and b) that longitudinal mixing of the adsorbent media that may occur during backwashing did not cause early breakthrough of trace organic contaminants.
Acknowledgements The lead author was funded by the Malcolm Pirnie Inc. Fellowship during a portion of the research. Austa Marie Parker assisted with the chloroform run while participating in the NSF Research Experience for Undergraduates Program.
references
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Saturated Groundwater flow: model Development and parameter sensitivity. Water Resour. Res. 22 (3), 271e284. Crittenden, J.C., Reddy, P.S., Arora, H., Trynoski, J., Hand, D.W., Perram, D.L., Summers, R.S., 1991. Predicting GAC performance with rapid small-scale column tests. J. Am. Water Works Assoc. 83 (1), 77e87. Cussler, E.L., 2009. Diffusion: Mass Transfer in Fluid Systems, third ed. Cambridge University Press. Gnielinski, V., 1978. Gleichungen zur Berechnung des Wa¨rmeund Stoffaustausches in durchstro¨mten ruhenden Kugelschu¨ttungen bei mittleren und großen Pecletzahlen. Verfahrenstechnik 12 (6), 363e367. Graham, M.R., Summers, R.S., Simpson, M.R., MacLeod, B.W., 2000. Modeling equilibrium adsorption of 2-methylisoborneol and geosmin in natural waters. Water Res. 34 (8), 2291e2300. Hand, D.W., Crittenden, J.C., Arora, H., Miller, J.M., Lykins, B.W., 1989. Designing fixed-bed adsorbers to remove mixtures of organics. J. Am. Water Works Assoc. 81 (1), 67e77. Hayduk, W., Laudie, H., 1974. Prediction of diffusion coefficients for nonelecrolytes in dilute aqueous solutions. J. Amer. Inst. Chem. Eng. 20 (3), 611e615. Hong, S., 1995. Activated Carbon Adsorption of Organic Matter: Backwashing, Desorption and Attenuation. Ph.D. dissertation. University of Cincinnati, OH. Hong, S., Summers, R.S., 2006. Effect of backwashing on activated carbon adsorption using plug flow pore surface diffusion model. Korean J. Chem. Eng. 23 (1), 57e62. Hubel, R.E., Howe, E.W., Wilczak, A., Wolfe, T.A., Tambini, S.J., 1992. Integrated pilot testing pays off. J. Am. Water Works Assoc. 84 (8), 43e51. Hutzler, N.J., Crittenden, J.C., Gierke, J.S., Johnson, A.S., 1986. Transport of organic compounds with Saturated Groundwater flow: experimental results. Water Resour. Res. 22 (3), 285e295. Jarvie, M.E., Hand, D.W., Bhuvendralingam, S., Crittenden, J.C., Hokanson, D.R., 2005. Simulating the performance of fixedbed granular activated carbon adsorbers: removal of synthetic organic chemicals in the presence of background organic matter. Water Res. 39 (11), 2407e2421. Knappe, D.R.U., Matsui, Y., Snoeyink, V.L., Roche, P., Prados, M.J., Bourbigot, M.M., 1998. Predicting the capacity of powdered activated carbon for trace organic compounds in natural waters. Environ. Sci. Technol. 32 (11), 1694e1698. Knappe, D.R.U., Snoeyink, V.L., Roche, P., Prados, M.J., Bourbigot, M.M., 1999. Atrazine removal by preloaded GAC. J. Am. Water Works Assoc. 91 (10), 97e109. Magnuson, M.L., Speth, T.F., 2005. Quantitative structureproperty relationships for enhancing predictions of synthetic organic chemical removal from drinking water by granular activated carbon. Environ. Sci. Technol. 39 (19), 7706e7711. Matsui, Y., Yuasa, A., Ariga, K., 2001. Removal of a synthetic organic chemical by PAC-UF systems e I: theory and modeling. Water Res. 35 (2), 455e463. Sander, M., Pignatello, J.J., 2007. On the reversibility of sorption to black carbon: distinguishing true hysteresis from artificial hysteresis caused by dilution of a competing adsorbate. Environ. Sci. Technol. 41 (3), 843e849. Sontheimer, H., Crittenden, J.C., Summers, R.S., 1988. Activated Carbon for Water Treatment. DVGW-Forschungsstelle am Engler-Bunte Institut der Universitat Karlsruhe, Karlsruhe, Germany. Snoeyink, V.L., Summers, R.S., 1999. In: Letterman, R.D. (Ed.), “Adsorption of Organic Compounds” in Water Quality and Treatment: A Handbook of Community Water Supplies, fifth ed. American Water Works Assn./McGraw Hill, New York (Chapter 13). Speth, T.F., Miltner, R.J., 1990. Technical note: adsorption capacity of GAC for synthetic organics. J. Am. Water Works Assoc. 82 (2), 72e75.
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Summers, R.S., Roberts, P.V., 1988. Activated carbon adsorption of humic substances .1. heterodisperse mixtures and desorption. J. Colloid Interface Sci. 122 (2), 367e381. Summers, R.S., DiCarlo, D., Palepu, S. 1990. GAC adsorption in the presence of background organic matter: pretreatment approaches and attenuation of shock loadings. In. Proceeding of the AWWA Annual Conference, Cincinnati, OH. Thacker, W.E., Snoeyink, V.L., Crittenden, J.C., 1983. Desorption of compounds during operation of GAC adsorption systems. J. Am. Water Works Assoc. 75 (3), 144e149.
To, P.C., Marinas, B.J., Snoeyink, V.L., Ng, W.J., 2008a. Effect of pore-blocking background compounds on the kinetics of trace organic contaminant desorption from activated carbon. Environ. Sci. Technol. 42 (13), 4825e4830. To, P.C., Marinas, B.J., Snoeyink, V.L., Ng, W.J., 2008b. Effect of strongly competing background compounds on the kinetics of trace organic contaminant desorption from activated carbon. Environ. Sci. Technol. 42 (7), 2606e2611. Yuasa, A., 1982. A Kinetic Study of Activated Carbon Adsorption Processes. Doctoral Thesis. Hokkaido University, Sapporo, Japan.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 2 7 e4 3 8
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QMRA and decision-making: Are we handling measurement errors associated with pathogen concentration data correctly? P.J. Schmidt, M.B. Emelko* Department of Civil and Environmental Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada
article info
abstract
Article history:
Knowledge of the variability in pathogen or indicator concentrations over time at
Received 16 April 2010
a particular location (e.g. in drinking water sources) is essential in implementation of
Received in revised form
concentration-based regulations and in quantitative microbial risk assessment. Microbial
21 August 2010
enumeration methods, however, are known to yield highly variable counts (even among
Accepted 24 August 2010
replicates) and some methods are prone to substantial losses (i.e. only a fraction of the
Available online 28 September 2010
target microorganisms in a sample are observed). Consequently, estimated microorganism concentrations may be biased and only a fraction of the variability that is observed in
Keywords:
temporally distributed concentration estimates is due to variability in concentration itself.
Quantitative microbial risk
These issues have often been ignored in the past, and approaches to integrate knowledge
assessment (QMRA)
about the measurement error associated with enumeration methods into decisions have
Recovery
not been standardized. Here, an existing model that describes variability in microorganism
Uncertainty
counts as a function of sample volume and the analytical recovery of the enumeration
Water treatment
method is expanded to include temporal concentration variability and sample-specific recovery information. This model is used to demonstrate that microorganism counts and analytical recovery are not independent (as has often been assumed), even if the correlation is obscured by other sources of variability in the data. It is also used as an experimental design tool to evaluate strategies that may yield more accurate concentration estimates. Finally, the model is implemented in a Bayesian framework (with a Gibbs sampling algorithm) to quantify temporal concentration variability with appropriate consideration of measurement errors in the data and the analytical recovery of the enumeration method. We demonstrate by simulation that this statistical approach facilitates risk analyses that appropriately model variability in microorganism concentrations given the available data and that it enables decisions based on quantitative measures of uncertainty. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Concentrations of waterborne microorganisms (including pathogens and indicator organisms) and other uniform discrete particles cannot be measured directly. Consequently, concentration estimates are commonly obtained by
enumerating microorganisms in specified volumes of water. Variability among concentration estimates can arise from spatial or temporal variability in concentration or from measurement errors (i.e. differences between concentration estimates and the actual concentrations that they represent) (Emelko et al., 2010). Consequently, not all of the variation
* Corresponding author. Tel.: þ1 519 888 4567x32208; fax: þ1 519 888 4349. E-mail address:
[email protected] (M.B. Emelko). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.08.042
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among concentration estimates can be attributed to variability in concentration: some of this variability is due to measurement errors. Additionally, enumeration methods that are susceptible to losses yield biased concentration estimates unless counts are adjusted to reflect analytical recovery (Emelko et al., 2010). Analytical recovery is “the capacity of the analyst to successfully count each microorganism or particle of interest in a sample using a specific enumeration method” (Schmidt et al., 2010). Measurement errors are particularly well-known in the analysis of Cryptosporidium and Giardia enumeration data, but all of the presented issues also apply to other types of microorganisms and discrete particles unless the associated enumeration methods have no losses or counting errors (Emelko et al., 2010). Appropriate quantitative analysis of temporally distributed enumeration data must consider measurement errors. Knowledge of pathogen concentration variability in drinking, recreational, or irrigation waters is essential to appropriately quantify and mitigate human health risks. Decisions such as the level of treatment required to consistently ensure adequately safe drinking water depend on pathogen concentration data (Haas et al., 1996; Medema et al., 2003). For example, the U.S. Long Term 2 Enhanced Surface Water Treatment Rule (USEPA, 2006) required public water systems using surface water or ground water under direct influence of surface water and serving >10,000 people to monitor source water Cryptosporidium concentrations at least monthly for two years. Each system was then assigned to a treatment bin with prescribed Cryptosporidium treatment requirements according to its calculated average raw water concentration (without consideration of issues such as analytical recovery or infectivity of the observed oocysts). Bin misclassification error was an important concern in the design of the prescribed monitoring program because sufficient treatment must be provided without incurring the costs of unnecessary treatment enhancements (USEPA, 2006). Appropriate analysis of monitoring data to quantify bin misclassification error must account for the uncertainty in the concentration estimates due to measurement errors (Emelko et al., 2008) as well as the temporal variability of the raw water oocyst concentration. Methods to evaluate the viability and epidemiological significance of observed (oo)cysts are also being developed (e.g. Aboytes et al., 2004; Di Giovanni et al., 2009). Quantitative microbial risk assessment (QMRA) is a tool that is increasingly advocated for use in risk management decisions (e.g. Regli et al., 1991; Gale, 1996; Haas et al., 1999; Medema et al., 2003). If a dose-response model (e.g. Haas, 1983; Rose et al., 1991; Regli et al., 1991; Haas et al., 1996; Teunis and Havelaar, 1999) is available to estimate the probability of infection associated with a particular pathogen dose in drinking water, then concentration and consumption data are needed to quantify the distribution of possible doses. For Cryptosporidium and Giardia, it has been demonstrated that unfeasibly large sample volumes (e.g. 100,000 L) would be required to confirm the safety of treated drinking water (Regli et al., 1991; Rose et al., 1995; Haas et al., 1996). Moreover, monitoring treated drinking water using the presently available protozoan methods has questionable value because the methods are expensive and inefficient and yield unreliable
data (Allen et al., 2000; Signor and Ashbolt, 2006). Consequently, information about raw water pathogen concentration and treatment efficiency is typically incorporated into QMRA models to enable calculation of acceptable pathogen concentrations, determination of minimum treatment requirements, or quantification of risk. Monte Carlo simulation is an increasingly common approach to integrate variability (and/or uncertainty) in the parameters of QMRA models into risk characterization; each parameter is represented by a distribution of values rather than a point or interval estimate (Haas et al., 1993, 1999; Medema et al., 1995; Teunis et al., 1997; Gale, 1998; Teunis and Havelaar, 1999; Masago et al., 2002; Medema et al., 2003; Pouillot et al., 2004; Signor and Ashbolt, 2006; Smeets et al., 2007; Jaidi et al., 2009; Cummins et al., 2010). Appropriate QMRA model development requires accurate conceptual and quantitative distinction between “variability” and “uncertainty”. Variability corresponds to the changing nature of a variable that changes over time, over space, or among samples, or to variation among data (e.g. concentration varies over time, concentration estimates vary). Uncertainty represents an imperfect state of knowledge about a parameter or a model (e.g. whether a concentration estimate is accurate, or whether the model for temporal concentration variability correctly represents reality). Several types of uncertainty and variability must be considered in a Monte Carlo QMRA model (Haas et al., 1999). To correctly evaluate temporal variability among pathogen concentration data, these include (1) uncertainty in the concentration estimates, (2) temporal variability of the pathogen concentration, and (3) uncertainty in the choice of temporal concentration variability model and its estimated parameters. Concentration estimates are inherently uncertain, as demonstrated by variability among fully replicated data from a well-mixed source. This is particularly true for methods with imperfect and variable analytical recovery, such as those typically used for the enumeration of Cryptosporidium oocysts and Giardia cysts (USEPA, 2005). Analytical recovery is an important consideration when analyzing enumeration data because methods with highly variable recovery will yield more uncertain concentration estimates and because concentration estimates that are not appropriately adjusted to reflect analytical recovery are biased (Emelko et al., 2010). Analytical recovery, however, has commonly been ignored in the analysis of protozoan data. This may be because (1) losses in the method have not been considered or were assumed to be negligible (e.g. plating methods, most probable number methods, molecular methods), (2) recovery was acknowledged as an important factor but appropriate recovery data were not available (Rose et al., 1991), or (3) under-estimation of microbial concentrations due to incomplete recovery was assumed to counteract over-estimation of the abundance of infectious microorganisms (Regli et al., 1991; USEPA, 2006; Smeets et al., 2007). Recovery data have been obtained and subsequently incorporated into analysis of microorganism enumeration data using two general approaches. In the first approach, variability in recovery among samples can be described by a distribution that is estimated from the enumeration of replicate samples into which known amounts of microorganisms
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were added (i.e. a recovery experiment) (Schmidt et al., 2010). Recovery experiments are often conducted to validate or compare enumeration methods (e.g. McCuin and Clancy, 2003; USEPA, 2005) and, if adequately representative of the enumeration of environmental samples, can be used to make inferences about the recovery of indigenous microorganisms (Nahrstedt and Gimbel, 1996; Teunis and Havelaar, 1999; Pouillot et al., 2004; Schmidt et al., 2010). The second approach is to obtain sample-specific recovery estimates (e.g. by using internal seeding). Petterson et al. (2007) evaluated the use of sample-specific recovery estimates obtained by seeding uniquely labeled (oo)cysts into environmental samples and enumerating them concurrently with indigenous (oo)cysts. They also addressed the estimation of parameters for a distribution describing variability in recovery among samples (i.e. when internal seed recovery data were not available for all environmental samples), and demonstrated that failure to incorporate information about analytical recovery into analysis of enumeration-based concentration data can lead to biased results. Historically, many approaches to integrate analytical recovery information into the analysis of microorganism count data have (often implicitly) treated microorganism counts and analytical recovery as statistically independent values (e.g. Medema et al., 1995, 2003; Teunis et al., 1997; Pouillot et al., 2004; Signor and Ashbolt, 2006; Cummins et al., 2010; Teunis and Havelaar (2002)) or have used built-in features in commercially available Monte Carlo-based software to apply user-specified levels of correlation (Jaidi et al., 2009; Pre´vost and Barbeau, 2010). The number of microorganisms observed in a sample, however, is necessarily dependent upon analytical recovery because fewer microorganisms will be observed (on average) when recovery is lower. Accordingly, the first objective of this work is to incorporate temporal concentration variability into an existing probabilistic model that addresses random measurement errors and to demonstrate the dependence between microorganism counts and analytical recovery using data simulated in accordance with the model. Data simulated by Monte Carlo are also used to demonstrate the bias associated with failing to address analytical recovery in concentration estimates and to evaluate strategies that may enable collection of more accurate concentration estimates. The results of monitoring programs to assess temporal variability of pathogen concentrations are often summarized using distributions that are fitted directly to the numbers of observed microorganisms (e.g. Medema et al., 1995, 2003; Teunis et al., 1997) or to the concentration estimates (e.g. Gale, 1998; Jaidi et al., 2009). A fundamental limitation of fitting methods is that they do not account for the impacts of measurement error upon the variability of counts or concentration estimates (i.e. they do not quantitatively describe the uncertainty in concentrations estimates). They also typically do not account for dependence between microorganism counts and the analytical recovery of the method. Furthermore, routine analytical and experimental issues such as nondetect samples, variable sample volumes, variable recovery information (e.g. different enumeration methods), and replication pose significant difficulties when fitting distributions. Non-detect samples (i.e. samples yielding zero-counts) are
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especially problematic when log-transforming concentration estimates, calculating geometric means, or fitting distributions that do not allow values of zero to sets of concentration estimates. All enumeration-based concentration estimates (i.e. not just non-detects) are uncertain due to measurement errors. Emelko et al. (2010) developed hierarchical Bayesian models that can be used to quantitatively describe the uncertainty in a concentration estimate (as a distribution) given knowledge about measurement errors. These models enabled analysis of single or replicate data (including nondetects and samples with different volumes) and explicitly accounted for dependence between microorganism counts and analytical recovery. Bayesian approaches have also been used to analyze dose-response data (Teunis and Havelaar, 1999; Messner et al., 2001), to analyze Cryptosporidium and Giardia concentration data associated with the Information Collection Rule (Messner and Wolpert, 2002; Crainiceanu et al., 2003) and to analyze temporally distributed pathogen monitoring data used in QMRA (Teunis and Havelaar, 1999; Petterson et al., 2007) because they enable modelling of random errors and quantitative assessment of uncertainty. The second objective of this work is to present a Bayesian hierarchical model that accounts for temporal concentration variability and known measurement errors in microorganism enumeration data, and to present the associated Gibbs sampling algorithm as a simple and easily implemented approach to rigorously analyze temporally distributed microorganism enumeration data. Simulated data are used to compare the results obtained using the proposed statistical approach with results from various conventional approaches to analyze temporally distributed enumeration data.
2.
Model development
2.1.
Probabilistic modelling of random errors
Four types of random variability that contribute to the overall variability of microbial enumeration data are addressed herein. These are (1) temporal concentration variability, (2) random sampling error, (3) analytical error, and (4) nonconstant analytical recovery. Counting errors are implicitly included in analytical recovery. The ‘beta-Poisson’ model in Emelko et al. (2010) is expanded herein to include temporal concentration variability and is also modified to enable sample-specific recovery information (i.e. the associated parameters are not necessarily equal for all data). Temporal concentration variability concerns variations in microorganism concentration among sampling events at a particular location. It is assumed herein that the microorganism concentrations during repeated sampling events are independent and follow a gamma distribution. Continuous random variables that take only positive values and typically have positively skewed distributions can often be adequately described by gamma distributions. The gamma distribution is also mathematically convenient because it is the conjugate of the Poisson distribution. Random sampling error represents variations in the numbers of microorganisms captured in repeated samples from a homogeneous suspension. If the microorganisms are randomly distributed (e.g. not clumped)
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and the sample volume is small relative to the size of the source it represents, then random sampling error follows a Poisson distribution. Analytical error is the difference between the number of microorganisms observed in a sample and the number of microorganisms actually present in the sample because the enumeration method has imperfect analytical recovery. If each microorganism in a sample has an equal probability of being observed (i.e. analytical recovery), then the number of observations is binomially distributed. Non-constant analytical recovery describes random variations in recovery among samples and is modelled herein by a beta distribution. Approaches to demonstrate each type of random measurement error described above are discussed in the Supplementary Content. The four distributions described above are summarized by Equations (1)e(4), in which ci is the microorganism concentration during the ith sampling event, nik is the number of microorganisms in the kth replicate sample of the ith sampling event, xik is the number of microorganisms observed in sample ik, pik is the analytical recovery of sample ik, r,l are shape and scale parameters of the temporal concentration variability distribution, Vik is the volume of sample ik, and aik,bik are recovery distribution shape parameters for sample ik. The recovery distribution parameters (aik,bik) may be set equal for all samples unless more detailed information is available. ci wGAMMAðr; lÞ
(1)
nik wPOISSONðci Vik Þ
(2)
xik wBINOMIAL nik ; pik
(3)
pik wBETAðaik ; bik Þ
(4)
Collection of sample-specific recovery data in environmental samples using internal seeding (e.g. Petterson et al., 2007) can enable enhanced inference about analytical recovery (particularly if analytical recovery can vary substantially or non-randomly among samples). If the number of seeded microorganisms (nik*) is precisely known, and analytical recovery (pik) is regarded as the probability that each particle will be observed (and is assumed to be equal for seeded and indigenous microorganisms in any given sample), then the number of uniquely identifiable seeded microorganisms that are observed (xik*) is binomially distributed as shown in Equation (5). xik wBINOMIAL nik ; pik
(5)
Given values of the model parameters (e.g. r,l,{Vik},{aik},{bik}), this probabilistic model describes the variability in microbial counts (e.g. {xik}) due to temporal concentration variability and random measurement errors. In this model, any (or all) of the counts of indigenous microorganisms can be paired with internal seed recovery data (i.e. {xik*},{nik*}). If Equation (5) is used without Equation (4), then the analytical recovery values (pik) need to be supplied. The hierarchical structure of this probabilistic model is shown schematically in Fig. 1. The model is hierarchical because, for example, the random variable in the first level (i.e. concentration) becomes a parameter
in the second. Further discussion of the assumptions and limitations of this model is included in the Supplementary Content. This model is used as a framework to simulate temporally distributed enumeration data in Section 3, and as the basis for a Bayesian hierarchical model in Section 2.2.
2.2.
Evaluation of temporal concentration variability
The probabilistic model presented in Section 2.1 depends upon specified values of the temporal concentration variability distribution parameters (r,l) and sample volumes {Vik}, and parameters that provide information about analytical recovery ({aik,bik} or {pik}). It describes the joint probability of obtaining specific sampling event concentrations {ci} and numbers of indigenous microorganisms in samples {nik}, analytical recovery values {pik} (when unspecified), and counts of microorganisms per sample ({xik}, and {xik*} where applicable). In reality, {xik} (and {xik*} where applicable) are measured, {Vik} is known, and {aik,bik} and/or {nik*} are specified, and the objective is to estimate concentrations and the variability therein. In this context, r,l are the parameters of interest, and the intermediate parameters ({ci},{nik},{pik}) are nuisance parameters that are unknown and generally not of interest. If the number of sampling events (r) is at least two, then the number of measured microorganism counts and specified parameter values is greater than or equal to the number of unknown parameters and inference about the unknown parameters is possible. Evaluation of temporal concentration variability is accomplished herein using Bayes’ theorem. Uncertainty in the unknown parameters is quantitatively described by a posterior distribution that expresses what the analyst is entitled to believe about these parameters given the available data, the model, and specified prior information. Bayesian methods differ from classical methods because probability is regarded as a measure of the strength of belief given the available data and because subjective information (informative priors) can be quantitatively incorporated into the analysis. In Bayesian models, the posterior distribution is proportional to the product of a prior (which quantitatively describes the analyst’s beliefs about the parameter(s) of interest before data were collected) and the likelihood function (which describes the joint probability of obtaining the experimental data given assumed values of all parameters). It is not necessary to specify priors for nuisance parameters ({ci} and {nik}, as well as all pik for which beta distributions are provided) because the prior information is fully specified by the probabilistic model. Relatively uninformative uniform priors are used herein for demonstration purposes. The semi-infinite uniform priors for r,l on the parameter space r > 0, l > 0 are ‘improper’ because their respective integrals are infinite (i.e. they cannot be arranged in the form of a distribution). The likelihood function is obtained from the model presented in Section 2.1. Because uniform priors were used, the joint posterior distribution is directly proportional to the likelihood function. Equation (6) is the joint posterior distribution if all data have (potentially sample-specific) beta-distributed recovery. Equation (7) is the joint posterior distribution if all counts are paired with internal seed recovery data and uniform priors for {pik} are used (as opposed to beta distributions).
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Fig. 1 e Hierarchical model for temporally distributed microorganism enumeration data. To distinguish between parameters that are known and unknown, the unknown parameters are shown in boxes while the parameters that have known or assumed values (including the beta distribution parameters) and the count data are not. The internal seed approach can be used for any sample (including any or all of the replicates within a sampling event). No additional information about the probability of recovery (pik) in internal seed samples is shown; a specific value of pik (or a distribution describing its possible values) must be specified. (" # r
r i n x Y cr1 eci =l Y eci Vik ðci Vik Þnik nik ! x i f r; l; fci g; fnik g; pik fxik g; fVik g; faik g; fbik g f pikik 1 pik ik ik r l GðrÞ k¼1 nik ! xik !ðnik xik Þ! i¼1
) b 1 Gðaik þ bik Þ aik 1 p 1 pik ik Gðaik ÞGðbik Þ ik (" # r
r i Y n x cr1 eci =l Y eci Vik ðci Vik Þnik nik ! x i pikik 1 pik ik ik f r; l; fci g; fnik g; pik fxik g; xik ; fVik g; nik f r l GðrÞ k¼1 nik ! xik !ðnik xik Þ! i¼1
) n x n ! x ik pikik 1 pik ik ik xik ! nik xik !
(6)
In hierarchical models, “nuisance” parameters that are not of interest can sometimes be integrated out to yield explicit likelihood functions (or posterior distributions) of the parameters of interest (e.g. Teunis and Havelaar, 1999; Emelko et al., 2010). When integration is computationally intractable, Markov Chain Monte Carlo methods such as Gibbs sampling are typically used. Gibbs sampling is a Monte Carlo technique in which an initial value of each unknown parameter is supplied and an iterative loop is constructed in which a new value for each parameter is drawn from its respective conditional posterior distribution (using the updated values of all other parameters). This iterative process forms a Markov Chain of the vector of unknown parameters in the model. Assuming convergence, the Markov Chain of parameter values is collectively representative of the joint posterior distribution. The conditional posterior distributions associated with Equations (6) and (7) are represented by Equations (8)e(13) (which were algebraically simplified, where possible, into the form of widely recognized distributions). r Y r r ci (8) DfcðrÞfðGðrÞÞ lr i¼1
1
DfcðlÞflrr $el
P
r1þ
Dfcðci Þfci ci
P
ci
r 1X / ci wGAMMAðrr þ 1; 1Þ l i¼1
(7)
(9)
P 1 $eci ðlþ Vik Þ / !
nik
! ri ri X 1 X Vik wGAMMA r þ nik ; 1 þ l k¼1 k¼1
n ci Vik 1 pik ik / ðnik xik Þ! nik xik wPOISSON ci Vik 1 pik
ð10Þ
Dfcðnik Þf
ð11Þ
x þa 1 n x þb 1 Dfc pik fpikik ik 1 pik ik ik ik / pik wBETAðxik þ aik ; nik xik þ bik Þ x þx n þn x x Dfc pik fpikik ik 1 pik ik ik ik ik / pik wBETA xik þ xik þ 1; nik þ nik xik xik þ 1
ð12Þ
ð13Þ
Equation (12) corresponds to enumeration data for which beta distribution parameters (aik,bik) are provided. These beta distribution parameters are obtained from the results of an
432
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independently conducted recovery experiment and can be obtained using maximum likelihood estimation (e.g. Teunis et al., 1999; Schmidt et al., 2010). A complete description of uncertainty in these parameters, rather than use of point estimates as described herein, can be obtained using Bayesian methods (Petterson et al., 2007). Equation (13) corresponds to enumeration data with associated internal seed recovery data. Given the uniform prior 0 < pik < 1 assumed herein and the substitution aik ¼ xik* þ 1 and bik ¼ nik*exik* þ 1, Equation (13) is a special case of Equation (12). If a beta distribution with parameters aik*,bik* is used as an informative prior (rather than the uninformative prior presented herein), then Equation (13) is still a special case of Equation (12) in which aik ¼ xik* þ aik* and bik ¼ nik*exik* þ bik*. Consequently, the model represented by Equations (8)e(12) is adaptable to data sets in which some (or all) counts of indigenous microorganisms are paired with internal seed recovery data. The conditional density function for r (Equation (8)) cannot be arranged in the form of a distribution for which sampling algorithms are widely available. A simple form of acceptanceerejection sampling is used herein. Because Equation (8) is unimodal, it is easy to obtain an accurate estimate of the mode (rmax) and associated probability density ( fmax) using a greedy optimization algorithm (a simple local optimization algorithm that will reliably return the mode because there are
no local maxima). Upper and lower boundaries for r were then found at which the probability density was between ten and eleven orders of magnitude lower than fmax. The sampling algorithm consisted of drawing a uniform deviate on this interval (rtest), and accepting it if a uniform deviate on the interval (0,fmax) was less than f(rtest). This process was repeated until a test value was accepted. Standard uniform deviates and random samples from Equations (9)e(13) were obtained using published algorithms. The objective in the proposed Bayesian hierarchical model is to quantify temporal concentration variability with appropriate consideration of measurement errors in the data. The posterior predictive distribution (Gelman et al., 2004) of concentration is used for this purpose. It represents the distribution of concentration over time with consideration of the uncertainty in the temporal concentration variability distribution parameters (r,l). The Gibbs sampling algorithm that was used to obtain this distribution is summarized in Fig. 2. To ensure rapid convergence of the Markov chains, the following initial values were used for {pik} and {ci}: pik0 ¼ aik/ (aik þ bik) and ci0 ¼ Sxik/S(Vikpik0). If internal seed recovery data were used, then the conversion aik ¼ xik* þ 1, bik ¼ nik* xik* þ 1 was used. If the sum of the counts for the ith sampling event was zero (i.e. Sxik ¼ 0), then a value of 0.5 was used to calculate an initial concentration value. The supplied initial values of the gamma distribution parameters (r0,l0) were obtained using the method of moments with the initial concentration values ({ci0}). In the Gibbs sampling examples presented herein, a total of 31,000 iterations were completed, of which the first 1000 were burn-in iterations (iterations from which generated values are discarded when evaluating posterior distributions) and the final 30,000 were used to evaluate the posterior predictive distribution of concentration. This Gibbs sampling algorithm was implemented in Microsoft Excel using the Visual Basic Editor. As shown in Fig. 2, the algorithm can be modified to incorporate uncertainty in the beta distribution parameters (e.g. by inputting parameter pairs from a posterior distribution if recovery experiment data are analyzed using Bayesian methods as in Petterson et al., 2007). Discussion of how this modelling approach contrasts with similar published models (Teunis and Havelaar, 1999; Crainiceanu et al., 2003; Pouillot et al., 2004; Petterson et al., 2007) is included in the Supplementary Content.
3.
Results and discussion
3.1. Assessment of correlation between microorganism counts and analytical recovery
Fig. 2 e Gibbs sampling algorithm used to evaluate temporal concentration variability. The sequence begins with a burn-in and ends after a user-specified number of iterations. c* is a value from the sequence that represents the posterior predictive distribution for concentration. Regeneration of the beta distribution parameters obtained from independent recovery experiments (using separate Markov Chain Monte Carlo approaches) is not addressed herein.
The model described in Section 2.1 was used to simulate enumeration data (with 100 L sample volumes, associated internal seeding of 100 microorganisms per sample, and betadistributed variability in recovery among samples) given hypothetical parameter values (r,l and a,b). Three temporal concentration variability distributions were used (Fig. 3a). Each distribution has a mean of 10 microorganisms/L and the associated standard deviations are 10, 5, and 1 microorganisms/L. Two non-constant analytical recovery distributions were used (Fig. 3b): the first has a mean of 0.30 and a standard
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Fig. 3 e Demonstration of correlation between counts and analytical recovery. (a) temporal concentration variability distributions used, (b) non-constant analytical recovery distributions used, (c) 100 L samples with high temporal concentration variability, (d) 100 L samples with moderate temporal concentration variability, (e) 100 L samples with low temporal concentration variability, and (f) 5 L samples with low temporal concentration variability.
deviation of 0.138 and the second has a mean of 0.70 and a standard deviation of 0.046. For each of the six combinations of temporal concentration variability and non-constant analytical recovery distributions, 24 data were generated (Fig. 3cee). The simulations shown in Fig. 3e were repeated with 5 L sample volumes (Fig. 3f). To evaluate the correlation between microorganism counts and analytical recovery, the 24 simulated pairs of indigenous and seeded microorganism counts were plotted (Fig. 3cef). This approach was used, rather than evaluating correlation between counts of indigenous microorganisms and the actual probability of recovery, because the probability of recovery associated with samples is never precisely known. These
results show that the numbers of observed indigenous and seeded microorganisms can be strongly correlated in some cases (as indicated by a steep slope in the trend of the plotted data), and seemingly uncorrelated in others (Fig. 3). In general, the correlation between counts of indigenous and seeded microorganisms is reduced by greater temporal concentration variability, lower sample volumes, or less variable nonconstant analytical recovery distributions: the correlation (which reflects the impacts of variability in recovery upon the indigenous microorganism count data) is confounded by other sources of variability. Furthermore, counts of indigenous microorganisms will be more strongly correlated with the actual probability of recovery than with counts of internally
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seeded microorganisms (results not shown) because there is analytical error in these counts as illustrated by Equation (13). Petterson et al. (2007) analyzed a data set in which it was demonstrated that numbers of observed (oo)cysts in 10 L samples were uncorrelated with internal seed recovery estimates, but raised the possible explanation that “the influence of variation in recovery was small relative to the variation in underlying (oo)cyst concentration”. The simulations presented in Fig. 3 demonstrate that observed levels of correlation are case-specific and should not be applied from one data set to another (because changing any factor that affects variability in the data will change the correlation). Statistical dependence means that the distribution of possible values of one random variable is influenced by the value of another. Microorganism counts and analytical recovery are dependent because the number of observed microorganisms depends upon the probability that each microorganism in a sample will be observed (analytical recovery). Correlation between microorganism counts and analytical recovery is merely an indicator of this omnipresent dependence. Statistical analyses that treat microorganism counts and analytical recovery as independent will over-predict the variability in concentration by pairing extreme numbers of observed microorganisms with extreme values of analytical recovery (e.g. unrealistically high concentration estimates may be obtained by pairing relatively high counts with relatively low recovery values). Monte Carlo risk assessments that regard counts and analytical recovery as correlated random variables may resolve this problem, but correlation is case-specific as demonstrated above. The Bayesian methods presented herein explicitly account for the dependence between microorganism counts and analytical recovery without the need for case-specific correlation information. The beta distribution describing nonconstant analytical recovery (Equation (4)) or the internal seed recovery data (Equation (5)) essentially provide prior information about each sample’s analytical recovery, and this is updated in light of the probabilistic model and the available data to yield a posterior distribution for each recovery value (which describes the possible values of recovery that are supported by the data). Replication within sampling events as well as the temporal concentration variability distribution describe what the concentration might have been during each sampling event (given the available data and the prior for r,l), and this determines what recovery values might have lead to each microorganism count (as represented by a posterior distribution of recovery values). Quantitative analyses of microorganism data must evaluate what portion of the variability in concentration estimates is due to actual variability in concentration (as opposed to measurement error), not artificially inflate measurement errors that are already reflected in the data by treating microorganism counts and analytical recovery as independent variables.
3.2. Strategies to obtain more accurate concentration estimates The model presented herein attributes variability in temporally distributed enumeration data to temporal concentration variability or measurement errors. More accurate concentration estimates can be obtained by reducing the effect of
measurement errors upon the variability in enumeration data. Strategies may include increasing sample volumes, obtaining sample-specific recovery estimates, or improving analytical recovery (i.e. using a method with more consistent recovery that is closer to 100%). Monte Carlo simulations using the models presented in Section 2.1 can be used to determine if sufficient measurement error is expected in the enumeration data to warrant changes in a sampling program (e.g. to determine whether the added complexity of larger sample volumes and/or acquiring sample-specific recovery estimates through internal seeding is worthwhile). Three approaches to calculate concentrations estimates from enumeration data are compared in the following analyses: counts per unit volume (i.e. assuming that analytical recovery is 100%), counts per unit volume divided by mean recovery, and counts per unit volume divided by internal seed recovery estimates. For demonstration purposes, it is assumed in these scenarios that the mean recovery of the method (30%) is precisely known. In practice, the mean recovery of a method is estimated from an independently conducted recovery experiment, and its associated uncertainty depends on the quality and quantity of available data (Schmidt et al., 2010). The internal seed recovery estimate (x* þ 1)/(n* þ 2), which is not unbiased, is used herein because it excludes recovery estimates of 0% (which cannot be used to adjust concentration estimates) and 100%. Four scenarios are shown in Fig. 4, each based on 10,000 model iterations. Fig. 4a is a scenario (against which each other part of the figure can be compared) with relatively constant concentration, highly variable analytical recovery, and relatively small processed sample volumes. Fig. 4b shows a scenario in which the mean concentration is unchanged (10 microorganisms/L) but the standard deviation of concentration over time is increased from 1 to 10 microorganisms/L. In Fig. 4c, the sample volumes are increased from 10 L to 100 L. Fig. 4d shows a scenario in which the mean recovery is unchanged, but the standard deviation of recovery is reduced from 13.8% to 4.6%. Each of the concentration estimation approaches yields a stepped cumulative relative frequency graph because the estimates have discrete values (e.g. a number of observed microorganisms divided by volume and recovery). This figure clearly demonstrates that failure to account for analytical recovery in concentration estimates is inappropriate (although the bias becomes small as recovery approaches 100%). If non-constant analytical recovery contributes substantially to the variability in temporally distributed enumeration data, then more accurate concentration estimates may result from dividing the count per unit volume by an internal seed recovery estimate than by the mean analytical recovery of the enumeration method. This concept is exemplified in Fig. 4a and c because the cumulative relative frequency plot for the ‘IS Recovery’ concentration estimates is closer to the actual concentration distribution than the ‘Mean Recovery’ concentration estimates. In Fig. 4b, the effect of non-constant analytical recovery upon the variability in concentration estimates is reduced because the concentration is more variable and the ‘IS Recovery’ concentration estimates are only marginally better than the ‘Mean Recovery’ concentration estimates. In Fig. 4d, the cumulative relative frequency plots of these two types of concentration estimates coincide because the variability in
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Fig. 4 e Demonstration of the variability and potential bias in concentration estimates (a) base scenario (against which other panels are compared) [c w GAMMA(100,0.1), p w BETA(3,7), V [ 10 L, n* [ 100], (b) scenario with more variable concentration [c w GAMMA(1,10), p w BETA(3,7), V [ 100 L, n* [ 100], (c) scenario with larger sample volume [c w GAMMA(1,10), p w BETA (3,7), V [ 100 L, n* [ 100], (d) scenario with less variable recovery [c w GAMMA(100,0.1), p w BETA(30,70), V [ 10 L, n* [ 100].
analytical recovery was reduced: the added complexity of obtaining internal seed recovery estimates yields negligible benefit in this scenario. Even if sample-specific recovery estimates do not yield more accurate concentration estimates, internal seeding of a random selection of samples may be a more convenient and representative approach to evaluate recovery than conducting a fully independent recovery experiment. Increasing sample volumes (where possible) can reduce measurement error because higher counts yield more precise concentration estimates. For example, 100 microorganisms in a 100 L sample is more suggestive of a concentration near 1 microorganism/L than 1 microorganism in a 1 L sample: when the count is small, a small random difference in the number of observed microorganisms has a larger impact on the concentration estimate. Increasing sample volumes from 10 L in Fig. 4a to 100 L in Fig. 4c only slightly decreased the variability in the concentration estimates. In this scenario, the added complexity of using 100 L samples (relative to 10 L samples) may not yield substantially better data. In other similar experiments, it has generally been found that increasing the sample volume is most beneficial when counts near zero are routinely observed. Emelko et al. (2008) demonstrated the value of choosing sample volumes so that at least 10 microorganisms were typically counted (where possible). Improving the mean recovery of enumeration methods (where possible) slightly reduces measurement error (because higher counts correspond to less uncertain concentration estimates) and improves sensitivity (i.e. reduces the frequency of false-negative samples). It also reduces the bias
associated with ignoring analytical recovery in enumeration data analysis. Measurement error can also be reduced by decreasing the variability in recovery among samples, most appreciably when non-constant analytical recovery is the primary cause of variability in the data. More accurate recovery-adjusted concentration estimates are obtained in Fig. 4d (relative to Fig. 4a) because of the decreased variability in recovery.
3.3. data
Analysis of temporally distributed concentration
The Gibbs sampling approach presented in Section 2.2 can be used to quantify temporal concentration variability with appropriate consideration of the measurement errors in the data. Simulated concentration data for 24 sampling events, each with a single 100 L sample containing an internal seed dose of 100 microorganisms, (Table S1 of the Supplementary Content) are analyzed using the Gibbs sampling approach and two alternative approaches. The analytical recovery was set to vary among samples according to a beta distribution, (a,b) ¼ (2,3), representing typical analytical recovery of Cryptosporidium oocysts in surface water by Method 1623 (Jaidi et al., 2009). The concentration (in oocysts/L) was set to vary according to a gamma distribution with parameters (r,l) ¼ (0.22,0.36) because the resulting model yielded counts with similar summary statistics to those presented for temporally distributed 100 L oocyst samples from a surface water source (Jaidi et al., 2009). These data include 16 non-
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detect samples, with a mean and standard deviation of 3.0 and 8.7 observed oocysts/100 L respectively. For comparison, the generated data were analyzed using the Gibbs sampling approach in two different ways: assuming beta-distributed non-constant analytical recovery (with beta distribution parameters precisely known and assuming that no internal seed recovery data were collected) and using the simulated internal seed recovery data (with uniform priors for analytical recovery). The Markov chains demonstrated rapid mixing and good convergence with 30,000 iterations following a 1000-iteration burn-in (results not shown). The resulting posterior predictive concentration distributions represent what the analyst is entitled to believe about the variability in concentration given the model and available data, and account for the measurement errors in the enumeration data as well as the uncertainty in the parameters of the temporal concentration variability model (r,l). Two bootstrapping approaches (‘with zeros’ and ‘no zeros’) were also used to compare the Gibbs sampling results with conventional approaches that assume that microorganism counts and analytical recovery are independent. For each bootstrapping iteration, one of the counts (xi) was drawn at random and then divided by its processed sample volume and a random value of analytical recovery (drawn from the beta distribution) to yield a concentration estimate. In the ‘no zeros’ approach, non-detect samples were assigned a count of 0.5 as an example of analyses in which non-detect samples are manipulated into non-zero values. Fig. 5 demonstrates that bootstrapping approaches (or similar Monte Carlo approaches) that treat microorganism counts and analytical recovery as independent values cannot correctly handle abundant non-detect data. The ‘with zeros’ bootstrapping approach often underestimates concentration because non-detect samples are treated as concentration estimates of 0 microorganisms/L (when possibly present oocysts may have been missed due to random sampling error or analytical error). Conversely, the ‘no zeros’ approach often overestimates concentration because it artificially inflates the values of non-detect samples. In contrast, either Bayesian approach is demonstrated to be more appropriate, because the posterior predictive distributions are close to the true concentration distribution that was used to generate the data. In general, the accuracy of posterior predictive distributions
obtained from the proposed model (assuming that the model is correct and that the chosen priors are not too restrictive), depends on the number and quality of available data (i.e. very inaccurate data contain very little information). In risk assessment, it is the high pathogen concentrations in the upper tail of the distribution that are most important (because larger doses pose a higher chance of infection). 75th, 90th, 95th, 99th, and 99.9th percentile oocyst concentrations for the five distributions presented in Fig. 5 are shown in Fig. 6 (and Table S2 of the Supplementary Content). These percentiles show that the posterior predictive distributions are in relatively good agreement with the true concentration distribution. The 95th, 99th, and 99.9th percentiles of the two bootstrapping approaches, however, are substantially higher than the same percentiles of the other distributions. This is because approaches that treat microorganism counts and analytical recovery as independent random variables will yield unrealistically high concentration values when high counts are paired with low recoveries.
4.
Implications
Random measurement errors in enumeration-based concentration estimates can be substantial, particularly when methods with low or highly variable analytical recovery are used. Problems associated with measurement errors include poor sensitivity (i.e. non-detect samples obtained when microorganisms are present in the source) and bias. Concentration estimates may be systematically lower than actual concentrations (due to poor analytical recovery) and substantially more variable than actual concentrations (due to measurement error). Standardized methods are often regarded as “good enough” and the associated data are used without consideration of measurement error (particularly if it is assumed that one unquantified measurement error counteracts another). It is always important to consider how accurate concentration estimates are and how much of the variability among non-replicate concentration estimates is real (i.e. not just measurement error). Accordingly, decisions and quantitative analyses based upon microbial enumeration data must appropriately consider measurement errors.
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Fig. 5 e Temporal variability distributions obtained using various modelling approaches.
Fig. 6 e Oocyst concentration percentiles obtained with various data analysis approaches.
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More robust risk analysis results and decisions can be obtained by correctly considering measurement errors in microbial enumeration data. In particular, the analytical recovery of enumeration methods must be addressed (unless it is consistently close to 100%) because concentration estimates based only upon numbers of observed microorganisms per unit volume are uncalibrated. These concentration estimates have limited relevance when they are systematically different from the underlying actual concentrations that matter for decision-making. Accordingly, infectivity (the fraction of enumerated microorganisms that are viable and of epidemiologically relevant species/genotype) is also an important consideration. Quantification of the infectivity of observed microorganisms (and the error therein) has not yet been fully addressed and requires continued research and consideration by laboratories, modelers, and regulators. Meanwhile, analytical recovery information should be incorporated into drinking water regulations so that the concentration metric that is used to evaluate compliance (or the need for mandatory mitigation) is equally stringent for all regulated water systems. For example, in the LT2ESWTR (USEPA, 2006), utilities that obtain higher analytical recovery (due to raw water quality, laboratory, or method used) are more likely to observe any (oo)cysts that are present and to incur mandatory treatment enhancement, while utilities that obtain lower analytical recovery are less likely to observe present (oo)cysts and may accordingly under-predict health risks (Emelko et al., 2008). In effect, failing to account for recovery may indirectly promote use of poor methodology by penalizing those who typically obtain higher recovery (i.e. observe a higher fraction of the parasites that are present). In a similar example, it has been demonstrated that coliform sampling results can be manipulated by strategically selecting inferior (though approved) methodology or by sampling out (collecting samples until a favorable result is obtained) (Olstadt et al., 2007; Bennear et al., 2009). It has been shown herein that data analyses that regard microorganism counts and analytical recovery as independent are biased (e.g. they may predict unrealistically high concentration values). Consequently, standardized approaches to quantitatively analyze microorganism enumeration data are needed. Statistics should not be regarded as a tool to manipulate data, but as a framework to develop informed decisions and quantitative conclusions under ever-present uncertainty. Accordingly, the probabilistic models presented herein address the impacts of various random measurement errors upon temporally distributed enumeration data and provide a framework to evaluate complicated data sets (e.g. with replication, differing sample volumes, non-detect data, and potentially sample-specific recovery information). Many decisions can be made directly from the posterior predictive distribution for concentration that is obtained herein: for example, the proportion of the time that the microorganism concentration is expected to be above a specific threshold. Monte Carlo QMRA can be enhanced by using the posterior predictive concentration distribution because it quantifies temporal concentration variability (and the uncertainty therein) while addressing measurement errors in the data and appropriately incorporating information about analytical recovery. QMRA must also correctly account for measurement
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errors and variability in the other parameters of the model (including dose-response) to correctly characterize variability and uncertainty in calculated risks.
5.
Conclusions
Microorganism counts and analytical recovery are not independent, even if their correlation is obscured by other sources of variability in the data. Enumeration-based concentration estimates can be much more variable than the underlying concentration distribution due to measurement errors (particularly when small processed sample volumes and imperfect analytical recovery routinely result in near-zero counts, recovery is highly variable, and/or variability in concentration among sampling events is low). Monte Carlo simulation of microorganism enumeration data can be used to evaluate monitoring programs and to determine if more accurate concentration estimates can be obtained with the available resources or, alternatively, if smaller sample volumes and/or less recovery data are sufficient. Routine analysis of enumeration data, as well as concentration-based regulations and quantitative microbial risk assessment, must correctly account for the analytical recovery of microorganism enumeration methods to avoid bias. Modelling approaches that account for measurement errors in enumeration data (and that are adaptable to replication, differing sample volumes, non-detect samples, and potentially sample-specific recovery information) are an essential tool to correctly interpret microorganism concentration data for decision-making or risk quantification purposes.
Acknowledgements We thank Dr. Park M. Reilly (Chemical Engineering, University of Waterloo) for his assistance, and the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canadian Water Network for funding.
Appendix. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.watres.2010.08.042.
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[email protected]. Regli, S., Rose, J.B., Haas, C.N., Gerba, C.P., 1991. Modeling the risk from Giardia and viruses in drinking water. J. AWWA 83 (9), 76e84. Rose, J.B., Haas, C.N., Regli, S., 1991. Risk assessment and control of waterborne giardiasis. Am. J. Public Health 81 (6), 709e713. Rose, J.B., Lisle, J.T., Haas, C.N., 1995. Risk assessment methods for Cryptosporidium and Giardia in contaminated water. In: Betts, W.B., Casemore, D., Fricker, C., Smith, H., Watkins, J. (Eds.), Protozoa Parasites and Water. The Royal Society of Chemistry, pp. 238e242. Schmidt, P.J., Emelko, M.B., Reilly, P.M., 2010. Quantification of analytical recovery in particle and microorganism enumeration methods. Environ. Sci. Technol. 44 (5), 1705e1712. Signor, R.S., Ashbolt, N.J., 2006. Pathogen monitoring offers questionable protection against drinking-water risks: a QMRA (quantitative microbial risk analysis) approach to assess management strategies. Water Sci. Technol. 54 (3), 261e268. Smeets, P.W.M.H., van Dijk, J.C., Stanfield, G., Rietveld, L.C., Medema, G.J., 2007. How can the UK statutory Cryptosporidium monitoring be used for quantitative risk assessment of Cryptosporidium in drinking water? J. Water Health 5 (S1), 107e118. Teunis, P.F.M., Medema, G.J., Kruidenier, L., Havelaar, A.H., 1997. Assessment of the risk of infection by Cryptosporidium or Giardia in drinking water from a surface water source. Water Res. 31 (6), 1333e1346. Teunis, P.F.M., Evers, E.G., Slob, W., 1999. Analysis of variable fractions resulting from microbial counts. Quant. Microbiol. 1 (1), 63e88. Teunis, P.F.M., Havelaar, A.H., 1999. Cryptosporidium in Drinking Water: Evaluation of the ILSIIRSI Quantitative Risk Assessment Framework. RIVM Report no. 284 550 006. RIVM, Bilthoven, The Netherlands. Teunis, P.F.M., Havelaar, A.H., 2002. Risk assessment for protozoan parasites. Int. Biodeter. Biodegr. 50 (3e4), 185e193. USEPA, 2005. Method 1623: Cryptosporidium and Giardia in Water by Filtration/IMS/FA. EPA 815-R-05-002. U.S. Environmental Protection Agency, Office of Water, Washington, DC. USEPA, 2006. Long term 2 enhanced surface water treatment rule; final rule. Fed. Regist. 71 (3), 654e786.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 3 9 e4 5 2
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Removal of bacterial fecal indicators, coliphages and enteric adenoviruses from waters with high fecal pollution by slow sand filtration Rosalie Bauer a, Halim Dizer b, Ingeborg Graeber a, Karl-Heinz Rosenwinkel c, Juan M. Lo´pez-Pila a,* a
Umweltbundesamt (Federal Environmental Agency), Corrensplatz 1, 14195 Berlin, Germany Helios Klinikum Berlin Buch, Clinic for Physical Medicine and Early Rehabilitation, Schwanebecker Chaussee 50, 13125 Berlin, Germany c Institute for Water Quality and Waste Management, Leibniz University of Hannover, Welfengarten 1, 30167 Hannover, Germany b
article info
abstract
Article history:
The aim of the present study was to estimate the performance of slow sand filtration (SSF)
Received 22 January 2010
facilities, including the time needed for reaching stabilization (maturation), operated with
Received in revised form
surface water bearing high fecal contamination, representing realistic conditions of rivers
3 August 2010
in many emerging countries. Surface water spiked with wastewater was infiltrated at
Accepted 25 August 2010
different pore water velocities (PWV) and samples were collected at different migration
Available online 28 September 2010
distances. The samples were analyzed for phages and to a lesser extent for fecal bacteria and enteric adenoviruses. At the PWV of 50 cm/d, at which somatic phages showed highest
Keywords:
removal, their mean log10 removal after 90 cm migration was 3.2. No substantial differ-
Slow sand filtration
ences of removal rates were observed at PWVs between 100 and 900 cm/d (2.3 log10 mean
River bank filtration
removal). The log10 mean removal of somatic phages was less than the observed for fecal
Coliphages
bacteria and tended more towards that of enteric adenoviruses This makes somatic phages
Enteric adenoviruses
a potentially better process indicator than Escherichia coli for the removal of viruses in SSF. We conclude that SSF, and by inference in larger scale river bank filtration (RBF), is an excellent option as a component in multi-barrier systems for drinking water treatment also in areas where the sources of raw water are considerably fecally polluted, as often found in many emerging countries. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Worldwide contamination of surface waters with enteric viruses is of considerable health concern, since insufficient removal of these pathogens during drinking water treatment might lead to viral infection and gastroenteritis (WHO, 2006a; Ashbolt, 2004; Van-Heerden et al., 2004; Glass et al., 2001). In emerging countries the problem of gastroenteritis poses a burden particularly on the very young, since the
concomitant diarrhea causes acute loss of water which, untreated, might quickly derange in life-threatening illness. There the production of healthy drinking water poses a bigger problem than elsewhere: as surface waters often receive untreated or poorly treated wastewater, the concentration of viral and other pathogens might be particularly high, creating an extraordinary challenge for drinking water treatment (Moe and Rheingans, 2006; Ashbolt, 2004; Gadgil, 1998). This situation will probably get worse, taking
* Corresponding author. Tel.: þ49 30 7978 947; fax: þ49 30 8903 1822. E-mail address:
[email protected] (J.M. Lo´pez-Pila). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.08.047
into account the water shortages expected in the future due to climate change, population increase, and to the water demand associated with industrial development. Soil filtration systems, as a rule are very efficient and provide water of very good microbiological quality (Ellis, 1985). Because of the capacity to reduce a variety of pathogens (Hijnen et al., 2004) and the low costs and easy operation, slow sand filtration (SSF) and river bank filtration (RBF), a water treatment procedure using a similar principle (Ray et al., 2003), seem especially well suited for the drinking water treatment in emerging countries (Grunert et al., 2008; Shamrukh and Abdel-Wahab, 2008) including small communities. A large number of studies on the removal of viruses through soil filtration have been conducted in the past (for reviews see Schijven, 2001, and Jin and Flury, 2002). Studies with fecally polluted waters are of special interest, as many surface waters in emerging countries are highly polluted and the composition of the infiltrate greatly influences the filtration performance of sand filters (Schijven and Hassanizadeh, 2000; Ray et al., 2002; Goldschneider et al., 2007). A high organic load or surfactants might increase the breakthrough of pathogens (Schijven, 2001). The results mentioned in the above reviews indicate that soil filtration can very efficiently reduce bacteriophages and viruses from fecally polluted waters under very different working conditions. The studies do not allow, however, general conclusions regarding maturation time, PWV, and filtration distance. In order to make an estimate of the removal of viruses in plants fed with highly polluted surface water, we have infiltrated wastewater-spiked surface water in two facilities, which represent a medium scale between laboratory experiments and applied sand filtration in the environment. Here we have studied the removal of somatic phages, K13-phages, enteric adenoviruses, and, in one of the test facilities, fecal bacteria. The dosage of wastewater was adjusted and was chosen in such a way as to reach a somatic phages concentration of approximately 104/100 ml, as this was a concentration observed in very polluted river waters (Grunert et al., 2008). The study of the influence of the PWV on the removal of microorganisms in RBF is hampered by the difficulty of adjusting the PWV under different field conditions. We have therefore studied such influence in an SSF-facility (Fig. 1A and B) that incorporates some properties of RBF (even if not all), including also partial lateral infiltration and tangential flow of the surface water. This seemed important, as especially the tangential flow of the water might influence the formation of the schmutzdecke, known to play an important role in the removal of microorganisms (Ellis, 1985). Other purposes of this study were to estimate the time such plants need to reach stability (maturation), and the influence of the (PWV) on the removal efficiency for the microorganisms studied. Because of the similar size and structure of coliphages and many human viral pathogens (Grabow, 2001), they are frequently used as process indicators in sand filtration processes (Schijven and Hassanizadeh, 2000). In order to evaluate the indicator function of phages, we have compared the removal of somatic phages, K13-phages, and enteric adenoviruses during SSF. Human enteric adenoviruses, worldwide responsible for a number of gastroenteric diseases (WHO, 2006a), are shed in high numbers with the feces and are
Fig. 1 e Test facilities for simulating SSF and some hydraulic aspects of RBF. A: Test facility used for conducting the experiments shown in Figs. 2 and 3. B: Test facilities for simulating SSF and some hydraulic aspects of RBF. Delta conductivity (real conductivity minus 950 mSi/ cm, the basis conductivity of the infiltrated water) at the different sampling points after infiltrating water with a conductivity of ca. 1,200 mS. The numbers in the right corner of the figure represent the depth of the sampling points in cm. The calculated values of the PWV at 30, 60, 90, 120 and 150 cm depth were respectively: 226.2 (68.5%); 238.0 (72.1%); 206 (62.6%); 129.1 (39.1%); and 89.4 (27.1%) cm/d. The values in parenthesis represent the PWV as % of the adjusted theoretical value of 330 cm/d. The sharp slender peak represents the salinity at the outlet of the drain. C: Test facility used for experiment of Table 2.
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found in relative high numbers in polluted waters (Grunert et al., 2008). Unpublished results of the European research project Virobathe, (Anonymous, 2010) revealed that the concentration on Escherichia coli in bathing waters significantly correlated with the number of samples positive for human enteric adenoviruses, suggesting that adenoviruses are reasonable indicators of human fecal pollution. The development of inexpensive and manageable concentration methods for viruses made this goal come closer (Calgua et al., 2008).
2.
Materials and methods
2.1. Test facilities for SSF and for simulating some features of RBF The sand chosen for filling the filters of Fig. 1A and C was free of clay material. It had a D10 coefficient of 0.269, i.e. 10% of the grains had a diameter below and 90% above 0.269 cm. The ratio between the D60 and D10 was 2.42; the Kf-value, a measure for the water permeability, was 1 103 m s1. The porosity was 0.32. Therefore, the pore water velocity, i.e. the water velocity within the filter body, was equal to the velocity above the filter divided by 0.32. All these values are typical of coarse sand. The unit shown in Fig. 1A, in which one part of the experiments was carried out, consists of a pond (3.35 m 6.85 m area) filled with sand to a height of 1.80 m above a 20 cm layer of gravel. It is divided in three sections; a section of 2.55 m height, 1.80 m length, 3.35 m width, and a horizontal surface of 6.03 m2; a second section with a slanted surface, a height between 2.55 and 1.30 m, a length of 325 m, and a width of 3.35 m; its surface was 11.67 m2 in the slanted part and 10.89 m2 in its horizontal projection (footprint); a third section of 1.30 m height, 1.80 m length, 3.35 m width, and a surface of 6.03 m2. The water was pumped into the top of the pond through an inlet (inflow) and the level of water was kept constant at approximately 30 cm above the highest part of the sand surface by means of an overflow situated at the opposite side of the inflow. The pore water velocity was regulated with a pump (not shown) interconnected with the drain pipe conducting the filtrate (backflow) towards a reservoir (not shown). In this reservoir, in addition to this backflow, surface water from a groundwater lake was pooled with 1% raw wastewater from a wastewater treatment plant and the overflow from the filtration pond. The dimensions of this reservoir were identical to that of the filtration pond and allowed a good mixing of surface water with wastewater before feeding the water to the filtration pond by a submerged pump. Five sampling pipes at different depths (30 cm, 60 cm, 90 cm, 120 cm, 150 cm), and a distance of 0.5 m from one of the small sides of the facility, carrying steel faucets at their opposite ends outside the filtration pond, allowed sampling of the water. Sampling was performed according to DIN EN ISO 19458 (2006). The pipe draining the water out of the filter was embedded in the gravel in the basis of the pond. The test facility was designed to reproduce some hydrogeological aspects of RBF. The filtration flow should have a vertical as well as a horizontal component and the flowing water should be kept in a constant current tangential to the
441
filter surface. To achieve this, the shape of the filter was designed as shown in Fig. 1A, with an upper and a lower horizontal filter surface and a transitional slanted surface connecting both. Consequently, the filter resistance in the upper horizontal part, being higher than in the lower part, allowed a filtration rate smaller than in the lower surface, the filtration rate in the slanted part being intermediate. The thickness of the arrows in Fig. 1A attempts to symbolize the strength of the infiltration rate entering the different parts of the facility. This situation generated a slight current of the supernatant along the slanted part and kept the water in gentle movement without having to resort to a stirring device which would have also stirred the sand, compromised the results in the superficially located sampling pipes, and possibly disturbed the formation of the schmutzdecke. Obviously, this construction did not generate a homogeneous, easy to calculate piston-like water infiltration into the filter surface. In order to estimate the true, effective PWV (PWVeff) above the different sampling pipes a tracer experiment was carried out. Sufficient NaCl was added to the water in the water reservoir feeding the test facility to raise the conductivity in its water to approximately 1200 mS/cm, and the salinity was distributed uniformly by a stirring pump. The inflow pump to the test facility was started and the drain valve opened to allow a water velocity of 100 cm/d above the filter, corresponding to a theoretical PWV over the horizontal projection of the filter surface of 320 cm/d (accounting for the porosity of 0.32 of the sand). Every 15 min samples of all sampling pipes were taken and their conductivity measured. Fig. 1B shows the results. The PWVeff above the sampling pipes was calculated from the time period at which 50% of the maximal conductivity was attained. The calculated values at 30, 60, 90, 120 and 150 cm depth were respectively: 226.2 (68.5%); 238.0 (72.1%); 206 (62.6%); 129.1 (39.1%); and 89.4 (27.1%) cm/d. The values in parenthesis represent the PWV as % of the adjusted theoretical value of 330 cm/d determined by the flow of the drain. As expected, the PWVeff above all sampling pipes were lower than the calculated for the horizontal projection of the filter surface. Consistent with these findings, the PWVeff at the drain level, strongly influenced by the filtration rate in the lower horizontal part, was 360 cm/d, higher than the theoretical PWV (sharp peak in the Fig. 1B). The PWVeff of the sampling sites at 30, 60 and 90 cm were similar, averaging 67.8% of the overall PWV, but at the two deeper sites at 120 and 150 cm the PWVeff were much lower. The second filtration unit was slightly conical in section (Fig. 1C). This unit allowed sampling only at 90 cm height. Its surface was 90 m2 at the top and 49 m2 at the bottom of the sand. Surface water was mixed with 1% wastewater in the mixing pond of 6 m3 volume (Fig. 1C), where it was continuously homogenized with a rotating pump. The water flowed by gravity through a series of weirs towards the filter surface, where it ponded up to approximately 20 cm height. From here, the water infiltrated vertically into the filter body. The bottom of the filter consisted of a layer of 20 cm gravel in which a net of perforated pipes was embedded to collect the filtrate in a main drain and to carry it further towards the sewer system. A faucet in the main drain allowed sampling under controlled conditions. The PWV was controlled with a pump at the outlet of the main drain.
442
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2.2. Estimation of the arithmetic mean values of the microorganism concentrations
DIN EN ISO 7899-2 (2000), which is based on MPN with 96 wells plates. The detection limit of the first method was 7, of the second one 1 cfu/100 ml. For the analysis of the somatic coliphages the USEPA method 1602 (2001), with the E. coli strain WG5 (ATCC700078) as indicator strain was used. The WG5 strain has a high sensitivity for somatic phages and is highly suitable for analyzing wastewater, since its resistance against the antibiotic nalidixic acid allows the suppression of most bacteria present in wastewater (Havelaar and Hogeboom, 1983). Instead of the agar formula stated in USPA1602, the double concentrated modified Scholtens-SoftAgar (DIN EN ISO 10705-2, 2002) was mixed with the sample before plating, as we had observed that the last formula enhanced sensitivity for detecting phages. For detecting the K13-phages, the same procedure was used, except that the E. coli strain K13 was the indicator. This strain is less sensitive for somatic phages than the ATCC700078, but has some sensitivity to Fþ phages (unpublished results of HD and JMLP). We chose the K13 strain because the broader spectrum of phages detected
Many samples at the 90 and 120 cm depths resulted in nondetects. Using the simplifying assumption that the actual phage concentrations were constant over time at each PWV, arithmetic means encompassing the entire experiment were calculated by dividing all recorded PFU or CFU for each particular PWV and depth by the total volume taken for the assays. The values obtained were expressed in the tables as PFU (or CFU)/100 ml. In the tables, the initial phage concentration above the filters is expressed as Co, the remaining concentration after filtration as C.
2.3.
Detection of E. coli, IE, and coliphages
E. coli (EC) was analyzed according to DIN EN ISO 9308-3 (1998). The detection limit was 7 MPN/100 ml. The detection of intestinal enterococci (IE) was carried out according to DIN EN ISO 7899-1 (1999), which is based on membrane filtration, or 1,E+05
Somatic Phages 1,E+04
pfu/100 ml
1,E+03
1,E+02
1,E+01
1,E+00 50 cm/day
50 cm/day
100 cm/day
200 cm/day
400 cm/day
1,E-01 1,E+05 K13-Phages 1,E+04
pfu/100 ml
1,E+03
1,E+02
1,E+01
1,E+00
1,E-01 0 5 Days
10
15
20
25
30
35
40
45
50
55
60
65
70
Fig. 2 e Removal of somatic and K13-coliphages, during sand filtration at pore water velocities between 50 and 400 cm/d and at different filtration depths. A mixture of surface water and wastewater was infiltrated in the facility of Fig. 1A; water samples were analyzed from inflow (A) and after migration distances of 30 cm (-), 60 cm (:), and 90 cm (3). The concentrations of the microorganisms are shown at various migration times. Table 1 is a companion to this figure. The detection limit was 1 pfu/100 ml (lowest gridline in the figure); symbols below this line represent values below detection limit. The experiment was conducted from October 11th till December, 17th 2004; the temperature of the infiltrate during this time was between 4 and 9 C.
Table 1 e Arithmetic mean values (C [ organisms/100 ml), removal (Llog(C/Co)), and removala rate (RR) at the different PWVs and depths, of the microorganisms in the experiment of Fig. 2. PWV
Arithmetic Means and Removal at 50 cm/day (day 1e10)
Arithmetic Means and Removal at 50 cm/day (day 11e24)
Arithmetic Means and Removal at 100 cm/day (day 25e37)
Arithmetic Means and Removal at 200 cm/day (day 38e59)
Arithmetic Means and Removal at 400 cm/day (day 60e67)
C
log(C/Co)
RR
C
log(C/Co)
RR
C
log(C/Co)
RR
C
log(C/Co)
RR
C
log(C/Co)
RR
0 30 60 90 0 30 60 90
24,471 2.275 613 218 3510 656 48 11
0.00 1.03 1.60 2.05 0.00 0.73 1.86 2.50
e 3.44 1.90 1.50 e 2.43 3.79 2.13
9320 262 87 6 1760 36 12 1
0.00 1.55 2.03 3.19 0.00 1.69 2.17 3.25
e 5.17 1.60 3.87 e 5.63 1.59 3.60
8355 720 336 47 3716 205 46 2
0.00 1.06 1.40 2.25 0.00 1.26 1.91 3.27
e 3.55 1.10 2.85 e 4.19 2.16 4.54
4530 314 146 20 1557 35 16 1
0.00 1.16 1.49 2.36 0.00 1.65 1.99 3.19
e 3.86 1.11 2.88 e 5.49 1.13 4.01
5520 212 149 23 1150 28 17 1
0.00 1.42 1.57 2.38 0.00 1.61 1.83 3.06
e 4.72 0.51 2.70 e 5.38 0.72 4.10
Somatic phages
K13-phages
a Removal Rate (RR) ¼ removal between two adjacent depths, d1 and d2, expressed as log(Cd2/Cd1)*m1. Cd ¼ concentration at the depth d.
Table 1a e Arithmetic mean values (C [ organisms/100 ml), removal (Llog(C/Co)), and removala rate(RR) at the different PWVs and depths, of the microorganisms in a replica of the experiment of Fig. 2. Velocity
Somatic phages
K13-phages
Arithmetic Means and Removal at 50 cm/day (day 1e10)
Arithmetic Means and Removal at 50 cm/day (day 11e38)
Arithmetic Means and Removal at 100 cm/day (day 39e51)
Arithmetic Means and Removal at 200 cm/day (day 52e73)
Arithmetic Means and Removal at 400 cm/day (day 74e88)
Depth
C
log(C/Co)
RR
C
log(C/Co)
RR
C
log(C/Co)
RR
C
log(C/Co)
RR
C
log(C/Co)
RR
0 cm 30 cm 60 cm 90 cm 0 cm 30 cm 60 cm 90 cm
7868 500 44 6.1 2822 112 10 <0.1
0.00 1.20 2.25 3.11 0.00 1.40 2.45 e
e 3.99 3.52 2.86 e 4.67 3.50 e
8071 163 15 4.0 1671 43 2.1 <0.1
0.00 1.69 2.73 3.30 0.00 1.59 2.90 e
e 5.65 3.45 1.91 e 5.30 4.37 e
8077 39 21 5.2 1390.8 6.1 2.6 0.3
0.00 2.32 2.59 3.19 0.00 2.36 2.73 3.67
e 7.72 0.90 2.02 e 7.86 1.23 3.13
4186 174 27 0.5 822 32.0 1.9 <0.1
0.00 1.38 2.19 3.92 0.00 1.41 2.64 e
e 4.60 2.70 5.77 e 4.70 4.09 e
12243 190 30 17 917 13 1.5 <0.1
0.00 1.81 2.61 2.86 0.00 1.85 2.79 e
e 6.03 2.67 0.82 e 6.16 3.13 e
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Depth(Cm)
a Removal Rate (RR) ¼ removal between two adjacent depths, d1 and d2, expressed as log(Cd2/Cd1)*m1. Cd ¼ concentration at the depth d.
443
444
During a period of 67 days, from October 11th till December, 17th 2004, the SSF-facility shown in Fig. 1A was continuously
0.31 0.38 0.46 0.08 0.15 0.23 0.62 0.77 0.92 0.15 0.30 0.45 1.24 1.54 1.84 0.30 0.60 0.90 0.94 0.94 0.94
ponded In the Total water sand residence time
0.19 0.23 0.28
0.47 0.47 0.47
ponded In the Total water sand residence time
0.09 0.12 0.14
0.23 0.23 0.23
ponded In the Total water sand residence time
0.05 0.06 0.07
Inactivation (log10) Residence time (days) at PWV 400 cm/d Inactivation (log10) Residence time (days) at PWV 200 cm/d Residence time (days) at PWV 100 cm/d
Inactivation (log10)
0.37 0.46 0.55 2.48 3.08 3.68 0.6 1.2 1.8 1.88 1.88 1.88
3.1. Removal of somatic and K13-phages by slow sand filtration at different depths and PWVs in the facility of Fig. 1A
30 60 90
Results
ponded In the Total water sand residence time
3.
Inactivation (log10)
To sediment the virus, 2 l samples were centrifuged at 15.000 rpm for 10 h using the A-621-Sorvall Rotor (Thermo Scientific, Germany). The supernatants were discarded; the sediments containing the viruses were resuspended in approximately 15 ml glycine buffer (0.25 M, pH 9.5) and pooled. To separate the viruses further from impurities this suspension was vortexed, and centrifuged 10 min at 7.000 rpm using the Sorvall HS-4 rotor (Thermo Scientific, Germany). The supernatants were spun down in 65 ml polycarbonate tubes at 40,000 rpm for 3 h using the Ti 45-Beckmann rotor (Beckman Coulter, Germany). The supernatants were discarded and the sediments containing the viruses were dissolved in 0.15 ml PBS buffer and stored at 20 C for further analyses. For isolation of the viral nucleic acids from the concentrated sediment, the QIAamp Viral Mini Kit was used according to the instruction of the manufacturer. The final volumes of the extracted nucleic acids were 100 ml. The real-time (RT) PCR-reactions were carried out with an ABI PRISM 7000 sequence detection system (Applied Biosystems, Germany). Enteric Adenoviruses genomes (AdV-Genomes) were detected using a TaqMan probe Ad:ACDEF (50 -(6FAM)-CCG GGC TCA GGT ACT CCG AGG CGT CCT-TAMRA30 ) and degenerated primers AdHexup 50 -CWT ACA TGC ACA TCK CSG G-30 (forward), AdHexdo 50 -CRC GGG CRA AYT GCA CCA G-30 according to Hernroth et al. (2002). The RT-PCR mixture contained 10e20 ml DNA, 0.75 ml each forward and reverse primer (20 mM), 0.75 ml TaqMan probe (10 mM), 12.5 ml 2xTaqMan Universal Mastermix (Applied Biosystems, Germany) and nuclease free water in a reaction mix of 15e25 ml. Amplification was performed 2 min at 50 C, 10 min at 95 C followed by 45 cycles (15 s 95 C, 1 min 60 C). Standard curves were generated by using serial dilutions (range, 1 to 106) of known amounts of linearized pBR322 plasmids containing the entire hexon region of Ad41 (Hernroth et al., 2002). Each sample was processed in triplicate together with a positive (plasmid) and a no-template control. The detection limit was 10 genome/100 ml of the original sample. Determination of the Chemical Oxygen Demand (COD) and of Total Suspended Solids (SS) was carried out according to DIN 38414-9 (1986) and DIN 38409-1 (1987), respectively.
Residence time (days) at PWV 50 cm/d
2.4. Detection of adenovirus genomes using real-time polymerase chain reaction, of Chemical Oxygen Demand (COD), and of Total Suspended Solids (SS)
Depth (cm)
might have made the results obtained with K13 more representative for the mixture of human-pathogenic viruses present in fecally polluted waters. For the detection of both, the somatic and the K13-phages, the sample volume was 0.1 and 1 ml for the infiltrate, and 100 ml for the exfiltrates. Accordingly, the detection limit was 1 pfu/ml and 1 pfu/100 ml respectively.
Table 2 e Residence time (days) of the infiltrated water and inactivation of the phages at the different PWV and depths (T50 of the phages [ 2 days), in the experiments of Tables 1 and 1a.
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Table 3 e COD and SS in the infiltrate and the filtrate at 90 cm during the experiment of Fig. 2 at different times after starting infiltration. Samples were taken every 2e3 days. Infiltrate
Number of samples COD (mg/l) Suspended Solids (mg/l)
Exfiltrate at 90 cm
Days 0e67
Days 0e2
Days 3e10
Days 11e24
Days 25e37
Days 38e59
Days 60e67
22 3.8 1.7 1.5 0.97
2 2.6 1.9 <0.2
3 4.4 0.8 <0.2
4 2.6 1.0 <0.2
3 2.25 0.7 <0.2
7 1.9 0.24 <0.2
2 2.7.0 0.5 <0.2
infiltrated with surface water spiked with 1% wastewater. The temperature of the infiltrate during this time ranged between 4 and 9 C. The course of the experiment is shown in Fig. 2. During the first 24 days the PWV was adjusted to 50 cm/d followed by a period of 13 days at 100 cm/d, 22 days at 200 cm/d, and 8 days at 400 cm/d. The removal of phages improved during the first 25 days, presumably as a consequence of maturation of the filter. The increase of the PWV from 50 to 100 cm/d reduced the removal, but further stepping up of the PWV to 200 and 400 cm/d had little effect on it. For a better comparison of the removal of the phages Table 1, companion to Fig. 2, summarizes the results quantitatively, showing the arithmetic mean values, the removal, and the removal rate (RR) for each microorganism at each velocity and depth. On the simple basis of presumptive counts the observed removal of the phages was highest at 50 cm/d (excluding the first 10 days), with log10 removals for somatic phages of 1.55, 2.03, and 3.19 at the 30, 60 and 90 cm depths respectively. Substantial differences in observed phage removal at 100, 200, and 400 cm/d were not observed. For the K13-phages, at 50 cm/d (days 11e24), the log10 removals (excluding the first 10 days) were 1.69, 2.17, and 3.25 at the 30, 60 and 90 cm depths respectively. The removals at the other PWVs were similar. A second experiment with identical design was carried out in the same facility during the period from 30 June to 24 October 2005, during which the temperature of the infiltrate ranged between 11 and 25 C. In this experiment the infiltration periods were 48 and 15 days at PWVs of 50 and 400 cm/d respectively. Phage removals during this second experiment were consistent with those presented in Fig. 2; however the observed mean reductions at each of the filtration distances were generally higher during the second experiment (Table 1a). In order to estimate the contribution that viral inactivation might have in the total removal of both experiments, Table 2 shows the residence times of the infiltrate while being above
the filter in the ponded water, inside the filter, and as the sum of both periods. In a former study (Dizer et al., 2005) the halftime (T50) for both, the somatic and the K13-phages, had been found to be approximately 2 days under the same conditions as used here, i.e. in 1% wastewater running in an artificial outdoor channel. Using this data, one obtains the inactivation stated in the Table 2. As can be observed comparing Table 2 with Tables 1 and 1a, inactivation contributes to a relatively small extent to the total removal. If, e.g., filtration of somatic phages at 400 cm and 90 cm depth in Table 1 is considered, 0.07 out of 2.38 log10 total removal might be ascribed to inactivation. At 50 cm/d (days 1e24) and 90 cm depth, the contribution due to inactivation was 0.55 log10, from a total removal of 3.19 log10. It becomes evident that the bulk of the removal takes place due to filtration processes other than inactivation. The oxygen concentration in the infiltrated water fluctuated between 8 and 12 mg/l during both experiments, and was always below 0.1 mg/l at all sampling points from two days on after the start of the experiments. During the experiment of Table 1 the COD of the infiltrate averaged 3.88 mg/l, the SS 1.5 mg/l. These parameters were determined also in the 90 cm exfiltrate. Whereas the SS were drastically reduced, the reduction of the COD, if any, was moderate (Table 3). No COD and SS were determined in the experiment of Table 1a. The residence times and the inactivation during them are shown in Table 2, the COD and SS concentrations were similar to the ones in Table 3.
3.2. Removal of somatic and K13-phages, and of E. coli, intestinal enterococci and adenovirus genomes by slow sand filtration at 90 cm depth and 900 cm/d PWV in the facility of Fig. 1C Since the removal rates observed at PWVs of 100e400 cm/d did not greatly differ from each other, a further experiment was conducted at a PWV of 900 cm/d. Because the facility of Fig. 1A
Table 4 e Arithmetic means (organisms or genomes/100 ml) and removal (Llog(C/Co)) values of somatic and K13bacteriophages, E. coli, intestinal enterococci, and AdV-genomes after infiltration of 1% wastewater at 900 m/d PWV and 90 cm depth.
Somatic phages (26 samples of both depths) K13-phages (26 samples of both depths) E. coli (26 samples of both depths) Intestinal enterococci (26 samples of both depths) AdV-Genomes (4 samples of both depths)
Depth
Arithmetic Means at 900 cm/day
log(C/Co)
0 cm 90 cm 0 cm 90 cm 0 cm 90 cm 0 cm 90 cm 0 cm 90 cm
19,735 36 7675 2 195,273 15 35,920 8 7600 2180 <10
0 2.74 0 3.58 0 4.11 0 3.65 0 >2.88
446
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Table 5 e Arithmetic means (organisms or genome copies), removal values (Llog(C/Co)) and removala rate (RR) of somatic and K13-phages (pfu/100 ml), and of AdV-genomes (genomes/100 ml) at different depths. COD and SS concentrations of the infiltrate and exfiltrates. Depth Somatic phages
K13-phages
AdV-Genomes
COD (mg/l) 0.12 samples each
SS (mg/l) 12 samples each
Arithmetic Mean
0 cm 30 cm 120 cm 0 cm 30 cm 120 cm 0 cm 30 cm 120 cm 0 cm 30 cm 120 cm 0 cm 30 cm 120 cm
2445 82 2.1 1708 6.1 <0.1 766 26 <10 2.8 2.4 3.1 1.5 <0.2 <0.2
0.7 0.8 1.0 1.0
log(C/Co)
RR
0 1.47 3.07 0 2.45 >4.23 0 1.47 >1.88 e e e e e e
e 4.91 1.77 e 8.16 >1.98 e 4.90 >0.46 e e e e e e
a Removal Rate (RR) ¼ removal between two adjacent depths, d1 and d2, expressed as log(Cd2/Cd1)*m1.Cd ¼ concentration at the depth d.
could not be operated at velocities of higher than 400 cm/ d without considerably risk of distorting the PWVs between the different depths, the filtration facility of Fig. 1C was used. This facility could only be sampled at 90 cm depth. As in the previous experiment, wastewater-spiked surface water, which had been homogenized in a reservoir using a pump stirring the water/wastewater mixture, was infiltrated at a PWV of 900 cm/d during 14 days. The experiment was conducted from 16th to 30th October 2005, the temperature of the infiltrate ranged from 12 to 14.5 C. Beside the somatic and the K13-phages, in this experiment the fecal indicators EC and IE were additionally examined. In addition, four samples of the infiltrate and exfiltrate were assayed for AdV-genomes in nonconsecutive days. The residence time above the filter surface (height of the water: 20 cm) was less than 2 h, the microorganisms decay therefore negligible (Dizer et al., 2005). The arithmetic means of the organisms and viral genomes and their log10 removals are given in Table 4. The removal of somatic phages was 2.74 log10, the ones of E. coli and intestinal enterococci 4.11 log10 and 3.65 log10 respectively. The log10 removal of AdV-genomes was more than 2.88.
3.3. Removal of somatic and K13-phages, and of adenovirus (AdV) genomes by slow sand filtration at 30 and 120 cm migration distance in the facility of Fig. 1A In order to estimate the removal of enteric AdV-genomes (as a potential surrogate for the AdV) during SSF, we carried out a similar experiment as the one shown in Fig. 2 in the same facility. The experiment was carried out from July 30th till August 15th 2005; the infiltrate temperature ranged between 18 and 25 C. The PWV was kept constant during 48 days at 50 cm/d (calculated for the depth at 30 cm). Due to the flow characteristics of the facility (see its description in the Material section) this PWV corresponded to approximately 29 cm/d at a depth of 120 cm. The arithmetic means, the removal, and the removal rates of both phages and of AdV-genomes are shown in Table 5. No AdV-genomes were detected at 120 cm
(values for 90 cm depth are not available), which corresponds to a log10 removal of at least 1.88. As in the first experiment shown in Fig. 2, the oxygen concentration in the effluents decreased below 0.1 mg/l after two days operation.
4.
Discussion
The concentration of phages remaining after the infiltration of wastewater-spiked surface water through different filtration distances is shown in Fig. 2 and in Tables 1 and 1a. From the tenth day on, removal between 0 and 30 cm was at least 1 log10 for both phages. Between 30 and 60 cm the removal was less pronounced. The higher removal of the upper layer presumably was due to the presence of a schmutzdecke above the top layer, known to significantly contribute to the removal efficiency (Ellis, 1985; Unger and Collins, 2006). Removal at 90 cm was at least 2.25 log10. The removal of the phages in the experiment of Fig. 3 and Table 4 were similar to the ones in Fig. 2 and Tables 1 and 1a. As Figs. 2 and 3 show, the estimated influent phage concentration varied from day to day, likely due to the changing quality of the wastewater used for spiking. The concentrations at the different migration distances varied as well. This variability made an accurate calculation of the quantitative removal very difficult since such a calculation would require that the phage concentration of the influent be constant at least within certain limits. Nonetheless, this has been done to obtain an approximate estimate of the extent of phage removal. Influent and effluent phage concentrations at a given PWV were averaged and log removals were subsequently calculated using those mean values as [log10(C/Co)] (Tables 1, 1a, 4, and 5). These mean concentrations are uncertain because of likely temporal variability (i.e., individual concentrations were estimated on different days, and concentration peaks or valleys might have been gone unnoticed if they happened to occur between two adjacent measurements). Moreover, the plating methods used to obtain the phage concentration estimates
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 3 9 e4 5 2
1,E+04
pfu/100 ml
1,E+03 1,E+02 Somatic phages 1,E+01 1,E+00 1,E-01 0
10
20
30
40
50
1,E+04 K13-phages
pfu/100 ml
1,E+03
1,E+02
1,E+01
1,E+00
1,E-01 0
10
20
30
40
50
Genome copies/100 ml
1,E+04 1,E+03 1,E+02 AdV-Genomes 1,E+01 1,E+00 1,E-01 0
Days
10
20
30
40
50
Fig. 3 e Removal of somatic and K13-coliphages, as well as enteric adenoviruses (as genome copies) during sand filtration at 50 cm/d at depths of 30 and 120 cm. A mixture of surface water and wastewater was infiltrated in the facility of Fig. 1A; water samples were analyzed from inflow (A), and after migration distances of 30 cm (-) and 120 cm (:). The concentrations of somatic phages (top), K13-phages (middle) and genome copies of enteric adenoviruses (AdV-genomes) (bottom) are shown at various migration times. Table 3 is companion to this figure. The detection limit for the phages was 1 pfu/100 ml, for the adenovirus DNA 10 genome copies/100 ml (lowest gridline in the figure); symbols below this line represent values below detection limit.
presented in Fig. 2 are prone to unavoidable measurement errors (Emelko et al., 2010; Schmidt et al., 2010). The uncertainty in these estimates increases when counts are low; particularly when counts are less than w10 pfu (Emelko et al., 2008), as shown in Fig. 2. As a result, the slight differences in the phage removals observed at 50 cm/d compared to those at higher
447
PWVs cannot necessarily be attributed to the differences in PWV because the relative contribution of measurement error is unknown. A number of removal studies have been conducted with groundwater, or with surface water low or free of fecal pollution, spiked with laboratory-grown viruses (Table 6, for a more extensive treatment of removal studies in saturated columns, see Jin and Flury, 2002). As a rule, the removal rates observed in the studies mentioned in Table 6 were less pronounced than in studies carried out using wastewater as the carrier water (Table 7), what suggests that contents in fecally polluted water enhances viral removal. Some workers also found strong evidence for a better removal at low PWVs (Lance et al., 1982; Quanrud et al., 2003). Using tertiary treated wastewater, Vaughn et al. (1981) found very little virus removal at a PWV of 21 m/d, but decreasing the PWV to 1.4 m/ d, or even lower, greatly improved the removal. However, tertiary treated wastewater, having been treated with precipitating salts and/or coagulating agents, might be not the proper representative matrix for the highly fecally polluted waters addressed in the present paper, since tertiary treated sewage has been found to foster adsorption of viruses to sand as compared to secondary effluent (Dizer et al., 1984). On the other side, polioviruses, used in the study Vaughn et al. do not seem to be conservative indicators for the removals of viruses, as they have been found to be much more efficiently removed by soil filtration than the MS2, PRD1 or FX174-phages (DeBorde et al., 1999). We have carried out the present work in order to obtain additional information to evaluate the viral removal of sand filtration plants that infiltrate fecally polluted or very polluted surface water, as often encountered in emergent countries. We have used phages in this study, but as laboratory-grown phages might not be in the same state of aggregation as indigenous phages (and pathogenic viruses), we have used indigenous phages as index organisms, in spite of the lower numbers sometimes found in the wastewater and of their somewhat erratic concentrations. Besides, indigenous phages, being not the clonal progeny of a single E. coli strain, but a mixture of clones generated in many different indigenous E. coli strains, avoid biases when a monoclonal population is used: Landry et al. (1979) have reported that a guanidine-resistant clone of poliovirus 1 displayed different soil adsorption properties than the parent strain. As tests organisms we have aimed at using a relatively broad spectrum of phages to make the study more representative for the human-pathogenic viruses present in fecally polluted water; in addition of using the E. coli-indicator strain WG5, which mainly sustains multiplication of somatic phages, we have used the strain K13 of E. coli. This strain multiplies both, Fþphages and T-phages, albeit at poorer efficiencies than other indicator strains that multiply either phage type only (unpublished results of HD and JMLP). Tables 1 and 1a show no clear dependency between the removals at different PWVs. Only when the PWV was raised from 50 to 100 cm/d the breakthrough of phages increased as well, but it receded back shortly thereafter to levels existing before raising the PWV. The increase of breakthrough might be explained by the enhanced fluid shear stress dislodging some of the phages from their attachment sites, the further building up of biomass
448
Table 6 e Transport of viruses in waters free of, or low in, fecal pollution. Virus infiltrated
Nature of the Infiltrate
PWV (m/d)
MS2; PRD1; VX174; Poliovirus 1 MS2; PRD1; VX174; Poliovirus 1 MS2; PRD1
Virus suspension in groundwater Same as above
Experimental field (Aquifer: clasts and gravel) Same as above
22e45 Bromide: 22.5e30 As above
7.5
Phage suspension in pretreated river water Same as above
Infiltration plant for groundwater recharge. (Aquifer: Fine sand) Experimental field. (Aquifer: sandy layers of fluvial sediments)
ca. 1.6
up to 30
3.33e1.01
up to 40
ca. 6 after 8 m for both phages, then 2.3 for MS2 after additional 32 m.
PRD1; MS2; VX174; Poliovirus 1 MS2
Same as above
Same as above
ca. 132
21.5
e
Natural aquifer
e
e
MS2
Phage suspension in pretreated (coagulation, rapid sand filtration, softening, etc.) surface water Phage suspension in pretreated (rapid sand filtration) surface water
Pilot plant, 2.56 m2, sand of 0.3 mm diameter
7.2
1.5
0.26; 0.77; 1.6; 2.9 respectively Ca. 0.6/day initial removal; 0.2/day afterwards 1.7e1.8
Pilot plant, 2.56 m2, sand of 0.3 mm diameter
7.2
1.5
Phage suspension in pretreated (coagulation, rapid sand filtration, softening, etc.) surface water Phage suspension in anoxic groundwater
Columns (0.4 m; 0.09 m diameter)
7.2
0.4
Experimental field. (Aquifer: coarse sand)
0.33e0.56
up to 37.7
MS2; PRD1
MS2
MS2
MS2, VX174
Filtration distance (m)
19.4
Total removal log10 (C/Co) 0.3; 0.54; 0.46; 2.0 respectively 0.82; 0.92; 1.22; 2.7 respectively ca 8.3 in 30 m
Removal rate (log10 (C/Co)/m) 0.034; 0.072; 0.061; 0.27 respectively 0.042; 0.047; 0.063; 0.14 respectively Ca. 0.7 in the initial part; ca. 0.15 in the linear part. Ca. 0.7 in the initial part for both phages. Ca 0.08 between 8 and 38 m for MS2 0.012; 0.036; 0.073; 0.136 respectively e
1.13e1.2
Reference DeBorde et al. (1999) DeBorde et al. (1999) Schijven et al. (1999)
Schijven et al. (2000)
Woessner et al. (2001) Medema and Stuyfzand (2002) cited by van der Wielen et al. (2008) Hijnen et al. (2004)
1.7e1.9 (4 days old schmutzdecke) 1.8e2.2 (81 days old schmutzdecke) 0.1 0.2 0.4
1.13e1.27 (4 days old schmutzdecke) 1.2e1.47 (81 days old schmutzdecke)
Hijnen et al. (2004)
0.25 0.5 1.0
Hijnen et al. (2004)
4.4 (after 37.7 m) and 2.8 (after 7.8 m) respectively
0.12 and 0.36 respectively
van der Wielen et al. (2008)
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Infiltration field
Table 7 e Transport of viruses suspended in wastewater. Infiltrated Virus(es)
Nature of the Infiltrate
Infiltration field
PWV (m/d)
Filtration distance (m)
Total removale(log10)
Removal rate (log10/m)
Reference
Soil basins (6.1 213.4 m) Fine loamy sand; gravel, cobbles.
0.25
9
>4
>0.45
Gilbert et al. (1976)
Viral suspension in secondary effluent
Saturated soil columns, 2.75 m and 10 cm diameter. Loamy sand 89%; silt 8%; clay 3%
15; 55
Up to 1.60
5.4 and 5.0 after 0.4 depth; 2.1 and 2.0 between 0.4 and 1.6 m respectively for 15 and 55 PWV
Lance et al. (1976)
Poliovirus 1
Viral suspension in tertiary effluent
0.24
Up to 0.75
10 during the first 10 cm; 6.7 during 10 and 25 cm; None between 25 and 75 cm
Landry et al. (1980)
Poliovirus 1
Viral suspension in tertiary effluent
Soil cores from a experimental recharge basin for tertiary effluent. Coarse sand/fine gravel with 1.25% silt and clay Experimental recharge basin for tertiary effluent (25 m2) t. Coarse sand/fine gravel with 1.25% silt and clay
2.1 and 2.0 for 15 and 55 after 0.4 m depth; 2.52 and 2.42 between 0.4 and 1.60 m respectively for 15 and 55 PWV 1 after 10 cm; 1 between 10 and 25. Between 25 and 75 cm no apparent removal
21; 1.4; 0.24; 0.12
Up to 7.62
0.42; 4.59; 4.29; 6.80 respectively (after an infiltration distance of 0.75 m)
Vaughn et al. (1981)
Poliovirus 1 Echo 1 Echo 29
Viral suspension in secondary effluent
0.5e0.6
Up to 2.75
5 between 0 and 0.4 m; >0.83 between 0.4 and 1.6 m
Lance, et al. (1982)
Echo 1
Viral suspension in secondary effluent
Saturated soil columns, 2.75 m 10 cm (i.d.). Loamy sand 89%; silt 8%; clay 3% Saturated soil columns, 2.75 m 10 cm (i.d.). coarse sand 93%; silt 6%; clay 1%
0.6; 1.2; 2.4
250
0.31; 3.44; 3.22; 5,10 respectively for the different PWV (Pulse experiments. Removal is expressed as the log10 of the ratio between the maximal concentration in the exfiltrates after 0.75 m infiltration to the concentration in the infiltrate) 2 after 0.4 m infiltration: >1 between 0.4 and 1.6 m infiltration. Undetectable afterwards Qualitatively: better removal at the lower PWV
e
Lance, et al. (1982)
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 3 9 e4 5 2
Secondary effluent
Enteroviruses, reoviruses indigenous in secondary effluent Poliovirus 1
449
450
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 3 9 e4 5 2
might have been responsible for the resumption of the removal. According to the results of Tables 1, 1a, and 2 the phage removal was similarly high at all pore water velocities between 1 and 9 m/d. This affirm regulations of the German Water Association (Deutscher Verein des Gas- und Wasserfaches, DVGW) which specify that the velocity of the water penetrating the filter should be between 0.05 and 0.30 m/h, corresponding to an approximate PWV between 3.6 and 21.6 m/d (Anonymous, 2005). The stabilization (maturation) of the slow sand filter in the experiment of Fig. 2 was apparently well advanced after 10 days. During this period the removals of the phages were, at all depths, lower than the ones corresponding to the following 14 days (Table 1). Maturation might take several more weeks, though, as suggested by Fig. 3 for the values at 30 cm depth. During this time, the removal efficiency for the organisms studied increased steadily. Maturation time probably greatly depends on the formation of a biological matrix not only above (schmutzdecke), but within the filter as well. During the experiments in the plant of Fig. 1A a vigorous development of biofilms was observed, and the density of biofilm bacteria decreased with filter depth (personal communication of Szewzyk and Szewzyk (2009)). As shown in Table 4 somatic phages (log10 removal: 2.74) as process indicators are more conservative than either K13phages (log10 removal: 3.58) or the bacterial indicators (log10 removal for IE: 3.58; for E. coli: 4.11). Somatic phages seem also to be more conservative than adenovirus DNA, since from the experiment in the facility of the Fig. 1C the log10 removal of viral genomes was higher than that of somatic phages. Although indigenous phages, taking advantage of the fecal bacteria present in the filter, might multiply to some extent during filtration, this possibility does not hamper their value as conservative indicators of viral attenuation. The concentration of both, somatic and K13-phages decreased with increasing filter depth. In the case of phage multiplication during filtration the filter performance for viral removal could be rather under than overestimated. Systems relying on sand or soil filtration might be a considerable help to reduce the incidence of water borne gastroenteritis in countries with highly polluted rivers. If, for instance, a stream contains 100 infectious particles of a pathogenic virus per liter which, after ingestion of one viral particle, causes illness in 10% of the cases, then, in order to reduce the risk of illness down to 104/d and person, the viral concentration will have to be reduced by five logs to a final concentration of 103 viruses/l (assuming that each person consumes 1 l water per day, which is an underestimate). Following the suggestions of WHO (2006b), this removal should be accomplished by a multiple-barrier system, each component of the system resilient to potential malfunctions of the other(s). Assuming that the last part of the system should be chemical disinfection with chlorine, hypochlorite or chlorine dioxide, the other part could be chosen to be SSF (or RBF) which, carried out with a filtration path of e.g. 10e20 m at PWV not above 10 m/d, would reduce the viral load of the water three to four logs and probably much more. The resulting filtrate, having been practically freed of the suspended solids and partially of the organic substances originally present in the surface water, would allow a much more
efficient chlorination than if the disinfection had been carried out directly with the surface water. In fact, chlorination has been reported to reduce indigenous somatic and male phages less than one log10 only, if it was carried out in secondary effluent (Tyrrell et al., 1995). Other alternatives for drinking water treatment which encompass filtration (as opposite to chemical treatment like e.g., lime softening) are fast sand filtration following precipitation/coagulation, and membrane filtration. Both filtration techniques require higher economic investments than SSF, both in equipment and maintenance. Membrane filtration plants, especially in the versions which efficiently remove viruses, seem prohibitive for low-income communities. Fast sand filtration, more affordable, does not outperform SSF in the removal of viruses (WHO, 2006c).
5.
Conclusions
Slow sand filtration removes indigenous somatic phages from fecally polluted water very efficiently; after 90 cm filter path the minimum removal was 2.25 log10 (Table 1), the maximum 3.92 log10 (Table 1a). The removal of indigenous K13-phages was even higher, Removal of indigenous AdV (as AdVgenomes) was >2.88 log10. The time for the filters to stabilize and reach a constant performance (maturation) was found to be approximately 10 days, but progression to maturity up to 40 days was also observed. At least in mature sand filters, PWVs between 1 and 9 m/d were equally efficacious in removing indigenous phages, an important fact if the filters are to be efficiently operated under cost/benefits aspects. SSF (and by inference RBF) is able to greatly reduce the viral concentration of highly fecally polluted surface waters, such as are found in many emergent countries.
Acknowledgments This investigation was carried out partly as a contribution to the Natural and Artificial Systems for Recharge and Infiltration project (NASRI project) with the financial support of the “Kompetenzzentrum Wasser Berlin” (KWB). The support of the German “Bundesministerium fu¨r Gesundheit” for the project “Kleinbadeteiche” is greatly acknowledged. The authors thank Mrs. Ch. Mekonnen and Mrs. Ch. Arndt for excellent logistical and technical assistance.
references
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 5 3 e4 6 0
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Immobilization of Hg(II) in water with polysulfide-rubber (PSR) polymer-coated activated carbon Eun-Ah Kim a, Angelia L. Seyfferth b, Scott Fendorf b, Richard G. Luthy a,* a b
Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305-4020, USA Environmental Earth System Science, Stanford University, Stanford, CA 94305-2220, USA
article info
abstract
Article history:
An effective mercury removal method using polymer-coated activated carbon was studied
Received 26 May 2010
for possible use in water treatment. In order to increase the affinity of activated carbon for
Received in revised form
mercury, a sulfur-rich compound, polysulfide-rubber (PSR) polymer, was effectively coated
24 August 2010
onto the activated carbon. The polymer was synthesized by condensation polymerization
Accepted 26 August 2010
between sodium tetrasulfide and 1,2-dichloroethane in water. PSRemercury interactions
Available online 21 September 2010
and HgeS bonding were elucidated from x-ray photoelectron spectroscopy, and Fourier transform infra-red spectroscopy analyses. The sulfur loading levels were controlled by the
Keywords:
polymer dose during the coating process and the total surface area of the activated carbon
Mercury
was maintained for the sulfur loading less than 2 wt%. Sorption kinetic studies showed
Polysulfide-rubber polymer
that PSR-coated activated carbon facilitates fast reaction by providing a greater reactive
Activated carbon
surface area than PSR alone. High sulfur loading on activated carbon enhanced mercury
Heavy metal adsorption
adsorption contributing to a three orders of magnitude reduction in mercury concentration. m-X-ray absorption near edge spectroscopic analyses of the mercury bound to activated carbon and to PSR on activated carbon suggests the chemical bond with mercury on the surface is a combination of HgeCl and HgeS interaction. The pH effect on mercury removal and adsorption isotherm results indicate competition between protons and mercury for binding to sulfur at low pH. ª 2010 Published by Elsevier Ltd.
1.
Introduction
Mercury ions exist in the environment as complex with inorganic and organic ligands or stabilized with sulfide ions forming cinnabar (Morel et al., 1998). Although inorganic mercury is less toxic than methylmercury or dimethylmercury, it is imperative that its concentration in sediment and waters be kept low since neutral dissolved inorganic mercury, such as neutral chloride species (Mason et al., 1996) can be transformed by sulfate reducing bacteria (Morel et al., 1998) into toxic and bioaccumulative methylmercury or dimethylmercury. Much effort has been expended on developing techniques for mercury removal from water such as precipitation with
* Corresponding author. E-mail address:
[email protected] (R.G. Luthy). 0043-1354/$ e see front matter ª 2010 Published by Elsevier Ltd. doi:10.1016/j.watres.2010.08.045
elemental sulfur, ion exchange, membrane separation, and adsorption with zeolites (Campbell et al., 2006), nanoparticles (Yantasee et al., 2007), and activated carbons (Mostafa, 1997). Utilizing an adsorbent such as zeolite or activated carbon has drawn attention for economical reasons (Namasivaysm and Kadirvelu, 1999). Since sulfur has a high affinity for mercury, reduced sulfur functional groups, such as sulfide, thiol, and elemental sulfur, have been incorporated into adsorbents to enhance mercury sorption efficiency (Zhu et al., 2009). Sulfur-impregnated activated carbon enhances mercury removal efficiency from flue gas (Karatza et al., 1996) and from the aqueous phase (Fitzpatrick et al., 1975). A general method for activated carbon sulfur impregnation is to treat the
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activated carbon with a mercury adsorbent such as elemental sulfur, polysulfide (S2 x ), thiol, or polythiol at high temperature (200e600 C) (His et al., 2001). Hg(0) removal efficiency on sulfur-impregnated activated carbon is dependent on sulfur impregnation method and temperature as well as the resulting micropore area of the sulfur-impregnated activated carbon (His et al., 2001). However, Wang et al. (2009) found that more than 20% of the elemental sulfur loading on activated carbon at reaction temperatures above 400 C resulted in the loss of Hg(II) removal efficiency due to the generation of less reactive sulfur species, blocking pores and reducing reactive surface area by the excessive sulfur loading. Surface modification of the inner pores of activated carbon is desirable for effective mercury immobilization in that the mercury species can be sequestered in the pores and physically isolated from influences of the outer environment, such as microbial transformation. Procedures involving the functional group modification of the activated carbon itself may be difficult. Alternatively, impregnation of mercury sorbent, such as by a polysulfide coating process, is conceptually more straightforward. In this case, the sulfur content may be directly related with reactive surface area and thus improve mercury removal efficiency. A simple surface coating method to control the sulfur content may allow provision for an optimal sulfur loading level. In this regard, we explore the incorporation of polysulfide-rubber polymer on activated carbon as an effective mercury sorbent. Polysulfide-rubber polymer is currently used as an adhesive, sealant, or dental impression material. These applications utilize the chemical inertness, poor solubility in most solvents, or malleability of the polymer. However, the specific binding affinity of the polysulfide group for mercury has not been studied to our knowledge. To date, there have been few studies (Sinner et al., 1998) regarding polymer coating on activated carbon in order to remove mercury from water. Having numerous SeS linkages, polysulfide-rubber itself is expected to be a strong mercury adsorbent. Furthermore, increasing the surface area of mercury binding sites by coating the polymer on highly porous activated carbon would increase the sorption capacity per mass of the polymer. Since natural organic matter in sediments is reported to reduce mercury bioavailability (Chen et al., 2009), it is expected that a surface coating of polysulfide-rubber would act as a surrogate for sulfur-rich natural organic matter, and will exhibit a comparable performance in reducing mercury bioavailability. This paper presents a technique for producing sulfur-rich activated carbon, and demonstrates greatly enhanced mercury ion removal efficiencies by using polysulfiderubber (PSR) coated activated carbon. The aim of the study is to report the PSR-activated carbon synthesis, to assess mercurysulfur binding on the PSR-coated activated carbon, and to show enhanced mercury removal capacity for Hg(II) from water.
2.
Materials and methods
2.1.
Reagents
Sulfur, sodium hydroxide, ethylene dichloride and methyl tributyl ammonium chloride (Fisher Scientific) were used as
received. Mercuric chloride (Sigma Aldrich) was used to prepare 8.11 g Hg L1 stock solution in a 2 M HCl solution, which was diluted from concentrated trace metal analysis grade HCl. HgS (black) was prepared by immediate filtration after mixing the HgCl2 stock solution with 1:1 M equivalent Hg:S using sodium sulfide (Sigma Aldrich). The HgCl2 solution was used for a standard in mXANES analysis and was prepared by diluting the stock solution to make 0.1 g Hg/g solution. The activated Carbon, TOG-NDS 50 200, had a particle size range of 75e300 mm (Calgon Carbon, Cattleburg, KY, USA).
2.2.
Synthesis of polysulfide
The polymer was synthesized following the procedure described recently by Kalaee et al. (2009). Condensation polymerization between sodium tetrasulfide and 1,2-dichloroethane, using methyltributylammonium chloride as a phase transfer catalyst, produced a yellowish elastic solid in water. The polymer block was boiled twice in deionized water for one day each to remove residual salts. The polymer was dried in an oven at 105 C over night. The polymer was characterized by 400 MHz (Varian) 1H NMR, 13 C NMR, and FT-IR (Nicholet 570) for the functional group analysis, and by a CHNS/O analyzer for an overall chemical composition.
2.3.
Polymer coating on activated carbon
0.5e2.0 g of the polysulfide polymer in 200 mL toluene was refluxed for 3 h. After the complete dissolution of the polymer, the solution was cooled to room temperature to which 1 g of the activated carbon was added. The slurry was stirred for 24 h, and collected by vacuum filtration on a Teflon membrane filter while rinsed with toluene. The filtered activated carbon was dried under vacuum at 50 C until constant mass. The modified activated carbon was characterized by BET (Coulter SA 3100) to estimate the changes in the pore size distribution and surface area. CHNS/O analysis (Perkin Elmer 2400 series II) provided the sulfur contents of the activated carbon before and after the modification. SEM (scanning electron microscopy) images obtained with an FEI Strata 235DB dual-beam FIB (focused ion beam)/SEM elucidated changes in the surface morphology by the polymer coating.
2.4.
Total mercury analysis
The mercury solutions with and without sorbent were shaken at 300 rpm. One mL of duplicate samples was taken at each time point and filtered through 0.45 mm polyvinylidene fluoride (PVDF) membrane filter. The filtrate was oxidized with 0.5% BrCl in order to stabilize the Hg(II) ion. The samples were diluted by 400e80000-fold to ensure mercury concentrations were within the detection range of 0.5e400 ng L1. The total mercury concentrations were measured by Tekran 2600 cold vapor atomic fluorescent spectrometry (CVAFS) following the US EPA (Environmental Protection Agency) method 1631revision E.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 5 3 e4 6 0
2.5.
Mercury contact scheme
Ten mg of PSR as a sole sorbent of mercury was prepared by dissolving in toluene and coating on glass vials by evaporating toluene. Ten ppm HgCl2 aqueous solution was prepared by dilution of 1.2 mL concentrated (8.11 g Hg L1) HgCl2 solution in 1 L of 0.01 M pH 7 phosphate buffer solution. Ten mg of virgin activated carbon or PSR-coated activated carbon of various sulfur contents was placed in each 40 mL vial to study the effect of polymer content and pH on mercury adsorption capacity; 10 ppm HgCl2 solution was in contact with each sorbent. Sodium biphthalate was used to prepare solutions at pH 3 and 4, potassium monophosphate for pH 7, and sodium bicarbonate for pH 11. For adsorption kinetics of activated carbon, 30 mg adsorbent was placed to a 200 mL 15 ppm HgCl2 solution and shaken for 30, 60, 720, and 1440 min. Initial HgCl2 aqueous solutions varying from 1 to 100 ppm in 0.01 M pH 7 phosphate buffer were used for adsorption isotherm studies. Ten mg of adsorbents were put into duplicate 40 mL glass vials. The vials were shaken at 300 rpm for 2 weeks. The mercury bound virgin AC and AC-PSR samples used for mXRF and mXANES analyses were prepared by putting 10 mg of virgin AC or AC-PSR (S: 8.03 or 2.58 wt%) in 10 ppm HgCl2 solution at pH 1, 7, and 13 for 1 month after which the particles were filtered, washed with DI-water, and dried in a glove box.
2.6.
X-ray photoelectron spectroscopy
AC-PSR (S: 8.03 wt%) before and after the 10 ppm Hg(II) contact for one month was washed with MilliQ water and acetone followed by drying in a vacuum for 10 min. PHI 5000 VersaProbe scanning XPS microprobe with Al Ka x-ray radiation (1486 eV) was used under high vacuum condition (below 1 105 Pa). Charging effects by the poor surface conductivity were minimized by applying a 10 eV argon ion gun. Analytical sample size for both survey and high resolution scans was 1000 1000 mm. An averaged spectrum from five survey scans over 0e1000 eV was obtained with a resolution of 1 eV. The high resolution spectra of S 2p orbital was obtained from 10 scans over 150e170 eV with a resolution of 0.1 eV.
2.7.
m-X-ray fluorescence imaging and mXANES
Micro-XRF images were obtained on AC-PSR (S: 8.03 wt%) and virgin AC samples at beam line 2e3. at the Stanford Synchrotron Radiation Laboratory (SSRL); additionally, Hg m-XANES spectra were obtained at select spots of the samples. This beam line is equipped with a bend magnet and a double crystal (Si 111) monochromator that delivers microfocused X-rays of 5 5 mm spot size. The AC-PSR sample was imaged at 12500 eV for Hg (L3 edge) and S utilizing 0.005 step size and 100 ms dwell time per pixel; fluorescence intensities of Hg and S were monitored with a seven element Ge solid-state detector placed 45 to the sample (90 to the incident beam). In addition to the element-specific images, Hg m-XANES spectra were obtained at select locations of Hg ‘hotspots’ and areas where Hg was less abundant. Five windows of 100 eV width were monitored in the region 2000e2500 eV, which picked up fluorescence intensities from
455
S and Hg (S: ka1, ka2 (2306.6 and 2307.8 eV) and Hg: Ma1 2195.3 eV). XRF standards (Micromatter, Vancouver, BC Canada) of S (CuSx S ¼ 11.8 mg/m2) and Hg (AgHg Hg ¼ 27.5 mg/ m2) were imaged at 12500 eV. The relative counts in each of the windows (2000e2100, 2100e2200, 2300e2400, 2400e2500 eV) from each of the standards were used to fit the sample data from each of those windows to produce fitted maps of S and Hg, which were free of depth effects of X-rays coming out of the sample which differ for S and Hg. The fitted S and Hg maps were then quantified using the AgHg and CuSx standards to produce maps in units of mg/cm2. First derivatives of normalized edge spectra were fitted in the region from 12250 to 12355 eV with two standards that may be present in the sample: HgCl2 and HgS (cinnabar). Mercuric chloride was analyzed as a freshly-prepared solution and cinnabar was analyzed as a solid.
3.
Results and discussions
3.1.
Characterization of polylsulfide-rubber polymer
Polysulfide-rubber polymer has repeating C2H4S4 units with a termination group of eC2H4Cl. 1H and 13C NMR (provided in the Supplemental material) and FT-IR spectra of the synthesized polysulfide agree with the previous report by Kalaee et al. (2009). Appearance of new peaks in FT-IR spectra between 1500 and 800 cm1, which are absent in dichloroethane spectrum, suggest the formation of CeS bonds. Disappearance of the proton peak corresponding to CH2Cl in 1 H NMR spectra also supports the claim that the polymerization reaction was successful. The relative peak area ratio is CH2Cl:CH2S4 ¼ 1:16, which corresponds to a molecular weight of about 2600 g/mol. The C:H:S mass ratio of the polymer obtained from CHNS/O analysis is 15:3:82, which is equivalent to the C2H4S4 formula.
3.2.
MercuryePSR interaction
The FT-IR spectra shown in Fig. 1 compare PSR before and after the contact with HgCl2. This shows that the chemical environment around the CH2 groups in PSR polymer has been changed by the mercury input. The disappearance of the peak at 711.6 cm1 after the mercury input suggests changes in the nature of CeS bonding (Lambert et al., 1987) while relative intensity changes at 1350e1500 cm1 and 2800e3000 cm1 indicate different CH2 bending vibration and CeH stretching energy due to changes in CH2eS bonding energy. These results support the engagement of mercury with sulfur atoms by which the process draws electrons from the sulfur and consequently replenishing the loss from the neighboring CeS bond.
3.3. Mercury adsorption kinetics with PSR polymer coated on glass The ability of PSR alone to sorb Hg(II) was demonstrated by coating PSR on glass vials and evaluating Hg(II) removed with 10 ppm mercuric chloride solution at pH 7. Fig. 2 shows that PSR polymer itself can remove mercuric chloride from water as
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Fig. 1 e FT-IR spectra of PSR polymer with and without mercuric chloride.
a sole sorbent, and the trend follows a first order reaction model with a single constant throughout the reaction, which may be expected when the number of mercury binding sites stays relatively constant with respect to substrate concentration. The PSR polymer adsorption has a different reaction mechanism from that with many mineralemetal binding pathways wherein electrostatic forces play an important role. Since the PSR polymer has zero formal charge on the sulfur atom, and the aqueous mercury species are either in a positively charged Hg(II) or neutral HgCl02 form, the mercuryepolymer binding should be favored not by electrostatic interactions, but by formation of the molecular orbital between sulfur 3s, 3p orbitals and mercury 5d, 6s orbitals (Scrocco, 1989; Stro¨mberg et al., 1991). Fig. 2 compares mercury removal by the same weight of PSR polymer coated on glass and PSR-coated activated carbon. Both show similar trends, but the AC-PSR has a rate constant k ¼ 0.0076 h1, while PSR coating on glass has a much smaller rate constant k ¼ 0.0024 h1. The normalized constants by the sulfur mass, 29 h1(g S)1 and 0.29 h1(g S)1 respectively, show the significant role of activated carbon to increase the reaction rate. The increase of the reaction constant by increasing the surface area of the polymer suggests an enhancement of the reaction rate by providing the polymer with larger overall surface area and ample macropores in the
activated carbon, which is supported by the gradual reduction of BET surface area resulting from the invasion of micropore area with the polymer (Table 1).
3.4.
Polymer coating on the activated carbon
Results presented in Table 1 shows trends in total and micropore surface areas as well as the sulfur content with respect to polymer dose. Within the polymer dose range of 0e1.64 g S/g AC, sulfur loading on activated carbon has a positive linear correlation with the polymer input. The total and micropore BET surface areas increase slightly to a polymer dose of about 0.41 g S/g AC and then decrease as the polymer dose increases. A small increase in the surface areas with 0e0.41 g S/g AC polymer dose may be due to the formation of polymer micro-clusters on the activated carbon surface or inside of the macropores. Polymer coating does not affect the overall pore structure of virgin AC as shown in Fig. 3, where the macropore is not blocked with the high dosage of polymer (S loading: 12.65 wt%). Microstructures on the activated carbon are also preserved after polymer coating, while the edges and steps are locally smoothed. This indicates a thin film-like character of the polymer that enables spreading of the polymer molecules without blocking macropores.
3.5.
X-ray photoelectron spectroscopy (XPS) analysis
Mercury immobilization on the surface is confirmed by XPS spectra (Fig. 4) of AC-PSR before and after HgCl2 contact for 1
Table 1 e Surface area and sulfur content changes with increasing polymer dose (1 g activated carbon was used at each experiment). Fig. 2 e Mercury adsorption kinetics with 10 mg PSR polymer-coat on glass (S: 82%) coated on 40 mL borosilicate glass vials in comparison with 10 mg AC-PSR particles (S: 2.58%) for the adsorption of 10 ppm HgCl2 at pH [ 7. First order reaction rates are given by the solid or dashed line.
Sulfur content (wt%) 0.57 1.41 1.84 4.28 8.03 12.65 Polymer dose 0 0.205 0.410 0.820 1.23 1.64 (g S/g AC) 724 832 832 640 516 306 Total surface area (m2 g1) Micropore area 433 530 519 346 237 102 (m2 g1)
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Fig. 3 e SEM images of a) virgin AC, and b) PSR-coated AC (Sulfur loading: 12.65 wt%). The macropore and fine structural features of activated carbon are preserved after the polymer coating.
month. A new peak at 101 eV corresponding to Hg (4f) orbital within the range of HgeS bond appears after the mercury contact while sulfur 2p and 2s peaks remain. The inset spectra of high resolution S (2p) orbital shows more peak area from the binding energy lower than 162 eV after the mercury contact, which indicates the newly-formed HgeS bond (Wang et al., 2009). Positive bonding energy shift of S 2p1/2 orbital after aqueous Hg(II) contact can be a result of sulfur oxidation or electron transfer (Mialki et al., 1981) from SeS bond toward the neighboring HgeS complex (Fig. 4).
3.6.
mXRF imaging and mXANES analysis
Fig. 5 shows Hg and S mXRF images obtained on the AC-PSR and AC samples. Since the color intensity indicates local concentrations, qualitative interpretation suggests stronger positive correlation between Hg and S concentrations in AC-PSR than that in virgin AC. Assuming that miscellaneous mercury bonding with various functional groups and trace elements on the AC is negligible, only HgeCl or HgeS bonding is investigated. Table 2 lists the proportions of HgeCl and HgeS bonding characteristics contributing to the mercury bonding to AC-PSR (S: 8.03 wt%) in comparison with that to virgin AC (S: 0.57 wt%) with respect to the HgCl2 and HgS (black) XANES spectra. Both AC and AC-PSR contain mixtures of HgeCl and HgeS properties; however the selected spots of various mercury concentrations in AC-PSR show more variation in HgCl2/HgS ratios (0.75e3.2) compared to those in virgin AC (1.1e1.8). This trend indicates that an Hg loaded AC-PSR particle contains heterogeneous local equilibrium between HgS and HgCl2 depending on the Hg/S ratio at the corresponding spot (Table 2).
3.7. Sulfur content and pH effects on adsorption capacities of AC-PSR According to the report by Wang et al. (2009), mercury (II) removal efficiencies on AC are reduced when the surface area decreases even though sulfur loading may increase. In the
case of Wang et al. (2009) this is believed to be a consequence of elemental sulfur deposition restricting access to sorption sites, and the amount of reactive sulfur compounds formed by thermal treatment, such as elemental sulfur, thiophene and sulfoxide generated at different temperatures. In contrast, in this study, the higher sulfur loading increases the mercury sorption capacity by three orders of magnitude up to a sulfur loading of 8 wt% after which the enhancement remains unchanged (Fig. 6). The difference between the findings of Wang et al. and this study may be due to the different extents of pore blocking between elemental sulfur and polysulfiderubber polymer. We infer that PSR polymer reduces the surface area by covering the micropores without blocking the larger channels that HgCl2 passes through, leaving the PSR accessible for binding Hg. As illustrated in Fig. 7, the removal of Hg from aqueous solutions with unmodified activated carbon is greatly influenced by pH, as much as 15 fold between pH 7 and 3. In contrast, PSR-coated activated carbon is less affected by pH, as
Table 2 e Linear combination fitting results of mXANES data showing the relative proportion of HgeCl and HgeS characteristics. Sample
Spot index
Percent of Reduced c2 fitted species HgCl2
HgS
AC-PSR (S: 8.03 wt%)
1-1 1-2 1-3 1-4 1-5
42.7 69.6 56.0 47.8 76.4
57.3 30.4 44.0 52.2 23.6
0.00012 0.00024 0.00015 0.00012 0.00019
Virgin AC (S: 0.57 wt%)
2-1 2-2 2-3 2-4 2-5
53.3 63.7 59.6 54.0 64.9
46.7 36.3 40.4 46.0 35.1
0.00038 0.00034 0.00040 0.00034 0.00029
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Fig. 4 e X-ray photoelectron spectroscopy of AC-PSR (S: 8.03%) a) before and b) after HgCl2 contact for 1 month.
the decrease in the sorption capacity at pH 3 is about a factor of 4 less from that at pH 7. AC-PSR exhibits a relatively strong Hg binding tendency at high pH compared to low pH. Hg(II) speciation over pH change calculated by MINEQLþ (see Supplemental material) reveals that HgCl2, HgClOH and Hg (OH)2 are the major species in acidic, neutral and basic conditions respectively. Protonation (Galardon et al., 2009;
Sellmann et al., 1996) of the sulfide chain in the polymer at low pH may explain the decreased binding with aqueous HgCl2. Although polysulfide chains are less affected by high pH, the negatively charged activated carbon surface at high pH will have low affinity to neutral mercury species Hg(OH)2. mXANES results (see Supplemental material) of AC-PSR in contact with mercuric chloride at pH ¼ 1 showed that
Fig. 5 e X-ray absorption elemental mapping of a) mercury on AC-PSR (S: 8.03 wt%), b) sulfur on AC-PSR, c) correlation between Hg and S concentrations in each pixel from AC-PSR map, d) mercury on virgin AC, e) sulfur on virgin AC (S: 0.57 wt%), f) correlation between Hg and S concentrations in each pixel from virgin AC map. The color scale is in picograms of Hg or S in each 5 3 5 mm spot size (note the different ranges for AC-PSR (0e100) and virgin AC (0e35). White scale bar corresponds to 100 mm.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 5 3 e4 6 0
Fig. 6 e Mercury concentration remaining after 1 month of contact with virgin AC or AC-PSR having 1.41e12.65 wt% sulfur contents. Error bars denote the maximum and minimum values of the duplicates. Note the logarithmic scale in which PSR on activated carbon reduces Hg by three orders of magnitude compared to virgin activated carbon.
the majority (82e99%) of the mercury compound bound on the surface to be HgCl2, which supports the speculation that the protonated polysulfide group has a very week binding affinity with mercury ions. On the other hand, mXANES results with AC-PSR in contact with HgCl2 at pH ¼ 13 showed comparable binding characteristics as with AC-PSR at pH ¼ 7, mainly (64e87%) existing in the HgS form. This suggests that the decrease in mercury removal at pH ¼ 13 is not because of the chemical change at polysulfide group, but because of the changes in mercury speciation under different pH conditions (Fig. S5).
3.8.
Mercury adsorption isotherm
Fig. 8 shows Hg adsorption isotherm data for aqueous concentration ranges to 25 mg Hg L1. The Langmuir model fits better with the adsorption of mercury on AC-PSR, indicating that the extent of mercury binding is a function of specific binding sites, a finite number of which are located on AC-PSR surface. The most noticeable feature of the Hg
a
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Fig. 7 e pH effect on mercury removal capacity. Error bars denote the maximum and minimum values of the duplicates.
adsorption isotherm is the very strong binding of Hg at low concentrations with PSR-coated activated carbon compared to virgin activated carbon. As aqueous mercury concentration goes infinite, the Hg/S ratio for AC-PSR (S: 2.58 wt%) converges to 0.9 while the Hg/S ratio for virgin AC (0.57 wt%) converges to 3.0. With AC-PSR, the mercury bonding is controlled by polysulfide groups, while the sulfur group on virgin activated carbon contributes less to the maximum sorption capacity.
4.
Conclusion
The chemisorption of mercury ions on PSR polymer is demonstrated by the characterization of chemical bonding using XPS, and FT-IR. Each spectrum exhibited newly-formed HgeS bonding and a reduced portion of HgeCl bonding. The mercury removal efficiency of PSR-coated activated carbon (AC-PSR) increases as sulfur loading increases, reducing HgCl2 concentration by as much as three orders of magnitude from aqueous solution buffered at pH 7. Having larger surface area, AC-PSR has 100-fold higher reaction rate per gram of sulfur than pure PSR polymer on glass. mXANES data indicate that
b
Fig. 8 e Mercury adsorption isotherm fitted with a) Langmuir and b) Freundlich models (Ce: aqueous concentration of total mercury, Qe: solid concentration of total mercury in activated carbon).
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depending on the local sulfur concentration in AC-PSR and pH of the solution, mercury ion forms various types of bonding with polysulfide groups. Adsorption isotherms show more favorable binding between mercury and AC-PSR compared with virgin AC at low aqueous concentrations. Based on these findings, effective mercury removal from liquid phases is anticipated for PSR-coated activated carbon.
Acknowledgments The authors acknowledge the National Institute of Environmental Health Sciences (NIEH), grant number R01 ES01614302, for the financial support of this study.
Appendix A. Supplemental material Supplementary data related to this article can be found online at doi:10.1016/j.watres.2010.08.045.
references
Campbell, L.S., Chimedtsogzol, A., Dyer, A., 2006. Species sensitivity of zeolite minerals for uptake of mercury solutes. Mineral. Mag. 70 (4), 361e371. Chen, C.Y., Dionne, M., Mayes, B.M., Ward, D.M., Sturup, S., Jackson, B.P., 2009. Mercury bioavailability and bioaccumulation in estuarine food webs in the gulf of Maine. Environ. Sci. Technol. 43 (6), 1804e1810. EPA, U.S., 2002. Mercury in Water by Oxidation, Purge and Trap, and Cold Vapor Atomic Fluorescence Spectrometry. EPA-821R-02e019 Method 1631 Revision E. Fitzpatrick, J.W., Berninger, C.J., Lewis, D.O., 1975. Process for reducing the level of contaminating mercury in aqueous solutions. United States Patent 3,873,581. Galardon, E., Tomas, A., Selkti, M., Roussel, P., Artaud, I., 2009. Synthesis, characterization, and reactivity of alkyldisulfanido zinc complexes. Inorg. Chem. 48 (13), 5921e5927. His, H., Rood, M.J., Rostam-Abadi, M., Chen, S., Chang, R., 2001. Effects of sulfur impregnation temperature on the properties and mercury adsorption capacities of activated carbon fibers (ACFs). Environ. Sci. Technol. 35 (13), 2785e2791. Kalaee, M.R., Famili, M.H.N., Mahdavi, H., 2009. Synthesis and characterization of polysulfide rubber using phase transfer catalyst. Macromol. Symp. 277 (1), 81e86.
Karatza, D., Lancia, A., Musmarra, D., Pepe, F., Volpicelli, G., 1996. Removal of mercuric chloride from flue gas by sulfur impregnated activated carbon. Hazard. Waste Hazard. Mater. 13 (1), 95e105. Lambert, J.B., Shurvell, H.F., Lightner, D.A., Cooks, R.G., 1987. Introduction to Organic Spectroscopy. In: Group FrequenciesInfrared and Raman, vol. 9. Macmillan Publishing Company, New York. Mason, R.P., Reinfelder, J.R., Morel, F.M., 1996. Uptake, toxicity, and trophic transfer of mercury in a coastal diatom. Environ. Sci. Technol. 30 (6), 1835e1845. Mialki, W.S., Stiefel, E.I., Bruce, A.E., Walton, R.A., 1981. X-ray photoelectron spectra of inorganic molecules. 28. Sulfur 2p binding energies of molybdenum complexes believed to contain a coordinated partial disulfide bond. Inorg. Chem. 20 (5), 1614e1616. Morel, F.M.M., Kraepiel, A.M.L., Amyot, M., 1998. The chemical cycle and bioaccumulation of mercury. Annu. Rev. Ecol. Syst. 29, 543e566. Mostafa, M.R., 1997. Adsorption of mercury, lead and cadmium ions on modified activated carbon. Adsorp. Sci. Technol. 15 (8), 551e557. Namasivaysm, C., Kadirvelu, K., 1999. Uptake of mercury(II) from wastewater by activated carbon from an unwanted agricultural solid waste by-product: coirpith. Carbon 37 (1), 79e84. Scrocco, M., 1989. Mercury(II) chalcogenides. X-ray and electronenergy-loss spectra. J. Electron Spectros. Relat. Phenomena 49 (2), 139e148. Sellmann, D., Kremitzl, H., Knoch, F., Moll, M., 1996. Protonation, alkylation, addition and redox reactions of 16, 17 and 18 valence electron [Mo(L)(NO)(‘S4’)] complexes [L ¼ SPh, PMe3, NO; ‘S4’2 ¼ 1,2-bis-(2-mercaptophenylthio)ethane(2-)]. J. Biol. Inorg. Chem. 1 (2), 127e135. Sinner, F., Buchmeiser, M.R., Tessadri, R., Mupa, M., Wurst, K., Bonn, G.K., 1998. Dipyridyl amide-functionalized polymers prepared by ring-opening-metathesis polymerization (ROMP) for the selective extraction of mercury and palladium. J. Am. Chem. Soc. 120 (12), 2790e2797. Stro¨mberg, D., Stro¨mberg, A., Wahlgren, U., 1991. Relativistic quantum calculations on some mercury sulfide molecules. Water Air Soil Pollut. 56 (1), 681e695. Wang, J., Deng, B., Wang, X., Zheng, J., 2009. Adsorption of aqueous Hg(II) by sulfur-impregnated activated carbon. Environ. Eng. Sci. 26 (12), 1693e1699. Yantasee, W., Warner, C.L., Sangvanich, T., Addleman, R.S., Carter, T.G., Wiacek, R.J., Fryxell, C.T., Warner, M.G., 2007. Removal of heavy metals from aqueous systems with thiol functionalized superparamagnetic nanoparticles. Environ. Sci. Technol. 41 (14), 5114e5119. Zhu, J., Deng, B., Yang, J., Gang, D., 2009. Modifying activated carbon with hybrid ligands for enhancing aqueous mercury removal. Carbon 47 (8), 2014e2025.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 6 1 e4 7 2
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Implications of land disturbance on drinking water treatability in a changing climate: Demonstrating the need for “source water supply and protection” strategies Monica B. Emelko a,*, Uldis Silins b, Kevin D. Bladon c, Micheal Stone d a
Civil and Environmental Engineering, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1 Renewable Resources, University of Alberta, Edmonton, Alberta, Canada T6G 2H1 c Natural Resource Sciences, Thompson Rivers University, Kamloops, British Columbia, Canada V2C 5N3 d Geography, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1 b
article info
abstract
Article history:
Forests form the critical source water areas for downstream drinking water supplies in
Received 30 April 2010
many parts of the world, including the Rocky Mountain regions of North America. Large
Received in revised form
scale natural disturbances from wildfire and severe insect infestation are more likely
26 August 2010
because of warming climate and can significantly impact water quality downstream of
Accepted 30 August 2010
forested headwaters regions. To investigate potential implications of changing climate and
Available online 17 September 2010
wildfire on drinking water treatment, the 2003 Lost Creek Wildfire in Alberta, Canada was studied. Four years of comprehensive hydrology and water quality data from seven
Keywords:
watersheds were evaluated and synthesized to assess the implications of wildfire and post-
Source water supply and protection
fire intervention (salvage-logging) on downstream drinking water treatment. The 95th
Treatability
percentile turbidity and DOC remained low in streams draining unburned watersheds
Adaptation
(5.1 NTU, 3.8 mg/L), even during periods of potential treatment challenge (e.g., stormflows,
Wildfire
spring freshet); in contrast, they were elevated in streams draining burned (15.3 NTU,
Climate change
4.6 mg/L) and salvage-logged (18.8 NTU, 9.9 mg/L) watersheds. Persistent increases in these
Integrated water management
parameters and observed increases in other contaminants such as nutrients, heavy metals,
Drinking water treatment
and chlorophyll-a in discharge from burned and salvage-logged watersheds present important economic and operational challenges for water treatment; most notably, a potential increased dependence on solids and DOC removal processes. Many traditional source water protection strategies would fail to adequately identify and evaluate many of the significant wildfire- and post-fire management-associated implications to drinking water “treatability”; accordingly, it is proposed that “source water supply and protection strategies” should be developed to consider a suppliers’ ability to provide adequate quantities of potable water to meet demand by addressing all aspects of drinking water “supply” (i.e., quantity, timing of availability, and quality) and their relationship to “treatability” in response to land disturbance. ª 2010 Elsevier Ltd. All rights reserved.
* Corresponding author. Tel.: þ1 519 888 4567x32208; fax: þ1 519 888 4349. E-mail address:
[email protected] (M.B. Emelko). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.08.051
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1.
Introduction
1.1.
Source water protection (SWP)
In 2008, supply and protection of drinking water sources was identified as the top strategic priority of North American water professionals (Runge and Mann, 2008). This is not surprising, given that rapidly expanding demand and associated increased competition over existing water supplies from industrial and municipal development is a major challenge facing water managers globally. This challenge is amplified by changes in water quality, quantity, and timing of availability that are caused by climate change and associated land disturbances. Accordingly, effective and sustainable use and management of water requires integration of water and land management that is specifically linked to characteristics of physiographic regions that supply water. Much of this integrated management has been historically considered in the context of “source water protection”. Traditional SWP plans are designed “to control or minimize the potential for introduction of chemicals or contaminants in surface water. that pose a threat to human health as well as aquatic life” (Alberta Environment, 2006). They also state or imply that “watershed protection approaches. safeguard drinking water supplies from potential contamination as a way to ensure the highest quality water and to reduce treatment costs” (USEPA, 1997). Although SWP plans may prevent anthropogenic water quality changes, they often cannot prevent or mitigate the water quality impacts associated with climate change and natural land disturbances (e.g., wildfire, severe flooding) nor are they focused on or able to increase quantity and/or control availability of water supplies.
1.2.
Treatment infrastructure design and operation
For surface water supplies of drinking water, the most common treatment approaches are conventional (coagulation, flocculation, clarification, granular media filtration, and disinfection), direct and inline filtration (conventional treatment without clarification), direct microfiltration (screening, microfiltration membranes, and disinfection), and reverse osmosis (RO) (screening, RO membranes, and disinfection) (MWH, 2005). Although treatment process selection, design, and operation are based on numerous factors that are not limited to source water quality, quality-based threshold values and ranges play a significant role in designing new or optimizing existing treatment processes. The basic principles of water treatment process design (Table 1) have been detailed by MWH (2005), who note that conventional water treatment processes are typically used to treat surface waters with high turbidity, color, or total organic carbon (TOC). Direct and inline filtration processes are typically used to treat higher quality surface waters with low turbidity, moderate to low color, and low TOC; while microfiltration processes are typically limited to treating good-quality surface waters with low turbidity, low color, and low TOC. Reverse osmosis is mainly used for desalination of seawater or brackish water and may be used for specific contaminant removal such as NOM (natural organic matter) from surface waters and color from
Table 1 e Key water quality thresholds associated with surface water treatment process selection and design (MWH, 2005). Process
Conventional Direct/Inline filtration Microfiltration
Turbidity (NTU)
Color (color units)
Dissolved Organic Carbon (DOC) (mg/L)
high >20 NTU low 15 NTU low 10 NTU
high >20 c.u.
high >4 mg/L low <4 mg/L low <4 mg/L
moderate-low 20 c.u. moderate-low 10 c.u.
groundwater (MWH, 2005). These process options represent widely differing infrastructure and operations costs that are not proportional to the amount of potable water produced. As a result, many technologies available to large systems may be too expensive or complicated for small systems to consider, sometimes making it difficult to meet all regulatory requirements. Land disturbance and/or climate-associated changes in source quality may present incremental cost increases for water treatment operations (e.g., increased chemical consumption), while others may necessitate new infrastructure to remove new target compounds (e.g., heavy metals, algae) or treat the associated challenges that they create (e.g., taste and odor compounds, toxic algal by-products). Some changes in source quality may not be significant in magnitude or from a health perspective (e.g., turbidity, DOC, color); however, they may produce shifts in source water quality beyond critical design threshold ranges (Table 1) so that treatment approaches must be modified; resulting in substantial infrastructure, operations, and personnel costs. Accordingly, it is critical to develop strategies that optimize treatment technology use, but also extend beyond technology dependence and traditional SWP to incorporate issues of drinking water supply and treatment.
1.3. Forested watersheds: a clear demonstration of the need to move beyond SWP In western North America, forested headwaters provide the vast majority of usable surface water supplies to downstream regions. These regions provide approximately 2/3 of all water supplies, including drinking water for w180 million people in the U.S. (Stein et al., 2005; Stein and Butler, 2004). In Alberta, Canada, the overwhelming majority of useable surface water supplies for communities originate from the forested Eastern Slopes of the Canadian Rocky Mountains. Ironically, the high quality and quantity of water resources from forested regions makes these source waters particularly vulnerable to impacts of climate change, which creates favorable conditions for catastrophic natural disturbances such wildfire, insect outbreaks and disease (Kurz et al., 2008; Kitzberger et al., 2007; Westerling et al., 2006; Flannigan et al., 2005; Dale et al., 2001). For example, the linkage between increased frequency and severity of large, catastrophic wildfires and climate change is now well established (Westerling et al., 2006; Flannigan et al., 2005). Over the past
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 6 1 e4 7 2
two decades, longer fire seasons and increased occurrence of large and severe wildfires have been attributable to warmer temperatures, earlier spring snowmelt, and drier vegetation (Westerling et al., 2006). Increases of 74e118% in wildfire season length, fire severity, and area burned in Canadian forests have been projected by the end of the century (Flannigan et al., 2005). Similar trends during inevitable dry years are anticipated in the U.S. (Lenihan et al., 2003; Bachelet et al., 2001). The mid-elevation areas of the northern Rocky Mountains are one of the most vulnerable regions in North America, accounting for as much as 60% of recent increases in large wildfires (Westerling et al., 2006). Forested landscape disturbances, such as wildfire, can significantly impact both water quality and quantity in headwater streams by a combination of hydrologic processes including dramatic decreases of evaporative losses (interception of precipitation and transpiration) from the forest canopy, increases in soil moisture and runoff generation from hillslopes. These, in turn, can produce greater storm runoff including large peakflows, and increase overall water production from fire-affected landscapes (DeBano et al., 1998). Large changes in physical/chemical stream water quality typically include increased concentration and export of sediments (Silins et al., 2009; Moody et al., 2008), nutrients (Bladon et al., 2008; Mast and Clow, 2008; Silins et al., in review), and some trace metals (Kelly et al., 2006). Thus, wildfires can produce a series of physical, chemical, and biological impacts on downstream river environments that have important design, operating, and cost implications for drinking water treatment processes. No substantive evaluation of how these source water impacts to water quality, which produce subsequent impacts on downstream drinking water treatment, has been reported. Moreover, although the assumption that “source protection ¼ no anthropogenic impacts on source watershed landscapes ¼ water quality stability” describes the essence of many current approaches to developing SWP strategies, it does not acknowledge the climate change-associated increased risk of catastrophic land disturbance that is particularly evident in forested regions.
1.4.
Research objectives
Here, impairment of water quality by wildfires in forested source water regions was examined as a critical vulnerability of downstream water treatment processes. In 2003, one of the most severe recorded fires (Lost Creek wildfire) occurred in the eastern slopes of the Rocky Mountains of southern Alberta, Canada and impacted several aspects of water quality and streamflow in the upper Oldman River Basin (ORB). Data from source watersheds with varying degrees of wildfire associated land disturbance (reference [unburned], burned, and post-fire salvage-logged) were collected and evaluated during the four years post-fire. Some of the water quality impacts during these recovery years have been reported elsewhere, while others are reported herein. Rather than attempt to predict or demonstrate the impacts of wildfire and salvage-logging on a specific downstream drinking water treatment plant, all of the studied water quality impacts of wildfire in the ORB are synthesized and analyzed to provide a holistic discussion of downstream threats to drinking water “treatability” that can
463
be associated with upstream wildfire and post-fire intervention (salvage-logging). Accordingly, this analysis of water quality impairment resulting from wildfire is used as a case study to demonstrate 1) the impacts of wildfire and post-fire salvage-logging on drinking water “treatability”, 2) a general approach for assessing potential drinking water “treatability” implications of land disturbance, and 3) the need for developing strategies for effectively and sustainably managing water resources in anticipation of local climate change and other natural or anthropogenic land disturbances.
2.
Methods
2.1.
Study sites and sampling approach
The Oldman, Crowsnest, and Castle Rivers flow eastward from the Rocky Mountain headwaters of the ORB, which has been closed to the issuing of new water extraction licenses due to a growing imbalance between demand and supply. Hydrologically, the southern Rockies in Alberta are the highest water yielding region of the province. Landscape associated impacts on water quality in the headwaters forests of the ORB are representative of increasing pressures related to land use change in many regions of North America. During JulyeSeptember 2003, the Lost Creek wildfire burned more than 21,000 ha in the headwaters of the Castle and Crowsnest Rivers, consuming practically all forest cover and floor organic matter in the burned watersheds. Seven research watersheds were established shortly after the fire. Hydrometric and water quality sampling stations were installed at the outlet of each watershed to document changes in water quantity and quality. Three burned (South York, Lynx, and Drum Creeks) and two unburned (reference) watersheds (Star and North York Creeks) were established prior to the first post-fire spring snowmelt in MarcheApril 2004 and two additional burned and salvage-logged watersheds were added in January 2005 (Lyons Creek East and West). Watersheds selected for study did not have significant historical logging disturbance prior to the fire. A multi-level hydrometric and water quality sampling program was employed to balance measurement of weather, streamflow, and water quality while optimizing the logistical and financial constraints of working in this remote environment. Details regarding the area, elevation, extent of burn, and hydrometric sampling program (categorized into three dominant flow periods: baseflow/non-event [summer and winter], snowmelt freshet, and stormflow [resulting from rainfall in each watershed]) are described in Bladon et al. (2008), Silins et al. (2009, in review). Water quality sampling involved collection of two separate (overlapping) data sets. The first data set was collected using manual (depth integrated) sampling consisting of instantaneous discharge and water quality measurements every 10 days during snowmelt freshet, every 14 days after the freshet during the ice-free periods, and approximately every 1e2 months throughout winter. Periodic storm events were also sampled. Collection of a second, continuous data set began in the spring of 2005. Automated water samplers were used to collect composite daily samples (four 250 mL sub-samples
464
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collected every 6 h) during ice-free periods from May to October in a sub-set of unburned (Star Creek), burned (South York and Drum Creek) and salvage-logged (Lyons Creek East) watersheds.
2.2.
Water quality and sediment analyses
Dissolved organic carbon (DOC) concentrations were determined using standard methods (Method 5310B; APHA et al., 2005). Stream turbidity was measured at 10-min intervals during ice-free periods using calibrated multiparameter sondes (YSI Models 6820 and 6920). Turbidity of samples collected by manual and automated sampling was measured with a benchtop turbidimeter (Hach 2100) using Standard Method 2130B (APHA et al., 2005). During the jar tests, turbidity was also measured using a benchtop turbidimeter (VWR Model 66120-200). Laboratory methods for dissolved organic nitrogen (DON) are described in Bladon et al. (2008). Total phosphorus (TP) and periphyton sampling and analysis are outlined in Silins et al. (in review). Microbial community analysis of biofilm formed on sediment collected from reference and wildfire-impacted streams after 2, 7, and 14 days of consolidation in an annular flume was described in Stone et al. (2010).
2.2.1.
Jar Testing
Standard jar tests were utilized to evaluate optimal polyaluminum chloride (PACl) (SternPAC, Kemira Water Solutions Inc., Brantford, ON) coagulant doses that would be required to effectively treat the source waters from the various catchments. These tests were conducted five years post-fire. Samples obtained during stormflow were collected on May 24, 2008 after 60e160 mm of rain had fallen during a 3-day period. Samples during baseflow were collected on August 21, 2008 (no precipitation during the preceding 14 days). The first series of jar tests was conducted at ambient conditions, with raw water temperatures of 8.2 C 0.2 C (mean one standard deviation) during stormflow. Each jar was filled with 2 L of raw water and rapid mixing commenced at 300 rpm [i.e., G z 300e400 s1] with PACl addition at varying concentrations to each of the jars. After 30 s, mixing was lowered to 70 rpm (i.e., G z 50e65 s1) for 3 min, followed by 10 min at 35 rpm (i.e., G z 23e28 s1). The particles/aggregates then settled for 15 min. Triplicate samples
of supernatant were collected for immediate pH and turbidity analyses and subsequent DOC analyses.
3.
Results and discussion
3.1.
Turbidity
During the first four post-wildfire years (2004e2007), all of the water sampling programs in the study watersheds indicated that water turbidities were relatively low most of the time. Nonetheless, watersheds affected by wildfire and post-fire salvage-logging did produce markedly higher turbidities on some occasions (Table 2). For example, the combined (manual, daily composite, and 10-min interval) data indicate that even during periods of potential treatment challenge (e.g., stormflows, spring freshet) turbidity typically remained low in reference catchments, with 95th percentile turbidity reaching 5.1 NTU; similar trends in TSS concentrations (Silins et al., 2009) were observed (Table 2). In contrast, discharge from burned and salvage-logged catchments demonstrated more variable water quality and considerable increases in turbidity (e.g., 95th percentile turbidities of 15.3 and 18.8 NTU respectively) (Table 2) that could deleteriously impact treatment by contributing to increased dependency on and/or difficulty in maintaining efficiency of solids removal processes such as coagulation/flocculation/sedimentation (C/F/S), sludge production, oxidant demand, etc. Overall, increased operating costs and compliance concerns could be expected in impacted areas depending on existing treatment approaches and capacities. For example, wildfire and salvage-logging near communities where membrane processes are relied upon would likely necessitate retrofitting of expensive conventional treatment infrastructure to meet such changes in solids treatment needs. Given a “very high confidence” in increased occurrence and severity of wildfire in North America due to climate change (IPCC, 2007), the extent of these impacts on drinking water treatment process performance and costs may likely depend on how infrastructure is adjusted in anticipation of their occurrence. Fig. 1 is a boxplot of the various water quality parameters measured using manual and daily composite sampling in the study watersheds. The contrast between the turbidity values reported in Table 2 and Fig. 1 underscores the importance of continuous monitoring (every few minutes) of rapidly
Table 2 e Median, mean, 90th percentile, 95th percentile and maximum turbidity and TSS in reference, burned, and salvage-logged watersheds during first four years after wildfire (2004e2007). Watershed
Turbidity (NTU) Median
Mean
90th percentile
95th percentile
Maximum
Reference Burned Salvage-logged
0.7 3.5 5.1
1.7 7.1 7.2
3.1 10.4 12.6
5.1 15.3 18.8
881 1311 1179
Reference Burned Salvage-logged
0.8 3.1 2.1
2.8 30.2 22.9
7.7 73.2 148.0
62.0 821.0 260.0
Total Suspended Solids (mg/L) 5.9 43.2 59.5
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 6 1 e4 7 2
465
Fig. 1 e Boxplot of (a) TSS (Silins et al., 2009), (b), turbidity, (c) DOC, (d) DON (Bladon et al., 2008), (e) TP (Silins et al., in review), and (f) chlorophyll-a (from attached periphyton) (Silins et al., in review) in concentrations in reference, burned, and salvagelogged watersheds in each of the four years after wildfire. Turbidity data obtained using manual and daily composite sampling only. The whisker indicates the range spanning 1.5 times the interquartile range.
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1000
a
Turbidity (NTU)
100
Reference Burned Burned & Salvaged
10
1
0.1
DOC Concentration (mg l-1)
0.01 12
b
10 8 6 4 2 0 Baseflow
Snowmelt Freshet
Stormflow
Fig. 2 e Boxplot of (a) turbidity and (b) dissolved organic carbon (DOC) concentration (2004e2007) in streams draining reference, burned, and salvage-logged watersheds during baseflow, snowmelt, and stormflow. Turbidity data obtained using manual and daily composite sampling only.
changing water quality parameters such as turbidity and reporting of extreme values (e.g., maxima, 95th or 99th percentiles) that are critical to treatment process design, but not commonly reported in watershed-scale studies.
3.2.
Dissolved organic carbon (DOC)
DOC discharge also increased from watersheds impacted by wildfire and post-fire salvage-logging. DOC is a common surrogate for describing aqueous levels of NOM, which is often considered a pollutant that governs coagulant dosing, particularly in low alkalinity and low turbidity source waters such as those flowing from mountain catchments. Elevated levels of NOM can introduce taste and odor-causing chemicals into water supplies because of algal and bacterial activity and can result in increased production of potentially carcinogenic disinfection by-products (DBPs) such as trihalomethanes (THMs) and haloacetic acids (HAAs) when residual organics react with chlorine (Stevens et al., 1990) and other disinfectants (Krasner et al., 2006). NOM/DOC often drives coagulant dosing (O’Melia et al., 1999) and increased levels can deleteriously impact “treatability” by contributing to increased dependency on and/or difficulty in maintaining efficiency of solids removal processes such as C/F/S, sludge production, oxidant demand, etc. As with turbidity, increased source water DOCs can result in overall increases in operating costs and compliance concerns in impacted areas, depending on existing treatment approaches and capacities. Median DOCs in discharge from watersheds affected by wildfire (3 mg/L) and post-fire salvage-logging (5 mg/L) markedly increased relative to reference levels (1e2 mg/L) in the
first two years post-fire and remained elevated in the third and fourth years post-fire (2 and 3 mg/L respectively) (Fig. 1). Similar to turbidity, extreme values of DOC concentrations are relevant to drinking water treatment process performance because they often dictate infrastructure and chemical consumption requirements. The 95th percentile DOC values observed during the study period were 3.8, 4.6, and 9.9 mg/L in the streams draining the respective unburned, burned, and salvage-logged catchments. The maximum DOC concentrations observed in discharge from the reference, burned, and post-fire salvage-logged catchments were 7.9, 8.1, and 19.8 mg/L respectively. Relative to the reference catchments, both turbidity (Fig. 2a) and DOC concentrations (Fig. 2b) remained elevated in discharge from the burned and salvagelogged watersheds during baseflow, snowmelt, and stormflow during the four post-fire years of investigation. In the hydroclimatic setting of the ORB, post-event hydrograph recession conditions can influence water quality for approximately 3e7 weeks over the typical ice-free season; snowmelt freshet flow conditions (rising and falling limbs) occur over approximately 8e10 weeks. Given the high variability in stream discharge in this physiographic setting and the observed increased concentrations of DOC and high turbidity from the legacy of wildfire, it can be expected that there will be significant impacts on water treatability for several years (possibly decades) post-fire. The changes in source water turbidity and DOC associated with wildfire and post-fire salvage-logging described herein (Table 2, Fig. 1) can clearly contribute to increased dependency on solids removal processes such as C/F/S. Fig. 3a and b respectively present examples of a jar test conducted during
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 6 1 e4 7 2
baseflow and stormflow five years post-fire. As would be expected, source water turbidities in all of the mountain catchments were relatively low during baseflow sampling. During that period, the source water turbidities were all <1 NTU: mean turbidities ( one standard deviation) were 0.21 0.09, 0.56 0.48, 0.62 0.16 and 0.43 0.15 NTU in discharge from the reference, burned, and Lyons Creek East and West salvage-logged catchments respectively. Fig. 3a shows that low turbidities resulted in non-discernable differences in coagulant dosing requirements in the study catchments. In contrast, during a stormflow event, mean source water turbidities were w1.1 NTU in reference catchments and w107, w247, and w162 NTU in discharge from the burned, first, and second salvage-logged catchments respectively (Fig. 3b). Although not indicated in Fig. 3b, subsequent jar tests indicated required doses of only approximately 5 mg/L of PACl for effective settling of coagulated water in the reference
Fig. 3 e Residual turbidity (mean ± one standard deviation) from jar tests of water obtained from reference, burned, and salvage-logged watersheds five years after wildfire during (a) baseflow (mean temperature ± one standard deviation [ 17.2 C ± 0.3 C and mean pH ± one standard deviation [ 8.45 ± 0.10) and (b) stormflow (mean temperature ± one standard deviation [ 8.2 C ± 0.2 C and mean pH ± one standard deviation [ 8.33 ± 0.17; modified from (Emelko et al., 2008)).
467
catchments, whereas 40 and 50e60 mg/L PACl were required for water from burned catchments and salvage-logged catchments respectively. While specific coagulant dosing impacts would likely vary for utilities downstream of wildfire-impacted source watersheds, these results are presented to illustrate the general potential for significant increases in operational costs that may be associated with such source watershed disturbances.
3.3.
Nitrogen and phosphorous
Nutrient releases to source waters can be expected as a result of wildfire because fire causes losses of TN from forest floors and surface soils and the release of inorganic P from soil organic matter. Elevated post-fire concentrations of dissolved and particulate N may decline relatively rapidly within several years post-fire (Hauer and Spencer, 1998); however, the magnitude of wildfire effects on N recovery varies greatly (Bladon et al., 2008; Minshall et al., 1997) and data from severe fires are limited (Turner et al., 2007). The nitrogen concentrations measured in the ORB during the four post-fire years represent some of the highest post-fire N concentrations and subsequent recovery reported by any study (Bladon et al., 2008). Nonetheless, changes in N-species concentrations and composition are important in source watersheds because increases in some species may impact water treatment process optimization, chemical costs, taste and odor formation, and the formation of potentially toxic disinfection by-products (DBPs), including N-containing DBPs (N-DBPs) (e.g., dichloroacetonitrile, trichloronitromethane, and N-nitrosodimethylamine). Moreover, increased nutrient levels may have substantial impacts on water quality in reservoirs with significant residence times. Here, higher levels of DON were measured in discharge from the post-fire salvage-logged catchments, relative to burned and unburned catchments, even four years post-fire (Fig. 1). The mean concentration of DON over the four post-fire years was 205.3 mg/L (95th percentile: 756.6 mg/L) from the burned watersheds and 166.4 mg/L (95th percentile: 409.7 mg/L) in the salvage-logged watersheds, compared to 120.0 mg/L (95th percentile: 358.5 mg/l) in the unburned. DON promotes the formation of N-DBPs (Lee et al., 2007). Increased levels of ammonia such as those that could be anticipated in conjunction with increases in other N species may also have significant water treatment process impacts. When chlorine is added to water in the presence of ammonia, chloramines can form; in the presence of nitrogenated organic matter, chlorinated organic nitrogen compounds with much less germicidal efficacy than inorganic chloramines can form. The production of monochloramine, dichloramine, and trichloramine is dependent upon pH, the ratio of chlorine to ammonia-nitrogen and, to a lesser extent, temperature and contact time (Wolfe et al., 1984). While monochloramine can be used to provide effective disinfectant residual, dichloramine is a less effective disinfectant and its formation causes taste and odor problems (Suffet et al., 1995). The chlorine demand of organic nitrogen compounds can upset the chlorine to ammonia ratios necessary for maximizing monochloramine formation while minimizing free ammonia available to form dichloramine or be
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discharged to the distribution system to act as a food source for nitrifying bacteria and contribute to nitrification problems. As a result, wildfire- and salvage-logging-associated increases in both DOC and DON (Fig. 1) may increase water utility challenges associated with providing adequate disinfection while limiting residual chlorine, balancing chlorine to ammonia ratios, and minimizing DBP formation. Wildfire impacts on phosphorous (P) were also evident with elevated levels observed in discharge from both burned and post-fire salvage-logged catchments (Fig. 1). Mean TP concentrations over the four post-fire years were 42.2 mg/L (95th percentile: 121.0 mg/L) and 41.2 mg/L (95th percentile: 210.0 mg/L) in streams draining the burned and salvage-logged watersheds respectively, compared to 5.7 mg/L (95th percentile: 12.4 mg/L) in streams draining unburned watersheds. In the initial two post-fire years, mean annual TP concentrations were 7e9 times higher in discharge from burned and salvagelogged catchments relative to unburned (Silins et al., in review). In the third and fourth post-fire years, TP concentrations remained 3e8 times higher in the disturbed watersheds, indicating slow recovery. This is likely the result of the strong affinity of P for sediment, which may prolong in-stream TP (Stone and English, 1993). Persistent elevated concentrations of TP are concerning because they have been frequently linked with the presence of microcystins produced by Cyanobacteria (Giani et al., 2005; Kotak et al., 2000), elevated levels of which have been associated with gastroenteritis (Hitzfeld et al., 2000) and, in some cases, liver toxicity and death (Chorus and Bartram, 1999). Increased concentrations of bioavailable phosphorus can increase microbial growth in distribution systems (Miettinen et al., 1997) and also prolong the survival of culturable Escherichia coli in water and biofilms (which can also act as a reservoir for microorganisms) in drinking water distribution systems (Juhna et al., 2007). Coagulation, flocculation, and sedimentation (or other clarification processes) may be necessitated for phosphorus removal.
3.4.
Mercury
Discharge after wildfire also produces pulsed exports of some heavy metals such as mercury (Hg). Two years after the wildfire, mean total Hg concentrations in discharge from postfire salvage-logged watersheds were w60% higher than those observed in only burned watersheds; chronic (0.005 mg/L) and acute (0.013 mg/L) total Hg provincial water quality guidelines (Alberta Environment, 1999), established to respectively protect the water body as a whole and to limit lethality to organisms, were exceeded in discharge from both burned and salvage-logged watersheds on 32 and 11 respective occasions during those years (Kelly, E.N., Schindler, D.W., Silins, U., Wagner, M., and Graydon, J., unpublished). Total Hg concentrations observed in these discharges were elevated relative to those in discharge from the reference catchment and notably exceeded both the U.S. EPA Maximum Contaminant Level (MCL) of 2 mg/L and Canadian Maximum Acceptable Concentration (MAC) of 1 mg/L (Health Canada, 2008) on at least one occasion (265 mg/L) after a large flood event during the first two post-fire years. The form of Hg was not determined; regardless, this confirmed result has implications regarding the
adequacy of monitoring requirements of Hg sampling every three months if Hg levels that exceed the MCL are observed. Fire characteristics (i.e., fire severity, proportion of catchment burned, and timing and intensity of runoff) influence limiting nutrient and contaminant release from burned catchments, altering the relative importance of Hg accumulation mechanisms (food web restructuring and increased Hg inputs and MeHg production) (Kelly et al., 2006). In such environments, event-associated (e.g., storm event) sampling may be more appropriate to determine if additional treatment is required for effective removal of Hg from source water.
3.5.
Biological impacts
A clear ecological response in mountain headwater streams was also associated with wildfire (Silins et al., in review). Periphyton-associated chlorophyll-a increased as a result of the wildfire, with similar levels observed in burned and salvage-logged catchments (Fig. 1). Periphyton is comprised of a complex mixture of algae and heterotrophic microorganisms that is attached to submerged substrata and represents an important general indication of water quality in lotic waters because community responses to pollutants can be measured at a variety of times scales representing physiological to community-level changes. Increased levels of algae and short-term algal blooms can cause many treatment challenges. The volatile organic compounds geosmin (trans1,10-dimethyl-trans-9-decalol) and MIB (2-methylisoborneol) are responsible for the majority of reported taste and odor events in surface waters and are secondary metabolites produced by actinomycetes (bacteria) and blue-green algae (Cyanobacteria) (Wnorowski, 1992). Pilot-scale investigations that involved growing sediment-associated biofilm demonstrated that sediments obtained from streams draining burned watersheds yielded increased levels of mid-chain branched saturated structures of phospholipid fatty acids (PLFA; associated with Actinobacteria) as compared to those obtained from unburned watersheds (Stone et al., 2011). In addition to producing taste and odor-causing compounds, algae can also impair coagulation and flocculation processes (Bernhardt, 1984), shorten filter run times because of filter clogging or breakthrough of algae and other particulate matter (Janssens et al., 1989; Bernhardt, 1984), contribute to microbial regrowth in the distribution system (Schmidt et al., 1998), produce microcystins (Chorus and Bartram, 1999), and increase oxidant demand. Algae-excreted metabolic products (which contribute to DOC), as well as the algal cells themselves, can be precursors for DBPs; even in systems that utilize ozonation for primary disinfection rather than chlorination (Plummer and Edzwald, 1998).
3.6. “Source water supply and protection”: integrating SWP and “treatability” assessment Many traditional SWP strategies would fail to adequately identify and evaluate many of the significant drinking water treatment implications of wildfire- and post-fire salvagelogging discussed above and summarized in Table 3. For example, it was demonstrated above that turbidity/TSS and DOC, which are critical water quality parameters known to
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Table 3 e Water quality parameters impacted by wildfire and their potential implications to 808 drinking water treatability (modified from Emelko et al., 2008). Impact on Treatment
Parameter
Need for solids removal (C/F/S) [ Coagulant demand [ Sludge production [ Oxidant demand [ DBPs [ Fluence required for UV [ microcystins [ Taste and odor concerns Compliance concerns [ Operating costs
Turbidity
TP
U U U U U
U
DON and TKN
Hg
U U U
DOC
Chl.-a
U U U U U U
U U U U U U U U U U
U U U
U
drive coagulant dosing and can potentially have substantial impacts on it as a result of wildfire and post-fire salvagelogging, (Table 2, Fig. 1), also vary with hydroclimatic conditions. These same water quality data can be presented as exceedance curves (Fig. 4) to highlight differences in the proportion of the time that DOC and turbidity in the study catchments exceeded key design thresholds. In contrast to
U U U
U U U
U U
Fig. 1, the data in Fig. 4 are more extensive and include three overlapping turbidity datasets: manual samples, daily composite samples, and samples collected at 10-min intervals using calibrated multiparameter sondes. The contrast between these figures underscores the need for adequate sampling of extreme water quality conditions (such as those linked to hydroclimatic conditions) that may impact design
20
Unburned Burned Salvage logged
-1
DOC (mg l )
10
1
1000
Unburned Burned Salvage logged
Turbidity (NTU)
100
10
1
0.1 0.0001
0.001
0.005 0.01
0.05
0.1
0.2
0.5
1
Proportion of time DOC or turbidity is greater than or equal to value on ordinate Fig. 4 e Residual DOC and turbidity exceedance curves for water in reference, burned, and salvage-logged watersheds during four post-fire years (n [ 105 per series). Turbidity data are based on manual samples, daily composite samples, and 10-min interval data obtained using calibrated multiparameter sondes.
470
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and operations decisions. Fig. 4 illustrates that reference catchment source water exceeded 10 NTU of turbidity or 4 mg/L of DOC w2% and 4% of the time during the study period respectively. In contrast, burned catchment source water turbidities and DOCs respectively exceeded those values 11% and 9% of the time (w1 month per year); these targets were respectively exceeded 16% and 48% of the time in post-fire salvage-logged catchments (Fig. 4). Many drinking water treatment plants are not designed or equipped to handle extreme water quality variations that exceed design thresholds and potentially necessitate different infrastructure requirements for periods of one month or more per year. Accordingly, plants at reasonable risk of experiencing such changes in source water quality would be faced with significant infrastructure and operational costs and decisions. Moreover, although it is commonly recognized that lower turbidity waters are likely safer from a health standpoint and the potential for harmful DBPs formation is lower when DOC is lower, these and other key water quality parameters discussed herein are not themselves generally considered “contaminants” of significant health risk, often making them relatively insignificant from an SWP standpoint. Our work demonstrates that changes in water quality that may occur as a result of land disturbances (e.g., wildfire or prescribed fire in forested watersheds) can have catastrophic effects on source water quality and drinking water treatment approach and/or capacity. Accordingly, a more holistic approach to evaluating critical vulnerabilities within source watersheds is necessary for enabling the development of essential adaptation and mitigation strategies to improve drinking water treatment system flexibility and resiliency to respond to the impacts of land disturbance and changing climate. A critical difference between the approach proposed herein and traditional SWP strategies is the necessary consideration of not only “contaminants” that impact human and ecosystem health, but also other water quality parameters (e.g., DOC, turbidity) that may considerably affect the cost of treatment of for potable water production. We propose that the concept of source water “supply” should be extended to consider not only the amount and quality of water available for potable water production but also the act (and cost) of providing that safe drinking water. Analogously, SWP strategies should be expanded into “source water supply and protection” (SWSP) strategies that also include “treatability” assessments that evaluate the costs and benefits of specific treatment process infrastructure selection, design, and operation as they relate to current and anticipated source water quality, which is governed by climate and the condition of source watershed landscapes. More specifically, “treatability” assessments should evaluate a drinking water suppliers’ ability to: 1) treat water to achieve appropriate quality standards, 2) provide adequate quantities of potable water to meet demand, 3) adequately respond to changing water quality conditions by either utilizing robust treatment processes that are resilient to changing water quality conditions and maintain the production of high quality potable water and/or adjusting treatment processes in a timely manner that does not disrupt the supply of high quality potable water, and 4) accomplish all of the above at reasonable cost. Addressing the substantial multi-regional and
jurisdictional challenges associated with the implementation of SWSP strategies into policy and governance frameworks is beyond the scope of this work; however, this work provides the scientific basis for justifying the critical need for SWSP or similar strategies.
4.
Conclusions
The reported impacts on water quality in Alberta, Canada’s ORB are representative of increasing threats to source waters from land disturbances that have been experienced along the entire North American Rocky Mountain range. This evaluation demonstrated that: 1. Turbidity/TSS, DOC, TP, DON, Hg, chlorophyll-a, and Actinobacteria-like microbial concentrations were all higher in streams draining burned and salvage-logged watersheds than in those draining reference watersheds and presented important infrastructure and operational challenges for water treatability; most notably, potentially increased dependence on expensive solids and DOC removal infrastructure. 2. TP, DOC, and chlorophyll-a remained elevated in the discharge from burned and salvage-logged catchments four years after the wildfire; this result was particularly evident during stormflow. Turbidity/TSS and DON demonstrated some recovery after the wildfire, with levels in the third and fourth post-fire years approaching reference levels. Postfire recovery of water quality must be evaluated cautiously, however, because it is inextricably linked with hydroclimatic setting. Accordingly, definitive conclusions regarding ecosystem recovery are premature. 3. The “recovery” of water quality parameters to levels observed prior to disturbances must be carefully interpreted because “recovery” is a matter of perspective. Much of the published literature considers recovery from watershed hydrology and ecological perspectives in which “rapid recovery” may occur over time frames of years. In contrast, “rapid recovery” during water treatment requires returns to baseline values within hours, days, or weeks, depending on available water storage capacity. Accordingly, when “rapid recovery” is not possible, robust design and operation of treatment processes is particularly critical. 4. Typically reported watershed-scale data obtained at greater time intervals (e.g., weekly, monthly, etc.) or at conditions that are not representative of periods of greatest treatment challenge (e.g., samples that do not represent extreme values of parameters such as turbidity or DOC), must be interpreted with caution because they are less relevant to water treatment design and practice. Detailed data collection regard extreme values is necessary for evaluating water “treatability”. 5. Water “treatability” assessments evaluate a drinking water suppliers’ ability to: 1) treat water to achieve appropriate quality standards (i.e., meet regulatory targets for the protection of public health), 2) provide adequate quantities of potable water to meet demand, 3) adequately respond to changing water quality conditions by either utilizing robust treatment processes that are resilient to changing water
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 6 1 e4 7 2
quality conditions and maintain the production of high quality potable water and/or adjusting treatment processes in a timely manner that does not disrupt the supply of potable water, and 4) accomplish these at reasonable cost. Therefore, water “treatability” assessments evaluate the costs and benefits of specific treatment process infrastructure selection, design, and operation as they relate to current and anticipated source water quality which, is governed by climate and the condition of source watershed landscapes. 6. Changes in water quality that may occur as a result of land disturbances such as wildfire can potentially have catastrophic effects on source water quality and drinking water treatment approach and/or capacity. Consequently, “Source Water Supply and Protection” (SWSP) or similar strategies that integrate SWP and “treatability” assessments are essential for enabling the development of adaptation and mitigation strategies to improve drinking water treatment system flexibility and resiliency to respond to the impacts of land disturbance and changing climate.
Acknowledgments The Southern Rockies Watershed Project is funded by Alberta Sustainable Resource Development Forest Management Branch, Natural Sciences and Engineering Research Council of Canada, Alberta Water Research Institute, Oldman Watershed Council, Alberta Ingenuity Centre for Water Research, Alberta Environment, Canadian Foundation for Innovation, and Fisheries and Oceans Canada. M. Wagner, C. Williams, L. Steinke, C. McCarthy, J. Farkvam, J. Sneddon, J. Howery, I. Tichkowsky, K. Geng, and E.F. Smith provided critical technical assistance.
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Stein, S.M., McRoberts, R.E., Alig, R.J., Nelson, M.D., Theobald, D.M., Eley, M., Dechter, M., Carr, M., 2005. Forests on the Edge: Housing Development on America’s Private Forests. In: Gen. Tech. Rep. PNW-GTR-636. USDA Forest Service, Pacific Northwest Research Station, Portland, OR. Stevens, A.A., Moore, L.A., Slocum, C.J., Smith, B.L., Seeger, D.R., Ireland, J.C., 1990. By-products of chlorination at ten operating utilities. In: Jolley, R.L., et al. (Eds.), Water Chlorination: Chemistry, Environmental Impact and Health Effects. Lewis Publishers, Chelsea, MI, pp. 579e604. Stone, M., English, M.C., 1993. Geochemical composition, phosphorus speciation and mass transport characteristics of fine-grained sediment in two Lake Erie tributaries. Hydrobiologia 253, 17e29. Stone, M., Emelko, M.B., Droppo, I.G., Silins, U., 2011. Biostabilization and Erodibility of Cohesive Sediment Deposits in Wildfire-affected Streams. Water Res 45 (2), 521e534. Suffet, I.H., Mallevialle, J., Kawczynski, E., 1995. Advances in Taste-and-Odor Treatment and Control. AWWA, Denver, CO. Turner, M.G., Smithwick, E.A.H., Metzger, K.L., Tinker, D.B., Romme, W.H., 2007. Inorganic nitrogen availability after severe stand-replacing fire in the Greater Yellowstone ecosystem. Proc. Natl. Acad. Sci. U.S.A. 104 (12), 4782e4789. USEPA, 1997. State Source Water Assessment and Protection Programs: Final Guidance. In: EPA 816-R-97e009. Westerling, A.L., Hidalgo, H.G., Cayan, D.R., Swetnam, T.W., 2006. Warming and earlier spring increase western U.S. forest wildfire activity. Science 313 (5789), 940e943. Wolfe, R.L., Ward, N.R., Olson, B.H., 1984. Inorganic chloramines as drinking water disinfectants: a review. J. AWWA 76 (5), 74e88. Wnorowski, A.U., 1992. Taste and odours in the aquatic environment. Water SA 18, 203e214.
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Comparison of humic acid rejection and flux decline during filtration with negatively charged and uncharged ultrafiltration membranes Jiahui Shao*, Juan Hou, Hongchen Song School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China
article info
abstract
Article history:
Increasingly stringent regulations for drinking water quality have stimulated the ultrafil-
Received 17 May 2010
tration (UF) to become one of the best alternatives replacing conventional drinking water
Received in revised form
treatment technologies. However, UF is not very effectively to remove humic acid due to
2 September 2010
the comparatively larger pore size compared to the size of humic acid. Fouling issue is
Accepted 3 September 2010
another factor that restricts its widespread application. In this study, rejection of humic
Available online 15 September 2010
acid and flux decline were compared with essentially neutral, negatively charged version of a regenerated cellulose membrane, in which electrostatic interaction was explored for
Keywords:
a better humic acid removal and less fouling. Solution environment, including ionic
Charged ultrafiltration membrane
strength, pH and calcium ion concentration, affecting humic acid removal and flux decline
Humic acid
on negatively charged and neutral membranes was also compared. Results indicated that
Rejection coefficient
the appropriate charge modification on the neutral UF membrane could be an effective way
Flux
for better removal of NOM and reduction of the membrane fouling due to the electrostatic
Membrane fouling
interactions with the combination effect of membrane pore size. Electrostatic interactions
Solution environment
are significant important to achieve high humic acid removal and less fouling, and to improve the water quality and protect people’s health. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Natural organic matter (NOM) is commonly found in surface and ground waters (Kilduff et al., 1996) and considered to react with the major disinfectants to produce a host of disinfection by-products (DBPs) (Krasner et al., 2006; Zularisam et al., 2006). Therefore, the removal of NOM effectively is of significant importance in meeting the stringent DBPs regulations and providing safe drinking water. Ultrafiltration (UF) allows the removal of particles, turbidity, microorganism and certain amount of the dissolved organic matter. It has received considerable attention in recent years and has been increasingly used in drinking water treatment as alternative
technology to convention filtration and clarification (Katsoufidou et al., 2005; Susanto and Ulbricht, 2008). A major fraction of NOM present in surface or ground waters is composed of humic substances (HS) (Nystro¨m et al., 1996; Zularisam et al., 2006). Humic acid generally makes up the major fraction of humic substance and has thus been studied by many researchers as a model compound for natural organic matter in water (Yuan, 2001; Zularisam et al., 2006; Susanto and Ulbricht, 2008; Campinas and Rosa, 2010). Humic acids are highly polydisperse, with molecular weights ranging from 2 kDa up to over 500 kDa (Stevenson, 1982). Compared to the size of humic acid, UF with comparatively larger pore size is not very effectively to remove humic acid. In
* Corresponding author. Tel.: þ86 21 54745634; fax: þ86 21 54740825. E-mail address:
[email protected] (J. Shao). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.006
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addition, humic acid has been recognized as one of the key foulants during water treatment using membrane processes (Nystro¨m et al., 1996; Yamamura et al., 2007; Huang et al., 2007; Gray et al., 2007). Not very high removal rate for humic acid and membrane fouling limit the further applications of ultrafiltration to remove NOM from water (Wei et al., 2006; Fan et al., 2001). Recent studies have shown that charged ultrafiltration membranes can provide much higher solute retention than conventional (relatively uncharged) membranes due to the electrostatic exclusion of the charged solute from the same charged membrane pores (Mehta and Zydney, 2006; Cho et al., 2000; Wei et al., 2006). The thin-film-composite (TFC) UF membrane with a negatively charged surface was found to have greater NOM rejection than the neutral membrane which was made of regenerated cellulose (Cho et al., 2000). Although the TFC membrane used was hydrophobic, the adsorbed NOM was less in quantity and less fouling was found than that on the hydrophilic RC membrane. The study done by Wei et al. (2006) showed that their modified membrane surfaces exhibited more hydrophilic and negatively charged features after the electrophoresis-UV grafting treatment of the original polyethersulfone (PES) membrane, which can improve NOM retention and present lower fouling tendency than the unmodified membrane. Several studies have shown that UF membrane with negative charge had a greater NOM rejection and less fouling tendency than the neutral membrane with similar nominal MWCO under identical conditions. However, this comparison was normally based on the different membrane materials. Also, there has been no study of comparing the effect of the solution environment including calcium ion concentration on the humic acid removal and fouling on charged and uncharged UF membranes. The objective of this study was to investigate the effect of membrane charge on the removal of humic acid and its fouling during ultrafiltration with charged regenerated cellulose (RC) membrane, and compare with the neutral unmodified version of RC membrane. Solution environment, including ionic strength, pH and calcium ion concentration, affecting humic acid removal and flux decline was also investigated.
2.
Materials and methods
2.1.
Experimental materials
The diluted humid acid (HA) solution of 2 mg/L was used as the feed water. Humic acid was from Aldrich Chemical Co. and deionized (DI) water was used. Salt solutions were prepared by dissolving pre-weighted quantities of sodium chloride and calcium chloride in DI water. Solution pH was adjusted to the desired value using small amounts of either HCl or NaOH as needed. Solutions were used immediately after initial preparation. All the chemicals used in this study were from Sinopharm Chemical Reagent Corp. (SCRC), China, otherwise being noted. The apparent molecular weight (MW) distribution of the humic acid sample was determined using the ultrafiltration fractionation method originally developed by Aiken (1984). Humic acid sample was fractionated in a 25 mm diameter
stirred cell (Model 8010, Amicon Corp.) using a series of RC ultrafiltration membranes (Millipore Corp.) with nominal molecular weight cutoffs (MWCOs) of 3, 5, 10, 30 and 100 kDa and microfiltration membranes of 0.22 mm and 0.45 mm. The filtration was performed at a constant pressure of 100 kPa, with the fractional amount of humic acid within each size range calculated from the difference in humic acid concentration between adjacent filtrate samples. The membranes used for the ultrafiltration were 25 mm diameter regenerated cellulose (RC) and Biomax polyethersulfone (Millipore Corp.) flat membranes, having MWCOs of 30 kDa and 100 kDa. A negatively charged version of the membrane used was made in the laboratory by the covalent attachment of negatively charged sulphonic acid groups to the surface of the membrane using the base-activated chemistry developed by van Reis (2001). Membranes were first flushed with deionized water to remove any residual storage agents. The membranes were then equilibrated with 0.1 M NaOH and immersed in a 2 M solution of 3-Bromopropanesulfonic acid sodium salt (Sigma Chemical) in 0.1 M NaOH for approximately 48 h. The membranes were then flushed with approximately 100 L/m2 of 0.1 M NaOH followed by 100 L/m2 deionized water.
2.2.
Experimental methods
2.2.1.
Static adsorption experiment
The hydraulic permeabilities were evaluated for Biomax polyethersulfone, neutral and negatively charged membranes before being soaked in 2 mg/L humic acid solutions of pH 7.5 at 4 C for 24 h. The hydraulic permeabilities were reevaluated after each membrane was removed from individual humic acid solution. The difference of the hydraulic permeability before and after adsorption shows the humic acid adsorption effect.
2.2.2.
Ultrafiltration experiment
UF experiments were performed in a dead-end 25 mm diameter stirred cell (Model 8010, Amicon Corp.) connected to an air-pressurized solution reservoir. The schematic diagram of UF experiment is shown in Fig. 1. The stirred cell and reservoir were initially filled with DI water. The hydraulic permeability (Lp) was evaluated by measuring the flux of water as a function of applied pressure (10e150 kPa), as shown in the following LP ¼
J DP
(1)
where DP is the applied pressure and J is the water filtrate flux. The stirred cell was then emptied and refilled with a desired humic acid solution. The system was repressurized, and the stirring speed was set to 600 rpm. The filtrate flow rate was measured by timed collection with the filtrate mass determined using an analytical balance. Filtrate samples were collected periodically for subsequent concentration analysis. At the end of the filtration experiment, the stirred cell was emptied, the membrane was gently rinsed with DI water to remove any labile humic acids, and the stirred cell and reservoir were refilled with fresh DI water. The membrane hydraulic permeability was then evaluated to provide a measure of any membrane fouling.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 7 3 e4 8 2
475
Fig. 1 e Schematic diagram of UF experiment.
2.2.3.
Zeta potential measurement
The surface charge property of the ultrafiltration membrane (both before and after charge modification) was examined using streaming potential. The streaming potential was evaluated using a device constructed from two Plexiglas chambers with Ag/AgCl electrodes inserted at each end. Data were obtained using 10 mM KCl at pH 7, with the fluid flow directed through the membrane pores. The schematic of the membrane streaming potential measurement is shown in Fig. 2. The streaming potential (Ez) was measured at a minimum of four applied pressures (DP) from 10 to 150 kPa. The apparent zeta potential (z) was evaluated from the slope using the HelmholtzeSmoluchowski equation
z¼
h^0 dEZ 30 3r dDP
(2)
where h is the solution viscosity, L0 is the solution conductivity, 30 is the permittivity of vacuum, and 3r is the dielectric
constant of the medium. Several studies have shown that Eq. (2) provides useful information on the charge characteristics of ultrafiltration membranes even though the HelmholtzeSmoluchowski equation neglects the effects of surface conductance and overlapping double layers (Nystro¨m et al., 1989, 1994). All results in this study are reported in terms of apparent zeta potential data as calculated from Eq. (2).
2.3.
Analytical methods
The total humic acid concentration was evaluated by a spectrophotometer from Shanghai MAPADA Instruments Co., Ltd with the absorbance measured at 254 nm. Fourier transform infrared spectroscopy with attenuated total reflection (FTIRATR, EQUINOX 55 from Bruker) was used to identify the organic functional groups on the surface of membranes. Contact angles for three kinds of membrane were measured using OCA 20 video-based contact angle meter (DataPhysics Instruments GmbH, Germany).
Fig. 2 e Schematic of the membrane streaming potential measurement.
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2.4.
Resistance analysis
The resistance-in-series model was applied to evaluate the portion of each resistance of the total resistance (Rt) as shown in Eqs. (3)e(7). Rt ¼
DP mJ DP mJi
(4)
DP Rm mJa
(5)
Rm ¼
Ra ¼
(3)
Rpp ¼
DP Rm Ra mJf
(6)
Rcp ¼
DP Rm Ra Rpp mJ
(7)
where Rm is the intrinsic membrane resistance, Ra is the adsorption resistance, Rpp is the deposition resistance caused by the humic acid deposited on the membrane surface and within the membrane pores and Rcp is the polarization resistance caused by concentration polarization effect; and J is the membrane permeate flux during filtration process, Ji is the water flux on new and clean membrane, Ja is the water flux after the clean membrane soaked in humic acid solution overnight and Jf is the water flux on the membrane after the filtration process.
3.
Results and discussion
3.1.
Adsorption of humic acid on membranes
The hydraulic permeability of each membrane was evaluated both before (Lp) and after adsorption (Lpads). The ratio of Lpads and Lp was used to quantitatively describe the effect of adsorption for each membrane. Zeta potential and contact angle were also measured on each membrane before and after adsorption. The results are summarized in Table 1. The hydraulic permeability had a small decrease after adsorption for both neutral RC and Biomax membrane, with the value of 7% and 5% decrease, respectively. In each case, the adsorption and hydraulic permeability experiments were performed using at least two membranes, with standard deviation of Lpads/Lp decrease less than 2.5%. Note that the water was spread out completely, that is, the contact angle decreased to
0 , in a few seconds after it was deposited on RC membranes. Since the contact angles at the time of deposition were very small (less than 20 ), together with the fast spreading of the water on RC membranes, the values of the contact angles were given as <20 . In this study, it is interesting to note that the hydraulic permeability for negatively charged RC membrane increased 14% after static adsorption procedure. Control experiment was performed with the negatively charged RC membrane incubated for 24 h in a KCl solution without HA added. The hydraulic permeability of this saline solution incubated membrane was measured with the decrease value of 2%, which concluded that the increase for the negatively charged RC membrane after HA adsorption is not associated with the better wetting of the membrane during the overnight incubation in the HA solution. Zeta potential of negatively charged RC membrane increased from 11.1 mV to 7.4 mV after adsorption of HA. Alternatively, the change in permeability might be due to the effects of counterelectro-osmosis since the permeability was measured using DI water. Fluid flow through the charged pores generates a voltage (streaming potential) across the membrane, which is needed to satisfy the condition of no net current flow due to the unequal convective transport of the co-ions and counter-ions. The solvent flow generated by the streaming potential is always in the opposite direction of the pressure-driven flow. This is known as the counterelectro-osmosis. We obtained permeability values of negatively charged RC membrane before and after HA adsorption using high ionic strength KCl solution (1000 mM), in which the back fluid flow associated with counterelectro-osmosis could be omitted. Our measured permeability value of negatively charged RC membrane using 1000 mM KCl solution increased 11% after HA adsorption compared to that before adsorption. It is concluded that though the adsorption of humid acid shields some of the membrane charge, thereby reducing certain extent of counterelectro-osmosis, the increase of the hydraulic permeability of negatively charged RC membrane after adsorption is not mainly caused by the effect of counterelectro-osmosis. Aoustin et al. (2001) observed that during humic substance (HS) ultrafiltration period, the flux had a distinct tendency to rise, showing that the HS makes the membrane more hydrophilic. Elimelech et al. (1997) reported a similar effect in nanofiltration. Similarly, we thought that the hydrophilic property of the negatively charged RC membrane might increase after adsorption (though the exact values of contact angle were not able to be obtained), causing the hydraulic permeability increased. The FTIR-ATR spectrum was obtained to determine qualitatively the types of functional groups on
Table 1 e Water flux, zeta potential and contact angle before and after pre-adsorption on neutral, negatively charged RC and Biomax membranes. Membrane
Native RC Charged RC Biomax
Contact angle ( )
Lpads/Lp
0.93 1.14 0.95
Zeta potential (mV)
Clean
After adsorption
Clean
After adsorption
<20 <20 56
<20 <20 60
1.1 11.1 12.3
2.1 7.4 9.1
477
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3.2.
Humic acid removal and fouling during filtration
90 80 negatively charged RC membrane(modified) 70
neutral RC membrane(unmodified) Biomax PES membrane
60 50 0
50
100
150
200
Fig. 4 e Rejection coefficients during filtration of Aldrich HA solution through UF 100 kDa membranes.
membrane charge. Humic acid is strongly negatively charged at a pH greater than 4.7 (Stevenson, 1982), the modified membrane with negative charge would reject the humic acid with the same kind of charge due to electrostatic repulsion and increase the rejection coefficient. Fig. 4 also shows the removal rate of HA through Biomax polyethersulfone membrane. It is observed that the initial removal rate of humic acid on Biomax is 66%, relatively higher than that on the neutral RC membrane. Though initially the removal rates of neutral and negatively charged RC membranes have big differences, they increased rapidly during filtration, approaching almost same values of 97% and 98% at long times. The Biomax membrane had the initial removal rate between neutral and negatively charged RC membranes, but the removal rate at longer times was the greatest one. Fig. 6 shows the normalized filtrate flux ratio (Jv/J0) during the constant pressure (69 kPa) filtration of the Aldrich HA through different membranes, Jv/J0 is the ratio of filtrate flux during the filtration process over the filtrate flux at the
70 60 50 40 30 20 10 0
Apparent Molecular Weight ( kD )
Fig. 3 e FTIR-ATR spectrogram of new negatively charged membrane and the one after static adsorption.
250
t(min)
Fra ct io nal Amo unt( %)
Experimental data for the humic acid rejection coefficient during the constant pressure (69 kPa) filtration through different 100 kDa UF membranes are shown in Fig. 4. Rejection coefficient (removal rate) is defined as (1 Cfiltrate/Cfeed), in which Cfiltrate and Cfeed are the total humic acid concentration in the filtrate and feed solution, respectively. Results showed that the initial removal rate of humic acid increased to 92% on the negatively charged version of RC membrane compared to only 59% removal of HA on the neutral one with the same MWCO. The initial rejection coefficient of HA on the neutral RC membrane was about 59%, which is consistent with certain amount (about 38%) of the low molecular weight components less than 100 kD as shown in Fig. 5. The apparent zeta potential of the clean charged RC membrane was measured at 11.1 mV, compared with almost neutral RC membranes at 1.2 mV. Negatively charged and neutral RC membranes had the similar pore size (the difference of hydraulic permeability within two membranes less than 10%) of the same material but quite different charges. Thus the difference in rejection for humic acid is almost certainly due to the difference in the
100
Rejectio n Co ef f icien ce (%)
the membrane surface as shown in Fig. 3. Compared with the FTIR spectrum of clean negatively charged membrane, a much higher band of absorption between 3000 and 3700 cm1, which is characteristic of hydroxide, was observed on the membrane surface after static adsorption. It was also observed the absorption around 1641 cm1, typically C]O stretching of amide group. The functional groups of both eOH and CeO are the characteristic property for hydrophilic organic matter. The presence of absorption in these regions further suggests a significant amount of hydrophilic portions of HA on the membrane surface. This confirms our hypothesis that more hydrophilic portions of HA adsorbed on the membrane and caused the hydraulic permeability increase. Further, hydraulic permeability was measured for 30 kDa membrane and 3% increase was observed after adsorption. For the larger pore size 100 kDa membrane, it has the ability to hold more hydrophilic portions of HA and thus the hydraulic permeability increased more than that of 30 kDa membrane.
Fig. 5 e Apparent molecular weight distribution for solutions of Aldrich humic acids at pH 7.0.
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Fig. 6 e Normalized filtration flux during filtration of Aldrich HA solution through UF 100 kDa membranes.
beginning of the filtration for each individual membrane. The flux after 4 h of filtration had declined by 71% for the Biomax membrane but only 32% for neutral RC membrane, even smaller at 25% for negatively charged RC membrane. In general, the main mechanisms responsible for NOM fouling in UF membranes are the interactions between the NOM species and the membrane surfaces. As mentioned before, humic acid is strongly negatively charged at a pH greater than 4.7 (Stevenson, 1982). Negatively charged RC membrane with zeta potential of 11.1 mV would reject the HA more compared to the neutral RC membrane due to the electrostatic repulsion between the negatively charged humic acid and the negatively charged membrane at pH 7. And thus, there is less fouling of HA on the negatively charged RC membrane. However, Biomax membrane is negatively charged with zeta potential measured at 12.3 mV, the fouling during filtration is the worst among these three kinds of membranes. Biomax membrane is more hydrophobic compared to RC membranes seen from the contact angle values. Because of the hydrophobic interactions between Biomax membrane and HA, Biomax membrane experienced the worst flux decline. This indicates that the potential approach used to reduce HA fouling should not only control the electrostatic interactions at the membrane surface but also hydrophobic/hydrophilic interaction. The flux decline observed during the humic acid filtration is due to the combined effects of humic acid adsorption on or within the membrane pores, humic acid deposition during filtration, and humic acid concentration polarization (Yuan
and Zydney, 2000). To determine the relative importance of these effects, the flux of DI water was evaluated for the clean membrane, for the same membrane after static adsorption in a 2 mg/L humic acid solution for 24 h to achieve equilibrium adsorption and then for the same membrane after a 4 h filtration. Using resistance-in-series model, various resistance results for the uncharged, charged RC and Biomax membranes are calculated and summarized in Table 2. The humic acid adsorption effects on three membranes were illustrated in Section 3.1. Contact angle data for the preadsorbed Biomax polyethersulfone membrane showed slight increase from 56 for the clean membrane to 60 , with a comparatively larger increase for zeta potential from 12.3 mV to 9.1 mV. This indicated that the adsorption for Biomax occurs in thin layer throughout the internal membrane pore structure. Similar experimental results and conclusions were obtained by Yuan and Zydney (2000) on OMEGA series polyethersulfone membranes (Filtron Technology Corp.). Contact angles on both native and negatively charged RC membranes do not show measurable difference. Zeta potential values of the native and charged RC membranes changed from 1.1 mV to 2.1 mV, from 11.1 mV to 7.4 mV, respectively. This also indicated that HA is probably adsorbed on the surface of RC membrane pores (not on the membrane surface). In contrast, contact angle and zeta potential data before and after filtration showed that the humic acid deposition during fouling occurs primarily on the upper surface for both RC and polyethersulfone membranes. The zeta potential of the negatively charged RC was 7.4 mV after HA adsorption and 6.2 mV after HA filtration compared to 11.1 mV for the clean membrane. Similarly, the zeta potential of Biomax changed from 12.3 mV to 9.1 mV after HA adsorption, with only a small additional change to 8.4 mV after HA filtration. Contact angle data for Biomax showed only a slight increase from 56 for the clean membrane to 60 , with a much larger increase to 78 after HA filtration. The data in Table 2 show that the hydraulic resistance of the HA deposit on the charged RC membrane decreased and accounted for only 1.27% of the total resistance compared to the native uncharged RC membrane with 6.12% resistance attributed from deposit. This is consistent with the fact that the electrostatic interaction between the same charged HA and RC membrane decreases the HA deposit on the membrane surface. It is also noticed that the deposit resistance accounted for 66.9% of the total resistance for Biomax membrane. The contribution from the deposit for Biomax membrane is much higher than that for both neutral and negatively charged RC membranes, which indicated that the hydrophobic property of the membrane has significant effect on the membrane fouling. One has to be very careful to choose
Table 2 e Resistance analysis on 100 kDa unmodified, modified RC membranes and Biomax membranes at pH [ 7.5 (R: 31010 mL1). Membrane
Rt/(% of total) resistance
Rm/(% of total) resistance
Ra/(% of total) resistance
Rpp/(% of total) resistance
Rcp/(% of total) resistance
Native RC Charged RC Biomax
7.68/(100) 7.36/(100) 11.73/(100)
5.34/(69.5) 6.08/(82.6) 2.30/(19.6)
0.37/(4.82) 0.48/(6.58) 0.22/(1.88)
0.47/(6.12) 0.09/(1.27) 7.85/(66.9)
1.50/(19.5) 1.68/(22.8) 1.36/(11.6)
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 7 3 e4 8 2
the membrane with the proper hydrophobic property. The relative contribution of concentration polarization to the total flux decline for the negatively charged RC membrane is 22.8%, a small increase compared to that of 19.5% for the native neutral RC membrane. The larger retention of the HA explained the larger contribution from the concentration polarization effect on the charged RC membrane.
3.3. Effect of solution environment on humic acid removal and fouling with charged ultrafiltration membrane 3.3.1.
Effect of solution pH
Fig. 7 shows data for the normalized filtrate flux and removal rate of humic acid during filtration 2 mg/L humic acid solution at pH 3.5 and 7.5 through neutral and negatively charged RC 100 kDa membranes at 69 kPa. The filtrate flux decline was more rapid at pH 3.5 for both neutral and negatively charged membranes. For neutral RC membrane, the flux decline at 4 h filtration time is 41% at pH 3.5 compared with filtration flux decline of 33% at pH 7.5 after the same filtration time. For charged RC membrane, the flux decline at 4 h filtration time is 36% at pH 3.5 compared with filtration flux of 26% at pH 7.5 after the same filtration time. These experimental data indicate that the effect of pH on the humic acid
fouling is larger for charged membrane than that for neutral membrane. It is observed that the removal rate of humic acid was smaller at pH 3.5 for both neutral and negatively charged membranes. For neutral RC membrane, the initial removal rate of humic acid is 55% at pH 3.5 compared with removal rate of 59% at pH 7.5. For charged RC membrane, the initial removal rate of humic acid is 79% at pH 3.5 compared with removal rate of 92% at pH 7.5. These experimental data clearly indicate that the effect of pH on the humic acid removal is much larger for charged membrane than that for neutral membrane. At low pH, due to the protonation of humic acid functional group -COOH, the net charge on humic acid decreases, causing the reduction in intra- or inter-molecular electrostatic repulsion and/or the increase in hydrophobicity of the humic molecules associated the reduced electrical charge. At the same time, the membrane net charge decreases, the electrostatic exclusion of the negatively charged humic acid from the negatively charged membrane pores decreases. All these lead to the increase in humic acid aggregation, more humic acids deposit on the membrane and in turn more humic acid fouling. In contrast, at high pH, the net charges on both humic acid and membrane increase, causing the electrostatic repulsion between humic acids, and the electrostatic repulsion between humic acid and membrane increase, and thus the humic acid fouling decreases, at the same time the removal rate of humic acid increases. Compared with the neutral membrane, the electrostatic interaction has more contribution to the humic acid filtration through modified negatively charged membrane, thus the effect of pH on the humic acid removal and fouling is greater. That is, the filtrate flux declines more during filtration and humic acid removal rate decreases more at low pH.
3.3.2.
Fig. 7 e Effect of solution pH on the normalized flux and removal rate of humic acid during filtration through neutral and negatively charged 100 kDa RC membranes.
479
Effect of ionic strength
Fig. 8 shows the effect of solution ionic strength on the normalized flux and removal rate of humic acid during filtration through neutral and negatively charged 100 kDa RC membranes. It is observed that the rate of flux decline increased with increasing ionic strength for both neutral and negatively charged membranes. For neutral membrane, the flux decline by 73% at 4 h filtration time for the humic acid solution with 100 mM NaCl compared to 67% for the solution with 3 mM NaCl and much smaller value of 32% for the solution without extra NaCl added. For negatively charged membrane, the flux decline by 63% at 4 h filtration time for the humic acid solution with 100 mM NaCl compared to only 35% for the solution with 3 mM NaCl and even smaller value of 26% for the solution without extra NaCl added. In addition, removal rate of humic acid decreased with increasing solution ionic strength for both neutral and negatively charged membranes. For neutral RC membrane, the initial removal rate of humic acid is 59% for the humic acid solution without extra NaCl added compared with removal rate of 32% for the solution with 100 mM NaCl and 43% for the solution with 3 mM NaCl. For charged RC membrane, the initial removal rate of humic acid is 92% for the humic acid solution without extra NaCl added compared with removal rate of 39% for the solution with 100 mM NaCl and 87% for the solution with 3 mM NaCl.
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Fig. 8 e Effect of solution ionic strength on the normalized flux and removal rate of humic acid during filtration through neutral and negatively charged 100 kDa RC membranes.
At low ionic strength, intra- and inter-molecular repulsion of the humic acid molecules increases, the humic molecules stretch to more linear chains, the deposition of these linear humic molecules on the membrane surface is loose, and thus the filtration flux is comparatively large. At high ionic strength, intra- and inter-molecular repulsion of the humic acid molecules decreases, the humic molecules transfer to a more coiled state and the net charge of humic acid decrease (Yuan, 2001). At the same time, the increasing electrostatic shielding causes a reduction in electrostatic repulsion between the charged humic acids and the membrane. These then result in the humic molecules densely accumulating on the membrane surface and fouling is more severe at high ionic strength. As to the rejection of humic acid, larger electrostatic shielding of humic acid from the membrane and less charge, more coiled molecules give reduced removal rate. Compared with negatively charged membrane, Fig. 8 also shows that the small solution ionic strength of 3 mM could have larger effect on the humic acid removal and fouling during filtration for neutral membrane, and this effect does not change much when the ionic strength increases further from 3 mM to 100 mM. This indicates that the effect of ionic strength on the humic acid removal and fouling is smaller for negatively charged membrane than neutral membrane when the ionic strength is comparatively small, which further confirms that electrostatic interactions between the charged humic acid and charged membrane are beneficial to the humic acid removal and anti-fouling.
3.3.3.
Effect of adding calcium
Fig. 9 shows the effect of CaCl2 concentration on the flux decline and removal rate of humic acid through both neutral and negatively charged 100 kDa RC membranes. For negatively charged membrane, as the Ca2þ concentration increases from 0 to only 0.5 mM, the rate of filtrate flux decreased sharply, with the flux decline at 4 h filtration time from 72% to 25%. As the Ca2þ concentration increases, the solution ionic strength increases. As explained previously, high ionic strength solution would decrease intra- and inter-molecular repulsion of the humic acid molecules and increase electrostatic shielding, which causes the increase in fouling. In addition, the role of Ca2þ is more than just an ionic strength effect (Yuan, 2001; Zularisam et al., 2006). Calcium could bind to the carboxylic acid functional groups, significantly reducing the net humic acid charge. It also could bind with the negatively charge sulfonic group of the membrane. These then cause a large increase in fouling. However, when the calcium concentration is larger than 0.5 mM, as the calcium concentration increased from 0.5 to 10 mM, the observed flux decline is becoming less. One possible explanation is that at this high calcium concentration the complexation/bridging interactions of humic acid with both humic acid and membrane are stronger, the formed humic acid aggregates deposition on the membrane surface becomes larger. The cake formed on the membrane surface however is looser with relatively high permeability, which then results in less flux decline. Katsoufidou et al. (2005) observed the similar phenomenon as ours while
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481
Fig. 9 e Effect of calcium concentration on the normalized flux and removal rate of humic acid during filtration through neutral and negatively charged 100 kDa RC membranes.
studying the effect of calcium concentration on the flux during humic acid filtration through RC membrane. Fig. 9 shows as the calcium concentration increased from 0 to 0.5 mM, the initial removal rate of humic acid decreased sharply from 92% to 64% and then the trend is reversed at 0.5 mM for negatively charged membrane. As the calcium concentration increased from 0.5 mM to 10 mM, the initial removal rate of humic acid increased from 64% to 74%. One possible explanation can be given as the following. At low calcium concentration of 0.5 mM, the binding effect of calcium reduces the net humic acid charge and electrostatic interactions, leading to the lower humic acid removal. As the calcium concentration increased further from 0.5 mM to 10 mM, on one hand, the size of the formed humic acid aggregates became larger, leading to the larger rejection of humic acid and lower density of the cake formed on the membrane surface; on the other hand, the binding effect of calcium with humic acid and membrane reduced the electrostatic interactions, leading to lower rejection of humic acid. At relatively high calcium concentration, the electrostatic interaction probably is not the dominant effect, and thus it is observed that the removal rate of humic acid increased as the calcium concentration increased. For neutral membrane, as the calcium concentration increased from 0 to only 0.5 mM, the rate of filtrate flux decreased and removal rate of humic acid decreased. However, as the calcium concentration increased further from 0.5 to 10 mM, the observed flux decline is becoming less and
the removal rate of humic acid increased. The results show that the effect of calcium on the flux decline and removal rate of humic acid through neutral 100 kDa RC membranes is similar to that observed for charged version membrane. The difference is that the filtrate flux as the function of filtration time for solution with Ca2þ concentration of 3 mM is close to that without extra Ca2þ added for neutral membrane, while only when Ca2þ concentration is 10 mM, the filtrate flux as the function of filtration time is close to that without extra Ca2þ added for charged version membrane. Compared to the negatively charged membrane, the electrostatic interactions between membrane and humic acid are smaller for neutral membrane and the binding effect is more dominant, thus the increase of Ca2þ concentration has more effect on filtrate flux and humic acid removal.
4.
Conclusions
This is the first reported attempt to study the rejection of humic acid and flux decline with same material made membranes but only charge difference, that is, essentially neutral and negatively charged version of a regenerated cellulose membrane. The effect of solution environment, including ionic strength, pH and calcium ion concentration, on humic acid removal and flux decline were also investigated and compared. The following conclusions can be drawn from this study.
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(1) The hydraulic permeability had a small decrease after adsorption for neutral RC membranes, while the hydraulic permeability for negatively charged RC membranes increased after static adsorption procedure. It is almost certain that the hydrophilicity of negatively charged RC membrane increased after adsorption, causing the hydraulic permeability increased, which is further confirmed by the FTIR-ATR spectrum. (2) The higher removal rate for humic acid and less fouling were observed on the charged RC membrane than that on the neutral unmodified RC membrane, which is confirmed to be due to the increased electrostatic interactions between charged humic acid and charged membrane. (3) The extent of humic acid fouling and removal on the charged RC membrane was also found to be strongly governed by solution environment. Though the degrees of the effects are not the same, the trends of the effect of solution environment on the humic acid removal and fouling are same on both neutral and negatively charged membrane. This indicates that the application of the neutral and charged membranes has to consider the solution environment differently in order to have its best membrane performance. (4) As the calcium concentration increased from 0 to 0.5 mM, both the rate of filtrate flux and the initial removal rate of humic acid decreased sharply and then the trend is reversed at 0.5 mM. As the calcium concentration increased from 0.5 mM to 10 mM, the removal rate of humic acid and flux increased. The explanation for this phenomenon probably has to be considered from two effects: both the electrostatic interactions and the permeability of the cake layer formed on the membrane. Results indicated that the appropriate charge modification on the neutral UF membrane could be an effective way for better removal of humic acid and reduction of the membrane fouling at the same time due to the electrostatic interactions with the combination effect of membrane pore size. In the application of ultrafiltration process for humic acid removal, one has to consider not only the properties of membrane itself (including MWCO, hydrophobicity and material), but also the solution environment, to achieve better humic acid removal rate and less membrane fouling.
Acknowledgement This research was supported by National High Technology Research and Development Program of China (2006AA06Z307).
references
Aiken, G.R., 1984. Evaluation of ultrafiltration for determining molecular weight of fulvic acid. Environ. Sci. Technol. 18 (12), 978e981. Aoustin, E., Scha¨fer, A.I., Fane, A.G., Waite, T.D., 2001. Ultrafiltration of natural organic matter. Sep. Purif. Technol. 22e23, 63e78.
Campinas, M., Rosa, M.J., 2010. Assessing PAC contribution to the NOM fouling control in PAC/UF systems. Water Res. 44, 1636e1644. Cho, J., Amy, G., Pellegrino, J., 2000. Membrane filtration of natural organic matter: factors and mechanisms affecting rejection and flux decline with charged ultrafiltration (UF) membrane. J.Membr. Sci. 164 (1e2), 89e110. Elimelech, M., Zhu, X., Childress, A.E., 1997. Role of membrane surface morphology in colloidal fouling of cellulose acetate and composite aromatic polyamide reverse osmosis membranes. J. Membr. Sci. 127, 101e109. Fan, L., Harris, J.L., Roddick, F.A., Booker, N.A., 2001. Influence of the characteristics of natural organic matter on the fouling of microfiltration membranes. Water Res. 35 (18), 4455e4463. Gray, S.R., Ritchie, C.B., Tran, T., Bolto, B.A., 2007. Effect of NOM characteristics and membrane type on microfiltration performance. Water Res. 41 (17), 3833e3841. Huang, H., Lee, N., Young, T., Gary, A., Lozier, J.C., Jacangelo, J.G., 2007. Natural organic matter fouling of low-pressure, hollow fiber membranes: effects of NOM source and hydrodynamic conditions. Water Res. 41 (17), 3823e3832. Katsoufidou, K., Yiantsios, S.G., Karabelas, A.J., 2005. A study of ultrafiltration membrane fouling by humic acids and flux recovery by backwashing: experiments and modeling. J.Membr. Sci. 266, 40e50. Kilduff, J.E., Karanfil, T., Weber, W.J., 1996. Competitive interactions among components of humic acids in granular activated carbon adsorption systems: effects of solution chemistry. Environ. Sci. Technol. 30 (4), 1344e1351. Krasner, S.W., Weinberg, H.S., Richardson, S.D., Pastor, S.J., Chinn, R., Sclimenti, M.J., Onstad, G.D., Thruston, A.D., 2006. Occurrence of a new generation of disinfection byproducts. Environ. Sci. Technol. 40 (23), 7175e7185. Mehta, A., Zydney, A.L., 2006. Effect of membrane charge on flow and protein transport during ultrafiltration. Biotechnol. Prog. 22, 484e492. Nystro¨m, M., Lindstro¨m, M., Matthiasson, E., 1989. Streaming potential as a tool in the characterization of ultrafiltration membranes. Colloid Surf. 36, 297e312. Nystro¨m, M., Pihlajama¨ki, A., Ehsani, N., 1994. Characterization of ultrafiltration membranes by simultaneous streaming potential and flux measurements. J. Membr. Sci. 87, 245e256. Nystro¨m, M., Ruohoma¨ki, K., Kaipia, L., 1996. Humic acid as a fouling agent in filtration. Desalination 106 (1e3), 79e87. Susanto, H., Ulbricht, M., 2008. High-performance thin-layer hydrogel composite membranes for ultrafiltration of natural organic matter. Water Res. 42, 2827e2835. Stevenson, F.J., 1982. Humus Chemistry. John Wiley & Sons, New York. van Reis, R., 2001. Charged Filtration Membranes and Uses Therefor. USA patent WO 01/08792 A2. Wei, X., Wang, R., Li, Z., Fane, A.G., 2006. Development of a novel electrophoresis-UV grafting technique to modify PES UF membranes used for NOM removal. J. Membr. Sci. 273 (1e2), 47e57. Yamamura, H., Chae, S., Kimura, K., Watanabe, Y., 2007. Transition in fouling mechanism in microfiltration of a surface water. Water Res. 41 (17), 3812e3822. Yuan, W., 2001. Fouling of Humic Acid during Ultrafiltration and Microfiltration for Water Treatment. Ph.D. Dissertation, University of Delaware, Delaware. Yuan, W., Zydney, A.L., 2000. Humic acid fouling during ultrafiltration. Environ. Sci. Technol. 34 (23), 5043e5050. Zularisam, A.W., Ismail, A.F., Salim, R., 2006. Behaviours of natural organic matter in membrane filtration for surface water treatment e A review. Desalination 194 (1e3), 211e231.
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Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Validation of two tropical marine bivalves as bioindicators of mining contamination in the New Caledonia lagoon: Field transplantation experiments Laetitia He´douin a,b,c, Olivier Pringault c,1, Paco Bustamante b, Renaud Fichez c,2, Michel Warnau a,* a
International Atomic Energy Agency e Marine Environment Laboratories (IAEA-MEL), 4 Quai Antoine 1er, MC-98000 Principality of Monaco, Monaco b Littoral, Environnement et Socie´te´s (LIENSs), UMR 6250 CNRS-Universite´ de La Rochelle, 2 rue Olympe de Gouges, F-17042 La Rochelle Cedex 01, France c UR 103 CAMELIA, Centre IRD de Noume´a, BP A5, 98848 Noume´a Cedex, Nouvelle Cale´donie, France
article info
abstract
Article history:
The bioaccumulation and retention capacities of some key local contaminants of the New
Received 13 March 2010
Caledonia lagoon (Ag, As, Cd, Co, Cr, Cu, Mn, Ni and Zn) have been determined in the oyster
Received in revised form
Isognomon isognomon and the edible clam Gafrarium tumidum during transplantation
9 July 2010
experiments. In a first set of experiments, oysters and clams from a clean site were
Accepted 6 September 2010
transplanted into contaminated sites. Uptake kinetics determined in the field indicated
Available online 15 September 2010
that for Cr and Cu in oysters and Co, Ni, and Zn in clams, concentrations in transplanted bivalves reached those of resident organisms after 100d, whereas for the other elements, it
Keywords:
would require a longer time for transplanted bivalves to reach the same levels as in the
Molluscs
resident populations (e.g., up to 3 years for Cd). However, the slow uptake rate for metals
Oyster
observed in the latter transplantation is rather related to low bioavailability of metals at the
Clam
contaminated sites than to low bioaccumulation efficiency of the organisms. Indeed,
Bioaccumulation
results of a second transplantation experiment into two highly contaminated stations
Biomonitoring
indicated a faster bioaccumulation of metals in both bivalves. Results of both trans-
Metals
plantations point out that the clam G. tumidum is a more effective bioindicator of mining contamination than I. isognomon, since it is able to bioaccumulate the contaminants to a greater extent. However the very efficient metal retention capacity noted for most elements indicates that organisms originating from contaminated sites would not be suitable for monitoring areas of lower contamination. Hence, geographical origin of animals to be transplanted in a monitoring perspective should be carefully selected. ª 2010 Elsevier Ltd. All rights reserved.
* Corresponding author. Present address. International Atomic Energy Agency, Technical Cooperation Department, Division for Africa Wagramer Strasse 5, PO Box 100 A-1400 Vienna, Austria. Tel.: þ43 1 2600 22301. E-mail address:
[email protected] (M. Warnau). 1 Present address. UMR 5119 ECOLAG, Universite´ Montpellier II, CNRS-IRD-IFREMER, Station Me´diterrane´enne de l’Environnement Littoral, F-34200 Se`te, France. 2 Present address. IRD, Universidad Autonoma Metropolitana Iztapalapa, DCBS, Departamento de Hidrobiologı´a, C.P. 09340, Iztapalapa, Mexico D.F., Mexico. 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.002
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1.
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Introduction
New Caledonia is a small South Pacific island whose main economic resources are derived from nickel exploitation. Among others, local mining activities result in large anthropogenic inputs of metals into the SW lagoon and thereby constitute a potential threat to the local coastal marine ecosystems (e.g., Bird et al., 1984) but it is only recently that relevant information was made available regarding levels of metal contamination and their possible impacts on the local marine ecosystems (e.g., He´douin et al., 2009; Metian et al., 2008a; Bustamante et al., 2000). Therefore monitoring of environmental contamination originating from mining activities in the lagoon still needs some significant scientific inputs. Among the common approaches used to study environmental contamination, the use of bivalve molluscs as bioindicator species has proved to be a valuable and informative technique (e.g., Mussel Watch, Goldberg et al., 1983). This approach has been particularly developed in temperate areas, whereas in sub-tropical and tropical areas the scarcity of available information makes the identification of species that could be used as suitable bioindicators difficult (e.g., Phillips, 1991). However, the screening of metal concentrations in a variety of marine organisms from several parts of the SW lagoon of New Caledonia has identified the oyster Isognomon isognomon, the edible clam Gafrarium tumidum, and the alga Lobophora variegata as potential bioindicators (He´douin et al., 2009). It has been shown recently that the alga L. variegata was an efficient bioindicator of metals in seawater in both controlled and in situ conditions (He´douin et al., 2008; Metian et al., 2008b). In addition, recent experimental works on the oyster I. isognomon and the clam G. tumidum have indicated that these two species bioconcentrate and efficiently retain several elements when exposed via seawater, sediments or their food (He´douin et al., 2010b). More importantly, both bivalve species were shown to concentrate As, Cd, Co, Cr, Mn, Ni, and Zn in direct proportion to their concentrations in seawater and food (He´douin et al., 2007, 2010b). Although the former experiments were carried out under controlled conditions simulating as closely as possible those in the natural environment, laboratory experiments cannot reproduce exactly the conditions in the field. In this respect in situ experiments offer a more ecologically-realistic approach, since they encompass all the factors that actually occur in the field and may possibly interfere with or influence bioaccumulation processes (e.g., Cain and Luoma, 1985; He´douin et al., 2008). Active biomonitoring using transplantation of organisms from one site to another is a very efficient way to follow the degree of contamination at various sites (e.g., He´douin et al., 2008). The main advantages over the traditional passive biomonitoring (viz. monitoring of metal concentrations using resident natural populations) are that (1) the sites to monitor may be chosen independently of the presence of natural populations and (2) the influence of external and internal factors (e.g., seasonal variation, size or age) susceptible to induce bias in data comparison is reduced (Phillips and Rainbow, 1993).
The aim of the present field study was to determine the relevance of using the oyster I. isognomon and the clam G. tumidum as bioindicator species of metal contamination in tropical waters. Through two different field transplantation experiments, the ability of both species to bioaccumulate and depurate 9 selected elements (Ag, As, Cd, Co, Cr Cu, Mn, Ni and Zn) under natural conditions has been assessed as well as their ability to inform about the contamination status of their surrounding environment. A power analysis was also carried out to determine the sample size required to allow differentiating among realistic field contamination levels.
2.
Materials and methods
Between March and June 2005, two series of transplantation experiments were performed in New Caledonia using the oyster I. isognomon and the clam G. tumidum. Based on previous field results (He´douin et al., 2009) sampling stations were selected according to their apparent degree of metal contamination. Maa Bay (subtidal station for oysters) and Ouano Beach (intertidal station for clams) were identified as clean stations with low element concentrations in bivalve tissues and sediments for all elements except As. In contrast, Boulari Bay (for oysters) and Grande Rade e GRInt e (intertidal station for clams) were designated as highly contaminated stations (Fig. 1).
2.1.
Experimental design
Since body size is well known to affect metal concentrations in marine invertebrates (e.g., Boyden, 1977), only individuals with shell length longer than 70 mm for I. isognomon (Metian, 2003) and shell width greater than 35 mm for G. tumidum (He´douin et al., 2006) were considered in order to minimize size-related variability. Two types of transplantations were conducted. A first reciprocal transplantation aimed at assessing metal bioaccumulation and depuration processes in natural populations living in two contrasted environmental conditions (see Fig. 2). A second transplantation was conducted to test the ability of both selected species to inform about the contamination level of their surrounding environment in a heavily polluted area (unidirectional transplantation in Grande Rade; Fig. 2).
2.1.1.
Experiment 1: reciprocal transplantations
Eighty oysters and 80 clams were collected from the two selected clean stations, Maa Bay and Ouano Beach, respectively. A sub-sample of 10 organisms from each station was used for determination of baseline concentrations of the 9 selected elements (Ag, As, Cd, Co, Cr Cu, Mn, Ni and Zn) at the beginning of the experiment. The remaining oysters and clams (n ¼ 70 per species) were transplanted for 100 d to the heavily contaminated stations, Boulari Bay and Grande Rade, (GRInt, intertidal station), respectively. The reciprocal transplantation was undertaken with another batch of 80 oysters and 80 clams collected in Boulari Bay and Grande Rade (GRInt), respectively, and transplanted to the clean stations, Maa Bay (for oysters) or Ouano Beach (for clams). Organisms (transplanted and control resident individuals) at each station were placed in plastic mesh cages
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485
Fig. 1 e Map showing the stations selected for transplantation experiments. OUANO: Ouano Beach; MAA: Maa Bay; BOULARI: Boulari Bay; GRInt: Grande Rade Interdital station; GR1: Grande Rade subtidal site 1; GR2: Grande Rade subtidal site 2; SLN: “Socie´te´ Le Nickel” Nickel ore processing plant.
(60 60 cm; 2-cm mesh size), which allowed free exchange of seawater. The plastic cages containing the oysters were placed at 5 m depth, which corresponds to their natural habitat; those with clams were fixed in an intertidal position and inserted within the sediments in order to reproduce to the best the living condition of the clams. In order to monitor possible natural variation in element concentrations at the different stations, resident organisms (n ¼ 5 per species) and superficial sediments (top 3-cm layer) were sampled simultaneously with the transplanted organisms (n ¼ 7) from clean and contaminated stations at different
times. Oysters were collected by SCUBA diving and the clams by hand picking at low tide.
2.1.2. Rade
Experiment 2: unidirectional transplantation in Grande
Grande Rade is locally influenced by anthropogenic inputs from the ‘Socie´te´ Le Nickel’ (SLN), a nickel processing plant. Two stations (GR1 and GR2) were chosen in Grande Rade for this experiment because they had different levels of metal contamination (Migon et al., 2007). GR1 station is a highly polluted site due to its proximity to the off-loading wharf of
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concentrations of elements at day 0 of transplantation; the remaining organisms (n ¼ 120 per species) were transplanted for 69 d into the two stations in Grande Rade (GR1 and GR2, n ¼ 60 per station per species) and held in 60 60 cm plastic cages (2-cm mesh size) immerged at 5 m depth for both clams and oysters. Transplanted organisms (n ¼ 30 per species) in GR1 and GR2, and resident organisms (n ¼ 20) from the clean stations (Maa Bay for oysters and Ouano Beach for clams) were collected by SCUBA diving after 35 and 69 d. Sediment samples (top 3-cm layer) were collected simultaneously with organisms from the clean and transplantation sites.
2.2.
Fig. 2 e Design of the transplantation experiments #1 (reciprocal transplantation) and #2 (unidirectional transplantation in Grande Rade) Arrows indicate the transplantation performed between two sites (A / B: Organisms from site A transplanted to site B).
the SLN, whereas the second station GR2, on the opposite side of the Rade just in front of the SLN factory, is less contaminated than GR1 (Fig. 1). The bivalves I. isognomon and G. tumidum (n ¼ 140 per species) were collected from the clean stations Maa Bay and Ouano Beach, respectively. Twenty organisms were used for element analyses in order to establish the baseline
Sampling preparation and analyses
Back to the laboratory, the bivalves were kept for 24 h in 30 l seawater from the same sampling station to allow depuration of gut contents and of particulate material present in the mantle cavity. Soft tissues were removed from the shells and were weighed (wet weight; wwt), dried at 60 C until constant weight, and weighed again (dry weight; dwt). They were then stored in acid-washed, hermetically sealed plastic containers until analysis. Sediments were similarly stored in acid-washed, hermetically sealed plastic bags and frozen at 20 C. Sediments were then dried at 60 C for 5 d. In order to eliminate heterogeneous materials (e.g., stones, fragment of corals), sediments were sieved (1-mm mesh size) prior to analysis. Aliquots of the biological samples (300e500 mg dwt) and sediment samples (300 mg dwt) were digested using a 3:1 (v:v) nitricehydrochloric acid mixture (65% suprapur HNO3 and 30% suprapur HCl, Merck). Acid digestion of the samples was carried out overnight at room temperature. Samples were then mineralized using a CEM Corp. MARS 5 microwave oven (30 min with constantly increasing temperature up to 100 C for sediments and 115 C for biological material, then 15 min at these maximal temperatures). Each sample was subsequently diluted with milli-Q water according to the amount of sample digested (10 ml/100 mg). Elements were analyzed using a Varian Vista-Pro ICP-OES (As, Cr, Cu, Mn, Ni, and Zn) or a Varian ICP-MS Ultra Mass 700 (Ag, Cd and Co). Three control samples (two Certified Reference Materials e CRM e and one blank) treated and analyzed in the same way as the samples were included in each analytical
Table 1 e ICP-OES and ICP-MS analysis of certified reference materials: certified values and measured values (mean ± SD mg gL1 dwt). Element
Ag As Cd Co Cr Cu Mn Ni Zn
Method
ICP-MS ICP-OES ICP-MS ICP-MS ICP-OES ICP-OES ICP-OES ICP-OES ICP-OES
TORT-2
DOLT-3
Found Mean SD
Certified Mean SD
% Recovery
No certified value 22.28 2.22 26.42 3.75 0.52 0.089 0.66 0.19 98.40 11.17 12.46 1.19 2.02 0.35 187.6 19.6
21.60 1.80 26.70 0.60 0.51 0.091 0.77 0.15 106.0 10.0 13.60 1.20 2.50 0.19 180.0 6.0
103.2 99.0 101.5 85.3 92.8 91.6 80.9 104.2
Found Mean SD
Certified Mean SD
% Recovery
1.07 0.092 9.45 0.97 17.01 3.12 No certified value No certified value 31.23 2.40 No certified value 3.05 0.76 97.67 6.97
1.20 0.07 10.20 0.50 19.40 0.60
89.3 92.7 87.7
31.20 1.00
100.1
2.72 0.35 86.60 2.40
112.1 112.8
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batch. The CRM were dogfish liver DOLT-3 and lobster hepatopancreas TORT-2 (NRCC). The results for CRM indicated recoveries of the elements ranging from 81% (Ni) to 113% (Zn) (Table 1). The detection limits were 31.0 (As), 1.3 (Cr), 3.8 (Cu), 0.15 (Mn), 1.1 (Ni) and 2.4 (Zn) mg g1 dwt for ICP-OES and 0.1 (Ag), 0.15 (Cd) and 0.1 (Co) mg g1 dwt for ICP-MS. All element concentrations are given on a dry weight basis (mg g1 dwt).
2.3.
Data treatment and statistical analyses
The uptake kinetics of the elements examined were described using either a simple linear regression model (eq. (1)) or a saturation exponential model (eq. (2)): Ct ¼ C0 þ ku t
(1)
Ct ¼ C0 þ C1 1 eke t
(2)
where Ct and C0 are the element concentrations in organisms at time t(d ) and 0, respectively (mg g1); C1 þ C0 is the concentration at steady state (Css; mg g1); ku is the uptake rate constant (mg g1 d1) and ke is the depuration rate constant (d1) (Whicker and Schultz, 1982). Depuration kinetics of elements was described by either a simple linear regression model (eq. (3)) or a single-component exponential equation (eq. (4)): Ct ¼ C0 ke t
(3)
Ct ¼ C0 eke t þ A
(4) 1
where A is a constant (mg g ). Model constants and their statistics were estimated by iterative adjustment of the model and Hessian matrix computation using the nonlinear curve-fitting routines in the Statistica software 5.2.1. Element concentrations of sediments and control organisms were plotted against time and fitted using simple linear regression. Statistical analyses of the data were performed using 1-way analysis of variance (ANOVA) followed by the multiple comparison test of Tukey (Zar, 1996). The level of significance for statistical analyses was always set at a ¼ 0.05. A power analysis was performed using the whole set of data in order to assess the minimal sample size of organisms (oysters and clams) required to detect realistic (field-observed) differences in element concentration with statistical significance ( p < 0.05) (Zar, 1996).
3.
Results
3.1.
Experiment 1: reciprocal transplantations
3.1.1.
Sediments
Comparison of element concentrations in sediments from the two stations naturally inhabited by the oysters I. isognomon (Maa Bay and Boulari Bay) indicated that levels of As, Co, Cr, Mn and Ni in sediments collected from Boulari Bay were significantly higher ( pTukey 0.0008) than those collected from Maa Bay, whereas concentrations of Cu and Zn were significantly higher ( pTukey 0.0002) in Maa Bay compared to Boulari Bay (Table 2). No significant difference was observed between Cd concentrations in sediments from the two bays. Element concentrations measured in sediments from the two stations naturally inhabited by the clams G. tumidum showed that concentrations of all elements in sediments collected in Grande Rade (GRInt, contaminated station,) were significantly higher ( pTukey always 0.0002) than those from Ouano Beach (clean station) (Table 2). Element concentrations in sediments collected from the four stations at the different times showed no significant variation with time.
3.1.2.
Oysters I. isognomon
At the beginning of the experiment, concentrations of all elements in oysters from Boulari Bay were significantly higher ( pTukey 0.0002, except for Zn: p ¼ 0.006) than those collected from Maa Bay, except for As, Cd and Mn for which no significant difference was found. Resident populations of I. isognomon from Maa Bay and Boulari Bay did not exhibit any significant variation in concentrations of any element during the experiment time course. In oysters transplanted to the Boulari Bay station, the concentrations of Cr, Cu and Ni showed a significant linear increase (ku: 0.054, 0.065 and 0.031 mg g1 d1; p < 0.003; R2 ¼ 0.14e0.24) with time (Fig. 3). At the end of the experiment, Ni concentrations in oysters were significantly lower ( pTukey ¼ 0.046) than those in resident oysters from the Bay. No significant difference was found for Cr and Cu. In oysters transplanted to the clean station (Maa Bay), only Ag, Co and Ni showed significant depuration. Ag and Co
Table 2 e Element concentrations (mean ± SD; mg gL1 dwt, n [ 3) in sediments collected in six sampling sites. GRInt: Grande Rade Interdital station; GR1: Grande Rade subtidal site 1; GR2: Grande Rade subtidal site 2.
Ag As Cd Co Cr Cu Mn Ni Zn
Ouano beach
Maa bay
Boulari bay
GRInt
GR1
GR2
0.02* 0.03 3.1* 1.2 0.4 0.2 0.8 0.4 7.8 2.4 1.4* 0.7 44.7 14.9 5.6 3.0 3.5 2.0
0.01* 0.01 6.4* 0.3 1.0 0.2 4.4 2.3 46.9 4.0 7.0 0.5 134 6.7 69.2 5.6 16.3 1.3
0.06* 0.04 16.7* 1.3 1.1 0.3 15.4 11.1 71.5 10.2 0.9* 0.1 545 53.0 101 12.9 7.1 1.6
0.35* 0.13 8.0* 1.2 2.5 0.2 49.2 5.2 309 39 27.0 3.6 304 15 848 78 148 11.0
0.17* 0.09 7.0* 5.9 3.7 1.2 366 145 1290 410 9.6 3.3 1600 600 10,500 3300 73.3 22.7
0.02* 0.02 15.4* 0.5 0.8 0.1 6.1 0.9 24.6 2.9 2.8* 0.4 76.7 8.1 66.4 15.8 12.1 1.8
*: lower than detection limit.
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concentrations showed a significant linear decrease over time (ke: 0.059 and 0.013 mg g1 d1; p < 0.03; R2: 0.08 and 0.10, respectively; Fig. 4). The depuration kinetics of Ni in oyster soft tissues was best fitted by an exponential model (ke: 0.19 d1, R2 ¼ 0.54, p < 0.0001). The concentrations of Ag, Co and Ni in transplanted oysters at the end of the experiment were still significantly higher (pTukey always 0.0001) than those in resident oysters.
3.1.3.
Clams G. tumidum
At the beginning of the transplantation experiment (day 0), concentrations of all elements in clams from Ouano Beach were significantly lower ( pTukey 0.001, except for Mn and Zn, p 0.02) than those from Grande Rade (GRInt). The only exceptions were As for which the highest concentration ( pTukey ¼ 0.0003) was measured in clams from Ouano Beach, and for Cd for which no significant difference was found between the clams of the two stations. Control resident G. tumidum in Ouano Beach and Grande Rade showed no significant variation for any element along the duration of the experiments. In clams transplanted to the contaminated station (GRInt), the concentrations of Ag, Cd, Co, Cr, Cu and Zn displayed
a significant linear increase (Ag, Cu and Zn ku: 0.092, 0.105 and 0.21 mg g1 d1, respectively; p < 0.0001; R2: 0.26e0.83; Cd, Co and Cr ku: 0.0014, 0.02 and 0.019 mg g1 d1, respectively; p < 0.02; R2 0.12) (Fig. 3). The uptake kinetics of Ni in clam soft tissues was best fitted by an exponential model (R2 ¼ 0.65, p < 0.0001) for which the estimated uptake rate constant, ku, was 1.28 mg g1 d1. The uptake rate of Ag, Cu, Ni and Zn was higher by one order of magnitude compared to that of the other elements (Fig. 3). When clams from GRInt were transplanted to the clean station, Ouano Beach, Ag and As concentrations displayed a significant linear increase (ku: 0.078 and 0.541 mg g1 d1; p < 0.001; R2: 0.17 and 0.56, respectively) (Fig. 4). For the other elements, no significant depuration was observed. When a significant increase/decrease in element concentration was observed, concentrations in transplanted organisms were compared to those of resident organisms. Statistical analyses indicated that at the end of the experiment, Ag, Cd, Cr and Cu concentrations in clams transplanted to GRInt were significantly lower ( pTukey 0.005, except for Ag, p ¼ 0.047) than in resident clams from GRInt (up to 3.9 fold lower for Cd and Cr). No significant difference was found for Co, Ni and Zn concentrations between transplanted and resident clams.
Fig. 3 e Experiment 1 e Reciprocal transplantation. Element concentrations (mean ± SD; mg gL1 dwt; n [ 7 for transplanted organisms and n [ 5 for control organisms) in oysters Isognomon isognomon and clams Gafrarium tumidum transplanted from clean stations, Maa Bay (I. isognomon) and Ouano Beach (G. tumidum), to the contaminated stations, Boulari Bay and Grande Rade (GRInt), respectively. (only data showing a significant regression, p < 0.05, are presented).
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Fig. 4 e Experiment 1 e Reciprocal transplantation. Element concentrations (mean ± SD; mg gL1 dwt; n [ 7 for transplanted organisms and n [ 5 for control organisms) in oysters Isognomon isognomon and clams Gafrarium tumidum transplanted from the contaminated stations, Boulari Bay (I. isognomon) and Grande Rade (GRInt, G. tumidum), to reference stations, Maa Bay and Ouano Beach, respectively. (only data showing a significant regression, p < 0.05, are presented).
At the end of the experiment, Ag concentrations in clams transplanted to Ouano Beach were significantly higher ( pTukey ¼ 0.0001) than those in resident clams at Ouano Beach, whereas for As, the opposite was observed ( pTukey ¼ 0.0003).
3.2. Experiment 2: unidirectional transplantation in Grande Rade 3.2.1.
Sediments
Sediments collected from Ouano Beach, Maa Bay, GR1 and GR2 revealed that concentrations of all elements were significantly higher (1e3 orders of magnitude higher) in sediments from GR1 (pTukey always 0.0002) compared to the other three stations, except for As that reached its highest concentration in GR2 ( pTukey ¼ 0.0002) (Table 2).
3.2.2.
Oysters I. isognomon
Element concentrations in resident oysters from Maa Bay showed no significant variation over the duration of experiment. At the most contaminated station (i.e., GR1), Co, Cr, Cu and Ni concentrations at 35 and 69 d were significantly higher than those at 0 d ( pTukey 0.0006 for Co, Cr, and Cu and p ¼ 0.005 for Ni; Fig. 5). Among these four metals, only Ni concentrations after 69 d were significantly higher than those after 35 d of transplantation. Ag concentration after 69 d was significantly higher than those at 0 d and after 35 d ( pTukey ¼ 0.03), but no significant difference was found between concentrations at 0 d and after 35 d of transplantation. Concentrations of As, Cd,
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Mn and Zn exhibited no significant differences in the oysters at station GR1 over the entire transplantation period. At station GR2, which displays a lower degree of contamination than GR1 according to the element concentrations in sediments (Table 2), Ni concentrations after 35 and 69 d were significantly higher than those at 0 d and concentrations after 69 d were significantly higher than those after 35 d ( pTukey 0.0001). Concentrations of Cr and Cu after 35 and 69 d were significantly higher than those at 0 d ( pTukey 0.0001), but no significant differences were found between 35 and 69 d. Ag concentrations after 69 d were significantly higher than those at 0 d ( pTukey ¼ 0.0002) and after 35 d ( pTukey ¼ 0.02), but no significant difference was found between concentrations at 0 d and after 35 d. No significant difference was found for the concentrations of As, Cd, Co, Mn and Zn in oysters over the entire transplantation period in GR2. After 35 d, oysters transplanted into GR1 displayed concentrations of Co, Cu and Ni significantly higher than those at GR2 ( pTukey 0.0001) whereas concentrations of Ag and Zn in GR1 oysters were significantly lower than those at GR2 ( pTukey ¼ 0.02 and 0.048, respectively). After 69 d of transplantation, concentrations of Co, Cr, Cu, Mn and Ni were significantly higher in oysters transplanted at station GR1 than those at GR2 ( pTukey 0.002 for Co and Cu, and <0.04 for Cr, Mn and Ni), whereas Ag concentrations at GR1 were significantly lower than those at GR2 ( pTukey ¼ 0.009).
3.2.3.
Clams G. tumidum
Element concentrations in resident clams from Ouano Beach showed no significant difference over time. At the most contaminated station (i.e., GR1), Ag, Co and Ni concentrations after 35 and 69 d were significantly higher than those in clams measured at 0 d ( pTukey 0.0001 for Ni and 0.02 for Ag and Co) (Fig. 6) and concentrations after 69 d were significantly higher than those after 35 d of transplantation. Concentrations of Cr and Cu after 69 d were significantly higher than those at 0 and after 35 d ( pTukey 0.0003), whereas no significant difference was found between the concentrations at 0 d and after 35 d of transplantation. No significant difference was found between the concentration of As, Mn and Zn after 35 and 69 d. At the second station, GR2, Ag and Ni concentrations after 35 and 69 d were significantly higher ( pTukey 0.0005) than those at 0 d, and concentrations after 69 d were significantly higher than those after 35 d ( pTukey ¼ 0.0005 and 0.03 respectively). Cr concentrations after 35 and 69 d were significantly higher ( pTukey 0.0001) than those at the beginning of the transplantation, but no significant differences were observed between 35 and 69 d. Cu and Mn concentrations after 69 d of transplantation were significantly higher ( pTukey ¼ 0.039 and 0.041) than those at the start of the experiment. No significant difference was found for Co and Zn concentrations at 0, and after 35 and 69 d. In contrast, As concentrations after 69 d were significantly lower than those at day 0 ( pTukey ¼ 0.014). Element concentrations after 35 and 69 d of transplantation were compared between stations GR1 and GR2. Results indicated that after 35 d, Co, Cu and Ni concentrations in clams at GR1 were significantly higher than those at GR2 ( pTukey 0.0002, except for Cu: p ¼ 0.01). For the other elements, no significant difference between GR1 and GR2 was
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Fig. 5 e Experiment 2 e Unidirectional transplantation in Grande Rade. Element concentrations (mean ± SD; mg gL1 dwt; n [ 30 for transplanted organisms and n [ 20 for control organisms) in oysters Isognomon isognomon from Maa Bay transplanted into stations GR1 and GR2 in the Grande Rade. (stars indicate that the concentration is significantly different from those in organisms at 0 d; *p < 0.05, **p < 0.001).
found after 35 d. After 69 d of transplantation, the concentrations of Cd, Co, Cr, Cu and Ni in clams at GR1 were significantly higher (pTukey always 0.0002) than those at GR2.
3). Generally, a sample of size 50 organisms would be required to detect realistic differences in element concentrations, ranging from 0.5 (Cd) to 150 (As) mg g1 dwt.
3.3. Estimation of the minimum sample size required to detect a significant difference in concentrations
4.
A power analysis was performed to determine the minimum sample size necessary to detect a significant difference (a ¼ 0.05) between concentrations of a given element in two batches of clams or oysters. The variability of the data was shown to be dependent upon the element, the species, the stations and the concentration levels. The highest variance was observed in the samples displaying the highest concentrations, consequently, minimum and maximum variance of the transplanted batches were used to determine the range of minimal sample size necessary to detect given differences of concentrations with statistical significance. Considered differences of concentrations were selected to be representative of those that are actually encountered in the field (Table
This field study investigated the in situ accumulation and depuration of 9 selected elements in two tropical bivalves in order to validate their relevance as biomonitoring species. Element concentrations in resident control organisms from each site showed no significant variation with time during the transplantation time course, indicating that any increase (or decrease) of element concentrations in tissues of the transplanted individuals would actually reflect a higher (or a lower) metal contamination level at a given site, and should not be due to seasonal factors. When the oysters and clams from the clean sites were transplanted into the contaminated sites (Experiment 1), the uptake of the selected elements displayed different trends
Discussion
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(Figs. 3 and 4). At the end of the transplantation period, concentrations observed in the organisms were either lower than or similar to those measured in resident populations of the contaminated site, or did not change compared to their initial levels. Concentrations of Cr and Cu in oysters and Co, Ni and Zn in clams reached values similar to those measured in resident organisms. Similar findings have been previously reported for Cu and Zn in the soft tissues of the mussel Mytilus edulis transplanted to a temperate polluted bay (Roesijadi et al., 1984). However, since metal uptake displayed linear kinetics over the transplantation period, the concentrations of these elements would most probably have continued to increase if the duration of the experiment was longer. This hypothesis is supported by the observations made in the second transplantation experiment, in which clams transplanted to GR1 and GR2 displayed Co and Ni concentrations (up to 15.7 4.8 and 140 46 mg g1 dwt, respectively) exceeding those of the resident clams from Grande Rade (7.2 2.3 and 63.2 13.5 mg g1 dwt for Co and Ni, respectively).
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In contrast, concentrations of Ni in transplanted oysters and, Ag, Cd, Cr, Cu in transplanted clams significantly increased during the transplantation period but did not reach the values measured in resident organisms. Taking into account the measured uptake rate constants of these elements in oysters and clams, it can be estimated that reaching the resident concentrations would require, for example, about 6 months for Ni in oysters and approximately 3 years for Cd in clams. Comparable results have been previously reported for the oysters Crassostrea rhizophorae (WallnerKersanach et al., 2000), the clam Macoma balthica (Cain and Luoma, 1985) and the mussel M. edulis from Greenland (Riget et al., 1997). However, our results from the second transplantation (Experiment 2) indicated that when both species were transplanted to a more contaminated site (GR1), accumulation of Ni in oysters and Cr in clams was faster than during the first transplantation experiment. Therefore, the slow uptake rate of Ni in oysters and Cr in clams observed in the latter transplantation is rather related to low bioavailability of these two metals at the contaminated site (Boulari
Fig. 6 e Experiment 2 e Unidirectional transplantation in Grande Rade. Element concentrations (mean ± SD; mg gL1 dwt; n [ 30 for transplanted organisms and n [ 20 for control organisms) in clams Gafrarium tumidum from Ouano Beach transplanted into the stations GR1 and GR2 in the Grande Rade. (stars indicate that the concentration is significantly different from those in organisms at 0 d; *p < 0.05, **p < 0.001).
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Table 3 e Minimal sample size of the oyster Isognomon isognomon and the clam Gafrarium tumidum necessary to detect with 9 significance a difference ( p < 0.05) of concentrations between two groups of organisms. Observed range of element concentrations represents concentrations that have been measured in the two species resident from different stations along the New Caledonia coast; number between brackets represents concentrations that have been reached during transplantation experiments. Element
Species
Observed Concentration range in tissues*
Difference (mg g1 dwt)
Sample size (number of individuals required) I. isognomon Concentration
G. tumidum Concentration
Low
High
Low
High
Ag
Oyster Clam
1.5e32.8 0.02e33.1
1 3 10 30
21 4 <3 <3
110 14 <3 <3
<3 <3 <3 <3
43 6 <3 <3
As
Oyster Clam
21.6e76.6 37.4e441
10 20 40 80 150 350
32 9 4 <3 <3 <3
111 29 8 <3 <3 <3
3713 921 231 59 18 5
8260 2065 517 130 38 8
Cd
Oyster Clam
1.2e2.5 0.17e1.8
0.2 0.5 1 2
220 36 10 4
894 144 37 10
4 <3 <3 <3
160 27 8 4
Co
Oyster Clam
0.5e2.5 1.1e7.2 (15.7)
0.2 0.5 1 2 5 10
8 <3 <3 <3 <3 <3
170 28 8 <3 <3 <3
780 126 32 9 <3 <3
>10,000 1945 487 122 21 6
Cr
Oyster Clam
1.6e9.0 1.1e10.5 (17.4)
1 2 4 8 15
9 4 <3 <3 <3
54 15 5 <3 <3
7 1 <3 <3 <3
993 248 63 17 8
Cu
Oyster Clam
3.1e17.3 5.6e88.2
2 4 8 15 30 60
6 <3 <3 <3 <3 <3
153 39 11 4 <3 <3
15 5 <3 <3 <3 <3
184 47 12 5 <3 <3
Mn
Oyster Clam
17.0e34.7 5.5e187.4
2 4 8 15 30 60 120
260 66 18 6 <3 <3 <3
1938 485 122 36 10 4 <3
727 183 47 14 5 <3 <3
8590 2148 538 154 40 11 4
Ni
Oyster Clam
2.2e16.0 (32.4) 8.1e63.2 (140)
2 4 8 15 30 60 120
7 <3 <3 <3 <3 <3 <3
963 242 62 19 5 <3 <3
36 10 4 <3 <3 <3 <3
6505 1627 410 117 30 9 4
Zn
Oyster Clam
1700 - 13,820 55.6e154
5 10 50 100 500 10,000
>10,000 722 182 9 4 <3
>10,000 4180 1045 43 12 <3
14 5 <3 <3 <3 <3
248 63 4 <3 <3 <3
* from Breau (2003), He´douin et al. (2008), Present study.
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Bay and GRInt for oysters and clams, respectively) than to low bioaccumulation efficiency of the organisms. In the case of Ag, As, Cd, Co, Mn and Zn in oysters and As and Mn in clams, concentrations did not show a significant increase during the transplantation from the clean site to the polluted one (Experiment 1). Even though similar observations were made for Cd and Zn concentrations in Crenomytilus grayanus after two months of transplantation (Shulkin et al., 2003), opposite trends have also been observed. For example, after 120 days of transplantation, a significant bioaccumulation of Cd and Zn was measured in tissues of oysters, clams and cockles (Baudrimont et al., 2005). Therefore, the lack of bioaccumulation of some elements in oysters and clams as observed in our study suggests that these elements were rather poorly bioavailable for the bivalves or that oysters and clams have efficient regulation mechanisms preventing these metals from being accumulated. In fact, when organisms were transplanted to GR1 and GR2 (Experiment 2), concentrations of Ag and Co in oysters and Mn in clams were actually efficiently bioaccumulated. In addition, in laboratory controlled conditions, metals including Ag, Co and Mn were efficient accumulated in oyster and clam tissues (He´douin et al., 2010a,b). Therefore, these results support the low bioavailability hypothesis, at least for Ag and Co in Boulari Bay and Mn at Grande Rade GRInt. When organisms were transplanted to a clean station (Experiment 1), the concentrations of all elements in both bivalves were almost the same after 100 d of transplantation, except for Ag, Co and Ni in oysters, which showed a low but significant decrease with time. However, Ag, Co and Ni concentrations in oysters were far from reaching the concentrations measured in natural resident populations by the end of the experiment. Such incomplete metal elimination has been reported by several authors when organisms from polluted areas were transplanted to clean areas (e.g., Zn in the mussel M. edulis, Roesijadi et al., 1984; Simpson, 1979; Cd and Cu in the oyster Crassostrea gigas, Geffard et al., 2002; Cr, Cu and Zn in the clam Mercenaria mercenaria, Behrens and Duedall, 1981). The biological half life (Tb½) of these elements has been previously determined from radiotracer experiments in I. isognomon and G. tumidum (He´douin et al., 2007, 2010b). Although, elements like Ag, Cd, Ni and Zn were very efficiently retained with Tb½ 5 months, the other elements displayed Tb½ ranging from 1 to 3 months in both bivalve species, independently of the uptake pathway tested (seawater, food or sediments). Comparison of the data indicates that, in the field, depuration processes would take longer for some metals than those previously estimated from laboratory experiments. This confirms that laboratory results cannot always be extrapolated directly to environmental situations, probably due to physiological adaptations of organisms living in contaminated conditions (e.g., sequestration mechanisms). Since oysters and clams showed very low depuration for most of the studied contaminants, bivalve tissues would be able to retain information of contamination events over very long periods of time. However, the subsequent drawbacks in a biomonitoring perspective are that (1) the element concentrations in transplanted organisms are not actually able to reflect the lower contamination levels occurring at a given location over a medium-scale time period (i.e., 3 months), and (2) the element concentrations in organisms
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collected from natural areas can reflect past contamination which is no longer occurring rather than actual contamination. These drawbacks arise from the fact that depuration is influenced by the past contamination history of the organisms. It was for example shown that Cu was more easily eliminated (30% after 30 d) by oysters temporarily transplanted into a metal-rich area for 60 d, than by resident oysters from the same metal-rich area (decrease limited to 9% after 30 d) (Wallner-Kersanach et al., 2000). This suggests that our specimens from the more contaminated area, which were exposed to high metal concentrations possibly for their whole life, may have developed more efficient sequestrating processes of metals to store them in their tissues as non-toxic forms (e.g., in granules, Mason and Jenkins, 1995). Such adaptive mechanisms could occur in both studied species, and hence explain the efficient retention observed in the field. Therefore, further studies should be focused on the long-term depuration of elements in both bivalves from contaminated and clean sites, in which bivalves would be previously exposed to contaminants in the field for 2e3 months before being transplanted into clean sites. Such experiments would demonstrate whether the past contamination history of I. isognomon and G. tumidum plays a role in the strong retention of elements observed in the field. Interestingly, when clams from Grande Rade (GRInt) were transplanted to Ouano beach (Experiment 1), a significant bioaccumulation of As was observed in clam tissues, although lower As concentration was reported in sediments from Ouano beach (3.1 mg g1 dwt). High level of As in clam tissues from Ouano beach has been recently reported (He´douin et al., 2009), and the authors suggested that food was the main pathway of As uptake in clams. Our transplantation experiment from Grande Rade (GRInt) to Ouano beach showed that As was highly bioavailable for clams in Ouano beach. In addition, due to the low levels of As in sediments from Ouano beach, this result supports the assumption that the high levels of As are most probably bioaccumulated from the diet of the organisms (Sanders et al., 1989; Warnau et al., 2007; He´douin et al., 2009). Since the clam G. tumidum is a seafood product in New Caledonia and that its tissues showed high levels of As, the sources of As in Ouano Beach and the potential toxicity of As for consumers should be further investigated. In the second transplantation (Experiment 2), element concentrations in sediments clearly indicated that GR1 is the most contaminated site, reaching very high level of Co, Cr, Mn, and Ni (up to 10,500 mg Ni g1 dwt). These high concentrations in metals, and more specifically in Ni, concur with the very high concentration of Ni observed in the particulate phase within the water column (Migon et al., 2007). Ag, Co, Cr, Cu and Ni were efficiently accumulated in transplanted oysters and clams. In addition, results indicate that bioaccumulation was dependent on sampling location and species, and difference in the contamination level of the two stations was easier to observe when organisms were transplanted for a longer time (69 vs 35 d). For example, our results showed that the concentrations of 5 elements in bivalve tissues (Co, Cr, Cu, Mn and Ni in oysters and Cd, Co, Cr, Cu and Ni in clams) were significantly higher at GR1 than at GR2 after 69 d, whereas differences were significant only for 3 elements (Co, Cu and Ni) after 35 d.
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In this second transplantation experiment, oysters and clams were transplanted to the same stations, hence exposed to the same environmental conditions. Their bioaccumulation capacities can thus be directly compared. Clams were more efficient than oysters in bioaccumulating the selected elements (e.g., concentrations measured after 69 d of transplantation increased by a factor 7 in oysters and by a factor 40 in clams). These findings were surprising considering previous results from laboratory radiotracer studies (e.g., He´douin et al., 2010a) which indicated a more efficient bioconcentration capacity in oysters than in clams when exposed to dissolved elements (concentration factors were higher by several orders of magnitude). Such a difference between laboratory and in situ experiments strongly suggests that the seawater pathway is not the major route of accumulation driving global metal uptake in these organisms. Rather, ingestion of particulate materials would be the main pathway for metal uptake (Metian et al., 2009), an hypothesis that is supported by a previous study of Cd, Co and Zn bioaccumulation modeling in I. isognomon and G. tumidum (He´douin, 2006, 2010b) This may indeed explain the higher metal levels in G. tumidum which lives buried in the sediment, and feeds mainly on organic (and metal)-rich particles at the seawateresediment interface. Combining the results from transplantations 1 and 2 demonstrated the usefulness of bioindicator species to assess the degree of contamination present in the marine environment. Indeed, for some elements, the high levels of metals reported in sediments were reflected in organism tissues (e.g., Cr, Ni) and a significant bioaccumulation of these metals was observed in the tissues of the clams and the oysters during the transplantation experiments. However, for some elements, the metal bioaccumulation trends observed in clams and oysters were different from those expected based on metal concentrations found in sediments at the different sites of transplantation. For example, in sites characterized with low As concentrations in sediments (Ouano beach), efficient bioaccumulation of As was observed in clam tissues (Experiment 1), suggesting that other sources of As uptake are available for organisms (e.g., food, see discussion above). In contrast, for Mn, although high levels were measured in sediments, almost no bioaccumulation was observed in organism tissues (Experiment 2). This clearly points out that only a fraction of the metals present in the sediments is bioavailable for organisms. Mn bound in the lattice of naturally occurring Mn-rich ores (e.g., laterite and garnierite) may be less available for uptake by marine organisms compared to water-soluble forms. The different patterns of metal bioaccumulation observed in clams and oysters during the two transplantation experiments carried out in this work pointed out that the metal contamination status cannot be based solely on metal analysis from the sediments and this is the reason why the use of bioindicator species is an important asset to better characterize the contamination status of a particular site. In addition, although it was not performed in the present study, metal analysis in seawater is also a useful complementary information to those obtained from sediments and organisms. However accurate analysis of metals in seawater is uneasy and expensive, and is therefore generally not
integrated in biomonitoring programmes. Nevertheless, nowadays the development of techniques such as the diffusive gradients in thin films (DGT) (e.g., Davison and Zhang, 1994; Webb and Keough, 2002) brings new insights to obtain time-integrated information on metal concentration in seawater. Ideally analysis of metals in sediments, seawater and organisms will be recommended for biomonitoring purposes since such combination enhances our understanding of the contamination status present in the marine environment, but also brings additional information for identifying the source of contamination. In order to obtain accurate and reliable data in biomonitoring programmes, the determination of optimal sample size to be collected is of fundamental importance. In this context, the present study has investigated the minimum sample size required to detect a given difference in concentration. Results shown in Table 3 indicate that the detection of a 0.5 mg g1 dwt difference in tissue concentrations in the highly contaminated organisms required the largest sample size. Relatively large variability in metal concentrations in organisms within a site has frequently been reported (e.g., Daskalakis, 1996; Gordon et al., 1980). In the present study, the concentration variability was higher with increasing average concentration. Consequently, detecting small differences in concentration among organisms with higher metal concentrations will require an increase in sample size. Nevertheless, it is important to keep in mind that to be feasible, the sample size required in a biomonitoring programme should always remain realistic. Compared to the actual metal concentration range measured in the New Caledonia lagoon waters and sediments, the minimum difference in concentrations detectable with sample sizes of 50e60 organisms would allow for an efficient differentiation among sites naturally inhabited by the two targeted bivalves. For example, a difference of 2 mg Ni g1 dwt can be detected with a sample size of 7 oysters and 36 clams in a population showing low Ni levels (Table 3). However, 62 oysters and 30 clams would be necessary to detect differences of 30 and 8 mg g1 dwt, respectively, in a population characterized by high Ni concentrations (Table 3). A sample size of 50e60 organisms was similarly recommended by other authors in order to facilitate the detection of significant changes in concentrations (e.g., Gordon et al., 1980; Topping, 1983). In current biomonitoring programmes, organisms collected (20 oysters and 30 mussels for the NOAA Mussel Watch, Beliaeff et al., 1998; 10 oysters and 50 mussels for the French RNO, Claisse, 1989) are pooled before analysis in order to reduce costs of sample preparation and analysis. However, pooling leads to the loss of statistical information on interindividual variability, which is obviously an important issue to assess significance of concentration differences among samples. These economic constraints are obvious in the case of large national and international biomonitoring programmes that assess the levels of numerous trace elements and organic contaminants in many stations. However, in New Caledonia, which is mainly impacted by mining activities, metal and metalloids are the contaminants of major concern. Therefore, analytical costs would be reduced compared to biomonitoring programmes that include the very expensive analysis of organic compounds. Hence, in the specific context of the New Caledonia lagoon, it is highly recommended to
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analyze individual samples in order to obtain information on inter-individual variability that would provide scientificallysupported best practices in environmental management.
5.
Conclusion
This study clearly indicates that the clam G. tumidum can be recommended for an active monitoring of contaminants in subtidal and intertidal stations of the New Caledonia lagoon on a spatiotemporal scale. Biomonitoring studies using transplanted organisms would be an efficient solution to survey environmental levels of key local metal contaminants in areas lacking resident bivalves. The advantage of using transplanted organisms (active biomonitoring) over sampling resident populations (passive biomonitoring) is that it allows selecting organisms of uniform initial element concentrations, of common origins and past history, and thus ensures comparable biological samples. However, if further studies confirm the observed very long element retention times in these organisms, organisms from sites displaying a low contamination will have to be used in order to prevent bias in element concentrations due to physiological adaptation of organisms (e.g., sequestration mechanisms).
Acknowledgements The authors thank E. Folcher, C. Geoffrey and J.L. Menou (IRDNoume´a Center) for their assistance at sea and C. Churlaud (LIENSs) for her assistance in ICP analyses. LH was beneficiary of a PhD grant (CIFRE, France) supported by the Goro-Nickel Company. MW is an Honorary Senior Research Associate of the National Fund for Scientific Research (NFSR, Belgium) and has benefited from a 2008-2009 Invited Expert position at LIENSs (CNRS-Universite´ de La Rochelle), supported by the Conseil Re´gional de Poitou-Charentes. This work was supported by the IAEA, the IRD, LIENSs and the French PNEC programme. The IAEA is grateful for the support provided to its Marine Environment Laboratories by the Government of Monaco.
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transplant field experiment. Marine Ecology Progress Series 18, 155e170. Sanders, J.G., Osman, R.W., Riedel, G.F., 1989. Pathways of arsenic uptake and incorporation in estuarine phytoplankton and the filter-feeding invertebrates Eurytemora affinis, Balanus improvisus and Crassostrea virginica. Marine Biology 103, 319e325. Shulkin, V.M., Presley, B.J., Kavun, V.I., 2003. Metal concentrations in mussel Crenomytilus grayanus and oyster Crassostrea gigas in relation to contamination of ambient sediments. Environment International 29 (4), 493e502. Simpson, R.D., 1979. Uptake and loss of zinc and lead by mussels (Mytilus edulis) and relationships with body weight and reproductive cycle. Marine Pollution Bulletin 10 (3), 74e78. Topping, G., 1983. Guidelines for the use of biological material in first order pollution assessment and trend monitoring. In: Scottish Fisheries Research Report. Department of Agriculture and Fisheries for Scotland, Marine Laboratory, p. 28. Wallner-Kersanach, M., Theede, H., Eversberg, U., Lobo, S., 2000. Accumulation and elimination of trace metals in a transplantation experiment with Crassostrea rhizophorae. Archives of Environmental Contamination and Toxicology 38 (1), 40e45. Warnau, M., Go´mez-Batista, M., Alonso-Herna´ndez, C., Regoli, F., 2007. Arsenic: is it worth monitoring in the Mediterranean Sea?. In: Marine Sciences and Public Health e Some Major Issues CIESM Workshop Monographs No. 31, pp. 83e86 (Monaco). Webb, J.A., Keough, M.J., 2002. Measurement of environmental trace-metal levels with transplanted mussels and diffusive gradients in thin films (DGT): a comparison of techniques. Marine Pollution Bulletin 44 (3), 222e229. Whicker, F.W., Schultz, V., 1982. Radioecology: Nuclear Energy and the Environment. CRC Press, Boca Raton, Florida, USA, 320 pp. Zar, J.H., 1996. Biostatistical Analysis, third ed. Prentice-Hall, Upper Saddle River, New Jersey, 662 pp.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 9 7 e5 0 8
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
The effect of UV/H2O2 treatment on biofilm formation potential D.H. Metz a,b,*, K. Reynolds a, M. Meyer a, D.D. Dionysiou b a b
Greater Cincinnati Water Works, 5651 Kellogg Avenue, Cincinnati, OH, USA University of Cincinnati, Department of Civil and Environmental Engineering, 765 Baldwin Hall, Cincinnati, OH, USA
article info
abstract
Article history:
Greater Cincinnati Water Works (GCWW) evaluated the efficacy of ultraviolet light/
Received 8 July 2010
hydrogen peroxide advanced oxidation (UV/H2O2) for reducing trace organic contaminants
Received in revised form
in natural water with varying water qualities. A year-long UV/H2O2 pilot study was con-
3 September 2010
ducted to examine a variety of seasonal and granular activated carbon (GAC) breakthrough
Accepted 7 September 2010
conditions. The UV pilot-scale reactors were set to consistently achieve 80% atrazine
Available online 17 September 2010
degradation, allowing comparison of low pressure (LP) and medium pressure (MP) lamp technologies for by-product formation. Because hydroxyl radicals react non-selectively
Keywords:
with organic compounds, unintended by-product formation occurred.
UV/H2O2
Total assimilable organic carbon (AOC) concentration increased through the reactors from
Advanced oxidation
14 to 33% on average, depending on water quality. Natural organic matter (NOM) contains
Biofilm potential
the precursors for AOC production, so when post-GAC water (versus conventionally treated
Assimilable organic carbon
water) served as reactor influent, less AOC was produced. No appreciable difference in AOC
AOC
concentration was observed between LP and MP UV reactors. The Spirillum strain NOX
Annular reactors
fraction of the AOC increased from 50 to 65% on average, depending on the quality of the water. The increase in this fraction of AOC occurred because oxidation of NOM yielded smaller more assimilable organic compounds such as organic acids that are necessary for NOX growth. The Pseudomonas fluorescens strain P17 AOC concentration increased only when conventionally treated plant water was used as pilot influent. This organism thrives in waters of differing organic energy sources, but does not thrive well in carboxylic acids alone. The CONV water had more overall TOC that could contribute to higher P17 AOC counts. Biofilm coupon studies indicated that biofilms with greater heterotrophic plate counts were observed in the granular activated carbon (GAC) effluent streams receiving UV/H2O2 pre-treatment. Biofilm coupon studies additionally indicated that the effluent stream of the GAC column proceeded by the MP reactor exhibited more viable biofilm than the other GAC effluent streams based on an ATP-bioluminescence method. The increased viability of the biofilm produced by the MP UV reactor is likely a result of the multiple UV wavelengths and higher energy input characteristic of this technology. ª 2010 Elsevier Ltd. All rights reserved.
* Corresponding author. Greater Cincinnati Water Works, 5651 Kellogg Avenue, Cincinnati, OH, USA. E-mail address:
[email protected] (D.H. Metz). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.007
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1.
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Introduction
Greater Cincinnati Water Works (GCWW) is designing a 908,500-m3/d (240-MGD) ultraviolet (UV) disinfection facility to be constructed by early 2012 at their Ohio River drinking water plant. GCWW additionally wished to determine the efficacy of ultraviolet light/hydrogen peroxide advanced oxidation process (UV/H2O2) for reducing pharmaceuticals and other organic contaminants at two points in the treatment process. A twelve-month UV/H2O2 study was conducted in order to cover seasonal variations and different levels of granular activated carbon (GAC) breakthrough. Low pressure (LP) and medium pressure (MP) UV lamp technologies were able to be compared for by-product formation, because the pilot-scale systems were normalized for 80% atrazine destruction. Two plant-process sources with varying natural organic composition and concentration were compared for UV/H2O2 and UV photolysis (without hydrogen peroxide) technologies. The enhanced removal of natural organic matter with UV/H2O2 followed by biologically active GAC also was evaluated. Biofilm formation potential was investigated through the UV/H2O2 reactors and through GAC following UV/ H2O2 process. UV/H2O2 is a promising technology for the destruction of endocrine disrupting compounds (EDCs) and pharmaceuticals and personal care products (PPCPs), which combines the effects of direct and indirect UV photolysis (Pereira et al., 2007). Direct photolysis takes place when a compound absorbs photons of an energy capable of breaking down bonds (Hovorka and Schoneich, 2001). Medium pressure lamps are more energy-intensive and emit a broad-spectrum of UV wavelengths, thus achieving direct UV photolysis at multiple wavelengths. Low pressure UV lamps primarily emit UV at 253.7 nm, and only achieve direct UV photolysis at this wavelength (Rosenfeldt and Linden, 2004). Light absorption behavior and direct UV photolysis of organic contaminants, however, is also a function of radiation wavelength. So, different wavelengths could influence the type, selectivity and yields of by-products formed. Indirect UV photolysis with hydrogen peroxide (H2O2) results in the cleavage of the HOeOH bond, causing the formation of hydroxyl radicals (OH). Although the UV absorption coefficient of H2O2 is a function of UV wavelength, both LP and MP UV lamps emit wavelengths that can cause photolysis of H2O2 to generate hydroxyl radicals. UV photolysis of H2O2 is a rapid process and the produced hydroxyl radicals react non-selectively with organic compounds yielding carbon-centered radicals. They target mainly unsaturated bonds or abstract hydrogen from CeH bonds (Buxton et al., 1988) especially those in a-position to p-systems, amines, ethers, thioethers, and carbonyl compounds (Hovorka and Schoneich, 2001). These carbon-centered radicals in turn rapidly react with dissolved oxygen to form peroxyl-radicals, followed by the breakdown of peroxyl-radicals to form oxylradicals, and the breakdown of oxyl-radicals to other radicals and stable reaction intermediates (Hovorka and Schoneich, 2001). In UV/H2O2 systems many radical-based reactions take place (i.e., generation, propagation, termination). The efficiency of the process is dependent upon the rate of formation
of hydroxyl radicals, the presence and concentrations of hydroxyl radical scavengers and other parameters (i.e., UV absorbance of the process water, type and concentration of other organic impurities in water such as natural organic matter, type and concentration of target organic contaminants, water temperature) (Antoniou et al., 2009). The most prominent scavengers are the dissolved organic compounds 2 (DOC), and alkalinity (HCO 3 , CO3 ), however, H2O2 will also react with hydroxyl radicals (Pereira et al., 2007). The pilot-scale systems included GAC columns to adsorb and potentially biodegrade intermediates and by-products. Organic biodegradation can be advantageous in drinking water treatment, and is usually accomplished through the soil and dunes as pre-treatment or through biologically active filtration or GAC. But, biodegradable organics leaving the plant can cause microbial regrowth in the distribution system, a potentially serious problem. Weinrich et al. (2009) states that “In distributed water, bacterial regrowth is perhaps the most significant mechanism for water quality deterioration between the treatment plant and the end user”. Coliform bacteria and pathogenic organisms can grow and be shielded in the biofilm and be difficult to eliminate. Biofilms can be responsible for disinfectant depletion and problems with taste and odor. In chloraminated systems nitrification also may occur. Even corrosion rate can be increased by the presence of biofilm under certain conditions (Geesey et al., 1989). Distribution system biofilm growth is caused by a combination of factors. Generally, four water quality parameters control microbial regrowth: temperature, assimilable organic carbon (AOC), availability of nutrients (trace inorganic compounds) and residual disinfectant presence (Reasoner, 1991). However, LeChevallier et al. (1996), investigated coliform regrowth in 31 drinking water systems. Their conclusion was that there was a complex interaction of physical, chemical operational and engineering factors involved in bacterial regrowth. Temperature, particulate protection of microorganisms, types of organisms colonizing the distribution system (e.g., resistance of microbes to disinfection) and nutrient concentrations are factors controlling the type and amount of biofilm (Baribeau et al., 2005). Kaplan et al. (2004) determined that source waters possess widely different quantities and qualities of biodegradable organic as carbon sources, and these differences in organics influence the community of heterotrophic bacteria in biofilm. Drinking water sources contain various levels of natural organic matter (NOM). While the composition of NOM varies from location to location, there are some similarities in the structure. Humic substances comprise up to 75% percent of the NOM (Volk et al., 1997). Organic matter originating from soils is derived from plant matter, which has a high lignin content. Lignin has a predominant aromatic fraction. NOM also provides reduced carbon that supplies energy and carbon for bacterial metabolism (Kaplan et al., 2004). Kaplan and Gremm (1995) determined 54% of the most biodegradable material in the waters sampled was humic in nature. Butterfield et al. (1997) additionally found that humic substances in the distribution system were the primary carbon source supporting distribution biofilm. However, while the formation of biofilms in the distribution system is believed to be ubiquitous, the degree of colonization varies from site to site.
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Research to determine the exact chemical composition of biodegradable organic matter is on-going. It is known that lower molecular weight compounds are more easily transported across cell membranes enabling enzymatic reactions to proceed. Assimilable organic carbon (AOC) is often used to quantify regrowth potential and to gain insight into the types of compounds comprising biologically degradable carbon. The AOC method developed by van der Kooij et al. (1982), and van der Kooij and Hijnen (1984) makes use of two specific strains of organisms that allow for universal comparison of biofilm potential among diverse utilities. Pseudomonas fluorescens strain P17 is able to utilize various compounds such as proteins, amino acids, carbohydrates, alcohols and aromatic acids, but does not grow well in carboxylic acids alone (LeChevallier et al., 1993). It has great nutritional variability. Spirillum strain NOX is more selective in its growth substrates. Only carboxylic acids and a few amino acids promote growth of NOX. In situations such as ozonation where compounds not utilized by P17 are present, Spirillum strain NOX is often used. Carboxylic acids promote more rapid growth of NOX than P17, thus NOX growth is a more sensitive indicator of the presence of these compounds. Also, in cases of low AOC, this organism tends to grow better than P17. Therefore, this method can be used to obtain information about the quantity and chemical composition of the assimilable materials (AwwaRF and KIWA, 1988). van der Kooij (1992) has recommended that unchlorinated systems maintain AOC values below 10 mg/L. LeChevallier et al. (1990, 1996) however, provided evidence that chlorinated systems may limit regrowth and coliform occurrence by maintaining AOC less than 50e100 mg/L Shi-hu et al. (2008) found that the AOC/TOC ratio increased with decreasing apparent molecular weight (MW). Hem and Efraimsen 2001 found 50e70% of the AOC fraction were <1000 Da MW. Other researchers observed good correlation between apparent molecular weight distribution (AMWD) and UV absorbance (at 254 nm) to TOC ratio and biodegradability of raw waters (Goel et al., 1995). The AOC fraction is generally less than 1000 MW, Hem and Efraimsen (2001), and can include sugars, fatty acids, amino acids and peptides (Haddix et al., 2004). These results would confirm the simpler lower MW fractions would be the most assimilable by biodegrading micro-organisms. The UV/H2O2 process forms hydroxl radicals that react nonselectively. Organic free radicals then can form small MW fractions such as aldehydes, ketones, alcohols, and carboxylic acids that can be used in microbial metabolism (Speitel et al., 1999). Wu (1991) studied the biodegradation of commercial humic acid after UV/H2O2 treatment. Wu was able to increase biodegradability by 17%. Biodegradable dissolved organic carbon (BDOC) increased from 0.1 to 1.3 mg/L in Lake Austin Water in continuous flow UV/H2O2 experiments and 0.52e0.87 mg/L in Lake Houston Water. Acetic and oxalic acids are often found as intermediates of the NOM oxidation process, and these acids biodegrade readily (Speitel et al., 1999). GAC has been used for the adsorption of organic compounds in drinking water. Organic adsorption onto GAC is known to be influenced by several variables including pore size distribution, internal surface area, GAC surface functional groups, electrostatic interactions, acidity, ash content, the size shapes and properties of the organic compounds and the
499
pH, dissolved oxygen and ions in solution (Moore et al., 2004). In general, less soluble organic compounds (hydrophobic) are better adsorbed than soluble compounds (hydrophylic). Therefore, polar compounds which tend to be hydrophilic are less well-adsorbed than non-polar compounds. Westerhoff et al. (2005) found a correlation between log Kow (measure of hydrophobicity) and the removal of 22 pharmaceutical and personal care products. Because of the surface area created by pores, GAC provides an excellent substrate for biological activity. GAC pores provide protection from shear forces and the functional groups of the adsorbed organic material provides a mechanism for chemical binding (Carvalho et al., 2001). Also, biofilms on a fixed media are less affected by organic loading changes than are suspended growth systems. Studies have shown that biologically active carbon can continue to be effective even when contaminant levels were low (Shi et al., 1995). This pilot study examined the use of GAC before and after the UV/H2O2 process.
2.
Methods
2.1.
Facilities
The source water for the UV/H2O2 pilot influent was drawn from two locations within Greater Cincinnati Water Works’ (GCWW) Ohio River treatment plant. The first location was after coagulation, settling and filtration, i.e., conventional treatment (CONV). The second location was from the GAC adsorber effluent (Post-GAC). Water exiting the filters was sent to the granular activated carbon (GAC) facility. The GAC contactors were filled with 11.4 ft (3.5 m) of carbon and were operated in a down-flow, gravity mode. Carbon contact time averaged about 20 min during the study. The GAC removed a broad-spectrum of organic compounds present in the Ohio River. Water entering the GAC facility had a TOC averaging 1.86 mg/L; water exiting the facility had a TOC averaging 0.89 mg/L. The GAC facility also served to significantly reduce disinfection by-product precursors and biodegradable organic carbon. After becoming exhausted (average combined effluent of 150 days, maximum combined effluent 200 days), the GAC was thermally reactivated onsite. This carbon treated water was used as the second of the two pilot influent process streams (Post-GAC process stream). A schematic of the full-scale process train, indicating these locations is shown in Fig. 1. Table 1 presents the pertinent water quality data for the two pilot influent streams. The organic parameters of the CONV pilot influent stream were more variable than those of the Post-GAC pilot influent stream. Temperature, alkalinity and anions did not change through the GAC adsorption process.
2.2.
Pilot plant design and operation
GCWW’s pilot plant consisted of a constant head tank, the peroxide and contaminant feed systems, the UV reactors, the GAC column skids, and the annular reactors. Fig. 2 depicts
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* chemical fed as needed
High Rate Sedimentation Alum Polymer* PAC *
Ohio River Supply
PAC * NaOH Poly-PO4 Chlorine Fluoride
Clearwells To Distribution System
Reservoirs
Inlet
Lime Ferric Sulfate* KMnO4 * PAC *
Secondary Sedimentation
GAC Adsorption
Post-GAC Pilot Inf.
Sand Filtration
CONV Pilot Inf.
Fig. 1 e Schematic of the Richard Miller Treatment Plant e Cincinnati, Ohio, U.S.A.
the layout of the pilot including the location of the chemical injection and sampling points. CONV or Post-GAC water was pumped by centrifugal pumps into the 600 L (160 gal) polyethylene constant head tank. The constant head tank was located about 6 m (20 ft) above the UV reactors to provide sufficient head for the water flow through the unit. The water flow split into two lines before entering the UV reactors. The atrazine solution and the 8% hydrogen peroxide solution were injected through two PVC inline injection mixers located three feet apart to ensure complete mixing. The contaminant solution was pumped from a polypropylene 19 or 115 L (5 gal or 30 gal) tank through a diaphragm pump into the inline mixer. The hydrogen peroxide was diluted down to 8% and was fed continually through a positive displacement pump and the second mixer. A 2 mg/L atrazine concentration and a 10 mg/L H2O2 were targeted. The LP reactor (Aquionics model ALT320 TOC reduction range) consisted of eight LP lamps oriented parallel to the central axis and placed equidistantly at about an 11 cm radius from that axis. The reactor’s internal diameter was about 31 cm and its chamber length is approximately 97 cm. The 80 W LP lamps and sleeves were standard disinfection models from Aquionics with an expected lifetime for the lamps of 12,000 h. The reactor included an immersed pre-calibrated UV monitor (Hanovia) sensitive to UVC wavelengths. A display on the power supply box indicated the UV intensity, run hours, and on/off lamps. The flow range through the reactor could vary between 1.8 and 10 m3/h (8e44 gpm).
The MP reactor (Aquionics model Photon II TOC reduction range) consisted of one MP lamp oriented parallel to the flow, and could be operated at 4 power levels ranging from 75 to 100% of the power. The reactor’s internal diameter was about 15 cm and its chamber length is approximately 97 cm. The 3.5 kW MP lamp and sleeve were Super TOC models from Aquionics with an expected lifetime for the lamp of 8000 h. The reactor included an immersed pre-calibrated UV monitor (Hanovia) sensitive to UVC wavelengths, and a manual rubber wiper. A digital display on the power supply box indicated the UV intensity, UV dose, run hours, and temperature, and allowed for flow and UVT input for the computation of the UV dose. The flow range through the reactor could vary between 1.8 and 10 m3/h (8e44 gpm). The effluent from both UV reactors and the control water (pilot influent water before the hydrogen peroxide injection point but after the contaminant injection point) were pumped to four GAC pilot columns. Two GAC columns were fed by the control water, control columns 1 and 2. Each of the remaining two columns received the effluent of the LP reactor or the effluent of the MP reactor. The GAC columns contained reactivated GAC acquired directly from the reactivation facility at Richard Miller Treatment Plant (RMTP) Cincinnati, Ohio. The GAC was bituminous coal, US mesh size 12 40 with 0.55e0.75 mm effective size, and apparent density of 0.48 g/cm3 (30 lbs/ft3). The GAC bed depth in the 10.2 cm (4 inch) diameter columns was about 173 cm (68 inches), yielding an empty bed contact time of 15 min. Finally, four annular reactors (Biosurface Technologies, model 1120 LS) were connected to the effluent lines of the GAC columns as shown in Fig. 3. The annular reactors were chosen to simulate a velocity of a typical water distribution main and are described further in the analytical methodologies section. The pilot study was structured to address the multiple research objectives within a period of 12 months. The pilot unit was constructed in the summer of 2007 and the tests began in the fall of 2007. Fig. 4 displays the process schematic of the pilot unit with the sampling points. During each quarter there were three phases of testing: (a) UV/H2O2 with CONV influent water, (b) UV/H2O2 with Post-GAC influent water, and (c) UV photolysis with Post-GAC influent water. For both UV/ H2O2 phases the operation of the system was based on performance. The goal was to operate the UV/H2O2 system so that atrazine would degrade by 80% through both the LP and MP reactor trains. The hydrogen peroxide concentration was maintained at 10 mg/L at all times (except during the UV
Table 1 e Water quality of CONV and Post-GAC water at RMTP e Pilot Plant Influents. Water quality parameter
September 2007eAugust 2008 CONV
pH TOC (mg/L) UV254 (L cm1) Total alkalinity (mg/L as CaCO3) Temperature
Post-GAC
Average
Minimum
Maximum
Average
Minimum
Maximum
7.8 1.86 0.046 64.3 16.3
7.2 1.22 0.024 49 4.5
8.2 2.64 0.086 82 28.4
7.7 0.89 0.016
7.3 0.37 0.01
8.3 1.35 0.035
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501
Fig. 2 e Pilot plant-process at GCWW.
photolysis testing) and the UV dose was adjusted by changing the flow through the reactors, and for the MP reactor by adjusting the power levels. Since the ultraviolet transmittance at wavelength 254 nm (UVT254) and TOC concentration of the water varied seasonally, tests were performed at the beginning of each phase using atrazine to determine the operational conditions of the UV reactors. An 80% atrazine was targeted for the UV/H2O2 phases to determine the operational conditions of the UV reactors. This protocol was followed during both CONV and Post-GAC pilot influent conditions. Following the Post-GAC UV/H2O2 phase, the hydrogen peroxide feed was discontinued, the UV dose at the reactors was set at the lowest level and the spiking was repeated. During this time period atrazine destruction of 80% was not achievable. When the three phases were completed, the pilot influent was switched to CONV water and the reactors and flows were set at the 80% atrazine degradation conditions until the next quarter began. During the 12 month study several water quality, operational and performance parameters were monitored for the pilot. The pilot was monitored on a daily basis for flows, UV reactor intensity and applied UV dose. The UVT of the pilot influent was monitored and the hydrogen peroxide concentration was determined before and after the reactors and after the GAC contactors. Additionally, several other water quality parameters, such as TOC and alkalinity were tested at various frequencies across the pilot. The analytical methods for these tests were from Standard Methods for the Examination of Water and Wastewater (American Public Health Service, 1998) (see Supplemental Table 1 in the online supplemental information).
However, it should be noted that carbon is not always the limiting nutrient (LeChevallier et al., 1987, 1991). Additionally, other distribution conditions are not considered. Annular reactors were used to assess biofilm potential after GAC adsorption in unchlorinated process streams as per Sharp et al. (2001) (see Fig. 3). Four model 1320LS Laboratory Annular Reactors from BioSurface Technologies Corp. received flow from the effluent of the four GAC columns. The experiment ran from September 4, 2008 to October 2, 2008, which corresponded to runday 300e328 of the GAC. The sterilized annular reactors were reassembled on site with motors and controllers and set to a flow rate of 8 mL/min and a carousel rotational speed of 90 revolutions per minute. These conditions simulated a pipe velocity of 0.30 m/s (1 ft/s). The biofilm was quantified by heterotrophic plate count (Standard Methods 9215 B) and ATP-bioluminescence analysis. The bioluminescence methodology is based on detection of adenosine-50 -triphosphate (ATP) in metabolically active cells. A luminometer was used to quantify the ATP-bioluminescence. It gives a direct measurement of the light intensity and therefore a direct quantification of ATP. The light is quantified as relative light units (RLU), and the intensity of the emission is proportional to the concentration of ATP. Bioluminescence was measured using a Hygiena System SURE Plus luminometer. The annular reactor methodology and the microbial tests are described in more detail in the Supplemental information section.
3.
Results and discussion
2.3.
3.1.
Background water/operational
Analytical methodologies
AOC was one of the methods used to assess biofilm formation potential. The analytical method performed was from the 9217B American Public Health Services Standard Methods for the Examination of Water and Wastewater (see Supplemental Table 1.). The AOC test has been found to be a useful tool for predicting bacterial growth in the distribution system.
The pilot unit was in continuous operation from October of 2007 until October 2008. During that period the pilot influent water showed seasonal variations or changes in water quality due to natural surface water fluctuations and the upstream treatment processes. The influent water quality parameters potentially affecting the performance of the UV advanced
502
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Fig. 3 e Annular Reactor for online biofilm tests.
oxidation process were UVT, TOC concentration, alkalinity and iron concentration. Influent UVT and TOC concentration were expected to fluctuate during the year, especially for the CONV pilot influent water, which was used most of the time during the pilot study. The changes in UVT for the CONV and the Post-GAC water can be seen in Fig. 4. The CONV water UVT ranged between 84 and 95%/cm, with its lowest points being in December 2007 and the summer of 2008. The UVT of the Post-GAC water was more stable and fluctuated only between 95 and 98%/cm. The variation in UVT greatly affected the operation of the UV reactors and changes in flow and power level were required in order to achieve the required 80% atrazine degradation. The TOC concentration of the pilot influent water also varied over the 12 month study, fluctuating between 1.2 and 2.6 mg/L for the CONV water and 0.6e1.0 mg/L for the PostGAC water. On average a slight 2e3% decrease in TOC concentration was observed through both reactors when CONV was the pilot influent water. When Post-GAC pilot influent water was used, the decrease in TOC concentration through the reactors averaged 4e7%. These consistent small decrease in TOC concentration through the UV reactors can be explained by the partial mineralization of natural organic matter (NOM) by the hydroxyl radicals formed in the reactors. Due to their redox potential of 2.8 V, hydroxyl radicals have
UV254 Transmittance (%)
Post-GAC Pilot Infl.
CONV Pilot Infl.
100 98 96 94 92 90 88 86 84 82
Fig. 4 e UV Transmittance of CONV and Post-GAC water during pilot study.
the potential of completely oxidizing organic molecules to carbon dioxide (Carr et al., 2000). Research has shown that under advanced oxidation conditions similar to the ones applied in this study, NOM was not mineralized, but partially oxidized, resulting in a shift of molecular weight distribution towards smaller organic molecules. However, when pretreatment processes remove higher molecular weight fractions of NOM, as indicated by the drop in specific ultraviolet absorbance (SUVA) values, then UV/H2O2 at similar conditions used in this study may cause mineralization of NOM (Sarathy and Mohseni, 2007, 2009). The CONV pilot influent water had been coagulated, flocculated and filtered which decreased the TOC concentration by 30% (from 2.5 to 1.7 mg/L), and SUVA by 25% (from 3.4 to 2.6 L * mg1 * M1) on average, while GAC adsorption reduced the TOC concentration of the raw water by 65% (from 2.5 to 0.85 mg/L), and the SUVA value of the raw water by 50% (from 3.4 to 1.7 L * mg1 * M1). The reduction in TOC and SUVA values between the river, CONV, and Post-GAC water is likely the reason that mineralization of TOC was observed through the UV reactors during the UV/H2O2 process. Fig. 5 represents TOC concentration through the pilot plant, including the effluent of the GAC pilot columns when CONV water was used as the pilot influent. The top three curves depict CONV influent TOC concentration and the two UV/H2O2 reactor effluent TOC values. The bottom four curves represent TOC concentrations for the GAC column effluent streams from the LP and MP reactor process trains and the two control GAC columns. Typical breakthrough curves were observed for all four GAC pilot column effluent streams. TOC concentration in the GAC effluent streams ranged from 0.2 to 1.6 mg/L over the study period. At the beginning of the GAC pilot column runs, there was excellent TOC removal, and over the first 140e150 days as the GAC became loaded with organics, the effluent TOC concentration exhibited a rising trend, even though the influent TOC concentration was declining. Steady-state was reached between 140 and 160 days. After this point, the GAC effluent TOC concentrations reflect the increases and decreases of the TOC concentration in the GAC influent. However, some removal was observed through all GAC columns during the study period. By run day 220 there was a clear separation in the TOC concentrations of the GAC effluent streams that had received UV/H2O2 pretreatment and those that had not as shown in Fig. 6. Overall, the GAC effluent following the UV/H2O2 reactors resulted in 8% less TOC concentration than the control GAC effluent streams. After GAC run day 220, the GAC effluent following the UV/H2O2 reactors averaged 16% less TOC concentration than the GAC effluent of the control process streams. It should be noted that after GAC run day 220 the water temperature was the warmest (26e29 C), reflecting summer conditions (June 18, 2008 e August 27, 2008). It is therefore likely that enhanced TOC concentration removal was attributable to more bioactivity caused by the warmer temperatures and more assimilable materials loaded onto the GAC after UV/H2O2 treatment. When UV/H2O2 is employed, changes to the molecular structure of dissolved organic matter occur. Larger molecules are fragmented into smaller molecular weight compounds and a decrease in aromaticity results. Additionally, the ratio of hydrophilic to hydrophobic compounds increases (Sarathy and Mohseni, 2007,2009). Smaller molecules of a hydrophilic
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 9 7 e5 0 8
CONV Influent Control # 1 GAC Effluent MP GAC Effluent
3.0
LP Reactor Effluent Control #2 GAC Effluent
In addition to the UV/H2O2 experiments, tests were performed to examine the degradation of contaminants by photolysis using Post-GAC as pilot influent. Since the two reactor types could provide significantly different UV dose ranges, the photolysis tests were performed at the low end of UV doses for each reactor, which were around 800 mJ/cm2 for the LP reactor (as estimated by the supplier’s UV dose tables) and 280 mJ/cm2 for the MP reactor.
MP Reactor Effluent LP GAC Effluent
2.5
TOC (mg/L)
503
2.0 1.5 1.0 0.5 0.0 0
50
100
150
200
250
300
350
Run Days Fig. 5 e TOC through pilot-CONV Influent.
nature tend to be more assimilable by micro-organisms and thus more biodegradable.
3.2.
Atrazine normalization
TOC (mg/L)
A primary operational goal of the pilot study was to consistently set the UV reactors at the proper UV dose that would achieve the benchmark 80% atrazine degradation. This became particularly challenging when CONV influent water was used, since the UVT of the water fluctuated significantly throughout the year, and different UV doses were required to keep the atrazine degradation constant. To achieve these conditions throughout the study UV doses between 1200 and 2000 mJ/cm2 were required for the LP reactor (based on the manufacturer’s UV dose tables) and 200e500 mJ/cm2 were required for the MP reactor. Atrazine reduction was between 75 and 85% for most of the study quarters, with the only exception being the first quarter of the study when it was measured at 62% for the LP reactor. The reason for the low value the first quarter was likely iron fouling of the LP reactor sleeves because of an improperly primed pump. After the sleeves were cleaned the LP reactor could provide sufficient UV dose to reach the benchmark. When the higher UVT Post-GAC water was used as influent to the pilot reactors, adjustments to their flow and power level were made to reach the 80% atrazine degradation conditions. These conditions were met closely for both reactor types for almost all study quarters.
1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 0
Control #1 GAC Effluent
Control #2 GAC Effluent
LP GAC Effluent
MP GAC Effluent
50
100
150
200
250
300
Run Days Fig. 6 e TOC breakthrough-GAC pilot columns.
350
3.3.
Biofilm formation potential
3.3.1. pilot
Assimilable organic carbon (AOC) through UV/H2O2
Non-chlorinated AOC samples were collected throughout the pilot run to reflect variations in water quality in the process streams when the pilot was normalized for 80% atrazine destruction. When using the CONV process stream as influent to the pilot plant, the total AOC concentration increased through the UV/H2O2 reactors from an average of 106 mg/L, to an average of 141 mg/L (33% increase) for the LP process train and from an average of 106 mg/L, to an average of 137 (30% increase) for the MP process train as presented in Fig. 7A and Table 2. However, GAC was very effective in reducing the total AOC concentration from an average of 106 mg/L, to an average of 39e45 mg/L (63%e58% reduction) through the control GAC contactors. Note that the total AOC concentration means of the two GAC control effluent streams were similar, 14% difference (see Fig. 7A and Table 2). GAC adsorption following the UV/H2O2 process was effective in reducing the total AOC concentration from an average of 141 mg/L for LP process train, to an average of 54 mg/L (62% reduction) in the associated GAC effluent and from an average of 137 mg/L for MP process train, to an average of 45 mg/L (67% reduction) after GAC adsorption, ultimately resulting in total AOC concentrations similar to the GAC control effluent streams (see Fig. 7A and Table 2). Overall, the removal of total AOC by GAC was very consistent regardless of whether the influent water had received UV/H2O2 treatment. The quantity and type of AOC formed or reduced by the pilot unit processes differed. P17 AOC concentration was measured by the growth of P. fluorescens. As was discussed in the introduction, the P17 organism is able to utilize various compounds to promote growth. This organism can survive using many carbon substrates as energy sources (van der Kooij et al., 1982 and AwwaRF and KIWA, 1988). Spirillum strain NOX is more selective in its growth substrates. Carboxylic acids primarily promote rapid growth of the NOX organism. In treatment techniques such as ozonation where compounds not readily utilized by P17 are formed, the growth of Spirillum strain NOX is a useful indicator of AOC concentration increases. Spirillum strain NOX has been shown to represent carboxylic acids. Therefore, it was selected for studying these advanced oxidation processes. An average of 83 mg/L P17 AOC concentration, and an average of 23 mg/L NOX AOC concentration was found in the CONV pilot influent as presented in Table 2. Both parameter concentrations increased through the UV/H2O2 reactors, but as would be anticipated, the NOX AOC concentration increased more. P17 AOC concentration increased 24% (from an average of 83e103 mg/L P17 AOC) through both the LP and MP reactors (see Table 2). NOX AOC concentration increased from 23 to 38 mg/L (65% increase)
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Table 2 e AOC formation through the pilot e UV/H2O2 and photolysis alone. UV/H2O2 with CONV pilot influent AOC mg/L as acetate
P17 avg. NOX avg. Total avg. Sample (n)
Pilot influent
LP effluent
83 23 106 4
103 38 141 4
LP GAC effluent
MP effluent
34 20 54 4
MP GAC effluent
103 35 138 4
27 18 45 4
LP GAC
MP GAC
GAC control #1
67% 47% 62%
74% 49% 67%
71% 35% 63%
GAC control #1
GAC control #2
24 15 39 4
30 15 45 4
Changes in AOC as acetate through treatment processes LP reactor P17 NOX Total
24% 65% 33%
MP reactor 24% 52% 30%
GAC control #2 64% 35% 58%
UV/H2O2 with Post-GAC pilot influent AOC mg/L as acetate Pilot influent P17 avg. NOX avg. Total avg. Sample (n)
44 20 64 4
LP effluent
MP effluent
42 31 73 4
43 30 73 4
Changes in AOC as acetate through treatment processes LP reactor 5% 55% 14%
P17 avg. NOX avg. Total avg.
MP reactor 2% 50% 14%
Photolysis with Post-GAC pilot influent AOC mg/L as acetate Pilot influent P17 avg. NOX avg. Total avg. Sample (n)
33 22 55 3
LP effluent
MP effluent
45 23 68 3
31 24 56 3
Changes in AOC as acetate through treatment processes LP reactor P17 avg. NOX avg. Total avg.
36% 5% 24%
through the LP reactor and from 23 to 35 mg/L through the MP reactor (52% increase). The greater magnitude of the NOX AOC concentration increase was reasonable considering the findings of Sarathy et al. (2007, 2009), i.e., the ratio of hydrophilic to hydrophobic compounds increases with UV/H2O2 treatment. As was previously discussed, the control GAC effluent concentrations for total AOC were similar, but P17 AOC was better removed than NOX AOC. This result was expected because GAC was less efficient in removing hydrophilic compounds that would be represented better by NOX AOC concentration (Westerhoff et al., 2005). The NOX AOC was better removed through GAC following the UV/H2O2 process (47e49%) than through the control GAC column (35%) because of the increased bioactivity of the GAC caused by the UV/H2O2 treatment and possibly because the increased NOX AOC
MP reactor 5% 10% 1%
concentration through the reactors represented different, more adsorbable compounds than represented by the NOX AOC concentration from the CONV treated process stream as shown in Table 2. The total AOC concentration in the pilot influent was 40% lower (64 mg/L vs. 106 mg/L) when Post-GAC water was used as the pilot influent rather than CONV treated water as presented in Fig. 7B and Table 2. Because the Post-GAC pilot influent contained less UV absorbable organics, 80% atrazine reduction was obtained with less UV energy. The total AOC concentration increased slightly through the reactors during the PostGAC influent phases, from 64 mg/L to 73 mg/L (14%) for both the LP and MP reactors. This increase in total AOC concentration was less than the 30e33% increase in total AOC concentration when CONV treated water was used as pilot influent. The
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 9 7 e5 0 8
lesser increase in total AOC concentration when Post-GAC served as pilot influent was because the TOC concentration was lower. Larger molecular weight humic compounds, potentially precursors of AOC, are well-removed by GAC (Sontheimer et al., 1988) and (Morris and Newcombe, 1993). Thus, there is a lower concentration of these humics in the Post-GAC pilot influent to act as AOC precursors. The P17 AOC concentration did not increase through the reactors when Post-GAC water was used as pilot influent. The lower concentration of organics in this process stream and the reduction of the larger molecule AOC precursors by the GAC pre-treatment contributed to this result. The NOX AOC concentration increased from 20 to 30e31 mg/L, about 50%, again indicative of carboxylic acid formation through the UV/ H2O2 reactors as demonstrated in Table 2. Three experimental pilot runs were performed using photolysis alone. However, these results were not able to be directly compared to the UV/H2O2 results for AOC concentration because 80% atrazine degradation was not achievable. Also because of the aforementioned technical considerations, the LP reactor UV dose (approximately 800 mJ/cm2) was higher than the MP reactor dose (approximately 280 mJ/cm2). So no direct comparison between LP and MP technologies can be made for the photolysis study. Nevertheless, the relative increases in P17 and NOX AOC concentrations are of interest. Photolysis using Post-GAC pilot influent created no increase in NOX AOC concentration, because without the H2O2, less oxidation takes place and few carboxylic acids are formed (see Fig. 7C and Table 2). The P17 AOC concentration increased through the LP reactor, but not through the MP reactor as demonstrated in Table 2. This increase was likely associated with the higher LP reactor dose focused near the 254 nm wavelength. This wavelength is known to be well-absorbed by humic materials. Photolysis of the humic materials would thus proceed. As was previously discussed, the P17 AOC represents a wide variety of smaller molecular weigh assimilable compounds. van der Kooij (1992) recommended that unchlorinated systems maintain AOC concentrations below 10 mg/L. Even the GAC effluent samples had total AOC concentrations above this value (see Table 2). If a utility with a source water similar to GCWW’s wished to maintain a total AOC concentration less than 10 mg/L, a GAC empty bed contact time greater than the pilot condition of 15 min may be required. LeChevallier et al. (1990, 1996) provided some evidence that chlorine disinfected systems may limit regrowth and coliform occurrence by maintaining AOC concentrations less than 50e100 mg/L. Chlorine provides some protection against regrowth. Only the UV/H2O2 reactor effluent streams and the CONV pilot influent samples had total AOC concentrations exceeding this range as presented in Figs. 7 and 8.
3.3.2.
Biofilm annular reactors after GAC pilot contactors
Because the pilot GAC effluent streams had such low AOC concentrations, annular biofilm reactors were employed to examine biofilm production more closely. The annular reactors were operated continuously on the undisinfected GAC effluent streams. The experiments were begun during the most biologically active stage of GAC, i.e., near the end of the run and during warm weather conditions. The experiment ran
505
A
B
C
Fig. 7 e AOC formation (mg/L as acetate) through the pilot e UV/H2O2 and photolysis alone. A) UV/H2O2 with CONV pilot influent. B) UV/H2O2 with Post-GAC pilot influent. C) Photolysis with Post-GAC pilot influent.
from September 4, 2008 to October 2, 2008, which corresponded to runday 300 to 328 of the GAC. The temperature ranged from 27 to 28 C and the TOC from 0.82 to 1.95 mg/L for this time period. The biofilm from the coupons was extracted from the reactors and analyzed by two methods: the traditional HPC method and an ATP-bioluminescence method developed internally. HPC is a microbiological parameter and tends to have more scatter in the data than a chemical parameter. Thus, a log scale was used to display the data. When analyzing biofilm by this method, one can only discern differences in magnitudes of order. Even with this caveat, the reproducibility of individual coupons was not as good as
506
A
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 9 7 e5 0 8
MP GAC Effluent
LP GAC Effluent
Control #1
Control #2
1E+8
HPC Count/mL
1E+7 1E+6 1E+5 1E+4 1E+3 1E+2 1E+1 1E+0 1
2
3
4
5
6
Coupon Number
Relative Light Units(RLU)
B
MP GAC Effluent
LP GAC Effluent
Control #1
Control #2
4.
80 70 60 50 40 30 20 10 0 7
8
9
10
11
12
13
14
15
16
17
the highest results. The HPC and the ATP-bioluminescence methods showed different results because the HPC method grew the organisms in a nutrient media under ideal conditions. Injured cells had the opportunity to repair (LeChevallier et al., 1990). The ATP-bioluminescence method results represented cell viability at the time that the coupons were removed from the annular reactors and biofilm extracted. The data would suggest that the organisms produced by the MP process stream were more viable than those produced by the LP process stream, even though the HPCs for the two streams were similar. The increased viability of the biofilm produced in the MP UV reactor train is potentially a result of the multiple UV wavelengths of the MP reactor yielding different growth producing materials than the LP reactor.
18
Coupon Number
Fig. 8 e Biofilm formation on annular reactor coupons. A) Pilot GAC effluent e heterotrophic plate counts (HPC/mL). B) Pilot GAC effluent e ATP measured as Relative Light Units (RLU).
would be desired. Overall, the GAC process streams for the two controls produced similar HPCs. The HPCs of these control samples were less than those receiving water from the UV/H2O2 reactors. The LP reactor process stream data was the least precise. However, the coupons from this process stream tended to have slightly lower HPCs than the coupons from the MP process stream (see Fig. 8A). The ATP-bioluminescence method of biofilm quantification was based on the amount of ATP present. This method was dependant on viability of the organisms. Because the ATP-bioluminescence method was a chemically based analysis, and does not have the problem of cell separation, the data tend to be more precise. For this reason a linear scale can be used for the concentration axis as displayed in Fig. 8B. However, there are still situations that can cause the test to produce outlying data points. ATP is common to all microbes and larger cells such as protozoa require more energy to thrive. Therefore, if larger cells are present, they can skew the ATP data. The data exhibited good precision, with two outlying data points, likely caused by the presence of a larger microorganism. Nevertheless, it was clear that the control GAC column effluent streams produced similar results. The GAC effluent following the LP reactor also produced results similar to the controls. The MP stream GAC effluent produced
Conclusions
Some slight mineralization of TOC occurred through the UV/ H2O2 reactors. After GAC run day 220, the GAC effluent streams that had received UV/H2O2 treatment produced 16% lower TOC concentrations than the control GAC effluent streams that had not received UV/H2O2 pre-treatment. The UV/H2O2 pre-treatment created microbially assimilable compounds, increasing the bioactivity of the organically loaded GAC. The warmer temperatures after run day 220 also increased bioactivity. The pilot reactors were able to consistently achieve the desired 80% atrazine degradation, allowing comparison of the LP and MP lamp technologies for by-product formation for this desired contaminant destruction. However, it is important to note that these pilot-scale reactors may give different results than optimized full-scale reactors. AOC concentration increased through the reactors. The degree of increase was related to the NOM concentration of the pilot influent. The total AOC concentration in the pilot influent was 40% lower when Post-GAC water was used as the pilot influent rather than CONV treated water. Larger molecular weight humic compounds, potentially precursors of AOC, are wellremoved by GAC. Thus, there is a lower concentration of these humics in the Post-GAC pilot influent versus the CONV pilot influent to act as AOC precursors. Because the Post-GAC pilot influent contained less UV absorbable organics, 80% atrazine reduction was obtained with less UV energy. Therefore, the total AOC concentration increased slightly through the reactors during the Post-GAC influent phases (14%) for both the LP and MP reactors, while the CONV pilot influent produced a 30e33% increase in total AOC concentration. The average P17 AOC concentration increased 24% through the LP and MP reactors when CONV water was used as pilot influent. The P17 AOC concentration did not increase through the reactors when Post-GAC water was used as pilot influent. As with the total AOC, the lower concentration of organics in this process stream and the reduction of the larger molecule P17 AOC precursors by the GAC pre-treatment contributed to this result. The average NOX AOC concentration increased 65% through the LP reactor and 52% through the MP reactor (CONV pilot influent). The average NOX AOC concentration increased 55% through the LP reactor and 50% through the MP reactor
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 9 7 e5 0 8
when Post-GAC water was used as pilot influent, indicative of carboxylic acid formation through the UV/H2O2 reactors. Carboxylic acids promote the growth of the NOX organism. LP UV photolysis (at a dose of approximately 800 mJ/cm2) produced a 36% average P17 AOC concentration increase when using Post GAC as pilot influent. No NOX AOC concentration increase was observed, because LP UV photolysis is not an advanced oxidation process that produces carboxylic acids and other oxygenated species. MP UV photolysis (at a dose of approximately 280 mJ/cm2) produced no appreciable AOC concentration increase when using Post GAC as pilot influent. This dose was not sufficient to chemically alter the NOM enough to produce AOC. GAC adsorption before or after the UV/H2O2 process greatly reduced the resulting AOC concentration. The final product in either case contained AOC concentrations below 75 mg/L. Biofilms with greater HPCs were observed in the GAC effluent steams receiving UV/H2O2 pre-treatment. These results are consistent with the AOC results. The effluent streams of the GAC column proceeded by the MP UV reactor exhibited more viable biofilm than the other GAC effluent streams based on an ATP-bioluminescence method. The increased viability of the biofilm produced by the MP UV reactor is likely a result of the multiple UV wavelength emissions characteristic of this technology. More research should be performed in this area.
Acknowledgements We would like to acknowledge the support of KWR Watercycle Research Institute, the Dutch Ministry of Economic Affairs and the Water Research Foundation (formerly the American Water Works Research Foundation) for their support of this research. Additionally, we would like to recognize the contributions of Nick Ashbolt and Tammie Gerke of the USEPA and the efforts of the GCWW staff.
Appendix. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.watres.2010.09.007.
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Nomenclature Acronym Meaning ADP: adenosine diphosphate AMWD: apparent molecular weight distribution AOC: assimilable organic carbon ATP: adenosine triphosphate BDOC: biodegradable dissolved organic carbon CONV: conventional treatment DOC: dissolved organic carbon EBCT: empty bed contact time EDC: endocrine disrupting compound GAC: granular activated carbon GCWW: Greater Cincinnati Water Works HPC: heterotrophic plate count LP: low-pressure MP: medium-pressure MW: molecular weight NOM: natural organic matter NOX: Spirillium strain NOX P17: Pseudomonas fluorescens strain P17 PPCP: pharmaceutical and personal care products RLU: relative light units RMTP: Richard Miller Treatment Plant SUVA: specific ultraviolet absorbance TCA: trichloroacetic acid TOC: total organic carbon UV/H2O2: UV/hydrogen peroxide advanced oxidation UVT: ultraviolet transmittance
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 5 0 9 e5 2 0
Available at www.sciencedirect.com
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Effects of temperature and dissolved oxygen on sludge properties and their role in bioflocculation and settling B.Q. Liao a,*, H.J. Lin a, S.P. Langevin a, W.J. Gao a, G.G. Leppard b a
Department of Chemical Engineering, Lakehead University, 955 Oliver Road, Thunder Bay, ON, Canada P7B 5E1 National Water Research Institute, Canada Centre for Inland Waters, Environment Canada, 867 Lakeshore Road, Burlington, ON, Canada L7R 4A6 b
article info
abstract
Article history:
Effects of temperature (mesophilic (35 C) vs. thermophilic (55 C)) and dissolved oxygen (DO)
Received 5 June 2010
concentration (under thermophilic conditions) on sludge properties and their role in bio-
Received in revised form
flocculation and settling were studied using well-controlled sequencing batch reactors fed
30 August 2010
with a synthetic wastewater comprised of glucose and inorganic nutrients. Under a similar
Accepted 9 September 2010
DO level, thermophilic sludge had a poorer bioflocculating ability and settleability than that
Available online 17 September 2010
of mesophilic sludge. Under a thermophilic condition, an increase in DO level led to a poorer settleability and a slightly improved bioflocculating ability. A poorer settleability was related
Keywords:
to a higher level of filaments. Analysis of bound extracellular polymeric substances (EPS)
Temperature
indicates that thermophilic sludge had a higher level of total bound EPS content than that of
Dissolved oxygen
mesophilic sludge under a similar DO level, and an increase in DO resulted in an increase in
Thermophilic treatment
total bound EPS content in thermophilic sludge. Surface analysis of sludge by X-ray photo-
Extracellular polymeric substances
electron spectroscopy (XPS) suggests that significant differences in the surface concentra-
Sludge properties
tions of elements N, C, O were observed between thermophilic and mesophilic sludge,
Flocculation
implying significant differences in bound EPS composition. The results of gel permeation
Settleability
chromatography indicate that the weight-averaged molecular weight (Mw) of bound EPS covered a range of 1159 Da to 13220 Da. The distribution of EPS “species” at floc surfaces was shown by transmission electron microscopy (TEM) to be uneven; different kinds of nanoscale materials were distributed in a patchy manner at the flocewater interface. The results suggest that it is the role of specific EPS molecules rather than the quantity of bound EPS that determine the difference in bioflocculation behavior between thermophilic and mesophilic sludge. The strategy of increasing the DO level could not solve the biomass separation problems associated with thermophilic sludge. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Thermophilic biological treatment of wastewater has received much attention in recent years. Wastewaters, including pulp and paper mill effluent, food processing wastewaters, livestock processing effluents, have been successfully treated
(LaPara and Alleman, 1999; Suvilampi and Rintala, 2003). Compared to analogous mesophilic treatment, thermophilic treatment has the advantages of low sludge yield, high reaction rate, and excellent process stability (LaPara and Alleman, 1999; Suvilampi and Rintala, 2003). The thermophilic treatment processes are extremely attractive for high temperature
* Corresponding author. E-mail address:
[email protected] (B.Q. Liao). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.010
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and high strength wastewater treatment with an aim for system closure. This eliminates the requirement of pre-cooling for mesophilic treatment and the post-heating for subsequent reuse of treated effluent. It is generally agreed in the literature (Tripathi and Allen, 1999; Vogelaar et al., 2002a) that thermophilic processes can achieve a similar removal efficiency of soluble chemical oxygen demand (COD), as compared to mesophilic processes. However, the thermophilic processes face challenges in terms of biomass separation. As compared to analogous mesophilic processes, thermophilic processes usually produce a treated effluent with a higher turbidity or effluent suspended solids (ESS) (Vogelaar et al., 2002b; Morgan-Sagastume and Allen, 2003). The settleability of thermophilic sludge is usually poorer with a higher level of filaments and sludge volume index (SVI) (Krishna and van Loosdrecht, 1999; Suvilampi and Rintala, 2002) in most cases. But a better settleability of thermophilic sludge was also observed too, although the cases are rare (Vogelaar et al., 2002a). Current strategies to solve the biomass separation problems associated with thermophilic treatment processes include the use of membrane bioreactors (MBR) (Be´rube´ and Hall, 2000) and post-mesophilic treatment (Vogelaar et al., 2002a). However, membrane fouling may be a challenge for thermophilic MBR treatment, due to the presence of a large amount of fine colloidal particles. A careful review of the literature shows that there are very limited well-controlled comparative studies between thermophilic and mesophilic treatment. The most frequent problem is a different DO level being used during the comparative studies. If a similar DO is used under thermophilic and mesophilic treatment, then a higher aeration intensity of air has to be used due to the lower solubility of DO under thermophilic temperatures, although DO and mixing intensity have been found to be important factors governing biomass separations. There is no report on the use of oxygen-enriched air to achieve a similar DO when comparing thermophilic and mesophilic treatment. It is generally believed that a minimum of DO level of 2.0 mg/L should be maintained to suppress the overgrowth of filaments in mesophilic processes (Jenkins et al., 2003; Martins et al., 2003). The strategy of increasing the DO level, which has been widely used in the mesophilic processes, has not been well studied under thermophilic conditions. Although thermophilic sludge faces the challenges of biomass separations, the causes of differences in biomass behavior between thermophilic and mesophilic sludge have not been well understood. The mechanisms of bioflocculation under thermophilic temperatures have not been explored. Vogelaar et al. (2005) hypothesized that polymer bridging may be the mechanism governing bioflocculation, but no detailed information on the comparison of extracellular polymeric substances (EPS) between thermophilic and mesophilic sludge is available in the literature. In particular, the role of surface properties, including bound EPS, of thermophilic sludge has not been well studied. The objectives of this study were to: 1.)compare the performance of thermophilic and mesophilic treatment using well-controlled sequencing batch reactors fed with a synthetic wastewater; 2.) compare the surface properties, including EPS production, composition and molecular weight distribution, surface composition of sludge, filaments level,
and zeta potential; 3.) understand the causes of different bioflocculation behaviors between thermophilic and mesophilic sludge; and 4.) test the feasibility of using an elevated DO level to improve the flocculating ability and settleability of thermophilic sludge.
2.
Materials and methods
2.1.
Aerobic sequencing batch reactors (SBRs)
The laboratory experimental system consists of two parallel SBRs (total and effective volume: 2.0 and 1.8 L/each, respectively), operated at either 55 C or 35 C with an on-line pH controller (pH ¼ 7.0) (Thermo/Barnant HD PH metering pump and pH controller, Model HD-pH-5e10, Barrington, IL, USA) using a 0.1 N NaOH solution. A refrigerator was used for storing the synthetic wastewater at 4 C. Two preheating tanks were used to increase the temperature of the synthetic feed from 4 C to 35 C and 55 C, respectively, before it entered the SBRs. Two water baths that circulated water at different temperatures, through the jacket of SBRs, resulted in each SBR being operated at a certain constant temperature (35 1 C, 55 1 C, respectively). The SBRs were operated at a cyclic time of 12 h at a feed COD of 1000 50 mg/L. The time of filling, reaction, settling and discharging was 10, 660, 40 and 10 min, respectively. The mixing intensity in each SBR was made similar by setting the same rotating speed of the magnetic stirring bar at the bottom of each SBR and the similar air flow rate. 1 L of the synthetic wastewater was added to each SBR in each cycle. The chemical composition of the synthetic wastewater and operational conditions of the SBR system are shown in Tables 1 and 2, respectively. To achieve a well-controlled comparative study on the effects of temperature and dissolved oxygen concentration on sludge properties and their role in bioflocculation and settling, two types of aeration sources were used to provide oxygen for biodegradation: air (21% oxygen) and oxygen-enriched air (32% oxygen þ68% nitrogen). The oxygen-enriched air (32% oxygen) provides a similarly saturated level of oxygen in water at 55 C (7.8ppm) as that of aerated using air at 35 C. The aeration conditions of the thermophilic SBR could be classified as having 3 phases, as shown in Table 3. The first phase (days 1e82) was aerated using air, the second phase (days 83e176) was aerated using oxygen-enriched air
Table 1 e Inorganic nutrients composition of the synthetic wastewater (glucose as C source, NH4Cl as N source and K2HPO4 as P source, COD:N:P [ 100:5:1, COD [ 1000 mg/L). Chemical NiCl2 CaCl2$7H2O CuCl2$2H2O FeCl3$6H2O MnCl2$4H2O ZnCl2 CoCl2$6H2O Na2SeO3
Concentration (mM) 0.1 5.0 0.01 5.0 0.1 0.01 0.1 0.01
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Table 2 e Operating parameters of the SBR system for the treatment of synthetic wastewater. Thermophilic Mesophilic Thermophilic low DO high DO Cyclic Time (hr) Solids Retention Time (day) Organic Loading (kg/m3d) Operating Temp (oC) Operating pH
12 10
12 10
12 10
1.1
1.1
1.1
55 1
35 1
55 1
7.2 0.2
7.2 0.2
7.2 0.2
(32% oxygen), and the third phase (days 177e209) was aerated using air again. The purpose of the third phase was to check if the results in the first phase could be repeated by switching from oxygen-enriched air back to regular air for aeration. To study the effect of temperature under a similar DO level, the results of the thermophilic SBR in the second phase were compared to those of the mesophilic SBR. The difference in DO level between thermophilic (phase 2) and mesophilic SBR was less than 0.5 ppm at anytime of the cycle. To investigate the effect of DO level under thermophilic conditions, the results from the first and third phases were compared to those of the second phase of the thermophilic SBR. To avoid washout of settled sludge due to sludge bulking in the second phase of the thermophilic treatment, 300e400 mL of mixed liquor was withdrawn before settling. The settled sludge was put back in the SBR at the end of the filling phase. Both the thermophilic and mesophilic sludge seeds were developed from a previous comparative study treating thermomechanical pulp (TMP) pressate, using thermophilic and mesophilic processes, whose activated sludge seeds were taken from a pulp and paper mill activated sludge plant. The thermophilic sludge in the previous study was developed from a mesophilic sludge by a step increase in temperature.
2.2.
Analytical methods
2.2.1. Mixed liquor suspended solids (MLSS), COD and ESS measurements MLSS, soluble COD, and ESS were determined according to Standard Methods (APHA, 2005). The samples for these measurements were taken from the SBRs at the end of the reaction (MLSS and COD) and at the end of settling (ESS) just before discharging.
Table 3 e Aeration conditions for the thermophilic and mesophilic SBR. Thermophilic SBR
Mesophilic SBR
Phase 1
Phase 2
Phase 3
Days 1e82 Air aeration
Days 83e176 32% O2 þ 68% N2 Aeration DO ¼ 3.5e5.7 mg/L
Days 177e209 Air aeration
DO ¼ 1.1e2.5 mg/L Day 1e209 Air Aeration DO ¼ 4.0e5.8 mg/L
DO ¼ 1.1e2.5 mg/L
2.2.2.
511
SVI
The settleability of sludge, evaluated by SVI, was measured using a 500 mL graduated cylinder filled with mixed liquor and being allowed to settle for 30 min.
2.2.3.
Filamentous microorganisms
The abundance of filamentous microorganisms was extensively examined (2e3 times/week) with a light microscope (Olympus IX51 inverted microscope, Tokyo, Japan) at a 10 magnification of the objective lens. The number of filaments was classified into levels 0 to 6 according to Jenkins et al. (2003). A smaller score corresponds to a lower level of filaments.
2.2.4.
Floc size distribution
Floc size distribution, of MLSS and ESS in treated effluent (after 40 min settling), was determined by using a Mastersize 2000E (measuring range 0.02e2000 mm) made by Malvern Instruments (Worcestershire, UK). The Malvern instruments use light scattering and the data is given as frequency by volume. Three measurements were taken for each SBR in each week of the experimental time.
2.2.5.
Zeta potential
Zeta potential of the non-settleable fraction of sludge flocs in the treated effluent as ESS was measured by Zetacompact Z8000 model (CAD Instrumentation, Les Essarts Le Roi, France). The electric field, added on the solution containing non-settleable flocs, was controlled by a cell voltage of Zetacompact Z8000 and was fixed at 80 V. The pH of the treated effluent was in the range of 7.2 0.2.
2.2.6.
Floc morphology
Morphology of sludge flocs was observed and recorded by an Olympus IX51 inverted microscope (objective lens:10 magnification) (Tokyo, Japan) at the same time for quantification of filaments. Three measurements were conducted for each SBR in each week of the experimental time.
2.2.7.
EPS extraction and analysis
The extraction of bound EPS was based on a cation exchange resin (CER) (Dowex Marathon C, Naþ form, SigmaeAldrich, Bellefonte, PA) method (Frølund et al., 1996): 100 mL of sludge suspension was taken and centrifuged at 18700 g for 20 min at 4 C. The sludge pellets were resuspended to their original volume using a buffer consisting of 2 mM Na3PO4, 4 mM NaH2PO4, 9 mM NaCl and 1 mM KCl at pH 7. Then, the sludge was transferred to an extraction beaker with buffer and the CER (80 g/g MLSS) added. The suspension was stirred (Corning 171 Scholar Stirrer, Corning, USA) for the selected stirring intensity and extraction time (1.5 h) at 4 C. The extracted EPS was recovered by centrifugation of the CER/sludge suspension for 20 min at 18700 g at 4 C in order to remove the CER and MLSS. The EPS was normalized as the sum of polysaccharide and protein, which were measured colorimetrically by the methods of Dubois et al. (1956) and Lowry et al. (1951), respectively. Bovine serum albumin (BSA) was used as a protein standard, and glucose was used as a polysaccharide standard.
Surface concentrations of elements, including C, O, and N etc. on sludge surface were determined by the XPS method, which detects the outermost molecular layers (e.g., EPS) of the surface (2e5 nm). The sludge samples were first filtered with 0.45 mm filter paper. The wet cake layers were carefully removed and placed into an alumina dish for freeze-drying. The sludge samples were placed in a freeze-dryer (Labconco Freezone 12, USA) at 35 C for one week (until freeze-dried). The freeze-dried powder sludge samples were placed in a sample well with the surface flush with the top, while the sheet samples were simply affixed to double sided adhesive tape. The sludge samples were then run on the thermo Scientific K-Alpha XPS Spectrometer (ThermoFisher, E. Grinstead, UK) and at a take-off angle (relative to the surface) of 90 . A monochromatic AlKa X-ray source was used, with a spot area (on a 90 sample) of 400 mm. Charge compensation was provided. The position of the energy scale was adjusted to place the main C 1s feature (CeC) at 285.0 eV, except for those samples where the CeO peak was more dominant. In this case the energy scale was adjusted to place this feature at 286.5 eV. A survey spectrum was taken at low resolution (PE 150 eV). In addition to C, N, and O, traces of Na, S and Si were observed. These regions were collected at low resolution for relative atomic percents. High resolution spectra were taken of C, N and O regions (PE 25 eV). All data processing was performed using the software (Avantage) provided with the instrument.
2.2.10. Transmission electron microscopy (TEM) Nanoscale observations of floc surfaces and internal floc architecture (surface roughness, channels, three-dimensional disposition of individual bacterial cells and filamentous bacteria, their microcolonies and their nanoparticulate EPS) were made on ultra-thin sections of whole flocs sampled from sequencing batch reactors (three mesophilic, three thermophilic with low DO, and three thermophilic with high DO). These sections were prepared for transmission electron microscopy (TEM) by the established methodology of Liss et al. (1996). Observations were made from flocs fixed initially in glutaraldehyde plus ruthenium red, then washed and postfixed in osmium tetraoxide plus ruthenium red (Liss et al., 1996). After the double fixation (designed to minimize extraction of macromolecules and shrinkage), the flocs were embedded in Spurr’s low-viscosity epoxy resin (Spurr, 1969),
2.2.11. Statistical analysis Statistical analysis was carried out using the Statistical Package for the Social Sciences (SPSS) V11.0 produced by SPSS Incorporation (America) with the aim to characterize the influence of temperature and DO on sludge properties. An analysis of variance (ANOVA) was used to test for differences between treatment means. The type I error rate was set at 0.05 (95% confidence interval) for all statistical tests performed in this study.
3.
Results and discussion
3.1. Overall performance of the thermophilic and mesophilic SBRs Fig. 1(a), (b), and (c) show the changes in SVI, ESS and residual soluble COD in treated effluent with experimental time,
a
Thermophilic Low DO
350
Thermophilic High DO
Thermophilic Low DO
Thermophilic Mesophilic
Acclimation
300 250 200 150 100 50
b 1800
c
Thermophilic Mesophilic
160 140 120 100 80 60 40 20 0 180 160
Thermophilic Effluent Mesophilic Effluent Influent
140
1400 1200 1000
120
800
100 80
600
60 40
400
COD (mg/L)
2.2.9. Surface composition of sludge by X-ray photoelectron spectroscopy (XPS)
and then sectioned with a diamond knife mounted in a Leica Ultracut UCT ultramicrotome (Leica Mikrosysteme, Wien, Austria). The 70 nm sections were mounted on formvarcovered copper TEM grids, and then counterstained (Liss et al., 1996). The searches of TEM views of flocs, to select representative images of ultrastructural features, were done systematically according to the protocol of Leppard et al. (2003). Documentation was performed with a JEOL JEM 1200 EX TEMSCAN scanning transmission electron microscope (JEOL, Peabody, MA) operated in transmission mode at 80 kV.
SVI (mL/g MLSS)
Molecular weight distribution of bound EPS
The molecular weight distribution (MWD) of bound EPS was determined using the gel permeation chromatography (GPC) method. The GPC was equipped with an Aquagel OH-40 column (Polymer Laboratories, Amherst, USA) and aqueous phosphate buffer solution (pH ¼ 7.4) was used as the mobile phase at 1 mL/min flow rate. The sample volume was 50 mL and the column was maintained at ambient temperature with a differential refractive index (RI) detector for detection of the separated compounds. Polyethylene oxide standards with a weight-averaged molecular weight (Mw) ranging from 1010 to 909,500 g/mol were used to calibrate the system. Therefore, the results obtained are quoted relative to these linear standards.
ESS (mg/L)
2.2.8.
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COD (mg/L)
512
200
20 0
0 0
20
40
60 80 100 120 140 160 Experimental Time (day)
180 200
Fig. 1 e Variation of (a) SVI, (b) ESS, and (c) soluble COD in treated effluent and feed with experimental time.
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Table 4 e Average sludge properties under different tested conditions. Mesophilic
SVI (mL/g MLSS) COD (% Removal) ESS (mg/L) MLSS (g/L)
32 92 49 3.5
8 2 7 0.5
Thermophilic Low DO
High DO
157 91 96 2.8
270 90 81 2.3
26 2 10 0.4
60 1 5 0.2
respectively. The average values of SVI, ESS and residual soluble COD under different conditions are presented in Table 4. As shown in Fig. 1(a), the settleability of thermophilic sludge was poorer (higher SVIs) than that of the mesophilic sludge. The thermophilic sludge had a filamentous bulking problem while the mesophilic sludge had excellent settleability. An increase in the DO level resulted in an increase in the SVI under the thermophilic temperature (55 C). A subsequent decrease in the DO level, after the period of time of higher DO level, led to an improvement in settleability. The result suggests that an increase in DO did not improve the settleability of thermophilic sludge. Overall, the results of the poorer settleability of the thermophilic sludge, as compared to the analogous mesophilic sludge, is consistent with the findings of a number of previous studies (Be´rube´ and Hall, 2000; Cenens et al., 2000; Rozich and Bordacs, 2002; Vogelaar et al., 2002a, 2002b). However, some other studies, although the cases are rare, found that the settleability of thermophilic sludge was better than or comparable to that of mesophilic sludge (LaPara and Alleman, 1999). The effect of DO on the settleability and structure of mesophilic sludge has been extensively studied (Wile´n and Balme´r, 1999; Jenkins et al., 2003; Martins et al., 2004). Literature data are contradictory and the relationship between DO and the settleability of mesophilic sludge is still unclear (Martins et al., 2003). It is generally believed that an increase in DO would improve the settleability of mesophilic sludge (Jenkins et al., 2003). A critical DO level of 2 mg/L is required to suppress the overgrowth of filaments in biomass separation (Jenkins et al., 2003; Martins et al., 2003). However, the results from this study suggest that an increase in the DO level resulted in a deterioration of the settleability of thermophilic sludge. This result is consistent with the findings of previous studies (Benefield et al., 1975; Houtmeyers et al., 1980; Palm et al., 1980) in that sludge bulking could occur at higher DO levels. The contradictory results suggest that the role of DO level in controlling the settleability of sludge is complex and may depend on the relative importance of factors other than DO. The results from this study suggest that the widely used strategy of raising DO level to minimize filamentous sludge bulking in mesophilic sludge may not be applicable to thermophilic sludge. As shown in Fig. 1(b), the flocculating ability of the thermophilic sludge was poorer (higher ESS level) than that of the analogous mesophilic sludge. An increase in the DO level led to a slightly improved flocculating ability under the thermophilic temperature (55 C) (Table 4). The results of poorer flocculating ability of thermophilic sludge are in good agreement with the findings of previous studies (Suvilampi and
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Rintala, 2003). Literature data suggests that more colloidal particles or free bacteria are present in the treated effluent of thermophilic processes (Suvilampi and Rintala, 2003). The slightly improved flocculating ability of thermophilic sludge at a higher DO level could be due to an increased level of filaments at the higher DO level, providing more binding sites from the filaments for free bacteria or colloidal particle attachments. Fig. 1(c) shows that the residual soluble COD in the treated effluent of the thermophilic SBR was slightly higher than that of the mesophilic sludge. Considering the fact that glucose is a readily biodegradable compound, the residual soluble COD might be soluble microbial products (SMPs) rather than glucose. Analysis of the treated effluent showed that proteins and carbohydrates were present in treated effluent, implying the presence of SMPs in treated effluent. A calculation of the COD removal rate, based on the data of COD measured at different reaction times in one operational cycle, under both thermophilic and mesophilic conditions, suggests that the COD removal rate of the thermophilic sludge was higher than that of the mesophilic sludge under a similar DO level. An increase in the DO level resulted in a higher COD removal rate under thermophilic conditions.
3.2.
Properties of thermophilic and mesophilic sludge
3.2.1.
Filaments level
A significant difference in the level of filaments was observed under different tested conditions. Fig. 2 shows the image analysis of typical samples of mesophilic and thermophilic sludge. The mesophilic sludge contained no or a few filaments (level: 0e1), while the thermophilic sludge had a significantly higher level of filaments (4e5). An increase in the DO level from 1 to 2.5 ppm to 3.5e5.7 ppm by using 32% oxygen gas led to an increase in the filaments level. The increase in the filaments level with an increase in the DO level is not consistent with the findings of previous studies (Jenkins et al., 2003; Martins et al., 2003) with mesophilic sludge. It is generally agreed that a minimum of DO level at 2 mg/L should be maintained to suppress the overgrowth of filaments in mesophilic sludge (Jenkins et al., 2003; Martins et al., 2003). A DO level of smaller than 2 mg/L will promote the growth of filaments (Jenkins et al., 2003; Martins et al., 2003). If this conclusion can be applied to thermophilic sludge, then it is not surprising to see the presence of a significant amount of filaments in thermophilic sludge at the lower DO levels (LaPara and Alleman, 1999; Rozich and Bordacs, 2002; Suvilampi and Rintala, 2003) aerated with air. But a further increase in the filaments level with an increase in DO level suggests that the thermophilic filaments may be more responsive to the change in temperature than to the change in DO level.
3.2.2.
EPS production and composition
Extensive studies on bound EPS production and composition were conducted over a period of 6 months. Under a similar DO level, the average total bound EPS of the mesophilic sludge (17.70 mg/g MLSS) was significantly lower than that (32.04 mg/ g MLSS) of the thermophilic sludge (ANOVA, p < 0.05) (Fig. 3). The difference in the total bound EPS between thermophilic
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Bound EPS (mg EPS/ g MLSS)
45 40 35
Thermophilic Low DO Thermophilic High DO Mesophilic
30 25 20 15 10 5 0 Protein
Carbohydrate
Total EPS
Fig. 3 e Comparison of bound EPS under different tested conditions (sample number n [ 24, 16 and 15 for mesophilic, thermophilic low DO and thermophilic high DO sludge, respectively) (ANOVA, P < 0.05).
findings of previous studies with activated sludge (Shin et al., 2001). Shin et al. (2001) found higher airflow rates increased the amount of carbohydrate in the bound EPS but the protein level was almost constant. Fig. 4 shows the soluble EPS in treated effluent under different tested conditions. There are no statistically significant differences among the tested conditions. However, there was an increasing trend in protein and total soluble EPS content with the thermophilic high DO condition, although not statistically significant. The presence of soluble EPS may change the surface tension of the solution and thus affect bioflocculation. The potential role of soluble EPS in bioflocculation needs to be further studied.
3.2.3.
MWD of bound EPS
As bound EPS plays an important role in controlling bioflocculation (Yu et al., 2009), further analyses of the MWD of bound EPS were conducted using the GPC method. The results, as shown in Fig. 5, indicate that there were 3 peaks in the MWD of bound EPS, corresponding to an Mw of 12227e13220, 4850e5420, and 1159e1678 Da, respectively. Most of the bound
30
Fig. 2 e Morphology of sludge under (a) mesophilic (b) thermophilic low DO (c) thermophilic high DO conditions.
and mesophilic sludge might be related to the different microbial communities and cell lysis rates. It is known that thermophilic sludge contains a different microbial community, as compared to the mesophilic sludge (LaPara et al., 2000). An increase in temperature would result in an increase in the cell lysis rates (LaPara et al., 2000). Under the thermophilic condition, an increase in the DO level resulted in an increase in the total bound EPS (Fig. 3) (ANOVA, p < 0.05). The increased total amount of bound EPS with an increase in the DO level in thermophilic sludge is consistent with the
Soluble EPS (mg/L)
25
Thermophilic Low DO Thermophilic High DO Mesophilic
20 15 10 5 0 Protein
Carbohydrate
Total Soluble EPS
Fig. 4 e Comparison of soluble EPS under different tested conditions (sample number n = 12 to 15) (ANOVA, P > 0.05).
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polydispersity, as compared to peaks 1 and 2. The polydispersities results revealed that in bound EPS, the constituents in the high MW peaks (peaks 1 and 2) had similar origin and/or properties, whereas the constituents in the low MW (peak 3) were relatively more heterogeneous (Yu et al., 2010). Overall, their Mw, Mn and Mw/Mn values were similar under all tested conditions. These results indicate that MWD of bound EPS could not explain the observed difference in bioflocculation and settling, and point in the direction of the specific roles of individual EPS molecules in bioflocculation and settling.
1.1 1
Thermophilic low DO Thermophilic high DO Mesophilic
peak 1
0.9
Specific height
0.8 0.7
peak 2
0.6 0.5
peak 3
0.4 0.3 0.2 0.1 0 25
26
27
28
29
30
31
Time (min)
Fig. 5 e GFC chromatograms of bound EPS under different tested conditions (sample number n [ 5 for each condition).
EPS had an Mw of over about 5000 Da and only a small fraction of bound EPS had an Mw in the range of 1159e1678 Da. The range of MWD of EPS is consistent with that reported in previous studies (Higgins and Novak, 1997; Esparza-Soto and Westerhoff, 2001; Zhang et al., 2008). These results suggest that bound EPS may not be considered only as macromolecules because a significant portion of bound EPS had a low Mw. Although there were some differences in the high Mw peak (peak 1) between mesophilic bound EPS and thermophilic high DO bound EPS or between thermophilic low DO bound EPS and thermophilic high DO bound EPS (Table 5) (ANOVA, p < 0.05), there were no significant differences in the peak 2 and peak 3 Mw of bound EPS among the tested conditions and peak 3 between mesophilic bound EPS and thermophilic low DO bound EPS. In addition, sometimes peak 3 (low Mw EPS) disappeared in some samples (2) of the thermophilic low DO bound EPS and mesophilic bound EPS while it appeared in all the tested bound EPS samples of thermophilic high DO sludge. The number-averaged molecular weight (Mn) of bound EPS was similar to the Mw of bound EPS (Table 5). The polydispersity (ratio of Mw to Mn) was in the range of 1.019e1.355. Peak 3 (low molecular weight) usually had a larger value of
3.2.4.
Surface composition of sludge measured by XPS
Surface composition, which represents the outermost molecular layers of sludge surface (2e5 nm) (mainly EPS), of the thermophilic and mesophilic sludge was measured by the XPS. Representative peaks of the major elements are presented in Fig. 6. According to previous publications (Dengis and Rouxhet, 1996; Dufrene et al., 1997; Badireddy et al., 2008), each peak corresponds to different bonds. The C peaks (C1s, C1sA, C1sB, C1sC) could be attributed to four different bonds: C bound only to C and H, C-(C,H) at a binding energy of 284.8 eV; C singly bound to O or N, C-(O, N), including ether, alcohol, amine, and amide, at a binding energy of 286.3 eV; C bound to O using two single bonds or one double bond, C]O or OeCeO, including amide, carbonyl, carboxylate, ester, acetal, and hemiacetal, at a binding energy of 288.0 eV. The O peaks (O1s, O1sA, and O1sB) could be decomposed into three bonds: OeC bond, including hydroxide (CeOH), acetal, and hemiacetal (CeOeC), at a binding energy of 532.7 eV, and O]C in carboxylic acid, carboxylate, ester, carbonyl and amide at a binding energy of 531.4 eV. The N peaks (N1s and N1sA) were attributed to the two different bonds: NeC bond in amide or amine at a binding energy of 400.12 eV and NeH bonds in ammonia or protonated amine at a binding energy of 402.10 eV. Table 6 presents the surface composition, in terms of atomic concentration, of C, O, N, Na, S and Si. A significant difference in the quantity of C, O, and N was observed between thermophilic and mesophilic sludge (95% confidence level, student t-test). The mesophilic sludge had more hydrocarbon moieties, more nitrogen and less oxygen than the
Table 5 e Molecular weight distribution of bound EPS. Source of bound EPSa
Peaks
Mwb
Mnb
Thermophilic high DO EPS
Peak 1 Peak 2 Peak 3 Peak 1 Peak 2 Peak 3 Peak 1 Peak 2 Peak 3
12447 235 5063 165 1413 195 12924 332 5094 95 1257 77 12888 239 5194 169 1291 210
12070 237 4818 193 1263 227 12068 370 4801 122 1086 145 12520 227 4921 194 1125 272
Thermophilic low DO EPS
Mesophilic EPS
a Note that sample number ¼ 5 under each tested condition. b Note that Mw and Mn are the weight-averaged molecular weight and number-averaged molecular weight.
Polydispersities (r ¼ Mw/Mn) 1.032 1.051 1.127 1.025 1.061 1.163 1.029 1.056 1.160
0.002 0.009 0.075 0.005 0.008 0.083 0.002 0.009 0.087
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Fig. 6 e XPS spectra of mesophilic sludge, (a) whole spectra, (b) C1s spectra, (c) N1s spectra, and (d) O1s spectra.
thermophilic sludges. No significant difference in the quantity of C, O, and N was observed between the thermophilic sludge at high DO level and thermophilic sludge at low DO level. It is well known that the bound EPS are mainly composed of sugar polymers (polysaccharides), proteins, DNA, lipids and uronic acid polymers; both the sugar and uronic acid monomers are carbohydrates (Li and Yang, 2007). The surface composition and the molecular constituents detected by the XPS are the building blocks of the bound EPS molecules. From the literature (Dengis and Rouxhet, 1996; Dufrene et al., 1997; Badireddy et al., 2008), the C-(C, H) bonds might originate from lipids or from amino acid side chains. Polysaccharides contain hydroxide and acetal or hemiacetal building blocks. Proteins and uronic acids in polysaccharides might contain carboxylate and carboxyl function groups. The amide may represent peptidic bonds in proteins. The ammonium might be a counter-ion of surface negative sites and the protonated amine could be due to basic amino acids (Dengis and Rouxhet, 1996). The higher concentration of C-(C, H), and N and lower concentration of O as observed in the mesophilic sludge might indicate a higher surface concentration of lipids and proteins and a lower surface concentration of carbohydrates in the mesophilic sludge. The significant difference in surface composition might explain the difference in bioflocculation behavior between thermophilic and mesophilic sludge, as discussed later. A comparison of the XPS data with the data from the biochemical extraction and analysis of bound EPS showed that the biochemical analysis of bound EPS had a relatively large variation in standard deviation, while the XPS data are
more consistent with each individual measurement. This is probably not surprising, as a number of factors will affect the bound EPS extraction and analysis and thus lead to a relatively large variation in the quantity of bound EPS determined. Since the XPS directly measures the outermost portions of cell surfaces and their coatings (2e5 nm), an alteration by physical and chemical extraction is avoided. Systematic differences between XPS analysis of thermophilic and mesophilic sludge point to the advantages of XPS analysis over biochemical extraction and analysis of bound EPS. The results suggest that XPS is a promising technique for bound EPS analysis in sludge flocs.
3.2.5.
Zeta potential
Under a similar DO level, the average zeta potential of the thermophilic sludge (12.6 0.8) was similar to that (13.2 0.9) of the mesophilic sludge. Under a thermophilic condition, the DO level had limited effect on the zeta potential of the thermophilic sludge. The results are consistent with the findings of Vogelaar et al. (2005) in that there was no significant difference in zeta potential between thermophilic and mesophilic sludge. Floc size distribution measurement indicates that floc sizes of thermophilic sludge were much smaller than that of mesophilic sludge and there was a larger amount of fine colloidal particles in thermophilic effluent (results not shown). The results suggest that the Derjaguin, Landau, Verwey and Overbeek (DLVO) theory is not valid in explaining the difference in flocculating behavior between thermophilic and mesophilic sludge, and point to a role for EPS in controlling flocculation.
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Table 6 e Surface composition of the thermophilic and mesophilic sludge determined by XPS: average atom fraction (%) excluding hydrogen. Element component
Mesophilic sludge (MS)
Thermophilic sludge (low DO) (TS-LD)
Thermophilic sludge (High DO) (TS-HD)
Total C C-(O,N) C-(C,H) C]O O]CeOH Total O CeOH and CeOeC O]C OeC]O Total N NeC Nþ Na S Si
70.89 1.09 22.81 1.48 37.11 2.21 7.41 1.26 3.56 1.03 21.78 1.19 11.12 0.68
63.81 3.07 23.77 2.72 28.66 3.36 7.89 1.51 3.49 1.25 27.59 2.19 14.96 3.70
7.43 0.58 3.23 1.55 6.56 0.41 6.02 0.55 0.54 0.19 0.23 0.06 0.29 0.03 0.24 0.23
8.47 1.15 4.16 1.48 5.76 0.61 5.23 0.45 0.53 0.17 0.18 0.14 0.63 0.40 2.03 2.06
Significant differencea MS and TS-LD
MS and TS-HD
TS-LD and TS-HD
65.34 1.78 24.92 1.78 28.51 3.50 8.87 0.83 3.03 0.84 27.27 1.89 14.48 1.26
Y (0.000) N (0.482) Y (0.002) N (0.555) N (0.921) Y (0.000) Y (0.017)
Y (0.001) N (0.103) Y (0.001) N (0.063) N (0.408) Y (0.000) Y (0.020)
N N N N N N N
8.34 0.67 4.44 0.66 5.75 0.58 5.17 0.57 0.58 0.10 0.16 0.07 0.65 0.26 0.84 0.75
N (0.078) N (0.286) Y (0.047) Y (0.047) N (0.286) N (0.447) N (0.073) Y (0.039)
N (0.082) N (0.132) Y (0.028) Y (0.022) N (0.132) N (0.191) Y (0.039) N (0.407)
N (0.803) N (0.803) N (0.970) N (0.875) N (0.727) N (0.637) N (0.903) N (0.135)
(0.264) (0.385) (0.941) (0.222) (0.498) (0.782) (0.725)
a sig. value shown in parentheses. Sample number n ¼ 5, 4 and 6 for mesophilic, thermophilic low DO and thermophilic high DO sludge, respectively.
3.2.6.
Ultrastructure of microbial flocs
Fig. 7(A, B, C) shows pertinent features of the ultrastructure of the thermophilic (high and low DO) and mesophilic flocs. Both types of flocs were irregular in shape, while the thermophilic flocs were less-densely packed. Filamentous microorganisms were very common in the thermophilic flocs but virtually nonexistent in the mesophilic flocs. Intra-floc channels were more common in the thermophilic flocs, while cell density was so great in mesophilic flocs that cell-to-cell contact sometimes predominated over celleEPSecell contact. Bacterial microcolony structure varied considerably within a given floc, while the thermophilic floc architecture was obviously different in comparison to mesophilic flocs. While TEM complemented the gross morphological information on the flocs obtained by conventional optical microscopy (COM), TEM also provided important additional information on nanoscale structure. Systematic correlation of such multi-scale observations was outlined in Mavrocordatos et al. (2007). A layer of nanoscale fibrils (Leppard, 1997), covering a portion of the surface of many flocs (Fig. 7(D)), can only be detected by TEM, because individual fibrils in the SBRs tended to be elongate flat ribbons having a diameter in the 7e17 nm range. Their demonstration indicates that some portions of a floc surface can be distinctively different from other portions, implying a potentially significant difference in chemistry from the overall EPS composition. Very fine channels within flocs (Fig. 7(B)) can be described in detail by TEM, as can the observation that some channels are partially filled with nanoscale fibrils of EPS (note the middle right portion of Fig. 7(E)). Such observations (not obtainable by COM) have a bearing on the internal porosity of flocs and an assessment of both channel and floc surface roughness. The near-micrometre size filaments (in least dimension) were determined to be chains of bacteria, since longisections revealed the structure of the individual cells to
be prokaryotic. The diversity and three-dimensional distribution of nanoscale materials was quite variable within a given large volume of a single floc, as was expected given the chemical evidence for two major families of EPS polymers, the presence of extracellular vesicles and ultrastructural evidence for cell lysis. The EPS nanoparticles were mainly fibrils, granules and recognizable cellular debris. The individual nanoparticles within the granular EPS were in the 3e10 nm range.
3.3. Relationships between sludge properties and settleability as well as flocculating ability A strong correlation between the level of filaments and SVI was observed for all the measured results. A higher level of filaments corresponded to a higher SVI. This is consistent with the findings of previous studies (Jenkins et al., 2003; Martins et al., 2003). The results suggest that sludge bulking in the thermophilic system was caused by the overgrowth of filaments. An increase in the DO level was not effective to minimize the growth of filaments and even further promoted the growth of filaments. Therefore, the strategy of using an elevated DO level to control filamentous bulking problems widely used in mesophilic systems might not be applicable to the thermophilic system. However, the slightly improved bioflocculation ability of thermophilic sludge (more EPS) at higher DO could be attributed to the slightly increased filaments level. Previous study found that the increased filaments level provide more binding sites on the backbone of filaments for free cells or smaller aggregates to attach and thus the level of free cells or colloids in the suspension is decreased with an increase in the level of filaments (Jobba´gy et al., 2000). In addition to the importance of filaments in sludge bulking, the EPS has been reported to be another important factor in controlling SVI and ESS (Liao et al., 2001; Li and Yang, 2007).
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Fig. 7 e Ultrastructure of representative portions of flocs from (A) a thermophilic SBR at low DO, (B) a thermophilic SBR at high DO and (C) a mesophilic SBR, supplemented with nanoscale examination of floc surface materials on a thermophilic floc at (D) high DO and at (E) low DO. The bar represents 1 mm, except for (C) where it represents 2 mm. The arrow in (A) points to a glancing section through a bacterial filament; many such glancing sections are seen in (B). The “W” in (D) indicates the aquatic milieu side of a layer of nanoscale fibrils at a floc surface.
From Fig. 1(a) and (b) and Fig. 4, it is clear that there were no significant correlations between SVI and total bound EPS or between ESS and total bound EPS, if all the results, including mesophilic and thermophilic results, are considered. However, an increase in the total bound EPS corresponded to an increase in the SVI and a decrease in the ESS, for the thermophilic results. This might not be surprising, as EPS could be very different in terms of molecular weight and composition, especially when they are produced by very different biological systems (thermophilic vs. mesophilic). The difference in surface composition between thermophilic and mesophilic sludge is confirmed by the XPS data (Fig. 6 and Table 6). The results suggest that, with potentially similar EPS composition and molecular weight produced in
the thermophilic system, an increase in total bound EPS corresponded to an increase in SVI, suggesting a role for bound EPS content in settling. The results are consistent with the findings of Liao et al. (2001) and Li and Yang (2007) in that an increase in the bound EPS quantity was related to an increase in SVI. The improved bioflocculation with an increase in the total bound EPS of the thermophilic sludges could be due to the fact that more bound EPS provided more opportunities for cells to become/remain embedded in EPS, so that free cells or small aggregates were less (Yu et al., 2009). However, for different biological systems, it seems that the properties of specific bound EPS molecules (composition and hydrophobicity, etc.) are more important than the total bound EPS in controlling bioflocculation and settling.
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4.
Conclusions
This study investigated the effects of temperature and DO on sludge properties and their role in bioflocculation and settleability using well-controlled SBRs. The main conclusions are summarized below. Under a similar DO level, the thermophilic sludge had a poorer flocculation ability and settleability than the mesophilic sludge. The poorer settleability of thermophilic sludge was related to a higher filaments level. Under the same thermophilic temperature (55 C), an increase in the DO level from 1.0 to 2.5 ppm to 3.5e5.5 ppm resulted in a slightly improved flocculating ability and even poorer settleability. This result suggests that an increase in DO was not effective in solving filaments bulking problems in thermophilic sludge. Temperature had a significant impact on bound EPS production and composition ratio. Under similar DO levels, the total bound EPS of the thermophilic sludge was significantly higher than that of the mesophilic sludge. Under thermophilic conditions, an increase in DO resulted in an increase in the quantity of total bound EPS. Under the same thermophilic temperature (55 C) but different DO levels, an increase in the quantity of total bound EPS corresponded to an increase in SVI and a decrease in ESS. But no correlations between SVI and total EPS and between ESS and total EPS were observed, if the results from both mesophilic and thermophilic systems are considered. The results suggest that the quantity of total bound EPS played some roles in bioflocculation and settling only with similar biological systems. For different biological systems (like mesophilic vs. thermophilic), the quantity of total bound EPS had a limited role in controlling bioflocculation and settling. The results from XPS data suggest that significant differences in surface concentrations of elements C, O and N were observed between the thermophilic and mesophilic sludge, implying a significant difference in bound EPS composition. This result suggests that there is a role for specific bound EPS molecules rather than the quantity of bound EPS in controlling bioflocculation. The difference in bound EPS composition might explain the difference in bioflocculation behavior between thermophilic and mesophilic sludge. The distribution of identifiable nanoscale materials (fibrils, granules, vesicles and bacterial parts) was patchy at the flocewater interface, as revealed by TEM at 3 nm resolution. This observation suggests that a research focus on the specific nature of these interfacial nanoscale components of bound EPS could be revealing.
Acknowledgements Financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC) is appreciated. Assistance of Marcia Reid at McMasterUniversity on the TEM work is highly appreciated. The authors thank Dr. Rana Sodhi at the Surface-Interface Ontario, University of Toronto, for the XPS work, and Ms. Patrycja Galka at the University of Western Ontario for the Gel Permeation Chromatography work.
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and thermophilic bioreactors treating pharmaceutical wastewater. Applied and Environment Microbiology 66 (9), 3951e3959. Leppard, G.G., 1997. Colloidal organic fibrils of acid polysaccharides in surface waters: electron-optical characteristics, activities and chemical estimates of abundance. Colloids Surfaces A: Physicochemical and Engineering Aspects 120 (1e3), 1e15. Leppard, G.G., Droppo, I.G., West, M.M., Liss, S.N., 2003. Compartmentalization of metals within the diverse colloidal matrices comprising activated sludge microbial flocs. Journal of Environmental Quality 32 (6), 2100e2108. Li, X.Y., Yang, S.F., 2007. Influence of loosely bound extracellular polymeric substances (EPS) on the flocculation, sedimentation and dewaterability of activated sludge. Water Research 41 (5), 1022e1030. Liao, B.Q., Allen, D.G., Droppo, I.G., Leppard, G.G., Liss, S.N., 2001. Surface properties of sludge and their role in bioflocculation and settleability. Water Research 35 (2), 339e350. Liss, S.N., Droppo, I.G., Flannigan, D.T., Leppard, G.G., 1996. Floc architecture in wastewater and natural riverine systems. Environmental Science Technology 30 (2), 680e686. Lowry, O.H., Rosebrough, N.J., Farr, A.L., Randall, R.J., 1951. Protein measurement with the folin phenol reagent. Journal of Biological Chemistry 193, 265e275. Martins, A.M.P., Heijnen, J.J., van Loosdrecht, M.C.M., 2003. Effect of dissolved oxygen concentration on sludge settleability. Applied Microbiology and Biotechnology 62 (5e6), 586e593. Martins, A.M.P., Heijnen, J.J., van Loosdrecht, M.C.M., 2004. Sludge bulking in biological nutrient removal systems. Biotechnology Bioengineering 86 (2), 125e135. Mavrocordatos, D., Perret, D., Leppard, G.G., 2007. Strategies and advances in the characterisation of environmental colloids by electron microscopy. In: Wilkinson, K.J., Lead, J.R. (Eds.), Environmental Colloids and Particles: Behaviour, Separation and Characterisation. John Wiley and Sons, Chichester, UK, pp. 345e404. Morgan-Sagastume, F., Allen, D.G., 2003. Effects of temperature transient conditions on aerobic biological treatment of wastewater. Water Research 37 (15), 3590e3601. Palm, J.C., Jenkins, D., Parker, D.S., 1980. Relationship between organic loading, dissolved oxygen concentration and sludge settleability in the completely-mixed activated sludge process. Journal Water Pollution Control Federation 52 (10), 2484e2506.
Rozich, A.F., Bordacs, K., 2002. Use of thermophilic biological aerobic technology for industrial waste treatment. Water Science and Technology 46 (4e5), 83e89. Shin, H.-S., Kang, S.-T., Nam, S.-Y., 2001. Effect of carbohydrate to protein in the EPS on sludge settling characteristics. Water Science and Technology 43 (6), 193e196. Spurr, A.R., 1969. A low-viscosity epoxy resin embedding medium for electron microscopy. Journal Ultrastructure Research 26, 31e43. Suvilampi, J., Rintala, J., 2002. Comparison of activated sludge processes at different temperatures: 35 C, 27e55 C and 55 C. Environmental Technology 23 (10), 1127e1134. Suvilampi, J., Rintala, J., 2003. Thermophilic aerobic wastewater treatment, process performance, biomass characteristics, and effluent turbidity. Reviews in Environmental Science and Biotechnology 2 (1), 35e51. Tripathi, C.S., Allen, D.G., 1999. Comparison of mesophilic and thermophilic aerobic biological treatment in sequencing batch reactors treating bleached kraft pulp mill effluent. Water Research 33 (3), 836e846. Vogelaar, J., Bouwhuis, E., Klapwijk, A., Spanjers, H., van Lier, J., 2002a. Mesophilic and thermophilic activated sludge posttreatment of paper mill process water. Water Research 36 (7), 1869e1879. Vogelaar, J.C.T., van Lier, J.B., Klapwijk, B., de Vries, M.C., Lettinga, G., 2002b. Assessment of effluent turbidity in mesophilic and thermophilic activated sludge reactors e origin of effluent colloidal material. Applied Microbiology and Biotechnology 59 (1), 105e111. Vogelaar, J.C.T., Keizer, A., De Spijker, S., Lettinga, G., 2005. Bioflocculation of mesophilic and thermophilic activated sludge. Water Research 39 (1), 37e46. Wile´n, B.-M., Balme´r, P., 1999. The effect of dissolved oxygen concentration on the structure, size and size distribution of activated sludge flocs. Water Research 33 (2), 391e400. Yu, G.H., He, P.J., Shao, L.M., 2009. Characteristics of extracellular polymeric substances (EPS) fractions from excess sludges and their effects on bioflocculability. Bioresource Technology 100 (13), 3193e3198. Yu, G.H., He, P.J., Shao, L.M., 2010. Reconsideration of anaerobic fermentation from excess sludge at pH 10.0 as an eco-friendly process. Journal of Hazardous Materials 175 (13), 510e517. Zhang, B., Sun, B., Jin, M., Gong, T., Gao, Z., 2008. Extraction and analysis of extracellular polymeric substances in membrane fouling in submerged MBR. Desalination 227 (1e3), 286e294.
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Biostabilization and erodibility of cohesive sediment deposits in wildfire-affected streams M. Stone a,*, M.B. Emelko b, I.G. Droppo c, U. Silins d a
Department of Geography and Environmental Management, University of Waterloo, Waterloo, Ontario, Canada N2L3G1 Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, Ontario, Canada N2L3G1 c National Water Research Institute, Environment Canada, Burlington, Ontario, Canada L7R4A6 d Department of Renewable Resources, University of Alberta, Alberta, Canada T6G2H1 b
article info
abstract
Article history:
The erosion characteristics and bed stability of wildfire-affected stream sediment were
Received 10 May 2010
measured in an annular flume. Biofilms were grown in the flume on cohesive streambed
Received in revised form
sediments collected from a wildfire affected stream and a reference undisturbed stream in
8 September 2010
southern Alberta, Canada. Examined factors that influence sediment erosion, settling and
Accepted 12 September 2010
bed stability included applied shear stress, geochemical and physical properties of the
Available online 21 October 2010
sediment, floc structural characteristics and consolidation period (2, 7, 14 days). Erosion characteristics and sediment properties were strongly influenced by wildfire, consolidation
Keywords:
period and bed biostabilization. The fire-modified sediment was more resistant to erosion
Wildfire
than the reference unburned sediment. Settling velocities were lower in the burned sediment
Sediment transport
due to higher organic content and porosity. The critical shear stresses for erosion were 1.6
Biostabilization
and 1.8 times higher for the burn-associated sediment after 7 and 14 days of consolidation.
Cohesive sediment
The differences are related to the greater degree and spatial extent (depth) of biofilm
Colmation
attachment in the burned sediment. Erosion depths were 4e8 times higher in burned sedi-
Critical shear stress
ment as a result of wildfire-associated biostabilization. ª 2010 Elsevier Ltd. All rights reserved.
Treatability Water treatment
1.
Introduction
In aquatic systems, many contaminants of concern are bound to and transported by cohesive sediment (inorganic and organic particles <63 mm). Depending upon the hydrodynamic and biogeochemical characteristics of the system, finegrained materials flocculate in the water column (Droppo, 2001) and upon settling are the building blocks of sediment deposits referred to as surficial fine-grained laminae (SFGL) (Stone and Droppo, 1994). These deposits contribute to both external and internal colmation that occurs at the interface between ground water and surface water (Brunke, 1999). The
stability of cohesive sediment deposits is governed by factors such as electrochemical reactions (Mehta, 1989), consolidation (Droppo and Amos, 2001), dewatering (Tolhurst et al., 2000) and biostabilization (Dade and Norwell, 1990; Paterson, 1997). When the critical shear stress for erosion (sc) is exceeded (Stone et al., 2008), these deposits are remobilized within the water column where they can adversely impact downstream lotic environments (Wood and Armitage, 1997). In aquatic systems, sediment resuspension is a function of shear stress and sediment characteristics such as grain size, density and mineralogy (Partheniades, 1990). Benthic flora and fauna are also being increasingly recognized as
* Corresponding author. E-mail address:
[email protected] (M. Stone). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.016
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important contributors to sediment stability (Paterson, 1997). Benthic bacteria, microalgae and macrofauna secrete polymers (extracellular polymeric substances; EPS) that bind mineral grains (biostabilization) together in a mucilaginous matrix (Dade and Norwell, 1990; Paterson, 1997). For biostabilization to occur, threshold concentrations of organic compounds that drive microbial metabolism must be available for energy maintenance and microbial enzyme induction (Schmidt et al., 1985). Biofilms develop on a variety of interfaces by attachment processes; aquatic sediments, in particular, provide excellent substrata for biofilm growth (Neu, 1994). The development of biofilms on aquatic sediments can change the characteristics of deposited sediment (i.e. particle structure, morphology, size, porosity, shape, degree of consolidation) and influence erosion rates (Droppo and Amos, 2001; Droppo et al., 1997; Lau, 1995). Several studies have demonstrated that biostabilization can significantly increase the energy required to erode sediments by horizontal shear (Amos et al., 2004; Gerbersdorf et al., 2008). While the influence of biogenic sediment stabilization on sediment erosion and contaminant transport has been extensively examined in marine environments (Underwood and Paterson, 2003; Friend et al., 2003), much less is known about the role of biofilms in stabilizing sediment deposits in freshwater systems (Droppo et al., 2007; Droppo, 2009; Gerbersdorf et al., 2008); particularly those formed in streams draining forested landscapes impacted by severe, landscape-altering disturbances such as wildfire. The frequency and severity of large-scale natural disturbances such as wildfire in many forested regions of the globe has significantly increased in recent decades (Westerling et al., 2006). Because of the severity and magnitude of wildfire related landscape disturbances, sediment fluxes (Silins et al., 2008, 2009) are modified at rates and magnitudes that cause profound and often irreversible changes in river system function (DeBano et al., 1998; Bladon et al., 2008) and drinking water treatability (Emelko et al., 2011). For example, Blake et al. (2009) demonstrated that post-fire sediment and nutrient transport dynamics in streams are strongly related to coarsening of the effective particle size distributions in burned material via the aggregation of fines into composite particles. They reported that burned composite particles (predominantly <63 mm) had higher concentrations of bio-available phosphorus than unburned sediment. The subsequent increased flux of bioavailable phosphorus and terrestrial organic matter from hillslopes to streams produced by wildfire disturbance increase instream storage and availability of particle-associated nutrients (Petticrew et al., 2006), which increases biofilm formation and growth rates, particularly in areas where the forest canopy has been lost (Minshall, 2003). Little information currently exists regarding biofilm formation and its potential role in stabilizing cohesive sediment deposits and associated contaminants in streams draining fire impacted catchments. Given the increasing severity and spatial extent of landscape disturbance by wildfire at the global scale and its impacts on sediment availability, transport and storage in streams, knowledge of processes that govern sediment transport is critical to quantify and model sediment and associated contaminant fluxes from fire impacted watersheds to downstream environments. Such
information is also highly relevant to the impacts of wildfire on reservoir management and drinking water treatability. Here, wildfire and bed age were studied using an annular flume to elucidate their impacts on the stability of cohesive stream sediment deposits. The objectives of this work were (1) to quantify the physical (particle size, morphology, density, porosity, settling velocity) and geochemical (major element composition, mineralogy, total carbon) properties of burned and unburned river sediment (2) to characterize the microbial communities comprising the sediment-associated biofilms and (3) determine the effect of biofilms on the transport (sc), deposition (settling behaviour) and erodibility (erosion rate) of the two sediment types.
2.
Methods
2.1.
Study area and sample collection
The 2003 Lost Creek wildfire burned >21,000 ha in the Crowsnest Pass, Rocky Mountain region of southwestern Alberta. It was particularly severe and consumed most forest cover and floor organic matter across much of the headwater regions of the Oldman River Basin. One of the watersheds, Lynx Creek, was severely impacted (67.3% burned), which dramatically altered the discharge and sediment and nutrient characteristics of the river (Silins et al., 2008, 2009; Bladon et al., 2008). Increased post-fire light levels and particle-associated nutrient inputs to the stream have significantly increased the presence and abundance of biofilms in Lynx Creek compared to neighbouring unburned watersheds (Silins et al., in review). Here, cohesive sediment and river water were collected in Lynx Creek (burned watershed) and the Castle River (reference unburned watershed) in 2007 (4 years post-fire). Surface deposits of fine sediment were collected with a plastic scoop, immediately refrigerated at 4 C then transported within one week to the Canada Centre for Inland Waters in Hamilton, Ontario where they were studied using an annular flume.
2.2.
Experimental procedure
A stainless steel annular flume was used to measure sc, erosion rate and erosion depth of burned and unburned river sediment (Lau, 1995). The outside diameter of the flume is 2 m and the trough is 20 cm wide and 12 cm high. A clear glass top, which fits inside the trough, is lowered until it touches the water surface then rotated to generate flow. Calibration of the flume was described by Lau and Droppo (2000). Sediment and river water were placed in the flume and then the cover was rotated at high speed. After the sediment bed was completely entrained and well mixed, the cover rotation was reduced gradually and then stopped. Suspended solids in the water column settled under low shear to form a cohesive sediment bed approximately 1 cm thick. The bed was then allowed to settle for three consolidation periods (2, 7 and 14 days) during which eight wide-spectrum fluorescent grow lights (40 W each totalling a measured 1250 Lx) were activated above the flume for a 12 h light and 12 h dark scenario to promote biofilm growth. The flume experiments were conducted in sequence to simulate sediment transport conditions that could occur in
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stream reaches where cohesive sediments are deposited and biofilms form and subsequently resuspend at conditions of higher flow. Accordingly, for each sediment type (burned/ unburned), the first consolidation period studied was 2-days. Thereafter, the flume was rotated and shear stress increased incrementally (every 10 min) until all of the bed material had eroded; thereby enabling the calculation of Tcrit. After the estimated critical bed shear level had been applied for 10 min the flume was stopped to monitor still water settling. Suspended solids were collected every 2 min for 10 min. Two subsequent samples were then collected at 5 min intervals and four samples collected at 10 min intervals thereafter. This process was repeated for subsequent, longer consolidation periods (i.e. the 7-day consolidation period experiment was conducted after the 2-day experiment; the 14-day experiment was conducted after the 7-day experiment). The same sediment remained in the flume for all three erosion experiments conducted for a given source sediment. The river water was not changed except when suspended solids were collected from the sampling port and then an equal amount of river water was added to the flume to maintain a constant water level. This approach is non-ideal for simulating the biogeochemical environment and biofilm growth in natural streams; nonetheless, it provides a rigorous quantitative assessment of sediment erosion under controlled hydrodynamic conditions and a relative comparison of the effects of biofilms on the erosion of the burned and unburned sediments. The light conditions (intensity and duration) in forested regions of the southern Rockies are highly variable and dependent upon site specific conditions such as the time of year, slope, aspect, canopy cover, forest age class and degree of burn. In this study, fluorescent lights were not used to simulate actual light conditions present at the stream surface at a specific location in the complex forested environment from which the sediment samples were obtained, but rather to generate biofilm growth in the flume under controlled conditions (12 h on and 12 h off) to compare and contrast potential biofilm generation in the two systems. Changes in sediment bed stability were observed through windows on the side of the flume and with a video imaging Lennox boreoscope. The suspended sediment concentration was monitored continuously using an optical backscatter (OBS) turbidimeter. Suspended solids concentrations were also measured gravimetrically from samples collected directly from the flume through a sample port at 1, 5 and 9 min after the start of every 10-min shear interval. Type I erosion is defined as an erosion rate that exponentially decays to zero and is comprised of both Type 1A (erosion of the loosely bound floc layer referred to as SFGL) and Type 1B (erosion of the stronger bed [or armour layer] below the SFGL) erosion (Villaret and Paulic, 1986). Here, the critical shear stress for erosion (sc) for Type 1B erosion was determined using visual and boreoscope observations as well as the temporal suspended sediment concentration data for each sediment type during each of the annular flume experiments.
2.3.
Settling analysis
Total suspended solids concentrations over time were plotted and the characteristic kinetic plots for still water settling were
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examined. Still water settling was considered as the period after the flume was stopped and the flow of water could no longer be observed (360 s and thereafter). Mass settling during each experiment was evaluated by calculating the appropriate (second order in all cases as discussed below in the results and discussion) kinetic decay constant (k), which is obtained by regressing the inverse of the suspended solids concentration (L mg1) at each sampling point (360, 480, 600, 900, 1200 and 1800 s; in some cases, also 2400, 3000, and/or 3600 s) on time (s) and determining the slope by least squares linear regression to yield k (L mg1 s1). The TSS mass settling rate at each point in time was then calculated by multiplying k by the square of the measured suspended solids concentration. The rates of change in mass settling rate during still water settling in the burned and unburned systems with the various consolidation periods were compared using an F-test for equality of slopes (SAS/STAT Version 9.1).
2.4.
Physical and geochemical sediment properties
Sediment bulk density after the growth/consolidation periods was used to evaluate the influence of consolidation (and potentially biofilm formation) on bed stability. Sediment samples were settled in 10 cm of river water in each of three glass beakers to form beds with similar thickness to those formed in the flume. Bulk density profiles were measured in 1 mm increments (Berkhout, 1994). Eroded floc settling velocity was determined using a settling column interfaced with a Nikon SMZ-2T (Nikon Canada Inc., Mississauga, Canada) stereo-scopic microscope and Open Lab image analysis system (Improvision, Coventry, UK) to size and track particle settling trajectories for the measurement of settling velocity (Droppo et al., 1997). Stokes’ law was used to obtain density and porosity estimates (Li and Ganczarczyk, 1987). Surface deposits of cohesive sediment were collected from a wildfire-affected stream and a reference undisturbed stream and immediately submitted to a commercial laboratory for grain size and geochemical analyses to characterize and compare the sediment geochemistry between the two rivers. The grain size distribution of the two sediment types was determined using a Malvern Mastersizer Model 2000. The concentration of major elements (Al2O3, CaO, Cr2O3, Co3O4, CuO, Fe2O3, K2O, MgO, MnO, Na2O, NiO, P2O5, SiO2, TiO2) was determined by X-ray fluorescence spectrometry. The sediment loss on ignition (LOI) was determined at 475 C for 12 h. The mineralogical composition of the sediments was evaluated by X-ray diffraction (Philips X’pert PW3040-PRD diffractometer with a Cu X-ray source operated at 40 kV and 50 mA). Certified reference materials USGS GXR-1, GXR-2, GXR-4 and GXR-6 were analyzed at the beginning and end of each batch of samples. Internal control standards were analyzed after every 10 samples and a duplicate was analyzed after every 10 samples. Volatile organic carbon was determined by heating a 0.1 g sample in a pure oxygen environment at 380 C, releasing the volatile organic carbon species, binding them with the oxygen to form CO and CO2, the majority being CO2. Carbon was measured as carbon dioxide in the IR cell.
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2.5.
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Microscopy
Eroded floc morphology (size distribution and structure) was examined by light microscopy (Zeiss Axiovert 100 fitted with a flow cell interfaced directly to the flume) (Droppo et al., 2007). Environmental scanning electron microscopy (ESEM; ElectroScan model 2020 operated at 20 kV, ElectroScan Corporation, Wilmington, MA, USA) was used to observe finescale structural characteristics of the eroded flocs using a Peltier stage cooled to 1 C (Leppard, 1986). Transmission electron microscopy (TEM) was used to determine the microstructure and internal composition of the eroded flocs. Ultrathin sections were imaged in transmission mode (TEM) at an accelerated voltage of 80 kV (JEOL 1200 Ex II TEMSCAN TEM) (Leppard, 1986).
2.6.
Biofilm mass
Biofilm is a heterogeneous and amorphous material comprised of living organisms and other solid materials (e.g., organic substances such as EPS, inorganic solids such as clays). Liu et al. (1994) suggested determination of total attached polysaccharide (carbohydrate) as an indirect measure of biofilm mass because biofilms contain relatively large amounts of polysaccharide. Here, water samples from the flume were collected in a polyethylene bottle and filtered onto Whatman GF/C glass-fiber filters and frozen (220 C) for subsequent analysis (performed on the filters within one week). The filter residue was extracted with acetone and assessed at a wavelength of 663 nm (Pacepavicuis et al., 1997). This method is particularly suitable for the determination of total polysaccharide content in suspended biofilm because it is sensitive (i.e. a few mg is sufficient) and specific (i.e. there is little interference from environmental factors such as salt and water hardness). The amount of eroded biofilm mass is expressed as mass of total polysaccharide as glucose per liter of water (mg L1) and is determined directly from a glucose calibration curve. The contribution of biofilm mass (as glucose) to suspended solids concentration in the burned and unburned sediment was evaluated using least squares linear regression and an F-test for equality of slopes (SAS/STAT Version 9.1).
2.7.
Microbial community analysis
Phospholipid fatty acids (PLFA) are the primary constituents of cell membranes of all living cells. Different groups of microorganisms synthesize varieties of PLFA through different biochemical pathways. Some PLFAs can be used as “signatures” to analyze changes in viable microbial biomass and community structure (Tunlid and White, 1992); accordingly, changes in PLFA profiles are indicative of changes in the total viable microbial community, thereby making PLFA an effective tool for monitoring microbial responses to environmental conditions. Here, lipid extraction and PLFA analyses were performed using the modified Bligh and Dyer-method (1959) as described by White et al. (1979). The microbial communities comprising the biofilm formed on the two sediment types (burned and unburned) after 2, 7 and 14 days of consolidation were determined using a phospholipid fatty acid (PLFA) technique (White et al., 1979).
Biofilm was formed using a plastic bin containing a 1 cm layer of bed sediment covered by a water layer of 2 cm. The bins were placed under the grow lights and frosted glass slides were placed on the surface of the sediment to allow biofilm growth for the designated consolidation periods of 2-, 7-, and 14-days. While biofilm growth on slides is likely different from that formed in streams where flow conditions, light and nutrient levels are variable over short temporal scales (days to weeks), this method provides a comparison of relative differences in biofilm growth that may form on various sediment types (Lau, 1995; Lau and Liu, 1993). Twenty-four slides were added to the plastic bins (8 for each consolidation period). Of these eight slides: the biofilm from four was combined and analyzed by PLFA (Microbial Insights; Rockford, Tennessee) to yield a composite determination of viable microbial biomass and a PLFA profile reflecting the proportions of organisms present in the sample, according to six PLFA structural groups (monoeonic, terminally branched saturated, branched monoeonic, mid-chain branched saturated, normal saturated, and polyeonic). Biofilm from the remaining four slides was analyzed by TEM and ESEM.
3.
Results and discussion
3.1.
Physical and geochemical sediment properties
The geochemical composition and clay mineral assemblage of sediment in aquatic systems are largely dependent upon the general characteristics of the source area which include geology, weathering, vegetation, soils, mass wasting processes and land use (Griggs and Hein, 1980). Resulting differences in the sediment properties (density, grain size, geochemistry) will influence sediment transport characteristics. The major element composition of the study sediments is presented in Table 1 and indicates that Lynx Creek sediment (burned) has higher concentrations of CaO (9.16%) and carbon (LOI ¼ 16.7%) but lower concentrations of SiO2 (53.74%) and Al2O3 (8.79%) than Castle River (unburned) sediment. Lynx Creek sediment contains less quartz than Castle River sediment, but elevated levels of dolomite and muscovite (Table 2). The measured volatile organic carbon content of Lynx Creek was 4.73% compared to <0.05% in Castle River. The median density, porosity and settling velocity of the burned and unburned sediments after consolidation are presented in Table 3. While the geochemical composition of both sediments varied slightly, their densities were similar. The median settling velocity and porosity of the unburned sediment were higher than of burned sediment. In contrast to the results presented herein, Blake et al. (2007) examined the impact of wildfires on the effective size distribution of burned and unburned soils and reported that burned soils had significantly higher settling velocities than unburned particles of equivalent diameter; they attributed this to increased density and decreased organic matter and pore space in burned soils. Wildfires can influence the structure and stability of soil aggregates but the degree of aggregation will vary as a function burn temperature, soil depth as well as the mineral and organic properties of the soil (Andreu et al., 2001; Fox et al., 2007). However, once soil aggregates enter receiving streams they are
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Table 1 e Major element composition of fine sediment deposits in Burned and Unburned Watersheds (%). Major element Al2O3 CaO Cr2O3 Co3O4 CuO Fe2O3 K 2O MgO MnO Na2O NiO P 2O 5 SiO2 TiO2 LOI
Burned
Unburned
8.79 9.16 0.01 0.005 0.005 2.92 3.05 4.87 0.055 0.86 0.003 0.12 53.74 0.33 16.66
10.49 2.02 0.01 0.005 0.005 3.59 3.24 2.88 0.052 0.99 0.003 0.11 71.26 0.51 4.44
influenced by a range of physical, chemical and biological processes that alter the grain size distribution and biogeochemical nature of the particles (Blake et al., 2009). Here, flocs were formed within the flume (i.e. not eroded soil aggregates) with a significant microbial component (biofilm interactions). The higher organic content (Table 1) for Lynx Creek is likely responsible for the larger floc size and increased floc porosity resulting in a slower settling velocity (although the density analysis showed no difference between rivers). Moreover, the river sediments assessed in this study are four years post-fire whereas Blake et al. (2009) investigated recently burned soils.
3.2. Morphology and physical characteristics of eroded flocs Eroded flocs were collected during the flume experiments to assess changes in floc structure between rivers and over different consolidation times. Within these experiments it is difficult to compare eroded floc morphology, size, settling velocity, porosity and density for a given shear, because for a given shear value, flocs will originate from different depths within the bed where different physicochemical and biological properties may exist. Further, a comparison of floc characteristics at sc also provides little insight as these comparisons are different for each consolidation period and bed type (burned and unburned) resulting in a wide range of properties given the different forces affecting the floc. Accordingly, differences in floc properties can only be viewed on an average trend basis.
Table 2 e Mineralogy of fine sediment deposits in Burned and Unburned Watersheds (%).
Quartz Calcite Dolomite Albite Microcline Muscovite Chlorite
Burned
Unburned
23 1 8 7 15 41 5
42 1 2 12 14 22 7
Table 3 e Physical characteristics of eroded sediment. Consolidation Median settling Median Median porosity density time velocity (%) (g cm3) (days) (mm s1) Burned
2 7 14
2.22 2.81 2.96
84.7 85.6 93.3
1.09 1.09 1.05
Unburned
2 7 14
3.18 3.26 3.82
89.3 91.3 93.4
1.08 1.07 1.04
In general, fire modified eroded flocs were larger than unburned flocs and both floc types decreased in size with increasing shear levels. Using three different microscopes with a range of resolution, it was observed (e.g., Fig. 1) that Castle Creek exhibited more inorganic networks relative to Lynx Creek, which possessed substantially more organic networks encompassing both cellular material and extracellular polymeric substances in the form of fibrils (see TEM image Fig. 1c and f). COM images (Fig. 1a and d) show the eroded Lynx Creek flocs to be more diffuse and irregular in shape while the ESEM images (Fig. 1b and e) show more organic coatings on the Lynx Creek flocs relative to the inorganic dominated Castle Creek flocs. The above differences relative to floc structure were consistent over time, however, the level of organic dominance increased with time of consolidation/growth.
3.3.
Biofilm analysis
Visual inspection of streambeds draining unburned (Fig. 2a) and burned (Fig. 2b) catchments even 5 years post-fire clearly indicated the possibility of significant differences in streambed-associated biomass amount, composition and activity. Biomass can attach to sediment in the form of biofilm and several studies have demonstrated that biostabilization can significantly increase the energy required to erode sediments by horizontal shear (Amos et al., 2004; Gerbersdorf et al., 2008; Droppo, 2009). A simple estimate of biofilm accumulation on the sediment bed within the flume expressed as mass of total polysaccharide as glucose per liter of water indicated that burned sediments had significantly higher biomass (glucose mass) per suspended solids mass than unburned sediments ( p < 0.0001; Fig. 3). PLFA analyses of the same biofilms did not yield a clear difference in accumulated biomass associated with the burned and unburned sediments (Fig. 4). However, only a very limited number of samples could be processed thereby precluding a more thorough assessment of intra- and inter-sample variability. The microbial community structure comprising the biofilms formed on the unburned and burned sediments with various consolidation periods is organized according to the PLFA structural groups and presented in Fig. 5. As observed with the total accumulated biomass evaluated using PLFA (Fig. 4), few differences in microbial community structure between the unburned and burned catchments and the various consolidation periods were observed. The only notable difference appeared to be an increase in the mid-chain branched saturated PLFA structural group indicative of Actinobacteria (e.g.,
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Fig. 1 e Eroded Castle River (a) and Lynx Creek (b) flocs using conventional optical microscopy. Representative ESEM images of 14-day Castle River (c) and 14-day Lynx Creek sediment (d). TEM images of 14-day Castle River (e) and 14-day Lynx Creek sediment (f).
Actinomycetes spp.) and sulfate-reducing bacteria in the biofilms formed on burned relative to unburned sediments (Fig. 6). Increased amounts of bacteria like Actinomycetes spp. in biofilms obtained on burned relative to unburned sediments are not surprising given that Actinomycetes spp. form conidia (asexual spores) that may help them better survive harsh soil environments such as those exposed to dessication and heat (Alexander, 1998) that result from wildfire. Increases in Actinomycetes spp. may be of concern when impacted waters are used as drinking water sources because these bacteria are associated with earthy/musty odors in water and may contribute to formation of geosmin and 2-
methylisoborneol (MIB), which are major sources of taste and odor causing compounds in drinking water (Zaitlin and Watson, 2006). Biofilm growth in plastic bins occurred under quiescent (no flow) conditions on a glass substratum which likely resulted in differences observed between the biomass amount, composition and activity in the lab compared to those found in nature. Glass is a relatively smooth substratum; however, in many circumstances biofilms accumulate more readily on rougher substrata (e.g., Percival et al., 1999). Accordingly, biomass growth in the source watersheds was likely underpredicted by the simple approaches used herein to estimate biomass accumulation. Nonetheless, caution should
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Fig. 2 e Typical biofilm accumulation in riverbeds draining (a) reference and (b) burned catchments five years post-fire.
be exercised when considering increases (or lack thereof) in amounts of total estimated biomass (Figs. 3 and 4) or in biomass associated with specific structural groups such as those indicative of Actinomycetes spp. (Fig. 6) because differences in biomass are not necessarily indicative of biomass activity. For example, even when substantial differences in substratum roughness exist and may contribute to significant differences in biomass accumulation, differences in biomass activity may still be insignificant (e.g., Emelko et al., 2006). Regardless of how representative biofilm mass formed on a glass substratum is of the biofilm formed in the source watersheds, it is essential to note that secondary metabolite (geosmin and MIB) production by Actinomycetes spp. in a given situation is also not necessarily linked to the amount of biomass present (Zaitlin and Watson, 2006). Accordingly, further investigation of the potential risks (e.g., increased propensity for taste and odor events as related to sedimentassociated microorganism transport) resulting from land disturbances to downstream drinking water supply and further in situ analysis and characterization of biofilms in fire impacted streams is warranted.
3.4.
increased consolidation time regardless of sediment type; however, this increase was more pronounced for the burnassociated sediment. The observed increase in shear stress required for Type 1A and 1B erosion to occur with increasing bed age is strongly related to the nature of the biofilm and its association with the underlying sediments. Boreoscope observations of sediment movement as a function of applied shear stress show that surface biofilm began to erode before the underlying sediments. The process of biofilm erosion typically began at low shear with the creation of small fractures on the biofilm surface. With increasing shear, segments of biofilm would partially dislodge and the biofilm would roll up upon itself in long narrow segments up to a few cm in length until it detached completely from the sediment bed. As it was being dislodged from the bed, the eroded Lynx Creek biofilm appeared to contain more sediment than Castle River biofilm. These visual observations coupled with the flume data suggest that the degree and spatial extent (depth) of biofilm attachment were greater on the burned (Lynx Creek) sediment. Table 4 shows that burned sediment was approximately twice as
Critical erosion values
Flume experiments were conducted to determine sc of cohesive sediment deposits in a wildfire impacted and reference stream. Suspended solids concentrations increased exponentially with applied bed shear stress, but the degree of erosion was influenced by bed age and sediment type (Fig. 7). The sc values for Type 1A and 1B erosion varied both within (i.e. different consolidation/biostabilization periods) and between sediment types (Table 4). The level of shear stress required for Type 1A erosion increased with consolidation time for the burned and unburned sediment, but higher shear stresses were required to erode the SFGL of the burned sediments compared to unburned (Fig. 7). During the 2-day consolidation experiments, the level of shear stress required to produce Type 1B erosion was comparable for both sediment types; however, the measured sc was 1.6 and 1.8 times higher for the burnassociated sediment after 7 and 14 days of consolidation, respectively than for unburned sediment (Table 4). Accordingly, sediment resistance to erosion generally increased with
Fig. 3 e Estimated contribution of biofilm mass (as glucose) to suspended solids concentration arising from the Castle River (unburned) and Lynx Creek (burned) sediments with various bed consolidation periods.
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Fig. 4 e Estimated amount of bacterial and eukaryotic biomass on Castle River (reference/unburned) and Lynx Creek (burned) sediment with various bed consolidation periods as determined by phospholipid fatty acid (PLFA) analysis.
resilient to the applied bed stress for the 7-day (1.6 times) and 14-day (1.9 times) deposit compared to the unburned sediment. The higher sc values required to erode Lynx Creek sediment suggests that the sediment-pore biofilm complex is more integrated and the biological community associated with the SFGL and eroded flocs in these deposits is more active than in unburned materials. Networks of EPS can permeate void spaces and promote inter-particle linkages (Gerbersdorf et al., 2008, 2009) and the resultant increased level of attachment within and between deposited flocs can lead to increased bed stability (Droppo, 2009). Recent sediment biostabilization studies in freshwater wave dominated (Droppo et al., 2007) and
riverine (Gerbersdorf et al., 2008, 2009) environments show that bacterial EPS production correlates well with bed stability. Droppo (2009) examined biofilm structure and bed stability of five contrasting freshwater sediments and demonstrated that
Fig. 5 e Relative percentages of total PLFA structural groups in Castle River (reference/unburned) and Lynx Creek (burned) sediments with various bed consolidation periods. Structural groups are assigned according to PLFA chemical structure, which is related to fatty acid biosynthesis.
Fig. 6 e Biomass composition of mid-chain branched saturated structures of PLFA (indicative of Actinobacteria [e.g., Actinomycetes spp.] and sulfate-reducing bacteria) comprising the biofilms formed on Castle River (reference/ unburned) and Lynx Creek (burned) sediments with various bed consolidation periods.
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Fig. 7 e Changes in suspended solids concentration as a function of applied bed shear stress for the 2-, 7-, and 14-day consolidation (Lynx Creek e A, B, C; Castle River e D, E, F).
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Table 4 e Comparison of erosion depths at Type 1A and 1B erosion for Castle River (unburned) and Lynx Creek (burned) sediment. Consolidation time (days)
1A Shear (Pa)
1A Erosion depth (mm)
1B Shear (Pa)
1B Erosion depth (mm)
Burned
2 7 14
0.08 0.16 0.18
0.0295 0.041 0.095
0.12 0.23 0.31
0.336 0.426 1.54
Unburned
2 7 14
0.039 0.097 0.094
0.0024 0.0009 0.0009
0.105 0.141 0.165
0.0126 0.008 0.014
the resultant increased level of attachment within and between deposited flocs increased bed stability. Wet weight bulk density measurements of the two sediment types were made to evaluate the influence of consolidation (and potentially biofilm formation) on bed stability. The wet density of sediment from the unburned watershed (Castle River) was approximately 1.28 g cm3 and changed very little with depth. In contrast, some compaction of the Lynx Creek sediment deposit is suggested by the increase in density of deposited sediment with depth (Fig. 8). For example, at a depth of 5 mm, the density of the burned sediment was 0.19, 0.16 and 0.14 g cm3 after 2, 7 and 14 days of consolidation, respectively. However, at a depth of 11 mm, the density increased to 1.16, 1.05 and 0.89 g cm3 after the respective consolidation periods. Droppo and Amos (2001) developed a general three-layer model to describe the formation processes and characteristics of SFGL deposits. The model describes a surface organic floc layer (Layer 1), a middle collapse zone (Layer 2) and a lower consolidated bed (Layer 3). They describe SFGL as a porous, low density and high water content deposit with high yield resistance due to biostabilization. Here, the bulk density (Fig. 8), the sc (Table 4) and floc morphology analyses indicate that cohesive sediment deposits in wildfire-impacted streams consist primarily of porous, low density flocs that are associated with an active biological community. Accordingly, the formational processes
and erosion dynamics in streams draining wildfire-impacted landscapes are consistent with the conceptual model proposed by Droppo and Amos (2001).
3.5.
Erosion rates
The erosion rates and depths for each flume experiment are shown in time series plots in Fig. 9. For all runs, the initial erosion event occurred at a lower applied shear than for subsequent runs with longer consolidation periods (Table 4). The peak in erosion rate occurred at the beginning of each shear increment but then decreased as the underlying more stable bed sediment was exposed. The maximum erosion rates for the 2, 7 and 14-day runs occurred at 0.22, 0.34 and 0.39 Pa for unburned sediment (Castle River) and 0.25, 0.33 and 0.30 Pa for burned sediment (Lynx Creek), respectively. The erosion depths at 0.25 Pa for the 2, 7 and 14-day consolidation periods were 0.084, 0.051 and 0.033 mm for Castle River and 2.78, 0.426 and 0.181 mm for Lynx Creek sediment, respectively. The time series plots show that increasing shear stress was required to erode the wildfire-affected Lynx Creek sediment over time with increasing bed age. However, this effect was less pronounced in the Castle River sediment. Erosion depths for Castle River sediments never exceeded 0.5 mm while maximum erosion depths in the wildfire-affected
Fig. 8 e Bulk density (g mL3) of Lynx Creek (burned) sediment.
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Fig. 9 e Changes in erosion rate and depth as a function of applied bed shear stress for the 2-, 7-, and 14-day consolidation (Lynx Creek e A, B, C; Castle River e D, E, F).
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Lynx Creek sediment were 1.4, 4 and 3.7 mm for 2, 7 and 14-day runs respectively.
3.6.
Still water settling
Still water settling rates have been described using zero (Amos et al., 2003; Droppo, 2009) and first order kinetic models elsewhere (Amos et al., 2004 based on Einstein and Krone, 1962). In contrast to single point-based approaches that have been utilized in the literature, the approach used herein is based on all of the collected still water settling data (5e8 points per land disturbanceeconsolidation period combination). Characteristic zero, first, and second order kinetic plots were made for still water mass settling of total suspended solids and indicated that still water settling (the change in TSS concentration over time) was best described as a second order reaction. For this type of reaction, the rate of reaction decreases rapidly (faster than linearly) as the suspended solids concentration decreases. All combinations of land disturbance (unburned/ burned) and bed consolidation period (2, 7, and 14 days) resulted in high (i.e. >98%) coefficients of determination for second order kinetic models. The still water TSS mass settling rates (mg TSS L1 s1) derived from the Castle River (unburned) and Lynx Creek (burned) eroded sediments after the various consolidation periods were calculated and evaluated over time. The TSS mass settling rates associated with the burned eroded sediments during still water settling were higher than those observed with unburned eroded sediments (Fig. 10). The temporal changes in TSS mass settling rates of the unburned and burned eroded sediments in still water (Fig. 10) were significantly different ( p ¼ 0.0053, p ¼ 0.0295, p ¼ 0.0054 for the 2, 7, and 14-day consolidation periods respectively). These analyses suggest that TSS mass settling rates of the unburned eroded sediments decreased more rapidly than those associated with the burned sediments, possibly implying that settling velocities of the unburned sediment were higher than those of the burned sediments, a result consistent with the settling velocities reported in Table 3. Differences in mass settling rates for the two sediment types are likely explained by differences in the
Fig. 10 e Temporal changes in still water TSS mass settling rates of Castle River (unburned) and Lynx Creek (burned) sediments after various bed consolidation periods.
floc size distribution and characteristics (i.e. porosity, settling velocity, floc strength and composition). Here, it is hypothesised that burned sediment and attached biofilm eroded into the water column formed organic rich (Table 1; Fig. 3), low porosity (Table 2) flocs that settled more slowly than those formed in the unburned system. From a water management perspective, mass settling rate data imply that suspended solids in wildfire-affected streams will remain in suspension longer than in streams draining unburned landscapes.
3.7.
Implications for water management
Results of the present study suggest that biogenic sediment stabilization will have a significant effect on the rates and magnitudes of sediment erosion and associated contaminant transport in wildfire-affected streams. The initial influx of fire modified sediment and associated nutrients from hillslopes (Blake et al., 2009) combined with the loss of forest canopy in wildfire-affected watersheds can create conditions which promote biostabilized in-channel deposits of cohesive sediment. Silins et al. (in review) deployed fixed area ceramic tiles in the wildfire-affected streams and reported that the mean algal biomass in these streams was 10e14 times higher than in reference streams. The flume experiments show that the erosion depth of wildfire-influenced sediments is higher, suggesting that more material will likely be eroded once the biofilm is removed compared to streams in unburned watersheds. Accordingly, once sc in the wildfire-affected streams is exceeded, the eroded cohesive materials, which have low settling velocities, will remain in the water column for prolonged periods of time and will more likely be transported to downstream reservoirs, potentially contributing to significant drinking water treatment challenges (Emelko et al., 2011).
4.
Conclusions
1. Burned sediment had lower settling velocities, increased C and carbohydrate levels compared to unburned sediment. 2. Biofilm formation on burned sediment increased sc for 7 and 14-day deposits by a factor of 1.6 and 1.8, respectively. 3. Erodibility (erosion depth) of burned sediment after 2, 7, and 14 days of consolidation was respectively 26, 53 and 110 times greater than unburned sediment. 4. Lower settling velocities of burned sediment are related to increased organic content and higher porosity compared to unburned sediment. 5. Changes in suspended solids concentrations as related to shear stress indicated that sc increased with consolidation period and as a result of wildfire-associated biostabilization. 6. Erosion depths significantly decreased with bed age; however, these depths were significantly greater as a result of wildfire-associated biostabilization. 7. As a result of upstream land disturbance, fine sediments (and their associated-contaminants) will stay in riverbeds for longer periods of time due to disturbance-associated biostabilization. 8. Increases in biofilm communities of Actinomycetes spp. (and other species) may be associated with wildfire and may be indicative of an increased propensity for taste and odor
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 5 2 1 e5 3 4
events related to sediment-associated microorganism transport to downstream water suppliers. 9. Temporal changes in TSS mass settling rates of the unburned and burned eroded sediments in still water were significantly different for the 2, 7, and 14-day consolidation periods, respectively.
Acknowledgements The assistance of S. Deignan, B. Trapp and C. Jaskot in conducting the flume experiments and K. Bladon in collecting sediment and water samples is greatly appreciated.
references
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 5 3 5 e5 4 4
Available at www.sciencedirect.com
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Virus inactivation by silver doped titanium dioxide nanoparticles for drinking water treatment Michael V. Liga a, Erika L. Bryant b, Vicki L. Colvin b, Qilin Li a,* a b
Department of Civil and Environmental Engineering, Rice University, 6100 Main St., Houston, TX 77005, United States Department of Chemistry, Rice University, 6100 Main St., Houston, TX 77005, United States
article info
abstract
Article history:
Photocatalytic inactivation of viruses and other microorganisms is a promising technology
Received 16 June 2010
that has been increasingly utilized in recent years. In this study, photocatalytic silver
Received in revised form
doped titanium dioxide nanoparticles (nAg/TiO2) were investigated for their capability of
4 September 2010
inactivating Bacteriophage MS2 in aqueous media. Nano-sized Ag deposits were formed on
Accepted 13 September 2010
two commercial TiO2 nanopowders using a photochemical reduction method. The MS2
Available online 19 September 2010
inactivation kinetics of nAg/TiO2 was compared to the base TiO2 material and silver ions leached from the catalyst. The inactivation rate of MS2 was enhanced by more than 5 fold
Keywords:
depending on the base TiO2 material, and the inactivation efficiency increased with
Drinking water
increasing silver content. The increased production of hydroxyl free radicals was found to
Nanotechnology
be responsible for the enhanced viral inactivation. ª 2010 Elsevier Ltd. All rights reserved.
Photocatalysis Silver Titanium dioxide Virus
1.
Introduction
The removal of viruses and other pathogens from drinking water (and the environment in general) is important for the maintenance of the health and well being of society. Pathogenic viruses such as adenovirus, norovirus, rotavirus, and hepatitis A commonly occur in both surface and groundwater sources (Abbaszadegan et al., 2003; Hamza et al., 2009; Wong et al., 2009) and must be effectively inactivated to provide safe water. In the United States just between 2003 and 2005 there were four reported waterborne disease outbreaks attributed to viruses in drinking water affecting 282 people (US Centers for Disease Control and Prevention, 2006, 2008). The USEPA requires treatment systems capable of providing 4 log (99.99%) removal of viruses for all surface water sources (US Environmental Protection Agency, 2006a) and groundwater
sources with a history of contamination or other deficiencies (US Environmental Protection Agency, 2006b). Traditional chlorine disinfection, while highly effective for viral inactivation, produces harmful disinfection byproducts (DBPs) when organic compounds are present in the water. This has prompted stricter regulations concerning the acceptable levels of these compounds (US Environmental Protection Agency, 2006c). Although UV disinfection has not been found to form DBPs (Liberti et al., 2003), some viruses such as adenoviruses are highly resistant to UV disinfection (Yates et al., 2006). As a result, the USEPA has increased the UV fluence requirements for 4 log removal of viruses from 40 mJ/cm2 to 186 mJ/cm2 (US Environmental Protection Agency, 2006a). The new high fluence requirement significantly increases the energy demand, which translates into a higher treatment cost.
* Corresponding author. Tel.: þ1 713 348 2046. E-mail address:
[email protected] (Q. Li). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.012
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The employment of a highly efficient photocatalyst for advanced oxidation could potentially enable effective virus inactivation in drinking water as chlorine can while limiting the formation of DBPs (Liu et al., 2008). It would also require less energy than UV disinfection. Therefore, photocatalytic oxidation is being actively researched as an alternative water disinfection method (Lydakis-Simantiris et al., 2010; Sordo et al., 2010). A highly efficient photocatalyst could also be utilized for air treatment or as an antimicrobial coating.
1.1.
Titanium dioxide photocatalysis
Titanium dioxide is an attractive photocatalyst for water treatment as it is resistant to corrosion and non toxic when ingested (Kaneko and Okura, 2002). The basic mechanism of TiO2 photoactivation and reactive oxygen species (ROS) generation is well known (Hoffmann et al., 1995). There are currently a few commercial treatment systems that utilize TiO2 photocatalysis (e.g. Wallenius AOT, Purifics). However their usage is not wide spread. One major reason for the limited application is the slow reaction kinetics as a consequence of charge recombination, which consumes the activated electrons and holes. The antibacterial properties of TiO2 have been well documented (Wei et al., 1994; Watts et al., 1995; Kikuchi et al., 1997; Cho et al., 2005; Benabbou et al., 2007; Page et al., 2007) and are attributed to the generation of ROS, especially hydroxyl free radicals (HO) and hydrogen peroxide (H2O2) (Kikuchi et al., 1997). While fewer studies have investigated the antiviral properties of TiO2, its potential for inactivating viruses has ova´ et al., 1999; been demonstrated (Watts et al., 1995; Belha´c Koizumi and Taya, 2002; Cho et al., 2005). However, the inactivation rates obtained in most of these studies were extremely low. For example, Cho et al. (2005) demonstrated only w1 log removal of MS2 after 2 h of irradiation using P25 TiO2 suspended at 1 g/L. The inactivation kinetics needs to be greatly improved in order to provide efficient drinking water disinfection. Metal doping has been used to enhance TiO2 photocatalysis by trapping excited electrons to prevent charge recombination (Mu et al., 1989; Choi et al., 1994; Haick and Paz, 2003; Iliev et al., 2006). Electron trapping can occur if the dopant has a lower Fermi level than the excited electron. Several metals including Fe, Mo, Ru, Os, Re, V, Rh, Au, Pt, and Ag have been shown to enhance TiO2 performance. Silver in particular has been shown to enhance the photocatalytic efficiency of TiO2 for both organic contaminant degradation and bacterial inactivation (Kondo and Jardim, 1991; Vamathevan et al., 2002; Zhang et al., 2003; Xin et al., 2005; Page et al., 2007; Seery et al., 2007). Tran et al. (2006) showed selective enhancement by silver, which increased degradation rates for short chain carboxylic acids but not for alcohols or aromatics. Silver coatings above the optimum amount can also decrease the photocatalytic activity (Sclafani et al., 1991; Sung-Suh et al., 2004). However, there is limited information on its impact on the antiviral capabilities of TiO2 (Kim et al., 2006). In addition to facilitating charge separation, silver is thought to enhance TiO2 photocatalysis by directly interacting with microorganisms and providing more surface area for adsorption (Sclafani et al., 1997; Sung-Suh et al., 2004),
although Vamathevan et al. (2002) found no increase in BET surface area after silver doping. Silver ions and nanoparticles have been shown to have antimicrobial properties themselves through a variety of mechanisms (Feng et al., 2000; Elechiguerra et al., 2005; Morones et al., 2005), which could also aid in bacterial or viral inactivation. Utilizing silver in conjunction with TiO2 photocatalysis could potentially allow several different inactivation mechanisms to work in concert. Therefore, it is possible that a synergism occurs between silver and TiO2 when silver doped titanium dioxide is used for inactivating microorganisms under UV radiation. The study reported here demonstrated that silver doping TiO2 greatly enhanced the photocatalytic inactivation of viruses primarily by increasing HO production in addition to slightly increasing virus adsorption.
2.
Materials and methods
2.1. Synthesis and characterization of nano-silver doped TiO2 (nAg/TiO2) nAg/TiO2 was prepared by depositing nano-sized silver islands via photochemical reduction of silver nitrate (Alfa Aesar) onto two commercially available TiO2: Aeroxide TiO2 P 25 (denoted hereafter P25 TiO2, Degussa) and Anatase TiO2 (denoted hereafter AATiO2, Alfa Aesar; CAS: 1317-70-0). A solution containing oxalic acid (SigmaeAldrich, anhydrous 99%) as a sacrificial electron donor, TiO2, and silver nitrate (SigmaeAldrich, 99.9999%) was stirred for 2 h at pH 1 under ambient light at room temperature while purged with nitrogen gas. The solution was then irradiated with a germicidal UV lamp for one day and the product purified by washing with excessive water four times (Iliev et al., 2006). The concentration of silver nitrate used in the reaction solution was varied to achieve 4, 8, and 10 wt.%; oxalic acid was added at a 25:1 acid to silver molar ratio. The AATiO2 was doped using 10% AgNO3 in solution. The doped particles were then dried and stored under vacuum in dark. Samples were prepared for TEM and XPS analysis by applying a drop of a nAgTiO2 suspension to a Silicon Monoxide/Formvar grid (Ted Pella; 01829) or a silicon wafer coated with gold (w68 nm). The grid was then used to analyze the sample in a JEOL 2100 field emission gun transmission electron microscope (JEM 2100F TEM) at 200 KV. The silicon wafer was used for x-ray photoelectron spectroscope (PHI Quantera XPS). The actual silver content of the nAg/TiO2 nanoparticles was determined by acid digestion and subsequent analysis of silver concentration using inductively coupled plasmaoptical emission spectroscopy (ICPeOES, PerkinElmer Optima 4300 DV). Aliquots of 0.01 g nAg/TiO2 nanoparticles were mixed with 5 mL of 50% HNO3, briefly bath sonicated, refluxed for 4 h and diluted to 50 mL with ultrapure water. The resulting suspensions were centrifuged and the supernatants filtered through a 0.22 mm-pore-size syringe filter. The filtrates were then analyzed by ICPeOES to determine the silver concentration.
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2.2.
Model virus
Bacteriophage MS2 (ATCC 15597-B1) was used as a model virus in this study due to its similarity to many waterborne pathogenic viruses (Koizumi and Taya, 2002; Mackey et al., 2002; Butkus et al., 2004) and the simplicity of its propagation and enumeration. MS2 has been found to be comparable or more resistant to chlorine and chloramines than Hepatitis A virus (Sobsey et al., 1988) and Poliovirus (Tree et al., 2003), and more resistant to UV disinfection than other bacteriophages (Sommer et al., 2001). Hence, using MS2 as a virus surrogate provides conservative assessment on treatment efficiency. The virus stock solution used in the disinfection procedures was obtained by infecting an incubation of the E. coli host (ATCC 15597) with a liquid MS2 suspension. The mixture was mixed with a molten LBeLennox (Fisher) medium containing 0.7% Bacto agar (Difco Laboratories) and poured over a Petri dish containing solid LBeLennox media. After incubating overnight at 37 C, sterile 0.1 M bicarbonate (Fisher) buffer was added to the plate which was gently rocked for 3 h. The solution was withdrawn from the plate, centrifuged, and the supernatant filtered through a 0.22 mM-pore-size PES syringe filter (Cho et al., 2005). The virus suspension contained w7 109 plaque forming units per milliliter (PFU/mL) and was stored at 4 C before use. MS2 samples were enumerated according to the double agar layer method (Adams, 1959). Samples were analyzed either immediately or stored at 4 C in dark and analyzed within 24 h. No change in viral titers was found within 24 h of storage in the presence or absence of the nanomaterials. To determine if the presence of nanoparticles interfered with virus enumeration, parallel samples containing nanoparticles were enumerated directly or after centrifugation at 10,900 g for 15 min to remove the nanoparticles. No significant difference was found between the two methods. Therefore, all data reported hereafter were obtained from direct enumeration of the samples without removing the nanoparticles. Control tests consisted of enumerating buffer solution to ensure that viral contamination was not present in any of the reagents.
2.3.
in the dark, and sampled at different times for virus enumeration. The virus/nanoparticle mixtures were subsequently kept in dark at 4 C for 24 h before enumerated again. The effect of leached silver was investigated by removing the catalyst particles from suspension after sonication by centrifugation and filtration. The resulting solution was added to an MS2 suspension, which was sampled periodically for the active MS2 titer.
2.3.2.
Photocatalytic virus inactivation
The photocatalytic viral inactivation experiments were carried out in a pre-stabilized Luzchem LZC-4V photoreactor (Luzchem Research, Inc., Ottawa, ON Canada) fitted with four 8 W UV-A (315e400 nm) lamps with peak emission at 350 nm (Hitachi). The total light intensity used in all experiments was 2.5 mW/cm2 as determined by a UV radiometer (Control Company, Friendswood, TX) with a NIST traceable 350 nm photosensor. Reactions were housed in sealed 25 mL Pyrex Erlenmeyer flasks. Sterile ultrapure water was combined with the MS2 stock solution and catalyst suspensions or leached Agþ solutions to achieve a final concentration of w7 107 PFU/mL MS2 and 100 mg/L TiO2 or nAg/TiO2. The volume of leached Agþ solution added was the same as that used with particles in suspension. The mixture was stirred for 1 min in the dark, after which a sample was taken representing the initial virus concentration after adsorption. The reaction flask was then placed in the reactor and 1 mL samples were taken at 30 s intervals. All samples were immediately enumerated or covered and refrigerated at 4 C to prevent further inactivation while waiting to be processed. To investigate the role of HO in MS2 inactivation, reactions were carried out in the presence of two HO scavengers, methanol (99.9%, Fisher spectranalyzed) or tert-butanol (Fisher, ACS Certified) at concentrations from 30 to 400 mM. Control experiments were performed by mixing MS2 in the corresponding alcohol solution for 10 min to account for any inactivation due to the alcohol. Samples were immediately diluted into 0.1 M bicarbonate buffer.
Virus inactivation experiments
All materials that came in contact with the virus solutions, media, and reagents were sterilized by autoclaving, filtering, or purchased sterile. Nanoparticle suspensions were freshly prepared in ultrapure water and were bath sonicated for 30e45 min to ensure good dispersion before each experiment. Particle size and zeta potential of each suspension was analyzed by dynamic light scattering (DLS) using a Zen 3600 Zetasizer (Malvern Instruments, Worcestershire, UK) to determine if differences in particle size and thus surface area were responsible for any observed differences in viral inactivation.
2.3.1.
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Dark inactivation of viruses
Dark inactivation of viruses was assessed using undoped P25 TiO2 and nAg/TiO2 synthesized with P25 and 10 wt.% AgNO3. A suspension of w7 107 PFU/mL MS2 was made in ultrapure water to which sonicated nAg/TiO2 or P25 TiO2 nanoparticles were added. The suspension was then stirred for up to 10 min
3.
Results and discussion
3.1.
nAg/TiO2 characterization
The color of the dried nAg/TiO2 nanoparticles varied from light brown to reddish brown. The degree of surface oxidation of the silver is likely responsible for the differences in color.
3.1.1.
Silver content
The amount of silver captured by the TiO2 varied with both the AgNO3 concentration and the base TiO2 material used. The P25 TiO2 based nAg/TiO2 made with 10, 8, and 4 wt.% AgNO3 had final Ag contents of 5.95, 4.36, and 2.46 wt.%. The AATiO2 based nAg/TiO2 made with 10 wt.% AgNO3 had a final Ag content of 3.94 wt.%. The nAg/TiO2 materials are hereafter designated by the final nAg content and base TiO2 material (e.g. 5.95%nAg/P25TiO2). Silver deposition was more efficient at higher AgNO3 concentrations. Deposition onto P25 was notably greater than that on the AATiO2: 90% of the Ag added
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was coated onto P25 while only 59% onto the AATiO2 when 10% AgNO3 was applied. The lower doping efficiency of AATiO2 is attributed to its limited photoactivity.
3.1.2.
TEM and XPS analyses
Fig. 1 presents representative TEM images of the Ag doped and undoped P25 samples. Silver islands of w2e4 nm in diameter were found on the TiO2 nanoparticles (w10e50 nm), although they were not apparent on all crystallites. No silver deposits were observed on any TiO2 particles not treated with silver. XPS analyses showed similar results for all samples. Fig. 2 presents the XPS spectra for the 5.95%nAg/P25TiO2 as an example. O 1s spectra (e.g., Fig. 2a) of all samples showed a major peak with a broad shoulder in the area for metal oxides (528e531 eV). The presence of the shoulder indicates the presence of multiple metal oxides, i.e., titanium dioxide and silver oxide. The Ag 3d spectra (e.g., Fig. 2b) confirm the presence of silver oxide (peak range 367.3e368.0). This may be due to silver adsorbing on the TiO2 surface at oxygen sites or the oxidation of the surface of the deposited silver.
3.1.3.
Dispersed particle size
Mean hydrodynamic diameters of all photocatalyst suspensions in ultrapure water are presented in Fig. 3. The sizes of P25 TiO2, AATiO2, and 3.94%nAg/AATiO2 stayed constant for at least 25 min, suggesting that these suspensions were stable during the virus inactivation experiments (w5 min). All the P25 based nAg/TiO2 materials, however, formed large aggregates and settled out gradually. The aggregation of the silver doped samples was consistent with the measured changes in zeta potential: 9.1e9.3 mV versus 38.7 mV for P25. The sizes presented in Fig. 3 for these materials are the average of data obtained in the first 5 min concurrent with the inactivation procedure.
3.2.
MS2 dark inactivation
Inactivation and removal of MS2 by the photocatalysts in dark (referred to as dark inactivation hereafter) is attributed to adsorption to the photocatalyst particles and inactivation by Agþ released from nAg/TiO2. Fig. 4 shows the total dark
Fig. 1 e TEM images of nAg/P25TiO2 with silver particles (w2e4 nm dia.) indicated by arrows. Silver particles are visible on all doped samples, although they are not apparent on all TiO2 crystallites (10e50 nm dia.). Top left undoped P25 (50 nm scale), top right 5.95% Ag (10 nm scale), bottom left 4.36% Ag (20 nm scale), bottom right 2.46% Ag (20 nm scale).
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Fig. 2 e Typical X-ray photoelectron spectra of (a) O 1s, which reveals the presence of multiple metal oxides through the observed peak shoulder and (b) Ag 3d, with peak between 367.3 and 368 eV which corresponds to silver oxide. Spectra shown for 5.95%nAg/P25TiO2.
Fig. 3 e Dispersed particle diameters of nanoparticles used for virus inactivation as measured by DLS. Silver doping P25 TiO2 was found to decrease the stability of the suspended particles, resulting in the observed aggregation.
539
Fig. 4 e Removal of MS2 by P25 TiO2, 5.95%nAg/P25TiO2, and leached AgD from 5.95%nAg/P25TiO2 after 10 min of contact in dark. TiO2 and nAg/TiO2 samples were enumerated both with particles in suspension and after their removal by centrifugation (data marked “supernatant”) to determine if adsorbed viruses remained infective. The limited difference in virus titers between solutions with particles suspended and removed suggests that MS2 is inactivated upon adsorption to the catalysts. After accounting for the effect of leached AgD, the 5.95% nAg/P25TiO2 removed 38% (75e37%) MS2 by adsorption as compared to only 26% by P25 TiO2.
removal after 10 min of exposure to P25 and 5.95%nAg/ P25TiO2. Leached Agþ was responsible for 37% (0.2 log) MS2 inactivation, with most inactivation occurring during the first 1 min of exposure. When enumerated with catalyst particles in suspension, a total of 75% (0.6 log) removal was observed with 5.95%nAg/P25TiO2. After accounting for the effect of leached Agþ (37% removal), the 5.95%nAg/TiO2 removed 38% (0.2 log) of the MS2 by adsorption, 12% more than that adsorbed by undoped P25, which inactivated 26% (0.13 log) of the MS2. The majority of dark inactivation occurred during the first minute of exposure. Therefore, the MS2 concentration measured after 1 min of dark contact in each photocatalytic inactivation experiment was used as the initial concentration for analysis of the photocatalytic inactivation data. The increased adsorptive removal by nAg/TiO2 may be explained by interactions of viral surface amino acids with silver. Silver has a high affinity for sulfur moieties, and there are 183 cysteine residues exposed on the MS2 capsid surface (Jou et al., 1972; Nolf et al., 1977; Penrod et al., 1996). Carboxyl
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groups on the amino acids are also known to interact with silver (Stewart and Fredericks, 1999). The minimum difference between virus titers with nanoparticles in suspension and nanoparticle free centrifuge supernatant (Fig. 4) suggests that adsorption of MS2 to the nAg/TiO2 or undoped TiO2 surface either inactivates these viruses or sterically inhibits access of the MS2 A protein to the E. coli pili, where infection occurs. The limited additional removal observed after centrifugation of samples is attributed to the interception of MS2 by the catalyst particles during centrifugation. The mixtures of the MS2 and the P25 TiO2 or 5.95%nAg/ P25TiO2 were further kept in dark at 4 C for 24 h and re-enumerated to assess the potential dark inactivation of stored samples. Negligible change in virus titer was observed during the 24 h period for either materials (data not shown), suggesting that further inactivation was absent in dark.
3.3.
Photocatalytic MS2 inactivation
The inactivation of MS2 by the different nanomaterials and UV-A alone is shown in Fig. 5. The plain P25 TiO2 achieved 1.6 log inactivation of MS2 in 2 min while UV-A irradiation alone showed negligible MS2 removal within the same time period (Fig. 5a), showing that the inactivation in the presence of P25 TiO2 is attributed to photocatalytic oxidation. Silver doping significantly enhanced MS2 inactivation by P25 TiO2 and the inactivation rate increased with silver content. The enhanced inactivation was also observed with the Ag doped AATiO2 (Fig. 5b), even though AATiO2 showed minimum inactivation. The photocatalytic inactivation kinetics data could be described by the ChickeWatson model (Equation (1)), where k is the rate constant (s1) and N0 and N are the titer of active viruses at time zero and t. Here, the virus titer after dark adsorption equilibrium was used as N0.
log
N N0
¼ kt_
(1)
The inactivation rate constants obtained from fitting the kinetics data with the ChickeWatson model are shown in Table 1. The silver doping increased the reaction rate constant by up to 584% as compared to the base TiO2. The inactivation rate was found to increase with the silver content on P25 TiO2, with rate constants of 0.089, 0.035, 0.017 and 0.013 s1, for the materials with 5.95, 4.36, 2.46 and 0% silver, respectively. The inactivation rate constant for 3.94%nAg/AATiO2 (0.024 s1) showed a 5 fold increase from the plain AATiO2 (0.004 s1); it also outperformed P25 TiO2 and 2.46%nAg/P25TiO2, even though the P25 TiO2 inactivated MS2 w3.2 times faster than the AATiO2. While silver was found to be beneficial when doped onto P25 TiO2, the increased aggregate size may have offset some enhancement in photoactivity. Also shown in Table 1 is the time required for each nanomaterial to achieve 4 log removal of MS2. With 5.95 wt.% nAg loading on P25, 4 log removal of MS2 could be obtained in 45 s, making it feasible to achieve virus removal from drinking water using a small photoreactor or to improve removal of UV resistant viruses of existing UV reactors. Experiments using solutions containing leached Agþ resulted in no notable photocatalytic inactivation. These results suggest that the enhanced inactivation was due to the increase in the photocatalytic activity of TiO2 instead of the antimicrobial property of nAg. Two mechanisms may be responsible for such enhancement: increased MS2 adsorption and greater ROS generation. MS2 inactivation has been shown to be directly proportional to the amount adsorbed to the TiO2 surface (Koizumi and Taya, 2002). Increased adsorption as demonstrated in Fig. 4 may enhance the inactivation rate by placing the virus in close proximity to newly generated HO
Fig. 5 e MS2 Inactivation by (a) UV-A alone and AgD, P25 TiO2, 2.46%nAg/P25TiO2, 4.36%nAg/P25TiO2, and 5.95%nAg/ P25TiO2 under UV-A irradiation, and by (b) UV-A alone and, AATiO2, 3.94%nAg/AATiO2 under UV-A irradiation. The inactivation rate was found to increase along with the silver content on P25 TiO2 up to the maximum amount tested (5.95%). 3.94% nAg on anatase TiO2 also dramatically increased the inactivation rate.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 5 3 5 e5 4 4
Table 1 e Actual silver contents on nAg/TiO2 particles and first order rate constants for MS2 inactivation. Material
P25 TiO2 2.46%nAg/P25TiO2 4.36%nAg/P25TiO2 5.95%nAg/P25TiO2 AATiO2 3.94%nAg/AATiO2
Rate Constant (s1)
R2
Time Required to Achieve 4 Log Removal (min)a
0.013 0.017 0.035 0.089b 0.004 0.024
0.91 0.99 0.97 0.99 0.98 0.99
5.1 3.9 1.9 0.75 16.7 2.8
a Times greater than 2 min obtained by projecting kinetic data. b Rate for first 60 s of inactivation.
(both surface bound and bulk) and may increase direct hole oxidation. In addition, silver doping has been proposed to facilitate charge separation in TiO2 resulting in more efficient ROS generation and consequently greater MS2 inactivation. MS2 inactivation by 5.95%nAg/P25TiO2 shows a tailing effect after 60 s (5.4 log removal), when the inactivation rate
541
constant decreased from 0.089 to 0.013. This was not observed when MS2 was inactivated by the other materials. This is likely due to the presence of the large number of inactivated viruses and their remnants, which compete with infective viruses for adsorption sites and ROS, since 99.9996% of the MS2 had been inactivated after 60 s.
3.3.1.
Effects of HO scavengers
As discussed above, one potential mechanism for the enhanced virus inactivation of nAg/TiO2 is higher HO production rate. To test this mechanism, methanol and tertbutanol were employed to elucidate the role of Ag doping in HO production and MS2 inactivation. Alcohols, especially methanol and t-butanol, are known HO scavengers. Methanol was reported to scavenge both surface bound and bulk HO, as well as holes (Cho et al., 2005). While t-butanol has been shown to competitively adsorb to TiO2 (Sun and Pignatello, 1995), research has demonstrated that it does not scavenge all surface bound HO (Kim and Choi, 2002). Using methanol and t-butanol as HO scavengers, Cho et al. (2005) showed that bulk HO was responsible for the inactivation of MS2 by TiO2. Singlet oxygen and superoxide anion were also found to
Fig. 6 e MS2 inactivation in the presence of HO scavengers methanol and t-butanol. (a) P25 TiO2 with methanol; (b) P25 TiO2 with t-butanol; (c) 5.95%nAg/P25TiO2 with methanol; (d) 5.95%nAg/P25TiO2 with t-BuOH. The inactivation rate was found to decrease in a concentration dependent manner when either alcohol was applied. When present at 400 mM, both alcohols completely stopped MS2 inactivation by P25 TiO2 while inactivation still occurred by 5.95%nAg/P25TiO2, but to a much lesser degree than the case when no HO scavenger is applied. Dark inactivation of MS2 by 5.95%nAg/P25TiO2 was enhanced when either alcohol was present at 400 mM, but the effect was reversed after 30 s of irradiation corresponding to the apparent initial rise in active virus titer.
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inactivate MS2 in a study using fullerol as the photocatalyst (Badireddy et al., 2007). Experiments were performed using P25 TiO2 (Fig. 6a,b) and 5.95%nAg/P25TiO2 (Fig. 6c,d) at different methanol or t-butanol concentrations. Both methanol and t-butanol completely stopped inactivation of MS2 by the plain P25 TiO2 at 400 mM and considerably slowed the reaction at 200 mM. P25 TiO2 showed higher sensitivity to t-butanol, as 30 and 100 mM t-butanol both decreased the inactivation rate while the same concentrations of methanol had no effect. Control experiments using methanol or t-butanol in the absence of any photocatalyst did not show any decrease in virus titer for methanol or t-butanol concentrations up to 400 mM. These results suggest that HO is primarily responsible for MS2 inactivation by P25 TiO2. Although singlet oxygen and superoxide anions are also produced by TiO2 (Hoffmann et al., 1995), they did not seem to cause notable MS2 inactivation in our study. Because there was no significant difference in reaction rates when either methanol or t-butanol was used at 400 mM and the inactivation rate was more sensitive to low t-butanol concentrations, the data suggests that bulk HO plays a more important role than surface bound HO in MS2 inactivation. This observation agrees with the conclusion by Cho et al. (2005). Both alcohols also reduced the inactivation rate of MS2 by 5.95%nAg/P25TiO2, but to a much less extent. With 400 mM methanol or t-butanol, 1.3 and 1.6 log of MS2 inactivation was achieved, respectively, suggesting that silver doping increases HO production and consequently MS2 inactivation. When nAg/TiO2 was used with 400 mM of either alcohol, the amount of viruses removed by dark stirring was greater than that observed without added alcohol (90e98%, data not shown). The active virus titer increased after 30 s of irradiation compared to that before UV exposure. Since the depression of initial virus concentration was not observed with undoped P25 TiO2, this effect is attributed to the interaction of the alcohol with the silver and the subsequent changes in viral adsorption capacity. Any increased adsorption of alcohol to silver/silver oxide as compared to TiO2 could change the electrostatic and/or hydrophilic properties of the catalyst, resulting in changes to its adsorptive capacity. From 30 s to 2 min, the MS2 was slowly inactivated. The inactivation rate by nAg/TiO2 was observed to be influenced by both alcohols in a concentration dependent manner, confirming the role of HO in MS inactivation.
4.
Conclusion
This study demonstrated that silver doping TiO2 nanoparticles is an effective way to increase TiO2 photocatalytic activity for virus inactivation. Silver doping enhances photocatalytic inactivation of viruses primarily by increasing HO production, although increased virus adsorption to silver sites and leaching of antimicrobial Agþ also contribute to virus removal. The fast virus inactivation kinetics of the nAg/TiO2 materials demonstrated in our study suggest that effective virus inactivation can be achieved using a small photoreactor and photocatalytic disinfection of drinking water at both point
of use and municipality scales could be a potential application of the nAg/TiO2 materials. Further research is needed to address issues such as photocatalyst fouling, impact of water quality, loss of silver, and need for catalyst regeneration to ensure the sustainability of the technology. Very importantly, the retention of the nAg/TiO2 materials in the treatment system is critical. This can be achieved by using a hybrid photoreactor/membrane system, where the photocatalyst is retained by a membrane unit down-stream of the photoreactor and recirculated, or by applying the photocatalyst as a coating on surfaces inside a photoreactor. Because UV-A is a significant component of the solar irradiation that reaches earth surface, coating transparent piping or shallow open channels with the photocatalyst at sunny locations could also be a low-cost solution to drinking water disinfection. Ag/TiO2 may also be activated by visible light through the silver surface plasmon resonance, however it is not clear how the catalyst will behave under both UV and visible radiation (Sung-Suh et al., 2004; Seery et al., 2007).
Acknowledgements This work was supported by the Center for Biological and Environmental Nanotechnology at Rice University (NSF Award EEC-0647452). Laboratory work was assisted by Zoltan Krudy.
references
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Application of fractal dimensions to study the structure of flocs formed in lime softening process Arman Vahedi, Beata Gorczyca* University of Manitoba, Department of Civil Engineering, E1-368, EITC, 15 Gillson Street, Winnipeg, MB, Canada
article info
abstract
Article history:
The use of fractal dimensions to study the internal structure and settling of flocs formed in
Received 16 April 2010
lime softening process was investigated. Fractal dimensions of flocs were measured
Received in revised form
directly on floc images and indirectly from their settling velocity. An optical microscope
19 August 2010
with a motorized stage was used to measure the fractal dimensions of lime softening flocs
Accepted 14 September 2010
directly on their images in 2 and 3D space. The directly determined fractal dimensions of
Available online 22 September 2010
the lime softening flocs were 1.11e1.25 for floc boundary, 1.82e1.99 for cross-sectional area and 2.6e2.99 for floc volume. The fractal dimension determined indirectly from the flocs
Keywords:
settling rates was 1.87 that was different from the 3D fractal dimension determined
Lime softening floc
directly on floc images. This discrepancy is due to the following incorrect assumptions
Fractal dimension
used for fractal dimensions determined from floc settling rates: linear relationship
Settling velocity
between square settling velocity and floc size (Stokes’ Law), Euclidean relationship between floc size and volume, constant fractal dimensions and one primary particle size describing entire population of flocs. Floc settling model incorporating variable floc fractal dimensions as well as variable primary particle size was found to describe the settling velocity of large (>50 mm) lime softening flocs better than Stokes’ Law. Settling velocities of smaller flocs (<50 mm) could still be quite well predicted by Stokes’ Law. The variation of fractal dimensions with lime floc size in this study indicated that two mechanisms are involved in the formation of these flocs: clusterecluster aggregation for small flocs (<50 mm) and diffusion-limited aggregation for large flocs (>50 mm). Therefore, the relationship between the floc fractal dimension and floc size appears to be determined by floc formation mechanisms. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
The success of removal of contaminants by coagulation/flocculation process is determined by the success of solid/liquid separation. In the coagulation process the suspended and dissolved impurities aggregate into flocs which subsequently are removed from the water typically by settling. Floc properties determine the settling of the sludge; this is especially pronounced at low sludge concentrations (type I settling) and
very high sludge concentration (type IV settling). In type I settling, flocs settle as individual aggregates; in type IV settling, a blanket of flocs settles. Effectiveness of type I settling depends on settling velocities of individual flocs, while type IV settling is predominantly determined by the dewatering properties of flocs. The sludge settling process is typically modeled using the sediment flux theory. In this theory, all properties of flocs are reduced to one parameter e sludge solid concentration. Attempts for extending the flux
* Corresponding author. Tel.: þ1 204 474 6674; fax: þ1 204 474 7513. E-mail addresses:
[email protected] (A. Vahedi),
[email protected] (B. Gorczyca). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.014
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DM DST
List of symbols 1
floc settling velocity, m s particle density, kg m3 water density, kg m3 gravitational acceleration, m s2 dynamic viscosity, kg m1 s1 kinematic viscosity, m2 s1 Drag coefficient correction factor for porous particles floc porosity particle diameter, m median size of the component particles within floc, m sludge settling velocity, m s1 sludge concentration constants in sludge settling model number of floc covering elements size of floc covering elements, m Hausdorff fractal dimension boundary fractal dimension cross-sectional area fractal dimension volume fractal dimension
vS rp rw g m V CD U 3 d d50 vzs C K,n Nr r DH DB DS DV
m0 v0 rp P A M V S DP DA d g l q r0 F Re
theory to include the low and high sludge concentration range do not incorporate information on the settling and dewatering behaviour of discrete flocs (Ekama et al., 1997). The fouling layer formed on the membrane during filtration is essentially a cake of compressed flocs. The properties of the individual flocs such as flow-through or compressibility may also determine the properties of the cake and the effectiveness of the membrane filtration (Li and Ganczarczyk, 1992). Many attempts to model the settling velocity of individual flocs have been undertaken (Winterwerp, 1998; Bushell et al., 2002; Khelifa and Hill, 2006). These models are mainly modifications of Stokes’ Law shown in Eq. (1) (Lee et al., 1996; Bushell et al., 2002; Gorczyca and Ganczarczyk, 2001):
v2S
¼
4g rp rw 3UCD rw
d
(1)
where vS is the settling velocity, rp is the particle density, rw is the water density, g is the gravitational acceleration, CD is the drag coefficient, U is a correction factor for porous particles and d is the particle size. There is much evidence showing that the Stokes’ Law does not adequately describe the floc settling velocity (Logan, 1999). The correction factors modifying the original Stokes’ Law have not been widely applied due to their limited effectiveness (Namer and Ganczarczyk, 1993; Brown and Lawler, 2003; Khelifa and Hill, 2006). The settling of concentrated suspensions of flocs, i.e. sludge, has also been modeled. Eq. (2) shows one of many mathematical expressions linking the sludge zone settling velocity to the sludge concentration (Ekama et al., 1997): vZS ¼ KenC
(2)
where vzs is the sludge settling velocity, C is the sludge concentration and K and n are constants related to the
mass fractal dimension mass fractal dimension estimated from settling tests mass of floc covering elements, kg volume of floc covering elements, m3 density of a fractal, kg m3 floc perimeter, m floc projected area, m2 floc mass, kg floc volume, m3 floc cross-sectional area, m2 perimeter-based fractal dimension fractal dimension associated with the projected area volume fractal dimension of the primary particles distance from self-similarity dimensionless floc size dimensionless particle shape factor density of the primary particles, kg m3 the effect of the size distribution of the primary particles particle Reynolds number
characteristics of particular sludge. The physical meaning of K and n is not known but it has been suggested that these parameters might represent some properties of flocs such as settling velocity and drainage through flocs (Gorczyca and Ganczarczyk, 2002). The internal arrangements of particles inside a floc that is the floc structure determines almost every property of the floc such as settling rate, drag force acting on the floc, mass, porosity, density and dewatering. The internal structure of a floc has been shown to be well-described by fractal models (Li and Ganczarczyk, 1989; Logan, 1999; Kim et al., 2001; Gorczyca and Ganczarczyk, 2001, 2002; Chung and Lee, 2003; Maggi, 2005). A fractal dimension measures the degree of meandering or irregularity of a geometric object (Gomes and Selman, 1999). A floc’s fractal dimensions may indicate a particular structural model that can be used to simulate floc’s hydrodynamic behaviours such as gravity settling and floc permeability. The linkage between the fractal dimensions of flocs and their settling velocities is well documented (Ganczarczyk, 1995; Lee et al., 1996; Gorczyca and Ganczarczyk, 1996, 2000; Logan, 1999; Bushell et al., 2002; Tang et al., 2002; Chu et al., 2004; Jarvis et al., 2005; Khelifa and Hill, 2006; Bugni, 2007; Chakraborti et al., 2007). However, the methods for measurement of fractal dimensions have not been wellestablished.
2.
Flocs fractal dimensions
Numerous methods have been suggested to determine the fractal dimension of flocs. These methods can be categorized into direct and indirect methods.
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2.1. Box-counting method: a direct method for determination of fractal dimensions The box-counting method can be used to determine fractal dimensions directly by covering the fractal image by Nr elements (pixels) of size r (Kaye, 1993): DB;S;V ¼ lim r/0
logðNr Þ . log 1 r
(3)
In this paper, the box-counting fractal dimensions are labelled as DB, DS and DV for floc boundary, cross-sectional area and volume respectively. DV is an example of a 3D fractal dimension which provides direct information of aggregate structure and can be used directly to model floc’s hydrodynamic behaviours such as gravity settling and flow through the floc. The projected area of floc is sometimes used to determine perimeter-based fractal dimension DP (Eq. (4)). 2
AfPDP
(4)
where A and P are floc projected area and floc perimeter respectively. Note that the projected area may be different from the cross-sectional area; the floc cross-sectional area is the floc intersection with a plane in 3D but the projected area of the floc is simply the 2D projection from any direction; projected area is what is usually captured by imaging of the entire floc but the cross-sectional area imaging requires floc sections revealing floc internal pores. Even though the perimeter-based fractal dimension has been used interchangeably with the boundary fractal dimension in some previous studies, it can be shown that DP is equal to the boundary fractal dimension (DB) only when A w d2. For fractals this assumption is not true (Imre, 2006). Several researchers have used Eqs. 3 and 4 to measure the boundary, cross-sectional area and perimeter-based fractal dimensions of the chemical coagulation and activated sludge flocs directly on the images of projections of these flocs (Li and Ganczarczyk, 1989; Thill et al., 1998; Kim et al., 2001; Gorczyca and Ganczarczyk, 2001; Maggi and Winterwerp, 2004). Of all the fractal dimensions discussed above only the 3D fractal dimension (DV) has practical applications for modeling the settling and other hydrodynamic characteristics of flocs. Unfortunately, this particular dimension is extremely difficult to measure directly as it requires 3D reconstruction of the floc structure. Confocal microscopy has been used for 3D imaging of biological aggregates (Snidaro et al., 1997; Thill et al., 1998; Schmid et al., 2003; Chu and Lee 2004). Chu and Lee (2004) calculated DV of activated sludge flocs directly on 3D images of flocs reconstructed by stacking 2D cross-sectional images. However, there are several limitations associated with the use of confocal microscopy for floc imaging. The fluorescent staining process can be considerably elaborative and change the moisture content of the flocs which may result in altering their fragile structure. Also, the stain attaches only to the organic parts of the floc making inorganic parts invisible. This is especially problematic for the analysis of chemical coagulation flocs which are predominantly inorganic. Therefore, DV has not yet been directly measured for chemical coagulation flocs. The indirect estimation of 3D volume fractal dimension (DV) of flocs from the boundary (DB), perimeter (DP) and cross-
sectional area fractal dimensions (DS) has been attempted by some researchers. Thill et al. (1998) used Eq. (5) to estimate the volume fractal dimension of activated sludge flocs. In derivation of Eq. (5) DS was assumed to be the fractal dimension of cross-sectional surface area determined by confocal microscopy and Eq. (3). DS ¼ DV 1
(5)
Maggi and Winterwerp (2004) used the perimeter-based fractal dimension (DP) to estimate the volume fractal dimension using the following relationship: DV ¼
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi aðlÞ for DP < 2 DP bðlÞ
(6)
where l ¼ d/r is the dimensionless floc size, d is the floc size, r is the pixel size in the two-dimensional projection of the floc and a(l ) and b(l ) are calculated from the following equations:
aðlÞ ¼ 9 zðlÞ
h i! 2 2 kðlÞ 9zðlÞ 2
½kðlÞ 9
;
bðlÞ ¼
h i 2 2 kðlÞ 9zðlÞ 2
½kðlÞ 9
(7)
where kðlÞ ¼ zðlÞ½zðlÞ 1 þ 1;
zðlÞ ¼ DP
log½4l 4 log½l
2.2. Free settling test: an indirect method for determination of 3D fractal dimension 3D mass fractal dimension can be derived directly from Eq. (3) by substituting the number of volume filling elements with the mass of filling elements. If the density of the primary particle (filling elements) of the floc is assumed constant, the floc mass and volume are correlated linearly and 3D mass fractal dimension and volume fractal dimension must be identical. Many researchers have attempted to determine the floc mass fractal dimension indirectly from the settling velocity of flocs using the approach explained below. Eq. (3) shows that the floc volume (V) scales with the floc size according to the principles of fractal geometry: VfdDV
(8)
Assuming constant density for primary particles, similar power law can be written for the mass fractal dimension: MfdDM
(9)
where M and DM are floc mass and floc mass fractal dimension respectively. Let’s consider v0 and m0 as constant volume and mass of primary particles (covering elements). Since, the mass of an aggregate is related to the number of elements in the aggregate by M ¼ Nm0, the following power law exists: NfdDM
(10)
For a floc made of N elements with mass of m0 and volume of v0, the solid fraction is defined as: 13¼
Nv0 V
where 3 is the floc porosity.
(11)
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The first critical assumption that has been used in previous reviewed derivations appears at this point. It has been assumed that the settling velocity of flocs follows the Stokes’ Law. According to Stokes’ Law (Eq. (1)) the following relationship exists: C v2 D S rp rw f d
(12)
Assuming the floc being composed of solids of constant density r0 and water, the following relation can be obtained by simple mass balance: rp rw ¼ ð1 3Þðr0 rw Þ ¼ ð1 3ÞDr Combining relationship:
Eqs.
(11)e(13)
results
(13) in
the
Nv0 CD v2S f V d
following
(14)
The drag coefficient acting on the settling floc is assumed the same as the one for impermeable sphere: CD ¼
24 ðRe 1Þ Re
(15)
where Re ¼ vsd/V is the particle Reynolds number in which V is the kinematic viscosity of the water. Therefore, the following relationship can be derived: CD f
1 vS d
(16)
Combining Eqs. (14) and (16) produces the following relationship: Nv0 vS f V d2 and Eqs. (10) relationship: dM fV
vS d2
(17) and
(17)
together
result
in
following
(18)
The second critical assumption is that the volume and size of a floc are related according to Eq. (19): Vfd3
(19)
Therefore, vS fdDM 1
(20)
Many researchers have estimated the volume fractal dimension of aggregates indirectly from their settling rates according to Eq. (20) (Li and Ganczarczyk, 1989; Logan, 1999; Bushell et al., 2002; Tang et al., 2002; Chu et al., 2004; Liao et al., 2005). However, Eq. (19) which is based on Euclidean geometry contradicts the Eq. (8) which is a fundamental relationship in fractal geometry. Also Eq. (12) is directly derived from Stokes’ Law that has been shown not to predict the settling velocity of flocs correctly. In order to distinguish between the directly determined mass fractal dimension and the mass fractal dimension obtained from settling velocity, in this study the mass fractal dimension from settling test is denoted as DST.
2.3.
The variability of fractal dimensions with floc size
Several studies have suggested that the floc fractal dimensions vary with the floc size (Gorczyca and Ganczarczyk, 2001; Chakraborti et al., 2003; Maggi and Winterwerp, 2004; Maggi et al., 2007; Khelifa and Hill, 2006). The variation of fractal dimensions with size is consistent with the theory of multi-level floc aggregation. Many studies have shown that the aggregation mechanism affects the morphology and fractal dimension of aggregates (Meakin and Jullien, 1988; Thouy and Jullien, 1994; Snidaro et al., 1997; Logan, 1999; Gorczyca and Ganczarczyk, 2002). The aggregation mechanism is determined by many factors such as the properties of primary particles, hydrodynamic conditions within the reactor and so on. As the aggregates grow, both clusterecluster aggregation and particleecluster aggregation might occur and form different structures. In the case of particleecluster aggregation the small primary particles can easily penetrate into and fill in a fractal structure and form a compact structure with fractal dimensions closer to Euclidean dimensions. On the other hand in clusterecluster aggregation the clusters cannot penetrate into each other but rather stick to each other and form a larger aggregate with lower fractal dimension (Meakin, 1984). The probability of penetration in particleecluster aggregation or sticking in clusterecluster aggregation depends on many factors. Floc restructuring for example due to breakup and reaggregation can also change the fractal dimension of aggregates (Meakin and Jullien, 1988). Since the breakup and restructuring pattern depends on the hydrodynamic conditions that define the aggregate size, the relationship between the size and fractal dimension of aggregates can be affected by many phenomena. Gorczyca and Ganczarczyk (1999, 2001) investigated Sierpinski fractal dimensions of alum coagulation and activated sludge flocs. Sierpinski fractal dimension is equivalent to DS as discussed in this paper. According to their study, the flocs form in different stages and the relationship between the size and fractal dimension is linear for each stage but different from other stages. Sierpinski fractal dimensions were higher for larger flocs. They also showed that flocs become more irregular and their fractal dimensions deviates more from Euclidean dimensions as their size increases. Maggi (2005) suggested that a floc’s volume fractal dimension changes with aggregate size according to the following power law: DV ¼ dlg
(21)
where d is the volume fractal dimension of the primary particles, which is normally close to or equal to 3 but no larger than 3, and g is a parameter called distance from self-similarity. The parameters d and g can be calculated by fitting Eq. (21) to the floc’s fractal dimension and size data.
2.4.
Floc fractal dimensions and settling rate
Efforts have been undertaken to incorporate various floc fractal dimensions into Stokes’ Law to improve settling rate predictions (Logan, 1999; Li and Logan, 2001; Bushell et al., 2002; Tang et al., 2002; Sterling, 2005; Khelifa and Hill, 2006).
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Khelifa and Hill (2006) modified Stokes’ Law by introducing variable floc fractal dimension (Eq. (22)). vS ¼
1 rS rw 3DV dDV 1 d50 f qg m 18 1 þ 0:15Re0:687
Raw Clarifier Clarifier Clarifier water influent effluent
(22)
where d50 is the median size of the component particles within the floc, i.e. primary particles, q is a dimensionless particle shape factor, m is the water dynamic viscosity, rs is the density of the primary particles forming the flocs and V represents the effect of the size distribution of f ¼ m3 =m3=D F P the primary particles in the floc where m3 ¼ ð ki¼1 d3i Þ=k and Pk DV mF ¼ ð i¼1 di Þ=k. The volume fractal dimension in this model is estimated from a power law function (similar to Eq. (21)). The fractal dimensions and settling rates obtained in previous studies were used in the model.
2.4.1.
Table 1 e Raw and clarifier water quality of the Portage la Prairie Plant.
Objectives of this study
The objective of this study was to determine the fractal dimensions of flocs formed in water lime softening process directly on floc images and indirectly from settling rates. These fractal dimensions are compared to those of other flocs formed in water/wastewater treatment processes. The application of fractal dimensions to study the structure and settling velocity of flocs is investigated.
pH Alkalinity (mg/l CaCO3) Total hardness (mg/l CaCO3) Temperature ( C) Total dissolved solid (mg/l) Turbidity (NTU) Color (TCU) Particle size range (mm) Standard deviation of particle size (mm) d50 of particle size distribution (mm)
Material and methods
Lime softening process is widely used for hardness removal in water treatment plants in Manitoba. Lime softening flocs were collected from Portage la Prairie (Manitoba, Canada) water treatment plant that utilizes sand ballasted flocculation prior to lime/soda softening. The lime softening process is followed by recarbonation, ozonation and filtration. The studied flocs were collected in May and August 2008 as well as January and August 2009 from the upper portion of the circular solid blanket clarifier where the solid concentration is relatively low and individual floc settling is predominant. The quality data as well as the size distribution characteristics of the raw and clarifier water are presented in Table 1 (The City of Portage la Prairie, 2009). The chemical composition analysis of flocs was performed by Ion Chromatography Inductivity-Coupled Plasma (ICP), X-Ray Diffraction and Energy Dispersive Spectroscopy. The lime flocs were mainly composed of calcium carbonate (85%), Mg (9%), Fe (5%) and small amount of other elements including Al (1% total).
3.1.
Floc size distribution analysis
The floc samples were preserved at 4 C and their size distributions were analyzed within 6 h from the time of sampling. The preliminary size distribution analysis of flocs that was conducted by using dynamic light scattering (Photocor Tech.) and Malvern Mastersizer (2000) had revealed that the number of flocs smaller than 2 mm was not significant in the samples; therefore it was decided to analyze only the flocs that are larger than 2 mm. A combination of two particle counting instruments and optical microscopy was used to determine the full size distribution of flocs with each instrument detecting particles in the
e e e
10.6 72.9 172
e e e
15 591
e e
e e
e e
4.8 45 10e90 3.05
e e 6e85 8.6
3.02 e e e
e e 2e62 8
64
17
e
16
specific size range, optimal for the particular method. The equivalent circular diameter (ECD) was considered as the representative floc size.
3.1.1.
3.
7.6 279 362
Particle counting
Two particle counters, the Dynamic Particle Analyzer (DPA 4100) and Spectrex 2200, were used to determine the size distribution of flocs larger than 2 mm. Previous experience showed the results from these two particle counters to be comparable (Gorczyca and Klassen, 2008). In the measurements with DPA 4100, the suspension passes through a flow cell where a high-speed camera takes a sequence of images of the sample. The DPA 4100 software analyzes the images and determines the size distribution of the particles. The 8 ml/s flow of suspension was analyzed for 60 s in five separate tests. About 3770 particles were counted in the range of 2e25 mm. The Spectrex 2200 utilizes the principles of light scattering to count the number and measure the sizes of particles in a suspension. In each of five tests, 100 ml of the suspension was analyzed. About 1530 particles were counted in the range of 2e92 mm.
3.1.2.
Microscopy and image analysis
The size distribution of flocs larger than 20 mm was obtained by using an optical microscope (SMZ 800) with the magnification of 6.3. The microscope was equipped with a digital camera (Olympus DP70) and Image ProPlus 5.4 for image processing. The numerical aperture (N.A.) of the microscope was 1.4 and the digital camera was capable of acquiring images with 4140 3096 pixels. About 1 ml of the diluted floc suspension in a Petri dish was placed on the microscope for imaging. 30 images were taken from 5 samples and about 1680 flocs were counted. The number of images was chosen to be 30 to achieve a standard error of less than 0.05 for the data (Parker, 1970).
3.1.3.
Combining the distributions at different size ranges
Previous experience showed that the results of microscopy and particle counting are comparable (Gorczyca and Klassen,
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2008). The linear opinion pool method (Eq. (23); Stone, 1961) was used to combine the distributions obtained from microscopy, DPA and Spectrex particle counters. pðxÞ ¼
n X
wi pi ðxÞ
(23)
i¼1
where pi(x) is the value of the probability distribution i for the uncertain quantity xi, the weightings (wi) sum to one, and p(x) represents the combined probability distribution. In this study the weightings were selected based on the number of the particles counted by each instrument and the size range covered.
3.2.
Direct determination of fractal dimensions
3.2.1.
Obtaining floc images
3D images of flocs were needed to measure the volume fractal dimension (DV) of flocs directly. A CCD camera and an optical microscope equipped with a motorized stage (Zeiss AxioImager Z1) were used for acquisition of floc’s section images at different depths. The microscope had inverted lenses that allowed direct imaging of the flocs in suspension with preserving the floc structure. The microscope was equipped with a temperature and moisture controlled chamber. A small volume of floc suspension (<1 ml) was placed in a Petri dish and viewed in bright field. The smallest possible distance between two focal planes was 50 nm but in this study the distance between the levels of imaging varied from 100 nm to 1000 nm. The digital camera was capable of acquiring images with 4080 3072 pixels and the numerical aperture (N.A.) of the microscope was 1.4. The magnifications of lenses used in this study were 10, 20 and 40. Axio Vision 4.5 was used for deconvolution of the images. Table 2 shows the floc sampling details.
3.2.2.
3D reconstruction of flocs
The 3D-doctor software made by Able Software Corporation (Lexington, MA, USA), commonly used for 3D reconstruction of MRI images, was used for reconstruction of 3D images of flocs. The 3D-doctor acquires the 2D section images as well as the distance between the sections and provides the 3D image of the object.
3.2.3.
Measurements of fractal dimensions
In order to simplify the box-counting fractal analysis, the images were first processed in MATLAB 7.4 (developed by MathWorks Inc., USA). The grayscale level of 128 (middle point in the range of 0 and 255) was used as threshold to obtain the bi-level images of floc sections. The boundary box-counting fractal dimension (DB) was determined on the images of flocs from Eq. (3) using the image
Table 2 e The experimental details of the flocs analyzed in the box-counting fractal measurement. Number of flocs analyzed Size of flocs (ECD for 2D images) Distance between the sections Number of the sections per floc
53 8 mme235 mm 100 nme1000 nm 50 or 75
analysis system Image ProPlus 5.4. The box-counting fractal dimension for the cross-sectional surface area (DS) was determined using the Eq. (3) and the built-in box-counting library of MATLAB 7.4. The holes inside the images were deducted from the area for measuring the cross-sectional area of flocs. For 3D fractal analysis, each section image was covered by pixels with the sizes r ranging from 0.16 mm to 3.2 mm. For each pixel size the number of 2D pixels covering the cross-sectional area of the floc section image was directly counted using the image-processing library in MATLAB 7.4. The number of cubes (3D voxels) filling the floc (Nr) was calculated by integration of the 2D pixels counts obtained for an individual floc’s cross-sections. The box-counting volume fractal dimension (DV) was then calculated from Eq. (3). For each individual floc, the perimeter-based fractal dimension (DP) was simply estimated from Eq. (4) assuming that A ¼ P2=Dp . This assumption has been used in some studies (Maggi and Winterwerp, 2004). The methods suggested by Maggi and Winterwerp (2004) and Thill et al. (1998) were applied to the data to estimate the volume fractal dimension of flocs from their other fractal dimensions (Eqs. (5) and (6)).
3.3. Indirect determination of mass fractal dimension of flocs from settling velocity Terminal settling velocities of flocs were measured in free settling tests and the mass fractal dimension of individual flocs was calculated by using Eq. (20). An optical microscope (SMZ800) on a vertical stand and a digital camera (Olympus DP70) were used to take sequence images of flocs falling in a cylinder. The camera was capable of acquiring images with 4080 3072 pixels that allowed the capture of images of flocs larger than 5 mm. About 50 tests were conducted but the velocity of only 34 individual flocs was measured as others were out of focus. The size and settling velocity of the flocs was calculated by the image analysis system Image ProPlus 5.4.
4.
Results
4.1.
Floc size distribution
Fig. 1 shows the equivalent diameter distribution obtained as a result of combining the data from microscopy, DPA and Spectrex. The equivalent circular diameters (ECD) of the flocs ranged from 2 mm to 250 mm with the arithmetic mean of 50 mm and standard deviation of 36 mm.
4.2. Floc fractal dimensions determined directly on the floc images Fig. 2 shows the bi-level section images of a lime softening floc at different focal planes obtained by the Zeiss AxioImager Z1and processed in MATLAB 7.4. The 3D reconstructed floc is shown in Fig. 3. Note that in Fig. 2 only 15 images out of 75 cross-sectional images are shown. There are some holes in cross-sectional images that might not be visible in the 3D image. The reason is that some holes may not be parts of conduits continuing
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) X g 2 ; min Dvi dli (
d3
(24)
i
where Dvi and li are the volume fractal dimension and dimensionless size of the ith floc. The calculated values for d and g were 2.989 and 0.0135 respectively; therefore the relationship between the size and volume fractal dimension of lime flocs can be written as: DV ¼ 2:989 l0:0135
Fig. 1 e Size distribution of lime softening flocs.
through different imaging planes; therefore, even though the holes are visible in all section images they are not visible in some 3D views. For the same reason the outlines of 3D and 2D images might look different. The results of the box-counting fractal analysis for 53 individual flocs in the range of 8.2e235 mm are listed in Table 3.
4.3.
(25)
According to Eq. (25) the volume fractal dimension of the lime softening flocs decreases as the floc size increases and becomes nearly constant for large floc sizes. The resulted curve from Eq. (25) had the R-squared of 0.52 and the range of estimated fractal dimensions was 2.68e2.835. A two-stage linear regression was also applied to volume fractal dimension data (Fig. 4). The flocs were divided to two groups of small flocs (50 mm) and large flocs (>50 mm) and for each group a linear relationship between size and fractal dimension was derived (Eq. (26)). DV ¼
0:0047d þ 3:0183 d 50 mm R2 ¼ 0:93 0:0007d þ 2:7529 d > 50 mm R2 ¼ 0:69
4.4.
Estimation of volume fractal dimension
(26)
Variation of floc volume fractal dimension with size
Two different relationships (power law and two-stage linear relationship) for flocs size and volume fractal dimension are investigated. A constrained optimization was performed to find values of d (the volume fractal dimension of the primary particles) and g according to Eq. (21):
Fig. 5 shows the estimation of volume fractal dimension of lime softening flocs according to Eq. (5) (Thill et al., 1998) and Eq. (6) (Maggi and Winterwerp, 2004). The results are compared with the measured values of volume fractal dimension.
Fig. 2 e Bi-level images of cross-sections of a lime softening floc. The size (ECD) of this floc that was measured on the projected area of the floc was 209 mm.
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Fig. 3 e (a) Projected 2D and (b, c) 3D images of the floc from Fig. 2 using 3D-doctor software.
4.5. Floc settling rates and indirectly determined mass fractal dimension The measured settling velocity of flocs ranged from 0.11 to 3.5 mm/s with the average of 1.71 mm/s for the flocs with equivalent diameters of 10 mme246 mm (average 118 mm). The mass fractal dimension of flocs calculated from Eq. (20) was 1.87.
4.6.
Van de Ven, 1995). In this study it was assumed that the primary particles were monosized (F ¼ 1). Using trial and error, fitting of different sizes of primary particles was tested and three primary particles sizes (0.5 mm, 1 mm, 5 mm) that fitted the data well were selected for the model. The volume fractal dimension (DV) was estimated for each floc by using relationships in Eq. (26). The settling velocity of the flocs was also fitted with the modified Stokes’ Law (Eq. (1)). Based on the suggested values by Bushell et al. (2002), a correction factor (U)
Predicting the settling velocity of flocs
The model suggested by Khelifa and Hill (2006) presented in Eq. (22) was applied to fit the data of the settling velocity of lime softening flocs. Based on the chemical composition analysis of flocs, the primary particles were assumed to be mainly composed of calcium carbonate. The density of calcium carbonate is usually around 2700 kg/m3 (Kamiti and
Table 3 e Fractal dimensions of lime softening flocs. Fractal dimension Boundary (DB) Cross-sectional surface area (DS) Perimeter-based (DP) Volume (DV)
Range
Average
1.150e1.275 1.82e1.995 1.217e1.380 2.55e2.99
1.199 1.898 1.276 2.73
Fig. 4 e Volume fractal dimension of lime softening flocs.
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Fig. 5 e Measured and estimated volume fractal dimension of lime softening flocs. Fig. 6 e Predictions of lime floc settling velocities by Stokes’ Law and Khelifa and Hill (2006) model. of 0.9 was used for the lime softening flocs. All the floc-settling models are shown in Fig. 6.
5.
Discussion
5.1.
Settling velocities of lime flocs
Table 4 shows the average size and settling velocity of lime flocs compared to other flocs encountered in water and wastewater treatment. Since the lime flocs were collected from the upper portion of the clarifier, their average size is smaller than settled alum and activated sludge flocs analyzed in other studies.
5.2.
Fractal dimensions and settling velocity of lime flocs
The models suggested by Khelifa and Hill (2006) (Eq. (22)) and modified Stokes’ Law (Eq. (1)) were used to model the settling velocity of lime softening flocs (Fig. 6). In order to have a better comparison of the models, the relative errors of two models are calculated and compared in Fig. 7. Stokes’ Law predicts the settling velocities of small lime flocs in the size range of 10e50 mm quite well. The small flocs have fractal dimensions close to Euclidean dimensions and this may be the reason that their settling velocity can also be well predicted by Stokes’ Law. It seems that the large (>50 mm) flocs are too complex to be described by the modified Stokes’ Law that is based on the Euclidean geometry. For larger flocs the Khelifa and Hill (2006) model provides much better estimation of settling velocities than the modified Stokes’ Law. In this model primary particles size and fractal dimensions are variable according to the results of this study. The settling velocities of flocs in the size range of 50e100 mm are predicted with the model assuming a size for primary particles of 5 mm. It is probable that these smaller lime flocs are formed by collisions of clusters of calcium carbonate precipitates. The settling velocities of flocs in the size range of 100e250 mm could be modeled assuming primary particles with a size of 0.5e1 mm (Fig. 6 and Fig. 7). This size of primary particles coincides with the size of calcium carbonate precipitates (Nason and Lawler, 2008). This would suggest small flocs formed by clusterecluster aggregation grow to
larger flocs by Diffusion Limited Aggregation due to precipitation of calcium carbonate.
5.3.
Floc fractal dimensions
In Table 5, the average box-counting fractal dimensions as well as the mass fractal dimension of the lime softening flocs are compared with the results for other types of flocs. Overall, the lime flocs have fractal dimensions similar to fractal dimensions of alum flocs and higher than fractal dimensions of activated sludge flocs. Compared to activated sludge flocs, the chemical lime flocs were found to have fractal dimensions for boundary, surface and volume closer to Euclidean dimensions i.e. 1, 2 and 3 respectively.
5.3.1. Estimation of 3D volume fractal dimensions from 2D fractal dimensions Fig. 5 shows that for flocs larger than 50 mm the Eq. (6) (Maggi and Winterwerp, 2004) gives a better estimation of volume fractal dimension than Eq. (5) (Thill et al., 1998) while for flocs smaller than 50 mm the Eq. (5) gives a closer estimation of volume fractal dimension of flocs. In derivation of Eq. (5), DS was assumed to be the fractal dimension of cross-sectional surface area resulting from the intersection of a plane and the floc in 3D space. If the primary particles are relatively homogeneously distributed within the aggregate, then intersection of any plane with the floc will result in cross-sectional
Table 4 e Average size and settling velocity of the lime softening flocs and some other flocs. Type of the aggregate
Average size (mm)
Average settling velocity (mm/s)
Lime softening flocs (this study) Alum coagulation flocs (Gorczyca and Ganczarczyk, 1996, 1999)
118 339
1.71 0.7
Activated sludge flocs (Ganczarczyk, 1995)
700
3.8
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Fig. 7 e Comparison of Khelifa and Hill (2006) and modified Stokes’ Law.
surfaces with similar morphology. Fig. 5 indicates that the smaller lime flocs in fact have fractal dimensions close to Euclidean dimension (Eq. (3)) and compared to the larger flocs have more homogeneously distributed primary particles within the aggregate.
5.3.2.
Variation of volume fractal dimension with floc size
It seems that the relationship between the size and volume fractal dimension of lime softening flocs can be explained by two-stage flocculation model (Fig. 4). The smaller flocs (50 mm) have fractal dimensions close to Euclidean dimension (Eq. (3)) while the fractal dimensions of larger flocs deviate from Euclidean dimension. This result is consistent with previous studies for alum and activated sludge flocs (Snidaro et al., 1997; Gorczyca and Ganczarczyk, 2001). Initial clusters are simply bundles of few primary particles and their fractal dimension is very close to Euclidean dimension (Eq. (3)). It seems that smaller flocs are formed by aggregation of these clusters and as the floc grows, the fractal dimension decreases. Eq. (26) and Fig. 4 show that there is a strong linear relationship between size and fractal dimension at the first stage of flocculation but linear correlation is not so strong for large flocs. Many factors such as breakup, restructuring and
precipitation of calcium carbonate in the pores and around the aggregate specifically affect larger flocs and cause the discrepancy from the simple linear correlation. For example, the precipitation of primary lime particles inside the flocs or their penetration inside the aggregate may be modelled by the Diffusion Limited Aggregation (DLA) mechanism. Theoretically, aggregates formed in this mechanism are highly heterogeneous with volume fractal dimension of around 1.78e2 (Thouy and Jullien, 1994). However, according to Witten and Sander (1981) model, the diffusion limited particleecluster aggregation can generate fractals with volume fractal dimension of about 2.45 which is close to the fractal dimensions of large lime flocs in this study. Therefore the relationship between the floc size and fractal dimension appears to be determined by the floc formation mechanism.
5.3.3.
Discrepancy between DST and DV
The lime flocs in this study were mainly composed of calcium carbonate; therefore of constant density. It was expected that the mass fractal dimension and volume fractal dimension of these flocs to be at least similar. Table 5 shows that for different types of flocs the indirectly determined mass fractal dimension from settling tests (DST) significantly is not within the range of directly determined volume fractal dimensions. Therefore, the power law relationship in Eq. (20) that was used for indirectly determination of mass fractal dimension needs to be critically evaluated. As discussed in Section 2.2, the derivation of Eq. (20) is based on two fundamentally incorrect assumptions of Euclidean geometry and Stokes’ Law. Further the Eq. (20) suggests that the mass fractal dimension is constant over a range of flocs sizes while DV determined directly on floc images decreases with increasing flocs size. Eq. (22) and Fig. 6 indicate that the relationship between the settling velocity and log(size) is not linear and not unique. The relationship between the floc size and its settling velocity depends on aggregation mechanism which is reflected by the relationship of floc fractal dimension and floc size.
5.4.
Floc internal structure
The average directly determined fractal dimensions describing the cross-sectional area and the volume of lime
Table 5 e Fractal dimensions in this study and some previous studies. Type of aggregate and source of data
Lime softening flocs (this study) Ferric coagulation flocs (Bahrami, 1997) Alum coagulation flocs (Gorczyca and Ganczarczyk, 1996, 1999) Activated sludge flocs (Li and Ganczarczyk, 1989; Ganczarczyk and Rizzi, 1996) Activated sludge flocs (Ganczarczyk, 1995) Activated sludge flocs (Cousin and Ganczarczyk, 1998) Activated sludge flocs (Chu et al., 2004)
Fractal Dimensions DB
DS
DP
DV
DST
1.15e1.27 (1.20) 1.11e1.16 1.13e1.25
1.82e1.99 (1.898) 1.94e1.99 1.91e1.99
1.22e1.38 (1.29) N.A. N.A.
2.55e2.99 (2.73) N.A. N.A.
1.87 N.A. 1.37e1.79
N.A.
1.88e1.97
1.21
N.A.
1.45e2.0
N.A. N.A.
N.A. 1.83e1.99
1.04e1.32 1.28e1.4
N.A. N.A.
N.A. N.A.
N.A.
1.72e1.81
1.35e1.48
2.46e2.70
1.3e1.55
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 5 4 5 e5 5 6
softening flocs (DS and DV) for the flocs in the size range of 60e120 mm are 1.9 and 2.72 respectively and are similar to those for the ideal 2D and 3D Sierpinski carpets with the constriction ratio of 3. The results of this study suggest that this particular model can be used to represent internal structure and simulate the behaviour of the lime softening flocs during settling in this size range. Similar models may exist to represent the structure of other flocs. The Sierpinski model or other mathematically known models may also be appropriate to simulate the behaviour of cakes of flocs accumulated on the membranes.
6.
Conclusions
Fractal dimensions of lime softening flocs were determined directly from floc images and indirectly from floc settling; application of floc fractal dimensions to model the flocs settling velocity was evaluated. The following conclusions can be made from this study: 1. For lime softening flocs larger than 50 mm the model incorporating variable floc fractal dimensions and variable primary particle size describes the settling better than the models assuming fixed values for these parameters (Stokes’ Law). 2. For smaller flocs (<50 mm) the Stokes’ Law predicts the settling velocity better than the model based on the fractal structure of flocs. This suggests that Stokes’ Law can still be applied to aggregates with fractal dimensions close to Euclidean dimensions such as small lime flocs and other chemical coagulation aggregates. 3. It appears that two aggregation mechanisms are involved in the lime flocs formation: Clusterecluster aggregation for small flocs (<50 mm) and Particle – Cluster Aggregation for large flocs (>50 mm). The precipitation of calcium carbonate particles inside the aggregates as well as their penetration into the floc pores appears to be predominant mechanism for large lime flocs formation. 4. Numerous assumptions contradicting fractal nature of flocs are used in indirect determinations of mass fractal dimensions from the floc settling rates. These assumptions result in large discrepancy found between the indirectly determined mass fractal dimension from settling tests and directly measured volume fractal dimensions of flocs. 5. For lime softening flocs in the size range of 60e120 mm the directly determined fractal dimensions are similar to those of an ideal Sierpinski carpet with the constriction ratio of 3.
Acknowledgement The authors would like to thank Prof. Jason Morrison and Prof. Barbara Sherriff for their scientific comments on this paper. This work was financed by the University of Manitoba as well as the City of Portage la Prairie and R.M. Macdonald water treatment plants and NSERC Collaborative Research and Development Grant.
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Namer, J., Ganczarczyk, J.J., 1993. Settling properties of digested sludge particle aggregates. Water Research 27, 1285e1294. Nason, J.A., Lawler, D.F., 2008. Particle size distribution dynamics during precipitative softening: constant solution composition. Water Research 42 (14), 3667e3676. Parker, B., 1970. Characteristics of Biological Flocs in Turbulent Regimes. PhD thesis, University of California, Berkeley. Snidaro, D., Zartarian, F., Jorand, F., Bottero, J.Y., Block, J.C., Manem, J., 1997. Characterization of activated sludge flocs structure. Water Science Technology 36 (4), 313e320. Schmid, M., Thill, A., Purkhold, U., Walcher, M., Bottero, J.Y., Ginestet, P., Nielsen, P.H., Wuertz, S., Wagner, M., 2003. Characterization of activated sludge flocs by confocal laser scanning microscopy and image analysis. Water Research 37, 2043e2052. Sterling Jr., M.C., 2005. Application of fractal flocculation and vertical transport model to aquatic solesediment systems. Water Research 39, 1818e1830. Stone, M., 1961. Linear opinion pool. The Annals of Mathematical Statistics 32 (4), 1339e1342. Tang, P., Greenwood, J., Raper, J.A., 2002. A model to describe the settling behaviour of fractal aggregates. Journal of Colloid and Interface Science 247, 210e219. Thill, A., Veerapaneni, S., Simon, B., Wiesner, M., Bottero, J.Y., Snidaro, D., 1998. Determination of structure of aggregates by confocal scanning laser microscopy. Journal of Colloid and Interface Science 204, 357e362. The City of Portage La Prairie, 2009. http://www.city.portage-laprairie.mb.ca/. Thouy, R., Jullien, R., 1994. A clusterecluster aggregation model with tunable fractal dimension. Journal of Physics A: Mathematical and General 27, 2953e2963. Winterwerp, J.C., 1998. A simple model for turbulence induced flocculation of cohesive sediment. Journal of Hydraulic Research 36, 309e326. Witten Jr., T.A., Sander, L.M., 1981. Diffusion-limited aggregation, a kinetic critical phenomenon. Physical Review Letters 47, 1400e1403.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 5 5 7 e5 6 4
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Modeling the decay of ammonium oxidizing bacteria Giulio Munz a,b,*, Claudio Lubello b, Jan A. Oleszkiewicz a a b
Department of Civil Engineering, University of Manitoba, Winnipeg, Manitoba, Canada R3T 5V6 Department of Civil and Environmental Engineering, University of Florence, Via S. Marta 3, 50139 Florence, Italy
article info
abstract
Article history:
A bench-scale sequencing batch reactor was used to study factors affecting the endoge-
Received 4 July 2010
nous decay of the ammonium oxidizing biomass (AOB) in different operating conditions.
Received in revised form
AOB decay was very sensitive to oxygen concentration, and increased up to 0.4 d1 for
9 September 2010
oxygen concentration of 7 mg O2 L1. The decay in anaerobic conditions was shown to be
Accepted 15 September 2010
very low (0.03 d1) when compared to literature data.
Available online 8 October 2010
The effect of nitrite and nitrate on AOB decay was also studied. The correlation was quite weak suggesting that both nitrate and nitrite absence had little impact on decay which is
Keywords:
contrary to what is typically assumed in some of the existing process models. A simple
Activated sludge models
expression for the decay of AOB was proposed, calibrated and validated using the results of
Endogenous decay
batch kinetic tests and of the continuous sequencing batch reactor monitoring.
Kinetic parameters estimation
ª 2010 Elsevier Ltd. All rights reserved.
Partial nitrification Sequencing batch reactors Nitrite
1.
Introduction and objectives
In the last two decades various, increasingly more complex, activated sludge models (ASM) were proposed in order to describe more accurately both the oxidation and the reduction of nitrogen as two-step processes. Initially, nitrification was described as a two-step process to consider the buildup of nitrite in dynamic conditions due to the presence inhibiting compounds (Nowak et al., 1995). The nitrifying biomass was divided into ammonium and nitrite oxidizing bacteria (AOB and NOB); their growth was described through the Monod-like kinetics limited by the electron acceptor (oxygen) and the electron donor (ammonia and nitrite) (Ossenbruggen et al., 1996), similarly to the growth of nitrifiers in the IWAASM1 (Henze et al., 1987). The decay processes were assumed, initially, as simple first order in respect to the biomass concentration process, as in the ASM1 approach. Decay rates were assumed to be slower in the absence of oxygen, again in an analogy to previous observations made
on the whole nitrifying biomass (Lee and Oleszkiewicz, 2003; Nowak et al., 1995). Separate AOB and NOB modeling became necessary with the development of partial nitrification processes such as SHARON (Hellinga et al., 1999). Free nitrous acid (FNA) was introduced as a variable to describe the inhibition of both AOB and NOB. The endogenous decay was not considered due to the very low solids retention time (SRT) required for the SHARON process. The model was further developed to introduce free ammonia (FA) as inhibiting compound of both AOB and NOB (e.g. Wett and Rauch, 2003; Jubany et al., 2008; Park and Bae, 2009). A variety of approaches were used to describe FA and FNA inhibition. The values of parameters used to describe inhibition were shown to vary in a wide range when similar models are used (Tora` et al., 2010). The importance of inorganic carbon limitation, in relation to the pH, was highlighted and modeled (Wett and Rauch, 2003; Guisasola et al., 2007).
* Corresponding author. Department of Civil Engineering, University of Manitoba, Winnipeg, Manitoba, Canada R3T 5V6. E-mail address:
[email protected] (G. Munz). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.022
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The initial application of partial nitrification at high temperatures and low SRT directed the attention towards growth rather than decay kinetics which should now be more carefully considered as processes such as Demon (Innerebner et al., 2007) also use partial nitrification, while being operated at higher SRT and lower temperatures. Partial nitrification was in fact shown to be stable up to 30 d of SRT (Jubany et al., 2009). Estimation of maximum specific growth rates of AOB and NOB, requires a preliminary estimation of the active (AOB or NOB) biomass based on yield, substrate consumed, SRT and the decay: the accuracy of the decay coefficient estimation increases in importance when SRT increases (Dold et al., 2005). The knowledge of the decay process is particularly important in optimizing the operational regime when nitrification process is operated in a side stream configuration, with the aim of bioaugmenting the mainstream treatment train. The objective of this research was to evaluate and model the effect of nitrite, nitrate and oxygen on the decay process of AOB in a sequencing batch reactor operating in the partial nitrification regime. A consistent procedure for the estimation of decay parameters will also be proposed.
2.
Materials and methods
2.1.
Reactor setup and operation
A sequencing batch reactors (SBR) was operated with a total liquid volume of 3 L, at 35 0.5 C. The reactor had a sequence of fill, react, sludge wasting, settle and decant. The reactor was operated in alternating aerobic/anoxic conditions during the reaction phase. The total duration of the cycles was 4 h: the aerobic (aerated) phase included the filling of the reactor (20 min) with a total duration of approx. 2 h; the anoxic phase lasted 1.5 h including at the beginning the dosing of 500 mg COD as methanol for 30 min; the settling and the decant durations were 20 min and 10 min, respectively. The hydraulic retention time (HRT) was 12 h and the SRT after a series of adjustments during the start-up was fixed at 12 days. The experiment lasted 180 days. Waste activated sludge (WAS) was removed at the end of the aerobic period during the first 150 d. In the last 30 d WAS removal took place at the beginning of the aerobic phase. Biomass from a previous bench-scale study on nitrification in alternating conditions was used as inoculum, blended with a biomass collected from a bench-scale partially nitrifying system. This was done in order to start with a mixed AOB and NOB populations. Dissolved oxygen (DO) was not controlled and resulted in an increase from 0.5 to 3 mg O2 L1 during the aerobic phase. Sufficient alkalinity for nitrification was provided and the pH was controlled above 7.2 by dosing of a 10 g L1 solution of NaHCO3. Synthetic wastewater used consisted of beef extract ¼ 90 mg L1; yeast extract ¼ 90 mg L1; MnSO4 ¼ 1.22 mg L1; FeSO4 ¼ 10.1 mg L1; KCl ¼ 3.125 mg L1; K2HPO4 ¼ 87.6 mg L1; NaHCO3 ¼ 163.5 mg L1; CaCl2 ¼ 1.68 mg L1; NH4Cl ¼ 1055 mg L1; MgSO4 ¼ 10.86 mg L1. The average COD of the feed was 290 mg COD L1 while the TN was 300 mg N L1.
2.2.
Process monitoring and control
The control strategy of the bench-scale SBR was to maximize the AOB concentration and to obtain a stable wash out of the NOB. During start-up the SRT was maintained initially at about 3 d and methanol was not dosed to avoid nitrification inhibition, with pH controlled at 7 as the minimum value. As it was not possible to obtain a complete nitritation due to the simultaneous inhibition by ammonia and nitrite, the SRT and methanol dosing were gradually increased to 12 days and to 1000 mg COD L1, respectively. The consumption of alkalinity was monitored and used to identify the decrease of ammonia oxidation during the aerobic phase. The aerobic phase was then reduced to facilitate NOB wash out. Once stable process conditions were reached, the end of the aerobic phase was moved back and forth in time (10 min) depending on the trend of the alkalinity consumption rate. Mixed liquor suspended solids (MLSS) and mixed liquor volatile suspended solids (MLVSS), NO 3 , NO2 and alkalinity were analyzed according to Standard Methods (1998). Hach Co. COD digestion vials were used to measure the COD. Dissolved phosphate and ammonium were measured using a Lachat Instrument Quik Chem 8500, following the Quik Chem orthophosphate method 10-115-01-1-O, and Quik Chem ammonia method 10-107-06-1-I. Dissolved oxygen (DO) was measured with HQ10 Hach Portable LDO dissolved oxygen meter.
2.3.
Batch tests for growth and decay rates evaluation
Two different types of batch tests were carried out to assess the ammonium oxidation rates and the decay rates of the ammonium oxidizing biomass. Twenty short-term batch tests were carried out in aerobic condition to evaluate nitrification rates, both at the same temperature of the SBR reactor (35 C) and at 20 C. At the beginning of each test the same concentration of nitrogen (as TKN) and COD present during each cycle of the SBR process was fed in. Nitrogen compounds were monitored every 30 min for 8 h, controlling the pH at 7.3 0.1. In order to evaluate the decay in the presence of oxygen, samples of biomass collected from the SBR were diluted 1:6 with the filtered SBR effluent and were transferred to different reactors and maintained in aerobic conditions (at various DO concentrations equal to 2e5e7 mg O2 L1) and fed once every 2 d for the duration of 8 d with 20 mg N L1 of NH4Cl. In the aerobic decay estimation tests the spikes were made and the nitrogen forms were measured in the same reactor. This approach allowed inclusion of the data in the simulation of the release of ammonia due to heterotrophic decay. The effect of low pH (pH ¼ 6) on the AOB decay was tested using the same procedure. Batch tests (lasting up to 14 d) were carried out with the selected biomass to evaluate the endogenous decay process in the absence of oxygen. In this case it was not possible to monitor the nitrosation rates trend always in the same reactor, as for the aerobic tests, in order to avoid oxygen presence during the storage. In order to evaluate the decay in the presence of nitrate and nitrite alone and in anaerobic conditions, the biomass sampled from the SBR was diluted
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1:10 with the SBR effluent and was stored in separate reactors and maintained in three different conditions: 1. With nitrite between 10 and 15 mg N L1 (in the absence of oxygen and nitrate); 2. With nitrate between 10 and 15 mg N L1 (in the absence of oxygen and nitrite); 3. In the absence of oxygen, nitrite and nitrate (anaerobic conditions). The concentration of nitrite and nitrate was frequently measured in order to ensure that both were in the range 10e15 mg N L1. Moreover, the storage conditions were designed (through the control of the dilution factor) to avoid the depletion of nitrite and nitrate below 10 mg N L1 also overnight. When the concentration reached 10 mg N L1, then either nitrite or nitrate was dosed again into the sample. The pH was controlled at 7.3 0.2 and the temperature at 20 0.5 C. A fixed volume of biomass was extracted from each reactor in endogenous conditions (after 0, 2, 4 and 14 days) to conduct the nitrification test after 2 h of adaptation to aerobic conditions. The concentration of 15 mg N L1 of nitrite was chosen as upper limit to avoid nitrosation inhibition. The concentration of nitrate was chosen in analogy to that of nitrite. The ammonia concentration for the tests was chosen as a compromise to avoid both substrate limitation and inhibition based on observation and literature data. Table 1 summarizes test conditions.
2.4.
Processes modeling
An Activated Sludge Model (ASM) with two-steps nitrificationedenitrification processes was used as the reference. The model e a modified version of the ASM3 of the International Water Association (IWA) e was described and validated by Kaelin et al. (2009). The model is given an acronym 2S-ASM in this work. The model structure has been modified as follows: - Stoichiometry and processes related to the heterotrophic biomass were identical to those of the 2S-ASM; - The AOB and NOB growth stoichiometry and processes were identical to those of the 2S-ASM, except for two additional terms that were introduced in the growth equations to take into account the inhibition by free ammonia (FA) and free nitrous acid (FNA), as described by Park and Bae (2009);
- Various processes to describe the AOB and NOB decay were evaluated as described in the results and discussion paragraphs. The parameters of the model were either calibrated or assumed from literature as follow: - The parameters related to the heterotrophic biomass were assumed from the 2S-ASM with the exception of the biomass decay coefficient; the heterotrophic decay was calibrated using the trend of VSS concentration, and validated with the trend of nitrogen released during the aerobic batch decay; - The parameters related to the NOB were assumed as in the 2S-ASM and were not calibrated due to the scarcity of biomass in the SBR; - The parameters related to the AOB were either assumed (yield coefficient), calibrated using the results of short-term tests (maximum specific growth rate and parameters related to FA and FNA inhibition) or calibrated using the results of long-term tests (parameters related to aerobic, anoxic and anaerobic decay). Endogenous decay was determined as the overall result of four different contributions: the decay in anaerobic conditions (bANAER), in the presence of nitrite (bNO2) and nitrate (bNO3) alone and in aerobic conditions (bAER). The aerobic decay was considered as a function of the oxygen concentration and different approaches were evaluated to describe the process in anaerobic and anoxic conditions; specifically nitrite and nitrate effect and concentration were evaluated. The calibration procedure for the parameters related to AOB decay was as follows. A first order decay kinetics was used to describe the decay in anaerobic conditions (bANAER), and for a given concentration of nitrite (bNO2) and nitrate (bNO3); the decay coefficient was calibrated using the least squares method with the decreasing maximum rates of ammonia consumption at different times as an objective function. Results of the tests described in Table 1 (Test type: decay rate with Nitrite; decay rate with Nitrate and
Table 1 e Summary of the tests type and conditions. Parameter measured
Growth rate
Number of test Duration d pH Sampling interval (minutes)
20 0.33 7.3 0.1 30
1 Initial NHþ 4 (mg N L ) Number of spikes/test Frequency of the spikes
50 1 n.a.
T ( C) 1 NO 2 (mg N L ) during storage 1 (mg N L ) during storage NO 3 DO (mg O2 L1) during storage
20e35 0.5 n.a. n.a. n.a.
Aerobic Decay rate
Decay rate with Nitrite
Decay rate with Nitrate
Anaerobic Decay rate
Aerobic Decay rate at low pH
5 8 7.3 0.2 30 (until NHþ 4 depletion) 20 4 t ¼ 0; t ¼ 2 d; t ¼ 4 d; t ¼ 8 d 20 0.5 Variable Variable 2e5e7
5 5 and 14 7.3 0.2 30 (until NHþ 4 depletion) 15 4 t ¼ 0; t ¼ 2 d; t ¼ 4 d; t ¼ 14 d 20 0.5 10 < NO 2 < 15 0 0
5 5 and 14 7.3 0.2 30 (until NHþ 4 depletion) 15 4 t ¼ 0; t ¼ 2 d; t ¼ 4 d; t ¼ 14 d 20 0.5 0 10 < NO 3 < 15 0
5 5 and 14 7.3 0.2 30 (until NH4 þ depletion) 15 4 t ¼ 0; t ¼ 2 d; t ¼ 4 d; t ¼ 14 d 20 0.5 0 0 0
3 5 6.0 0.2 30 (until NHþ 4 depletion) 20 3 t ¼ 0; t ¼ 2 d; t ¼ 4 d; 20 0.5 Variable Variable 7
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Anaerobic decay rate) were used. The results were then used as a reference for further decay estimation in aerobic conditions. The decay in aerobic conditions was estimated as the sum of decay in the absence of oxygen and in its presence. In order to calibrate the aerobic decay coefficient of AOB the model was calibrated in dynamic conditions at different DO concentrations using the results of the aerobic batch tests described in Table 1; the values of estimated coefficients of anoxic and anaerobic decay were used as default values for a “background” decay (see Equation (1)); a default value for the “half-saturation” for oxygen of the decay (Kb,AOB), was estimated from literature data (Yuan et al., 2000). Calibrating the model it was possible to include the effect of the release (and oxidation by AOB) of ammonia by the heterotrophic biomass during its decay. The necessary calibration of the decay of the heterotrophic biomass was accomplished using the results of the SBR monitoring. This followed a similar approach applied for the decay coefficient estimation of other autotrophic (including nitrifiers) and heterotrophic biomasses (Munz et al., 2008, 2009; Scaglione et al., 2009). The subsequent step was the application of the decay modeling to estimate the concentrations of AOB (considering SRT, HRT, temperature and oxidized nitrogen) at the beginning of each kinetic test that were in turn used to estimate the maximum specific growth rates mmax,AOB and the AOB halfsaturation constant for NHþ 4 (KAOB,NH4). Aquasim was used (Reichert et al., 1998) as the modeling tool.
3.
Results
3.1.
SBR monitoring results
Effluent ammonia, nitrite and nitrate are shown in Fig. 1 while the volatile and total suspend solids trends in the SBR mixed liquor are shown in Fig. 2. The start-up phase lasted about 70 days. The start-up of partial nitrification/denitrification process is typically slow as the ammonia concentration is high at the beginning and inhibits both AOB and NOB (Jubany et al., 2008). With increased SRT, the ammonia oxidation starts, but nitrite becomes the cause of inhibition and methanol is required to carry out denitrification. Methanol may in turn inhibit both AOB and NOB and, as a consequence, the increase of methanol dosing has to be gradual. When methylotrophic bacteria (which are relatively slower than other heterotrophs) are grown, and both nitrite and methanol concentrations are low, then the SRT needs to be decreased in order to avoid NOB
Fig. 1 e Ammonia, nitrite and nitrate in the effluent of the SBR.
Fig. 2 e Volatile and total suspend solids in the SBR mixed liquor during the experiment.
growth. The DO and pH play an important role in the selection process, together with ammonia and nitrite concentration. The wash out of NOB is partially accomplished with DO due to their lower affinity e in this case the average DO during the aerobic phase was about 1.7 mg O2 L1, which is potentially limiting both for AOB and NOB. In the investigated conditions, the main reasons for NOB wash out were high ammonia concentration at the beginning and the increasing concentration of nitrite during the aerobic phase of the SBR cycle. In order to discourage NOB growth and to allow complete ammonia oxidation, the aerobic SRT was used as a control parameter through small adjustments of the duration of the aerobic phase. In order to monitor the minimum required SRT for the growth of AOB, the consumption rate of alkalinity during the aerobic phase has proven to be more effective than the monitoring of ammonia. The sporadic presence of nitrite and ammonia in the effluent (Fig. 1) after day 70 was due to technical problems (rapidly solved) such as alkalinity or methanol dosing failures. The concentration of nitrate at the end of the aerobic cycle was in average 1.5 mg N L1, indicating an almost complete wash out of the NOB. After the start-up period, and in the absence of technical problems, the process reached stable conditions and the AOB were able to completely oxidize ammonia (Fig. 1) while NOB were almost completely washed out. The nitrite at the end of the aerobic cycle (see Fig. 7 as an example) averaged 1.5 mg N L1.
3.2. Decay tests results in anoxic and anaerobic conditions The decay coefficients estimations indicated that the presence of nitrite or nitrate does not have a significant impact on the decay coefficients. The decrease of ammonia concentration (at time d ¼ 0; d ¼ 2; d ¼ 4) due to the oxidation by the same sample of biomass stored in different conditions: anaerobic (R1), in the presence of nitrite (R2) and in the presence of nitrate (R3) is shown in Fig. 3. There was practically no difference among the three trends. Even after 14 d of storage in the above conditions the difference was very small (Fig. 4). The decay was found to be slightly higher in anaerobic conditions than in the presence of nitrite or nitrate. Various authors (Iacopozzi et al., 2007; Kaelin et al., 2009; Nowak et al., 1995) hypothesized that nitrate and nitrite affected the decay in their models and that there is no decay of
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Fig. 3 e Ammonia concentration during the endogenous decay aerobic tests made with sludge samples collected at different times from the reactors in anaerobic conditions (R1), in the presence of Nitrite (R2) and in the presence of Nitrate (R3) respectively.
the AOB in the absence of oxygen and/or nitrite and/or nitrate. This approach is analogous to that used to describe the decay of the heterotrophic biomass. The results of this research, compiled in Table 2, suggest that a more simplified model with a constant anaerobic decay coefficient, not affected either by nitrite or nitrate, is more reliable, similarly to what was assumed by Wett and Rauch (2003). Depending on the operating conditions, consideration of only the aerobic decay (Jubany et al., 2009), is an acceptable approximation.
3.3. Decay test results and modeling in aerobic conditions Estimation of the endogenous decay of the AOB in aerobic conditions is shown in Fig. 5. The model accurately fits the experimental data. The decrease in the ammonia oxidation rate over time is much more pronounced in aerobic conditions (Fig. 5) than in anoxic or anaerobic conditions (Fig. 3). The aerobic decay was observed to increase with oxygen concentration and, given the result of the tests in anaerobic and anoxic conditions; the following expression was proposed to describe the process (Eq. (1)): bAOB
S0 þ bAOB;ANAER S0 þ Kb;AOB
(1)
Fig. 6 shows the result of the calibration using Eq. (1) and the experimental data. As a reference for DO ¼ 0 mg O2 L1 the value of the decay in anaerobic conditions (Table 2) was used. The optimal values for the parameters bAOB,AER and Kb,AOB were estimated to be 0.4 d1 and 1.6 mg O2 L1 respectively. The aerobic decay increased with oxygen concentration increasing up to 7 mg O2 L1. The almost linear relation between oxygen and the decay coefficients for values of DO above 0 mg L1 suggested that Monod kinetics (as in Eq. (1)) did not represent the process accurately. It is possible that intensive mixing due to aeration plays an increasing role when DO increases in the experimental conditions. To reach a complete understanding of the process it would be necessary to use gases with DO concentration higher than air. The pH was shown to affect the decay, particularly in aerobic conditions, with an increase up to 0.6 d1 when the samples were stored at pH ¼ 6 (DO ¼ 7 mg O2 L1).
Fig. 4 e Ammonia concentration during the aerobic tests after 14 days with the sludge collected from the reactors in anaerobic conditions (R1), in the presence of Nitrite (R2) and in the presence of Nitrate (R3) respectively.
4.
Discussion
Although nitrite modeling in activated sludge was recently reviewed (Sin et al., 2008) the decay process was not considered in detail. The decay coefficients were considered either as a first order kinetic with a constant coefficient or with different coefficients in anoxic and aerobic conditions. The difference between anoxic and anaerobic conditions, that was previously highlighted for one-step nitrification (Siegrist et al., 1999), was not further considered in any of the models of the two-step nitrification. The equations used for decay process in the recently published models of the two-step nitrification are reported in Table 3. There are a number of inconsistencies and/or differences between the cited models. - The simplest model, used by Jubany et al. (2009) considered the decay as decreasing with oxygen concentration, with an anoxic/anaerobic decay equal to zero. This could be an oversimplification at high SRT; - Model by Kaelin et al. (2009) had the decay present only when nitrate and/or oxygen were present. Nitrite and nitrate were considered different in their role in decay while anaerobic decay was absent altogether; - Wett and Rauch (2003) considered decay as half of the aerobic decay when oxygen was absent. The presence of nitrite and nitrate was not considered important, however anaerobic decay was taken into account. - Iacopozzi et al. (2007) and Sun et al. (2009) considered the AOB as decaying in the presence of nitrite and/or oxygen (and not decaying when nitrate alone is present). The NOB were considered to decay in the presence of nitrate and/or
Table 2 e AOB decay coefficients in anaerobic conditions (R1), in the presence of nitrite (R2) and in the presence of nitrate (R3) (at T [ 20 C and pH [ 7.3). Reactor Storage Conditions Symbols Average St. Dev.
R1
R2
Anaerobic bAOB,anaer 0.031 0.006
Nitrite only bAOB,NO2 0.025 0.007
R3
Units
Nitrate only bAOB,NO3 0.019 d1 0.011 d1
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Fig. 5 e Example of test for estimation of the endogenous decay coefficient in aerobic conditions (pH [ 7.5; T [ 20; DO [ 7 mg LL1), modeling (Ammonia_mod) and experimental (Ammonia_exp) results.
Fig. 7 e Model calibration with the results of R1 cycle monitoring.
in aerobic and anaerobic conditions and a different ratio can be found between aerobic and anoxic decay. oxygen e and not to decay when nitrite alone was present. The anaerobic decay was absent. - In Nowak et al. (1995) the decay of AOB was present only when nitrate and/or oxygen were present; while anaerobic decay and the decay in the presence of nitrite were absent. For NOB the decay was considered to depend on oxygen concentration; the presence of nitrite and nitrate was not considered important. The anaerobic decay was present - The choice of the half-saturation constant for oxygen/ nitrite/nitrate (Table 3) was in many cases related to either heterotrophic or autotrophic biomass with no experimental evidence. The assumption of an equal decay in anoxic and anaerobic conditions (Wett and Rauch, 2003) was not in agreement with what was previously observed on the overall nitrifying biomass (Siegrist et al., 1999), and proposed by many authors (Table 3), but was in agreement with the results of this work. The values of the decay coefficient used should be carefully considered; in particular the value of the oxygen “half-saturation” constant was much higher in this work than the values suggested by other authors. The anoxic decay of AOB was considered by some authors to be about 50% (Wett and Rauch, 2003) of the aerobic decay, while others estimated values in the range of 10% of the aerobic one (Manser et al., 2006). The research reported here showed very low decay in the absence of oxygen (Table 2), however it should not be ignored as the selective pressure on the nitrifiers could affect the decay both
4.1. Modeling of the SBR cycle and estimation of the maximum specific growth rate The ability of describing the decay process should facilitate the estimation of the biomass concentration in the SBR and verification if the proposed approach is valid to describe the results of the SBR monitoring. The maximum specific growth rate of AOB was estimated through calibration of the model (data not shown) with the results of the kinetic tests carried out in non-limiting and non-inhibiting conditions. As a result an average value of mmax, AOB (35 C) ¼ 2.04 d1 was estimated. When brought to 20 C, the AOB were shown to have
Table 3 e Equations used to describe decay process of AOB of NOB in recently published papers on two-step nitrification. Symbols are as reported in the cited papers. Kinetics AOB
Reference
0 bAOB S0 þKSAOB;S 0
K
SNO3 þKH;NO3
H;S0 ;inib S0 þ bAOB ; hH;end S0 þK ; bAOB S0 þK H;S H;S ;inib SNO 0
3
0
K
H;S0 ;inib S0 þ bAOB;ANOX ; S0 þK ; bAOB S0 þK H;S H;S ;inib SNO 0
SNO3 þKH;NO3
3
0
0 Þ bAOB 0:5ð1 þ S0 Sþ0:1
S0 þ bAOB;ANAER bAOB S0 þK b;AOB K
AOB;S0 bAOB;NO2 S0 þK AOB;S
NOB
0 bNOB S0 þKSNOB;S
S0 bNOB S0 þK H;S0
SNO2 SNO2 þKAOB;NO2
0
0 þ bAOB S0 þKSAOB;S
0
0
K
H;S0 ;inib þ bNOB ; hH;end S0 þK ; H;S ;inib SNO 0
SNO3 þKH;NO3
3
K
H;S0 0 þ bNOB S0 þKSNOB;S bNOB;ANOX ; S0 þK H;S 0
0
KNOB;S0 SNO3 bNOB;NO3 S0 þK NOB;S0 SNO3 þKNOB;NO3
S0 Þ bAOB 0:5ð1 þ S0 þK O b 2
Fig. 6 e Experimental data and model assumed to describe endogenous decay coefficient as a function of the oxygen concentration.
S0 S0 þKb;NOB
þ bNOB;ANAER
a suggested in analogy to AOB.
0 þ bNOB S0 þKSNOB;S
0
Jubany et al., 2009 Kaelin et al., 2009; Nowak et al., 1995 Wett and Rauch, 2003 This work Iacopozzi et al., 2007; Sun et al., 2009 Jubany et al., 2009 Kaelin et al., 2009 Nowak et al., 1995 Iacopozzi et al., 2007; Sun et al., 2009 Wett and Rauch, 2003 This worka
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 5 5 7 e5 6 4
a relatively low maximum specific growth rate (mmaxAOB ¼ 0.55 d1), showing at the same time a high Arrhenius coefficient qm ¼ 1.1. The parameters representing the inhibition of the AOB by free ammonia and free nitrous acid were calibrated using an uncompetitive inhibition model to describe free ammonia inhibition and a noncompetitive model to describe free nitrous acid inhibition (Park and Bae, 2009). The results (Kinib,FA,AOB ¼ 4 mg L1; Kinib,FNA,AOB ¼ 0.15 mg L1) showed a relatively high sensitivity of the AOB biomass to these compounds. This indicated that adaptation and/or selection of the biomass was not important during this experiment which lasted 6 months. The model, once calibrated, showed a good agreement between simulated and experimental results of the monitoring of the SBR cycles (Fig. 7).
5.
Conclusions
The AOB decay process was studied through the monitoring of an SBR operating nitritation and denitritation at 35 C. The following conclusions were obtained: - The decay coefficient in anaerobic conditions was found to be very low (0.031 d1); - Nitrite and nitrate were shown not to be limiting the decay and the difference in the decay coefficients due to the presence of nitrite and nitrate was not shown to be significant; - The decay of AOB was shown to increase with oxygen concentration to relatively high values (e.g. 0.35 d1 at 7 mg O2 L1); - A simple expression for the AOB decay was introduced and calibrated through short-term tests and validated through long-term data analysis; - Estimation of the AOB decay requires first the determination of the anaerobic decay. This should be followed by the estimation of the aerobic decay in the presence of the target dissolved oxygen concentration.
Acknowledgements The authors thank the Department of Foreign Affairs and International Trade Canada (DFAIT) (gs1), the Natural Sciences and Engineering Research Council of Canada (NSERC) (gs2), the City of Winnipeg’s Water and Waste Department and Regione Toscana (Italy) (gs3) for co-funding the research project. The authors are grateful to Qiuyan Yuan, PhD candidate at the University of Manitoba, to Peter Nemcek and to Dr. Simone Caffaz for their valuable contribution to the research.
references
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kinetics. Water Science and Technology 52 (10e11), 469e477. Guisasola, A., Petzet, S., Baeza, J.A., Carrera, J., Lafuente, J., 2007. Inorganic carbon limitations on nitrification: experimental assessment and modeling. Water Research 41 (2), 277e286. Hellinga, C., van Loosdrecht, M.C.M., Heijnen, J.J., 1999. Model based design of a novel process for nitrogen removal from concentrated flows. Mathematical and Computer Modelling of Dynamical System 5 (4), 351e371. Henze, M., Grady Jr., C.P.L., Gujer, W., Marais, G.v.R., Matsuo, T., 1987. Activated Sludge Model No.1. IAWPRC Scientific and Technical Report No. 1. IAWPRC, London. Iacopozzi, I., Innocenti, V., Marsili-Libelli, S., Giusti, E., 2007. A modified activated sludge model no. 3 (ASM3) with two-step nitrificationedenitrification. Environmental Modelling & Software 22 (6), 847e861. Innerebner, G., Insam, H., Franke-Whittle, I.H., Wett, B., 2007. Identification of anammox bacteria in a full-scale deammonification plant making use of anaerobic ammonia oxidation. Systematic and Applied Microbiology 30 (5), 408e412. Jubany, I., Carrera, J., Lafuente, J., Baeza, J.A., 2008. Start-up of a nitrification system with automatic control to treat highly concentrated ammonium wastewater: experimental results and modelling. Chemical Engineering Journal 144 (3), 407e419. Jubany, I., Lafuente, J., Baeza, J.A., Carrera, J., 2009. Total and stable washout of nitrite oxidizing bacteria from a nitrifying continuous activated sludge system using automatic control based on oxygen uptake rate measurements. Water Research 43, 2761e2772. Kaelin, D., Manser, R., Rieger, L., Eugster, J., Rottermann, K., Siegrist, H., 2009. Extension of ASM3 for two-step nitrification and denitrification and its calibration and validation with batch tests and pilot scale data. Water Research 43, 1680e1692. Lee, Y., Oleszkiewicz, J.A., 2003. Effects of predation and ORP conditions on the performance of nitrifiers in activated sludge systems. Water Research 37, 4202e4210. Manser, R., Gujer, W., Siegrist, H., 2006. Decay processes of nitrifying bacteria in biological wastewater treatment systems. Water Research 40 (12), 2416e2426. Munz, G., Gori, R., Mori, G., Lubello, C., 2009. Monitoring biological sulphide oxidation processes using combined respirometric and titrimetric techniques. Chemosphere 76, 644e650. Munz, G., Gori, R., Cammilli, L., Lubello, C., 2008. Characterization of tannery wastewater and biomass in a membrane bioreactor using respirometric analysis. Bioresource Technology 99, 8612e8618. Nowak, O., Svardal, K., Schweighofer, P., 1995. The dynamic behaviour of nitrifying activated sludge systems influenced by inhibiting wastewater compounds. Water Science and Technology 31 (2), 115e124. Ossenbruggen, P.J., Spanjers, H., Klapwik, A., 1996. Assessment of a two-step nitrification model for activated sludge. Water Research 30 (4), 939e953. Park, S., Bae, W., 2009. Modeling kinetics of ammonium oxidation and nitrite oxidation under simultaneous inhibition by free ammonia and free nitrous acid. Process Biochemistry 44 (6), 631e640. Reichert, P., Ruchti, J., Simon, W., 1998. Aquasim 2.0. Swiss Federal Institute for Environmental Science and Technology (EAWAG), CH-8600 Duebendorf, Switzerland. Scaglione, D., Caffaz, S., Bettazzi, E., Lubello, C., 2009. Experimental determination of anammox decay coefficient. Journal of Chemical Technology and Biotechnology 84, 1250e1254.
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Siegrist, I., Brunner, G., Koch, G., Phan, L.C., Le, V.C., 1999. Reduction of biomass decay rate under anoxic and anaerobic conditions. Water Science and Technology 39 (1), 129e137. Sin, G., Kaelin, D., Kampschreur, M.J., Taka´cs, I., Wett, B., Gernaey, K.V., Rieger, L., Siegrist, H., van Loosdrecht, M.C., 2008. Modelling nitrite in wastewater treatment systems: a discussion of different modelling concepts. Water Science and Technology 58 (6), 1155e1171. Sun, P., Wang, R., Fang, Z., 2009. Fully coupled activated sludge model (FCASM): model development. Bioresource Technology 100, 4632e4641.
Tora`, J.A., Lafuente, J., Baeza, J.A., Carrera, J., 2010. Combined effect of inorganic carbon limitation and inhibition by free ammonia and free nitrous acid on ammonia oxidizing bacteria. Bioresource Technology 101 (15), 6051e6058. Wett, B., Rauch, W., 2003. The role of inorganic carbon limitation in biological nitrogen removal of extremely ammonia concentrated wastewater. Water Research 37 (5), 1100e1110. Yuan, Z., Bogaert, H., Leten, J., Verstraete, W., 2000. Reducing the size of a nitrogen removal activated sludge plant by shortening the retention time of inert solids via sludge storage. Water Research 34 (2), 539e549.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 5 6 5 e5 7 2
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Release of organic P forms from lake sediments J. Ahlgren a,*, K. Reitzel a, H. De Brabandere b, A. Gogoll c, E. Rydin d a
Institute of Biology, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark Department of Analytical Chemistry, Uppsala University, Box 599, 751 24 Uppsala, Sweden c Department of Organic Chemistry, Uppsala University, Box 599, 751 24 Uppsala, Sweden d Erken Laboratory, Institute of Ecology and Evolution, Uppsala University, Norr Malma 4200, 761 73 Norrta¨lje, Sweden b
article info
abstract
Article history:
The effects of different physical and chemical conditions on the decomposition and release
Received 24 February 2010
of organic and inorganic P compound groups from the sediment of Lake Erken were
Received in revised form
investigated in a series of laboratory experiments. Conditions investigated were temper-
14 September 2010
ature, oxygen level, and the effects of additions of carbon substrate (glucose) and poison
Accepted 15 September 2010
(formalin). The effects on the P compound groups were determined by measurements with
Available online 12 October 2010
31
P NMR before and after the experiments, as well as analysis of P in effluent water
throughout the experiment. Phosphate analysis of the effluent water showed that oxygen Keywords:
level was the most influential in terms of release rates, with the sediments under anoxic
Organic phosphorus
conditions generally releasing more phosphate than the other treatments. 31P NMR showed
Release
that the various treatments did influence the P compound group composition of the
Sediments
sediment. In particular, the addition of glucose led to a decrease in orthophosphate and
31
polyphosphate while the addition of formalin led to a decrease in phosphorus lipids, DNA-
P NMR
phosphate and polyphosphate. Oxic conditions resulted in an increase in polyphosphates, and anoxic conditions in a decrease in these. Temperature did not seem to affect the composition significantly. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
The importance of phosphorus (P) as a nutrient in aquatic environments is well understood (e.g. Schindler, 1977; Wild, 1988; Hecky, 1998), and recent years have highlighted organic P compounds as an important part of the active P pool (e.g. Hupfer et al., 1995; Reitzel et al., 2006a; Ahlgren et al., 2005). Organic P compounds, which earlier were regarded as part of the refractory P pool, are now known to contain many labile species which may play an important role in the aquatic P cycling (e.g. Turner et al., 2005; Reitzel et al., 2007). In most aquatic environments, the sediment plays an important role in P cycling as it is able to store a large part of the
P that settles out from the water body, either temporary or permanently. Phosphorus stored temporary will eventually be recycled to the water column. In areas where the external P input has been significant and the temporary storage of sediment P is large, sediment P recycled may under certain conditions be larger than external input from e.g. rivers (Ahlgren et al., 2006). Since recent studies have demonstrated that organic P compounds play an important role in the process of sustaining eutrophication (Ahlgren et al., 2005, 2006), more detailed knowledge on factors controlling the release of sediment P is of great importance. Several studies concerning inorganic P have been presented (e.g. Bostro¨m et al., 1982; Jensen and Andersen, 1992; Rydin, 2000; Søndergaard et al.,
* Corresponding author. Present address: Department of Aquatic Science and Assessment, Swedish University of Agricultural Sciences, P.O. Box 7050, SE-750 07 Uppsala, Sweden. Tel.: þ46 18 673136; fax: þ46 18 673156. E-mail address:
[email protected] (J. Ahlgren). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.020
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 5 6 5 e5 7 2
2003; Hansen et al., 2003; Ahlgren et al., 2005; Hupfer and Lewandowski, 2005) but investigations concerning specific organic P compounds, their diagenesis and potential mobility in aquatic sediments are absent as far as we know. It is thus of great importance to investigate what factors influence organic sediment P turnover. These mechanisms however, are difficult to monitor under natural conditions and more detailed answers may be available from controlled laboratory experiments. Laboratory studies investigating the release of P from the sediment under various conditions have identified the amount of potentially mobile P, but not in what form it is released (e.g. Rydin, 2000; Hansen et al., 2003). The phosphorus nuclear magnetic resonance spectroscopy (31P NMR) technique has the potential to distinguish between different organic P compound groups (e.g. Newman and Tate, 1980; Turner et al., 2003) and thereby provide information on which species of P are lost from the sediment under the investigated conditions. The overall goal of this study is to increase our understanding of which organic P forms can be considered to be stored temporary or permanently in the sediment under various conditions, by performing controlled manipulated P release experiments. Using the 31P NMR technique, we primarily aim at identifying the mobility of different P compounds at different oxygen levels, as redox conditions are known to be a major driving force behind P related processes within sediments, including internal loading. In addition to this, the impact of the microbial community was investigated, since micro organism activity is likely to have as great an impact on P turnover as redox conditions. These processes are indeed closely intertwined, and for example bacteria release P under reducing conditions, a process that may constitute a large part of P release during anoxic conditions (e.g. Gachter et al., 1988; Ga¨chter and Meyer, 1993). Furthermore, the fact that bacterium are dependent on electron acceptors to utilize and decompose organic matter means that the redox potential will play a central role in the sediment, reflecting the activity of microorganisms as they reduce the electron acceptors (Golterman, 2004). Temperature is another vital parameter for biological processes in the sediment, which is proven by the seasonal variations in internal loading (e.g. Jensen and Andersen, 1992; Søndergaard et al., 1999). This is most likely due to increased mineralization of organic matter by microorganisms as the temperature increases. Apart from stimulating mineralization of organic P compounds, a rise in temperature also increases the release of inorganic phosphate from sediment (e.g. Bostro¨m et al., 1982). We simulated these scenarios by exposing the sediment to different levels of oxygen, different temperatures, and different levels of microbial activity by adding glucose (high activity level) or formalin (low level). These additions were made to investigate the impact of the microbial community, and specifically whether it is possible to distinguish between microbial and chemical decomposition of the various P species in the sediment, since the addition of glucose should act as substrate for the microbial communities in the sediment, accelerating the microbial decomposition of P species. This could on the one hand lead to an increased mineralization and release of P from the sediment or on the other hand lead to an increased uptake of P and hence, a lower release of P if the microbial community is P limited. In contrast, the addition of
formalin was used to terminate all microbial activity in the sediment, releasing microbes and thereby microbial P, thus making chemical decomposition the dominant process in these treatments. Additions of glucose and formalin were made at both high and low oxygen levels to investigate the redox potentials influence on the microbial community.
2.
Materials and methods
2.1.
Sampling and study site
In this study, sediment from the moderately eutrophic Lake Erken (Sweden) was used to follow potential diagenetic changes under laboratory conditions. The average total P concentration in the lake water is 27 mg L1 and the lake has a surface area of 24 km2, and mean and maximum depths are 9 and 21 m, respectively. The drainage area (137 km2) is mostly forested and consists of nutrient-rich glacial and post-glacial clay deposits. During summer stratification, bottom water occasionally becomes anoxic. The lake has been studied extensively, and Rydin (2000) argue that the lake has been in a stable trophic state since observations began in 1930. Twelve sediment cores were collected with a gravity core sampler (Willner sampler) at a depth of 16 m within an accumulation bottom area (50 m2). The 0e1 cm layer of the cores was collected, pooled, and homogenized to obtain a representative sample of sufficient size for the experiment. The sediment in this layer had a total P concentration of 2.0 mg g1 dry weight, water content of 93%, organic matter content 200 mg g1 dry weight and total Fe content was 24 mg g1 dry weight. pH in the benthic water varied between 7 and 7.5.
2.2.
Laboratory set-up
The pooled sediment sample was divided into 20 sub-samples of 15 g (wet sediment) which each was encapsulated into water filled sediment chambers (Fig. 1) similar to the one used in Rydin (2000). Through these chambers water with different characteristics was pumped upwards through the sediment at a rate of 0.3 L day1. The slow flow rate and chamber set up with frits at the inflow ensured minimum physical impact on the sediment, without channelling or compaction. The sediment was treated with different conditions i.e. oxiceanoxic water, different temperatures (4 C and 20 C) as well as oxic and anoxic water with addition of glucose (1% weight/weight (w/w)) and formalin (3.7% w/w) at 20 C. Temperatures were chosen to simulate potential maximum high and low conditions at the sediment surface. Oxygen conditions were monitored through redox potential measurements and corrected by additions of a reducing agent (dithionite). Oxic conditions were considered as when the inflowing water was in equilibrium with the surrounding air, and anoxic conditions when the redox potential was below 0 mV. To ensure correct and consistent oxygen levels, pH and concentrations of additives throughout the experiment, new aliquots of the various inflow water solutions were mixed twice a day. The pH value was adjusted to 7 in all solutions. Treatments were made in triplicates, except for treatments with addition of glucose and formalin, which due to space restrictions were made in duplicates. From each of
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 5 6 5 e5 7 2
Outflow water for analysis
25 mm GF/F filter
5 cm plexiglass pipe Sediment 25 mm GF/F filter
Inflow water Fig. 1 e Schematic picture of the sediment chamber. the sub-sample set ups, samples of the effluent water were collected for phosphate and TP analysis once daily, in order to calculate the accumulated P release during the experiment. TP (after digestion at 120 C in 1 M HCl for 1 h) and phosphate content was analyzed according to Murphy and Riley (1962). The experiment was conducted for 21 days, after which the sediment samples were removed from the chambers for extraction and subsequent NMR analysis.
2.3.
Alkaline extraction
All the samples for 31P NMR analysis were pre-extracted in 0.067 M EDTA (di-sodium salt, Merck, Switzerland) for 1 h in order to improve extraction efficiency in the Lake Erken sediment, according to Ahlgren et al. (2007). After the preextraction, the sediment was separated from the EDTA solution by centrifugation at 4000 rpm for 10 min. The remaining sediment was extracted in 0.1 M NaOH for 16 h at room temperature, following Ahlgren et al. (2007), and the NaOH extract was collected following centrifugation. All extractions were made with a 1:3 sediment to solvent ratio. Samples were concentrated ten times by rotary evaporation at 35 C. The concentrated extracts were frozen until analysis, a procedure proven not to affect the extracted P compounds (Hupfer et al., 2004). Total P in the extracts was measured by inductively coupled plasmaeatomic emission spectrometry (ICPeAES).
2.4.
NMR analysis
The 31P NMR spectra were measured at 121.5 MHz on a Varian MercuryPlus NMR spectrometer (Varian, Palo Alto, CA) at
567
ambient temperature. An amount of D2O sufficient to obtain a stable lock signal was added to the extracts before measurement. Spectra were recorded using a 63 observe pulse, acquisition time 0.4 s, relaxation delay 1.2 s, acquiring around 30000 transients (12 h). Chemical shifts were indirectly referenced to external 85% H3PO4 (at d ¼ 0.0) via the lock signal. Assignment of peaks was done from spectra of sediment extracts spiked with standard solutions (Na2HPO4$7H2O for orthophosphate and Na2P2O7$10H2O for pyrophosphate), added to one of the sediment extracts, as well as comparisons with literature (e.g. Hupfer et al., 1995; Cade-Menun and Preston, 1996; Makarov et al., 2002; Turner et al., 2003). To obtain peak areas, peaks in the raw spectrum, with a signal to noise ratio exceeding 4, were fitted with Lorentzian line shapes using the deconvolution subroutine of the NMR software (Vnmr 6.1C). From these peak areas, the contribution of individual P compound groups was calculated relative to total extracted P. EDTA pre-extracts were not suitable for 31P NMR analysis, due to low concentrations of P and high concentrations of paramagnetic metals such as Mn and Fe. However, we assume that mainly inorganic P bound to metals such as Fe, Al, and calcium was extracted by EDTA.
3.
Results
The accumulated releases of phosphate and TP showed similar trends between the various treatments, with the phosphate release being about 50% of the total release (Table 1, Fig. 2a, b). Exceptions to this were the relatively small releases from the sediment treated at 4 C, where all of the release was made up of phosphate. Phosphorus release from most sediment samples had ceased at termination of the experiment (Fig. 2a and b). Exceptions to this pattern were three of the anoxic treatments (formalin, glucose, and 20 C), which continued to release P at the end of the experiment. These three treatments were also the ones that showed the highest accumulated P release, corresponding to over 75% of the sediment TP content in the case of glucose. In comparison, only 10% was released from the oxic sub-sample kept at 20 C. Accumulated TP release ranged between about 10 and 1600 mg g1, whereas accumulated phosphate ranged between 20 mg g1 and 700 mg g1 for the various treatments (Fig. 2b). Relative standard deviations generally ranged between 10 and 30% for the TP and phosphate release respectively. The original sample contained a total extractable amount of 0.9 mg P g1 dry sediment (Table 1) that could be divided into seven P groups (Fig. 3): orthophosphate (Ortho-P); orthophosphate monoesters (Mono-P); three different groups within the orthophosphate diester area, most likely deoxyribonucleic acid P (DNA-P), P lipids (P-lipids), and a signal possibly indicating teichoic acid P (Teichoic-P), but that also may be the result of another form of phospholipid, as well as pyrophosphate (Pyro-P) and polyphosphate (Poly-P). The appearance of an additional peak in the immediate vicinity of the Pyro-P peak in some of the spectra may indicate the presence of Poly-P end groups. After exposure, the 7 different groups were recovered in most sub-samples. Exceptions to this were the anoxic
568
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 5 6 5 e5 7 2
Table 1 e Average amounts (mg gL1 DW) including standard deviation (STD), based on replicate or triplicate analyses, of the 3L identified P compound groups, with percentual values in italic, and accumulated release for both TP and phosphate (PO 4 ). Accumulated release mg/g DW (STD) TP
PO3 4
Original
20 C Oxic
78 (11)
55 (11)
496 (172)
332 (28)
12 (2)
23 (5)
4 C Anoxic
155 (37)
156 (46)
20 C Oxic Glucose 20 C Anoxic Glucose 20 C Oxic Formalin 20 C Anoxic Formalin
377 (105)
303 (103)
1612 (185)
594 (171)
372 (23)
76 (4)
633 (83)
348 (7)
20 C Anoxic 4 C Oxic
Sediment P mg/g DW (STD) % Ortho-P
Mono-P
Teichoic-P
P-lipids
DNA-P
Pyro-P
Poly-P
Extracted tot-P
277 32.6
210 24.7
13 1.5
28 3.3
82 9.6
9 1.1
230 27.1
849
275 (37) 31.4 229 (35) 33.0 238 (19) 26.8 240 (57) 33.2 115 (16) 14.7 123 (11) 24.5 249 (112) 49.2 217 (4) 40.8
237 (29) 27.0 269 (37) 39.1 211 (50) 23.7 223 (34) 30.9 416 (5) 53.4 253 (20) 50.3 145 (70) 28.8 216 (25) 40.5
10 (3) 1.2 26 (6) 3.8 16 (3) 1.8 21 (15) 2.9 9 (3) 1.1 17 (7) 3.4 0 0.0 14 (1) 2.6
44 (8) 5.0 13 (11) 1.8 38 (9) 4.3 27 (4) 3.8 50 (16) 6.4 33 (5) 6.5 0 0.0 0 0.0
85 (17) 9.6 85 (9) 12.3 80 (7) 9.1 76 (6) 10.5 115 (9) 14.7 66 (12) 13.0 0 0.0 0 0.0
74 (9) 8.5 35 (13) 5.0 77 (11) 8.6 49 (20) 6.8 50 (12) 6.4 0 0.0 85 (51) 16.2 86 (10) 16.1
153 (28) 17.4 35 (5) 5.0 227 (24) 25,7 85 (21) 11.8 26 (4) 3.4 12 (1) 2.3 24 (12) 5.8 0 0.0
878 (106)
sub-sample containing glucose, where Pyro-P was missing, and the formalin treated sub-samples, where the oxic subsample lacked Teichioc-P, P lipids and DNA-P, and the anoxic sub-sample lacked P lipids, DNA-P and Poly-P (Table 1; Fig. 3). The anoxic sub-sample containing glucose and the oxic subsample containing formalin only had 500 mg P g1 DW of extractable P remaining after exposure. This is in contrast to the two oxic sub-samples, without additions, where virtually no P was released, and P corresponding to the original concentration of 0.9 mg P g1 DW was recovered. The composition of the sub-samples from the various treatments varied considerably (Table 1; Fig. 3). In general, Ortho-P and Mono-P were the P groups found in highest concentration, and in most cases Ortho-P was larger than Mono-P. Exceptions to this were the glucose samples and the anoxic treatment at 20 C where the Mono-P was the largest group. Of the less prevailing P compound groups, Poly-P was the most prominent in the cases of the oxic treatments without additions; DNA-P was the third largest group in most of the other sub-samples. Compared to the original sample Poly-P in general decreased during the experiment, with the exception of the oxic treatment at 4 C were the Poly-P level remained unchanged, while Pyro-P and Mono-P generally increased. Concerning the smaller constituents of the samples, the largest difference compared to the original sample was shown by Pyro-P in the treatments with formalin additions. On the other hand, Teichoic-P generally changed only marginally compared to the original sample (Table 1; Fig. 3), except in the oxic treatment with added formalin, were it was absent. In general, relative standard deviation was around 10e15% for the different P compound groups in most of the sub-samples, with the exception of the oxic sub-sample with formalin addition, where the standard deviation was higher (Table 1).
692 (61) 887 (43) 722 (22) 780 (40) 504 (40) 502 (213) 533 (40)
The influence of the investigated factors (oxygen level, temperature and additives) on the results was analyzed with ANOVA, using a GLM (General Linear Model) in Minitab 15 (Minitab inc., State College, PA, USA). These investigations proved that the differences between the identified P compound groups from the various treatments were significant in all cases for the set ups where additives were used, and for all compound groups except Ortho-P and Mono-P concerning the oxidation level. In contrast, the only P compound group showing significant difference concerning the temperature chosen was Poly-P (Table 2). It should be noted that although additives are used as a single factor in the ANOVA, the two additives investigated had very different impact on the organic P composition in the sediment. To facilitate easier interpretation of the results of the individual P compound groups in Table 2, a principal component analysis (PCA) was performed using the Unscrambler, version 7.6 (ASA Camo, Oslo, Norway). The PCA was made on autoscaled data, using the concentrations of the individual P compound groups, with PC1 describing 38% of the variation, and PC2 29% (Fig. 4). In order to elucidate the influence of the factors on the variation patterns found by PCA, ANOVA was performed for the score values in the same way as for the individual P compounds. According to the results, PC1 describes the effects of additives ( p < 0.001), while PC2 mainly describes the effects of the oxygen level ( p < 0.001) but to a certain degree the effects of additives as well ( p ¼ 0.006). Along PC1 in Fig. 4a, groupings according to additives can be found, with set ups with added formalin to the left, set ups without addition in the middle and set ups with added glucose to the right. Corresponding differences can be seen in the loading-plot (Fig. 4b), i.e. that added formalin induces higher levels of Ortho-P and Pyro-P, but lower levels of Lipid-P, DNA-P and Mono-P. Although the oxygen condition has a highly
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 5 6 5 e5 7 2
a
569
1800 anoxglu
1600
TP (µg g -1)
1400 1200 1000 800 anoxfor 20anox oxglu oxfor 4anox 20ox 4ox
600 400 200 0
b
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Day 700 anoxglu
PO4 (µg g-1)
600 500 400
anoxfor 20anox oxglu
300 200
4anox
100 0
oxfor 20ox 4ox 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Day
Fig. 2 e a. Total accumulated release of P from the laboratory set ups over time, as measured in the effluent water. 20 and 4 designates the temperatures used, 20 C and 4 C, respectively. When no number is present, the temperature used was 20 C. Ox and Anox designates the oxygen level, oxic and anoxic; and glu and form designates the additives used, glucose and formalin. b. Accumulated release of phosphate from the laboratory set ups over time, as measured in the effluent water. 20 and 4 designates the temperatures used, 20 C and 4 C, respectively. When no number is present, the temperature used was 20 C. Ox and Anox designates the oxygen level, oxic and anoxic; and glu and form designates the additives used, glucose and formalin.
significant influence on the PC2 scores, no obvious grouping can be seen in the score-plot (Fig. 4) due to the concurrent influence of the additives.
4.
Discussion
4.1.
Overall release
This study demonstrates that specific organic P compounds that may reflect the microbial community, such as orthophosphate diesters, (DNA-P, P lipids and Teichoic acids) all disappeared when the microbial biomass was deceased, indicating the microbial origin of these compounds. Judging from comparisons with the original sample, in terms of the individual P compound groups, it seems P was primarily lost from the Poly-P pools in the various treatments, confirming that poly-P is readily released from the sediments, as found in previous papers (e.g. Reitzel et al., 2006b). This indicates that there is no net production of Poly-P in the sediment, under any
Fig. 3 e Typical solution 31P NMR spectra from the various set ups, including the original.
570
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 5 6 5 e5 7 2
Table 2 e Analysis of variance (ANOVA) for the identified P compound groups in regard to their dependence on the variables in the laboratory set ups, temperature (Temp) oxygen level (Ox) and additions (Add). Stars (1, 2 or 3) indicate 5%, 1% and 0.1% significance levels ( p-values 0.05, 0.01 and 0.001), respectively. Results are given as average of duplicate or triplicate analyses.
Ortho-P Mono-P Teichoic-P P-lipids DNA-P Pyro-P Poly-P
Temp
Ox
Add
e e e e e e **
e e ** ** * ** ***
*** ** * *** *** ** **
of the investigated conditions. This could in turn indicate that Poly-P found in surface sediment are to a large degree remnants of production in the water column, as seen in earlier studies at Lake Erken (Reitzel et al., 2007), and supporting the findings made by Hupfer et al. (2004) in 22 European lakes. It should be noted that the treatment where the environmental parameters most resembled those in the lake at the time of sampling was the oxic treatment at 4 C, which is also the treatment most resembling the original sample in organic P species composition at the end of the experiment. This treatment can thus in many ways be considered as a control treatment, and conclusions regarding the release and decomposition of organic P species during the experiment may thus be made either comparing to the original sediment sample or the oxic 4 C treatment.
4.2.
a
PC2 No adtive (20ºC) With glucose (20ºC) With formalin (20ºC) No adtive (4ºC)
2
* Anoxic conditions
1 PC2(29%)
* *
0 *
* *
-1 * *
* *
-2
*
PC1 -3
-2
-1
0 PC1(38%)
b
2
3
PC2 Poly-P
0.6
0.4 PC2 (29%)
1
Ortho-P
P-lipids
Pyro-P
DNA-P
0.2
0 Mono-P
Oxygen status
An important result was the pronounced release of phosphate under anoxic conditions compared to the oxic treatments. Anoxic treatments generally induced higher release, particularly when glucose was added. Our results indicate that the general hypothesis proposed by Mortimer (1941) that the internal P release under anoxic conditions results from a dissolution of the FeeP complex can be blurred by a concomitant release of phosphate resulting from mineralization of e.g. Poly-P, as suggested by Gachter et al. (1988) or iron associated organic compounds as suggested by Golterman et al. (1998). That part of the phosphate may result from mineralization of Poly-P is supported by the observation that Poly-P has decreased drastically in some of the treatments were all the release was shown to be made up by phosphate (Table 1). It should be noted, however, that some of this phosphate might be the result of hydrolysis during the molybdate analysis. When comparing the anoxic to the oxic treatments, the well-known rapid release of P from sediments under naturally occurring anoxic conditions is mirrored by the lower content of the labile species of Poly-P and P lipids in the anoxic treatments. This most likely indicates a higher decomposition and subsequent release of these species, which is supported by the higher amounts of possible intermediate products in the decomposition of Poly-P and P lipids such as Pyro-P and Mono-P in the anoxic treatments (Table 1). The anoxic conditions used in these laboratory experiments were deliberately chosen to be very reducing to make certain that the natural conditions that may arise during summer stagnation were covered. This might mean that the effects on the organic P compound groups are slightly more pronounced than they would be in the lake, but the general principles of decomposition and release are the same.
Teichoic-P
PC1 -0.2
0
0.2
0.4
Fig. 4 e PCA of the organic P groups extracted from the sediments from the various treatments. Fig. 4a shows the score-plot of PC1 and PC2, with variance in % on the axis, and Fig. 4b shows the loadings to Fig 4a. The score-plot indicates how the different treatments vary from each other, and the loading-plot indicates on which organic P compounds this depends.
4.3.
Temperature
The treatments showing the lowest release, both in terms of phosphate and TP, occurred at low temperature, thus indicating that the microbial activity is limited under these circumstances. The presence of microbial activity, albeit low, under these conditions is supported by the NMR results, which show that there is a transformation between Poly-P and Pyro-P in the 4 C treatments. In addition to this, an
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 5 6 5 e5 7 2
independent increase in Pyro-P does seem to occur, which is more difficult to explain. Comparing the treatments at 4 C with 20 C, it is seen that the higher temperature removes more of the poly-P found in the original. This could be due to higher mineralization rates, which would be consistent with the seasonal variation in internal loading reported by e.g. Jensen and Andersen (1992). However, the temperature does not seem to have the same large effect on the P release as does the oxygen levels, although a slightly faster release at higher temperature can be seen in Fig. 2 a & b.
4.4.
Formalin
In the formalin treated anoxic treatment, phosphate only comprised about 20% of the total release, indicating that a large amount of the P lost from the sediment may be in organic (nrP) form. This corresponds well with the fact that formalin kills the microorganisms resulting in a release of these from the sediment matrix. Consequently, in this treatment, both inorganic P, such as iron bound P, and microbial P are released to the water as phosphate and nrP, respectively. While all treatments with additions showed a large decrease in Poly-P, only those with added formalin showed a total lack of DNA-P and P lipids. In addition to this, the oxic treatment with added formalin also showed a decrease in all measured P compound groups except Pyro-P, which most likely reflects an intermediate step in the decomposition of Poly-P. This illustrates that the majority of the poly-P is associated with microbial biomass, but as there is no net production of poly-P in any of our treatments, this does not compromise our hypothesis that poly-P in this sediment is mainly originating from the water column. While chemical decomposition of organic P, Poly-P, and Pyro-P may be significant under certain circumstances, it is not likely that all of the changes in the treatments with added formalin are due to purely chemical processes. A more likely scenario is that the lack of the labile species of Poly-P, DNA-P and P lipids reflects the loss of microorganisms in these treatments, due to the toxic properties of formalin, as these species are most often incorporated in various bacterial communities. When these bacteria die, there will under these toxic circumstances be no degradation and recycling of the labile P species that are now available; instead they are most likely flushed out of the sediment chambers with the effluent water. The decrease in Mono-P under oxic conditions might however reflect chemical decomposition as this group of compounds contains compounds not normally prone to fast microbial decomposition (Ahlgren et al., 2005; Reitzel et al., 2007), such as e.g. inositol hexaphosphate.
4.5.
571
the microbial community previously adapted to oxic conditions as known from waste water treatment plants (e.g. Davelaar, 1993) and from when a lake stratifies and turn anoxic, as demonstrated by e.g. Hupfer et al. (2004). Since the original sample was surface sediment sampled during spring circulation, oxic conditions would be prevailing and the microbial communities present at the time of sampling would be adapted to and be most efficient under these conditions. This may be reflected in the fact that both DNA-P and P lipids increase with added glucose in the oxic treatment, which could document a build up of microorganisms in the sediment. Furthermore, the microbial origin of these two P compound groups is in agreement with the results from the formalin amended treatments, where they are completely lost. The increase in Mono-P during the glucose treatment could be a direct microbial signal from, for instance, the production of nucleotides such as glycerol6phoshate (found in cell membrane), which has recently been identified in many lake sediments by 31P NMR spectroscopy (unpublished data from Reitzel).
5.
Conclusions
The results of this study show that the presence and decomposition of organic P compound groups is dependent on the prevailing conditions in the sediment. The oxygen level, and thus the redox potential, is of great importance, with anoxic conditions giving rise to faster release and a depletion of labile P groups in the sediment. Release of P at anoxic conditions is 5e10 times higher than at oxic conditions, when other conditions are similar. Poly-P seems to be the P species contributing the most to this release. Temperature does not affect the P variation as much, but a slightly higher release rate can be detected at the higher temperature. Additions of glucose or formalin considerably increase the rate of release, as well as alter the P composition. The total release increases the most at oxic conditions, from about 80 mg g1 without additions to about 400 mg g1 with both glucose and formalin additions, although the largest release was found at anoxic conditions when glucose was added. NMR analysis indicates that Poly-P again was the P species contributing the most to the release, in the case of additions together with primarily P lipids and DNA-P. This work demonstrates that it is possible to track changes in sedimentary P compound groups with changing conditions, such as oxygen level, temperature, carbon status and microorganisms, using model set ups and established analytical techniques. This allows the correlation of P compound groups to various conditions, which has important predictive consequences.
Glucose
In the case of glucose addition, the most obvious change compared to the original sample is the large decrease in OrthoP and poly-P in both the oxic and the anoxic set up. This might be explained by the role of glucose as an available substrate for the microbial communities in the sediments. Hence, this fertilization of the sediment could lead to an increased requirement for P and as a consequence mobilization of P from the ortho-P and poly-P pools and subsequent release of P from
Acknowledgements This study was supported by Formas, the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning. We thank Rolf Danielsson, Uppsala University, for help with statistical evaluation. Kasper Reitzel was supported by Villum Kann Rasmussen Centre of Excellence: Centre for Lake Restoration (CLEAR) and the Carlsberg foundation.
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references
Ahlgren, J., Tranvik, L., Gogoll, A., Waldeba¨ck, M., Markides, K., Rydin, E., 2005. Depth attenuation of biogenic phosphorus compounds in lake sediment measured by 31P NMR. Environmental Science and Technology 39, 867e872. Ahlgren, J., Reitzel, K., Tranvik, L., Gogoll, A., Rydin, E., 2006. Degradation of organic phosphorus compounds in anoxic Baltic Sea sediments; a 31P NMR study. Limnology and Oceanography 51 (5), 2341e2348. Ahlgren, J., De Brabandere, H., Reitzel, K., Rydin, E., Gogoll, A., Waldeba¨ck, M., 2007. Sediment phosphorus extractants for phosphorus-31 nuclear magnetic resonance analysis: a quantitative evaluation. Journal of Environmental Quality 36 (3), 892e898. Bostro¨m, B., Jansson, M., Forsberg, C., 1982. Phosphorus release from sediments. Archiv fu¨r HydrobiologieeBeiheft Ergebnisse der Limnologie 18, 5e59. Cade-Menun, B.J., Preston, C.M., 1996. A comparison of soil extraction procedures for P-31 NMR spectroscopy. Soil Science 161, 770e785. Davelaar, D., 1993. Ecological significance of bacterial polyphosphate metabolism in sediments. Hydrobiologia 253 (1e3), 179e192. Gachter, R., Meyer, J.S., Mares, A., 1988. Contribution of bacteria to release and fixation of phosphorus in lake-sediments. Limnology and Oceanography 33 (6), 1542e1558. Ga¨chter, R., Meyer, J.S., 1993. The role of microorganisms in mobilization and fixation of phosphorus in sediments. Hydrobiologia 253, 103e121. Golterman, H., Paing, J., Serrano, L., Gomez, E., 1998. Presence of and phosphate release from polyphosphates or phytate phosphate in lake sediments. Hydrobiologia 364, 99e104. Golterman, H.L., 2004. The Chemistry of Phosphate and Nitrogen Compounds in Sediments. Kluwer Academic Publishers. Hansen, J., Reitzel, K., Jensen, H.S., Andersen, F.O., 2003. Effects of aluminum, iron, oxygen and nitrate additions on phosphorus release from the sediment of a Danish softwater lake. Hydrobiologia 492, 139e149. Hecky, R.E., 1998. Low N: P ratios and the nitrogen fix: why watershed nitrogen removal will not improve the Baltic. In: “Effects of Nitrogen in the Aquatic Environment”. The Royal Swedish Academy of Sciences, pp. 85e115. Report 1. Hupfer, M., Gachter, R., Ru¨egger, H., 1995. Polyphosphate in lakesediments e P-31 NMR-spectroscopy as a tool for its identification. Limnology and Oceanography 40, 610e617. Hupfer, M., Rube, B., Schmieder, P., 2004. Origin and diagenesis of polyphosphate in lake sediments: a P-31-NMR study. Limnology and Oceanography 49, 1e10. Hupfer, M., Lewandowski, J., 2005. Retention and early diagenetic transformation of phosphorus in Lake Arendsee (Germany) e
consequences for management strategies. Archiv fu¨r Hydrobiologie 164 (2), 143e167. Jensen, H.S., Andersen, F.O., 1992. Importance of temperature, nitrate and pH for phosphate release from aerobic sediments of four shallow, eutrophic lakes. Limnology and Oceanography 37 (3), 577e589. Makarov, M.I., Haumaier, L., Zech, W., 2002. Nature of soil organic phosphorus: an assessment of peak assignments in the diester region of P-31 NMR spectra. Soil Biology & Biochemistry 34, 1467e1477. Mortimer, C., 1941. The exchange of dissolved substances between mud and water in lakes. Journal of Ecology 29, 280e329. Murphy, J., Riley, J.P., 1962. A modified single-solution method for the determination of phosphate in natural waters. Analytica Chimica Acta 27, 31e36. Newman, R.H., Tate, K.R., 1980. Soil phosphorus characterisation by 31P nuclear magnetic resonance. Communications in Soil Science and Plant Analysis 11, 835e842. Reitzel, K., Ahlgren, J., DeBrabandere, H., Waldeback, M., Gogoll, A., Tranvik, L., Rydin, E., 2007. Degradation rates of organic phosphorus in lake sediment. Biogeochemistry 82, 15e28. Reitzel, K., Ahlgren, J., Gogoll, A., Jensen, H.S., Rydin, E., 2006a. Characterization of phosphorus in sequential extracts from lake sediments using P-31 nuclear magnetic resonance spectroscopy. Canadian Journal of Fisheries and Aquatic Sciences 63, 1686e1699. Reitzel, K., Ahlgren, J., Gogoll, A., Rydin, E., 2006b. Effects of aluminum treatment on phosphorus, carbon, and nitrogen distribution in lake sediment: a P-31 NMR study. Water Research 40, 647e654. Rydin, E., 2000. Potentially mobile phosphorus in Lake Erken sediment. Water Research 34, 2037e2042. Schindler, D.W., 1977. Evolution of phosphorus limitation in lakes. Science 195, 260e262. Søndergaard, M., Jensen, J.P., Jeppesen, E., 1999. Internal phosphorus loading in shallow Danish lakes. Hydrobiologia 408/409, 145e152. Søndergaard, M., Jensen, J.P., Jeppesen, E., 2003. Role of sediment and internal loading of phosphorus in shallow lakes. Hydrobiologia 506e509, 135e145. Turner, B.L., Mahieu, N., Condron, L.M., 2003. Phosphorus-31 nuclear magnetic resonance spectral assignments of phosphorus compounds in soil NaOH-EDTA extracts. Soil Science Society of America Journal 67, 497e510. Turner, B.L., Frossard, E., Baldwin, D.S., 2005. Organic Phosphorus in the Environment. CABI Publishing. Wild, A., 1988. Plant nutrients in soil: phosphate. In: Wild, A. (Ed.), Russell’s Soil Conditions and Plant Growth, tenth ed. Longman Scientific and Technical, Harlow, UK, pp. 695e742.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 5 7 3 e5 8 2
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Technical, economic and environmental assessment of sludge treatment wetlands Enrica Uggetti a, Ivet Ferrer a,*, Jordi Molist b, Joan Garcı´a a a
Environmental Engineering Division, Department of Hydraulic, Maritime and Environmental Engineering, Technical University of Catalonia, C/Jordi Girona 1-3, Building D1, E-08034 Barcelona, Spain b Age`ncia Catalana de l’Aigua, c/Provenc¸a 204-208, E-08036 Barcelona, Spain
article info
abstract
Article history:
Sludge treatment wetlands (STW) emerge as a promising sustainable technology with low
Received 26 May 2010
energy requirements and operational costs. In this study, technical, economic and envi-
Received in revised form
ronmental aspects of STW are investigated and compared with other alternatives for sludge
8 September 2010
management in small communities (<2000 population equivalent). The performance of full-
Accepted 15 September 2010
scale STW was characterised during 2 years. Sludge dewatering increased total solids (TS)
Available online 21 September 2010
concentration by 25%, while sludge biodegradation lead to volatile solids around 45% TS and DRI24h between 1.1 and 1.4 gO2/kgTS h, suggesting a partial stabilisation of biosolids. In the
Keywords:
economic and environmental assessment, four scenarios were considered for comparison:
Constructed wetlands
1) STW with direct land application of biosolids, 2) STW with compost post-treatment,
Biosolids
3) centrifuge with compost post-treatment and 4) sludge transport to an intensive waste-
Composting
water treatment plant. According to the results, STW with direct land application is the most
Drying reed beds
cost-effective scenario, which is also characterised by the lowest environmental impact. The
Sanitation, wastewater
life cycle assessment highlights that global warming is a significant impact category in all scenarios, which is attributed to fossil fuel and electricity consumption; while greenhouse gas emissions from STW are insignificant. As a conclusion, STW are the most appropriate alternative for decentralised sludge management in small communities. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
A major concern of intensive sewage treatment processes is the large production of waste sludge, which is generally managed by complex and costly operations. Its production is highly variable depending on the wastewater treatment used, for instance conventional activated sludge processes produce from 60 to 80 g of total solids (TS) per person per day (Von Sperling and Gonc¸alves, 2007). During the last years, sludge generation has increased dramatically by the fast growth of world population and industrialisation (Hong et al., 2009). According to Fytili and Zabaniotou (2008), sludge production
in the European Union has increased by 50% since 2005. Therefore, optimisation of sludge management becomes a key element in the wastewater treatment sector. Secondary sludge consists of excess biomass produced during biological wastewater treatment. It is characterised by high organic matter (50e80% TS) and low dry solids (0.5e2% TS) contents (Wang et al., 2008). According to these properties, sludge treatment processes may be separated into stabilisation and dewatering techniques. Sludge stabilisation aims at reducing the biodegradable fraction of organic matter, thus the risk of putrefaction, while diminishing the concentration of pathogens (Luduvice, 2007). On the other hand, the aim of
* Corresponding author. Tel.: þ34 934016463; fax: þ34 934017357. E-mail address:
[email protected] (I. Ferrer). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.019
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dewatering is to decrease sludge volume, hence disposal costs and environmental risks associated. Besides, sludge dewatering is required prior to composting, incineration or landfilling. Conventional sludge stabilisation and dewatering technologies (i.e. anaerobic digestion followed by centrifugation or filtration) are costly and energy demanding, which is troublesome particularly in small facilities (<2000 population equivalent (PE)). This is a matter of concern, since the number of small wastewater treatment plants (WWTP) in operation will continue to increase within the next years, including municipalities below 500 PE (Council of the European Union, 2000). Nowadays, the solution adopted in many small facilities is sludge transport to the nearest WWTP with a conventional sludge treatment line, posing high operation costs and potential environmental impacts. In this context, simplified in situ treatments are needed. Sludge treatment wetlands (STW) consist of shallow tanks filled with a gravel layer and planted with emergent rooted wetland plants such as Phragmites australis (common reed). Sludge is spread and stored on the surface of the beds where most of its water content is lost by evapotranspiration of the plants and by water draining through the gravel filter layer, leaving a concentrated sludge residue on the surface. When the maximum storage capacity is reached, after a final resting period, the final biosolids are withdrawn to start a new operating cycle. Evolution of sludge composition results from dewatering and mineralisation processes (Nielsen, 2003). The resulting final product is suitable for land application (Nielsen and Willoughby, 2005); although in practice in some cases it is post-treated to improve sludge stabilisation and hygienisation (Zwara and Obarska-Pempkowiak, 2000). In comparison with common mechanical dewatering technologies like centrifuges, sludge treatment wetlands emerge as a promising alternative (Uggetti et al., 2010), which has low energy requirements, reduced operation and maintenance costs, and in principle causes little environmental impact. However, a systematic evaluation of the environmental performance of this technology has not yet been reported. Life Cycle Assessment (LCA) is a useful tool for investigating the environmental impacts of a product or system over its whole life cycle. As established by the ISO 14040 and 14044 guidelines (ISO, 2006a,b), LCA gives overall information on resource consumption and environmental emissions by including extraction of raw materials, processing, manufacture, use and end of life of a product or a process. The LCA method has been previously used to assess the environmental impact of sewage sludge management scenarios (Suh and Rousseaux, 2002; Lundin et al., 2004; Houillon and Jolliet, 2005; Tarantini et al., 2007) and treatment technologies (Svantro¨m et al., 2004; Hospido et al., 2005; Peregrina et al., 2006). Besides, LCA studies are at times supported by economic analysis (Murray et al., 2008; Hong et al., 2009). In this study, the performance of STW is investigated by means of a field study carried out over a period of two years in one STW located in Spain. The system’s efficiency is then compared to literature results from conventional treatments for sludge management in small communities (<2000 PE). Data collected from field campaigns and from the literature are the basis for subsequent economic and environmental assessment, assuming design and operation criteria of full-
scale systems located in Spain. Four scenarios are compared: 1) STW with direct land application of the final product, 2) STW with compost post-treatment, 3) centrifugation with compost post-treatment, 4) sludge transport to an intensive WWPT without previous treatment. To our knowledge, this is the first time that an economic and environmental assessment of STW is conducted and compared with other alternatives for sludge management. Our aim is to demonstrate the suitability of STW for small communities, from a technical, economic and environmental point of view.
2.
Materials and methods
2.1.
Sludge treatment wetlands’ performance
The performance of STW was studied by monitoring a fullscale facility (1500 PE) located in Seva, province of Barcelona (Catalonia, Spain). The wastewater treatment line consists of a contact-stabilisation unit. Secondary sludge is stored in a tank and pumped to the STW. In this facility, 7 drying reed beds were set-up in 2000 by transforming conventional drying beds. The total surface area of the STW is 175 m2 and the sludge-loading rate around 125 kgTS/m2/year. Each bed is fed semi-continuously during alternate days. Other details on the design and operation of these wetlands may be found in Uggetti et al. (2009). Operating cycles last on average 5 years; after a resting period of some 4 months, the final product is removed with a power shovel and transported to a composting plant. During 2 years, 5 field campaigns were carried out in one bed to characterise the properties of the influent and sludge from the wetlands. Composite samples were taken from three sampling points located along the bed. The biosolids obtained (final product) were also characterised in two STW at the end of the operating cycle after a resting period of 4 months. Sludge dewatering was determined by the TS concentration, while organic matter was analysed in terms of Volatile Solids (VS) and Chemical Oxygen Demand (COD). The stability of biosolids was measured by the Dynamic Respiration Index (DRI), as proposed by Adani et al. (2000) and Barrena et al. (2009). Nutrients (nitrogen(NTK), phosphorus(TP) and potassium (K)), heavy metals and faecal bacteria indicators (Salmonella spp. and Escherichia coli) contents in biosolids were also determined. All parameters were analysed in triplicate following Standard Methods (APHA-AWWA-WPCF, 2001). Methane and odour emissions from wetlands were measured both after feeding and between feedings; corresponding to the maximum emission rate and the average emission rate, respectively. These measurements were carried out as described by Sarkar and Hobbs (2003). Samples were collected from representative STW by positioning a Linvall Hood of 1 m2 surface area. A controlled airflow (0.1 m/s) was passed over the chamber surface and samples of inlet and exhaust air were collected in Nalophan NA sample bags. Odour concentration was determined according to the European Standard EN13725:2003 (Committee for European Normalization, 2003), as a function of the number of required dilutions to be detectable by 50% of the odour panel. According to this method, the odour concentration is expressed as unit of
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odour per m3 of air (ouE/m3 s). CH4 was analysed by gas chromatography (Thermo Finnigan Trace, GC 2000).
Table 2 e Sludge flow rates and emissions considered in the economic and environmental assessment. Wastewater treated
2.2.
Economic evaluation
500 PE 1000 PE 2000 PE
Economic aspects of STW are compared with sludge management alternatives which are currently used in small WWTP in our zone: centrifugation, as representative of mechanical dewatering techniques, and transport to a larger WWTP with sludge treatment line. Besides, the need for post-treatment after STW is also accounted for. Consequently, the following scenarios are considered: 1) STW with direct land application of biosolids, 2) STW with compost post-treatment, 3) centrifuge with compost post-treatment, 4) transport to an intensive WWTP. Each scenario is evaluated for sewage treatment capacities of 100, 200 and 400 m3/d of wastewater treated, theoretically corresponding to 500; 1000 and 2000 PE. The results are expressed in m3/d of wastewater treated. Design and operation criteria of STW located in Spain are adopted (Table 1). In this sense, 5-year operating cycles are assumed, although longer operating cycles are reported in other countries like Denmark (Nielsen, 2003). Emptying procedures involve biosolids withdrawal with a power shovel and transport to final destination. STW operation is thereafter re-started without replanting. STW investment costs (Table 5) include soil occupation and excavation, wetlands construction, pump and pipe installation, gravel placement and plantation. Table 2 summarises sludge flow rates for each scenario. Secondary sludge generation in the WWTP is calculated by the Huisken equation. The difference between sludge production in STW and centrifuge is due to the TS concentration of the final product, 25% TS and 20% TS, respectively (Uggetti et al., 2010).
2.3.
Life cycle assessment
The aim of the LCA model developed is to compare the environmental impact of STW with sludge management alternatives commonly used in small WWTP in our zone. Therefore, the same scenarios as in the economic analysis are considered.
Table 1 e Sludge treatment wetlands’s design and operation parameters considered in the economic and environmental assessment (scenarios 1 and 2). Wastewater treated
Population equivalent Sludge-loading rate (kgTS/m2/year) Total surface area (m2) Number of beds Wall height (m) Gravel volume per wetland (m3) Sludge storage capacity per wetland (m3) Sludge withdrawn (t) Operating cycle (years)
Scenario
500 PE
1000 PE
2000 PE
500 50 167 4 1.6 16 45
1000 50 331 6 1.6 22 59
2000 50 662 12 1.6 22 59
182 5
361 5
724 5
Waste activated sludge (sludge generation) (m3/year) Sludge production in STW (m3/year) Sludge production in centrifuge (m3/year) Pump electricity consumption in STW (kWh/year) Pump electricity consumption in centrifuge (kWh/year) Centrifuge electricity consumption (kWh/year) CH4 emission rate from STW (mg/m2 s) Odour emissions (ouE/m2 s)
275
550
1100
1e4
33
66
132
1e2
41
82
165
3
25
50
105
1e2
30
60
125
3
140
280
560
3
<88
<88
<88
1e2
5.7e7.3
1e2
5.7e7.3 5.7e7.3
The function of the system is to manage secondary sludge produced in an activated sludge unit with extended aeration, which is commonly used in small facilities of the zone (Uggetti et al., 2009). For this reason, the functional unit is defined as the management of 1 ton of sewage sludge (wet weight). Taking into account the functional unit, the system boundaries are as follows: a) The wastewater treatment line is not included in the model, because it is the same in all scenarios. b) Since the study is focused on sludge management, secondary sludge is selected as input material; and only the impact generated by sludge management in the facility is accounted for. This includes the sludge treatment line of the WWTP (STW or centrifuge) and transport to posttreatment in a composting plant (scenarios 2 and 3) or treatment in an intensive WWTP (scenario 4), assuming a distance of 30 km in all cases. c) Treatments outside the WWTP (composting in scenarios 2 and 3; and sludge treatment in a larger WWTP in scenario 4) are not included in the model. d) Final transport and disposal are not included either, bearing in mind that they would be approximately the same in all scenarios. e) Raw materials required for systems’ construction and energy consumption for systems’ operation are taken into account. f) The boundaries exclude the construction phase, which only accounts for minor environmental impacts compared to the operation phase of WWTP, according to previous LCA studies (Lundie et al., 2004; Lassaux et al., 2007). g) The end of life is included for the centrifuge, as it should be replaced over the period considered (20 years). This aspect has not been taken into account for STW since their lifespan is longer than the 20 years period considered in this study.
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System boundaries and scenarios defined in the model are shown in Fig. 1. Inventory data on systems’ design and operation are the same as for the economic analysis, collected in full-scale facilities from Spain (Tables 1 and 2). Data concerning the embodied environmental aspects of materials, transport use and other processes were taken from the Ecoinvent system process database. The LCA analysis was carried out with the software SimaPro 7.1 by PRe´ Consultant, using the CML 2 baseline method (Guine´e, 2001). Impact categories evaluated include Abiotic Resource Depletion, Acidification, Eutrophication and Global Warming Potential (Climate Change). The results are expressed as a quantification of the potential contribution of materials and processes to each impact category.
3.
Results and discussion
3.1.
Sludge treatment wetlands’ performance
The main results from the sampling campaigns of the full-scale STW are summarised in Table 3. Notice that Campaign VI corresponds to the biosolids obtained at the end of the operating cycle. In general terms, TS increase from 1% in the influent to 15e16% in the wetlands, with a maximum concentration of 25% in the final product after a resting period of 4 months. Dewatering efficiency is generally increased during the summer season, reaching high TS concentration (25%) (campaign III), being generally lower (around 16%) during the rest of the year. Notice that even the lowest dewatering efficiency found in this study (14.8% in autumn) is competitive compared to centrifuges, which are capable of achieving 14e18% TS with conventional waste activated sludge (Gonc¸alves et al., 2007) and 18% TS with waste activated sludge from extended aeration units (Uggetti et al., 2010). Organic matter biodegradation is shown by some 10% decrease in VS and COD contents during sludge treatment; VS and CODconcentrations being quite stable all over the year.
SCENARIO 1
On average, during the treatment the VS content is reduced from 55% VS/TS in the influent to 48% VS/TS in the wetlands. The same can be said for COD which is decreased from an average value of 770 g/kgTS to 720 g/kgTS. The lowest VS concentration is reached in summer (45% VS/TS), although in this case the values are also a result of the low influent concentration (39.5% VS/TS). Besides, the seasonal variability that influences the efficiency of the treatment is minimised by the resting period, which enhances organic matter biodegradation before biosolids removal leading to VS over 40% VS/TS and COD around 500 g/kgTS in biosolids (Table 3). Final values are within the range obtained after conventional sludge stabilisation techniques, such as anaerobic digestion (Ferrer et al., 2010). However, in compost samples organic contents are usually higher, around 60% VS/TS for compost of sewage sludge mixed with vegetable wastes (Bertran et al., 2004), due to humic-like substances produced during composting. Even if the properties of biosolids in terms of total solids and organic matter suggest their suitability for land application, the need for post-treatments depends on the stability and hygienisation degree of the final product. Biological stability determines the extent to which readily biodegradable organic matter has been decomposed (Lasaridi and Stentiford, 1998). For agricultural uses, higher biological stability implies lower environmental impacts (like odour generation, biogas production, leaching and pathogen’s re-growth) during land application of the product (Muller et al., 1998). The DRI is based on the rate of oxygen consumption and is a useful indicator of the biological stability of a sample:lower oxygen consumption (DRI value) corresponds to higher biological stability. In this study, the DRI24h from STW biosolids ranged between 1.1 and 1.4 gO2/kgTS h. Such a stability degree is much higher than the values reported for a mixture of primary and activated sludge (6.7 gO2/kgTS h) and for anaerobically digested sludge (3.7 gO2/ kgTS h) (Pagans et al., 2006). Values around 1 gO2/kgTS h are found in compost (Ponsa´ et al., 2008) and partially digested material (Scaglia and Adani, 2008). Biosolids from the studied STW achieve almost the same stabilisation degree as compost;
Sludge Treatment Wetlands (land application)
SCENARIO 2
Sludge Treatment Wetlands (land application after stabilisation post-treatment)
Transport to a composting plant (30 km)
SCENARIO 3
Centrifuge (land application after stabilisation post-treatment)
Transport to a composting plant (30 km)
SCENARIO 4
No treatment (treatment required before land application)
Transport to a WWTP plant (30 km)
Secondary sludge
Fig. 1 e System boundaries and scenarios of the Life Cycle Assessment.
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Table 3 e Sludge characteristics (mean value ± SD) from samples taken during 2 years. Note that Campaigns IeV correspond to sludge samples taken during the treatment in wetlands, while Campaign VI refers to the final product after 4 months resting period (biosolids). Campaign I (autumn) TS (%) VS (%TS) COD (g/kgTS)
Influent Wetland Influent Wetland Influent Wetland
1.7 16.1 57.7 47.7 940 880
0.01 3.3 0.9 3.5 180 440
Campaign II (spring) 1.2 14.8 59.0 50.2 820 660
0.01 2.5 0.7 3.2 110 100
therefore by prolonging the resting time to ensure stabilisation, the final product could be valorised in agriculture without posttreatment in a composting plant. Concerning the main nutrients, a certain amount of nitrogen (4.4%TKN/TS) is found in biosolids, indicating the potential use of the final product as organic fertilizer. However, the concentration of phosphorus (0.26%TP/TS) and potassium (0.15%K/TS) are relatively low. For compost of sewage sludge, Bertran et al. (2004) give slightly lower nitrogen (2.53%TS) but higher phosphorus (2.3%TS) contents. In general, sludge is characterised by a considerable variability in nutrient’s content, depending on the wastewater source and treatment process (Moss et al., 2002). The concentration of nutrients is needed to ensure appropriate dosages of the sludge prior to land application. On the other hand, the main hazard associated to sludge application on agricultural soils is the potential long-term accumulation of toxic elements (Singh and Agrawal, 2008), which may then be uptaken by crops. Such elements include both inorganic pollutants, like heavy metals, and organic micropollutants. Currently, only heavy metals concentrations are regulated for land application of sewage sludge (Council of the European Union, 1986). Since treated sludge may have considerable amounts of pathogens, depending on the treatment processes used, limit values for faecal bacteria indicators have also been proposed (Environment DG, EU, 2000). According
Campaign III (summer) 0.3 25.7 39.5 45.0 580 670
0.02 13.4 4.7 4.4 20 70
Campaign IV (winter) 1.7 14.3 58.7 49.5 740 620
0.06 3.03 0.2 4.0 20 200
Campaign V (spring) 0.6 16.7 45.7 48.9 780 720
Campaign VI (autumn)
0.01 4.2 5.5 5.8 60 90
1.1 25 51.5 43.7 709 520
0.0 1.6 0.8 7.1 11 50
to this proposal, conventionally treated sludge has to contain 3 log10 E. coli gTS1 and Salmonella spp. has to be absent in 2 gTS. The concentration of heavy metals and faecal bacteria indicators in STW biosolids is compared to the limit values for unrestricted land application according to current legislation (Council of the European Union, 1986) and more restrictive values proposed (Environment DG, EU, 2000) (Table 4). Notice that there are only little differences between influent sludge and the final product with regards to heavy metals, suggesting that heavy metals accumulation is negligible. Furthermore, in all cases the concentrations are clearly below the limits proposed. With regards to pathogens, Salmonella spp. was not detected, but small quantities E. coli were present in all cases (Table 4). Both faecal bacteria indicators are well below the limits proposed. On the whole, the characteristics of biosolids analysed (total solids, organic matter contents and nutrients) put forward their suitability for land application especially as organic amendment. Moreover, according to the concentration of heavy metals and faecal bacteria indicators, biosolids from STW studied in this work fulfil the requirements for agricultural application. Nevertheless, biosolids from STW are post-treated in composting plants before agricultural re-use in the case of facilities located in our zone, while they are directly spread on fields in countries like Denmark or France (Nielsen and Willoughby, 2005; Lie´nard et al., 2008).
Table 4 e Concentration of heavy metals and faecal bacteria indicators in the influent and biosolids from sludge treatment wetlands (STW). Parameter
Influent
STW
Council Directive 86/278/EEC limits
Environment DG, EU, 2000 proposed limits
Heavy metals Cr (ppm) Ni (ppm) Cu (ppm) Zn (ppm) Cd (ppm) Hg (ppm) Pb (ppm)
51 39 252 719 1.7 <1.5 53
57 31 265 615 0.8 <1.5 75
e 400 1750 4000 e e 1200
800 200 800 2000 5 5 500
Faecal bacteria indicators Salmonella spp. (presence/absence in 25 g) E. coli (MPN/g)
Absence <3
Absence <3
e e
Absence in 50 g <500 MPN g1
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Table 5 e Investment and operation costs for all scenarios expressed in V/year: (1) sludge treatment wetlands (STW), (2) STW D compost, (3) centrifuge D compost and (4) transport to wastewater treatment plant (WWTP). Cost (V/year)
Scenario 1
STW investment costs Personnel costs Materials replacement Emptying procedure, biosolids transport and agriculture application Total investment and operation cost
500 PE
1000 PE
2000 PE
50.563 1.125 502 1745
83.606 1.830 801 3468
159.442 2.840 1.304 6948
53.935
89.705
170.534
83.606 1.830 801 5.131
159.442 2.840 1.304 10.277
Scenario 2
STW investment costs Personnel costs Materials replacement Emptying procedure, biosolids transport and compost post-treatment Total investment and operation cost
50.563 1.125 502 2.581 54.771
91.368
173.863
Scenario 3
Centrifuge investment costs Personnel costs Materials replacement Sludge treatment, transport and compost post-treatment Electricity Total investment and operation cost
74.557 2.256 1.160 2.839
76.007 4.512 1.560 4.786
96.587 5.716 2.100 8.976
427 81.232
541 87.406
804 114.183
Personnel costs Transport Sludge treatment in WWTP Total cost
540 5.700 3.296 9.536
1.080 9.990 6.592 17.662
1.350 17.760 13.185 32.295
3.2.
Economic evaluation
The technical analysis of STW demonstrates that the efficiency of such a technology is comparable to that of conventional treatments in terms of sludge dewatering and stabilisation. Furthermore, the stability index observed suggests that biosolids can reach a high stabilisation degree if sufficient resting time is left at the end on each operating cycle. This means that biosolids’ post-treatment is not needed before agricultural application. Nevertheless, it has been included in the economic and environmental assessment to compare the impacts of STW with and without of post-treatment (scenarios 1 and 2), versus conventional treatments (centrifuge) (scenario 3) and transport to an intensive WWTP with sludge treatment line (scenario 4). The most significant costs of the centrifuge (Table 5) include machine assembly and installation, room construction and polyelectrolyte preparation. Notice that STW investment costs increase with the treatment capacity, from 50,000 to 160,000 V for 500 and 2000 PE systems, respectively. On the other hand, centrifuge costs increase only slightly, from 75,000 to 97,000 V. Therefore, the difference between investment costs is more evident for 2000 PE facilities; with centrifuges becoming more competitive. Regarding operation costs, there are little differences between STW with direct land application (scenario 1) and with compost post-treatment (scenario 2) (Table 5). Note that only one-year cost is considered. However, this difference increases with the treatment capacity, from 500 PE (1000 V) to 2000 PE (5000 V); which is attributed to the higher cost of composting (35 V/ton) with respect to the agriculture application canon
(12 V/ton) in our zone (Catalonia, Spain). In all cases, centrifuge operation costs are higher, increasing with the treatment capacity (from 7.000 to 17.000 V for 500 and 2000 PE, respectively). Transport (scenario 4), which does not have investment costs, is characterised by the highest operation cost (from 9.000 V for 500 PE up to 32.000V for 2000 PE). The economic analysis considering a life cycle of 20 years is shown in Fig. 2. It is calculated assuming 3% increase of operation costs and applying 5% interest tax to the total cost. In this case, amortisation of investment and STW emptying
0,45 Transport without treatment 30 km far away Centrifuge+composting STW+composting STW
0,40 Tre a tme nt c o s t (€/m3 )
Scenario 4
0,35 0,30 0,25 0,20 0,15 0,10 0,05 500
1000
2000
Population Equivalent
Fig. 2 e Investment and operation costs over a 20 years period of all scenarios: (1) sludge treatment wetlands (STW), (2) STW D compost, (3) centrifuge D compost and (4) transport to wastewater treatment plant.
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a
b
Abiotic depletion 42000
17000
25000
10000
g SO2 eq
g Sb eq
Acidification
10000 15
3500 20
10 10
5 5
2
1
Total Raw materials Energy
c
Tranport
Total Raw materials Energy
d
Eutrophication
Transport
Global warming 6000
3000,0 2000,0
kg CO2 eq
g PO4eq
3000 1000,0 1,0
1000
2 0,5
1 0,1 Total Raw materials Energy
Scenario 1
Total Raw materials Energy
Transport
Scenario 2
Scenario 3
Transport
Scenario 4
Fig. 3 e Life Cycle Assessment results grouped according to CLM 2 impact categories for all scenarios: (1) sludge treatment wetlands (STW), (2) STW D compost, (3) centrifuge D compost and (4) transport to wastewater treatment plant.
costs are also included. From a long-term perspective, the benefit of biosolids’ direct land application (scenario 1) emerges versus compost post-treatment (scenario 2), with lower costs (0.021 V/m3) in all cases. Investment and operation costs of the centrifuge (0.28 V/m3) are more expensive than other solutions (0.24 V/m3 for transport and 0.16e0.18 V/m3 for STW) for communities of 500 PE. However, centrifugation costs decrease at increasing treatment capacity (to 0.20 and 0.15 V/m3 for 1000 and 2000 PE systems, respectively), hence treatment costs are the same as STW for 2000 PE systems. Transport may be considered as an alternative to centrifugation only for systems with less than 850 PE or 170 m3/d (0.28 V/m3 versus 0.24 V/m3). Likewise, STW costs are 0.05e0.07 V/m3 lower than transport. It is worth mentioning that the economic evaluation of this scenario is correlated with sludge production (and humidity), as well as the distance to nearest WWTP with sludge treatment line. In this study, an average distance of 30 km was adopted, based on circumstances generally observed in our zone. This analysis underlines the economic advantage of STW with respect to conventional treatments exemplified by centrifugation in facilities up to 2000 PE. However, this technology is currently adopted for sludge management in systems up to 30,000 PE in Italy (Peruzzi et al., 2007) and 60,000e125,000 PE in
Denmark (Nielsen, 2003). Certainly, the results depend on local circumstances, including the costs and taxes of energy in each country, as well as design and operation criteria of STW and weather conditions, affecting the efficiency of the treatment. For instance, operating cycles of 5 and 10 years are described in Spain and Denmark, respectively. Longer operating cycles reduce operation costs of STW, resulting in additional economic advantage for communities above 2000 PE.
3.3.
Life cycle assessment
In LCA analysis the environmental impacts attributed to materials or processes are grouped according to the so-called impact categories. Fig. 3 shows the main impact categories of this LCA model (Abiotic Resource Depletion, Acidification, Eutrophication and Global Warming Potential (Climate Change)), with comparative results for each scenario. The results are presented in Fig. 3 in absolute values in the units corresponding to each impact category. Within each impact category, the total impact as well as the individual contribution of raw materials, energy and transport are included separately. This interpretation is useful to determine the most influent element of the process that could eventually be modified to reduce the global impact.
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In general, within each category the total impact is distributed following the same pattern: transport (scenario 4) has the highest impact, from 3 to 6 times higher than centrifuge with compost post-treatment (scenario 3) and STW with compost post-treatment (scenario 2). The impact of STW with direct use of the final product (scenario 1) is negligible in comparison with the other scenarios, with values between 1000 and 6000 times lower. According to this analysis, STW appear as the most favourable solution in every impact category. For scenario 1, the biggest impact is caused by raw materials employed in system’s construction; while direct greenhouse gas emissions (Table 2), as well as indirect emissions derived from energy consumption and transport, have a smaller contribution. If post-treatment is required, the total impact of STW (scenario 2) and centrifuge (scenario 3) is similar, due to sludge transport to post-treatment. From an environmental point of view, centrifuges and filter bands do not have relevant differences (Gallego et al., 2008), therefore scenario 3 should be representative of conventional mechanical dewatering treatments. Global Warming Potential accounts for a high contribution mainly in scenarios 2, 3 and 4 (1100; 1300 and 6000 kgCO2eq/t wet weight, respectively) due to fossil fuel and electricity consumption. In STW, the contribution of CH4 emissions to this impact category is negligible, as a result of the low CH4 found in these type of systems (Table 2). If we look at individual contributions of raw materials, energy and transport within each scenario (Fig. 3), other trends are observed. Scenario 1 is characterised by a high consumption of raw materials (basically steel and gravel), which accounts for the highest contribution in all impact categories. On the other hand, lower impacts are attributed to the energy consumption for sludge pumping into the STW, and transport during STW emptying operation. Scenario 2 has the same contribution as scenario 1 with respect to raw materials and energy, but in this case transport accounts for the highest impact, which is attributed to the compost posttreatment. In scenario 3, the centrifuge has low raw materials requirements, but significantly higher energy consumption for sludge dewatering and pumping. Like in scenario 2, transport to compost post-treatment has the highest contribution to the total impact. As in the economic study, sludge transport to an intensive WWTP (scenario 4) is characterised by the highest environmental impact in all categories. Indeed, the reduction of sludge volume after dewatering (scenarios 1e3) has a positive environmental impact with respect to untreated sludge transport. The results of this assessment show the economic and environmental benefits of STW compared to conventional mechanical dewatering and transport of untreated sludge. STW are less advantageous if compost post-treatment is required, as with mechanical dewatering techniques, due to the impact associated to sludge transport. However, the impacts of composting may differ between partially stabilised sludge from STW and dewatered sludge from centrifuges. For this reason, further LCA studies should include the post-treatment stage as well as final disposal of biosolids. As indicated by Cambell (2000), the most important criterion in the selection between sludge management alternatives is that the solution must be appropriated to the local conditions of each site.
4.
Conclusions
This study looked at technical, economic and environmental aspects of sludge treatment wetlands for small communities (500e2000 PE). The system was then compared with conventional treatments for sludge management. From this evaluation, the following conclusions can be drawn: In STW, sludge dewatering and stabilisation result in biosolids with around 25% TS and 40e45% VS/TS; with DRI24h between 1.1 and 1.4 gO2/kgTS h, indicating a partial stabilisation of the sludge treated and suggesting that with sufficient resting time the final product could be valorised in agriculture without post-treatment in a composting plant. According to the economic and environmental assessment, STW with direct land application is the most costeffective scenario, which is also characterised by the lowest environmental impact (almost negligible in comparison with the other options evaluated). If compost post-treatment is required, the costs increase only slightly but environmental impacts increase significantly. Centrifugation costs are higher than STW for systems up to 1800 PE, but become similar for 2000 PE systems. However, environmental impacts are higher in all categories compared to STW with direct land application. Sludge transport to external treatment is always the most expensive and environmentally unfriendly scenario. The LCA highlights that in all scenarios global warming has a significant impact, which is attributed to fossil fuel and electricity consumption; while gases emissions from STW are insignificant. As a conclusion, sludge treatment in constructed wetlands with direct land application is the most appropriate solution to manage waste sludge in decentralised small communities.
Acknowledgements This work was carried out with financial support of the Spanish Ministry of Environment (MMARM, Project 087/PC08). Paula Aguirre and Technicians of Depuradores d’Osona S.L. are kindly acknowledged for their contributions. E. Uggetti is grateful to the Technical University of Catalonia for her PhD scholarship.
references
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As(V) removal using carbonized yeast cells containing silver nanoparticles R. Selvakumar a,b,*, N. Arul Jothi a, V. Jayavignesh c, K. Karthikaiselvi a, Geny Immanual Antony a, P.R. Sharmila a, S. Kavitha d, K. Swaminathan a,c a
Department of Microbial Biotechnology, School of Biotechnology and Genetic Engineering, Bharathiar University, Coimbatore 641 046, India PSG Institute of Advanced Studies, P.B. No: 1609, Peelamedu, Coimbatore 641 004, India c Environmental Management and Biotechnology Division, DRDO-BU Centre for Life Sciences, Bharathiar University, Coimbatore 641 046, India d Department of Biotechnology, Karunya School of Biotechnology, Karunya University, Coimbatore 641 114, India b
article info
abstract
Article history:
The present study involves the development of adsorbent containing silver nanoparticles
Received 5 August 2010
for arsenate removal using silver reducing property of a novel yeast strain Saccharomyces
Received in revised form
cerevisiae BU-MBT-CY1 isolated from coconut cell sap. Biological reduction of silver by the
17 September 2010
isolate was deduced at various time intervals. The yeast cells after biological silver
Accepted 25 September 2010
reduction were harvested and subjected to carbonization at 400 C for 1 h and its properties
Available online 12 October 2010
were analyzed using Fourier Transform Infra-Red spectroscopy, X-ray diffraction, scanning electron microscope attached with energy dispersive spectroscopy and transmission
Keywords:
electron microscope. The average size of the silver nanoparticles present on the surface of
Saccharomyces cerevisiae
the carbonized silver containing yeast cells (CSY) was 19 9 nm. The carbonized control
Silver nanoparticles
yeast cells (CCY) did not contain any particles on its surface. As(V) adsorption efficiency of
Characterization
CCY and CSY was deduced in batch mode by varying parameters like contact time, initial
As(V) removal
concentration, and pH. Desorption studies were also carried out by varying the pH. The
Isotherm
experimental data were fitted onto Langmuir and DeR Isotherms and Lagergren and
Kinetics
pseudo second order kinetic models. The CSY was more efficient in arsenate removal when compared to CCY. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Ground water contamination by arsenic has been a major problem in the northeastern parts of India like West Bengal, Assam and in few pockets of Orissa (Singh, 2004, 2007). Continuous consumption of such arsenic contaminated water has lead to hyperkeratosis, skin cancers and pigmentation of palm. Due to these clinical manifestations caused by the arsenic contaminated Drinking water, World Health Organization (WHO) has recommended 0.01 mg L1 as maximum
contaminant level (MCL) in drinking water (Zouboulis and Katsoyiannis, 2002). Considering its clinical significance, extensive studies have been carried out for As(III) and As(V) removal using various methods. Scientific evidences suggested that adsorption is an efficient method to control the mobility and bioavailability of arsenic when compared to other methods. Recently nano/micro scale sorbents and nanoparticles have been reported to exhibit greater pollutant separation/adsorption property; thus providing unprecedented opportunities to
* Corresponding author. PSG Institute of Advanced Studies, P.B. No: 1609, Peelamedu, Coimbatore 641 004, India. Tel.: þ91 9944920032. E-mail addresses:
[email protected],
[email protected] (R. Selvakumar). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.034
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develop more efficient and cost effective water purification system (Diallo and Savage, 2005). Adsorption of arsenic has been tried with various nano based adsorbents like aluminosilicates treated with FeII nanoparticles (Dousova´ et al., 2006), TiO2, Fe2O3 and NiO metal oxide nanomaterials (Hristovski et al., 2007), nanoscale zerovalent iron (Giasuddin et al., 2007) and mixed magnetiteemaghemite nanoparticles (Chowdhury and Yanful, 2010). ArsenXnp, a hybrid sorbent consisting of hydrous iron oxide nanoparticles distributed throughout a porous polymer bead was used for arsenic removal (Sylvester et al., 2007). Iron oxide nanoparticles assisted arsenic removal from aqueous solution was studied by De et al. (2009). Martinson and Reddy (2009) evaluated CuO nanoparticles as an adsorbent for the removal of As(III) and As(V) from ground water. Synthesis and preparation of such nanoparticles/nanosorbents is usually achieved using chemical reactions involving various toxic chemicals and the process remains non-ecofriendly and energy intensive. The use of highly structured physical and biosynthetic activities of microorganism for the synthesis of nanosized materials has recently emerged as a novel approach (Gericke and Pinches, 2006). Many microbes are known to produce nanostructured mineral crystals and metallic nanoparticles with properties similar to that of chemically synthesized materials (Roh et al., 2001). Examples include the formation of magnetic nanoparticles by mangnetotactic bacteria (Roh et al., 2001), gold nanoparticles by Verticillium fusarium, Pichia jadini, Verticillium luteoalbum DMS 63545 (Mukherjee et al., 2001; Gericke and Pinches, 2006) and silver nanoparticles using Fusarium oxysporum (Ahmad et al., 2003), yeast strain MKY3 (Kowshik et al., 2003), Pseudomonas stutzeri (Klaus et al., 1999), Aspergillus fumigatus (Bhainsa and D’Souza, 2006) and Rhizopus stolonifer extract (Binupriya et al., 2010). In the present study, attempts were made to explore the silver reducing property of wild type yeast strain isolated from cell sap of coconut inflorescence (Cocos nucifera). The yeast cells were identified as Saccharomyces cerevisiae BU-MBT-CY1 strain. Yeast cells after silver nitrate reduction were harvested and subjected to carbonization at 400 C. Its properties were analyzed using Fourier Transform Infra-Red spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscope (SEM) attached with energy dispersive spectroscopy (EDS) and transmission electron microscope (TEM). The size of the nanoparticles present on the surface was measured using Image J software. Efficiency of CCY and CSY to adsorb arsenate from aqueous solution was studied in batch mode and the experimental data were fitted onto Langmuir and DeR isotherms and Lagergren first order and pseudo second order kinetic models.
2.
Materials and methods
2.1.
Organism
Indigenous yeast strain was isolated from the cell sap of coconut inflorescence (C. nucifera) collected from Palakkad district of Kerala, India. The yeast strain was identified as S. cerevisiae BU-MBT-CY1 at Bangalore Genei Pvt. Ltd.,
Bangalore, based on partial sequence of 5.8S ribosomal RNA gene present within the internal transcribed spacer 2 and partial sequence of 28S ribosomal RNA gene. The % homology analysis based on nucleotide sequence showed that the organism was 99% homologous to S. cerevisiae st H51960 (Accession No: EF432781), S. cerevisiae st YNC20-151205 (Accession No: DQ347487), and S. cerevisiae st Sake yeast KVOKAI No.1 (Accession No: AB180466). The sequence (372 bp) was submitted to GenBank, NCBI (Accession No: EU182579). The organism was maintained on yeast peptone dextrose (YPD) agar slant and used for further study.
2.2.
Preparation of CCY and CSY
The biomass for the preparation of CCY and CSY was grown in yeast, peptone and dextrose (YPD) broth (containing (g L1) yeast extract: 10; peptone: 20; dextrose: 20). The flasks were inoculated, incubated on orbital shaker at 25 C and agitated at 150 rpm. The biomass was harvested after 72 h of growth by centrifugation at 10,000 rpm at 4 C for 20 min, followed by extensive washing (5 times) with milli Q water to remove any media components. Typically 1% w/v (1 g/100 mL) of completely washed yeast biomass was dispensed in milli Q water containing 104 M silver nitrate (the yeast cells exhibited maximum tolerance towards silver nitrate at this concentration during the preliminary screening studies) (data not shown) and agitated in shaker at 25 C for 72 h. A known quantity of sample was withdrawn at a regular time interval of 12 h and biotransformation was routinely monitored using UVevisible spectrophotometer (Shimadzu, 1601). After 72 h of incubation, the biomass was harvested by centrifugation at 10,000 rpm at 4 C for 20 min. The yeast cells were made into carbon using muffle furnace at 400 C for 1 h. Carbonized yeast cells were sieved to a mesh size of 125e250 mm and used for further studies.
2.3.
Characterization of CCY and CSY
CCY and CSY were characterized using FTIR, XRD, SEM attached with EDS and TEM. The FTIR analysis was carried out using Shimadzu model FTIR-8400 PC. The completely dried samples were treated with spectral grade KBr for pelleting in the ratio of 1: 50 and were used for the FTIR analysis. The spectra were recorded in the range of 4000e400 cm1 at a scanning speed of 2 mm s1 at a resolution of 4 cm1. The nature of material (either amorphous or crystalline) was analyzed through XRD using Shimadzu XRDe6000/6100 models operating in transmission mode at 30 kV, 30 mA with Cu-ka radiation. The scanning was performed at an axis of 2q at 10e80 with a scanning pith of 0.02 . The scan speed was maintained at 5 per min. The surface morphology, energy dispersive spectroscopy (EDS) and silver mapping of CCY and CSY were analyzed using JEOL JSM 6360 model SEM having 3 nm resolution capacity at an operating voltage of 20 kV. Imaging and selected area electron diffraction (SAED) analysis of silver nanoparticles in CSY were performed using JEOL JEM2100 transmission electron microscopy (TEM) at an operating voltage of 200 kV.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 5 8 3 e5 9 2
2.4.
Estimation of As(V)
As(V) estimation was carried out according to Lenoble et al. (2003). For brief protocol please refer our earlier research paper (Selvakumar et al., 2008).
2.5.
Batch adsorption studies
Batch mode studies were carried out by agitating 100 mg of CCY and CSY in 50 mL of As(V) solution of desired concentration and pH at 150 rpm at room temperature. Concentration of As(V) was estimated spectrophotometrically. The samples were withdrawn at predetermined time intervals; supernatant solution was separated from the adsorbent by centrifugation at 15,000 rpm for 20 min and the residual arsenate was analyzed. Effect of pH was studied in the range of 3e11 by adjusting the pH of the solution using 0.1 N HCl and NaOH and 2-[4-(2-hydroxyethyl)-1-piperazinyl] ethanesulfonic acid (HEPES) was used as a buffer reagent. The initial arsenate concentration of 2.0 mg L1 and 100 mg 50 mL1 of adsorbent dose were used to examine the pH effect. Langmuir and DeR isotherm and Lagergren first order and pseudo second order kinetics were employed to study the adsorption efficiency.
by the biosorption process, some reducing groups in the biomass such as aldehyde, ketone, and some reducing hydrolysates of polysaccharides should have reduced Agþ to Ag(0) nuclei (Zhang et al., 2005). These nuclei then grew into Ag(0) clusters or crystals as biosorption and bioreduction proceeded. As the surface morphology and organic structure of yeast cells hindered the migration and aggregation of these crystals along the cell surface, further growth of these crystals was limited and nanosized particles should have been formed (Zhang et al., 2007). Lower fungi like Penicillium and Aspergillus species have number of simple hydroxy/methoxy derivatives of benzoquinones and toluquinones in its membrane. Since yeast also belongs to these lower fungi, similar membrane bound quinones in the yeast may also have played a role in the reduction process (Prasad and Jha, 2010). In the absence of an externally added electron donor, the electrons for nitrate reduction are provided by coenzyme NADPH, whereas quinone (oxidized form) and hydroxyquinone (reduced form) serve as an electron shuttle for the reduction of silver cation to its elemental form (Duran et al., 2005). After 72 h of bioreduction, yeast cells were subjected to carbonization at 400 C for 1 h. The carbonized yeast containing silver particle was further used for characterization studies.
3.2. 2.6.
585
Characterization
Desorption studies
The CSY used for the adsorption of 2.0 mg L1 of As(V) was separated by centrifugation. The As(V) loaded adsorbent was filtered through Whatman No. 1 filter paper and washed gently to remove any unadsorbed arsenic. Several such samples were prepared and the spent adsorbent was mixed with 50 mL of distilled water containing different pH (3e11) (pH adjusted using 0.1 N HCl and NaOH solutions) and agitated at equilibrium time (3.5 h). The desorbed arsenic was estimated spectrophotometrically. The experiments were carried out in duplicate and mean values were taken for calculations.
3.
Results and discussion
3.1.
Synthesis of CCY and CSY
The use of physical and biosynthesis activities of microorganism for the synthesis of nanosized materials has recently emerged as a novel approach (Gericke and Pinches, 2006). The S. cerevisiae BU-MBT-CY1 biomass before and after exposure to aqueous AgNO3 solution was visually analyzed for a period of 72 h. The appearance of pink colour in the yeast suspended AgNO3 solution is a clear indication for the biological reduction of silver ions and formation of reduced silver nanoparticles on the yeast biomass. This colour change may be due to excitation of surface plasmon vibration (Henglein, 1993). The time dependent increase in the intensity of plasmon resonance at 440 nm observed in the Erlenmeyer flask containing yeast cells confirmed the formation of silver nanoparticle. The process of bioreduction of Agþ could be hypothesized as following. Silver ions should have been biosorbed by the cell wall functional groups like ionized carboxyl of amino acid residues and amide of peptide chains, providing active sites for binding. Followed
CCY and CSY were characterized using FTIR, XRD, SEM attached with EDS and TEM. FTIR analysis permits spectrophotometric observation of the CCY and CSY surface in the range of 400e4000 cm1 and serves as a direct means for the identification of functional groups on the surface. Fig. 1 shows a comparison of FTIR transmitting spectra of CCY and CSY. While the basic skeletal of both the spectra remained unchanged, few shifts in peak were observed in CCY and CSY. The CH2 asymmetric stretching of control shifted from 2921.96 cm1(m) and 2950.52 cm1(m) to higher region of 2923.88 cm1(s) and 2956.67 cm1(s) with the formation of silver nanoparticles. The CH2 symmetric stretching frequency at 2852.52 cm1 remained unchanged in both CCY and CSY.
Fig. 1 e a) FTIR spectrum of CCY b) CSY with silver nanoparticles on its surface.
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Fig. 2 e Scanning electron microscopic image of carbonized yeast cells a) CSY showing multiple pores on its surface b) CCY showing smooth surface.
The absorbance band at 1683.74 cm1 (Fig. 1a) was associated with the carbonyl stretch vibration (ReCOeNH2) in the amide linkage of control in solution. The band at 1533.30 cm1 was identified as another amide band. After silver nanoparticles formation, these two bands shifted to 1670.74 cm1 corresponding to secondary amide CO stretching and 1544.88 cm1 corresponding to secondary amide NH bending vibration (Fig. 1b), which demonstrated that amide linkages, bound with Agþ in the process of silver nanoparticle formation changed its vibration frequencies. The band at 1382.87 cm1 was attributed to the symmetrical stretching vibration of C]O of some amino acid (NHþeCeCOO). After formation of silver nanoparticle the carboxyl group shifted from 1382.87 cm1 to 1377.08 cm1 indicating the ionized carboxyl group was combined with Agþ(NHþeCeCOOAgþ). Zhang et al., (2007) observed similar type of band shift from 1396 cm1 to 1385 cm1 during the binding of Agþ to the carboxyl group. In our present study, we observed that the intensity of most of the shifted peaks in CSY spectra increased when compared to control, indicating that the AgNO3 has undergone reduction to Ag0, where as only the binding of AgNO3 cannot cause such changes (Zhang et al., 2005). X-ray diffraction technique is a powerful tool to analyze the nature of the material. If the material under investigation is crystalline, well-defined peaks are observed while noncrystalline or amorphous system show hallow instead of welldefined peak. The X-ray diffraction pattern can also be used to deduce the average size of the crystallite in the material (Moon et al., 2005). The silver reduction process may lead to change in structure of carbonized yeast cells after thermal decomposition and resulting changes thereof would provide valuable information regarding the formation of crystalline silver nanoparticles. No crystalline signature of silver was observed in CCY sample indicating that the complex is amorphous. However, a signature of silver was observed in CSY XRD spectra. The broadened peaks were obtained due to the crystalline nature of material containing silver nanoparticles. The XRD spectra showed four peaks at 2q values of 37.829 , 44.002 , 64.185 and 77.153 indicating the presence of silver. The signature of the silver is due to the formation of silver
nanoparticles on its surface. Large crystallite gives rise to sharp peaks; as the crystallite size decreases, the peak width increases and the intensity decreases (Moon et al., 2005). The narrow peaks in the XRD spectra of CSY indicated the growth of silver nanoparticle with some regular shape. If the formation of large particles were caused by agglomeration, then the peak in the XRD pattern would not become narrow and the shape of the particles would not be regular. Therefore, we posit that the subsequent growth is caused by reduction of Agþ ions, which are present in the form of residual free ions on the yeast surface (Chu et al., 2005). Moon et al. (2005) reported formation of silver nanoparticles of w42 nm when annealed at 250 C. The SEM images (Fig. 2) revealed the presence of multiple pores in the CSY surface formed due to thermal activation (Fig. 2a) which were not observed in CCY (Fig. 2b). The EDS spectrum of the SEM image confirmed the presence of silver, phosphorous and oxygen on the surface of the CSY (Fig. 3). The distribution of silver on the surface of CSY was again confirmed using mapping technique. These studies clearly indicate the presence of silver nanoparticles on the surface of the CSY.
Fig. 3 e EDS spectra of CSY.
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Fig. 4 e a) Transmission electron image of the silver nanoparticles on carbon. b) SAED pattern of CSY showing {111}, {200}, {220}, and {311} reflection rings. c) Histogram showing particle size distribution on CSY surface. The average size of silver nanoparticles was 19 ± 9 nm.
100 Adsorbent dosage: 100 mg 50mL -------- CCY _____ CSY
90
-1
80
% Removal
70 0.5 mg L-1
60
1.0 mg L
50
1.5 mg L 2.0 mg L
40
-1 -1 -1 -1
2.5 mg L
30 20 10 0 0
0.5
1
1.5
2
2.53
3.5
4
4.5
Contact time, h
Fig. 5 e Effect of agitation time and concentration of As(V) on removal (Temperature, 30 C; Adsorbent dosage: 100 mg 50 mLL1; Error bars, in most cases smaller than the symbols, represent standard deviations).
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120
60 Adsorption Desorption
50
Equilibrium time: 3.5 h
80
40
60
30
40
20
20
10
0
% Desorption
% Removal
100
0 3
4
5
6
7
8
9
10
11
pH
Fig. 6 e Effect of pH on adsorption and desorption of As(V) (Adsorbent dosage, 100 mg 50 mLL1; temperature, 30 C; Error bars, in most cases smaller than the symbols, represent standard deviations).
The percent removal for CSY at equilibrium decreased from 99.52 to 73.58% as the concentration increased from 0.5 to 2.5 mg L1; whereas in case of CCY, the percent removal ranged from 28 to 13.66% (Fig. 5). The initial rapid phase of adsorption may be due to increased number of vacant sites available for adsorption at initial stages; as a result, there exists an increased concentration gradient between adsorbate in solution and adsorbate in adsorbent (Kavitha and Namasivayam, 2007). Dousova´ et al., (2006) obtained more than 95% of As(V) adsorption using Fe nanoparticles-treated aluminosilicates. TiO2, Fe2O3 and NiO nanomaterials have been reported to exhibit higher arsenate removal in surface water, achieving >98% efficiency at an initial As(V) concentration of 100 mg L1 and the contact time of 7 days (Hristovski et al., 2007). Ansari and Sadegh (2007) reported 95% arsenic adsorption onto silver impregnated powdered activated carbon with increase in contact time. Mixed magnetiteemaghemite nanoparticles were reported to remove 98% of As(V) at initial concentration of 1 mg L1 with an equilibrium time of 3 h (Chowdhury and Yanful, 2010).
Fig. 4a shows the typical bright-field TEM micrograph of the synthesized Ag nanoparticles. It is observed that most of the Ag nanoparticles were spherical in shape. The SAED pattern (Fig. 4b) shows intense circular rings corresponding to the {111}, {200}, {220}, and {311} planes revealing a high crystalline structure with FCC phase (Philip, 2009). The size of the nanoparticle present on the surface of the CSY was determined using Image J software. The histogram taken from representative micrographs evident the variation in nanoparticle sizes and the average size was estimated to be 19 9 nm (Fig. 4c). The nanoparticles size ranged from 7 nm to 54 nm. Increase in size of silver nanoparticle and the distribution of the crystallites in the sample may be due to dependency on the growth temperature, consistent with surface free energy consideration (Lee and Kang, 2004).
3.3.
Adsorption of As(V) using CCY and CSY
3.3.1. Effect of contact time and initial concentration on As(V) adsorption CSY showed effective As(V) removal when compared to CCY in aqueous solution. The As(V) uptake was found to increase with contact time and remained constant after equilibrium. The equilibrium time was found to be 4, 4, 3, 3.5 and 4 h for 0.5, 1.0, 1.5, 2.0 and 2.5 mg L1 of As(V) concentration, respectively.
3.3.2.
Effect of pH on As(V) adsorption
The extent of adsorption of ions is strongly influenced by the pH of the solution. The pH studies were carried out only with CSY considering its higher As(V) adsorption capacity than
Table 1 e Langmuir and DeR plots for adsorption of As(V). Adsorbent
Langmuir plot 1
CCY CSY
1
DeR isotherm
Q0 (mg g )
b (L mg )
R
qm (mg g )
E (kJ mol1)
R2
0.236 0.975
1.422 16.64
0.9592 0.9818
3.319 2.926
12.909 22.360
0.9072 0.9581
2
1
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Table 2 e Equilibrium parameter (RL) for adsorption of As (V). Adsorbate concentration (mg L1)
0.5 1.0 1.5 2.0 2.5
RL value CCY
CSY
0.584 0.413 0.319 0.260 0.219
0.107 0.056 0.039 0.029 0.023
RL ¼ 1=ð1 þ bC0 Þ
(2)
Where b is the Langmuir constant (L mg1) and C0 is the initial metal ion concentration (mg L1). RL value between 0 and 1 indicates favorable adsorption for all the initial concentration (Hall et al., 1966). The RL value for different concentrations indicate favorable adsorption of As(V) onto the CCY and CSY (Table 2).
3.4.2.
DubinineRadushkevich isotherm
The DubinineRadushkevich(DeR) isotherm (Dubinin and Radushkevich, 1947) has the following form: CCY. The effect of pH on removal of As(V) using CSY is shown in Fig. 6. The percent removal was more than 90% in the pH range of 3e5 where after percentage removal started decreasing. Higher adsorption at pH 3 could be related to the strong electrostatic attraction between positively charged surface sites for the predominant As(V) species H2AsO 4, whereas a decrease of As(V) adsorption above pH 7 could be due to electrostatic repulsion between the negatively charged As(V) species, HAsO2 4 , and the adsorbent surface (Mohapatra et al., 2006). Our result is in harmony with previous findings (Ghimire et al., 2002; Yang et al., 2007).
3.4.
Adsorption isotherm
3.4.1.
Langmuir isotherm
Langmuir isotherm is represented by the following equation (Langmuir, 1918). Ce =qe ¼ 1=Q o b þ Ce =Q 0
(1)
where Ce is the concentration of As(V) solution (mg L1) at equilibrium. The constant Q0 signifies the adsorption capacity (mg g1) and b is related to the energy of adsorption (L mg1). The linearity of the plots indicates that adsorption of As(V) follows Langmuir isotherm model, supporting monolayer formation on the surface of the adsorbent. The values of Q0 and b were calculated from the slope and intercept of the Langmuir plot and are presented in Table 1. The CSY showed higher Q0 value (0.975 mg g1) when compared to CCY (0.236 mg g1). The CSY exhibited increased adsorption capacity (Q0 value) when compared to natural iron ore (0.4 mg g1) (Zhang et al., 2004), and sulphate modified iron oxide coated sand (0.2 mg g1) (Vaishya and Gupta, 2004) and hence remains superior to these adsorbents. The essential characteristics of a Langmuir isotherm can be expressed in terms of a dimensionless constant separation factor or equilibrium parameter, RL.
lnqe ¼ lnqm ge2
(3) 1
where qm is the theoretical saturation capacity (mol g ), g is a constant related to the mean free energy of adsorption per mole of the adsorbate (mol2 J2), and e is the Polanyi potential which is related to the equilibrium concentration. e ¼ RTlnð1 þ 1=Ce Þ
(4)
where R is the universal gas constant (8.314 J1 mol1 K), Ce is the equilibrium concentration of adsorbate in solution (mol L1) and T (K) is the absolute temperature. The DeR constants qm and g were calculated from the intercept and slope of the plot lnqe vs e2 and is given in Table 1. Mean free energy, E (kJ mol1) of adsorption per mole of the adsorbate when it is transferred to the surface of the solid from infinity in the solution can be calculated using the relationship: pffiffiffi E ¼ 1= 2g
(5)
This parameter gives information whether the adsorption mechanism is ion-exchange or physical adsorption. If the magnitude of E is between 8 and 16 kJ mol1, the adsorption process follows ion-exchange, while for the values of E < 8 kJ mol1, the adsorption process is of a physical nature. A higher value of E (24.7 3.2 kJ mol1) shows the formation of strong chemical bond between adsorbate and adsorbent (Yang et al., 2007). The mean free energy (E) of CCY was lower (12.909 kJ mol1) when compared to CSY (22.360 kJ mol1) indicating that the process of adsorption is due to the chemical interaction between CSY and the arsenic in the aqueous solution. The energy of adsorption observed with CSY is comparatively higher than hydrous nanostructure iron(III)e titanium(IV) binary mixed oxide (13.51 kJ mol1) used for arsenic removal (Gupta and Ghosh, 2009). The R2 value of CSY for Langmuir isotherm (R2 ¼ 0.9818) showed higher linearity when compared to DeR isotherm (R2 ¼ 0.9581) indicating that the CSY adsorption data have
Table 3 e First order and second order kinetics for As(V) adsorption by CSY. Initial As(V) conc (mg L1) qe(Exp) (mg g1)
First order kinetic model 1
1
k1 (L min ) qe (cal) (mg g ) 0.5 1.0 1.5 2.0 2.5
0.249 0.461 0.657 0.812 0.919
0.656 0.718 1.332 0.975 1.086
0.079 0.297 0.552 0.782 1.148
Second order kinetic model 2
R
0.8945 0.9521 0.9639 0.9916 0.8831
k2(g mg1 min1) qe (cal) (mg g1) 0.064 0.309 0.382 0.922 1.122
0.259 0.514 0.756 1.008 1.14
R2 0.9979 0.9888 0.9925 0.9987 0.9969
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fitted better with Langmuir isotherm than DeR isotherm. The adsorption capacity value ascertained using DeR isotherm (qm) was found to be high when compared to Langmuir isotherm (Qo value) and qe value indicating that adsorption data did not fit well with DeR isotherm model (Tables 1,3).
3.4.3.
chemisorptions involving valence forces through sharing or exchange of electrons between the adsorbent and As(V) ions (Gucek et al., 2005). The value of qe increased as the initial concentration of the adsorbate increased. The value of the overall rate constant, k, also varied as the initial concentration was varied (Table 3).
Adsorption kinetics
The rate constant of adsorption is determined from the first order rate expression given by Lagergren (Lagergren, 1898),
3.5.
log qe q ¼ logqe ðk1 t=2:303Þ
The recovery of the adsorbed material as well as the regeneration of adsorbent is an important aspect of water treatment. Attempts were made to desorb As(V) from CSY surface using acid or alkali (0.1 N HCl or NaOH). The maximum desorption (49.2%) occurred at pH 11 (Fig. 6). This result reveals that CSY is economically feasible to use in treatment plants used for arsenic removal.
(6)
where qe and q are the amounts of As(V) adsorbed (mg g1) at equilibrium and at time t (min), respectively and k1 is the rate constant of adsorption (min1). Values of k1 were calculated from the plots of log (qe q) vs t for different concentrations of As(V) (Figure not shown). For the adsorption of As(V) by CSY, the experimental qe values did not agree with the calculated ones obtained from the first order kinetics. This shows that the adsorption of As(V) does not follow pseudo first order kinetics. The second order kinetic model (Ho and McKay, 1999) is represented as t=q ¼ 1=k2 q2e þ t=qe
(7)
where k2 (g1 mg1 min) is the rate constant of second order adsorption. The second order rate constant k2 and qe were calculated from the intercept and slope of the plots of t/q vs t (Fig. 7). The linear plots of t/q vs t show an agreement of experimental qe values with those derived from second order kinetic model for different initial As(V) concentrations (Table 3). The correlation coefficient for the first order model was low and the calculated qe values obtained from the first order kinetic model did not give a reasonable value. However correlation coefficient for the second order model was higher than 0.98 and the calculated qe values obtained from second order kinetic model were near to the experimental qe values for all the cases (Table 3). This shows that the mechanism of the adsorption can be described by the pseudo second order kinetic model, based on the assumption that the rate-limiting step may be
18 0.5 1.0 1.5 2.0 2.5
16 14
4.
Desorption studies
Conclusion
The CSY prepared using biomimetic approach showed increased As(V) adsorption capacity (Q0 ¼ 0.975 mg g1) when compared to CCY (Q0 ¼ 0.236 mg g1). The CSY exhibited multiple pores with silver nanoparticles on its surface when observed under SEM attached with EDS and TEM with SAED. CSY was further characterized using FTIR and XRD. CSY was much porous when compared to CCY which might be the possible reason for higher adsorption of As(V) onto CSY along with its interaction with the silver nanoparticles present on its surface. The adsorption data fitted well with Langmuir isotherm than DeR isotherm. The silver particles present on the surface should have induced increased adsorption of As(V) through chemical interaction as evident through mean free energy value (E) in DeR isotherm model. The kinetic data followed pseudo second order reaction and failed to obey first order. The As(V) adsorbed to the CSY could be desorbed (upto 49.2%). Since the CSY contain silver nanoparticles on its surface, high level of water purification could be achieved with respect to both arsenic removal and microbial decontamination of drinking water in water purification plant.
Acknowledgement
mg L-1 mg L-1 mg L -1 mg L -1 mg L -1
Dr. R.Selvakumar is thankful to Council for Scientific and Industrial Research (CSIR), Government of India for providing the financial support to the author through CSIR-SRF fellowship. The author acknowledges the help rendered by Dr.Anuradha M Ashok and Dr.K.R.Ravi of PSG Institute of Advanced Studies, Coimbatore for their suggestions and TEM analysis.
12 t/q
10 8 6 4 2
references
0 0
0.5
1
1.5
2
2.5 Time (h)
3
3.5
4
4.5
Fig. 7 e Second order kinetic plot for the removal of As(V) from aqueous solution(adsorbent dosage, 100 mg 50 mLL1; temperature, 30 C).
Ahmad, A., Mukherjee, P., Senapathi, P., Mandal, D., Islam Khan, M., Kumar, R., 2003. Extracellular biosynthesis of silver nanoparticles using the fungus Fusarium oxysporum. Colloids and Surfaces B: Biointerfaces 28, 313.
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FeS-coated sand for removal of arsenic(III) under anaerobic conditions in permeable reactive barriers Young-Soo Han, Tanya J. Gallegos 1, Avery H. Demond, Kim F. Hayes* Department of Civil and Environmental Engineering, University of Michigan, 1351 Beal Avenue, Ann Arbor, MI 48109-2125, USA
article info
abstract
Article history:
Iron sulfide (as mackinawite, FeS) has shown considerable promise as a material for the
Received 1 July 2010
removal of As(III) under anoxic conditions. However, as a nanoparticulate material,
Received in revised form
synthetic FeS is not suitable for use in conventional permeable reactive barriers (PRBs).
15 September 2010
This study developed a methodology for coating a natural silica sand to produce a material
Accepted 25 September 2010
of an appropriate diameter for a PRB. Aging time, pH, rinse time, and volume ratios were
Available online 8 October 2010
varied, with a maximum coating of 4.0 mg FeS/g sand achieved using a pH 5.5 solution at a 1:4 volume ratio (sand: 2 g/L FeS suspension), three days of aging and no rinsing.
Keywords:
Comparing the mass deposited on the sand, which had a natural iron-oxide coating, with
Iron sulfide
and without chemical washing showed that the iron-oxide coating was essential to the
Mackinawite
formation of a stable FeS coating. Scanning electron microscopy images of the FeS-coated
Permeable reactive barrier
sand showed a patchwise FeS surface coating. X-ray photoelectron spectroscopy showed
Arsenic
a partial oxidation of the Fe(II) to Fe(III) during the coating process, and some oxidation of S
Sand coating
to polysulfides. Removal of As(III) by FeS-coated sand was 30% of that by nanoparticulate FeS at pH 5 and 7. At pH 9, the relative removal was 400%, perhaps due to the natural oxide coating of the sand or a secondary mineral phase from mackinawite oxidation. Although many studies have investigated the coating of sands with iron oxides, little prior work reports coating with iron sulfides. The results suggest that a suitable PRB material for the removal of As(III) under anoxic conditions can be produced through the deposition of a coating of FeS onto natural silica sand with an iron-oxide coating. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Permeable reactive barriers are an increasingly common remediation technology for the treatment of contaminated groundwater. Treatment by this technology involves the emplacement of a hydraulically permeable reactive medium in situ; as the water flows through it under the natural hydraulic gradient, the reactive medium removes the contaminants from the groundwater. The possibility of in situ treatment, obviating the need to manage large quantities of water with low contaminant concentrations (Blowes et al.,
1999) and the resultant minimization of operation and maintenance expenses (Powell et al., 1998), makes the technology particularly attractive for groundwater remediation. The vast majority of PRBs currently in use utilize zero-valent iron (ZVI) as the reactive medium (Henderson and Demond, 2007). They have been installed to remove chlorinated organics such as TCE, PCE and 1,1-DCA, as well as heavy metals and radionuclides (see RTDF (2001) for details). ZVI has also been proposed for the removal of arsenic from groundwater in PRBs (Lackovic et al., 2000; Manning et al., 2002). Yet, arsenic presents a challenge for removal under
* Corresponding author. Tel.: þ1 734 763 9661; fax: þ1 734 763 2275. E-mail address:
[email protected] (K.F. Hayes). 1 U.S. Geological Survey, Denver, CO, USA. 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.033
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the anoxic conditions that often prevail in groundwater. It is generally thought that the iron oxyhydroxide corrosion products of ZVI provide the reactive surface for arsenic uptake (Furukawa et al., 2002; Manning et al., 2002). But, it is well documented that the reductive dissolution of ferric oxyhydroxide solids causes the release of arsenic back into solution (Cummings et al., 1999; Tufano and Fendorf, 2008). On the other hand, iron sulfides such as troilite, pyrite and mackinawite have been demonstrated as effective for the removal of arsenic from water under anoxic conditions (Bostick and Fendorf, 2003; Wolthers et al., 2005). A highly reactive nanoparticulate mackinawite forms as the first iron sulfide phase under reducing conditions (Wolthers et al., 2003; Rickard and Morse, 2005; Jeong et al., 2008), which can efficiently remove As(III) from anoxic water over the pH range of 5.0e10.5 through precipitation and adsorption (Gallegos et al., 2007, 2008). At a pilot-scale ZVI-based PRB, emplaced in 2005 for the treatment of groundwater contaminated by a smelter plant in Helena, MT, analysis of the solids provided evidence of the removal of As(III) through sorption to FeS surfaces (Beak and Wilkin, 2009). This is consistent with other studies that have linked effective arsenic removal in ZVI systems with the production of sulfide (e.g., Kober et al., 2005). Although synthetic mackinawite is highly reactive towards arsenic, it is not suitable as formed for conventional PRBs, due to its small particle size. Estimates of the particle size of synthetic mackinawite vary depending on the measurement method, ranging from 3.5 nm (Jeong et al., 2008) to 400 nm (Wolthers et al., 2003). Yet, the median hydraulic conductivity of ZVI PRBs is about 31 m/day (Henderson, 2010), which based on the KozenyeCarman equation (Freeze and Cherry, 1979) and a porosity of 0.35, yields a typical particle diameter for a PRB of about 2.5 104 m. To form suitably-sized particles, it may be possible to coat larger particles with synthetic mackinawite. Studies investigating the formation of coatings of iron oxides on silica sands, for example, have found that the presence of such coatings increases the ability of natural sands to remove the targeted contaminants. To optimize the coatings, these studies have varied parameters such as pH, aging time and initial iron concentration (e.g., Scheidegger et al., 1993; Lo et al., 1997; Xu and Axe, 2005). The effect of pH seems to be critical. The maximum coating for goethite was achieved at a pH near its pHpzc, with an abrupt decrease occurring at values of pH higher than the pHpzc where the surface charge of both silica sand and goethite becomes negative (Scheidegger et al., 1993). pH influenced not only the quantity deposited, but also the coating strength, with the oxide coating showing an optimum coating pH between 8 and 12 (Scheidegger et al., 1993; Kuan et al., 1998). Aging time has been varied from several hours (Gupta et al., 2005; Herbel and Fendorf, 2006) to several days (Szecsody et al., 1994; Xu and Axe, 2005). In addition, the initial Fe concentration has been varied (Lo et al., 1997); but both aging time and initial Fe concentration seem to have a lesser effect than pH (Xu and Axe, 2005). Another concern is whether the coating has reactivity similar to that of the unattached materials. Edwards and Benjamin (1989) found them to be comparable, whereas Herbel and Fendorf (2006) compared their data for the uptake of As(III) on ferrihyrdite-coated sands
to that of Raven et al. (1998) for nanoparticulate ferrihydrite, and noted an 80% decrease. To develop FeS as a PRB material, the coating of synthetic mackinawite onto natural silica sand was investigated as a function of pH and initial FeS concentration. Previous studies have focused on coatings of iron and other oxides; comparatively few have focused on sulfide coatings. Vaishya and Gupta (2002) examined the possibility of coating quartz sand by adding a combination of BaSO4 and Fe2(NO3)3 to increase the sorption of As(III). This medium was developed for an above ground reactor; the introduction of a reactive medium containing barium into the subsurface would prove problematic as barium is a regulated water contaminant. Thus, this study evaluated an alternative method for the deposition of FeS onto sand. To determine the amount of FeS deposited as a coating, the iron sulfide must be extracted. Extraction methods for determining the amount of iron sulfide in soils have been summarized by Cornwell and Morse (1987). While 100% recoveries were reported for amorphous FeSeS and mackinawite-S using hot or cold 6 N HCl extractions, incomplete recoveries were noted for greigite (Fe3S4) unless a reducing agent was added. Furthermore, dried FeS was found to be less extractable in HCl than wet FeS, which Rickard and Morse (2005) explained as resulting from less available pore space and a stronger static charge for dried FeS. To overcome the limitations of single acid extractions, Cooper and Morse (1998) showed that sequential HCl and HNO3 additions produced a more effective digestion for those iron fractions that were not completely extracted in a single step. Alternatively, a single extraction with a 3:1 concentrated solution of HCl and HNO3 (i.e., aqua regia) can also be effective for extracting metals from natural soils (Ramos, 2006; Tume et al., 2006). Because of the various methods for the extraction of Fe, it was necessary to evaluate these methods to quantify the effectiveness of various coating conditions. Additionally, to compare the morphology and chemical properties of synthetic nanoparticulate mackinawite with the FeS-coated sands, surface characterization was implemented. Surface coatings were analyzed by scanning electron microscopy and X-ray photoelectron spectroscopy. These two methods have been commonly applied to characterize iron oxide coatings on sands (Scheidegger et al., 1993; Xu and Axe, 2005). Finally, to evaluate whether the reactivity of synthetic mackinawite is similar as a coating, arsenic removal by FeScoated sand was investigated as a function of pH and compared to that of suspensions of nanoparticulate FeS.
2.
Materials and methods
2.1.
Sand preparation and mackinawite synthesis
Wedron 510 silica sand (Wedron Silica, Wedron, IL) was used as the natural sand substrate. This sand is comprised of rounded, predominantly silica grains, coated with a thin film of iron oxide as evidenced by the slight orange tint (99.65% SiO2, 0.065% Al2O3 and 0.018% Fe2O3; Wedron Silica, Wedron, IL). To prepare the sand for coating, it was sieved to obtain a geometric mean grain size of 0.15e0.22 mm and then rinsed repeatedly with Milli-Q water (obtained by passing distilled,
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deionized water through Milli-Q cartridge filters [Millipore, Billerica, MA]) until the rinse water was clear. To assess the impact of the natural iron-oxide coating in the formation of the FeS coating, chemically-washed sand (CWS) was prepared using a three-step procedure. The sand was soaked sequentially in 1 N sodium dithionite, then 12 N HCl and finally 15% H2O2, for 24 h each, based on the recommendations by Edwards and Benjamin (1989) and Kunze and Dixon (1986). After each step, the sand was rinsed with MilliQ water 20e30 times. The sand prepared in this manner contained less than 1.1 103 mg Fe/g sand, about 1% of that which existed originally (1.2 101 mg Fe/g, based on sequential extraction with 12 N HCl þ HNO3). FeS was synthesized inside an anaerobic chamber, maintained with a 5%H2/95%N2 atmosphere, by mixing 2.0 L of a 0.57 M FeCl2 solution with 1.2 L of 1.1 M Na2S solution (Butler and Hayes, 1998). The mixture was mechanically stirred for three days with a magnetic stirrer and then centrifuged at 10,000 rpm for 15 min to separate the solid from liquid. After decanting the supernatant liquid, the solid was rinsed with Milli-Q water multiple times until the electrical conductivity of rinsing solution was below 1 mS/cm (typically within 5 rinses). After freeze-drying, the FeS solids were sealed in glass vials capped with Teflon-coated butyl rubber septa and stored inside the anaerobic chamber until used. The specific surface area and particle size of mackinawite prepared using this method were determined by Jeong et al. (2008) as 103e424 m2/g and 3.5e21.7 nm (diameter or length), respectively, depending on the measurement technique.
2.2.
Coating of sands with FeS
To coat the Wedron sand with FeS, 32.5 mL of a 2 g/L FeS solution and 32.5 mL (w50 g) of either the natural or chemically-washed sand were combined in a 50 mL batch reactor tube and continuously mixed with an end-over-end rotator for three days. The aging time of three days was determined based on the clarity of the solution, which suggested that the majority of the FeS particles were either attached on the sand surface or had aggregated into larger particles and settled out. Then, the pH of the FeS/sand mixture was measured, the mixture centrifuged, and the supernatant discarded. The coated sands were then subjected to either no rinsing, short-
term rinsing (six consecutive Milli-Q water rinses within 1 h), or long-term rinsing (an additional one-day contact with MilliQ water following the short-term procedure). The coated sands were then dried inside the anaerobic chamber. The optimum coating pH was determined by contacting the 2 g/L FeS suspension in a 1:1 volume mixture with the natural Wedron sand over the pH range of about 4.5e6.5, as the optimal coating pH was expected to be near 5 given that this is the pHpzc of mackinawite synthesized with the same method utilized here (Gallegos et al., 2007). The pH of the solution was adjusted within this range by titrating with 0.8 N HCl. After three days of aging, the solids were then separated from the solution by decanting the supernatant, dried without rinsing, and analyzed for the amount of Fe. The impact of varying the volume ratio (from 1:1e1:4) of sand to FeS suspension was tested at the optimum coating pH, with a 1:1 ratio corresponding to 32.5 mL of sand (w50 g) to 32.5 mL of the 2 g/L FeS suspension. Small amounts of 0.08 N HCl were added as needed to maintain a clear supernatant to promote selfaggregation and particle sticking to the sand. After three days, the solids were separated from the solution, dried without rinsing, extracted and analyzed for the total amount of Fe. In order to measure the amount of Fe on the sand after coating with FeS, six acid extraction procedures were evaluated (Table 1). All were performed by mixing 3 g of the coated sand with the acid extractants listed in Table 1 in 15 mL polypropylene centrifuge tubes and allowing the mixture to sit without further agitation for a specified amount of time (Table 1). Subsequently, the extracting solution and solid were separated and the solid rinsed with Mill-Q water. The extracting and rinsing solutions were combined and the total amount of Fe was measured using an inductively coupled plasma mass spectrometer (ICP-MS) (Perkin Elmer, Waltham, MA). In all cases, the amount of the coating is reported as the total mass of Fe present on the surface minus the mass of Fe originally present in the natural iron-oxide coating on the Wedron 510 sand.
2.3. Microscopic and spectroscopic characterization of sand coatings Scanning electron microscopy (XL30FEG, Philips, Hillsboro, OR) was utilized to study the surface morphology of natural
Table 1 e Relative efficiency of acid extraction methods. Extraction acid 1 N HCl 6 N HCl 12 N HCl HNO3 12 N HCl þ HNO3 sequential extraction
Aqua regia
Extracted range [avg] (%)a
Standard error (%)
Description of method
Exposure time
Soaked in 1 N HCl Soaked in 6 N HCl Soaked in 12 N HCl Soaked in concentrated HNO3 Soaked in HCl for 15 min, decanted HCl in separate jar for Fe analysis, repeated same with concentrated HNO3 Soaked in 3:1 sol’n HCl þ HNO3
1 day 1 day 1 day 1 day 30 min
42.9e62.2[56.3] 68.4e78.5[73.4] 69.2e82.4[78.2] 63.8e87.3[76.7] 99.0e101.0 [100]
4.74 2.11 2.05 3.80 0.50
1h 1 day
81.5e86.5[84.0] 99.2e100.0 [99.6]
2.50 0.38
a Expressed as a percent normalized by the maximum amount of Fe extracted using aqua regia for 1 day of 1.22 mg Fe/g sand.
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Wedron sand and FeS-coated sand. The samples were prepared in the anaerobic chamber and transferred to the microscope using air-tight containers to minimize the exposure of the sample surface to oxygen. A Kratos (Manchester, England) Axis Ultra X-ray photoelectron spectrometer (XPS), with an Al-Ka line (1486.6 eV) as the radiation source, was used to examine the chemical composition and oxidation state of Fe, S, O, Si and C on the surface of FeS, FeS-coated sand (coated at the optimum pH using a 1:4 volume ratio), natural Wedron sand and chemically-washed Wedron sand. Survey and narrow XPS spectra scans were obtained with analyzer pass energies of 160 and 20 eV, respectively. Energies were corrected for charging effects using the reference peak of adventitious carbon C ls with a binding energy of 284.6 eV. Raw spectra were smoothed and then fit using a Shirley baseline and a GaussianeLorentzian peak shape. XPS spectra were fit using the binding energies and FWHM (full width at half maximum) values reported in the literature for the four primary iron species for iron sulfides: Fe(II)eS, Fe (II)eO, Fe(III)eS, and Fe(III)eO (Pratt et al., 1998; Mullet et al., 2004). In the Fe 2p3/2 spectra, Fe(II)eS and Fe(II)eO were modeled as single peaks at 707.3 0.1 and 708.3 0.1 eV representing a low-spin configuration of Fe(II) while the presence of Fe(III)eS was fit using the multiplet peaks located at 708.5, 709.5, 710.6 and 711.6 eV and peak area ratios of 1, 0.68, 0.24 and 0.11, respectively (Herbert et al., 1998; Mullet et al., 2004). These multiplet peaks are expected for high spin Fe(III) (Gupta and Sen, 1974). The Fe(III)eO contribution was also fit with four components located at 711.0 eV, 712.0 eV, 713.1 eV and 714.1 eV, using the same peak area ratio as reported by others (Herbert et al., 1998; Mullet et al., 2004). The S 2p spectra were modeled as a doublet (2p1/2 and 2p3/2) separated by a spin-orbit splitting of 1.2 eV. The peak area of 2p1/2 was constrained to be half that of the 2p3/2. The O 1s spectral contributions from FeS were fit with three components at 529.5, 531.0 and 532.2 eV for lattice oxide oxygen, hydroxide oxygen, and adsorbed water oxygen, respectively (Mullet et al., 2002,2004), while the O 1s from the silicate sands was modeled with contributions at 533, 532 and 531 eV, representing >SiOHþ, >SiOH0, and >SiO surface groups, respectively (Duval et al., 2005). Since the Si 2p spectra did not show any shift from one sample to another, they were fit using one primary peak at 102.7 eV.
2.4.
PHREEQC simulation of FeS coating
The FeS coating procedure was simulated using PHREEQC thermodynamic software (Parkhurst and Appelo, 1999). The simulation was set up assuming 1 L volume of 2 g/L FeS as mackinawite at pH 7 and 1 kg of Wedron sand. Wedron sand was assumed to contain the average amount of 2.0 106 mol Fe/g sand and the naturally existing iron-oxide coating was assumed to be goethite (aeFeOOH). Except for mackinawite, the MINTEQ.V4 thermodynamic database for the PHREEQC was used. In the case of mackinawite, however, a log K value of 3.6 for the reaction FeS þ Hþ ¼ Fe2þ þ HS was assumed based on fitting iron solubility data for mackinawite-coated sand as a function of pH (Hayes et al., 2009). To simulate the coating procedure, the amount of HCl added during the titration of the coating was calculated in moles and added in
the PHREEQC modeling. The acid added from this model titration lead to a pH of 5.5, the typical coating pH. Iron solid phases that were considered included a variety of ferric oxyhydroxides such as ferrrihydrite, maghemite, hematite, goethite, lepidocrocite, mixed-iron hydroxides including magnetite, several ferrous hydroxides solids, and iron sulfide phases including am-FeS, mackinawite, and greigite. Additional details on the modeling can be found in Supporting information.
2.5.
As(III) uptake on FeS-coated sand
The uptake of arsenic on nanoparticulate FeS and FeS-coated sand was measured at pH 5, 7, and 9. To maintain the pH at the desired value, buffer solutions were utilized consisting of 0.1 N acetate (pH 5), 0.1 N 3-(N-morpholino)proponesulfonic acid (MOPS) (pH 7), and 0.1 N 2-(cyclohexylamino)ethanesulfonic acid (CHES) (pH 9 for FeS-coated sand) or 0.1 N borate (pH 9 for nanoparticulate mackinawite). Aliquots from a 50 g/L stock solution of mackinawite were added to each buffer solution to achieve 10 mL of a 1 g/L mackinawite suspension in 15 mL polypropylene reactor tubes. Each tube was then spiked with varying amounts of NaAsO2 stock solution to achieve a final concentration ranging from 6.7 107 M to 3.0 103 M As(III). Test tubes with 5 g of FeS-coated sand and 10 mL of buffer solution were spiked with As(III) stock solution to achieve a concentration range of 1.3 106 M e 6.7 104 M initial As(III) concentrations for pH 5, and 1.3 106 M e 2.6 104 M for pH 7 and 9. The reaction tubes were then mixed on an endover-end rotator for two days. A rapid equilibration time has been reported for the uptake of As(III) onto mackinawite (Wolthers et al., 2005); preliminary studies conducted here confirmed an equilibration time of under an hour (Gallegos, 2007). The supernatant in tubes was then filtered through a 0.1 mm nylon filter, diluted, acidified with HNO3 and then analyzed for arsenic by ICP-MS. All the experimental steps, except the acidification of the supernatant for ICP-MS analysis, were performed in the anaerobic chamber. The As(III) removal capacity of FeS-coated sand was compared to that of the suspensions of nanoparticulate FeS on a per gram of FeS basis.
3.
Results and discussion
3.1.
Quantifying the amount of Fe by acid extraction
The relative quantities of Fe extracted using the various methods evaluated in this study are presented in Table 1. For the single acid extractions, the best results were obtained for the 1 day 6 N and 12 N HCl digestion, although only about 70% of the total Fe coated on the sand was extracted. The incomplete dissolution using these single acid extractions may be due to one or a combination of the following reasons: the inaccessibility of some of the FeS due to aggregation or deposition into small pores or crevices of the sand; the formation of strong bonds between the FeS particles and the iron oxides on the natural sand surface; or the partial oxidation of FeS during the coating process, producing iron sulfur and oxide phases that were more resistant to acid extraction. Such incomplete extraction of metal sulfides by single strong
597
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1.2 Relative mass of Fe in coating
acid extractions has been reported previously (Huerta-Diaz and Morse, 1990; Cooper and Morse, 1998) with larger particle aggregates and resistant sulfide phases being implicated in lower acid extraction efficiencies. Considerably greater removals were achieved using sequential acid extraction, similar to the results of Cooper and Morse (1998). Interestingly, comparably high removals were achieved using a single extraction utilizing a 3:1 concentrated solution of HCl and HNO3 together (aqua regia) rather than sequentially, although the contact time needed to be increased from 30 min to one day. Because this extraction solution achieved high removals, but was simpler in that it eliminated the transfer of the solids from one solution to another, this technique was adopted.
1 0.8 CWS-short term rinse CWS-long term rinse
0.6
CWS-no rinse NS-short term rinse
0.4
NS-long term rinse NS-no rinse
0.2 0
0
1
2
3
4
Time (day)
Effect of natural iron-oxide coating
To assess the impact of the natural iron-oxide coating of the Wedron 510 sand, both the natural and the chemicallywashed sands were coated with FeS. During the coating procedure, some attachment of FeS began within minutes (as evidenced by the settling of black sand in the bottom of the reaction vessel), but the majority of the FeS particles still existed as a stable suspension in a solution phase. As the solutions aged over three days, the FeS suspension became progressively clearer, and finally completely clear, suggesting that most of the FeS particles had settled out of solution attached to the sand surface or as self-aggregated particles. Even though the initial pH value of the FeS solutions was between pH 5 and 5.5, it should be noted that, after mixing with the natural Wedron sand, the final equilibrium pH increased to between 6.8 and 7.5. This pH increase was not observed during the coating procedure for the chemicallywashed sand. A logical explanation for this result is that the ferric-oxide coating on the natural sand undergoes a redox reaction with FeS with protons being consumed during this process. Some of the FeS easily detached when the sand was subsequently rinsed with Milli-Q water (Fig. 1). The loss of coating by rinsing was especially prominent with the chemically-washed sands, with only 2e3% of coating retained regardless of the rinsing procedure. This result was somewhat surprising in light of previous studies where acidwashed sands were coated using procedures that included rinsing, with, for example, Edwards and Benjamin (1989) reporting a retention of 75% of the added iron. To obtain this level of retention, the natural Wedron sand needed to be utilized without treatment. The untreated sand retained about 65% and 90% with long-term and short-term rinsing, respectively. These results demonstrate that, although the quantity of the iron oxide measured on the sand surface was relatively small, 0.12 mg Fe/g sand, (the range of Fe in natural oxide coatings on geologic materials was reported as 0.07e44 mg Fe/g sand Xu and Axe, (2005)), its presence was critical to the formation of a stable coating of FeS on the sand. Alternatively, perhaps the requisite layer might have been formed through sequential exposures to the coating solution, as the successful iron-oxide coating of acid-washed sand that Edwards and Benjamin (1989) reported involved 15 coating cycles, rather than the single cycle used here.
Fig. 1 e Mass of Fe coated on chemically-washed Wedron 510 sand (CWS) and untreated Wedron 510 sand (NS [natural sand]) as a function of the length of aging and the amount of rinsing, normalized by the mass of Fe coated onto untreated Wedron 510 sand without rinsing as extracted by aqua regia, equal to 1.22 mg Fe/g sand.
3.3.
Optimal pH of coating
Because the natural Wedron sand with no rinsing provided the maximum coating, this variation of the procedure was further optimized as a function of pH and volume mixture ratios. Fig. 2 shows the change in coating quantity as a function of pH over the range of about 4.3e6.2. Between pH values of 5 and 5.5, visibly black coatings formed on the natural sand, but the amount of FeS coverage varied, with the sand coated at pH 5.5 containing about 25% more than the sand coated at pH 5 (Fig. 2). In this pH range, mackinawite particles self-aggregated in solution, as evidenced from the particle settling behavior. At higher pH values, the FeS particles tended to be
1.4
Mass of Fe in coating (mg Fe/g sand)
3.2.
1.2 1.0 0.8
0.6 0.4 0.2 0.0
4
4.5
5
5.5
6
6.5
7
pH of 2g/L FeS suspension Fig. 2 e Effect of pH of the FeS suspension on the quantity of Fe coated onto Wedron 510 sand with no rinsing. The optimum coating occurred at a pH value of 5.5, as indicated by the arrow.
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highly dispersed, stable with respect to settling, and did not deposit on the Wedron sand, as evidenced by the blackcolored solution left in the supernatant of the FeS/sand mixture at the higher values of pH. This optimum coating pH value is thought to be related to the pHpzc of the coating as well as to the pHpzc of the surface to be coated. For FeS, the pHpzc value is near pH 5, while for ferric-oxide-coated sand it is expected to be near pH 8 (Gallegos et al., 2007). In this system, the solubility of FeS may also be an important consideration in determining the optimum coating pH. Below pH 6, the solubility of FeS begins to increase dramatically and at pH 5, it can be significantly dissolved (Gallegos, 2007). Thus, the optimum pH of the FeS solution for coating was determined to be pH 5.5, which is near the pHpzc but where FeS solubility is not excessive. The FeS-coated sand so produced yielded an average coating amount of 1.26 0.044 mg Fe/g sand.
3.4.
Effect of mass of FeS solid on coating
To evaluate the impact of mixture volume conditions, the ratio of the volume of sand and 2 g/L FeS suspension was adjusted from 1:1 to 1:4. The amount of FeS coating was found to increase by adding a greater total mass of FeS solid (Fig. 3). Since the addition of larger amounts of FeS resulted in higher values of pH, increasing amounts of acid were needed to titrate the system back near pH ¼ 5.5 for optimal coating. The maximum coating was found to be 1.2, 2.5 and 4.0 mg Fe/g sand for the 1:1, 1:2 and 1:4 ratios, respectively. This indicates that the coating amount could be enhanced by over a factor of three by increasing the FeS to sand ratio by a factor of four. However, these data also indicate that there is a point of diminishing returns in that doubling the amount of FeS in suspension doubled the amount of Fe in the coating; yet increasing the amount by a factor of four did not increase the amount in the coating commensurately, but rather by a factor of 3.3.
3.5.
Microscopic and spectroscopic sand characterization
SEM images of FeS-coated sand illustrate patchwise coating, with aggregated nanoparticles of FeS partially covering the sand surface (Fig. 4). The indentations on the silica surface appear to be effective locations for the build-up of FeS aggregates; a similar observation was made by Xu and Axe (2005) for iron-oxide coatings on silica. An increasingly thicker layer of FeS coating is clearly visible as the coating amount increased from 1.2 to 4.0 mg FeS/g of coated sand (Fig. 4bed). To characterize the surface of the coated sands, XPS spectra were obtained for FeS, FeS-coated sand (4.0 mg FeS/g sand), natural Wedron sand and chemically-washed Wedron sand. The XPS survey scan of the FeS and FeS-coated sand indicated the presence of O (38.1%), C, Na (28.2%), S (17.4%), and Fe (16.3%) and the presence of O (57.3%), C, Na (1.4%), S (12.9%), Fe (10.6%) and Si (17.8%), respectively (with the normalized surface atomic composition shown in parentheses for O, Na, S, Fe, and Si). The presence of Na was from incomplete rinsing of the FeS after its synthesis and C was adventitious. The scan of natural Wedron sand showed the presence of O, C, Na, Al, Ca, Fe and Si, while that of the chemically-washed sand consisted of only O, C and Si. The narrow scan region spectra for Fe 2p3/2, S 2p and O 1s are shown in Fig. 5. The binding energies and the peak areas of the species as a percentage of the total areas are listed in Table 2.The Fe(III)eO species is the sole contributor for the natural Wedron sand sample but represents only 6.2% of the spectrum for the nanoparticulate FeS (Fig. 5). The Fe(III)eO surface species contribution increases to 14.2% for the Wedron sand coated with FeS, relative to the nanoparticulate FeS sample. At the same time, the Fe(II)eO and Fe(II)eS species contributions in the FeS spectrum, which are 21.5% and 35.1%, respectively, decrease to 19.3% and 29.4% in the spectrum for FeS-coated Wedron sand. These results suggest that, upon coating, a partial oxidation of the Fe(II) of FeS occurs. The presence of significant contributions of Fe(III)eS in both the nanoparticulate FeS (37.2%) and FeS-coated sand (36.7%) samples suggests that this partial surface oxidation of FeS occurred during the preparation of the samples. In the coating procedure, the FeS suspension was acidified to pH 5.5 with HCl. Acid addition may cause the oxidation of mackinawite to greigite (Fe3S4, e.g., FeIIFeIII2S4) through the following oxidation reaction: 4FeS þ 2Hþ ¼ H2(g) þ Fe3S4 þ Fe2þ
(1)
This transformation of mackinawite to greigite at pH 5 was previously hypothesized based on XRD evidence (Gallegos et al., 2007) and later supported by modeling results (Gallegos et al., 2008). It is also possible that oxidation of FeS by water could result in the formation of mixed-iron oxides, such as magnetite, via anoxic corrosion as follows: Fig. 3 e Effect of the volume ratio on the FeS coating of untreated Wedron 510 sand. The ratio represents 32.5 mL of sand (w50 g) to varying volumes of a 2 g/L FeS suspension, with the 1:1 ratio representing 32.5 mL of sand to 32.5 mL of the 2 g/L FeS suspension. Circled points represent the maximum coverage at each volume ratio and correspond to the SEM images in Fig. 4.
3FeS þ 4H2O ¼ Fe3O4 þ H2(g) þ 3H2S
(2)
However, the observations that the reaction of FeS suspensions with the natural Wedron sand led to an increase in pH (but not the reaction of FeS with the chemically-washed sand), suggest in addition, that a redox reaction with the
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 5 9 3 e6 0 4
599
Fig. 4 e SEM images of (a) natural Wedron 510 sand, (b) FeS-coated sand with 1.2 mg Fe/g sand (maximum coating at a 1:1 volume ratio), (c) FeS-coated sand with 2.4 mg Fe/g sand (maximum coating at a 1:2 volume ratio) and (d) FeS-coated sand with 4.0 mg Fe/g sand (maximum coating at a 1:4 volume ratio).
ferric-oxide coating on the sand (illustrated with goethite) may have occurred: FeS þ 2FeOOH ¼ Fe3O4 þ H2S
(3)
At pH values less than 7, loss of H2S gas to the head space would lead to an increase in pH while producing magnetite. While the above reactions are consistent with the XPS data and coating process, exposure to trace amounts of oxygen could also lead the production of magnetite and greigite, as reported by Boursiquot et al. (2001) in studies of the dry oxidation of mackinawite by oxygen. Additional spectral features collected by XPS further confirm the nature of the surface coating. For example, both FeS and FeS-coated sand displayed similar S 2p spectra (Fig. 5), indicating the predominant presence of sulfide on the surface with some conversion of sulfide to polysulfide species during the coating process, possibly due to the presence of trace amounts of oxygen. The O 1s spectra of each sample support the notion of the patchiness of the FeS coating on the sand (Fig. 5). The main peak of O 1s of FeS-coated sand has the same position as both the chemically-washed and the natural Wedron sand. However, the O 1s spectra show a broadening caused by the OH component, consistent with the presence of OH from the hydroxylation of FeS.
3.6.
Simulation of solid phase composition of coating
To provide additional support for the above reactions, thermodynamic calculations simulating the coating experiments
were performed with equilibrium modeling using PHREEQC (Parkhurst and Appelo, 1999). The modeling results (see SI for details) indicate that a majority of the FeS remains as mackinawite when reacted with iron oxides with the formation of small amounts of greigite and magnetite at pH 5.5, the coating pH. All of the original ferric iron coating modeled as goethite dissolved to form the reaction products. This is consistent with the interpretation of the Fe 2p XPS data that the FeScoated sand consists of at least two different surface iron phases, predominantly mackinawite with smaller amounts of iron oxidation products such as greigite and magnetite.
3.7.
Arsenic removal capacity of FeS-coated sand
As(III) removals at pH 5, 7 and 9 were measured using nanoparticulate FeS and FeS-coated sand (coated at the optimum pH and a 1:1 volume ratio) and compared on an FeS unit mass basis (Fig. 6). To derive removal capacities, the Langmuir sorption model was fit to the data. Although As(III) removal is known to be caused by a combination of adsorption and bulk precipitation over this pH range (Gallegos et al., 2007, 2008), the Langmuir model fit the data well, with values of R2 of equal to or greater than 0.89. Therefore, qm, the total sorption capacity (Table 3) obtained by fitting the Langmuir model to the data was considered to be a good estimate of the total amount of As(III) removed by adsorption and/or precipitation. Fig. 6 shows that As(III) removal by both FeS and FeScoated sand is highly pH dependent. At pH 5, bulk precipitation of realgar (AsS) from a reaction between dissolved sulfide
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Fig. 5 e Narrow scan region spectra of Fe 2p, S 2p and O 1s spectra of (a) FeS (mackinawite), (b) FeS-coated sand, (c) untreated Wedron sand and (d) chemically-washed Wedron sand surfaces.
and aqueous As(III) has been previously proposed as the primary mechanism for the high uptake (Gallegos et al., 2007, 2008): 3FeS þ H3AsO3 þ 3Hþ ¼ 1/2Fe3S4 þ AsS þ 3/2Fe2þ þ 3H2O (4)
However, at pH 5, the arsenic removed per g of FeS was 70% less as a coating, similar to the results of Herbel and Fendorf (2006). This reduction may be attributed to the change in the iron mineral composition based on XPS results above, or the possible reduction in accessibility of FeS due to its aggregation
Table 2 e Binding energies (BE), and peak areas (I), expressed as a percent of the total area, for Fe 2p3/2, S 2p and O 1s X-ray photoelectron spectra of nanoparticulate FeS (mackinawite), FeS-coated sand, untreated Wedron sand and chemicallywashed Wedron sand. Sample
Fe BE (eV)
Nanoparticulate FeS
FeS-coated sand
Wedron-sand
Chemically-washed Wedron sand
I (%)
S Species
706.0 706.6 709.5 711.1 706.8 708 709.4 711.5 711.5
33.4 13.2 44.7 8.7 26.4 25.6 37.8 10.1 100.0
Fe(II)eS Fe(II)eO Fe(III)eS Fe(III)eO Fe(II)eS Fe(II)eO Fe(III)eS Fe(III)eO Fe(III)eO
e
e
e
BE (eV)
I (%)
O Species
BE (eV)
I (%)
2-
Species
160.9 162.5 164.3
87.1 11.0 2.0
S S22 S2n
259.5 531.0 532.5
9.5 84.2 6.3
O2 OH H2 O
160.9 161.9 164.0
68.2 25.0 6.8
S2S22 S2n
529.7 531.3 532.1
9.5 20.2 70.3
O2 OH, SiOH0 H2O, >SiO-
e
e
e
e
e
e
e 531.3 532.1 532.1
e 15.7 82.5 100
O2 OH, SiOH0 H2O, >SiOH2O, >SiO-
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Table 3 e Langmuir isotherm model parameters for arsenic uptake by nanoparticulate FeS and FeS-coated sand (1:1 volume ratio). qm
Kl
pH (mg As(III)/g FeS) (L/mg As(III)) Coated sand
Nanoparticulate FeS
5 7 9 5 7 9
41.7 10.7 12.6 137.0 16.0 3.2
1.39 0.78 1.08 6.08 8.23 1.45
R2 e 0.99 0.95 0.96 0.99 0.99 0.89
Parameters obtained by fitting the Langmuir model, qeq ¼ qm Kl Ceq =ð1 þ Kl Ceq Þ where, qeq ¼ amount of As(III) sorbed by solid (mg/g); qm ¼ total As(III) sorption capacity (mg/g); Kl ¼ Langmuir constant (L/mg); Ceq: the As(III) concentration left in solution(mg/L), to the data presented in Fig. 6.
As uptake (mg As/g FeS)
B
FeS -coated sand 50 40 30 pH 5
20
pH 7
10
pH 9
0
0
10
20
30
As concentration in solution (mg As/L) Fig. 6 e Mass of As removed versus the solution concentration after 2 days equilibration at pH 5, 7 and 9 using (A) FeS (dose [ 1 g FeS/L) and (B) FeS-coated sand (dose [ 500 g FeS-coated sand/L). Error bars represent the standard deviation of the measurements.
on the sand surface. Another possibility is that arsenic is being removed by the formation of a different solid phase such as orpiment (Han, 2009). As the pH increases, As(III) removal due to bulk precipitation of arsenic sulfide decreases as the FeS solubility abruptly decreases above pH 6. Instead, a surface sorption mechanism is thought to become increasingly more important as the pH increases above 6 (Gallegos et al., 2007). At pH 7, the FeS-coated sand has about 70% less capacity than the nanoparticulate FeS, perhaps resulting from less accessible FeS surface functional groups in the aggregated nanoparticles. Interestingly, at pH 9, the FeS-coated sand shows comparable removals to that at pH 7 and approximately four times more removal than the nanoparticulate FeS. This result may be attributed to the presence of other oxidized iron mineral phases such as the naturally existing oxide coating on the Wedron sand or the presence of a secondary mineral phase from mackinawite oxidation. Recent work has shown that As(III) uptake is enhanced in the presence of magnetite, although this enhancement may only be temporary if prolonged exposure to
reducing conditions prevails (Tufano and Fendorf, 2008) in the absence of sulfide. FeS has been demonstrated as effective for the removal of arsenic from water under anoxic conditions (Bostick and Fendorf, 2003; Wolthers et al., 2005; Gallegos et al., 2007, 2008). To create a PRB material using FeS, this study demonstrates that sands with a natural iron-oxide coating can be successfully coated with mackinawite. Since the sand substrate can be sieved in a range of particular diameters, it is possible to create PRB media that, when packed, will have the desired design permeability of a factor of 10 greater than the surrounding aquifer (Gavaskar et al., 1998). The As(III) removal capacities of FeS-coated sand (based on fitting the Langmuir isotherm model to the data) obtained in this study are 41.6, 10.7 and 12.7 mg As/g FeS (or 0.052, 0.013, and 0.016 mg As/g FeS-coated sand) using sands (d ¼ 0.15e0.22 mm) coated at pH 5.5, no rinsing and a volume ratio of 1:1 at pH 5, 7 and 9, respectively. Presumably greater capacities could be achieved for the sands coated at higher volume ratios since more FeS would be present on the sand surface. The evaluation of these capacities by comparing them to that of other materials is not straightforward. Even though much work has been devoted to the study of arsenic sorption using various absorbents (Mohan and Pittman (2007) present an extensive summary), the studies utilize a wide range of concentration ratios between adsorbate/adsorbent, pH, and redox conditions, all of which influence the sorption capacity (Hartzog et al., 2009). Removal capacities for As(III) for ironcoated sands under oxic conditions around neutral pH are, for example, 0.041 mg/g (d ¼ 0.6e0.8 mm) (Thirunavukkarasu et al., 2003), 0.028 mg/g (d50 ¼ 0.5 mm) (Gupta et al., 2005) and 0.096 mg/g for a barium sulfate-modified iron-oxide-coated sand (d50 ¼ 0.5 mm) (Vaishya and Gupta, 2002). These removal capacities are of the same order of magnitude as those for FeScoated sands, notwithstanding the fact that they were obtained under oxic conditions where the removal of As(III) is much more efficient than under anoxic conditions (Bang et al., 2005). While these results show promise for removing As(III) when present in simple background electrolyte solutions, further work is needed to assess the effectiveness of FeS-coated sand for removing As from natural groundwater and when As(V) species are present.
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For the uptake of As(III) by ZVI under anoxic conditions, removal capacity ranged from 0.8 to 7.5 mg/g, depending on the solution conditions and the ZVI used (Nikolaidis et al., 2003; Lien and Wilkin, 2005). Interestingly, in the ZVI column studies reported by Nikolaidis et al. (2003), high concentrations of sulfide as well as a hint of hydrogen sulfide gas were found, apparently due to the presence of sulfate-reducing organisms. Spectroscopic measurements found that solid phase As was highly correlated with S, confirming that the presence of sulfide was key to the effective removal. In the study by Lien and Wilkin (2005), the formation of oxidation products on ZVI (in this case green rust) was found to be responsible for As uptake. As mentioned earlier, arsenic may ultimately be released by reductive dissolution from such passivation products under prolonged reducing conditions (Tufano and Fendorf, 2008). Furthermore, unintended reactions that occur with ZVI (Liang et al., 2005), but not with FeS-coated sands (Henderson, 2010), that result in excessive solids precipitation may reduce the effectiveness of ZVI in a PRB. Given these circumstances, FeS-coated sands may provide an attractive alternative to ZVI for arsenic removal in PRB applications in which long-term reducing conditions prevail.
Comparison of the XPS spectra of nanoparticulate FeS and FeS in the coating showed some oxidation of Fe, but no oxidation of S, suggesting a partial conversion of mackinawite to greigite or the formation of magnetite. Removal of As(III) by the FeS-coated sand showed similar pH dependent behavior as nanoparticulate FeS, with maximum removals occurring at pH 5. However, with nanoparticulate FeS, the removals at pH 9 are less than those at pH 7, whereas with the FeS-coated sands, the removals are comparable. The removal capacity of the FeS-coated sand was 30% that of the nanoparticulate FeS at pH 5 and 7, respectively. At pH 9, the removal was 400%, perhaps due to the sand’s natural oxide coating or to the presence of a secondary mineral phase from mackinawite oxidation. FeS-coated sands can provide comparable removals of As (III) under anoxic conditions to iron-oxide-coated sands under oxic conditions, despite the increased difficulty of removal under anoxic conditions. ZVI has been cited as having higher removal capacities, but these removal capacities might result from the formation of corrosion products which may ultimately release As(III) back into solution under prolonged anoxic conditions. Thus, FeScoated sands may provide an attractive alternative for arsenic removal in PRB applications in which long-term reducing conditions prevail.
4.
Acknowledgements
Conclusions
FeS has been shown to be effective at removing As(III) from water under the anoxic conditions that often prevail in groundwater. Because of its nanoparticulate nature, FeS must be deposited onto a substrate to form an appropriately sized material for a permeable reactive barrier. The focus of this study was to evaluate a range of conditions to optimize the mass of FeS deposited on a natural silica sand and then to evaluate the efficacy of the coated sand at removing As(III) relative to nanoparticulate suspensions of FeS. A sequential acid extraction method or a combined acid extraction treatment using 12 N HCl and concentrated HNO3, rather than a single acid extraction, is necessary for effective extraction of Fe incorporated as a part of an iron sulfide coating. A range of coating conditions were evaluated and the maximum amount of FeS that was deposited as a coating in this study was 4.0 mg Fe/g sand which occurred under the conditions of a volume ratio of 1:4 of sand titrated to a pH 5.5, 2 g/L FeS suspension, with no rinsing following solidliquid separation after a three-day contact period between the FeS suspension and the sand. Chemically washing the natural silica sand significantly reduced the stability of the coating, indicating that the natural iron-oxide coating on the sand was critical in the formation of optimum coating. Surface characterization by SEM and XPS showed the FeScoated sand has a patchwise coating that is predominantly comprised of FeS self-aggregrates along with a small fraction of an oxidized magnetite phase and portions of uncoated sand surface exposed.
The authors gratefully acknowledge the assistance of Tom Yavaraski (Department of Civil and Environmental Engineering) for help in developing the analytical methods for As and Fe analyses, and Udo Becker and Devon Renock (Department of Geological Sciences) for their guidance on the XPS data collection and analysis. This research was supported by the Strategic Environmental Research and Development Program (SERDP) under Department of Defense, Department of Army (gs1), Contract Number W912HQ-04-C-0035. This paper has not been subject to agency review; it therefore does not necessarily reflect the sponsor’s view, and no official endorsement should be inferred.
Appendix. Supplementary material Supplementary data related to this article can be found online at doi:10.1016/j.watres.2010.09.033
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 0 5 e6 1 7
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Kinetic assessment and modeling of an ozonation step for full-scale municipal wastewater treatment: Micropollutant oxidation, by-product formation and disinfection Saskia G. Zimmermann a,b,1, Mathias Wittenwiler a,b, Juliane Hollender a,2, Martin Krauss a,3,7, Christoph Ort a,6,4, Hansruedi Siegrist a,5, Urs von Gunten a,b,* a b
Eawag, Swiss Federal Institute of Aquatic Science and Technology, P.O. Box 611, 8600 Du¨bendorf, Switzerland Institute of Biogeochemistry and Pollutant Dynamics, ETH Zu¨rich, 8092 Zu¨rich, Switzerland
article info
abstract
Article history:
The kinetics of oxidation and disinfection processes during ozonation in a full-scale reactor
Received 23 March 2010
treating secondary wastewater effluent were investigated for seven ozone doses ranging
Received in revised form
from 0.21 to 1.24 g O3 g1 dissolved organic carbon (DOC). Substances reacting fast with
28 June 2010
ozone, such as diclofenac or carbamazepine (kP;O3 > 104 M1 s1), were eliminated within
Accepted 28 July 2010
the gas bubble column, except for the lowest ozone dose of 0.21 g O3 g1 DOC. For this low
Available online 5 August 2010
dose, this could be attributed to short-circuiting within the reactor. Substances with lower ozone reactivity (kP;O3 < 104 M1 s1) were only fully eliminated for higher ozone doses.
Keywords:
The predictions of micropollutant oxidation based on coupling reactor hydraulics with ozone
Micropollutant oxidation
chemistry and reaction kinetics were up to a factor of 2.5 higher than full-scale measurements.
Rct
Monte Carlo simulations showed that the observed differences were higher than model
Modeling
uncertainties. The overestimation of micropollutant oxidation was attributed to a protection of
Full-scale ozonation
micropollutants from ozone attack by the interaction with aquatic colloids. Laboratory-scale
Oxidation by-products
batch experiments using wastewater from the same full-scale treatment plant could predict
Disinfection
the oxidation of slowly-reacting micropollutants on the full-scale level within a factor of 1.5. The Rct value, the experimentally determined ratio of the concentrations of hydroxyl radicals and ozone, was identified as a major contribution to this difference. An increase in the formation of bromate, a potential human carcinogen, was observed with increasing ozone doses. The final concentration for the highest ozone dose of 1.24 g O3 g1 DOC was 7.5 mg L1, which is below the drinking water standard of 10 mg L1. N-Nitrosodimethylamine (NDMA) formation of up to 15 ng L1 was observed in the first compartment of the reactor, followed by a slight elimination during sand filtration. Assimilable organic carbon (AOC) increased up to 740 mg AOC L1, with no clear trend when correlated to the ozone dose, and decreased by up to 50% during post-sand filtration. The
* Corresponding author. Tel.: þ41 44 823 5270; fax: þ41 44 823 5210. E-mail addresses:
[email protected] (S.G. Zimmermann),
[email protected] (M. Wittenwiler), juliane.
[email protected] (J. Hollender),
[email protected] (M. Krauss),
[email protected] (C. Ort),
[email protected] (H. Siegrist),
[email protected] (U. von Gunten). 1 Tel.: þ41 44 823 5083; fax: þ41 44 823 5028. 2 Tel.: þ41 44 823 5493; fax: þ41 44 823 5893. 3 Tel.: þ49 341 235 1530; fax: þ49 341 235 45 1530. 4 Tel.: þ61 7 3345 4730; fax: þ61 7 3365 4726. 5 Tel.: þ41 44 823 5054; fax: þ41 44 823 5389. 6 Present address: The University of Queensland, Advanced Water Management Centre (AWMC), Qld 4072, Australia. 7 Present address: UFZ e Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig, Germany. 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.07.080
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disinfection capacity of the ozone reactor was assessed to be 1e4.5 log units in terms of total cell counts (TCC) and 0.5 to 2.5 log units for Escherichia coli (E. coli). Regrowth of up to 2.5 log units during sand filtration was observed for TCC while no regrowth occurred for E. coli. E. coli inactivation could not be accurately predicted by the model approach, most likely due to shielding of E. coli by flocs. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
In recent years, mitigation strategies to lower micropollutant loads from wastewater treatment plant (WWTP) effluents to receiving waters have been discussed intensively. Ozonation is a promising technique to upgrade WWTPs with a tertiary treatment step because it has been demonstrated to be efficient for the oxidation of pharmaceuticals and other micropollutants in drinking water (Ternes et al., 2002; Westerhoff et al., 2005) and wastewater (Hollender et al., 2009; Huber et al., 2005; Nakada et al., 2007; Ternes et al., 2003; Wert et al., 2009a,b). Oxidation does not usually result in complete mineralization, but in the formation of transformation products, which in many cases have a much lower biological activity than the parent compounds (Dodd et al., 2009; Huber et al., 2004; Lee et al., 2008). In addition to the formation of transformation products, other undesired by-products can be formed such as bromate, a potential human carcinogen, formed during ozonation of bromide-containing waters by a complicated mechanism involving both ozone and hydroxyl radicals (von Gunten, 2003a). N-Nitrosodimethylamine (NDMA) formation was reported during ozonation of drinking water (Schmidt and Brauch, 2008) and wastewater (Hollender et al., 2009; Yang et al., 2009), while N-Nitrosomorpholine (NMOR) formation occurred during ozonation of lake water (Zhao et al., 2008). Other organic by-products are formed from the oxidative breakdown of complex dissolved organic matter (DOM), which are usually readily biodegradable and can be measured as e.g. assimilable organic carbon (AOC) (Hammes et al., 2006, von Gunten, 2003a). AOC is an important water quality parameter for the biological stability of drinking water and can influence river water quality after discharge of ozonated wastewater. For all of these known oxidation by-products, no information is available on the extent and kinetics of their formation during ozonation of secondary wastewater effluent. Concerning the toxicity of ozonated secondary wastewater effluent, a decrease in specific and unspecific toxicity after ozonation was demonstrated by an in vitro test battery using different toxicological endpoints (Escher et al., 2009), while in vivo tests with rainbow trout suggested an increased toxicity that disappeared after sand filtration (Stalter et al., 2010). This can be explained by the formation of potentially toxic by-products from DOM oxidation, such as aldehydes, ketones and other oxygen-rich compounds (von Gunten, 2003a), which might not be enriched during solid phase extraction necessary for the in vitro test battery. These oxygen-rich compounds are typically removed during sand filtration (Hammes et al., 2006). Besides its role as an oxidant, ozone is mainly used in drinking water production because it is an effective disinfectant for viruses, bacteria and protozoa (von Gunten, 2003a). In
the present study, the main focus is on micropollutant oxidation, however, disinfection is an additional benefit especially in reference to the amended bathing water directive of the European Union (2006/7/EC). Both objectives can be achieved by ozonation at a reasonable and manageable cost as demonstrated by Hollender et al. (2009). Prediction of the oxidation and disinfection capacity of a full-scale ozonation step is of practical and scientific interest to ensure optimum performance. The design of the reactor as well as site-specific wastewater composition are important parameters influencing oxidation and disinfection performance of ozonation steps. von Gunten et al. (1997, 1999) developed a concept to predict the behavior of oxidants and micropollutants in an ozone reactor treating lake water for drinking water purposes. This concept is based on the coupling of reactor hydraulics with ozone chemistry and reaction kinetics, and has been successfully applied for the simulation of ozone reactors treating spring (Boller et al., 2000) and river water (Gallard et al., 2003). The objectives of the present study were to assess the oxidation and disinfection capacity of a full-scale ozonation step by quantitatively describing the kinetics of the following processes occurring during ozonation of secondary wastewater effluent: (i) micropollutant oxidation, (ii) oxidation byproduct formation, (iii) AOC formation, and (iv) disinfection. (v) Finally, two different approaches to predict ozone reactor performance in terms of micropollutant oxidation were tested: (a) a model based on the concept of von Gunten et al. (1997, 1999) which could also be used for disinfection prediction, and (b) spiked laboratory-scale batch experiments using secondary effluent from the same full-scale WWTP.
2.
Materials and methods
2.1.
Standards and reagents
All chemicals and solvents were of analytical purity (95%) and purchased from various suppliers. The micropollutants analyzed in the sampling campaigns are compiled in Table S1 of the Supplementary Information (SI).
2.2.
Ozone reactor
A detailed description of WWTP Wu¨eri in Regensdorf, Switzerland, including rapid sand filtration as final biological treatment step after ozonation was given by Hollender et al. (2009). A mixing chamber for final flocculation was retrofitted to a full-scale six compartments ozone reactor with a volume of 38 m3 (Fig. 1). A gaseous ozone/oxygen mixture was added in counter-current mode through a series of disc
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 0 5 e6 1 7
607
Fig. 1 e Scheme of the ozone reactor at WWTP Wu ¨ eri in Regensdorf, Switzerland.
diffusers into the first compartment. Since DOM e measured as dissolved organic carbon (DOC) - is the main matrix component determining the ozone consumption in wastewater, a DOC load-proportional ozone dosing was chosen to account for varying DOC concentrations in the secondary effluent. Hence, the flow to the ozone reactor was determined online by an ultrasonic device (Prosonic, Endress þ Hauser, Germany), and the DOC concentration was measured online by an S::can sensor (S::can, Austria) using UV absorption. The S::can sensor was calibrated using grab samples filtered to 0.45 mm and measured by a total organic carbon analyzer (Shimadzu, Switzerland). Seven sampling points (P1eP7) were distributed along the ozone reactor (Fig. 1), each consisting of a stainless steel pipe with an inner diameter of 5.5 mm reaching 80 cm into the ozone reactor. This resulted in residence times between 5 and 22 s in the sampling tubes, depending on the length and the flow within the tubes.
2.3.
Tracer tests
The hydraulic behavior of the ozone reactor was characterized using fluoresceine as a conservative tracer during Dirac pulse tracer tests and as a reactive tracer during a continuous tracer test. Further information can be found in Text S1 of the SI.
2.4.
ozone reactor (IN), along the ozone reactor (P2, P3, P7 or P3, P5, P7), and after sand filtration (SF). Depending on the ozone dose, either sampling point P2 or P5 was sampled. Dissolved ozone was quenched with sulfite (nitrosamine analysis) or nitrite (all other analyses) in at least 10-fold excess during sampling. Samples along the ozone reactor were taken accounting for the different travel times between sampling points in order to sample from the same water package each time.
2.5. Analyses of micropollutants and oxidation byproducts A total of 22 micropollutants (Table S1 of the SI without nitrosamines) were analyzed by online-SPE-LC-MS/MS (Hollender et al., 2009), while NDMA and NMOR (Table S1 of the SI) were extracted using offline SPE and analyzed by LCMS/HRMS (Krauss and Hollender, 2008). Bromide and bromate concentrations were determined using ion chromatography followed by a post-column reaction (Salhi and von Gunten, 1999). Quenching the wastewater samples with sulfite (nitrosamines) or nitrite (micropollutants and bromide/bromate) did not influence quantification. Relative residual concentrations [%] were calculated by dividing the measured concentration at each sampling point with the concentration in the ozone reactor influent.
Sampling campaigns 2.6.
Seven ozone doses were applied ranging from 0.21 to 1.24 g O3 g1 DOC by adding a fixed gas volume of either 15 or 20 m3 h1 and by automatically adjusting the ozone concentration in the process gas (Table 1, Table S2 of the SI). Each ozone dose was injected for at least 20 h prior to sampling to guarantee equilibration of the sand filter (hydraulic retention time (HRT) 0.5e1 h). Grab samples were taken from the influent to the
Disinfection and AOC measurement
The concentration of Escherichia coli (E. coli) was determined according to the Swiss standard method (Ettel, 2000). Total cell counts (TCC) were analyzed by flow cytometry using a nucleic acid staining by SYBR Green (Hammes et al., 2008). The AOC concentration was measured by inoculation of the wastewater samples with a natural microbial consortium for 4 d at
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Table 1 e Ozonation process and wastewater quality parameters of the influent grab samples collected during the sampling campaigns. Transferred ozone dosea [g O3 g1 DOC] 0.21 Transferred ozone dosea Ozone exposureb Ozone exposureb kO3 Rct Temperature DOC pHc
1
0.41
0.60
0.74
0.81
0.90
1.24
[mg O3 L ]
0.8
1.2
2.6
3.0
3.8
4.3
5.2
[M s]
n.d.
6.9 0.7 104
3.1 0.6 103
4.1 0.4 103
6.1 0.4 103
1.1 0.1 102
1.9 0.1 102
[mg min L1]
n.d.
0.5 0.1
2.5 0.5
3.3 0.4
4.9 0.4
8.9 0.4
14.9 0.7
[s1] [-] [ C] [mg L1] [-]
n.d. n.d. 20 3.4 7.0
2.1 0.3 102 1.3 0.3 107 20 2.4 7.0
9.3 1.2 103 3.6 0.2 108 13 4.4 7.0
8.4 0.7 103 4.6 0.4 108 20 3.4 7.0
6.0 0.5 103 2.6 0.1 108 13 4.7 7.0
3.8 0.0 103 1.7 0.0 108 13 4.8 7.0
3.6 0.2 103 2.2 0.3 108 22 4.1 7.2
n.d. e not determined. a Applied ozone dose minus the ozone lost in the off-gas ððcO3 process gas Qprocess gas Þ ðcO3 offgas Qoffgas Þ=Qwastewater =cDOC wastewater Þ. b Measured average ozone exposure after complete ozone decay with standard deviation based on 2 or 3 laboratory batch experiments with the same wastewater. c No change in pH by ozonation was observed.
30 C and determination of the cell numbers by flow cytometry. The value of 1 107 bacteria to 1 mg AOC L1 allowed the conversion of the natural microbial consortium growth into AOC concentrations (Hammes et al., 2006; Hammes and Egli, 2005).
2.7. Determination of pseudo first-order ozone decay constants, ozone and hydroxyl radical exposures and Rct values The pseudo first-order ozone decay constants kO3 as well as the ozone and hydroxyl radical exposures were determined by means of a laboratory dispenser system (Hoigne´ and Bader, 1994) (Table 1). p-Chlorobenzoic acid ( pCBA) was spiked as ozone-resistant probe compound to measure the transient hydroxyl radical concentrations and to calculate the Rct values (Table 1). Further experimental details can be found in Text S2 of the SI.
2.8. Modeling of micropollutant oxidation and E. coli inactivation The oxidation of a micropollutant P typically follows a secondorder rate law (von Gunten, 2003b). During an ozonation process, both ozone and hydroxyl radicals have to be considered as oxidants: d½P ¼ kP;O3 ½O3 ½P þ kP; OH ½OH ½P dt
(1)
with kP;O3 and kP; OH being second-order reaction rate constants for the reaction of the micropollutant with ozone and hydroxyl radicals, respectively, and [O3] and [OH] as ozone and hydroxyl radical concentrations. To model micropollutant oxidation as well as E. coli inactivation in the full-scale ozone reactor, a four-step approach based on the combination of reactor hydraulics, laboratoryscale characterization of the wastewater with regard to ozone
chemistry, and reaction kinetics was used (von Gunten et al., 1997, 1999): (i) Characterization of the hydraulics of the ozone reactor with a conservative tracer and fitting of the breakthrough curves by a series of ideal reactors (Section 3.1). (ii) Laboratory-scale experiments to determine the pseudo first-order rate constant for ozone decomposition and the Rct value (ratio between hydroxyl radical and ozone concentrations, (Elovitz and von Gunten, 1999)) at the respective wastewater pH and temperature (Section 2.7). (iii) Determination of the second-order rate constants for the oxidation of micropollutants by ozone (kP;O3 ) and hydroxyl radicals (kP;OH ) and for E. coli inactivation by ozone for the respective wastewater pH and temperature. The secondorder rate constants were compiled from literature (Table S3 of the SI) and corrected for pH by taking the acid-base speciation of the respective micropollutant into account and by using the species-specific reaction rate constants with ozone. Furthermore, the Arrhenius equation was used for temperature correction. An activation energy of 50 kJ mol1 was assumed which is in the range of typical activation energies for reactions with ozone (Hoigne´ and Bader, 1983). (iv) Coupling of reactor hydraulics with chemical kinetics by using software capable of solving coupled differential equations such as Berkeley Madonna (Macey et al., 2001). The hydraulic model as well as eqs (1)e(3) were used to describe the chemical kinetics during ozonation and were solved simultaneously: dO3 ¼ kO3 ½O3 Ozone decay: dt
(2)
Hydroxyl radical concentration: ½OH ¼ Rct ½O3
(3)
The arithmetic mean and standard deviation of the model were calculated by running Monte Carlo simulations (batch
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runs with n ¼ 1000) with kP;O3 ; kP; OH ; kO3 and Rct as random uniform distributions between the published or experimentally determined uncertainties of the respective parameter (Table 2).
2.9. Laboratory-scale batch experiments for micropollutant oxidation In addition to the hydraulic model, a second approach based on laboratory-scale batch experiments was tested to predict micropollutant oxidation on the full-scale level. The concept included the use of wastewater from the same WWTP and the application of ozone under the same conditions (pH, T) as in the full-scale campaign, but with spiked micropollutants to allow for direct HPLC-UV/FL analysis. Accordingly, grab samples from the influent to the ozone reactor were taken, filtered (0.45 mm) and stored at 4 C until use. Batch experiments were prepared and carried out as described in Section 2.7, however, with spiked micropollutants (0.2e1.0 mM) and by applying ozone doses yielding the same ozone exposures as in the full-scale campaigns. Relative residual concentrations of micropollutants were subsequently determined by HPLC-UV/ FL, while ozone and hydroxyl radical exposures and Rct values were determined as described in Section 2.7. In addition, micropollutant transformation within the laboratory batch system was predicted according to eq (4), the integrated form of eq (1), which is only valid for a constant Rct and for perfectly mixed and homogeneous solutions such as in a batch reactor: Z Z ½P ¼ kP;O3 ½O3 dt kP; OH ½OHdt ln ½P0 Z ½O3 dt ¼ kP;O3 þ kP; OH $Rct
(4)
with ![O3] dt and ![OH] dt being the ozone and hydroxyl radical exposures, respectively. Eq (4) shows that the relative elimination of a micropollutant reacting with second-order kinetics during an ozonation process is concentration-independent with regard to micropollutants concentration, as long as the micropollutant does not significantly affect the oxidant stability.
3.
Results and discussion
3.1.
Hydraulics of the ozone reactor
The HRT of the ozone reactor varied between 3 and 15 min for storm (250 L s1) and dry weather conditions at night (35 L s1), respectively. For dry weather during daytime hours (80e100 L s1), the HRT was determined to be 7e8 min. The breakthrough curves derived from the Dirac pulse tracer tests could be modeled empirically for two flows as a series of five continuously stirred tank reactor (CSTR) cascades starting from P2 or P3 to P7 (Fig. 2). Table S4 of the SI summarizes the number of CSTRs in each cascade for the two flows and shows that the difference is marginal. Inclusion of a recirculation/ back-mixing did not significantly improve the model fit. The hydraulic model started at P2 or P3, depending on the selected first sampling point within the ozone reactor for micropollutants or E. coli analysis. The first compartment of the ozone reactor was not included because short-circuiting occurred from P1 to P2 which was stronger when wastewater flows were <150 L s1. In case of ideal mixing within a CSTR, breakthrough curves at P2 should decrease in height and increase in width as compared to P1. However, the breakthrough curve at P1 was only as high as and slightly wider than the breakthrough curve at P2 under these flow conditions (Fig. 2a). Results from a reactive continuous tracer test support the hypothesis of short-circuiting (Helbing et al., in preparation). In addition, 0.1 0.02% of the tracer mass was detected prior to the main breakthrough curves at P7 (Figure S1 of the SI), but not at P1eP6. This hints on short-circuiting of a water fraction through the ozone reactor directly to P7, most likely on top of the reactor along the concrete walls. It remains unclear whether this small water fraction was exposed to ozone or not.
3.2.
Kinetics of full-scale micropollutant oxidation
Fig. 3 shows the relative residual concentrations of three representative micropollutants with different second-order rate constants for their reaction with ozone. The fast-
Table 2 e Ozone chemistry parameters used within the Monte Carlo simulations for ozone doses of 0.41 and 0.74 g O3 gL1 DOC, including the respective range of their random uniform distributions. Transferred ozone dosea [g O3 g1 DOC] 0.41 kO3 ;benzotriazole b kOH;benzotriazole c kO3 ;atenolol kOH;atenolol kO 3 Rct
[M1 [M1 [M1 [M1 [s1] [-]
s1] s1] s1] s1]
0.74 1.86e2.70 7.10e8.10 1.30e2.10 7.50e8.50
1.97e2.42 102 1.14e4.71 107
Reference
102 109 103 109 7.60e8.80 103 4.26e28.50 108
Lutze (2005) Naik and Moorthy (1995) Benner et al. (2008) Benner et al. (2008) Present study Present study
a Applied ozone dose minus the ozone lost in the off-gas ððcO3 process gas Qprocess gas Þ ðcO3 offgas Qoffgas Þ=Qwastewater =cDOC wastewater Þ. b kO3 ;benzotriazole determined at pH 2 and 5 (18 and 22 M1 s1, respectively, Karpel Vel Leitner and Roshani, 2010) support the kO3 ;benzotriazole value determined by Lutze (2005) at pH 7. Standard deviation of kO3 ;benzotriazole at pH 7 was set as difference between the measured (2.28 102 M1 s1) and modeled (1.86 102 M1 s1) value determined in Lutze (2005), and the measured value was used as average value. c Standard deviation of kOH;benzotriazole was estimated to be equal to the standard deviation of kOH;atenolol .
610
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Fig. 2 e Breakthrough curves from the Dirac pulse tracer test of the conservative tracer fluoresceine at the ozone reactor for wastewater flows of a) 87 L sL1 and b) 170 L sL1, both with 15 m3 hL1 oxygen gas flow. Experimental data are shown as solid curves for P1eP7 and hydraulic model calculations are depicted as dashed lines from a) P3 and b) P4 to P7.
reacting substance diclofenac with a kO3 > 104 M1 s1 (Table S3 of the SI) was completely oxidized for ozone doses 0.41 g O3 g1 DOC within the first compartment of the ozone reactor (Fig. 3a). Based on eq (4), this was expected from the ozone exposures (Table 1) and its kO3 . Surprisingly, w8% of diclofenac resisted ozonation at a dose of 0.21 g O3 g1 DOC. According to eq (4), an ozone exposure of 6.7 106 M s is needed to achieve 99% oxidation of diclofenac with kO3 ¼ 6.8 105 M1 s1. Although the ozone exposure resulting from the 0.21 g O3 g1 DOC dose could not be determined by means of the laboratory dispenser system (Hoigne´ and Bader, 1994), because ozone was consumed too fast, the ozone exposure needed for 99% oxidation is less than 1% of the final exposure of the 0.41 g O3 g1 DOC dose. Hence, a 99% oxidation was expected even for an ozone dose of 0.21 g O3 g1 DOC. The incomplete oxidation of diclofenac and other fastreacting substances such as carbamazepine, sulfamethoxazole and clarithromycin (Figure S2 of the SI), can be attributed to short-circuiting from the first into the second compartment (Section 3.1). 8e21% of the mentioned micropollutants were not oxidized at a dose of 0.21 g O3 g1 DOC which is in
agreement with the percentage of non-oxidized water (about 15%) obtained during the reactive continuous fluoresceine tracer test carried out under identical conditions (Helbing et al., in preparation). The short-circuiting is a hydraulic phenomenon and therefore independent of the ozone dose. However, for the 0.21 g O3 g1 DOC dose, dissolved ozone was completely consumed up to P2, leading to the observed residual fraction of fast-reacting micropollutants due to non-ozonated water packages. For all higher ozone doses, residual ozone was measured at P2. This may have led to a mixing of water packages containing dissolved ozone with others not containing ozone before reaching P2. As a consequence, the intrinsically fast oxidation of the fast-reacting micropollutants resulted in their complete oxidation. The other fast-reacting substances clindamycin, erythromycin, mefenamic acid, naproxen, sulfapyridine and trimethoprim as well as sotalol (kO3 > 103 M1 s1), were oxidized below their limit of quantification (LOQ) (Table S1 of the SI) at a dose of 0.21 g O3 g1 DOC at P2. This is not caused by a different oxidation behavior as compared to diclofenac, carbamazepine or sulfamethoxazole, but by their low influent concentrations which render them non-detectable even for 70% relative residual concentration. Mefenamic acid with an unknown kO3 was classified as fast-reacting compound from its structure. Its kO3 was estimated to be higher than that of diclofenac because the two methyl groups increase the electron density of the aromatic system in contrast to the two electron-withdrawing chlorine atoms of diclofenac (Table S1 of the SI). Micropollutants reacting slower with ozone (kP;O3 < 104 M1 s1), such as atenolol and benzotriazole, were oxidized along the ozone reactor with increasing ozone and hydroxyl radical exposure (Fig. 3b and c). The degree of transformation (kO3 ;atenolol ¼ also increased with increasing kO3 1.7 103 M1 s1 > kO3 ;benzotriazole ¼ 2.3 102 M1 s1 at pH 7). Ibuprofen, paracetamol and sulfamethazine were not detected in the influent of the ozone reactor, and sulfadiazine and phenanzone were only detected once and as expected fully oxidized at a dose of 0.81 and 1.24 g O3 g1 DOC, respectively. The slowlyreacting bezafibrate was detected in the influent on three sampling days, and was oxidized below the LOQ at the three highest ozone doses 0.60 g O3 g1 DOC (Figure S2 of the SI).
3.3. Prediction of micropollutant oxidation based on a model coupling reactor hydraulics with ozone chemistry and reaction kinetics The modeled ozone and hydroxyl radical concentration profiles for the two ozone doses 0.41 and 0.74 g O3 g1 DOC are illustrated in Fig. 4. The relatively high number of reactors in the CSTR cascades (Table S4 of the SI) indicates that the ozone reactor behaves similar to a plug flow reactor (PFR) after the first compartment. Hence, the ozone reactor was also modeled as a PFR by combining eqs (1)e(3) to determine the influence of the hydraulic model on the prediction of ozone concentration profiles and micropollutant oxidation. The CSTR and PFR models predicted very similar ozone profiles, showing that the hydraulic model structure was of minor importance for this specific ozone reactor. Resulting ozone exposures were predicted to be 60% (0.41 g O3 g1
611
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120 -1
Rel. Res. Diclofenac [%]
Cl
100
0.21 0.41 0.60 0.74 0.81 0.90 1.24
NH
80
OH
Cl
60
O
40 20
b
120 NH2
100
O N H
O OH
80 60 40 20
n.d.
0 In
c
g O3 g DOC
Rel. Res. Atenolol [%]
a
P2
P3
P5
P7
SF
P7
SF
0 In
P2
P3
P5
P7
SF
120
Rel. Res. Benzotriazole [%]
H N
100
N N
80 60 40 20 0 In
P2
P3
P5
Fig. 3 e Relative residual concentrations of a) diclofenac, b) atenolol and c) benzotriazole for seven ozone doses ranging from 0.21 to 1.24 g O3 gL1 DOC in the influent to the ozone reactor (IN), along the ozone reactor (P2, P3, P7 or P3, P5, P7) and after sand filtration (SF). Error bars were calculated by linear error propagation derived from duplicate measurements. n.d. [ not determined.
DOC) and 26% (0.74 g O3 g1 DOC) lower by the hydraulic models than for the full-scale measurements (Fig. 4). Based on the lower ozone exposures and eq. (3), the modeled hydroxyl radical exposures were significantly smaller than the effective exposures in the full-scale system. The different ozone addition and mixing in the full-scale reactor (bubble column, non-homogeneously mixed) compared to the batch reactor (dosage of a stock solution, homogeneously mixed) may have affected the ozone behavior during the second phase of ozone decay, leading to different kO3 values in both set-ups. However, differences in the initial phase of ozone decay (first w20 s, Buffle et al., 2006) can be excluded as a factor for the observed deviations of the ozone profiles because the ozone decay in the full-scale ozone reactor already followed the second phase of ozone decay at the starting point of the hydraulic model (P2: HRT 18 s relative to P1), and can hence be described by kO3 (von Gunten, 2003b). Fig. 5 shows the results of the CSTR and PFR models for benzotriazole and atenolol. As for the ozone profiles, predictions by the PFR model were very similar to the CSTR model. For an ozone dose of 0.41 g O3 g1 DOC, the measured and predicted values correlated well, while for an ozone dose of 0.74 g O3 g1 DOC, model predictions were about a factor of 2.5 higher than full-scale measurements. Monte Carlo simulations showed that deviations between measurements and model predictions were larger than model uncertainties (standard deviations CSTR and PFR models Fig. 5). This is an
independent evidence that the model overestimated micropollutant oxidation for the ozone dose of 0.74 g O3 g1 DOC. The underestimation of oxidant exposures (Fig. 4) should have led to underestimation of micropollutant oxidation in the model predictions. However, the contrary was observed. Therefore, counteracting processes must have lowered micropollutant oxidation in wastewater at the full-scale level, to compensate for the underestimation of oxidant exposures in the model predictions. Huber et al. (2005) attributed the overestimation of micropollutant oxidation based on ozone and hydroxyl radical exposures to sorption of some compounds to sludge particles or the interaction with colloids which might offer some protection against ozone attack. We did not distinguish between micropollutants in the colloidal (particles between 1 nm and 1 mm) and dissolved phase in this study, but micropollutants in both phases were concentrated on and eluted from SPE cartridges (Zhang and Zhou, 2007). The deviations between model predictions and measurements for benzotriazole and atenolol at a dose of 0.74 g O3 g1 DOC correspond to 45% and 19% at P5, respectively, and to 22% for benzotriazole at P7. These deviations are in the range of the percentage of chemicals sorbed to aquatic colloids (Maskaoui et al., 2007; Zhou et al., 2007). Since both benzotriazole and atenolol show sorption capabilities (Hinterbuchner, 2006; Yamamoto et al., 2009), it might be possible that these micropollutants were protected from ozone attack by the interaction with aquatic colloids.
612
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Ozone [mg L-1]
-1 0.24 mg min L
1.0 0.8 0.6
10-12
-1 2.14 mg min L
0.4
-1 1.63 mg min L
]
P3
1.2
0.2
-1 0.22 mg min L P2
-1
10-14
0.1
0.0
10-13
10-15 P4
P5
P6
10-11
-1
0.74 g O3 g DOC
-1 1.58 mg min L
0.0 P3
P7
P4
P5
P6
.OH Radicals [mol L-1]
-1 0.56 mg min L
.OH Radicals [mol L
10-12
0.3
0.2
b 1.4
10-11
0.41 g O3 g-1 DOC
-1
0.4
Ozone [mg L ]
a
10-13 P7
Ozone measured Ozone CSTR Ozone PFR
.OH Radicals CSTR .OH Radicals PFR
Fig. 4 e Measured ozone (black circles) and modeled ozone and hydroxyl radical profiles for the continuously stirred tank reactor (CSTR) model (grey circles and triangles) and the plug flow reactor (PFR) model (open circles and triangles) along the ozone reactor (P2/P3eP7) for an ozone dose of a) 0.41 g O3 gL1 DOC and b) 0.74 g O3 gL1 DOC. Measured and modeled standard deviations are given but smaller than the respective symbols. The ozone exposures from P2/P3 to P7 are indicated in mg min LL1. Storm weather conditions occurred during the 0.74 g O3 gL1 DOC campaign, leading to a HRT of only 3e4 min (Table S2) and a DOC of 3.4 mg LL1 (Table 1).
In contrast to these slower-reacting substances, fastreacting substances with a kO3 > 104 M1 s1 were already completely oxidized within the first compartment of the ozone reactor (Section 3.2). This may be attributed to their intrinsic fast oxidation, leading to lower concentrations in the aqueous phase. This in turn causes a shift in the colloid/ micropollutant equilibrium leading to quick desorption of micropollutants from colloids. Maskaoui et al. (2007) determined 5 min to be sufficient to reach the colloid/pharmaceutical equilibrium. Although desorption kinetics have not been reported, quick desorption and subsequent fast oxidation by ozone are possible, leading to the observed oxidation of fastreacting micropollutants below the LOQ in both the colloidal and dissolved phase. The same processes (sorption equilibrium, oxidation, desorption, further oxidation) apply to slower-reacting micropollutants as well. However, micropollutant desorption from the colloidal to the aqueous phase is much slower due to a slower oxidation of these compounds in aqueous solution. In addition, model predictions were calculated based on minimum and maximum Rct values (1 108e5 107) determined in secondary effluent from WWTP Wu¨eri sampled under varying weather conditions over a period of 16 months (light grey bars, Fig. 5). These calculations show that the Rct value is highly important for the oxidation of slowly-reacting micropollutants, since hydroxyl radicals contribute significantly to their oxidation. Predictions for the relative residual concentrations at P7 for the two modeled ozone doses varied from 39 to 93% and 0 to 58% for benzotriazole, respectively, and from 27 to 64% and 0 to 6% for atenolol, respectively. Hence, differences between the Rct value in the full-scale reactor and laboratory batch experiments could directly contribute to the observed variations. Different Rct values may be derived from different
modes of ozone addition and mixing in the full-scale reactor (bubble column, non-homogeneously mixed) compared to the batch experiment (dosage of a stock solution, homogeneously mixed). In conclusion, the two counteracting processes i) underestimation of modeled oxidant exposures (leading to an underestimation of micropollutants oxidation in the hydraulic model) and ii) protection of the sorbed micropollutants fraction from ozone attack (leading to an overestimation of micropollutants oxidation in the hydraulic model) may have compensated each other to varying extents. In combination with possibly different Rct values in the fullscale vs. batch reactor, this resulted in a good prediction of micropollutants oxidation for the 0.41 g O3 g1 DOC dose, and in an overestimation within the factor of 2.5 for the 0.74 g O3 g1 DOC dose. The factor of 2.5 hence represents the range of possible deviations of the hydraulic model from the full-scale measurements at the ozone reactor at WWTP Wu¨eri in Regensdorf.
3.4. Prediction of full-scale micropollutant oxidation by laboratory-scale batch experiments Fig. 5 illustrates that micropollutant oxidation derived from laboratory-scale batch experiments and the respective laboratory-scale predictions based on eq. (4) are in good agreement, but deviate up to a factor of 1.5 from full-scale measurements. Since different wastewater grab samples were used during laboratory- and full-scale experiments, and the comparison was based on the same ozone exposures, the Rct value was identified as a major source for the observed deviations. It was 3e5 times lower in the batch experiments (data not shown) compared to batch experiments with wastewater from the full-scale sampling campaigns. Hence, it is highly
613
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120 100
Benzotriazole 0.41 g O3 g-1 DOC
b 120 Rel. Res. Benzotriazole [%]
Rel. Res. Benzotriazole [%]
a
80 60 40 20
80 60 40 20
0 P2
Rel. Res. Atenolol [%]
100
0
P7
Atenolol 0.41 g O3 g-1 DOC
d 120 100
Rel. Res. Atenolol [%]
c 120
P3
80 60 40
Benzotriazole 0.74 g O3 g-1 DOC
100
20
P3
P5
P7
Atenolol 0.74 g O3 g-1 DOC
80 60 40 20
0
0 P2
P3
P7
P3
P5
P7
Laboratory-scale measurement Batch model CSTR model for Rct range 1 x 10-8 - 5 x 10-7
Full-scale measurement CSTR model PFR model
Fig. 5 e Relative residual micropollutant concentrations in various scenarios for benzotriazole with a) 0.41 and b) 0.74 g O3 gL1 DOC and for atenolol with c) 0.41 and d) 0.74 g O3 gL1 DOC starting from sampling point P2 or P3. Abbreviations: CSTR Model [ Continuously Stirred Tank Reactor Model; PFR Model [ Plug Flow Reactor Model. Laboratory-scale measurements were carried out in a batch experiment with secondary effluent from WWTP Wu ¨ eri under the same experimental conditions (ozone dose, pH, temperature) as in the full-scale measurement. The batch model is based on oxidant exposures determined during laboratory-scale measurements. Error bars from full- and laboratory-scale measurements were calculated by linear error propagation derived from duplicate measurements. Standard deviations of CSTR and PFR model predictions were calculated by Monte Carlo simulations.
important to determine the possible range of Rct values, which strongly depends on the organic and inorganic matrix, of a specific wastewater under all conditions (rain, temperature, season, daytime). Due to the important contribution of hydroxyl radicals to the oxidation of slowly-reacting micropollutants, the Rct is a critical parameter for these cases. This is underlined by a recent study investigating the uncertainty during micropollutant ozonation (Neumann et al., 2009). The Rct value (and not kP;O3 or kP;OH ) was found to be the most influential parameter for slowly-reacting substances, while the accuracy of the hydraulic model of a reactor was determined to be most important for fast-reacting substances. Finally, the complete oxidation of the fast-reacting substances carbamazepine, diclofenac, sulfamethoxazole and 17a-ethinylestradiol (kO3 ;17aethinylestradiol ¼ 1.6 106 M1 s1 at pH 7 (Deborde et al., 2005)) in the laboratory-scale batch experiments was achieved for ozone exposures occurring at
P2/P3 during full-scale sampling campaigns (data not shown), and is in accordance with full-scale measurements. Micropollutants interacting with colloids were of minor importance within the batch experiments since their respective concentrations in the aqueous phase were much higher (spiking of micropollutants), and accordingly, the fraction of micropollutants interacting with colloids was much lower. In addition, low residual concentrations could not be detected due to higher LOQs of the LC-UV/FL method as compared to the online-SPE-LC-MS/MS method.
3.5.
Oxidation by-products
Bromate, a potential human carcinogen, forms during ozonation of bromide-containing waters through a combination of reactions involving ozone and secondary oxidants such as hydroxyl and carbonate radicals (von Gunten, 2003a). Bromide
614
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 0 5 e6 1 7
levels in the influent to the ozone reactor varied from 6 mg L1 (due to dilution) to 32 mg L1 (Table S2 of the SI). These values are in accordance with low bromide levels (<5e50 mg L1) found in a survey of 85 water resources for drinking water treatment in Switzerland (von Gunten and Salhi, 2003). Fig. 6a shows that only ozone doses 0.74 g O3 g1 DOC yielded bromate concentrations above the LOQ of 2 mg L1. Bromate formation is a slow process (von Gunten, 2003a), reflected in a bromate increase from P3 to P7 for the highest ozone dose. As expected, aerobic sand filtration did not remove bromate. The final bromate concentrations (max. 7.5 mg L1) were below the drinking water standard of 10 mg L1 for all ozone doses and hence also below a proposed ecotoxicological threshold value of 3 mg L1 (Hutchinson et al., 1997). The concentrations of NDMA and NMOR in the reactor are illustrated in Fig. 6b for the highest ozone dose (1.24 g O3 g1 DOC). A previous study showed that NDMA formation from secondary effluent did not correlate with the ozone dose (Hollender et al., 2009) and might strongly depend on the concentrations and conversion rate of precursors as well as matrix composition. In the present study, NDMA formation was observed to be a fast process (strong increase up to P3), which was followed by a slight decrease within the ozone reactor to P7. The fast formation of NDMA points towards specific precursors rather than moieties of DOM as precursors (von Gunten et al., 2010). As observed in a previous study (Hollender et al., 2009), sand filtration resulted in a decrease of NDMA concentration due to biodegradation, leading to concentrations slightly above the notification level of 10 ng L1 for NDMA in drinking water in California (CDPH, 2009). An ecotoxicological threshold value does currently not exist for NDMA. In contrast to NDMA, neither ozonation nor sand filtration did have an influence on NMOR concentrations (Fig. 6b).
3.6.
AOC formation
Fig. 7a shows that the AOC levels in the secondary effluent varied between 40 and 200 mg L1, which is in the range of
10
-1
Bromate [µg L ]
8
6
4
Ozone is an excellent disinfectant and inactivates a broad range of microorganisms (von Gunten, 2003a). TCC in the influent to the ozone reactor were relatively stable and varied from 3.3 to 8.4 106 cells ml1 (Fig. 7b), as determined by flow cytometry. Ozonation decreased TCC by 1e4.5 log units, not consistently correlating with the ozone doses. A distinct regrowth of TCC was observed during sand filtration, since AOC formed during ozonation could act as a carbon source for microorganisms which were subsequently released from the sand filter. The release of TCC from the sand filter seemed to be independent of the TCC or AOC level after ozonation (Fig. 7) and DTCC could not be correlated with DAOC concentrations (data not shown). Overall, TCC could be
b
-1
g O3 g DOC 0.21 0.41 0.60 0.74 0.81 0.90 1.24
2
0
Removal of TCC and E. coli
3.7.
Nitrosamines [ng L-1]
a
concentrations found in lake water (Hammes et al., 2006). Hence, AOC was either not completely consumed during biological wastewater treatment or it might have been released from lysating cells due to a natural turnover of the microbial sewage sludge community. During ozonation, AOC levels increased up to a factor of 6, reaching a maximum concentration of 740 mg L1. AOC formation was a fast process since no substantial increase was observed after the initial increase at P1. Similar results were found during ozonation of lake water (Hammes et al., 2006). Still, no clear trend could be found when correlating AOC formation with the ozone dose, ozone exposure or influent AOC concentrations. The rapid sand filter finally decreased the AOC levels by 14e46%, leading to AOC concentrations above the level before ozonation in some cases, as previously observed for ozonated drinking water followed by GAC/sand filtration (van der Kooij et al., 1989). Interestingly, the AOC levels after sand filtration depended on the wastewater temperature, and not on the AOC levels prior to sand filtration. AOC levels after sand filtration of 390e440 mg L1 were found in April 2008 (wastewater temperature 13 C), while 120e150 mg L1 AOC were determined in August 2008 (wastewater temperature 20e22 C).
20
NDMA NMOR
15
10
5
0 In
P3
P7
SF
In
P3
P7
SF
Fig. 6 e Oxidation by-products concentrations in the influent to the ozone reactor (IN), along the ozone reactor (P3, P7) and after sand filtration (SF): a) Bromate concentrations for seven ozone doses ranging from 0.21 to 1.24 g O3 gL1 DOC. The limit of quantification (LOQ) was 2 mg LL1 and bromide concentrations varied between 6 and 32 mg LL1 (Table S2 of the SI). b) N-Nitrosodimethylamine (NDMA) and N-Nitrosomorpholine (NMOR) concentrations for an ozone dose of 1.24 g O3 gL1 DOC (LOQ [ 1 ng LL1). Error bars were calculated by linear error propagation derived from duplicate measurements. Note the different concentration units in Figures a) and b).
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 0 5 e6 1 7
615
Fig. 7 e a) Assimilable organic carbon (AOC) concentrations and b) total cell counts for seven ozone doses ranging from 0.21 to 1.24 g O3 gL1 DOC in the influent to the ozone reactor (IN), along the ozone reactor (P1, P3, P7) and after sand filtration (SF). Error bars indicate standard deviations based on triplicate measurements.
lowered by 0.5e1.5 log units by the combination of ozonation and sand filtration. E. coli concentrations were lowered by 0.5e2.5 log units during ozonation (Fig. 8, Table S2 of the SI), and only partially correlated with the ozone exposure (Table 1). After a fast initial inactivation during the first compartment of the ozone reactor up to P3, E. coli was not further eliminated substantially from P3 to P7. This was unexpected for ozone doses 0.41 g O3 g1 DOC since dissolved ozone was still apparent from P3 to P7. E. coli inactivation by ozone is a fast process and well predicted in drinking water treatment based on the concept of reactor hydraulics coupled with ozone chemistry and reaction kinetics (von Gunten, 2003a). However, in the present study, the same concept applied to wastewater treatment strongly overestimated E. coli inactivation (Figure S3 of the SI). The water fraction of 0.1% short-circuiting directly to P7 cannot be the main reason for the large discrepancy. In a worst case scenario with 0.1% of the water not getting in contact with ozone at all, a maximum E. coli inactivation of 3 log units would be achieved. In case of the
g O3 g-1 DOC
0.21 0.41 0.60 0.74 0.81 0.90 1.24
100
E/E0
10-1
10-2
10-3
10-4 In
P3
P7
SF
Fig. 8 e Relative residual concentrations of E. coli for seven ozone doses ranging from 0.21 to 1.24 g O3 gL1 DOC in the influent to the ozone reactor (IN), along the ozone reactor (P3eP7) and after sand filtration (SF).
short-circuiting water packages getting in contact with ozone, the overall expected inactivation should be higher. However, a maximum inactivation of w0.5 log units from P3 to P7 was observed (Figure S3 of the SI). The large differences between measured and modeled E. coli inactivation could be explained by the shielding of E. coli in activated sludge flocs, which can experience lower ozone exposures than the aqueous solution (Huber et al., 2005). Accordingly, E. coli present in activated sludge flocs might not be inactivated during ozonation, but could be released from the activated sludge flocs at a later stage and lead to a positive result in plating techniques. Finally, no regrowth of E. coli was observed upon sand filtration. Combining ozonation and sand filtration is hence an efficient barrier against E. coli and possibly other pathogens.
4.
Conclusions
The predicted oxidation of slowly-reacting micropollutants based on coupling reactor hydraulics with ozone chemistry and reaction kinetics was up to a factor of 2.5 higher than full-scale measurements. This overestimation was attributed to a protection of micropollutants from ozone attack by the interaction with aquatic colloids in the real system. Laboratory-scale batch experiments using wastewater from the same full-scale WWTP could predict the oxidation of slowly-reacting micropollutants (kO3 < 104 M1 s1) on the full-scale level within a factor of 1.5. The Rct value (depending strongly on the organic and inorganic wastewater matrix) was identified as a major source to this deviation. Hence, it is important to determine the spectrum of Rct values for various conditions in wastewater of a specific WWTP. Oxidation of fast-reacting substances could be well predicted since their oxidation does not significantly depend on the formation of hydroxyl radicals. An increase in bromate and NDMA concentrations to nonproblematic levels was observed during ozonation, while no influence on NMOR concentrations could be shown. AOC was formed during ozonation of wastewater of up to 740 mg L1 and subsequently degraded by up to 46% during
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rapid sand filtration. Along with the slight decrease of NDMA during sand filtration, this finding highlights the importance of sand filtration after an ozonation step for removal of oxidation by-products (AOC, NDMA). Disinfection during ozonation in the full-scale reactor was demonstrated by the fast decrease of the TCC and inactivation of E. coli. However, predictions of E. coli inactivation did strongly deviate from full-scale measurements, which can be attributed to the shielding of E. coli by activated sludge flocs.
Acknowledgements This study was part of the project Strategy MicroPoll funded by the Swiss Federal Office for the Environment (FOEN). Support for S.G. Zimmermann was provided by the EU project Neptune (Contract No 036845, SUSTDEV-2005-3.II.3.2), which was financially supported by grants obtained from the EU Commission within the Energy, Global Change and Ecosystems Program of the Sixth Framework (FP6-2005-Global-4). The authors gratefully acknowledge S. Brocker and D. Rensch for installation and help with maintenance of the ozone reactor, the staff at WWTP Wu¨eri for general support, S. Koepke for SPE-LC-MS/MS measurements, E. Salhi for laboratory assistance and bromate measurements, F. Hammes and H. P. Fu¨chslin for AOC and TCC determination, E. Gansner and M. Koch for E. coli measurements, J. Helbing and C. Abegglen for help with the tracer tests, M. Gresch for support during modeling, and J.L. Kormos for language corrections. The authors also thank CWQRC at Curtin University, Australia, and BfG, Germany, where the modeling and writing took place, Jaehong Kim for fruitful discussions as well as two anonymous reviewers for their helpful comments.
Appendix. Supplementary material Supplementary data associated with this article can be found in the online version, at doi:10.1016/j.watres.2010.07.080.
references
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 1 8 e6 2 4
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Combined anaerobic and aerobic digestion for increased solids reduction and nitrogen removal John T. Novak a,*, Sarita Banjade a, Sudhir N. Murthy b a b
Department of Civil & Environmental Engineering, Virginia Tech, Blacksburg, VA 24061, United States DC Water & Sewer Authority, Washington DC 20032, United States
article info
abstract
Article history:
A unique sludge digestion system consisting of anaerobic digestion followed by aerobic
Received 5 April 2010
digestion and then a recycle step where thickened sludge from the aerobic digester was
Received in revised form
recirculated back to the anaerobic unit was studied to determine the impact on volatile
13 July 2010
solids (VS) reduction and nitrogen removal. It was found that the combined anaerobic/
Accepted 10 August 2010
aerobic/anaerobic (ANA/AER/ANA) system provided 70% VS reduction compared to 50% for
Available online 17 August 2010
conventional mesophilic anaerobic digestion with a 20 day SRT and 62% for combined anaerobic/aerobic (ANA/AER) digestion with a 15 day anaerobic and a 5 day aerobic SRT.
Keywords:
Total Kjeldahl nitrogen (TKN) removal for the ANA/AER/ANA system was 70% for sludge
Sludge treatment
wasted from the aerobic unit and 43.7% when wasted from the anaerobic unit. TKN
Nitrogen removal
removal was 64.5% for the ANA/AER system. ª 2010 Elsevier Ltd. All rights reserved.
Anaerobic digestion Aerobic digestion
1.
Introduction
Minimization of sludge generated from wastewater treatment plants is of interest because of the cost, health concerns and environmental factors associated with the transport and disposal of biosolids. Under 40 CFR 503 Part (b) sludge reuse and disposal regulations (U.S EPA, 1992), specific levels of treatment of sludge are required for pathogen deactivation prior to land application. One of the major processes for achieving acceptability for land application is anaerobic digestion. While this process is effective for reducing pathogens and destroying organic matter, solids reduction above 50% is often difficult to achieve. For this reason, advanced digestion processes have gained interest in recent years. Combined anaerobic and aerobic digestion has been investigated by several researchers. Pagilla et al., (2000) investigated the use of a thermophilic aerobic pretreatment stage prior to anaerobic digestion and found improved solids
reduction and coliform destruction with the combined system compared to anaerobic digestion alone. They also saw better dewatering properties for the pretreated sludges. Akunna et al., (1994) investigated the combined anaerobiceaerobic treatment of synthetic wastewater and found that the COD in anaerobic effluent from an upflow filter could be degraded an additional 30% by aerobic treatment. Subramanian et al., (2007) conducted batch aerobic digestion studies for anaerobically digested sludges’ and found that, in addition to increased solids reduction, sludge dewatering properties improved. Recent research in our laboratory has focused on combined anaerobic/aerobic sludge digestion. There are many advantages to combined anaerobic/aerobic sludge digestion and these include better solids reduction, improved sludge dewatering properties and reduction of nitrogen (Kumar et al., 2006). The combination of both an anaerobic and an aerobic step seems to provide for additional solids reduction that is
* Corresponding author. Tel.: þ1 540 231 6132; fax: þ1 540 231 7916 E-mail address:
[email protected] (J.T. Novak). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.08.014
619
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not possible by one digestion mode alone (Park et al., 2006). Typically, conventional anaerobic digestion is followed by an aerobic step as short as 3 days to gain an additional 10% VS destruction and removal of up to 90% ammonia nitrogen (Kumar et al., 2006). It was thought that by adding an additional anaerobic sequence to the anaerobic/aerobic process, additional solids destruction could occur without additional tankage volume. Solids from the aerobic digester were thickened, the liquid discharged and the concentrated solids recycled back to the anaerobic unit. The addition of the recycled sludge back to the anaerobic unit does not increase the reactor size but does require a separate thickening and recycle step. Solids can be wasted from either the anaerobic or the aerobic reactor. The goal of this study was to determine if a combined anaerobic/aerobic/anaerobic digestion system could provide additional solids destruction beyond conventional single stage mesophilic digestion and combined anaerobic/aerobic digestion and to determine the impact on nitrogen removal. The combined anaerobic-aerobic-anaerobic system was operated so that sludge could be wasted from either the aerobic unit or the anaerobic unit.
1.1.
Objectives
The specific objectives of this study were: a) To determine the impact of combined anaerobic/aerobic/ anaerobic digestion on anaerobic digestion efficiency as measured by volatile solids and COD reduction. b) To determine the effect of combined anaerobic/aerobic/ anaerobic digestion on nitrogen removal. c) To determine the best location for sludge wastage from the ANA/AER/ANA system, the anaerobic unit or the aerobic unit.
2.
Materials and methods
2.1.
Research approach
Three separate digestion combinations were run. A conventional mesophilic digester with an SRT of 20 days, a combined anaerobic/aerobic system with an anaerobic SRT of 15 days and an aerobic stage of 5 days and an anaerobic/aerobic/ anaerobic system. All of the units were set up in a 35 C constant temperature room. The anaerobic units were at 35 C and the aerobic units were at 32e34 C due to cooling from aeration. Flow diagrams for the ANA/AER/ANA systems are shown in Fig. 1 with sludge wastage from either the anaerobic unit or the aerobic unit. The control anaerobic digester was a single unit, identical to the digesters used for the anaerobic phase of the combined ANA/AER/ANA studies. The digesters used for the ANA/AER study were the same digesters used for the combined ANA/AER/ANA study, but these were operated without a recycle step from the aerobic to the anaerobic digester. The SRT for the systems is shown in Table 1. The units were operated in a similar manner with regard to the sludge
Fig. 1 e Combined ANA/AER/ANA with sludge wastage from either the anaerobic or aerobic unit.
volumes in the reactors and the sludge feed volume and wastage. However, since 2 L of sludge was fed to the aerobic unit when wastage was from the aerobic unit, the SRT in the aerobic digester was half that of the system that wasted sludge from the anaerobic unit. Plastic, egg-shaped fermenters supplied by Hobby Beverage Equipment Company, were used as anaerobic digesters. Mixing was by gas recirculation from top to bottom through a port provided at the bottom of the reactor and an outlet drilled into the top. For the aerobic digesters, 9.5 L glass digesters, approximately 21 cm diameter with a narrow screw cap top (Fisher Scientific) were used. Bubble diffusers were used for maximum oxygen transfer and a compressor was used to supply oxygen. The dissolved oxygen concentration in the aerobic reactor varied from zero immediately after feeding to 2.5e3.0 mg/L just prior to feeding. Distilled water was added each day to counter any water loss due to evaporation in the aerobic reactor. All the reactors were slug fed once per day. Typically, sludge was withdrawn from the reactor and then feeding followed. For the anaerobic/aerobic system, sludge was first removed from the aerobic reactor for testing and wastage and then sludge was removed from the anaerobic reactor and fed to the aerobic reactor. The anaerobic reactor then received the raw sludge feed. For the anaerobic/aerobic/anaerobic systems, sludge to be recycled back to the anaerobic reactor was removed, centrifuged using a lab centrifuge, the centrate wasted, and the solids combined with raw sludge and fed to the anaerobic reactor.
Table 1 e SRTs of the anaerobic and aerobic digesters in the systems. System
Conventional MAD Sequential ANA/AER ANA/AER/ANA e anaerobic waste ANA/AER/ANA e aerobic waste
System Anaerobic Aerobic SRT SRT SRT (days) (days) (days) 20 20 35 35
20 15 15 15
e 5 5 2.5
620
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For mixing in the anaerobic digesters, peristaltic pumps (Cole Parmer 6e600 rpm) were used to recirculate gas from the headspace to the bottom of the digesters. The pumps were operated at 50% of their maximum speed. To ensure greater mixing of the digesters before and after feeding, gas recirculation in the digesters was increased by increasing the speed of the pumps to 100% for 10 min before sampling and also for 10 min after feeding. The anaerobic digesters were seeded with mesophilic anaerobically digested sludge taken from Pepper’s Ferry Regional Wastewater Treatment Facility, Radford, Virginia, USA. The digesters were monitored for steady-state. Steadystate was determined to have been reached when the VS reduction and gas production showed less than 5% variation. Once steady-state occurred, complete sampling and analysis took place. The feed for the anaerobic digester was a mixture of primary and secondary sludge (gravity thickened sludge and air flotation thickened waste activated sludge). The sludge was supplied weekly by DCWASA Blue Plains Advanced Wastewater Treatment Facility and shipped overnight. Total solids percentages of both the sludges’ were measured and a mixture of 1:1 by weight of the sludges with a total solid percentage of 5% was made by dilution. The sludge was blended and was stored in a 4 C room until used. To maintain the SRT of both the anaerobic and aerobic digesters, constant volume was maintained and same amount of sludge was fed and wasted daily from the digesters. The daily biogas production by the anaerobic digesters was measured using a RebelTM wet-tip gas flow meters.
2.2.
Analytical methods
Liquid samples were analyzed for total solids (TS), volatile solids (VS), pH, total Kjeldahl nitrogen (TKN), and ammonium (NH3-N), according to Standard Methods (APHA, 1998). An ORP probe (Model 96-78-BN) was used to measure oxidationereduction potential of the aerobic digesters. The oxidationereduction potential (ORP) was measured using an ORP probe (Model 96-78-BN).
3.1.1.
Volatile solids reduction
A mixture of primary and secondary sludge in a ratio of 1 to 1 by dry solids from the DC Water and Sewer Authority was anaerobically digested in a constant temperature room at 35 C to determine the volatile solids reduction. The single stage mesophilic (control) digester data operated at a 20 day SRT is shown in Fig. 2. It can be seen that the average volatile solids reduction (VSR) was 50% over a period of approximately 6 months of operation. Variations were expected because of variations in the feed solids. The VSR for the ANA/AER system is shown in Fig. 3. It can be seen that the VSR for this system was approximately 62%. The overall SRT for the combined ANA/AER digester was 20 days, the same as for the single stage conventional digester, with the aerobic portion being 5 days. The two systems operated as ANA/AER/ANA provided a VSR of approximately 70% (Fig. 3) and the removal of waste sludge from either the anaerobic reactor or the aerobic reactor did not appear to make a difference in the VSR. However, as shown in Table 1, when wastage was from the aerobic reactor, the aerobic SRT was 2.5 days compared to an SRT of 5 days when wastage was from the anaerobic reactor. The overall SRT for the system was 35 days. The higher SRT resulted from the thickening and recirculation of solids from the aerobic reactor back to the anaerobic reactor. The SRT was calculated assuming that the centrate from the solid/liquid separation process for the recycle of solids from the aerobic unit to the anaerobic unit did not contain any solids. COD removals were similar to the VS removals for all the systems (data not shown). Total solids removals were lower than the VS removal. The MAD has a TS removal of 42%, the ANA/AER TS removal was 54%, the ANA/AER/ANA with wastage from the anaerobic unit was 64% and the ANA/AER/ ANA with wastage from the aerobic unit was 63%. Two factors are thought to account for the increased solids removal by the combined ANA/AER/ANA. First, the SRT increased from 20 to 35 days. Second, by combining an additional anaerobic step, aerobically digested sludge was exposed to an additional degradation cycle where biopolymer that was generated by aerobic growth could be further degraded anaerobically. Novak et al., 2003 suggested that some fractions of biological floc could be degraded only aerobically while
Results and discussion
3.1.
Performance of the digesters
Lab-scale anaerobic and aerobic digestion systems were operated to determine the performance of anaerobic digestion followed by aerobic digestion and aerobic/anaerobic digestion. The analyses were performed after determination of steady-state conditions and were evaluated by monitoring pH, biogas production and solids removal. Different performance parameters such as volatile solids destruction, COD removal, nitrogen removal, biogas production and VFA production and destruction were measured. VFA and biogas data are not included in this paper, but were consistent with the volatile solids removal data. All the digesters performed well during the steady-state phases.
Vo la tile So lids Reductio n (%)
60
3.
MAD (20d SRT)
Average VSR 20d SRT (50%)
25
100
55
50
45
40 0
50
75
125
150
175
Time (days after reaching steady state) Fig. 2 e Volatile solids removal data for the control digester.
200
621
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65
T K N (m g -N /L )
60 55 Ana/Aer/Ana-Ana wastage
50
Ana/AerAna-Aer wastage
45
Ana/Aer
40 0
20
40
60
7000
TKN in Anaerobic Digester
6000
TKN in Aerobic Digester
80
% TKN Removal
5000 4000
60
3000
40
2000 20
1000 80
100
120
0
0 0
Steady State Operating Time (days)
Fig. 3 e Volatile solids removal data for combined digestion systems.
others could only be degraded anaerobically. It is thought that some of the additional aerobic floc generated in the aerobic digester was degradable anaerobically so by recycling sludge from the aerobic digester back to the anaerobic digester, more solids would be destroyed.
3.1.2.
100
Nitrogen removal
Of interest to this study was the effect of the combined digestion processes on nitrogen removal. Kumar et al. (2006) reported that combined anaerobic/aerobic digestion could remove up to 90% of the ammonia nitrogen by nitrification/ denitrification. Kumar et al. (2006) operated laboratory digesters in the same manner as this study with sludge being fed to the digestion units once per day. They showed that immediately after feeding anaerobically digested sludge into the aerobic digester, denitrification took place over several hours. When the readily degradable organic material was depleted, the dissolved oxygen increased and nitrification occurred. Kumar et al. (2006) measured nitrite and nitrate in the aerobic digesters and found little nitrate, suggesting that the high temperature of the reactors (32e34 C) of the reactors slowed nitrite oxidation. A similar process was thought to occur in this study. Data for nitrogen speciation in the control reactor is shown in Fig. 4. It can be seen in Fig. 5 that the TKN was approximately 3000 mg/L entering the control digester and the ammonia content was about 20% of the TKN. After anaerobic digestion, the ammonia content increased to about 50% of the
10
20
30
40
60
Operating Time (days)
Fig. 5 e TKN in the reactors for the ANA/AER system.
TKN and as expected, the TKN did not change. The only change was that organic nitrogen was converted to ammonia. For the ANA/AER system, it can be seen in Fig. 5 that the removal of TKN was approximately 65% in the aerobic digester. In Fig. 6, the ANA/AER system is shown to remove in excess of 80% of the ammonia. This combined digestion system has many advantage over conventional anaerobic digestion including additional VS reduction and a high degree of ammonia removal. Therefore, the impact of recycle streams from sludge dewatering processes would be reduced if a combined anaerobic/aerobic system is used for digestion. When an additional anaerobic step is added, as shown in Fig. 3, additional solids reduction occurred, even though the complete reactor system was no larger than the anaerobic/ aerobic system. However, the fate of nitrogen is of interest and the reactor selected for wastage determined the overall nitrogen removal. In Fig. 7, the fate of TKN in the system with wastage from the anaerobic system is presented. The nitrogen data is presented in terms of gm/day instead of mg/L because the recycle affects the concentration in the reactors. It can be seen in Fig. 7 that the overall TKN removal is slightly above 40%. However, it can also be seen that the TKN in the aerobic digester is low relative to the influent concentration, but because the wastage is from the anaerobic reactor, removal of TKN is less than 50%. In comparison, if removal is from the aerobic reactor (Fig. 8), the TKN removal increases to 70%. This is to be expected because when sludge is removed from the anaerobic
2500
2500
100 90
A m m o n i a ( m g - N /L )
2000 Nitrogen (mg/L)
50
1500 TKN in TKN out
1000
NH3 in 500
NH3 out
0 0
50
100
150
Operating Time (days)
Fig. 4 e Nitrogen Speciation in the Control Anaerobic Digester.
2000
80 70
1500 1000
60 Ammonia in Anaerobic Digester
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Ammonia in Aerobic Digester
40
% Ammonia Removal
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500
20
% A m m o n ia R e m o v a l
VSR Reduction (%)
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% T K N R emo val
75
10
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0
0 Operating Time (days)
Fig. 6 e Ammonia in the reactors for the ANA/AER system.
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TKN in feed TKN in Ana Dig
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TKN in Aer Dig
N itro g e n (m g /d /L o f fe e d )
T K N ( m g - N /d ay )
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% TKN Removal
Fig. 7 e TKN removal from ANA/AER/ANA system with sludge wastage from the anaerobic reactor.
digester, feed sludge that has not undergone aerobic digestion is part of the material that is wasted. When sludge is wasted from the aerobic digester, all sludge in the waste stream has undergone some degree of aeration. In addition, more TKN is converted to ammonia and more of the ammonia is converted to nitrogen gas. It can also be seen from the data in Figs. 7 and 8 that the concentration of TKN in the aerobic reactor is lower in the system which has wastage from the anaerobic reactor while the TKN is higher in the system where wastage occurs from the aerobic reactor. In Table 1, it can be seen that the SRT in the aerobic reactor is lower for the system which undergoes wastage from the aerobic system. It may be that additional TKN removal could occur if the aerobic reactor was operated at 5 days instead of 2.5 days. No attempt was made to assess the impact of varying SRT’s for the systems that were studied in this research. The approach in this study was to make the overall volume of the reactors the same, although the combined systems used two reactors instead of one. The data in Figs. 9 and 10 also provide for a comparison of the wastage from the two systems. In Fig. 9, the nitrogen data is shown for wastage from the anaerobic unit and in Fig. 10, nitrogen in the wastage from the aerobic unit can be seen. The major difference in is the effluent TKN and effluent ammonia. Recent data from out lab suggests that the removal of ammonia in the aerobic digester can be improved by
3000
40
2000 20
1000 0
0
0 TKN in feed 10 20 30 Operating Time (days)
40
Effluent Amm (mg/d/L)
3000 2500 2000 1500 1000 500
TKN in Aer Dig % TKN Removal
Fig. 8 e TKN removal from ANA/AER/ANA system with sludge wastage from the aerobic reactor.
20
40
60
80
100
Fig. 9 e Influent TKN and ammonia from the system with wastage from the anaerobic reactor in units of mg/d/L of feed.
continuous feeding and by cycling air on and off. Ammonia concentrations of 100e150 mg/L have been obtained using this approach. If the ammonia removal process was optimized, TKN concentrations of 600e700 mg/L could be expected and this would increase the overall TKN removal from the system with wastage from the aerobic digester to 80% and most of the remaining TKN would be relatively non-biodegradable organic nitrogen. Nitrate and nitrite were measured several times in the aerobic reactor just before feeding from the anaerobic unit and were always below the detection limit of 0.1 mg/L which is consistent with the data of Kumar et al., 2006. The oxidationereduction potential (ORP) over one cycle of the aerobic digester was measured and the data are shown in Table 2. These data are for the system with wastage from the aerobic unit. It can be seen from the data that the ORP declines for the first 7 h after feeding and then increases to a maximum at 24 h when feeding again takes place. Nitrogen data indicate that ammonia is converted to nitrite and then to nitrogen gas. Nitrate production is minimal. The data in Table 2 also indicate that readily degradable organic matter enters the aerobic digester and is rapidly degraded over the first 4e8 h. The unique characteristics of the combined anaerobic/ aerobic system provided optimal conditions for both nitrogen
N it ro g e n ( mg /d /L o f f e e d )
60
% T K N R emo val
T K N (m g /d a y )
4000
3500
Influent TKN (mg/d/L)
4500
80
5000
Influent Amm (mg/d/L)
Operating Time (days)
100
6000
Effluent TKN (mg/d/L)
4000
0 0
Operating Time (days)
7000
Influent TKN (mg/d/L)
4500 % T K N R em o v al
6000
Effluent TKN (mg/d/L)
4000
Influent Amm (mg/d/L)
3500
Efflent Amm (mg/d/L)
3000 2500 2000 1500 1000 500 0
0
10
20
30
Operating Time (days)
Fig. 10 e Influent TKN and ammonia from the system with wastage from the aerobic reactor in units of mg/d/L of feed.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 1 8 e6 2 4
Table 2 e Oxidationereduction potential in the aerobic digester after feeding from the anaerobic digester. Time after Feeding (h) 0 1 2 3 4 5 6 7 8 9 17 18 19 20 21 22 23 24
ORP (mV) 240 114 173 200 270 288 287 87 25 58 15 20 24 40 83 188 238 230
and solids removal. With regard to nitrogen removal, the increased solids destruction resulted in more conversion of organic nitrogen to ammonia. The ammonia was oxidized to nitrite effectively in the aerobic digester, but conversion to nitrate was slowed by the operational temperature of 32e34 C which has been shown to limit nitrite oxidation (Hellinga et al., 1998). It is thought that combined nitrification/denitrification occurs in the aerobic digester in the 6e8 h after the feeding cycle when the ORP drops to less than 100 mv (Zeng et al., 2004).
3.2.
Summary
Anaerobic digesters were operated using three anaerobic/ aerobic combinations and these were compared to a conventional mesophilic anaerobic digester. The parameters of interest were volatile solids reduction and nitrogen removal. It was found that conventional anaerobic digestion with a 20 day SRT resulted in 50% VS reduction for sludge from the DC Water and Sewer Authority. When a combined anaerobic/aerobic digestion system was used, the Vs destruction increased to 62%. When a recycle step was added in which sludge from the aerobic digester was concentrated and returned to the anaerobic unit, VS destruction increased to 70%. TKN removal in the anaerobic/aerobic unit was approximately 65%. For the ANA/AER/ANA systems, nitrogen removal depended on the unit from which sludge was wasted. If sludge was wasted from the anaerobic unit, TKN removal was approximately 45%, but if wastage was from the aerobic unit, it was approximately 70%. Data from other studies suggest that nitrogen removal can be improved in the ANA/AER/ANA system by increasing the SRT in the aerobic unit, providing continuous feeding and cycling air on and off. Additional research is needed to optimize this process. Overall, the combination of ANA/AER/ANA digestion appears to be a cost effective approach to achieve high VS destruction and effective ammonia removal from digested sludge. In addition, data from Banjade, (2008) indicates that
623
the sludge dewatering properties for this system are better than for conventional anaerobic digestion and odors are greatly reduced.
4.
Conclusions
This study was conducted to compare the performance of combined anaerobic/aerobic digestion systems to conventional mesophilic anaerobic digestion. In particular, it was of interest to evaluate a digestion subsystem in which anaerobic digestion was followed by aerobic digestion and then some of the aerobically digested sludge was thickened and recycled back to the anaerobic unit. Based on the data collected, the following conclusions are drawn: 1. Combined anaerobic/aerobic digestion increased VS reduction from 50% to 62% using the same overall SRT and the conventional digester. TKN removal was approximately 65%. 2. For the combined anaerobic/aerobic/anaerobic digestion system, VS destruction was 70% compared to the conventional digester at 50%. 3. For the ANA/AER/ANA system with wastage from the anaerobic unit, TKN removal was approximately 45% but increased to 70% when sludge was wasted from the aerobic unit.
Acknowledgements Support for this study was provided by the District of Columbia Water and Sewer Authority. The assistance of Chris Wilson and Charan Tanneru with laboratory operation and analysis is gratefully acknowledged.
references
Akunna, J., Bizeau, C., Moletta, R., Bernet, N., Heduit, A., 1994. Combined organic carbon and complete nitrogen removal using anaerobic and aerobic upflow filters. Water Sci. Technol. 30 (12), 297e306. American Public Health Association, 1998. In: Clesceri, L.S., Greenberg, A.E., Eaton, A.D. (Eds.), Standard Methods for Examination of Water and Wastewater, twentieth ed. American Public Health Association, Washington, D.C. Banjade, S., 2008. Anaerobic/Aerobic Digestion for Enhanced Solids and Nitrogen Removal. M.S. Thesis. Virginia Polytechnic Institute & State University, Blacksburg, VA, USA. Hellinga, C., Mulder, J.W., van Loosdrecht, M.C.M., Schellen, A.A.J. C., 1998. The SHARON process: an innovative method for nitrogen removal from ammonium-rich waste water. Water Sci. Technol. 37 (9), 135e142. Kumar, N., Novak, J.T., Murthy, S.N., 2006. Sequential AnaerobicAerobic Digestion for Enhanced Volatile Solids Reduction and Nitrogen Removal WEF Residuals and Biosolids Management Conference 2006, Cincinnati, OH, March 12e14, 2006. Novak, J.T., Sadler, M.E., Murthy, S.N., 2003. Mechanisms of floc destruction during anaerobic and aerobic digestion and the
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effect on conditioning and dewatering of biosolids. Water Res. 37 (13), 3136e3144. Pagilla, K.R., Kim, H., Cheunbarn, T., 2000. Aerobic thermophilic and anaerobic mesophilic treatment of swine waste. Water Res. 34 (10), 2747e2753. Park, C., Abu-Orf, M.M., Novak, J.T., 2006. Predicting the digestability of waste activated sludges. Water Environ. Res. 78 (1), 59e68. Subramanian, S., Kumar, N., Murthy, S., Novak, J.T., 2007. Effect of anaerobic digestion and anaerobic/aerobic digestion
processes on sludge dewatering. J. Residuals Sci. Technol. 4 (1), 17e24. US EPA, 1992. Environmental Regulations and Technology: Control of Pathogens and Vector Attraction in Sewage Sludge Under 40 Cfr Part 503. EPA/625/R-92/013, Washington, DC. Zeng, R.J., Lemaire, R., Yuan, Z., Keller, J., 2004. A novel wastewater treatment process: simultaneous nitrification, denitrification and phosphorus removal. Water Sci. Technol. 50 (10), 163e170.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 2 5 e6 3 1
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Photochemical fate of atorvastatin (lipitor) in simulated natural waters Behnaz Razavi a, Sihem Ben Abdelmelek a, Weihua Song a, Kevin E. O’Shea b, William J. Cooper a,* a b
Urban Water Research Center, Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697-2175, USA Department of Chemistry and Biochemistry, Florida International University, Miami, FL 33199, USA
article info
abstract
Article history:
Cholesterol-lowering statin drugs are among the most frequently prescribed for reducing
Received 9 May 2010
human blood cholesterol and they have been detected as contaminants in natural waters. In
Received in revised form
this study the photochemical behavior of atorvastatin (lipitor) was investigated at two
4 August 2010
different concentrations of 35.8 mM (20 mg L1) and 35.8 nM (20 mg L1) using a solar simulator
Accepted 10 August 2010
and a UV reactor. Photochemical fate in natural waters can be described in most cases by the
Available online 17 August 2010
sum of the loss due to hydrolysis, direct photolysis, and, reaction with hydroxyl radical (OH),
Keywords:
absolute bimolecular reaction rate constant with OH was measured, using pulsed radiolysis,
Atorvastatin
(1.19 0.04) 1010 M1 s1. The reaction rate constant of 1O2 was determined to be
Photodegradation
(3.1 0.2) 108 M1 s1. Under the experimental conditions used, at high atorvastatin
Solar simulator
concentration (35.8 mM) the contribution of singlet oxygen (1O2) to the photodegradation of
Singlet oxygen
atorvastatin in natural waters was higher than that of hydroxyl radical, and accounted for up
Hydroxyl radical
to 23% of the loss in aqueous solutions. Whereas, at a concentration of 35.8 nM, 1O2 (and OH)
Dissolved organic matter
both played a minor role in the removal of this compound. Lastly, it also appears that ator-
singlet oxygen (1O2) (or O2 (1D)), and excited state dissolved organic matter (DOM). The
vastatin reacts with 3DOM* contributing to its loss in simulated natural waters. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
In recent years, the environmental occurrence of pharmaceutically active compounds (PhACs), human and veterinary medication, has been a source of growing concern. Once these compounds are in aquatic systems, it has been shown that they can adversely affect both aquatic and non aquatic organisms and thus the ecosystem (Bendz et al., 2005; Brain et al., 2006; Dussault et al., 2008; Khetan and Collins, 2007; Kostich and Lazorchak, 2008; Lam et al., 2004; Liu and Williams, 2007; Liu et al., 2009). Current water and wastewater treatment systems in most cases do not completely remove low concentrations of PhACs (Doll and Frimmel, 2005; Huber et al., 2005; Lam and Mabury, 2005; Ternes et al., 2003; Vanderford and
Snyder, 2006). Although the concentrations of these PhACs are well below the medical dose (Wong and MacLeod, 2009; Pomati et al., 2006), there are concerns that the presence of mixtures of pharmaceuticals in drinking water may have long term consequences to human health especially to children, women of child bearing age, elderly and people with compromised immune systems (Brody et al., 2006). Statins have been reported to adversely affect a plant Lemna gibba, also known as duckweed (Brain et al., 2006). Exposure to environmentally relevant concentrations has also produced effects in fish, resembling pathology observed in wild populations of similar species (Kostich and Lazorchak, 2008; Ramirez et al., 2007). Recent studies have found that low concentrations of pharmaceuticals in drinking water, including
* Corresponding author. Tel.: þ1 949 824 5620. E-mail addresses:
[email protected] (B. Razavi),
[email protected] (S. Ben Abdelmelek),
[email protected] (W. Song), osheak@ fiu.edu (K.E. O’Shea),
[email protected] (W.J. Cooper). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.08.012
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statins, affected human embryonic kidney cells (Pomati et al., 2006; Wesierska-Gadek, 2006), human blood cells (de Voogt et al., 2009; Cunningham et al., 2009) and human breast cancer cells (Brody et al., 2006). The focus of this study was atorvastatin, widely used for the treatment of hypercholesterolemia (Prueksaritanont et al., 2002). Atorvastatin was chosen due to its large production volume and widespread use. Atorvastatin has been detected in untreated sewage samples at 76 ng L1, in treated sewage samples at 32 ng L1, and in surface water samples in the ng L1 range (Vanderford and Snyder, 2006; Miao and Metcalfe, 2003). Sunlight mediated photodegradation may occur via direct or indirect reactions and may play an important role in the fate of these compounds in natural waters (Khetan and Collins, 2007; Lam and Mabury, 2005; Cermola et al., 2006; Montanaro et al., 2009). Direct photolysis requires that the compound absorb light at above 300 nm and this has been reported to occur with atorvastatin (Lam et al., 2004; Lam and Mabury, 2005). In natural waters, reactions involving indirect photolysis, mainly resulting from photolysis of dissolved organic matter in surface waters with atorvastatin, have not been studied in detail. Indirect photochemical pathways include reaction with singlet oxygen (1O2), hydroxyl radical (OH), and photoexcited organic matter (1DOM* and/or 3DOM*). In this study the photochemical behavior of atorvastatin in simulated natural waters was investigated. The bimolecular reaction rate constants for the reaction with 1O2 and OH were evaluated. Experiments were performed in a solar simulator and a UV (300e400 nm) reactor. Studies suggesting that at least 3DOM* is involved in the loss of atorvastatin were also conducted. The percent contribution of hydroxyl radical and singlet oxygen to the photodegradation of atorvastatin was determined from the data.
NH
OH
O N
OH O
OH
F
Atorvastatin (MW 558) Fig. 1 e Chemical structure of atorvastatin [CAS 134523-005]. IUPAC name, (3R,5R)-7-[2-(4-fluorophenyl)-3-phenyl-4(phenylcarbamoyl)-5-(propan-2-yl)- 1H-pyrrol-1-yl]-3,5dihydroxyheptanoic acid.
a 1O2 scavenger. Studies were also conducted in D2O. Singlet oxygen has a significantly longer half-live in D2O (when compared to H2O) and therefore, the steady-state concentration is higher, and if 1O2 is involved then the rate of loss of the compound would increase. Solutions were transferred to 47 mL glass vials and the photolysis was conducted in a Luzchem Solar Simulator equipped with a 300 W ceramic xenon lamp (Luzchem Research Inc.). Similar experiments were conducted in borosilicate test tubes on a turn-table apparatus inside a Rayonet photochemical reactor equipped with sixteen 350-nm bulbs (Southern New England Ultraviolet Co. RPR-3500 A).
2.
Materials and methods
2.3.
2.1.
Materials
A Cary 100 Bio UV/visible, dual beam spectrometer was used to obtain the absorption spectra of atorvastatin as shown in supporting information, Fig. S1. The details are described in the supporting information, Text S2.
The pharmaceutical compound atorvastatin (Fig. 1) was purchased from Shanghai FWD Chemicals Limited in China. Atorvastatin was shown to be 99% pure by high performance liquid chromatography (HPLC). Suwannee River Fulvic Acid standard (SRFA) (1S101F) was obtained from the International Humic Substance Society (IHSS, 1991 Upper Buford Circle, St. Paul, MN 55108, U.S.A.) and used as received. Furfuryl alcohol (FFA), sorbic acid, sodium azide (NaN3), Rose Bengal and dipotassium phosphate were purchased from Sigma Aldrich and the deuterium oxide (D2O) was purchased from Cambridge Isotope Laboratories, Inc. The 2-hydroxyl terephthalic acid (2OHTA), used for calibration, was synthesized (Mason et al., 1994) as described in the supporting information (Text S1).
2.2.
2.4.
Molar absorption coefficient spectra of atorvastatin
Steady-state concentration of singlet oxygen (1O2)
To determine the reaction rate of 1O2 with atorvastatin, it was first necessary to determine the steady state concentration of 1 O2 under the reaction conditions. A solution containing 200 mM FFA and 20 mg L1 SRFA were prepared and photolyzed. Aliquots were removed at various time points and analyzed by HPLC. The steady-state concentration of singlet oxygen was determined by dividing the observed FFA degradation rate by the FFA reaction rate constant with 1O2 (krxn ¼ 8.3 107 M1 s1) (Latch et al., 2003).
Photolysis of atorvastatin
For this study, solutions were prepared with 35.8 mM (20 mg L1) and 35.8 nM (20 mg L1) atorvastatin concentrations. These solutions, buffered at pH ¼ 7 with a 5.0 mM phosphate buffer, were prepared with the addition of SRFA (20 mg L1), to simulate waters with dissolved organic matter, and NaN3 (20 mg L1), as
2.5. (1O2)
Bimolecular reaction rate constant of singlet oxygen
Samples containing 100 mM atorvastatin, 100 mM furfuryl alcohol (FFA), and 40 mM Rose Bengal (RB) were prepared and transferred to glass vials (Boreen et al., 2008). The solution was
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½FFAt krxn;FFA ½atorvastatint ln ¼ ln ½FFA0 krxn;atorvastatin ½atorvastatin0
(1)
2.6. Bimolecular reaction rate of hydroxyl radical (OH) and solvated electron (e aq ) Electron pulse radiolysis was used to measure the absolute bimolecular reaction rate of hydroxyl radical and the solvated electron with atorvastatin as shown in supporting information, Text S3.
2.7.
Concentration of hydroxyl radical (OH)
To determine the contribution of hydroxyl radicals to the loss of atorvastatin, the steady state concentration of OH in solutions of SRFA was determined. A solution containing 600 mM (99.7 mg L1) terephthalic acid (TA) and 20 mg L1 SRFA was prepared and photolyzed, as above. Aliquots were removed at various time points and analyzed by HPLC using a fluorescence detector (lexcitation ¼ 315 nm; lemission ¼ 425 nm). The observed formation rate of 2-hydroxy terephthalic acid (2OHTA) was converted to OH concentration by dividing TA concentration and its hydroxyl radical reaction rate (krxn ¼ 4.94 109 M1 s1, this reaction rate has also been measured using electron pulse radiolysis, and experimental results are shown in Fig. S2, supporting information). The method of initial rates was used to obtain the steady state concentration of OH as the direct photolysis of the 2OHTA occurs at wavelength above 360 nm (Page et al., in press).
2.8.
HPLC and LC-MS-MS analysis
Atorvastatin was analyzed using an Agilent 1200 series HPLC, equipped with Quaternary Gradient, Autosampler, UV/Vis and fluorescence detectors, under the following conditions: column, Phenomenex Gemini C18 250 4.6 mm i.d.; the mobile phase consisted of isocratic mixtures varying between 2 and 50% CH3OH and 98e50% 10 mM phosphate buffer solution (pH 3.0). The detector was operated at wavelengths of 241 nm for atorvastatin. The LC-MS-MS system used in this experiment was a Waters Acquity UPLC system equipped with an Acquity BEH C18 1.7 mm column (2.1 50 mm). The mobile phases A (water supplemented with 2% acetonitrile and 0.2% acetic Acid) and B (acetonitrile acidified with 0.2% acetic acid) were used at a rate of 0.3 mL min1. The gradient began at 90% A with a linear increase to 90% of B in 1 min, where it was held for 1 min followed by a return to 90% A in 0.05 min and held for 0.95 min. The total cycle time was 3 min. The column oven temperature was set at 50 C. An injection volume of 10 mL was used for all analyses. The UPLC system was coupled to a Waters Quattro Premier XE triple quadrupole mass spectrometer (MS). The samples were run in positive ionization
MSeMS mode. Experimental conditions for the mass spectrometer were the following: desolvation and ESI source block temperatures, 400 C and 125 C, respectively; capillary voltage: 3.3 kV; argon collision gas 7 103 mbar. Nitrogen was used as both the nebulization and desolvation (800 L/h) gas. The cone voltage and collision energy were optimized for atorvastatin using MassLynx 4.1 QuanOptimize software. For quantitative analysis the MS was operated in multiple-reaction monitoring (MRM) mode. One transition between the precursor ion and the most abundant fragment ion was recorded (559 / 440).
3.
Results and discussion
3.1.
Photodegradation of atorvastatin (35.8 mM)
The results of the photolysis of 35.8 mM atorvastatin in the solar simulator are summarized in Fig. 2. Photolysis in distilled water under our experimental conditions (k ¼ 9.55 106 s1) suggested that direct photodegradation was very slow (Lam and Mabury, 2005). The results of indirect photolysis, atorvastatin and SRFA (20 mg L1), gave a pseudofirst order rate constant of 2.34 104 s1 or a half-life under those conditions of 4.89 103 s. The addition of NaN3, 0.308 mM, (k ¼ 5.6 108 M1 s1 for reaction with 1O2) reduced the loss rate constant to 1.41 104 s1 suggesting that 1O2 was involved in the indirect photolysis (Latch et al., 2003). To further confirm this, solutions of SRFA and atorvastatin were prepared in D2O and photolyzed. The rate constant for the disappearance of atorvastatin in D2O was 1.10 103 s1, significantly faster than in H2O, suggesting that indeed, 1O2 was involved in the loss of the compound due to its increased lifetime in D2O as compared to that in H2O (Boreen et al., 2008). To further understand the impact of UV-A and UV-B on the degradation of atorvastatin, a similar series of solutions were photolyzed in a second photochemical reactor, where the central wavelength of the light source was 350 nm which tailed off on both sides in the range 300e400 nm. The results 0.0 Ln([atorvastatin]t /[atorvastatin]0)
photolyzed in the solar simulator with a UV filtered (a commercial photographic UV filter was used) light source to reduce the irradiance. Aliquots of the solutions were removed at various time points and analyzed by HPLC. The loss of the atorvastatin was recorded in the presence and absence of FFA, and the reaction rate constant of atorvastatin with 1O2 was obtained as shown in equation (1).
-0.6 -1.2 -1.8 -2.4 -3.0 -3.6 0
1000
2000 Time (sec)
3000
Fig. 2 e Photodegradation of 35.8 mM atorvastatin in various solutions in the solar simulator. The lines represent the photodegradation of atorvastatin in distilled water (-), and, in water to which were added, SRFA (C), SRFA and NaN3 (:), and, SRFA in D2O (;).
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from these experiments are summarized in the supporting information, Text S4 and are shown in Fig. S3, Supporting information.
Stead state concentration of O2 1
The steady state concentration of singlet oxygen, [ O2]ss, in solutions of 20 mg L1 SRFA, was determined in the photochemical reactor and solar simulator to be 8.7 1013 M and 1.8 1013 M, respectively. Fig. 3 summarizes the results obtained in the solar simulator for determining the [1O2]ss.
3.3.
Steady state concentration of OH
The steady state concentration of hydroxyl radical, [OH]ss, for 20 mg L1 solutions of SRFA was determined in the photochemical reactor and solar simulator (Fig. 5) to be 9.6 1017 M and 2.9 1017 M, respectively. The initial rate of formation of 2OHTA was used to calculate the [OH]ss (Page et al., in press). The fluorescence of the 2OHTA allowed quantification of the cumulative concentration of OH and from that the steady state concentration was derived. The reaction is shown in Scheme 1.
3.5.
Kinetic measurements for OH radical and e aq
To determine the absolute bimolecular reaction rate of OH with atorvastatin a transient absorption spectra was obtained
-0.2 -0.3 -0.4 -0.5
Reaction rate constant of 1O2
The steady state photolysis of the sensitizer Rose Bengal, as a source of 1O2, was used to determine the reaction rate constant of 1O2 and atorvastatin. The conditions employed were optimized to promote the reaction with 1O2, using shorter exposure times and a UV filtered light source to reduce the irradiance. Fig. 4 summarizes the data used to calculate the rate constant for the reaction of atorvastatin with 1O2, (3.1 0.2) 108 M1 s1. This value is in close agreement with that determined by others, (1.5 0.2) 108 M1 s1 (Montanaro et al., 2009).
3.4.
-0.1 Ln([FFA]t /[FFA]0)
3.2.
1
0.0
-1.6 -1.2 -0.8 -0.4 0.0 Ln([atorvastatin]t /[atorvastatin]0)
Fig. 4 e Competitive 1O2 degradation of atorvastatin and furfuryl alcohol in pH 5.0 buffered H2O with RB using as sensitizer. krxn values were determined by multiplying the slope obtained from these plots (0.269) by the krxn of FFA.
(Fig. S4, Supporting information). The maximum absorbance at 330 nm was used for the rate determination. The absorption coefficient, 11,600 M1 cm1 was calculated using a hydroxyl radical G value of 0.59 mmol J1, based upon the intraspur scavenging model calculations of LaVerne and Pimblott (1993). (The G value is the efficiency with which radicals are formed in the radiolysis of water.) The absolute hydroxyl radical reaction rate constant was measured by fitting exponential curves to the pseudo-firstorder growth kinetics (Fig. S5a, Supporting information) and then plotting these values as a function of the concentration of atorvastatin (Fig. S5b, Supporting information). The bimolecular rate constant for the reaction between OH and atorvastatin was determined to be (1.19 0.04) 1010 M1 s1, which is consistent with a previously reported value of (1.9 0.5) 1010 M1 s1 (Lam and Mabury, 2005). The absolute bimolecular reaction rate of atorvastatin with the solvated
0.35
-0.02
0.30
-0.04
[2OHTA] (μΜ)
Ln ([FFA]t /[FFA]0)
0.00
-0.06 -0.08
0.25 0.20 0.15
-0.10 0
1000 2000 3000 4000 5000 6000 7000 Time (sec) 1
Fig. 3 e The concentration of O2, in solar simulator, was determined by the loss of furfuryl alcohol. By dividing the slope of the line (1.48 3 10L5 sL1) by the reaction rate of furfuryl alcohol with 1O2, the steady state concentration of 1 O2 in the reactor was determined.
0
2000
4000
6000
8000
10000
Time (sec)
Fig. 5 e The steady state concentration of OH, in the solar simulator was calculated by dividing the initial slope of the graph (3.38 3 10L5 M sL1) by the hydroxyl radical reaction rate of terephthalic acid and its concentration used in the experiment.
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 2 5 e6 3 1
O
OH
O
O
OH
H
O
OH
OH OH
H
OH
OH
OH H
O2
+
HO2
O O O
OH
O
O
OH
O
OH
OH 2OHTA
TA
Scheme 1.
9 1 1 electron (e s aq ) was determined to be (1.42 0.02) 10 M using the transient absorption of the eaq at 700 nm (Fig. S6, Supporting information).
3.6. Photodegradation of atorvastatin near environmental concentrations (35.8 nM) Atorvastatin has been observed in the environment at relatively low concentrations. Therefore, a series of experiments were performed in the solar simulator using 35.8 nM atorvastatin solutions, and the results are summarized in Fig. 6. The disappearance rates for atorvastatin were 4.02 104, 2.73 104 and 2.28 103 s1, for the solutions with only SRFA, SRFA and sodium azide, and in D2O with SRFA, respectively. For each treatment the loss rate for the disappearance of atorvastatin was faster than at the higher concentration (see above, Fig. 2). The absolute concentration of SRFA and sodium azide was the same in both studies, and the only variable was the concentration of the atorvastatin. The ratio of the reaction rates between the solution with only SRFA in it and the one with SRFA and sodium azide were comparable, suggesting that the contribution of 1O2 to the loss of atorvastatin in both solutions was similar. However, the degradation rate for the loss of the atorvastatin in the solution containing 35.8 nM, was 58% faster. This suggested that
0.0 Ln([atorvastatin]t /[atorvastatin]0)
0 Ln([atorvastatin]t /[atorvastatin]0)
another reactive species accounted for a substantial portion of the loss of the atorvastatin. Although it was initially thought that the solvated electron (e aq ) was produced in surface waters via sunlight meditated photolysis of natural organic matter (Fischer et al., 1985, 1987; Zepp et al., 1987a), later it was shown in three independent studies that, at least for the reduction of dioxygen to superoxide anion radical (as the precursor to H2O2 in natural waters), e aq is apparently not formed under normal sunlight irradiation conditions (Cooper et al., 1994; Sturzenegger, 1989; ThomasSmith and Blough, 2001). Therefore, even though the absolute bimolecular reaction rate of atorvastatin with the solvated 9 1 1 s , electron (e aq ) was determined to be at (1.42 0.02) 10 M however it is not likely to be involved in the loss of the pharmaceutical in natural waters. From studies probing the reduction of dioxygen in natural waters it has been suggested that a direct reaction between the triplet excited state of the DOM (3DOM*) and ground state triplet dioxygen (Cooper et al., 1994) was a possible mechanism. The data summarized in Fig. 2, suggest that a significant loss of atorvastatin is not accounted for by reaction with 1O2 or OH or e . Therefore, a direct reaction of the atorvastatin with aq excited state DOM is proposed. As these solutions were made in high purity water there is no possibility of NO 3 involvement (Zepp et al., 1987b) suggesting that the major reactant with atorvastatin is an excited state of DOM with atorvastatin.
-1
-2
-3
-4
-0.5 -1.0 -1.5 -2.0 -2.5 -3.0 -3.5 0
0
2000
4000
6000
8000
10000
Time (sec)
Fig. 6 e Indirect photodegradation of 35.8 nM atorvastatin in various solutions in the solar simulator. The solid lines represent the photodegradation of atorvastatin in D.I. H2O (-), in SRFA (C), in SRFA and NaN3 (:), and in SRFA and D2O (;).
750
1500
2250
3000
3750
Time (sec)
Fig. 7 e Comparison study of indirect photodegradation of 35.8 mM atorvastatin in various solutions in the solar simulator. The solid lines represent the photodegradation of atorvastatin in water to which were added SRFA (-), SRFA in saturated O2 (C), SRFA in saturated N2 (:), and, SRFA and sorbic acid (;).
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 2 5 e6 3 1
To study this hypothesis solutions of 35.8 mM atorvastatin were prepared with the addition of 1 mM sorbic acid, a known triplet state quencher (Conceicao et al., 2000; Mateus et al., 1994), 20 ppm SRFA, and by supersaturating the solution with O2 and removing O2 by vigorous bubbling of N2. The solutions were photolyzed in the solar simulator and aliquots were removed at various time points and analyzed by HPLC. The results are summarized in Fig. 7. The addition of sorbic acid slowed the loss of atorvastatin, suggesting that 3DOM* was responsible for a portion of the observed loss. The addition of O2 resulted in a marked increase in the removal of atorvastatin, likely as a result of an increase in 1O2, while at the same time quenching the 3DOM*. The removal of oxygen by bubbling N2 into the solution increased the loss rate, suggesting that the 3 DOM* was indeed reacting directly with atorvastatin.
4.
Conclusion
The indirect photodegradation using a solar simulator, of both 35.8 mM and 35.8 nM atorvastatin in the presence of SRFA, was investigated. The results showed that 1O2 contributed to the loss of atorvastatin, in 35.8 mM and 35.8 nM solutions, 23% and 14%, respectively. The OH contributed 0.15% and 0.085%, to the degradation of atorvastatin in the 35.8 mM and 35.8 nM solutions, respectively (Table S1). From the results it can be concluded that at high atorvastatin concentration (35.8 mM) nearly 23% of atorvastatin is photodegraded through reaction with 1O2; and OH has very little role in photochemical removal of this compound from natural compounds. It is proposed that the other 77% of the indirect photodegradation of atorvastatin in natural waters is due to the reaction of this compound with excited state dissolved organic matter either 1 DOM* or 3DOM*. Quenching of the 3DOM* clearly showed a reduced loss rate while enhancing 3DOM* (by saturating the solution with N2) enhanced the loss. It is not possible to determine the intermediacy of 1DOM* with these experiments; however, we speculate that because of the short lifetime of the singlet state the reaction with the 3DOM* may be the dominant pathway. The results that were obtained for atorvastatin at the more environmentally representative concentration (35.8 nM of atorvastatin solution) suggest that at low atorvastatin concentration, almost all of this compound is sorbed to natural organic matter, and therefore both 1O2 and OH have a minor little role in the removal of this compound. An alternative hypothesis is that the sorbed atorvastatin reacts directly with 1O2 in the “microenvironment” in the dissolved organic matter, as suggested by Latch and McNeill (2006). Further studies are required to elucidate the details of these alternative mechanisms. From the data presented it is likely that photodegradation of atorvastatin is a major mechanism in determining its overall environmental fate. The extent to which any photochemical reaction occurs in natural waters will depend on many variables. These variables include the amount of DOM, latitude, flow (laminar vs turbulent) regimes, time of day and year. i.e. the higher the concentration of DOM the less penetration in a natural water. However, in many streams there are areas of turbulent flow and these areas mix the water column
resulting in a redistribution of the pollutant. The data presented provide a more detailed assessment of the underlying photochemistry of atorvastatin in natural waters and they would need to be coupled with a hydraulic model to assess the extent of degradation in a particular water system. The one reaction rate constant that was not evaluated was that of 3 DOM* with Atorvastatin. The methodology for that measurement is not straightforward and is the subject of ongoing studies.
Acknowledgments Work performed at the Radiation Laboratory, University of Notre Dame, is supported by the Office of Basic Energy Sciences, U.S. Department of Energy. We thank The Newkirk Center at the University of California, Irvine for partial support of this study. This is contribution 57 from the University of California, Irvine, Urban Water Research Center.
Appendix. Supplementary data Supplementary data associated with this article can be found in online version at doi:10.1016/j.watres.2010.08.012.
references
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 3 2 e6 3 8
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Photosensitized degradation of amoxicillin in natural organic matter isolate solutions Haomin Xu a, William J. Cooper a, Jinyoung Jung b, Weihua Song a,* a
Urban Water Research Center, Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697-2175, USA Department of Environmental Engineering, Yeungnam University, 214-1 Dae-Dong, Gyeongsan-Si, Gyeongsangbuk-Do 712-749, Republic of Korea b
article info
abstract
Article history:
Amoxicillin is a widely used antibiotic and has been detected in natural waters. Its envi-
Received 4 May 2010
ronmental fate is in part determined by hydrolysis, and, direct and indirect photolysis. The
Received in revised form
hydrolysis rate in distilled water and water to which five different isolated of dissolved
10 August 2010
organic matter (DOM) was added, were evaluated. In the five different DOM solutions
Accepted 11 August 2010
hydrolysis accounted for 5e18% loss of amoxicillin. Direct and indirect photolysis rates
Available online 17 August 2010
were determined using a solar simulator and it appeared that indirect photolysis was the dominant loss mechanism. Direct photolysis, in a solar simulator, accounted for 6e21%
Keywords:
loss of amoxicillin in the simulated natural waters. The steady-state concentrations of
Photodegradation
singlet oxygen, 1DO2 (w1013 M) and hydroxyl radical, OH (w1017 M) were obtained in
Amoxicillin
aqueous solutions of five different dissolved organic matter samples using a solar simu-
Reactive oxygen species
lator. The bimolecular reaction rate constant of 1DO2 with amoxicillin was measured in the
Triplet excited state DOM
different solutions, kDO2 ¼ 1.44 104 M1 s1. The sunlight mediated amoxicillin loss rate with 1DO2 (w109 s1), and with OH (w107 s1), were also determined for the different samples of DOM. While 1DO2 only accounted for 0.03e0.08% of the total loss rate, the hydroxyl radical contributed 10e22%. It appears that the direct reaction of singlet and triplet excited state DOM (3DOM*) with amoxicillin accounts for 48e74% of the loss of amoxicillin. Furthermore, the pseudo first-order photodegradation rate showed a positive correlation with the sorption of amoxicillin to DOM, which further supported the assumption that excited state DOM* plays a key role in the photochemical transformation of amoxicillin in natural waters. This is the first study to report the relative contribution of all five processes to the fate of amoxicillin in aqueous solution. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
As our reliance on manufactured chemicals becomes increasingly important in an overpopulated world, of particular interest are pharmaceutical compounds which have potential ecological and human health implications. There are hundreds of these compounds in use and are now being found
in rivers, lakes and reservoirs (Snyder et al., 2003). Antibiotics, designated for human and veterinary use, are one of the most important groups of pharmaceuticals (Kuemmerer, 2009). They are excreted partially unmetabolized and enter wastewater treatment plants (Calamari et al., 2003; Rizzo et al., 2009), while those used in aquaculture may enter natural waters directly. Due to their high water solubility (with
* Corresponding author. E-mail addresses:
[email protected] (H. Xu),
[email protected] (W.J. Cooper),
[email protected] (J. Jung),
[email protected] (W. Song). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.08.024
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log Kow 1) and low biodegradability, these substances and their active metabolites may pass through conventional wastewater treatment plants and ultimately end up in natural waters (Ternes, 1998; Halling-Sorensen, 2000; Trovo et al., 2008; Grujic et al., 2009). The long term health effects of these low concentrations of biologically active compounds are unknown; however, evidence is accumulating that they do pose a concern in receiving waters and ecosystems (Kuemmerer et al., 1997; Kolpin et al., 2002; Alaton et al., 2004). In general, little is known about the environmental occurrence, transport, and ultimate fate of pharmaceuticals (Kolpin et al., 2002; Kuemmerer, 2003). Biodegradation, sorption and photodegradation are the main removal processes in surface waters (Poiger et al., 1999; Zwiener, 2007). In some cases, pharmaceuticals have been designed to be resistant to biodegradation, thereby inhibiting one of the major elimination pathways (Khetan and Collins, 2007). The sediment type has been shown to significantly affect the sorption of pharmaceuticals, thus implying it is site specific (Scheytt et al., 2005). The other potential major pathway is via solar mediated photodegradation (Boreen et al., 2003; Robinson et al., 2007). Sunlight initiated photolysis is direct, where their absorption spectra overlap the solar spectrum and the rates usually vary in natural waters (Liu, 2007), or indirect, by reaction with reactive oxygen species (ROS), e.g. hydroxyl radical (OH) (Vaughan and Blough, 1998) and singlet oxygen (1DO2) (Haag and Hoigne, 1986; Latch and McNeill, 2006; Boreen et al., 2008; Grandbois et al., 2008; Cory et al., 2009), as a result of dissolved organic matter (DOM) photolysis in the water (Zepp and Cline, 1977; Zepp et al., 1981; Andreozzi et al., 2004; Miller and Chin, 2005; Chen et al., 2009; Ge et al., 2009; Vione et al., 2006). Prior studies have suggested that OH plays a significant role in the fate of pharmaceuticals in natural waters (Lam et al., 2003). Direct reactions with triplet excited state DOM (3DOM*) may also contribute to the fate of pharmaceuticals (Boreen et al., 2005). It is proposed that triplet excited state DOM (3DOM*) reacts with pharmaceutical compounds by energy and/or electron transfer, and/or hydrogen abstraction (Gerecke et al., 2001). However, in some cases, DOM interferes with the indirect photolysis of pharmaceuticals by screening reactive wavelengths of light and by scavenging ROS (Guerard et al., 2009). Amoxicillin has a b-lactam ring structure and is considered to be one of most important penicillin-like antibiotics (Laengin et al., 2009). It is used to treat bacterial infection and to promote animal growth. Although its concentration in surface waters is in the ng L1 range (Cha et al., 2006), well below the acutely toxicity level to humans, its continuous input into environment leads to chronic exposure of aquatic organisms which may pose risks (Andreozzi et al., 2004; Bound and Voulvoulis, 2006). Andreozzi et al. (2004) determined its biodegradation and adsorption rate constants; however, biodegradation was studied in synthetic activated sludge solution, with microbial levels much higher than in the environment, and therefore not particularly relevant to natural waters, while the indirect photo-transformation of amoxicillin and its reaction pathway in natural waters remains unclear. They also studied the direct photolysis and showed that the rate was somewhat higher at pH of 7.5 when compared to 5.5. They also reported the solar quantum yields
and predicted that, at 37 oN latitude, the half life was 1.13 and 1.69 days, pH of 7.5 and 5.5, respectively. Preliminary data suggested that the addition of both NO 3 (15 mg/L) and humic acid (5 mg/L) enhanced the photodegradation rate (Andreozzi et al., 2004). Amoxicillin was selected as the pharmaceutical compound of interests in this study and five different reactions that build the study of Andreozzi et al. (2004) were determined. The rate constant for the reaction with 1DO2 was measured and the contributions of the direct and indirect photodegradation to the total photolysis assessed. Thus, the reactions of amoxicillin with 1DO2, $OH and 3DOM*, were determined. The photodegradation rate of amoxicillin was also shown to be positively correlated with its sorption on DOM.
2.
Materials and methods
2.1.
Materials
Amoxicillin (99%), furfuryl alcohol (FFA, 99%), furfuraldehyde (furan-2-carbaldehyde, FAD, 99%), terephthalic acid (TA, 98%), Rose Bengal (RB), and sorbic acid (SA, 99%), were purchased from SigmaeAldrich and used as received. 2-hydroxyl terephthalic acid (2HTA) was synthesized using a literature method (Mason et al., 1994) as described in the supporting information (Text S1). Deuterium oxide (D2O, 99.9%) was obtained from Cambridge Isotope Laboratories, Inc. Methanol, 2-propanol, and phosphoric acid (Fisher Science) were of HPLC grade. Compressed nitrogen was purchased from Airgas, Inc. For all of the studies, solutions were prepared using water filtered with a Millipore Milli-Q system, which includes constant illumination by a Xe arc lamp at 172 nm to keep total organic carbon concentrations below 13 mg L1. Amoxicillin solution, 0.06 mM, was prepared in Milli-Q water Scheme 1. Aquatic organic matter, humic acid (2S101H), fulvic acid (2S101F), a peat derived fulvic acid (1R107F), and Suwannee River NOM (1R101N) were obtained from the International Humic Substances Society (IHSS). A second peat derived humic acid (HA) was obtained from SigmaeAldrich. All of the studies were conducted in 25 mg L1 solutions of the five DOM samples.
2.2.
Analytical methods and experimental details
The concentration of amoxicillin was determined by HPLC, Agilent 1200 equipped with UVevisible and fluorescence
NH2 H N
HO
H S
N
O O
COOH
Scheme 1 e Molecular structure of amoxicillin.
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detectors, and a Phenomenex Gemini C18 column (5 mm, 250 4.6 mm). The isocratic mobile phase was 85% 10 mM phosphate buffer solution (pH 3.0) and 15% CH3OH. The flow rate was 1.0 mL min1 and the detection wavelength was 230 nm.
2.2.1.
Hydrolysis
Amoxicillin solutions were stored in 40 mL amber vials at 20 C in the dark. Aliquots of samples (500 mL) were withdrawn at time intervals, 4e12 h, and analyzed by HPLC.
2.2.2.
Direct photolysis
Amoxicillin solutions were prepared in 15 mL quartz cells and exposed to simulated sunlight in a Luzchem (Model LZC-PMV) solar simulator equipped with a 300 W ceramic xenon lamp. Data for the determination of the photochemical rate were obtained by withdrawing aliquots of samples (500 mL) over 4e12 h, and analyzing amoxicillin by HPLC.
2.2.3.
where k1 DO2 ;FAD ¼ 8.4 104 M1 s1 (Gollnick and Griesbeck, 1985) (v) Steady-state concentrations of OH. Solutions of TA (0.6 mM) and 25 mg L1 of the DOM samples were prepared and irradiated as above. In the air-saturated solution, hydroxylation of TA formed 2-Hydroxyterephthalate (2HTA), and the reaction yield was 35% (Mark et al., 1998). The concentration of 2HTA was monitored by HPLCFluorescence (lexcitation ¼ 315 nm; lemission ¼ 425 nm) (Samuni et al., 2002). The observed formation rate of 2 HTA was converted to OH concentration by diving TA concentration, reaction yield and its hydroxyl radical reaction rate constant, using following equations.
TA þ OH / 2HTA
(4)
d[2HTA]/dt ¼ 0.35$kOH,TA$[TA][OH]
(5)
Indirect photolysis
where kOH,$TA ¼ 3.3 109 M1 s1 (Mark et al., 1998)
1
(i) Roles of DO2 and OH. Solutions of amoxicillin containing various DOM (1R107F, 2S101F, 2S101H, 1R101 N and HA respectively) were prepared and exposed to simulated sunlight as above. Experiments were conducted in H2O, D2O (1DO2 enhancer) and with the addition of 2-propanol (65 mM, OH scavenger) and analyzed by HPLC. (ii) Role of 3DOM*. Solutions of amoxicillin containing 25 mg L1 DOM (2S101F) were prepared and exposed to simulated sunlight as above. Experiments were conducted with the addition of sorbic acid (0.18 mM) and at deoxygenated condition by purging nitrogen gas into the solution, and, analyzed by HPLC. (iii) Steady-state concentrations of 1DO2. Solutions of DOM and FFA (1.5 mM), as a 1DO2 kinetic probe, were irradiated in the solar simulator. The loss of FFA was analyzed using a gradient elution starting at 2% CH3OH and increased to 30% CH3OH in 15 min. The UV/Vis detection wavelength was 219 nm. The steady-state concentration of 1DO2 was determined using the following equations (Boreen et al., 2005):
FFA þ 1DO2 / Substrate
(1)
d½FFA=dt ¼ k1 DO2 ;FFA ½FFA½1 DO2 ss
(2)
where k1 DO2 ;FFA ¼ 8.3 107 M1 s1 (Latch et al., 2003) (iv) Reaction rate constant of 1DO2 and amoxicillin. Rose Bengal (RB, 0.103 mM), as a photosensitizer for 1DO2 production, was photolyzed simultaneously in solutions of amoxicillin and FAD (1.6 mM) in solar simulator respectively. The bimolecular reaction rate constant of amoxicillin and 1DO2 was determined using competitive kinetics study (Boreen et al., 2008), as Eq. (3). k1 DO2 ;AMO d½AMO=dt ¼ k1 DO2 ;FAD d½FAD=dt
(3)
2.2.4.
Amoxicillin sorption determinations
A series of experiments were conducted to study the sorption of amoxicillin on DOM. Solutions of amoxicillin (0.06 mM) containing 25 mg L1 of the five DOMs were thoroughly mixed and stored in dark for 3 h. The samples were transferred to centrifuge tubes fitted with a 3000 molecular weight cut off filter (MWCO) and centrifuged at 5000 rpm (10 minutes) using a Fisher Scientific (Model AccuSpin 4000) centrifuge (Song et al., 2007). The samples were analyzed by HPLC and the percentage of amoxicillin association with DOM was determined.
3.
Results and discussion
Initially, studies were conducted to explore the hydrolysis rate (dark control) in pH 7.0 buffered distilled water, the direct photochemical degradation of amoxicillin, and the indirect photolysis in the presence of DOM, using a solar simulator (Fig. 1). The rate constant of hydrolysis, at pH 7.0 and room temperature was: khydr ¼ (4.45 0.24) 107 s1 or half life ¼ 18 days. The pseudo first-order rate constant for the direct photolysis in the solar simulator and pH 7.0 buffered distilled water was computed as shown in Eq. (6), (kphot ¼ (5.24 0.33) 107 s1):
d[AMO]/dt ¼ khydr[AMO]ekphot[AMO]
(6)
Both the rate of hydrolysis and direct photolysis are consistent with previous studies (Andreozzi et al., 2004). Sunlight mediated photolysis in (simulated) natural waters results from DOM derived reactive species, e.g. 1DO2, OH, and excited state DOM, presumably, 3DOM*. A significant increase in the loss rate of amoxicillin in irradiated solution containing DOM (2S101F) (Fig. 1) indicated that indirect photolysis was important to its photodegradation, in agreement with previously reported results for other compounds (Chin et al., 2004).
635
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 3 2 e6 3 8
a
0.0
0.0 -0.2 -0.4
Ln ([AMO]/[AMO]0)
Ln ([AMO]/[AMO]0)
-0.2
-0.4
-0.6
0
10
20
-0.8 -1.0 -1.2
AMO (H2O) AMO + 2S101F (H2O) AMO + 2S101F + 2-Propanol (H2O) AMO + 2S101F (D2O)
-1.4
AMO (Dark) AMO (Photolysis) AMO + 2S101F (Photolysis)
-0.8
-0.6
-1.6
30
40
0
50
10
Time Elapsed (hr)
1
To qualitatively demonstrate the role of DO2 and OH in indirect photolytic processes, in a solar simulator, amoxicillin solutions containing 25 mg L1 DOM (2S101F) were studied in H2O and D2O, and, with the addition of 2-propanol, an OH scavenger (Behnajady et al., 2008), respectively (Fig. 2a). Singlet oxygen (1DO2) has a longer life time in D2O than in H2O (Merkel and Kearns, 1972; Zepp et al., 1977; Matheson et al., 1978), and, therefore, if the reaction with 1DO2 is important the amoxicillin loss rate should be increased. However, the amoxicillin solution in D2O showed little enhancement over that in H2O, suggesting a slow reaction rate of 1DO2 and amoxicillin. The addition of 2-propanol (65 mM) resulted in a significant decrease in the observed amoxicillin loss rate, indicating that OH was of importance. However, the addition of scavengers for OH did not completely inhibit the indirect photolysis of amoxicillin, which implies that direct reaction with excited state DOM (1DOM* or 3DOM*) might be an important loss mechanism for amoxicillin. The influence of 3DOM* on the photodegradation of amoxicillin was then investigated and the results shown in Fig. 2b. As O2 is known to be a triplet quencher, an experiment was conducted using a nitrogen purged (deoxygenated) solution. The absence of oxygen would lead to increased concentrations of 3DOM* (Chen et al., 2009). The results indicated the loss rate of amoxicillin was significantly enhanced, suggesting a direct reaction with 3DOM*. The addition of sorbic acid, another triplet excited state quencher (Zepp et al., 1998; Sortino and Scaiano, 1999; Mateus, 2000), suppressed the depletion of amoxicillin in DOM solution. These two observations strongly suggest that the photodegradation of amoxicillin proceeds via reaction with 3DOM*, and, the contribution of reactive oxygen species, e.g. $OH and 1DO2, might be of less importance. The pseudo first-order photodegradation of amoxicillin in five different DOM samples (1R107F, 2S101F, 2S101H, 1R101N
b
30
40
0.00 -0.05 -0.10
Ln ([AMO]/[AMO]0)
Fig. 1 e Loss of amoxicillin (0.06 mM) in pH 7.0 buffered distilled water (-) dark control; (C) direct photolysis in pH buffered distilled water; and, (:) containing 25 mg LL1 DOM (2S101F). All solutions were at room temperature (20 C).
20 Time Elapsed (hr)
-0.15 -0.20 -0.25 -0.30 -0.35 -0.40
AMO + 2S101F AMO + 2S101F + Sorbic Acid AMO + 2S101F (N2)
-0.45 -0.50
0
2
4
6
8
10
Time Elapsed (hr)
Fig. 2 e Overall photolysis of amoxicillin (0.06 mM) containing 25 mg LL1 DOM (2S101F) irradiated in solar simulator at room temperature (20 C): (a) Control experiment without DOM (-), 25 mg LL1 DOM in distilled H2O (C), in D2O (;), and with the addition of 2-propanol (65 mM) (:). (b) Photolysis with DOM (2S101F) (-), with addition of sorbic acid (0.18 mM), a 3DOM* quencher (C); purged with nitrogen (to enhance the formation of the 3 DOM*) (;).
and HA) of simulated natural water was then studied. The rate constants were determined from the slopes of the lines in Fig. 3. It was reasoned that the observed differences in the loss rates of amoxicillin among different DOM solutions were possibly due to the different characteristics of the DOM. Singlet Oxygen Reaction. The steady-state 1DO2 concentration, in five different solutions of DOM, was determined (Figure S1 of the Supporting Information) and summarized in Table l. The 1DO2 concentrations and its rate constants in sunlight are not affected in the range of pH 5 to 10 (Zepp et al., 1981), and pH variations were not evaluated in the present study. The steady-state 1DO2 concentration, coupled with the bimolecular reaction rate constant (k1 DO2 ¼ 1.44 104 M1 s1 obtained from data shown in Figure S2 of the Supporting
636
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 3 2 e6 3 8
0.0
Table 2 e Contributions (%) of hydrolysis, direct photolysis, reactions with ½1 DO2 , OH and 3DOM* to the overall fate of amoxicillin at presence of 25 mg LL1 DOM (1R107F, 2S101F, 2S101H, 1R101N and HA) respectively.
-0.2
DOM
-0.6
1R107F 2S101F 2S101H 1R101N HA
-1.2 -1.4 0
10
20
30
40
Time Elapsed (hr)
Fig. 3 e Photolysis of amoxicillin solutions (0.06 mM) in 25 mg LL1 DOM (1R107F, 2S101F, 2S101H, 1R101 N and HA) in the solar simulator at room temperature (20 C).
Information) of amoxicillin and 1DO2, allowed for the calculation of the pseudo first-order rate constants (in 109 s1 range) for the reaction of amoxicillin with 1DO2 in DOM solutions (Table 1). The evaluation of the reaction rate of amoxicillin with 1DO2 accounts for 0.03e0.08% of the total sunlight mediated loss of amoxicillin in different simulated natural waters, which supports our preliminary evaluation of the role of 1DO2 in the photochemical fate of amoxicillin. Hydroxyl Radical Reaction. The steady-state concentration of hydroxyl radical in five different DOM solutions was determined (from the slopes of the lines in Figure S3 of the Supporting Information) and summarized in Table 1. (Note: These concentrations were determined in a solar simulator by using the initial time of formation of the hydroxylated product to avoid photolysis of the product (Page et al., in press)). The steady-state OH concentration, coupled with the known bimolecular rate constant with amoxicillin (k$OH ¼ 6.94 109 M1 s1 (Song et al., 2008)), allowed for the calculation of the pseudo first-order loss rate (in 107 s1 range) for the amoxicillin via OH, in the irradiated solutions. The hydroxyl radical accounted for 10e22% of the sunlight mediated loss of amoxicillin in the simulated natural waters.
Table 1 e Measured steady-state concentrations (M) and pseudo first-order rate constants (sL1) of reactions of amoxicillin (0.06 mM) with singlet oxygen and hydroxyl radical at presence of 25 mg LL1 DOM (1R107F, 2S101F, 2S101H, 1R101 N and HA) respectively. DOM
½1 DO2 ss (1013 M)
k1 DO2 (109 s1)
[OH]ss (1017 M)
kOH (107 s1)
1R107F 2S101F 2S101H 1R101N HA
1.41 0.04 2.16 0.04 1.57 0.05 1.58 0.03 1.51 0.03
2.03 0.05 3.11 0.05 2.26 0.07 2.27 0.04 2.17 0.04
5.08 0.05 7.09 0.10 6.50 0.05 20.12 0.43 14.25 0.20
3.53 0.03 4.92 0.07 4.51 0.04 13.97 0.30 9.89 0.14
18 9 11 5 9
21 10 13 6 10
0.08 0.06 0.05 0.03 0.04
13 10 13 15 22
3
DOM* 48 71 63 74 59
The solar mediated photodegradation is a combination of direct and indirect photolysis, where indirect photolysis plays an important role for a compound that has no or little overlap between its absorption and the solar spectrum (Mill, 1999). Table 2 summarizes the contribution, of the five variables studied, to the sunlight mediated loss of amoxicillin. Hydrolysis accounted for 5e18% of amoxicillin in different DOM solutions. Direct photolysis accounted for 6e21% of the loss. The direct photolytic pathway is likely to be less important in natural waters due to the light screening effects (Chin et al., 2004). The role of 1DO2 can be excluded from the DOM mediated photo-transformation of amoxicillin because of its slow reaction. Based on the results from 1DO2 and OH probes, and our previous findings, we thus propose that a large fraction of amoxicillin loss (48e74%) was depleted via other photodegradation pathways, possibly by the direct reaction with 3 DOM*, which has not yet been characterized, and, the exact mechanism(s) not elucidated. Chen et al. (2009) reached the similar conclusion for amine drugs that reacted exclusively with 3DOM* in aqueous solutions. It was hypothesized that the efficiency for the reaction of amoxicillin with 3DOM* might be relevant with the proximity of these two substrates. At neutral pH of natural waters, the binding of amoxicillin to DOM might be due to the interaction of amine groups of DOM with the carbonyl groups of amoxicillin (Holten Lutzhoft, 2000). DOM bound amoxicillin is closer to DOM than unbound molecules in the bulk solution, thereby -5
1.0x10
-1
-0.8 -1.0
Hydrolysis Direct Photolysis ½1 DO2 OH
1R107F 2S101F 2S101H 1R101N HA
Overall AMO Photodegradation Rate (s )
Ln ([AMO]/[AMO0 ])
-0.4
-6
9.0x10
1R101N
-6
8.0x10
-6
7.0x10
-6
6.0x10
2S101F
-6
5.0x10
-6
4.0x10
HA 2S101H
-6
3.0x10
1R107F
-6
2.0x10
6
8
10
12
14
% AMO sorbed to DOMs
Fig. 4 e The photodegradation rate of amoxicillin (0.06 mM) in five solutions of DOM (1R107F, 2S101F, 2S101H, 1R101 N and HA, 25 mg LL1) as a function of the percentage sorbed on DOM.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 3 2 e6 3 8
enhancing the energy transfer and thus the degradation. To support this proposed pathway the sorption of amoxicillin on DOM was studied. A straight line was obtained when the pseudo first-order degradation rate was plotted against the percent sorption of amoxicillin on DOM (Fig. 4). Our results indicated that the association of amoxicillin with DOM is likely to have a pronounced effect on its photodegradation. The energy transfer from excited state DOM to amoxicillin, possibly facilitated by the binding, might play a key role in the photo-transformation of amoxicillin (Song et al., 2007). An alternative possibility for the linear correlation could also be the result of the proximity of the site of OH formation.
4.
Conclusions
Our study results suggested that the reaction of excited state DOM is a predominant route of amoxicillin loss in sunlit aquatic environments. The half life of amoxicillin varied from 0.9 to 3.3 days in the NOMs isolate solutions under simulated sunlight irradiation. Its photodegradation rate was shown to be related to the extent of sorption of amoxicillin on DOM. The other photodegradation pathways for amoxicillin, e.g. direct photolysis and reactions with 1DO2 and OH radicals, were of less importance, due to their lower concentration, lower rate constants and the possibility of light screening effects. Studies are underway to determine the second order reaction rate constants of 3NOM* with amoxicillin and eventually to extend theses results to photochemical fate modeling.
Acknowledgments This is contribution 58 from from the Urban Water Research Center at University of California, Irvine.
Appendix. Supplementary data Supplementary data associated with this article can be found in the online version, at doi:10.1016/j.watres.2010.08.024.
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Global sensitivity analysis in wastewater treatment plant model applications: Prioritizing sources of uncertainty Gu¨rkan Sin a,*, Krist V. Gernaey b, Marc B. Neumann c,d, Mark C.M. van Loosdrecht e, Willi Gujer c,f a
CAPEC, Department of Chemical and Biochemical Engineering, Technical University of Denmark, Building 229, DK-2800 Kgs. Lyngby, Denmark b Center for Process Engineering and Technology (PROCESS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Building 229, DK-2800 Kgs. Lyngby, Denmark c Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Du¨bendorf, Switzerland d modelEAU, De´partement de Ge´nie Civil, Universite´ Laval, 1065 Avenue de la Me´decine, Que´bec, QC, G1V 0A6, Canada e Department of Biotechnology, Delft University of Technology, Julianalaan 67, 2628 BC Delft, The Netherlands f Institute of Environmental Engineering, ETH Zurich, 8093 Zurich, Switzerland
article info
abstract
Article history:
This study demonstrates the usefulness of global sensitivity analysis in wastewater
Received 7 March 2010
treatment plant (WWTP) design to prioritize sources of uncertainty and quantify their
Received in revised form
impact on performance criteria. The study, which is performed with the Benchmark
9 August 2010
Simulation Model no. 1 plant design, complements a previous paper on input uncertainty
Accepted 12 August 2010
characterisation and propagation (Sin et al., 2009). A sampling-based sensitivity analysis is
Available online 20 August 2010
conducted to compute standardized regression coefficients. It was found that this method is able to decompose satisfactorily the variance of plant performance criteria (with R2 > 0.9)
Keywords:
for effluent concentrations, sludge production and energy demand. This high extent of
BSM1
linearity means that the plant performance criteria can be described as linear functions of
Design
the model inputs under the defined plant conditions. In effect, the system of coupled
Influent fractions
ordinary differential equations can be replaced by multivariate linear models, which can be
Sensitivity
used as surrogate models. The importance ranking based on the sensitivity measures
Uncertainty
demonstrates that the most influential factors involve ash content and influent inert
WWTP
particulate COD among others, largely responsible for the uncertainty in predicting sludge
Plant performance
production and effluent ammonium concentration. While these results were in agreement
Wastewater treatment
with process knowledge, the added value is that the global sensitivity methods can quantify the contribution of the variance of significant parameters, e.g., ash content explains 70% of the variance in sludge production. Further the importance of formulating appropriate sensitivity analysis scenarios that match the purpose of the model application needs to be highlighted. Overall, the global sensitivity analysis proved a powerful tool for explaining and quantifying uncertainties as well as providing insight into devising useful ways for reducing uncertainties in the plant performance. This information can help engineers design robust WWTP plants. ª 2010 Elsevier Ltd. All rights reserved.
* Corresponding author. Tel.: þ45 45 25 2806; fax: þ45 45 93 2906. E-mail addresses:
[email protected] (G. Sin),
[email protected] (K.V. Gernaey),
[email protected] (M.B. Neumann),
[email protected] (M.C.M. van Loosdrecht),
[email protected] (W. Gujer). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.08.025
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 3 9 e6 5 1
Abbreviations and Notations ANOVA ASM ASM1 BNR BOD5 BSM1 CDF COD DO HRT
1.
analysis of variance activated sludge model activated sludge model no 1 biological nutrient removal biochemical oxygen demand benchmark simulation model no. 1 cumulative distribution function chemical oxygen demand dissolved oxygen hydraulic retention time
Introduction
In an earlier paper, we focused on uncertainty analysis of WWTP models when used for wastewater treatment plant design (Sin et al., 2009). To this end, the MonteeCarlo (MC) simulation procedure was used to propagate uncertainties from model inputs to outputs. One challenge with using an MC type procedure is the initial step where the uncertainty of model inputs is identified and characterised by assigning an appropriate range and (statistical) distribution (Helton and Davis, 2003). This step is mostly performed based on expert-judgment (so-called expert review of input uncertainty) and specifies the degree of certainty about the values of model inputs (McKay et al., 1999; Saltelli et al., 2008). In response to this issue, the previous study analysed the importance of framing and the context to ensure that the resulting estimates of output uncertainties are interpreted appropriately. The results demonstrated how, depending on the way the uncertainty analysis was framed (i.e., which sources of uncertainties included, which of them excluded, how the range of uncertainties was assigned, and so on), the estimated uncertainty of design performance criteria differed significantly. Hence, the study concluded that the framing of the uncertainty analysis should match the particular purpose of the specific model application. In this manuscript, the focus is on sensitivity analysis of WWTP models. Most sensitivity analysis studies done in the field of wastewater treatment modelling are of local nature and use a differential analysis of outputs with respect to parameters. This is understandable as this concept of sensitivity analysis is related to the control/identification problem, which has long been part of the research agenda in this scientific community (Holmberg, 1982; Weijers and Vanrolleghem, 1997; Brun et al., 2002; Van Veldhuizen et al., 1999). An alternative definition of sensitivity analysis relates to uncertainty analysis and can be viewed as an analysis of variance (ANOVA) problem (Helton and Davis, 2003; Saltelli et al., 2006). Hereby the output variance is decomposed into fractions, which are attributed to the single model inputs. This helps elucidating which inputs are the major causes of the uncertainty in the outputs, thereby identifying the underlying major sources of uncertainty. Examples of such sensitivity analysis methods (often termed global sensitivity analysis) include Morris Screening (Morris, 1991), linear regression of MonteeCarlo outputs (Helton and Davis, 2003) and variance decomposition (Saltelli et al., 2008).
IWA KLa LHS MLTSS NH4eN PI SRC SRT SS TN TSS WWTP
International Water Association oxygen transfer coefficient (d1) latin hypercube sampling mixed liquor total suspended solids ammonium nitrogen proportional e integral standardized regression coefficients sludge retention time suspended solids total nitrogen total suspended solids wastewater treatment plant
In this study, we adopt this alternative sensitivity analysis paradigm, as a means to identify which model inputs are mainly responsible for the observed uncertainty of model outputs. As such this paper complements the previous study (Sin et al., 2009) where the uncertainty analysis was performed. The global sensitivity analysis provides results, which inform the engineer on how to think about reduction of the uncertainty in the predicted system performance (Neumann et al., 2009). At the same time, the engineer must take into account that some uncertainties of the system are reducible through further investigations (epistemic uncertainty) and some are irreducible (aleatory uncertainty) (Walker et al., 2003). For example, uncertainties related to microbial growth stoichiometry and kinetics cannot be reduced as microorganisms exhibit natural variability within a certain range. The substrate saturation/ affinity coefficient is such a parameter since it depends on the type of organism but also on floc size and mixing intensity in the reactor (Chu et al., 2003). However, uncertainties related to mass-transfer in surface aeration can be reduced by dedicated experiments. In either case, before discussing the reducible or irreducible nature of uncertainty, one needs to identify which uncertain model inputs are the key contributors to output uncertainty. This sets the objective for this study. We perform linear regression on MonteeCarlo simulation outputs (also known as Standardized Regression Coefficient (SRC)) (Saltelli et al., 2008) as sensitivity analysis method. As in the previous study, the benchmark (BSM1) plant layout and its operational and influent characterisation are used as a case study (Copp, 2002). In the WWTP field, there are some applications of global methods for sensitivity analysis, for example factorial sensitivity analysis (Abusam and Keesman, 2002) or the Standardized Regression Coefficient (SRC) method (Flores-Alsina et al., 2009). However, both studies focused on importance ranking of the parameters and not on variance decomposition.
2.
Materials and methods
2.1. Problem statement and scenarios for sensitivity analysis The problem statement for the sensitivity analysis reads as follows: given a plant design with its layout, operational
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configuration and influent profile, what are the most significant inputs contributing to the uncertainty of key plant performance criteria such as effluent quality, sludge production and energy consumption? As in the uncertainty analysis (Sin et al., 2009), we investigate three scenarios from engineering practice as shown in Box 1. The three scenarios (shown in Box 1) formulated for the uncertainty analysis in the previous study are also employed in this study for the sensitivity analysis. In each scenario, different model inputs (such as biokinetic model parameters, influent fractions, mass-transfer parameters and the like), are assumed to be either uncertain or known (Table 1, Box 1). The value range and distribution type for each model input (mostly taken as uniform distribution when there is lack of information) are identical to the characterisation of input uncertainty reported in Sin et al.
Box 1 Framing of sensitivity analysis: three scenarios from engineering practice.
Scenario 1 An engineer has some experience with dynamic models of activated sludge systems. He decides to use the ASM1 for the design of a new treatment plant. Since he has never worked with the wastewater in question he feels quite uncertain about the stoichiometric and kinetic parameters of this model. He would like to know how this uncertainty affects the target variables in his design. He may want to reduce uncertainty in the target variables, and he would like to know which parameters to identify using pilot-scale studies. Scenario 2 An engineer has the task to expand an existing treatment plant. He wants to use the ASM1 and he feels quite comfortable with his knowledge about the stoichiometric and kinetic parameters of this model. However he asks himself whether he should use Computational Fluid Dynamics (CFD) to optimize the design of the new biological reactors and whether he should perform any pilot tests in order to better characterize the new aeration system that he considers using. He is also aware of the fact that it is difficult to control return and recycle streams accurately. Scenario 3 An engineer wants to quantify the uncertainty that remains in the design of a new treatment plant. He asks himself how he could best reduce this uncertainty. He identifies the stoichiometric and kinetic parameters of his biokinetic model, the performance of the aeration equipment and the hydraulic performance of his biological reactor as the main sources of uncertainty. He is an expert in the design of secondary clarifiers, thus he does not expect any additional uncertainty from this aspect of his design.
(2009), and are provided here as supplementary information (Tables S1 and S2).
2.2. Sensitivity analysis: linear regression of MonteeCarlo simulations To employ this sensitivity analysis method two elements are required. First, a MonteeCarlo simulation is conducted where uncertainty is propagated from model inputs to outputs. Then a linear regression is performed on the MC results describing each model output of interest as a multivariate linear function of the model inputs.
2.2.1.
MonteeCarlo simulations
For notational convenience, the WWTP model structure is represented by f, the state variables by x, the input variables by u, the parameters by P, the output vector of the target variables by y (target variables y being aggregate measures g (x) of the states x), and time is represented by t: dx dt
¼ fðx; u; t; PÞ; y ¼ gðxðtÞÞ
xðt0 Þ ¼ x0 ;
(1)
After specifying an appropriate mathematical model structure the subsequent uncertainty analysis involves the following steps: (1) Specifying input uncertainty, (2) Sampling input uncertainty and, (3) Simulating the model (f) using the sampling matrix (q). Firstly, appropriate models are specified based on the previous experience of the engineers in question (see Box 1 and BSM1 below). For Step 1, above an expert review process was used bearing in mind that the model was intended for plant design (see above). For Step 2, the Latin Hypercube Sampling (LHS) method of Iman and Conover (1982) was applied, where 500 samples were drawn (this number was found reproducible in the previous study; Sin et al., 2009) and MonteeCarlo simulation was used for Step 3. Notice that the sampling matrix, q, has the following format: the columns refer to each model input (specified uncertain in Step 1) and each row contains values for each model input (values obtained by sampling in Step 2). The number of rows depends on the number of samples (Step 2). The number of columns depends on the number of model inputs specified as uncertain, which will be different according to the scenario formulated for the sensitivity analysis (see Box 1). For instance in Scenario 1, the biokinetic parameters of the model and influent fractions are specified as uncertain, hence the number of columns in q contains some model parameters (a subset of P) as well as influent fractions (a subset of u) (see Complimentary information S1).
2.2.2.
Linear regression of MonteeCarlo simulations
This method is also termed “Standardized Regression Coefficients” -method (SRC). The sensitivity measure in this method is obtained by performing linear regression for each of the model outputs of interest obtained from the MonteeCarlo procedure. Since this technique requires scalar output, a certain aggregate property of the dynamic model outputs needs to be used, e.g., the mean of a time-series profile (see Section 2.3).
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Table 1 e Different sources of input uncertainties considered in the three engineering scenarios shown in Box 1 (the table is taken from Sin et al., 2009). Sources of uncertainty/ Scenario: Y
Influent Dynamic load
1 2 3
Known Known Known
Fractionation
a
Uncertain Known Uncertain
Kinetics & stoichiometry
Settling, temperature
Hydraulics & mass-transfer
Uncertain Known Uncertain
Known Known Known
Known Uncertain Uncertain
a When parameters are taken as known they are set to their default values in the BSM1 plant layout (see Supplementary information).
2.3. BSM1 plant layout, simulation strategy and plant performance evaluation
For each model output of interest, a first order linear multivariate model is fitted to the (scalar) output of the MonteeCarlo simulations relating model output y to model inputs qi : X yreg ¼ a þ bi $qi (2)
The BSM1 plant is a pre-denitrification system for nitrogen removal (See Fig. 1). The activated sludge unit, modelled using the Activated Sludge Model no 1 (ASM1, Henze et al., 2000) consists of 5 compartments, in which the first two are anoxic with a total volume of 2000 m3, while the last three are aerated with a total volume of 3999 m3. The settling unit, modelled using the Taka´cs settling model (Taka´cs et al., 1991), is a nonreactive secondary settler with a volume of 6000 m3 (area of 1500 m2, depth of 4 m) subdivided into 10 layers. The plant has an SRT of about 10 days with an HRT of approximately 15 h. The default aeration control strategies use a DO set point of 2 mg/l in the aerobic tanks. More information on the benchmark plant description can be found in Copp (2002) and (http://www.benchmarkwwtp.org/). The modifications of the BSM1 (particularly sludge wastage controller from the aeration tank) were described in Sin et al. (2009). The benchmark simulation strategy as well as the plant performance criteria calculation is performed as in the previous study (Sin et al., 2009).
i
To obtain the standardized regression coefficients, bi, the regression coefficients bi are scaled using the standard deviations of model input and output of the MC simulations: bi ¼
sqi $bi sy
(3)
For a linear model, the b2i are the relative variance contributions to the model output variance. Therefore, in the case of linearity, the b2i are equal to the 1st order sensitivity index Si obtained with variance decomposition (Saltelli et al., 2008). The bi can take on values between 1 and þ1. For these coefficients to be considered a valid measure of sensitivity, the coefficient of determination should be sufficiently high, e.g., R2 0.7, which implies the model is sufficiently linear (Campolongo and Saltelli, 1997; Saltelli et al., 2006). When the model is linear, the following condition holds for standardized P P regression coefficients: ðbi Þ2 ¼ 1. In general, ðbi Þ2 1 and is i
i
equal to the model coefficient of determination R2 (Saltelli et al., 2008). The sensitivity measure, bi, has the following meaning: (i) a high absolute value indicates a large effect of the corresponding parameter on the output, (ii) a negative sign indicates a negative effect and vice versa a positive sign indicates a positive effect on the output, and (iii) coefficients close to zero mean that the output is not sensitive to that parameter (i.e., negligible effect) (Campolongo and Saltelli, 1997; Helton and Davis, 2003).
3.
Results
3.1.
Sensitivity analysis
3.1.1.
Presentation of the results for Scenario 1
The time-series data (dynamic simulations) obtained from the MonteeCarlo simulations were averaged flow proportionally following the BSM1 data pre-processing strategy in which the last 7 days of two weeks dynamic simulation data PI
kLa
Influent (Qin)
Anoxic V= 1000 m3 Reactor 1
Anoxic V= 1000 m3
Aerobic DO V= 1333 m3
Reactor 2
Reactor 3
PI
PI kLa
kLa
Aerobic DO V= 1333 m3
MLSS
Reactor 4
Aerobic DO V= 1333 m3
Settler
Effluent
Reactor 5 PI Waste sludge Qw =385 m3/d
Internal recycle, Qintr = 3*Qin Sludge recycle, Qr = 1*Qin
Fig. 1 e General overview of the modified BSM1 plant (http://www.benchmarkwwtp.org/). Please see text for explanations about the modifications introduced.
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are considered as evaluation period for the analysis. The results are plotted as histograms (Fig. 2a) for the four plant performance criteria considered: effluent nitrate, effluent ammonia, sludge production and aeration energy requirement. These results indicate that there is considerable variance in predicted plant performance criteria. This variance is decomposed with respect to the uncertain parameters by linear regression whose coefficients are provided in Table 2, while the regression model fit for sludge production is shown in Fig. 2b. Moreover, the histograms (Fig. 2a) show that the effluent ammonium concentration is skewed compared to the other three criteria, corresponding to the degree of linearization (R2) obtained with the multivariate regression (see Table 2). Linear regression models are fitted for each of the averaged plant performance criteria, hence resulting in four multivariate linear models e essentially predicting the plant performance criteria as linear functions of the BSM1 model parameters (Fig. 2b). The corresponding (scaled) coefficients of the linear models, bi’s, are shown in Table 2. Important to note is that the linear model determination coefficients (R2) are
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found to be >0.9 (Table 2). This means that the time-averaged model outputs could be linearized to a high degree hence satisfying the requirement for bi to be used as a reliable index of the sensitivity measure (R2 > 0.7). In other words, the nonlinear complex model consisting of coupled ordinary differential equations can be replaced by a simple multivariate linear model for further analysis. This is discussed further in Section 5.3 as well. Since R2 > 0.7, we can calculate the individual contribution of each model input to the total variance of the output by taking the square of the standardized regression coefficients, b2i . For instance, the X2TSS parameter (representing the ash content of the influent) is responsible for 70% (¼0.84 0.84 100%) of the total variance of the sludge production in the plant.
3.1.2.
Interpretations of the sensitivity analysis results
To allow a detailed interpretation of the results, we restrict the further analysis to the parameters that have an absolute bi value greater than 0.1 for each process performance criterion (Fig. 3). This cut off value corresponds roughly to a 1%
Fig. 2 e Sensitivity analysis results: (a) Averaged plant performance criteria obtained from MonteeCarlo simulations in Scenario 1 plotted as histogram (top-four plots), (b) Linear model fit to MonteeCarlo simulations (shown for the averaged sludge production data) (500 MonteeCarlo simulations in total).
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Table 2 e Standardized regression coefficients (bi) of linear models of the four plant performance criteria for Scenario 1 (sensitive parameters (abs(bi)>0.1) shown in italics). ID Parameter Effluent Effluent Sludge Aeration nitrate ammonium production energy R2 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 Sum
mH KS KOH KNO bH mA KNH KOA bA hg ka kh KX hhyd YH YA fP iXB iXP X2TSS fSI fSS fXI fXBH iSND_SS iXND_XS of b2i ,a
0.97
0.92
1.00
0.99
0.02 0.04 0.31 0.09 0.01 0.06 0.13 0.03 0.08 0.19 0.08 0.17 0.11 0.44 0.17 0.01 0.02 0.36 0.00 0.09 0.18 0.44 0.50 0.02 0.01 0.01 1.06
0.02 0.01 0.01 0.00 0.04 0.23 0.44 0.18 0.31 0.02 0.00 0.00 0.01 0.02 0.21 0.00 0.09 0.07 0.01 0.59 0.06 0.02 0.32 0.08 0.00 0.03 0.89
0.00 0.01 0.00 0.00 0.06 0.01 0.01 0.00 0.01 0.00 0.00 0.01 0.01 0.00 0.32 0.01 0.11 0.01 0.00 0.84 0.09 0.00 0.45 0.08 0.00 0.00 1.04
0.00 0.02 0.14 0.04 0.12 0.04 0.10 0.03 0.04 0.08 0.06 0.06 0.03 0.18 0.53 0.03 0.19 0.25 0.00 0.46 0.03 0.19 0.52 0.15 0.01 0.01 1.02
a When the model is linear then the following statistics hold: P ðbi Þ2 ¼ 1.The deviations from 1 could be attributed to the random i
sampling or numerical accuracy such as round-off errors.
effluent. Next, the effluent nitrate concentration was also found to be sensitive towards several process kinetics related parameters: (a) hydrolysis rate (hhyd, kh), (b) denitrification rate (hg ,KOH) and (c) nitrogen assimilation for the growth of heterotrophic biomass (iXB, YH and KNH). While the nitrification kinetics are important, their significance ranking is considerably lower (see Table 2).
3.1.2.2. The most significant parameters for sludge production. With respect to the sludge production, only 4 parameters had an bi higher than 0.1. These significant parameters include: {X2TSS, fXI, YH, fP}. This shows that the ash content of the solids (X2TSS) and the inert particulate fraction of the influent ( fXI) are by far the most important parameters. These two parameters are specific to the input to the sewer and the plant characteristics, and will, for example, depend on whether or not there is a grit removal or primary clarifier upstream of the activated sludge plant. From a process engineering point of view the significance of these two parameters on sludge production is known (see e.g., STOWA protocol for WWTP model calibration in Hulsbeek et al., 2002) and can be explained as follows: increasing the X2TSS and fXI in the system leads to accumulation of solids in the plant (this is implied by the positive sign of the corresponding bi values in Table 2) and hence to more sludge wastage by the MLSS controller to maintain a constant MLSS in the system. In other words, varying ash content and influent insoluble solids mean varying sludge production in the system, which in turn means varying SRT of the system. The latter phenomenon has consequences on the nutrient removal efficiency, particularly for the ammonium, and also on the aeration energy requirement as discussed below. In addition, the heterotrophic growth yield (YH) and the inert particulate fraction of biomass generated upon death ( fP) are the other two influential parameters.
3.1.2.3. The most significant parameters for effluent ammonium. (0.1 0.1) fraction of the total variance. The results are compared to the expectations arising from expert process knowledge.
3.1.2.1. The most significant parameters for effluent nitrate. The following parameters were found to have a significant impact on the effluent nitrate concentration (listed in decreasing order of importance): {fXI, fSS, hhyd, iXB, KOH, hg, fSI, YH, kh, KNH}. This ranking indicates that by far the most important source of uncertainty determining the variance in the effluent nitrate concentration are the influent fractions, especially the non-biodegradable and readily biodegradable fractions. Noteworthy is that the sign of the bi is meaningful. For example, the standardized regression coefficient of fXI is 0.5, which means that fXI is responsible for 25% of the total variance of effluent nitrate. Further, the positive sign means that increasing fXI will cause an increase in the effluent nitrate concentration. This observation is in accordance with process engineering knowledge: high fXI means that the influent wastewater has a lower degradable COD fraction. All other things being equal, this consequently means reduced denitrification and hence higher nitrate concentrations in the
As regards effluent ammonium concentration, the following parameters were found to be the most influential: {X2TSS, KNH, fXI, bA, mA, YH, KOA}. One notices that the significant parameter list associated with the sludge production, {X2TSS, fXI, YH, fP}, also appears in the list along with the nitrification kinetics parameters {KNH, bA, mA, KOA}. This means that the effluent ammonium concentration is equally affected by sludge production as well as nitrification kinetics. We first explain the impact of sludge production. Noteworthy is that the signs of the bi {X2TSS, fXI, YH, fP} are positive for both sludge production and effluent ammonium. This means that an increase in the value of these parameters leads to increased sludge production, which leads to a lower SRT and thus to an increased effluent ammonium concentration. This can be explained as follows: higher sludge production (under the action of the MLSS controller to maintain a constant MLSS in the system) means a decrease in the system SRT with accompanying decrease in the amount of nitrifying organisms in the plant. The decrease in the nitrifying capacity consequently leads to higher ammonium concentration in the effluent. The parameters related to the nitrification kinetics such as KNH, bA, mA and KOA were also found influential on the effluent ammonium concentration. The sensitivity of the latter two
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Fig. 3 e the 10 most significant parameters influencing the plant performance: the parameters are ranked according to absolute bi, whereas the sign (positive/negative) given in Table 2.
parameters {mA and KOA} strongly depends on the operating DO concentration in the tanks which were controlled at 2 mg O2/l. The heterotrophic growth yield (YH) also appeared to be important. The impact of YH on the effluent ammonium theoretically could be in two ways: (i) nitrogen assimilation to biomass and (ii) sludge production. The sign of the bi value of YH is positive (bYH ¼ 0.21), which suggests that the sludge production hypothesis is the primary cause for the ammonium variation as argued above. The nitrogen assimilation may play a role in the variation of the effluent ammonium, however, this seems to be a weak cause, which is read from the bi value for iXB (0.07): the negative sign means that higher nitrogen content of biomass would decrease the effluent ammonium concentration, and the magnitude 0.07 means the contribution to the variance is much lower (nine times less than the contribution from the YH).
3.1.2.4. The most significant parameters for aeration energy demand. As regards aeration energy demand the following parameters were found significant: {fXI, YH, X2TSS, iXB, fSS, nhyd, fP, fXBH, KOH, bH, KNH}. This can be explained by a combination of mechanisms: (i) sludge production (X2TSS, fXI, YH), which has a strong impact (ii) denitrification process ( fXI, fSS), which is moderately important, (iii) the nitrification (KNH, iXB, bA) and (iv) endogenous respiration ( fP and bH) processes which have a relatively lower impact on the energy demand. The sludge production is found to have a significant impact on the aeration energy demand, which corresponds to process
knowledge. This is implied from the high magnitude of bi for X2TSS, fXI, YH. Further, their sign is negative, which means that less sludge production (under MLSS controller) leads to lower observed biomass yield (Yobs), which in turn requires more oxygen for the respiration of the organics (1 Yobs). Moreover, less sludge production also means higher SRT hence better nitrification, which also requires more oxygen for oxidation. Concerning nitrification, one also observes the effect of nitrogen content of biomass, iXB, on the aeration demand. That can be understood from the point of view of oxygen required by the nitrification process too. Since iXB affects the amount of ammonium nitrogen assimilated for biomass growth, it also affects the ammonium nitrogen left available for aerobic oxidation by nitrifying autotrophic biomass. As regards the denitrification (ii), the anoxic hydrolysis rate reduction factor (hhyd) and the oxygen switch/affinity coefficient (KOH) e are also found influential on the aeration energy demand. Increasing these two parameters will increase the denitrification rate. This in turn causes more degradable COD to be channelled to nitrate instead of oxygen as electron acceptor. Overall, this leads to less oxygen demand, which is a desirable outcome in itself from a process economics point of view. Notice that the bi of these two parameters are negative, supporting the above explanation. For endogenous respiration processes, biomass decay rate related parameters such as fP and bH are found important. This can be explained again from the sludge production point of view, the lower the SRT, the less the decay kinetics and hence less oxygen consumption due to endogenous respiration.
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4. Sensitivity analysis results obtained from scenarios 2 and 3 The sensitivity analysis results obtained from Scenarios 2 and 3 are shown in Figs. 4 and 5, respectively. The mean and variance of each plant performance criterion are presented in Table 3 as obtained for each scenario. In general, the mean values of the performance criteria remained largely unchanged (with the exception of ammonium in Scenario 3), while the calculated variances differed significantly in each scenario.
4.1. Scenario 2: impact of hydraulics & aeration on plant performance In Scenario 2, the focus is on the evaluation of sensitivity of plant performance criteria to hydraulic and mass-transfer related factors in the plant. To this end, 7 factors (Vanx, Vaer, klamax, klaanx, SOsat, Qr, Qintr) were identified to contribute to uncertainty in the plant performance (Fig. 4). One notices that the most significant factors contributing to the variability for effluent nitrate and ammonium are the aeration capacity (klamax and klaanx) and effective volume of aerobic tanks (Vaer). This outcome is not surprising as nitrification capacity is limited in this particular design (BSM1), which makes it sensitive to variation in the oxygen supply and also to the residence time of nitrifying organisms in the system (which is related to the size of the aerobic-fraction of the total tank volume). As expected, aeration energy requirements are tightly related to the volumetric oxygen transfer capacity as well as the aerobic volume in the plant. The resulting variance of the sludge production is significantly lower compared to Scenario 1 (about a factor 1000). This variance is mainly caused by the
uncertainties in the following parameters: recycling rate of biomass (Qr) as well as the total volume (aerobic and anoxic) of the tanks in the plant. However, the significance of these parameters, in terms of the magnitude of the variance they induce in the output, is quite negligible compared to the variance induced by X2TSS and fXI as discussed above in Scenario 1 (See Table 3 for the comparison of the variance in Scenarios 1 and 2). A caveat has to be mentioned in Scenario 2. The uncertainty of hydraulics considers only the short-circuiting phenomenon, i.e., reduced effective volumes of aerobic and anoxic tanks. This is obviously only an approximation of hydraulic uncertainties. A comprehensive analysis of uncertainties related to hydraulics would require computational fluid dynamics (CFD), which is beyond the scope of this study.
4.2. Scenario 3: combined impacts of biokinetic model assumptions, hydraulics and aeration Scenario 3 considers simultaneously the input uncertainties identified in Scenarios 1 and 2, which is in total 33 factors (see Supplementary information). The resulting sensitivity measures of these input uncertainties on the plant performance criteria are quantified using the SRC method and shown in Fig. 5 for the 10 most significant factors. Compared to the ranking of parameter significance obtained in Scenario 1 (see Fig. 3), one observes two additional parameters appearing in the list, namely klamax and Vaer. Concerning uncertainty about the nitrate and ammonium effluent concentration, these two parameters are naturally important as explained above: since nitrification is kinetically limited in this particular plant design, the effluent ammonium as well as nitrate concentration become sensitive to uncertainty about the oxygen supply (i.e., the aeration capacity
Fig. 4 e The most significant parameters influencing the plant performance in Scenario 2: the parameters are ranked according to absolute SRC.
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Fig. 5 e The 10 most significant parameters influencing the plant performance in Scenario 3: the parameters are ranked according to absolute SRC.
klamax,) as well as uncertainty about the aerobic-fraction of the SRT (i.e., Vaer). Likewise, the aeration capacity naturally is a determining factor in calculating the energy demand for the aeration (the higher the aeration capacity, the more energy is required for oxygen transfer). The significance of the hydraulic and aeration related factors appears to be quite low in determining uncertainty on sludge production e this fact was commented already in the evaluation of Scenario 2.
5.
Discussion
5.1. Interpretation of sensitivity analysis results: context and framing In this plant design, three different scenarios were formulated corresponding to three possible situations encountered in
engineering practice. Each scenario yielded a different importance ranking of factors on the plant performance criteria. Relative to the three scenarios defined, it appears that Scenario 3 provided robust results, as it considered a broader scope (open mind) when deciding which input uncertainties to be included in the analysis. 1. To sum up, the interpretation of sensitivity analysis results depends very much on how the problem is defined and which uncertainties are considered in the input, hence the framing of the analysis. This conclusion is in agreement with the sensitivity analysis practice in other disciplines (Helton, 1993; Walker et al., 2003; Refsgaard et al., 2007). While this conclusion could be obvious in hindsight, it has important messages for the good sensitivity analysis practice in the wastewater treatment industry: The sensitivity analysis results are conditional to the way the problem is framed, hence they should be interpreted within this context.
Table 3 e Summary of plant performance uncertainties obtained from Scenarios 1,2 and 3: uncertainty represented as variance (s2). Plant performance criteria
Effluent nitrate (mgN/l) Effluent ammonium (mgN/l) Aeration energy (kWh/d) Sludge production (kgSS/d)
Scenario 1
Scenario 2
Scenario 3
Mean1
s21
Mean2
s22
Mean3
s23
12.90 1.80 3660.30 2593.20
5.50 0.89 44462.00 1.48Eþ05
12.60 1.90 3503.20 2309.90
2.53 0.76 55823.00 293.10
12.60 2.60 3451.80 2635.10
7.17 1.94 66556 1.48Eþ05
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2. It is better to consider all input uncertainties simultaneously in the analysis, which provides a more realistic picture of relative importance ranking of input uncertainties to the output (in this case plant performance). Hence a more realistic understanding of output variability can be obtained by least ignorance of input uncertainties.
5.2. Coupling sensitivity analysis with process engineering knowledge The interpretation of the results from the sensitivity analysis agrees well with process engineering knowledge, however, more importantly the global sensitivity analysis quantified how much variance the significant parameters induce in the plant performance, e.g., the ash content causes 70% of the variance of sludge production in Scenario 1. Hence the global sensitivity analysis methods complement well process engineering knowledge. Also the global sensitivity analysis method formally quantified and hence reasserted the significance of the influent inert fraction, fXI, for solids calibration in the plant e which is proposed by some calibration protocols for WWTP models (e.g., STOWA protocol). However, the global sensitivity analysis revealed the ash content to be more important than the inert influent wastewater fraction; hence it should also be considered by the WWTP plant calibration protocols (Sin et al., 2005).
5.3. Multivariate linear models as metamodels for WWTPs Sensitivity analysis using the standardized regression coefficient method shows that the considered plant performance criteria can, in this case study, be replaced by multivariate linear models as R2 is close to 1. The linear model built on the daily average sludge production data obtained in Scenario 1, is given below considering only the inputs that contribute at least 1% to the total variance (i.e., {X2TSS, fXI, YH, fP}) (using Eq. (3), Tables 2, 3 and Table S1): bsludge ¼ 6278 YH þ 3068 fp þ 4464 X2TSS þ 4459 fXI y 5349
(4)
It is important to note that the ysludge stands for the average sludge production (kg TSS/d), and to predict the sludge production the values of the linear model coefficient, bi, are used in Eq. (4). This is an important outcome as it implies that for the given inputs, uncertainty ranges, model structure and plant configuration (this forms the linear model validity region) one can replace the entire plant simulator by a simple linear model for each of the performance indicators. This means that further calculations with the plant model can be done using these multivariate models as surrogate models of the original model. This can save considerable computational efforts for further analysis and also from the system understanding point of view, it is much easier to interpret a linear plant model. It is important to bear in mind that the validity of the multivariate linear model is confined to a certain boundary, which is defined by the upper and lower ranges for the uncertain parameters (a higher dimensional parameter
space) for a given plant configuration. This means that one can use the model safely to interpolate within this boundary range, however outside of this validity range the extrapolation power of the linear model is questionable.
5.3.1. How to reduce the variance of the uncertainty about the plant performance by the help of global sensitivity analysis? The availability of a linear plant model helps generating and testing different scenarios for reducing plant performance uncertainty by quantitative variance analysis. This can be illustrated by a simple example in which 50% reduction in the sludge production variance found in Scenario 1 is studied. One solution can be to focus on the largest source of uncertainty in Scenario 1, that is the ash content (X2TSS) with bi equal to 0.84. All other things being equal, the 50% reduction in total variance can be made possible by reducing the uncertainty of X2TSS, which has a mean of 0.825 and a standard deviation of 0.0723 g SS/g COD (see Supplementary material). Using variance decomposition as point of departure, one finds that the standard deviation of X2TSS needs to be reduced to 0.037 g SS/g COD (see Box 2).. This reduction in the uncertainty of X2TSS may be possible by more accurate measurements. However, it can also be that some uncertainty may remain, inherent to X2TSS, due to fluctuations in sewage characteristics in the influent to the plant. As shown above the sensitivity analysis is able to indicate how variance in the predictions can be reduced by reducing the uncertainty of the significant parameters, particularly the influent fractions, e.g., by external carbon dosage to decrease the variance of effluent nitrate (Flores-Alsina et al., 2008) or a better estimation/measurement of influent wastewater fractions. However, some sources of uncertainty are beyond the control of the process engineer, e.g., the uncertainty of parameters related to the anoxic growth. Such sources of uncertainty need to be dealt with in other ways such as increasing plant capacity, i.e., an increase of the anoxic tank volume.
5.3.2. Strenghts and weaknesses of the standardized regression coefficient (SRC) method The standardized regression coefficient (SRC) method is an intuitive and simple method and relatively straightforward to perform (MonteeCarlo simulations and linear regression). However, the method requires the degree of linearization to be satisfactory (R2 > 0.7). In case this condition does not hold, which may be the case for non-linear models with parameter interactions, then other global sensitivity analysis methods need to be used. To this end, Morris screening (Morris, 1991) appears a reliable alternative method for factors prioritisation purposes, which is also computationally efficient. More sophisticated methods for variance decomposition include (extended-) FAST and Sobol’s indices (Saltelli et al., 2008). (Extended-) FAST approximates first order effect and total effect indices and Sobol’s method can be used to compute all possible interaction effects. However, both methods are computationally expensive requiring many model runs.
5.4. Sensitivity analysis for studying extreme conditions in the WWTP performance The sensitivity analysis in the preceding sections focused on weekly average plant performance prediction. Hence,
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Box 2 Calculation of the required reduction in the model input uncertainty of X2TSS to achieve a 50% reduction in the model output uncertainty for the sludge production. Step 1 Decomposition of variance for sludge production in Scenario 1 Variance decomposition for linear additive models (Saltelli et al., 2008): 2 2 X sqi bi sy ¼ i
hence the variance decomposition for the linear model given in Eq. (4): 2 2 2 2 2 ¼ ðsYH bYH Þ þ sfp bfp þ sX2TSS bX2TSS þðsXI bXI Þ sSC1 sludge In Scenario 1, the variance of sludge production is 147740 (kgTSS/d)2. Step 2 New scenarios to reduce the variance in sludge production To obtain 50% reduction in the variance, 0.5 147740 needs to be removed from the left hand side of the variance decomposition equation (mentioned above). This reduction can be achieved in many combinations, yet one sensible scenario is by focusing on the major contributor, i.e., X2TSS: 2 2 2 2 2 SC ¼ snew ðsYH bYH Þ þ sfp bfp þðsXI bXI Þ snew X2TSS bX2TSS sludge 2 ¼ 0:5 147740 ð0:0194 6278Þ2 þð0:0135 3068Þ2 þð0:0388 4456Þ2 snew X2TSS 4464 ffiffiffiffiffiffiffiffiffiffiffiffiffiffi r ffi 27484 snew ¼ 0:037ðdown from initially 0:072 gSS=gCOD of standard deviationÞ: X2TSS ¼ 44642
studying which factors are driving the uncertainty of the average plant performance. The global sensitivity analysis can of course be extended to study extreme conditions such as plant performance under peak loading, diurnal flow pattern and so on. In this section, an example of such an application is
provided. To this end, the sensitivity analysis (SRC method) was applied to interpret what drives the uncertainty in the maximum (peak) effluent ammonium concentration in the plant (Fig. 6). The distribution (histogram) of peak effluent ammonium concentration is shown in Fig. 6 (top-left), which has a normal distribution shape with mean around 5.8 mgN/l and a standard deviation of 2.6 mgN/l. The linear regression models showed a high degree of linearization (R2 ¼ 0.99). The parameter significance ranking based on absolute bi’s is shown in Fig. 6 bottom-left. One finds that the variance of peak effluent ammonium concentration is influenced mostly by the SRT of the system as well as nitrification kinetics, which is a similar finding to the parameters affecting the variance of average effluent ammonium concentration (see Fig. 3). However, additionally, one notices that the nitrogen assimilation process (YH and iXB) as well as the influent nitrogen fractionation related parameters becomes significant contributors to the variance of the peak effluent ammonium uncertainty.
6.
Fig. 6 e Application of the global sensitivity analysis to study extreme situations in the plant performance (see text).
Conclusions
This work investigates the (global) sensitivity analysis as a means to explain uncertainties in predicted plant performance. To this end, the BSM1 plant layout is used as a design case study. Global sensitivity analysis was performed by linear regression on MonteeCarlo simulation model output. The following conclusions are inferred from the results obtained:
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The linear regression of the MonteeCarlo simulation output is found to be a useful method for (global) sensitivity analysis. In Scenario 1 (biokinetics only) the time-averaged plant performance criteria (effluent ammonium, nitrate, sludge production and aeration energy), could be linearized satisfactorily (model determination coefficient R2 > 0.9), making the standardized regression coefficients (bi) a valid (global) sensitivity measure. For the present application the model can be replaced by a simple multivariate linear model. The relative importance ranking of input uncertainties on the model output (plant performance criteria) is found conditional on the way the problem is formulated, i.e., on which input uncertainties were included in the analysis. Hence the results of sensitivity analysis can only be interpreted within the context of the analysis. Both the process knowledge and global sensitivity analysis methods identify correctly the most significant parameters driving the plant performance. However, the added value of the global sensitivity analysis methods is that they quantify how much variance each significant parameter contributes to the variance in the plant performance, e.g., ash content contributes to 70% of the variance in the sludge production. Sensitivity analysis based on variance decomposition is demonstrated to be a powerful tool for explaining and quantifying uncertainties in the system outputs (e.g., plant performance) and also for gaining valuable insight into interactions between plant inputs and outputs. The latter helps devising and testing efficient ways of reducing uncertainties in the plant performance, thereby helping engineers design robust WWTP plants.
Acknowledgment Dr. Ulf Jeppsson is gratefully acknowledged for providing the BSM1 Matlab/Simulink code.
Appendix. Supplementary information Supplementary information available about the following: (i) range and distribution assigned for input uncertainties considered in the three scenarios for sensitivity analysis, (ii) standardized regression coefficient (bi) values obtained in the Scenario 2 and 3. The supplementary data associated with this article can be found in the on-line version at doi:10.1016/j. watres.2010.08.025.
references
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Campolongo, F., Saltelli, A., 1997. Sensitivity analysis of an environmental model: an application of different analysis methods. Reliab. Eng. Syst. Saf. 57, 49e69. Copp, J.B., 2002. The COST Simulation Benchmark: Description and Simulator Manual. Office for Official Publications of the European Community, Luxembourg. Chu, K.H., van Veldhulzen, H.M., van Loosdrecht, M.C.M., 2003. Respirometric measurement of kinetic parameters: effect of activated sludge floc size. Water Sci. Technol. 48 (8), 61e68. Flores-Alsina, X., Rodrı´guez-Roda, I., Sin, G., Gernaey, K.V., 2008. Multi-criteria evaluation of wastewater treatment plant control strategies under uncertainty. Water Res. 42, 4485e4497. Flores-Alsina, X., Rodrı´guez-Roda, I., Sin, G., Gernaey, K.V., 2009. Uncertainty and sensitivity analysis of control strategies using the Benchmark Simulation Model No1 (BSM1). Water Sci. Technol. 59 (3), 491e499. Helton, J.C., 1993. Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive-waste disposal. Reliab. Eng. Syst. Saf. 42 (2e3), 327e367. Helton, J.C., Davis, F.J., 2003. Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliab. Eng. Syst. Saf. 81, 23e69. Henze, M., Gujer, W., Mino, T., van Loosdrecht, M.C.M., 2000. Activated Sludge Models ASM1, ASM2, ASM2d and ASM3. IWA Scientific and Technical Report 9. IWA Publishing, London. Holmberg, A., 1982. On the practical identifiability of microbial growth models incorporating MichaeliseMenten type nonlinearities. Math. Biosci. 62, 23e43. Hulsbeek, J.J.W., Kruit, J., Roeleveld, P.J., van Loosdrecht, M.C.M., 2002. A practical protocol for dynamic modelling of activated sludge systems. Water Sci. Technol. 45 (6), 127e136. Iman, L.R., Conover, W.J., 1982. A distribution-free approach to inducing rank correlation among input variables. Commun. Statist.-Simula. Computa. 11, 311e334. McKay, M.D., Morrison, J.D., Upton, S.C., 1999. Evaluating prediction uncertainty in simulation models. Comput. Phys. Commun. 117, 44e51. Morris, M.D., 1991. Factorial sampling plans for preliminary computational experiments. Technometrics 33, 161e174. Neumann, M.B., Gujer, W., von Gunten, U., 2009. Global sensitivity analysis for model-based prediction of oxidative micropollutant transformation during drinking water treatment. Water Res. 43, 997e1004. Refsgaard, J.C., van der Sluijs, J.P., Højberg, A.L., Vanrolleghem, P.A., 2007. Uncertainty in the environmental modelling process e a framework and guidance. Environ. Modell. Soft. 22, 1543e1556. Saltelli, A., Ratto, M., Tarantola, S., Campolongo, F., 2006. Sensitivity analysis practices: strategies for model-based inference. Reliab. Eng. Syst. Saf. 91, 1109e1125. Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S., 2008. Global Sensitivity Analysis: The primer. John Wiley & Sons, West Sussex, England. Sin, G., Gernaey, K.V., Neumann, M.B., van Loosdrecht, M.C.M., Gujer, W., 2009. Uncertainty analysis in WWTP model applications: a critical discussion using an example from design. Water Res. 43, 2894e2906. Sin, G., Van Hulle, S.W.H., De Pauw, D.J.W., van Griensven, A., Vanrolleghem, P.A., 2005. A critical comparison of systematic calibration protocols for activated sludge models: a SWOT analysis. Water Res. 39, 2459e2474. Taka´cs, I., Patry, G.G., Nolasco, D., 1991. A dynamic model of the clarification thickening process. Water Res. 25 (10), 1263e1271. Van Veldhuizen, H.M., Van Loosdrecht, M.C.M., Heijnen, J.J., 1999. Modelling biological phosphorus and nitrogen removal in a full scale activated sludge process. Water Res. 33, 3459e3468.
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Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Addressing uncertainty in fecal indicator bacteria dark inactivation rates Andrew D. Gronewold a,*, Luke Myers c, Jenise L. Swall b, Rachel T. Noble c a
Great Lakes Environmental Research Laboratory, NOAA, Ann Arbor, MI 48108, USA National Exposure Research Laboratory, USEPA, Research Triangle Park, NC 27711, USA c Institute of Marine Sciences, UNC Chapel Hill, Morehead City, NC 28557, USA b
article info
abstract
Article history:
Assessing the potential threat of fecal contamination in surface water often depends on
Received 22 February 2010
model forecasts which assume that fecal indicator bacteria (FIB, a proxy for the concen-
Received in revised form
tration of pathogens found in fecal contamination from warm-blooded animals) are lost or
6 August 2010
removed from the water column at a certain rate (often referred to as an “inactivation”
Accepted 13 August 2010
rate). In efforts to reduce human health risks in these water bodies, regulators enforce
Available online 25 August 2010
limits on easily-measured FIB concentrations, commonly reported as most probable number (MPN) and colony forming unit (CFU) values. Accurate assessment of the potential
Keywords:
threat of fecal contamination, therefore, depends on propagating uncertainty surrounding
Water quality modeling
“true” FIB concentrations into MPN and CFU values, inactivation rates, model forecasts,
Fecal contamination
and management decisions. Here, we explore how empirical relationships between FIB
Bayesian
inactivation rates and extrinsic factors might vary depending on how uncertainty in MPN
MPN
values is expressed. Using water samples collected from the Neuse River Estuary (NRE) in
Decay rate
eastern North Carolina, we compare Escherichia coli (EC) and Enterococcus (ENT) dark inactivation rates derived from two statistical models of first-order loss; a conventional model employing ordinary least-squares (OLS) regression with MPN values, and a novel Bayesian model utilizing the pattern of positive wells in an IDEXX Quanti-Tray/2000 test. While our results suggest that EC dark inactivation rates tend to decrease as initial EC concentrations decrease and that ENT dark inactivation rates are relatively consistent across different ENT concentrations, we find these relationships depend upon model selection and model calibration procedures. We also find that our proposed Bayesian model provides a more defensible approach to quantifying uncertainty in microbiological assessments of water quality than the conventional MPN-based model, and that our proposed model represents a new strategy for developing robust relationships between environmental factors and FIB inactivation rates, and for reducing uncertainty in water resource management decisions. Published by Elsevier Ltd.
1.
Introduction
Fecal contamination is a leading cause of surface water quality degradation in the United States (Mostaghimi et al., 2002; Noble et al., 2003a) and throughout the world (Ashbolt * Corresponding author. Tel.: þ1 734 741 2444; fax: þ1 734 741 2055. E-mail address:
[email protected] (A.D. Gronewold). 0043-1354/$ e see front matter Published by Elsevier Ltd. doi:10.1016/j.watres.2010.08.029
et al., 1993; Ghinsberg et al., 1994). Roughly 20% of all total maximum daily load (TMDL) assessments approved by the United States Environmental Protection Agency (USEPA) since 1995, for example, address water bodies with unacceptably high fecal indicator bacteria (FIB) concentrations (a proxy for
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Nomenclature Bi No Po St c, ct c0 i k, k0 ln n p t
binomial distribution normal distribution Poisson distribution Student distribution fecal indicator bacteria concentration at time t (organisms per 100 ml) fecal indicator bacteria concentration at time t ¼ 0 (organisms per 100 ml) index of dilution series number first-order bacteria dark inactivation or loss rate (1/day) natural logarithm number of wells in a dilution series probability of a positive well in a dilution series time (days)
the measurement of fecal contamination-associated pathogens), the highest percentage of any pollutant category (for more on the TMDL program, see National Research Council, 2001). Fecal contamination water quality assessments (within the context of the TMDL program and similar comprehensive water resource management programs) typically compare model-derived (or measured) FIB concentrations in a water body to a set of health risk-based numeric water quality standards (Benham et al., 2006; Gronewold et al., 2008). Models supporting these assessments play a critical role by helping managers understand the potential threat waterborne pathogens pose to human health. This is true even for very simple models, such as those for calculating a geometric mean or 90th percentile, as outlined in the Food and Drug Administration and Interstate Shellfish Sanitation Conference (2005) proceedings and discussed further in Boehm et al. (2009). In addition, models provide the foundation for large-scale management decisions, such as whether or not to restrict access to a water body, or the extent to which pollutant loading levels must be reduced (through best management practices associated with the TMDL process, for example) so that receiving water bodies will comply with pertinent water quality standards. Methodological variability associated with FIB concentration quantification methods is well-documented (see, for example Griffin et al., 2001; Noble et al., 2003b; McBride, 2003; Gronewold and Wolpert, 2008) and, along with other extrinsic factors, can have a significant impact on water quality-based management actions. Fully understanding and acknowledging these sources of variability represents an important step towards generating robust management decisions, such as the closing of a shellfish harvesting area or beach. More importantly, when uncertainty and variability are ignored or incorrectly quantified, they may lead to either overlyconservative management decisions, such as the closure of a beach or shellfish harvesting area when no true threat exists, or inadequate management interventions leading, perhaps, to human illness or the outbreak of disease. Models
v y
653
volume of each well in a dilution series experiment (ml) number of positive wells in a dilution series
Greek letters model residual error terms e, e0 l mean and variance of the Poisson probability distribution m location parameter in Student (St) probability distribution n degrees of freedom in Student (St) probability distribution p prior probability distribution standard deviation of e, e0 (ln organisms per s, s0 100 ml) s scale parameter in Student (St) probability distribution
that appropriately propagate uncertainty and variability from monitoring observations and environmental phenomena into water quality forecasts, therefore, could lead to more robust water resource management decisions, alleviate the need for intensive water quality sampling, and minimize detrimental impacts on human health. Models which fail to account for these potential sources of variability may lead to decisions with unfortunate human health consequences, and are therefore of limited practical use to water resource managers. We know of no studies, however, which perform a retrospective analysis of the strength of the relationship between model-based FIB concentration forecasts and actual human illness derived from contact with contaminated water. We believe this type of comparison would provide critical information towards improving model-based management decisions, and should be pursued in future research.
1.1.
FIB inactivation rates and the first-order loss model
Models used to support FIB water quality assessments often include a parameter reflecting the effective rate of FIB loss over time due to natural die-off, settling, and other factors (Auer and Niehaus, 1993; Ferguson et al., 2003). The magnitude of this rate, and its relationship to extrinsic factors, is typically assessed by calibrating a first-order loss model (see Section 2.4) using FIB concentration data collected in a controlled (e.g. laboratory) setting. Other model structures (second-order, for example) could be used, such as those discussed in Borsuk and Stow (2000) and Huang and McBean (2007). Here, we focus on the first-order loss model because it is commonly applied in FIB dark (i.e. in the absence of sunlight) inactivation rate studies, and because it provides an ideal template for us to explore alternative approaches to quantifying uncertainty. In addition to Auer and Niehaus (1993) and Ferguson et al. (2003), Sinton et al. (1999) and Noble et al. (2004) suggest that FIB inactivation rates (also referred to as a “die-off” or “decay” rate) vary under different environmental conditions, including solar radiation and water temperature (from here
654
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forward, we refer to this rate as an “inactivation” rate). FIB inactivation rate variability in response to other factors, however, including initial FIB concentration and water column depth, is not as well-understood, and has been recommended as an area for future research. An implicit and more general objective of these studies, however, is to incorporate inactivation rates into comprehensive models with “real-world” data to forecast future FIB concentration dynamics over broad spatial (e.g. estuarine) and temporal (e.g. multiple years) scales. Despite this goal, documented inactivation rates (Bowie et al., 1985, for example) are rarely accompanied by an indication of the structure (e.g. first- or second-order) or performance (assessed, perhaps, through model confirmation) of the calibration model from which they were derived (Gronewold et al., 2009). This common oversight is particularly problematic because the calibration model may not be an appropriate representation of FIB concentration dynamics, leading to inaccurate estimates of inactivation rate magnitude and variability which could then propagate into undesired uncertainty and variability in “real-world” model applications.
1.2.
FIB measurement uncertainty and variability
The two most common FIB concentration metrics are the most probable number (MPN) and the colony-forming unit (CFU). MPN and CFU values, when used to calibrate FIB inactivation rate models (such as the first-order loss model), contribute to variability in inactivation rate estimates and to discrepancies between observed and model-predicted concentration values (i.e. model error) in different ways due to unique intrinsic sources of bias and variability associated with MPN- and CFU-based testing procedures. Here, we focus on uncertainty and variability in MPN values alone. For more on addressing CFU value variability and incorporating it into FIB water quality models, see Gronewold et al. (2009). There are a variety of MPN-based testing procedures, however the two most common for quantifying FIB concentrations are multiple-tube fermentation and chromogenic substrate tests. MPN values derived from these procedures are known to be positively biased (Garthright, 1993,1997) and have varying degrees of uncertainty depending on the design of the testing procedure, such as the number and volume of wells or tubes in a dilution series. Furthermore, each procedure can yield multiple sets of “raw” data (such as the pattern of positive wells in a dilution series) which, while leading to the same MPN value, might imply very different uncertainty bounds on the value of the “true” FIB concentration. Put differently, the “raw” data from an MPN-based experiment includes all of the information needed to quantify uncertainty and variability in the FIB concentration and to calculate an MPN value. Unfortunately, water quality scientists commonly report only MPN values, an approach which effectively discards valuable uncertainty and variability information contained in the “raw” data (Woodward, 1957; McBride, 2003). Here, we explore ways to improve the estimation and representation of FIB inactivation rates for the purpose of increased accuracy in water quality management decisions. In the following section, we describe our approach to collecting and analyzing water quality data from an estuary in
eastern North Carolina. We then present a novel Bayesian model calibration procedure for quantifying FIB inactivation rates in estuarine waters, and explore the effect of potential extrinsic and intrinsic factors, including uncertainty in monitoring data, environmental conditions, and specific members of the FIB group being studied. We then compare the results of our proposed Bayesian model to those from a more conventional regression analysis, and conduct a model confirmation procedure (commonly referred to as a validation procedure) to assess model performance.
2.
Methods
2.1.
Monitoring plan and site description
Our study area is the Neuse River Estuary (NRE) near the city of New Bern in eastern North Carolina (NC). This area has been intensively studied and is selected for this study because it has historically high FIB concentrations relative to other sites in the NRE (Fries et al., 2006). The NRE is a typical Atlantic, lagoonal, largely wind-mixed estuary, and the water quality in the upper NRE is of economic and recreational importance to the surrounding area (Borsuk et al., 2001). Previous studies have indicated that NRE water quality suffers from anthropogenic FIB loading through stormwater runoff and upstream fecal contamination sources (Fries et al., 2006,2008).
2.2.
Sample collection and inoculation
In order to assess dark inactivation rates of FIB populations at different concentrations, we inoculated environmental samples (i.e. collected in situ) from the NRE using inocula of influent from a nearby sewage treatment plant to achieve two different initial (i.e. post-inoculation) concentrations: 1) a “high” concentration (approximating a spill from a sewage treatment plant, for example), and 2) a “moderate” concentration approximating chronic stormwater runoff or a similar pollutant loading source. Morehead City Wastewater Treatment Plant (MCWWTP) influent was collected in order to inoculate the environmental NRE samples. To enumerate FIB concentrations in the influent, duplicate 1.0 ml aliquots were diluted 100-fold along a four-point serial dilution. Escherichia coli (EC) and Enterococcus (ENT) concentrations (in organisms per 100 ml) were determined from a 100 ml diluted sample using appropriate IDEXX media and the IDEXX Quanti-Tray/2000 kit (see Section 2.3 for details). The estimated MCWWTP influent FIB concentration was then used to calculate the appropriate volume of influent to inoculate water samples in an effort to achieve “target” final (post-inoculation) ENT concentrations of 3000 (“high”) and 300 (“moderate”) organisms per 100 ml. Environmental grab samples (5 L each) were collected from both the surface and bottom water of the NRE study site on three dates (August 6, September 17, and October 16) in 2007. Three 1-L subsamples were poured from each sample, and placed in separate 1 L polycarbonate Nalgene bottles. Two of the three 1 L subsamples were inoculated with MCWWTP influent to achieve target concentrations. The third 1 L sample
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 5 2 e6 6 4
from each set was not inoculated with MCWWTP influent, and is referred to as the sample with a “baseline” FIB concentration. After inoculation, each 1 L subsample was evenly split into two 500 ml subsamples which were then incubated in the dark at temperatures comparable to those in ambient NRE waters (22e28 C), and individually shaken and uncapped to simulate environmental mixing and aeration every 8 h thereafter. Finally, water from the 500 ml subsamples was tested (as described in the following section) for EC and ENT concentrations (in organisms per 100 ml) at time intervals of roughly 0, 1, 2, and 3 days post-inoculation.
2.3.
Sample enumeration
EC and ENT concentrations were quantified using the IDEXX Quanti-Tray/2000 chromogenic substrate test (CST) kit with Colilert-18 and Enterolert media. To ensure that FIB concentrations did not exceed the upper bound of the IDEXX Quanti-Tray/2000 test (i.e. a test result with all positive wells in both dilution series), we followed manufacturer’s recommendations for implementing a 1:10 dilution factor by extracting duplicate 10.0 ml aliquots from each subsample and pipetting them into IDEXX 100 ml polycarbonate bottles containing 90 ml of deionized (DI) water and either Colilert18 and Enterolert media. Each bottle was shaken 25 times to ensure that the media was completely dissolved. The liquid was poured into an IDEXX Quanti-Tray/2000 tray, and the tray was sealed. Then, the trays containing the sample and the dissolved Colilert-18 and Enterolert media were incubated at 35 C for 18 h, and 41 C for 24 h, respectively. “Raw” data, including the numbers of positive small and large wells for each set of trays, and the dilution factor (in this study, a dilution factor of 10 was used for all samples) was recorded according to manufacturer’s instructions. Finally, the number of positive large and small wells, along with the applicable dilution factor, were used to calculate the MPN (in organisms per 100 ml) for EC, ENT, and total coliforms. Total coliform results, however, were not used in this study.
2.4.
Model calibration
655
organisms per 100 ml) are interchangeable with the “true” FIB concentration c, leading to the following calibration model: lnMPNt ¼ lnc0 k t þ et
(2)
et wNoð0; sÞ
(3)
where MPNt represents the average MPN (based, in our study, on the average of two split samples) at each time step. The model in equations (2) and (3) reflects the common assumption that discrepancies between the expected and observed values of the (logarithm of the) MPN can be described by a normally-distributed error term e (with mean 0 and standard deviation s). Although our calibration procedure allows different values of k and c0 for each experiment, we use subscripts in equations (2) and (3) to differentiate between times t after inoculation only (for clarity). We estimate k and s in equations (2) and (3) using a classical ordinary-least squares (OLS) regression procedure (for more on OLS regression, see Weisberg, 2005) in the lm package (Chambers and Hastie, 1991: Chapter 4) in the statistical software program R (Ihaka and Gentleman, 1996).
2.4.2.
Proposed “raw” data-based approach
In our proposed “raw” data-based approach, we deliberately avoid calculating or using MPN values, and instead consider the pattern of positive wells from the IDEXX Quanti-Tray/2000 test kit as data (hereafter referred to as “raw” data). In this approach, we calibrate the first-order loss model by assuming (following Hurley and Roscoe, 1983; McBride, 2003) that the number of positive wells in each dilution series of an IDEXX Quanti-Tray/2000 test kit are independent binomial random variables, yi w Bi(ni, pi), where (in dilution series i from any given sample) yi is the number of positive wells, ni is the total number of wells, pi ¼ 1 eðcvi =100Þ is the probability of a positive well, c (in organisms per 100 ml) is the “true” but unobserved FIB concentration, and vi is the volume (in ml) of each well in dilution series i. For the IDEXX Quanti-Tray/2000 kit, i ˛ {1, 2}, n1 ¼ 49, n2 ¼ 48, v1 ¼ 1.86 ml, and v2 ¼ 0.186 ml. These assumptions, when combined with equation (1), lead to the following calibration model (for clarity, subscripts differentiate only between dilution series i and times t after inoculation):
FIB inactivation rates are often quantified by calibrating the following well-known first-order loss model (Fischer, 1979; Thomann and Mueller, 1987, pp. 145e147 and 56e59, respectively):
yi;t jni ; pi;t wBi ni ; pi;t
(4)
pi;t ¼ 1 ect vi =100
(5)
dc ¼ kc; dt
lnct ¼ lnc00 k0 t þ e0t
(6)
e0t wNoð0; s0 Þ
(7)
cðt ¼ 0Þ ¼ cð0Þ ¼ c0
(1)
lnc ¼ lnc0 k t where c ¼ ct is the “true” (but unobserved) FIB concentration (in organisms per 100 ml) at time t (in days), c0 is the “true” FIB concentration at time t ¼ 0 (also in organisms per 100 ml), and k (in 1/days) is the first-order loss (or inactivation) rate. Here, we use k to represent FIB dark inactivation. In the following sections, we describe the two procedures used to calibrate this model.
2.4.1.
Conventional MPN-based approach
In the conventional MPN-based calibration approach, we assume (following common practice) that EC and ENT MPN values (in
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where e is a normally-distributed error term with mean 0 and standard deviation s0 . The rationale for equations (4) through (7), and for distinguishing a “raw” data-based model from the conventional MPN-based model, can be explained, in part, through the graphical model in Fig. 1. This graphical model reflects the assumed relationship between the FIB concentration c in a sample at time t, the dark inactivation rate (k, k0 ), the associated pattern of positive wells ( y1 and y2) given c, and an MPN value. The variables and parameters included in our proposed
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it reflects the range of previously documented inactivation rates and accommodates potential values which might be supported by our analysis (including both very high and negative inactivation rates). We estimate k0 and s0 in equations (6) and (7) by simulating samples from their respective posterior distributions (for definitions, see Press, 2003; Bolstad, 2004; Chapter 4 and Section 4.6, respectively) using Markov chain Monte Carlo (MCMC) procedures in the software program WinBUGS (Lunn et al., 2000). We ran each MCMC chain until it reached convergence, indicated by a potential scale reduction factor b ¼ 1.0 (Gelman et al., 2004, pg. 297). Computer code for our R Bayesian model is included in the appendix.
2.5. Fig. 1 e Graphical representation of first-order FIB loss model and the IDEXX Quanti-Tray/2000 procedure. Dashed lines bound variables and parameters (both ovals) and constants (rectangles) in our proposed “raw” data-based model (equations (4)e(7)), while the shaded regions include variables, parameters, and constants in the conventional MPN-based model (equations (2) and (3)). Single-lined arrows lead to stochastic (i.e. defined by a probability distribution) model variables and parameters, while double-lined arrows lead to deterministic model variables.
“raw” data-based calibration model are bounded by dashed lines in Fig. 1, while those included in the conventional MPNbased calibration model are shaded. In particular, Fig. 1 indicates how our proposed “raw” data-based procedure infers the dark inactivation rate (k0 ) by first quantifying uncertainty in the true but unobserved FIB concentration c. In contrast, the dark inactivation rate derived from the conventional MPNbased procedure (k) is based on an assumption that the MPN and c are interchangeable. We encode equations (4)e(7) within a Bayesian framework (for more on Bayesian statistics, see Berry, 1996; Bolstad, 2004), an approach which allows us to utilize all of the “raw” data from a serial dilution experiment and to express the number of positive wells in a dilution series as a binomial random variable (equations (4) and (5)). Encoding equations (4)e(7) in a Bayesian framework also allows us to infer the FIB dark inactivation rate (k0 ) by combining a priori beliefs with empirical evidence using Bayes’ theorem (Bayes, 1763). In this approach, a priori beliefs regarding potential values of k0 are expressed through a prior probability distribution, p(k0 ), while values of k0 supported by empirical evidence are expressed through a likelihood function. Bayes’ theorem combines these two sources of information into a posterior probability distribution. Here, we specify an informative prior probability distribution for k0 drawing from documented values in previous FIB inactivation rate studies (Bowie et al., 1985). Using these historic values as a guide to likely values of k0 , we choose a Student (St) prior distribution, p(k0 ) w St(m, s, n), with location m ¼ 0.15, scale s ¼ 1, and degrees of freedom n ¼ 3. The Student (St) distribution (Bernardo and Smith, 1994, pp. 122e123), particularly when compared to more common prior probability distributions, is ideal for our study because
Model confirmation
We evaluate the predictive performance of the conventional MPN-based model and our proposed “raw” data-based model using a “leave-one-out” cross-confirmation procedure (for details, see Efron and Tibshirani, 1993, pps. 240e241). We recognize that while this procedure is commonly referred to as validation, we prefer the term confirmation, since validation implies an ascertainment of truth, and only applies when the model is compared to independent observations (Reckhow and Chapra, 1983). To confirm the MPN-based model, we begin by using the “leave-one-out”-based parameter sets to predict MPN values at each time step. This approach is based on the common assumption that the MPN-based model, because it is calibrated using MPN values from the IDEXX Quanti-Tray/2000 procedure, implicitly predicts MPN values (as opposed to FIB concentration values or CFU values). We then construct 95% prediction intervals (Weisberg, 2005) for each MPN value using the 0.025 and 0.975 quantiles of the predictive probability distribution implied by the model in equation (3). Finally, we calculate the fraction of observed MPN values within each interval. We confirm our proposed “raw” data-based model by following the logic of equations (4)e(7). First, we use the “leaveone-out” parameter sets to simulate 10,000 samples from the FIB concentration (c) probability distribution (equations (6) and (7)) at each time step t. We then simulate the pattern of positive wells ( y1, t, y2, t) from an IDEXX Quanti-Tray/2000 procedure (equations (4) and (5)) for each simulated sample and calculate the MPN value (Woodward, 1957; Hurley and Roscoe, 1983). Using the 10,000 simulated MPN values for each time t in each experiment we then construct a 95% MPN prediction set which, following Gronewold and Wolpert (2008), is defined as the set of highest probability MPN values for which the cumulative probability mass is at least 0.95. Finally, we calculate the fraction of observed MPN values which coincide with each discrete 95% prediction set.
3.
Results
3.1. FIB concentration analysis and inactivation rate assessment Our water quality analysis results indicate that inoculated samples corresponding to “high” and “moderate” EC and ENT
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concentrations decrease at rates approximately represented by a log-linear model, although the rate of decrease appears greater for higher starting concentrations (first and fourth rows in Fig. 2). Dynamics for relatively low EC and ENT concentrations, however, are less clear, in part because the rate of change appears to fluctuate across different sampling dates, and in part because the uncertainty in monitoring data is more pronounced at low concentrations (third and sixth rows in Fig. 2). We also note that the native bacteria organisms
in the baseline sample may be from a different population than those in the “high” and “moderate” concentration samples, and that this difference may limit our comparison between the corresponding inactivation rates. Our inactivation rate assessment (i.e. model calibration) results (Fig. 3) indicate that EC dark inactivation rates (left two panels) decrease as initial EC concentration decreases, and this relationship is consistent across sample depth and between the two model calibration procedures. In particular,
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our results indicate that EC dark inactivation rates are likely to be negative at concentrations at or below approximately 30e50 organisms per 100 ml (suggesting the potential for sustained populations or regrowth at low concentrations). Our results also indicate that the “raw” data-based model leads to slightly narrower credible interval estimates for EC dark inactivation rates (black horizontal lines) when compared to confidence intervals derived from the conventional MPNbased model (grey horizontal lines). The relationship between ENT dark inactivation rates (right panels in Fig. 3) and initial concentration, depth, and model selection is not as clear. Most noticeably, our results indicate that the magnitude and uncertainty associated with ENT dark inactivation rate estimates depends on the model from which the estimate was derived. In particular, uncertainty in ENT dark inactivation rates derived from the MPN-based model (grey lines) is relatively consistent across all extrinsic factors, while the uncertainty in estimated ENT dark inactivation rates derived from our “raw” data-based model (black lines) increases as FIB concentration decreases. Furthermore, while our results do not indicate a significant overall trend in the magnitude of ENT dark inactivation rates relative to initial ENT
concentration, they do indicate that ENT dark inactivation rates derived from the conventional MPN-based model may be significantly lower at ENT concentrations close to 1 organism per 100 ml when compared to higher ENT concentrations. Finally, our results indicate that the two calibration models lead to very different estimates of model residual standard deviation (s, s0 ) and that the magnitude of the difference depends on which FIB organism is studied (Fig. 4). For example, the residual standard deviation in the MPN-based model ranged between 0.72 and 1.1 when using EC data (grey lines in top panel, Fig. 4) and between 0.75 and 1.15 when using ENT data (grey lines in bottom panel, Fig. 4). The residual standard deviation in the “raw” data-based model, meanwhile, ranged between 0.6 and 0.96 when using EC data (black lines in top panel, Fig. 4) and between 0.12 and 0.35 using ENT data (black lines in bottom panel, Fig. 4).
3.2.
Model confirmation results for 20 representative EC samples are presented in Fig. 5, which compares EC MPN values (circles in Fig. 5 with duplicates, if analyzed, distinguished by color)
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Dark inactivation rate, k (1/day) Fig. 3 e Relationship between FIB dark inactivation rate estimates (k) and measured (as opposed to “target”) initial FIB concentration for each combination of sample depth (surface or bottom) and FIB species (EC or ENT). Dark inactivation rate interval estimates (horizontal lines) were derived from experiments with either “high” (top black and grey lines in each panel), “moderate” (middle black and grey lines in each panel), or “baseline” (bottom black and grey lines in each panel) target post-inoculation concentrations. Black lines represent 95% (thin lines) and 50% (thick lines) credible intervals derived from our “raw” data-based model, while grey lines represent 95% and 50% confidence intervals derived from the conventional MPN-based model. The vertical dashed line at k [ 0 is included to help distinguish between positive and negative inactivation rates.
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an MPN value in the 95% prediction set derived from the “raw” data-based model. Of the 137 samples analyzed (72 samples, 65 analyzed in duplicate) for both EC and ENT concentration, 118 (86%) EC MPN values and 124 (91%) ENT MPN values were within 95% crosseconfirmation prediction intervals derived from the conventional MPN-based model. Similarly, 125 (91%) EC MPN values and 121 (88%) ENT MPN values coincided with 95% prediction sets derived from our “raw” dataebased model. Fig. 4 e 95% (thin lines) and 50% (thick lines) intervals for model residual standard deviation (s) from the “raw” databased model (black lines) and the conventional MPN-based model (grey lines). Results are presented separately for models calibrated to EC data (top panel) and ENT data (bottom panel).
with cross-confirmation 95% confidence intervals derived from the MPN-based model (left-hand panel) and 95% prediction sets derived from the “raw” data-based (i.e. Bayesian) model (right-hand panel). Sample 1, for example, which was not analyzed in duplicate, yielded an MPN value of 31 organisms per 100 ml (bottom blue dot in both panels of Fig. 5) which is within the 95% prediction intervals derived from the conventional MPN-based model, and coincides with
4.
Discussion
We have presented two approaches to calibrating a first-order FIB loss model to assess FIB dark inactivation rates, and have demonstrated that the magnitude and uncertainty of the assessed rates may vary depending on FIB species and initial FIB concentration, but that these relationships might be contingent upon how the inactivation rate model is calibrated and, consequently, how uncertainty is expressed in the estimated inactivation rate. Applicability of these findings beyond the scope of this study (to largeescale water quality model-based assessments, for example), however, depends not only on environmental conditions (such as sunlight intensity and salinity, among others) but also on the predictive performance of the calibration models from which they were derived (Gronewold
Fig. 5 e Model confirmation results for 20 representative (of 72 total) EC analysis events, including 95% prediction intervals derived (via cross-confirmation) from the MPN-based model (horizontal black lines in left panel) and 95% prediction sets derived from the “raw” data-based model (grey circles in right panel). The diameter of the grey circles in the right panel is proportional to the probability mass of the corresponding MPN value (Gronewold and Wolpert, 2008). Observed MPN values are represented by red and blue circles (with duplicates distinguished by color). Samples 1e6 were not analyzed in duplicate.
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correctly reflects the discrete multi-modal nature of the MPN probability distribution by including only values which, given the design of the testing procedure, are feasible. As a result, the “raw” data-based model prediction sets represent a more effective, defensible, and realistic approach to assessing model predictive performance. To further clarify this point, we compare two approaches to calculating the predictive distribution of the MPN for a hypothetical “true” FIB concentration with a lognormal LN(c jm ¼ ln 300, s ¼ 0.16) probability distribution (Fig. 6). The first approach assumes that the MPN and FIB concentration (c) are exchangeable and, therefore, have the same predictive distribution. This approach to calculating the MPN predictive distribution is consistent with the logic of equations (2) and (3), and is represented in Fig. 6 by a curved black line (MPN predictive distribution) and shaded grey area (MPN 95% prediction region). The second approach uses equations (4) and (5) to simulate the pattern of positive wells from an IDEXX Quanti-Tray/2000 test, and then calculates (for each simulated pair of positive wells) an MPN value. The predictive distribution of the MPN based on the second approach is represented in Fig. 6 by vertical lines. All red vertical lines (regardless of height or location) represent the MPN 95% prediction set, defined as the set of highest probability MPN values for which the cumulative probability is at least 0.95 (Gronewold and Wolpert, 2008). Black vertical lines represent MPN values outside of the MPN 95% prediction set. Fig. 6 suggests that the discrepancies between expected and observed model predictive performance in Fig. 5 may be caused
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et al., 2009). Indeed, both model calibration procedures appear to lead to reasonable confirmation results (proposed 95% intervals and sets collectively include 86e91% of all MPN values), yet we suspect that the inability of these intervals to capture more of the variability in MPN values is strongly associated with how the MPN and “raw” data from a serial dilution analysis procedure are used (or not used) in model inference. Consequently, understanding how and why the MPN might contribute to poorer-thanexpected model performance and, conversely, how the use of “raw” data might address those causes, has significant implications for both “real-world” (i.e. management action-based) and research-oriented applications. To begin, we note that 95% prediction intervals derived from the conventional MPN-based model (left-hand panel in Fig. 5) are commonly assumed to reflect all sources of variability in MPN values, including those arising from FIB fate and transport, and through the MPN analysis procedure. The conventional MPN-based model, however, is often calibrated using average MPN values from two split samples (a practice we follow in this study). Thus, the 95% prediction intervals in the left-hand panel in Fig. 5 are, in fact, prediction intervals for the average MPN value, and the fact that a relatively high percentage (86e91%, depending on FIB species) of the non-averaged MPN values (i.e. blue and red circles in Fig. 5) are within these intervals indicates that the MPN-based model may lead to unnecessarily large prediction intervals and, potentially, to overly-conservative management decisions. In contrast, the “raw” data-based model did not utilize average values, and instead treated the pattern of positive wells from the serial dilution analysis as independent observations arising from a common FIB concentration, an approach which ultimately allows us to propagate uncertainty in the FIB concentration estimate into an estimated inactivation rate, a quantification of model error and, ultimately, into a model-based forecast of likely MPN values. Another complication in the conventional MPN-based model is the absence of a protocol for reporting an MPN value when an IDEXX kit (or other serial dilution analysis test) yields no positive wells in either dilution series (i.e. y1 ¼ y2 ¼ 0), yet we recognize this result is often reported as an MPN value 1.0 organism per 100 ml (i.e. the MPN value when only one well from only one dilution series is positive). Following Gronewold and Wolpert (2008), we argue that a result of all wells negative is best explained by a concentration of 0 organisms per 100 ml, though other values are possible, and the corresponding MPN values should be 0 organisms per 100 ml (for details, see Hurley and Roscoe, 1983; McBride, 2003). Of course, an MPN value of 0 organisms per 100 ml is incompatible with the conventional MPN-based model (equation (2)) because the logarithm of 0 is not finite. Furthermore, the range of feasible MPN values in this study is limited because the MPN probability distribution is intrinsically discrete, and because a 1:10 dilution factor was used in the study design, limiting the range of potential MPN values. Thus, while the discrete nature of the MPN probability distribution and the value assigned to an “all wells negative” result clearly impact our water quality analysis results and potential assessments derived from those results, neither is adequately reflected in the MPNbased model confidence intervals (left panel Fig. 5). For example, all of the MPN-based model intervals imply a continuous range of potential MPN values with the most likely values closest to the center of each interval. The “raw” data-based model, however,
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Fig. 6 e Comparison between two approaches to calculating the predictive distribution of the MPN for a hypothetical FIB concentration c with a lognormal LN (cjm [ ln 300, s [ 0.16) probability distribution. The MPN predictive distribution and 95% prediction intervals associated with this FIB concentration based on the conventional MPN model are represented by the curved black line and the grey shaded area, respectively. The MPN distribution (for the same FIB concentration c) derived from our “raw” data-based model is represented by vertical lines. All red vertical lines (regardless of height or location) represent the MPN 95% prediction set, defined as the set of highest probability MPN values for which the cumulative probability is at least 0.95 (Gronewold and Wolpert, 2008). Black vertical lines represent MPN values outside of the MPN 95% prediction set.
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by problems associated with using the MPN and the MPN-based model, a finding with significant implications for water quality assessments and management decisions. In particular, the relatively high number (and height, indicating probability mass) of vertical red lines in Fig. 6 which fall to the right or left of the shaded (grey) region indicates there is a significant chance for an MPN value to be both outside the prediction interval derived from the conventional MPN-based model and within the 95% prediction set of the proposed “raw” data-based model. Adjacent vertical red lines with very different heights reflect the multi-modal discrete nature of the MPN probability distribution. The vertical black lines in Fig. 6 within the shaded grey region indicate it is possible for an MPN value to be within the 95% prediction interval of the conventional MPN-based model yet not within the 95% prediction set derived from our proposed “raw” data-based model. These discrepancies are unacceptable to water quality managers making human health risk-based management decisions. Aside from explicitly addressing these complications associated with modeling MPN values and providing a more realistic basis for assessing model predictive performance, our proposed “raw” data-based modeling approach explicitly distinguishes between intrinsic bias and variability introduced through a serial dilution analysis procedure (as discussed in Best and Rayner, 1985; Garthright, 1993, 1997), and variability in FIB fate and transport. These two sources of variability affect inactivation rate estimates and model forecasts in very different ways, yet they are not distinguishable in the conventional MPN-based model (equations (2) and (3)), potentially leading to overly conservative prediction intervals (see, for example, the relatively high calculated value of the residual error standard deviation for the ENT MPN-based model in Fig. 4) and an inappropriate level of confidence in the estimate of the inactivation rate (as indicated by the variability in the width of ENT inactivation rate confidence intervals in Fig. 3). Advantages of the proposed “raw” data-based model arise, in part, from an explicit acknowledgement that the pattern of positive wells from any serial dilution analysis experiment is a sufficient statistic (for definitions, see Bernardo and Smith, 1994, pp. 191e192) which can be expressed in a probabilistic framework through a binomial probabilistic distribution. In other words, the pattern of positive wells (along with the dilution factor, and volume of each well) contains all of the information necessary to quantify the likelihood function for the “true” FIB concentration c. In contrast, the MPN is not a complete summary of that information. We argue, therefore, that there is little relative benefit to the existing practice of calculating and reporting an MPN value when compared to the less common practice of reporting the pattern of positive wells (or tubes) alone. This approach, of course, would allow modelers and water resource managers alike to better understand the sources of uncertainty and variability in water quality measurements, and to more appropriately propagate them into model parameter estimates and model forecasts. Finally, the “raw” data-based model could be improved by assuming that the dispersion of FIB cells in a sample aliquot is greater than that represented by equations (4) and (5), which are based on the assumption that the number of FIB cells in a sample has a Poisson Po(l) probability distribution with mean and variance l. Previous authors (El-Shaarawi et al., 1981; Christian and
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Pipes, 1983, for example) have suggested that the dispersion of organisms in a sample aliquot may be more appropriately reflected by a negative binomial probability distribution. We leave exploration of the negative binomial model and its effect on inactivation rate assessments for future research, but suspect that it might improve model performance. Ongoing and future opportunities for applying our modeling approach are found in a broad range of environmental and public health-related disciplines. For example (Harris et al. (1998), utilize MPN data in the analysis of planktonic diatom concentrations in sediment samples and cite similar studies using MPN calculations (e.g. Larrazabal et al., 1990; An et al., 1992). Eckford and Fedorak (2005) use an MPN method to assess nitrate-reducing bacteria growth in oil fields, and Fegan et al. (2004) present a series of studies enumerating E. coli 0157 in cattle feces using MPN procedures. Additional examples of MPN-based environmental assessment include soil and groundwater composition analysis (Menyah and Sato, 1996; Papen and von Berg, 1998) and aquifer contamination studies (Bekins et al., 1999). A specific example of an MPN-based assessment of fecal contamination in recreational water bodies is the Oregon Beach Monitoring Program (Neumann et al., 2006). This program, while acknowledging environmental conditions as potential sources of data variability, applies MPN point estimates of FIB concentration rather than probabilistic estimates, and therefore represents the type of study which could utilize, and potentially be improved by, our modeling strategy.
5.
Conclusions
The following is a list of conclusions drawn from our study: FIB dark inactivation rates may vary with initial FIB concentration, but the relationship depends on FIB species, and the choice of a calibration model. We find, for example, that EC dark inactivation rates tend to decrease as initial EC concentrations decrease, but that ENT dark inactivation rates are relatively consistent across different ENT concentrations. We have demonstrated potential benefits of a new modeling strategy which uses the pattern of positive wells or tubes from a serial dilution FIB quantification experiment as “raw” data in a FIB water quality assessment. This approach helps propagate uncertainty in FIB concentration estimates into inactivation rates and model-based water quality forecasts while potentially simplifying the data recording process. Our proposed “raw” data-based model represents a more general class of Bayesian hierarchical and multi-level modeling strategies (for a detailed description, see Gelman and Hill, 2007) which provide an ideal structure for encoding the pattern of positive wells or tubes from a serial dilution analysis experiment as random variables. This model structure also allows us to infer inactivation rates (and other model parameters) by combining previously documented values with empirical evidence from our own study using Bayes’ theorem. We have demonstrated that our proposed “raw” data-based modeling approach performs as well as (if not better than) a conventional MPN-based model, yet avoids much of the burden of appropriately interpreting MPN values and their confidence limits.
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Laboratory-scale inactivation rate estimates are not necessarily transferable to more complex “real-world” mechanistic models. These estimates, instead, should be critically evaluated depending on the modeling context in which they were derived. Minor variations in how uncertainty is addressed, for example, can lead to very different parameter estimates for a given model, and can subsequently effect empirical relationships, model forecasts, and model-based management decisions.
United States Environmental Protection Agency, through its Office of Research and Development, partially funded and collaborated in the research described here. It has been subjected to Agency review and approved for publication. This paper is GLERL contribution number 1573. The authors thank Jim Wickham, Ibrahim Alameddine, James Christian, and two anonymous reviewers for their comments on the manuscript.
Acknowledgements
Appendix
This study was partially funded by the North Carolina Division of Water Quality (Contract No. EW05049). In addition, the
The following code was used in WinBUGS to calibrate the proposed “raw” data-based (Bayesian) model:
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Zebra mussel (Dreissena polymorpha) parasites: Potentially useful bioindicators of freshwater quality? Lae¨titia Minguez a, Daniel P. Molloy b, Franc¸ois Gue´rold a, Laure Giambe´rini a,* a
Universite´ Paul Verlaine e Metz, Laboratoire des Interactions, Ecotoxicologie, Biodiversite´, Ecosyste`mes (LIEBE), CNRS UMR 7146, Campus Bridoux, Rue du Ge´ne´ral Delestraint, F-57070 Metz, France b Division of Research and Collections, New York State Museum, Albany, NY 12230, USA
article info
abstract
Article history:
In environmental quality bioassessment studies, analysis of hosteparasite interactions
Received 23 March 2010
may well be a valuable alternative to classical macroinvertebrate sampling approaches.
Received in revised form
Herein, we investigated whether zebra mussel (Dreissena polymorpha) parasites could be
9 August 2010
useful biomonitoring tools. Mussel populations were sampled twice at two sites in
Accepted 13 August 2010
northeastern France representing different levels of contamination and were characterized
Available online 21 August 2010
for parasite infection following standard histological methods. Our results indicated that sites of different environmental quality (i.e. chemical contamination) exhibited different
Keywords:
parasite communities characterized by different trematode species and parasite associa-
Dreissena polymorpha
tions. An additional significant finding was the positive correlation established between
Hosteparasite interactions
the prevalence of Rickettsiales-like organisms and metal contamination. Multivariate
Environmental contamination
analyses were valuable in examining parasite communities. ª 2010 Elsevier Ltd. All rights reserved.
Bioassessment
1.
Introduction
The constant threat of anthropogenic pollution to aquatic habitats requires ongoing and comprehensive monitoring efforts. In this regard, biological methods have proven valuable in assessing environmental impacts. Such methods have included quantification of changes in biological responses by using biochemical, physiological, morphological, and behavioural biomarkers, as well as the measurement of ecological parameters focusing on the structures and the taxonomic composition of communities (e.g. diversity indices), indicator species (e.g. biotic indices) (Hawkes, 1997), or life history traits (Bournaud et al., 1992). As indicator species, macroinvertebrates are by far the most commonly used and convenient group for these investigations (Connell et al., 1999). Among freshwater macroinvertebrates, zebra mussels, Dreissena polymorpha, are considered a reliable bioindicator
species for use in such studies (Sures et al., 1997). D. polymorpha is an invasive species which has successfully colonized a wide range of ecosystems throughout Europe and North America where it became common over wide areas (McMahon, 1996). These mussels have been documented to have a variety of parasites (Molloy et al., 1997), and in the study reported herein we investigate whether these parasites could also be useful as indicator species of aquatic pollution. The concept of using parasites as a bioassessment tool is not new, but has focused mainly on fish parasites (Dusek et al., 1998; Broeg et al., 1999, 2005; Sures et al., 1999; Dzikowski et al., 2003; Schmidt et al., 2003; Blanar et al., 2009; Vidal-Martı´nez et al., 2010). Up to now, little attention has been paid to parasitism in molluscs, and when studies exist, they deal mostly with marine organisms (Kim et al., 1998; Heinonen et al., 2000; Chu et al., 2002; Moles and Hale, 2003). Parasites are important in ecosystem structure
* Corresponding author. Tel.: þ33 (0) 387 378 415; fax: þ33 (0) 387 378 512. E-mail address:
[email protected] (L. Giambe´rini). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.08.028
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(Poulin, 1999; Lafferty et al., 2006), and their ubiquitous presence in virtually all animal populations provide the opportunity to conveniently use them for bioassessment purposes. Many parasites have diverse life history strategies involving more than one host, thereby informing about the presence in the same ecosystem of other organisms participating in their life cycles and their trophic interactions (Pietrock et al., 2002; Marcogliese, 2005). How might infectious organisms be useful indicator species of aquatic pollution? As stressed by Sures (2004), parasites can respond to pollution in a variety of ways, including: (i) by their presence/ absence or a variation in their numbers and/or diversity, i.e. as “effect indicators”, and (ii) by accumulating some contaminants, i.e. as “accumulation indicators”. Studies documenting their response as “effect indicators” have reported either direct effects on parasites themselves, e.g. pollution reducing the viability and infectivity of free-living parasitic stages such as miracidia and cercariae (Siddall and Sures, 1998; Pietrock et al., 2002; Pietrock and Marcogliese, 2003; Morley et al., 2005) or indirect effects through impacts on their intermediate and/or definitive hosts (Lafferty, 1997; Halmetoja et al., 2000; Marcogliese, 2005). Parasites useful as “accumulation indicators” tend to be those that are less directly impacted by contaminants, and whose tissues can accumulate these pollutants, e.g. often heavy metals (Sures and Taraschewski, 1995; Thielen et al., 2004). It is for their accumulation capacity that zebra mussels have been frequently used for pollution biomonitoring (Gossiaux et al., 1998). It has been demonstrated, however, that parasites of other macroinvertebrates in a habitat can be even more effective as “accumulation indicators” than zebra mussels, e.g. Sures et al. (1997) observed adults of the acanthocephalan Acanthocephalus lucii, an acanthocephalan parasite of perch (Perca fluviatilis), to have higher lead and cadmium concentrations than that found in zebra mussel tissues. This raises the question of whether zebra mussel parasites themselves could in some way be useful pollution bioindicators. In the present investigation we inventoried and analysed the composition and the structure of the parasite communities of zebra mussel populations at two locations in northeastern France, characterized by different levels of contamination. Our specific goal was to determine if zebra mussel parasites could be useful as “effect indicators” of environmental quality.
Commercy and Sierck-les-Bains, respectively) and in April before the spawning period (n ¼ 130 and n ¼ 110 at Commercy and Sierck-les-Bains, respectively). The shell lengths of the mussels collected for the study were about 24 mm at Commercy and 20 mm at Sierck-les-Bains. To document the different contamination levels at these two sites, water and sediment were collected in January and April to determine the main organic and metallic burdens. Sediment analyses were performed after sieving (4 mm) according to the standards EN 13346/ISO11885 for metals and XP X 33012 for PAH. Several additional water analyses were made: major cations were determined by flame AAS (PerkineElmer Aanalyst 100) after water acidification (1% HNO3), except for NHþ 4 analyzed by graphite furnace AAS (Varian Spectra-300). Major anion concentrations were measured by ion exchange chromatography. The chemical oxygen demand (COD), 5-day biochemical oxygen demand (BOD5) and suspended matter were evaluated, respectively, by volumetric, oxymetric and gravimetric methods. Mussels were examined for parasite identification and counting following standard histological methods, i.e. fixing in Bouin’s Fixative, rinsing in water, dehydrating in a graded series of ethanoletoluene, embedding in paraffin, tissue sectioning (5 mm thickness), and staining in Gill II hematoxylin/eosin. A semi-quantitative parasite inventory was made by observing 30e40 tissue sections per individual. Parasites were identified following Molloy et al. (1997, 2001). The level of infection was assessed using standard epidemiological parameters (Bush et al., 1997): prevalence (percentage of mussels infected) and mean intensity (mean number of parasites per infected mussels) only for parasites that could be enumerated, e.g. individual cells of Ophryoglena spp., ciliates, and cytoplasmic inclusion bodies of Rickettsiales-like organisms (RLOs). Mussel gender was also taken into account during data analysis. Parasites are not the only type of endosymbiont documented in zebra mussels (Molloy et al., 1997), and to be comprehensive all such organisms observed in association with zebra mussel tissues at the two sampling sites were evaluated for their bioindicator potential. For this reason, infection with the ciliate Conchophthirus acuminatus was included in this “parasite” study, even though all evidence suggests this species is far more likely to be commensal in nature (Karatayev et al., 2007).
2.2.
2.
Materials and methods
2.1.
Data collection
Zebra mussels were collected in northeastern France in 2007 at two sites representing different levels of contamination: Commercy on the Meuse River (48 450 21.2900 N, 05 360 25.3700 E) was selected as the relatively “unpolluted” reference site because of its low levels of urbanisation and industry, and Sierck-les-Bains on the Moselle River (49 260 32.8200 N, 06 210 13.2400 E) as our “polluted” site since it was known to be impacted by different industrial activities such as salt mining or steel. Two sampling periods were chosen: in January during the mussel reproductive resting period (n ¼ 196 and n ¼ 120 at
Data analysis
All the statistical analyses were undertaken with R software version 2.8.1, except the non-metric multidimensional scaling (NMDS) made with SPSS software version (SPSS statistics 17.0). In order to illustrate the contamination of the sites, a principal component analysis (PCA) and ManneWhitney Utests were carried out on the individual values of the sediment contaminants (metals and PAHs). The c2 test and the nonparametric KruskaleWallis test, performed on original data, were used to evaluate differences in the prevalence and intensity of infection, respectively, followed by the ManneWhitney U-test. The UPGMA (unweighted pairgroup method with arithmetic mean) clustering was generated from the presence/absence data of each parasite species observed
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from Commercy and Sierck-les-Bains, transformed in a Jaccard distance matrix (NMDS) analysis of parasite prevalence data was used to assess differences among sites in parasite assemblages. This ordination method is a robust procedure for analysing ecological data (Minchin, 1987). We used the BrayeCurtis coefficient to quantify the dissimilarity among sites based on joint occurrence and prevalence of taxa (Clarke and Warwick, 1994). Scores of the sites from the NMDS analysis were correlated (Pearson correlation) with biological and chemical data to identify variables most strongly corresponding to among-site differences in parasite assemblages.
3.
Results
Our experimental design required parasite data comparisons between unimpacted and impacted sites. Compared to the study’s relatively unpolluted reference site at Commercy, Sierck-les-Bains was, as expected, characterized by an elevated organic and metallic contamination (Table 1, Fig. 1). For example, fluoranthene concentration was five times higher in sediments at Sierck-les-Bains than at Commercy (respectively: 4.6 and 1.4 mg kg1, U-test; p < 0.05). Similarly, this site showed a significantly higher metal contamination (i.e. Cr, Ni, Pb, Cu and Zn, U-test: p < 0.05). Moreover, the PCA carried out on the contaminant values of the sediment allowed to discriminate the sites (Fig. 1). The first axis (F1) which explained 88% of the total variance was strongly defined by the pollution level of the sites (all the metals and PAHs measured) (see correlation circle Fig. 1A). The contamination gradient was reflected on this axis as shown by the positions of the sites on the factorial map F1eF2 (Fig. 1B), with a distinction between the 2 river sites (Sierk-les-Bains with negative loadings corresponding to higher levels of contamination and Commercy corresponding to unpolluted and reference site). The second principal axis (9% variability explained) seems to be defined by the seasons (January and April). The infection prevalence at Commercy was significantly higher than at Sierck-les-Bains (c2 test, p < 0.001, Fig. 2B). The number of infected mussels varied between sampling periods, with higher mean infection prevalence in January. At Commercy, differences between months were more pronounced, i.e. 97% of infection in January and only 50% in April. During this study, we observed infection in 9 taxa (4 protozoans, 4 helminths, and 1 bacterium), each with different life history traits (Table 2). Parasite community composition differed at each sampling site and period (Fig. 2A). A total of 8 taxa were found at the relatively unpolluted reference site at Commercy, and 7 were found at the polluted site at Sierck-les-Bains. Among these, some taxa were found at both sites and sampling periods, e.g. RLOs, the trematode Echinoparyphium and the ciliates C. acuminatus and Ophryoglena spp. Others were observed only at one site and with a low prevalence rate, like the trematodes Phyllodistomum folium and Bucephalus polymorphus at Commercy, or Aspidogaster sp. at Sierck-les-Bains (only one infected mussel in January). Whereas Commercy was especially characterized by a high relative prevalence of Ophryoglena spp., this ciliate species and RLOs were the two predominant parasites observed at Sierck-les-Bains.
Table 1 e Results of the physico-chemical analysis carried out on sediment and water at each sampling site. Parameters
Sierck-les-Bains
Commercy
Moselle River
Meuse River
Sediment: (mg kg Dried Matter) Cr* Cu* Hg Ni* Pb* Zn* Acenaphtene Anthracene Benzo(a)anthracene* Benzo(a)pyrene* Benzo(b)fluoranthene* Benzo(e)pyrene* Benzo(ghi)perylene* Benzo(k)fluoranthene* Chrysene* Dibenzo(ah)anthracene Fluoranthene* Fluorene* Indeno(1.3 cd)pyrene* Naphthalene Phenanthrene Pyrene*
45.5 2.1 49.0 2.8 <1 29.0 4.2 107.5 23.3 375.0 29.7 <0.5 1.17 1.8 1.1 1.6 0.7 1.9 0.6 4.3 1.9 1.5 0.5 0.8 0.3 1.9 1.1 <0.5 4.6 2.3 <0.5 1.3 0.6 <0.5 1.7 1.2 3.5 2.0
28.0 7.1 16.0 1.4 <1 19.5 4.9 19.0 4.2 78.5 4.9 <0.5 <0.5 0.7 0.56 0.66 0.9 0.59 <0.5 0.78 <0.5 1.4 1.1 <0.5 0.54 <0.5 1.2 1.83
Water: (mg L1) pH Conductivity 25 C (mS cm1) NH4þ NO2 NO3 N kjedahl PO4 P total Cl SO2 4 Suspended matter Ca2þ Mg2þ Naþ Kþ COD BOD5
8.2 0.2 1295 92 0.14 0.01 0.02 0.01 2.5 0.6 1.2 0.3 0.04 0.05 0.21 0.07 260.0 28.3 89.5 24.7 26.4 17.6 153.5 9.2 14.7 2.4 71.1 11.7 5.4 0.5 9.0 2.8 2.9 0.7
8.1 0.0 501 65 0.08 0.01 0.02 0.0 2.8 0.1 0.7 0.3 0.04 0.05 0.19 0.15 13.5 1.6 35.6 14.0 32.4 38.3 88.9 12.5 8.8 1.6 7.1 0.7 1.6 1.3 12.5 7.8 2.2 0.1
1
Results are given as mean S.D. * significant differences in contamination parameters between the 2 sites (ManneWhitney test, p < 0.05).
In both sampling periods, infected mussels from Commercy displayed significantly more Ophryoglena spp. infection than at Sierck-les-Bains ( p < 0.001, Fig. 2C). Ophryoglena spp. infection intensity differed significantly ( p < 0.001) between sampling periods only at Commercy, with twice as many ciliates per mussel in January. In contrast, the mussels at Sierck-les-Bains exhibited significantly more ( p < 0.001) RLOs than at Commercy i.e. 4 and 2 RLO inclusion bodies per mussel, respectively (Fig. 2D). RLO infection intensity was not different (Sierck-les-Bains: p ¼ 0.40; Commercy: p ¼ 0.52) at either site in the two sampling periods. In regard to host gender, no significant differences (all p > 0.05) were observed between male and female mussels in terms of infection
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Fig. 1 e Results of the PCA of the two dominant components carried out on the contaminant parameters of the sediments of the study sites (97% of the total variance). A. Correlation circle defined by metallic and PAH contaminants in the sediment. B. Factorial plane 1e2 of the sampling units, showing variability of scores among sites (C: Commercy, S: Sierck-les-Bains, 1: January, 4: April).
Fig. 2 e Parasite inventory in Dreissena polymorpha collected in January and April 2007 at Commercy and Sierck-les-Bains. (A) Diversity and relative prevalence of parasite species (pie-charts) and total prevalence of infection (histogram, c2 test, p < 0.05). (B) Mean (±S.D.) intensity of Ophryoglena spp. and (C) Rickettsiales-like organisms (RLO) observed in the 30e40 tissue sections per mussel examined. White and black bars in histograms represent, respectively, January and April samples, and different letters indicate significant differences between groups (t-test, p < 0.05).
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Table 2 e Life history characteristics of the nine taxa observed infecting zebra mussels and their presence (D) or absence (L) at the two study site (C: Commercy, S: Sierck-les-Bains) (adapted from Molloy et al., 1997). Species
Number of hosts
Host sp.
Specificity of infection
Target organs
Site (C/S)
Bacterium Rickettsiales-like organisms
Parasite
1a
Freshwater invertebrates
Generalist
Digestive cells
þ/þ
Ciliates Conchophthirus acuminatus Ophryoglena spp. b Sphenophrya dreissenae
Commensal Parasite Parasite
1 1 1
ZM ZM ZM
Specialist Specialist Specialist
Pallial cavity Digestive gland Gills
þ/þ þ/þ þ/þ
Trematodes Echinoparyphium sp.
Parasite
3
Generalist
Connective tissue near gonads and digestive gland
þ/þ
Phyllodistomum folium
Parasite
2
Specialist
Gills
þ/
Bucephalus polymorphus
Parasite
3
Specialist
Gonads
þ/
Aspidogaster sp.
Parasite
1
Generalist
Renal and pericardial cavities
/þ
1st: Freshwater snails e.g. Lymnea stagnatilis) or tadpoles 2nd: ZM or tadpole or snail (e.g. Radix auricularia) 3rd: Bird (e.g. Greater scaup) 1st: ZM 2nd: Fish (e.g. European bleak) 1st: ZM 2nd: Fish (e.g. Bream) 3rd: Carnivorous fish (e.g. European pike-perch) Bivalves and snails
a Life cycle not yet comprehensively studied: one host only suspected. b Two species have been observed: O. hemophaga and a smaller undescribed Ophryoglena sp.
prevalence or infection intensity with either Ophryoglena spp. or RLOs (data not shown). The UPGMA clustering method performed on parasite diversity (Fig. 3) revealed that at both sites, infected mussels primarily contained one parasite taxon, and when two parasite taxa were present the associations differed between the sites. At Commercy (Fig. 3A) the principal parasite association was co-infection with the ciliates Ophryoglena spp. and C. acuminatus while in contrast at Sierck-les-Bains (Fig. 3B) RLOs were primarily associated with Ophryoglena spp. In addition, the dendrograms of both parasite communities indicated that the four species of trematodes were especially observed only as single infections. The MNDS, based on prevalence rates well discriminated sites and sampling period (stress ¼ 0.000, Fig. 4). The dimension 1 was strongly defined by the infection rate (Pearson RP ¼ 0.97, p ¼ 0.03) and especially by the prevalence of Ophryoglena spp. (RP ¼ 0.99, p ¼ 0.01), with positive loads corresponding to a higher infection rate (e.g. in C1 the prevalence reached 97%). Dimension 2 which clearly separated the study sites was defined by the level of metallic contamination, especially chromium and nickel (RP ¼ 0.99, p ¼ 0.001; RP ¼ 0.96, p ¼ 0.04, respectively), and the intensity of RLO, with higher values corresponding to negative coordinates, characterizing the site of Sierk-les-Bains. In addition, RLO prevalence was strongly correlated both with metallic (e.g. particularly with Cr: RP ¼ 0.98, p ¼ 0.02 and less with Pb: RP ¼ 0.93, p ¼ 0.073; Cu: RP ¼ 0.92, p ¼ 0.075; Zn: RP ¼ 0.92, p ¼ 0.084) and organic contamination (Benzo(b)fluoranthene: RP ¼ 0.95, p ¼ 0.045; Benzo(ghi)perylene: RP ¼ 0.95, p ¼ 0.044; Benzo(e)pyrene: RP ¼ 0.9, p ¼ 0.09). Moreover, it is interesting to note that the prevalence of B. polymorphus was negatively correlated with
pollution (Cu: RP ¼ 0.98; p ¼ 0.021; Zn: RP ¼ 0.99, p ¼ 0.013; Pb: RP ¼ 0.95, p ¼ 0.05; Benzo(k)fluoranthene: RP ¼ 0.93, p ¼ 0.067).
4.
Discussion
This is the first study to examine the relationship between D. polymorpha parasites and pollution and to evaluate their possible use as indicators of freshwater quality. In terms of picking an ideal host for use in parasite pollution studies, zebra mussels meet several of the criteria recommended by Overstreet (1997) and Williams and MacKenzie (2003), including low mobility, organism ubiquity and the ease of sampling. Moreover, the wide assemblage of infectious organisms that have been recorded from zebra mussels, e.g. more than 30 species, provides an ample number of parasites as candidates for evaluation as bioindicators, including some species that use zebra mussels as the first or second intermediate host in their life cycle (Molloy et al., 1997). Once attached by their byssal threads, zebra mussels typically remain at that location for their entire life span. Thus, once the infection process begins, their internal parasites also have a relatively fixed location (e.g. upriver versus downriver), thereby facilitating the assessment of contaminant effects on organisms at different spatial and temporal scales (Blanar et al., 2009). In studies focusing on the use of fish parasites as tools for environmental quality assessment, several ecological parameters have been used, such as biodiversity indices (Broeg et al., 1999; Dzikowski et al., 2003; Schmidt et al., 2003; Nachev and Sures, 2009). For the zebra mussel parasite community, however, only semi-quantitative parasite inventories are
670
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Fig. 3 e Parasite assemblages observed at Commercy (A) and Sierck-les-Bains (B) in the form of UPGMA dendrograms from the presence/absence data of each parasite species observed in D. polymorpha and transformed in a Jaccard distance matrix.
possible, and thus the above-mentioned indices cannot be used. However, we propose that other methods, such as clustering and non-metric analysis, are useful in characterizing parasite assemblages in D. polymorpha. Our experimental design required that to adequately compare the zebra mussel parasite communities, the two chosen sampling sites had to be different in their physicochemical characteristics. Indeed, concerning environmental pollution, both non-parametric tests and PCA carried out on the contamination parameters of the sediment clearly discriminated the sites, with Sierck-les-Bains shown to be highly impacted by heavy metals and hydrocarbons compared with Commercy.
Fig. 4 e NMDS plots based on BrayeCurtis similarities in parasite prevalences for the four site 3 time couples. (C: Commercy, S: Sierck-les-Bains, 1: January, 4: April).
Our initial sampling period (i.e. in January) indicated that mussels from Sierck-les-Bains were less infected than those from Commercy, in terms of total prevalence rates. These results challenge the theory that organisms at contaminated sites should have more, not less, infection or diseases due to a decrease in their immune defences from pollution stress, e.g. as observed by Khan (1990) and Khan and Thulin (1991). To the contrary these data support the theory that healthy ecosystems are high-parasite ecosystems, with healthy communities and assemblages (Kennedy, 1997; Marcogliese, 2005; Hudson et al., 2006). Like other organisms, parasites can be affected by exposure to contaminants, either directly within the host or indirectly through biochemical and physiological changes to the host. Poulin (1992) and Lafferty (1997) suggested that contamination (e.g. heavy metals) negatively affected parasites. There was also evidence that infection data reflected environmental quality since the study sites differed in terms of their parasite prevalence, communities and assemblages. Although some species were present at both sites, e.g. RLOs, all the ciliate taxa (Ophryoglena spp., C. acuminatus and Sphenophrya dreissenae), and the trematode Echinoparyphium sp., other parasites seemed to be more specific to one site. Infections with the digenean trematodes P. folium and B. polymorphus were found only at Commercy, and the infection with Aspidogaster sp. only occurred at the impacted site. To consider parasites as bioindicators in the assessment of water quality, they must be sensitive or resistant to environmental changes. It is necessary, however, not only to take into account a parasite’s various development stages (particularly the free-living phases), but also its host specificity, since these parameters could explain different parasite responses to pollution stress (Poulin, 1992; Lafferty, 1997). Several studies have reported different behaviours between microparasites and digeneans which, respectively, tended to increase and decrease with contamination levels, e.g. by heavy metals (Lafferty, 1997; Sures, 2004; Blanar et al., 2009). In the polluted site, Sierck-les-Bains, we
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recorded more RLOs and less trematode infection, with B. polymorphus negatively correlated with pollution, mainly metallic contaminants. Several investigations have demonstrated that pollution can compromise heteroxenous species by preventing the completion of their life cycles (MacKenzie, 1999). Morley et al. (2006) highlighted the complexity of the relationship between pollutant and parasites (trematode) that involved numerous factors influencing the immune response of snail hosts. In this case, the prevalence of the trematode species may increase or decrease depending on the toxicant-induced pathology of the host immune system. Both digeneans P. folium and B. polymorphus are obligate and host specific parasites of the zebra mussel (using it as an intermediate host), with infection initiated by the free-living miracidial stage (Molloy et al., 1997). In agreement with the theories of Sures (2004), the absence of these two trematode species at Sierck-les-Bains could be explained by: (i) the increased susceptibility of infected mussels to contamination, e.g. P. folium induces both gill deformation and loss of soft tissues (Molloy et al., 1997), and/or (ii) the direct pollution effect on parasites or on one host in the life cycle. Several studies have demonstrated a negative effect of heavy metals on the survival and infectivity of miracidia and cercariae (e.g. Schistosoma mansoni, Echinoparyphium recurvatum, or Zoogonoides viviparous), involving a decrease in infection rates (Siddall and Clers, 1994; Morley et al., 2003, 2006; Pietrock and Marcogliese, 2003). Pathologies have also been frequently reported in fish (e.g. bleak, other cyprinids) used as hosts and exposed to different contaminants like heavy metals or hydrocarbons (Gu¨l et al., 2004; Raldu´a et al., 2007). Moreover, Galli et al. (2001) have shown that B. polymorphus was less prevalent in highly polluted versus non-impacted or moderately polluted habitats. Due to their high host specificity in their first intermediate host and definitive host, the presence of digeneans can be a valuable indicator of the presence of their other obligate hosts within the same ecosystem. These trematodes are also valuable as bioindicators since they integrate the effects of pollutants at multiple levels in the food web (Lafferty, 1997; MacKenzie, 1999; Lafferty and Kuris, 1999; Morley et al., 2003). In contrast, the digenean Echinoparyphium is potentially a less useful bioindicator because zebra mussels are only one of many possible second intermediate hosts (Molloy et al., 1997; Laruelle et al., 2002). This could explain its presence at both sites at almost the same prevalence rate. Likewise, the trematode Aspidogaster requires only one host to complete its life cycle and is not specific to D. polymorpha (Laruelle et al., 2002). Furthermore, when this trematode is observed, its prevalence rate typically is low (Molloy et al., 1997). Trematodes were generally observed in single infections. This could possibly be explained by their pathogenicity and/or the parasite size since: (i) mussels infected by P. folium have been reported to have one-third less dry weight than their uninfected congeners due to diminished feeding caused by gill deformation (Molloy et al., 1997); (ii) B. polymorphus induces the complete castration of zebra mussels, in which gonads are completely replaced by sporocysts (Laruelle et al., 2002); (iii) Aspidogaster may cause pathological changes at the cellular level (Molloy et al., 1997). When zebra mussels had concurrent infection with more than one parasite taxon, the association differed between the
671
two sites. At Commercy the main association was primarily composed of the ciliates Ophryoglena spp. and C. acuminatus, whereas at Sierck-les-Bains Ophryoglena spp. were associated primarily with RLOs. The two study sites could be discriminated on the basis of the prevalence and intensity of RLO infection, thereby suggesting that RLOs may play a significant role in structuring parasite assemblages in D. polymorpha. However, we cannot dismiss the possibility that all zebra mussels were infested by the commensal C. acuminatus (i.e. higher prevalence rates), in the pallial cavity. The numerous rinsing of tissues during histological procedures likely eliminated many of them since C. acuminatus is considered as the most widespread symbiont of D. polymorpha, often being the only one detected in many specimens (Molloy et al., 1997; Karatayev et al., 2007; Conn et al., 2008). An additional significant finding of the present study was the correlation established between the prevalence of RLOs infection and metal contamination, especially by chromium and nickel and to a lesser extent but not significantly with lead, copper and zinc. A similar pattern was previously observed by Kim et al. (1998), whose study positively correlated the prevalence of RLOs in oysters with nickel concentration. The presence of this type of bacteria is also known to be associated with the decomposition of faecal matter and thus related to rural wildlife or municipal discharges (Reible, 1999). RLOs may be a better bioindicator than faecal coliforms, classically measured to assess the water quality, since they could be the sign of faecal and metallic contaminations. Several factors can perturb the distribution of parasites in host population, not only biotic factors like the size or the gender of the host, but also abiotic factors such as season and pollution (MacKenzie et al., 1995). Minguez et al. (2009) have demonstrated that the gender of zebra mussels can be a factor affecting their physiological response to parasitism, but the gender of the relatively uniform sized mussels in the present study did not appear to influence the measured epidemiological parameters. Concerning abiotic factors, we observed that Ophryoglena spp. displayed seasonal variations in their prevalence rates and infection intensity, with higher values in winter. A slight negative correlation between temperature and the prevalence of Ophryoglena spp. infection in zebra mussels was also reported by Karatayev et al. (2003), and we have observed this also in other field samples (authors, unpublished data).
5.
Conclusion
The specific goal of the present study was to investigate if zebra mussel parasites could be used as bioindicators in the assessment of freshwater quality. Parasitism was studied as a function of parasite species, infection prevalence and intensity, and parasite associations. Our results suggest that sites of different environmental quality (i.e. chemical contamination) exhibited different parasite communities characterized by different trematode species and parasite associations. According to the seasons, total infection could be lower in mussels from the more contaminated area. In addition, the significant correlations between the metallic and organic contamination and prevalence rates of RLO (positive)
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and B. polymorphus (negative) allowed discrimination between the two field sites sampled. Due to their high host specificity, the presence of the two digeneans, P. folium and B. polymorphus can represent a valuable bioindicator. Clustering methods and NMDS analysis appear to be valuable methods to examine parasite communities. Thus, we obtained promising results for the future use of zebra mussel parasite communities and hosteparasite interactions as a complementary and/or alternative method in the biological assessment of water quality. Further investigations, however, are necessary at other sites to confirm the observed correlations and to assess the normal seasonal variations of epidemiological parameters.
Acknowledgement This work was supported by grants from CNRS-INSU (programme EC2CO) and CPER Lorraine-ZAM (Contrat Projet Etat Re´gion Lorraine, Zone Atelier Moselle). We thank Philippe Rousselle for physico-chemical analysis of water samples. The authors wish to thank the two reviewers for their helpful suggestions which improve the manuscript.
references
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Evaluating the influence of process parameters on soluble microbial products formation using response surface methodology coupled with grey relational analysis Juan Xu, Guo-Ping Sheng*, Hong-Wei Luo, Fang Fang, Wen-Wei Li, Raymond J. Zeng, Zhong-Hua Tong, Han-Qing Yu Department of Chemistry, University of Science and Technology of China, Hefei 230026, China
article info
abstract
Article history:
Soluble microbial products (SMPs) present a major part of residual chemical oxygen
Received 24 May 2010
demand (COD) in the effluents from biological wastewater treatment systems, and the SMP
Received in revised form
formation is greatly influenced by a variety of process parameters. In this study, response
29 July 2010
surface methodology (RSM) coupled with grey relational analysis (GRA) method was used
Accepted 18 August 2010
to evaluate the effects of substrate concentration, temperature, NHþ 4 -N concentration and
Available online 26 August 2010
aeration rate on the SMP production in batch activated sludge reactors. Carbohydrates were found to be the major component of SMP, and the influential priorities of these
Keywords:
factors were: temperature > substrate concentration > aeration rate > NHþ 4 -N concentra-
Activated sludge
tion. On the basis of the RSM results, the interactive effects of these factors on the SMP
Effluent quality
formation were evaluated, and the optimal operating conditions for a minimum SMP
Grey relational analysis (GRA)
production in such a batch activated sludge system also were identified. These results
Response
provide useful information about how to control the SMP formation of activated sludge and
surface
methodology
ensure the bioreactor high-quality effluent.
(RSM)
ª 2010 Elsevier Ltd. All rights reserved.
Influential priority Operating factor Soluble microbial products (SMPs)
1.
Introduction
Effluents from biological wastewater treatment systems contain a variety of soluble organic compounds, among which soluble microbial products (SMPs) constitute a major part of the residual chemical oxygen demand (COD) for most welloperated biological treatment systems (Schiener et al., 1998; Barker and Stuckey, 1999). SMPs are defined as the pool of organic compounds that are released into solution through substrate metabolism and microbial decay (Noguera et al., 1994; Barker et al., 2000; Aquino and Stuckey, 2004).
Carbohydrates, proteins and humic substances are the main components of SMP. SMP formation involves a very complex process with many influential factors, such as substrate type and concentration, hydraulic retention time, nutrients, temperature, solid retention time, etc (Pribyl et al., 1997; Huang et al., 2008; Krasner et al., 2009; Fernando and Allen, 2003). The contents and characteristics of SMP from different wastewater treatment reactors usually vary significantly, attributed to the different process parameters and operational conditions applied. The influences of process parameters on SMP production have been investigated using the conventional
* Corresponding author. Fax: þ86 551 3601592. E-mail address:
[email protected] (G.-P. Sheng). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.08.032
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‘change-one-factor-at-a-time’ method (Barbosa et al., 2001; Eroglu et al., 1999), which requires a large quantity of experiments to yield comprehensive information. In these studies, only single influential factor was individually evaluated. This probably leads to controversial results, because the interactions between these factors could not be taken into account. For example, some researchers observed a linear increase in SMP production with the increasing influent COD concentration, while others found that there existed an optimum range of organic loads for minimized SMP production (Pribyl et al., 1997; Barker et al., 2000). In this regard, response surface methodology (RSM) presents an alternative and more efficient approach based on statistical principles. RSM has been widely used in analyzing various biological processes, designing the experiment, building models, evaluating the effects of several factors, and searching for optimum conditions to give desirable responses and reducing the number of experiments (Oh et al., 1995; Cruz et al., 1999; Yang et al., 2003). Since many operating factors are involved in the SMP formation in wastewater treatment bioreactors, RSM can be employed as an appropriate approach to analyze the SMP formation process. Furthermore, it is essential to understand the most significant influential parameters for SMP production. Biological systems can usually be considered as grey systems due to their high complexity and lacking of sufficiently defined or precise information. For such systems, grey relational analysis (GRA), as one of the most important contents of grey theory, has been applied extensively. The principle of GRA is to estimate the similarity and degree of the compactness among factors based on the geometric shape of the different sequences (Deng, 1989). It has been employed to evaluate the significance of the influencing factors to complex biological processes (Chen and Syu, 2003; Moran et al., 2006), and to qualitatively and quantitatively identify the interrelationships between multiple factors and variables with minimal information needed (Chou and Tsai, 2009). Thus, this study aimed to explore the effects of substrate concentration, NHþ 4 -N concentration, temperature and aeration rate on SMP production, and to find the optimum conditions for minimizing the SMP production in a batch reactor with the RSM method. Then, the GRA was used to quantitatively evaluate the significance of these influencing factors on the SMP formation. This work shall hopefully provide a useful approach for optimizing the parameters of a batch activated sludge system to minimize the SMP production and accordingly improve the effluent quality from wastewater treatment bioreactors.
Table 1 e Levels of the variable tested in the central composite designs. Variable
X1, X2, X3, X4,
xi ¼
Materials and methods
2.1.
Experimental design with RSM
Based on RSM with a central composite design (CCD) as shown in Table 1, the variables Xi were coded as xi according to the following equation:
(mg COD/L) NHþ 4 -N (mg/L) Temperature ( C) Aeration rate (m3/h)
-2
-1
0
1
2
100 15 4 0.050
300 20 12 0.075
500 25 20 0.100
700 30 28 0.125
900 35 36 0.150
Xi Xi0 dXi
(1)
where Xi is the uncoded value of the ith independent variable (i ¼ 1,2,.,4), Xi0 is the value of Xi at the centre point of the investigated area and dXi is the step change. Substrate concentration (X1), NHþ 4 -N (X2), temperature (X3) and aeration rate (X4) were chosen as the independent input variables. The effluent SMP concentration was used as the dependent output variable. The response variable was fitted by a second-order model in the form of quadratic polynomial equation: y ¼ b0 þ
k X
bi xi þ
i¼1
k X
bi x2i þ
i¼1
i<j X X i
bij xi xj ði ¼ 1; 2/4; j
j
¼ 1; 2; .; 4Þ
(2)
where xi refers to input variable that influences the response variable y; b0, bi, bii and bij are the constant regression coefficients of the equation.
2.2.
Data analysis
The parameters of the response equation and corresponding analysis of variances were evaluated using Minitab Version 14 (Minitab Inc., USA). Probability (P) values were used to check the significance of the coefficients, which are necessary to understand the pattern of the mutual interactions between the test variables. A smaller magnitude of the probability means a more significant correlation coefficient. The significance of the regression coefficient was tested by a t-test with the confidence of 95%. The quality of the fit of the polynomial model equation was expressed by the coefficient of determination (R2), and its statistical significance was checked by an F-test. Response surface plots were generated by Matlab 7.0 (MathWorks Inc., USA). Subsequently, two additional confirmation experiments were conducted to verify the validity of the statistical experimental strategies.
2.3.
2.
Range and levels
GRA
To evaluate the importance of various factors on the production of SMP, independent input variables, i.e., substrate concentration, NHþ 4 -N concentration, temperature and aeration rate, were chosen as the compared series and defined as: zj ¼ zj ðkÞj ¼ 1; 2; .; m; k ¼ 1; 2; .; n
(3)
The output variable (e.g., SMP concentration) was set as the reference series and expressed as:
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z0 ¼ fz0 ðkÞjk ¼ 1; 2; .; ng
(4)
where m is the total number of factors to be considered, and n is the total number of observation data. In this study, m ¼ 4, and n ¼ 31. It is necessary to normalize the original data prior to GRA analysis. Thus, the input factors and the output variables were normalized to the same order of magnitude to reduce error. The normalized data zj(k) were calculated as follows (Noorul et al., 2008): h i ðoÞ ðoÞ max zj ðkÞ zj ðkÞ i h i h zj ðkÞ ¼ ðoÞ ðoÞ max zj ðkÞ min zj ðkÞ
(5)
(o) where z(o) j (k) is the original data, max[zj (k)] is the maximum (o) (o) value of zj (k), min[zj (k)] is the minimum value of z(o) j (k). Thus, an m n matrix Z could be composed by m series:
0
z1 ð1Þ B z2 ð1Þ B Z¼@ . zm ð1Þ
z1 ð2Þ z2 ð2Þ . zm ð2Þ
1 . z1 ðnÞ . z2 ðnÞ C C . . A . zm ðnÞ
(6)
The elements in each row of the above matrix were subtracted by the elements of the reference series, and another matrix D was thus formed. 0
D01 ð1Þ D01 ð2Þ B D02 ð1Þ D02 ð2Þ B D¼@ . . D0m ð1Þ D0m ð2Þ
1 . D01 ðnÞ . D02 ðnÞ C C . . A . D0m ðnÞ
minj mink z0 ðkÞzj ðkÞ þxmaxj maxk z0 ðkÞzj ðkÞ ¼ jz0 ðkÞzj ðkÞjþxmaxj maxk z0 ðkÞzj ðkÞ g z0 ðkÞ;zj ðkÞ Dmin þx,Dmax ¼ D0j ðkÞþx,Dmax (8) where Dmin is the element of minimum value in the matrix D and Dmax is defined as the element of maximum value in the matrix D, x (0 < x 1) is a distinguishing coefficient to adjust the range of the comparison environment, which was selected as 0.5 in this study (Chen and Syu, 2003). Finally, the grey relational grade g was obtained by calculating the average values of all the grey relational coefficients. (9)
With the values above, the influential degree of the factors on the system could be identified.
2.4.
SMP ¼ Carbohydrates þ Proteins þ Humic substances
(10)
The content of carbohydrates in SMP was measured with the anthrone method using glucose as the standard, while the
(7)
where D0j ðkÞ ¼ jz0 ðkÞ zj ðkÞj. The grey relational coefficients g(z0(k) and zj(k)), which were used to express the relationship between the factors and the concentration of SMP, were computed as follows (Deng, 1989):
n 1X g z0 ðkÞ; zj ðkÞ ðj ¼ 1; 2; .; mÞ g z0 ; zj ¼ n k¼1
Synthetic wastewater was used in this study. The concentrations of substrate and NHþ 4 -N (dosed as sodium acetate and NH4Cl) in the influent were adjusted to the predetermined values based on CCD design (Table 2). The other components of the wastewater were: KH2PO4, 40 mg/L, MgSO4, 90 mg/L, KCl, 37 mg/L and trace element solution (in mg/L): EDTA, 50, ZnSO4$7H2O, 22, CaCl2$2H2O, 8.2, MnCl2$4H2O, 5.1, FeSO4$7H2O, 5.0, (NH4)6Mo7O24$4H2O, 1.1, CuSO4$5H2O, 1.8, CoCl2$6H2O, 1.6. The working volume of the batch reactor was 1 L. The mixed liquor suspended solid (MLSS) concentration was kept at about 2500 mg/L. The temperature was controlled by water bath as specified. The aeration rate was adjusted by a gas flow meter. Thirty-one trial experiments under different conditions were performed. After aeration for 5 h and settling for 30 min, 100 mL of sample was collected from each reactor. The samples were filtered through 0.45-mm acetate cellulose membranes to separate the residual biomass, and the filtrates were subjected to SMP analysis. The SMP was calculated as the sum of the following three components:
Experimental conditions and analytical methods
The activated sludge used in this study was obtained from Wangtang Municipal Wastewater Treatment Plant, Hefei, China. Prior to the experiments, the sludge was aerated for 10 h and washed three times with tap water and three times with distilled water to remove external soluble substances.
Table 2 e CCD and experimental results for four variables in coded units. Trial
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Factor
SMP (mg/L)
x1
x2
x3
x4
Measured
Predicted
0 0 0 1 0 1 2 0 1 1 1 0 0 2 1 1 1 1 0 0 1 0 1 1 0 1 0 1 1 0 1
0 0 0 1 0 1 0 0 1 1 1 0 0 0 1 1 1 1 0 0 1 0 1 1 2 1 2 1 1 0 1
2 0 0 1 0 1 0 0 1 1 1 0 0 0 1 1 1 1 2 0 1 0 1 1 0 1 0 1 1 0 1
0 0 2 1 2 1 0 0 1 1 1 0 0 0 1 1 1 1 0 0 1 0 1 1 0 1 0 1 1 0 1
9.9 7.3 9.4 9.2 9.8 8.6 10.1 7.8 10.1 9.9 7.8 7.8 7.5 8.5 10.5 15.3 9.3 10.4 19.7 8.2 8.8 7.7 10.9 8.8 12.6 20.8 14.9 14.2 14.4 7.9 12.6
11.3 7.7 9.4 11.1 9.1 7.6 11.1 7.7 9.5 10.8 9.2 7.7 7.7 6.9 8.4 14.3 9.9 10.7 17.7 7.7 8.9 7.7 10.1 10.8 12.8 20.3 14.0 14.9 15.7 7.7 12.1
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25 20 SMP (mg/L)
Table 3 e Estimated regression coefficients.
Humic substances Proteins Carbohydrates
15 10 5 0 0
5
10
15 Trial
20
25
30
Fig. 1 e Production and the compositions of SMP at various trials.
contents of proteins and humic substances were determined using the modified Lowry methods using chicken egg albumin and humic acid, respectively, as the standards (Frolund et al., 1996).
3.
Results and discussion
3.1.
SMP production and components
The contents of the main SMP components in the 31 trials are shown in Fig. 1. The total SMP contents in these experiments varied from 7.5 mg/L to 20.8 mg/L. The concentration of carbohydrates varied from 4.7 mg/L to 14.8 mg/L, proteins from 0 mg/L to 6.7 mg/L, and humic substances from 1.2 mg/L to 5.5 mg/L. Though the fraction of SMP varied significantly under various operating conditions, carbohydrates were found to be the major component in all the cases.
3.2.
RSM analysis
SMP concentrations under the various experimental conditions are given in Table 2, and the regression coefficient values, standard deviations, T values, and probability (P) values are given in Table 3. It could be seen that the b0, b1, b3, the quadratic coefficients (b22 and b33) and the interactive coefficients (b12, b13, b14 and b34) all exhibit significant influences. Among these parameters, the linear effect of substrate concentration (b1) and temperature (b3), the quadratic effects (b22 and b33) and the interactive coefficients (b12, b13, b14 and b34) are the most influential ones. In addition, the values of these coefficients (b1, b3, b22, b33, b12, b13, b14 and b34) were larger than zero, indicating a positive effect of these parameters on the SMP formation. Statistical testing of the model was performed with the F-test for analysis of variance (ANOVA) and the results are shown in Table 4. The quadratic regression indicates a high significance of the model, because the value of F-statistic
Term
Value
Standard deviation
T
P
b0 b1 b2 b3 b4 b11 b22 b33 b44 b12 b13 b14 b23 b24 b34
7.7419 1.0526 0.3022 1.5997 0.0743 0.3117 1.4187 1.6848 0.3722 0.9955 1.6032 1.1307 0.5688 0.4572 1.0310
0.5384 0.2908 0.2908 0.2908 0.2908 0.2664 0.2664 0.2664 0.2664 0.3561 0.3561 0.3561 0.3561 0.3561 0.3561
14.378 3.620 1.039 5.501 0.255 1.170 5.326 6.324 1.397 2.795 4.502 3.175 1.597 1.284 2.895
0.000a 0.002a 0.314 0.000a 0.802 0.259 0.000a 0.000a 0.181 0.013a 0.000a 0.006a 0.130 0.218 0.011a
T value was obtained from the t-test, which indicates the significance of the regression coefficients. a Means highly significant.
(the ratio of the mean square attributed to regression to mean square to the real error) of 11.23 was much greater than the tabular F0.001,14,16(3.45). The high value of the correlation coefficient (R2 ¼ 0.908) suggests a good agreement between the measured and predicted values of SMP formation. The low P-values also confirm the adequacy of the model. Then, the optimum conditions for minimizing SMP formation were calculated by setting the partial derivatives of Eq. (2) to zero with respect to the corresponding variables. As a result, the optimal conditions in this study were obtained to be follows: substrate concentration of concentration of 23.9 mg/L, 654.8 mg COD/L, NHþ 4 -N temperature 13.7 C, aeration rate of 0.1 m3/h. In this case, the minimum response value for SMP formation was estimated to be 7.5 mg/L. As shown in Fig. 2, a random distribution was observed for the residual plots for the models and dataset on SMP formation, indicating that the residual distribution of the regression equation follows normal and independent patterns (Hwang et al., 2001). This suggests the high adequacy of the quadratic models for evaluating SMP formation by activated sludge. With SMP formation as the response, the contour plots of the quadratic model with two variables kept at their central levels and the other two varying within the experimental ranges are, respectively, shown in Fig. 3. The shapes of the
Table 4 e ANOVA analysis. Degree Sum Mean F statistic P value of freedom of square square Model Residual
14 16
319.060 32.47
Total
30
351.53
2
R ¼ 0.908. a Means highly significant.
22.790 2.0294
11.23
0a
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useful for optimizing the activated sludge systems, in terms of minimizing the effluent SMP content.
Residuals
2 1
3.4. Influential priority of the four factors for SMP formation
0
GRA method was used to evaluate the influential degrees of substrate concentration, NHþ 4 -N concentration, temperature and aeration rate on the SMP production in the batch activated sludge reactor. The grey relational grades g of these factors for the SMP formation were determined as:
-1 -2
gðSMP productionÞ ¼ ð0:626 0:584 0:646 0:605Þ
8
10
12 14 16 Observed SMP (mg/L)
18
20
Fig. 2 e Residual plots of quadratic model.
contour plots, circular, elliptical or saddle, indicate whether the interactions between the variables are significant or not. A circular contour plot shows that the interactions between the corresponding variables are negligible, whereas an elliptical or saddle pattern indicates significant interactions (Celik et al., 2004; Hwang et al., 2001). The elliptical contour plot in Fig. 3(a) suggests that the interactive effects of substrate and NHþ 4 -N concentrations on SMP production were significant. Similar counter plots were also observed in Fig. 3(b), (c) and (f). The corresponding two-dimensional contours show a considerable curvature, implying that these three factors were interdependent. In other words, there were significant interactive effects on SMP formation between substrate concentration and temperature, substrate concentration and aeration rate, as well as temperature and aeration rate. The obvious trough in the plot in Fig. 3(d) indicates that the optimal conditions are exactly located inside the design boundary. SMP formation decreases with the NHþ 4 -N concentration or temperature to its optimum conditions, and then increases with a further increase in NHþ 4 -N concentration or temperature. However, the two-dimensional contour shows a rounded ridge running diagonally on plot, implying that NHþ 4 -N concentration and temperature are slightly interdependent, and that their interactive effects on SMP formation are not significant. Similarly, the contour lines in Fig. 3(e) present a continuous rounded shape elongated and diagonal, implying that the interaction of NHþ 4 -N concentration and aeration rate is not significant.
3.3.
Confirmation experiments and model validation
To confirm the validity of the statistical experimental strategies and offer a better understanding on the SMP formation in activated sludge reactors, two confirmation experiments based on the optimized conditions above were conducted. The experimental conditions and results are listed in Table 5. The measured value of SMP concentration was found to be very close to those estimated using the RSM model. This demonstrates that RSM with a central composite design analysis is
(11)
Thus, these factors all have significant effects on the SMP production, and their influential priorities on the SMP formation are: temperature > substrate concentration > aeration rate > NHþ 4 -N concentration. Temperature shows the most significant effect on the SMP formation, followed by the substrate concentration. Aeration rate and concentration exhibit similarly less-significant NHþ 4 -N effects. Rittmann and McCarty (2001) found that a severe limitation of nitrogen and phosphorus could lead to the release of a large flow of organic molecules to the environment. The results in this work also demonstrated that the NHþ 4 -N concentration could affect the SMP formation. However, its influence was less significant than the other factors. Nagaoka et al. (1996) observed that a high aeration rate might stimulate the secretion of SMP by activated sludge due to the enhanced endogenous metabolism. But our study showed that its effect on the SMP formation was also less significant than temperature and substrate concentration. Temperature and substrate concentration were found to be the two crucial factors influencing the SMP formation in this study. Among them, temperature is usually uncontrollable for real wastewater treatment plants. Results show that there existed an optimum range of the organic load for the minimized SMP production, indicating that carbon source control is essential for the SMP formation by activated sludge. For instance, a step-feeding operating mode might be able to reduce the production of SMP and thus to improve the effluent quality. However, it warrants a further investigation. Furthermore, the SMP formation is a complicated process and is influenced by many factors. To study the influences and priorities of other factors on SMP production, e.g., phosphorus concentration, trace nutrients, substrate types and toxicants, the integrated RSM and GRA approach proposed in this study could also be used after additional experiments are conducted. Their interactive influences on the SMP production activated sludge warrant further investigations.
4.
Conclusions
In the present work, RSM coupled with GRA was used to evaluate both the individual and interactive effects of substrate concentration, NHþ 4 -N concentration, temperature and aeration rate on SMP formation in batch activated sludge
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Fig. 3 e Two-dimensional contour plots for SMP: effect of (a) substrate concentration and NHD 4 -N concentration; (b) substrate concentration and temperature; (c) substrate concentration and aeration rate; (d) NHD 4 -N concentration and temperature; (e) NHD 4 -N concentration and aeration rate; and (f) temperature and aeration rate.
reactors. The results showed that the formation of SMP could be minimized under the following conditions: substrate concentration of 654.8 mg COD/L, NHþ 4 -N concentration of 23.9 mg/L, temperature 13.7 C, aeration rate of 0.1 m3/h. GRA
analysis results showed that the influential priorities of the four factors on the SMP formation by activated sludge were: temperature > substrate concentration > aeration rate > NHþ 4N concentration.
Table 5 e Results of confirmation experiments. Trial
1 2
Substrate (mg COD/L)
654.8 654.8
NHþ 4 -N (mg/L)
23.9 23.9
Temp ( C)
13.7 13.7
Aeration rate (m3/h)
0.1 0.1
SMP concentration (mg/L) Measured
Calculated
7.5 7.8
7.5 7.5
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Acknowledgements The authors wish to thank the Natural Science Foundation of China (50625825, 50708106, 50738006 and 50978243) for the partial support of this study.
references
Aquino, S.F., Stuckey, D.C., 2004. Soluble microbial products formation in anaerobic chemostats in the presence of toxic compounds. Water Res. 38, 255e266. Barbosa, M.J., Rocha, J.M.S., Tramper, J., Wijffels, H., 2001. Acetate as a carbon source for hydrogen production by photosynthetic bacteria. J. Biotechnol. 85, 25e33. Barker, D.J., Salvi, S.M.L., Langenhoff, A.A.M., Stuckey, D.C., 2000. Soluble microbial products in ABR treating low-strength wastewater. J. Environ. Eng. 126, 239e249. Barker, D.J., Stuckey, D.C., 1999. A review of soluble microbial products (SMP) in wastewater treatment systems. Water Res. 33, 3063e3082. Celik, D., Bayraktar, E., Mehmetoglu, U., 2004. Biotransformation of 2-phenylethanol to phenylacetaldehyde in a two-phase fedbatch system. Biochem. Eng. J. 17, 5e13. Chen, M.Y., Syu, M.J., 2003. Film analysis of activated sludge microbial discs by the Taguchi method and grey relational analysis. Bioprocess Biosyst. Eng. 26, 83e92. Chou, J.R., Tsai, H.C., 2009. On-line learning performance and computer anxiety measure for unemployed adult novices using a grey relation entropy method. Inform. Process. Manage 45, 200e215. Cruz, O.S., Castaneda, G.S., Hach, J.L.P., Rojas, M.G., Torres, E.F., 1999. Effect of substrate composition on the mycelial growth of Pleurotus ostreatus, an analysis by mixture and response surface methodologies. Process Biochem. 35, 127e133. Deng, J.L., 1989. Introduction to Grey system theory. J. Grey Syst. 1, 1e24. Eroglu, I., Aslan, K., Gunduz, U., Yucel, M., Turker, L., 1999. Substrate consumption rates for hydrogen production by Rhodobacter sphaeroides in a column photobioreactor. J. Biotechnol. 70, 103e113. Fernando, M.S., Allen, D.G., 2003. Effects of temperature transient conditions on aerobic biological treatment of wastewater. Water Res. 37, 3590e3601.
Frolund, B., Palmgren, R., Keiding, K., Nielsen, P.H., 1996. Extraction of extracellular polymers from activated sludge using a cation exchange resin. Water Res. 30, 1749e1758. Huang, G.T., Jin, G., Wu, J.H., Liu, Y.D., 2008. Effects of glucose and phenol on soluble microbial products (SMP) in sequencing batch reactor systems. Int. Biodeterior. Biodegrad. 62, 104e108. Hwang, S.K., Lee, Y.S., Yang, K.Y., 2001. Maximization of acetic acid production in partial acidogenesis of swine wastewater. Biotechnol. Bioeng. 75, 521e529. Krasner, S.W., Westerhoff, P., Chen, B., Rittmann, B.E., Nam, S., Amy, G., 2009. Impact of wastewater treatment processes on organic carbon, organic nitrogen, and DBP precursors in effluent organic matter. Environ. Sci. Technol. 43, 2911e2918. Moran, J., Granada, E., Miguez, J.L., Porteiro, J., 2006. Use of grey relational analysis to assess and optimize small biomass boilers. Fuel Process. Technol. 87, 123e127. Nagaoka, H., Ueda, S., Miya, A., 1996. Influence of bacterial extracellular polymers on the membrane separation activated sludge process. Water Sci. Technol. 34, 165e172. Noguera, D.R., Araki, N., Rittmann, B.E., 1994. Soluble microbial products (SMP) in anaerobic chemostats. Biotechnol. Bioeng. 44, 1040e1047. Noorul, H.A., Marimuthu, P., Jeyapaul, R., 2008. Response optimization of machining parameters of drilling Al/SiC metal matrix composite using grey relational analysis in the Taguchi method. Int. J. Adv. Manuf. Technol. 37, 250e255. Oh, S., Rheem, S., Sim, J., Kim, S., Baek, Y., 1995. Optimizing conditions for the growth of Lactobacillus casei YIT 9018 in tryptone-yeast extract-glucose medium by using response surface methodology. Appl. Environ. Microbiol. 61, 562e568. Pribyl, M., Tucek, F., Wilderer, P.A., Wanner, J., 1997. Amount and nature of soluble refractory organics produced by activated sludge microorganisms in sequencing batch and continuous flow reactors. Water Sci. Technol. 35, 27e34. Rittmann, B.E., McCarty, P.L., 2001. Environmental Biotechnology: Principles and Applications. McGraw-Hill Int. Editions, London. Schiener, P., Nachaiyasit, S., Stuckey, D.C., 1998. Production of soluble microbial products (SMP) in an anaerobic baffled reactor: composition, biodegradability and the effect of process parameters. Environ. Technol. 19, 391e400. Yang, K., Yu, Y., Hwang, S., 2003. Selective optimization in thermophilic acidogenesis of cheese-whey wastewater to acetic and butyric acids: partial acidification and methanation. Water Res. 37, 2467e2477.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 8 1 e6 9 3
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Release of antibiotic resistant bacteria and genes in the effluent and biosolids of five wastewater utilities in Michigan Mariya Munir, Kelvin Wong, Irene Xagoraraki* Department of Civil and Environmental Engineering, Michigan State University, A124 Engineering Research Complex, East Lansing, MI 48824, United States
article info
abstract
Article history:
The purpose of this study was to quantify the occurrence and release of antibiotic resistant
Received 9 April 2010
genes (ARGs) and antibiotic resistant bacteria (ARB) into the environment through the
Received in revised form
effluent and biosolids of different wastewater treatment utilities including an MBR
3 August 2010
(Membrane Biological Reactor) utility, conventional utilities (Activated Sludge, Oxidative
Accepted 19 August 2010
Ditch and Rotatory Biological Contactors-RBCs) and multiple sludge treatment processes
Available online 27 August 2010
(Dewatering, Gravity Thickening, Anaerobic Digestion and Lime Stabilization). Samples of raw wastewater, pre- and post-disinfected effluents, and biosolids were monitored for
Keywords:
tetracycline resistant genes (tetW and tetO) and sulfonamide resistant gene (Sul-I) and
Antibiotic resistant genes
tetracycline and sulfonamide resistant bacteria. ARGs and ARB concentrations in the final
Antibiotic resistant bacteria
effluent were found to be in the range of ND(non-detectable)-2.33 106 copies/100 mL and
Tetracycline
5.00 102e6.10 105 CFU/100 mL respectively. Concentrations of ARGs (tetW and tetO) and
Sulfonamide
16s rRNA gene in the MBR effluent were observed to be 1e3 log less, compared to
Wastewater treatment
conventional treatment utilities. Significantly higher removals of ARGs and ARB were
Biosolids
observed in the MBR facility (range of removal: 2.57e7.06 logs) compared to that in
Effluent
conventional treatment plants (range of removal: 2.37e4.56 logs) ( p < 0.05). Disinfection (Chlorination and UV) processes did not contribute in significant reduction of ARGs and ARB ( p > 0.05). In biosolids, ARGs and ARB concentrations were found to be in the range of 5.61 106e4.32 109 copies/g and 3.17 104e1.85 109 CFU/g, respectively. Significant differences ( p < 0.05) were observed in concentrations of ARGs (except tetW ) and ARB between the advanced biosolid treatment methods (i.e., anaerobic digestion and lime stabilization) and the conventional dewatering and gravity thickening methods. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
The escalating problem of emergence of antibiotic resistant bacteria and their resistant genes is becoming a major global health issue (Levy, 2002; Chee-Sanford et al., 2001). The use of numerous antimicrobial agents as treatments in animal, human, and plant health maintenance, is a worldwide practice providing both desirable and undesirable consequences. Links have been found to exist between antibiotic use and the
emergence of antibiotic resistant bacterial pathogens (Aminov et al., 2001; Levy, 2002; Peak et al., 2007; Se´veno et al., 2002). Studies have proven increase in antibiotic resistance strains that belong to pathogenic bacteria (Blasco et al., 2008) and over the years, nearly every bacterial pathogen has developed resistance to one or more clinical antibiotics (Todar, 2008). The general observation published in different studies is that the environmental compartments which are most directly impacted by human or agricultural activities showed higher
* Corresponding author. Tel.: þ1 517 353 8539; fax: þ1 517 355 0250. E-mail address:
[email protected] (I. Xagoraraki). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.08.033
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Table 1a e Wastewater treatment characteristics. East Lansing
Imlay
Romeo
Traverse city
Lansing
Wastewater treatment process (Biological treatment)
Activated Sludge (AS)
Oxidation Ditch (OD)
Membrane Biological Reactor (MBR)
Activated Sludge (AS)
Capacity Average flow Discharge Rate Disinfection
18.8 MGD 13.4 MGD 14.1 MGD Chlorine (Cl)
0.9 MGD 0.4 MGD 0.02 MGD Ultra-Violet (UV)
Rotating Biological Contactors (RBCs) 2.1 MGD 0.8 MGD 0.8 MGD Chlorine (Cl)
17.0 MGD 8.5 MGD 4.0 MGD Ultra-Violet (UV)
37.0 MGD 20.0 MGD 19.0 MGD Ultra-Violet (UV)
MGD-Millions gallon per day.
concentrations of antibiotic resistant bacteria and antibiotic resistant genes (Pruden et al., 2006; Chee-Sanford et al., 2001). Large amounts of antibiotics are released into municipal wastewater due to incomplete metabolism in humans or due to disposal of unused antibiotics (Nagulapally et al., 2009), which finally find their ways into different natural environmental compartments. Antibiotic resistant genes and antibiotic resistant bacteria have been detected in wastewater samples (Zhang et al., 2009a,b; Auerbach et al., 2007; Brooks et al., 2007; Pruden et al., 2006; Reinthaler et al., 2003). Also, the release of antibiotic resistant organisms through wastewater effluents into streams has been previously reported (Gallert et al., 2005; Iwane et al., 2001). Iwane and their colleagues reported approximately 8% and 6.7% of tetracycline resistant bacteria to be found in the pre- and post-chlorinated samples of a wastewater treatment plant respectively and then close to discharge location in the river water, similar percentages of bacteria were found to be resistant to tetracycline (Iwane et al., 2001). In addition, biosolids samples were reported to contain a high concentration of antibiotic resistant bacteria (Brooks et al., 2007). Also, the role of wastewater treatment plants in reducing the load of antibiotic resistant bacteria present in raw sewage is not well known (Rijal et al., 2009). However, it has been suggested that certain conditions within the wastewater treatment plants might increase the number of antibiotic resistant bacteria during the treatment process (Silva et al., 2006; Reinthaler et al., 2003). To the best of our knowledge, comparisons between different wastewater and biosolids treatment processes have not been studied so far. The objective of this study was to quantify the release of antibiotic resistant genes (ARGs) and antibiotic resistant bacteria (ARB) in the effluent and biosolids of wastewater treatment plants (WWTPs). This is the first study that surveys the release of ARGs and ARB into the environment through the
effluent and biosolids of different wastewater treatment utilities including an MBR (Membrane Biological Reactor), conventional wastewater utilities and multiple sludge treatment processes. This study has attempted to provide comparisons between different wastewater treatment processes and biosolid treatment processes along with the comparison of release loads of ARGs and ARB in the environment through the effluent and biosolids. In this study, samples of raw wastewater, effluent and biosolids were monitored for tetracycline and sulfonamide resistant bacteria, tetracycline resistant genes (tetW and tetO) and sulfonamide resistant gene (SulI) using quantitative polymerase chain reaction (qPCR) assays and conventional heterotrophic plate count methods. Tetracycline and sulfonamide resistance genes (tetW, tetO and SulI) were chosen in this study because tetracycline and sulfonamide are the most commonly used antibiotics in human and veterinary medicine (Boxall et al., 2003; Chopra and Roberts, 2001). In addition, quantitative detection systems already exist for this class of genes (Pei et al., 2006; Aminov et al., 2001). TetW and tetO genes are common in intestinal and rumen environments (Aminov et al., 2001) and have been cited as being promiscuous in their ability to spread among and across populations (Pei et al., 2006; Smith et al., 2004; Billington et al., 2002). SulI gene is also one of the most commonly detected sulfonamide resistant genes in the environment (Pei et al., 2006).
2.
Materials and methods
2.1.
Sample collection
Samples of raw wastewater, effluent prior to disinfection, and final effluent after disinfection were collected from five
Table 1b e Biosolid treatment characteristics.
Sludge treatment
Disposal of sludge Disposal rate (dry tons per year) % solid
East Lansing
Imlay
Romeo
Traverse city
Lansing
Dewatering (No Anaerobic Digestion) Landfill 3596 18.05%
Gravity Thickening (No Anaerobic Digestion) Agricultural land 118 1.49%
Anaerobic Digestion
Anaerobic Digestion
Lime Stabilization
Agricultural land 125 7.98%
Agricultural land 850 4.85%
Agricultural land 4380 9.20%
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Table 2 e Primers and probes used in the study. Target
Sequences (50 e30 )
Primers
Annealing temperature ( C) Amplicon Size (bp) PCR
Tet-W
tet(W)-FV tet(W)-RV Tet-O tet(O)-FW tet(O)-RV Sul-I sul(I)-FW sul(I)-RW Bacteria16s rRNA BACT1369F PROK1492R TM1389F (Probe)
GAGAGCCTGCTATATGCCAGC GGGCGTATCCACAATGTTAAC ACGGARAGTTTATTGTATACC TGGCGTATCTATAATGTTGAC CGCACCGGAAACATCGCTGCAC TGAAGTTCCGCCGCAAGGCTCG CGGTGAATACGTTCYCGG GGWTACCTTGTTACGACTT CTTGTACACACCGCCCGTC
different WWTPs located in Michigan (U.S.A.). Biosolid samples were also collected from the same treatment plants. Characteristics of the different WWTPs based on wastewater treatment processes, disinfection methods and sludge treatment methods are given in Tables 1a and 1b. Two or three sampling events were conducted from each of these treatment plants starting from December 2008 till October 2009. Samples were kept in ice and were transported to the Water Quality Laboratory at Michigan State University (East Lansing, U.S.A.) for immediate processing.
2.2.
Sample processing
Bacteria in the effluent samples were concentrated by filtration with 0.45 mm HA filters (Millipore, Billerica, MA). The volume of effluent samples filtered was 1 L. The filters were collected in a 50 mL tubes and 50 mL Phosphate Buffer Water (PBW) was added in each tube containing a filter. The tubes were then vortexed for 5 min to allow the biomass layer on the filters to mix with water. For influent raw samples, 50 mL sample volumes were directly collected into the tubes. All the tubes were then centrifuged for 20 min at 4500rpm to
References
QPCR
64
60
168
Aminov et al., 2001
60
50
171
Aminov et al., 2001
55.9
55
163
Pei et al., 2006
56
55
143
Suzuki et al., 2001
concentrate the sample down to 2 mL. Supernatant was discarded and the concentrates were stored at 80 C until DNA extraction was performed for molecular analysis. Biosolid samples were directly stored at 80 C. The volume of all the samples initially collected for processing was taken into account when calculating the final concentrations.
2.3.
DNA extraction
DNA was extracted from the concentrated samples using MagNA pure Compact DNA extraction machine (Roche) following the protocol in the manufacturer’s manual. Before DNA extraction, a lysis step was carried out with the samples using Lysis Buffer and Proteinase K solution and the mixture was then placed in the heating block at 65 C for 30 min. The lysed samples were used for DNA extraction and the extracts were stored in a freezer at 20 C.
2.4.
Quantification
Real-time Polymerase Chain reaction was used for quantification of two tetracycline ARGs (tetW and tetO) and one
Table 3a e : Reported concentrations of antibiotic resistant genes in different samples of WWTPs detected by Quantitative PCR Method. Type of WWTP
Type of Sludge treatment
AS þ UV, Cl
AnD,GrT
AS þ Cl
AnD
AS, OD, RBCs, MBR þ UV, Cl
DeW, GrT, AnD, LS
Antibiotic resistant genes detected
Raw influent (copies/mL)
Tet-Qb Tet-Gb Tet C Tet A Tet-W Tet-O Sul-I
107.2e109 106.4e107.8 108.13e108.3 107.78e108.2 105.37e107.4 105.51e107.61 105.46e107.54
PrePostdisinfected disinfected effluent effluent (copies/mL) (copies/mL) e e 105.36e105.57 104.38e104.81 100.37e104.03 NDe103.96 102.98e104.78
103.8e106.2 104.2e105.9 NDe104.12 NDe104.33 NDe103.63 NDe103.96 104.37e106.75
Biosolids (copies/g)
References
108.4e109a 108.5e109.2a 108.49e108.97 108.09ae109.11 105.37e107.4 106.8e109.24 106.75e109.4
Auerbach et al., 2007 Zhang et al., 2009a This Study
Note: ND ¼ non-detectable; Tet ¼ tetracycline-resistant gene, Sul ¼ Sulfonamide-resistant gene. Wastewater treatment type: AS ¼ Activated Sludge process; OD ¼ Oxidative ditch; RBCs ¼ Rotatory Biological Contactors; MBR ¼ Membrane Biological Reactors. Disinfection type: UV ¼ Ultraviolet radiation disinfection; Cl¼Chlorination disinfection. Biosolid treatment: DeW ¼ Dewatering; GrT ¼ Gravity Thickening; AnD ¼ Anaerobic Digestion; LS ¼ Lime Stabilization. a Units are expressed as copies/mL. b data approximated from the published graphs.
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Table 3b e Reported concentrations of antibiotic resistant bacteria in different samples detected by Plating (HPC) Method. Type of WWTP
Type of Sludge treatment
AS þ Cl
DeW
e
AnD
AS, OD, RBCs, MBR þ UV, Cl
Antibiotic targeted
24 different antibiotics
Ampicillin, cephalothin, ciprofloxacin, tetracycline DeW, GrT, AnD, LS Tetracycline-resistant Sulfonamide-resistant
Raw influent PrePost(CFU/mL) disinfected disinfected effluent effluent (CFU/mL) (CFU/mL)
Biosolids (CFU/g)
103.9e105.45
e
100.78e103.15
e
e
e
e
105.83e1010.95a
104.18e105.36 105.23e107.08
101.18e102.73 102.18e104.03
100.7e102.48 102.02e103.79
104.5e109.07 106.09e109.27
References
Reinthaler et al., 2003 Brooks et al., 2007 This Study
a ¼ data approximated from the published graphs. Wastewater treatment type: AS ¼ Activated Sludge process; OD ¼ Oxidative ditch; RBCs ¼ Rotatory Biological Contactors; MBR ¼ Membrane Biological Reactors. Disinfection type: UV ¼ Ultraviolet radiation disinfection; Cl¼Chlorination disinfection. Biosolid treatment: DeW ¼ Dewatering; GrT ¼ Gravity Thickening; AnD ¼ Anaerobic Digestion; LS ¼ Lime Stabilization.
sulfonamide ARGs (SulI) using the SYBR Green approach. The primers and the probes along with the annealing temperatures used for the tetracycline and sulfonamide resistant genes were previously developed (Aminov et al., 2001; Pei et al., 2006). The Eubacterial 16s rRNA genes were quantified according to the protocol described by Suzuki et al. (2001) using a TaqMan QPCR method. All QPCR analyses were performed using a Roche Light Cycler 1.5.
QPCR reactions were performed with a temperature program of 15 min at 95 C (initial denaturing), followed by 50 cycles of 15 s at 95 C; 30 s at the annealing temperature (given in Table 2) followed by a melting curve stage with temperature ramping from 60 to 95 C and a final cooling for 30 s at 40 C. The primer sequences used for quantification of antibiotic resistant genes and 16s rRNA genes are summarised in Table 2.
Fig. 1 e Log concentration (copies/100 mL) of tetracycline resistant genes (tetW, tetO), sulfonamide resistant gene (SulI) and 16s rRNA gene abundance at different sampling points of all the five wastewater utilities. Note: n [ no. of samples, X-axis labels indicate different sampling points, Rectangular boxes indicate the interquartile range of the data, Median value is indicated by the horizontal line inside the box, Small circles‘q’ represent the mean values.
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Fig. 2 e Log concentration (number of CFU/100 mL) of tetracycline resistant bacteria, sulfonamide resistant bacteria and total heterotrophic plate count at different sampling points of all the five wastewater utilities. Note: n [ no. of samples, X-axis labels indicate different sampling points, Rectangular boxes indicate the interquartile range of the data, Median value is indicated by the horizontal line inside the box, Small circles‘q’ represent the mean values.
2.5.
Standard curves
Positive controls were used to construct the standards by transforming gene bearing plasmids into the Escherichia coli using TOPO Cloning kit (Invitrogen). Biosolids sample were taken from a wastewater treatment plant (East Lansing, MI) at different times and were analysed for the presence of antibiotic resistant genes by PCR and Gel electrophoresis. PCR reaction was performed with initial denaturation at 94 C for 5 min, followed by 25 cycles of 94 C for 30 s, annealing for 30 s at the annealing temperature (Table 2), extension at 72 C for 30 s and a final extension step at 72 C for 7 min. Fresh PCR product from the samples with confirmed presence of the target gene was mixed with the cloning solution containing the vector. This mixture was then transformed into the competent E. coli cells followed by growth of these cells on media. Culture suspension was prepared using the transformed colonies, screened by PCR again to verify cloning of the target gene. Plasmid was extracted according to the QlAprep Spin Miniprep Kit (QIAGEN). The concentration of the purified plasmid DNA was determined using NanoDrop spectrophotometer (NanoDrop ND-1000, Wilmington, DE). Standards with different range of concentrations were prepared by serial dilutions of purified plasmid extracts. Absolute quantification
was done using QPCR. The CT value (threshold cycle) in the quantification graphs for each respective concentration was used to finally generate the standard curve.
2.6.
Culture method
The conventional approach of heterotrophic plate count (HPC) method was used to evaluate the concentration of antibiotic resistant bacteria in the samples. The analysis was done within 24e48 h of sample collection. The concentration of resistant microorganisms was determined by plating samples on media amended with two different antibiotics: (1) tetracycline, 16 mg/mL (Sigma Aldrich) and (2) sulfonamide, 50.4 mg/ mL (sulfamethoxazole, Sigma Aldrich). R2A plating media (Difco Laboratories, Franklin Lakes, NJ) were used and each antibiotic was individually amended into the media along with antifungal additive cyclohexamide, 200 mg/mL (Sigma Aldrich). The samples were serially diluted and 0.1 mL of the dilution was used for spread plating. Plates were incubated for 2 days at 37 C and then for a period of 5 days at 27 C (Brooks et al., 2007). Total hetrotrophic culturable bacterial population was determined by plating samples on media without antibiotics.
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Fig. 3 e Log removals of tetracycline resistant gene (tetW, tetO), sulfonamide resistant gene (SulI) and 16s rRNA gene abundance from wastewater sample of different wastewater utilities. Abbreviations: OX [ Oxidative ditch; RBCs [ Rotatory Biological Contactors; AS [ Activated Sludge process; MBR [ Membrane Biological Reactors; Cl [ Chlorination disinfection; UV [ Ultraviolet radiation disinfection; Note: n [ no. of sampling events, Error bars indicate standard deviation around mean values.
2.7.
Statistical analysis
RLbiosolid ¼ Cbiosolid Qbiosolid
Student t-test was used to conduct the statistical analysis of the results (i.e., for comparison of concentration means). The null hypothesis which is the concentration of ARGs (or ARB) was not different between different samples was rejected at a p-value less than or equal to 0.05.
2.8.
Estimation of overall release
Estimation of the ARGs and ARB released into the environment was conducted based on the discharge through the effluent and biosolids of all the WWTPs and the concentrations measured in this study. Information about average daily discharge rates of the effluent and the biosolids produced was obtained from the managers of all the five WWTPs (personal communication). To compare the daily release loads of ARGs (or ARB) from effluent and biosolids, number of copies (or CFU) were calculated using equations (1), (2) and (3), respectively. Release loads from individual WWTPs were calculated and averaged. Contribution of effluent and biosolids in the release of ARGs (or ARB) was then calculated using equations (4) and (5) respectively. IL ¼ Cin Qin
(1)
RLeff ¼ Ceff Qeff
(2)
FARG ðeffÞ or FARB ðeffÞ ¼
(3)
RLeff ðcopies=dayÞ IL ðcopies=dayÞ
FARG ðbiosolidÞ or FARB ðbiosolidÞ ¼
RLbiosolid ðcopies=dayÞ IL ðcopies=dayÞ
(4)
(5)
where, IL ¼ Number of copies (or CFU) per day in the influent, RLeff, RLbiosolid ¼ Release load (copies or CFU) released per day through effluent and biosolids respectively, Cin, Ceff, Cbiosolid ¼ Concentration of ARGs or ARB in influent, effluent and biosolids respectively, Qin ¼ Inflow rate, Qeff, Qbiosolid ¼ Outflow rate of effluent and biosolids respectively, FARG (eff) or FARB (eff) ¼ Fraction of contribution of ARGs or ARB through effluent, FARG (biosolid) or FARB (biosolid) ¼ Fraction of contribution of ARGs or ARB through biosolids.
3.
Results
3.1. Overall concentrations of ARGs and ARB in wastewater treatment plants Concentrations of ARGs and ARB found in this study are presented in Tables 3a and 3b respectively. Variations among
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Fig. 4 e Log removals of tetracycline resistant bacteria, sulfonamide resistant bacteria and total heterotrophic plate count from wastewater sample of different wastewater utilities. Abbreviations: OX [ Oxidative ditch; RBCs [ Rotatory Biological Contactors; AS [ Activated Sludge process; MBR [ Membrane Biological Reactors; Cl [ Chlorination disinfection; UV [ Ultraviolet radiation disinfection; Note: n [ no. of sampling events, Error bars indicate standard deviation around mean values.
different WWTPs in the raw influent concentration for different genes are expected because of different locations and related human activities. Also wastewater treatment plants receive inflow from a wide variety of sources beyond human population including industrial, hospital and animal waste. Overall, the trends observed in concentration ranges at different sampling points from all the wastewater treatment plants are: raw influent > pre-disinfected effluent > post-disinfected effluent (Figs. 1 and 2). The concentration ranges of raw influent and biosolids had no significant difference ( p > 0.05) for both tetO and SulI genes (Fig. 1b and c). However, higher concentration of tetW genes were observed in biosolids (Fig. 1a) compared to concentrations in raw samples ( p < 0.05). Significantly higher ( p < 0.05) concentration of tetracycline
resistant bacteria were observed in biosolids as compared to raw samples (Fig. 2a). However, sulfonamide resistant bacteria show no significant difference ( p > 0.05) between biosolids and raw (Fig. 2b).
3.2.
ARGs and ARB in effluent
Concentration of ARGs (tetW and tetO) and 16s rRNA gene in the effluent from a MBR (Membrane Biological Reactor) utility were 1e3 log less compared to conventional treatment utilities, but no significant differences ( p > 0.05) could be drawn using t-test analysis due to smaller sampling events at the MBR facility. Similarly, no significant difference ( p > 0.05) was observed for ARB among different utilities.
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Fig. 5 e Relative concentrations (copies/100 mL) of tetracycline resistant gene (tetW, tetO), sulfonamide resistant gene (SulI) normalized with 16s rRNA gene abundance at different sampling points of all wastewater utilities. Note: X-axis labels indicate sampling points, Rectangular boxes indicate the interquartile range of the data, Median value is indicated by the horizontal line inside the box, Small circles represent the mean values.
3.3.
ARGs and ARB removals
Log removal values were calculated based on concentrations of ARGs and ARB in the raw influent samples and the final effluent samples and are shown in Figs. 5 and 6, respectively. Among different WWTPs, the highest removals of tetW, tetO and 16s rRNA genes were observed in the Traverse City WWTP which is a MBR facility with a UV disinfection process (Fig. 3 a, b and d). The highest removals of SulI genes were observed in activated sludge wastewater utilities (Lansing and East Lansing) (Fig. 3c). Significant difference ( p < 0.05) was observed in the log removals between conventional methods and MBR for tetW, tetO and 16s rRNA genes. Findings in this study show that the MBR facility provided the highest removal efficiency for most of the ARGs from the wastewater stream. For tetracycline resistant bacteria, the highest removal was detected by activated sludge process (Fig. 4a) whereas for sulfonamide resistant bacteria, highest removal was observed in the MBR utility (Fig. 4b). However, there was no significant difference observed in log removals for antibiotic resistant bacteria ( p > 0.05) between conventional methods and MBR. Overall disinfection did not prove to have significant contribution to ARGs and ARB reduction (Figs. 3 and 4). Very little change in concentrations of ARGs and ARB was observed between preand post-disinfected effluents from all treatment plants. Also, the statistical t-test between concentrations of ARGs in pre- and postdisinfected effluent does not show a significant difference between UV and chlorination disinfection process ( p > 0.05).
Normalization of the concentration of ARGs with that of total 16s rRNA genes, showed a reduction in ratio from the raw to the effluent samples for both the tetW and tetO genes, suggesting that there is a better reduction in concentrations of tetracycline resistant genes compared to that of total 16s rRNA genes during the wastewater treatment process (Fig. 5). However, for SulI genes, the ratio with 16s rRNA genes remained the same throughout the treatment process. Also the concentrations of ARB normalized with the total hetrotrophic culturable bacterial count showed approximately same ratios throughout the treatment (Fig. 6).
3.4.
ARGs and ARB in biosolids
High concentrations of ARGs and ARB have been found in the biosolid samples. Significant difference ( p < 0.05) was observed in concentrations of both tetO and SulI genes in biosolids samples between the advanced treatment methods (anaerobic digestion and lime stabilization) and the conventional treatment methods (dewatering and gravity thickening) (Fig. 7). For tetW gene, the concentration was found to be lowest in the lime-stabilized biosolid samples (1.75 107e1.85 108 copies/g) but there was no significant difference ( p > 0.05) observed between the advanced and conventional treatment methods. Also there was no significant difference ( p > 0.05) observed for 16s rRNA genes between different advanced and traditional treatment processes.
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Fig. 6 e Relative concentrations (number of CFU/100mL) of tetracycline resistant bacteria and sulfonamide resistant bacteria normalized with total heterotrophic plate count at different sampling points of all wastewater utilities. Note: X-axis labels indicate sampling points, Rectangular boxes indicate the interquartile range of the data, Median value is indicated by the horizontal line inside the box, Small circles represent the mean values.
Both ARB and hetrotrophic culturable bacterial concentrations in biosolids were also observed to be significantly ( p < 0.05) different between the advanced and the conventional sludge treatment methods (Fig. 8). Overall, results of this study showed that the advanced sludge treatment methods provide better reduction of ARGs and ARB.
effluent (Fig. 9). Assuming steady flow for all the treatment plants, FARG (eff) (1.37 106e9.29 104) and FARB (eff) (6.38 106e2.27 103) were much lower than FARG (biosolid), 10þ1) and FARB (2.09 103e1.15 (biosolid), 3 þ1 (3.81 10 e6.38 10 ), which indicates the majority of ARGs and ARB coming into the WWTP would eventually present in the sludge rather than effluent.
3.5. Comparison of ARGs and ARB release in effluent and biosolids
4. Release loads of biosolids were observed to be significantly higher than the effluent loads for all the ARGs and ARB analysed ( p < 0.05) showing biosolids to have higher contribution in the release of the ARGs and ARB in the environment relative to
Discussion
This study documents the occurrence of ARGs and ARB at different points in multiple conventional WWTPs and an MBR facility in Michigan. Tables 3a and 3b illustrate reported
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Fig. 7 e Log concentration (copies/g) of tetracycline resistant gene (tetW, tetO), sulfonamide resistant gene (SulI) and 16s rRNA gene abundance in biosolid sample of different wastewater utilities by real-time PCR. Sludge treatment processes include: DeW [ Dewatering; GrT [ Gravity Thickening; AnD [ Anaerobic Digestion; LS [ Lime Stabilization. Note: n [ no. of sampling events, X-axis labels indicate type of treatment process, Rectangular boxes indicate the interquartile range of the data, Median value is indicated by the horizontal line inside the box, Small circles represent the mean values.
ranges of ARGs and ARB presented in different published studies along with a summary of concentration ranges detected in this study. We observed that even though the concentrations of ARGs and ARB in raw wastewater are significantly reduced with wastewater treatment, high concentration is discharged into the effluent. Discharge of final effluent from wastewater treatment plants, still contaminated with ARGs and ARB, is a potential route for entry of ARGs and ARB into the natural environment. It was reported in the literature that percentages of antibiotic resistance in a treated wastewater effluent were found to be mostly higher than the percentages in the river water and were observed to be increasing downstream due to discharges from a wastewater treatment plant (Iwane et al., 2001). It has been reported that the wastewater treatment process can have an influence on antibiotic resistance through selective pressures and can lead to increase in concentrations of antibiotic resistant bacteria (Zhang et al., 2009b; Silva et al., 2006; Reinthaler et al., 2003). Wastewater has been said to stimulate horizontal gene transfer among microbial species (Aminov et al., 2001; Lorenz and Wackemagel, 1994). Therefore, wastewater treatment plants could increase the antibiotic resistance of surviving bacteria, and serve as important reservoirs for the spread of antibiotic resistance to opportunistic pathogens if the treatment processes were not effective. However, in our study we observed significant reduction in the concentration of ARGs and ARB. Similar findings have also
been reported by Rijal et al. (2009) which supports the reduction of antibiotic resistant fecal coliform bacteria in a wastewater treatment facility. Differences in removals of ARGs and ARB were found in this study from different wastewater treatment utilities which might be attributed to multiple selective pressures in the environment. In our study, advanced wastewater treatment in an MBR utility was observed to provide better treatment efficiency (range of overall log removal of ARGs and ARB: 2.57e7.06) compared to other treatment techniques (range of overall log removal of ARGs and ARB: 2.37e4.56). Based on the observed low standard deviations in log removals for all the WWTPs, it is likely to observe similar log reductions if more sampling was done. Very little change was observed in concentrations of ARGs and ARB between pre- and post-disinfected effluents, therefore the disinfection process did not prove to contribute much in the ARGs and ARB reduction. This was stated by a previous study (Auerbach et al., 2007). Several studies have found that chlorination selects for ARB (Murray et al., 1984; Armstrong et al., 1982), while some other studies demonstrated that disinfection does not select ARB but instead induces the development of antibiotic resistance (Rutala et al., 1997; Murray et al., 1984). However, the mechanism involved in chlorine-induced antibiotic resistance in bacteria is still unknown (Xi et al., 2009). Additional study is needed to understand the effect of disinfection on concentration of ARGs and ARB in wastewater treatment plants.
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Fig. 8 e Log concentration (number of CFU/g) of tetracycline resistant bacteria, sulfonamide resistant bacteria and also total heterotrophic plate count in biosolid sample of different wastewater treatment utilities. Sludge treatment processes include: DeW [ Dewatering; GrT [ Gravity Thickening; AnD [ Anaerobic Digestion; LS [ Lime Stabilization. Note: n [ no. of sampling events, X-axis labels indicate type of treatment process, Rectangular boxes indicate the interquartile range of the data, Median value is indicated by the horizontal line inside the box, Small circles represent the mean values.
High concentrations of ARGs and ARB were detected in the biosolids samples which may potentially spread in the natural soil environment via agricultural land application of biosolids. The concentrations of ARB detected in our study observed to be within the range of the previously published concentration of 6.78 105e4.46 108 CFU/g in biosolids (Brooks et al., 2007) and were consistent with the range reported by other research studies (Auerbach et al., 2007; Zhang et al., 2009a). In this study, advanced biosolids treatment methods (anaerobic digestion and lime stabilization) were found to significantly reduce the ARGs and ARB concentrations in the biosolids as compared to simple dewatering and gravity thickening. It was found that the tetW and tetO gene concentrations were lower than SulI gene concentration in different samples which was similar to previous observations (Pei et al., 2006). Concentrations of bacteria (CFU/g or CFU/mL) were mostly found to be 1e2 log smaller than concentrations of their respective resistant genes (copies/g or copies/mL) in same samples because not all bacteria are cultivable. Human exposure to ARGs and ARB, which might be pathogenic in nature, could occur in number of ways. The water environment is considered to play an important part in providing a medium for the transfer of the resistant genes and resistant bacteria to the environment (Baquero et al., 2008; Iwane et al., 2001). Wastewater treatment plants hold an important place in the elimination or the spread of antibiotic
resistant microbes as the treatment systems and their operational conditions might influence the fate of resistant bacteria or resistant genes (Iwane et al., 2001). Although, treated effluents with trace amount of ARGs and ARB from the treatment plants discharged into rivers or streams can add to the contamination of the environment, comparison of release loads of ARGs and ARB calculated in this study, showed that biosolids application seems to be a major source of entry of ARGs and ARB into the natural environment from WWTPs. However, the extent of human exposure to ARGs and ARB is still not well examined. Future studies on human exposure to these resistant contaminants are needed. These may include the ability of bacterial species to survive in the soil and aquatic environment, the biological fitness of the resistance genes they carry, the opportunities to reach new hosts, and the ability of bacterial species to colonize and/or transfer resistance genes.
5.
Conclusions
Wastewater utilities seem to be a potential sources of emerging tetracycline and sulfonamide resistant genes and bacteria in our environment. All raw influent, effluent and biosolid samples analysed in this study were found to contain high concentrations of tetracycline and sulfonamide resistant
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Fig. 9 e Release of copies or CFU of ARGs (Tet-W, Tet-O, SulI) and ARB (Tet R2A and Sul R2A) respectively through Effluent and Biosolids into the environment on a daily basis from the WWTPs. Note: Error bars indicate standard deviation around mean values from all WWTPs.
genes and bacteria. The concentration levels of ARGs and ARB in raw sewage were found be much higher than their respective concentrations in treated effluent. The concentrations of these resistant microbes and genes were observed to decline several orders of magnitude in the treated effluent. No significant difference in concentrations of both ARGs and ARB was observed in pre-disinfected and post-disinfected effluents. Significant difference ( p < 0.05) was observed in the log removals for the tetW, tetO and 16s rRNA genes between conventional wastewater utilities and an MBR facility. The MBR facility provided the highest removal efficiency for most of the ARGs from the wastewater stream. Comparisons of concentrations of ARGs and ARB in biosolids and raw influent samples showed that in the case of lime stabilization, concentrations of different ARGs and ARB in biosolids samples appeared to be less, compared to that in the influent raw samples. Significant difference ( p < 0.05) was observed in concentration of ARGs (tetO and SulI), and ARB in biosolids samples between the advanced treatment methods (anaerobic digestion and lime stabilization) and the conventional dewatering and gravity thickening methods. Daily release loads of ARGs and ARB in the environment were found to be higher through biosolids relative to effluents.
Acknowledgements We would like to thank the managers of all the wastewater treatment plants for providing the samples and information needed for this study. Also, we would like to acknowledge sampling assistance and help provided by Frederick J. Simmons and Arun Kumar.
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 9 4 e7 0 4
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Biocontrol of biomass bulking caused by Haliscomenobacter hydrossis using a newly isolated lytic bacteriophage Shireen M. Kotay a, Tania Datta b, Jeongdong Choi a, Ramesh Goel a,* a b
Department of Civil and Environmental Engineering, University of Utah, 122 S. Central Campus Drive, 104 CME, Salt Lake City, UT, USA CH2M Hill, Salt Lake City, UT, USA
article info
abstract
Article history:
This research demonstrates the first ever application of lytic bacteriophage (virus) medi-
Received 11 June 2010
ated biocontrol of biomass bulking in the activated sludge process using Haliscomenobacter
Received in revised form
hydrossis as a model filamentous bacterium. Bacteriophages are viruses that specifically
15 August 2010
infect bacteria only. The lytic phage specifically infecting H. hydrossis was isolated from the
Accepted 20 August 2010
mixed liquor of a local wastewater treatment plant. The isolated bacteriophage belongs to
Available online 27 August 2010
the Myoviridae family with a contractile tail (length-126 nm; diameter-18 nm) and icosa-
Keywords:
to be 5.2 0.3 105 PFU/mL and burst size was found to be 105 7 PFU/infected cell. The
H. hydrossis
phage was considerably stable after exposure to high temperature (42 C) and pH between
Filamentous bacteria
5 and 8, emphasizing that it can withstand the seasonal/operational fluctuations under
Sludge bulking
real-time applications. Phage to host (bacteria) ratio for the optimal infection was found to
Bacteriophages
be 1:1000 with w54% host death. The isolated phage showed no cross infectivity with other
Biocontrol
bacteria most commonly found in activated sludge systems, thus validating its suitability
hedral head (diameter-81 nm). Titer of the isolated phage with H. hydrossis was calculated
for biocontrol of filamentous bulking caused by H. hydrossis. Following the phage application, successful reduction in sludge volume index (SVI) from 155 to 105 was achieved, indicating improved biomass settling. The application of phage did not affect nutrient removal efficiency of the biomass, suggesting no collateral damage. Similar to phage therapy in medical applications, phage-mediated biocontrol holds a great potentiality for large-scale applications as economic agent in the mitigation of several water, wastewater and environmental problems. Present study in this direction is a novel effort. Published by Elsevier Ltd.
1.
Introduction
Bacteriophages are viruses that infect bacteria and are known to be very important components of freshwater and marine water ecosystems. Phages have the ability to drive and control bacterial community structure (Breitbart and Rohwer, 2005). Metagenomic analyses have shown that viral communities in the environment are incredibly diverse (Weinbauer, 2004). There are an estimated 5000 viral genotypes in 200 L of * Corresponding author. Tel.: þ1 801 581 6110; fax: þ1 801 585 5477. E-mail address:
[email protected] (R. Goel). 0043-1354/$ e see front matter Published by Elsevier Ltd. doi:10.1016/j.watres.2010.08.038
seawater and possibly a million different viral genotypes in 1 kg of marine sediment (Breitbart and Rohwer, 2005). Abundance and diversity of phages have been reported in natural, marine ecosystems, oceanic ice, sediments, soil (Weinbauer, 2004; Wommack and Colwell, 2000) and also in engineered systems such as drinking water distribution systems, wastewaters and activated sludge bioreactors (Otawa et al., 2007; Weinbauer, 2004; Wu and Liu, 2009). The published information on bacteriophages in aforementioned natural and
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engineered systems provides sufficient evidence that bacteriophages play a pivotal role in all nutrient cycles and engineered bioprocesses. Activated sludge process is one of the widely used methods for municipal wastewater treatment. The performance of activated sludge process is driven by a complex community of prokaryotes. The community composition in activated sludge systems is as complex and as diverse as that in many other aquatic systems including the oceans. Activated sludge systems have been shown to contain 108e109 phages per mL (Ewert and Paynter, 1980; Otawa et al., 2007), a number comparable to or greater than the number of phages found in most of the aquatic systems. It wouldn’t be an overstatement to say that the “viruses rule the world”. However, phagemediated changes in the bacterial community in activated sludge systems and the effect of phages on the process performance have not been investigated hitherto. Albeit, activated sludge process is an established treatment technique that is being employed worldwide; frequent process upsets, biomass bulking and foaming are a few undisputed problems that still haunt these processes. Biomass bulking in activated sludge processes is caused due to the over-development of filamentous bacteria (Eikelboom, 1977; Eikelboom and van Buijsen, 1981; Ziegler et al., 1990) and is one of the main operational problems in activated sludge systems. From wastewater treatment plants several filamentous bacteria have been identified, isolated and found responsible for biomass bulking viz. Microthrix parvicella, Sphaerotilus natans, Eikelboom type 1702, Haliscomenobacter hydrossis, Nocardia, Thiothrix spp. etc. (da Motta et al., 2003; Eikelboom, 1975; Kampfer, 1995; van Veen et al., 1973; Williams and Unz, 1985). Biomass bulking causes poor settling in the secondary clarifier and allows the unsettled biomass to escape with the effluent. Engineering manipulations are conventionally employed to solve the problem of biomass bulking and these manipulations are primarily based on the past engineering observations without sufficient microbiological insight. Thus, a cause and an effect relationship between the specific microorganisms and their role in filamentous bulking is unclear. In efforts towards controlling the biomass bulking, physicoechemical methods like manipulation of flow rates of the return activated sludge, increased aeration, the addition of flocculants/coagulants and oxidants have been tried in the past (Xie et al., 2007). Surfactants and chlorine are also used to control the filamentous bulking in different attempts (Caravelli et al., 2007; Se´ka et al., 2003 and Xie et al., 2007). For various reasons most of the above mentioned attempts in mitigating filamentous bulking are not acknowledged as sustainable and/or cost-effective. For example, the addition of chlorine may trigger the formation of halogenated organics, which could pose potential threat to the receiving waters. Lou and de los Reyes (2008) proposed a conceptual framework where they attribute kinetics of substrate (nutrients) concentration and substrate diffusion rates as primary factors driving the growth of filamentous bacteria. An engineering parameter often used to distinguish between good and bad biomass settling is sludge volume index (SVI). SVI value of between 50 and 100 is considered to be good, that between 100 and 150 is filamentous growth and above 150 is bulking (Lee et al., 1983).
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It is an established concept that bacteriophages can infect single host or multiple hosts (Weinbauer, 2004; Jensen et al., 1998). Based on the host specificity of bacteriophages, phage therapy has been used in medical applications and in meat industry where the objectives have been to infect the target bacteria to cure the disease and to disinfect the meat respectively (Kropinski, 2006; Withey et al., 2005). Likewise, biomass bulking is most often caused due to the over growth of filamentous bacteria (called filaments) and therefore could be controlled using phage therapy or biocontrol using phages to reduce the filamentous bacterial populations. There are no published reports on the application of phage-based biocontrol to regulate the population of filamentous bacteria and to improve the biomass settleability. Furthermore, the information on the types of phages and their genetic diversity in activated sludge systems is scarce (Breitbart and Rohwer, 2005). Nevertheless, there is evidence that lytic phages infecting these filamentous bacteria exist in nature (Jensen et al., 1998; Kampfer, 1995; Winston and Thompson, 1979; Wommack and Colwell, 2000). Withey et al. (2005) provided an excellent review on the possibility of using bacteriophages to mitigate several problems related to the bacterial ecology in activated sludge systems. With this driving force, we approached the idea of using bacteriophage-based biocontrol of biomass bulking. H. hydrossis is a sheathed filamentous bacterium that has been detected worldwide in activated sludge samples because of its easily recognizable morphological appearance: rigid straight filament, length between 10 and 200 mm, diameter between 0.3 and 0.5 mm extending from the floc surface (thin needleshape appearance) and Gram negative staining (Eikelboom, 2006). This study is limited to H. hydrossis as the model filamentous bacterium and bulking caused by this bacterium. The objectives of this study were to; (i) isolate, purify and characterize a lytic phage specifically infecting H. hydrossis, a model filamentous bacterium, (ii) demonstrate the application of biocontrol using the isolated phage in laboratory scale set-ups and, (iii) investigate whether the addition of the newly isolated phage will have any effect on the activated sludge process performance for organic and nutrient removals. After the proof of concept of the biocontrol of the bulking caused by H. hydrossis, the application will be transformative to be applied to several other filamentous as well as to foamforming bacteria that are also found predominantly in activated sludge systems. Furthermore, since phages are host specific and are not pathogenic to higher organisms (Weinbauer, 2004; Withey et al., 2005), use of phages to biocontrol the population of filamentous bacteria and other unwanted organisms does not pose any health related threats.
2.
Materials and methods
2.1.
Bacterial strains
H. hydrossis (ATCC # 27776) was obtained from the American Type Culture Collection (ATCC) and was grown as per ATCC’s instructions in 733 SCY medium (Tripticase soy broth without dextrose 0.25 g, casitone 0.75 g in 1 L deionized water) at 37 C. Escherichia coli K12, Pseudomonas aeruginosa, Nitrosospira
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 6 9 4 e7 0 4
multiformis (ATCC # 25196), Nitrosomonas europea (ATCC # 19718) and Desulfovibrio desulfuricans ND132 were used for cross infectivity studies.
2.2. host
Isolation of bacteriophage with H. hydrossis as the
Biomass sample (called the mixed liquor) from a full-scale wastewater treatment plant was used as a source of virulent phages. Mixed liquor sample was sequentially filtered through 0.45 mm and 0.2 mm filters (Millipore, CA) to remove the bacteria and other suspended impurities. The filtrate, which mostly contained dissolved substances and phages, was collected in a pre-autoclaved glass flask. The filtrate was concentrated for phages using Amicon Ultra-4 (Millipore, CA) Uitracel-30k with MWCO 30,000. The concentrated phage extract was stored at 4 C until further use. To isolate the lytic phage specific to the filamentous bacterium an established method top-agar plating technique is used. The model filamentous bacterium, H. hydrossis was grown overnight and 10 mL of this overnight grown culture was mixed with 1 mL of the concentrated phage extract and 3 mL of 0.75% SCY agar (SCY medium þ 0.75% w/v bacto-agar). This mixture was vortexed thoroughly and overlaid on a premade 2% SCY base agar plate. The top layer of 0.75% agar doesn’t solidify as quickly as the base 2% agar and therefore it would allow uniform distribution of host bacterium and phages within the agar. This results in formation of isolated plaques following incubation. The plate was allowed to solidify and was incubated at 37 C overnight. A negative control plate was also prepared following same protocol. The control plate contained everything mentioned above except the concentrated phage extract. One of the capillary stubs of the plaques that appeared on the plate following the incubation was carefully picked and re-suspended in the SMG buffer (5.8 g/L NaCl, 2.0 g/ L MgSO4-7H2O, 5.0 mL/L of a 5% solution of gelatin, 50 mL/L of 1 M TriseHCl pH 7.5). The plaque was further purified by repetitive infections on the fresh H. hydrossis plates. The purified isolated phage was stored at 4 C in the SMG buffer until further use.
2.3. (EFM)
Viral enumeration using epifluorescence microscopy
From the bacteriaephage mixture, 1 mL triplicate samples were taken for the viral enumeration. The samples were first filtered through 0.22 mm pore size filter (Millipore Co. Bedford, MA) to remove the dead unlysed bacteria and other suspended solids. The filtrate was collected in a fresh pre-autoclaved flask. In order to exclude the free bacterial DNA present in the phage extract, 900 mL of phage solution was transferred into a fresh polypropylene tube containing 100 mL of RQ1 reaction buffer and 2 mL of RNase free DNase I (Invitrogen). After the addition of DNase, samples were incubated at 37 C for 20 min and 35 mL of 0.5 M EDTA was added to stop the DNase activity. Aliquots of DNase treated sample (100 mL) were suspended in 900 mL of sterile deionized water and were vacuum filtered through a stack of 25 mm filters consisting of 0.02 mm Anodisc (Whatman Int’l Ltd., Maidstone, England), a 0.22 mm Durapore membrane filters (Millipore, Ireland), and a glass fiber prefilter
(Millipore, Ireland). Anodisc containing captured virus-like particles were stained by adding 10 SYBR Gold dye (Invitrogen Co.). Anodisc was incubated for 20 min in the dark. The anodisc was viewed under an Olympus BX 51 epifluorescence microscope (Olympus, Japan) using a Cy3 filter. At least 10 fields were captured from each Anodisc digitally at a magnification of 1000 with an Olympus DP-71 camera. Virus-like particles (VLPs) were enumerated manually from the micrographs and the average VLPs were calculated for all micrographs. Uninfected H. hydrossis culture was considered as a control for the VLPs enumeration.
2.4.
Live and dead bacterial enumeration
Live and dead bacterial cell analysis was done using Baclightä bacterial viability kit (Molecular Probes Inc.). The samples (1 mL) for bacterial enumeration were filtered through 0.22 mm (PCTE black, GE Water & Process Technologies) using a vacuum manifold. The bacteria captured on the membrane filters were stained with a mixture of two dyes supplied with the Baclightä bacterial viability kit (Molecular Probes Inc.). The bacterial cells with the dye mixture were incubated in dark for 20 min and were analyzed using BX 51 microscope (Olympus, Japan) using Cy3 and a FITC filters to capture live and dead cells respectively. At each parameter tested, multiple pictures were captured and each picture was divided into four compartments using a grid system. Number of live and dead cells in each compartment was manually counted and was averaged.
2.5.
Preparation of lysate stock
The newly isolated bacteriophage (>108 plaque forming units (PFU)/mL) was mixed with 0.1 mL of the H. hydrossis suspension in 1:1 ratio and the mixture was incubated for 20 min at 37 C. To the incubated culture, 3 mL of the molten SCY agar (0.7% w/v) was added and the mixture was immediately poured into petri dishes containing solidified SCY agar (1.5% w/v). After the overnight incubation at 37 C, the plates were removed from the incubator and 5 ml SMG buffer was added to these plates. The plates were stored at 4 C for 12 h with intermittent gentle shaking. A Pasteur pipette was used to collect the overlying SMG buffer from the plate after the overnight incubation. The collected buffer was transferred into a fresh and pre-autoclaved polypropylene tube (13 100 mm). Fresh SMG buffer (1 mL) was again added on the top of each incubated plate slowly. The plates were allowed to sit for 15 min. Thereafter, the overlying buffer was collected and was added to the previously collected buffer. Chloroform (0.1 mL) was added to the collected SMG buffer solution followed by gentle vortexing for 1 min. The tube containing the SMG buffer and the chloroform was centrifuged for 10 min at 4000g and 4 C. The supernatant containing phages was carefully transferred to a fresh tube and stored at 4 C.
2.6.
Phage titer
In order to estimate the phage titer for the isolated bacteriophage, 102, 104, 106, 108 dilutions of the original phage
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extract in triplicates were prepared using SMG buffer. Subsequently 200 mL of the fresh H. hydrossis culture (O.D.600 1) was added to 10 mL of each set of the diluted phage extract. The mixture was vortexed thoroughly and incubated for 30 min at room temp. To each tube containing this mixture, 3.0 mL of 0.75% SCY agar (45 C) was added, vortexed thoroughly and was poured onto premade 2% SCY base agar plates. Triplicate plates for each dilution were incubated overnight at 37 C for 16e24 h. Plaques formed on each plate were counted and the plaque numbers were averaged from three plates to calculate the phage titer. Phage titer was calculated by multiplying number of plaques with the corresponding dilution factor (Madigan et al., 2000).
2.7.
One-step growth curve of bacteriophage
In order to construct the one-step growth curve of the isolated phage, 1 mL of freshly grown H. hydrossis culture (ca. 1 109) was taken into sterile polypropylene tubes (13 mm 100 mm) in triplicate. Subsequently 1 1010 plaque forming units (PFU) of the isolated phage was added to each tube and the tubes were incubated at 37 C for 1 min. The bacterial culture containing the isolated phage particles was immediately centrifuged at 3000g for 1 min at 4 C. The supernatant was discarded and the pallet was re-suspended in fresh SCY growth medium. The re-suspended pallet was added to freshly grown 1 L cell suspension of H. hydrossis in log growth phase. The mixture was incubated at 37 C on a shaker and triplicate samples at every 10 min were taken from the mixture. Each sub-sample from the set of triplicate sample at each time step was divided into two equal halves, of which, one half was used to calculate the total PFU (free phage plus any infectious intracellular phage particles) after adding the chloroform and the other half was used to calculate the free PFU (extracellular/non-adsorbed). The counts obtained from triplicate values were averaged and were plotted to obtain the one-step growth curve (Ellis and Delbru¨ck, 1939). The burst size was calculated by dividing the number of virus-like particles released from the cell with the number of virus particles initially added.
2.8.
Transmission electron microscopy (TEM)
Phages were allowed to grow for 12 h in 1 L fresh cultures of H. hydrossis. Chloroform (10 mL) was added to the culture and the mixture was kept on a gyratory shaker for 2 h. Once the cell lysis was visible, the mixture was centrifuged at 5500g for 20 min to pellet down the lysed bacteria. The supernatant containing the phage particles was carefully transferred to a fresh tube and was centrifuged at 8890g overnight at 4 C. The clear pellet was re-suspended in the SMG buffer. The phage particles were further purified by isopycnic centrifugation at 115000g for 3 h in sucrose cesium chloride gradient. Subsequently, 5 mL of the purified phage was loaded on 400 grid formvar coated copper grids (Fisher Scientific) and was allowed to settle for 1 min. Excess liquid was soaked by holding bibulous paper (Fisherbrand) at 90 to the grid. The grids were stained for 1 min using 2 mL freshly prepared filtered 1% uranyl acetate and excess stain was soaked by holding bibulous paper at 90 to the grid. Dried grids were
697
subsequently examined under Tecnai T12 Transition Electron Microscope (FEI, Japan). The accelerating voltage used for imaging was 80 kV and images of negatively stained phage particles were recorded subsequently.
2.9. Phage-based biocontrol of biomass bulking caused by H. hydrossis Fresh biomass without any settling problem was obtained from an ongoing laboratory scale sequencing batch reactor. Subsequently, 50 mL of this sludge in triplicates was mixed with an equal volume of overnight grown culture of H. hydrossis in 250 mL in an Erlenmeyer flask. The resulting mixture was grown under limited dissolved oxygen (DO) and nutrient conditions for 48 h to acclimatize the spiked H. hydrossis with the biomass. After which, the biomass was divided into two halves and one of those was spiked with the isolated phage extract at 1000:1 host to phage ratio and was further allowed to sit for 3 h. The second half without the phage addition was considered as the control. The samples were poured in separate measuring cylinders. For each case, sludge volume index for 30 min settling (SVI30), settling velocity and turbidity were determined at 0 and 3 h time intervals (Lee et al., 1983). Turbidity of the supernatant was measured using a turbidometer (2100N Turbidimeter, HACH, CO, USA) and reported in terms of nephalometric turbidity units (NTU). The supernatant was also examined qualitatively for microbial population by staining with 40 ,6-diamidino-2phenylindole (DAPI) (Sigma) and observing under 100 objective of the epifluorescent microscope (Olympus, Japan) using DAPI filter. DAPI is a fluorescent stain (excitation maximum 358 nm/emission maximum 461 nm) that binds strongly to DNA. Samples in triplicates at each location were collected from the supernatant at different depths during settling tests using a Pasteur pipette and were examined using DAPI staining technique. During sample collection, care was taken as not to disturb the settled biomass.
2.10. Stability of isolated phage, cross infectivity tests and its effect on biomass performance Stability of the isolated phage was tested under the storage medium, temperature and pH changes. Storage media considered were water and SMG buffer over a storage period of 9 months. The temperature range of 20 C to 45 C and the pH range of 5e8 were considered for stability studies. Stability in terms of infectivity titer was determined for each parameter in triplicates. Cross infectivity tests were performed to evaluate the possibility of infection by the isolated phage to selected model bacteria found in activated sludge systems. For cross infectivity studies, bacterial strains that are relevant in the wastewater treatment were considered. Not all of the considered strains (viz. Nitrosomonas) could grow easily as lawn on agar plates, so they were tested for infectivity in liquid cultures. To conduct the cross infectivity tests, 100 mL of each bacterial culture was grown overnight in their respective nutrient media and subsequently spiked with 1 mL SMG buffer containing the isolated phage (resulting in phage to host ratio of w1:1000). The resulting mixture was incubated for 3e6 h at 37 C and was tested for the plaque formation
698
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using standard plating technique or for the presence of VLPs using the epiflourescence microscopy. The effect of the addition of the isolated phage on the biomass performance in terms of chemical oxygen demand (COD) and nutrient removals was evaluated using batch tests. Three batch tests were performed over a period of 12 h simultaneously. The first batch contained the biomass without any other addition and served as a control. The second batch contained the biomass and H. hydrossis. The last batch contained the biomass, H. hydrossis and the isolated phage. Care was taken that all three batch tests contained same concentrations of biomass, had identical final volume and were incubated under same temperature and agitation. Samples for inorganic species estimation were taken at the beginning of the experiment and at the end of 12 h.
2.11.
Other analytical methods
COD was quantified using HACH’s low range COD method (Hach, Loveland, CO). Dissolved P, NH3-N, NO3-N and NO2-N were quantified using HACH methods 8048 (Ascorbic Acid method), 10020 (Chromotropic Acid method), 10031 (Salicylate method), and 8153 (Ferrous Sulfate method) respectively.
3.
Results and discussion
The purpose of this research was to demonstrate bacteriophage-mediated biocontrol of filamentous bulking using lytic bacteriophages. For this purpose, H. hydrossis was chosen as model filamentous bacterium and the biocontrol of biomass bulking caused by it was investigated.
3.1. Isolation and characterization of H. hydrossis phage Lytic phage specific to H. hydrossis (referred to as HHY-phage onwards) was isolated from the mixed liquor sample from a full-scale wastewater treatment plant. The purification of the HHY-phage was performed using repeated plaque assay technique. Fig. 1a shows an agar plate containing several plaques obtained after serial infection of H. hydrossis. From the figure it is also evident that the plaques were distinct, clear and round shaped with 1e2 mm diameter. Distinct bacteriolysis was visible when H. hydrossis in suspension was infected with the HHY-phage. Significant number of virus like particles (VLPs) were also observed when the phage extract was viewed under epifluorescent microscope (Fig. 1b). The titer of the HHY-phage with H. hydrossis was calculated to be 5.2 0.3 105 PFU/mL. Compared to the reported values (Synnott et al., 2009; Uchiyama et al., 2009; Verma et al., 2009), the HHY-phage was found to have lower titer which may be attributed to the slow growth rate of H. hydrossis. Furthermore, no turbid or bull-eyed plaques, which are characteristics of lysogenic phages (Ellis, and Delbru¨ck, 1939; Uchiyama et al., 2009), were observed (Fig. 1b). Before the phage can be employed for the biocontrol, it is very important to know whether the isolated phage is lytic or lysogenic. Lysogenic phages cannot be employed in effective biocontrol applications. Distinct plaque characteristics and the absence of
turbidity demonstrate the lytic nature of HHY-phage and its suitability for biocontrol of filamentous bulking. TEM analysis (Fig. 2) revealed that the isolated phage belongs to the Myoviridae family with hexagonal head, collar, contractile tail, tail fibers and icosahedral symmetry. The HHY-phage was around 203 nm long and depicted icosahedral head (diameter w81 nm) and contractile tail (length w126 10 nm; diameter w18 nm). Myoviruses are typically virulent phages, meaning they do not integrate their genetic material with their host cells (lysogenise), and they usually kill their host cell (Suttle, 2005). This trait of HHY-phage (myoviruses) would be most beneficial for the biocontrol of H. hydrossis in wastewaters because, it negates the risk of lytic phage turning into lysogenic phage under low host populations. One-step growth curve experiment performed on the isolated phage confirmed that the phage was lytic in nature. Fig. 3 shows the one-step growth curve for the HHY-phage. The latent and eclipse period of the bacteriophage were found to be 30 min and 20 min respectively which are typically similar to that of a T4 type bacteriophage. Latent and eclipse periods are important parameters for bacteriophage growth, especially during the process of biocontrol using the bacteriophage. The latent time period spans from the point of phage adsorption to the point at which host lysis occurs and the eclipse time period spans from the point of phage adsorption to the point at which the first phage progeny have matured within an infected cell. The burst size was calculated to be 105 7 PFU/infected cell.
3.2. Stability under different conditions and cross infectivity of HHY-phage The biggest challenge for the sustainability of biocontrol would be the resistance of the host towards phage infection over time (Holmfeldt et al., 2007). Therefore virulence studies were conducted before the HHY-phage could be used as remedial biocontrol for the biomass bulking. In this direction, the infectivity titer of the HHY-phage was monitored across a 9 month time period. HHY-phage was stable in sterile SMG buffer at 4 C during the storage time period. No appreciable decrease in infectious titer was observed following the storage of the HHY-phage in the SMG buffer. However, the storage of the HHY-phage in water reduced the infectious titer to a considerable extent (w80%). Negligible change in the infectivity titer after 9 months suggested the stability of the phage and the absence of any developed resistance of the host towards the phage infection. An alternative solution to the problem of host resistance against the lytic phage would be to isolate more than one lytic phage for the host filamentous bacterium. That will ensure long-term suitability of the demonstrated biocontrol strategy for biomass bulking caused due to a specific filamentous bacterium. Exposure to temperatures ranging from 20 C to 35 C had no effect on the infectious titer of the HHY-phage. The phage was relatively stable upon exposure to the high temperature for about 2 h. The decrease in infectious titer after incubation at 42 C for 2 h was negligible. However, exposure to temperatures higher than 42 C resulted in negative effect on the phage infectivity suggesting possible denaturation of
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Fig. 1 e a) Plaque obtained from phages infecting H. hydrossis (arrow pointing the plaque originating from isolated phages) b) Micrographs obtained from EFM of phage extract stained with SYBR GolddVLPs can be seen as tiny dots.
capsid proteins and nucleic acids at higher temperatures (Caldeira and Peabody, 2007). The HHY-phage remained equally infectious after overnight incubation (8e10 h) at pH 5 and 8. The average value of the HHY-phage titer after 9 months at different pH values was 5.0 0.1 105 PFU/mL, which was very close to the original titer value of 5.2 (0.3) 105 PFU/mL. The isolated phage was sufficiently stable under varying pH and temperature conditions that are commonly encountered in activated sludge processes. The observed stability characteristics of the HHY-phage endorse the fact that this newly isolated phage is a potential candidate for an intended biocontrol application in activated sludge
process, which typically encounters pH and temperature fluctuations. For the biocontrol using the bacteriophage in activated sludge processes, it is very important that the applied phage is host specific and it does not have any cross infectivity with other important indigenous bacterial communities in activated sludge process. In cross infectivity studies using pure cultures of several bacteria, it was observed that HHY-phage did not produce any plaques when attempted to infect Escherichia coli, Pseudomonas aeruginosa, N. multiformis, N. europea and D. desulfuricans cultures. These bacteria represented communities responsible for COD removal, ammonia
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Fig. 2 e Micrographs obtained from TEM of phage extract infecting H. hydrossis (bar represents 100 nm).
oxidation and anaerobic sulfate reduction. Live-dead assay for each of these bacteria after addition of HHY-phage showed no death caused due to phage infection. No signs of bacteriolysis in suspended growth experiments or plaques on agar plates were observed for these pure strains of bacteria. This demonstrated the suitability of the isolated phage for the biocontrol of filamentous bacteria in activated sludge processes. The phages leaving with the effluent after the
1E10
Untreated Chloroform treated
1E9 1E8
biocontrol may pose a threat to the receiving waters. However, given the host specificity of the isolated phages, the phages leaving with the effluent will not multiply further and will die when the phages will not find their host in receiving waters. Phage to host ratio (PHR) was another critical parameter that was evaluated in this research. Suitable PHR for infection was found to be 1:1000 where the bacterial death of w54% was recorded and final viral count was 1.89 0.3 1012 per mL (Table 1). Higher PHR caused more than 90% mortality of H. hydrossis and PHR lower than 1:1000 resulted in mortality less than 10%. It is worth mentioning that just the presence of filamentous bacteria is not sufficient to cause the biomass bulking. In fact, at lower concentrations, filamentous bacteria form an integral part of the floc and helps in floc integrity.
1E7
PFU/mL
1000000 100000
Table 1 e Effect of initial phage/host ratio on bacteriolysis and VLP count after 12 h.
10000 1000
Initial phage/ host ratio
100 10 1 0
10
20
30
40
50
60
Time(min)
Fig. 3 e One-step growth curve of HHY-phage.
70
Blank 0.01:100 0.1:100 1:100 10:100
Final O.D.600 0.080 0.100 0.072 0.012 0.003
0.03 0.01 0.001 0.004 0
Final Bacteria Live/Dead ratio 97.8 89.9 54.5 6.1 0.5
0.5 0.4 0.5 0.3 0.3
Final VLP count (/mL) 7.03 1.89 9.12 2.99
e 0.4 0.3 0.5 0.6
1010 1012 1012 1013
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Fig. 4 e Settled biomass after 30 min a) biomass acclimatized with H. hydrossis b) biomass acclimatized with H. hydrossis and spiked with HHY-phage. Settling curves of mixed liquor of two batches at c) 0 h d) 3 h after phage addition.
Hence, the over death of filamentous bacteria could cause negative effects on the biomass settleability. Activated sludge bioreactor consists of a complex community of a variety of organisms. Hence a higher ratio may be needed to achieve optimum lytic effect of the bacteriophage on the targeted filamentous. Furthermore, more than one filamentous may also exist in activated sludge mixed liquor. Under such a case, it may be necessary to isolated lytic phage for each filamentous and then use the isolated phages for the biocontrol of bulking caused by multiple filamentous bacteria.
3.3.
Effect of biocontrol on sludge settling
Upon visual examination, difference in height of settled sludge of batches was recorded following the application of the HHY-phage (Fig. 4a and b). Relatively clear supernatant (Fig. 4b) observed after application of the HHY-phage also suggested better sludge settling. Distinct difference in sludge settling characteristics was also encountered when the two batches were compared at 0 h and 3 h of infection with the HHY-phage (Fig. 4c and d respectively). As it may be seen from Fig. 4d, in the batch spiked with the HHY-phage, the
settling velocity of the sludge was higher and the height of final settled sludge was lower. The biomass acclimatized with H. hydrossis showed a SVI >156 3, (Fig. 5a) resulting in poor settling. On the other hand, the biomass sample containing H. hydrossis and spiked with the HHY-phage at 1:1000 PHR showed signs of bacteriolysis. A SVI value 105 2 for the biomass sample containing H. hydrossis and the HHY-phage was recorded after 12 h. This is a significant finding and demonstrates that phage therapy indeed can be applied for a complex system such as activated sludge bioreactor. At the time of dividing the H. hydrossis culture into two halves for the purpose of the control and the phage spiked biomass settling tests, care was taken to divide the biomass sample such that the resulting sub-samples will have nearly identical cell concentrations. Hence, the possibility of different H. hydrossis numbers in the control and the phage spiked biomass can be ignored. One possibility to monitor the numbers of H. hydrossis in the control and the phage spiked biomasses was to count H. hydrossis cells. However, it was not practically easy because H. hydrossis is filamentous in morphology and integrates itself within biomass flocs, thus making it difficult to count the number.
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Fig. 5 e a) SVI and b) Turbidity profiles of supernatant, of the two batches: biomass acclimatized with H. hydrossis (:) and biomass acclimatized with H. hydrossis and spiked with HHY-phage (;).
The turbidity profiles of the supernatant in both batches are shown in Fig. 5b. It is evident from this figure that the turbidity of the supernatant after 3 h in the batch containing the biomass and the H. hydrossis spiked with HHY-phage decreased (from 58.4 NTU to 11.6 NTU) with time. This reduction in turbidity may be attributed to the bacteriolysis of H. hydrossis as a result of infection by HHY-phage. Turbidity of the supernatant provided another strong evidence of bacteriolysis of H. hydrossis. Turbidity of the supernatant in the batch test containing the biomass and H. hydrossis was greater than that in the batch containing the biomass and H. hydrossis spiked with the HHY-phage. The higher turbidity of the supernatant in the batch containing the biomass and the HHY-phage may be due to the presence of unsettled smaller flocs containing H. hydrossis which were not sufficiently heavier to settle to the bottom. The turbidity of the supernatant in the batch containing the biomass and H. hydrossis spiked with HHY-phage further dropped after 12 h of settling (data not shown). It is noteworthy that the settling tests were done with a much greater population of H. hydrossis to demonstrate the concept of biocontrol. However, in real-time scenario, the number of filamentous bacteria will be present in much smaller number as compared to other bacteria in the activated sludge process mixed liquor and the turbidity of the supernatant should not pose a problem. Microscopic examination of the supernatant was performed to investigate the presence of H. hydrossis in the supernatant. Since the purpose of DAPI staining was only qualitative, cells were not counted but the floc size and the morphology were recorded. The representative micrographs of DAPI stained supernatant samples from both the batches are presented in Fig. 6. In the supernatant from the settling cylinder with H. hydrossis but no phage extract, pin flocs (<10 mm diameter) were observed which were typical for filamentous bulking caused by H. hydrossis (Eikelboom and van Buijsen, 1981, van Veen et al., 1973). However, no pin flocs were present in the supernatant in the settling cylinder containing biomass with H. hydrossis and spiked with the phage extract. Bigger floc sizes around 50 mm diameter were observed in settled biomass with H. hydrossis and spiked with the phage extract. This finding further strengthens the earlier speculation that following application of the HHY-phage, the
Fig. 6 e Epifluorescent micrographs of DAPI stained samples of (a) supernatant of batch containing biomass acclimatized with H. hydrossis and (b) settled biomass acclimatized with H. hydrossis and spiked with HHY-phage.
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decrease in the population of H. hydrossis occurred resulting in the increase of the floc size, leading to clearer supernatant and better sludge settling. The reduction in the SVI that led to a better settling was due to the increase in settling velocity, which was possibly a result of the increase in floc sizes due to the killing of H. hydrossis by the HHY-phage. It has been reported earlier that sludge settling velocity is dependent on filamentous population and overpopulation can resulted in decreased settling velocity (da Motta et al., 2002). It is also an established fact that the overpopulation of filamentous bacteria like H. hydrossis can cause smaller floc sizes and poor settling (Eikelboom, 1975). Good settling in the batch spiked with H. hydrossis and HHY phage was noticed only after 30 min of settling tests using batch experiments (Fig. 4c). This may be due to the reason that latent period for the HHY-phage was around 30 min. During this period HHY-phage may have only adsorbed on to the H. hydrossis cells but, no lysis and reduction in population had taken place. Furthermore, a higher phage to bacteria ratio may be needed in full wastewater treatment plants experiencing filamentous bulking problems because of the mixed liquor complexity and the transport limitations that the added phage/phages will face to come in contact with the filamentous bacteria.
3.4. Effect of phage application on COD and nutrient removal efficiency Cross infectivity studies of the HHY-phage with selected pure strains of bacteria showed no infectivity of HHY-phage on these strains. However, it was necessary to determine whether the COD and nutrient removing efficiencies of the original biomass were affected due to the addition of HHYphage. The sequencing batch reactor from which the biomass was sampled was efficiently removing COD, nitrogen and phosphorus. It was observed that the phage application did not affect the COD and the nutrient removal efficiencies for 24 h of treatment (Table 2). The COD and ammonia nitrogen removals in the control batch (no H. hydrossis and HHY-phage) test were 74% and 100% respectively. The corresponding
Table 2 e pH and nutrient profile (at 0, 12, 24 h) of the two scenarios in comparison with original biomass. Sludge þ H. Sludge þ H.hydrossis hydrossis þ virus
Time
Sludge
pH
0 12 24
7.6 0.1 7.4 0.2 7.1 0.2
7.5 0.1 6.8 0.1 6.9 0.1
COD (mg/L)
0 12 24
31.4 0.4 9.0 0.3 8.3 0.3
30.9 0.5 12.3 0.4 11.4 0.4
30.9 0.5 7.5 0.6 7.3 0.4
Turbidity (NTU)
0 12 24
31.5 0.4 28.2 0.2 21.5 0.2
58.7 0.2 50.4 0.2 50.2 0.2
50.4 0.2 11.6 0.3 5.9 0.2
NH3-N (mg/L)
0 12 24
27.8 0.1 0.5 0.1 0 0.05
27.8 0.2 1.1 0.1 0.2 0.05
27.8 0.1 0.6 0.1 0 0.05
7.3 0.2 6.9 0.05 7.1 0.3
703
removal efficiencies over 24 h of these species in the batch containing the biomass and H. hydrossis were 63 and 96% respectively, which indicates that the COD and ammonia nitrogen removal efficiencies in this batch slightly went down as compared to the control. The marginal difference in COD and nutrient removal efficiencies between the control batch and the batch containing H. hydrossis could possibly be due to the interference of H. hydrossis with other bacteria putting substrate diffusion limitations to both organics and ammonia nitrogen. The removal efficiencies of COD and ammonia nitrogen in the batch containing the biomass with H. hydrossis and spiked with HHY phage was nearly close to the control batch test indicating that the HHY phage did not infect other key organisms responsible for COD and ammonia nitrogen removals. This finding further demonstrates that the addition of phage in fact reduced H. hydrossis population to overcome the substrate diffusion limitations caused by the presence of H. hydrossis.
4.
Conclusions
So far, the phage-based biocontrol (phage therapy) using lytic phages has been researched and practiced for medical and sanitation applications only (Kropinski, 2006). Although, some review papers (Withey et al., 2005) have discussed the possibility of using bacteriophages as biocontrol agents for problems such as sludge dewatering and anaerobic digestion in wastewater treatment application in activated sludge processes, the actual research efforts in this direction are wanting. Biomass bulking resulting in poor biomass settling in the secondary clarifier due to the presence of filamentous bacteria is one of the most common operational problems, which is still encountered at many full-scale biological wastewater treatment plants. Although several engineering parameters have been linked to the biomass bulking, a definite engineering solution to this is yet to be evolved. This research demonstrated the biocontrol of the biomass bulking using a lytic bacteriophage as an alternative solution to this problem. The proposed idea is innovative and falls outside the existing paradigm of current engineering practices in municipal wastewater treatment for biomass bulking control. Furthermore, this is the first report on the isolation, characterization and application of a lytic bacteriophage for the biocontrol of filamentous bacterium H. hydrossis responsible for sludge bulking. Hence, the research has both microbiological as well as environmental engineering significance. Currently, characterization of HHY-phage at genetic and proteomic level is underway which would provide further insight into this phage. Further work to obtain the complete genome sequence of the isolated phage is also in progress. This would not only help understand the newly isolated bacteriophage but also may provide ways to genetically engineer the phage for better performance and extended applications. Unlike bacterial genomes, there are limited phage genomes available in the common public databases, which pose a challenge to researchers in conducing blast searches for genes specific to and conserved in bacteriophages. Such database would also be useful to compare phage genomes among themselves and also with bacterial genomes.
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In the least it would promote the development of phage-based biocontrol applications to a new level. Parallel research work is also in progress for the isolation of lytic phages infecting other important filamentous and foam-forming bacteria. This is aimed at constructing a pool of bacteriophages that could together form a consortium of lytic phages that can infect any filamentous bacteria that is responsible for biomass bulking or foaming. An ideal solution however would be to have a single (natural/engineered) bacteriophage which has broad host range to infect all filamentous bacteria without any collateral damage to nutrient/COD removal efficiency.
Acknowledgement We deeply acknowledge Eddie B. Gilcrease and Dr. Sherwood Casjens, University of Utah for their help in isopynic purification of phages. We are also grateful to Dr. Jeanette Norton, Utah State University for providing us the N. multiformis strain.
references
Breitbart, M., Rohwer, F., 2005. Here a virus, there a virus, everywhere the same virus? Trends Microbiol. 13, 278e284. Caldeira, J.C., Peabody, D.S., 2007. Stability and assembly in vitro of bacteriophage PP7 virus-like particles. J. Nanobiotechnol. 5, 10. Caravelli, A., Giannuzzi, L., Zaritzky, N., 2007. Inhibitory effect of a surfactant on pure cultures of a filamentous and a floc forming micro-organism. Environ. Technol. 28, 137e146. da Motta, M., Pons, M.N., Roche, N., 2002. Study of filamentous bacteria by image analysis and relation with settleability. Water Sci. Technol. 46, 363e369. da Motta, M., Pons, M.N., Roche, N., 2003. Monitoring filamentous bulking in activated sludge systems fed by synthetic or municipal wastewater. Bioprocess Biosyst. Eng. 25, 387e393. Eikelboom, D.H., 1975. Filamentous organisms observed in activated sludge. Water Res. 9, 365e388. Eikelboom, D.H., 1977. Identification of filamentous organisms in bulking activated sludge. Progr. Water Technol. 8, 153e161. Eikelboom, D.H., 2006. Identification and Control of Filamentous Microorganisms in Industrial Wastewater Treatment Plants. IWA Publishing, London, UK. Eikelboom, D.H., van Buijsen, H.J.J., 1981. Microscopic Sludge Investigation Manual. TNO Res. Inst. for Env. Hygiene, Delft, The Netherlands. Ellis, E.L., Delbru¨ck, M., 1939. The growth of bacteriophage. J. Gen. Physiol. 22, 365e384. Ewert, D.L., Paynter, M.J., 1980. Enumeration of bacteriophages and host bacteria in sewage and the activated-sludge treatment process. Appl. Environ. Microbiol. 39, 576e583. Holmfeldt, K., Middelboe, M., Nybroe, O., Riemann, L., 2007. Large variabilities in host strain susceptibility and phage host range govern interactions between lytic marine phages and their Flavobacterium hosts. Appl. Environ. Microbiol. 73, 6730e6739. Jensen, E.C., Schrader, H.S., Rieland, B., Thompson, T.L., Lee, K.W., Nickerson, K.W., Kokjohn, T.A., 1998. Prevalence of broad-hostrange lytic bacteriophages of Sphaerotilus natans, Escherichia coli, and Pseudomonas aeruginosa. Appl. Environ. Microbiol. 64, 575e580.
Kampfer, P., 1995. Physiological and chemotaxonomic characterization of filamentous bacteria belonging to the genus Haliscomenobacter. Syst. Appl. Microbiol. 18, 363e367. Kropinski, A.M., 2006. Biocontrol e everything old is new again. Can. J. Infect. Dis. Med. Microbiol. 17, 297e306. Lee, S.E., Koopman, B., Bode, H., Jenkins, D., 1983. Evaluation of alternative sludge settleability indices. Water Res. 17, 1421e1426. Lou, I.C., de los Reyes III, F.L., 2008. Clarifying the roles of kinetics and diffusion in activated sludge filamentous bulking. Biotechnol. Bioeng. 101, 327e336. Madigan, M.T., Martinko, J.M., Parker, J., 2000. Brock: Biology of Microorganisms, ninth ed. Prentice-Hall International Limited, London. Otawa, K., Lee, S.H., Yamazoe, A., Onuki, M., Satoh, H., Mino, T., 2007. Abundance, diversity, and dynamics of viruses on microorganisms in activated sludge processes. Microb. Ecol. 53, 143e152. Se´ka, M.A., Hammes, F., Verstraete, W., 2003. Predicting the effects of chlorine on the micro-organisms of filamentous bulking activated sludges. Appl. Microbiol. Biotechnol. 61, 562e568. Suttle, C.A., 2005. Viruses in the sea. Nature 437, 356e361. Synnott, A.J., Kuang, Y., Kurimoto, M., Yamamichi, K., Iwano, H., Tanji, Y., 2009. Isolation from sewage influent and characterization of novel Staphylococcus aureus bacteriophages with wide host ranges and potent lytic capabilities. Appl. Environ. Microbiol. 75, 4483e4490. Uchiyama, J., Rashel, M., Matsumoto, T., Sumiyama, Y., Wakiguchi, H., Matsuzaki, S., 2009. Characteristics of a novel Pseudomonas aeruginosa bacteriophage, PAJU2, which is genetically related to bacteriophage D3. Virus Res. 139, 131e134. van Veen, W.L., van der Kooij, D., Geuze, E.C., van der Vlies, A.W., 1973. Investigations on sheathed bacterium Haliscomenobacter hydrossis gen. nov., sp. nov., isolated from activated sludge. Anton Van Leeuwenhoek 39, 207e216. Verma, V., Harjai, K., Chhibber, S., 2009. Characterization of a T7like lytic bacteriophage of Klebsiella pneumoniae B5055: a potential therapeutic agent. Curr. Microbiol. 59, 274e281. Weinbauer, M.G., 2004. Ecology of prokaryotic viruses. FEMS Microbiol. Rev. 28, 127e181. Williams, T.M., Unz, R.F., 1985. Isolation and characterization of filamentous bacteria present in bulking activated-sludge. Appl. Microbiol. Biotechnol. 22, 273e282. Winston, V., Thompson, T.L., 1979. Isolation and characterization of a bacteriophage specific for Sphaerotilus natans which contains an unusual base in its deoxyribonucleic acid. Appl. Environ. Microbiol. 37, 1025e1030. Withey, S., Cartmell, E., Avery, L.M., Stephenson, T., 2005. Bacteriophages e potential for application in wastewater treatment processes. Sci. Total Environ. 339, 1e18. Wommack, K.E., Colwell, R.R., 2000. Virioplankton: viruses in aquatic ecosystems. Microbiol. Mol. Biol. Rev. 64, 69e114. Wu, Q., Liu, W.T., 2009. Determination of virus abundance, diversity and distribution in a municipal wastewater treatment plant. Water Res. 43, 1101e1109. Xie, B., Dai, X.-C., Xu, Y.-T., 2007. Cause and pre-alarm control of bulking and foaming by Microthrix parvicella e a case study in triple oxidation ditch at a wastewater treatment plant. J. Hazard. Mat. 143, 184e191. Ziegler, M., Lange, M., Dott, W., 1990. Isolation and morphological and cytological characterization of filamentous bacteria from bulking sludge. Water Res. 24, 1437e1451.
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Submerged anaerobic membrane bioreactor for low-strength wastewater treatment: Effect of HRT and SRT on treatment performance and membrane fouling Zhi Huang a, Say L. Ong a, How Y. Ng b,* a
Department of Civil Engineering, National University of Singapore, 7 Engineering Drive 1, Singapore 117576, Singapore Centre for Water Research, Division of Environmental Science and Engineering, Block EA #03-12, 9 Engineering Drive 1, Singapore 117576, Singapore b
article info
abstract
Article history:
Three 6-L submerged anaerobic membrane bioreactors (SAnMBRs) with solids retention
Received 3 June 2010
times (SRTs) of 30, 60 and infinite days were setup for treating synthetic low-strength
Received in revised form
wastewater at hydraulic retention times (HRTs) of 12, 10 and 8 h. Total COD removal
17 August 2010
efficiencies higher than 97% were achieved at all operating conditions. Maximum biogas
Accepted 20 August 2010
production rate was 0.056 L CH4/g MLVSS d at an infinite SRT. A shorter HRT or longer SRT
Available online 27 August 2010
increased biogas production due to increased organic loading rate or enhanced dominancy of methanogenics. A decrease in HRT enhanced growth of biomass and accumulation of
Keywords:
soluble microbial products (SMP), which accelerated membrane fouling. A drop in carbo-
Anaerobic membrane bioreactor
hydrate to protein ratio also inversely affected fouling. At 12-h HRT, the effect of SRT on
Biogas production
biomass concentration in SAnMBRs was negligible and membrane fouling was controlled
Low-strength wastewater
by variant surface modification due to different SMP compositions, i.e., higher carbohy-
Soluble microbial products
drate and protein concentrations in SMP at longer SRT resulted in higher membrane
Extracellular polymeric substances
fouling rate. At 8 and 10-h HRTs, infinite SRT in SAnMBR caused highest MLSS and SMP
Membrane fouling
concentrations, which sped up particle deposition and biocake/biofilm development. At longer SRT, lower extracellular polymeric substances reduced flocculation of particulates and particle sizes, further aggravated membrane fouling. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
In recent years, interests in anaerobic biological treatment for low-strength wastewater have increased because of merits such as lower energy consumption, low sludge production and biogas generation. The difficulty in retaining slow-growth anaerobic microorganisms with short hydraulic retention time (HRT) is an issue of concern (Haandel and Lettinga, 1994). However, this issue could be resolved by applying membrane separation in anaerobic processes as the membrane can
retain biomass effectively, producing a solids-free effluent and prevent unintended sludge wasting. Short HRT coupled with long solids retention time (SRT) to achieve high biomass concentration in a bioreactor is now possible through the use of membrane for solidseliquid separation. Among the reported anaerobic MBR studies, most of them combined cross-flow membrane modules with anaerobic reactors and focused on high strength wastewater. Typically, extremely high biomass concentrations are achieved in anaerobic MBRs, which leads to very high COD removal efficiency of
* Corresponding author. Tel.: þ65 68744777; fax: þ65 67744202. E-mail address:
[email protected] (H.Y. Ng). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.08.035
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around 90e98% (Ince et al., 1997; Fuchs et al., 2003). However, due to a high membrane flux or trans-membrane pressure, rapid fouling development becomes an obstacle to full-scale application of cross-flow anaerobic MBR (Ince et al., 1995, 1997; Choo and Lee, 1996; Kang et al., 2002; Fuchs et al., 2003; He et al., 2005). In addition, high hydraulic shear force stemmed from high-speed circulation pumps reduces the activity of microorganisms that, in turn, leads to reduction in biogas production (Kim et al., 2001, 2005). Only a few are related to low-strength wastewater treatment. The technical and economic feasibility of treating relatively low strength wastewater using membrane biological reactors (MBRs) under anaerobic conditions had been assessed (Sutton et al., 2004; Hall and Be´rube´, 2006). Compared to cross-flow anaerobic MBR, submerged anaerobic membrane bioreactor (SAnMBR) has attracted more interest recently with the positive experiences gained from the successful application of aerobic submerged MBR for wastewater treatment. Membrane fouling, the main operational issue, is governed mainly by membrane flux, membrane pore size and materials, operation pressure and temperature, hydrodynamics and sludge characteristics (Liao et al., 2006). To control fouling development, different strategies had been studied, including interval operation, sub-critical flux operation, periodic physical or chemical cleaning, etc. (Chang et al., 2002; Jeison and Lier, 2006a; Liao et al., 2006). Although it is expected that certain fundamental principles derived from aerobic submerged MBR are similar in SAnMBR, more detailed investigation of SAnMBR with regards to optimizing operating conditions, elucidating membrane fouling mechanism and developing fouling control strategy is required. An earlier work done by Vallero et al. (2005) has shown that SAnMBR could retain sulfate-reducing bacteria with slow-growth rate and achieve high sulfate reduction rate. Based on the concept of critical flux (Field et al., 1995), fouling mechanisms and controlling strategies were also studied (Vallero et al., 2005; Jeison and Lier, 2006b; Liao et al., 2006). However, all of these studies were conducted with high strength wastewater and under different operational conditions. As controllable operation parameters, HRT and SRT are two major factors that contribute to different treatment performance and biomass characteristics, which inevitably affect membrane fouling development in a SAnMBR (Haandel and Lettinga, 1994; Liao et al., 2006). Membrane fouling is significantly affected by extremely high MLSS concentrations, which are found either in an aerobic MBR with long SRT (Ng and Hermanowicz, 2005) or from an anaerobic MBR treating high strength wastewater at long SRT (Jeison and Lier, 2006b). However, for a SAnMBR treating low-strength wastewater, the impacts of HRT and SRT on treatment performance and membrane fouling are still unclear. In addition, to investigate membrane fouling mechanism, soluble microbial products (SMP) and extracellular polymeric substances (EPS), which were identified as two key factors affecting membrane fouling in aerobic MBR, need to be further studied in SAnMBR systems. In this study, a new configuration of SAnMBR was introduced. The aim of this paper was to investigate the influences of HRT and SRT on treatment performance of the SAnMBR for treatment of low-strength wastewater. Secondly, the mechanism of membrane fouling in terms of the effect of biomass concentration, SMP and EPS at different HRTs or SRTs were elucidated.
2.
Material and methods
2.1.
SAnMBR setup and operating conditions
By changing the volume of daily sludge wasting, three benchscale SAnMBRs with different SRTs, namely 30, 60 and infinite days (denoted as R30, R60 and RN, respectively. Infinite SRT means no sludge wasting was carried out except a smallvolume sludge was sampled for analysis) were operated at HRTs of 12, 10 and 8 h, successively. The ambient operating temperature varied from 25 to 30 C. As shown in Fig. 1, each SAnMBR system consisted of a 5-L completely mixed anaerobic reactor coupled with a 1-L gas lifter, in which a submerged plate and frame membrane module with a membrane surface area of 0.059 2 m2 was installed. This design of having the membrane unit external to the anaerobic rector allowed ease of membrane cleaning and replacement while maintaining a strictly anaerobic environment in the bioreactor at all time. The membrane module was fabricated by mounting 2 pieces of PES microfiltration (MF) membrane (GE Osmonics, pore size 0.45 mm), with one on each side, on an acrylonitrile butadiene styrene (ABS) risen plate. Three peristaltic pumps (Masterflex, L/S) were individually used to feed influent into the anaerobic reactor, recycle mixed liquid from the anaerobic reactor to the gas lifter and withdraw permeate from the membrane module. Produced biogas was recycled by a diaphragm gas pump (KNF, NMP850) to scour the membrane surface for fouling control via an air diffuser (located below and inline with the membrane plate). Membrane fouling would be indicated by an increase in the normalized trans-membrane pressure (TMP) which was recorded by a digital pressure switch (SMC, ZSE50F) installed between the membrane module and the permeate pump and was normalized by deducting initial TMP from temporal TMP. Biogas production was measured according to the volume of biogas collected in the wetted gas collector, in which the gas pressure was maintained at 1 atm pressure. A conductancetype point level controller was applied to balance the influent
Fig. 1 e Schematic diagram of a SAnMBR and operating conditions of the three SAnMBRs.
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and effluent fluxes, and a pH controller (ETATRON, HD-PH/P) was used to maintain the pH at 7.0 0.5. All three SAnMBRs were started up with seeding biomass collected from a sludge digester of a local wastewater treatment plant and fed continuously with identical synthetic wastewater for effective comparisons. The synthetic influent contained 636 mg L1 glucose as carbon source, 95.53 mg L1 NH4Cl and 28.06 mg L1 K2HPO4 as nutrients, which resulted in a total COD of 550 mg L1 and a C:N:P ratio of 100:5:1. Other compounds and trace elements were also added (600 mg L1 NaHCO3, 5 mg L1 MgCl2$6H2O, 14.6 mg L1 CaCl2, 13.5 mg L1 FeCl3, 4 mg L1 CoCl2$2H2O, 1.23 mg L1 Na2MoO4$2H2O, 0.002 mg L1 CuSO4$5H2O, 0.16 mg L1 MnSO4$H2O, 0.002 mg L1 ZnSO4$7H2O, 0.002 mg L1 H3BO3, 0.002 mg L1 KI). No biomass reseeding was carried out when the HRT was changed. At each HRT, the three SAnMBRs with different SRTs were simultaneously operated for 5 months to ensure a steady state condition was achieved, which was also estimated by two samples t-test (at 95% confidence interval) of treatment performance results. More detailed operating parameters are summarized in Fig. 1. When normalized TMP increased to 30 kPa, membrane was taken out from the gas lifter, soaked in 0.5% NaClO solution overnight and followed by thorough flushing with deionised (DI) water.
2.2.
was measured by a laser diffraction particle size analyzer (Coulter LS230, Beckman Coulter, USA). Two samples t-test at 95% confidence interval was applied on all data analysis. Using the modified Lowry method (Frølund et al., 1995) and Dubios phenolesulphuric acid method (Dubois et al., 1956), protein and carbohydrate concentration of SMP and EPS were determined with bovine serum albumin (BSA) and glucose as the standard references, respectively. The absorbance was measured by a spectrophotometer (DR/4000U, Hach). Periodically, fresh samples of mixed liquor from the three SAnMBRs were examined using an optical microscope (BX41, Olympus) equipped with a digital camera. Specific methanogenic activity (SMA) of biomass was measured and evaluated by incubating biomass samples in 117-mL serum bottles (Coates et al., 1996; Jawed and Tare, 1999). 10 mL of biomass sampled from each SAnMBR was mixed with 70 mL of a mixture containing PBS buffer and sodium acetate at 1.5 g COD L1. The produced biogas volume was recorded by a wetted gas collector fabricated with a graded pipette and cylinder. Gas composition was measured by a gas chromatograph (GC17A, Shimadzu, Japan).
3.
Results and discussion
3.1.
Start-up stage
Sample collection and preparation
Influent, effluent (membrane permeate) and mixed liquor were regularly taken from the outlets of feed pump, permeate pump and main anaerobic reactor, respectively, for analysis. The supernatant of mixed liquor (SML) was obtained using the centrifugationefiltration procedure with centrifugation at 9000 rpm for 10 min followed by filtration through a 0.45-mm filter. The SML sample was also used to determine SMP compositions. EPS was extracted using heating method at 80 C for 10 min in a water bath, then prepared by the same centrifugationefiltration procedure.
2.3.
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Analytical methods
The Standard Methods (APHA-AWWA-WEF, 2005) were used for the measurement of COD, MLSS and MLVSS concentrations. Total removal efficiency, Rt and secondary removal efficiency, RS, were calculated between influent and effluent, influent and SML based on Eqs. (1) and (2), respectively. Rt ¼
CODinf CODeff 100% CODinf
(1)
RS ¼
CODinf CODSML 100% CODinf
(2)
where CODinf, CODeff and CODSML are the COD of the influent, the effluent (membrane permeate) and SML, respectively. Four volatile fatty acids (VFA) in the supernatant of the mixed liquor, i.e., acetate acid, propionic acid, putyric acid and n-valuric acid, were determined by a gas chromatograph (GC2010, Shimadzu, Japan). Biogas composition (H2, N2, CH4 and CO2) was measured using a gas chromatograph (GC17A, Shimadzu, Japan). Particle size distribution of mixed liquor
Identical synthetic wastewater and seed biomass were first added into the three SAnMBRs and a strictly anaerobic environment was established by flushing nitrogen gas through the whole systems. During the first 2e3 weeks of the startup period, serious foaming in the gas lifters caused biomass overflow through the gas lines back to the main reactors and disturbed the system operation. Fig. 2 shows the microscopic images of fresh biomass samples. Well-flocculated microorganisms were found in the seeding biomass (Fig. 2a). In contrast, floc sizes reduced greatly and became fine and dispersive in the SAnMBR biomass (Fig. 2bed). On the other hand, short filaments proliferated and formed a supporting framework for dispersed microorganisms. The startup foaming could be attributed to the high shear force generated by the gas scouring for membrane fouling control. The shear force broke up the original flocs (Fig. 2a) into smaller flocs or fine particles (Fig. 2bed), which had much higher specific surface area and preferred to attach on gas bubbles. According to Ganidi et al. (2009), the attachment of fine particles makes the bubble surface more hydrophilic and reduces the surface tension of it, which would make the particle-coated bubbles difficult-to-be broken, especially under the gas-blowing condition in MBR. The proliferated short filaments (Fig. 2bed) linked dispersive fine particles to form bigger flocs and foaming gradually disappeared. Compared to R60 and RN, R30 biomass contained more filaments, which would benefit floc formation, which will be discussed later.
3.2.
Treatment performance
Table 1 shows a summary of results pertaining to treatment performance at steady state, including biomass concentration, removal efficiency and biogas production rate.
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Fig. 2 e (a) Flocculated microorganisms in seeding sludge; (b), (c) and (d) Fine dispersive microorganisms combined with short filaments in R30, R60 and RN, respectively (bar [ 1 mm).
3.2.1.
Effect of HRT
Regardless of the operating SRT, some common phenomena were observed: (1) Very high total COD removal efficiencies (>97%) were achieved under all investigated HRTs with insignificant differences among the three SRTs. This was attributed to the MF membrane that can completely retain biomass present in the mixed liquor to produce a high quality effluent with non-detectable solids; (2) Rt was higher than Rs. This observation indicated that although the membrane pore size was 0.45 mm, the MF membrane could further remove soluble COD present after biological treatment. This additional removal was attributed to internal and external membrane adhesion, interception, as well as possible organic degradation by the biofilm which was developed on the membrane surface; (3) Higher MLSS or MLVSS concentrations and more methane production were observed when HRT was successively reduced from 12 to 8 h. Higher organic loading rate (OLR) in shorter HRT induced more biomass multiplication and more carbon conversion from organic compounds to methane gas.
3.2.2.
Effect of SRT
In general, the confidence tests of total COD removal rates did not show significant differences under different operating SRTs due to the high quality of permeates produced by the MF membranes. On the other hand, at the same HRT, MLSS or MLVSS concentration increased with prolonged SRT e R30 had
the lowest biomass concentration followed by R60 and RN in ascending order. For the longer HRT of 12 h, even though the average MLSS concentration at steady state ranged from 5582 to 6497 mg L1 for SRT ranging from 30 to infinite days, the MLVSS concentrations were rather similar among the three SAnMBRs (Table 1). This could be due to a very low OLR (1.1 kg COD/m3 d) at a long HRT of 12 h, wherein anaerobic microorganisms growth was extremely limited. However, at confidence level of 95%, methane production and Rs differed for the three SAnMBRs operated at different SRTs but a similar HRT of 12 h. R30 was observed to have the lowest methane yield rate of 0.670 0.203 L CH4 d1, followed by R60 (0.906 0.357 L CH4 d1) and RN (1.290 0.267 L CH4 d1). For the purpose of comparison study, only the distinction of collected gas-phase methane production of three reactors was significant and could reflect the impact of SRT. Although dissolved methane can be calculated by Henry’s Law, the loss of it in waste sludge and effluent was not accounted for in this study because the volume of waste sludge was small and the loss of dissolved methane in the effluents of the three SAnMBRs would be similar at any particular HRT. In addition, the methane yield rate in terms of COD removal (Table 1) was comparable to other studies (Ince et al., 1997; Wen et al., 1999; Saddoud et al., 2007),ranging from 0.05 to the theoretical value of 0.35 L CH4 g COD1 (Hall, 1992),which indicated the feasibility of energy recovery by this SAnMBR system. Conversely, the Rs in R30 was higher than
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10 30 6974 560 6352 501 0.91 0.02 97.9 3.1 92.5 3.2 0.965 0.160 0.124 0.012 0.026 0.006
10 60 8865 955 7948 835 0.90 0.02 98.2 1.5 70.9 5.3 1.437 0.307 0.178 0.036 0.031 0.007
10 infinite 9069 906 7906 868 0.87 0.03 98.4 1.3 90.5 2.2 1.474 0.287 0.219 0.027 0.031 0.006
8 30 7074 781 6500 737 0.92 0.02 98.8 1.1 95.0 1.7 1.250 0.273 0.136 0.017 0.033 0.008
8 60 8698 1220 7324 1085 0.90 0.04 98.0 1.6 81.4 8.4 2.079 0.605 0.24 0.043 0.037 0.010
8 infinite 10452 788 9378 717 0.84 0.02 94.5 4.9 86.2 5.3 2.194 0.735 0.25 0.041 0.053 0.020
the corresponding values of R60 and RN at an HRT of 12 h (also at HRT of 10 and 8 h). This could possibly due to a higher organic matter adsorption rate but a lower degradation rate in R30, since organic substrates were removed by the adsorptionedegradation pathway and microorganisms at the exponential growth stage, such as in R30 with short SRT, had more absorbability. The lower biogas production in R30 suggested a lower degradation rate, in that less carbon source or energy was converted to methane. Meanwhile, RN showed higher total and specific methane yield rates, which suggested that SAnMBR with a longer SRT at HRT of 12 h would benefit the growth of methanogenesis and become more efficient in terms of methane production. As HRT was reduced from 12 to 8 h (resulting in OLR increase from 1.10 to 1.65 kg COD m3 d1), average MLVSS concentration increased and eventually stabilized at 7074, 8698 and 10,452 mg L1 for R30, R60 and RN, respectively. Statistical tests showed that at HRTs of 10 and 8 h, at 5% significance level, R30 had the highest Rs, the lowest MLVSS concentration and the lowest methane yield rate. This phenomenon was similar at HRT of 12 h due to the same reasons mentioned earlier. Hence, a lowest methanogenic activity was occurring in R30 than the other two SAnMBRs e R60 and RN. To determine the different biomass activity in the three SAnMBRs, batch testing of SMA by cultivating biomass with SRT of 30, 60 and infinite day and HRT of 10 h was carried out. Because approximately 70% of the methanogenesis is converted through acetate route (Haandel and Lettinga, 1994), sodium acetate was used as the substrate of SMA test and a higher methane production would imply a higher acetoclastic-mehanogenesis activity. Fig. 3a showed that more methane was produced by the biomass from RN and R60 than R30. This observation implied that the acetoclastic methanogens became more dominant in microorganisms at longer SRT. Except the differences mentioned above, similar higher biomass concentration, biogas yield rate and specific biogas yield rate were observed in R60 and RN at HRTs of 10 and 8 h (Table 1). This suggested that the effect of SRT on the treatment performance of SAnMBR was significant when the SRT was short, e.g. 30 d. But with a prolonged SRT, especially longer than 60 d, the effect became less pronounced.
a Mean value standard deviation. b n ¼ 23 for 12-h HRT, 22 for 10-h HRT, 10 for 8-h HRT.
12 Infinite 6497 1140 5383 1039 0.82 0.03 97.6 2.1 87.9 3.3 1.290 0.267 0.205 0.049 0.056 0.012 12 60 5724 663 5218 717 0.91 0.05 98.7 0.9 87.0 9.2 0.906 0.357 0.171 0.039 0.028 0.012 12 30 5582 561 5132 520 0.92 0.04 99.1 0.6 94.2 3.2 0.670 0.203 0.138 0.031 0.023 0.008 HRT (h) SRT (d) MLSSa,b (mg L1) MLVSS (mg L1) MLVSS/MLSS Rt (%) Rs (%) CH4 yield (L CH4 d1) CH4 yield (L CH4 g COD1) Specific CH4 yield (L CH4 g MLVSS1 d1)
R60(12) R30(12) Parameter
Table 1 e Treatment performance at different SRT and HRT.
RN(12)
R30(10)
R60(10)
RN(10)
R30(8)
R60(8)
RN(8)
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3.3.
Membrane fouling
Although only a slight difference in biomass concentration was observed among the three SAnMBRs, they experienced very different fouling development.
3.3.1.
Effect of HRT
The influence of HRT on TMP was observed to be similar for all the SAnMBRs. That is, when HRT was decreased, TMP increased faster to the value of 30 kPa, a point when chemical cleaning was required. For example, at an HRT of 12 h, the membrane fouling happened within around 90 d, which was shortened to two months when the HRT was reduced to 8 h (Fig. 4a and c). This phenomenon was due firstly, to the increase of biomass concentration resulted from an increase in OLR as the HRT was reduced, which greatly enhanced membrane fouling rate; Secondly, membrane fouling was significantly affected by SMP compositions. For instance, in
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a
surface and into the membrane pores. Based on the finding of Meng et al. (2006), proteins are more hydrophobic, adhere more easily to membrane surface and induce membrane fouling. Therefore, a lower C/P ratio in the SAnMBR with an HRT of 8 h would contribute to more severe membrane fouling (Fig. 4). Thirdly, EPS compositions also played a primary role in membrane fouling. According to Yamamoto et al. (1989), the major fraction of SMP was the soluble phase of EPS, and SMP consistently varied with EPS in the aerobic MBR. But based on statistical analysis, there was no significant difference in EPS composition for the three HRTs of R60 (Fig. 5c). The protein and carbohydrate concentration, as well as C/P ratio in EPS were not affected by the change of HRT.
b
3.3.2.
Effect of SRT
The impact of SRT on membrane fouling was distinctive at different HRTs. Fig. 4a shows the normalized TMP profile of the three SAnMBR at HRT of 12 h, which consisted of two
a
c
b
Fig. 3 e (a) Specific methanogenic activity (SMA); (b) and (c) Particle size distribution of biomass with different SRT at HRT of 10 and 8 h, respectively.
R60, although the absolute carbohydrate concentrations of SMP were rather similar at HRT of 8, 10 and 12 h, the absolute protein concentration was the highest at HRT of 8 h, followed by HRTs of 10 and 12 h in decreasing order (Fig. 5a). Due to the highest OLR at HRT of 8 h, more un-degraded substrates/SMP were present in the supernatant of mixed liquor (Fig. 5a), which increased the possibility of external and internal pore plugging and biocake/biofilm growth when it was drawn onto the membrane surface by permeation. However, because of the highest biomass concentration occurring in the SAnMBR with an HRT of 8 h, the specific SMP concentration, which represents the SMP concentration per gram MLVSS, showed a reverse trend, i.e., lowest specific SMP concentration at HRT of 8 h (Fig. 5b). In addition, the Carbohydrate/Protein (C/P) ratio was found to reduce from 1.11 to 0.49 with decreasing HRT. This means, to produce per unit volume of effluent, more protein-type components were introduced to the membrane
c
Fig. 4 e TMP profiles at HRT of (a) 12 h; (b) 10 h; and (c) 8 h for the three SAnMBRs operated under three different SRTs.
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a
b
c
Fig. 5 e (a) SMP; (b) specific SMP; and (c) EPS compositions at different HRTs for R60.
stages: horizontal and exponential increase. During the initial 60 d, TMPs increased negligibly in R30, R60 and RN. However after 60 d, TMPs climbed up exponentially and fastest TMP increase was observed in RN, followed by R60 and R30. The initial similarity was possibly due to similar MLSS concentrations (Table 1). Although prolonged SRT could abate the amount of waste sludge and retain more biomass in the reactor, there was only a slight increase of MLSS concentration by about 1000 mg,L1 when the SRT was increased from 30 d to infinite days. Due to a much low OLR at HRT of 12 h, multiplication of slow-growth anaerobic microorganisms were significantly limited regardless of how long the SRT was. Therefore, during the first initial 60 d (Fig. 4a), the effect of SRT on biomass concentrations was not significant, which resulted in negligible increase in TMP for the three SAnMBRs at different SRTs. The initial stage was characterized as modification of membrane surface by the gradual accumulation of biomass, soluble compounds, and insoluble organic and inorganic particles on the surface or within the membrane
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pores. At the end of this stage, membrane modification e pore size reduction and thin biocake/biofilm development on membrane surface e would aggregate membrane fouling propensity and result in different subsequent TMP exponential increases. Fig. 6a and d compared the EPS and absolute SMP composition at HRT of 12 h for three different SRTs. No significant differences were found in the protein and carbohydrate concentrations of EPS, although the C/P ratios at RN were slightly lower than those at R30 and R60 (Fig. 6a). As EPS composition closely affects particle flocculation (Meng et al., 2006), particle size distribution was measured. Similar median particle sizes, ranging from 24 to 31 mm, were detected and the results showed that SRT did not affect EPS composition as well as particle size significantly at an HRT of 12 h. However, the comparison of SMP showed higher protein and carbohydrate concentrations in R60 and RN with longer SRTs (Fig. 6d). At a long operating SRT, 60 d or infinite days in this case, cultured microorganisms were in endogenous growth which had lower metabolism rates e less nutrition take-up and degradation. This weak activity led to higher SMP concentrations remaining in the SAnMBRs, which accelerated the modification of membrane surface, promoted the growth of biocake/biofilm layer and finally caused faster TMP increases in R60 and RN. Nevertheless, the TMP profiles at HRTs of 10 and 8 h developed differently. As shown in Fig. 4b and c, regardless of the operating HRT, RN experienced most rapid membrane fouling followed by R60 and R30. The initial horizontal stage became shorter with increased SRT. At shorter HRT (i.e., 10 and 8 h), the differences of MLSS concentration under the three SRTs became significant. Owing to increased OLR (reduced HRT), much higher biomass growth was observed, particularly in SAnMBRs with longer SRTs. The stable MLSS concentrations, in ascending order, were 6974, 8865 and 9069 mg L1 at HRT of 10 h, and 7074, 8698 and 10,452 mg L1 at HRT of 8 h for R30, R60 and RN, respectively. Higher MLSS concentration stemmed from longer SRT would enhance membrane fouling rate and induce a faster TMP increase. In addition, the analysis of SMP components showed consistently higher protein concentrations, higher carbohydrate concentrations and higher C/P ratios in R60 and RN, and much lower values in R30 (Fig. 6e and f). Considering that microorganisms in short SRT (30 d) metabolized more actively, more organic compounds were metabolized and less SMP were produced, which restricted particulate deposition, biofilm growth as well as membrane fouling. For R60 and RN, higher C/P ratios might be explained by two reasons. Firstly, because no proteins were added in the synthetic influent, proteins found in the supernatant only had one source e products of microorganisms by utilizing inorganic nitrogen (NH4Cl) and metabolically releasing into the liquid medium. But carbohydrates had two sources e original substrates and metabolism products. Secondly, the microorganisms under specific endogenous stage had lower substrate utilization, appearing as higher carbohydrate concentrations in SMP. Therefore, higher carbohydrate concentration coupled with rather constant protein concentration induced a higher C/P ratio at longer SRT. Fig. 6b and c shows the result of EPS compositions for HRT of 10 and 8 h, respectively. In contrast to absolute SMP, a reverse decrease trend was found in EPS with increase in
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a
d
b
e
c
f
Fig. 6 e (a), (b) and (c) EPS compositions at HRT of 12, 10 and 8 h, respectively; and (d), (e) and (f) SMP compositions at HRT of 12, 10 and 8 h, respectively.
SRT. The lowest carbohydrate and protein concentrations of EPS in RN were observed, followed by R60 and R30 in ascending order. In addition, based on the knowledge of microorganism growth, the biomass in RN had a lower metabolism rate - consuming less food and generating less microbial products. Therefore, EPS concentrations in RN were comparably lower than those at the other two SRTs. This observation suggested that finer particles would be present in RN with infinite SRT due to reduced flocculation in the presence of lesser amount of EPS. The measurement of particle size had proven this hypothesis. The biggest median particle sizes were found in R30 at 47 and 42 mm for 10 and 8-h HRTs, respectively (Fig. 3b and c). One the other hand, the smallest median particle sizes were found in RN at 26 and 29 mm for 10 and 8-h HRTs, respectively (Fig. 3b and c). Hence, even though lower EPS was reported to limit the attachment of particles to form biofilm/biocake on membrane surface and slow membrane fouling (Ng and Hermanowicz, 2005), finer particle induced by lower EPS in RN inversely accelerated fouling development. There was no significant difference in the C/P ratio of EPS for the three SAnMBRs.
4.
Conclusions
This study showed that during the startup of the SAnMBRs, foaming and short filaments proliferated and formed a supporting framework for dispersed microorganisms. This phenomenon could be attributed to the high shear force generated by the gas scouring. From the performance analysis of the three SAnMBRs operated at different SRTs and HRTs, it was shown that the SAnMBR could achieve excellent treatment performance in terms of COD removal and biogas production for treating lowstrength synthetic wastewater in long-term operation. A longer SRT operation achieved a better treatment performance. Biomass concentration and biogas production rate were affected significantly by both SRT and HRT. With a shorter HRT, biomass concentration was higher, which consequently resulted in higher methane production. On the other hand, a longer SRT would benefit methanogenesis and lead to more biogas generation. The effect of SRT on membrane fouling can be classified based on different HRT conditions. At a longer HRT (12 h), membrane fouling development consisted of horizontal and
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 0 5 e7 1 3
exponential increase stages, in which the former was due to the similar MLSS concentrations at different SRTs, and the latter was resulted from discrepant membrane surface modifications by soluble organic compounds and biomass deposition. However, at a shorter HRT (10 or 8 h), infinite SRT resulted in a higher MLSS concentration, which induced faster membrane fouling. It was also found that a longer SRT would cause higher SMP production, which introduced more nutrients to the membrane surface that induced more pore blocking in membrane and enhanced biofilm/biocake development. In addition, with prolonged SRT, lower protein and carbohydrate concentrations in EPS resulted in less particle flocculation and smaller particle size, which could explain an accelerated fouling development at longer SRT. For application of SAnMBR, too short an HRT or too long an SRT would not be recommended due to higher biomass concentrations, higher SMP and lower EPS concentrations that had a negative impact on membrane fouling. The optimized operation conditions e HRT and SRT should be determined by excellent treatment performance and minimized membrane fouling potential. Adjusting HRT or SRT can be an effective approach to avoid unexpected performance deterioration and for membrane fouling control in SAnMBR systems. This study showed that through the optimization of operating parameters in SAnMBR using synthetic wastewater, SAnMBR could achieve satisfactory treatment efficiency, biogas production and membrane fouling control though biogas scouring. This study also suggested that SAnMBR could be potentially used for treatment of real domestic wastewater.
Acknowledgement The authors acknowledge the financial support of the Ministry of Education, Singapore (WBS: R-288-000-025-133) for this project.
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a stirred tank reactor coupled with a membrane filtration unit. Water Res. 37 (4), 902e908. Ganidi, N., Tyrrel, S., Cartmell, E., 2009. Anaerobic digestion foaming causes - A review. Bioresour. Technol. 100 (23), 5546e5554. Haandel,., A.C.v, Lettinga, G. (Eds.), 1994. Anaerobic Sewage Treatment: a Practical Guide for Regions with a Hot Climate. J. Wiley, Chichester, New York. Hall, E.R., 1992. In: Malina, J.F., Pohland, F.G. (Eds.), Anaerobic Treatment of Wastewaters in Suspended Growth and Fixed Film Processes, Design of Anaerobic Processes for the Treatment of Industrial and Municipal Wastes. Technomic Publishing Co. Ltd., Lancaster, PA, pp. 41. Hall, E.R., Be´rube´, P.R., 2006. Membrane bioreactors for anaerobic treatment of wastewaters: Phase II, WERF and IWA. He, Y., Xu, P., Li, C., Zhang, B., 2005. High-concentration food wastewater treatment by an anaerobic membrane bioreactor. Water Res. 39 (17), 4110e4118. Ince, O., Anderson, G.K., Kasapgil, B., 1995. Control of organic loading rate using the specific methanogenic activity test during start-up of an anaerobic digestion system. Water Res. 29 (1), 349e355. Ince, O., Anderson, G.K., Kasapgil, B., 1997. Composition of the microbial population in a membrane anaerobic reactor system during start-up. Water Res. 31 (1), 1e10. Jawed, M., Tare, V., 1999. Microbial composition assessment of anaerobic biomass through methanogenic activity tests. Water SA 25 (3), 345e350. Jeison, D., Lier,van, J.B., 2006a. On-line cake-layer management by trans-membrane pressure steady state assessment in anaerobic membrane bioreactors for wastewater treatment. Biochem. Eng. J. 29 (3), 204e209. Jeison, D., Lier,van, J.B., 2006b. Cake layer formation in anaerobic submerged membrane bioreactors (AnSMBR) for wastewater treatment. J. Memb. Sci. 284 (1e2), 227e236. Kang, I.J., Yoon, S.H., Lee, C.H., 2002. Comparison of the filtration characteristics of organic and inorganic membranes in a membrane-coupled anaerobic bioreactor. Water Res. 36 (7), 1803e1813. Kim, J.O., Kim, Y.H., Ryu, J.Y., Song, B.K., Kim, I.H., Yeom, S.H., 2005. Immobilization methods for continuous hydrogen gas production biofilm formation versus granulation. Process Biochem. 40 (3e4), 1331e1337. Kim, J.S., Lee, C.H., Chang, I.S., 2001. Effect of pump shear on the performance of a crossflow membrane bioreactor. Water Res. 35 (9), 2137e2144. Liao, B.Q., Kraemer, J.T., Bagley, D.M., 2006. Anaerobic membrane bioreactors: applications and research directions. Crit. Rev. Environ. Sci. Technol. 36 (6), 489e530. Meng, F., Zhang, H., Yang, F., Li, Y., Xiao, J., Zhang, X., 2006. Effect of filamentous bacteria on membrane fouling in submerged membrane bioreactor. J. Memb. Sci. 272 (1e2), 161e168. Ng, H.Y., Hermanowicz, S.W., 2005. Membrane bioreactor operation at short solids retention times: performance and biomass characteristics. Water Res. 39 (6), 981e992. Saddoud, A., Ellouze, M., Dhouib, A., Sayadi, S., 2007. Anaerobic membrane bioreactor treatment of domestic wastewater in Tunisia. Desalination 207 (1e3), 205e215. Sutton, P.M., Be´rube´, P., Hall, E.R., 2004. Membrane bioreactors for anaerobic treatment of wastewaters: Phase I, WERF and IWA. Vallero, M.V.G., Lettinga, G., Lens, P.N.L., 2005. High rate sulfate reduction in a submerged anaerobic membrane bioreactor (SAMBaR) at high salinity. J. Memb. Sci. 253 (1e2), 217e232. Wen, C., Huang, X., Qian, Y., 1999. Domestic wastewater treatment using an anaerobic bioreactor coupled with membrane filtration. Process Biochem. 35 (3e4), 335e340. Yamamoto, K., Hiasa, M., Mahmood, T., Matsuo, T., 1989. Direct solidliquid separation using hollow fiber membrane in an activatedsludge aeration tank. Water Sci. Technol. 21 (4e5), 43e54.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 1 4 e7 2 0
Available at www.sciencedirect.com
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Reactivity of mucohalic acids in water Rafael Go´mez-Bombarelli, Marina Gonza´lez-Pe´rez, Emilio Calle, Julio Casado* Departamento de Quı´mica fı´sica, Facultad de Ciencias Quı´micas Universidad de Salamanca, Plaza de los Caı´dos, s/n E-37008 Salamanca, Spain
article info
abstract
Article history:
One group of disinfection byproducts of increasing interest are the halogenated furanones,
Received 5 May 2010
which are formed in the chlorination of drinking water. Among these halofuranones is
Received in revised form
mucochloric acid (MCA, 3,4-dichloro-5-hydroxyfuran-2(5H )-one), and mucobromic acid
13 July 2010
(MBA, 3,4-dibromo-5-hydroxyfuran-2(5H )-one). Both mucohalic acids (MXA) are direct
Accepted 22 August 2010
genotoxins and potential carcinogens, with the capacity to alkylate the DNA bases
Available online 27 August 2010
guanosine, adenosine and cytosine, and they have been measured in concentrations ranging up to 700 ng/l in tap water. MCA and MBA react in basic aqueous medium to form
Keywords:
mucoxyhalic acids (4-halo-3,5-hydroxyfuran-2(5H )-one). Since: i) this reaction may
Mucochloric acid
represent the first step in the abiotic decomposition of mucohalic acids, ii) mucoxyhalic
Mucobromic acid
acids have been proposed as possible intermediates in the reaction of MXA with DNA,
Chlorination by-products
a kinetic study of the reaction mechanism is of interest. Here, the following conclusions
MX
were drawn: a) At moderately basic pH, the reaction of mucohalic acids with OH to form mucoxyhalic acids is kinetically significant. b) The nucleophilic attack of hydroxide ions on MXA occurs through a combination of two paths: one of them is first-order in hydroxide whereas the other is second-order and are proposed to occur through the deprotonation of the hydrate of MXA. c) The hydration constants of mucohalic acids 0.23 and 0.17 for MCA and MBA respectively e corresponds to the very significant hydrate concentrations. Since hydrates are not electrophilic, these values imply a decrease in the alkylating capacity of mucohalic acids. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Disinfection by-products (DBPs) are formed when disinfectants such as chlorine, ozone, chlorine dioxide or chloramines react with naturally occurring organic matter, anthropogenic contaminants, bromide, and iodide during the production of drinking water (Richardson et al., 2007). These DBPs are responsible for most of the observed mutagenicity of chlorinated tap water (Kanniganti et al., 1992; Kronberg et al., 1991, 1988; Meier et al., 1988). A group of DBPs of increasing interest and yet to be regulated are the halofuranones, which are formed in the chlorination of organic matter (i.e., humic substances), and whose
genotoxic and carcinogenic properties are well known (IARC, 2004; McDonald and Komulainen, 2005). Among these halogenated furanones formed in chlorination is mucochloric acid (MCA, 3,4-dichloro-5-hydroxyfuran-2(5H )-one), which, like its analogue mucobromic acid (MBA, 3,4-dibromo-5-hydroxyfuran-2(5H )-one), is a direct genotoxin and a potential carcinogen (Fekadu et al., 1994; Jansson et al., 1995; Knasmuller et al., 1996; Liviac et al., 2009). Both mucohalic acids (MXA) are also known to alkylate the DNA bases guanosine, adenosine and cytosine, both in the form of monomers and forming part of DNA, giving rise to etheno, oxalo etheno and halopropenal derivatives (Kronberg et al., 1996, 1993, 1992; LeCurieux et al., 1997).
* Corresponding author. Tel.: þ34 923 294486; fax: þ34 923 294574. E-mail address:
[email protected] (J. Casado). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.08.040
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 1 4 e7 2 0
Mucochloric acid, along with other halogenated furanones, such as the highly mutagenic MX and its chlorinated and brominated analogues, has been detected in drinking waters (plant effluent) at concentrations up to 1 mg/l (Krasner et al., 2006; Kronberg and Franzen, 1993; Kronberg et al., 1988; Smeds et al., 1997). The total concentration of MCA in plant effluent from U.S. water treatment plants ranges from below detection limit (0.02 mg/l) up to 0.71 mg/l, depending on the location of the plant, the season, the amount of natural organic matter and the chlorination product used (Onstad et al., 2008). Mucohalic acids are known to react in basic aqueous media to form mucoxyhalic acids (MOXA): the a halogen atom of the carboxyl group undergoes nucleophilic substitution by a hydroxide ion (Wasserman and Precopio, 1952, 1954). The direct product of this reaction is the enol tautomer of MOXA (MOXAen), which is in tautomeric equilibrium with the keto tautomer (MOXAket), as is shown in Scheme 1. The reaction of MXA to form MOXA is of ecological importance, since it may represent the first step in a possible abiotic pathway for the degradation of mucohalic acids in the environment. Little is known about the reaction products, mucoxyhalic acids, and their potential environmental and health effects. For instance, mucoxyhalic acids are known to be unstable in acidic conditions (Mowry, 1950; Wasserman and Precopio, 1952) and have been suggested to undergo decarboxylation, yielding highly toxic malondialdehydes (Kronberg et al., 1996, 1993, 1992; LeCurieux et al., 1997). These effects could become significant, should the formation of mucoxyhalic acids be favored under environmentally relevant conditions or during water treatment processes. In addition, the formation of mucoxyhalic acids has been proposed as a possible initial step in the reaction of MXA with DNA bases (Klika et al., 2006; Kronberg et al., 1996, 1993; LeCurieux et al., 1997). Whereas currently some evidence has discarded mucoxyhalic acids as intermediates in the alkylation reaction of mucohalic acids (Asplund et al., 1995; Ma¨ki et al., 1999), a kinetic study of the formation of MOXA is still lacking.
2.
Materials and methods
Mucochloric acid (99%), mucobromic acid (99%) and D2O were purchased from Aldrich. NaOH (99%) was from Panreac. Water was purified using a MiliQ system.
2.1.
Kinetics of MXA decomposition
Reactions were carried in NaOH solutions (pH 11e13.5) that had been previously normalized with potassium hydrogen phthalate. The final concentration of MXA was in the
Scheme 1 e Reaction of Mucohalic Acids with OHL.
715
1 105e1 104 M range and the applied concentration of sodium hydroxide was between 0.01 and 0.50 M for MCA, and 0.005 and 0.10 M for MBA. Reactions were monitored between 15 and 35 C. The experimental procedure was as follows: 50 ml of MXA (5 104e5 103 M) in slightly acidic aqueous solution (pH w4.5) was delivered with a Hamilton syringe to a Hellma quartz UVeVis cuvette (10.0 mm optical length) containing 3 cm3 of the thermostated NaOH solution. Immediately after the mixture was complete, data acquisition was started. Reaction kinetics were monitored at l ¼ 255 and 290 nm for MCA and l ¼ 275 nm for MBA. UVeVis spectra and kinetic measurements were carried out on a Shimadzu UV-2401-PC spectrophotometer equipped with a thermoelectric six-cell holder temperature control system ( 0.1 C). The temperature of the reaction mixtures prior to mixing was kept constant ( 0.05 C) with a Lauda Ecoline RE120 thermostat. The UVeVis, 1H NMR and mass spectra of the reaction products were consistent with those reported for MOXA in the literature.
2.2.
Hydration constants of MXA
The experimental determination of the hydration constants of the two mucohalic acids in neutral aqueous solution was performed as suggested in the literature (Greenzaid et al., 1967; Hooper, 1967). Briefly, solutions of mucohalic acids (0.15 M) were prepared in D2O, the pH values of these were adjusted to approximately 7.0 by the addition of small amounts of concentrated sodium hydroxide in D2O, and their 1H NMR were recorded at 25 C using a Brucker 400 MHz apparatus. By dividing the area of the peak corresponding to the hydrate (AreaMXA hyd) by the area of the main peak (AreaMxA), which corresponds to un-hydrated MXA, it is possible to calculate the hydration constants. Khyd ¼
3.
½MXA hyd AreaMXA hyd ¼ AreaMXA ½MXA
(1)
Results and discussion
Mucohalic acids are known to exist as an equilibrium between two tautomers: the open-chain aldehyde-acid and the closedchain lactone-lactol (Scheme 2). In neutral or basic medium, as used in this work, the open-chain form is the most abundant tautomer (Franzen et al., 1999; Mowry, 1950, 1953). The pKapp values for this equilibrium (Scheme 2) are 3.95 and 4.27 for MCA and MBA respectively, as measured by optical titration (Franzen et al., 1999). The reaction products, MOXA, have received only modest attention in the literature, and little is known about their toxicity, stability or tautomeric equilibria. Since the openchain tautomers of halogenated furanones are strong acids (due to the inductive and conjugative effects of halogen atoms and the double bond, respectively) the species of interest are anionic at the working pH, which hinders the use of the easily available models such as SPARC (SPARC Performs Automated Reasoning in Chemistry) to predict their properties, and ab initio or DFT computational approaches are needed.
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A ¼ l½MXAo 3MXA ekexp t þ 3MOXA 1 ekexp t ¼ l½MXAo 3MXA þ ð3MOXA 3MXA Þ 1 ekexp t A ¼ Ao þ DA 1 ekexp t Scheme 2 e Tautomerization of MXA.
Thus, we computed the free energy of MOCAen and MOCAket in aqueous solution at the DFT B3LYP 6e31þþG(2df, 2pd) level of theory with the Polarizable Continuum Model for solvation, using the Gaussian 03 suite of programs (Frisch et al., 2003). The difference in free energy between these two species is less than 1 kJ mol1 (DG ¼ Genol Gketo ¼ 0.3 kJ mol1), which suggests an equilibrium constant very close to unity, and hence, that neither of the two is major and both have similar concentrations in the reaction conditions.
(5)
(6)
Ao is the initial absorbance and DA is the difference in absorption between MXA and MOXA. Fig. 2 shows the excellent fit of the results to eq. (6). The reaction orders were determined from the slope of the logarithmic plots of logkexp against log [OH] (Fig. 3). The observed reaction orders with respect to hydroxide ions in the experimental conditions are 1.2 for MCA and 1.8 for MBA. These values suggest that the reaction takes place by both first-order and second-order reaction mechanisms, with rate and kOH constants kOH 1 2 , respectively (eq. (7)). OH
kexp ¼ k1
2 OH OH þ k2 OH
(7)
Since both MXA and MOXA show significant absorption at the wavelengths of measurement, the total absorbance is
Fig. 4 shows the excellent fit of the experimental kexp values to OH OH eq. (7). The values obtained for k1 and k2 are shown in Table 1. The variation in the rate constants with temperature allows calculation of the activation energies using the Arrhenius equation; the values are shown in Table 2. are somewhat higher for MBA, which The values for kOH 1 together with the lower activation energies is consistent with is almost bromide as the better leaving group. Moreover, kOH 2 two hundred times larger in the case of MBA, which can be interpreted in terms of the proposed mechanism (see below and Scheme 3). In general, the high activation energies are consistent with the attack of a hard-charged nucleophile such as the hydroxide ion. These results show that the reaction of mucohalic acids with hydroxide ions to form mucoxyhalic acids becomes kinetically significant only at moderately high concentrations of OH, and hence the reaction rate when the pH value is close to neutrality can be considered negligible. Since the extracellular pH is approximately 7.40 (Ross and Boron, 1981) and pH levels inside human cells are about 7.00e7.40 (Bright et al., 1987) the formation of MOXA from MXA in vivo is expected to be negligible. This is in agreement with current evidence suggesting that MOXA do not participate in the alkylation reactions of mucohalic acids (Asplund et al., 1995; Ma¨ki et al., 1999).
Fig. 1 e Variation with time in the UVeVis spectra of MXA in the reaction with OHL; T [ 25.0 C, [OHL] [ 3.3 3 10L2 M; a) X [ Cl, [MCA] [ 7.5 3 10L5 M, b) X [ Br, [MBA] [ 1.1 3 10L4 M.
Fig. 2 e Kinetic profiles of the reaction of MXA with OHL; T [ 25.0 C; a) X [ Cl, [OHL] [ 2.6 3 10L2 M, l [ 290 nm; [MCA] [ 3 3 10L5 M; b) X [ Br, [OHL] [ 6.5 3 10L3 M, [MBA] [ 4 3 10L5 M, l [ 275 nm.
3.1.
Kinetics of MXA decomposition
The reactions were followed by UVeVis spectroscopy. In the case of MCA, its disappearance was monitored at l ¼ 255 nm, and the appearance of MOCA at l ¼ 290 nm (Fig. 1a); equal rate constants were obtained in both cases. In the case of MBA, owing to the low molar absorption of MOBA only the disappearance of MBA was observed (l ¼ 275 nm; Fig. 1b). The following rate equation describes the formation of MOXA from MXA as illustrated in Scheme 1: d½MOXA ¼ kOH OH ½MXA dt
(2)
Since hydroxide ions were present in large excess, their concentration was considered to remain constant, and thus the pseudo-first-order approximation was used, such that kexp ¼ kOH [OH], as in eq. (3), and its integrated counterpart, eq. (4). d½MOXA ¼ kexp ½MXA dt ½MOXA ¼ ½MXAo 1 ekexp t
(3)
(4)
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Table 1 e Rate Constants for the Reaction of MXA with OHL. T ( C)
MCA 4 OH
10 k1 (M1 s1) 15.0 20.0 25.0 30.0 35.0
Fig. 3 e Variation in kexp with pH. Reaction order with respect to [OHL]. T [ 25.0 C.
MXA do not decompose to MOXA at the common pH levels of tap water, 6.5e8.5 being the values recommended by the US Environmental Protection Agency in the National Secondary Drinking Water Regulations (US-Cfr, 2002), or those of surface waters, since the global median pH value is 7.7, most average annual pH values are between 6.5 and 8.3, and very rarely exceed 9.0 (GEMStat, 2010). Therefore, the amount of time elapsed since water treatment is not expected to modify exposure to MXA or MOXA. A situation worth noting could arise in the case of prechlorination followed by lime softening, where the pH values are raised to high levels such as those used in this work (pH > 10). At these high concentrations of OH, the formation of mucoxyhalic acids is kinetically significant. Whereas the environmental fate of MOXA is unknown, in the acidification following lime softening, they could undergo decarboxylation and evolve into malondialdehydes, which are known carcinogens. The quadratic dependence of kexp on [OH] suggests a mechanism involving two successive reactions with hydroxide ions. Such a dependence has often been documented in the hydrolysis of carbonyl compounds such as anilides (Biechler and Taft, 1957; Pollack and Bender, 1970), arylaminoacrolein (Ono et al., 1989), and acylpyrroles (Menger and Donohue, 1973) or sulfonyl compounds such as sultams
3.5 7.0 13.0 22.9 41.2
MBA 4 OH
10 k2 (M2 s1)
10 k1 (M1 s1)
102kOH 2 (M2 s1)
0.5 0.4 0.7 2.3 3.2
7.0 1.1 13.4 1.6 20.8 1.6 35.0 2.1 59.6 1.1
3.4 0.3 5.6 0.2 10.6 0.2 15.9 0.3 28.1 1.2
0.1 0.2 0.3 1.0 1.3
9.8 17.7 31.1 49.6 88.4
4 OH
(Baxter et al., 2000; King et al., 1996; Page, 2004). The existence of a second-order term is generally interpreted as coming from the reversible addition of the first hydroxide anion to the carbonyl group and the subsequent deprotonation of the hydroxyl group formed by the second OH, resulting in a dianion, which expels the leaving group. The second-order term measured in this work is, to our knowledge, a novelty with respect to those described before, since in the reaction of mucohalic acids with hydroxide ions the leaving group is not located on the carbonylic carbon. Therefore, some variation in the accepted hydrolysis mechanism can be proposed, such as two successive attacks of the hydroxide ion on different electrophilic sites, the first one being a fast equilibrium and the second one the actual nucleophilic substitution. Alternatively, a deprotonation followed by substitution of the halogen atom could be proposed. Since the only hydrogen atom in MXA is not expected to dissociate in the working pH interval, and, since aldehydes have high hydration constants, we suggest a possible reaction mechanism that could help to understand the observed results (Scheme 3). In the proposed mechanism, mucohalic acids react directly in a first-order with hydroxide ions, with rate constant kOH 1 reaction. At the same time, MXA can add water to form the corresponding hydrate, the equilibrium constant for the formation of the gem-diol being Khyd ¼ MXAhyd =MXA. The hydrate hyd can, in turn, undergo deprotonation ðKa Þ; the sum of these two first steps is equivalent to the reversible attack of a hydroxide anion on the carbonyl group. The deprotonated hydrate can subsequently react with OH ðkOH hyd Þ. The predicted rate equation arising from this mechanism is eq. (8), which has both first-order and second-order terms, as observed experimentally. d½MOXA OH OH 2 ½MXA ¼ k1 OH þ Khyd Khyd a khyd OH dt
(8)
OH
The product Khyd Khyd a khyd would equal the experimental rate OH OH constant k2 . The large difference observed in k2 for MCA
OH Table 2 e Activation Energies for kOH 1 and k2
Ea (kJ mol1) Fig. 4 e Fit of experimental values of kexp to eq. (7). a) X [ Cl; b) X [ Br. - 35.0 C, 6 30.0 C,; 25.0 C, B 20.0 C, C 15.0 C.
MCA MBA
kOH 1
kOH 2
90.0 1.1 77.3 2.2
80.1 1.5 77.4 2.4
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Table 3 e Hydration Constants of Mucohalic Acids. Khyd (unitless) 0.23 0.04 0.17 0.03
MCA MBA
Scheme 3 e Proposed reaction mechanism of MXA with OHL.
OH
and MBA can be explained in terms of a difference in khyd (suggesting an inherently higher reactivity of MBA as (suggesting that the hydrate of compared with MCA), in Khyd a MBA is more acidic than that of MCA), or in Khyd (suggesting greater electrophilicity of the aldehyde group in MBA) or any combination of the three. To gain more information about the proposed mechanism, the hydration constants of mucohalic acids were determined using 1H NMR spectroscopy.
3.2.
Hydration constants of mucohalic acids
Since aldehydes in their hydrate form cannot react with nucleophilic sites in DNA, the hydration of aldehydes is also significant in their role as alkylating and potentially mutagenic and carcinogenic agents. For instance, at any given pH, 99.997% of chloral in aqueous solution is in its hydrate form, which significantly reduces its potential reactivity as an electrophile, and this in turn influence its genotoxic potential (Daniel et al., 1992; Haselkorn et al., 2006; Salmon et al., 1995; Seng et al., 2003). The 1H NMR spectra of mucohalic acids in neutral and basic aqueous solution show one major signal, belonging to the hydrogen atom of the aldehyde group (dMCA ¼ 9.696 ppm; dMBA ¼ 9.409 ppm). A minor signal is also observed, and its chemical shift (dMCA ¼ 6.110 ppm; dMBA ¼ 5.715 ppm), similar to that of gem-diols, hemiacetals, and acetals, suggests that this peak corresponds to the hydrate of the aldehyde group. The ratio of the two peak areas are time- and concentration- independent. By introducing the peak areas in eq. (1), the values shown in Table 3 were obtained. The hydration constants are somewhat lower than those of other haloaldehydes, such as chloroacetaldehyde, trichloroacetaldehyde, 2-chlorobutyraldehyde or 2-bromobutyraldehyde, which have Khyd values of 101.6, 104.45, 101.2 and 100.6, respectively (Bell, 1966; Go´mezBombarelli et al., 2009; Hilal et al., 2005). This suggests that the presence of the double bond, together with the conjugation with the carboxylate group, decreases the electrophilicity of the aldehyde group, and hence the hydration constant. Nevertheless, the amount of hydrate in the equilibrium is
significant, since close to 20% of MXA is present in this form. Because aldehyde hydrates lack the electrophilicity of the free aldehyde group, these values imply a decrease in the alkylating capacity of mucohalic acids. This high concentration of hydrate is also consistent with the hypothesis of hydrates playing a role in the proposed mechanism. Since the hydration constants of both compounds are very similar, the large difference in kOH 2 between MBA and MCA e one hundred-fold e could be due both to the acidity of the hydrates hyd ðKa Þ and to the inherent reactivity of the deprotonated hydrate OH ðkhyd Þ. A higher kOH hyd for MBA should be expected since, as stated above, bromide is a better leaving group, and this tendency may be increased by the presence of one extra negative charge in the molecule, as in the deprotonated hydrate. hyd Whereas the experimental determination of Ka is challenging, standard procedures exist for the computational calculation of acid dissociation constants (Ho and Coote, 2010). We have estimated the values at the DFT B3LYP 6e31þþG(d,p) level of theory with the Polarizable Continuum Model for solvation, using the Gaussian 03 suite of programs (Frisch et al., 2003). The hyd pKa values predicted were 23.0 for MCA and 23.6 for MBA. Absolute computational pKa values are known to show significant errors systematic errors and, as regards to the present work, only the difference between MCA and MBA is of interest. Since both values are almost equal within the error of the methodology applied (w1 log unit), the large difference in kOH 2 can only be attributed to bromide as the better leaving group ðkOH hyd Þ.
4.
Conclusions
From the present work, the following conclusions were drawn: (i) At moderately basic pH, the reaction of mucohalic acids with OH to form mucoxyhalic acids is kinetically significant. (ii) At the pH of natural or tap water (6.5 > pH > 8.5), mucohalic acids are not expected to degrade spontaneously. In cellular conditions (pH w7.40), mucohalic acids do not yield mucoxyhalic acids. Thus, MOXA are not expected to participate in the in vivo genotoxicity mechanism of the acids. (iii) The nucleophilic attack of hydroxide ions on mucohalic acids occurs through a combination of two paths, one of them first-order in [OH] and the other second-order in [OH], and could occur through the deprotonation of the hydrate of mucohalic acids. (iv) The hydration constants of mucohalic acids 0.23 and 0.17 for mucochloric and mucobromic acid respectively e correspond to very significant hydrate concentrations. Since aldehyde hydrates are not electrophilic, these values imply a decrease in the alkylating capacity of mucohalic acids.
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Acknowledgements The authors thank the Spanish Ministerio de Ciencia e Innovacio´n and European Regional Development Found (Project CTQ2007-63263) and the Junta de Castilla y Leo´n (Project SA040A08) for supporting the research reported in this article. R.G.B. thanks the Spanish Ministerio de Educacio´n and M.G.P. also thanks the Junta de Castilla y Leo´n for PhD grants. Thanks are also given to the reviewers for their valuable and insightful suggestions.
references
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Menger, F.M., Donohue, J.A., 1973. Base-catalyzed hydrolysis of N-Acylpyrroles. A measurable acidity of a steady-state tetrahedral intermediate. J. Am. Chem. Soc. 95 (2), 432e437. Mowry, D.T., 1950. Mucochloric acid .1. Reactions of the pseudo acid group. J. Am. Chem. Soc. 72 (6), 2535e2537. Mowry, D.T., 1953. Mucochloric acid .2. Reactions of the aldehyde group. J. Am. Chem. Soc. 75 (8), 1909e1910. Ono, M., Todoriki, R., Mitake, A., Yukawa, M., Goto, Y., Tamura, S., 1989. A kinetic study of alkaline hydrolysis of b-Arylaminoacrolein. Chem. Pharm. Bull. 37 (11), 2902e2908. Onstad, G.D., Weinberg, H.S., Krasner, S.S., 2008. Occurrence of halogenated furanones in U.S. Drinking waters. Environ. Sci. Technol. 42 (9), 3341e3348. Page, M.I., 2004. b-Sultams - mechanism of reactions and use as inhibitors of serine proteases. Acc. Chem. Res. 37 (5), 297e303. Pollack, R.M., Bender, M.L., 1970. The Alkaline hydrolysis of pNitroacetanilide and p-Formylacetanilide. J. Am. Chem. Soc. 92 (24), 7190e7194. Richardson, S.D., Plewa, M.J., Wagner, E.D., Schoeny, R., DeMarini, D.M., 2007. Occurrence, genotoxicity, and carcinogenicity of regulated and emerging disinfection by-
products in drinking water: a review and roadmap for research. Mutat. Res. 636 (1e3), 178e242. Ross, A., Boron, W., 1981. Intracellular pH. Physiol. Rev. 2, 296e434. Salmon, A.G., Kizer, K.W., Zeise, L., Jackson, R.J., Smith, M.T., 1995. Potential carcinogenicity of chloral hydrate e a review. J. Toxicol. Clin. Toxicol. 33 (2), 115e121. Seng, J.E., Agrawal, N., Horsley, E.T.M., Leakey, T.I., Scherer, E.M., Xia, S.J., Allaben, W.T., Leakey, J.E.A., 2003. Toxicokinetics of chloral hydrate in ad libitum-fed, dietary-controlled, and calorically restricted male B6C3F(1) mice following short-term exposure. Toxicol. Appl. Pharmacol. 193 (2), 281e292. Smeds, A., Vartianinen, T.M.-P., Ma¨ki-Paakkanen, J., Kronberg, L., 1997. Concentrations of Ames mutagenic chlorohydroxyfuranones and related compounds in drinking waters. Environ. Sci. Technol. 31 (4), 1033e1039. Us-Cfr, US Code of Federal Regulations. Title 40, vol. 19, Chapter I, Part 143.1e4 (2002). Wasserman, H.H., Precopio, F.M., 1952. Studies on the mucohalic acids .1. The structure of mucoxychloric acid. J. Am. Chem. Soc. 74 (2), 326e328. Wasserman, H.H., Precopio, F.M., 1954. Studies on the mucohalic acids .4. Replacement of halogen in the pseudo ester series. J. Am. Chem. Soc. 76 (5), 1242e1243.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 2 1 e7 3 1
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Multi-scale temporal and spatial variation in genotypic composition of Cladophora-borne Escherichia coli populations in Lake Michigan Brian D. Badgley a,b, John Ferguson a, Amy Vanden Heuvel c, Gregory T. Kleinheinz c, Colleen M. McDermott c, Todd R. Sandrin c, Julie Kinzelman d, Emily A. Junion d, Muruleedhara N. Byappanahalli e, Richard L. Whitman e, Michael J. Sadowsky a,b,* a
University of Minnesota, Department of Soil, Water, and Climate, 1991 Upper Buford Circle, 439 Borlaug Hall, St. Paul, MN 55108, United States b BioTechnology Institute, University of Minnesota, St. Paul, Minnesota, United States c Department of Biology and Microbiology, University of Wisconsin-Oshkosh, Oshkosh, WI, United States d City of Racine Health Department, Racine, WI, United States e Lake Michigan Ecological Research Station, United States Geological Survey, Porter, IN, United States
article info
abstract
Article history:
High concentrations of Escherichia coli in mats of Cladophora in the Great Lakes have raised
Received 24 May 2010
concern over the continued use of this bacterium as an indicator of microbial water quality.
Received in revised form
Determining the impacts of these environmentally abundant E. coli, however, necessitates
21 August 2010
a better understanding of their ecology. In this study, the population structure of 4285 Clado-
Accepted 23 August 2010
phora-borne E. coli isolates, obtained over multiple three day periods from Lake Michigan Cla-
Available online 28 September 2010
dophora mats in 2007e2009, was examined by using DNA fingerprint analyses. In contrast to
Keywords:
large time scales and distances, the extensive sampling done here on free-floating mats over
E. coli
successive days at multiple sites provided a large dataset that allowed for a detailed exami-
Cladophora
nation of changes in population structure over a wide range of spatial and temporal scales.
Population structure
While Cladophora-borne E. coli populations were highly diverse and consisted of many unique
Variability
isolates, multiple clonal groups were also present and accounted for approximately 33% of all
Indicator bacteria
isolates examined. Patterns in population structure were also evident. At the broadest scales,
Great Lakes
E. coli populations showed some temporal clustering when examined by year, but did not show
previous studies that have been done using isolates from attached Cladophora obtained over
good spatial distinction among sites. E. coli population structure also showed significant patterns at much finer temporal scales. Populations were distinct on an individual mat basis at a given site, and on individual days within a single mat. Results of these studies indicate that Cladophora-borne E. coli populations consist of a mixture of stable, and possibly naturalized, strains that persist during the life of the mat, and more unique, transient strains that can change over rapid time scales. It is clear that further study of microbial processes at fine spatial and temporal scales is needed, and that caution must be taken when interpolating short term microbial dynamics from results obtained from weekly or monthly samples. ª 2010 Elsevier Ltd. All rights reserved.
* Corresponding author. Department of Soil, Water, and Climate, University of Minnesota, 1991 Upper Buford Circle, 439 Borlaug Hall, St. Paul, MN 55108, United States. Tel.: þ1 612 624 2706. E-mail addresses:
[email protected],
[email protected] (M.J. Sadowsky). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.08.041
722
1.
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Introduction
Cladophora glomerata (L.) Ku¨tz is a branching, filamentous, green alga that grows in Lake Michigan and most of the other Laurentian Great Lakes (Taft, 1975). The alga primarily grows attached to hard substrates, such as rocks, but also attaches to clams and mussels (Dodds and Gudder, 1992). The primary growth period of Cladophora is late spring through summer, when temperature, solar radiation, wave action, and water levels are most favorable (Blum, 1982). In recent decades, however, rapid and excessive growth of Cladophora has occurred, which is thought to be stimulated by nitrogen- and phosphorus-induced eutrophication (Dodds and Gudder, 1992), as well as increased water clarity and light penetration that likely resulted from invasions of dense populations of dreissenid mussels (Hecky et al., 2004). Detachment of Cladophora thalli from the benthos, resulting from wave action, storms, and seasonal sloughing, leads to the accumulation of large, free-floating, algal mats along the shoreline and in swash zones (Whitman et al., 2003; Herbst, 1969), and in deposition on beaches. The accumulation and decomposition of algal matter is an annual nuisance in the nearshore waters of Lake Michigan (Taft, 1975), where it produces noxious odors, negatively impacts recreational activities, such as swimming, fishing, and boating, and decreases shoreline water quality (Whitman et al., 2003). Rock-attached and free-floating Cladophora thalli can harbor relatively large numbers of culturable Escherichia coli and enterococci, both of which are used as fecal indicator bacteria (Whitman et al., 2003; Byappanahalli et al., 2003, 2007). In one study, Whitman et al. (2003) surveyed Cladophora samples from 10 beaches in Wisconsin, Illinois, Indiana, and Michigan, U.S.A., and reported the presence of E. coli at mean concentrations ranging from 103 to 106 CFU/g dry weight (dw) algal tissue. Water samples from within Cladophora mats can also contain significantly higher concentrations of E. coli than water located outside of mats, suggesting that the algae may be a source of E. coli to nearshore water (Englebert et al., 2008). In addition to E. coli, Ishii et al. (2006b) reported that Cladophora from lower Lake Michigan also harbors Shiga toxin-producing E. coli (STEC), Salmonella, Shigella, and Campylobacter, bacterial species known to cause human diseases. Moreover, members of the CytophagaeFlavobacteriumeBacteroides group, constituting up to 40% of the bacterial community, have also been reported to associate with Cladophora (Olapade et al., 2006), highlighting the broad array of bacterial communities associated with this alga. While the ecological basis for the relationship among these diverse bacteria and Cladophora is unknown, it is likely driven, in part, by the alga’s provision of nutrients (Marks and Power, 2001; Malinsky-Rushansky and Legrand, 1996), and its ability to protect these bacteria from predation and the harmful effects of ultraviolet radiation (Byappanahalli et al., 2003). Cladophora also provides a habitat for a variety of epiphytes, such as cyanobacteria and diatoms, as well as grazers such as protozoa, mollusks, rotifers, and young crayfish (Chilton et al., 1986; Stevenson and Stoermer, 1982; Taft, 1975). A variety of methods has been used to differentiate among environmental E. coli strains and those associating with Cladophora, including ribotyping (Carson et al., 2001; Anderson
et al., 2005), antibiotic resistance patterns (Parveen et al., 1997; Harwood et al., 2000), and rep-PCR DNA fingerprinting (Johnson et al., 2004; Dombek et al., 2000). Many of these same methods have also been used in microbial source tracking (MST) studies, which attempt to use phenotypic or genotypic variation among the isolates to track contamination to a specific host source. However, in order for these methods to work properly, they require a complete characterization of isolates from known animal and human sources (i.e. librarydependent methods) and are based on the assumption that populations of E. coli isolated from the environment do not change over short time scales. Several studies have now shown that genetic variability among E. coli can result in the differential survival of genotypes growing in various environmental substrates (Topp et al., 2003; Anderson et al., 2005), and that ‘naturalized’ strains may exist in several environments (sand, water, sediments and soils) (McLellan, 2004; Power et al., 2005; Ishii et al., 2006a). Consequently, the presence of environmentally adapted E. coli strains in these matrices undermines correlations between E. coli concentrations and the presence of pathogens, raising concerns over the use of this organism as an indicator of microbial water quality. The population structure and genetic diversity of E. coli associated with rock-attached Cladophora in lower Lake Michigan have been investigated in detail (Byappanahalli et al., 2003, 2007). These studies, however, were generally done using strains obtained from disparate sites over longer time scales. Initial studies done at Indiana Dunes National Lakeshore in northwest Indiana, using a limited number of isolates (n ¼ 44), indicated that E. coli genotypes recovered from rock-attached Cladophora were highly related to each other (>80% similarity), but not genetically identical (Byappanahalli et al., 2003). A subsequent study, however, done using a larger sample size (n ¼ 835), indicated that while the E. coli isolates from Cladophora were genetically related, the Shannon diversity index for the population was relatively high (5.39). Moreover, the population structure and composition were strongly influenced by location and year of sampling (Byappanahalli et al., 2007). This suggested that there was temporal and geographic variation in the population structure of Cladophora-attached E. coli at the test site, and that the population structure of E. coli associating with the alga was largely determined by the dominant strains in water. Recently, Vanden Heuvel et al. (2010) examined the spatial and temporal relationship of E. coli concentrations within and near free-floating Cladophora mats at two Lake Michigan beaches in Door County, Wisconsin. They examined the same mats over a three to four day period at the same site, and reported that E. coli concentrations present in water underlying Cladophora mats were frequently 400-fold greater than those triggering beach closures, and that there was a relationship between Cladophora-attached E. coli concentrations within mats and those in the water underlying mats during a one year period (Vanden Heuvel et al., 2010). They did not, however, conduct any DNA fingerprinting analyses or report data on the population structure of the isolates. Currently, there is no information on the short- and long-term changes, or geographic variation, in the population structure of E. coli associating with free-floating Cladophora mats.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 2 1 e7 3 1
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The overall objective of the current study was to examine the short- and long-term changes in the population structure of E. coli obtained from free-floating Cladophora mats over multiple years (2007e2009) by using DNA fingerprint analyses. The study presented here also extends the initial results reported by Vanden Heuvel et al. (2010) by adding additional years of E. coli count data, and characterization of the population structure of mat-associated E. coli over a three year period at multiple sites. The frequent and extensive sampling scheme used resulted in the acquisition of a large dataset consisting of 4285 E. coli isolates that is uniquely suited to examine short term variability in environmental E. coli populations across ecologically relevant scales.
2.
Materials and methods
2.1.
Site description and sampling
In 2007, sampling sites included Lakeside Park Beach and Whitefish Dunes Beach on the eastern side of the Door Peninsula, Door County, WI, and Wind Point Beach, approximately 8 km North of Racine, Wisconsin (Fig. 1). Further details of the sampling sites and strain collection were as previously published elsewhere (Nevers et al., 2009; Vanden Heuvel et al., 2010). In 2008 and 2009, these sites remained the same, except in 2008 the Newport State Park site, in Door County, Wisconsin, was added and Lakeside Park site was removed from the study, due to the lack of Cladophora mats at the Lakeside beach site in 2008. Sample collections took place in July and August in 2007, 2008, and 2009. Each site was monitored daily for the appearance of a new Cladophora mat (accumulated algal matter drifting into the site) and, when present, individual Cladophora mats were sampled daily for the duration of their occurrence, typically for three to four consecutive days. Isolates were taken from a total of 21 mats for all of the samples analyzed in the study. At the Door County sites, three separate mats were analyzed at each site during each year, while at the Wind Point site, only one mat was sampled each year. Sampling occurred each day before 0900 h in order to minimize disturbance by beach visitors and damage due to excessive UV irradiation. Randomly-selected thalli from 10 evenly spaced locations within each Cladophora mat were sampled as previously described (Vanden Heuvel et al., 2010). Samples were divided into duplicate sub-samples, placed in sterile Whirlpak containers, and held on ice in the dark for a maximum of 6 h prior to analysis. The concentration of E. coli in water samples was determined by membrane filtration using 0.45 mm nitrocellulose membranes. Membranes were incubated on modified mTEC agar medium for 2 h at 35 C, followed by 20e24 h at 44.5 C (USEPA, 2002). The counts for the 2007 data were previously reported by Vanden Heuvel et al. (2010), while the 2008 and 2009 data was generated in the present study. Data are presented as colony forming units (CFU)/100 mL of water. Cladophora-borne E. coli were first released from thalli by shaking l g aliquots of algae sub-samples in 9 mL of phosphate buffered water, (PBW, pH 6.8) as described previously (Byappanahalli et al., 2003; Whitman et al., 2003). If greater numbers of
Fig. 1 e Map of Wisconsin beaches in Door County (upper) and near Racine (lower right) from which Cladophora-borne E. coli isolates were collected. Sites were monitored daily for the appearance of Cladophora mats, and mats were sampled daily when present.
E. coli were needed for DNA fingerprinting, homogenized subsamples (10e25 g) were shaken in 40e50 mL PBW containing 0.01% hydrolyzed gelatin (Ishii et al., 2006a). Bottles were shaken for 30 min on a horizontal shaker, allowed to stand for 20 min, and the upper aqueous phase served as the initial dilution (2 101 g wet algae/mL) for processing via membrane filtration as described above. Algal samples were dried for 24 h at 105 C to obtain dry weight, and data are presented as CFU/g dry weight algal material. E. coli isolates obtained from mTEC plates were randomly selected for genetic fingerprint analysis to determine population structure. Four to five colonies were randomly selected from membrane filters from each of the ten locations sampled on a given day, and no more than three isolates were chosen from any one membrane filter. Since spatial variation in population structure within an individual algal mat was not considered in this study, the collected isolates were pooled into a group representing a single day’s population in the mat. The taxonomic identity of approximately 1% of the isolates was confirmed by using API 20E test strips (bioMe´rieux, Inc., Hazelwood, Missouri, USA), according to the manufacturer’s instructions. However, because mTEC agar uses b-glucuronidase activity as a means to
724
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detect E. coli, it is possible that some of the isolates were members of novel clades of Escherichia (Walk et al., 2009). Individual colonies were streaked for purification on mTEC agar medium, and cells were frozen at 80 C until used. A total of 4285 E. coli isolates were obtained in this manner, including 611, 994, 2009, and 671 isolates from the Lakeside Park Beach, Newport Beach, Whitefish Dunes Beach, and Wind Point Beach sites, respectively.
2.2.
DNA fingerprint analyses
The population structure and diversity of all E. coli isolates attached to Cladophora samples, from 2007 to 2009,was determined by using the horizontal, fluorophore-enhanced, repetitive-PCR (HFERP) DNA fingerprinting technique and the BOXA1R primer as previously described (Johnson et al., 2004; Ishii et al., 2006a). The PCR conditions used were for the PTC100 Thermal Cycler (MJ Research) as described by Rademaker et al. (1998). Electrophoresis, visualization, and image analyses were done as previously described (Johnson et al., 2004; Ishii et al., 2006a) using BioNumerics (Version 3.0) software (Applied-Maths, Sint-Martens-Latem, Belgium). DNA fingerprint similarities were calculated by using Pearson’s product moment correlation coefficient, with 1% optimization, and dendrograms were generated by using the unweighted pair group method with arithmetic means (UPGMA) as previously described (Johnson et al., 2004; Ishii et al., 2006a). Isolates having overall genetic relatedness values of 92%, as defined using a Pearson’s product moment correlation coefficient, were defined as the same (genotype) strain (Johnson et al., 2004). Clonal groups are defined as any group within a population that contains at least 10 isolates of the same strain.
2.3.
Statistical analysis and diversity calculations
Clustering of isolates was performed using multivariate analysis of variance (MANOVA) and jackknife analyses. Groupings of genotypes were visualized using multidimensional scaling (MDS) and discriminant analysis as previously described (Johnson et al., 2004; Vogel et al., 2007). These analyses were used to examine multiple scales, including large scale comparisons of sample sites and years, as well as finer scaled temporal comparisons across individual mats and individual days within a single mat. For some samples, common ecological indicators of diversity were also calculated. The Shannon diversity index (H0 ) was calculated as: H0 ¼
S X
pi lnpi ½ðS 1Þ=2N
(1)
i¼1
Where N ¼ the total number of isolates in a sample; S ¼ the total number of genotypes in a sample; and pi ¼ the proportion isolates belonging to a given genotype. The Jaccard similarity coefficient (J ) was used to compare the similarity of two samples, in terms of the presence of common genotypes, and was calculated as: J¼
c aþbþc
(2)
where a ¼ the number of genotypes present in the first sample, but not the second; b ¼ the number of genotypes
present in the second sample, but not the first; and c ¼ the number of genotypes common to both samples.
3.
Results and discussion
3.1.
E. coli densities in mats and water
The concentrations of E. coli observed in water and Cladophora samples obtained from the Lakeside and Whitefish Dunes sites in 2007 were reported previously (Vanden Heuvel et al., 2010). Briefly, in 2007 the density of E. coli attached to algal tissue averaged >104 CFU/g dry weight of mat material, which is similar to other reported densities of Cladophora-borne E. coli (Whitman et al., 2003; Byappanahalli et al., 2003). In addition, E. coli concentrations were significantly greater, by 1.5e2 orders magnitude, in water samples taken from inside Cladophora mats, compared to samples taken adjacent to the mats. Although it was not the primary focus of the current study, a brief summary of E. coli concentrations observed in the 2008 and 2009 samples taken in this study is provided here for context. The general pattern of cell density differences was consistent throughout the three year study period, although cell numbers were lower in 2008 and 2009, than in 2007. In 2008 and 2009, the density of E. coli on algal tissue averaged 3800 and 16 CFU/g, respectively. The concentration of E. coli in water within the mats averaged 540 and 180 CFU/100 mL in 2008 and 2009, respectively, and were higher than concentrations outside of mats, which were 10 CFU/100 mL in 2008 and 16 CFU/100 mL in 2009. This pattern of elevated E. coli concentrations associated with Cladophora mats was also common across sites, even though differences in E. coli concentrations were observed among the sites. For example, even though the average density of Cladophora-attached E. coli was considerably greater at Whitefish Dunes (1.4 103 CFU/g) than at the Newport Beach site (31 CFU/g), mean concentrations in water from within the mat (2600 and 81 CFU/100 mL at Whitefish Dunes and Newport, respectively) were consistently higher than in water adjacent to the mat (36 and 4 CFU/100 mL at Whitefish Dunes and Newport, respectively). Vanden Heuvel et al. (2010) reported a significant correlation between the concentration of Cladophora-attached E. coli and that found in the underlying water column in 2007 ( p < 0.001; r2 ¼ 0.42), providing further evidence that the mats may serve as a source of E. coli to nearby water.
3.2. Large-scale population structure of Cladophoraassociated E. coli If Cladophora mats are serving as sources of E. coli to Lake Michigan beach waters, it is important to understand the population structure of E. coli attached to mats and those in the underlying water. The large number of DNA fingerprints of Cladophora-borne E. coli that were obtained and analyzed for this study not only helped elucidate the overall population structure, but provided valuable insight into the dynamics that might be occurring at multiple scales. Among the various fingerprinting methods available, rep-PCR has been found to be highly sensitive, able to differentiate at the genotype, strain
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 2 1 e7 3 1
or isolate level, and recent advances have allowed increased reproducibility and throughput (Ishii and Sadowsky, 2009). DNA fingerprint analyses of E. coli isolates attached to algal thalli indicated that there was a high degree of genetic diversity in mat populations. However, clonal groups were also found to comprise a significant portion of a population. To examine large scale trends in population structure, all of the Door County isolates (Whitefish Dunes, Lakeside, and Newport) were analyzed together, for all three years (n ¼ 3614). Within this population, similarity values between isolates ranged from 5.6 to 100%, which was comparable to that previously found in Cladophora mats by Byappanahalli et al. (2007). In contrast to the observed high degree of genetic diversity, however, 55 different clonal groups were identified, containing 33% (1195 of 3614) of all of the isolates examined. Clonal groups were defined as those containing 10 or more isolates having overall genetic relatedness values of 92%, as defined using a Pearson’s product moment correlation coefficient (Johnson et al., 2004). Four of the groups contained at least 50 isolates, and were comprised of genotypes obtained from multiple sites and during different years. The presence of relatively large clusters of identical or genetically near-identical E. coli genotypes that spanned large spatial and temporal scales suggested that at a portion of mat-associated E. coli populations were likely comprised of persistent or naturalized strains that adapted to survival or even growth in these mats. Substantial evidence exists for the presence of naturalized E. coli strains in other environmental matrices, including soils (Ishii et al., 2006a; Ksoll et al., 2007), sand (Byappanahalli et al., 2006; Ishii et al., 2007), water (McLellan, 2004; Power et al., 2005), algae (Byappanahalli et al., 2007) and periphyton (Ksoll et al., 2007). While the potential source(s) of the mat-attached and planktonic E. coli was not addressed in the studies reported here, a previous study of Byappanahalli et al. (2007) found little similarity between Cladophora-borne isolates and those from known animal sources. It has been suggested that E. coli isolates have the ability to grow on algal exudates obtained from Cladophora (Byappanahalli et al., 2003), and thus the attached cells exhibiting high genetic similarity may be comprised of reproducing clones. Such reproducing populations of E. coli in Cladophora mats represent a significant threat to the utility of E. coli as a reliable indicator of fecal pollution on beaches exposed to Cladophora, as they are less likely to correlate well with the presence of human pathogens that result from fecal pollution. When compared across all samples, MANOVA analyses of DNA fingerprint data showed that there was little spatial distinction between samples at the scales examined in this study, but there was stronger temporal clustering of isolates. The isolates from the three Door County sites were not well distinguished by MANOVA, even though both discriminants were highly significant ( p < 0.001). This was evident when data from all years were combined, or when data from each year was examined individually (data not shown). The lack of clustering of isolates at each site may be due to the drifting and ephemeral nature of Cladophora mats, and that the sampling sites were separated by about 50 km of coastline. Thus, our study sites may have been too close to be affected by mats of significantly different geographic origins.
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In contrast, the E. coli isolates exhibited strong temporal clustering when compared by year (Fig. 2). The isolates obtained over the various years of the study, particularly in 2007, showed significant separation on both axes ( p < 0.001), and formed more coherent clusters in MANOVA analyses than did isolates grouped by site. This was true for both the Door County and the Racine sites, which exhibited the same general pattern of population structure (Fig. 2A and B, respectively) and indicated that shifts in population structure were occurring on an annual scale. While Byappanahalli et al. (2007) also found temporal clustering when E. coli isolates
Fig. 2 e Multivariate analysis of variance (MANOVA) of HFERP DNA fingerprints from E. coli isolates obtained from Lake Michigan Cladophora mats. The two graphs represent discriminant analyses with all isolates, grouped by year, from the combined Door County sites (panel A) and Wind Point Beach site (panel B), respectively. Binary bandmatching character tables were analyzed by MANOVA, accounting for variance.
from rock-attached Cladophora mats in Lake Michigan were compared over a two year period, E. coli strains were only lightly sampled in one year of their study and the number of isolates examined was relatively small. Our analyses done with a large number of E. coli isolates obtained from freefloating mats over successive days at multiple sites confirm this pattern on a broader scale. In a temperate climate like that of the Laurentian Great Lakes, declining temperatures and irradiance are likely factors leading to E. coli and Cladophora die back each winter (Whitton, 1970; Blum, 1982). It is reasonable, therefore, to assume that genetically different populations of E. coli colonize Cladophora when it reemerges in the spring. Likewise, genotypes of Salmonella, another enteric bacterium associated with Cladophora also strongly clustered by year (Byappanahalli et al., 2009), suggesting that new isolates appearing in the spring dominate Cladophora during the growth season.
3.3.
Fine-scale genetic structure
The population structure of E. coli isolates associating with individual floating Cladophora mats at the Door County sites was examined in more detail. Overall, the genetic diversity of E. coli isolates present in an individual algal mat was high. Due
Table 1 e Measurements of diversity for daily populations of E. coli isolates obtained from beached Cladophora mats in Lake Michigan. Site/Mata
Day
Total No. of Isolates
Whitefish Dunes 2007: Mat 1 1 2 3 Total Mat 2 1 2 3 Total Mat 3 1 2 3 Total Newport Beach 2009: Mat 1 1 2 3 Total Mat 2 1 2 3 Total Mat 3 1 2 3 Total
Genotype Richnessb
Shannon Diversity Index
69 62 76 207 80 80 80 240 86 96 90 272
38 29 44 108 39 53 46 131 55 59 52 161
3.2 2.6 3.1 3.8 3.5 3.8 3.6 4.6 3.8 3.8 3.6 4.8
91 36 80 207 42 42 40 124 37 15 39 91
55 28 43 105 26 35 22 67 27 14 23 62
3.8 3.2 3.5 4.4 3.1 3.5 2.9 4.0 3.1 2.6 2.9 4.0
a Values are for each day from each mat at Whitefish Dunes in 2007 and Newport in 2009. b Genotype richness refers to the number of unique fingerprint types.
Fig. 3 e Dendrogram showing genetic diversity of E. coli isolates obtained over a three day period from a single Cladophora mat at the Whitefish Dunes site in 2007. The top axis represents the percent similarity among genotypes, as indicated by the Pearson correlation coefficients. The dendrogram has been collapsed at 80% similarity, and the values associated with each cluster indicate the number of isolates contained within that cluster.
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to the extensive size of the dataset collected, it is impossible to show a fine-scaled analysis of all of the samples. Therefore, we have chosen to present only two sampling seasons at two sites, Whitefish Dunes in 2007 and Newport Beach in 2009, as examples of typical patterns seen among all of the sites. Accumulation curves for E. coli genotypes obtained on individual days at these sites, based on the acquisition of new genotypes, showed that despite our significant sampling effort much of the genetic diversity among the mat-borne E. coli was not captured (data not shown). Thus, very deep sampling would be required to characterize the majority of the population structure in Cladophora mats. This point is important to consider when designing library-dependent studies that attempt to characterize populations of E. coli from various animal, human or environmental sources. Table 1 lists indicators of diversity for the Whitefish Dunes Newport Beach mats in 2007 and 2009, respectively, including genotype richness and Shannon diversity values. Shannon diversity values typically ranged from 2.5 to 4.0 when examined at the level of individual days within a given mat, and between 3.5 and 5.0 when E. coli obtained from all three days of mat presence were considered as a single population. While these values are comparable to those reported by McLellan (2004) and Byappanahalli et al. (2007) for environmental E. coli, they were considerably higher than seen for enterococci (Brownell et al., 2007).
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Dendrograms offer additional insight into viewing the population structure of Cladophora-borne E. coli. However, the extensive size of the DNA fingerprint dataset generated in this study precluded presenting all isolates in a single figure. Results in Fig. 3 show a representative dendrogram from a Whitefish Dunes mat in 2007, with the clusters collapsed at the 80% similarity level for clarity. The resulting dendrogram structure shows that the isolates displayed a wide range of genetic similarity values and the mat was comprised of a mixture of relatively unique isolates existing as singletons, clusters of a few isolates, and larger clusters of clonal and closely related E. coli isolates. All of the mats examined similarly contained a mixture of unique isolates and up to 4 clonal groups of isolates with a mean of 1.5 1.2 clonal groups/mat. These groups represented up to 29% of the total E. coli isolates obtained from each mat, with an average of 8 3.3% of the total genotypes. This complex population structure was typical for all of the analyzed mats at each site. Discriminant analyses conducted on a mat-by-mat and day-by-day basis showed fine-scale temporal dynamics in the population structure of E. coli isolates. MANOVA analyses consistently showed a significant separation of isolates by mat, when examined by both site and year. Results in Figs. 4A and 5A show examples of this relationship using DNA fingerprint data of E. coli obtained from Whitefish Dunes in 2007 and Newport Beach in 2009. The degree of clustering
Fig. 4 e MANOVA of HFERP DNA fingerprints from all Whitefish Dunes E. coli isolates obtained from Cladophora mats in 2007 and grouped by (A) each Cladophora mat and by (BeD) each day within each individual Cladophora mat. Binary bandmatching character tables were analyzed by MANOVA, accounting for variance.
728
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Fig. 5 e MANOVA of HFERP DNA fingerprints from all Newport Beach E. coli isolates obtained from Cladophora mats in 2009 and grouped by (A) each Cladophora mat and by (BeD) each day within each individual Cladophora mat. Binary bandmatching character tables were analyzed by MANOVA, accounting for variance.
varied from relatively distinct, as for isolates obtained from the Whitefish site in 2007 (Fig. 4A) to less distinct, as exemplified by the isolates from Newport site in 2009 (Fig. 5A). Temporal changes in the population structure of E. coli were also evident on a more rapid time scale. MANOVA analyses of each individual mat showed that isolates also clustered according to sampling day, within the duration that each mat was present (Figs. 4BeD and 5BeD). This analysis indicated that E. coli isolates present within a mat, on any given day, were more similar to each other, on average, than to those isolates found either the day before or the day after in the same mat. This finding is further supported when Jaccard similarity values are used to examine E. coli population structure within a given mat on individual days. Results in Table 2 show that Jaccard values, which represent the percentage of genotypes that are shared between two samples, ranged from 0 to 19, indicating that on any given day,
at least 80% of the genotypes present were unique in relation to the other days during the lifespan of the mat. This suggests that new genotypes were associating and disassociating with Cladophora mats on a daily basis, and likely reflect the loss of genotypes from the Cladophora coupled with genotypes moving from the water column onto the surface of Cladophora. Moreover, while some genotypes were always present on the same mat over time, i.e. a persisting subpopulation contributing to clonal structure, others were more ephemeral and varied on a short time scale. Results of these studies indicate that the E. coli populations in the tested Cladophora mats were comprised of a mixture of genotypes that persisted for the duration of mat coalescence, as well as genotypes that change over shorter time scales. Thus, a significant proportion of the E. coli population within an individual mat appeared to change rapidly, even on a daily time scale. To our knowledge, temporal variability in
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Table 2 e Percent similarity of genotypes in Cladophoraborne E. coli populations among different days within a single Cladophora mat. Site/Mata Day 1 vs. Day 2 Day 1 vs. Day 3 Day 2 vs. Day 3 Whitefish Dunes 2007: Mat 1 5 Mat 2 8 Mat 3 3 Newport 2009: Mat 1 3 Mat 2 6 Mat 3 0
1 6 0
4 6 10
15 9 2
10 19 3
a Values were for day-by-day comparisons for each mat at Whitefish Dunes in 2007 and Newport Beach in 2009. Values are Jaccard similarity coefficients multiplied by 100, and represent the percentage of the total number of genotypes appearing on either day that are common to both days.
population structure of E. coli has not been investigated at such fine time scales in environmental samples, but rapid fluctuations in E. coli and coliform densities have been previously documented (Boehm et al., 2002; Desmarais et al., 2002; Whitman and Nevers, 2004; Whitman et al., 2004), as well as rapid change over of portions of E. coli populations in experimental mesocosms (Anderson et al., 2005). Taken together, these results highlight the need for further study of microbial processes at fine spatial and temporal scales, as well as the need to be cautious about extrapolating results obtained from microbial samples obtained at weekly or monthly intervals to phenomena that are likely occurring in shorter time scales. Although the great amount of temporal change in E. coli populations that was seen in these studies was somewhat surprising, it may be explained, at least in part, by considering the life cycle of a typical Cladophora mat. As opposed to more stationary substrates and matrices, such as soils and aquatic sediments, Cladophora thalli detach from rock surfaces, due to wave action and storm events, coalesce, and drift as algal mats, eventually making their way onshore where they decompose. This process affects the physical and chemical characteristics of the algal matter over time (Paalme et al., 2002), and probably differentially promotes the growth and survival of various E. coli genotypes over the lifespan of the mat. Furthermore, as mats move onshore, they come into closer contact with terrestrial sources of nutrients and new E. coli genotypes, which can colonize the mat and dramatically change the overall population structure. Therefore, when interpreting the population structure of E. coli in an environmental sample, which is generally required for MST studies, it is important to be cognizant that the sample population may reflect a complex mosaic of various inputs, as well as a snapshot of only the most recent history of fecal loading at any given location.
4.
Conclusions
Understanding the ecology of environmental E. coli is important for the use of this bacterium as an indicator of water quality and to protect public health. The large dataset
729
obtained and analyzed in this study allowed for a much more detailed view, at multiple scales, of the population structure of E. coli attached to free-floating Cladophora mats than was possible in previous studies. Important conclusions from these analyses are: E. coli populations obtained from free-floating Cladophora mats included a mixture of unique and transient isolates, as well as clonal and highly related genotypes that persisted for the duration of the algal mat. These results strongly suggest that Cladophora mats harbor naturalized strains of E. coli, and may serve as a source of indicator bacteria to Great Lakes beaches that do not correlate well with the presence of fecal pollution. While the population structure of mat-borne E. coli varied annually, it also displayed high levels of short term variability, with isolates often changing daily. The rapid changes in the population structure, the inability to capture all of the genetic diversity even with a very large sample size, and the persistence of mat-associated E. coli all limit the use of this bacterium for MST studies and as an indicator of fecal contamination.
Acknowledgments This study was supported, in part, from grants from the University of Wisconsin Sea Grant Research Program and from the University of Minnesota Agricultural Experiment Station (to MJS). This article is contribution number 1607 of the USGS Great Lakes Science Center.
references
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 3 2 e7 4 0
Available at www.sciencedirect.com
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Occurrence of androgens and progestogens in wastewater treatment plants and receiving river waters: Comparison to estrogens Hong Chang, Yi Wan, Shimin Wu, Zhanlan Fan, Jianying Hu* Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
article info
abstract
Article history:
Research has shown that exposure to androgens and progestogens can cause undesirable
Received 15 March 2010
biological responses in the environment. To date, however, no detailed or direct study of their
Received in revised form
presence in wastewater treatment plants has been conducted. In this study, nine androgens,
3 August 2010
nine progestogens, and five estrogens were analyzed in influent and final effluent wastewaters
Accepted 25 August 2010
in seven wastewater treatment plants (WWTPs) of Beijing, China. Over a period of three weeks,
Available online 17 September 2010
the average total hormone concentrations in influent wastewaters were 3562 (Wujiacun WWTP)e5400 ng/L (Fangzhuang WWTP). Androgens contributed 96% of the total hormone
Keywords:
concentrations in all WWTP influents, with natural androgen (androsterone: 2977 739 ng/L;
Androgens
epiandrosterone: 640 263 ng/L; and androstenedione: 270 132 ng/L) being the predominant
Estrogens
compounds. The concentrations of synthetic progestogens (megestrol acetate: 41 25 ng/L;
Progestogens
norethindrone: 6.5 3.3 ng/L; and medroxyprogesterone acetate: 6.0 3.2 ng/L) were
Wastewater treatment plants
comparable to natural ones (progesterone: 66 36 ng/L; 17a,20b-dihydroxy-4-progegnen-3one: 4.9 1.2 ng/L; 21a-hydroxyprogesterone: 8.5 3.0 ng/L; and 17a-hydroxyprogesterone: 1.5 0.95 ng/L), probably due to the wide and relatively large usage of synthetic progestogens in medical therapy. In WWTP effluents, androgens were still the dominant class accounting for 60% of total hormone concentrations, followed by progestogens (24%), and estrogens (16%). Androstenedione and testosterone were the main androgens detected in all effluents. High removal efficiency (91e100%) was found for androgens and progestogens compared with estrogens (67e80%), with biodegradation the major removal route in WWTPs. Different profiles of progestogens in the receiving rivers and WWTP effluents were observed, which could be explained by the discharge of a mixture of treated and untreated wastewater into the receiving rivers. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Estrogenic substances have attracted significant interests in studies on reproductive endocrine disruption in aquatic environments (Purdom et al., 1994; Lye et al., 1997; Allen et al., 1999). Biological responses from exposure to androgenic substances have been associated with effluent from wastewater treatment
plants (WWTPs) and paper mills (Howell et al., 1980; Bortone et al., 1989; Cody and Bortone, 1997; Larsson et al., 2000; Jenkins et al., 2001; Borg et al., 1993). In vitro androgenic activity (Jakobsson et al., 1999) and the masculinization of fish have been observed downstream from pulp mill effluent in Sweden and the United States (Howell et al., 1980; Bortone et al., 1989; Cody and Bortone, 1997; Larsson et al., 2000).
* Corresponding author. Tel./fax: þ86 10 62765520. E-mail address:
[email protected] (J. Hu). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.08.046
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 3 2 e7 4 0
Several studies have reported that some androgens and progestogens are important hormonal odorants and reproductive pheromones, which can affect the reproductive physiology and behavior of many fish species (Kolodziej et al., 2003). The occurrence of estrogens in wastewaters and surface waters has been investigated in numerous studies (Belfroid et al., 1999; Baronti et al., 2000; Labadie and Budzinski, 2005; Kolpin et al., 2002). Natural estrogens (estrone and 17b-estradiol), as well as the synthetic estrogen 17a-ethynylestradiol, were identified as the compounds responsible for estrogenic activities in WWTP effluents and sewage runoff from agriculture and livestock (Hoffmann and Evers, 1986; Kolodziej et al., 2003; Orlando et al., 2004). The wide occurrence of trace level (ng/L) estrogens in wastewater and receiving waters has been well documented. Compared to estrogens, the environmental levels of androgens and progestogens should be much higher, since their excretion amount in human urine are 100e1000 times higher than those of estrogens (Shore and Shemesh, 2003). In addition, many hormone drugs, especially synthetic progestogens, are widely used in human and veterinary therapies. Synthetic progestogens such as megestrol acetate, medroxyprogesterone acetate, norethindrone, and norgestrel are used in contraceptive treatments for the promotion of menstrual cycles, correction of abnormal uterine bleeding, controlling the symptoms of menopause, and preventing certain types of cancer. In contraceptive treatments, norethindrone and norgestrel are often associated with estrogens at concentrations 5 to 10-fold of estrogens, and even higher concentrations of megestrol acetate and medroxyprogesterone acetate are often used (Labadie and Budzinski, 2005). However, only limited data on a narrow range of androgens and progestogens has been reported from surveys of pharmaceuticals and endocrine disruptor substances in wastewaters (Kolodziej et al., 2003; Vulliet et al., 2007; Fernandez et al., 2007; Batt et al., 2008) and surface waters (Jenkins et al., 2001; Yamamoto et al., 2006; Kolpin et al., 2002). We recently developed an original analytical method for monitoring five classes of steroid hormones including estrogens, androgens, and progestogens from one water sample using liquid chromatography-electrospray tandem mass spectrometry. We found that androgens and progestogens were ubiquitously detected in urban rivers (Chang et al., 2009). To further explore the occurrence and removal of these compounds in WWTPs, nine androgens, nine progestogens, and five estrogens were analyzed in influent and final effluent wastewaters in seven WWTPs of Beijing, China. Degradation of androgens and progestogens in WWTP slurry was conducted to explore the removal mechanisms of androgens and progestogens. The contributions of WWTP effluents to the receiving rivers for all compounds were also assessed.
2.
Experimental section
2.1.
Materials
Twenty-three sex hormones as shown were targeted in this study: 19-nor-4-androstene-3,17-diol (NAD), trenbolone (TBL), nandrolone (NDL), androstenedione (ADD), norethindrone (NTD), 17a-hydroxyprogesterone (17-HPT), testosterone (TTR),
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21a-hydroxyprogesterone (21-HPT), norgestrel (NGT), 17a, 20b-dihydroxy-4-progegnen-3-one (DPO), methyl testosterone (MTTR), epiandrosterone (EADR), stanozolol (SZL), 6a-methylhydroxyprogesterone (MHPT), megestrol acetate (MTA), medroxyprogesterone acetate (MPA), progesterone (PGT), androsterone (ADR), 17a-estradiol (aE2), 13C2-NTD, 13C2-TTR, NGT-d6 and PGT-d9 were purchased from Sigma (St Louis, MO, USA). Ethinylestradiol (EE2), 17b-estradiol (bE2), estrone (E1), diethylstilbestrol (DES), E2-d3, E1-d2, and EE2-d4 were purchased as powders from Wako (Tokyo, Japan). Formic and acetic acids were analytical grade (Beijing Chemicals, China). Methanol, acetonitrile, ethyl acetate, hexane, and dichloromethane were all HPLC grade purchased from Fisher Chemical Co. (Beijing, China). Mercuric chloride (HgCl2) was purchased from SigmaeAldrich (St. Louis, MO, USA). HPLC grade water was prepared using a Milli-Q RC apparatus (Millipore, Bedford, MA, USA).
2.2.
Sample collection
By using flow proportional samplers, 24-h composite samples of the influents and effluents were collected each day during the 3-week study period (June 26eJuly 16, 2006) from seven operating WWTPs in Beijing, China. These WWTPs are operated with primary and secondary treatment processes with no post-disinfection or additional filtration step. All plants receive mainly domestic waters, and detailed information on the WWTPs and sampling dates are summarized in Table S1. We also collected water samples from Tonghui River and Qing River on the same bank (their width is between 15 and 25 m) once in a week as these two rivers receive the effluents from the Gaobeidian and Qinghe WWTPs, respectively. The sampling locations along the Tonghui River were 2 km upstream and 0.5, 0.55, and 2.5 km downstream from the discharge point of Gaobeidian WWTP. The sampling locations for Qing River were 4 and 2 km upstream, and 2 and 4 km downstream of the Qinghe WWTP. Formaldehyde (final concentration 1%, v/v) was added to each sample immediately after collection. Samples were extracted on the same day after being filtered by a glass microfiber filter GF/C 1.2 mm (Whatman, Maidstone, UK).
2.3. Degradation of androgens and progestogens in slurry An aerobic degradation test was conducted to investigate the degree of biodegradation/adsorption of androgens and progestogens. Slurry was collected from the aeration basin of Qinghe WWTP and incubated within 4 h of collection. In the test, about 200 mL of slurry with a sludge concentration of 4 mg/L was incubated with mixtures of androgens and progestogens (10 mg/L for each compound in the suspension) in 250 mL flasks. The flasks were shaken horizontally (120 rpm) at 28 C for 24 h. Three treatments performed in duplicate were included in the test: (I) slurry, (II) slurry þ hormones, and (III) slurry þ hormones þ HgCl2. Treatment I was used to monitor the target hormones in the slurry. For treatment II, biodegradation and sorption were the major removal routes, but biodegradation was excluded in treatment III due to the prevention of biological activities by the addition of HgCl2 (Fu et al., 1996; De Weert et al., 2010). The incubated hormones included all target androgens and
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progestogens except for NTD and NGT. These two compounds could not be detected in our preliminary experiments after HgCl2 was added, probably due to the reaction between their methenyl groups and Hg2þ. Supernatant samples (10 mL) were removed from the flasks at 0, 1, 2, 5, 10, and 24 h and filtered by a glass microfiber filter GF/C 1.2 mm before analysis.
2.4.
Sample extraction and cleanup
In this study, estrogens, androgens, and progestogens were simultaneously extracted by using one Oasis HLB cartridge (6 mL, 60 or 500 mg, Waters, USA). The cartridge was preconditioned with 6 mL of ethyl acetate, 6 mL of acetonitrile, and 12 mL of distilled water. 70 mL of influent, 200 mL of effluent, 2 L of river water, and 10 mL of incubated slurry water spiked with 7, 10, and 50 ng of E1-d2, and 0.7, 2, and 10 ng of other surrogate standards were extracted using HLB cartridges at a flow rate of 5e10 mL/min. The cartridges were rinsed with 10 mL of distilled water, and were then dried under a flow of nitrogen. All hormones were eluted with 15 mL of ethyl acetate. The 7-day elutants of influents and effluents were then pooled as composite samples for a complete week. The extracts were dried and redissolved in 0.2 mL of ethyl acetate and 1.8 mL of hexane. The mixed solutions were applied to silica cartridges (3 mL, 500 mg, Waters), which had been preconditioned with 4 mL of water-saturated ethyl acetate and 4 mL of hexane/ethyl acetate (90:10, v/v). After the cartridges were rinsed with 3 mL of hexane/ ethyl acetate (90:10, v/v), all hormones were eluted with 3 mL of hexane/ethyl acetate (38:62, v/v). The solution was evaporated to dryness under a gentle stream of nitrogen, and reconstituted with 0.5 mL of methanol to determine androgens and progestogens by LC-ESI-MS/MS. For estrogens, 0.2 mL of reconstituted methanol resolution was dried and redissolved with 1 mL of hexaneemethylene chloride (DCM) (1:1, v/v), and then passed through the preconditioned Florisil cartridges (6 mL, 1 g, Waters). Ten millilitres of hexaneeDCM (1:1, v/v) were discarded, and the fraction containing target estrogens was eluted with 6 mL of acetoneeDCM (1:9, v/v). The solution was evaporated to dryness under a gentle stream of nitrogen, and reconstituted with 0.2 mL of acetonitrile.
2.5.
LC-ESI-MS/MS analysis
The LC apparatus was an Acquity Ultra Performance LC (Waters). All hormones were separated using a Waters Acquity UPLC BEH C18 column (100 2.1 mm, 1.7 mm particle size). The column was maintained at 40 C at a flow rate of 0.3 mL/ min, and the injection volume was 5 mL. Acetonitrile and 0.1% acetic acid in water were used for estrogen analysis. Gradient conditions were increased linearly from 20% to 80% acetonitrile in 4.5 min, and then to 100% acetonitrile in 0.1 min (held for 1 min). For the separation of androgens and progestogens, methanol and water containing 0.1% formic acid were chosen as mobile phases. Gradient conditions were initiated with 60% methanol followed by a linear increase to 65% methanol in 2.5 min. After being increased to 70% in 3.5 min, methanol was increased sharply to 100% in 0.1 min and then held for 1 min. Mass spectrometry was performed using a Premier XE tandem quadrupole mass spectrometer (Waters) equipped with a Z-Spray ionization (ESI) source and operated in the
positive ion (PI) mode. The following instrument conditions were used: capillary voltage, 2.5 kV; source temperature, 120 C; desolvation temperature, 450 C; source gas flow, 50 L/h; and desolvation gas flow, 900 L/h. Data acquisition was performed by multiple reaction monitoring (MRM). Table S2 summarizes the optimized ESI-MS/MS conditions for analysis of target hormones. For androgens and progestogens [M þ H]þ was selected as the precursor ion. For estrogens [M þ H H2O]þ was chosen as the precursor ion for E2, aE2, and EE2, while [M þ H]þ was chosen for E1, DES.
2.6.
Analytical procedure and method performance
The efficiency of the extraction and purification procedure was assessed by spiking all samples with standard solutions of target analytes and surrogate standards. The surrogate standards were used to automatically correct for the loss of analytes during sample preparation and the matrix induced change in ionization and to compensate for variations in instrumental response from injection to injection. E1-d2, EE2d4 and bE2-d3 were used as surrogate standards for estrogens; and 13C2-TTR, 13C2-ethyl-NTD, NGT-d6 and PGT-d9 for androgens and progestogens. Analyte addition was at least three times the original concentration determined prior to the fortification experiment. The mean overall recoveries of the surrogate standards and target steroids ranged between 78 and 100% with an RSD lower than 15% (n ¼ 3). During the recovery experiment, the spiked influent samples were analyzed in a 10-day period, and the typical RSD was lower than 12% for day-by-day replicate determinations. No significant ionization suppression was observed from this analysis. Since many target steroids were expected to occur in influent and effluent samples, estimation of method detection limits (MDLs) was based on peak-to-peak noise of the baseline near the analyte peak (selected precursor ion production-ion transition with lower sensitivity) obtained by analyzing field samples and also on a minimum value of 3 for signal-to-noise. For the non-contaminated samples, target steroids were spiked at a concentration range of 0.005e100 ng/L using mixtures of standard solution. Table 1 lists the MDLs of each steroid in aqueous matrices considered. Identification of the target steroids was accomplished by comparing the retention time (within 2%) and the ratio (within 20%) of the two selected precursor ioneproduct ion transitions with those of standards. Quality control also included at least one distilled water blank, one duplicate sample, and one matrix spike sample with a mixture of target analytes and surrogate standards per 10 samples. Throughout the whole determination procedure, contamination of blanks was never detected as indicated by the distilled water blanks. The standard deviations of the field duplicates were within 10%.
3.
Results and discussion
3.1.
Occurrence of sex hormones in WWTPs
Table 2 shows the concentrations of nine androgens, nine progestogens, and five estrogens in the influent and effluent samples collected from seven Beijing WWTPs in 2006.
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Table 1 e Method detection limits (MDLs, ng/L) for target sex hormones. Hormone
Influent
Effluent
River water
Estrogens DES E1 aE2 bE2 EE2
1 1 0.1 0.5 0.5
0.35 0.30 0.03 0.14 0.14
0.25 0.20 0.02 0.10 0.10
Androgens ADD ADR EADR NAD NDL MTTR SZL TBL TTR
2.2 20 40 0.8 2.4 0.8 0.24 0. 5 0.1
0.90 7.0 15 0.3 0.85 0.3 0.09 0.2 0.04
0.63 5.0 12 0.20 0.60 0.20 0.06 0.15 0.03
2.0 0.3 0.3 0.2 0.12 0.1 0.3 1.2 0.5
0.7 0.14 0.14 0.07 0.04 0.03 0.1 0.4 0.19
0.5 0.10 0.10 0.05 0.03 0.02 0.08 0.30 0.13
Progestogens DPO 17-HPT 21-HPT MHPT MTA MPA NGT NTD PGT
Typical MRM LCeMS/MS chromatograms for a composite influent and corresponding effluent are shown in Figs. S1eS3. Over three weeks, the average total hormone concentrations in influent wastewaters were highest in Fangzhuang WWTP (5400 1544 ng/L), followed by Qinghe WWTP (4206 904 ng/L), Jiuxianqiao WWTP (3912 680 ng/ L), Beixiaohe WWTP (3759 1018 ng/L), Gaobeidian WWTP (3753 583 ng/L), Xiaohongmen WWTP (3882 677 ng/L), and Wujiacun WWTP (3562 816 ng/L). The relatively high concentrations in Fangzhuang WWTP could be due to the large portion of domestic wastewater, since all other WWTPs have additional industrial influence. As shown in Table 2, the concentrations of estrogens were lower than other two groups of hormones, and the contribution to total hormone concentrations was only 0.3 0.1% in WWTP influents. Of the five estrogens analyzed, DES and EE2 were both under detection limits. But the three natural compounds, bE2 (1.5 1.5 ng/L), aE2 (0.76 0.49 ng/L), and E1 (8.7 7.5 ng/L), were detected in almost all influent samples with concentrations in the range of previous investigations (Baronti et al., 2000; Johnson et al., 2000; Ternes et al., 1999). There is well-established evidence of the dominance of E1 in estrogens (Baronti et al., 2000; Johnson et al., 2000; Ternes et al., 1999), but aE2 has seldom been investigated due to lower excretion from the human body and/or lower estrogenic activity compared to bE2 (Hutchins et al., 2007). In the present study, ubiquitous occurrence of aE2 was found, which could be due to the conversion of E1 to aE2 under anaerobic conditions (Hutchins et al., 2007). Further research is required to clarify this hypothesis.
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Among the three classes of sex hormones detected, androgens accounted for 96.5 0.5% of the total hormone concentrations in all WWTP influents. Natural androgens, including ADR (2977 739 ng/L), EADR (640 263 ng/L), and ADD (270 132 ng/L), were the most abundant compounds. These results are consistent with the high excretion of androgens in humans compared to other hormones (Shore and Shemesh, 2003). Thus, androgens are an important group of environmental hormones for future studies. To the best of our knowledge, androgen levels have only been reported in one unique investigation in Canadian WWTPs (Fernandez et al., 2007). The only androgen investigated, TTR, had average levels of 0e46 ng/ L from three WWTPs, which are similar to those of this study (34 23 ng/L). The concentrations of natural androgens (ADR, EADR, and ADD), however, were significantly higher (up to a thousand times) than those of TTR in the present study. The relatively high concentrations of ADR, EADR, and ADD have also been found in natural rivers of Japan in our previous study (Chang et al., 2008), suggesting that the presence of androgens in the environment, especially natural androgens, should receive more research attention. Besides natural androgens, lower levels of synthetic androgens (NAD and SZL) were also detected in influent wastewaters with concentrations of 1.8 1.2 ng/L and 0.54 0.17 ng/L, respectively. The hormones MTTR, TBL, and NDL were all under the detection limit. For progestogens, seven compounds were detected in the influents of all WWTPs with average contributions of 3.3 0.4% of the total hormone concentrations. Natural progestogens (PGT, DPO, 21-HPT, and 17-HPT) were detected with concentrations of 66 36 ng/L, 4.9 1.2 ng/L, 8.5 3.0 ng/ L, and 1.5 0.95 ng/L, respectively. In a previous study, PGT was the predominant progestogen (Pauwels et al., 2008), but the concentrations (4.8e33 ng/L) were lower than those from the current study. Different from the findings of estrogens and androgens, synthetic progestogens (MTA: 41 25 ng/L, NTD: 6.5 3.3 ng/L, and MPA: 6.0 3.2 ng/L) were detected with comparable levels to natural progestogens. This could be due to the wide and relatively large usage of synthetic progestogens in medical therapies. For example, NTD, MTA, and MPA are often associated with estrogens in contraceptive treatment at concentrations >5-fold exceeding those of estrogens (Labadie and Budzinski, 2005). In previous research, NTD has been detected with concentrations of 0e92 ng/L (Fernandez et al., 2007), much higher than those of the present study. Both MPA and MTA have also been detected in urban rivers with concentrations up to 25 ng/L and 34 ng/L, respectively (Chang et al., 2009). These findings indicate that the presence of these progestogens in the environment, especially synthetic ones, requires future study. As shown in Table 2 and Fig. 1, the WWTP effluents discharging into the receiving waters still contained all three classes of sex hormones. Androgens were still dominant in effluents, with concentration contributions of 60 14%, followed by progestogens (24 8.3%), and then estrogens (16 13%). The average concentrations were 0.10 (aE2)e8.6 (E1) ng/L for estrogens, 0.20 (TTR)e12 (ADD) ng/L for androgens, and 0.10 (17-HPT)e2.3 (PGT) ng/L for progestogens. While E1 was still the dominant estrogen compound in the effluents, the concentrations of bE2 became comparable to those of aE2. Both ADR and EADR, which occurred in WWTP
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Table 2 e Average concentrations (ng/L) of steroid hormones entering and leaving seven Beijing WWTPs in June and July 2006. Beixiaohe
Fangzhuang
Gaobeidian
Jiuxianqiao
Qinghe
Wujiacun
Xiaohongmen
Effluent
Influent
Effluent
Influent
Effluent
Influent
Effluent
Influent
Effluent
Influent
Effluent
Influent
Effluent
9.1 2.5 2.1 1.5 1.0 0.6
1.3 0.8 0.4 0.5 0.5 0.7
11.8 5.3 3.8 3.4 1.1 0.6
0.2 0.1 0.3 0.4 0.7 1.1
8.8 1.3 0.9 0.4 0.9 0.4
0.5 0.2 0.2 0.2 0.5 0.8
7.5 1.9 1.1 1.0 0.7 0.5
1.0 1.2 0.3 0.2 0.3 0.4
19.1 21.6 1.6 1.7 1.1 1.0
8.6 1.0 0.8 0.7 0.4 0.5
6.5 1.1 1.0 0.5 0.7 0.2
0.6 0.6 0.2 0.3 0.1 0.2
6.9 2.6 1.5 0.9 0.7 0.1
0.9 0.3 0.5 0.7 0.6 0.9
Androgens ADD 177 38 ADR 2767 945 EADR 763 210 NAD 1.4 0.9 SZL ND TTR 26 11
4.5 1.2 ND ND ND ND 0.7 0.4
330 104 3700 1539 977 411 1.8 0.7 0.2 0.4 76.7 20
11.2 5.9 ND ND 0.6 0.5 ND 1.0 0.6
220 91.7 2700 300 407 131 1.7 1.8 ND 22 21
4.9 1.7 ND ND ND ND 1.1 0.8
203 59 2800 436 537 56.9 3.0 2.7 0.1 0.2 27 13
4.8 4.0 ND ND ND ND 0.5 0.1
267 68 2867 611 553 129 2.1 0.6 ND 27 9.2
8.9 1.0 ND ND ND ND 0.2 0.4
293 123 2667 751 357 139 1.2 0.1 ND 21 10
6.8 2.6 4.3 7.5 ND ND ND 0.8 0.2
157 55 2767 404 577 110 1.4 0.4 ND 25 11
12 3.4 ND ND ND ND 1.2 0.8
Progestogens 3.1 0.8 DPO 17-HPT 1.4 1.0 21-HPT 5.8 0.6 MHPT ND MPA 40 30 MTA 3.4 1.0 NTD 7.0 2.6 PGT 35 14
ND ND 0.9 1.1 1.2 0.4 1.1 0.9 0.7 0.7 ND 1.4 0.2
8.4 1.0 1.8 1.5 13 3.1 ND 58 17 7.8 3.8 12 0.6 57 47
ND 0.1 0.1 0.6 0.2 1.2 0.4 0.1 0.2 ND ND 1.3 0.3
3.9 1.2 0.9 0.6 6.7 4.8 ND 30 25 9.3 3.3 4.6 4.7 69 27
ND 0.1 0.3 0.1 0.1 0.4 0.2 ND ND ND 1.0 0.6
4.9 1.6 1.3 1.0 8.0 1.8 ND 38 30 7.8 4.5 7.0 3.5 58 24.6
ND 0.1 0.1 0.7 0.3 0.9 0.5 ND 0.1 0.1 ND 0.8 0.1
4.8 1.4 1.5 0.5 10 3.3 ND 18 13 5.1 1.2 5.3 1.7 108 89
ND ND 1.0 0.3 0.7 0.5 0.7 0.6 0.4 0.3 ND 1.2 0.3
3.1 1.4 1.7 1.8 7.5 2.3 ND 34 40 1.9 1.6 6.1 6.0 62 31
ND 0.1 0.1 0.7 0.5 0.3 0.1 0.2 0.2 ND ND 2.3 0.5
4.5 2.2 1.0 0.5 7.3 1.1 ND 35 24 5.3 0.9 7.3 1.2 61 8.7
ND 0.1 0.2 1.1 0.4 1.8 0.7 0.2 0.2 0.1 0.2 ND 1.8 0.3
Estrogens E1 bE2 aE2
ND: under detection.
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Influent
1.2
PGT
1
EADR
0.8
NAD MTTR
0.6
TBL NDL
0.4
0
0
1.4
b 1.4
1.2
1.2
1
1
Ct/C0
Ct/C0
0.2
0.8
0.6
0.4
0.4
0.2
0.2
0 15
20
25
MTA
0
30
0
Time (h) Fig. 1 e Degradation of androgens in activated sludge during 24 h incubation. (a) Slurry D androgens; and (b) slurry D androgens D HgCl2.
influents at very high levels, were under the detection limits in all effluents. Both ADD and TTR were the main androgens detected in all effluents, with the proportion of ADD in androgens increasing from 6% in influents to 85% in effluents. As for progestogens, PGT was the dominant compound in effluents, with the proportion of 21-HPT in progestogens increasing from 6% (influents) to 19% (effluents). It is interesting to note that MHPT was detected in all effluent samples despite not being detected in influents, suggesting possible biological conversion during wastewater treatment.
3.2.
MPA
0.8
0.6
10
MHPT
0.6
0.2
5
DPO
0.4
SZL
0
17-HPT 21-HPT
TTR
0.8
b
1.2
ADR
1
Ct/C0
a
ADD
Ct/C0
a
Removal of sex hormones in WWTPs
Removal efficiency of all target hormones was calculated by comparing influent and effluent concentrations. High removal efficiency of androgens and progestogens were found during the WWTP process: 100 0% for ADR and EADR, 97 3.0% for ADD, 96 7.9% for TTR, 90 22% for NAD, 100 0% for SZL, 96 9.4% for MTA, 98 6.5% for MPA, 97 1.7% for PGT, 96 11% for 17-HPT, 91 7.1% for 21-HPT, 100 0% for DPO and NTD, and 100 0% for MHPT. Similar high removal efficiency (100%) has also been reported for two androgens (TTR and ADD) and one progestogen (PGT) (Esperanza et al., 2004, 2007) in two pilot-scale WWTPs. But ADD was the dominant compound of all hormones in effluent despite its high removal efficiency in the present study. This can be explained by the significantly higher
5
10
15
20
25
30
Time (h) Fig. 2 e Degradation of progestogens in activated sludge during 24 h incubation. (a) Slurry D progestogens; and (b) slurry D progestogens D HgCl2.
Table 3 e Degradation parameters of first-order kinetics model. Hormones
First-order kinetics C0 (mg/L)
k1 (h1)
t1/2 (h)
r2
ADD ADR EADR TTR NAD MTTR TBL NDL SZL
15.4 11.2 10.9 13.3 17.5 12.2 7.5 8.8 10.6
0.93 0.71 0.64 1.24 0.81 0.67 1.03 0.9 0.21
0.7 1.0 1.1 0.6 0.9 1.0 0.7 0.8 3.3
0.9997 0.9768 0.9322 0.981 0.9994 0.9993 0.9583 0.99 0.9405
PGT 17-HPT 21-HPT DPO MHPT NTD NGT MPA MTA
11.8 20.4 13.9 17.3 18.4 11.4 14.0 13.7 11.8
0.69 0.86 0.84 0.86 0.66 0.57 0.47 0.42 0.23
1.0 0.8 0.8 0.8 1.1 1.2 1.5 1.7 3.0
0.9728 0.9984 0.9964 0.9998 0.9982 0.9783 0.9983 0.9545 0.9558
First-order kinetic model was applied to fit the degradation results. The equation was Ct ¼ C0 ek1$t, where, C0 is initial concentration of androgens and progestogens; Ct is the concentrations of compounds at time t; and k1 is the first-order rate constant; t1/2 can be calculated as 0.693/k1.
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influent concentrations of ADD, and its relatively low removal efficiency (97%) compared to ADR and EADR (100%). Estrogen removal was 80 19% for bE2, 67 51% for aE2, and 76 46% for E1, which was in the range reported in a previous investigation (Baronti et al., 2000). The relatively low removal efficiency of estrogens compared to androgens and progestogens were also found by Labadie and Budzinski (2005), possibly due to the fact that estrogens with benzene rings are more resistant to degradation during WWTP processes (Labadie and Budzinski, 2005). Generally, biodegradation and adsorption on sludge were the two main processes used to remove pollutants in WWTPs. Complete information on estrogens in WWTPs has been presented in previous studies, and this is invaluable when trying to understand the removal process of other hormones. Slow sorption kinetics of estrogens was observed in WWTPs due to their relatively low logKow (3.43e3.94, Lai et al., 2000; Andersen et al., 2003), thus similar logKow values of androgens and progestogens (2.55e4.09; KowWin Program, 1999, used in October 2008) may indicate a low tendency for adsorption on sludge particles (Fu¨rhacker et al., 1999). The rapid degradation of PGT in spiked effluent wastewaters (Labadie and Budzinski, 2005) may suggest the
12 10
3.3. Occurrences of sex hormones in receiving river waters The contribution of sex hormones in WWTP effluents to its receiving river waters was analyzed from samples taken in
10
A
E1 α E2 β E2
high removal of androgens and progestogens in WWTPs through biodegradation processes. To test the removal mechanism hypothesis for androgens and progestogens, aerobic degradation tests were conducted with fresh slurry collected from the WWTPs. As shown in Figs. 1(a) and 2(a), target androgens and progestogens were significantly (>99%) removed within 24 h. The degradation half-lives calculated by first-order kinetics model were 0.6e3.3 h for all test compounds (Table 3). Conversely, in the treatment where biological activity of the sludge was inhibited by HgCl2, 78e110% of the test compounds, except for SZL (29%), were still detected in the slurry after 24 h incubation (Figs. 1(b) and 2(b)). These results imply that elimination of most androgens and progestogens was achieved by biodegradation in WWTP, which is in accordance with our hypothesis. As for SZL, adsorption or reaction with HgCl2 may be the major removal routes and further studies are needed.
B
30
C
8 20
8
6
6 4
4
2
2
Concentration (ng/L)
0
10 8
10
0
0
-4
-2
NAD TTR ADD EADR ADR
E
2
4
10
-2
E
0.50 0.55 2.55
5000
Influent
4000
8
3000 5
5
2000 3
3
0
0 6
1000
-4 MTA -2
E MPA 2
PGT 21-HPT MHPT
4
4
17-HPT DPO NTD
6
0 -2
E
0.50 0.55 2.55
250
Influent
200
4
150 100
2
2
50 0
0
0 -4
-2
E
2
4
-2
E
0.50 0.55 2.55
Influent
Distance from STP (km) Fig. 3 e Concentrations of sex hormones (androgens, progestogens and estrogens) in the River Qing (A) and River Tonghui (B) at different distances from the WWTPs in the rivers. E: STP effluent. The levels and profiles of sex hormones in influents of Qing and Gaobeidian WWTPs were similar (C), thus the average concentrations were showed.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 3 2 e7 4 0
Tonghui River and Qing River, which receive the effluents from Gaobeidian and Qinghe WWTPs, respectively. Fig. 3 shows the levels and profiles of sex hormones detected in WWTP influents, effluents, and river water samples (Tables S3 and S4). In river samples, ADD was still the dominant androgen. The ubiquitous occurrence of ADD in the river environment was also reported by Yamamoto et al. (2006) and our recent publication (Chang et al., 2009). In addition, toxicity identification and evaluation studies indicated that ADD could be responsible for in vitro androgenic activity as well as the masculinization of female fish downstream of pulp mill effluent discharges (Jenkins et al., 2001; Thomas et al., 2002). These results imply that ADD could be one of the most important androgens in the environment. Although it was not detected in corresponding effluents, NAD (0.44e1.6 ng/L, 21 of 32 river samples) was present upstream and downstream of Qing and Tonghui rivers. Two progestogens (MPA and MTA) were dominant in rivers samples from Tonghui River, which was different from the progestogen profiles in WWTP effluents. The different profiles between river water and WWTP effluents indicate that sex hormones at the sampling locations of Qing River could be contributed by the discharge of a mixture of treated wastewater and naturally attenuated untreated wastewater during the study period. This agrees with our recent report (Chang et al., 2009) that about 29.4% of sex hormones in several Beijing Rivers was estimated to be contributed by treated wastewater and naturally attenuated untreated wastewater.
4.
Conclusion
The presence of androgens and progestogens were investigated in seven WWTPs and two receiving rivers. Significantly higher levels of androgens and progestogens compared to estrogens occurred in all samples. Natural androgens were ubiquitous and dominant in the WWTPs and receiving rivers, while synthetic progestogens were present in WWTP influents at comparable levels of natural ones. Biodegradation was the major removal route for the high removal efficiencies of most androgens and progestogens in WWTPs. The presence of target hormones in the receiving rivers was mainly attributed to the discharge of a mixture of both treated and untreated wastewater.
Acknowledgments Financial support from the National Basic Research Program of China (2007CB407304) and the National Natural Science Foundation of China (20837003, 40632009) is gratefully acknowledged.
Appendix A. Supplementary information Supplementary information associated with this article can be found in the online version at doi:10.1016/j.watres.2010.08.046.
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Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Advances in on-line drinking water quality monitoring and early warning systems Michael V. Storey a,*, Bram van der Gaag b, Brendan P. Burns c a
Customer Strategy and Planning, Sydney Water, 1 Smith Street, Parramatta NSW 2150, Australia KWR Groningenhaven 7, 3433 PE Nieuwegein, The Netherlands c School of Biotechnology and Biomolecular Sciences, The University of New South Wales, Sydney 2052, Australia b
article info
abstract
Article history:
Significant advances have been made in recent years in technologies to monitor drinking
Received 11 March 2010
water quality for source water protection, treatment operations, and distribution system
Received in revised form
management, in the event of accidental (or deliberate) contamination. Reports prepared
27 August 2010
through the Global Water Research Coalition (GWRC) and United States Environment
Accepted 28 August 2010
Protection Agency (USEPA) agree that while many emerging technologies show promise,
Available online 6 September 2010
they are still some years from being deployed on a large scale. Further underpinning their viability is a need to interpret data in real time and implement a management strategy in
Keywords:
response. This review presents the findings of an international study into the state of the
Water quality
art in this field. These results are based on visits to leading water utilities, research orga-
On-line monitoring
nisations and technology providers throughout Europe, the United States and Singapore involved in the development and deployment of on-line monitoring technology for the detection of contaminants in water. ª 2010 Elsevier Ltd. All rights reserved.
1. On-line monitors and early warning systems There exists a need for better on-line monitoring of water systems given that existing laboratory-based methods are too slow to develop operational response and do not provide a level of public health protection in real time. There is a clear need to be able to rapidly detect (and respond) to instances of accidental (or deliberate) contamination, due to the potentially severe consequences to human health. Detecting this in real time is the most optimal way to ensure an appropriate and timely response. However the need for real-time monitors should be assessed on a case-by-case basis based on the requirements of an individual water management body. Water utilities worldwide therefore employ on-line monitoring tools
and early warning systems at all stages of the urban water cycle, through intake protection, treatment operations and distribution systems. Through the use of these tools water utilities have the potential to detect contaminants (either natural or artificial, and accidental or deliberate) in a drinking water system in near to real-time thus improving system management responses to events. General water quality parameters including pH, chlorine, temperature, flow and turbidity are commonly monitored using on-line instrumentation (Frey and Sullivan, 2004). These are used by operators in process control and regulatory compliance, and in some cases as early warning systems for contaminant detection. Early warning systems (EWS) are generally an integrated system consisting of monitoring instrument technology, with an ability to analyse and interpret results in real time (Grayman
* Corresponding author. Tel.: þ612 8849 5432; fax: þ612 8849 3143. E-mail address:
[email protected] (M.V. Storey). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.08.049
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Table 1 e Summary of some advantages and limitations of various reviewed technologies. Sensor J-Mar Biosentry
UVeVIS s::can spectro::lyser
Hach Event Monitor (Guardian Blue)
YSI Sonde
Censar
s::can Water Quality Monitoring Station
TOXcontrol (microLAN)
Algae Toximeter (BBE)
Daphnia Toximeter (BBE)
Advantages Directly integrated into the water supply through continuous, slip-stream flow analysis Process is virtually instantaneous providing for real-time detection and classification Fully automated and remotely accessible, and minimal maintenance requirements Fully submersible UV/Vis probe suitable for monitoring wide range of liquids
Single probe can be used to measure multiple parameters, including COD, COD-filtered, BOD, TOC, DOC, UV-254, NO3, NO2, ozone, H2S, TSS, and turbidity Distribution and plant personnel can troubleshoot remotely Programmed to recognise future occurrences of the same event and notify operations Simultaneous measurement of conductivity, salinity, temp, depth, pH, dissolved oxygen, turbidity, chlorophyll and blue-green algae Provides an immediate on-site measurement with good sensitivity at natural levels Ideal as early warning of algae blooms Simultaneously measures colour, turbidity, and temperature On-line access to all sensor readings Applicable for wastewater, drinking water, and environmental water Multiple parameters measured
Real-time assessment of microbial populations; quick response time High sensitivity of Vibrio fischeri to cyanide Precise determination of algae concentrations in water Highly sensitive with regard to detection of herbicides and their by-products Highly sensitive biological system for early detection of potentially dangerous unknown substances Low maintenance
ToxProtect (BBE)
Rapid detection of toxic substances in water
Fish Activity Monitoring System (FAMS)
Capable of detecting low levels of cyanide High toxic response relation between fish and humans exists Round-the-clock monitoring of fish activity for continuous water quality monitoring Quicker response time after event occurrence Non-invasive and reagentless Highly specific microbial identification
Surface enhanced Raman spectroscopy
Laser tweezer Raman spectroscopy
Surface acoustic wave (SAW) devices
Allows discrimination between different strains if bacteria and single Bacillus spores Bacteria selected from random growth phases can be classified Highly specific and low-cost Array-based sensors and data processing schemes provide increased utility
Limitations
Source
Does not provide viability data System cannot differentiate between live and dead organisms, motile or non-motile, organic or inorganic When the instrument was installed at the intake of a treatment plant, where surface water is monitored, fouling of the flow-cell observed Flow velocity determined the rate of sediment build-up
(USEPA, 2010)
The instrument has little maintenance problems however has created several false alarms
(Hohman, 2007)
Despite best practices sometimes not possible to clean sonde to a point where the standard is not contaminated by some small amount Depth sensor can be affected by biological fouling that grows in the water passage tube Limited parameters measured
(Atkinson and Mabe, 2006)
Colour measurement not highly sensitive Some issues with running system without the use of air cleaning have been identified Vibrio fischeri less sensitive to sodium fluoroacetate
(Chow et al., 2008)
Lag time in cultivating slow-growing algae
(de Hoogh et al., 2006; Mons, 2008)
Not suitable for finished (chlorinated) water as Daphnia magna is sensitive to chlorine Adjustable high sensitivity may lead to false positive alarms in some cases Incapable of detecting considerably high levels of fluoroacetate Maintenance time, size required to house fish stocks
(de Hoogh et al., 2006; Jeon et al., 2008; Mons, 2008)
Maintenance time, size required to house fish stocks
(van der Gaag and Volz, 2008; Mons, 2008) (Sengupta et al., 2006; van der Gaag and Volz, 2008)
Spectral deviations caused by metabolic or environmental factors needs to be smaller than spectral deviation between strains Associated biochemical and biomolecular methods required at same time to confirm effectiveness of Raman spectral method Reduced sensitivity and stability suggests they are not ready to fully replace analytical methodologies
(van den Broeke, 2005, USEPA, 2010)
(USEPA, 2009)
(Zurita et al., 2007; Mons, 2008)
(van der Gaag and Volz, 2008; Mons, 2008)
(Xie et al., 2005; van der Gaag and Volz, 2008) (van der Gaag and Volz, 2008)
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 4 1 e7 4 7
et al., 2001, USEPA, 2005a). The goal of an EWS is to identify lowprobability/high-impact contamination events in sufficient time to be able to safeguard the public. EWS should provide a fast and accurate means to distinguish between normal variations, contamination events and differences in quality due to biochemical and physical interactions. EWS should be able to detect deliberate as well as accidental contamination events and ideally should be reliable, with few false positives and negatives, inexpensive, easily maintainable and easily integrated into network operations (Brussen, 2007). A new generation of on-line monitoring tools based on sensor technology has emerged in recent years. Effective implementation of these tools however has not been realized for a number of reasons, least of which (i) they do not meet practical utility needs, (ii) their cost, reliability and maintenance are unsatisfactory, and (iii) data handling and management and an ability to produce meaningful operational information is yet to be realized (van der Gaag and Volz, 2008). There is a need, however, to better understand the opportunities provided by the latest on-line monitors and best practices the field has to offer to improve the capability of a utility in contaminant detection and water security management. The aim of this paper is therefore to describe existing and emerging technologies, water utility experiences and approaches to water security management. This review is by no means exhaustive, though is aimed at identifying current global trends as well as future needs and considerations for the urban water industry. Furthermore this review is not intended to evaluate specific technologies, nor imply endorsement to their use. For many of the emerging technologies the data simply is not available yet to allow a sound and statistically significant appraisal of these systems. However, to allow some comparison and critique of the technologies reviewed, some of the available advantages and limitations of each system are outlined in Table 1. Finally, it should be stressed that some online monitors may be more useful than others to particular water quality managers depending on location and/or the different and changing guidelines present in a given country. Indeed in some instances a combination of monitors may be most beneficial. Thus it is necessary and appropriate that each on-line monitor is assessed in the context of an individual water management body.
2.
Existing technologies
Many commercially-available technologies used for the detection of routine water quality parameters continue to provide the most reliable means of detecting anomalies within water systems. Whilst there exists a need for more robust instrumentation for the measurement of ammonia and fluoride, many solid state instruments including those for pH, chlorine, total organic carbon (TOC), conductivity and temperature continue to provide the most reliable means by which changes in drinking water quality can be measured in real time. More recently technologies have been developed including the J-Mar Biosentry, a laser-based technology that is designed for the continuous on-line measurement of particles in water, and the submersible UVeVIS s::can spectro::lyser, which is designed to measure multiple water
743
quality parameters including turbidity, TOC equivalent, biochemical oxygen demand (BOD), nitrate, nitrite and aromatic compounds. Other multiparametric instruments, including Hach Event Monitor, YSI Sonde, Censar and s::can Water Quality Monitoring Station technologies have been deployed at various water utilities throughout Europe and the United States for the real-time on-line analysis of water quality and contamination events. Furthermore, instrumentation including gas chromatographyemass spectrometry (GCeMS), an automated system that can be used to detect volatile trace organic micropollutants, liquid chromatographyeMS (LCeMS) and high performance liquid chromatography (HPLC), have also been used in an on-line capacity by water utilities and can provide reliable information on micropollutants, particularly in water intake monitoring, in near to real time. In addition to changes in water quality, a number of monitors have been deployed by water utilities within source and treated waters in recent years to detect contamination events in real time (Hall et al., 2007, 2009). Biological monitors such as bacterial bioluminescence and fish monitors have been in use for many decades, however significant advances have been made in this field in recent years over a range of tropic levels including those for bacterial bioluminescence, Daphnia (water flea), algal cell and fish monitoring. TOXcontrol (microLAN) is a realtime biological toxicity monitor used to measure toxicity in environmental samples and is based on the ability of Vibrio fischeri, a luminescent bacterium, to produce light as a byproduct of its cellular respiration (Meighen, 1991). Bacteria react rapidly to toxins changing their metabolism and therefore emitted amount of light. In more recent years this device has been coupled with the s::can spectro::lyser to increase the sensitivity in the detection of chemical contaminants in water at low concentrations. The combination of two instruments further allows for the verification of alarm signals from one instrument with the signal of the other, thereby reducing false alarm rates. Other devices such as the Algae Toximeter (BBE) continuously measure the photosynthetic activity of algae to detect the presence of toxic substances. The presence of toxins reduces the activity of the algae, decreasing the amount of natural (auto) fluorescence. Herbicides are the most important class of toxins detected by this type of on-line monitor. The analogue of this instrument used for the detection of pesticides is the Daphnia Toximeter (BBE). The Daphnia Toximeter is based on the sensitivity of the water fleas Daphnia magna to changes in water quality and observes Daphnia behaviour (speed, movement, swimming height and growth rate) under the influence of constantly running sample water. The live images obtained using a CCD-camera are evaluated on-line using digital image analysis with an integrated PC to analyse changes in the behaviour of the Daphnia. As with most biological assays this test is not yet suitable for finished (chlorinated) water, since D. magna is sensitive to chlorine (OECD 2004). One of the limitations (and findings of this review) is that there are no chlorine-resistant bioassays that could be proposed in this case. However, some emerging alternatives to D. magna include the cladoceran Simocephalus mixtus. S. mixtus, which has been used as a test organism in acute and chronic toxicity assays of untreated waste waters and sediments, was found to be a more
744
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sensitive test organism than D. magna in this application (Martı´nez-Jero´nimo et al., 2008). A photobacterium bioassay has also been successfully used in the testing of wastewater chlorination disinfection processes (Wang et al., 2007). Though they have been in use in water utilites worldwide for many decades, significant advances have been made in fish monitoring in recent years. Fish, often local indigenous species such as the Australian Rainbow trout, are generally placed in chambers through which water continually flows. In the event of erratic behaviour in one or more, the presence of toxins in the water is assumed. The ToxProtect (BBE) fish monitor is used to detect toxins in water by analysing the swimming activity of up to 20 fish across an array of 80 photoelectric light diode barriers in real time. Other fish monitors analyse electrical signals generated by the movements of fish muscles, as well as ventilation rate and depth, gill purge frequency and whole body movement. Research being conducted through the Agency for Science and Technology Research (A-Star) in Singapore has combined complex video surveillance algorithms and CCTV (closedcircuit television) in its Fish Activity Monitoring System (FAMS) for use in the Singapore water distribution system. As with each of the other biological monitors, the major limitation of this technique is that fish are generally sensitive to chlorine, and that the test is neither selective nor specific. The Singapore water utility (PUB) is circumventing this by dechlorinating the water, though this has its own limitations by altering the properties of the source water. In many instances, while behavioural tests are the current benchmark in terms of rigorously tested bioassays, cell based bioassays (including microbial and human) are beginning to emerge as important tests particularly in the assessment of the effects of combinations of toxins/contaminants in a system (Pomati et al., 2008). These authors have shown that both the bacterium Escherichia coli and human cells (embryonic and tumor) were valid and highly reproducible models for testing the effects of a range of micropollutants found in waterways, as well as their antagonisticesynergistic interactions. Water utilities throughout Europe and the United States have deployed many of the technologies described in this section. The focus in Europe has been for the most part on the protection of source waters, largely river intake monitoring, whilst in the United States a greater emphasis has been placed on distribution system protection in regard to homeland security in recent years. The efficacy of many of these commerciallyavailable technologies and their response to changes in water quality through simulated contamination events has been evaluated in a number of programs, most notably in Europe during the EU Techneau Programme, and by the USEPA, Office of Ground Water and Drinking Water, Water Security Division (http://water.epa.gov/infrastructure/watersecurity/lawsregs/ initiative.cfm). The latter was performed by the National Homeland Security Research Center, a research center within the U.S. EPA’s Office of Research and Development.
3.
Emerging technologies
In addition to many commercially-available technologies used for the detection of contaminants in drinking water systems,
there are a number of emerging sensor technologies in various stages of research and development that could find future applications in the urban water industry. Many emerging biological sensors rely on the detection of specific biomolecules including adenosine triphosphate (ATP), enzymes and other proteins, as well as immunoassay and polymerase chain reaction (PCR) techniques (e.g. Pomati et al., 2004; Hawkins et al., 2005). The major limitation of these and many other biological systems lies in their sensitivity and their ability to detect low concentrations of microorganisms, which unlike chemicals, are not uniformly distributed in aqueous environments. Other biological sensors rely on the optical properties of water and analytes and include those based on evaporative light scattering detection, refractive index measurement, fluorescence detection, and Raman spectroscopy. Fluorescence is a technique that uses emitted light to measure the excitation spectra of specific compounds such as chlorophyll, aromatic compounds, pesticides and humic acids, and can be used to identify compounds using the combination of the wavelength of the emitted light and the wavelength of the irradiated light. The application of fluorescence as a monitoring tool for the detection of cross-connection in dual reticulation systems, where recycled water had been inadvertently introduced into a drinking water system, has been recently explored (Henderson et al., 2009). As there is a potential for accidental (or deliberate) cross-connection from recycled water to a potable water distribution system, there is a need for monitoring to ensure water safety and to maintain public confidence (Storey et al., 2007). There have been several incidents of non-potable to potable cross-connections leading to disease outbreaks including a large incident in the Netherlands and several reports from the United States (Liang et al., 2006). Raman spectroscopy has been further developed into two technologies for microbial detection, surface enhanced Raman spectroscopy (SERS) and laser tweezer Raman spectroscopy (LTRS). SERS is the identification of microorganisms from the spectra produced at the surface of the organism which has reacted with antibodies. The LTRS technique produces an optical “tweezer” to ‘catch’ a microorganism and then laser light is used to produce a unique Raman spectrum that can be used to discriminate between different strains of bacteria or bacterial spores. From this spectrum the dynamic changes in biological molecules such as proteins, nucleic acids, lipids, and carbohydrates can be monitored (Marshall et al., 2007). Other sensor-based technologies that rely on the optical properties of water and contaminants include infra-red (IR) spectroscopy which relies on the ability of various organic functional groups including proteins, carbohydrates, lipids and nucleic acids to absorb infra-red light at specific wavelengths. Sensors based on surface acoustic wave (SAW) devices include the electronic nose and tongue and mChemLab. In SAW devices an acoustic wave is generated which produces a mechanical wave that travels through the surface of the device (Groves et al., 2006). The surface changes due to analytes that are bounded on the surface and changes in frequency provide information about the concentration of the compound. The electronic nose or tongue consists of a number of non-specific biological or chemical sensors whose responses are analysed with pattern recognition routines or artificial neuronal
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networks (Krantz-Ruckler et al., 2001). Other sensors used in the electronic nose or tongue include optical chemical detectors, quartz microbalance devices, conducting polymers, mass spectroscopy and electrochemical devices. mChemLab monitors consist of gas and liquid-phase types that are able to detect biotoxins and other inorganic and high molecular weight chemical compounds. The gas phase type consists of GC channels and SAW sensors, while the liquid type combines various chip-based techniques with fluorescence detectors. Further developments are being made in this technology to detect viruses and bacteria, and the ultimate goal is to develop a low-cost, fast deployable and real-time sensor for on-line water quality measurements. Other emerging sensor technologies are based on electrochemical detection techniques and include ion selective electrodes, photoionisation/mass spectrometry and amperometric sensors. Amperometric sensors are widely used to detect free chlorine and comprise a flow-through cell and electrode layer that is able to conduct amperometric measurements due to changes in analyte concentrations. Amperometric sensors can be used for on-line measurements and their integration into onchip optics is currently under development.
4. Sensor placement, data handling, and communications Significant advances have been made in recent years in tools for the optimal placement of on-line monitoring stations and the real-time management of data and communications. Sensor placement optimization tools including optiMQ-S and TEVASPOT (Berry et al., 2008), in combination with event detection software CANARY have been developed and may improve operations and assist in the detection of contamination events within water systems. Software such as optiMQ-S was developed to determine the optimal location of monitoring stations aimed at detecting deliberate external terrorist hazard intrusions into a water distribution system (Ostfeld and Salomon, 2005). The algorithm takes into consideration hydraulic demands and water quality conditions, as well as contaminant transport and points of contaminant introduction. The main difference between the two tools is that TEVA-SPOT is an open source software tool available for free on the web. The TEVASPOT software contains multiple optimizers (heuristic, lagrangian, and integer programming) while optiMQ-S uses genetic algorithms to optimize sensor placement. Recent collaboration of USEPA, SANDIA National Laboratories, and Argonne National Laboratory has seen the development and application of Threat Ensemble Vulnerability Assessment (TEVA) Sensor Placement Optimization Tool (TEVA-SPOT). As its name implies TEVA-SPOT can be used in the design of contamination warning systems for improving the security of drinking water distribution systems (Hart et al., 2008; Murray et al., 2010a), and amongst other benefits can be used to (i) recommend optimal sensor placement (ii) assess the consequences of contamination events and (iii) improve water distribution system network models.
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TEVA-SPOT relies on a number of input parameters including the required detection (and response) time, and as part of the tool calculates the health impacts. Both optiMQ-S and TEVA-SPOT require utility-specific input (e.g. a water distribution system network model such as EPANET), and through the application of this software, improvements can be made to distribution system models, which can in turn benefit the water utility’s understanding and management of the distribution system. A range of software has been developed to assist in the detection of water quality anomalies or events within water systems and the handling of large volumes of water quality data. Event monitors such as Hach Event Monitor and s::can Water Quality Monitoring Station are generally supported by proprietary event detection software, while others such as CANARY was developed by the EPA’s National Homeland Security Research Center, though not specifically for the WSi (Murray et al., 2010b). CANARY software uses a range of detection algorithms to evaluate standard water quality parameters such as free chlorine, pH and total organic carbon, and uses mathematical and statistical techniques to identify the onset of anomalous water quality incidents (Hart et al., 2009). Event detection software such as CANARY can utilize ongoing operational data or training data acquired to determine the natural variation of these water quality parameters, and assist utility personnel to understand the expected false alarm rates. In addition to anomalous conditions or potential contamination events, CANARY can detect unexpected “normal” events, such as a sensor malfunction or a pipe break. Recent advances in automated metering and wireless technology has presented a platform on which improved data handling and management can be made. In addition to providing a communications platform that can feed back information in real time, automated meter reading (AMR) has an added benefit of being dual purpose, where it can be used in other network operations such as billing and leak detection. Research currently being undertaken through the University of Cincinnati in collaboration with the USEPA is using AMR technology to build a real-time network model that can be used to improve existing hydraulic models and estimate contaminant transport.
5.
Water security initiative
Previously termed the “Water Sentinel” project, the Water Security Initiative (WSI) is a program of the USEPA, Office of Ground Water and Drinking Water, Water Security Division aimed at addressing the risk of intentional contamination of drinking water distribution systems (USEPA 2005b, USEPA 2008). Five major water utilities (in Cincinnati, San Francisco, New York, Philadelphia, and Dallas) were recruited for the WSI, which involves the deployment of real-time monitors and early warning systems to detect possible contamination in drinking water distribution systems. Water quality monitoring stations and analytes (pH, turbidity, temperature, conductivity, TOC and chlorine) were chosen on the basis of their sustainability for long-term operation and to provide
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“dual-use” benefits to drinking water utilities, such as improved water quality management. The WSI consists of 5 components including: (i) on-line water quality monitoring stations located throughout the distribution system, combined with (ii) public health surveillance such as over the counter pharmaceutical sales, hospital admission reports and infectious disease surveillance (iii) field and laboratory analysis of distribution system samples (iv) enhanced security monitoring and (v) customer complaint data in real time. The WSI also uses TEVA-SPOT and CANARY software packages for the optimal placement of sensors and analysis of data. TEVA-SPOT has been used to design the monitoring network for all five WSi pilots, and CANARY is used for realtime monitoring at one of the five WSi pilots, and is under evaluation at three WSi pilots.
6.
Conclusions
Despite recent advances in biological monitors and microsensor technologies, there is no universal monitor for water quality monitoring and contaminant detection. Whilst technologies are emerging from the microsensor and nanotechnology sector, they are not at a stage where they can be readily deployed within existing operations. Technology therefore needs to co-evolve and become less expensive and more reliable. Although operations and technology go hand-inhand, it is likely that technology in terms of monitoring will need to evolve to meet the many operational constraints. Ultimately it will be a balance between cost and ease of implementation. In the meantime though, water utilities should focus on addressing existing operational needs such as nitrification control and cross-connection detection using available technologies, improved data handling and interpretation, as well as improved communications. In this way a platform can be laid for the future deployment of an early warning system. To ensure their survival in network operations, early warning systems must furthermore demonstrate operational benefits (such as better water quality, decreased operating costs or reduced customer complaints). A focus on water security alone does not provide sufficient grounds for its survival in operations, given the maintenance, technical expertise and cost required, and the number of false alarms often associated with them. Utilities should also focus their efforts on the real-time management of large amounts of data. One way to validate any technology (either existing or emerging) is for water companies to make data available for research, and there is thus a need to build an information platform that could be provided by automated meter reading (AMR) and wireless technologies. AMR has an added benefit in that it is dual purpose, given its intended use in billing and potential use in leak detection. Solid-state instrumentation that measures traditional water quality parameters including pH, chlorine, temperature, flow and turbidity continues to provide the most
reliable information and should form the focus of water utility attention. There is a need for predictive models that better describe distribution system dynamics and contaminant transport within a distribution system. Furthermore, there is a need for improved incident management strategies to restore operations and public confidence in the event of contamination of source waters and distribution systems.
Acknowledgments This work was supported through Sydney Water and Degre´mont’s Science and Technology agreement. The staffs at both organisations are gratefully acknowledged for their invaluable assistance. The authors would also like to gratefully acknowledge the contribution of Professor Nicholas Ashbolt (US Environment Protection Agency) and the staff of more than 60 water utilities, government agencies, technology companies and universities who graciously gave their time to contribute to this study. The authors are very grateful to John Hall, Regan Murray, Robert Janke of the USEPA, Jeff Szabo of NHSRC, and Steve Allgeier and Dan Schmelling of EPA’s Office of Water for their critical review of this manuscript.
references
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Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Control of mineral scale deposition in cooling systems using secondary-treated municipal wastewater Heng Li a, Ming-Kai Hsieh b, Shih-Hsiang Chien a, Jason D. Monnell a, David A. Dzombak b, Radisav D. Vidic a,* a b
Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA 15261, United States Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, United States
article info
abstract
Article history:
Secondary-treated municipal wastewater (MWW) is a promising alternative to freshwater
Received 10 June 2010
as power plant cooling system makeup water, especially in arid regions. A prominent
Received in revised form
challenge for the successful use of MWW for cooling is potentially severe mineral depo-
19 August 2010
sition (scaling) on pipe surfaces. In this study, theoretical, laboratory, and field work was
Accepted 30 August 2010
conducted to evaluate the mineral deposition potential of MWW and its deposition control
Available online 9 September 2010
strategies under conditions relevant to power plant cooling systems. Polymaleic acid (PMA) was found to effectively reduce scale formation when the makeup water was concentrated
Keywords:
four times in a recirculating cooling system. It was the most effective deposition inhibitor
Scaling
of those studied when applied at 10 mg/L dosing level in a synthetic MWW. However, the
Mineral deposition
deposition inhibition by PMA was compromised by free chlorine added for biogrowth
Precipitation
control. Ammonia present in the wastewater suppressed the reaction of the free chlorine
Cooling water
with PMA through the formation of chloramines. Monochloramine, an alternative to free
Municipal wastewater
chlorine, was found to be less reactive with PMA than free chlorine. In pilot tests, scaling
Antiscaling
control was more challenging due to the occurrence of biofouling even with effective control of suspended bacteria. Phosphorous-based corrosion inhibitors are not appropriate due to their significant loss through precipitation reactions with calcium. Chemical equilibrium modeling helped with interpretation of mineral precipitation behavior but must be used with caution for recirculating cooling systems, especially with use of MWW, where kinetic limitations and complex water chemistries often prevail. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
With increasing shortages of freshwater, wastewater is now being recognized as a significant source of water for nonpotable uses (Miller, 2006; Metcalf & Eddy, 2007). Among different types of wastewater, secondary-treated municipal wastewater (MWW) is of increasing interest, primarily because it holds promise as a viable alternative source of cooling water in terms of quantity and geographical proximity
to existing and future power plants in the US (Vidic and Dzombak, 2009). A number of power plants already use MWW as makeup water in their recirculating cooling water systems (Ehrhardt et al., 1986; EPRI, 2008). The majority of these power plants are in regions of the US most susceptible to freshwater constraints, i.e., the southwest and Florida. These power plants typically use MWW only as a fraction of the total makeup water needed or only after significant additional treatment to obtain better water quality.
* Corresponding author. Tel.: þ1 412 624 9870; fax: þ1 412 624 0135. E-mail address:
[email protected] (R.D. Vidic). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.08.052
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The primary challenges with MWW reuse for cooling arise from its low quality. Secondary-treated MWW usually contains appreciable amounts of hardness, phosphate, ammonia, dissolved solids, and organic matter compared to the amounts in freshwater (Weinberger et al., 1966; Williams and Middlebrooks, 1982). In recirculating cooling systems, the water constituents become concentrated many times (typically 4e8 times) because of the evaporative loss of water. The elevated concentrations and high water temperature can cause severe mineral deposition (scaling) problems, along with the problems of corrosion and biofouling. Because of these challenges, intensive chemical control programs are usually implemented (EPRI, 2003). Neither the mineral deposition characteristics of MWW under cooling system conditions nor the feasibility of controlling deposit formation through chemical addition when using MWW as the sole source of makeup water in a recirculating cooling system have been studied. Presently, three types of deposit inhibition chemicalsdantiscalantsdare widely used to prevent mineral deposition on pipe and heat exchanger surfaces in cooling systems: carboxylic polymers, such as polyacrylic acid (PAA), polyacrylamide, and polymaleic acid (PMA); phosphonates; and polyphosphates. Polymeric antiscalants often incorporate functional groups in addition to carboxylate such as sulfonate or benzenesulfonate (Shakkthivel and Vasudevan, 2006; Du et al., 2009). Multiple antiscaling mechanisms working together contribute to the effectiveness of the antiscaling chemicals. First, the precipitation propensity of minerals is mitigated through complexation with antiscalant molecules to increase the operational solubility of cationic species, primarily Ca and Mg, the most common potential scale forming species in water (Eriksson et al., 2007). Second, the antiscalants can interact with newly formed mineral nuclei to disrupt the crystallization process, thereby hindering the growth of the precipitating particles (Frayne, 1999; Shakkthivel and Vasudevan, 2006). Antiscalants for which this mechanism is dominant are commonly referred to as threshold inhibitors. Third, antiscalant molecules can stabilize the mineral particulates through electrostatic and/or steric interactions to keep them dispersed in the aqueous suspension, rendering them less prone to sedimentation or deposition (Eriksson et al., 2007). A fourth mechanism of scale inhibition involves adsorption of antiscalants onto pipe surfaces to prevent mineral deposition onto the surfaces. For example, phosphorous-bearing groups exhibit strong interactions with surfaces of metals and metal oxides (Nowack, 2003). Based on the similar mechanism of surface adsorption, some phosphonates and polyphosphates are used as corrosion inhibitors as well because a surface layer of these molecules retards surface redox reactions (Kielemoes et al., 2000). However, many of the phosphorous-based compounds, particularly the polyphosphates, suffer from hydrolysis reactions that produce orthophosphate (Snoeyink and Jenkins, 1980), potentially exacerbating phosphate scaling when the water contains significant hardness. The effectiveness and fate of phosphorous-based inhibitors when applied in cooling systems using MWW have not been well studied. Numerous polymer antiscalants with varied molecular weight, structural features, and effectiveness in different
749
waters are available commercially. PMA was selected in this study as a model polymer antiscalant based on a literature survey and consultation with practitioners in cooling system design and operation. PMA is believed to act as both a colloid dispersant and a crystal distorter, particularly for Ca-containing precipitates, the potentially dominant scale formers in MWW (Christophersen, 2007; Metcalf & Eddy, 2007; EPRI, 2008; Scandolari, 2008; Beardwood, 2009). Besides PMA, representative antiscalants containing phosphonates or polyphosphates, including 2-phosphonobutane-1,2,4-tricarboxylic acid (PBTC) and tetrapotassium pyrophosphate (TKPP), were also tested for their effectiveness in MWW. The influence of orthophosphate and ammonia present in MWW on scaling control is of particular interest because phosphate can precipitate with di- and trivalent cations while ammonia is a strong complexing agent for copper and iron, both of which are common pipe/heat exchanger materials in cooling systems (Stumm and Morgan, 1996). Another concern with the use of MWW for cooling lies in the need to control biogrowth. The use of chlorine as a biocide may potentially compromise the effectiveness of organic antiscalants because free chlorine, a strong oxidant, is aggressive toward many aqueous organic compounds and pipe materials. For example, studies show that large doses of chlorine significantly increase mild steel corrosion (Nalepa et al., 1999), which leads to iron dissolution and precipitation on the pipe. Ammonia, on the other hand, can combine with free chlorine to form chloramines, which poses less risk for metal alloy corrosion (Zhang et al., 2008). The influence of chloramines on scaling control in cooling systems that use MWW has not been investigated. Quantitative analytical methods for studying mineral scaling in cooling systems are not readily available in the literature. There is a general lack of well-documented methods suitable for in situ measurements of mineral deposition kinetics. Most established techniques pertaining to mineral scaling phenomena confine themselves to means of static observations and analysis only after solid scales have formed and been collected. For example, ASTM standard methods D 1245-84, D 2331-80, D 933-84, D 934-80, and D 88782 only deal with the procedures of removing water-formed deposits from sample tubes by specified mechanical or chemical means, and with qualitative identification of deposits by spectroscopy-based analysis. Very limited effort has been devoted to the study of mineral scaling kinetics in terms of how scales form, at what rate(s) they form, and the mechanisms and conditions influencing their formation in waters of varying quality. The objectives of this study were to investigate the effects of orthophosphate and ammonia on the performance of scaling control by PMA in cooling systems using MWW, and to test the feasibility of biocontrol by chlorine and corrosion control by phosphorous-bearing chemicals without interfering with scale inhibition under recirculating cooling conditions. Equilibrium calculations were performed to evaluate the mineral deposition potential of MWW under a range of cooling water conditions. Laboratory studies were conducted to determine effective deposition control strategies under conditions relevant to industrial cooling systems, i.e., elevated temperature, circulating flow, and concentrated water constituents. In addition, pilot-scale cooling tower tests
750
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were conducted to study the effectiveness of the model polymer antiscalant, PMA, and the potential applicability and implications of an integrated chemical regimen for the successful control of scaling, corrosion, and biofouling in using treated MWW for cooling.
Table 1 e Chemical composition of the secondary-treated municipal wastewater (MWW) from Franklin Township Municipal Sanitary Authority, Murrysville, PA. Analyte
Unit
Result (unfiltered) (filtered)
2.
Materials and methods
2.1.
Secondary-treated municipal wastewater
A secondary-treated municipal wastewater effluent (i.e., biological trickling filter followed by secondary clarification) was collected for use in bench-scale experiments. The effluent was collected at the Franklin Township Municipal Sanitary Authority (FTMSA) wastewater treatment plant located in Murrysville, PA. Polyethylene (PE) containers (1 L bottles or 5gallon jars) were used for temporary storage of the MWW before experiments. Typical storage time was less than 24 h, otherwise the water was refrigerated. To characterize the water quality, both filtered (0.45 mm) and unfiltered water samples were transferred to different PE containers that were prepared with the addition of appropriate acid preservatives. Metal concentrations were determined by inductively coupled plasma mass spectroscopy (ICP-MS) at a commercial lab (Test America, Pittsburgh, PA). Other parameters were determined either in our laboratory or at the commercial lab, using appropriate standard test procedures (Li, 2010). The concentrations of calcium and magnesium, the two principal cationic species, were measured by Atomic Absorption Spectroscopy (AAS). The water quality data are provided in Table 1. As indicated in Table 1, the wastewater represents well the typical secondary-treated municipal effluent (Metcalf & Eddy, 2007). For bench-scale experiments, the wastewater effluent sample was concentrated in the laboratory by evaporation at 40 C to reach 4 cycles of concentration (CoC 4) as determined by 75% volume reduction, prior to use in a bench-scale water recirculating system (Fig. 1). However, it was discovered from preliminary tests that pre-concentrating the MWW resulted in a loss of mineral content due to precipitation that took place during the concentration step. This premature precipitation made the concentrated water less representative of MWW at CoC 4. As such, a synthetic municipal wastewater was prepared that truly represented CoC 4 in terms of its mineral content (i.e., four times more concentrated than the MWW) for detailed investigation in the bench-scale studies. The synthetic MWW (CoC 4) was made using DI water (resistivity > 18 MU cm) with the addition of desired chemical constituents (reagent grade or better). The chemical recipe of the synthetic MWW (CoC 4) is provided in Table 2. The composition of the synthetic MWW was chosen to represent the typical secondary-treated municipal effluent (Metcalf & Eddy, 2007).
2.2.
Antiscalants and other chemicals
PMA and PBTC, both in 50% active content, were provided by Kroff Chemical Company (Pittsburgh, PA). TKPP (48% active content) was provided by Crown Solutions/Veolia Water
Al Ca Cu Fe K Mg Mn Na SiO2 Zn pH NH3 NO3 HCO3 Alkalinity Total Alkalinity BOD Cl SO4 Total P TOC TDS TSS Conductance Turbidity
mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg N/L mg N/L mg CaCO3/L mg CaCO3/L mg/L mg/L mg/L mg P/L mg/L mg/L mg/L mS/cm NTU
0.2 42 0.028 0.5 16.3 10.7 0.32 94 8.54 0.07 7.1 21.0 3.6 177 177 32 106 86 4.5 27 661 NA 1.03 16.7
e 41 e 0.37 NAa 10 e NA NA NA 7.2 NA NA NA NA NA NA NA NA NA NA 41 1.02 NA
Detection limit 0.2 5 0.025 0.1 5 5 0.015 5 1.07 0.02 0.5 0.1 5 5 10 1 0.5 1 10 5 0.01 1
a NA: Not Analyzed.
(Vandalia, OH). Free chlorine was used as concentrated sodium hypochlorite (NaOCl) solution (5%). Monochloramine was pre-formed by mixing NaOCl and ammonium chloride (NH4Cl) at 4:1 Cl2:NH3 mass ratio and was used immediately (Kirmeyer et al., 1993). PMA concentrations were determined colorimetrically at 505 nm using a commercial test kit (MCI analytical test procedure, Masters Company, Wood Dale, IL). The concentrations of PBTC and TKPP were monitored by following Standard Method 4500-P (American Public Health Association et al., 2005). Free chlorine and monochloramine were measured with a chlorine photometer (HF Scientific Inc., FL).
2.3.
Scaling study in bench tests
A customized bench-scale water recirculating system was equipped with removable stainless steel (SS316) circular disc specimens to provide surfaces for scaling/deposition in the recirculating water (Fig. 1). Mineral mass deposited on the SS surfaces (5.61 cm2 per disc) was determined to track the scaling process with varied water chemistries and scaling control strategies. Water temperature and flow velocity were 40 C (105 F) and 0.6 m/s, respectively, to reflect actual conditions of industrial cooling systems. In a typical test, the recirculating water was exposed to air so that the alkalinity may approach equilibrium with CO2(g), as is the case with actual cooling system operation. Before use, the SS specimens were cleaned by ultrasonic wash for 5 min in an acetone/ethanol solution (1:1 v/v ratio), rinsed with DI water and air-dried in a laminar
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 4 8 e7 6 0
751
Fig. 1 e Customized bench-scale water recirculation system for examining mineral deposition. (a) Schematic flow chart. (b) Picture of the experimental setup with a pipe flow section showing the circular metal disc used to collect mineral deposits.
flow hood. At predetermined time intervals during an experiment, the SS specimens were taken out of the recirculating water through the sampling ports. The water remaining on the disc surface was carefully removed by paper tissue without disturbing the solid deposits on the surface. The discs were then air-dried for at least 48 h and the mass of each disc was measured using an analytical balance (Mettler AE163, detection limit 0.01 mg). Final weighing was performed only after a constant mass was achieved (mass measurement variation < 0.05 mg/h). Three measurements were taken for each specimen and the average value was reported as the mineral mass on the disc. After weighing, the morphology of the scale samples was inspected using Scanning Electron Microscopy (SEM, Philips XL30, FEI Co., Hillsboro, OR), and the scale elemental compositions were determined by Energy Dispersive X-ray Spectroscopy (EDS, EDAX Inc., Mahwah, NJ). Samples were not sputtered with Au or Pd prior to the SEM/EDS analyses. These examinations were useful to identify the scale characteristics and facilitated the selection of effective scaling control approaches. For example, the identification of the mineral deposits by SEM/EDS provided evidence for the selection of the appropriate antiscaling chemicals to inhibit the formation of the specific minerals identified in the scales. After each experiment, the recirculating system was cleaned by running HCl solution (pH 2e3) for about 1 h,
Table 2 e Chemical composition of synthetic MWW (simulating CoC 4) used for MINEQLD modeling calculations and bench-scale experiments. Cation
Ca2þ Mg2þ Naþ Kþ NHþ 4 (as N)
Concentration mM
mg/L
7.60 7.16 26.88 0.70 7.01
305 174 618 27 98
Anion
SO2 4 HCO 3 Cl PO3 4
Concentration mM
mg/L
2.84 13.44 31.13 0.21
273 820 1105 20
The initial level of TDS of the water, before any precipitation takes place, is 3455 mg/L.
followed by a DI water rinse times, with 0.5 h of water recirculation each time.
2.4.
Pilot-scale cooling tower tests
Pilot-scale cooling towers were constructed and operated with secondary-treated MWW (Fig. 2) to test the optimal chemical treatment regimen that was identified from the bench-scale experiments. The pipe section for scale collection on disc specimens had a similar design as used in the bench-scale tests. For simultaneous evaluation of different scaling control programs, three towers were operated side by side at the wastewater treatment plant of the Franklin Township Municipal Sanitary Authority (Murrysville, PA). Chemical control of biofouling and corrosion was also implemented. All three towers were operated at CoC 4, using a flow velocity of 0.6 m/s. The temperature of water entering the tower was 40 C (105 F) and leaving the tower was 35 C (95 F). The loading rate of each tower was 123 L/min/m2 (the crosssectional area of the tower was 0.093 m2). The tower fill was fabricated from rigid, corrugated PVC sheets and had a surface area of 147.8 m2/m3 (Brentwood Industries, Reading, PA). The volume of each tower fill was 0.085 m3, or 10 W 10 L 30 D. Under steady state operations at CoC 4, 120e160 L of MWW makeup water was added daily to each tower. The cooling towers were tested for two consecutive 21-day periods. The first run was a full operation with all three towers and the second run used two towers. The primary purpose for the second run was to test the biocontrol by pre-formed monochloramine instead of free chlorine (Chien et al., submitted for publication). Between the two tests, the towers were cleaned with an acetic-acid solution and treated with free chlorine as a biocide. Detailed information on tower operations was recorded, including the water temperature profile at different locations, the airflow rate inside the cooling tower, the conductivity of circulating water, the flowrates of makeup water, recirculating water, and blowdown stream, as well as the ambient conditions (weather, air temperature, relative humidity, etc.). The rates of solid deposition on stainless steel disc specimens were measured during both runs. In addition, the corrosion of selected metal alloys and the bioactivity in the towers were monitored by a coupon weight loss method (Hsieh et al., in press) and by
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Fig. 2 e Schematic of pilot-scale cooling tower. The disc coupon rack had a similar design as in the bench-scale recirculation system (Fig. 1).
heterotrophic planktonic/sessile bacteria counts (Chien et al., submitted for publication), respectively. A planktonic heterotrophic bacteria plate count was performed following the spread plate count method. Plate count agar was used as the culture medium and the plates were incubated for at least 48 h at 35 C.
2.5.
MINEQL modeling
Chemical equilibrium modeling using MINEQLþ (Schecher and McAvoy, 1992, 1999) version 4.6 was performed to estimate the mineral precipitation potential of the secondarytreated MWW, the chemical composition of solid precipitates, and their relative abundance. System parameters specified for the modeling included temperature of 40 C and a closed system with respect to carbonate equilibria.
3.
Results and discussion
3.1. Bench-scale recirculating experiments with synthetic MWW (CoC 4) The use of a synthetic municipal wastewater allowed the representation of the recirculating cooling conditions in which secondary-treated MWW as the sole makeup water was concentrated to CoC 4. A series of experiments was conducted to test the effectiveness of the antiscalants PMA and PBTC at CoC 4 and to evaluate the impact of chlorine-based biocides, ammonia, and phosphate on the performance of the antiscalants. In cooling systems using freshwater, typical doses of
antiscalants are 5e10 mg/L, depending on the specific water quality (Christophersen, 2007; Scandolari, 2008). In this study using MWW, higher doses were used considering the impaired water quality.
3.1.1.
Effect of antiscalants addition
Without antiscalants addition, the mineral deposits collected on a disc specimen at CoC 4 during the recirculation of the synthetic MWW were, on average, more than 2 mg (Fig. 3). As a comparison, the deposits collected at CoC 1 were between 0 and 0.2 mg (data not shown in figure). The antiscalant PBTC dosed at 10 mg/L suppressed deposition to about 0.2 mg, which was more effective than when dosed at 5 mg/L (Fig. 3), suggesting that the MWW concentrated to CoC 4 demands higher doses of antiscalants for scaling control, as compared with typical values used for freshwater (Christophersen, 2007; Scandolari, 2008). PMA dosed at 10 mg/L inhibited scaling nearly completely, demonstrating its superior antiscaling effect in the synthetic MWW (CoC 4). Based on data in Fig. 3, the initial scaling rate (within the first 12 h or so) of MWW with 10 mg/L of PMA addition was estimated to be near zero while the initial scaling rate with 10 mg/L of PBTC was 0.025 mg/h. The overall average scaling rate with 10 mg/L of PMA was calculated to be 0.0018 mg/h and that with 10 mg/L of PBTC was 0.0085 mg/h. It appeared that scaling occurred within about 12 h beyond which no significant amount of additional scale formed. In these bench-scale tests, supersaturated synthetic MWW representing CoC 4 was subjected to recirculating in a water loop where external heat was provided to raise the water temperature to 40 C. Mineral precipitation and deposition (scaling)
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 4 8 e7 6 0
modeling) and ammonia adsorption onto mineral surfaces to disrupt particle growth (Gehrke et al., 2005). Also, given the predominance of ammonium ions in the experimental pH range (pH 7.5e8) where the water recirculating system operated, some complexation between NHþ 4 and carbonate species could also occur to further decrease the precipitation potential of carbonate minerals.
Deposits (mg)
3
2
1
3.1.3.
0 0
24
48
72
Water recirculation time (hr) Fig. 3 e Scaling behavior of a synthetic municipal wastewater (CoC 4) in bench-scale tests with inhibitors at different dosing (40 C): No inhibitors (C), 5 mg/L of PBTC (,), 10 mg/L of PBTC (-), 10 mg/L of PMA (:).
took place until the supersaturation level was substantially decreased. Because no additional CoC 4 water was added to the system during mass gain data acquisition, no continued scaling was observed after about 12 h.
3.1.2.
753
Influence of ammonia and phosphate
As can be seen in Fig. 4, the removal of ammonia, which was present as 100 mg N/L in the other tests, resulted in significant scale formation compared with the case in which the addition of antiscalants substantially reduced scaling in the presence of ammonia. Conversely, the removal of orthophosphate (as 20 mg PO4/L) did not exhibit a profound impact on scaling control by PBTC, which implies that the addition of the antiscalant (10 mg/L) was sufficient to reduce phosphate mineral scale formation. The beneficial role of ammonia for scaling inhibition was due to both complexation reactions between ammonia and calcium (confirmed by chemical equilibrium
Fig. 4 e Influence of ammonia and phosphate on scaling control in bench tests with a synthetic MWW at CoC 4 (40 C). No inhibitors (C), 10 mg/L of PBTC (-), 10 mg/L of PBTC, no ammonia (A), 10 mg/L of PBTC, no phosphate (6).
Interference of chlorine biocides
As shown in Fig. 5, the addition of chlorine biocides for biogrowth control negatively impacted scaling inhibition by either PBTC or PMA. In the absence of the biocides, PBTC substantially inhibited scale formation while PMA nearly completely inhibited scaling. However, the addition of free chlorine caused a significant decrease in the antiscaling efficiency as both antiscalants were significantly impaired by the oxidizing biocides. It is noteworthy that free chlorine was more detrimental than monochloramine to compromise the antiscaling effects of PBTC and PMA, even for the case with elevated dosing of 20 mg/L of PMA (Fig. 6). The interaction between PMA and free chlorine is explained by the data shown in Fig. 6. In the absence of free chlorine, the PMA concentration remained stable during the entire period of experiment (6 days). After 3 days of interaction with free chlorine, PMA started to deplete for both doses tested. Furthermore, during the experiment with 20 mg/L of PMA addition, total chlorine demand was much greater than in the experiment with 10 mg/L of PMA. To maintain a constant level of chlorine in solution, i.e., 1 mg/L, a total of 26 mg/L of chlorine was added over the 6 days of the experiment. In comparison, only 6 mg/L of chlorine was needed to maintain the 1 mg/L concentration level over the same period of experiment with 10 mg/L of PMA addition. This explains, at least in part, the sharp decrease in PMA after day 3. PMA was substantially consumed by chlorine, especially after 3 days, and consequently, the scaling inhibition efficiency was greatly reduced, as evidenced by the appreciable increase in the mineral mass deposited on test coupons after 3 days. A number of studies have used PMA as a model compound of natural organic matter (NOM) owing to its resemblance of the chemical and structural characteristics of natural humic and fulvic acids (Anderson and Russell, 1976; Hess and Chin, 1996; Wang et al., 1997). It is hence not surprising to observe the destruction of PMA by free chlorine, given the extensivelystudied formation pathways of disinfection by products (DBPs) from NOM and chlorine biocides (Li et al., 2000; Kitis et al., 2001; Hassan et al., 2006; Roccaro et al., 2008; Chowdhury et al., 2009; Johnstone and Miller, 2009). Separate batch tests of PBTC in the presence of chlorine additives showed stability of PBTC up to 150 h. As such, deposit data beyond 72 h were not collected in Fig. 5.
3.2.
Pilot-scale study with secondary-treated MWW
3.2.1.
Changes of water chemistry due to scaling
Table 3 shows the chemical treatment program for simultaneous control of scaling, corrosion, and biofouling in pilotscale cooling towers. The program included the addition of PMA and PBTC as scaling inhibitors, TKPP and Tolyltriazole (TTA) as corrosion inhibitors, and free chlorine (the first run) or
754
Deposi ts (mg)
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 4 8 e7 6 0
3
3
2
2
1
1
0
0 0
24
48
72
0
24
Water recirculation time (hr)
48
72
96
120
144
Water recirculation time (hr)
Fig. 5 e Interference of chlorine-based biocides on scaling control in bench tests with a synthetic MWW at CoC 4 (40 C). Left: No inhibitors, no biocides (C), 10 mg/L of PBTC, no biocides (-), 10 mg/L of PBTC, 1 mg/L of free chlorine (*), 10 mg/L of PBTC, 1 mg/L of monochloramine (3). Right: No inhibitors, no biocides (C), 10 mg/L of PMA, no biocides (:), 10 mg/L of PMA, 1 mg/ L of free chlorine (*), 10 mg/L of PMA, 1 mg/L of monochloramine (3).
monochloramine (the second run) as biocides. The results of the pilot-scale studies provided evidence for the effectiveness of PMA, PBTC, and TKPP in preventing scaling from the MWW at larger scale. Water samples, obtained from the recirculating loop of each cooling tower operated at steady state, were analyzed for key constituents and chemical parameters (Tables 4 and 5). Before reaching the steady state of CoC 4, a sharp increase of water pH from 7.2 of the makeup water (secondary effluent) to 8.3 was observed in each tower, primarily due to an aeration effect of the cooling towers that liberated CO2 from the water. It is well known that effluent from the secondary clarifier in a wastewater treatment plant is commonly over saturated with CO2 yielded by the continuing aerobic biodegradation of residual organic carbon (Sperandio and Paul, 1997; Ficara and Rozzi, 2004; Weissenbacher et al., 2007; Zhang et al., 2009). We analyzed the pH behavior of the cooling towers through measurements and modeling. Detailed description about the pH changes will be reported separately. After reaching CoC 4, the alkalinity in the recirculating water was generally 2e4
times higher than in the makeup water, further raising the pH to 8.5e9. The concentration factor of 2e4 for alkalinity was lower than the volume-based CoC because part of the alkalinity was lost to the precipitation of carbonate solids. Concentrations of chloride in the cooling water were typically 6e7 times higher than in the makeup water. This ratio is higher than the expected ratio based on the water volume reduction (i.e., CoC 4e5). The extra chloride of some 350 mg/L (estimation based on Table 5) in the recirculating water came from the addition of chlorine biocides (either free chlorine for the first run or monochloramine for the second run). Concentrations of sulfate in the cooling water were generally 4e5 times higher than in the makeup water, which corresponded well to the volume-based CoC because there was no additional sink or source for sulfate, and as such, sulfate behaved as a conservative species. Total phosphate concentrations in the tower water were much lower than that in the makeup waterdnearly 90% of the phosphate precipitated out of water due to its low solubility in the presence of high calcium (e100 mg/L, or 2.5 mM) under the
25
3
PM A (mg/ L)
Deposit s (mg)
20 2
1
15 10 5
0
0 0
24
48
72
96
120
144
Water recirculation time (hr)
0
24
48
72
96
120
144
Water recirculation time (hr)
Fig. 6 e Interference of chlorine biocides with PMA on scaling control in bench tests with a synthetic MWW at CoC 4 (40 C). No inhibitors, no biocides (C), 10 mg/L of PMA, no biocides (:), 10 mg/L of PMA, 1 mg/L of free chlorine (*), 20 mg/L of PMA, 1 mg/L of free chlorine (*). Left: Scaling behavior. Right: Depletion of PMA concentration in the aqueous solution.
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Table 3 e Chemical treatment program (target concentration) for pilot-scale cooling tower tests with secondary-treated MWW at Franklin Township, PA (unit: mg/L). Chemical addition
Corrosion Control Scaling Control Biocontrol
First Run
Second Run
Tower Tower Tower Tower Tower A B C A B TTA TKPP PMA PBTC Free Cl2 MCA
1 0 0 0 1.5 0
2 10 10 5 1.5 0
2 10 20 10 1.5 0
1 0 0 0 0 3
2 0 10 0 0 3
TTA: Tolyltriazole; TKPP: tetrapotassium pyrophosphate; PMA: polymaleic acid; PBTC: 2-phosphonobutane-1,2,4-tricarboxylic acid; MCA: monochloramine.
PMA was added to Towers B and C for scaling control during the first run. The concentrations and fate of PMA were monitored periodically. The target levels of PMA in Towers B and C were 10 mg/L and 20 mg/L, respectively (daily addition was based on the volume of blowdown water). Detected PMA in Tower A, however, was 7 mg/L on average, suggesting that about 30% of the PMA was removed with precipitated mineral solids and settled out of the liquid phase, or was degraded by chlorine. Furthermore, free PMA (the filterable fraction) accounted for about 60% of total aqueous PMA. The rest (40%) was most likely associated with suspended solids. Studies have shown that anionic polyelectrolytes such as PMA tend to adsorb onto mineral particles and prevent solids settling by providing an electrostatic and/or steric stabilization mechanism (Wang et al., 1997; Shakkthivel et al., 2005; Eriksson et al., 2007; Sonnenberg et al., 2007).
3.2.2. cooling tower conditions investigated. For example, TKPP, a polyphosphate, was added primarily as a corrosion inhibitor (10 mg/L as PO4). However, the measured TKPP concentration in the recirculating water was less than 1 mg/Ldmost of it precipitated and became unavailable for corrosion control. Therefore, the feasibility of using phosphate-based corrosion inhibitors such as TKPP in secondary-treated municipal wastewater is questionable because it may only add to more challenges for scaling control. Tower A, which received no PMA or PBTC for scaling inhibition, precipitated the greatest amount of calcium. The amount of calcium in the recirculating water accounted for 60e70% of the amount fed with the makeup water, i.e., 30e40% of the calcium precipitated. The degree of calcium removal by precipitation will be discussed in more detail. Similar to sulfate, magnesium was 4e5 times more concentrated in the cooling water than in the makeup water, suggesting that magnesium precipitation was minimal. This was confirmed by the EDS analysis, which revealed nearly undetectable amount of magnesium in the collected solids.
Mass deposition measurement
Fig. 7 shows the accumulated scale solids deposited on stainless steel disc specimens in the three cooling towers with different dosing strategies. Tower A, as a control tower, received no antiscaling chemicals. In Tower B, the addition of 10 mg/L of PMA and 5 mg/L of PBTC resulted in the least scaling among the three towers. However, when the dosing of PMA and PBTC was doubled in Tower C, expected better scaling inhibition was not observed and actually the scales accumulated as much as those in the control tower without antiscaling treatment (Tower A). It appeared that overdosing had occurred. Given the small increment between the two dosings, the ionic strength of the cooling water was not increased significantly, and as such, compression of the electrical double layer of suspended particles was unlikely to be important in destabilizing them in Tower C. It is more likely, however, that interparticle bridging due to the double dosing of PMA might cause particle destabilization and subsequent deposition (scaling), an effect similar to enhanced coagulation by polymers. The main difference between bench-scale experiments (Figs. 3e6) and pilot-scale cooling towers (Fig. 7) was the
Table 4 e Concentrations of cationic species and PMA in makeup water (secondary effluent) and recirculating water (CoC 4e5) in field testing with pilot-scale cooling towers (unit: mg/L). Species
Ca Mg Fe Cu PMA
Raw water
Total Filterable Total Filterable Total Filterable Total Filterable Total Filterable
35.2 1.5 34.5 1.1 10 1 10 1 0.37 0.11 0.12 0.03 0.06 0.03 0.06 0.03 e e
First Run
Second Run
Tower A
Tower B
Tower C
Tower A
Tower B
97 7 91 7 47 8 45 8 0.59 0.23 0.06 0.02 0.12 0.03 0.10 0.03 e e
112 8 100 9 58 5 55 4 0.81 0.25 0.05 0.04 0.13 0.03 0.10 0.03 6.8 1.9 4.3 1.3
111 10 102 11 57 5 54 5 0.68 0.25 0.07 0.03 0.13 0.04 0.11 0.04 14.6 2.6 9.7 2.1
105 3 98 4 46 3 44 3 0.74 0.24 0.06 0.03 0.28 0.14 0.23 0.11 e e
113 7 103 6 43 3 42 3 0.86 0.28 0.08 0.05 0.22 0.09 0.18 0.09 6.9 1.6 4.5 1.3
Data are mean values 1 sd. Sample size for raw water n ¼ 7. Samples for recirculating water in the cooling towers were from day 4 to day 24 during the tower operation (sample size for tower A: 10, tower B: 10, tower C: 11).
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Table 5 e Concentrations of anionic species and other chemical additives (for corrosion and biofouling control) in makeup water (secondary effluent) and recirculating water (CoC 4e5) in field testing with pilot-scale cooling towers (unit: mg/L). Species
Raw water
ALK SO4 Cl PO4 TKPP PBTC TTA Total Cl2
113 75 142 11.5 e e e e
First Run
34 7 22 1.8
Tower A
Tower B
283 357 955 5.9 e e 1.0 1.2
364 388 937 4.1 0.6 0.8 2.0 1.0
54 39 135 1.1
0.8 0.9
53 49 74 1.0 0.4 0.3 0.9 0.7
Second Run Tower C
Tower A
324 378 917 5.2 0.6 0.9 1.8 1.5
232 323 859 7.5 e e e 3.2
25 76 152 0.6 0.4 0.7 1.0 0.8
68 30 133 2.7
1.3
Tower B 244 356 1050 8.1 e e 1.8 3.6
79 27 115 3.3
1.0 2.2
For ALK, the unit is mg/L as CaCO3.
Depositson sp ecimen (mg)
continuous addition of makeup water (secondary-treated MWW) to the cooling towers. Unlike the scaling patterns observed in bench-scale tests where a plateau was obvious after about 12 h, scale was continuously formed over the entire test period (24 d) in cooling towers. After day 8 (or 4 days after reaching steady state of CoC 4), an accelerated mass accumulation of solids on the coupon discs in all three towers was obvious. An analysis of solid composition revealed that biomass (the fraction burnable at 500 C) accounted for 30e50% of the total accumulated solids, indicating that biofilm growth played an important role in surface fouling in the cooling towers using secondary-treated MWW. However, the total amount of biomass cannot be completely accounted for by the volatile organic matter burnable at 500 C. For example, the mineral content of cells remaining after burning still contributed to the mass gain measurements. Moreover, the dynamic process of simultaneous biofouling and mineral scaling might enhance each other mutuallydthe mineral scales can provide a coating layer conducive to biofilm development compared to a smooth metal surface, and at the same time the organic matrix consisting of extracellular polymeric substances (EPS)
30 25 20 15
of the biofilm can help trap more mineral solids. For example, the inorganic mineral fraction of EPS can be as much as 77% of the EPS dry weight (d’Abzac et al., 2010).
3.3.
Experimental observation vs. equilibrium prediction
The use of chemical equilibrium modeling with the model MINEQLþ (Schecher and McAvoy, 1992, 1999) allowed estimation of the mineral precipitation potential, the chemical composition of solid precipitates, and their relative abundance. In this study, mineral precipitates predicted by MINEQLþ were compared with the actual species that comprised the deposits collected from experiments conducted at both bench-scale water recirculating systems and pilot-scale cooling towers. The precipitated solids from the bench- and pilot-scale tests were inspected using Scanning Electron Microscopy (SEM) and their elemental composition determined by Energy Dispersive X-ray Spectroscopy (EDS) analyses. The total mass of the solids collected in various tests was also compared to the amount predicted by MINEQLþ for the conditions tested. The information obtained from these comparisons was used to discuss the usefulness of the equilibrium modeling as a predictive tool in assessing the cooling water scaling behavior. Based on the chemical composition of the synthetic MWW (Table 2), chemical equilibrium modeling predicted that hydroxyapatite (HAP) and dolomite would precipitate at CoC 4 (modeling condition: ionic strength corrected, 40 C, and closed system) with the following amounts:
10
HAP [Ca5(PO4)3OH](s): 0.07 mM (35.2 mg/L) Dolomite [CaMg(CO3)2](s): 6.44 mM (1187.5 mg/L)
5 0 0
5
10
15
20
25
Cooling tower operating day
Fig. 7 e Deposit mass measurements in the pilot-scale cooling tower tests using secondary-treated MWW. Tower A, no inhibitors (B), Tower B, 10 mg/L of PMA (:), Tower C, 20 mg/L of PMA (C). Deposits were collected on stainless steel disc specimens immersed in recirculating pipe flow. Effective collection area 5.61 cm2, flow velocity 0.6 m/s (3 GPM flowrate in 3/400 pipe), water temperature 40 ± 1 C (104 ± 2 F), measured pH 8.5 ± 0.3. Error bars indicate the data range of measurements from duplicate tower tests.
Based on the modeling results, the elemental composition of the predicted solids is shown in Table 6 (Condition (1)). For the simulations of synthetic MWW, the initial TDS of the water (CoC 4) was 3455 mg/L, of which 1223 mg/L were predicted to precipitate at equilibrium (35.4 wt%), leaving 65% of the initial TDS in solution (roughly, a 1:2 distribution in terms of precipitated vs. soluble total solids). Of particular interest is the distribution of Ca and Mg at equilibrium: precipitated, complexed, and free ions. Equilibrium prediction by MINEQLþ for synthetic MWW illustrated that almost 90% of the initial Ca and Mg should precipitate out of solution. Ca and
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Table 6 e Elemental composition of the precipitates from synthetic MWW: Modeling prediction vs. experimental observation. Condition
Modeling without kinetic constraints (1) Modeling with kinetic constraints (2)a Observed in bench experiments (3)b
Elemental Percentage
Molar Mass Molar Mass Molar Mass
Ca
Mg
P
C
O
H
10.3 22.3 20.1 40.0 28.7 2.4 52.2 3.1
9.8 12.8 0 0 1.3 0.2 1.4 0.2
0.3 0.5 0.6 0.9 0.0 0.1 0.0 0.1
19.5 12.7 19.2 11.4 24.2 1.1 13.2 1.0
60.0 51.8 60.0 47.7 45.8 3.7 33.2 3.4
0.1 0.0 0.2 0.0 0.0 0.1 0.0 0.1
a No Mg precipitation. b Data are mean values 1 sd based on triplicate measurements.
Mg are thus disproportionately removed from the solution compared to other aqueous species. Stainless steel disc specimens immersed in the benchscale recirculating system using the synthetic MWW were used to collect mineral deposits. After 6 days, the discs were removed from the recirculating water and air-dried prior to SEM/EDS analysis. The SEM image shows well-shaped crystalline morphologies of calcite (Fig. 8). Based on the EDS analysis, the average abundance of the elements in the collected solids is listed in Table 6 (Condition (3)). Both molar and mass concentrations were directly obtained from the EDS analysis (triplicate measurements). Compared to the model prediction (Table 6 Condition (1)), the sampled solids contained excess Ca but were deficient in Mg. This observation that Mg did not participate in the solids formation was confirmed by the essentially unchanged aqueous concentration of Mg over the course of experiment (Fig. 9). What the model predicted may be the most stable crystalline phases under equilibrium conditions. Deposits precipitated from the experimental water, while ultimately driven by thermodynamics, can experience different pathways of mineral formation which involved different kinetic constraints and/or inhibitory factors imposed by water chemistry amendments.
Since Mg was only marginally observed in the collected deposits, a second set of modeling calculations was performed with the added modeling constraints: 1) Mg-containing solids (e.g., dolomite, huntite, artinite, brusite, and magnesite) were not allowed to form, and 2) Calcium carbonate takes the form of aragonite, a faster-forming crystalline phase of CaCO3(s) that is also more soluble than calcite (Ogino et al., 1990; Gutjahr et al., 1996). Under these conditions, a total of 759 mg/L of precipitates in the form of HAP and aragonite were predicted to form, resulting in a 22% decrease in solution TDS. HAP [Ca5(PO4)3OH](s): 0.07 mM (35.2 mg/L) Aragonite [CaCO3](s): 7.23 mM (723.6 mg/L) The elemental composition of the solids predicted under these conditions is shown in Table 6 (Condition (2)). The result is in a closer agreement with the experimental observation in terms of elemental composition. However, the total amount of solids predicted by modeling (759 mg/L) was still significantly greater than that precipitated experimentally (150e200 mg/L), implying that precipitation equilibrium had not been established during the experimental conditions, i.e., 3e4 days of limiting hydraulic residence time in the cooling water.
Fig. 8 e SEM image (left) and quantitative 1D EDS analysis (right) of the deposits collected on a stainless steel disc immersed in synthetic MWW in bench-scale water recirculating system. The arrow line (10 mm in length) on the SEM image indicates the scan line for the EDS analysis of elemental abundance. P and H are not detected.
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Fig. 9 e Changes in the aqueous concentrations of Ca and Mg in bench-scale water recirculating system using a synthetic MWW (without chemical addition). Closed data points represent concentrations of unfiltered water samples while open points filtered samples. The filtration is carried out using 0.45 mm HA type membrane filters (Millipore) to remove suspended solids. Ca unfiltered (C), Ca filtered (B), Mg unfiltered (:), Mg filtered (6).
Compared to the modeling prediction of 90% Ca precipitation, only about 35% of Ca actually precipitated during the 6 days of bench-scale tests with synthetic MWW (Fig. 9). A similar percentage of Ca precipitation (35e40%) was observed in pilotscale cooling tower tests with secondary-treated MWW. Tower A, which received no PMA, precipitated the greatest amount of Ca, while Towers B and C, with PMA addition, retained higher amounts of Ca in water. This suggests that PMA retarded Ca precipitation, resulting in higher Ca concentrations during the course of tower tests. It is clear therefore that kinetic constraints
of precipitation exerted by the PMA addition are not captured by the equilibrium modeling that is entirely based on thermodynamic calculations. For pilot-scale cooling tower experiments, SEM/EDS analyses were performed on deposits collected from Tower A after 6 days of operation at CoC 4 (Fig. 10). The EDS spectra show very low amounts of Mg, thereby confirming the results shown in Figs. 8 and 9 from bench tests with synthetic MWW. However, the SEM data indicated solids of more amorphous character as opposed to those depicted in Fig. 8. Alvarez et al. (2004) observed that the CaeP complexes preferentially precipitate in amorphous forms in the presence of soil organic matter. In addition, amorphous CaCO3(s) has been collected on steel surfaces when organic additives are present in solution (Kjellin et al., 2001; Kjellin, 2003; Wei et al., 2004, 2005). The interactions of mineral precipitates with organic matter present in the actual MWW suggest more complex chemistries occurring in pilot-scale cooling tower water than in the bench-scale system where synthetic MWW was used to simulate only the inorganic constituents. The EDS analyses conducted on the solids collected from pilot experiments indicate that the deposits consisted primarily of calcium carbonates and phosphates, which is in qualitative agreement with the revised model predictions discussed earlier. However, the quantity of phosphates appears to be greatly enriched when compared to that in the deposits collected from the bench-sale studies using synthetic MWW. This is likely because of the higher P concentration in the actual MWW (i.e., 12 mg/L vs. 5 mg/L in the synthetic water). In addition, P-containing chemicals, in the form of TKPP (10 mg/L) and PBTC (5 mg/L), were also added to the cooling towers for corrosion/scaling control. Chemical analyses indicated that these added phosphates quickly became undetectable in the liquid phase, suggesting their precipitation
Fig. 10 e SEM image and the elemental composition of the solid deposits collected on a stainless steel disc immersed in the secondary-treated MWW in the pilot-scale cooling tower (Tower A) operated at CoC 4. EDS scan was performed on the area outlined by the square box on the SEM image.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 4 8 e7 6 0
that further contributed to the relatively high P signal in the EDS spectra (Fig. 10).
4.
Conclusions
This study demonstrates the feasibility and challenges of using secondary-treated municipal wastewater as an alternative cooling system makeup water to replace freshwater. The scaling behavior and control of it in recirculating cooling systems was evaluated. Based on the results from bench-scale experiments performed in this study, it was determined that commonly used polymer-based scaling inhibitors can be effective in controlling potentially severe scaling when using this impaired water as makeup in recirculating cooling systems. PMA reduced scaling significantly in the absence of chlorine biocide but was only partially effective in the presence of chlorine. Ammonia present in the wastewater could suppress the aggressiveness of free chlorine on PMA. Preformed monochloramine was found to be less aggressive than free chlorine, while still being an effective biocide. Pilot-scale cooling tower experiments indicated that mineral scaling control by PMA was much more challenging due to biofouling. Overall, for scaling control of MWW that is concentrated to CoC 4 in recirculating cooling systems, 1) PMA can be applied at 10 mg/L level for effective mineral scaling inhibition in the absence of biofouling, 2) monochloramine is better suited as biocide than free chlorine because of the reduced impact of monochloramine on antiscaling programs, and 3) phosphorous-based scaling and corrosion inhibitors are not appropriate due to their precipitation with Ca.
Acknowledgements This work was supported by the U.S. Department of Energy, National Energy Technology Laboratory, Grant No. DE-FC2606NT42722. The authors gratefully acknowledge the Franklin Township Municipal Sanitary Authority, and especially manager James Brucker, for allowing and supporting performance of the pilot-scale cooling tower tests at their facility.
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Direct observation of solid-phase adsorbate concentration profile in powdered activated carbon particle to elucidate mechanism of high adsorption capacity on super-powdered activated carbon Naoya Ando, Yoshihiko Matsui, Taku Matsushita, Koichi Ohno* Graduate School of Engineering, Hokkaido University, N13W8, Sapporo 060-8628, Japan
article info
abstract
Article history:
Decreasing the particle size of powdered activated carbon (PAC) by pulverization increases
Received 14 March 2010
its adsorption capacities for natural organic matter (NOM) and polystyrene sulfonate (PSS,
Received in revised form
which is used as a model adsorbate). A shell adsorption mechanism in which NOM and PSS
27 August 2010
molecules do not completely penetrate the adsorbent particle and instead preferentially
Accepted 30 August 2010
adsorb near the outer surface of the particle has been proposed as an explanation for this
Available online 6 September 2010
adsorption capacity increase. In this report, we present direct evidence to support the shell adsorption mechanism. PAC particles containing adsorbed PSS were sectioned with
Keywords:
a focused ion beam, and the solid-phase PSS concentration profiles of the particle cross-
SPAC
sections were directly observed by means of field emissionescanning electron microscopy/
FIB
energy-dispersive X-ray spectrometry (FE-SEM/EDXS). X-ray emission from sulfur, an index
SEM
of PSS concentration, was higher in the shell region than in the inner region of the parti-
EDXS
cles. The X-ray emission profile observed by EDXS did not agree completely with the solid-
Isotherm
phase PSS concentration profile predicted by shell adsorption model analysis of the PSS
PSS
isotherm data, but the observed and predicted profiles were not inconsistent when the
NOM
analytical errors were considered. These EDXS results provide the first direct evidence that PSS is adsorbed mainly in the vicinity of the external surface of the PAC particles, and thus the results support the proposition that the increase in NOM and PSS adsorption capacity with decreasing particle size is due to the increase in external surface area on which the molecules can be adsorbed. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Powdered activated carbon (PAC) is used for the treatment of drinking water because this versatile adsorbent removes a broad range of organic pollutants, including pesticides and other organic chemicals, taste and odor compounds, cyanobacterial toxins, and total organic carbon (Suffet and McGuire, 1980; World Health Organization, 2006). Although activated
carbon is widely used, it is expensive, and the higher the grade and quality of the activated carbon, the greater its cost (Babel and Kurniawan, 2003). While attention has been focused on investigation of various replacements for activated carbon, enhancing its adsorption has also been studied. For example, reducing the particle size of activated carbon increases the rate of adsorbate uptake and thereby reduces the amount of activated carbon required (Randtke and Snoeyink, 1983; Najm
* Corresponding author. Tel./fax: þ81 11 706 7280. E-mail address:
[email protected] (K. Ohno). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.08.050
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et al., 1990; Jia et al., 2005; Matsui et al., 2005, 2009,); therefore, pulverizing activated carbon has been used as a strategy for cost reduction. Although decreasing the particle size increases the adsorption rate, it has been long assumed that the adsorption capacity does not change with particle size (Randtke and Snoeyink, 1983; Najm et al., 1990). However, several studies have shown that activated carbon with a small particle size (e.g., 100/200 US sieve size, Weber et al., 1983; and 0.73 mm median diameter, Ando et al., 2010) has a higher adsorption capacity for NOM. This NOM adsorption capacity increase is explained by means of a mechanism whereby NOM is adsorbed more in the shell region close to the external surface of the particles than in the region inside the particles because NOM forms aggregates in activated carbon pores and does not fully penetrate the carbon particle (Ando et al., 2010), and a shell adsorption model (SAM) based on this mechanism is proposed and verified by means of adsorption isotherm data (Matsui et al., submitted for publication). The radial profile of adsorbate concentration throughout a particle, however, has not been confirmed by direct observation. Generally, activated carbon adsorption data is interpreted on the basis of measurement of the adsorbate concentration in the bulk water phase, and the solid-phase concentration profile inside activated carbon particles has rarely been observed. Ahn et al. (2005) recently used microprobe laser-desorption laser-ionization mass spectroscopy to spatially resolve the intraparticle concentration profile within several granular adsorbents, including granular activated carbon particles (40 mm). The study demonstrated the complexity of the intraparticle diffusion process. The objective of the current study was to directly observe the solid-phase adsorbate concentration profiles of PAC particles and thus verify the shell adsorption mechanism. PAC particles containing PSS as a model adsorbate were sectioned with a focused ion beam (FIB), and the intraparticle solidphase concentration profiles were directly quantified at a 0.4-mm scale by means of energy-dispersive X-ray spectrometry (EDXS) in a field emissionescanning electron microscope (FE-SEM).
Dionex Corp., California, USA). PSS (Polymer Standard Service, Mainz, Germany; Mw ¼ 1100 Da; Mw/Mn < 1.2) was used as a model adsorbate (Matsui et al., submitted for publication).
2.2. Preparation of PSS solutions and measurement of adsorption isotherms PSS solutions (initial concentrations, 4.7 and 104 mg/L) were prepared by dissolving PSS in sulfate-ion-free water containing NaHCO3 (20 mg/L as alkalinity) and CaCl2 (4.9 mg/L as Ca). The PSS solutions were adjusted to pH 7.0 0.1 by the addition of HCl or NaOH as required, and the solutions were filtered through 0.2-mm PTFE (Polytetrafluoroethylene) membrane filters before being used for adsorption isotherm tests. The bottle-point technique was used to conduct the adsorption isotherm tests. Various amounts of activated carbon were added to the PSS solution with efficient mixing, and 125 mL aliquots from the solution containing PSS and activated carbon were transferred to 125 mL vials. The vials were then agitated on a shaker for 3 weeks. After the contents of the vials were filtered through a 0.2-mm PTFE membrane filter, the liquid-phase PSS concentrations were measured. The PSS concentrations were determined by UV absorption at a wavelength of 262 nm (UV-1700, Shimadzu Co., Kyoto, Japan) with a calibration line.
2.3. Solid-phase PSS concentration profile in a PAC-T particle 2.3.1.
Preparation of PAC-T samples
2.
Materials and methods
To prepare PAC-T particles with adsorbed PSS (PSS-loaded PAC-T), we conducted batch adsorption in a 125 mL vial containing a suspension of PSS (103 mg/L), PAC-T (20 mg/L), NaHCO3 (0.2 mmol/L), and CaCl2 (0.12 mmol/L). Because the PAC concentration profiles were determined by means of EDXS analysis of the sulfur in PSS, no sulfate-ion was added. To prepare PAC-T particles without PSS (blank PAC-T), we conducted a blank test without PSS in a 125 mL vial containing a suspension of PAC-T (20 mg/L), NaHCO3 (0.2 mmol/L), and CaCl2 (0.12 mmol/L). After the vials were shaken for 3 weeks, the PAC-T particles were recovered from the vials by means of centrifugal separation (1000 rpm, 190 g, 10 min). The particles were dried for 24 h at about 40 C.
2.1.
Adsorbents and adsorbate
2.3.2.
Commercially available PAC (Taikou-W, Futamura Chemical Industries Co., Gifu, Japan) was used as received (designated PAC-T) or pulverized in a bead mill (Metawater Co., Tokyo, Japan) to produce super-powdered activated carbon (SPAC) samples of various particle sizes, designated SPACa-T, SPACbT, SPACc-T, and SPACd-T in order of increasing particle size. A slurry of each activated carbon sample was prepared in pure water and stored at 4 C before use. The median particle diameters of the samples were 0.7 mm for SPACa-T, 1.1 mm for SPACb-T, 1.9 mm for SPACc-T, 3.0 mm for SPACd-T, and 11.8 mm for PAC-T (as determined with an LA-700 size distribution analyzer, Horiba, Kyoto, Japan). The sulfur content of PAC-T was determined by combustion (International Organization for Standardization, 1998; TOX-100, Mitsubishi Chemical Analytech Co., Mie, Japan) and ion chromatography (DX-120,
FIB sectioning of PAC-T particles
PAC-T particles were placed on a silicon (Si) wafer that was mounted with double-stick carbon tape (Nisshin EM Co., Tokyo, Japan) on the specimen holder of the FIB system (FB2100, Hitachi, Ltd., Tokyo, Japan). After desiccation at 40 C, the sample surface was coated with a 20-nm layer of platinum (Pt) to avoid charge-up effects by means of ion sputtering equipment (JEC-1600, JEOL, Ltd., Tokyo, Japan). A PAC-T particle was selected arbitrarily during real-time scanning-ion microscope imaging, and tungsten (W) was FIB-deposited on the particle for 20 min to prevent damage to the PAC-T particle during FIB milling, which was performed at a beam energy of 40 kV (Fig. 1a). Deep trenches were grooved around the PAC-T particle by means of gallium (Ga) FIB milling such that a small cubic portion (micro-sample) of the Si wafer with the PAC-T particle on top remained; a corner of the small cubic portion (micro-bridge) was left to connect the micro-sample to the Si
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 6 1 e7 6 7
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Fig. 1 e Micro-sampling and sectioning of a PAC-T particle using FIB: (a) PAC particles on silicon wafer; (b) micro-sample (small cube with a PAC particle on top) and a tungsten probe; (c) micro-samples on Si wafer, and (d) cross-section of PAC particle.
wafer. Subsequently, the specimen holder was tilted to 60 , and the bottom of the micro-sample was cut with a Ga FIB; as a result, the micro-sample was supported by a micro-bridge at one corner. A mechanical probe was inserted, and the tip of the probe was welded to the micro-sample by means of FIBassisted W deposition (Fig. 1b). After the micro-bridge was cut with a Ga FIB, the micro-sample was separated from the Si wafer and placed on a newly inserted Si wafer by means of W deposition (4 min). The mechanical probe was cut with a Ga FIB (Fig. 1c) after the newly inserted wafer was mounted on the sample holder of the FIB system by double-stick carbon tape. About half of the PAC-T particle on the micro-sample was then cut away with a Ga FIB to leave a cut PAC-T particle (Fig. 1d). The cut particle was coated with a 2 nm layer of Pt by ion sputtering to avoid charge-up effects during SEM.
2.3.3.
cross-sectioned particle in the FE-SEM (acceleration voltage, 10 kV; number of sweeps, 25; dwelling time, 0.1 s; magnification, 10,000; working distance, 8.0 mm; JED-2300, JEOL). The penetration depth of electron for the EDXS analysis could be considered large (Castaing, 1960). Lee et al. (2006)
FE-SEM/EDXS of a PAC-T particle
The Si wafer with the FIB-cut PAC-T particle was mounted with double-stick carbon tape on an L-shaped holder (JEOL) for FE-SEM (JSM-7400F, JEOL) observation of the crosssection of the particle. EDXS line-scan chemical analysis was performed in 0.047-mm increments on the surface of the
Fig. 2 e Adsorption isotherms of PSS.
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Fig. 3 e Left panel: SEM image of the cut face of a PSS-loaded PAC-T particle. EDXS line-scan analysis was conducted along the broken line in 0.047-mm increments. Right panel: Profile of X-ray counts from S obtained by EDXS line-scan analysis. The dashed lines indicate the particle boundaries.
estimate the penetration depth of 1.2 mm using 10-kV acceleration voltage for bulk carbon. Therefore, the EDXS would be regarded as center-weighted average metering scheme in the circle of around 0.6 mm radius if the penetration depth of 1.2 mm and the spherical penetration region were assumed and the diameter of the spherical penetration region was assumed to be equal to the penetration depth.
3.
Results and discussion
3.1. Adsorption isotherms and solid-phase concentration profile Our goal was to verify the hypothesis that organic macromolecules are adsorbed mainly on the shell region close to the external surface of powdered activated carbon particles and not in the inner region. Because the specific outer surface area (surface area per unit mass) available for adsorption is greater for smaller particles than for larger particles, this hypothesis would explain why the adsorption capacity of SPAC is larger than that of PAC. To verify the hypothesis, we used PSS as a model adsorbate and directly observed the
cross-sectional profile of the PSS concentration in PAC-T particles by FE-SEM/EDXS. In previous work (Ando et al., 2010; Matsui et al., submitted for publication), in which we observed that the adsorption capacity of SPAC was larger than that of PAC, we used a PSS solution with a natural ion composition (including sulfate-ion). In contrast, in this study, we observed the PSS concentration profiles of PAC-T particles with adsorbed PSS in sulfate-ion-free water. Therefore, we first confirmed that in sulfate-ion-free water, the amount of PSS adsorbed on the SPAC was higher than that adsorbed on PAC-T (Fig. 2). Fig. 2 also suggests that a higher initial liquidphase concentration lead to a lower solid-phase concentration. The reason for this phenomenon is not known, but this might be due to the slight heterogeneity of the PSS (Matsui et al., submitted for publication): for heterogeneous adsorbate, the isotherm results depend on the initial concentration (Sontheimer et al., 1988). Because the sulfur (S) in PSS is in a sulfonic acid group and activated carbon contains little S (0.28 mg-S/g), the characteristic X-ray emissions from S (Ka, 2.307 keV; He et al., 1999) were counted as a measure of PSS concentration. The X-ray emission counts were scanned in 0.047 mm increments along the FIB-cut surface of a PSS-loaded PAC-T particle (Fig. 3, left
Fig. 4 e Left panel: SEM image of the cut face of a blank PAC-T particle. EDXS line-scan analysis was conducted along the broken line crossing. Right panel: Profile of X-ray counts from S obtained by EDXS line-scan analysis. The dashed line indicates the particle boundary.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 6 1 e7 6 7
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Fig. 5 e Comparison of X-ray counts in the shell and inner regions of PAC-T particles. Left panel: X-ray counts for PSS-loaded PAC-T particles aei. Right panel: X-ray counts for blank PAC-T particles jep. X-ray counts obtained from 0.024 to 0.400 mm (0.376-mm range) from the outer surface were summed as the counts for the shell region, and the counts obtained from 2.024 to 2.400 mm (0.376-mm range) from the interface were summed as the counts for the inner region.
panel, broken line). The characteristic X-ray emission from S was weak because X-ray production is inherently low for light elements such as S and because the amount of PSS adsorbed on the PAC-T was small (63 mg-S/g) relative to the amount of carbon. Therefore, the X-ray emission counts obtained for each 0.047-mm increment were small, and the counts were summed for each 0.376-mm interval and are plotted in Fig. 3 (right panel). The data showed some scatter, but the X-ray counts for S were clearly higher in the region close to the outer surface of the particle than in the inner region. High X-ray counts were also observed beyond the external surface of the particle (beyond Edge R in Fig. 3), but these high counts may have been due to high PSS loading on the external particle surface or to irregular X-ray scattering arising from surface roughness, which can be seen in the SEM image. The X-ray emission profile for a blank PAC-T particle did not show higher X-ray emission counts outside the particle relative to the counts inside; the emission count profile was roughly flat for the blank PAC-T particle (Fig. 4).
3.2.
Statistical hypothesis testing
Although the FE-SEM/EDXS results (Figs. 3 and 4) supported the SAM, analytical errors were associated with the data. To verify the SAM, we prepared additional PSS-loaded PAC-T and
Fig. 6 e Mean X-ray counts in shell and inner regions of PSS-loaded PAC-T (n [ 9) and blank PAC-T (n [ 7) particles. Error bars indicate maximum and minimum values.
blank PAC-T particles, cut the particles with the FIB, conducted EDXS line-scan analyses, and measured X-ray counts for the shell regions (0.024e0.400 mm from the outer surface) and the inner regions (2.024e2.400 mm from the outer surface). Comparison of the X-ray counts for the shell region and the inner region of each PSS-loaded PAC-T particle (Fig. 5, left panel) indicated that the X-ray counts for each particle were always higher in the shell region than in the inner region. For the blank particles, the X-ray counts in the shell region were not consistently higher or lower than the counts in the inner region (Fig. 5, right panel). Note that for the PSS-loaded PAC samples, the X-ray counts in the shell regions of some of the particles were lower than the counts in the inner region of other particles: for example, the X-ray counts in the shell region for PSS-loaded PAC particle “c” were lower than the X-ray counts in the inner region for PSS-loaded PAC particle “a”; that is, the ranges for the shell region and the inner region overlapped each other (Fig. 6). Therefore, we cannot definitively say that the X-ray counts in the shell region were higher than the counts in inner region. However, the overlap may have resulted from data scattering arising from the low sensitivity of S in EDXS. To determine whether the difference between the X-ray counts in the shell region and the inner region was statistically significant, we conducted statistical hypothesis tests. The tests indicated that for PSS-loaded PAC-T, the mean number of X-ray counts in the shell region was 23.4, whereas the number of counts in the inner region was 10.2 (Fig. 6). For blank PAC-T particles, in contrast, the mean number of X-ray counts in the shell region was 12.1, whereas the number of counts in the inner region was 10.9. The statistical significance of the difference between the two mean counts was evaluated by means of the equal-variance Student’s T-test after the homogeneity of variances was tested by means of the F-test. For PSS-loaded PAC-T, the P-value for the null hypothesis H0 (that is, the hypothesis that there was no difference in the variance of counts between the shell and inner regions) was 12.9%, and the corresponding P-value for blank PAC-T particles was 75.1% (Table 1). Therefore, the null hypothesis could not be rejected for either PAC sample. By assuming the variances of counts were the same for the shell region and the inner region, we conducted the equal-variance
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Table 1 e Results of F-test. PSS-loaded PAC-T 86.3 27.7
Blank PAC-T
Variance of counts
Shell region Inner region
Outside Inside
H0: Null hypothesis F-value P-value (%)
No difference in mean counts 3.12 1.31 12.9 75.1
31.1 40.8
Student’s T-tests for the null hypothesis H0 (that is, the hypothesis that there was no difference in the population means of counts between the shell and inner regions). The null hypothesis was rejected at the 0.2% level for the PSS-loaded PAC-T, whereas the null hypothesis could not be rejected for the blank PAC-T particles (P-value ¼ 69.5%; Table 2). In summary, the statistical tests clearly indicated that the X-ray counts were different between the shell region and the inner region of PSS-loaded PAC-T particles, but for the blank PAC-T particles, the difference between the X-ray counts in the shell and inner regions was not statistically significant. Thus, we conclude that for the PSS-loaded PAC-T particles, the solid-phase concentration of PSS was higher on the outside than in the inside of the particles. Therefore, the results of the EDXS line-scan analysis clearly supported the hypothesis that the PSS was adsorbed mainly in the vicinity of the outer surface. Consequently, PAC particles, which have less outer surface area than SPAC particles, can be expected to show reduced adsorption capacity.
3.3.
Comparison with shell adsorption model
The SAM assumes a pattern of adsorbate concentration profile described by two parameters: d, which is the thickness of the shell (penetration depth), and p, which is a dimensionless parameter that defines availability of internal porous structures for adsorption. Once these parameter values are known, the solid-phase adsorbate concentration profile can be depicted. The determination of these parameter values requires the isotherm data for adsorbent particle of different sizes. In this study, the isotherm data for SPACb-T, SPACc-T and SPACd-T (Fig. 1S in the supplementary information) as well as SPACa-T and PAC-T (Fig. 2) were obtained with same PSS solution prepared for the analysis of the PAC-T samples by FE-SEM/EDXS, and the isotherm data were analyzed by means of the SAM (Matsui et al., submitted for publication). The PSS concentration profile in a PAC-T particle is depicted in Fig. 7 (solid line). The SAM results suggest that the shell was thin (shell thickness, 0.16 mm) and that the PSS load was high in the
Table 2 e Results of student’s T-test. PSS-loaded PAC-T 23.4 10.2
Blank PAC-T
Mean counts
Shell region Inner region
Outside Inside
H0: Null hypothesis Pooled variance T-value P-value (%)
No difference in mean counts 57.0 36.0 3.72 0.40 0.2 69.5
Fig. 7 e Comparison of characteristic X-ray profile (see Fig. 3) and PSS solid-phase concentration profiles. Circles: characteristic X-ray profile for S. Solid line: PSS solid-phase concentration profile obtained by SAM and PSS adsorption isotherm data. Dashed gray line: Moving-average profile of the PSS solid-phase concentration. Vertical dotted lines: Particle boundaries.
shell region. From the profile of the X-ray counts for S, however, we could not confirm the thinness of the shell, because of the low density of the data (as mentioned earlier, the X-ray emission counts were summed for each 0.376-mm interval). For comparison, we determined a moving-average PSS concentration profile for 0.376-mm intervals so that we could compare the results of the SAM analysis and the X-ray counts over the same data-acquisition interval. The movingaverage PSS profile was similar to the characteristic X-ray profile, but because of data scattering and the small number of data points over the distance, we could not unequivocally conclude that the two profiles were consistent. If the two profiles were different, there are two possible explanations. First, PSS molecules may have diffused into the inner region during preparation of PAC-T samples and the FIB cutting process. Second, the effect of the large evolution area of the characteristic X-rays must be considered (Lee et al., 2006); the large area means that the EDXS method is center-weighted average metering scheme rather than spot metering scheme and the obtained X-ray counts were inevitably area-averaged counts (Castaing, 1960).
4.
Conclusions
PSS-loaded and blank PACs were cut by FIB, and the solidphase PSS concentration profiles of the particle cross-sections were directly observed by means of FE-SEM/EDXS line-scan analysis. PSS was adsorbed mainly in the shell region close to the outer surface of the particles and less so in the inner region. These results confirmed that the shell adsorption mechanism can explain the higher adsorption capacity of SPAC relative to that of PAC.
12.1 10.9
Acknowledgements This study was supported by a Grant-in-Aid for Scientific Research A (21246083) from the Ministry of Education, Science,
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 6 1 e7 6 7
Sports and Culture of the Government of Japan; a research grant from the Ministry of Health, Labor and Welfare; and by Metawater Co., Tokyo, Japan.
Appendix. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.watres.2010.08.050
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Ahn, S., Werner, D., Karanagioti, H.K., Mcglaothlin, D.R., Zare, R.N., Luthy, R.G., 2005. Phenanthrene and pyrene sorption and intraparticle diffusion in polyoxymethylene, coke, and activated carbon. Environmental Science and Technology 39 (17), 6516e6526. Ando, N., Matsui, Y., Kurotobi, Y., Nakano, Y., Matsushita, T., Ohno, K., 2010. Comparison of natural organic matter adsorption capacities of super-powdered activated carbon. Water Research 44 (14), 4127e4136. Babel, S., Kurniawan, T.A., 2003. Low-cost adsorbents for heavy metals uptake from contaminated water: a review. Journal of Hazardous and Materials 97 (1e3), 143e219. Castaing, R., 1960. Electron probe microanalysis. Advances in Electronics and Electron Physics 13, 317e386. He, A.J., Yang, K.V., Dolukhanyan, T., Sung, C., Kumar, J., Tripathy, K.S., Samuelson, L., Balogh, L., Tomalia, A.D., 1999. Electrostatic multilayer deposition of a gold-dendrimer nanocomposite. Chemistry of Materials 11 (11), 3268e3274. International Organization for Standardization (ISO 11632:1998), 1998. Stationary Source EmissionseDetermination of Mass Concentration of Sulfur DioxideeIon Chromatography Method. International Organization for Standardization, Geneva. Jia, Y., Wang, R., Fane, A.G., Krantz, W.B., 2005. Effect of air bubbling on atrazine adsorption in water by powdered
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activated carbonsecompetitive adsorption of impurities. Separation and Purification Technology 46 (1e2), 79e87. Lee, S., Younan, H., Siping, Z., Zhiqiang, M., 2006. Studies on electron penetration versus beam acceleration voltage in energy-dispersive X-ray microanalysis. Proc. ICSE 2006, IEEE International Conference, Kuala Lumpur, Malaysia, pp. 610e613. Matsui, Y., Murase, R., Sanogawa, T., Aoki, N., Mima, S., Inoue, T., Matsushita, T., 2005. Rapid adsorption pretreatment with submicron powdered activated carbon particles before microfiltration. Water Science and Technology 51 (6e7), 249e256. Matsui, Y., Ando, N., Sasaki, H., Matsushita, T., Ohno, K., 2009. Branched pore kinetic model analysis of geosmin adsorption on super-powdered activated carbon. Water Research 43 (12), 3095e3103. Matsui, Y., Ando, N., Yoshida, T., Kurotobi, R., Matsushita, T., Ohno, K. Modeling high adsorption capacity and kinetics of organic macromolecules on super-powdered activated carbon. Water Research, submitted for publication. Najm, I.N., Snoeyink, V.L., Suidan, M.T., Lee, C.H., Richard, Y., 1990. Effect of particle size and background natural organics on the adsorption efficiency of PAC. Journal of American Water Works Association 82 (1), 65e72. Randtke, S.J., Snoeyink, V.L., 1983. Evaluating GAC adsorptive capacity. Journal of American Water Works Association 75 (8), 406e413. Sontheimer, H., Crittenden, J.C., Summers, R.S., 1988. Activated Carbon for Water Treatment, second ed. DVGWForschungsstelle, Karlsruhe, Germany. Suffet, I.H., McGuire, M.J., 1980. Activated Carbon Adsorption of Organics from the Aqueous Phase, vol. 1e2. Ann Arbor Science, Ann Arbor. Weber, W.J.J., Voice, T.C., Jodellah, A., 1983. Adsorption of humic substances: the effects of heterogeneity and system characteristics. Journal of American Water Works Association 75 (12), 612e619. World Health Organization, 2006. Guidelines for Drinking-Water Quality. third ed., vol. 1. recommendations, Word Health Organization, Geneva.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 6 8 e7 8 0
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
A screening level fate model of organic contaminants from advanced water treatment in a potable water supply reservoir Darryl W. Hawker a, Janet L. Cumming a, Peta A. Neale b, Michael E. Bartkow c, Beate I. Escher b,* a
School of Environment, Griffith University, Nathan, QLD 4111, Australia The University of Queensland, National Research Centre for Environmental Toxicology (Entox), 39 Kessels Rd, Coopers Plains, QLD 4108, Australia c Queensland Bulk Water Supply Authority (trading as Seqwater), Brisbane, QLD 4000, Australia b
article info
abstract
Article history:
Augmentation of potable water sources by planned indirect potable reuse of wastewater is
Received 12 May 2010
being widely considered to address growing water shortages. Environmental buffers such
Received in revised form
as lakes and dams may act as one of a series of barriers to potable water contamination
29 August 2010
stemming from micropollutants in wastewater. In South-East Queensland, Australia,
Accepted 31 August 2010
current government policy is to begin indirect potable reuse of water from reverse osmosis
Available online 8 September 2010
equipped advanced water treatment plants (AWTPs) when the combined capacity of its major storages is at 40% capacity. A total of 15 organic contaminants including NDMA and
Keywords:
bisphenol A have been publically reported as detected in recycled water from one of South-
Indirect potable reuse
East Queensland’s AWTPs, while another 98 chemicals were analysed for, but found to be
Advanced water treatment
below their detection limit. To assess the natural attenuation in Lake Wivenhoe, a Level III
Organic contaminant
fugacity based evaluative fate model was constructed using the maximum concentrations
Fugacity
of these contaminants detected as input data. A parallel aquivalence based model was
Recycled water
constructed for those contaminants, such as dichloroacetic acid, dalapon and triclopyr, which are ionised in the environment of Lake Wivenhoe. A total of 247 organic chemicals of interest, including disinfection by-products, pesticides, pharmaceuticals and personal care products, xenoestrogens and industrial chemicals, were evaluated with the model to assess their potential for natural attenuation. Out of the 15 detected chemicals, trihalomethanes are expected to volatilise with concentrations in the outflow from the dam approximately 400 times lower than influent from the AWTPs. Transformation processes in water are likely to be more significant for NDMA and pharmaceuticals such as salicylic acid and paracetamol as well as for caffeine and the herbicides dalapon and triclopyr. For hydrophobic contaminants such as cholesterol and phenolic xenoestrogens such as 4-nonylphenol, 4-t-octylphenol and bisphenol A, equilibrium between water and sediments will not be attained and hence fate processes such as removal in outflow are predicted to become relatively important. ª 2010 Elsevier Ltd. All rights reserved.
* Corresponding author. Tel.: þ61 7 3274 9180; fax: þ61 7 3274 9003. E-mail address:
[email protected] (B.I. Escher). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.08.053
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1.
Introduction
Potable water is becoming an increasingly scarce resource. This is particularly so in South-East Queensland, Australia, where drought and a rapidly growing population have been placing pressure on surface water supplies. The most recent drought that lasted from 2001 to 2008 was described in 2007 as the worst on record (QCCCE, 2007). This region is the fastest growing in Australia with the population forecast to more than treble by 2026 with commercial and industrial demand for water also increasing (Traves et al., 2008; Ying et al., 2008). The region has few accessible groundwater resources and until recently, has relied on surface reservoirs to store water for potable treatment. The largest such reservoir is Lake Wivenhoe which was formed by damming the Brisbane River and is fed by the surrounding watershed as well as the Somerset Dam on an upstream tributary. The storage capacity of this reservoir is 1.2 109 m3 and the major potable water treatment plant in the area is located on the Brisbane River downstream of Wivenhoe Dam (Freeman et al., 2008). There is now little scope for further large dams in the region, and recently a 1.25 105 m3 d1 capacity desalination plant was commissioned as an additional climate independent source of potable water (Traves et al., 2008). A further initiative to address the potable water situation in the region was the construction of a recycled water project that has been described as the largest in the Southern Hemisphere and the third largest in the world (Freeman et al., 2008; Lawrence et al., 2009). This project takes treated wastewater from six existing wastewater treatment plants in the South-East Queensland area (http://www.westerncorridor.com.au). These plants all practice biological nutrient removal and deliver disinfected
water of secondary standard to advanced water treatment plants (AWTPs) (Queensland Water Commission, 2009; Traves et al., 2008). The AWTPs adopt a dual membrane approach, using microfiltration and reverse osmosis, followed by advanced oxidation (H2O2/UV) and finally stabilisation and disinfection with chlorine. The maximum capacity of the project is approximately 2.30 105 m3 d1 but at present recycled water from these AWTPs is being used only by coalfired electricity generating stations (Queensland Water Commission, 2008). Following recent rains, current government policy is that this recycled water will only be added to Lake Wivenhoe for indirect potable reuse (IPR) when combined dam levels in South-East Queensland fall below 40% (Rodriguez et al., 2009). A testing campaign of the recycled water produced by the AWTPs was carried out from May to November 2008. It was undertaken for disinfection by-products (DBPs), hormones, pharmaceuticals and personal care products (PPCPs), and pesticides together with other organic micropollutants as well as inorganic and microbial analytes that were detected in source wastewaters (Queensland Water Commission, 2009). A total of 113 organic chemicals were analysed for during this sampling campaign, with 98 not detected and 15 chemicals detected and quantified at least once (Table 1). Of the detected compounds, bromodichloromethane, dibromochloromethane, chloroform, dichloroacetic acid and NDMA (N-Nitrosodimethylamine) may be categorised as DBPs. 4-t-Octylphenol, bisphenol A, and 4nonylphenol are regarded as good indicators of industrial contribution to wastewater (Queensland Water Commission, 2009) and exhibit weak estrogen-like activity (Jobling and Sumpter, 1993). Paracetamol (N-(4-hydroxyphenyl)ethanamide), salicylic acid and DEET (N,N-Diethyl-m-toluamide) are
Table 1 e Physicochemical properties of organic contaminants detected in recycled water from AWTPs. Chemical
Bromodichloromethane Dibromochloromethane Chloroform Dichloroacetic acid NDMA 4-t-Octylphenol Bisphenol A 4-Nonylphenol Paracetamol Salicylic acid DEET Dalapon Triclopyr Caffeine Cholesterol
Log KOW Log KH (Pa m3 mol1)
2.33 1.90 2.57 3.07 0.73 0.34 6.03 0.54 7.19 3.13 2.68 2.04 4.01 5.44 1.23
2.00 2.16 1.97 0.92 0.57 5.28 3.32 5.76 0.46 2.26 2.18 1.68 2.54 0.07 8.74
Acidity constant ( pKa) e e e 1.41 (acid) e 10.25 (acid) 9.78/10.53 (acid) 10.25 (acid) 9.48 (acid) 3.08/13.1 (acid) 0.91 (base) 1.38 (acid) 3.22 (acid) 0.05 (base) e
Half-life Half-life Fraction of Log KOC Log KH in water t½ (d) in sed t½ (d) neutral (Pa m3 mol1) of all species (corrected species at pH 7.86, an for ionisation) 38 38 38 15 0.5 ha 6.2b 38 4.9c 15 15 38 38 1.3d 15 60
338 338 338 135 338 338 338 135 135 135 338 338 542 135 542
100% 100% 100% 0.00004% 100% 100% 99% 100% 98% 0.00166% 100% 0.00003% 0.0023% 100% 100%
2.33 1.90 2.57 9.52 0.73 0.34 6.04 0.54 7.20 7.91 2.68 8.52 8.65 5.44 1.23
1.61 1.77 1.58 0.47 0.96 4.89 2.93 5.37 0.46 0.87 1.79 0.29 1.14 0.46 8.35
a 38 d for biodegradation and 0.5 h for photodegradation. Literature data for photodegradation 0.5e1 h (Mackay et al., 1992) and 15 min (Plumlee et al., 2008). b 38 d for biodegradation and 7.4 d for photodegradation (4-t-octylphenol assumed to have the same photodegradation rate as 4-nonylphenol). c 15 d for biodegradation and 7.4 d for photodegradation (Neamtu and Frimmel, 2006). d 60d for biodegradation and 1.3 d for photodegradation (Woodburn et al., 1993).
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classified as “Pharmaceuticals and Personal Care Products” (PPCP), and triclopyr ([(3,5,6-Trichloro-2-pyridinyl)oxy]acetic acid) and dalapon (2,2-dichloropropionic acid) as pesticides (subclass herbicides). Cholesterol is a natural product excreted by mammals including humans and caffeine is a consumer product. Both are good markers of wastewater and categorised under “others” (Queensland Water Commission, 2009). Rodriguez et al. (2009) have noted that use of environmental buffers such as lakes, rivers and aquifers is considered world’s best practice as part of IPR schemes. Within such buffers, dilution and further removal of organic contaminants is expected to occur. However, the extent of attenuation and the processes by which attenuation might occur are often unclear or uncertain. It is of interest to investigate what attenuation, if any, would occur in Lake Wivenhoe and to predict what factor decrease would occur with water released from the dam as this water subsequently serves as influent for potable water treatment. As source apportionment is not possible, i.e. chemicals introduced into Lake Wivenhoe with PRW cannot be differentiated from chemicals introduced through other pathways, e.g. from tributaries, run-off from agricultural applications or stormwater input, it is unclear what contribution recycled water would have to the overall loads of organic micropollutants. Further, purified recycled water (PRW) has not yet been added to Lake Wivenhoe because the capacity has not fallen to the prescribed level and if it was introduced, the concentrations of many micropollutants are expected to be below detection limits. Therefore, an exposure model will be used for an assessment of the role of natural attenuation in IPR schemes. The aim of this work is to construct a fugacity based model of Lake Wivenhoe as a screening level tool to evaluate the fate of these contaminants, with concentration in the released water as one model output. In addition to running the fate model for the 15 chemicals detected in PRW, we provide model estimates for an additional 232 organic chemicals, including all those that were detected in source water for the AWTP (but not detected in PRW; limits of detection were 5e20 ng/L for nitrosamines, 1e10 mg/L for haloacetic acids, 1 mg/L for trihalomethanes, 250 ng/L for phenols, pesticides and 10 ng/L for PPCPs and 1 ng/L for industrial organics) (Queensland Water Commission, 2009), all the organic chemicals currently listed in the Australian Guidelines for Recycled Water (NRMMC/NHMRC, 2008) and a group of perfluorinated chemicals because those have been recognized as relevant drinking water contaminants in Australia (J. Mueller, The University of Queensland, unpublished results) (Table SI-2 in the Supplementary Information). A particular challenge was the fact that a large number of chemicals are acids or bases or compounds that possess multiple ionisable substituents, therefore speciation had to be considered in the fate model.
2.
Materials and methods
2.1. Selection of test chemicals and their physicochemical properties Table 1 contains the list of 15 organic chemicals detected and quantified at least once in the recycled water, together with
relevant physicochemical parameters such as octanolewater partition coefficients (KOW), Henry’s Law constants (KH), biodegradation half-lives in water and sediment estimated using EPI Suite (U.S.EPA, 2008) and phototransformation half-lives from the literature. The additional 232 chemicals modelled but not detected in AWTP effluent are listed in the Supplementary Information (Table SI-2). Physicochemical data were collected using the procedures outlined below. The physicochemical data used as model input were measured at or estimated for 298 K. The temperature of Lake Wivenhoe shows a seasonal variation by approximately 10 K with a median of 295 K (see Supplementary Information), and the published measured depth-integrated annual average temperature of the surface waters of Lake Wivenhoe is 299.2 2.4 K (Burford et al., 2007). The temperature of the PRW is 298 K (Gibbes et al., 2009).
2.1.1.
Henry’s law constant search
The database of HENRYWIN V3.20 (U.S.EPA, 2008) was checked first for experimental Henry’s Law constants. When experimental values were not available, Henry’s Law constants derived from the Bond method (Meylan and Howard, 1991) were used as this data was available for all compounds.
2.1.2.
logKow-search
The databases of KOWWIN v. 1.67 (U.S.EPA, 2008), ChemPlusID (http://chem.sis.nlm.nih.gov/chemidplus/chemidlite. jsp), and PhysProp (http://www.epa.gov/oppt/exposure/pubs/ episuitedl.htm, also accessible via http://www.syrres.com) were surveyed for experimentally derived octanolewater partition coefficients KOW. If no experimental value was found for a compound, the KOW was estimated by KOWWIN v. 1.67 (U.S.EPA, 2008) and SPARC (Hilal et al., 2005). If the experimental or the estimated value from KOWWIN was not more than 10 times greater or smaller than the value estimated by SPARC, the former was used for all further calculations. If the two KOW predictions differed by more than an order of magnitude, additional estimations compiled by VCC labs (Virtual Computational Chemistry Laboratory, 2009) were compared and the KOW from either KOWWIN or SPARC closest to the mean value reported by VCC labs was used.
2.1.3.
Acidity constants
SPARC (Hilal et al., 2005) was used to calculate the acidity constants and therefore determine the fraction that is neutral at the pH of Lake Wivenhoe (average pH 7.86, see Section SI-1 for details). The pKa-values of single functional groups of a compound were also extracted from SPARC and where possible, experimental values from PhysProp database were collected as a comparison. For mono-functional compounds the fraction of neutral species, an, was calculated using the equations described in Section 2.4, and for polyfunctional compounds an was predicted by SPARC.
2.1.4.
Half-life values in water and sediment
Biodegradation half-life values for water and estimated using the BIOWIN 3 model in (U.S.EPA, 2008). BIOWIN 3 estimates ultimate or mineralization half-life values based on
sediment were BIOWIN v4.1 biodegradation regressions of
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biodegradability estimates from a survey of experts for a suite of organic chemicals against chemical substructure plus molecular weight. The mean value of the estimated range of half-lives from BIOWIN 3 is converted to a half-life for the compound using a series of conversion factors (Aronson et al., 2006). These conversion factors assume that six half-lives constitute ultimate biodegradation and that sediments are anaerobic with the rate of ultimate biodegradation in this phase one-ninth that in the water compartment. The potential for abiotic hydrolysis was assessed for compounds with applicable hydrolysable functional groups (e.g., esters, carbamates, alkylhalides) using HYDROWIN v2.0 (U.S.EPA, 2008). For the remainder, there is little information on hydrolysis half-lives under environmental conditions in the literature, suggesting this pathway is not significant. There is no satisfactory way of a priori estimation of phototransformation rates unless factors such as the absorption spectra of the compound, solar emission spectrum, quantum yields and water attenuation factors are known. In their absence the literature was surveyed to find relevant phototransformation kinetic data. These were integrated in the half-lives in water (Table 1). To facilitate comparisons, and because many are non-detected and do not currently warrant more intensive scrutiny, the evaluation for all 227 chemicals presented in the Supplementary Information was done using biodegradation as the sole degradation process.
2.2.
Fugacity based mass balance model
A Level III fugacity-based, steady state model of the attenuation of chemicals of concern in Lake Wivenhoe Dam was constructed based on the QWASI (Quantitative Water Air Sediment Interaction) model of chemical fate in lakes and impoundments (Mackay, 2001; Mackay and Diamond, 1989). The evaluative environment (Lake Wivenhoe) is assumed to be at steady state for water i.e. inflows equal outflows. The releases from Lake Wivenhoe are approximately 660 Ml d1 or 2.75 104 m3 h1 (personal communication, Seqwater). The magnitude of the inflows is therefore the same. For the purposes of this work, it is assumed that recycled water from the AWTPs contributes 230 Ml d1 or 9.58 103 m3 h1 (or approximately 35%) to this inflow, and that the organic contaminants are present only in this portion of the inflows. Phases considered are water and sediment. The atmosphere is treated only as a loss pathway from the water. Since a steady state situation is assumed to exist for the chemicals of interest within the water and sediment phases the following mass balance equation can be written for the water phase. Vw Zw
Fig. 1 e Illustration of the processes considered in the fugacity model of organic contaminants from AWTPs in Lake Wivenhoe.
represents the input flux and the D values (mol Pa1 h1) are fate and transport parameters, with their magnitude an indicator of the importance of that particular fate or transport pathway for the chemical of interest in water (Seth et al., 2008). The subscripts R,T,D,W,V and O refer to sediment resuspension, sedimentewater diffusion, sediment deposition, transformation in water, volatilisation and loss in outflow respectively. This is illustrated in Fig. 1. Consistent with the QWASI approach of Mackay (2001), the bulk water and sediment phases are assumed to exist as continuously stirred tank reactors (CSTRs) i.e. there is a homogenous distribution of the chemical contaminants of interest. This means that the concentration in the outflow over the dam wall is the concentration in the lake. An analogous mass balance expression can be constructed for the sediment compartment. Vs Zs
dfs ¼ 0 ¼ fw ðDD þ DT Þ fs ðDR þ DT þ DS þ DB Þ dt
0fw ðDD þ DT Þ ¼ fs ðDR þ DT þ DS þ DB Þ
(3) (4)
Explicit equations for both fw and fs may be obtained by solving Eq. (2) and (4) simultaneously, affording fw ¼
fs ¼
I ðDD þ DT ÞðDS þ DB Þ D W þ DV þ D O þ ðDR þ DT þ DS þ DB Þ
(5)
IðDD þ DT Þ ðDW þ DV þ DO ÞðDR þ DT þ DS þ DB Þ þ ðDD þ DT ÞðDS þ DB Þ (6)
2.3. Estimation of fate and transport parameters (D values)
dfw ¼ 0 ¼ I þ fs ðDR þ DT Þ fw ðDT þ DD þ DW þ DV þ DO Þ dt (1)
0I þ fs ðDR þ DT Þ ¼ fw ðDT þ DD þ DW þ DV þ DO Þ
771
(2)
In Eq. (1), Vw is the volume of the water compartment (Lake Wivenhoe at 30% capacity), Zw the fugacity capacity constant of the water (Pa m3 mol1) and fw and fs the fugacities (Pa) in water and sediment respectively. The parameter I (mol h1)
Eq. (5) and (6) describe the fugacities in water and sediment ( fw and fs) in terms of fate and transport parameters (D values) and input flux (I). The concentrations of the organic contaminants in any phase, i, is given by Ci ¼ fiZi in order to calculate concentrations it is necessary to know fugacity and in turn to be able to estimate D values. Details of the calculation of these values for water transformation, volatilisation, outflow,
772
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sediment burial, sedimentewater diffusion as well as sediment transformation, resuspension and deposition are provided in the Supplementary Information.
2.4.
Accounting for speciation of ionisable compounds
2.4.1.
General approach for ionisable compounds
concept for transport and partitioning of ionizing compounds (Trapp et al., 2010). Mathematically we can just use the same equations as the fugacity approach but employ partition coefficients that are corrected for speciation.
2.4.2. A large number of micropollutants investigated in this study are acids or bases or possess multiple ionisable substituents. Therefore their speciation has to be taken into account when assessing their fate. The pH of the Lake Wivenhoe had a mean value of 7.86 between 2003 and 2009 with relatively large fluctuations, ranging from pH 6.2 to 9.5 (see Supplementary Information for more details). Thus for acids with an acidity constant pKa of approx. 9 and lower and bases with a pKa of 6 and higher, the role of the charged species cannot be neglected. For the 15 detected and quantified chemicals, charged species need to be considered for dichloroacetic acid, dalapon, triclopyr and salicylic acid. In the fugacity approach to prediction of environmental fate and behaviour, compounds are considered to have a measurable vapour pressure (Mackay, 2001). All ionic species are likely to have a negligible vapour pressure and sorption characteristics different from the unionised acid species. It is possible to address this by adopting the aquivalence approach (Mackay and Diamond, 1989). The criterion of equilibrium is now not fugacity, but aquivalence (A mol m3) that has units of concentration. A distinct advantage is that the relevant mass balance expressions are analogous to Eqs. (2) and (4). Assuming steady state, the equations in aquivalence format for water and sediment compartments are as follows.
I þ As DR þ DT ¼ Aw DT þ DD þ DW þ DO Aw DD þ DT ¼ As DR þ DT þ DS þ DB
(7) (8)
Volatilisation is ignored in Eq. (7) because dichloroacetic acid, dalapon, triclopyr and salicylic acid would be present almost entirely as the non-volatile ionised conjugate base under the mean pH conditions prevailing in Lake Wivenhoe. Instead of fugacity capacity constants (Z mol m3 Pa1), this approach employs aquivalence capacity constants (Z) that are dimensionless. Here, Zw is defined as unity, and the constants for the other phases are simply the appropriate dimensionless partition coefficients. For example, with sediment, Zs is the dimensionless sedimentewater partition coefficient. The fate or transport parameters are denoted by D to distinguish them from those associated with the fugacity framework. They are calculated in the same way but have units of flow rate (m3 h1). The aquivalence concept is employed here if there is one species present that is fully charged. The concentrations of the fully ionised compounds in water derived using aquivalence are effectively identical to those derived using fugacity, but are obtained using a more satisfactory theoretical basis. The fugacity concept is applicable if there is only one species present that is neutral and has a measurable vapour pressure. To account for multiple species one has to adopt a multispecies approach, as has been detailed in Seth et al. (2008) and which is also consistent with the very recently published activity-based
Speciation and partitioning to the air
The Henry’s Law constants can be corrected for speciation by assuming that the ions are non-volatile (Schwarzenbach et al., 2003) (Eq. (9)): KH ðall speciesÞ ¼ an $KH ðneutralÞ
(9)
where an is the fraction of neutral species.
2.4.3. Speciation and partitioning to suspended solids and sediments Prediction of sorption for the charged species identified above is problematic. Spadotto and Hornsby (2003) investigated the pH dependent sorption of 2,4-dichlorophenoxy acetic acid to soil. They concluded that the overall sorption or partition coefficient (KD) is the sum of contributions from the neutral acid form and the anionic conjugate base form (Eq. (10)). KD ¼ an KDðnÞ þ ð1 an ÞKDðiÞ
(10)
In this expression, KD,n is the sorption coefficient for the neutral species and KD,i that of the charged (ionic ¼ i) species, an is the fraction of neutral species, and (1an) the sum of the fractions of all charged species (including zwitterions). For the neutral species KD,n was estimated with Eq. (11), KDðnÞ ¼ fOCðsÞ KOC ¼ fOCðsÞ 0:41Kow
(11)
and the fraction of neutral species an with Eqs. (12) and (13) for acids and bases, respectively (Schwarzenbach et al., 2003). an ¼
1 1 þ 10pHpka
(12)
an ¼
1 1 þ 10pka pH
(13)
Franco and Trapp (2010) used this model but corrected the KD of the charged species using the correction factor of KOW, i.e. assuming that sorption was 3.5 orders of magnitude lower for the charged species compared to the corresponding neutral species by analogy to the respective KOW values (Jafvert et al., 1990). A recent publication investigated the pHdependent sorption of various acidic organic chemicals to soil organic matter (Tu¨lp et al., 2009). It was shown that the investigated anions sorbed between factors of 7e60 less than the corresponding neutral species. Since we are dealing not only with acids but also with bases and multifunctional speciating compounds, we used a factor of 10 in this study to estimate the decreased sorption of the charged species to organic carbon (Eq. (14)) and assumed that even for the charged species the sorption to sediment and suspended solid is only due to partitioning to organic matter. KOCðiÞ ¼ 0:1KOCðnÞ
(14)
With the QSPR used to estimate the KOC(n) from KOW (Eq. (11)), we obtain the following prediction model for the sedimentewater distribution of ionisable compounds (Eq. (15)).
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Note that there was no distinction made between positively charged, negatively charged and zwitterionic species; they were all treated as ionic species. KOC ¼ ð0:041 þ 0:369$an ÞKOW
2.5.
CS ¼ fS ZS
(17)
The concentration on suspended solids (CSS) is found from CSS ¼ fW ZSS
Model parameterisation
(18)
Amounts in these phases are therefore VWfWZW for water, VSfSZS for sediment and VSSfWZSS for suspended solids. The importance of each process considered is found by comparing the fluxes (mol h1) of the various processes. The fluxes are the product of the fate or transport parameter (D) and the appropriate fugacity. A measure of the persistence of the compound is residence time. For the sedimentewater system as a whole, the overall residence time of a compound is the ratio of the total amount in the system to the input (or output) flux (Mackay, 2001). It follows that the residence time in the water compartment is simply the amount present at steady state divided by the input (or output flux). Residence time in the sediment is defined analogously.
Model inputs
The concentrations presented for each of the 15 detected compounds are maximum values based on LCeMS or GCeMS analyses of 60 separate samples (Queensland Water Commission, 2009). They thus represent a worst-case scenario, not an average. The samples cover a three-month period of AWTP validation and verification from May to August 2008 and subsequently, normal operation up to December 2008 (Queensland Water Commission, 2009). For chemicals not detected but which are in the list of chemicals of concern (Table SI-2), a concentration of 10 ng/L in recycled water was used for modelling to be able to compare relative attenuation between all compounds.
2.7.
(16)
(15)
A volume of 30% for this major reservoir was selected for the purposes of this modelling scenario based upon current government policy of adding recycled water when the combined dam levels fall below 40%. The characteristics of Lake Wivenhoe (at 30% capacity) necessary for the calculation of these D values are compiled in Table 2. The data were either supplied by Seqwater (unpublished results) or are default values from Mackay (2001) and other literature sources. The system temperature was taken as 298 K as is discussed in Section 2.1.
2.6.
CW ¼ fW ZW
3.
Results and discussion
3.1. General results and discussion of model uncertainties As mentioned previously in Section 2.2, it is assumed that PRW contributes approximately 35% to inflow. Under steady state conditions, the PRW added to the lake is diluted by natural inflows resulting in an initial attenuation by a factor of approximately 3. Further attenuation is caused by volatilisation, sorption and degradation. Fig. 2 gives an overview on the predicted attenuation of all chemicals. For most of the chemicals, concentrations are expected to be reduced by more than a factor of 10 as compared to PRW (Fig. 2, left axis, black line), i.e. in addition to dilution there is natural attenuation by at least a factor of 4. For 50% of the chemicals attenuation causes a reduction of more than a factor 30. However, removal
Model outputs
Chemical fugacities in water and sediment are found from evaluation of Eqs. (5) and (6). From these, concentrations of the chemical of interest in the water and sediment (CW and CS) are given by
Table 2 e Physical characteristics of Lake Wivenhoe (at 30% capacity) used in derivation of Level III fugacity and aquivalence models. Characteristics Lake Volume (VW) Lake Surface Area (AW) Sediment Area (AS) Sediment Volume (VS) Sediment organic carbon content (expressed as a fraction) ( foc(s)) Suspended solids organic carbon content (expressed as a fraction) ( foc(ss)) Suspended solids concentration kg m3 Sediment and suspended solids density (rs) Sediment Burial Rate (UB) Sediment Deposition Rate (UD) Sediment Resuspension Rate (UR) a Seqwater, unpublished results.
Value 3.53 4.63 4.63 4.63 0.03
8
10 107 107 106
3
m m2 m2 m3
0.20 7.9 2.4 3.4 4.6 1.1
103 kg m3 103 kg m3 108 m3 m2 h1 108 m3 m2 h1 108 m3 m2 h1
Ref. and/or uncertainty/variability Seqwater storage curves for a capacity of 30%a Same source as VW Same value as AW From sediment area and depth Mean value from 57 sites sampled in the lake with a standard deviation of 0.01a Mean value from 57 sitesa
(Mackay and Diamond, 1989) (Mackay, 2001) (Mackay, 2001) (Mackay, 2001)
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Fig. 2 e Overview of the predicted natural attenuation of all 247 compounds under consideration. The data are sorted with increasing ratio of outflow concentration to PRW concentration (left y-axis, bold line) and related to the ratio of amount in sediment to amount in water (right y-axis, diamonds). The fraction in suspended solids is generally negligible and was therefore omitted in this plot. All data are reported in the Supplementary Information Table SI-3.
from the aqueous phase is not entirely related to biodegradation or volatilisation. For many compounds a significant fraction of the remaining chemicals is predicted to reside in the sediment phase. This is indicated in Fig. 2 by the ratio of amount in sediment to amount in water (right axis, diamonds), which covers 10 orders of magnitude. The perfluorinated compounds, and a number of very hydrophobic chemicals, cypermethrin, 4,40 -DDT, 4,40 -DDE, chlordane, musk xylene and chlorpyrifos, would be strongly absorbed to sediments and consequently enriched in the sediments. A well-mixed lake model was used in the present study, although Lake Wivenhoe is definitely not well mixed and is also stratified from September to April. An alternative model would be a plug flow model. Both models are extreme cases that are not applicable in reality. The hydrodynamics and the extent of mixing are extremely complex and unknown, and moreover likely to vary with storage volume. Given this and the fact that once PRW is introduced it will be continuously introduced and its quality will be relatively constant, we chose a steady state well-mixed model over a plug flow model. The analysis of Warren et al. (2009) suggests that the output concentrations from the well-mixed model are greater than those from the assumption of plug flow. The difference is a function of ks, the product of the summed first order loss rate constants and the hydraulic residence time (s) meaning that the greatest discrepancy would occur with compounds with relatively short half-lives such as NDMA. The model currently predicts considerable attenuation for this compound, but this is almost certainly very conservative, with even greater attenuation likely. As Mackay has emphasized in many of his publications (see e.g., Mackay, 2001 or Mackay et al., 2009) a model is only an approximation of the reality and fidelity must be well balanced with complexity. For example Warren et al. (2002) showed that subdividing the lake into smaller, well-mixed compartments lead to calculated maximum concentrations that are still within a factor of 5 for 12 compartments versus one single compartment. Thus increasing complexity of the model and introducing further uncertainty with respect to compartment number, size and properties would not greatly
improve the fidelity of the models, while making it less useful for screening-type applications. Sources of uncertainty are variability and errors in input data and model parameters. The physicochemical parameters can be predicted with reasonable precision and the influence of their uncertainty on the output of Level III fate models should be relatively small (Ku¨hne et al., 1997; Citra, 2004). In contrast, variability or error in degradation half-lives would lead to large uncertainty in residence times (Citra, 2004). The measured system parameters (Table 2) are subject to spatial and temporal variability and we used the means as input parameters. The estimated system parameters are sediment burial, deposition and resuspension rates and according to the source these are order-of-magnitude-estimates. Therefore, predicted fate on the chemicals cannot be more than an order-of-magnitude estimation but allows for comparison of different chemicals and ranking of chemicals for further in-depth investigation. Most of the 247 chemicals that were assessed will never enter the lake, therefore the following discussion focuses on those that were detected in PRW, albeit in very low concentrations.
3.2.
All chemicals detected in recycled water
The 15 organic contaminants detected in recycled water cover a wide range of physicochemical properties. The three halomethanes are semivolatile with high Henry’s Law constants and appreciable water solubility. Four chemicals (dichloroacetic acid, salicylic acid, dalapon and triclopyr) are relatively strong acids ( pKa < 3.22), which are fully dissociated and of moderate hydrophobicity (expressed as Kow) and therefore neither volatile nor sorptive to sediments. Four other chemicals, NDMA, paracetamol, DEET and caffeine, are also water soluble and not volatile and should therefore also remain in the aqueous phase. Three chemicals, 4-t-octylphenol, 4-nonylphenol and cholesterol, are very hydrophobic and not volatile and should therefore partition to the sediments and suspended solids. The remaining one, bisphenol A, cannot easily be classified, as it is not volatile but of moderate hydrophobicity, therefore attention has to be directed to both water and sediment compartments. A common feature of all 15 organic contaminants is that they have relatively short half-lives in water but persist once they reach the sediment compartment (Table 1). Using the European Union Persistent, Bioaccumulative and Toxic (PBT) criteria (European Parliament and European Council, 2006), compounds with a half-life 40 days in freshwater are considered persistent (P), while compounds with a halflife 60 days are considered very persistent (vP). From the modelled values, only cholesterol can be considered very persistent in water. In freshwater sediment, compounds with a half-life 120 days are considered persistent, while compounds with a half-life 180 days are listed as very persistent. All 15 organic contaminants can be considered persistent or very persistent in sediments (Table 1). The maximum concentrations of the 15 organic contaminants detected in recycled water from the AWTPs were all below the relevant local public health standards after normal operations commenced (Queensland Parliamentary Counsel, 2005) (Table 3). There is further attenuation predicted for all
Table 3 e Concentrations of organic contaminants detected in recycled water from AWTPs that is the influent to Lake Wivenhoe, modelled outflow concentration and relevant local public health standards (QPC, 2005). Outflow concentrations were modelled using the fugacity model for the neutral chemicals and the aquivalence approach for the completely ionised chemicals. Chemical
3 4 3 21 44/0.03 76/28 64 25/17 21 21 51 49 75/2 21 147
3 4 3 21 44/0.03 18/7 47 6/4 21 21 49 49 74/2 21 0.01
29 41 27 0.3 0.1 480 275 194 1 5 42 1 10 0.3 775
8.0 2.0 16.0 0.90 0.010 0.040 0.022 0.069b 0.010 0.010 0.010 0.020 0.030 0.030 0.011
Modelled Public Health MOS Removal by outflow standard (margin dilution concentration (mg/L) of safety) and natural from attenuation (%) Lake Wivenhoe (mg/L) with photodegradation 1.89 5.51 3.63 1.34 2.15 1.92 7.39 1.85 1.49 1.49 3.50 7.04 3.93 4.47 1.07
104 103 102 102 107 104 104 104 104 104 104 104 105 104 107
6 100 200 100 0.01 50 200 500 175 105 2500 500 10 0.35 7
320 18,160 5500 7450 46,500 260,870 270,770 2,702,660 1,173,860 704,780 7,152,570 709,980 254,180 780 6,544,4280
99.8% 99.7% 99.8% 98.5% 100% 99.5% 96.6% 99.7% 98.5% 98.5% 96.5% 96.5% 99.9% 98.5% 100%
Dominant removal process
Volatilisation Volatilisation Volatilisation Water transformation Water transformation Outflow Water transformation Various processes Water transformation Water transformation Water transformation Water transformation Water transformation Water transformation Outflow
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 6 8 e7 8 0
Bromodichloro-methane Dibromochloro-methane Chloroform Dichloroacetic acid NDMA 4-t-Octylphenol Bisphenol A 4-Nonylphenol Paracetamol Salicylic acid DEET Dalapon Triclopyr Caffeine Cholesterol
Residence time Residence Residence time Maximum in water (d) time in system (d) input without/with in without/with concentration photo-degradationa photo-degradationa sediment detected in (d) PRW (mg/L)
a Two scenarios were modelled e with and without photodegradation; all notes in text and figures refer to the model with photodegradation. b Note for 4-nonylphenol, the analysis result is referring to total nonylphenols since there are too many isomers to identify individually using the analytical procedure adopted.
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compounds in Lake Wivenhoe (Table 3). The assumption of a well-mixed water compartment is a common one in chemical fate models (Mackay, 2001; Mackay and Diamond, 1989; Warren et al., 2002). It is unlikely that this assumption is always valid or reasonable for lakes and impoundments however. As discussed above, recent work suggests outflow concentrations predicted assuming a well-mixed compartment are over-estimates, particularly for compounds with extensive loss (Warren et al., 2009). Biodegradation is the main degradation pathway predicted for most chemicals. Hydrolysis half-lives, as derived from HYDROWIN v2.0 and a literature search, showed that hydrolysis is negligible under the prevailing environmental conditions for the 15 detected chemicals. However, phototransformation must be considered, and NDMA in particular has a relatively short half-life. Phototransformation is therefore accounted for in the aggregate water transformation rate constant for NDMA as well as compounds such as triclopyr, 4-nonylphenol and 4-t-octylphenol (Table 1). Predicted sediment concentrations were all less than 2 ng L1, except for the sterol cholesterol and the phenols (20e125 ng L1) and the trihalomethanes (20e95 ng L1). Comparison of the fugacities of the chemicals in water and sediment gives an indication of how close the different chemicals are to equilibrium (Figure SI-2, Table SI-3). Most chemicals have different fw and fs implying non-attainment equilibrium between these two compartments. Only for the DBPs and some of the PPCPs, such as caffeine and paracetamol, are the values of fw and fs within 5% of each other indicating that equilibrium has effectively been attained. For these compounds the magnitude of the water to sediment diffusive flux for a compound is balanced by the magnitude of the water to sediment flux. For the remaining compounds, equilibrium will not be attained, with fs invariably less than fw and therefore a net diffusive flux from water to sediment exists. The greatest difference was observed for cholesterol and the phenols bisphenol A, 4-toctylphenol and 4-nonylphenol where fs is 30% or less than the value of fw.
Table 4 e Numbers of chemicals analysed (detected and not detected in AWTP effluent) and total number of chemicals modelled. Group of chemicals Detected Total number Total number analysed modelled Disinfection by-products Endocrine disrupting chemicals Pharmaceuticals and personal care products Pesticides Others Total
3.3.
5
12
16
3
11
17
3
52
100
2 2 15
27 11 113
53 61 247
Disinfection by-products (DBPs)
The trihalomethanes (THMs), bromodichloromethane, dibromochloromethane and chloroform that were found in recycled water are disinfection by-products. The treated wastewater that comes into the AWTPs is chloraminated, and as part of the process train in the AWTPs, water is chlorinated (Queensland Water Commission, 2009), which leads to the formation of disinfection by-products. In Lake Wivenhoe, the model predicts the dissolved concentration of these THMs to be reduced by a factor of approximately 400. Given the relative high Henry’s Law constant of these compounds, it is not unexpected that volatilisation is the major fate process. Photodegradation is not relevant (Mackay et al., 1992) and biodegradation is slow (half-life 38 d), thus >90% of the input flux is expected to volatilise and approximately 7% will undergo transformation in the water. Less than 1% is anticipated to be in the outflow from the dam. Concentrations in the outflow for these and other chemical compounds of interest (Table 3) would be expected to undergo further attenuation, mainly due to volatilisation, in the 40 km of the Brisbane River between the dam wall and the potable water treatment intake (Fono et al., 2006; Traves et al., 2008).
Fig. 3 e Absolute and relative importance of the various fate processes for the 15 chemicals detected in AWTP effluent (Table 1). Panel A represents the absolute values of the fluxes (calculated from D times f ) between all compartments and Panel B is scaled to percentage of total flux so the contribution from the different processes is more discernible. The legend is valid for both panels.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 6 8 e7 8 0
Concentrations in water and residence times in water and sediment as well as the system as a whole are found in Table 3, where it can be seen that the system residence times of THMs (3e4 days) are the lowest of all the compounds of interest (apart from NDMA and triclopyr, for which photodegradation need to be additionally considered). NDMA is a probable human carcinogen and has received considerable recent attention since it has been reported as amongst the most potent carcinogens detected in potable water (Sedlak et al., 2005; Pehlivanoglu-Mantas and Sedlak, 2006). While the compound occurs naturally, it has anthropogenic sources. NDMA is characterised as a DBP because of formation from organic nitrogen precursors during chlorination and chloramination (Queensland Water Commission, 2008). The organic nitrogen precursors typically occur in wastewater effluent regardless of the level of treatment (Sedlak et al., 2005), and disinfection is necessary to prevent membrane biofouling in the AWTPs. NDMA is poorly rejected by reverse osmosis membranes (Plumlee et al., 2008). Advanced oxidation processes (H2O2/UV) are necessary for further removal due to its susceptibility to oxidation by hydroxyl radicals, as well as photolability (Queensland Water Commission, 2008). It has been noted that in the aquatic environment, NDMA undergoes photodegradation and biotransformation (Pehlivanoglu-Mantas and Sedlak, 2006). Based on the half-life data, photodegradation dominates (Mackay et al., 1992; Plumlee et al., 2008) over biotransformation. From Fig. 3, it can be seen that transformation processes in water are predicted to be effectively the sole fate. System and water residence times are negligible (<1 h) due to the fact that the standing mass in the system is negligible. The predominance of water transformation in the predicted fate of NDMA indicates that confirmatory studies should be undertaken to assess its fate and behaviour in Lake Wivenhoe given the sitespecific nature of contributory processes. In addition, the performance of EPI Suite in categories such as degradation half-life data is often modest (Gouin et al., 2004) and the literature data on photodegradation span a quite wide range and might be different under the particular conditions existing in Lake Wivenhoe. The remaining DBP detected was dichloroacetic acid, which is present as the anionic conjugate base under the pH conditions prevailing in Lake Wivenhoe. It was therefore modelled using the aquivalence framework. The compound has a slow biodegradation half-life and even slower photodegradation (79e790 d, (Mackay et al., 1992)) but nevertheless the degradation in the water phase remains the dominant elimination pathway as volatilisation is negligible for such charged compounds and sorption to particles and sediment is relatively unimportant due to the low hydrophobicity of the charged species. A total of 12 disinfection by-products were analysed but 7 remained below detection limit (Table 4), and the fate model was run for an additional 4 chemicals of this group. The results are presented and discussed in the Supplementary Information.
3.4.
Endocrine disrupting chemicals
While none of the natural hormones could be detected in recycled water, the xenoestrogens 4-t-octylphenol,
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4-nonylphenol and bisphenol A were present. They are widely found in the effluent of wastewater treatment plants including those providing treated wastewater to the AWTPs of interest in this work (Tan et al., 2007; Ying et al., 2008). Though their estrogenic potency is much lower than that of natural estrogens, their abundance warrants concern regarding their estrogenic activity (Ying et al., 2008), especially as they can act additively together with natural hormones (Thorpe et al., 2006). All three detected xenoestrogens should be reduced substantially due to natural attenuation (by factors of 30e370). However, they constitute the only group of detected chemicals where no single fate process was dominant (Fig. 3). Even volatilisation has a small but non-negligible contribution in case of the alkylphenols. The alkylphenols do show appreciable photodegradation (Neamtu and Frimmel, 2006), while for bisphenol A biodegradation is the only relevant degradation process (Chin et al., 2004). Since all of the studied xenoestrogens are very hydrophobic, the sediment compartment plays a role with sediment deposition and sediment transformation becoming relevant (Fig. 3). Driven by the hydrophobicity and persistence, the fraction in suspended solids also becomes significant, and contributes to the outflow. Thus outflow is a relevant removal process. A better understanding of the fate of this group of chemicals would require increased knowledge of their sorption behaviour to suspended particles and sediments. As mentioned above, none of the natural hormones were detected in recycled water although they were in the list of analytes investigated (Queensland Water Commission, 2009). The dominant role of water transformation confirms that they are easily degraded. For detailed results refer to the Supplementary Information.
3.5.
Pharmaceuticals and personal care products (PPCPs)
Jones et al. (2005) have highlighted the increasing awareness of the importance of pharmaceuticals in IPR schemes. Problems such as unintended effects on non-target organisms or receptors at sub-therapeutic doses and synergism of mixture components need to be considered. Compounds detected in product water from AWTPs are paracetamol and salicylic acid as well as the insect repellent DEET (Queensland Water Commission, 2009). Paracetamol is claimed to be the most dispensed pharmaceutical (by mass) in Australia (Khan and Ongerth, 2004) while salicylic acid is a metabolite of aspirin (acetylsalicylic acid) as well as having other pharmaceutical uses in its own right. Their presence in effluent streams of many wastewater treatment plants is well known (Costanzo and Watkinson, 2007). Nikolaou et al. (2007) reported that although rates of transformation of many pharmaceuticals are relatively large, release from treatment facilities occurs because of elevated influent concentrations and insufficient detention and residence times. The occurrence of trace levels of these pharmaceuticals in water from the AWTPs is therefore not surprising, and the model suggests that Lake Wivenhoe influent concentrations would be reduced by factors of approximately 30e70 (Table 3). Transformation processes in water dominate the predicted
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fate of these compounds and most of the PPCPs that do not undergo this process are advected from the dam. While the insect repellent DEET is relatively water soluble despite being present as neutral species, Bernhard et al. (2006) found little removal in a municipal wastewater treatment plant with activated sludge treatment. However, increased removal was found in a membrane bioreactor utilising primary effluent from this plant. No photodegradation has been observed for DEET (Kim and Tanaka, 2009) and biodegradation is predicted to be very slow (Table 1). Therefore, a residence time of around 50 d in Lake Wivenhoe is expected and reduction of concentration in the outflow is mainly due to dilution (Table 3). Given these properties and despite its low hydrophobicity that suggests negligible bioaccumulation and negligible sorption to sediments, DEET should be further investigated to evaluate if it is more degradable in Lake Wivenhoe than the literature data indicates. A large number of pharmaceuticals and personal care products can be detected in wastewater but not in recycled water (Table SI-2). A total of 52 PPCPs were in the list of analytes, but only three were detected. Another 48 PCPPs are considered of environmental interest and were run with the model (Table 4) and the results are presented in the Supplementary Information.
3.6.
Pesticides
The pesticides dalapon and triclopyr are both substituted acetic acids and as can be seen from their acidity constants (Table 1) they are essentially ionised at the mean pH of Lake Wivenhoe. Their fate was therefore also assessed using the aquivalence approach. The reduced sorption of the anionic conjugate base of these compounds means that there is a diminished presence in the sediment compartment. Model results show considerable attenuation in Lake Wivenhoe due to both biotic and abiotic transformation processes in the water. Triclopyr has a photodegradation half-life of 1.3 d (Woodburn et al., 1993), which dominates overall transformation. No photodegradation data were found in a Web of Science search for dalapon (http://apps.isiknowledge.com, accessed on 14 August 2010, search terms ((photo* or degradation) and dalapon)) but from the overlap of its spectrum with the emission spectrum of sunlight, some degradation can be expected (not quantified in model) (Howard, 1991). Dalapon is hydrolysable but the half-life is months and therefore biotransformation is more important (Howard, 1991). The flux in the outflow over the dam wall is minor (<10%) compared to transformation in water. A total of 53 pesticides were modelled (Table SI-7), 25 of which were in the list of analytes not detected (see Supplementary Information).
3.7.
concentrations (Ternes et al., 2001). In Lake Wivenhoe, model output indicates transformation processes in water are also the most important with over 95% predicted to undergo this fate, with the remainder being removed through advection. The sterol cholesterol is ubiquitous in municipal wastewater effluent (Garric et al., 1996). Of all the compounds detected in recycled water from the AWTPs, the dissolved phase concentration of cholesterol is predicted have the greatest reduction in concentration in Lake Wivenhoe apart from NDMA, by a factor of almost 105 (Table 3). However, outflow is also important (Fig. 3) with some 16% of cholesterol predicted to undergo this fate due to of the contribution of suspended solids. Aside from the THMs, NDMA and triclopyr, modelled residence times in water of this group are the smallest of the suite of detected organic contaminants, but system and sediment residence times are generally amongst the largest (Table 3). Cholesterol is by far the most hydrophobic of these compounds as characterised by KOW, followed by 4-nonylphenol and 4-t-octylphenol. Sorption would normally be expected to be extensive but equilibrium between water and sediment is far from complete. A total of 61 additional chemicals were run through the model (see Supplementary Information). While few of them would be expected to be present in recycled water, they have been included only to ensure a comprehensive database.
Other chemicals
The alkaloid caffeine has been proposed as a marker of wastewater contamination of surface waters (Buerge et al., 2003). It is quite water soluble as evidenced by its KOW value (Table 1). The compound has been reported to undergo rapid biodegradation during wastewater treatment, but it is still commonly observed in effluent due to relatively large influent
4.
Conclusions
The IPR process is based on multiple barriers for organic chemicals and other contaminants. The results of the present study will assist in assessing the performance of the barrier represented by Lake Wivenhoe as it was possible to identify contaminants with reduced or negligible attenuation or enhanced residence times for closer scrutiny and in situ confirmatory work. The concentrations of organic contaminants including DBPs, PPCPs and herbicides in recycled water produced by AWTPs in the South-East Queensland area were all below relevant public health guidelines after normal operations commenced. Assuming that all the water from the AWTPs is added to Lake Wivenhoe and that a steady state situation exists, the fugacity model predicts concentrations of these compounds in the outflow from the dam to be reduced by a factor of at least 30 and confirms the utility of environmental buffers. On average, the concentrations in recycled water would be reduced to 3.5% of the input concentration in recycled water (25th percentile 1.5%, 75th percentile 5.0%). As all assumptions were very conservative, in reality natural attenuation is expected to reduce the chemicals’ concentrations even further. In particular, for THMs, the model predicts them to be reduced by a factor of approximately 400 with volatilisation the major fate. The potent carcinogen NDMA is also classified as a DBP and transformation processes in water are likely to be much more important than for THMs to the extent that negligible levels are predicted in the evaluative environment due to photolability. However, in situ confirmatory data is needed. For PPCPs such as DEET and paracetamol, transformation in water also dominates their fate, while the majority of molecules that do not undergo this fate are
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 6 8 e7 8 0
advected from the dam. The sterol cholesterol and the phenolic xenoestrogens 4-t-octylphenol, 4-nonylphenol and bisphenol A are more hydrophobic and while sorption may be expected to be more important, equilibrium between water and sediment is not attained. Some of the organic contaminants are ionisable and present as organic anions, cations or zwitterions in the water of Lake Wivenhoe. For these, the model shows transformation processes in water to be predominant. However, the half-lives collected from literature are likely to refer to the neutral species, so there is some uncertainty related to the input data. For these chemicals it would be highly desirable to improve the input database by experiments, ideally undertaken in situ in the lake. Future work will aim to develop a more detailed hydrodynamic model of Lake Wivenhoe to assess the chemical fate in water storages.
Acknowledgments Entox is a partnership between Queensland Health and the University of Queensland. This work was supported by Queensland Bulk Water Supply Authority (trading as Seqwater), Brisbane, QLD 4000, Australia. The authors thank Ben Reynolds, Amber Klawitter and Leanne Bowen of Seqwater and Badin Gibbes and Alistair Grinham of the University of Queensland for provision of Lake Wivenhoe water quality and quantity data. We also thank three anonymous reviewers for their constructive comments.
Appendix. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.watres.2010.08.053.
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Sedlak, D.L., Deeb, R.A., Hawley, E.L., Mitch, W.A., Durbin, T.D., Mowbray, S., Carr, S., 2005. Sources and fate of nitrosodimethylamine and its precursors in municipal wastewater treatment plants. Water Environment Research 77, 32e39. Seth, R., Webster, E., Mackay, D., 2008. Continued development of a mass balance model of chemical fate in a sewage treatment plant. Water Research 42, 595e604. Spadotto, C.A., Hornsby, A.G., 2003. Soil sorption of acidic pesticides: modeling pH effects. Journal of Environmental Quality 32, 949e956. Tan, B.L.L., Hawker, D.W., Mu¨ller, J.F., Leusch, F.D.L., Tremblay, L.A., Chapman, H.F., 2007. Modelling the fate of selected endocrine disruptors in a municipal wastewater treatment plant in South East Queensland, Australia. Chemosphere 69, 644e654. Ternes, T., Bonerz, M., Schmidt, T., 2001. Determination of neutral pharmaceuticals in wastewater and rivers by liquid chromatographyeelectrospray tandem mass spectrometry. Journal Chromatography A 938, 175e185. Thorpe, K.L., Gross-Sorokin, M., Johnson, I., Brighty, G., Tyler, C.R., 2006. An assessment of the model of concentration addition for predicting the estrogenic activity of chemical mixtures in wastewater treatment works effluents. Environmental Health Perspectives 114, 90e97. Trapp, S., Franco, A., Mackay, D., 2010. Activity-based concept for transport and partitioning of ionizing organics. Environmental Science and Technology 44, 6123e6129. Traves, W.H., Gardner, E.A., Dennien, B., Spiller, D., 2008. Towards indirect potable reuse in South-East Queensland. Water Science & Technology 58, 153e161. Tu¨lp, H., Fenner, K., Schwarzenbach, R.P., Goss, K.-U., 2009. pH-dependent sorption of acidic organic chemicals to soil organic matter. Environmental Science & Technology 43, 9189e9195. U.S.EPA, 2008. EPISuite Exposure Assessment Tools and Models. http://www.epa.gov/opptintr/exposure/pubs/episuite.htm. Virtual Computational Chemistry Laboratory, 2009. ALOGPS2.1. http://www.vcclab.org. Warren, C.S., Mackay, D., Bahadur, N.P., Boocock, D.G.B., 2002. A suite of multi-segment fugacity models describing the fate f organic contaminants in aquatic systems: application to the Rihand Reservoir, India. Water Research 36, 4341e4355. Warren, C.S., Mackay, D., Webster, E., Arnot, J.A., 2009. A cautionary note on implications of the well-mixed compartment assumption as applied to mass balance models of chemical fate in flowing systems. Environmental Toxicology and Chemistry 28, 1858e1865. Woodburn, K., Batzer, F.R., White, F.H., Schultz, M.R., 1993. The aqueous photolysis of triclopyr. Environmental Toxicology and Chemistry 12, 43e55. Ying, G.-G., Kookana, R.S., Kumar, A., Mortimer, M., 2008. Occurrence and implications of estrogens and xenoestrogens in sewage effluents and receiving waters from South-East Queensland. Science of the Total Environment 407, 3147e3155.
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Impact of nickel and cobalt on biogas production and process stability during semi-continuous anaerobic fermentation of a model substrate for maize silage Herbert Pobeheim a, Bernhard Munk a, Harald Lindorfer b, Georg M. Guebitz a,* a b
Institute of Environmental Biotechnology, Graz University of Technology, Petersgasse 12, 8010 Graz, Austria Schaumann BioEnergy GmbH, Pinneberg, Germany
article info
abstract
Article history:
The importance of nickel and cobalt on anaerobic degradation of a defined model substrate
Received 8 February 2010
for maize was demonstrated. Five semi-continuous reactors were operated for 250 days at
Received in revised form
35 C and a well-defined trace metal solution was added to all reactors. Two reactors each
30 August 2010
were limited regarding the concentration of Ni2þ and Co2þ, respectively, for certain time
Accepted 1 September 2010
intervals. The required nickel concentration was depending on the organic loading rates
Available online 15 September 2010
(OLR) while, for example, above 2.6 g ODM L1 d1 nickel concentrations below 0.06 mg kg1 FM in the process significantly decreased biogas production by up to 25% compared to
Keywords:
a control reactor containing 0.8 mg Ni2þ kg1 FM. Similarly, limitation of cobalt to
Biogas
0.02 mg kg1 FM decreased biogas production by about 10%. Limitations of nickel as well as
Volatile fatty acids
cobalt lead to process instability. However, after gradual addition of nickel till 0.6 mg and
Cellulose
cobalt till 0.05 mg kg1 FM the OLR was again increased to 4.3 g ODM L1 d1 while process
Nickel
stability was recovered and a fast metabolisation of acetic and propionic acid was detected.
Cobalt
An increase of nickel to 0.88 mg kg1 FM did not enhance biogas performance. Furthermore, the increase of cobalt from 0.05 mg kg1 FM up to 0.07 mg kg1 FM did not exhibit a change in anaerobic fermentation and biogas production. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Many biogas plants in central Europe are operated with maize silage as sole substrate while often these plants suffer from a dramatic decrease of biogas production after a certain time of operation (Hinken et al., 2008). This phenomenon could be due to lack of trace elements such as nickel and cobalt which are essential co-factors of enzymes involved in the anaerobic degradation of biomass (Takashima and Speece, 1989; Goodwin et al., 1990; Zandvoort et al., 2006). For example, methyl-coenzyme M contains the nickel containing cofactor F430 (Friedman et al., 1990) while the acetate converting enzyme complex carbon monoxide dehydrogenase (CODH)
contains a nickeleironesulfur component (Ferry, 1999). On the other hand, the methyl-H4SPT: coenzyme M methyltransferase complex contains cobalt (Thauer, 1998). Previously, critical nickel and cobalt concentrations of 0.6 mg kg1 and 0.02 mg kg1 FM, respectively have been reported (Pobeheim et al., 2010; Jarvis et al., 1997). However, most previous data resulted from batch experiments. In addition, bioavailability of these obviously essential trace elements is significantly lower than their total content in the fermentation medium (Oleszkiewicz and Sharma, 1990). Therefore, the aim of this study was to determine the influence of nickel and cobalt on anaerobic fermentation dynamics before and after element-limited conditions in a semi-continuous reactor
* Corresponding author. Tel.: þ43 316 873 8312; fax: þ43 316 873 8815. E-mail address:
[email protected] (G.M. Guebitz). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.001
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system. The process was monitored by measuring the conversion of volatile fatty acids (VFAs), the production of biogas and the stability in organic dry matter (ODM) concentration. For the first time, a well-defined model substrate for maize silage was semi-continuously fermented to allow exact setting of Ni2þ and Co2þ concentrations in the process. In addition, long-term semi-continuous experiments should provide important information on the effect of Ni2þ and Co2þ on process stability besides biogas production.
2.
Methods
2.1.
Inoculum and defined model substrate
Digester sludge from a fullscale biogas plant (Fu¨rstenfeld, Styria) fed with maize silage was used for reactor start up. The sludge was filtered to eliminate particles larger than 4 mm. The inoculum sludge was diluted with distilled water to a concentration of 5% dry substance (DS). The defined model substrate (dS) was designed with respect to the composition of the maize plant. The main components of the maize plant are cellulose, starch and hemicellulose. Consequently, microcrystalline cellulose and starch from maize were used as carbon source in a mixture of 50% cellulose and 46% starch. The basic nutrients carbon, nitrogen and phosphorus were mixed in aC:N:P ratio of 125:5:1. Urea was applied as nitrogen source. The adequate amount of phosphorus was supplied with a 0.1 M potassium phosphate buffer. Chemicals were purchased from Roth and Merck chemicals, Germany.
2.2.
Trace elements
Basic concentrations of trace elements were adjusted at fermentation start by addition of 1 mL kg1 FM of the trace element solution (Table 1). At each feeding event an equal amount of the trace element solution was added to keep the appointed concentration level. Nickel (NiCl2$6H20) and cobalt (CoCl2$6H20) were also applied as single elements in concentrations as indicated below (Table 2). Calcium and magnesium were blended as CaCl2 and MgCl2.
2.3.
Reactor set up and operation
Semi-continuous experiments were conducted in 5 L glass reactors (GL 45, 355 186 mm). Five reactors were operated at
Table 1 e Composition of added trace element solution. Metal
Compound
Conc. [mM]
Fe2þ Zn2þ Mn2þ B3þ Cu2þ Se6þ Mo6þ W6þ
FeCl2$4H2O ZnCl2 MnCl2$4H2O H3BO3 CuCl2$2H2O Na2SeO3$5H2O Na2MoO4$2H2O Na2WO4$2H2O
7.5 0.5 0.5 0.1 0.01 1.0 0.15 0.1
35 C (1 C) for about 250 days. The reactor content was mixed manually by shaking the fermentors two times per day. Experiments with nickel and cobalt limitation were carried out in duplicate. The reactors were equipped with a distributor cap to remove generated biogas from the fermentors and a valve for substrate feeding and sample collection. The fermentors were linked to a gas flow meter (Milligascounter, Ritter, Germany) for continuous measurement of produced biogas. Furthermore, a 27 L gas-sampling bag (Linde Gas, Graz, Austria) was connected to the gas flow meter to collect the biogas for determination of gas quality. The CH4, CO2, H2S and O2 content of the biogas was detected with the gas analyzer Visit-03 from the Messtechnik EHEIM GmbH (Schwaigern, Germany). The gas analyzer was calibrated with a mixture of 50% CH4 and 50% CO2 (Linde Gas, Graz, Austria). The reactors were initially filled with 3000 g of the inoculum sludge with a concentration of 5% DS. Thereafter, the fermenters were flushed with nitrogen gas to obtain anaerobic conditions. All reactors were operated semi-continuously and fed 3 times per week with amounts of the defined model substrate and the described trace element solution (Table 1) diluted with distilled water to give 100 g. In addition 100 g samples were directly taken from the changed reactor sludge. After day 40 of operation, addition of trace elements was started while the actual amount to be added varied between reactors and feeding times. The amount was calculated based on the current concentration measured. Consequently, for example 0.39 and 0.41 mg Ni kg1 FM were added to reactors 1 and 4 on day 40. All values given in Table 2 thus represent the final concentration measured after adjustment. The trace element solution and extra nickel and cobalt were added to reactor R1 while the reactors R2 and R3 were limited for a certain time period by nickel and reactors R4 and R5 by cobalt, respectively. The organic loading rate (OLR) was continuously increased and trace elements were specifically added.
2.4.
Analytical methods
All trace metal measurements were conducted by ICPeOES (inductively coupled plasmaeoptical emission spectrometer, iCAP 6300 Duo, Thermo Fisher Scientific Inc. Waltham, MA. USA) at the ISF Wahlstedt, Germany. Samples were first prepared with HNO3/H2O2 for following microwave pressure disintegration at 180 C. The pH was determined with a two point calibrated (pH 4 and pH 7) WTW pH 540 GLP pH-meter. A preliminary and rapid testing of produced VFA’s were carried out with the two-point titration TVA/TIC technique (Burchard et al., 2001). The pH and TVA/TIC (total volatile acids/total inorganic carbonate) were determined immediately after sample collection. Nitrogen content was measured according to the method of Kjeldahl with a Vapodest Vap 50 (Gerhardt Analytical Systems, Ko¨nigswinter, Germany) data not shown. Further, from the collected samples the organic acids acetic, propionic, butyric isobutyric, isovaleric and valeric acid were measured by HPLC (High-performance liquid chromatography). Thus 1 g of mixed reactor sludge was centrifuged for 10 min at 16,000g and the supernatant was collected. An aliquot of the supernatant was pretreated according to Carrez precipitation to remove proteins and fat components. For
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Table 2 e Summary of nickel and cobalt concentration, organic loading rate (OLR), biogas amount and methane during anaerobic digestion of a maize model substrate in 5 semi-continuous reactors during operation time of 250 days. Operation (day)
1
40
42
70
86
107
133
135
149
163
168
194
228
250
R1 Ni (mg kg1 FM) Co (mg kg1 FM) OLR (g ODM L1 d1) Biogas (NL d1) CH4 (%)
0.28 0.10 0.4 0 0
0.19 0.03 1.7 2.4 31
0.59 0.09 1.7 1.8 35
0.59 0.10 1.7 2.4 54
0.69 0.19 1.7 2.5 48
0.64 0.17 2.6 4.4 46
0.88 0.17 3.0 5.1 49
0.83 0.19 3.0 5.4 48
0.78 0.17 3.0 3.0 51
0.75 0.17 2.6 5.2 47
0.71 0.16 2.6 5.2 52
0.63 0.12 3.0 5.3 52
0.61 0.14 3.0 5.7 1
0.60 0.15 4.3 7.5 54
R2 Ni (mg kg1 FM) Co (mg kg1 FM) OLR (g ODM L1 d1) Biogas (NL d1) CH4 (%)
0.28 0.10 0.4 0 0
0.22 0.04 1.7 2.3 39
0.15 0.09 1.7 2.0 41
0.12 0.10 1.7 2.3 47
0.13 0.19 1.7 3.0 48
0.10 0.17 2.6 4.9 44
0.06 0.18 3.0 4.5 41
0.14 0.18 3.0 4.2 45
0.18 0.17 3.0 2.7 45
0.18 0.13 2.6 3.4 35
0.35 0.13 nf 0.6 36
R3 Ni (mg kg1 FM) Co (mg kg1 FM) OLR (g ODM L1 d1) Biogas (NL d1) CH4 (%)
0.28 0.10 0.4 0 0
0.20 0.04 1.7 2.1 42
0.15 0.09 1.7 1.6 39
0.13 0.10 1.7 2.9 47
0.14 0.19 1.7 3.4 48
0.09 0.17 2.6 4.3 44
0.06 0.16 3.0 3.7 42
0.12 0.18 3.0 3.6 30
0.31 0.16 nf 1.6 45
0.33 0.13 2.6 4.2 46
0.34 0.13 2.6 4.2 48
0.39 0.10 3.0 4.5 55
0.51 0.13 nf 2.8 58
0.65 0.13 4.3 8.0 55
R4 Ni (mg kg1 FM) Co (mg kg1 FM) OLR (g ODM L1 d1) Biogas (NL d1) CH4 (%)
0.28 0.10 0.4 0 0
0.21 0.04 1.7 1.8 35
0.60 0.04 1.7 1.7 32
0.55 0.02 1.7 1.9 46
0.70 0.02 1.7 2.4 45
0.63 0.05 2.6 4.8 42
0.75 0.06 3.0 5.7 36
0.78 0.07 3.0 5.4 45
0.74 0.06 3.0 4.5 47
0.59 0.04 2.6 4.3 47
0.55 0.04 2.6 5.5 46
0.56 0.03 3.0 5.1 51
0.53 0.06 3.0 4.6 47
0.42 0.07 4.3 5.8 46
R5 Ni (mg kg1 FM) Co (mg kg1 FM) OLR (g ODM L1 d1) Biogas (NL day1) CH4 (%)
0.28 0.10 0.4 0 0
0.22 0.04 1.7 2.3 33
0.59 0.03 1.7 2.2 32
0.56 0.02 1.7 1.9 44
0.69 0.02 1.7 2.4 46
0.61 0.05 2.6 5.8 43
0.79 0.06 3.0 4.9 48
0.82 0.07 3.0 5.7 50
0.79 0.06 3.0 5.5 46
0.61 0.05 2.6 4.4 48
0.62 0.04 2.6 4.5 49
0.58 0.04 3.0 3.8 51
0.54 0.07 3.0 5.4 50
0.51 0.07 4.3 7.0 51
Values in italilc indicate final Ni/Co concentrations after addition of trace elements measured on this day. Operation of R2 stopped after day 168. FM ¼ fresh mass. ODM ¼ organic dry mass. NL ¼ norm litre. nf ¼ no substrate feeding.
HPLC a Hewlett Packard HPLC System 1100 was used. The system was equipped with a TRANSGENOMIC, ICSep ION-300, ¨ FFLER, ICSep IONArt Nr. ICE-99-9850 column and WAGNER LO 300, Art Nr. CH0-0800 column respectively. As pre-column TRANSGENOMIC, GC-801/C, Art Nr. ICE-99-2364 and WAGNER ¨ FFLER, Interaction Replacement Cart. GC-801/C, 24 4, LO 0 mm, Art Nr. CH0-0831 respectively were used. Measurements were carried out at the following operating conditions: 0.005 M H2SO4 as mobile phase and a flow rate of 1 mL min1, injection volume 40 mL, column temperature 42 C. For reporting of chromatograms special software was used (HP chemstation).
3.
Results and discussion
3.1. Effect of nickel and cobalt limitation on anaerobic fermentation of a defined model substrate In a first stage, all reactors were operated with a model substrate for maize silage. Further, for a certain time period
the reactors R2 and R3 were limited by nickel and reactors R4 and R5 by cobalt, respectively. On the other hand, starting at operation day 40, nickel was added to reactor R1 with increasing concentrations up to a maximum of 0.88 mg kg1 fresh mass (FM). In contrast, in R2 and R3 the nickel concentrations were reduced from the initial value of 0.28 mg kg1 FM to 0.06 mg kg1 FM at day 133 (Table 2). During this period the cobalt concentration was adjusted from 0.03 mg kg1 FM (R1) and 0.04 mg (R2 and R3), respectively, to 0.19 mg kg1 FM in R1, R2 and R3. Concomitantly, the organic loading rate (OLR) of the defined model substrate was increased stepwise in all five reactors to 3 g ODM L1 d1 (Table 2). Surprisingly, during this period of fermentation no significant differences in biogas production and methane yield were detected between R1 supplied with nickel and cobalt and R2 and R3 which were limited by nickel (Table 2). Moreover it seems that amounts of nickel available in R2 and R3 are sufficient for a stable fermentation of the defined model substrate up to a loading rate of 2.6 g ODM L1 d1 while a 5 fold higher nickel concentration in R1 even slightly decreased the biogas yield (Table 3). These results were also reflected by
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 8 1 e7 8 7
Table 3 e Comparison of the theoretical biogas yield to the effective biogas yield during anaerobic digestion of a maize model substrate with different concentrations of nickel and cobalt. Operation (day)
1
40
42
70
86
107
133
135
149
163
168
194
228
250
0.4
1.7
1.7
1.7
1.7
2.6
3.0
3.0
3.0
2.6
2.6
3.0
3.0
4.3
Calculated biogas yield (NL L FM1 d1)
0.3
1.3
1.3
1.3
1.3
2.0
2.3
2.3
2.3
2.0
2.0
2.3
2.3
3.2
Effective biogas yield R1 (NL L FM1 d1) (%)
0 0
0.8 63
0.6 47
0.8 63
0.8 66
1.5 75
1.7 75
1.8 79
1.0 44
1.7 88
1.7 88
1.8 78
1.9 84
2.5 77
R2 (NL L FM1 d1) (%)
0 0
0.8 61
0.7 53
0.8 61
1.0 79
1.6 83
1.5 66
1.4 62
0.9 40
1.1 58
0.2 10
R3 (NL L FM1 d1) (%)
0 0
0.7 55
0.5 42
1.0 76
1.1 89
1.4 73
1.2 54
1.2 53
0.5 24
1.4 71
1.4 71
1.5 66
0.9 41
2.7 82
R4 (NL L FM1 d1) (%)
0 0
0.6 47
0.6 45
0.6 50
0.8 63
1.6 81
1.9 84
1.8 79
1.5 66
1.4 73
1.8 93
1.7 75
1.5 68
1.9 60
R5 (NL L FM1 d1) (%)
0 0
0.8 61
0.7 58
0.6 50
0.8 63
1.9 98
1.6 72
1.9 84
1.8 81
1.5 75
1.5 76
1.3 56
1.8 79
2.3 72
OLR (g ODM L
1
1
d )
Note: calculated biogas yield are theoretical biogas yield corresponded to ODM of substrate and was calculated according to Buswell and Mueller (1952) and Boyle (1976). FM ¼ fresh mass. ODM ¼ organic dry mass. NL ¼ norm litre.
The best biogas performance with OLRs up to 4.3 g ODM L1 d1 was obtained with a nickel concentration of 0.6 mg kg1 FM in R1 as well as R3 (Tables 2 and 3). This result was in agreement with findings in a previous study where batch digestion tests of a similar maize model substrate and nickel were conducted (Pobeheim et al., 2010).
8,00
4,5
7,75
4,0 3,5
7,50
-1
2,5
ODM L d
-1
3,0
7,25
pH
a comparison of the theoretical and the effective biogas yield (Table 3). In a study of Murray and Berg (1981) it was also described that already a nickel addition of 6 103 mg L1 to a biowaste digester had a significant influence on enhancement of biogas production. However, considering the fact that the model substrate did not contain the studied trace elements, a limitation should also occur at lower loading rates after prolonged incubation. A decrease in process stability and biogas production was observed after increasing the OLR to 3 g ODM L1 d1. Consequently, the nickel concentration in R2 and R3 was adjusted to 0.35 mg kg1 FM (Table 2) while nickel concentrations in R1 were reduced constantly from day 135 (0.83 mg kg1 FM) to day 250 down to 0.6 mg kg1 FM. Nevertheless, R2 showed a rapid decrease of biogas production starting at day 160 with a drop of the biogas yield down to 10% at day 168 (Table 3). Results regarding biogas production from R3 between operation day 135 and day 250 indicate an increase in process stability due to the increase of the nickel dosage up to 0.5 mg kg1 FM. Interestingly, during this increase of the nickel concentration up to 0.5 mg kg1 FM the addition of the defined model substrate had to be suspended twice for several days (around day 149 and 228, Table 2). Furthermore, up to day 228 in reactor R3 an approximately 20% lower substrate conversion to biogas were observed compared to control reactor R1 (Table 3). However, these findings together with other parameters indicating process instability as discussed below further suggest the importance of nickel addition right from the beginning of the fermentation process.
7,00
R1
2,0
R2
6,75 6,50
R3
1,5
R4
1,0
R5
6,25
OLR
6,00
0,5 0,0
0
30
60
90
120
150
180
210
240
day
Fig. 1 e pH profile and change of the organic loading rate (OLR) during anaerobic digestion of a maize model substrate with different concentrations of nickel and cobalt. The dots on the interrupted OLR line show the gradual substrate increase and not every feeding event. Arrows at day 133 and 149 indicate a change in nickel concentrations at R2 and R3.
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R2
3,0
R5
2,10
2,5
OLR
1,80 1,50
2,0
1,20
1,5
0,90
-1
3,5
R3 R4
2,40
-1
2,70
ODM L d
3,00
TVA/TIC
4,0
R1
1,0
0,60 0,5
0,30 0,00
0,0 0
30
60
90
120
150
180
210
240
day
Fig. 2 e TVA/TIC (total volatile acids/total inorganic carbonate) values during anaerobic digestion of a maize model substrate with different concentrations of nickel and cobalt.
The reactors R4 and R5 were limited by cobalt. As reported in Table 2 measurements of cobalt in R4 and R5 at day 70 and 86 lead to a cobalt concentration of 0.02 mg kg1 FM. During this operation period the constantly high TVA/TIC and low pH-values indicated process disturbance (Figs. 1 and 2). For instance, in a study of Jarvis et al. (1997) with grasseclover silage as substrate a strong influence of cobalt was reported with a critical total concentration of about 0.02 mg kg1 FM. Moreover, Kida et al. (2001) described in their work that both Ni2þ and Co2þ were essential for the methane-producing reactions by increases of coenzymes F430 and corrinoids. These results suggest that the influence of nickel especially in combination with cobalt on anaerobic fermentation could not be considered separately. Upon adjustment of the cobalt concentration to 0.05 mg kg1 FM in R4 and R5 at an OLR of 2.6 g ODM L1 d1 (day 107), in both reactors the biogas production increased up to 81% at R4 and even 98% at R5 (Table 3). An increase of cobalt up to 0.07 mg kg1 FM actually did not further enhance biogas production in R4 and R5 (Table 2). However, with cobalt concentrations between 0.04 and 0.07 mg kg1 FM (operation day 135e250) and OLRs between 2.6 and 4.3 g ODM L1 d1 R4 and R5 produced biogas about 75%. Moreover, a decreased conversion rate of R4 till day 250 (Table 3) in relation to R5 could be attributed to the lower nickel concentration of 0.42 mg kg1 FM (Table 2). Furthermore conversion rates of 98% (day 107) in R5 and 93% (day 168) in R4 were detected with nickel and cobalt concentrations about 0.6 mg kg1 FM and 0.05 mg kg1 FM, respectively (Tables 2 and 3).
a decrease of the concentration of organic acids (Fig. 2). However, a similar but less pronounced change of these parameters after exclusive addition of the trace element solution was also seen for the other reactors. Nickel limitation in reactor R2 and R3 to 0.06 mg kg1 FM at day 133 (Fig. 1, Table 2) and the parallel substrate increasing at a higher loading rate of 3.0 g ODM L1 d1 led to a drastic process instability (Figs. 1 and 2). This finally resulted in accumulation of organic acids with concomitant decrease of substrate conversion (Fig. 3) requiring termination of the fermentation in R2 at day 168. Reactor R3 displayed also process instability and decreased substrate degradation (Fig. 3) until operation day 149 (Fig. 1) when Ni concentrations reached 0.31 mg kg1 FM (Table 2). Moreover, a gradual increase of the nickel concentration to 0.65 mg kg1 FM till day 228 offered a further substrate increase to 4.3 g ODM L1 d1 (Table 2). Due to an increase of cobalt to 0.05 mg kg1 FM in R4 and R5 at day 107 (Table 2) the accumulated organic acids were metabolized immediately (Fig. 2) with a strong increase of biogas production (Table 3) and improved process stability until stop of the anaerobic fermentation after 250 days. The pH, TVA/TIC and ODM profiles during digestion of the maize model substrate clearly indicate that appropriate amounts of nickel and cobalt enhance sustainable process stability upon increasing loading rates apart from conversion of the substrate into intermediates and finally biogas. In a previous study of Gonzalez-Gil et al. (1999) it was reported that a continuous addition of nickel and cobalt to an anaerobic bioreactor increased bioavailability and process stability in contrast to a step-by-step donation of these elements. This is also reflected by the profile of acetic and propionic acid during fermentation (Fig. 4). After addition of nickel and cobalt at day 40 to R1 in concentrations up to approximately 0.6 and 0.1 mg kg1 FM, respectively; acetic and propionic acid were immediately metabolized (Fig. 4a). However, upon adjustment of nickel to a level of 0.88 mg kg1 FM and an increased loading rate of 3 g ODM L1 d1 at operation day 133 acetic and propionic acid started to increase indicating no improvement of fermentation rate with nickel concentrations as mentioned. Consequently, readjustment to a lower concentration of 0.75 mg kg1 FM measured at day 163 4,50
R1 R2 R3 R4 R5
4,00 3,50
ODM (%)
4,5 3,30
3,00 2,50 2,00
3.2. Study on process stability during anaerobic fermentation of the defined model substrate
1,50 1,00 10
The monitoring of the continuous digestion of maize model substrate additionally included the measurement of the pH value and TVA/TIC (total volatile acids/total inorganic carbonate). An addition to the trace element solution and extra dosage of nickel and cobalt to R1 at operation day 40 (Table 2) induced a subsequent increase of the pH (Fig. 1) and
30
50
70
90 110 130 150 170 190 210 230 250
day
Fig. 3 e Organic dry matter of R1eR5 during anaerobic digestion of a maize model substrate with different concentrations of nickel and cobalt. Arrow shows the start of the trace element solution (day 40) at all reactors.
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a
6000 acetic acid R1
5000
-1
mg kg FM
propionic acid R1
4000 3000 2000 1000 0 0
20
40
60
80 100 120 140 160 180 200 220 240
day
b 11000 10000
acetic acid R3 propionic acid R3
9000
acetic acid R2 propionic acid R2
-1
m g kg FM
8000 7000 6000 5000 4000 3000 2000 1000
250 allowed also substrate feeding of 4.3 g ODM L1 d1 (Fig. 4b) with a conversion rate of 82% (Table 3). The limitation of cobalt in reactor R4 and R5 to 0.02 mg kg1 FM between operation day 42 and 107 did also decrease the conversion of acetic and propionic acid to biogas (Fig. 4c). Moreover, in reactor R4 a further increase of propionic acid upon approximately 2500 mg kg1 FM with a measured cobalt concentration of 0.03 mg kg1 FM could be detected between operation day 180 and 230. However, results from R4 and R5 indicated that limitation of cobalt below 0.03 mg kg1 FM led to process disturbance (Fig. 4c, Tables 2 and 3). Summarizing the results of this study, the dramatic decrease of biogas production together with arising process instability seen in our lab scale experiments operated with the defined model substrate for maize can be amongst others attributed to limitations by nickel and cobalt. Moreover, previous batch scale investigation indicated a negative effect of Ni/Co limitations on biogas production (Jansen et al., 2007; Murray and Berg, 1981). In the presented data the impact of Ni and Co on process stability could be demonstrated as well. Furthermore, a stable pH and low complexation are related to bioavailability of these trace elements (Mosey et al., 1971).
0 0
c
20
40
60
80 100 120 140 160 180 200 220 240
day 7000
acetic acid R4 propionic acid R4 acetic acid R5 propionic acid R5
6000
-1
mg kg FM
5000 4000 3000 2000 1000 0 0
20
40
60
80 100 120 140 160 180 200 220 240
day
Fig. 4 e Effect of variation of nickel and cobalt concentration on the formation of acetic and propionic acid. The first graph (a) displays results from reactor R1 with full Ni/Co dosage. The second graph (b) shows results from reactor R2 and R3 with variation in nickel concentration and graph (c) represents data from reactor R4 and R5 with variation in cobalt concentration. Arrows indicate enhancement of the nickel concentration (a, b) and cobalt concentration (c), see also Table 2.
supported again the conversion of these intermediates culminate in an increased feeding rate of 4.3 g ODM L1 d1 with nickel concentration of 0.6 mg kg1 FM at the end of fermentation trial (Fig. 4a). However, in general the limitation of nickel destabilized process performance and conversion of intermediates like acetic and propionic acid as shown in Fig. 4b. In comparison to reactor R1, in R2 and R3 accumulation of acetic acid increased in a peak at day 133 with a measured nickel concentration of 0.06 mg kg1 FM (Fig. 4a and b). The gradual nickel increase (Table 2) up to 0.65 mg kg1 FM till day
4.
Conclusion
In anaerobic semi-continuous fermentations of a defined model substrate for maize, limitation of nickel as well as cobalt showed a negative impact on process stability and biogas production. Especially nickel concentrations below 0.1 mg kg1 FM at OLRs above 2.6 g ODM L1 d1 and general cobalt concentrations below 0.02 mg kg1 FM enhanced accumulation of organic acids and lead to a strong decrease of the pH value with a concomitant decrease of methanogenic activity. With nickel and cobalt levels around 0.6 and 0.05 mg kg1 FM, respectively, stable fermentation was possible up to an OLR of 4.3 g ODM L1 d1. An increase of nickel and cobalt beyond these concentrations did not further enhance biogas production. Concluding these findings, organic loading rates can be increased with an appropriate addition of a selected trace element solution, nickel and cobalt, respectively. Nevertheless, to evaluate this lab scale experiments following tests in fullscale biogas plants should be conducted. Also, investigations should be extended to other elements which could be limiting in certain substrates, the effect of which may, however, be interdependent.
references
Boyle, W.C., 1976. Energy recovery from sanitary landfills e a review. In: Schlegel, H.G., Barnea, S. (Eds.), Microbial Energy Conversion. Pergamon Press, Oxford. Burchard, C.H., Groche, D., Zerres, H.P., 2001. ATV Handbuch einfacher Messungen und Untersuchungen auf Kla¨rwerken, 10. Auflage. Hirthammer Verlag, Mu¨nchen. Buswell, A.M., Mueller, H.F., 1952. Mechanism of methane formation. Ind. Eng. Chem. 44, 550e552.
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Ferry, J.G., 1999. Enzymology of one-carbon metabolism in methanogenic pathways. FEMS Microbiol. Rev. 23, 13e38. Friedman, H.C., Klein, A., Thauer, R.K., 1990. Structure and function of the nickel porphinoid, coenzyme F430, and its enzyme, methyl coenzyme M reductase. FEMS Microbiol. Rev. 87, 339e348. Gonzalez-Gil, G., Kleerebezem, R., Lettinga, G., 1999. Effects of nickel and cobalt on kinetics of methanol conversion by methanogenic sludge as assessed by on-line CH4 monitoring. Appl. Environ. Microbiol. 65, 1789e1793. Goodwin, J.A.S., Wase, D.A.J., Forster, C.F., 1990. Effects of nutrient limitation on the anaerobic upflow sludge blanket reactor. Enzyme Microb. Technol. 12, 877e884. Hinken, L., Urban, I., Haun, E., Urban, I., Weichgrebe, D., Rosenwinkel, K.H., 2008. The valuation of malnutrition in the mono-digestion of maize silage by anaerobic batch tests. Water Sci. Technol. 58 (7), 1453e1459. Jansen, S., Gonzalez-Gil, G., van Leuuwen, H.P., 2007. The impact of Co and Ni speciation on methanogenesis in sulfidic media e biouptake versus metal dissolution. Enzym. Microb. Technol. 40, 823e830. Jarvis, A., Nordberg, A., Jarlsvik, T., Mathisen, B., Svensson, B.H., 1997. Improvement of a grasseclover silage-fed biogas process by the addition of cobalt. Biomass Bioenergy 12, 453e460.
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Kida, K., Shigematsu, T., Kijima, J., Numaguchi, M., Mochinaga, Y., Abe, N., Morimura, S., 2001. Influence of Ni2þ and Co2þ on methanogenic activity and the amounts of coenzymes involved in methanogenesis. J. Biosci. Bioeng. 6, 590e595. Mosey, F.E., Swanwick, J.D., Hughes, D.A., 1971. Factors affecting the availability of heavy metals to inhibit anaerobic digestion. Water Pollut. Contr 70, 668e680. Murray, W.D., Berg, L., 1981. Effects of nickel, cobalt, and molybdenum on performance of methanogenic fixed-film reactors. Appl. Environ. Microbiol. 42, 502e505. Oleszkiewicz, J.A., Sharma, V.K., 1990. Stimulation and inhibition of anaerobic processes by heavy metals. Biol. Wastes 31, 45e47. Pobeheim, H., Munk, B., Johansson, J., Guebitz, G.M., 2010. Influence of trace elements on methane formation from a synthetic model substrate for maize silage. Biores. Technol. 101, 836e839. Takashima, M., Speece, R.E., 1989. Mineral nutrient requirements for high-rate methane fermentation of acetate at low SRT. J. Water Pollut. Control Fed. 61, 1645e1650. Thauer, R.K., 1998. Biochemistry of methanogenesis: a tribute to Majory Stephenson. Microbiology 144, 2377e2406. Zandvoort, M.H., Hullebusch, E.D., Fermoso, F.G., Lens, P.N.L., 2006. Trace metals in anaerobic granular sludge reactors: bioavailability and dosing strategies. Eng. Life Sci. 6, 293e301.
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Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Antimicrobial resistance of heterotrophic bacteria in sewage-contaminated rivers Tamara Garcia-Armisen a,b,*, Ken Vercammen b, Julien Passerat a, David Triest b, Pierre Servais a, Pierre Cornelis b a
Ecologie des Syste`mes Aquatiques, Universite´ Libre de Bruxelles, Campus de la Plaine, CP 221, Bd du Triomphe, 1050 Brussels, Belgium Microbial Interactions, Department of Molecular and Cellular Interactions-VIB, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium b
article info
abstract
Article history:
Sewage-contaminated rivers are ecosystems deeply disturbed by human activity due to the
Received 31 March 2010
release of heavy metals, organic pollutants and pharmaceuticals as well as faecal and
Received in revised form
pathogenic micro-organisms, which coexist with the autochthonous microbial population.
16 August 2010
In this study, we compared the percentage of resistance in faecal and heterotrophic
Accepted 4 September 2010
bacteria in rivers with different degrees of sewage pollution. As a matter of fact, no
Available online 15 September 2010
correlation was found neither between the degree of sewage pollution and the percentage of antimicrobial resistant heterotrophic bacteria nor between the number of resistant
Keywords:
faecal bacteria and that of resistant heterotrophic bacteria. Most of the resistant isolates
Sewage-polluted river
from the Zenne river downstream Brussels were multi-resistant and the resistance
Antimicrobial
patterns were similar among the strains of each phylogenetic group. The total microbial
Antimicrobial resistance
community in this polluted river (as evaluated through a 16S rRNA gene clone library
16S clone library
analysis) appeared to be dominated by the phyla Proteobacteria and Bacteroidetes while
Bacterial community composition
the phylum TM7 was the third most represented. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
During the past sixty years, antimicrobials were extensively used as growth promoters in breeding practices as well as in human and veterinary medicine. Nevertheless concerns about the use and disposal of these pharmaceuticals have been rising during the past decade (Kummerer, 2003) because of their impact on both human health and for the environment. Killing or growth inhibition of constituents of the native bacterial community by antimicrobials is generally detrimental to the ecosystem since this community plays key roles in biogeochemical processes. It has been shown for example that some antimicrobials can inhibit important microbial
processes as denitrification (Costanzo et al., 2005) or primary production by cyanobacteria (Halling-Sorensen et al., 1998). Drug concentrations sufficiently high to represent an ecological threat have already been reported in different rivers worldwide (Managaki et al., 2007; Baquero et al., 2008 and Tamtam et al., 2008). Their presence in the environment could also be linked to the spread of resistance genes among environmental bacteria (Alonso et al., 2001; Baquero et al., 2008 and Martinez, 2009), especially among emergent pathogens, which represent a major challenge for public health in the modern world (Sharma et al., 2003). Additionally, antimicrobial resistance (AR) determinants may be considered as a form of pollution (Martinez, 2009)
* Corresponding author. Ecologie des Syste`mes Aquatiques, Universite´ Libre de Bruxelles, Campus de la Plaine, CP 221, Bd du Triomphe, 1050 Brussels, Belgium. Tel.: þ322 650 29 89; fax: þ322 650 59 93. E-mail address:
[email protected] (T. Garcia-Armisen). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.003
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 8 8 e7 9 6
when they are introduced into the environment by the release of faecal bacteria that have been exposed to high levels of antimicrobials in the human or animal digestive track (Alonso et al., 2001). An extensive literature describes and analyses the resistance pattern of faecal bacteria, mainly Escherichia coli and enterococci, in aquatic environments (Witte, 2000; Reinthaler et al., 2003; Hamelin et al., 2007; Kimiran-Erdem et al., 2007; Laroche et al., 2009 and Servais and Passerat, 2009). Most of these authors consider that faecal bacteria released by wastewaters (treated or not) could play a key role in AR determinants dissemination. Indeed, in sewagecontaminated rivers, the autochthonous microbiota are in contact with potentially multi-resistant faecal and pathogenic micro-organisms, with heavy metals from industry, and with pharmaceuticals (Baquero et al., 2008). Horizontal gene transfer as well as cross and co-selection between heavy metal and antibiotic resistance (Baker-Austin et al., 2006) within this complex population represent a health risk. The transfer of AR determinants could occur in two directions: either pathogenic strains could acquire resistance genes from autochthonous bacteria, either AR genes present in faecal bacteria could be spread over to the autochthonous bacterial community (Linares et al., 2006; Fajardo et al., 2008; Fajardo and Martinez, 2008 and Aminov, 2009). In this study we addressed four main questions: 1) Is there any relationship between the degree of sewage pollution of a river and the occurrence of antibiotic resistant heterotrophic bacteria? 2) Is there any quantitative correlation between the level of faecal bacteria resistance and that of heterotrophic bacteria in sewage-contaminated rivers? 3) What is the phylogenetic composition of the AR heterotrophic bacteria and what are their patterns of resistance? 4) Which are the more important phylotypes composing the bacterial community in sewage-polluted rivers? In order to answer the first two questions, rivers characterised by different levels of recent sewage pollution were sampled; the degree of pollution was estimated by the concentration of E. coli taken as an indicator of faecal contamination. The proportion of AR E. coli was calculated as well as the proportion of AR heterotrophic bacteria. For the other two questions, a sewage-polluted urban river was sampled and AR heterotrophic strains were isolated; the 16S rRNA gene of these isolates was sequenced and their resistance pattern to 9 antibiotics was analysed. In parallel the bacterial community composition was explored using a culture-independent method (16S rRNA gene clone library sequencing).
2.
Materials and methods
2.1.
Sampling sites
The occurrence of AR E. coli and AR heterotrophic bacteria was measured from 27 water samples collected between September 2008 and December 2009 in rivers from two drainage networks: the Seine River (France) and the Scheldt River (Belgium) in both dry and wet weather conditions.
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Samples were harvested from various rivers in order to cover a large range of sewage pollution level. Resistant heterotrophic bacterial strains were isolated from a sample collected in the Zenne River downstream Brussels (Belgium) in February 2009 and two samples collected in November 2008 and February 2009, were used for the analysis of the bacterial community composition in this site. The Zenne is a small size river (average discharge 4 m3 s1) from the Scheldt drainage network, receiving in the Brussels area effluents from two large wastewater treatment plants (total capacity of 1.4 million inhabitant-equivalents) and some untreated wastewater during combined sewer overflows. This river is also known for its historically high level of pollution by heavy metals (Baeyens et al., 2007), due to industrial activities in the watershed. All samples were collected in sterile 2 L bottles, kept at 4 C and analysed within 12 h.
2.2. Quantitative analysis of AR occurrences in E. coli and heterotrophic bacteria Resistances to amoxicillin (AMX), tetracycline (TE) and nalidixic acid (NA) were tested in parallel for E. coli and heterotrophic bacteria. These antibiotics were chosen because they belong to three different families and have different mechanisms of action. For that purpose, E. coli was grown 24 h at 36 C on Chromocult Coliform Agar (Merck KGaA, Darmstadt, Germany) and heterotrophic bacteria were grown 7 days at 20 C on R2A agar (Merck KGaA, Darmstadt, Germany). Media were used as such or supplemented with one of the three antibiotics: AMX (4 mg ml1), TE (4 mg ml1) or NA (8 mg ml1) (Sigma Chemical Company, St. Louis, MO). These concentrations are the breakpoint concentrations established for E. coli by the French committee for antimicrobial standards (Comite´ de l’Antibiogramme de la Socie´te´ Franc¸aise de Microbiologie). Three ten-fold serial dilutions were filtered (or spread) for each sample in order to obtain proper counts for at least one of them. Triplicates were made for each volume filtered or dilution spread. Results are expressed in number of bacteria per sample volume independently of the volume analysed. The ratio of the colony counts on the antimicrobial-supplemented medium to the colony counts on the un-supplemented medium gives an estimate of the proportion of AR E. coli or heterotrophic bacteria in the sample.
2.3. Analysis of antibiotic resistance patterns and identification of heterotrophic bacteria isolated from a sewage-polluted river Briefly, 100 ml of water sample, pre-filtered through a 1.2 mm porosity glass-fibre filter, were plated on 20 different media using two basic media: R2A agar medium and Nutrient Agar Diluted 200-fold (NAD), a very poor medium suitable for the development of slow-growing oligotrophic bacteria (Hiroyuki and Tsutomu, 1983). For each medium duplicates were prepared containing a single selective agent, including 5 heavy metals (HgCl2 10 mg ml1, CdCl2 100 mg ml1, ZnCl2 100 mg ml1 and K2CrO4 100 mg ml1) and 5 antibiotics (chloramphenicol [CM] 25 mg ml1, ampicillin [AMP] 50 mg ml1, tetracyclin [TE] 150 mg ml1, kanamycin [KM] 150 mg ml1 and
790
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 8 8 e7 9 6
streptomycin [STR] 250 mg ml1) according to De Souza et al. (2006). These concentrations, significantly higher than those advised by the French committee for antimicrobial standards, were used in order to isolate bacteria presenting a resistance mechanism and not only tolerance. The plates were incubated at 20 C for 7 days for the R2A and 15e21 days for the NAD and five resistant colonies per selective agent were selected for isolation using the streaking procedure. To maximise the chances of recovery from the master plates in sub-culturing, different media were tested without antibiotics: R2A, NAD, Casamino Acids (CAA) and Luria Bertani (LB). Isolated bacteria surviving sub-culturing were further used as template for 16S rDNA amplification. Briefly, a small amount of bacterial cells was transferred into an Eppendorf tube containing 50 ml of sterile water. The tubes were subsequently frozen at 80 C for 10 min and heated 5 min at 100 C. This procedure was repeated twice. The resulting lysate was used directly for 16S rRNA gene amplification as described in Section 2.4. PCR products were sent for bidirectional sequencing using the same primers used for the amplification. The sequences were assembled and the full-length 16S rRNA gene sequence was used for phylogenetic identification. The BLAST (Basic Local Alignment Search Tool) program was used to search in the GenBank database for the closest known relatives. The identified bacteria were tested for their resistance to 9 antibiotics using the disc diffusion method as described elsewhere (Miller et al., 2003). The antibiotics disks (BD Benelux N.V. Dendermonde), were selected to cover different classes of antibiotics, each having a different mechanism of action. The concentrations used were: chloramphenicol [CM] 30 mg, erythromycin [ERY] 15 mg, gentamicin [GEN] 15 mg, meropenem [MER] 10 mg, aztreonam [AZT] 30 mg, tetracyclin [TE] 30 mg, amoxicillin [AMX] 30 mg, ofloxacin [OFL] 5 mg and ceftazidime [CEF] 30 mg. Bacteria were classified as resistant to an antibiotic on the basis of a halo of 2 mm around each disc according to Boon and Cattanach (1999). Based on the resistance pattern to the different antimicrobials, a distance matrix and UPGMA (Unweighted Pair Group Method with Arithmetic mean) were performed using the program NTSYS (Numerical Taxonomy Systematic). This approach allows the analysis of the similarity in the resistance patterns and to link them to certain taxonomic levels.
of Taq DNA polymerase (Qiagen, Hilden, Germany), 10 mM TriseHCl, 50 mM KCl, and 1.5 mM MgCl2. Amplified 16S rDNA fragments were cloned, using a TOPO TA Cloning Kit for sequencing (Invitrogen, Carlsbad, CA, USA), according to the manufacturer’s instructions. The cloned inserts were reamplified using the vector primers M13 forward and reverse (25 cycles at 94 C for 1 min, 48 C for 1 min and 72 C for 2 min) and PCR products were sequenced in a commercial sequencing facility (VIB Genetic Service Facility, Antwerp, Belgium) using the forward M13 primer.
2.5.
16S rDNA sequence analysis
Sequences were trimmed using the GREENGENES trim tool (http://greengenes.lbl.gov/trim). Putative chimeric sequences were detected and removed using a combination of three programs: MALLARD, BELLEROPHON and PINTAIL (Ashelford et al., 2006). The query sequences were assigned using the RDP classifier (http://rdp.cme.msu.edu/classifier/) with a bootstrap cut-off of 80% and rarefaction curves and nonparametric richness estimation (Chao2) were calculated by using MOTHUR (Schloss et al., 2009). Sequences were aligned using the SILVA database as reference. The distance matrix was calculated and served as an input for the estimation for the diversity using a furthest neighbour-clustering algorithm. The definition of operational taxonomic units (OTUs) was taken using a cut-off level of 97% identity.
3.
Results
3.1. Quantitative analysis of AR occurrence between E. coli and heterotrophic bacteria: impact of the sewage pollution level Fig. 1 shows the percentage of AR heterotrophic bacteria for the three tested antimicrobials (AMX, TE, NA) plotted against the abundance of E. coli ranging from 100 CFU (100 ml)1, corresponding to the cell densities encountered in forest or agricultural small streams, to 1,00,000 CFU (100 ml)1, which
2.4. Construction of a 16S rRNA gene clone library from metagenomic DNA Water samples (at least 1 L) were pre-filtered through a 1.2 mm porosity glass-fibre filter to remove particles and the largest eukaryotic organisms, and then filtered through a 0.2 mm porosity to retain bacteria. Genomic DNA was extracted using the “Metagenomic DNA isolation kit for water” from Epicentre (Madison, WI), according to manufacturer’s instructions. The universal primers 27f and 1492r (Frank et al., (2008)) were used to amplify bacterial 16S rRNA genes. PCR amplifications (20 cycles at 94 C for 1 min, 48 C for 1 min and 72 C for 2 min) were done in an Eppendorf thermocycler in a final volume of 50 ml, containing 5 ml of extracted DNA (approximately 500 ng), 0.2 mM of dNTPs, 0.4 mM of each primer, 1.25 IU
Fig. 1 e Percentages of resistant heterotrophic bacteria to amoxicillin (circles), nalidixic acid (triangles) and tetracycline (squares), plotted against the E. coli concentration in the river samples (in Log scale). The circled points correspond to samples collected in the Seine River downstream from Paris and in the Zenne River downstream from Brussels.
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corresponds to the number of cells found in sewage-polluted rivers. Considering E. coli as an indicator of the degree of recent sewage discharge, we didn’t find any relation between the level of sewage pollution and the proportion of AR in heterotrophic bacteria for any of the tested antimicrobial (AMX: R2 ¼ 0.051p ¼ 0.254; TE: R2 ¼ 0.096 p ¼ 0.114; NA: R2 ¼ 0.002 p ¼ 0.826). An Anova test suggested significant differences in the % of AR heterotrophic bacteria between the three tested antibiotics F (2, 78) ¼ 10.58, p < 0.0001. To precisely quantify these differences, a Tukey’s test of multiple comparisons was used; this test indicated the absence of any significant difference in the % of AR between AMX and NA but a highly significant difference ( p < 0.001) between both of them and TE. Indeed the percentage of TE resistant bacteria was always under 10%, except in three samples where it was higher than 20% (Fig. 1). In two of them (circled in Fig. 1), the percentage of resistance was high for the three tested antimicrobials (maximum values observed for TE and AMX). These samples corresponded to rivers heavily polluted by sewage (as estimated by the E. coli cell densities encountered), downstream from large cities, the Seine downstream Paris, and the Zenne downstream Brussels. Fig. 2 shows for each sample the percentage of AR heterotrophic bacteria plotted against the percentage of AR E. coli for the three antimicrobials. We didn’t observe any relation between the percentage of resistance of the two types of bacteria for any of the three tested antimicrobials (AMX: R2 ¼ 0.003 p ¼ 0.777; TE: R2 ¼ 0.103 p ¼ 0.103; NA: R2 ¼ 0.002 p ¼ 0.819).. An Anova test suggested a significant difference in the % of AR E. coli between any of the three antibiotics F (2, 78) ¼ 8.74, p < 0.0001. The Tukey’s test indicated that there was no significant difference between the % of AR to TE and NA, but a highly significant difference ( p < 0.001) between both of them and AMX.
3.2. Analysis of the AR pattern of heterotrophic bacteria isolates from a sewage-polluted river To establish the AR pattern of culturable bacteria present in a sewage-polluted river downstream from urban and industrial areas, samples from the Zenne River collected downstream
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Brussels were inoculated on agar media containing antimicrobials or heavy metals. Despite the fact that 80 colonies were initially selected for purification, only 41 survived repeated subculturing (supplementary material 1). The majority of them were those recovered from R2A agar, and only 8 strains were isolated from NAD agar. Three phyla and nine different orders were represented. The genus Pseudomonas was prevalent, representing 44% of the isolates. The other two dominating genera were Pedobacter and Flavobacterium. One isolate was an Actinomicetales Gram þ, Curtobacterium herbarum. Three of the four strains of Flavobacterium were isolated on media supplemented with Zn and all 6 strains of Pedobacter were selected on media supplemented with either KM or AMP. For Pseudomonas, we didn’t observe any special trend regarding the selective agents. Some of the identified isolates failed to grow in liquid media and thus to make a proper mat for the disc diffusion test. As a consequence, from the 41 isolates only 32 could be used to perform the multiple resistance pattern test (Table 1). All strains were resistant to at least two of the antimicrobials with 97% of the strains being resistant to at least 3 antimicrobials, 72% to 4, 47% to 6 and 22% to the 9 tested antimicrobials. Five out of the seven strains of this last group were Pseudomonas sp., the others being Pedobacter cryoconitis and Stenotrophomonas sp. The highest percentages of resistance were observed for aztreonam (84% of the strains) followed by erythromycin and chloramphenicol. The lowest percentages of resistance were observed for meropenem and ofloxacine with 31% in both cases. To compare the resistance pattern of different isolates, a phenogram was established using a distance matrix and UPGMA (Fig. 3). Similarities in the resistance patterns were found at high taxonomic levels. There were 4 major groups that clustered together. The first one comprises most of the Bacteroidetes with the exception of F2 (Sphingomonas sp.). The second group comprises a majority of the Pseudomonadales, plus three species from other orders (G5: Serratia; A8: Ochrobactrum and F6: Delftia). The third group was built up solely of Enterobacteriales, with the exception of G5 (Serratia) and the fourth group corresponded to the multi-resistant or highly resistant clade. This group contained strains from the Bacteroidetes, Pseudomonadales and one Stenotrophomonas (F7).
3.3. Bacterial community composition of a sewagepolluted river
Fig. 2 e Percentages of resistant heterotrophic bacteria to amoxicillin (circles), nalidixic acid (triangles) and tetracycline (squares), plotted against the percentages of resistant E. coli to the same antimicrobial in the same sample.
In order to analyse the total bacterial community in the Zenne River downstream Brussels, two 16S rRNA gene clone libraries were constructed. The PCR protocol was optimised according to Ishii and Fukui (2001) to minimise biases and to get a better picture of the relative abundance of the different phylotypes. A total of 113 clones were sequenced: 44 from the November 2008 sample and 69 from the February 2009 one. Using a cutoff level of 97% identity, a total of 67 different operational taxonomic units (OTUs) were detected. Among them, 28 corresponded to the November 2008 sample and 44 to the February 2009 sample while only 5 were common to both samples. The rarefaction curves built either with the independent or combined data from both libraries (supplementary material 2)
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Table 1 e Identification and resistance pattern of the 32 resistant isolates based on 16S rRNA gene sequence analysis using Megablast. Nine antimicrobials were used: chloramphenicol (CM), erythromycin (ERY), gentamicin (GEN), meropenem (MER), aztreonam (AZT), tetracycline (TE), amoxicillin (AMX), ofloxacin (OFL) and ceftazidime (CEF). D: resistant, s: sensitive. The number of antimicrobials to which each strain is resistant is indicated in the last column. The % of resistant strains to each antibiotic is indicated in the last line. Phylum/Order
CM
ERY
GEN
OFL
CEF
No. Resist
D6eFlavobacterium sp. (99) D7eFlavobacterium sp. (99,2) G4eFlavobacterium sp. (98,6) B1eEpilithonimonas lactis (96,6) Chryseobacterium sp. (96) C3eChryseobacterium ginsengisoli (99,2) B8eChryseobacterium sp. (98,8)
0 þ þ s
0 þ s þ
þ þ s þ
s s s s
þ þ þ þ
s s s s
s s s s
s s s s
þ þ þ s
3 5 3 3
þ
þ
þ
þ
þ
þ
þ
s
þ
8
þ
þ
þ
þ
þ
þ
s
s
þ
7
Bacteroidetes/ Sphingobacteriales
A6ePedobacter cryoconitis (98,7) E2ePedobacter alluvionis (99,5) E1ePedobacter westerhofensis (99,2) P. cryoconitis (99,2)
þ s s
þ s s
þ þ þ
þ þ s
þ þ þ
þ s s
þ s s
þ þ þ
þ þ s
9 5 3
Proteobact (b)/Burkholderiales
F6eDelftia acidovorans (99,9)
þ
þ
þ
s
þ
þ
s
s
s
5
Proteobact(a)/ Sphingomonadales
F2eSphingomonas sp. (99,3)
s
þ
þ
s
s
þ
s
s
s
3
Proteobact(a)/Rhizobiales
A8eOchrobactrum sp. (98,8)
þ
þ
s
s
þ
þ
s
s
s
4
Proteobact(g)/ Enterobacteriales
B5eEscherichia coli (99,5) E3eE. coli (99,7) G5eSerratia marcescens (99,7) F1eKlebsiella sp (99) F4eKlebsiella terrigena (99,5)
s þ þ s s
þ þ þ þ þ
þ s s s þ
s s s s s
s s þ s s
þ þ þ þ þ
s s þ s s
s s s s s
s s þ s s
3 3 6 2 3
Proteobact(g)/ Xanthomonadales
F7eStenotrophomonas sp. (99,8)
þ
þ
þ
þ
þ
þ
þ
þ
þ
9
Proteobact(g)/ Pseudomonadales
D3eAcinetobacter johnsonii (99,8) B6ePseudomonas sp. (98,5) C4ePseudomonas sp. (99,5) C6ePseudomonas sp. (99,9) C8ePseudomonas sp. (98,2) D4ePseudomonas sp. (98,8) E8ePseudomonas sp. (98,5) G7ePseudomonas sp. (98,6) G8ePseudomonas sp. (98,1) C7ePseudomonas veronii (99,6) H2ePseudomonas veronii (99,7) D5ePseudomonas aeruginosa (100) C5ePseudomonas putida (99,9) H1ePseudomonas veronii (99,6)
þ þ þ þ þ þ þ þ þ þ þ þ þ þ
þ þ þ þ þ þ þ þ þ þ þ þ þ þ
s þ þ s þ s þ þ þ þ þ þ s þ
s þ s s þ s þ s þ s þ s s s
þ þ þ þ þ þ þ þ þ þ þ s þ þ
þ þ þ þ þ þ þ þ þ þ þ þ þ þ
s þ þ þ þ þ þ s þ þ þ þ s þ
s þ s s þ s þ þ þ s þ s s s
þ þ þ s þ s þ s þ s þ þ þ s
5 9 7 5 9 5 9 6 9 6 9 6 5 6
75
84
69
31
84
78
47
31
56
Bacteroidetes/ Flavobacteriales
Strains code - Species assignment (score in %)
% of resistant strains
were not saturated and the upper level of the confidence interval of the diversity estimators Chao 2 suggested that more than 478 different species could be expected to be present in the February and 116 in the November sample, respectively. Most of the sequences were assigned to six phyla: Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria, SR1 and TM7. Some sequences corresponded to unclassified bacteria (Fig. 4). The most represented phylum was the Proteobacteria followed by Bacteroidetes (previously Cytophaga-Flavobacterium-Bacteroidetes [CFB] group). Interestingly, the candidate phylum TM7 was the third most represented suggesting that bacteria belonging to this candidate phylum could play important roles in the ecosystem. The distribution of the
MRP
AZT
TE
AMX
sequences in both samples (Fig. 4) did not show much variation, Proteobacteria, Bacteroidetes and TM7 being the most important phyla. In both samples, 16% of the sequences corresponded to unclassified bacteria. Firmicutes were however more abundant in the February (15%) than in the November sample (5%).
4.
Discussion
Sewage-polluted rivers have been reported by many authors as a potential reservoir of AR determinants (Auerbach et al., 2007; Baquero et al., 2008; Martinez, 2009, and Zhang et al., 2009). To test this hypothesis, small streams crossing fields
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 8 8 e7 9 6
793
Fig. 3 e Phenogram based on the similarity of the AR patterns of the isolates with indication of the phylum and the order of each isolate.
and forest as well as urban rivers receiving important amounts of treated and untreated wastewater were sampled. No correlation was found between the abundance of AR in heterotrophic bacteria and the level of recent sewage
pollution, as estimated by E. coli abundance for three antimicrobials. However data corresponding to the sampling stations located downstream important urban areas (Paris and Brussels; Fig. 1) may be singled out as the proportion of AR
Fig. 4 e Distribution of the sequences among the different phyla in the two 16S rRNA gene clones libraries (A: November 2008 and B: February 2009).
794
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heterotrophic bacteria was the highest for two of the three tested antibiotics. These values were nevertheless not different from what was reported in the literature (McArthur and Tuckfield, 2000; Baquero et al., 2008; Blasco et al., 2008). One possible explanation for this lack of correlation could be that E. coli numbers are not necessarily correlated with antibiotic concentrations or with the antibiotic resistance prevalence among faecal bacteria. Possible correlations between the percentage of AR E. coli and the percentage of AR among heterotrophic bacteria to the same antibiotic were investigated. No correlation was found between those two parameters for the three tested antibiotics although it is often assumed that transfer from resistant strains of human origin to native bacteria is an important process (Baquero et al., 2008). Moreover, the percentage of AR resistance of heterotrophic bacteria was significantly lower for TE while E. coli were significantly more resistant to AMX. To the best of our knowledge there are only very few researchers who checked simultaneously for antibiotics resistance patterns in faecal and heterotrophic bacteria. One report (Boon and Cattanach, 1999) described significantly higher level of resistance in native heterotrophic bacteria compared to E. coli, but didn’t find any correlation between the heterotrophic bacteria resistance and sewage pollution. Another study (Goni-Urriza et al., 2000) described an increase in acquired resistance after sewage discharge among isolates of two model bacteria: Aeromonas for waterborne bacteria and E. coli for faecal bacteria, but the authors were unable to demonstrate any exchange of resistance determinants between the two types of bacteria. Comparison of resistance patterns to different antimicrobials with other reports is however difficult, since the data depend on the antimicrobials tested, the concentrations and the methods used (isolates, counts, enrichments). To get some insight on the phylogenetic composition of AR heterotrophic bacteria and their resistance pattern and on the total bacterial community composition, the Zenne River downstream Brussels, considered here as a model of extreme sewage-polluted river, was sampled. Although minimal culture media designed to optimise the recovery of heterotrophic environmental bacteria were used, it was very difficult to recover the majority of bacteria in subcultures and purification procedures, confirming reports that cultivation remains a critical step to study environmental bacteria through isolates (Whitman et al., 1998). It is noticeable that many of our resistant isolates are phylogenetically related to opportunistic or nosocomial pathogens, such as Klebsiella terrigena and Serratia marcescens (Hejazi and Falkiner, 1997), Pseudomonas aeruginosa (Lyczak et al., 2000), Stenotrophomonas sp. (Ryan et al., 2009), Sphingomonas sp. (Heung et al., 2006), and species of the genus Flavobacterium and Chryseobacterium which are also associated with diseases in fishes and humans (Jooste and Hugo, 1999). Many of the strains isolated are also known to be tolerant and/ or able to degrade aromatic compounds: Pseudomonas sp. (Wang et al., 2009), Delftia acidovorans and Acidovorax sp. (Schulze et al., 1999), Sphingomonas sp. (San Miguel et al., 2009) and Stenotrophomonas sp. (Ryan et al., 2009). This is consistent with the historical industrial pollution of the Zenne River with inter alia heavy metals (Baeyens et al., 2007) which could have
represented a selective pressure for AR (Alonso et al., 2001; Stepanauskas et al., 2005 and Baker-Austin et al., 2006). The statistical analysis of the multi-resistance pattern of our isolates showed high correlations within each taxonomic group which is consistent with the fact that horizontal gene transfer is facilitated between phylogenetically related strains (Gogarten and Townsend, 2005 and Choi and Kim, 2007). This is also consistent with the absence of correlation between the AR of faecal and heterotrophic bacteria as reported above. On the other hand, the abundance of phylotypes related with emergent, opportunistic and nosocomial pathogens in the resistant strains highlights the health risk that sewagecontaminated rivers represent as a reactor of AR and pathogenesis evolution. Another important concern related to sewage pollution and antimicrobials release is its potential negative impact on the native microbial community. To test this impact, two 16 rRNA gene clone libraries from samples from the Zenne downstream Brussels were analysed and 67 phylotypes were observed in the 113 clones sequenced. Nevertheless the rarefaction curves were not saturated; the diversity should thus be even higher than what was reported by Kemp and Aller (2004) ie. 70 phylotypes per 100 clones sequenced. The bacterial community of the Zenne River reveals two major differences with those reported by Logue et al. (2008), Hahn (2006) and Cottrell et al. (2005) by the occurrence of an important number of sequences associated with the candidate phylum TM7 as well as by the abundance (16%) of unclassified bacteria. The TM7 candidate division is one of the several newly described bacterial divisions exclusively characterised by environmental sequence data (Hugenholtz et al., 2001). This phylum appears to be present in a variety of terrestrial and aquatic habitats but is also widely associated with bulking problems in the activated-sludge process used in wastewater treatment (Hugenholtz et al., 2001). The phylogenetic composition of the bacterial community described by the 16S rRNA gene clone libraries sequencing and the resistant strains enlighten the same groups at high phylogenetic level. The Proteobacteria were the most abundant, followed by Bacteroidetes. Nevertheless at the genus level, Flavobacterium is the only one to be well represented in both the 16S rRNA gene clone library and among the cultivable resistant isolates. A high proportion (44%) of the resistant isolates belonged to the Pseudomonas genus, while this genus was not detected in the culture independent analysis. This overrepresentation suggests that Pseudomonas sp. are more prone to become antimicrobial resistant and/or easier to cultivate than other genus. This is in accordance with numerous reports describing Pseudomonas as ubiquitous, opportunistic and resistant or even multi-resistant to antimicrobials within environmental bacteria (Pirnay et al., 2005, 2009; De Souza et al., 2006; Blasco et al., 2008). So far most of the reports dealing with the identification of AR heterotrophic bacteria were based on isolates and the same types of opportunistic bacteria were reported everywhere (De Souza et al., 2006). The few studies, using culture independent methods, consider the bacterial community as a whole (Stepanauskas et al., 2005) or search for resistance genes independently (Zhang et al., 2009). Our work suggests
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that one might expect that a culture independent approach allowing the phylogenetic identification of AR heterotrophic bacteria could reveal other groups or strains, and potentially novel resistance mechanisms.
5.
Conclusions
Sewage pollution and horizontal gene transfer between faecal and environmental bacteria are often mentioned as important processes for the dissemination of antimicrobial resistant determinants. However in this study, we established the absence of correlation between the resistance of heterotrophic bacteria to the three tested antibiotics and the recent sewage pollution as estimated by E. coli abundance. On the other hand, in the Zenne River downstream Brussels, almost all the recovered resistant isolates were multiresistant. The culture independent analysis of the total microbial community of the same sample showed that the phylotypes retrieved among the resistant isolates were not among the most abundant in the total community.
Acknowledgements This study was performed in the scope of different research projects:the project “Tracing and Integrated Modelling of Natural and Anthropogenic effects on Hydrosystems” (TIMOTHY), funded by the Belgian Federal Science Policy Office; the GESZ project funded by the IRSIB and the PIRENSeine program of the CNRS (France). T. Garcia-Armisen and P. Cornelis were supported by a GOA grant from the VUB. The authors also wish to thank the anonymous reviewers for their valuable comments and suggestions.
Appendix. Supplementary material Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.watres.2010.09.003.
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Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Tracking natural organic matter (NOM) in a drinking water treatment plant using fluorescence excitationeemission matrices and PARAFAC S.A. Baghoth a,b,*, S.K. Sharma a, G.L. Amy a,b,c a
UNESCO-IHE Institute for Water Education, Westvest 7, 2611 AX Delft, The Netherlands Delft University of Technology, Faculty of Civil Engineering and Geosciences, 2600 GA Delft, The Netherlands c King Abdallah University of Science and Technology, Jeddah, Saudi Arabia b
article info
abstract
Article history:
Natural organic matter (NOM) in water samples from a drinking water treatment train was
Received 19 May 2010
characterized using fluorescence excitation emission matrices (F-EEMs) and parallel factor
Received in revised form
analysis (PARAFAC). A seven component PARAFAC model was developed and validated
3 September 2010
using 147 F-EEMs of water samples from two full-scale water treatment plants. It was
Accepted 6 September 2010
found that the fluorescent components have spectral features similar to those previously
Available online 15 September 2010
extracted from F-EEMs of dissolved organic matter (DOM) from diverse aquatic environments. Five of these components are humic-like with a terrestrial, anthropogenic or
Keywords:
marine origin, while two are protein-like with fluorescence spectra similar to those of
Natural organic matter
tryptophan-like and tyrosine-like fluorophores. A correlation analysis was carried out for
Parallel factor analysis
samples of one treatment plant between the maximum fluorescence intensities (Fmax) of
Size exclusion chromatography
the seven PARAFAC components and NOM fractions (humics, building blocks, neutrals, biopolymers and low molecular weight acids) of the same sample obtained using liquid chromatography with organic carbon detection (LC-OCD). There were significant correlations ( p < 0.01) between sample DOC concentration, UVA254, and Fmax for the seven PARAFAC components and DOC concentrations of the LC-OCD fractions. Three of the humic-like components showed slightly better predictions of DOC and humic fraction concentrations than UVA254. Tryptophan-like and tyrosine-like components correlated positively with the biopolymer fraction. These results demonstrate that fluorescent components extracted from F-EEMs using PARAFAC could be related to previously defined NOM fractions and that they could provide an alternative tool for evaluating the removal of NOM fractions of interest during water treatment. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Natural organic matter (NOM) is a major concern in drinking water treatment since it causes adverse aesthetic qualities such
as colour, taste and odour. It negatively affects the performance of water treatment processes such as granular activated carbon filtration and membrane filtration and it may promote biogrowth in water distribution networks. Furthermore, it can
* Corresponding author. UNESCO-IHE Institute for Water Education, Westvest 7, 2611 AX Delft, The Netherlands. Tel.: þ31 15 2151826; fax: þ31 152122921. E-mail addresses:
[email protected],
[email protected] (S.A. Baghoth). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.005
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decrease the effectiveness of oxidants and disinfectants and produce undesirable disinfection by-products during oxidation processes (Owen et al., 1993). Thus, in order to minimise these undesirable effects, it is essential to limit the concentration of NOM in treated water. However, both the amount and composition of NOM affect the efficiency of its removal during water treatment. Therefore, in order to design and operate drinking water treatment processes for optimal NOM removal, a better understanding of its character is necessary. Many studies and reviews have been undertaken on the structural characterization of aquatic NOM (Frimmel, 1998; Abbt-Braun et al., 2004; Leenheer, 2004) but its structure and fate in drinking water treatment (individual processes and process trains) are still not fully understood. Because NOM may contain literally thousands of different chemical constituents, it is not realistic to characterize it on the basis of a thorough compilation of the individual compounds (Croue´ et al., 2000). Therefore, researchers have found it more practical to characterize NOM according to operationally defined chemical groups having similar properties. These groups are commonly isolated by methods which involve concentration and fractionation of bulk NOM (Frimmel and Abbt-Braun, 1999; Peuravuori et al., 2002). Whereas these methods provide valuable insight into the nature of NOM from diverse aquatic environments, they are often laborious, time consuming and may involve extensive pre-treatment of samples which could modify the NOM character. Thus, they are not commonly used for monitoring of NOM in drinking water treatment plants. A technique that has recently gained popularity for NOM characterization is fluorescence spectroscopy. It is a simple, relatively inexpensive and very sensitive tool that requires little or no sample pre-treatment. Since fluorescence is a function of structure and functional groups in molecules, it can be used to extract a lot of information about the chemical characteristics of NOM. Hudson et al. (2007) carried out an extensive review of the use of fluorescence spectroscopy to measure organic matter fluorescence and the application of dissolved organic matter (DOM) fluorescence in marine waters, freshwaters and wastewaters. They concluded that whereas the investigation of the source, character and reactions of marine organic matter is common, the investigation of the behaviour of organic matter in freshwaters still lags marine waters. Three-dimensional fluorescence excitation-emission matrix (F-EEM) spectroscopy has been used to distinguish different types and sources of dissolved organic carbon (DOC) in natural waters (Coble et al., 1990). It has been used to characterize DOC and to identify humic-like and protein-like fluorescent signals in water samples from different aquatic environments (Coble, 1996). In a study of sewage impacted rivers using F-EEM spectroscopy, protein fluorescence was found to be a better indicator of sewage pollution than ultraviolet (UV) absorbance at 254 nm (UVA254) (Baker, 2001). Various methods have been used to analyze F-EEMs. The traditional peak picking method involves the use of excitationeemission wavelength pairs to identify fluorophores based on the location of the maximum fluorescence intensity (Coble, 1996). The fluorescence intensity peaks are picked from a contour plot of F-EEMs and the excitation and emission
wavelength pairs at which they occur are used to characterize the NOM fluorescence. A review of recent literature demonstrated the potential of F-EEMs as a successful monitoring tool for recycled water systems (Henderson et al., 2009). Bieroza et al. (2009a) used F-EEMs for the assessment of TOC removal and organic matter characterization of surface waters and they found that F-EEMs could be used to predict TOC removal during surface water treatment by clarification. F-EEMs have also been used to distinguish between allochthonous and autochthonous DOC on the basis of a fluorescence index (FI), which is calculated as a ratio of fluorescence intensity at emission wavelength of 450 nm to that at 500 nm, obtained with an excitation wavelength of 370 nm (McKnight et al., 2001). More recent methods for the analysis of DOM EEMs include fluorescence regional integration (FRI) (Chen et al., 2003), multivariate data analysis (e.g. Principal Component Analysis, PCA, and Partial Least Squares regression, PLS) (Persson and Wedborg, 2001), and multi-way data analysis using parallel factor analysis (PARAFAC) (Stedmon et al., 2003). Peiris et al. (2010) used PCA of fluorescence EEMs to identify major foulants for ultrafiltration (UF) and nanofiltration (NF) membranes and to assess the performance of feed water pre-treatment by roughing filters and biofilters and the subsequent UF/NF membrane filters. Recently, F-EEMs have been used with selforganising maps for determination of NOM removal efficiency in water treatment works (Bieroza et al., 2009b). More detailed information about NOM character of water samples can be obtained by using F-EEMs and PARAFAC, a statistical method used to decompose multi-dimensional data. F-EEMs may be arranged in three dimensions comprising fluorescence measurements at several excitation and emission wavelengths for several samples and the resulting threeway data modelled with PARAFAC. In this way, individual components have been extracted some of which have been attributed to protein-like, fulvic-like or humic-like fractions of NOM. Although the method was first used for NOM characterization only recently (Stedmon et al., 2003), it has been used in several studies of DOM (Stedmon and Markager, 2005a; Hunt and Ohno, 2007; Yamashita and Jaffe, 2008). In a study of DOM from a wide variety of aquatic environments, F-EEMs and PARAFAC were used to identify thirteen components, seven of which were attributed to quinone-like fluorophores (Cory and McKnight, 2005). However, unlike all the other studies involving PARAFAC analysis of DOM fluorescence, which used oxidized materials only, Cory and McKnight (2005) used reduced and oxidized samples, thus resulting in the extraction of more components in their study. Another property which is important for understanding the physical and chemical characteristics of NOM is molecular size (MS) or molecular weight (MW). It influences the adsorption, bioavailability as well as other water treatment processes for the removal of NOM. Lower MW NOM molecules tend to be more hydrophilic and thus more biolabile, while higher MW NOM molecules tend to be more aromatic and more hydrophobic, and have higher affinity for adsorption. High performance size exclusion chromatography (HPSEC), which separates molecules according to molecular size or molecular weight, has been widely applied in characterization of NOM in aquatic environments (Chin et al., 1994; Her et al., 2003; Croue´, 2004). It has been shown to be very effective in following
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changes in the NOM distribution along drinking water treatment trains (Vuorio et al., 1998; Matilainen et al., 2002). HPSEC may be coupled with detectors such as UV, fluorescence or DOC detectors. An HPSEC system coupled with an organic carbon detector (LC-OCD) (Huber and Frimmel, 1994) has been used to fractionate NOM into five fractions: biopolymers (such as polysaccharides, polypeptides, proteins and amino sugars); humic substances (fulvic and humic acids); building blocks (hydrolysates of humic substances); low molecular weight (LMW) humic substances and acids; and low molecular weight neutrals (such as alcohols, aldehydes, ketones and amino acids). HPSEC has been used with online F-EEM to study fluorescence properties of NOM as a function of MS and polarity (Wu et al., 2003; Her et al., 2003). Allpike et al. (2005) used HPSEC with online DOC, UV and fluorescence detectors to compare the removal of different molecular weights of DOC in two water treatment processes. Whereas Allpike et al. (2005) and Her et al. (2003) used a single pair of ex/em wavelengths for the fluorescence measurements, Wu et al. (2003) used threedimensional F-EEM. In this study, the LC-OCD system used was coupled with online UV and DOC detectors and the F-EEMs were measured separately. As well as contributing to a better understanding of NOM, identification of fluorescent components using PARAFAC could be used to track the fate of problematic NOM fractions and to optimise the design and operation of drinking water treatment processes for their removal. Recently, fluorescence spectroscopy has been used for organic matter characterization and assessment of TOC removal in drinking water treatment (Bieroza et al., 2009a, 2010). However, they used F-EEMs alone, which were collected from fewer treatment processes, while this study uses F-EEMs and LC-OCD. Furthermore, they used fluorescence intensity peaks identified from the composite EEMs, while this study uses PARAFAC to identify fluorescence intensity peaks for individual fluorescent components. The main objective of this study was to characterize NOM in samples from a drinking water treatment train using F-EEMs and PARAFAC. A further objective was to examine the relationship between the extracted PARAFAC components and the NOM fractions of the same samples obtained using LC-OCD.
2.
Methods
2.1.
Sampling
Water samples were collected monthly from different points along the process trains (Fig. 1) of two drinking water treatment plants, operated by Waternet, which supply water to Amsterdam city and its environs. Samples were collected between January and December 2007 from Loenderveen/ Weesperkarspel treatment train consisting of two stages: a pre-treatment plant at Loenderveen, which treats surface water by coagulation and flocculation, retention in surface water reservoir for about 100 days and rapid sand (RS) filtration; and a post treatment plant at Weesperkarspel, about 10 km away, which comprises ozonation, pellet softening, biological activated carbon (BAC) filtration and slow sand (SS) filtration. On average, twelve samples were collected from
799
Loenderveen/Weesperkarspel process train and from the distribution network every month. Samples were also collected from June to September in 2008 and in August and September of 2009 from the other treatment train, at Leiduin, which treats surface water which has been pre-treated by coagulation, rapid sand filtration at Nieuwegein, and then by infiltration in sand dunes. The Leiduin process train comprises aeration, RS filtration, ozonation, softening, BAC filtration and SS filtration. Seven samples were collected each time. The samples were collected in clean glass bottles and then filtered through pre-washed 0.45 mm regenerated cellulose membrane filters within 24 h of arrival in the laboratory. The filtered samples were then stored at 5 C until analysis, which was done within one week. The samples were analyzed for DOC, UV absorbance at 254 nm (UVA254) and F-EEM.
2.2.
DOC and UV254 measurements
DOC concentrations of all pre-filtered samples were determined by the combustion method using a Shimadzu TOCVCPN organic carbon analyzer. UVA254 absorbance of each sample was measured in a 1 cm quartz cell using a Shimadzu UV-2501PC UVeVIS spectrophotometer. For each sample, SUVA was determined by dividing the absorbance UVA254 by the corresponding DOC concentration.
2.3.
Fluorescence EEM measurements
To minimise fluorescence quenching resulting from the relatively high concentrations of DOC (inner filter effects), the prefiltered samples were diluted to a DOC concentration of 1 mg C/L using 0.01 M KCl solution prior to fluorescence measurements. To minimise possible metal-NOM complexation, the pH of diluted samples was adjusted to 2.8 0.1 using 0.1 M HCl and the fluorescence intensities measured in a 1.0 cm quartz cell using a FluoroMax-3 spectrofluorometer (Horiba Jobin Yvon) at room temperature (20 1 C). EEMs were generated for each sample by scanning over excitation wavelengths between 240 and 450 nm at intervals of 10 nm and emission wavelengths between 290 and 500 nm at intervals of 2 nm. The bandwidths on excitation and emission modes were both set at 1 nm. An EEM of the 0.01 M KCl solution was obtained and subtracted from the EEM of each sample in order to remove most of the Raman scatter peaks. Since samples were previously diluted to a DOC concentration of 1 mg C/L, each blank subtracted EEM was multiplied by the respective dilution factor and Raman-normalized by dividing by the integrated area under the Raman scatter peak (excitation wavelength of 350 nm) of the corresponding Milli-Q water and the fluorescence intensities reported in Raman units (RU).
2.4.
PARAFAC modelling
PARAFAC was used to model the dataset of F-EEMs. It uses an alternating least squares algorithm to minimise the sum of squared residuals in a trilinear model, thus allowing the estimation of the true underlying EEM spectra (Bro, 1997). It reduces a dataset of EEMs into a set of trilinear terms and a residual array (Andersen and Bro, 2003)
800
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a
b Raw water
Raw water
Coagulation
Coagulation Pre-treatment (Loenderveen)
Surface reservoir
Pre-treatment (Nieuwegein)
Rapid sand filtration Dune filtration
Rapid sand filtration Rapid sand filtration
Ozonation Ozonation
Pellet softening Pellet softening
BAC filtration
Slow sand filtration
Post-treatment (Weesperkarspel)
BAC filtration
Post-treatment (Leiduin)
BAC filtration
BAC filtration
Slow sand filtration
Finished water
Finished water
Distribution
Distribution
Fig. 1 e Water treatment scheme and sampling points (arrows) for (a) Loenderveen/Weesperkarspel, and (b) Leiduin.
xijk ¼
XF f ¼1
aif bjf ckf þ 3ijk i ¼ 1; .; I; j ¼ 1; .; J; k ¼ 1; .; K
where xijk is the fluorescence intensity of the ith sample at the kth excitation and jth emission wavelength; aif is directly proportional to the concentration of the fth fluorophore in the ith sample (defined as scores), bjf and ckf are estimates of the emission and excitation spectra respectively for the fth fluorophore (defined as loadings), F is the number of fluorophores (components) and 3ijk is the residual element, representing the unexplained variation in the model (Stedmon et al., 2003). Some components extracted by PARAFAC can be ascribed to specific species of organic matter present in water samples, but they more likely represent groups of organic compounds having similar fluorescence properties. While component scores indicate the relative concentrations of groups of organic fractions represented by the components, excitation and
emission loadings indicate their characteristic excitation and emission spectra. However, since most of the components that have been extracted from aquatic samples thus far cannot be ascribed to specific organic compounds, the scores cannot be converted to concentrations. Nevertheless, differences in component scores can be used to illustrate variations in the organic matter composition of water samples within a given dataset. But it should be noted that these differences may also be due to changes in the local environment of the analyte, such as polarity and temperature. In this study, differences in scores due to solution environment were minimised by performing fluorescence measurements at the same pH (2.8 0.1) and temperature (20 1 C). The maximum fluorescence intensity for each component was obtained and used to illustrate the quantitative and qualitative differences between samples. Several diagnostic tools can be used to determine the appropriate number of PARAFAC components. In this study,
Table 1 e Variation of DOC and SUVA of samples from Loenderveen/Weesperkarspel and Leiduin drinking water treatment trains. Loenderveen/Weesperkarspel process train Sample Raw water Coagulation effluent Surface reservoir effluent RS filtration effluent Ozonation effluent Pellet softening effluent BAC filtration effluent Finished water
pH 7.9 0.3 7.7 0.3 7.8 0.3 7.8 0.3 7.8 0.3 7.9 0.3 8.1 0.2 8.1 0.2
a
Leiduin process train a
DOC (mg C/L) SUVA (L/mg/m) 9.0 0.8 7.1 0.6 6.5 0.2 6.0 0.3 5.7 0.3 5.4 0.3 3.0 0.5 2.7 0.3
3.5 0.3 3.0 0.1 3.0 0.1 2.8 0.1 1.8 0.1 1.7 0.1 1.5 0.1 1.5 0.1
Sample
pH
Pre-treated water RS filtration effluent Ozonation effluent Pellet softening effluent BAC filtration effluent Finished water
8.0 0.1 7.9 0.2 8.0 0.1 7.9 0.1 8.1 0.3 8.2 0.2
DOCa (mg C/L) SUVAa (L/mg/m) 2.5 2.1 2.0 1.9 1.2 1.0
a Mean value standard deviation, for n ¼ 13 and 7 for Loenderveen/Weesperkarspel and Leiduin, respectively.
0.4 0.3 0.2 0.3 0.2 0.1
2.7 0.4 2.6 0.2 1.8 0.2 1.7 0.2 1.2 0.1 1.2 0.2
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however, only two methods were mainly employed: split-half analysis (Harshman, 1984) and examination of residual error plots (Stedmon and Bro, 2008). For split-half analysis, the data were split in the first mode comprising of water samples. The samples were divided into two halves and a PARAFAC model obtained for each half. The excitation and emission spectral loadings of the two halves were then compared to ascertain whether they were similar. A series of PARAFAC models consisting of between three and seven components were generated using the DOMfluor toolbox (Stedmon and Bro, 2008), which was specifically developed to perform PARAFAC analysis of DOM fluorescence, and contains all of the tools used to identify outlier samples as well as to perform split-half and residual errors diagnostics. A dataset of F-EEMs for 137 samples collected from Loenderveen/Weesperkarspel water treatment train over 12 months in 2007, and for 46 samples collected from Leiduin water treatment train during two campaigns in 2008 and 2009 was used to develop the PARAFAC model.
2.5. Liquid chromatography with organic carbon detection (LC-OCD) NOM separation by size exclusion was performed with an LCOCD system (DOC-LABOR, Germany) at Het Waterlaboratorium, Haarlem, The Netherlands. In the system, a column TSK HW-50S is connected to a Graentzel thin-film reactor (Huber and Frimmel, 1994) in which NOM is oxidized to CO2 by UV before it is measured by infrared detection. The column separates NOM, according to molecular size/weight, up to five fractions: (i) biopolymers (BP), comprising polysaccharides, proteins and colloids, (ii) humic substances (HS), (iii) building blocks (hydrolysates of humics) (BB), (iv) low molecular weight humics and acids (LMW), and (v) low molecular weight neutrals (such as alcohols, aldehydes, ketones and amino acids). Besides the organic carbon detector, the system also incorporates a UV detector, which may be used to assess the aromaticity of the sample as well as of the humic fraction by computing the respective SUVA values, and a dissolved organic nitrogen (DON) detector. Water samples were analyzed without any pre-treatment other than filtration through 0.45 mm filters prior to injection in the chromatographic column. The classification of LC-OCD fractions is based on empirical as well as systematic studies. For identification of HS fraction, up to five criteria may be used: (i) retention time, (ii) peak width, (iii) peak symmetry, (iv) the ratio of the peak area for the UV signal to that of the peak area for the DOC signal, and (v) DON. Definition of the fraction boundaries and quantification of the fractions by area integration of chromatograms was done with FIFFIKUS software (DOC-LABOR), which uses data for calibration standards as some of the inputs, and the results are published elsewhere (Baghoth et al., 2009).
2.6.
Correlation analysis
Spearman rank-order correlation coefficients were used to investigate the relationships between sample DOC, UVA254, maximum fluorescence intensity of the PARAFAC components (Fmax), and the five LC-OCD fractions (biopolymers,
Table 2 e Comparison of the spectral characteristics of the seven components identified in this study with those of similar components from previous studies. Values in brackets represent secondary peaks or shoulders. Component of this study
Excitation/ Emission wavelength
C1
260(360)/480
C2
250(320)/410
C3
<250(330)/420
C4
<250(290)/360
C5
250(340)/440
C6
<250(300)/406
C7
270/306
a b c d e f g
Description and source assignment (References) Terrestrial humic substances Peak P3: <260(380) /498c Component 3: 270(360) /478d Terrestrial/anthropogenic humic substances Component 6: <250 (320)/400e Component C2: 315/41b Marine and terrestrial humic substances Peak M, Coblea Component P1: <260(310) /414c Amino acids, free or protein bound Component C7: 240(300) /338c Component 4: <260(305) /378g Terrestrial humic substances Component P8: <260(355) /434c Component 4: 250(360)/440e Marine and terrestrial humic substances Component 1: <260(305)/428g Component 3:295/398f Peak C or Ma Amino acids, free or protein bound Component 4: 275/306f Component 8: 275/304e Peak B: 275/310a
Coble, 1996. Murphy et al., 2006. Murphy et al., 2008. Stedmon et al., 2003. Stedmon and Markager, 2005a. Stedmon and Markager, 2005b. Yamashita and Jaffe, 2008.
humics, building blocks, neutrals and LMW acids). The analysis was performed with SPSS statistical software.
3.
Results and discussion
3.1.
DOC, UVA254 and SUVA
Table 1 shows a summary of the means and standard deviations of the pH, DOC and SUVA of water samples collected from the Loenderveen/Weesperkarspel and Leiduin drinking water treatment trains. There were some slight seasonal variations
802
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Fig. 2 e Contour plots of the seven components identified from the complete F-EEMs dataset. The line plots on the right show split-half validations of excitation (thin) and emission (thick) loadings between the complete dataset (solid) and one of the independent halves (dotted).
in the DOC of the raw water at the pre-treatment plant at Loenderveen, with a minimum of 7.6 mg C/L in autumn, a maximum of 9.8 mg C/L in winter and a monthly average of 9.0 mg C/L. The retention in a surface reservoir for a period of about 100 days dampens the seasonal variation and after RS filtration, the monthly average DOC is 6.0 mg C/L. At the final treatment plant of Weesperkarspel, the DOC is further reduced by 55% to 2.7 mg C/L. The DOC levels in Leiduin were generally lower, with a mean of 2.5 mg C/L for influent pre-treated water, and a mean of 1.0 mg C/L for treated water, representing a total reduction of 60% across the process train. The average SUVA for raw water of Loenderveen/Weesperkarspel treatment train was a of 3.5 L/mg/m, an indication of NOM of moderate aromaticity, while that of finished water was 1.5 L/mg/m, which is typical of NOM with low aromaticity (SUVA < 2 L/mg/m). For Leiduin treatment train, the SUVA for the influent water, previously pre-treated by coagulation and filtration followed by infiltration in sand dunes, was 2.7 L/mg/m, while that of finished water was 1.2 L/mg/m. Thus, both Loenderveen/
Weesperkarspel and Leiduin treatment trains significantly reduce the aromatic character of the NOM in the treated water.
3.2.
PARAFAC components
A total of 183 F-EEMs of water samples from Loenderveen/ Weesperkarspel and Leiduin water treatment trains were used for PARAFAC analysis. An initial exploratory analysis was performed in which outliers were identified and removed from the dataset. A sample was considered an outlier if it contained some instrument error or artefact, or if it was properly measured but was very different from the others (determined by calculating its leverage using DOMfluor). The latter was removed in order to facilitate the modelling process as well as the model validation using the split-half method; otherwise, the dataset would need to contain a sufficient number of the unique samples, which are evenly divided between the split halves.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 9 7 e8 0 9
803
Fig. 3 e Maximum fluorescence intensities (Fmax) of PARAFAC components across Loenderveen/Weesperkarspel drinking water treatment train.
PARAFAC analysis with 3e7 components was performed on the remaining 147 samples. However, only the models containing three, four and seven components could be split-half validated. These were split-half validated in the sense that the corresponding components in the split halves had equal excitation and emission loadings as verified by the corresponding Tucker’s congruence coefficients being greater than 0.95 (Lorenzo-Seva and Ten Berge, 2006). For a complete dataset model to be validated, the Tucker’s congruence coefficients between the split halves, as well as between the complete dataset and a split half should be greater than 0.95 and only the seven component model could be validated in this manner. Whereas the PARAFAC model in this study uses F-EEMs of samples collected at a low pH (w2.9), which would inevitably result in the non-uniform quenching of fluorescent peaks of the different components, the seven components extracted have spectral features similar to those previously extracted from F-EEMs of DOM (Stedmon et al., 2007; Murphy et al., 2008; Borisover et al., 2009), all of which were collected at ambient pH (normally above 7.0). Table 2 shows excitation and emission
wavelength pairs of the main peaks of the seven components as well as descriptions of similar components that were identified in previous studies. Comparison of previously identified components with the spectral contours shown in Fig. 2 indicates that the samples in this study contain humic-like as well as protein-like fluorophores. Two of the components (C4 and C7) have previously been ascribed to protein-like fluorophores (Cory and McKnight, 2005): component C4 to tryptophan-like fluorophore, and component C7 to tyrosine-like fluorophore (Yamashita and Tanoue, 2003). Components C1, C2, C3, C5 and C6 are humic-like fluorophores which may have a terrestrial or anthropogenic origin.
3.3.
PARAFAC component scores across treatment
After validation of the seven component model, the fate of the components across the Loenderveen/Weesperkarspel treatment train was tracked using their maximum fluorescence intensities (Fmax). Fmax gives estimates of the relative concentrations of each component; however, direct comparison of
Fig. 4 e Mean percentage reduction of maximum fluorescence intensities (Fmax) of PARAFAC components across Loenderveen/Weesperkarspel water treatment train.
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 9 7 e8 0 9
Ratio of component Fmax
10
8 C1/C2 6
C1/C4
4
C1/C7 C2/C4
2 C2/C7 0 Raw water Coagulation effluent
Reservoir effluent
RS filtration Ozonation effluent effluent
Softening effluent
BAC filtration effluent
Finished water
Fig. 5 e Variation of ratios of Fmax across the treatment train of the two dominant humic-like components (C1 and C2) to that of protein-like components (C4 and C7), as well as of component C1 to that of C2.
relative concentrations between different components depends on the magnitude of their quantum efficiencies as well as on their individual responses to quenching effects. Fig. 3 shows the mean Fmax of each component across the treatment train. For all the water samples analyzed, Fmax was higher for terrestrial humic-like components C1 and C2 than for humic-like components C3, C5 and C6, and for protein-like components C4 and C7. For raw water samples, the mean Fmax was: 1.63 and 1.64 R.U. for C1 and C2, respectively; 0.50, 0.42 and 0.39 for humic components C3, C5 and C6, respectively; and 0.57 and 0.25 for protein-like components C4 and C7, respectively. Fmax of the tyrosine-like component C7 was almost always lower than that of any of the other components, while that of the tryptophan-like component C4 was comparable to those of humic-like components C3, C5 and C6. Whereas these results appear to indicate that the samples were dominated by humic-like fluorescent compounds, they are not sufficient to permit conclusions to be drawn about the relative concentrations of all the seven components without prior knowledge of their respective quantum yields. Since fluorescence intensity is proportional to concentration as well as quantum yield of the fluorophores, differences in the relative fluorescent intensities of the components may be a reflection of differences in concentrations and/or quantum efficiencies of the components. However, results of NOM characterization of the same set of samples using LC-OCD showed quantitatively that, on average, humic substances comprised about 70% of all samples analyzed (Baghoth et al., 2009). In order to evaluate the effect of water treatment on fluorescence characteristics of NOM, the mean percentage reduction of Fmax across each treatment process (Fig. 4) was computed. Fmax may be reduced in two ways: (i) intact removal of fluorescent compounds by, for example, coagulation and BAC filtration or (ii) transformation of fluorescent compounds by, for example, ozonation. Ozonation and BAC filtration reduced Fmax by 50% or more for all components, while coagulation and storage in surface reservoir reduced it by between 5 and 50%, depending on the component. For all components, softening and slow sand filtration did not reduce Fmax while rapid sand filtration reduced it by less than 10%. Whereas the
mean percentage reduction of fluorescence is comparable to that of DOC in the case of BAC filtration (w50e70% for fluorescence and w40% for DOC), it is disproportionately higher than that of DOC in the case of ozonation (w50e70% for fluorescence and 5% for DOC). This is explained by the fact that ozonation transforms large molecular weight NOM into smaller and less aromatic organic compounds (Swietlik et al., 2004) which have lower UV absorptivities and fluorescence. Coagulation significantly reduced Fmax of all humic-like components as well as of the tyrosine-like component C7 but not of the tryptophan-like component C4. The reduction in humic-like NOM is consistent with a previous study (Allpike et al., 2005) showing effective removal of larger molecular weight, hydrophobic humic-like NOM by coagulation. The ratios of Fmax of components, particularly those of humic-like to protein-like, may be used to compare the removals, across different treatment processes, of the related NOM fractions. Therefore, these ratios were computed and attempts were made to find out whether they are consistent with what is known about the removal (by different processes) of specific NOM fractions to which some of these fluorescent components have been ascribed. Paired t-tests were performed to ascertain whether there were statistically significant changes in the ratios of Fmax across coagulation, ozonation and BAC filtration processes. Fig. 5 shows the variation of ratios of Fmax across the treatment train of two dominant humic-like components (C1 and C2) to that of protein-like components (C4 and C7), as well as of component C1 to that of C2. During coagulation, the ratios of Fmax of humic-like to that of protein-like components decreased for all cases except for humic-like component C3, which did not show a significant change relative to either of the protein-like components C4 or C7. The preferential reduction of humic-like components is consistent with the preferential removal of hydrophobic high molecular weight humic NOM by coagulation (Allpike et al., 2005; Bolto et al., 2002). Components C1 and C2 were preferentially removed relative to all the other humic-like components except in one case: there was no significant difference between the reduction of components C2 and C6. This may be
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Table 3 e Correlation matrix of DOC, UVA254, and Fmax with the seven PARAFAC components and concentrations of LC-OCD fractions for all samples from the water treatment train. C1
DOC UVA254 DOC UVA254 C1 C2 C3 C4 C5 C6 C7 Humics Building blocks Neutrals Biopolymers LMW acids
1.00
0.88* 1.00
C2
C3
C4
C5
C6
C7
0.96* 0.97* 0.94* 0.94* 0.95* 0.93* 0.91* 0.89* 0.91* 0.88* 0.92* .89* 0.91* 0.86* 1.00 0.97* 0.98* 0.95* .97* 0.96* 0.89* 1.00 0.95* 0.97* .97* 0.94* 0.93* 1.00 0.96* .97* 0.96* 0.86* 1.00 .96* 0.95* 0.93* 1.00 0.96* 0.89* 1.00 0.86* 1.00
Humics Building Neutrals Biopolymers blocks 0.98* 0.85* 0.93* 0.96* 0.91* 0.90* 0.93* 0.90* 0.89* 1.00
0.82* 0.67* 0.77* 0.78* 0.78* 0.76* 0.78* 0.74* 0.72* 0.77* 1.00
0.96* 0.84* 0.95* 0.92* 0.92* 0.91* 0.92* 0.92* 0.86* 0.92* 0.82* 1.00
0.78* 0.78* 0.76* 0.76* 0.76* 0.81* 0.72* 0.76* 0.76* 0.69* 0.66* 0.81* 1.00
LMW acids 0.68* 0.69* 0.72* 0.72* 0.71* 0.73* 0.71* 0.72* 0.68* 0.65* 0.40* 0.65* 0.53* 1.00
*Correlation is significant at the 0.01 level (2-tailed).
an indication that C1 and C2 are representative of larger molecular weight and more humic compounds, which have been found to be preferentially removed by coagulation (Haberkamp et al., 2007; Humbert et al., 2007). Ozonation degraded humic-like components C1, C2, and C6 more than protein-like components. While degradation of C5 was less, that of C3 did not differ significantly from that of protein-like components. There was no significant difference between the rates of ozone-degradation of protein-like components C4 and C7. The preferential reduction of humic fluorescence is consistent with the lower reactivity of microbially derived NOM (represented by protein-like fluorophores) with ozone (Bose and Reckhow, 2007). During BAC filtration, the ratios between Fmax of humiclike components did not show significant changes except for two cases: the ratio of Fmax of C1 to that of C3 decreased ( p < 0.01), while the ratio of Fmax of C2 to that of C6 increased ( p < 0.05). The ratios of humic-like to protein-like components did not change significantly except for components C1 and C5 ( p < 0.05 and p < 0.01, respectively), which decreased relative to the tyrosine-like component C7. There was significant ( p < 0.05) reduction in fluorescence intensity of the tryptophan-like component C4 relative to that of tyrosine-like component C7.These results appear to indicate that humic-
Conc entration of biopoly m ers , m g C/L
Conc entration of hum ic s , m g C/L
8
6
4
like components were removed by BAC filtration just as effectively as protein-like components. Because the BAC filters at Weesperkarspel water treatment plant are operated for extended periods before regeneration (more than six months), NOM is considered to be removed mainly by biodegradation, although adsorption may also play a role. It would therefore be expected that, because it is generally not easily biodegradable, aromatic humic NOM (represented by humic-like components) would not be as well removed as microbially derived NOM (represented by protein-like components). That this is not apparent from the results could be due to one or a combination of factors: release of fluorescent bacterial exudates from the biofilter could offset the preferential reduction of humic-like fluorescence; presence of considerable variances in the analytical measurements of protein-like fluorescence; and persistence of protein-like fluorescence signature, which has been used as a tracer of microbial organic matter from wastewater pollution. In a comparative study of removal of effluent organic matter from tertiary effluent of a wastewater treatment plant by direct nanofiltration (NF) and powdered activated carbon/NF, the signature of wastewater was detectable as protein-like fluorescence even at a very low DOC concentration of <0.5 mg C/L in the permeate (Kazner et al., 2008).
R = 0.97
2
0.4
0.3
0.2 R = 0.54 0.1
0.0
0 0
2
4
6
8
DOC concentration, mg C/L
10
12
0
2
4
6
8
10
12
DOC concentration, mg C/L
Fig. 6 e Regressions describing the relationship between DOC concentrations and concentrations of humics and biopolymers.
806
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 9 7 e8 0 9
0.5
abs orbanc e, 1/c m
1.5
R = 0.84
1.0
0.4
0.3
0.2 R = 0.78
UV
M ax im um fluores c enc e intens ity of Com ponent 2, RU
2.0
0.5
0.0
0.1
0.0 0
2
4
6
8
10
12
0
2
4
DOC concentration, mg C/L
2.0
8
10
12
abs orbanc ed, 1/c m
0.5
1.5
R = 0.82
1.0
0.4
0.3
0.2 R = 0.77
0.5
UV
M ax im um fluores c enc e intens ity of Component 2, RU
6
DOC concentration, mg C/L
0.0 0
1
2
3
4
5
6
7
8
0.1
0.0 0
1
2
Concentration of humics, mg C/L
3
4
5
6
7
8
Concentration of humics, mg C/L
Fig. 7 e Regressions describing the relationships between (top) DOC, Fmax of PARAFAC component C2 and UVA254 absorbance, and (bottom) humic fraction concentration, Fmax of PARAFAC component C2 and UVA254 absorbance.
3.4.
Correlations
All samples from the pre-treatment and post treatment plants were included in the determination of Spearman’s correlation coefficients. Table 3 is a correlation matrix obtained with SPSS statistical software. There were significant correlations ( p < 0.01) among sample DOC concentration, UVA254, and Fmax for the seven PARAFAC components (C1, C2, C3, C4, C5, C6, and C7) and DOC concentrations for the five LC-OCD fractions (humics, building blocks, neutrals, biopolymers and LMW acids). The correlations of humics, building blocks and neutral fractions were higher with DOC than with UVA254 or with Fmax for any of the PARAFAC components. This result would be expected since measurements of DOC and LC-OCD fractions are all based on detection of carbon dioxide (CO2) produced by photo-oxidation of organic carbon, while UVA254 and Fmax measure only a part of organic matter responsible for UV absorption and fluorescence, respectively. The biopolymer
fraction did not display a similar trend but, rather, correlated more or less equally with DOC, UVA254 and Fmax. DOC correlated nearly perfectly (r ¼ 0.98, p < 0.01) with humic fraction but not as highly (r ¼ 0.78, p < 0.01) with biopolymer fraction; the latter displayed more variability for pre-treatment plant water samples (DOC > 6.0 mg C/L) (Fig. 6). This difference in degree of correlation with DOC between humic and biopolymer fractions could be due to a lower oxidation efficiency of the latter in the DOC detector of the LC-OCD system, which uses UV oxidation to decompose organic carbon to CO2 which is then measured by non-dispersive infrared absorption. In a study to evaluate the performance of an online DOC detector for detection of NOM samples using a similar LC-OCD system, the highest molecular weight biopolymer fraction (attributed mainly to polysaccharides) was found to be poorly oxidized, thus underestimating its concentration on the basis of the detected DOC (.Lankes et al., 2009). DOC correlated slightly higher with Fmax than with UVA254. DOC, UVA254 and Fmax correlated more strongly with humics
0.5 Max imum fluores c enc e intens ity of Com ponent 7, RU
Max im um fluores c enc e intens ity of Com ponent 4, RU
1.0
0.8
0.6
0.4 R = 0.61 0.2
0.0 0.00
0.05
0.10
0.15
0.20
Concentration of biopolymers, mg C/L
0.25
0.30
0.4
0.3
0.2 R = 0.57 0.1
0.0 0.00
0.05
0.10
0.15
0.20
0.25
0.30
Concentration of biopolymers, mg C/L
Fig. 8 e Regressions describing the relationship between concentrations of biopolymer fraction and (left) Fmax of tryptophanlike component C4 and (right) Fmax of tyrosine-like component C7.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 7 9 7 e8 0 9
and neutrals than with building blocks and biopolymers. The terrestrial humic-like component C2 (as well as C1 and C3) showed slightly better predictions of DOC and humic fraction concentrations than did UVA254 (Fig. 7). The latter showed more variability for pre-treatment plant samples (DOC > 6.0 mg C/L and humics > 4.0 mg C/L). The tryptophan-like and tyrosine-like components C4 and C7 correlated positively with biopolymer fraction (Fig. 8). Since tryptophan-like and tyrosine-like fluorescence have been found to correlate with protein-like NOM, this might be an indication of some input of microbial NOM in the samples analyzed. In an evaluation of pyrolysis gas chromatography mass spectrometry (Py/GC/MS) products of soil-water and stream water NOM, it was found that PARAFAC protein-like components correlated significantly ( p < 0.05) with nitrogen containing compounds but PARAFAC components correlated poorly with polysaccharide content (Fellman et al., 2009). However, in spite of the strong correlation of the protein-like fluorescence with biopolymer concentration, there was still a high percentage of variation in biopolymer measurements which could be partly attributed to the presence of non-fluorescing polysaccharides in the biopolymer fraction. The higher predictive power of Fmax provides an opportunity for its use as an alternative to UVA254 as a surrogate measure of DOC for online monitoring of its concentration in drinking water treatment plants. Furthermore, the higher sensitivity of fluorescence measurements allows measurements of very low NOM concentrations. The correlation of Fmax of protein-like components with the biopolymer fraction, which may include nitrogen containing compounds, further demonstrates its potential for online monitoring of sub-fractions of DOC which are known to be more labile, thus promoting biogrowth in distribution systems, and to contribute to irreversible protein fouling of polymeric water filtration membranes. Whereas this study used offline measurements to generate F-EEMs, it is possible to develop online methods for near real time monitoring, thus allowing operational changes to be made whenever required.
4.
Conclusions
Based on the characterization of NOM in water samples from a drinking water treatment plant using F-EEMs and PARAFAC and investigation of the correlation between extracted fluorescent component and NOM fractions obtained using LCOCD, the following conclusions can be drawn from this study: F-EEMs, of samples from Loenderveen/Weesperkarspel drinking water treatment plants, and PARAFAC were used to develop a 7-component model in which the components have fluorescence spectra similar to those of fluorescent components extracted in previous studies 5 of the components are humic-like and two are protein-like (tryptophan-like and tyrosine-like) There were significant correlations ( p < 0.01) between sample DOC concentration, UVA254, and Fmax for the seven PARAFAC components and DOC concentrations of the five LC-OCD fractions.
807
Three of the humic-like components showed slightly better predictions of DOC and of humic fraction concentrations than UVA254. Tryptophan-like and tyrosine-like components correlated positively (r ¼ 0.78 and 0.75, respectively) with biopolymer fraction. Except for component C3, which did not change significantly relative to protein-like components, there was preferential reduction of humic-like relative to protein-like components during coagulation There was a reduction in the ratio of Fmax of all humic-like, except C3 and C5, relative to that of protein-like components by ozonation, indicating, in general, stronger reactivity of ozone with humic-like NOM. During BAC filtration, the ratios of Fmax of humic-like to protein-like components did not change significantly except for components C1 and C5, which decreased relative to tyrosine-like component C7. There is need for further research on the identities of fluorescent components obtained with PARAFAC and how they relate with NOM characteristics determined using alternative techniques.
Acknowledgement The authors acknowledge the financial support of the IS-NOM project sponsored by Senter Novem, The Netherlands.
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Humbert, H., Gallard, H., Jacquemet, V.r., Croue, J.-P., 2007. Combination of coagulation and ion exchange for the reduction of UF fouling properties of a high DOC content surface water. Water Res. 41, 3803e3811. Hunt, J.F., Ohno, T., 2007. Characterization of fresh and decomposed dissolved organic matter using excitationeemission matrix fluorescence spectroscopy and multiway analysis. J. Agric. Food Chem. 55 (6), 2121e2128. Kazner, C., Baghoth, S., Sharma, S., Amy, G., Wintgens, T., Melin, T., 2008. Comparing the effluent organic matter removal of direct NF and powdered activated carbon/NF as high quality pretreatment options for artificial groundwater recharge. Water Sci. Technol. Water Supply 57 (6), 821e827. Lankes, U., Mueller, M.B., Weber, M., Frimmel, F.H., 2009. Reconsidering the quantitative analysis of organic carbon concentrations in size exclusion chromatography. Water Res. 43 (4), 915e924. Leenheer, J.A., 2004. Comprehensive assessment of precursors, diagenesis, and reactivity to water treatment of dissolved and colloidal organic matter. Water Sci. Technol. Water Supply 4 (4), 1e9. Lorenzo-Seva, U., Ten Berge, J.M.F., 2006. Tucker’s congruence coefficient as a meaningful index of factor similarity. Methodology 2, 57e64. Matilainen, A., Lindqvist, N., Korhonen, S., Tuhkanen, T., 2002. Removal of NOM in the different stages of the water treatment process. Environ. Int. 28, 457e465. McKnight, D.M., Boyer, E.W., Westerhoff, P.K., Doran, P.T., Kulbe, T., Andersen, D.T., Andersen, D.T., 2001. Spectrofluorometric characterization of dissolved organic matter for indication of precursor organic material and aromaticity. Limnol. Oceanogr. 46 (1), 38e48. Murphy, K.R., Ruiz, G.M., Dunsmuir, W.T.M., Waite, T.D., 2006. Optimized parameters for fluorescence-based verification of ballast water exchange by ships. Environ. Sci. Technol. 40 (7), 2357e2362. Murphy, K.R., Stedmon, C.A., Waite, T.D., Ruiz, G.M., 2008. Distinguishing between terrestrial and autochthonous organic matter sources in marine environments using fluorescence spectroscopy. Mar. Chem. 108 (1e2), 40e58. Owen, D.M., Amy, G.L., Chowdhary, Z.K. (Eds.), 1993. Characterization of Natural Organic Matter and Its Relationship to Treatability. AWWARF, Denver, CO. Peiris, R.H., Halle, C., Budman, H., Moresoli, C., Peldszus, S., Huck, P.M., Legge, R.L., 2010. Identifying fouling events in a membrane-based drinking water treatment process using principal component analysis of fluorescence excitationeemission matrices. Water Res. 44, 185e194. Persson, T., Wedborg, M., 2001. Multivariate evaluation of the fluorescence of aquatic organic matter. Anal. Chim. Acta 434 (2), 179e192. Peuravuori, J., Koivikko, R., Pihlaja, K., 2002. Characterization, differentiation and classification of aquatic humic matter separated with different sorbents: synchronous scanning fluorescence spectroscopy. Water Res. 36, 4552e4562. Stedmon, C.A., Markager, S., Bro, R., 2003. Tracing dissolved organic matter in aquatic environments using a new approach to fluorescence spectroscopy. Mar. Chem. 82, 239e254. Stedmon, C.A., Markager, S., 2005a. Resolving the variability in dissolved organic matter fluorescence in a temperate estuary and its catchment using PARAFAC analysis. Limnol. Oceanogr. 50 (2), 686e697. Stedmon, C.A., Markager, S., 2005b. Tracing the production and degradation of autochthonous fractions of dissolved organic matter by fluorescence analysis. Limnol. Oceanogr. 50 (5), 1415e1426. Stedmon, C.A., Thomas, D.N., Granskog, M., Kaartokallio, H., Papadimitriou, S., Kuosa, H., 2007. Characteristics of dissolved
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journal homepage: www.elsevier.com/locate/watres
Effects of aeration patterns on the flow field in wastewater aeration tanks Markus Gresch a,b, Martin Armbruster c, Daniel Braun b, Willi Gujer a,b,* a
Swiss Federal Institute of Aquatic Science and Technology, Eawag, 8600 Du¨bendorf, Switzerland Institute of Environmental Engineering, ETH Zurich, 8093 Zurich, Switzerland c Hydrograv GmbH, Eisenstuckstrasse 46, 01069 Dresden, Germany b
article info
abstract
Article history:
Due to the high energy input of aeration, the spatial distribution of air diffusers largely
Received 29 April 2010
determines the flow field in aeration tanks. This has consequences on the efficiency of the
Received in revised form
aeration system, the performance of the aeration tank and on tank operation and control.
7 September 2010
This paper deals with these effects applying both Computational Fluid Dynamics (CFD)
Accepted 8 September 2010
enhanced with a biokinetic model and full scale validation using velocity and reactive
Available online 17 September 2010
tracer measurements with high temporal and spatial resolution. It is shown that small changes in the diffuser arrangement drastically change the overall flow field. Using
Keywords:
different aeration patterns in the same tank may lead to large scale instabilities in the flow
Activated sludge process
field that lower plant performance and produce strong variations in concentration signals
Aeration
impeding their use for plant control. CFD is a valuable tool to analyze the interaction of
Ammonium
flow field and aeration and their effects on plant performance and operation. But, in
CFD
complex flow situations experimental validation is needed and strongly suggested.
Reactor hydraulics
ª 2010 Elsevier Ltd. All rights reserved.
Reactive tracer
1.
Introduction
Aeration is a key process in wastewater treatment. Its principle role is to supply oxygen needed for all aerobic treatment processes. In activated sludge systems, aeration also ensures mixing and homogenization of the sludge suspension and strips CO2 and other gases produced by the degradation processes. Aeration is the dominant source of kinetic energy in an aeration tank. If oxygen transfer is the limiting factor, aeration consumes 10e30 W/m3 of energy (Tchobanoglous et al., 2003). This is one order of magnitude more than suitable mixing devices transfer into the water and several orders of magnitude more than the kinetic energy of the inflowing water or the hydraulic head loss. Therefore the flow field in an aeration tank is clearly dominated by the energy sources of aeration
and mixers whereas flow structures that are related to the inflow and outflow are generally small. For bubble aeration systems, the development of the flow field is mainly driven by buoyancy effects. Hence, the air distribution is of prime interest. Consequently, the spatial distribution of air diffusers, the air flow rate and the reactor geometry decide about the nature of the flow field. This finally largely determines: - the efficiency of the aeration system - the overall flow field in the tank and hence the performance of the tank - the local pollutant concentrations at sensors used for plant control
* Corresponding author. Swiss Federal Institute of Aquatic Science and Technology, Eawag, 8600 Du¨bendorf, Switzerland. Tel.: þ41 44 823 5036; fax: þ41 44 823 5389. E-mail address:
[email protected] (W. Gujer). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.009
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Studies about the efficiency of the aeration system often point out the influence of the diffuser layout. Poor oxygen transfer efficiency is reported for spiral flow systems where basically just a small part of floor close to one of the sidewalls is aerated (Bewtra and Nicholas, 1964; Leary et al., 1969; Schmit et al., 1978). This arrangement produces a strong circulation due the vertical movement of bubbles at the aerated side of the tank. The circulation accelerates the vertical movement of the bubbles which reduces their contact time and therefore also the oxygen transfer efficiency. More efficient aeration systems are obtained by a grid arrangement of the diffusers or with total floor coverage aeration systems (Deronzier et al., 1998; Duchene et al., 2001; Thiersch and Valentin, 2002; Wagner and Popel, 1998). With a dense diffuser arrangement, the spiral flow phenomenon (stable circulation) is suppressed and the flow field is unstable with no preferential direction of liquid motion. For the transition from stable circulation to an unstable flow field, the extent of nonaerated zones at the sidewalls is important (Julia et al., 2007). Non-aerated areas serve for a stable downwelling of the liquid leading to a contraction of the bubble plume at the center of the aerated zone producing an air lift. In the past, this type of studies always involved large scale experiments. With recent developments in computer technology and multiphase flow research, Computational Fluid Dynamics (CFD) is progressively used to analyze flow patterns in various water treatment reactors (Armbruster et al., 2001; Craig et al., 2002; Do-Quang et al., 2001; He et al., 2008; Saalbach and Hunze, 2008; Templeton et al., 2006). Aeration tanks have been studied with CFD for their overall mixing (Le Moullec et al., 2008). Fayolle et al. (2007) also incorporated aeration and oxygen transfer. Even biokinetic models have been successfully incorporated in CFD simulations (Hunze, 1996; Le Moullec et al., 2010; Zima et al., 2009). For closed loop reactors, these simulations also report spiral flow structures at the transition of aerated and non-aerated sections (Fayolle et al., 2007). Although CFD is mostly based on physical principles, the simulation of a multiphase flow problem is still not straightforward. Closure terms, in particular interaction forces between the phases and the turbulence closure terms are widely discussed in literature (Jakobsen et al., 2005; Tabib et al., 2008; Zhang et al., 2006). Therefore, experimental validation of a CFD study, especially in complex flow situations, is still desired. We analyzed the flow field of a full scale rectangular aeration tank by means of CFD and extensive measurements of flow velocities and reactive tracer distribution. In this paper, we will focus on the role the aeration pattern has on the flow field and subsequently on the aeration efficiency and on the distribution of ammonium and discuss implications for plant operation and control.
2.
Plant description and methods used
2.1.
Plant description
An aeration tank of the wastewater treatment plant Werdho¨lzli, Zurich serves as an example for the complex flow
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features that evolve in such tanks. The aeration tank consists of an anoxic zone with a volume of 1400 m3 followed by an aerobic zone with a volume of 3500 m3. The denitrification zone is separated from the aerated zone by an under-flowed textile wall. There are no physical separations within the aerobic tank. The oxygen is introduced by 1800 ceramic air diffusers, arranged in five equally sized sections with 420, 420, 360, 300 and 300 diffusers. The first two sections are identical. From the second to the third aeration section, not only the number of diffusers change but also the layout changes from a rather uniform distribution to a more aligned one with wider spacing between air supplying pipes and with distinct nonaerated zones at the sidewalls. The last two sections have a diffuser arrangement very similar to the third section. Fig. 1 shows a scheme of the aeration tank and the details of the diffuser layout for the second and third aeration section. Measurement locations are indicated. The following analysis will solely focus on the aerobic part of the tank.
2.2.
Reactive tracer measurements
Ion selective electrodes (Nadler Analysetechnik, ZuzwilSwitzerland) were used to continuously detect ammonium. A multiple point in-situ calibration procedure was chosen to produce reliable results also for low concentrations (0e2 mg/L). In addition to ammonium, temperature, pH and potassium were continuously monitored at P3c (Fig. 1) in the tank. Variations in these parameters were generally slow during a measurement period. Their effects on the ammonium concentration were just implicitly included through the calibration procedure applied.
2.3.
Flow velocity measurements
Acoustic Doppler Velocimeters (ADV sensors of type Nortek 10 MHz) were used to directly measure 3D flow velocities at different locations in the aeration tank. Velocities were measured based on the Doppler-shift between the transmitted acoustic signal and the detected signal after scattering at particles moving with the fluid. In the bubbly flow environment of a wastewater aeration tank, scattering occurs not only at solid particles having the same velocity as the water but also at air bubbles having a different vertical flow velocity than the water (Nielsen et al., 1999). Therefore, the vertical flow component needs filtering. In aeration tanks of wastewater treatment plants, gas holdup is at a low level of approximately 1% producing a characteristic spike in the recording of the vertical flow velocity when a bubble travels through the measurement volume. This allows efficient filtering of the recorded data (Thiersch and Valentin, 2002). We simultaneously used four ADV sensors. The sensors were fixed in vertical direction on a movable aluminium rack system stabilized at the sidewalls of the aeration tank (Ortmanns and Minor, 2006). The actual measurement locations were situated 0.3 m upstream of the rack to minimise flow perturbation by the measurement system. Within a cross section, flow velocities were measured at 30 locations (Fig. 1C). Flow velocities were recorded with a frequency of 25 Hz (with an internal sample frequency of 250 Hz). Recorded data was filtered with a despiking algorithm of Goring and Nikora
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A
0m
20m
10m
50m
40m
P3a
Influent Denitrification
P3b P3c
Section 1 Return sludge
B
30m
Section 2
Section 2 Section 3
Section 4
Section 5
Section 3
C P3a
Ammonium measurement 3D Flow velocity measurement 11 m P3c
P3b 4.2 m
Fig. 1 e Scheme of the aeration tank. Top view (A) and cross sectional view (C) of the aeration tank. The sensor positions for ammonium (cross) and flow velocity measurements (quad) are shown in both views. The two different aeration patterns of section 2 and section 3 are shown in subplot B.
(2002). In average, 5% of the data was removed. Since the focus of this study was on the mean flow velocity or on slow variation of flow velocities, data was smoothed with a moving average window of 30 s. Based on preliminary ammonium measurements of Braun and Gujer (2008), we expected to find flow velocity oscillation with a period of 6e7 min. We therefore used measurement periods of at least 20 min capturing three periods of the expected flow variation. Inflow to the activated sludge tank (0.19 m3 s1), return sludge rate from the secondary clarifier (0.17 m3 s1) and total air flow rate (1.1 m3 s1) were kept constant during the whole period of flow velocity measurements. These values were also taken as boundary conditions for the CFD simulation outlined below. The flow velocity measurements were used to validate the CFD simulations.
2.4.
Computational fluid dynamics (CFD)
CFD provides very detailed insight into the hydraulics of reactor systems. Mainly based on fundamental physical principles, predictions about the flow field, including the transport of reactive species are made. The CFD simulation was performed as an EulereEuler multiphase flow simulation using the commercial code ANSYS CFX 11.0 which uses a finite volume method for discretisation of the Reynolds-Averaged NaviereStokes equations. The two phases involved are activated sludge as the continuous phase and air as the dispersed phase with a bubble diameter of 3 mm. Activated sludge was considered to be a single phase having a constant viscosity of 0.0081 Ns/m2 following the model of Bokil and Bewtra (1972) and using 3.0 kg/m3 as the concentration of suspended solids. Turbulence of the continuous phase is modelled by the SST keu model of Menter (1994). We used the default values for all the model constants. For the dispersed phase turbulent viscosity is specified to be equal to the continuous phase. Bubble induced turbulence is accounted for according to the model proposed by Sato and Sekoguchi (1975).
The interacting forces that were considered here are the drag force and the buoyancy force which are the dominant forces acting in bubble column-type flows (Sommerfeld et al., 2008). Drag force is modelled using the correlation of Ishii and Zuber (1979) which is adapted to modelling of gas bubbles taking also into account the bubble deformation towards an ellipsoidal shape. Standard wall functions are used to bridge the near wall region. Walls are treated as no-slip boundaries and the water surface is modelled as a degassing boundary condition. This means that the continuous phase sees this boundary as a freeslip wall and does not leave the domain whereas the gas phase sees this boundary as an outlet. At the inlet boundary, the velocity distribution according to the flow velocity measurements and at the outflow boundary, an average static pressure is specified. The precise shape of the diffusers and the piping are neglected in the model. Therefore, they are physically not present in the computational domain but the diffusers are imprinted on the floor and air inflow is specified at these boundaries. A total air flow rate of 1.1 m3 s1 was used, resulting in an air flow rate of 0.61 L/s for each of the total 1800 diffusers. Due to the variable diffuser arrangement in the different sections of the tank (Fig. 1), air inflow becomes inhomogeneous. We used a hexahedral grid with a total of approximately 400,000 nodes. A time step of 0.25 s is used and the simulation captures a period of 5 h (2.5 hydraulic residence times). In addition to the hydrodynamic equations, a transport equation for ammonium is solved. A sink term is used to describe nitrification. Monod-type kinetics (Equation (1)) is assumed and the maximal activity rmax was determined by batch experiments with sludge from the aeration tank. The half saturation constant KNH is taken from the literature (Gujer et al., 1999). rNH4 ¼ rmax $
cNH4 gN rmax ¼ 196 3 ; KNH ¼ 1 mg=L KNH þ cNH4 m d
(1)
We examined the sensitivity of the grid size by using three different grid sizes and the sensitivity of the turbulence model by using the standard ke3 model (Launder and Spalding, 1974) instead of the SST keu model for the continuous phase.
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To asses the impact of the diffuser layout on the distribution of air and subsequently on aeration efficiency and ammonium distribution, two more simulations were performed one using the diffuser layout of section 1e2 throughout the reactor and one using the diffuser layout of section 3e5 throughout the reactor.
3.
Results
3.1.
CFD analysis
The CFD simulation predicts the distribution of flow velocities, pressure, turbulence quantities and volume fractions for both activated sludge and air and the ammonium distribution. The two different diffuser layouts used in this aeration tank cause a significantly different distribution of air volume fractions (Fig. 2). The air distribution for the first two aeration sections with a uniform distribution of diffusers is homogenous. Individual bubble plumes keep their identity to a large extent. These bubble plumes are unstable and they meander which partially leads to merging of individual plumes. For the other aeration sections, a distinct air distribution is formed. The air concentrates in the center of the reactor whereas at the sidewalls, zones with almost no air develop. The bubble plumes combine in the center of the reactor and produce an upwelling region. The flow field that accompanies the distribution of air volume fraction is illustrated in Fig. 3. In the left panel the flow field at the 30 m cross section (aeration section 3) is shown. Arrows show the flow components in the cross sectional plane, contours refer to the velocity in longitudinal direction. At this cross section, the flow field in longitudinal direction shows an oscillatory behavior for which three typical situations are shown (subplot A, B, C). The right panel depicts the
Fig. 2 e Snapshot of air volume fractions at two cross sections with different diffuser patterns. The diffuser pattern as encountered in the first two aeration sections (top) produces a more homogenous distribution of air than the diffuser pattern at section 3e5 of the aeration tank (bottom).
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longitudinal flow velocities and the ammonium concentrations at three locations at this cross section over a period of 15 min. The vertical line marks the time for the plot in the left panel. In cross sectional directions, the flow field shows two countercurrent rotating cells. This flow feature is visible at the aeration sections 3e5, but is not present in the front part of the aeration tank (section 1e2). In longitudinal direction, the flow field also drastically changes at the intersection of the aeration patterns: Whereas in the front part of the reactor no preferential flow paths evolve, the forward flow alternates between the left and the right side of the tank with a period of approximately 5 min. This flow alternation starts at the intersection of the two aeration patterns. The left panel of Fig. 3 shows the situation with strong forward flow at the left side of the tank (subplot A), an intermediate situation with approximately equal flow distribution (subplot B) and the situation with strong forward flow at the right side of the tank (subplot C). This flow alternation is also represented in the distribution of ammonium in the reactor. At a specific location in the cross section, the concentration oscillates according to the flow alternation which transports water that is rich in ammonium from the front part of the tank during the forward flow periods and which transports almost no water during the stagnation periods. The right panel of Fig. 3 shows the time series of longitudinal flow velocity and ammonium at three locations at this cross section. These are locations where ammonium and flow velocity measurements were performed.
3.2.
Flow velocity and ammonium measurements
Flow velocities could be measured simultaneously at four locations. These measurements confirm the oscillatory flow behavior as predicted by the CFD simulation. To get information on the flow velocity distribution for a cross section, subsequent velocity measurements were combined. Therefore, they needed to be synchronized in time which was done using an ammonium measurement that recorded the flow oscillation during the entire measurement campaign. The results are shown in Fig. 4. They are presented the same way as Fig. 3. The left panel shows linearly interpolated longitudinal flow velocities at the 30 m cross section. Again, three typical situations of the oscillatory flow phenomenon are shown: The situation with strong forward flow at the left side of the tank is presented in subplot A, an intermediate situation with approximately symmetric flow distribution in subplot B and finally subplot C shows the situation with strong forward flow at the right side of the tank. The flow pattern is also expressed in the ammonium distribution. The measured ammonium concentration as well as the flow velocity oscillate with a period of approximately 7 min. The right panel of Fig. 4 shows the time series of longitudinal flow velocity and ammonium at three locations at this cross section. During the flow velocity measurements, the air flow rate was kept constant. However, in regular operation aeration intensity is adjusted dynamically based on an oxygen concentration measurement. Since ammonium was long term monitored, the dependency of the oscillation period on the aeration flow rate could be determined during
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Fig. 3 e Predicted flow field and ammonium concentrations at the 30 m cross section. The left panel shows the flow velocities at three points in time capturing the main characteristics of the oscillatory flow phenomenon. Vectors illustrate cross sectional flow components and contours refer to the flow velocity in longitudinal direction. The three points in time are indicated in the right panel by a vertical line together with the time series of longitudinal flow velocities and ammonium at the three locations marked in the left panel plots.
regular plant operation. At an air flow rate of 1.1 m3 s1, the oscillation period is found to be 7 min in average whereas it drops to 6 min for a 25% increase in air inflow and rises to 8 min for 25% decrease in air inflow. This flow pattern is seen at the intersection of the two aeration patterns. Extensive ammonium measurements that have been performed in longitudinal direction of the tank (Gresch et al., 2010) show, that this flow pattern essentially covers the center and back part of the tank. It is not expressed in the front part, where the aeration pattern is homogenous.
4.
Discussion
4.1.
CFD simulation
The CFD simulation is validated by measurements of the flow velocity and by reactive tracer measurements. The main focus lays on the prediction of the main characteristics of the flow field and less attention is given to a direct comparison of
predicted and measured values at specific locations. Especially the ammonium measurements are not suited for direct comparison since only volumetric flow rate of the inflow was kept constant but not the ammonium inflow concentration. In the real plant, the latter is subjected to a diurnal pattern which was not considered in the CFD simulation. For the CFD simulations, a constant inflow concentration was used that allowed to make performance predictions. The measurements confirm the oscillatory flow behavior that occurs at the intersection of the different aeration patterns as predicted by the CFD simulation. A deviation is still found in the period of the oscillation which is approximately 5 min for the simulation and 7 min for the measurements. The value of 7 min is found on both sides of the tank (Fig. 4A,C) by independent measurements and proved to be stable for constant air flow rates. The deviation is more likely attributed to the CFD simulation. In particular, the fact that no other interaction forces than drag and buoyancy have been addressed in the simulation may cause this deviation. Introducing additional forces will affect the distribution of air and subsequently also the oscillation period.
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Fig. 4 e Results of flow velocity and ammonium measurements at the 30 m cross section. The left panel shows a linear interpolation of longitudinal flow velocities (based on 30 measurement locations) at three points in time capturing the main characteristics of the oscillatory flow phenomenon. The three points in time are indicated in the right panel by a vertical line together with the time series of longitudinal flow velocities and ammonium at the three locations marked in the left panel plots.
4.2. Role of aeration pattern and impact on plant operation and control To investigate the effects of the aeration pattern on the flow field two additional simulations with modified diffuser layouts
Fine grid, SST-turbulence model Coarse grid, SST-turbulence model Fine grid, k-ε turbulence model
0.2 Flow velocity (m/s)
A grid independency study was performed focusing again on the overall flow field. Three hexahedral grids with a total number of 150,000, 300,000 and 400,000 computational nodes were used. The oscillatory behavior could be observed for the two finer grids but not for the coarse one. In a further sensitivity study, the standard k3 model with default model constants is used instead of the SST keu model for the continuous phase. The finest grid is used in both simulations. Using the k3 turbulence model, the flow variations are heavily damped and the oscillatory behavior disappears. This is in accordance with many studies that show that the k3 turbulence tends to overpredict turbulence viscosity which smoothes out dynamic effects of the flow (Pope, 2000). Fig. 5 illustrates time series of longitudinal flow velocities for the simulations that were performed for this sensitivity study.
0.1
0.0
-0.1 0
5
10
15 20 Time (min)
25
30
Fig. 5 e Sensitivity of mesh size and turbulence model on simulation results. Time series of the longitudinal flow velocities at location P3a are shown. The oscillation as experimentally observed occurs only when a fine grid and the SST keu turbulence model are used.
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were performed. Both simulations use only one uniform aeration pattern throughout the tank. The first simulation uses the aeration pattern of section 2 (Fig. 1B) of the original diffuser setting for the whole reactor and will be called the homogenous layout in the further discussion. The second simulation used the pattern of section 3 (Fig. 1B) and will be called the line layout. These simulations are only different with regard to the spatial arrangement of air inflow. The results of these simulations were analyzed for the overall flow field, ammonium distribution and average air volume fraction. For both uniform layouts, the flow oscillation as reported earlier disappears. Using the line layout throughout the reactor produces a very stable flow field which is dominated by two countercurrent rotating cells. Velocities in longitudinal directions are generally small. Using the homogenous layout throughout the reactor produces a flow field that is dominated by the single bubble plumes and their inherent dynamics. This flow field is generally more dynamic on all scales. This again shows that the spatial arrangement of diffusers largely determines the flow field in aeration tanks. Non-aerated zones at the sidewalls, as they are present in the line layout, serve for a stable downwelling of the water which introduces and supports the existence of the two countercurrent rotating cells. A diffuser layout that covers also the near wall region disturbs the evolution of a distinct large scale flow field. Therefore, the flow field remains dominated by the individual bubble plumes. Accordingly, the oscillation in the ammonium concentration is also not present any more for both simulations with uniform aeration patterns. Fig. 6 shows the ammonium concentration, as predicted by the CFD simulation for both aeration patterns as well as a section of the measured ammonium concentration in the front part of the reactor. This part is dominated by the homogenous aeration layout. The measurements serve as a reference for the dynamics in ammonium concentration that occur in this tank. The dynamics are Simulation, homogenous layout Simulation, line layout Measurement
NH4-N (mg/L)
5
4
3
2 0
10
20
30 40 Time (min)
50
60
Fig. 6 e Time series of ammonium for the two different diffuser layouts. The simulated ammonium concentration with the homogenous layout (black line) and the ammonium concentration with the line layout (gray line) are compared with the ammonium measurements at the front part of the reactor (aeration section 2, corresponding to the homogenous layout).
well captured using the homogenous layout throughout the tank. However, when using the line layout, the ammonium concentration is more stable. This is in accordance with the longitudinal flow velocities that are also more stable in that situation. The different flow fields directly determine the contact time of air and therefore the efficiency of aeration. At low air contact times as encountered in this aeration tank with a depth of 4.2 m, oxygen transfer is directly proportional to the contact time. From the CFD simulations, the average air volume fraction can be extracted and the contact time can be calculated: For the line layout, an average air volume fraction of 0.4% (average contact time of 12 s) results. With the homogenous layout the volume fraction raises to 0.5% (contact time of 14.5 s). From the sensitivity study that was performed for the grid size and the turbulence model, we have evidence that this difference clearly exceeds model uncertainties. A 20% increase in contact time proportionally translates to an increase in oxygen transfer. With regard to the high costs of aeration for a wastewater treatment plant, energy savings of 20% are significant. The average air volume fraction for the real reactor with changing diffuser layouts lies in between the two uniform patterns (Table 1). Following the CFD results, a direct impact of the flow field on plant performance can be assessed. These results are summarized in Table 1 for the different diffuser arrangements. Hydraulic shortcuts and strong longitudinal mixing may considerably affect plant performance. In particular for substances with high degradation rates (> 90%) which is also the case for nitrification, this can be of importance (Gresch et al., 2010). The line layout produces a very stable flow field with low longitudinal mixing tending towards a plug-flow type flow. For reactions with a non-zero reaction order, this type of reactor shows a better performance than systems with more longitudinal mixing as e.g., the reactor having a homogenous aeration pattern. The real reactor that exhibits the oscillatory flow field shows the lowest performance. The flow oscillation produces preferential flow paths that negatively affect ammonium degradation. It has to be noted that the nitrification process is described by a Monod-type kinetics (Equation (1)). The reaction rate gradually changes its order from zero-order at high ammonium concentrations to first-order at low concentrations. As we expect a degradation rate in the order of 90% for the nitrification process, the concentration range in the reactor covers both the zero-order (front part) and the first-order (back part) regime. For zero-order reactions, there is no effect of the flow field on the performance. Therefore, the degradation rate reacts more sensitive on the flow field at low inflow concentrations (corresponding to a larger area of non-zero reaction rate in the reactor) than at high concentrations. For the analysis presented here, we used an average situation with regard to the flow rate and ammonium inflow concentration. In addition to that, oxygen transfer is not included in the CFD simulation which means that a limitation of ammonium degradation due to a lack of oxygen cannot be considered. Oxygen transfer may limit the nitrification rate in the highly loaded front part of the reactor. In this situation, improved aeration efficiency (homogenous layout) might be more decisive for ammonium degradation than a plug-flow type flow
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Table 1 e Prediction of mean air volume fractions and ammonium degradation for different diffuser arrangements. All results are based on CFD simulations. An average ammonium inflow concentration of 12.5 mg/L is used. Diffuser arrangement
Mixed layout Homogenous layout Line layout
Mean air volume fraction (%)
Ammonium outflow concentration (mg/L)
Ammonium degradation (%)
0.46 0.50
0.45 0.38
96.4% 97.0%
0.41
0.25
98.0%
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aerated zones at sidewalls reduce not only the aeration efficiency but also the longitudinal mixing. - CFD is able to reproduce the complex interactions of aeration and flow field in such a tank. However, in such complex flow situations, experimental validation is required. Sensitivities of grid size and turbulence model were tested and turned out to be of importance. - besides direct measurement of flow velocities, measurements of the distribution of reactive tracers are a valuable method for validation of CFD studies.
Acknowledgements field (line layout). To include this interaction, the CFD simulation needs to be expanded by a validated oxygen-transfer model. However, the relative importance of either improved flow field or improved oxygen transfer is strongly case-specific and will strongly depend on the actual geometry of the tank, the exact diffuser arrangement and on plant operation and control. Changes in the diffuser layout in the flow direction of aeration tanks may lead to preferential flow paths or short circuiting type of flow structures. In closed loop reactors where aerated zones and non-aerated zones alternate such undesired flow features are suppressed by installing slow speed mixers (Gillot et al., 2000). In the presented case of a fully aerated rectangular tank, the change in the aeration layout leads to a flow oscillation, producing not only the observed variations in ammonia but similarly also in oxygen concentration. Since an oxygen measurement device in the transition zone of the aeration patterns is used for process control, such oscillations are crucial. Braun and Gujer (2008) showed how these variations may destabilize an aeration control loop, a situation plant operators try to avoid.
5.
Conclusions
We successfully applied CFD enhanced with a biokinetic model for nitrification to a full scale aeration tank and validated it by measurements of flow velocities and ammonium distribution with high temporal and spatial resolution. This validated model allows discussing the interaction of aeration pattern, flow field, aeration efficiency and ammonium degradation. The main conclusions to be drawn from this work are: - the aeration pattern largely determines the flow field in aeration tanks. Due to the high energy input, aeration is the main driving force for the flow field. In this context, the flow field reacts very sensitive to details of the aeration pattern, such as non-aerated zones at sidewalls. - changes in the diffuser layout can lead to oscillations in the flow field. These oscillations negatively affect the performance of the reactor and are potential sources of unstable control loops. - longitudinal mixing and aeration efficiency are linked to the aeration pattern through the flow field that is induced. Non-
We thank the operational staff of the WWTP Werdho¨lzli, Raphael Bru¨gger and Luzia Sturzenegger for their help with the experiments. We acknowledge the Laboratory of Hydraulics, Hydrology and Glaciology at ETH Zu¨rich for the use of their acoustic Doppler velocimetry equipment and the support by Daniel Gubser. This work was supported by the Swiss National Science Foundation Grant 200021-113298, as well as by Eawag and ETH.
references
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Gresch, M., Braun, D., Gujer, W., 2010. The role of the flow pattern in wastewater aeration tanks. Water Science and Technology 61 (2), 407e414. Gujer, W., Henze, M., Mino, T., van Loosdrecht, M., 1999. Activated sludge model no. 3. Water Science and Technology 39 (1), 183e193. He, C., Wood, J., Marsalek, J., Rochfort, Q., 2008. Using CFD modeling to improve the inlet hydraulics and performance of a storm-water clarifier. Journal of Environmental EngineeringASCE 134 (9), 722e730. Hunze, M., 1996. Numerische Modellierung reaktiver Stro¨mungen in oberfla¨chenbelu¨fteten Belebungsbecken. (Numerical Modeling of Reactive Flows in Activated Sludge Tanks with Surface Aerators). Institut fu¨r Stro¨mungsmechanik und Elektronisches Rechnen im Bauwesen der Universita¨t Hannover, Hannover (in German). Ishii, M., Zuber, N., 1979. Drag coefficient and relative velocity in bubbly, droplet or particulate flows. Aiche Journal 25 (5), 843e855. Jakobsen, H.A., Lindborg, H., Dorao, C.A., 2005. Modeling of bubble column reactors: progress and limitations. Industrial & Engineering Chemistry Research 44 (14), 5107e5151. Julia, J.E., Hernandez, L., Chiva, S., Vela, A., 2007. Hydrodynamic characterization of a needle sparger rectangular bubble column: homogeneous flow, static bubble plume and oscillating bubble plume. Chemical Engineering Science 62 (22), 6361e6377. Launder, B.E., Spalding, D.B., 1974. The numerical computation of turbulent flows. Computer Methods in Applied Mechanics and Engineering 3 (2), 269e289. Le Moullec, Y., Potier, O., Gentric, C., Pierre Leclerc, J., 2008. Flow field and residence time distribution simulation of a crossflow gaseliquid wastewater treatment reactor using CFD. Chemical Engineering Science 63 (9), 2436e2449. Le Moullec, Y., Gentric, C., Potier, O., Leclerc, J.P., 2010. CFD simulation of the hydrodynamics and reactions in an activated sludge channel reactor of wastewater treatment. Chemical Engineering Science 65 (1), 492e498. Leary, R.D., Ernest, L.A., Katz, W.J., 1969. Full scale oxygen transfer studies of 7 diffuser systems. Journal Water Pollution Control Federation 41 (3P1), 459e473. Menter, F.R., 1994. 2-Equation eddy-viscosity turbulence models for engineering applications. AIAA Journal 32 (8), 1598e1605. Nielsen, K.D., Weber, L.J., Muste, M., 1999. Capabilities and limits for ADV measurements in bubbly flows. In: 28th IAHR Congress, Graz, Austria, Proceedings.
Ortmanns, C., Minor, H.-E., 2006. Entsander von Wasserkraftanlagen (Desilting chambers of hydro power plants). Versuchsanstalt fu¨r Wasserbau Hydrologie und Glaziologie ETH-Zentrum, Zu¨rich (in German). Pope, S.B., 2000. Turbulent Flows. Cambridge University Press, Cambridge. Saalbach, J., Hunze, M., 2008. Flow structures in MBR-tanks. Water Science and Technology 57 (5), 699e705. Sato, Y., Sekoguchi, K., 1975. Liquid velocity distribution in twophase bubble flow. International Journal of Multiphase Flow 2 (1), 79e95. Schmit, F.L., Wren, J.D., Redmon, D.T., 1978. Effect of tank dimensions and diffuser placement on oxygen-transfer. Journal Water Pollution Control Federation 50 (7), 1750e1767. Sommerfeld, M., Van Wachem, B., Oliemans, R., 2008. Best Practice Guidelines for Computational Fluid Dynamics of Dispersed Multiphase Flows. ERCOFTAC. S.l. Tabib, M.V., Roy, S.A., Joshi, J.B., 2008. CFD simulation of bubble column e an analysis of interphase forces and turbulence models. Chemical Engineering Journal 139 (3), 589e614. Tchobanoglous, G., Burton, F.L., Stensel, H.D., 2003. Wastewater Engineering (Treatment Disposal Reuse), fourth ed. Metcalf & Eddy, Inc., Boston. McGraw-Hill Book Company. Templeton, M.R., Hofmann, R., Andrews, R.C., 2006. Case study comparisons of computational fluid dynamics (CFD) modeling versus tracer testing for determining clearwell residence times in drinking water treatment. Journal of Environmental Engineering and Science 5 (6), 529e536. Thiersch, B., Valentin, F., 2002. Stro¨mungsstrukturen in Belebungsbecken und Einfluss auf den Sauerstoffeintrag. (Hydrodynamic structures of aeration tanks and their influence on aeration efficiencies). Wasserwirtschaft (1e2), 34e38 (in German). Wagner, M.R., Popel, H.J., 1998. Oxygen transfer and aeration efficiency e influence of diffuser submergence, diffuser density, and blower type. Water Science and Technology 38 (3), 1e6. Zhang, D., Deen, N.G., Kuipers, J.A.M., 2006. Numerical simulation of the dynamic flow behavior in a bubble column: a study of closures for turbulence and interface forces. Chemical Engineering Science 61 (23), 7593e7608. Zima, P., Makinia, J., Swinarski, M., Czerwionka, K., 2009. Combining computational fluid dynamics with a biokinetic model for predicting ammonia and phosphate behavior in aeration tanks. Water Environment Research 81 (11), 2353e2362.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 1 9 e8 2 7
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Characteristics of adsorbents made from biological, chemical and hybrid sludges and their effect on organics removal in wastewater treatment Zhi-hui Pan, Jia-yu Tian*, Guo-ren Xu, Jun-jing Li, Gui-bai Li* State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, PR China
article info
abstract
Article history:
Meso-macropore adsorbents were prepared from biological sludge, chemical sludge and
Received 27 May 2010
hybrid sludge of biological and chemical sludges, by chemically activating with 18.0 M
Received in revised form
H2SO4 in the mass ratio of 1:3, and then pyrolyzing at 550 C for 1 h in anoxic atmosphere.
5 September 2010
The physical and chemical characteristics of the sludge-based adsorbents were examined
Accepted 9 September 2010
in terms of surface physical morphology, specific surface area and pore size distribution,
Available online 17 September 2010
aluminum and iron contents, surface functional groups and crystal structure. Furthermore, the adsorption effect of these adsorbents on the organic substances in wastewater was also
Keywords:
investigated. The results indicated that the adsorption capacities of the sludge-based
Sludge-based adsorbent
adsorbents for UV254 were lower than that of commercial activated carbon (AC), whereas
Chemical sludge
the adsorption capacities of the adsorbents prepared from hybrid sludge (HA) and chemical
Hybrid sludge
sludge (CA) for soluble CODCr (SCODCr) were comparable or even higher than that of the
Pore size distribution
commercial AC. The reasons might be that the HA and CA possessed well-developed
Metal-oxides
mesopore and macropore structure, as well as abundant acidic surface functional groups.
Acidic surface functional groups
However, the lowest adsorption efficiency was observed for the biological sludge-based
Wastewater treatment
adsorbent, which might be due to the lowest metal content and overabundance of surface acidic functional groups in this adsorbent. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Due to the rapid urbanization and stringent effluent standard implemented for municipal wastewater treatment in recent decades, more and more sewage sludge is being generated all over the world. The sludge will cause the pollution problem in the environment in turn, if proper treatment and disposal are not implemented. Sewage sludge is carbonaceous (Martin et al., 2004) in nature. Hence, it might be a promising way to prepare adsorbent from sewage sludge. Thus, not only the problem of second pollution caused by the sludge could be eco-friendly
solved, the prepared adsorbent could also be applied in wastewater treatment. In previous studies, many researchers (Rozada et al., 2005; Wang et al., 2008; Chiang et al., 2009) have used activated sludge generated from biological treatment for the preparation of adsorbent, because secondary biological treatment technology is prevalent in urban wastewater treatment plant. The results showed that the sludge-adsorbent had different kinds of applications with high efficiencies, such as adsorbing different dyes (Rozada et al., 2007), organic compounds (Przepiorski, 2006), heavy metals (Seredych and Bandosz, 2006) and even removing acidic gases (Ansari et al., 2005).
* Corresponding authors. Tel.: þ86 451 86284512; fax: þ86 451 86282100. E-mail addresses:
[email protected] (J.-y. Tian),
[email protected] (G.-b. Li). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.008
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However, because of the vast investment and high operation costs required by the secondary biological treatment units, chemical enhanced primary treatment (CEPT) is now attracting more and more attentions for urban wastewater treatment (Guida et al., 2007; Harleman and Murcott, 1999; Poon and Chu, 1999). In developing countries and underdeveloped countries, as recommended by the International Review Panel (IRP) (EPD, 1995), CEPT should be adopted in the newly-building works to produce an effluent with the quality that is almost comparable to that treated by biological methods. But one of the problems evoked by CEPT is the production of large amount of chemical sludge, which is 1.5e2 folds of that produced by conventional primary treatment (Semerjian and Ayoub, 2003). Xu et al. (2005) employed chemical sludge produced in the laboratory to prepare a useful adsorbent, which was successfully applied in sewage treatment. However, there are few researches that have focused on the preparation of adsorbent from chemical sludge generated by CEPT, which deserves further studies. On the other hand, more stringent regulations are often implemented in developed countries for effluent discharge. In this case, CEPT is usually combined with secondary biological treatment for advanced municipal wastewater treatment (Haydar and Aziz, 2009; Song et al., 2001). Therefore, the final sludge from these plants contains both the chemical sludge and biological sludge. At present, no researcher has ever investigated the preparation of adsorbent from the hybrid sludge, and the application of the adsorbent in wastewater treatment. Therefore, the objective of the present work is to compare the characteristics of the adsorbents prepared from the sludges generated in biological, chemical and chemicalebiological wastewater treatment, and to investigate the effectiveness of these three adsorbents for applying in wastewater treatment.
2.
Materials and methods
2.1.
Materials
After that, 5 g of each sludge was transferred into a covered ceramic cup placed in an iron recipient. The inter-space between the cup and the recipient was filled up with activated carbon particles, to prevent O2 from entering the ceramic cup (Gasco´ et al., 2005). Then, the samples were pyrolyzed in a muffle furnace. The furnace temperature was gradually increased to 550 C at a rate of 18 C/min, and the final temperature of 550 C maintained for 1 h. Thereafter, the iron recipient containing the pyrolyzed samples was taken out of the furnace and placed into another bigger iron recipient, where high pure N2 (99.999%) was in-poured until the pyrolyzed products were cooled to the room temperature. After being pyrolyzed, the products were ground and sieved to powder approximately 0.1 mm and washed with 3.0 M HCl to remove the acid-soluble inorganic impurities. Then, the samples were thoroughly washed with deionized water until the pH of rinsed water became constant. Finally, the samples were dried at 103 C for 24 h. The adsorbent prepared from the biological sludge, chemical sludge and hybrid sludge were designated as BA, CA and HA, respectively.
2.3.
Characterization of adsorbents
2.3.1.
Physical characterization
To investigate the surface morphology characteristics of the adsorbents prepared from different sewage sludges, the samples were gold-coated by a sputter and observed under a scanning electron microscopy (SEM, HITACHI S4800 HSD, Japan). To determine the surface area and pore size distribution of the adsorbents, the samples were analyzed with a surface area analyzer (ASAP 2020 M, Micromeritics Instrument Co.) by using the adsorption isotherms of gas adsorption (N2, 77 K). The specific surface area (SBET) was calculated by BrunauereEmmetteTeller (BET) equation (Braunauer et al., 1938). Micropore volume (Vmi) was obtained by t-plot method (Lowell ˚) and Shields, 1984). Macropore and mesopore (17.00e3000.00 A volume (Vmeþma) as well as the average pore diameter (Dp) and pore size distribution were calculated by the Barrette JoynereHalenda (BJH) method (Barrett et al., 1951).
2.3.2. The biological sludge (BS) was obtained from Taiping wastewater treatment plant (Harbin, China), where the conventional secondary biological treatment by activated sludge was employed. On the other hand, the chemical sludge (CS) and the hybrid sludge of biological and chemical sludges (HS) were collected from the Wenchang wastewater treatment plant (Harbin, China), where the hybrid processes of enhanced primary treatment by dosing polymeric ferric aluminous chloride (PFAC) and secondary biological treatment by activated sludge was implemented.
2.2.
Sludge-based adsorbents
The dried sludges were ground and sieved to obtain the powder with the diameter < 0.1 mm, and then impregnated in H2SO4 (18.0 M) with the mass ratio of 1:3 which were kept in contact with the acid in air for 24 h. Then, the mixtures were dried at 103 C in air for another 24 h.
Chemical characterization
The elemental contents of aluminum and iron in the adsorbents were measured by an Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES, Optima 5300DU, Perkin Elmer Inc). To make the metals soluble, the adsorbents were pre-digested by soaking 0.3 g of each adsorbent in the mixture of 8 ml HF and 3 ml HNO3 at 180 C for 30 min in a microwave digestion device (Mars-5 CEM, USA). In order to determine the quantities of metals leached from the adsorbents, a leaching test was carried out. Adsorbents (1.0 g) were soaked in 1.0 L of deionized water (pH: 7.0) and mixed for 30 min. The metal concentrations in the resultant leachate were then analyzed ICP-AES. The carbon and hydrogen (CH) elemental composition of the sludge-based adsorbents was measured by an Elemental Analyzer (CHNS-Vario EL). The amount of acidic surface functional groups (carboxyl, lactone, hydroxyl and carbonyl) of the sludge-based adsorbents and AC could be estimated by using the titration method
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 1 9 e8 2 7
of Tessmer et al. (Chen et al., 2002). According to the method, 0.5 g of the adsorbents was added into each of the following base solution (100 mL): NaHCO3 (0.05 M), Na2CO3 (0.05 M) and NaOH (0.05 and 0.25 M), and agitated for 48 h with a rotary shaker at room temperature. After that, the adsorbents were separated by filtration and each of 10 mL of the filtrate was back titrated by HCl (0.1 M) to determine the amount of base consumed by the acidic functional groups on the adsorbents surface. The crystal structures of the adsorbents were analyzed by a powder X-ray diffractometer (XRD, Rigaku D/max-2000) with monochromatic Cu Ka radiation (45 kV, 50 mA). To qualitatively estimate the surface functional groups, the infrared spectra of the adsorbents were obtained by Fourier Transform Infrared Red (FTIR) spectroscope (Perkin Elmer Spectrum One B) in the range of 4000e400 cm1 region using potassium bromide (KBr) pellet method (Ros et al., 2006).
2.4.
The jar tests of aqueous adsorption of wastewater
The wastewater sample (pH: 7.2, soluble CODCr (SCODCr) value: 125.6 mg/L, UV254 value: 0.199 cm1) was collected from the grit chamber of Taiping wastewater treatment plant (Harbin, China). The adsorption experiments were performed under a Programmable Jar Tester (Shenzhen, China) with six acrylic square beakers having a capacity of 1.0 L. In this research, the adsorbents prepared from sewage sludges were mainly intended to apply in the primary treatment unit. To better simulate the practical process, the adsorption procedure of the jar test was that rapid mixing at 300 rpm for 60 s, followed by slow mixing at 80 rpm for 15 min and settling for 20 min. Preliminary experiment showed that the adsorption process was rather close to the equilibrium in terms of SCODCr and UV254 removal. Thus, the above mentioned contact time was used in the adsorption experiments. During the experiments, 0.05e1.5 g of the adsorbents was added to 1.0 L of the wastewater. After adsorption, supernatants were collected and filtered through 0.45 mm microfiltration (MF) membrane (cellulose acetate material) to measure their UV254 and SCODCr. UV254 was detected by spectrophotometer 752 (Shanghai, China) at the wavelength of 254 nm; while SCODCr was determined according to the method described by Xu et al. (2005). To determine the size fractionation of UV254 and SCODCr in raw and treated wastewater, the samples were first filtered through 0.45 mm MF membrane. Then, the samples were fractionated by a series of ultrafiltration membranes (polyamide material, Millipore Inc., USA) with different molecular weight (MW) cut-offs (30, 10, 5, and 1 kDa, respectively). The raw and treated wastewater were filtered through 0.45 mm MF membrane and adjusted to pH 2 before being passed through XAD-8 resin (Beijing, China) which adsorbed the hydrophobic organic matter. The unadsorbable fraction (hydrophilic) was adjusted to pH 7 and determined by detecting its DOC which was measured by the TOC analyzer (TOC-VCPH, SHIMADZU, Japan). And the hydrophobic fraction was calculated by the difference between the wastewater and the hydrophilic fraction. A commercial powdered activated carbon (Jiangsu, China. Mean particle diameter of 0.1 mm) was investigated in parallel
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with the adsorbents produced from sewage sludges for comparative purpose. Hereinafter, the commercial activated carbon was referred as AC.
3.
Result and discussions
3.1.
Surface physical morphology
Scanning electron microscopy (SEM) was employed to observe the surface physical morphology of the adsorbents prepared from different sewage sludges. Fig. 1 showed the SEM photographs of the sludges and corresponding adsorbents. When comparing the SEM pictures of the adsorbents with those of the corresponding sludges, it could be seen that the surface morphologies were changed greatly after being pyrolyzed. The surface of the sludges was dense and relatively smooth, and no noticeable pore could be identified. However, after the activation, visible pores were developed on the adsorbents, with the increase of the surface roughness.
3.2.
Surface area and pore size distribution
Table 1 gave the structural parameters obtained from N2 adsorption isotherms. Due to the cautery effect of high concentration of H2SO4, the micropores of the BA, CA and HA were destroyed and enlarged during the activation. Thus, the pores in these absorbents were mainly composed of mesopores and macropores and even supermacropores, as shown in Table 1 and Fig. 2. Fig. 2 illustrated the pore size distribution of the sludgebased adsorbents and the commercial AC. The BA exhibited two pore diameter characteristics: one was in the vicinity of ˚ (mesopore) and the other was above 1000 A ˚ (macropore). 20 A On the other hand, the pore volume of CA was mainly ˚ . While the HA distributed in the mesopore range of 20e100 A ˚ , which was had the wide pore diameter band of 100e1000 A principally in the mesopore and macropore range. By contrast, the commercial AC possessed a narrow mesopore distribution ˚ . As shown in Table 1, predominantly in the range of 20e30 A the average pore diameter of these absorbents was in the following sequence: HA > BA > CA > AC. The larger pore diameter might facilitate the transport of molecules into the pores of the sludge-based adsorbents, thus increase the adsorption capacity for organic substances with high MW.
3.3.
Chemical composition
Table 2 revealed the contents of aluminum and iron in the adsorbents produced from the sewage sludges and the amounts leached into water. For comparison purpose, aluminum and iron contents in the raw sludges before pyrolysis were also determined. It could be noticed that although the HCl washing desorbed a portion of the metals from the adsorbents during the preparation process, the majority of them still remained in the absorbents. As shown in Table 2, the remained aluminum and iron showed only slight leaching characteristics, which implied that either the adsorbents had strong affinity for metals adsorption or these metals were bound up with the adsorbents structure.
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Fig. 1 e SEM of the sludges and corresponding adsorbents.
It could also be seen that the metal content of CA was the highest among the adsorbents, because its precursor sludge was produced from CEPT where inorganic coagulant PFAC was dosed. On the other hand, the aluminum and iron contents in BA were much lower, as the raw material was secondary biological sludge. While the metal content of HA fell between that of CA and BA, as its precursor was the hybrid sludge of the biological and chemical sludges. Furthermore, it could be noticed that the amounts of aluminum and iron in AC were much lower than that in CA and HA, and were comparable to that in BA.
Table 1 e Surface area and porosity of the adsorbents and activated carbon. ˚) Adsorbent SBET (m2/g) Vmi (m3/g) Vmeþma (m3/g) Dp (A BA CA HA AC
89.5 126.9 103.4 1114.7
0.015 0.021 0.027 0.147
0.32 0.25 0.33 0.39
335.7 92.5 376.3 33.5
3.4.
Surface chemistry structure
The carbon content and surface chemistry characteristics (in terms of functional groups) of the sludge-based absorbents and the commercial activated carbon (AC) were also measured, as shown in Table 3. It could be noticed that the carbon contents in the three sludge-based adsorbents were rather similar, which were much less than that in the AC. However, the sludge-based adsorbents exhibited much higher contents of acidic surface functional groups due to the activation by high concentration of H2SO4. Furthermore, it could also be seen that different raw materials of sludges produced adsorbents with different surface functional groups contents. The HA and BA possessed higher amount of surface functional groups in comparison with the CA, which might be contributed to the higher biomass proportion in their precursors. Fig. 3 showed the FTIR spectrum of the three sludge-based adsorbents and the commercial AC. The absorption bands and peaks on the FTIR spectrum demonstrated the existence of some surface functional groups on the adsorbents. Similar peaks were obtained for the adsorbents prepared from the three kinds of sewage sludges, indicating that there were
823
0.04
0.0012
3
BA CA HA AC
3
Adsorbents dV/dD (cm /g.A)
0.0016
Activated Carbon dV/dD (cm /g.A)
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 1 9 e8 2 7
0.03
0.0008
0.02
0.0004
0.01
0.0000
0.00 10
100
Table 3 e Contents of C and H as well as acidic surface functional groups of the sludge-based adsorbents and activated carbon. Ultimate analysis (wt. %)
BA CA HA AC
C
H
42.64 36.31 37.17 70.95
3.198 2.272 3.431 1.703
Acidic surface functional groups (mmol/L)
Carboxyl Lactone Hydroxyl Carbonyl Total 2.331 1.026 2.191 0.128
0.525 0.62 0.333 0.096
0.617 0.549 0.343 0.238
1.676 1.313 2.163 0.089
5.149 3.508 5.030 0.551
1000
Pore Diameter (A)
similar surface functional groups among them. The flat and broad band at approximate 3500 cm1 was assigned to eOH vibration, which could be ascribed to adsorbed water or hydroxyl groups (Demirbasa et al., 2009). While another strong band at 1640 cm1 was assigned to eC¼Ce, which was probably in enolic form (Duggan and Allen, 1997). A stronger and broader band was further observed between 1050 and 1100 cm1, which might be attributed to the structure of either SieOeC or SieOeSi groups (Duggan and Allen, 1997). It was also noticed that this peak on the AC was difficult to be distinguished as compared with the sludge-based adsorbents, implying that there was almost not SieOeC or SieOeSi groups on AC, i.e. not much silicon was presented in the commercial activated carbon. However, there was a certain amount of silicon element in the sludge-based absorbents, which could give rise to the less polar characteristics of the adsorbents and thus higher affinity for non-polar organic matter as compared with the commercial AC (Chen et al., 2002). The main functional groups on the sludge-based adsorbents such as eOH, eC¼Ce and SieOeC or SieOeSi indicated that the adsorbents could uptake organic matter through surface complexation mechanisms. As shown in Fig. 4, the XRD patterns of the adsorbents produced from sewage sludges pointed out that quartz (JCPDS 47-1144) was the main inorganic constituent of the adsorbents. The other two diffraction peaks of the adsorbents could be assigned to FeC (JCPDS 03-0411) and Al2O3 (JCPDS 50-1496). The XRD analysis proved that there was metal-oxide presented on the surface of the three adsorbents. The AC diagram
Table 2 e Contents of Al and Fe in the raw sludges, corresponding adsorbents and leachate from the adsorbents (mg/g). BS
CS
HS
BA CA
showed in Fig. 4 represented the typical XRD pattern of the commercial activated carbon produced from coke, coconut shell, coal or wood.
3.5.
Aqueous adsorption tests
Fig. 5 (a and b) presented the experimental results on the adsorption of UV254 and SCODCr from wastewater by the three sludge-based adsorbents and the commercial AC. It was obvious that UV254 and SCODCr removals continuously increased with the increase of adsorbents dosage. Excellent adsorption for UV254 was achieved by the commercial AC, with the efficiency reaching to 66.7% even at a low dosage of 0.5 g/L. The removal efficiencies of CA and HA for UV254 were 67.1% and 51.7% at the dosage of 1.5 g/L, respectively. However, the adsorption capacity of BA for UV254 was much lower, with the removal rate as low as 30.4% at the same dosage of 1.5 g/L. On the other hand, as for the adsorption of SCODCr, it could be observed from Fig. 5b that the sludge-based absorbents exhibited comparable or even higher adsorption capacity as compared with the commercial AC. The adsorption efficiency of SCODCr by AC was 62.2% at the dosage of 1.5 g/L; while CA achieved better SCODCr removal, with the efficiencies reaching to 66.7% at the same dosage. And the removal efficiency of HA was 55.6% at this dosage, which were comparable with that of the commercial AC. Xu et al. (2005) had also investigated the effectiveness of the adsorbent prepared from chemical sludge in sewage treatment, and reported that the adsorbent could achieve 77.8% removal for UV254 and 50.6% 4000
3500
3000
2500
1.21 0.68 <0.05 0.37 0.14 <0.05
1500
1000
500
1500
1000
500
CA
BA
HA AC BALa CAL HAL ACL
Al 19.8 38.8 30.1 5.17 20.1 13.1 3.62 0.23 Fe 3.25 12.9 7.37 1.92 9.42 3.38 1.96 0.11
2000
HA
Adsorbance
Fig. 2 e BJH mesopore size distribution of the adsorbents and activated carbon.
AC 4000
3500
3000
2500
2000 -1
a The abbreviation codes of BAL, CAL, HAL and ACL were used for the leachate concentration of BC, CA, HA and AC after mixing with deionized water, respectively.
Wavenumber(cm ) Fig. 3 e FTIR spectra of the adsorbents and activated carbon.
824
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 1 9 e8 2 7
Q
A
AC
F,Q
Intensity
Q Q
A
F,Q
A
F,Q
A Q
A
10
20
30
A F
Q
A
F
Q
A
F
40
A
A
A
50
F
F
HA
F
F
CA
F
F
BA
60
70
80
2 Thata (deg) Fig. 4 e X-ray diffraction patterns of the adsorbents and activated carbon. Band labeling: Fe, Ferrite; Al, Aluminum Oxide; Q, Quartz.
for CODCr at the dosage of 3.0 g/L, respectively. It could be noticed that although the removal efficiency of the CA for UV254 at the lower dosage of 1.5 g/L was less than the adsorbent made by Xu et al. (67.1% vs. 77.8%), CA achieved much higher SCODCr removal even at the lower dosage (66.7% vs. 50.6%), which might be attributed to the different pore size distribution between the two adsorbents resulting from 100
UV254 removal (%)
80
a
BA CA HA AC
60
40
20
0 0.0
0.3
0.6
0.9
1.2
1.5
Dosage of adsorbent (g/L) 70
SCODCr removal (%)
60
b
BA CA HA AC
50 40
different preparation procedures. However, the removal efficiency of BA for SCODCr was only 42.2% at 1.5 g/L, which was much lower than that of CA and HA at the same dosgae. Similar result (about 45% of removal efficiency) was also obtained during the adsorption of COD from wastewater by the biological sludge-adsorbent of mesopore structure at the same dosage of 1.5 g/L (Yu and Zhong, 2006). The sludge-based adsorbents, especially the CA and HA, would release the aluminum and iron when soaked in water (as shown in Table 2), which could work as coagulant and remove the supercolloidal- and colloidal-COD through flocculation. On the other hand, the aluminous and ferric hydroxide in the sludges would be dehydrated and turned into aluminum and iron oxides during the preparation of the absorbents. These aluminum and iron oxides were able to facilitate the adsorption of organic matter in the wastewater. The structural metal-oxides on the surface were hydrated and were converted into surface hydroxide groups in the following way (Stumm and Morgan, 1970): MeO þ Hþ %MeOH
(1)
MeOH þ Hþ %MeOHþ 2
(2)
These hydrolysis products of multivalent ions would be adsorbed more readily at particleewater interfaces than nonhydrolyzed metal ions, which could react with cations or anions as follows: Me OH þ HAO2 4 / Me OAO3 H þ OH
(3)
Me OH þ A2þ / Me OAþ þ Hþ
(4)
It could be seen that the metal hydrolysis products possessed high adsorption capacities for cations and anions. Due to the high reactive activity of the metal hydroxo with the organic matter in wastewater, the CA and HA which originated from chemical sludge and hybrid sludge achieved higher removal efficiencies for SCODCr and UV254 as compared with BA. This might also be the reason that the adsorption capacity of commercial AC for SCODCr was lower than that of the CA, even though it had much higher specific surface area. Furthermore, it could be noticed that the three sludge-based absorbents achieved better removal for SCODCr than that for UV254; while the adsorption efficiency of the commercial AC for UV254 was higher than that for SCODCr. This might be due to the different characteristics of pore size distribution of the sludgebased absorbents and the commercial AC, which would be discussed in detail in Section 3.6.
30
3.6.
20
Fig. 6 (a and b) presented the size distribution of organic substances in the raw wastewater and treated effluent by the sludge-based adsorbents and the commercial activated carbon (0.3 g/L). From Fig. 6, it could be clearly noticed that the organic substances in raw wastewater were mainly composed of two opposite portions in terms of size distribution, i.e. one was colloidal-COD fraction (30 kDae0.45 mm), and the other was the soluble portion (<1 kDa). The size-fractionation characteristic
10 0.0
0.3
0.6
0.9
1.2
1.5
Dosage of adsorbent (g/L) Fig. 5 e Effect of adsorbents dosage on the removal efficiencies for (a) UV254 and (b) SCODCr in 1.0 L wastewater (initial pH: 7.2, SCODCr: 125.6 mg/L and UV254: 0.199 cmL1).
Size fractions of organic substance
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 1 9 e8 2 7
70 60
Wastewater BA CA HA AC
50 40 SCODCr (mg/L)
a
30 20 10 0 -10 30k-450nm
10k-30k
5k-10k
1k-5k
<1k
Molecular Weight 0.16 0.14 0.12 0.10 -1
UV254 (cm )
b
Wastewater BA CA HA AC
0.08 0.06 0.04 0.02 0.00 30k-450nm
10k-30k
5k-10k
1k-5k
<1k
Molecular Weight
Fig. 6 e Size distribution of (a) SCODCr and (b) UV254 of the raw and treated wastewater after adsorption by adsorbents/activated carbon (0.3 g/L) (initial pH: 7.2, SCODCr: 125.6 mg/L and UV254: 0.199 cmL1).
of raw wastewater was similar with the result obtained by Karahan et al. (2008). The two fractions of organic substances accounted for 39.0% and 49.8% of the total SCODCr, respectively. The 11.2% of remaining organic compounds was distributed in the colloidal range. On the other hand, as for UV254, the majority of the organic matter existed in the soluble fraction, as illustrated by the fact that 67.0% of UV254 came from the fraction below 1 kDa, 19.2% fell between 30 kDa and 0.45 mm, and 13.8% was distributed in the range of 1 kDae30 kDa. Fig. 6a illustrated the results of SCODCr removal by the sludge-based adsorbents and commercial AC. Due to the large amount of macro- and meso-pores on the sludge-based absorbents (discussed in Section 3.2), colloidal-COD fraction (30 kDae0.45 mm) could be easily eliminated through adsorption. On the contrary, the commercial AC with micropore structure was not as effective as the sludge-based adsorbents for the removal of organic substances with large MW, especially for the colloidal fraction (30 kDae0.45 mm). In previous studies, several researchers had found that the higher SBET and larger Vmi of the sludge-based adsorbents were favorable for the adsorption of phenolic compounds (Chen et al., 2002)
825
and dyes such as Acid Brilliant Scarlet GR (Wang et al., 2008) and Orange II (Chiang et al., 2009). However, in preliminary experiments of this study, it was found that the meso- and macropore structure of the adsorbents were more suitable for COD removal when applying in wastewater treatment, which leaded to the low SBET. This result was in accordance with Yu and Zhong (2006), who reported that it was beneficial for removing COD in wastewater treatment by the adsorbent with high mesoporosity. For the low MW SCODCr, especially for the fraction below 1 kDa, the CA achieved the highest adsorption removal efficiency, followed by the HA. However, the BA exhibited a low removal for the low MW SCODCr. The result might be attributed to the roles of functional metal-oxides on the surface of CA and HA, which had been discussed in Section 3.5. Although AC had a well-developed micropore and much higher specific surface area, the removal efficiency of AC for SCODCr was lower than that of CA and HA (including the <1 kDa fraction), which might be ascribed to the structural metal-oxides on the surface of the sludge-based adsorbents. The AC might be only preferable to adsorb some particular organic matter, such as UV-active substances, as discussed below. Fig. 6b showed the UV254 removal through adsorption treatment by the sludge-based adsorbents and commercial AC. Due to the abundant macro- and meso-pores on the BA, CA and HA (discussed in Section 3.2), these three absorbents achieved similar removal for UV254 in the range of 30 kDae0.45 mm, with the efficiencies of 41.0%, 53.8% and 46.1% respectively. The relatively higher efficiency of CA for UV254 removal as compared with BA and HA (by 5e10%) could be attributed to the more metal-oxides presented on the surface of this adsorbent. As for the UV254 below 1 kDa, the adsorption capacities of these absorbents became lower due to the lack of micropores. However, owing to the metal-oxides on the surface of CA and HA, their removals for UV254 in this fraction were better than that of BA. On the other hand, in comparison with the sludge-based adsorbents, the commercial AC exhibited higher capacity for the adsorption of UV254 in the whole size range, especially for the fraction below 1 kDa. The reason was that the AC had well-developed microporous structure and high surface area (shown in Table 1), which was favorable for adsorbing low MW organic substances. Furthermore, the commercial AC might have selective-adsorption characteristics for UV-active substances, such as aromatic (Vilge´-Ritter et al., 1999) and unsaturated double-bond compounds (Bolto et al., 1999).
3.7. Hydrophilic and hydrophobic fractions of organic substance Fig. 7 presented the adsorption performance of the sludgebased adsorbents and commercial AC (0.3 g/L) for different organic fractions in the wastewater. Through fractionation by XAD-8 resin, it was found that the organics in the wastewater comprised of 59.1% of hydrophilic fraction and 40.9% of hydrophobic fraction in terms of DOC. The three sludge-based adsorbents achieved similar adsorption efficiencies for the hydrophobic fraction, which were comparable with that of the AC. Thus, the different adsorption efficiencies of these
826
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 1 9 e8 2 7
from hybrid sludge and chemical sludge for SCODCr were comparable or even higher than that of the AC. Under the role of the structural metals-oxides and the acidic functional groups on the surface, the adsorbent made from the chemical sludge exhibited the highest removal for organic substances, followed by HA; while the adsorption capacity of the biological sludge-based adsorbent was the lowest. From the results, it could be noticed that the adsorbent made from the sludge in wastewater treatment plants, especially the chemical sludge and hybrid sludge, possessed excellent adsorption capacity for the organic substances in wastewater. Thus, it might be a reasonable method to dispose sewage sludge by preparing adsorbent and applying in wastewater treatment.
70 BA CA HA AC
60
DOC removal (%)
50 40 30 20 10 0 Hydrophilic
Hydrophobic
Fig. 7 e Removal efficiencies of adsorbents/activated carbon (0.3 g/L) for different organic fractions in teams of DOC (initial pH: 7.2, DOC value of hydrophilic: 39.62 mg/L and hydrophobic: 27.14 mg/L).
adsorbents for total organics might be attributed to their different adsorption capacities for hydrophilic organics. The CA achieved the highest removal efficiency for the hydrophilic organic fraction, which amounted to 51.4% in terms of DOC. While the BA only obtained 20% of removal for the hydrophilic fraction, which was also lower than the 36.6% removal by HA and 45.1% by AC, respectively. The amounts of acidic functional groups reflected the hydrophilic property of the adsorbents, which had been indicated to have a positive effect on the uptake of hydrophilic organic compounds (Franz et al., 2000). Therefore, the highest adsorption capacity for hydrophilic organics by CA might be explained by the abundant acidic groups on it (Table 3). However, the overabundance of acidic groups on the surface would exert some negative effect on the adsorption of hydrophilic compounds in turn (Mahajan et al., 1980 and Franz et al., 2000), which might account for the lower adsorption efficiency of BA.
4.
Conclusion
Adsorbents could be prepared from biological sludge, chemical sludge and the hybrid sludge of biological and chemical sludges by 18.0 M H2SO4 activation with the mass ratio of 1:3, followed by pyrolysis at 550 C for 1 h. The pore size distributions of the adsorbents were mainly in the meso- and macro-pore range due to the cautery effect of high concentration of H2SO4. High contents of aluminum and iron were retained in the adsorbents, especially for CA and HA. The surface of these sludge-based adsorbents was also rich in acidic functional groups, due to the activation by high concentration of H2SO4. The aqueous adsorption tests indicated that these adsorbents had remarkable adsorption capacities for both UV254 and SCODCr. The adsorption capacities of these adsorbents for UV254 were lower than that of the investigated commercial AC; whereas the adsorption capacities of the adsorbents prepared
Acknowledgements This work was financially supported by the China National Natural Science Foundation (No. 50778051), the Open Project of State Key Laboratory of Urban Water Resource and Environment (HIT, No. QA201017), and the National Postdoctoral Science Foundation of China (No. 20100471074).
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Boron-doped diamond anodic treatment of landfill leachate: Evaluation of operating variables and formation of oxidation by-products A´ngela Anglada a, Ane Urtiaga a, Inmaculada Ortiz a, Dionissios Mantzavinos b, Evan Diamadopoulos b,* a b
Department of Chemical Engineering, University of Cantabria, Avenida de los Castros s/n, 39005 Santander, Cantabria, Spain Department of Environmental Engineering, Technical University of Crete, GR-73100 Chania, Greece
article info
abstract
Article history:
Landfill leachate with a low BOD/COD ratio was electrochemically oxidized by means of
Received 26 March 2010
a boron-doped diamond anode. In addition to organic matter removal, this study addressed
Received in revised form
the issue of formation of both chlorinated organic compounds and nitrate ions as a result
8 September 2010
of organic matter and ammonia and/or organic nitrogen electro-oxidation in the presence
Accepted 12 September 2010
of chloride ions. A factorial design methodology was implemented to evaluate the statis-
Available online 19 September 2010
tically important operating variables: treatment time (1e4 h), pH (5e8), current intensity (6.3e8.4 A) and addition of chloride (2500e4500 mg L1). The process was evaluated on
Keywords:
COD, total nitrogen (TN) and colour removal, as well as on the formation of nitrate, nitrite
Boron-doped diamond
and chlorinated organics. Of the four variables studied, treatment time and pH had
Electrolysis
a considerable influence on COD and colour removal. On the contrary, none of the variables
Leachate
had a significant effect on the elimination of TN for which an average removal of 61 mg L1
Factorial design
was obtained. The studied variables exhibited different effects on the four groups of
Treatment
organo-chlorinated compounds considered in this study, namely trihalomethanes (THMs), haloacetonitriles (HANs), haloketons (HKs) and 1,2-dichloroethane (DCA). Further analysis at more intense conditions, i.e. current intensity up to 18 A and reaction time up to 8 h revealed that high levels of decolourization (84%) could be achieved followed by low COD (51%) and ammonia (32%) removals. Apart from DCA, the concentration of chlorinated organics increased continuously with treatment time reaching values as high as 1.9 mg L1, 753 mg L1 and 431 mg L1 of THMs, HANs and HKs, respectively. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Municipal landfill leachate is a complex effluent that contains organic compounds, heavy metals, ammonia, chloride and many other soluble compounds (Kjeldsen et al., 2002). In particular, leachate from an old landfill site is usually characterised by a low BOD/COD ratio and biological processes are
ineffective. As a result, the use of different integrated processes such as bio-physico-chemical (Rivas and Beltra´n, 2005) and bio-advanced-oxidation processes (Wang et al., 2003) has been investigated. Among the advanced-oxidation processes studied, electrochemical oxidation stands out for its robustness, versatility, amenability to automation and for its little or no need for addition of chemicals.
* Corresponding author. Tel.: þ30 28210 37795; fax: þ30 28210 37846. E-mail address:
[email protected] (E. Diamadopoulos). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.017
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Different types of anode materials (i.e. TiO2/RuO2, PbO2/SnO2, Ti/Pt, SPR, PbO2/Ti and BDD) and the effect of several operating factors have been assessed (Deng and Englehardt, 2007; Anglada et al., 2009a). Current density, pH, chloride concentration and added electrolytes have been found to influence the removal efficiency of pollutants. Regarding the effect of pH and chloride concentration on COD removal, an overview of previous studies does not permit to reach a conclusion on whether an increase in their value has a beneficial or detrimental effect. For instance, Wang et al., (2001a,b) reported an increase in COD removal upon addition of chloride in the range 2010e4010 mg L1. On the contrary, Cabeza et al. (2007b) found that the addition of chloride ions resulted in lower COD oxidation rates. The discrepancies observed could derive from the different anode materials and/or current densities employed that determine the type of oxidation mechanism involved in the electro-oxidation process (Deng and Englehardt, 2007). In most studies electrochemical oxidation was applied as a post-treatment method, while various methods were used for the pre-treatment of leachate: Sequencing batch reactors (SBR), upflow anaerobic sludge blanket (Wang et al., 2001a,b), membrane bioreactor (Feki et al., 2009), coagulation, carbon adsorption (Chiang et al., 2001) and Fenton reaction (Urtiaga et al., 2009). The results demonstrated that under appropriate conditions electrochemical oxidation can significantly eliminate organic contaminants, ammonia and colour from leachate. However, the formation of undesirable oxidation by-products such as nitrate anions (Cabeza et al., 2007a) and chlorinated organic compounds (Lei et al., 2007) has been detected. For this reason, speciation of oxidation by-products, especially of chlorinated organics, as well as the identification of the main factors that affect their formation needs to be determined. At boron-doped diamond anodes, COD oxidation is thought to take place mainly by mediated hydroxyl radical oxidation, whereas ammonia removal occurs through indirect oxidation by means of electro-generated active chlorine. Thus, COD removal could be enhanced by operating at acidic and pH as this would decrease the concentration of CO2 3 HCO 3 ions, species that are always present in the leachate. These ions are effective OH scavengers and thus may decrease the oxidation rate of COD (Cossu and Polcaro, 1998). Acidic pH may also favour the oxidation of ammonia. During indirect oxidation of ammonia, chlorine evolution occurs at the anode. At pH < 3.3, aqueous chlorine is the predominant species whereas at higher bulk pH values, its diffusion away from the anode is coupled to its dismutation reaction to form hypochlorous acid at pH < 7.5 and hypochlorite ions at pH > 7.5. Then, HOCl reacts with NH3 through breakpoint chlorination reactions to regenerate chloride ions. It should be noted that the relative distribution of HOCl and OCl, which depends on the pH value, is very important, because the oxidation efficiency of HOCl is significantly greater than that of OCl. After all, as the initial pH value of the landfill leachate is typically alkaline, hypochlorite ions are expected to be the predominant species. Consequently, higher ammonia oxidation rates may be achieved by lowering the pH to 5 where HOCl will prevail. Regarding nitrate formation, Vlyssides et al. (2001) observed that acidic conditions (pH ¼ 6) favoured the formation of nitrate ions during electrochemical oxidation of landfill leachate on a Pt/Ti anode.
829
The aim of this work was to study the oxidation of landfill leachate over a BDD anode regarding the effect of various operating conditions such as treatment time, current intensity, initial pH and initial concentration of chloride ions on COD, total nitrogen and colour removal, as well as on the formation of nitrate, nitrite and chlorinated organics. The formation of organo-chlorinated compounds is an important issue in electro-oxidation of wastewaters, when indirect oxidation processes by means of active chlorine take place, as is the case of landfill leachate. The analysis of operating factors on the formation of these compounds is the first step that has to be taken to minimise their formation. The chlorinated organics analysed in this study are major disinfection by-products; i.e. trihalomethanes (THM), haloacetonitriles (HANs), haloketons (HKs), trichloroethylene (TCE), tetrachloroethylene (PCE), 1,2dichloroethatne (DCA) and chloropicrin (CPN). A factorial design methodology was adopted to determine the statistical significance of each parameter. This methodology could also provide mathematical models for the processes under study. In addition, two mathematical models previously used to describe the oxidation kinetics of landfill leachate have been used to predict the evolution of COD and ammonia (Anglada et al., 2009b). In the model for ammonia oxidation, the pseudo-kinetic parameter has to be re-defined in order to take into account the effect of the initial concentration of COD.
2.
Materials and methods
2.1.
Landfill leachate
The leachate used in this study was taken from the intermunicipal sanitary landfill site (DEDISA) of Chania prefecture, W. Crete, Greece. The site, which serves a population of over 150,000 inhabitants in eight municipalities, has an onsite facility capable of treating daily up to 100 m3 of leachate. The leachate is collected in equalization tanks and then fed to Sequencing Batch Reactors (SBRs) to undergo activated sludge treatment prior to subsequent treatment in constructed wetlands. The treated effluent is finally sent to an irrigation pond. The leachate used in this work was collected from the equalization tank in July and August 2009 and its main characteristics were determined. Each sample was analysed three times and mean values are shown in Table 1.
2.2.
Electrochemical experiments
Experiments were conducted in a DiaCell (type 100) single compartment electrolytic flow-cell manufactured by Adamant Technologies (Switzerland). The anode and cathode were made of boron-doped diamond (BDD) on silicon. Both electrodes were circular with a geometric area, A, of 70 cm2 each and an electrode gap of 10 mm. All experiments were carried out in batch mode and under galvanostatic conditions. In each run, a 10 L leachate volume (V) was batch loaded in a vessel and continuously recirculated in the cell by means of a peristaltic pump at a flow rate of 600 L/h. A spiral coil immersed in the effluent vessel and connected to tap water supply was used to remove the heat released from the electrolytic process. In those experiments in
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Table 1 e Physico-chemical characteristics of the raw leachate studied in this work. Property
Value 1
COD (mg L ) BOD5 (mg L1) Total nitrogen (TN) (mg L1) 1 NeNHþ 4 (mg L ) 1 NO 3 (mg L ) 1 NO 2 (mg L ) Cl (mg L1) Br (mg L1) 1 PO3 4 (mg L ) 1 (mg L ) SO2 4 True colour (TCU) pH Conductivity (mS cm1)
3385 500 1547 1235 1.9 <2 2587 9 31 10.8 8334 8.4 22.6
which the initial concentration of chloride ions was increased, the appropriate amount of NaCl was added to the effluent. 98% w/w H2SO4 was used in those cases in which the initial pH was adjusted to acidic conditions.
2.3.
Analytical measurements
Chemical oxygen demand (COD) was determined by the closed reflux and colorimetric method. Commercially available digestion solutions (Hach Europe, Belgium), in the range 0e1500 mg L1, were used for this purpose. Ammonia and total nitrogen (TN) were also measured by means of Hach Test Kits that work in the range 0e50 mg L1 NH3eN and 0e25 mg L1 TN, respectively. Colour was determined according to the method proposed by Hongve and Akesson (1996). Ion chromatography was used to determine the concentration of inorganic ions. Chlorinated volatile organics in the aqueous phase can easily be extracted using pentane as an extraction solvent. In this study, 10 mL of sample was pipetted into 40 mL screw cap vials. Then, 2 mL of pentane was added and the vials were shaken vigorously for 1 min and allowed to stand for 3 min to facilitate phase separation. 2 mL of pentane extract was analysed by gas chromatography on a Carlo Erba gas chromatograph equipped with a Ni63 electron capture detector (ECD) at 300 C. The column used was DB-5, 60 m 0.32 mm I.D., 0.25 mm film thickness (J&W Scientific). The temperature programme was from 35 C (15 min) to 100 C (1 min) at 5 C/ min and from 100 C to 260 C (2 min) at 15 C/min. The injector temperature was set at 250 C. The luminescent marine bacteria Vibrio fischeri was used to assess the acute ecotoxicity of landfill leachate samples prior to and after treatment. The inhibition of bioluminescence of V. fischeri was measured using a LUMIStox analyser (Dr. Lange, Germany). Toxicity is expressed as EC50, which is the effective concentration of a toxicant causing 50% reduction of light output after 15 min at 15 C.
2.4.
Experimental design methodology
In this work, a factorial experimental design was chosen to investigate the effect of treatment time, current intensity,
pH and chloride concentration on electrochemical oxidation of landfill leachate. The results obtained were evaluated in terms of: concentration of COD oxidized in mg L1 (response factor Y1), colour removal (response factor Y2), concentration of TN eliminated in mg L1 (response factor Y3), concentration of NeNO 3 formed (response factor Y4) and concentration of organo-chlorinated compounds formed (response factors Y5eY8). Each one of the four independent variables received two values: a high value (indicated by the þ sign) and a low value (indicated by the sign). These are presented in Table 2. Three repeat runs were also performed at the centre of the design (mean value indicated by zero in Table 2) in order to estimate the standard error. Therefore, the experimental design followed in this work was a full 24 experimental set and required a total of nineteen experiments. Treatment for up to 4 h is a reasonable timescale for advanced-oxidation processes applied to the treatment of industrial effluents, while it was decided to operate in the range of 900e1200 A m2 (6.3e8.4 A) in order to work at current densities higher than the limiting current density calculated for the initial COD concentration, which is namely 714 A m2 ( jlim,COD ¼ 4FkmCODo, where F is the Faraday constant, km is the mass transport coefficient reported in Section 3.2 and CODo is the initial concentration). If ammonia and COD are to be eliminated simultaneously, current densities higher than the limiting one have to be applied (Anglada et al., 2009b). Unfortunately, this may enhance the formation of undesirable by-products, such as nitrate and chlorinated organic compounds. Higher concentrations of chloride ions have been reported to favour ammonia oxidation (Cabeza et al., 2007a). However, higher chloride concentrations may bring about the formation of organo-chlorinated compounds. Consequently, it was decided to increase the concentration of Cl up to 4500 mg L1, remaining within the range of concentrations of chloride ions usually present in municipal landfill leachate, i.e. 150e4500 mg L1 (Kjeldsen et al., 2002).
3.
Results and discussion
3.1.
Effect of operating factors
The results obtained are shown in Table 3. As seen in Table 3, the extent of COD removal was below 20%. In addition, COD removal was lower than colour removal suggesting the presence of compounds that are resistant to oxidation. Tauchert
Table 2 e Independent variables of the 24 factorial design of experiments. Level of value þ 0
X1 Treatment time (h)
X2 Intensity (A)
X3 pH
X4 [Cl]o (mg L1)
1 4 2.5
6.3 8.4 7.35
5 8.3 6.65
2574 4476 3525
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Table 3 e Design matrix of the 24 factorial design and observed response factors, as well as percent removal of COD and TN. TTHM is the total concentration of trihalomethanes calculated as the sum of the concentration of chloroform (TCM), bromodichloromethane (BDCM), dibromochloromethane (DBCM) and bromoform (TBM); [HANs]T is the sum of the concentration of trichloroacetonitrile (TCAN), dichloroacetonitrile (DCAN), bromochloroacetonitrile (BCAN) and dibromoacetronitrile (DBAN); [HKs]T is the sum of the concentration of 1,1-dichloroacetone (1,1-DCP) and trichloropropanone (1,1,1-TCP). Y3, TN Y4, NeNO3 Y5, Y6, Y7, Y8, COD TN Run X1, X2, X3, X4, Y1, COD Y2, colour order Time, I, A pH [Cl]o oxidized, removal % eliminated, formed, [TTHM], [HANs]T, DCA, [HKs]T, removal removal mg L1 mg L1 mg L mg L1 h mg L1 mg L1 mg L1 % % 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
þ þ 0 þ 0 þ þ þ þ 0 þ
þ 0 þ þ þ 0 þ þ þ þ 0
þ 0 þ þ 0 þ þ þ þ þ 0
þ þ 0 þ 0 þ þ þ þ 0 þ
107 618 698 383 527 181 85 405 229 224 501 405 309 234 21 224 299 380 698
8 91 47 35 28 32 19 33 27 34 57 29 39 31 20 29 14 34 91
1 90 40 40 180 0 0 30 110 60 140 90 20 4 0 0 190 50 60
et al. (2006) also observed that COD oxidation occurred at a slower rate than colour removal. Electro-oxidation of 1 L of raw landfill leachate by means of a DSA anode resulted in 50% and 20% colour and COD removal, respectively, after 5 h of treatment. Similar levels of COD removal were also obtained by Chiang et al. (1995) over a wide variety of anode materials; from 21% to 30% COD removal at graphite, PbO2/Ti, DSA and SPR anodes. However, in the latter study addition of 5000 mg L1 of chloride brought about around 45% COD removal when an SPR anode was used. Electro-oxidation of raw landfill leachate at a BDD anode has been previously carried out by Cabeza et al. (2007b) at 6.3 A without addition of extra chloride. The results showed that complete COD removal was achieved after 6 h of treatment. The lower COD oxidation rate obtained in this study is due to the lower A/V ratio employed: only 1 L of landfill leachate was electrooxidized by Cabeza et al. (2007b) in contrast to the 10 L treated in this study yielding an A/V ratio of 7 m1 and 0.7 m1, respectively. As will be demonstrated in Section 3.2, the rate of COD oxidation is directly proportional to the “anode area/ volume” ratio. Regarding the elimination of TN, similar or lower levels of removal as compared to COD removal were generally achieved. Moreover, in some cases, TN was not eliminated. However, nitrate ions were formed under all operating conditions in concentrations that ranged from 7 mg L1 NeNO3 to 62 mg L1 NeNO3. This may be because only the speciation of the nitrogen species is changing: (1) ammonia is being converted into nitrate ions and/or (2) oxidation of organic matter yields ammonia which may afterwards be oxidized to NO 3 . After all, around 80% of the initial concentration of TN was ammonia with the rest being
7.4 61.8 27.7 27.6 36.4 7.7 14.3 23.6 31.2 11.1 24.4 30.3 45.8 17.5 9.4 7 10.4 23.6 51.6
202 2144 214 935 786 246 785 981 311 136 897 238 1053 382 185 86 309 964 1422
2.3 498 105 284 3.4 79 8.2 280 3.6 52 276 0.9 14 113 1.4 41 2.2 300 391
115 211 111 480 271 142 475 488 92 346 275 54 422 30 169 108 273 427 367
10 268 121 221 16 66 18 224 17 54 287 17 31 102 8.7 44 10 230 331
3 17 15 11 15 5 2 11 7 7 15 12 9 7 0.6 3 9 11 19
0.1 6 2.8 2.6 10.7 0 0 2 7 4.2 8.8 6.3 1.1 0.3 0 0 11.3 3.3 4.2
organic nitrogen. On the contrary, no nitrite ions were detected. It should be mentioned that in one case (exp.1), an increase in the concentration of COD was observed; this could be due to the oxidative polymerisation of certain landfill leachate constituents like phenols (Chatzisymeon et al., 2009) and it is consistent with the relatively low colour removal recorded. Other undesirable by-products such as chlorinated organic compounds were also detected (Table 3). A detailed characterisation of the chlorinated organic compounds measured in this work is given in Table A of the supplementary material. These compounds are formed as a result of addition and substitution reactions between organic compounds and active chlorine species or chlorine radicals (Costa et al., 2009). It is worth mentioning that none of the chlorinated compounds analysed in this work were detected in the raw leachate. As expected, chloroform was the main DBP formed with concentrations in the range 0.08e2.1 mg L1, followed by 1,2dichloroethane (DCA) in concentrations of up to 488 mg L1. Among the haloacetonitriles, dichloroacetonitrile (DCAN) was formed at relatively high concentrations (104e424 mg L1) when the initial pH value was adjusted to 5. On the contrary, trichloroacetonitrile (TCAN), trichloroethylene (TCE), CPN and bromoform (TBM) were either not detected or they were present at ng L1 levels. Statistical analysis of the response factors according to the factorial design technique involves the estimation of the average effect, the main effects of each individual variable, as well as their two and higher order interaction effects (Box et al., 1978). To assess the significance of the effects, an estimate of the standard error is required. Repeat runs at the centre point of the factorial design were used in this study to
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calculate the standard error. In this work, the effects and the standard error were estimated by means of the statistical package Minitab 14 and the results are summarised in Table B of the supplementary material. If an effect is about or below the standard error, it may be considered insignificant. However, the contribution of a variable whose effect is above the standard error is not necessarily very large. One way to identify the most important effects is to construct the normal probability plot. Effects that are small can be explained as white noise, following a normal distribution with a mean of zero. In the normal probability plot these effects will appear on a straight line. Any effects with a significant contribution will lie away from the normal probability line. The normal probability plots for the response factors are shown in Figs. 1 and 2. Any effects with a significant contribution lie away from the normal probability line and are represented by squares. However, all points lying on a straight line can be considered as coming from a normal distribution with a mean of zero (white noise) and therefore the influence of the corresponding variables on the response could be considered insignificant (it cannot be differentiated from randomness). As can be observed, there are three effects that lie away from a conceptual straight line in the COD (Fig. 1a) probability plot. These are the treatment time (X1), pH (X3) and the interaction of pH with current density (X2X3). Treatment time has a positive effect on COD reduction
indicating that an increase in its level brings about an increase in the amount of COD removed. On the other hand, treatment efficiency in terms of COD removal appears to be enhanced at acidic conditions. Similarly, Vlyssides et al. (2003) found that a low pH favoured COD removal and energy consumption within pH 5.5e7.5 during electrooxidation of landfill leachate. Operating at alkaline pH has also been reported to lead to less efficient processes during BDD electro-oxidation of 4-nitrophenol and several phenolic aqueous wastes. This was attributed to the fact that the extent of polymerisation and the concentration of organic intermediates increased at alkaline conditions (Can˜izares et al., 2004, 2005). The positive value of the interaction term indicates that the effect of current density is stronger at higher pH values. Regarding colour reduction (Fig. 1b), treatment time and pH were the significant effects. Treatment time had a positive effect on colour reduction indicating that an increase in its level brings about an increase in the amount of colour removed. On the other hand, treatment efficiency in terms of colour removal appeared to be enhanced at acidic conditions. It should also be pointed out that the initial pH value seems to have a much more significant effect on colour than on COD removal. The effect of the other variables and interaction of variables was not significant, because they can be explained as white noise.
b
a
99
99 A
60 50 40 20 10 5
Factor A B C D
Name Time I pH [Cl-]
A
95 90 80
BC
Percent
Percent
95 90 80
60 50 40 20 10 5
C
Factor A B C D
C
1
1 -5,0
-2,5
0,0
2,5
5,0
7,5
-20
10,0 12,5
-10
0
10
20
Standardized Effect
Standardized Effect
c
d 99
99
95 90 80
95 90 80
60 50 40
Factor A B C D
20 10 5 1 -7,5
-5,0
-2,5
0,0
2,5
Standardized Effect
Name Time I pH [Cl-]
5,0
Percent
Percent
Name Time I pH [Cl-]
A D
60 50 40
Factor A B C D
20 10 5
Name Time I pH [Cl-]
1 -10
0
10
20
30
Standardized Effect
Fig. 1 e Normal probability plot of the effects for the removal of (a) COD, (b) colour and (c) ammonia and for the formation of (d) nitrate ions.
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b
99
99
95 90 80
95 90 80 70 60 50 40 30 20 10 5
60 50 40
Factor A B C D
20 10 5
Percent
Percent
a
Name Time I pH [Cl-]
1 -3
-2
-1
0
1
2
3
AC C
Name Time I pH [Cl-]
1
4
-3
Standardized Effect
-2
-1
0
1
2
3
Standardized Effect
c
d
99
99 A
60 50 40 20 10 5
Factor A B C D
AC
Name Time I pH [Cl-]
1 -2
-1
0
1
2
BCD B D
60 50 40
Factor A B C D
20 10 5
C
-3
BC
95 90 80
Percent
95 90 80
Percent
Factor A B C D
3
Standardized Effect
1 -1
0
1
Name Time I pH [Cl-]
2
Standardized Effect
Fig. 2 e Normal probability plot of the effects for the formation of (a) trihalomethanes, (b) haloacetonitriles, (c) haloketons and (d) 1,2-dichloroethane.
Fig. 1c displays the normal probability plot for elimination of TN. All points seem to lie on a straight line indicating that, for the range of operating conditions in question, no significant effect of the variables was observed. In this case, an average removal of TN amounting to 61 mg L1 was obtained. On the other hand, treatment time (X1) and chloride concentration (X4) had significant effects on nitrate formation (Fig. 1d). Longer treatment times and higher chloride concentrations resulted in higher concentrations of nitrate ions as indicated by the positive sign of these two effects. On the contrary, current intensity and pH did not seem to have a significant effect. Regarding the formation of chlorinated organic compounds, different behaviours were observed concerning the effect of the studied variables on the formation of trihalomethanes (Fig. 2a), haloacetonitriles (Fig. 2b), haloketons (Fig. 2c) and 1,2-dichloroethane (Fig. 2d). Firstly, it can be observed that the formation of THM was not affected by any of the studied variables as all the points seem to lie on a straight line (Fig. 2a). Consequently, only the average value, namely 660 mg L1, seems to be important.
Secondly, pH (X3) and the interaction of pH with treatment time (X1X3) seem to have a significant effect on the formation of HANs (Fig. 2b). The formation of HAN seems to be favoured at lower pH values. In addition, the negative value of the interaction term indicates that the concentration of HANs increases more strongly with time at lower pH values. Similarly to HAN formation, the formation of HKs (Fig. 2c) depends on pH (X3) and on the interaction of pH with treatment time (X1X3). Their effect is also negative. However, in this case, treatment time (X1) also seems to have a significant influence. Its effect is positive indicating that prolonged electrolysis results in the formation of higher concentrations of haloketons. Costa et al. (2009) reported that the kinetics of formation of adsorbable organic halogens (AOX) during electro-oxidation of acid black 210 depended on the pH value; concentrations amounting to 120 mg L1 and 50 mg L1 of AOX were detected at pH 1.9 and 6.8, respectively. Finally, as far as the formation of DCA is concerned, four effects seem to lie away from the conceptual line. These are, in order of significance, the second-order interaction of intensity with pH (X2X3), the third-order interaction between
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3.2.
Kinetic experiments
3.2.1.
COD and NeNH3 oxidation kinetics
Based on the above results, further treatment optimisation was needed in terms of reaction time and intensity. For this reason, the effect of intensity was further investigated by performing experiments at 8.3, 12 and 18 A. These experiments were carried out at pH ¼ 5 and at the effluent’s inherent chloride concentration for an extended period of time (8 h). The evolution of the concentration of COD and ammonia with treatment time was determined and the results are shown in Figs. 3 and 4, respectively. Only slight differences were observed between the COD and ammonia profiles at 8.4 and 12 A, whereas a further increase to 18 A led to higher removal values. In all cases, ammonia removal was low during the first 4 h of treatment after which it accelerated (Fig. 4). This is because at the initial stages of electro-oxidation of landfill leachate on BDD anodes direct oxidation is dominant. As electro-oxidation proceeds, indirect oxidation gains importance and the oxidation rate of ammonia increases (Cabeza et al., 2007a,b; Anglada et al., 2009b). As organic characteristics vary from landfill to landfill, two models previously proposed in the literature for COD (Cabeza et al., 2007b) and ammonia oxidation (Anglada et al., 2009b) during BDD electro-oxidation of biologically treated landfill leachate were tested to confirm that these results were not site-specific. COD oxidation has been described by means of a model that assumes that the oxidation of organic matter on a BDD anode takes place mainly on the electrode surface (either by hydroxyl radicals or by direct oxidation) and that the limiting step is the pollutant transfer from the waste to the anode (Eq. (1)). A$km $t COD ¼ COD0 exp V
(1)
The model has only one parameter: the mass transport coefficient, which in this study has a value of 1.75 105 m s1 (Chatzisymeon et al., 2009). In Fig. 3, the experimental data are
1 0.9
COD /C OD o
0.8 0.7 0.6 0.5 0.4
I=8.4A
0.3
I=12A
0.2
I=18A
0.1
Eq.1
0 0
1
2
3 4 5 Time (h)
6
7
8
Fig. 3 e Evolution of the concentration of COD with treatment time during electrochemical oxidation of landfill leachate at 8.4, 12 and 18 A. The results are compared with the profile predicted by Eq. (1). Operating conditions: pH [ 5 and [ClL]o [ 2574 mg LL1.
compared with the COD profile predicted by Eq. (1). Fig. 3 shows that this model satisfactorily predicts the evolution of COD for intensities lower than 12 A, but at higher intensities the oxidation rate is higher than that predicted by the model. A change of the oxidation mechanism of organic matter at high current densities is thought to be responsible for this behaviour (Anglada et al., 2009b). Ammonia oxidation has been reported (Anglada et al., 2009b) to depend on ammonia and free chlorine concentration (Eq. (2)). The concentration of free chlorine can be calculated by means of Eq. (3). Substitution of the integrated form of Eq. (3) in (2) gives Eq. (4) which shows a linear fitting of þ the logarithm of [NHþ 4 ]/[NH4 ]o with the square of electrolysis time. This model has been validated for BDD electro-oxidation of landfill leachate at different current densities.
1
[N-NH4+]/[N-NH4+]o
intensity pH [Cl] (X2X3X4) and the main effects intensity (X2) and chloride concentration (X4). The positive value of the main effects and of the second-order interaction term indicate that higher current intensities and chloride concentrations enhance DCA formation being the former effect stronger at higher pH values. It should also be pointed out that in this case the system is probably non-linear as second- and third-order interaction terms are significant. At this point, it can be concluded that higher chloride concentrations should not be used as they do not favour pollutant removal. On the contrary, the formation of undesired oxidation by-products such as nitrate ions and DCA is enhanced. Regarding the initial pH value, acidic conditions (pH ¼ 5) favour the elimination of COD and colour, but boost the formation of HANs and HKs. In any case, pH values lower than 5 should not be used to increase the removal of COD and colour due to safety issues; at pH values of around 3, gaseous chlorine may be formed. Higher intensities slightly enhance the removal of COD, but also the formation of DCA. At any rate, extended electro-oxidation is necessary to remove COD, colour and TN.
0.8 0.6 I=8.4A I=12A I=18A Eq.4 and Eq.5, I=8.4A Eq.4 and Eq.5, I=12A Eq.4 and Eq.5, I=18A
0.4 0.2 0 0
1
2
3 4 5 Time (h)
6
7
8
Fig. 4 e Evolution of the concentration of NeNHD 4 with treatment time during electrochemical oxidation of landfill leachate at 8.4, 12 and 18 A. The results are compared with simulated data predicted by Eqs. (4) and (5). Operating conditions: pH [ 5 and [ClL]o [ 2574 mg LL1.
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Table 4 e Regression parameters obtained by fitting the experimental data obtained in this and in a previous study (Cabeza et al., 2007b) to Eq. (4). The last column shows the standard deviation (s) between experimental and modelled data from Eqs. (4)e(6).
Meruelo (Spain)
Chania (Greece)
Exp. 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Pre-treatment/ dilution No/No No/No No/Yes No/Yes No/Yes No/Yes Biological/No Biological/No Biological/No Biological/No Biological/No No/No No/No No/No
d NHþ 4 ¼ k$ NHþ 4 $½‘‘Cl2 ’’ dt
CODo (mg L1)
[NHþ 3 ]o (mg L1)
[Cl]o (mg L1)
J (A m2)
V (L)
k0 (h2)
R2
s
3799 3275 1728 1813 853 773 1497 1448 1398 1349 1430 3142 3177 3344
2076 2453 1348 1145 591 591 933 980 980 910 980 1380 1380 1380
2760 3030 2160 2060 1790 1900 2127 1820 2041 2020 2127 2587 2587 2587
900 600 600 300 300 150 300 450 600 900 1200 1200 1714 2571
1 1 1 1 1 1 1 1 1 1 1 10 10 10
0.060 0.018 0.103 0.016 0.087 0.019 0.030 0.094 0.143 0.241 0.355 0.0036 0.0044 0.0065
0.992 0.995 0.996 0.998 0.994 0.996 0.987 0.972 0.990 0.978 0.981 0.993 0.993 0.999
8.5 1.3 5.2 4.1 13 3.8 6.8 9.1 9.9 11 8.4 1 1.3 2.1
(2)
d½‘‘Cl2 ’’ 4jA ¼ dt nFV
(3)
NHþ k4jAt2 4 ¼ ln ¼ k0 t2 þ 2nFV NH4 o
(4)
In this work, ammonia oxidation followed the same behaviour since the logarithm of the normalised ammonia concentration against the square of electrolysis time followed a linear trend for all the applied intensities. The values of k0 obtained together with the correlation coefficients are shown in Table 4. In order to assess if these results were site-specific and to quantify the influence of the initial COD concentration, k0 values in Table 4 were compared with those obtained during BDD electro-oxidation of raw and biologically pre-treated landfill leachate (Cabeza et al., 2007b). The landfill leachate used in the latter experiments came from the municipal landfill site of Meruelo (Cantabria, Spain) and the experiments were carried out in an electrochemical cell with a BDD area of 70 cm2, the same as that used in this study. It can be observed that ammonia oxidation is faster at higher current intensities and lower concentrations of COD. It should be pointed out that although the effect of current density on the rate of ammonia oxidation has been previously quantified, the influence of the initial COD concentration still needs to be calculated. For this reason, in Fig. 5, k0 was not only plotted as a function of applied current intensity and volume of effluent treated but also as a function of the initial COD concentration (expressed as jlim,COD which was defined in Section 2.4). It can be observed that k0 always depended linearly on A$Jappl/(Jlim,COD$V). However, for values of A$Jappl/ (Jlim,COD$V) higher than approximately 9 m1, the degree of dependency of k0 on the A$Jappl/(Jlim,COD$V) ratio increases as indicated by an increase in the slope (Eqs. (5) and (6)). The data obtained in this work follow the trend marked by experiments 2, 4 and 6 of Table 4 (Eq. (5)) in which different dilutions of raw landfill leachate were electro-oxidized at a current density
near the limiting current density for the initial concentration of COD. It is worth mentioning that the lower ammonia oxidation rates obtained in this work in comparison with those obtained during electro-oxidation of leachate from the other landfill site (Meruelo), are due to the higher volume of effluent treated in this study. A$Jappl < 9m1 ; V$Jlim;COD
R2 ¼ 0:998
A$Jappl k0 ¼ 0:0019 þ 0:0011 V$Jlim;COD
(5) A$Jappl > 9m1 ; V$Jlim;COD
R2 ¼ 0:988
A$Jappl k0 ¼ 0:0102 0:0662 V$Jlim;COD
(6) The ammonium concentration profiles predicted by means of Eqs. (4) and (5) for the experiments performed in this study are plotted in Fig. 4. Good agreement between the experimental and simulated data is observed. The standard deviations between the experimental and modelled data are given in Table 4. Regarding colour removal, electrochemical oxidation was unable to decolorize landfill leachate completely; no colour
0.40 0.35
k ' ( h-2)
Source of leachate
0.30 0.25 0.20 0.15 0.10 0.05 0.00 0
10
20
30
40
50 -1
A*J appl/(J lim,COD*V) (m ) Fig. 5 e Experimental values of k0 as a function of A$Jappl/ (Jlim,DQO$V).
836
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 2 8 e8 3 8
b
a 2500
800
-1
[HANs] (µg L )
-1
[T-THMs] (µg L )
700 2000 1500 1000
200
I=12A
I=12A I=18A
100
I=18A
0 0
2
4
6
8
10
Time (h)
0
2
4
6
8
10
Time (h)
d
c 525
300
450
250
I=8.4A
375
-1
[DCA] (µg L )
-1
400 I=8.4A
0
[HKs] (µg L )
500 300
I=8.4A
500
600
300 225 I=8.4A
150
I=12A
200
I=18A
150 100
I=12A
75
50
I=18A
0
0
0
2
4
6
8
10
Time (h)
0
2
4 6 Time (h)
8
10
Fig. 6 e Effect of current intensity on (a) trihalomethane, (b) haloacetonitrile, (c) haloketons and (d) 1,2-dichloroethane formation.
removal occurred from 5 h of treatment onwards (data not shown for brevity). The maximum colour removal achieved was 84%.
3.2.2.
Formation of chlorinated organic compounds
The effect of current intensity and prolonged electrolysis on the formation of chlorinated organic compounds was also analysed. Chloroform was again the main compound formed representing on average 55% of the total concentration of chlorinated organic compounds detected. Haloacetonitriles, namely DCAN and BCAN, were also formed at high concentrations and constituted 19e25% of the total concentration of organo-chlorinated compounds. On the other hand, electrooxidation of landfill leachate did not result in the formation of TBM, PCE and TCE, and TCAN was detected at levels below the quantification limit (0.5 mg L1). In Fig. 6, the evolution of the concentration of TTHM, HANs, DCA and HKs with treatment time is displayed. Overall, longer treatment times led to continued formation of trihalomethanes (Fig. 6a), haloacetonitriles (Fig. 6b) and haloketons (Fig. 6c) although their formation seemed to slow down after extended electrolysis. As a matter of fact, the concentration of haloketons remained constant at the end of the process at current intensities higher than 8.4 A, indicating
resistance towards electrochemical decomposition. Lei et al. (2007) also observed a faster formation of AOX during the initial stages of electro-oxidation of landfill leachate that had been previously treated in a two-stage aged refuse bioreactor; around 15 mg L1 and 22 mg L1 of AOX were detected after 30 min and 2 h of electrolysis. In contrast, DCA formation (Fig. 6d) occurred over time, but after reaching a maximum concentration its concentration decreased over longer time periods. Scialdone et al. (2010) demonstrated that DCA could be eliminated by anodic oxidation on a BDD anode. Current intensity was observed to influence differently the formation of the four groups of DBPs considered in this study. On one hand, similar T-THMs and HANs concentration profiles were obtained at 8.4 and 12 A, whereas operation at 18 A resulted in much higher concentrations. On the other hand, DCA formation decreased at higher current intensities. To assess if the levels of chlorinated organic compounds formed increased the ecotoxicity of the effluent, acute ecotoxicity of landfill leachate before and after electro-oxidation at 12 A was assessed using V. fischeri. The toxicity of the raw sample yielded EC50 values of 18% while after 8 h of treatment the EC50 increased to 43%. Thus, it seems that the electrooxidation process decreased the toxicity even though chlorinated volatile organic compounds were formed.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 2 8 e8 3 8
400
W (kWh m-3)
350 I=8.4A
300 250
I=12A
200
I=18A
150 100 50 0 0
20
40
60
COD eliminated (%) Fig. 7 e Cumulative energy consumption profiles at 8.4, 12 and 18 A. Operating conditions: pH [ 5 and [ClL]o [ 2574 mg LL1.
3.2.3.
Energy consumption
In order to assess the efficiency of the electro-oxidation process its energy consumption needs to be determined. In Fig. 7, the cumulative energy consumption is shown against the percentage of COD eliminated. Operation at 8.4 A yielded the lowest energy consumption, whereas similar profiles were obtained at 12 and 18 A. Energy consumptions of 98 kWh m3 (or 102 kWh kg1 COD) and 134 kWh m3 (or 134 kWh kg1 COD) were needed to eliminate 30% of COD at 8.4 and 18 A, respectively. However, at 18 A lower treatment times are required; 7 and 3 h were needed to eliminate 30% COD at 8.4 and 18 A, respectively. Thus, a compromise must be reached between maximising the oxidation rate and minimising energy consumption. A possible alternative would be to carry out multiple step electro-oxidation (Panizza et al., 2008).
4.
Conclusions
Electrochemical oxidation of landfill leachate from a municipal landfill site was carried out in this work. The effect of various operating parameters such as treatment time, current intensity, initial pH and chloride concentration on treatment efficiency was evaluated by means of a factorial design technique. Treatment efficiency was assessed on the basis of COD, colour and nitrogen removal with special emphasis on chlorination by-product formation. The following main conclusions can be drawn from this work: Electrochemical oxidation was capable of achieving, under the operating conditions used in this work, satisfactory levels of decolourization followed by COD and NeNHþ 4 removals of up to 51% and 34% after 8 h of treatment, respectively. COD and ammonia oxidation kinetics could be described by means of two mathematical models previously used to predict BDD electro-oxidation of leachate from a different landfill site, demonstrating that the results are not site-specific. Regarding ammonia oxidation, the model was re-written in order to quantify the effect of the initial COD concentration and was then validated for landfill leachates with different compositions and for a wide range of operating conditions.
837
Chloroform, dichloroacetonitrile, 1,2-dichloroethane and 1,1-dichloroacetone were the main chlorinated organic compounds formed as a result of organic matter oxidation, whereas oxidation of ammonia and/or organic nitrogen brought about the formation of nitrate ions. Regarding the formation of DBPs, the concentration of chlorinated organics increased continuously with treatment time with the exception of DCA. Acidic conditions were found to favour the formation of haloacetonitriles and haloketons, while intensity and chloride concentration influenced significantly the formation of DCA. On the other hand, nitrate formation was enhanced at higher concentrations of Cl.
Acknowledgements Financial support of projects CTM2006-00317 and CTQ20080690 is gratefully acknowledged. A. Anglada would like to thank the Spanish Ministry of Innovation and Science (MICINN) for an FPU research grant.
Appendix. Supplementary material Supplementary material related to this article can be found online at doi:10.1016/j.watres.2010.09.017.
references
Anglada, A., Urtiaga, A., Ortiz, I., 2009a. Contributions of electrochemical oxidation to waste-water treatment: fundamentals and review of applications. Journal of Chemical Technology and Biotechnology 84 (12), 1747e1755. Anglada, A., Urtiaga, A., Ortiz, I., 2009b. Pilot scale performance of the electro-oxidation of landfill leachate at boron-doped diamond anodes. Environmental Science and Technology 43 (6), 2035e2040. Box, G.E.P., Hunter, W.G., Hunter, J.S., 1978. Statistics for Experiments. John Wiley and Sons, New York. Cabeza, A., Urtiaga, A., Rivero, M.J., Ortiz, I., 2007a. Ammonium removal from landfill leachate by anodic oxidation. Journal of Hazardous Materials 144 (3), 715e719. Cabeza, A., Urtiaga, A., Ortiz, I., 2007b. Electrochemical treatment of landfill leachates using a boron e doped diamond anode. Industrial and Engineering Chemistry Research 46 (5), 1439e1446. Can˜izares, P., Sa´ez, C., Lobato, J., Rodrigo, M.A., 2004. Electrochemical treatment of 4-nitrophenol-containing aqueous wastes using boron-doped diamond anodes. Industrial and Engineering Chemistry Research 43 (9), 1944e1951. Can˜izares, P., Lobato, J., Paz, R., Rodrigo, M.A., Sa´ez, C., 2005. Electrochemical oxidation of phenolic wastes with borondoped diamond anodes. Water Research 39 (12), 2687e2703. Chatzisymeon, E., Xekoukoulotakis, N., Diamadopoulos, E., Katsaounis, A., Mantzavinos, D., 2009. Boron-doped diamond anodic treatment of olive mill wastewaters: statistical analysis, kinetic modeling and biodegradability. Water Research 43 (16), 3999e4009.
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Chiang, L.C., Chang, J.E., Chung, C.T., 2001. Electrochemical oxidation combined with physical-chemical pretreatment processes for the treatment of refractory landfill leachate. Environmental Engineering Science 18 (6), 369e378. Chiang, L.C., Chang, J.E., Wen, T.C., 1995. Indirect oxidation effect in electrochemical oxidation treatment of landfill leachate. Water Research 29 (2), 671e678. Cossu, R., Polcaro, A.M., 1998. Electrochemical treatment of landfill leachate: oxidation at Ti/PbO2 and Ti/SnO2 anodes. Environmental Science and Technology 32 (22), 3570e3573. Costa, C.R., Montilla, F., Morallo´n, E., Olivi, P., 2009. Electrochemical oxidation of acid black 210 dye on the borondoped diamond electrode in the presence of phosphate ions: effect of current density, pH and chloride ions. Electrochimica Acta 54 (27), 7048e7055. Deng, Y., Englehardt, J.D., 2007. Electrochemical oxidation for landfill leachate treatment. Waste Management 27 (3), 380e388. Feki, F., Aloui, F., Feki, M., Sayadi, S., 2009. Electrochemical oxidation post-treatment of landfill leachates treated with membrane bioreactor. Chemosphere 75 (2), 256e260. Hongve, D., Akesson, G., 1996. Spectrophotometric determination of water colour in Hazen units. Water Research 30 (11), 2771e2775. Kjeldsen, P., Barlaz, M.A., Rooker, A.P., Baun, A., Ledin, A., Christensen, T.H., 2002. Present and long-term composition of MSW landfill leachate: a review. Critical Reviews in Environmental Science and Technology 32 (4), 297e336. Lei, Y., Shena, Z., Huang, R., Wang, W., 2007. Treatment of landfill leachate by combined aged-refuse bioreactor and electrooxidation. Water Research 41 (11), 2417e2426. Panizza, M., Kapalka, A., Comninellis, Ch, 2008. Oxidation of organic pollutants on BDD anodes using modulated current electrolysis. Electrochimica Acta 53 (5), 2289e2295.
Rivas, F., Beltra´n, F., 2005. Study of different integrated physicalchemical þ adsorption processes for landfill leachate remediation. Industrial and Engineering Chemistry Research 44 (8), 2871e2878. Scialdone, O., Galia, A., Gurreri, L., Randazzo, S., 2010. Electrochemical abatement of chloroethanes in water: reduction, oxidation and combined processes. Electrochimica Acta 55 (3), 701e708. Tauchert, E., Schneider, S., Morais, J.L., Peralta-Zamora, P., 2006. Photochemically-assisted electrochemical degradation of landfill leachate. Chemosphere 64, 1458e1463. Urtiaga, A., Rueda, A., Anglada, A., Ortiz, I., 2009. Integrated treatment of landfill leachates including electrooxidation at pilot plant scale. Journal of Hazardous Materials 166 (2e3), 1530e1534. Vlyssides, A., Karlis, P., Loizidou, M., Zorpas, A., Arapoglolu, D., 2001. Treatment of leachate from a domestic solid waste sanitary landfill by an electrolysis system. Environmental Technology 22 (12), 1467e1476. Vlyssides, A.G., Karlis, P.K., Mahnken, G., 2003. Influence of various parameters on the electrochemical treatment of landfill leachates. Journal of Applied Electrochemistry 33 (2), 155e159. Wang, F., Smith, D.W., Gamal El-Din, M., 2003. Application of advanced oxidation methods for landfill leachate treatmenta review. Journal of Environmental Engineering and Science 2, 413e427. Wang, P., Lau, I.W.C., Fang, H.H.P., 2001a. Electrochemical oxidation of leachate pretreated in an upflow anaerobic sludge blanket reactor. Environmental Technology 22, 373e381. Wang, P., Lau, W.C.I., Fang, H.H.P., 2001b. Landfill leachate treatment by anaerobic process and electrochemical oxidation. Environmental Science 22, 70e73.
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Aggregation and transport of nano-TiO2 in saturated porous media: Effects of pH, surfactants and flow velocity Itzel G. Godinez, Christophe J.G. Darnault* Department of Civil and Materials Engineering, Hydromechanics and Water Resources Engineering Laboratory, University of Illinois at Chicago, 842 W. Taylor St., Chicago, IL 60607, USA
article info
abstract
Article history:
Transport of manufactured nano-TiO2 in saturated porous media was investigated as
Received 12 May 2010
a function of morphology characteristics, pH of solutions, flow velocity, and the presence
Received in revised form
of anionic and non-ionic surfactants in different concentrations. Surfactants enhanced the
10 September 2010
transport of nano-TiO2 in saturated porous media while a pH approaching the point of zero
Accepted 14 September 2010
charge of nano-TiO2 limited their transport. The deposition process, a retention mecha-
Available online 19 September 2010
nism of nano-TiO2 in saturated porous media was impacted by surfactant and pH. In
Keywords:
presence of surfactants. The presence of non-ionic surfactant (Triton X-100) induced a size
Dispersion 1 systems (pH 7), the size of the nano-TiO2 aggregates was directly related to the Titanium dioxide
reduction of nano-TiO2 aggregates that was dependent on the critical micelle concentra-
Transport
tion. In Dispersion 2 systems (pH 9), the stability provided by the pH had a significant effect
Aggregation
on the size of nano-TiO2 aggregates; the addition of surfactants did impact the size of the
Saturated porous media
nano-TiO2 aggregates but in less significance as compared to Dispersion 1 systems. The
pH
electrostatic and steric repulsion forces in connection with the size of nano-TiO2 aggre-
Surfactant
gates and flow velocity impacted the single-collector efficiency and attachment efficiency which dictated the maximum transport distance of nano-TiO2 for the Dispersion 1 and Dispersion 2 systems. By doubling the flow velocity at pH 9, the No Surfactant, 50% CMC Triton X-100, 100% CMC Triton X-100 and 100% CMC SDBS dispersion systems allowed nano-TiO2 to attain maximum transport distances of 0.898, 2.17, 2.29 and 1.12 m, respectively. Secondary energy minima played a critical role in the deposition mechanisms of nano-TiO2. Nano-TiO2 deposited in the secondary energy wells may be released because of changes in solution chemistry. The deposition of nano-TiO2 in primary and secondary energy minima, the reversibility of their deposition should be characterized to analyze the transport of nanoparticles in porous media. This is necessary to assess the risk of nanoparticles to the environment and public health. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Novel nano-sized materials and particles are revolutionizing science and engineering due to their enhanced physicochemical properties compared with their bulk counterparts. By manufacturing nano-size materials and particles of specific
size, shape and crystalline configuration, nanotechnology possesses the capacity to transform the inherent characteristics of the majority of man-made objects and activities. Nanotechnology’s uttermost societal impact is anticipated to be as drastic as that of the first industrial revolution (Mansoori, 2005).
* Corresponding author. Tel.: þ1 312 996 2429; fax: þ1 312 996 2426. E-mail address:
[email protected] (C.J.G. Darnault). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.013
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As the production and application of manufactured nanoparticles in commercially available products are increasing tremendously (USEPA, 2007), titanium dioxide nanoparticles (nano-TiO2) are widely utilized (Wiesner et al., 2006). Nano-TiO2 have been implemented as a pigment in inks, paints, paper, plastics, cosmetics and nano-fibers (USEPA, 2007) and proven to be a superior photocatalyst characterized by large photostability, porosity, ion exchange capability and high specific surface area-to-volume ratio (Hoffmann et al., 1995). These physico-chemical properties lead to the application of nanoTiO2 in advanced oxidation technologies (AOTs) to treat toxins such as organic and inorganic compounds that may not be easily eradicated by conventional technologies in water treatment plants (Antoniou et al., 2009) and subsurface environments (Quan et al., 2005). The proliferation of applications and products containing nano-TiO2 will inherently result in sources of these nanoparticles to the environment with the potential to pollute the air, surface and groundwater as well as soil. NanoTiO2 is toxic to bacteria (Adams et al., 2006), algae (Aruoja et al., 2009), mice (Liu et al., 2009) and rats (Warheit et al., 2007), and their toxicity may be related to their size, structures or surface properties (Warheit et al., 2007; Aruoja et al., 2009). The extent of the threat of nano-TiO2 to the environment and public health is correlated to their ability to remain dispersed (i.e. stable) in the environment or to form submicron aggregates, and consequently their mobility. The aggregation of nano-TiO2 and the electrostatic interaction between nanoTiO2 and the surfaces of porous media are impacted by the solution ionic strength and pH (Guzman et al., 2006; Fang et al., 2009; French et al., 2009). As the pH of nano-TiO2 dispersions approached the point of zero charge (pHpzc) of nano-TiO2, their aggregates size increased while their mobility decreased (Guzman et al., 2006). At or near the pHpzc, the surface ionization of nanoparticles is suppressed or reduced to zero, thereby diminishing the repulsive forces between nanoparticles and allowing the formation of nanoaggregates. The morphology and aggregation rate of nano-TiO2 is a function of the ionic strength and electrolyte solution characteristics (French et al., 2009). An increase of the ionic strength and/or the valence charge of the cations diminished the magnitude of the electrostatic repulsion resulting in an intensification of the aggregation phenomena (Saleh et al., 2008; French et al., 2009). Fang et al. (2009) investigated the stability of nano-TiO2 in different soil suspensions and their transport in saturated homogeneous soil columns. It was observed that retention of nano-TiO2 was favored in soils containing higher clay content and salinity. The stability of nanoparticles in aquatic and subsurface environments is also impacted by the presence of natural organic matter (NOM). Previous studies revealed similar interaction characteristics between NOM and fullerenes, carbon nanotubes or quantum dots as compared to NOM and colloids in the submicron range, an indication of the effects of NOM on the stability and mobility of nanoparticles in porous media (Chen and Elimelech, 2007; Espinasse et al., 2007; Navarro et al., 2008). In the case of colloids, the NOM adsorbed at their surface may modify the physico-chemical properties of the underlying solid and impact the stability and behavior of colloids (Franchi and O’Melia, 2003; Mylon et al., 2004; Chen et al., 2006; Pelley and Tufenkji, 2008). NOM stabilizes colloids by means of electrostatic and steric repulsions in the presence
of monovalent electrolytes (Mylon et al., 2004). Nevertheless, divalent electrolytes at high concentrations do not contribute to colloid stability because of cation complexation with NOM (Amirbahman and Olson, 1995; Kretzschmar and Sticher, 1997; Mylon et al., 2004). The adsorbed NOM surface coatings have shown to enhance the transport of natural (Akbour et al., 2002) and synthetic colloids (Deshiikan et al., 1998; Franchi and O’Melia, 2003), along with biocolloids (Johnson and Logan, 1996) in saturated porous media. Surfactants may also adsorb to nanoparticles and affect their solubility and transport in porous media by modifying their electrostatic, hydrophobic and steric interactions (Tiraferri and Sethi, 2009; Lin et al., 2010; Tian et al., 2010). Surfactant may also sorb onto the surfaces of porous media and affect its hydraulic properties as well as interactions forces (Brown and Jaffe, 2001; Abu-Zreig et al., 2003; Wiel-Shafran et al., 2006; Mingorance et al., 2007). For example, the use of an anionic surfactant, sodium dodecylbenzene sulfonate (SDBS), to disperse and stabilize engineered nanoparticles (i.e. silver nanoparticles and carbon nanotubes) led to surfactant-solubilized nanoparticles that were highly mobile in saturated sandy porous media. Nonionic and ionic surfactants are commonly used to coat nanoTiO2 to remain dispersed (i.e. stable) during the fabrication of paints and cosmetics (Tkachenko et al., 2006). The stability and transport of nano-TiO2 may be influenced not only by the intentional and controlled generation of nanoTiO2 with specific surface coating that may modify their behavior in environmental systems compared to natural and air-borne nanoparticles (Nowack and Bucheli, 2007; Navarro et al., 2008), but also to the presence of surfactant and/or humic substances in subsurface environments. Information regarding the transport of nano-TiO2 through porous media is essential to create a coherent notion of their mobility through soil matrices as well as to assess the potential threats to groundwater pollution. The objective of this study is to explore the effects of environmental physico-chemical parameters on nano-TiO2 aggregation and transport mechanisms in saturated porous media. Laboratory scale column experiments were performed to investigate and characterize the effects of pH, anionic and non-ionic surfactants and flow velocity on the aggregation and transport of nano-TiO2 in saturated porous media. The single-collector efficiency for physico-chemical filtration in saturated porous media was also applied to characterize the particle deposition and maximum transport distance of the nano-TiO2 under favorable and non-favorable physical and chemical transport conditions. The parameters selected for these experiments fall within the ranges found in natural subsurface environments. Techniques of UV/Vis Spectrophotometer, Dynamic Light Scattering (DLS) and Zeta potential were implemented to determine nanoparticle concentration, changes in particle size and surface charge, respectively.
2.
Materials and methods
2.1.
Column system
The porous medium used in these experiments consisted of ASTM 20/30 unground quartz silica sand from U.S. Silica
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 3 9 e8 5 1
Company, Ottawa, Illinois. The sand was sieved through a stainless steel U.S. 30 mesh to achieve a collector diameter dc of 0.600 mm. The coating of the sand by metal oxides (e.g. Fe, Al, Ti, Ca, Mg, Na, K) may affect the surface charge of the porous media and induce colloid deposition at its surface (Litton and Olson, 1996). Therefore, to eliminate the metal oxides coating, the sand was chemically treated according to a procedure developed by Lenhart and Saiers (2002) that included a series of acid and base washes. For each experimental trial, 18.2 g of cleaned sand was packed into a Spectra/Chrom Aqueous column (ColeParmer Inc., Vernon Hills, Illinois) with an inner diameter and length of 0.9 cm and 15 cm, respectively. The sand was supported on a Polypropylene Spectra/Mesh screen with pore size of less than 0.500 mm. The sand was introduced into the column in 1 cm intervals. After each addition of sand, the sides of the column were tapped continuously to achieve uniform packing and avoid settling during the experiments. The sand column was saturated by applying deionized (DI) water to the column from the bottom utilizing an L/S brushless digital drive (Cole-Parmer Vernon Hills, Illinois) connected to an L/S multi-channel pump head (Cole-Parmer Vernon Hills, Illinois) and Tygon Autoanalysis Tubing (ColeParmer Vernon Hills, Illinois). To assure a homogenous compaction of the sand grains, at least 25 pore volumes (PVs) of DI water were passed through the column. The average porosity of the sand equaled 0.271. The permeability coefficient of the sand was determined according to the ASTM D 2434-68 (2006) method.
2.2.
Nano-TiO2 dispersions
Aeroxide TiO2 P 25 was obtained from Evonik Degussa Corporation (Parsippany, NJ). Aeroxide TiO2 P 25 is a mixture of two crystalline phases, anatase (80%) and rutile (20%), and its purity is in excess of 99.5%. According to the manufacturer, the density at 20 C, specific surface area (BET) and average particle size of TiO2 are 3.8 g/cm3, 50 15 m2/g and 21 nm, respectively. Two sets of nanoparticle dispersions were compounded for the study. The chemical characteristics of the electrolyte solution in both dispersions consisted of 0.01 M NaBr (SigmaeAldrich Co.) and 0.001 M NaOH (SigmaeAldrich Co.) with a final ionic strength of approximately 0.011 M. The concentration of nano-TiO2 in both dispersions was 25 mg/L. Dispersions 1 and 2 were prepared through sonication of nano-TiO2 in an ultrasonic bath for a period of 1 h in electrolyte solution with adjusted pH values of 7.0 and 9.0, respectively. The 0.001 M NaOH was used to target the desired pH. The pH values and ionic strength of the dispersion systems were based on the typical characteristics of most fresh groundwater aquifers (Davis and de Wiest, 1966). Upon sonication, the dispersions were cooled to room temperature (21e22 C) and each was separated into four100 mL volume parts. A non-ionic surfactant, Triton X-100 (SigmaeAldrich Co.) and an anionic surfactant, sodium dodecylbenzene sulfonate (SDBS) (SigmaeAldrich Co.) were incorporated at 50% (e.g. only Triton X-100) and 100% (e.g. both surfactants) critical micelle concentrations (CMC) in some of the dispersions. If additional pH adjusting was
841
required to maintain the dispersions at the specific pH, 0.001 M NaOH was utilized. Adsorption of surfactants and the role of surfactants in stabilizing colloidal dispersions have been studied extensively, as these phenomena are relevant to a range of industrial and environmental applications such as processing of minerals, personal care applications and soil remediation (Romero-Cano et al., 2002; Shao et al., 2010). Triton X-100 and SDBS are effective surfactants capable of adsorbing to the surface of colloids providing them with higher capacity to disperse in aqueous environments. We have selected these two surfactants to facilitate the dispersion of TiO2 in the electrolyte solutions. Dispersion 1 (pH 7) and dispersion 2 (pH 9) each consisted of the following four systems: No Surfactant, 50% CMC Triton X-100, 100% CMC Triton X-100 and 100% CMC SDBS. These systems became the feeding solutions for the different experiments conducted in this study. All feeding dispersions were prepared before conducting the experiments to diminish spontaneous aggregation of nano-TiO2 and its unstable behavior in the aqueous dispersions. Prior to performing an experiment, the corresponding 100 mL feed dispersion was further sonicated for 45 min in the ultrasonic bath, cooled to room temperature and stirred.
2.3.
Characterization of nano-TiO2
All characterization techniques of nano-TiO2 were conducted at room temperature. To corroborate the average primary particle size provided by the manufacturer, an X-ray diffraction (XRD) analysis of Aeroxide TiO2 P 25 was conducted. An X-ray Diffractometer, D5000 (Siemens), with monochromatic ˚ ) operated at 40 kV and 30 mA Cu Ka radiation (l ¼ 1.5418 A was utilized. Diffraction patterns were recorded in the 2q angular range of 24.5 e26 with step sizes of 0.01 and dwell time of 1 s. The average zeta potential of the different nanoTiO2 solution dispersions was determined by a Zeta-Meter System 3.0þ (Zeta Meter, Inc., Staunton, VA) apparatus. Dispersion systems were prepared, sonicated for 1 h, cooled to room temperature and stirred prior to taking the zeta potential measurements. The zeta potential value of a dispersion system was obtained by averaging ten zeta potential measurements. The average radius of the nanoTiO2 aggregates in the various dispersion systems was measured using a DynaPro Titan Dynamic Light Scattering (DLS) probe from Wyatt Technology Corporation. The solution dispersions were prepared as indicated for the zeta potential measurements with one additional step: dilution after the sonicating/cooling process to avoid multiple scattering of the DLS instrument. To not disturb the aggregate dimension, the dilutions were conducted with the same chemical characteristics of its corresponding solution minus the TiO2. To ensure a representative sample of the aggregates subjected to the DLS analysis, the solution dispersions were agitated vigorously for 2 min utilizing a vortex mixer prior to creating the dilutions. Before taking the DLS readings, the dilutions were again agitated for 30 s. Two DLS scattering analyses were taken per dispersion system; each run consisted of 20 DLS readings. The radius of the nano-TiO2 aggregates in each dispersion system was determined by taking the mean value of the measurements.
842
2.4.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 3 9 e8 5 1
Nano-TiO2 transport column experiments
2.5.
Table 1 provides a list of parameters utilized in the nano-TiO2 transport experiments. Once the soil matrix was homogenously compacted and completely saturated, the corresponding nano-TiO2 dispersion (i.e., 10 PVs) was loaded into the supply tank (i.e. a Pyrex beaker with cover) of the experimental set-up. A steady and almost pulse-free flow of dispersion was maintained through the soil matrix at a flow velocity (i.e. Darcy velocity) of 4.83 104 m/s (flow rate of 0.5 mL/min) or 9.67 104 m/s (flow rate of 1 mL/min) with the L/S brushless digital drive-pump system. Tygon Autoanalysis Tubing was used to deliver the dispersion into the column. The dispersion was introduced from the bottom of the column as described in Fig. 1. The moment the feed dispersion entered the column system, effluent samples were taken for nano-TiO2 concentration measurements. Samples were hand-collected in 5 mL BD Falcon roundbottom disposable polypropylene tubes (Fisher Scientific, Pittsburg, PA). Ten additional PVs of DI water were added to the supply tank immediately after the initial feed dispersion was depleted, to flush out any remaining nano-TiO2 or nanoTiO2 aggregates from the porous media. The pump was turned off after the flushing period was concluded. A total of 45 effluent samples were collected for each of the experiments conducted. The influent and effluent concentrations of nano-TiO2 were analyzed using a Cary 300 Bio UV/Vis Spectrophotometer (Varian, Inc., Walnut Creek, CA). The absorbance of the samples was measured for the wavelength range of 220e420 nm. The scanning control parameters of the spectrophotometer were set for data intervals corresponding to 1.000 nm, with average scanning time of 0.100 s and a scanning rate of 600.0 nm/min. The absorbance of nano-TiO2 was correlated to the feed dispersion characteristics. For each system, a “most relevant” wavelength value for absorbance was selected. The later turned out to be at 330 nm wavelength. The concentration of nano-TiO2 dispersions entering the column, C0, and in the effluent, C, were used to generate breakthrough curves (BTCs) of C/C0 as a function of PVs passing through the soil matrix. To create the nano-TiO2 BTCs, only the odd-numbered effluent-collected samples were utilized.
Table 1 e Experiment conditions and column properties. Parameter Description
Unit
Inner diameter of column Length of column Pore volume of column Collector diameter Porosity of filter media Ionic strength (0.01 M NaBr & 0.001 M NaOH) Initial concentration of TiO2 Flow Velocity 1 Flow Velocity 2 Temperature
cm cm mL mm
Value
M
0.9 15 2.54 0.600 0.271 0.011
mg/L m/s m/s C
25 4.83 104 9.67 104 22
Tracer breakthrough experiments
The hydraulic properties of the porous media within the column systems were evaluated through tracer tests. The even-numbered effluent samples collected in each experimental trial were used to determine the bromide (Br) concentration. An IC25 and AS 50 Ion Chromatograph System (Dionex Corporation) were used to perform the different isocratic Br ion-trace analyses via conductivity detection. The concentration of Br ions entering the column, C0, and in the effluent, C, were used to produce BTCs of C/C0 as a function of PVs.
2.6.
Sedimentation experiments
The stability of nano-TiO2 in the different dispersion systems was evaluated through sedimentation experiments. The different dispersion systems were prepared following procedures described in Section 2.2. The dynamic aggregation process was monitored utilizing a Cary 300 Bio UV/Vis Spectrophotometer, measuring the sedimentation process of nano-TiO2 via time-resolved optical absorbance. The absorbance of the samples was measured at a wavelength of 300 nm. Optical absorbency was recorded every 6 min for 180 min. For each dispersion system, the experiments were carried out in duplicates and the results presented are the average of the runs.
3.
Colloids transport theory
The theoretical framework originally developed to analyze colloidal transport in porous media was utilized to investigate the transport of nano-TiO2 through saturated soil. The mean particle sizes of the nanoaggregates in this study are within the submicron range. Therefore, we can characterize nanoTiO2 as miniature colloids, as also suggested in the case of various nanoparticle aggregates (Lecoanet et al., 2004; Lecoanet and Weisner, 2004; Brant et al., 2007; Guzman et al., 2006; Phenrat et al., 2007; French et al., 2009). Tufenkji and Elimelech (2004a) formulated a correlation equation to prognosticate the single-collector contact efficiency for physico-chemical filtration in saturated porous media. The theoretical approach takes into account colloid deposition on soil grains as a result of colloid transport to the non-mobile surface collector (i.e. soil grain) followed by attachment (Tufenkji and Elimelech, 2004a). The three mechanisms responsible for colloids transport are Brownian diffusion, interception, and gravitational sedimentation (Yao et al., 1971). The general expression for the single-collector contact efficiency (h0) is given by Tufenkji and Elimelech (2004a) as: h0 ¼ hD þ hI þ hG
(1)
where hD represents the transport by diffusion, hI is the transport by interception and hG is the transport by gravitational sedimentation. The actual single-collector removal efficiency (h) is defined as the product between attachmentecollision efficiency (a)
843
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 3 9 e8 5 1
Fig. 1 e Experimental set-up.
and h0 (Equation (1)). Note that a can be calculated in terms of the physico-chemical parameters of the systems as (Tufenkji and Elimelech, 2004a): 2 dc lnðC=C0 Þ a¼ $ 3 ð1 f ÞLh0
(2)
where L represents the length of the filtering porous media, and C/C0 is the ratio between the colloid effluent concentration and the colloid influent concentration, dc is the diameter of the spherical collector and f is the porosity of the porous medium. The maximum distance (Lmax) that colloids may be transported in saturated porous media can be estimated as the distance where 99.9% colloids removal from the pore solution occurred, Lmax is expressed as (Fang et al., 2009; He et al., 2009): 2 dc Lmax ¼ $ lnðC=C0 Þ 3 ð1 f Þah0
(3)
where C/C0 is 0.01.
4.
Results
computed mean particle size distribution of the nanoparticles was 18.9 nm. This crystallite size differs by 2.1 nm from the one provided by the manufacturer. The average zeta potential of nano-TiO2 in Dispersion 1 and 2 systems are presented in Table 2. The DLS tests provided hydrodynamic size of nano-TiO2 in Dispersion 1 and 2 systems (Tables 3 and 4). Since the hydraulic particle size is greater than the average particle size of TiO2, it can be concluded that the nanoparticles aggregated in the different dispersion solutions. Less aggregation was observed in the dispersion systems at pH 9 compared to those at pH 7. As the pH of the dispersion system approaches the pHpzc of nano-TiO2 which is reported in literature to be between 6.7 and 7.0 (Leong and Ong, 2003; Pelton et al., 2006; Liu et al., 2008; Boncagni et al., 2009), the aggregation phenomena of nano-TiO2 occurred. The size of nano-TiO2 aggregates in the dispersions containing surfactants was smaller than the one without surfactant regardless of the pH, demonstrating that surfactants allow nano-TiO2 to remain dispersed or stable. Note that surfactants adsorb to the surface of nano-TiO2 creating steric repulsion between nanoaggregates thus inhibiting interactions and preventing more aggregation which can cause an increase in particle size.
4.1. Nano-TiO2 characterization, porous medium permeability coefficient and tracer BTCs The size distribution of Aeroxide TiO2 P 25 was calculated based on X-ray diffraction analysis and Scherrer’s formula (Cullity and Stock, 2001): dhkl ¼
0:9l BcosqB
Table 2 e Zeta potential measurements of dispersion systems. pH 7
(4)
where dhkl represents the mean size particle distribution (nm), l is the wavelength of Cu Ka radiation (l ¼ 0.15418 nm), B (radians) constitutes the full width at half-maximum (FWHM) of the broadened diffraction line observed on the 2q angular range, and qB is the Bragg angle of diffraction. The
9
Dispersion System
Zeta Potential (mV)
No Surfactant 50% CMC Triton X-100 100% CMC Triton X-100 100% CMC SDBS No Surfactant 50% CMC Triton X-100 100% CMC Triton X-100 100% CMC SDBS
20.0 13.6 21.2 11.6 16.8 17.9 17.6 19.2
844
0.189 0.205 0.244 0.246 0.820 0.814 0.649 0.721 5.15 4.60 2.94 2.67 0.782 0.688 0.899 0.862 2.59E-2 3.41E-2 5.89E-2 6.03E-2 0.431 0.428 0.345 0.384 2.59E-03 2.69E-03 3.53E-03 3.84E-03 3.94E-03 4.51E-03 4.33E-03 4.06E-03 4.98E-04 4.00E-04 1.18E-04 8.18E-05 8.24E-05 6.15E-05 6.55E-05 7.00E-05 2.57E-04 2.15E-04 9.04E-05 7.11E-05 7.03E-05 5.59E-05 5.91E-05 6.35E-05 1.84E-03 2.07E-03 3.32E-03 3.69E-03 3.79E-03 4.40E-03 4.21E-03 3.93E-03 253 223 122 103 102 86.8 90.4 95.4 9
No Surfactant 50% CMC Triton X-100 100% CMC Triton X-100 100% CMC SDBS No Surfactant 50% CMC Triton X-100 100% CMC Triton X-100 100% CMC SDBS 7
Maximum Transport Distance, Lmax (m) Attachment Efficiency, a C/C0 Single-Collector Contact Efficiency, h0 ¼ hD þ hI þ hG Gravitational Sedimentation hG Interception hI Diffusion hD
The presence of Triton X-100, a non-ionic surfactant, and SDBS, an anionic surfactant, enhanced the transport of nanoTiO2 in saturated porous media for Dispersion 1 (pH 7) systems. Fig. 3 describes the nano-TiO2 BTCs for the No Surfactant, 50% and 100% CMC Triton X-100, and 100% CMC SDBS systems at 4.83 104 m/s. The C/C0 effluent fraction concentration of nano-TiO2 increased with the presence of surfactant and percent CMC. During the flushing period of the experiment, a sudden increase in C/C0 was registered in the nano-TiO2 BTCs. The increase in C/C0 registered during the flushing period of the experiments will be referred from now on as the flushing peak of the BTCs. To determine the quantity of nanoTiO2 that exited through the column, the percentage transport recovery for each experiment was computed (Table 5). The percentage transport recovery of nano-TiO2 for dispersion systems with No Surfactant, 50% and 100% CMC Triton X-100, and 100% CMC SDBS at 4.83 104 m/s and pH 7 were 3.80%, 5.92%, 8.59% and 7.72%, respectively. Fig. 4 shows the nanoTiO2 BTCs of these same dispersion systems under a varied flow velocity of 9.67 104 m/s. The increase in flow velocity translated to an augmentation in nano-TiO2 C/C0 effluent fraction concentration. At a flow velocity of 9.67 104 m/s, the percentage transport recovery of nano-TiO2 for dispersion systems with No Surfactant, 50% and 100% CMC Triton X-100, and 100% CMC SDBS were 4.98%, 11.6%, 21.2% and 29.6%, respectively. The concentration of nano-TiO2 in the effluent was once more correlated to the presence of surfactant and percent CMC. The BTCs in Fig. 4 also registered an increase in the flushing peak.
Particle Radius ap (nm)
4.3. Effect of surfactants vs. no surfactant in the transport of nano-TiO2
Dispersion System
The sedimentation experiments demonstrated that there is no significant sedimentation taking place in the inlet reservoir during the feeding of nano-TiO2 into the column. According to the optical absorbency results (Fig. 2), sedimentation is irrelevant for all the different dispersion systems for the first 60 min of the experiments. Considering that the feeding of nano-TiO2 into the column at 4.83 104 m/s and 9.67 104 m/s took 52 and 26 min, respectively, we can attest that the application of the transport theory for steady-state systems is correct and the results supported.
Table 3 e Experimental parameters from BTC experiments calculated with filtration theory for a flow velocity of 0.0483 cm/s.
Sedimentation analysis
pH
4.2.
Deposition Rate Coefficient, kd (h1)
The ASTM D 2434-68 (2006) method for permeability of granular soils at constant head was utilized to determine the coefficient of permeability of the column system. The permeability coefficient k was calculated as 3.86 104 m/s, indicating a significant flow of water through the porous media which is characteristic of clean sand (Holtz and Kovacs, 1981). The Br BTCs in all experiments included a monotonic increase with a C/C0 value greater than 0.5 attained at 1.5 PVs and a plateau value of passage through the sand column of 1. A monotonic trend was also observed in the Br BTC descent. The BTCs of the dispersion systems under the same pH and flow velocity turned out to be extremely close in appearance. Thus, the Br BTCs displayed in Figs. 3e6 represent the mean tracer curve of the dispersion systems presented in the figure.
156 145 121 120 36.1 36.3 45.5 41.0
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 3 9 e8 5 1
280 201 140 118 65.9 27.3 25.8 53.0 0.211 0.294 0.422 0.503 0.898 2.17 2.29 1.12 7.55 5.24 2.77 2.14 1.17 0.423 0.417 0.912 1.59E-03 1.64E-03 2.16E-03 2.35E-03 2.41E-03 2.76E-03 2.65E-03 2.49E-03 2.31E-04 1.85E-04 5.47E-05 3.79E-05 3.82E-05 2.85E-05 3.04E-05 3.24E-05 2.35E-04 1.97E-04 8.29E-05 6.52E-05 6.45E-05 5.13E-05 5.42E-05 5.83E-05
3.79E-02 9.51E-02 1.95E-01 2.53E-01 0.463 0.727 0.739 0.538
Attachment Efficiency, a C/C0 Single-Collector Contact Efficiency, h0 ¼ hD þ hI þ hG Gravitational Sedimentation, hG Interception, hI
To determine the effect that pH had on the transport of nano-TiO2 through the saturated porous medium, the experiments were also conducted at pH 9. Fig. 5 corresponds to the nano-TiO2 BTCs of No Surfactant, 50% and 100% CMC Triton X-100, and 100% CMC SDBS at pH 9 and 4.83 104 m/s. Based on the C/C0 fraction concentration of nano-TiO2 measured in the effluent, it was observed that the increase in pH induced better transport conditions for the nanoaggregates of all dispersions. At pH 9, the nanoaggregates in the dispersions are approximately two pH units apart from the pHpzc of nanoTiO2. At 4.83 104 m/s, the addition of non-ionic and anionic surfactant did not necessarily improve the transport behavior of nano-TiO2 compared to the No Surfactant dispersion. No particular dispersion system dominated the transport of nanoTiO2 under the present conditions. During the flushing period, similarly to the case at pH 7, the dispersion systems presented a well-established flushing peak. The presence and concentration of surfactant seemed to control the flushing peak maximum. The percentage transport recovery of nano-TiO2 for dispersion systems with No Surfactant, 50% and 100% CMC Triton X-100, and 100% CMC SDBS at 4.83 104 m/s and pH 9 were 42.5%, 45.9%, 41.1% and 45.4%, respectively (Table 5). Fig. 6 shows the nano-TiO2 BTCs of the dispersion systems described above under 9.67 104 m/s flow velocity. The increase in flow velocity and presence of non-ionic and anionic surfactant enhanced the transport of nano-TiO2 and the patterns of the nano-TiO2 BTCs were much more well-defined. The flushing peaks in Fig. 6 were not as pronounced as those in Fig. 5, which may be the result of much greater transport of nano-TiO2 during the first ten PVs. At pH 9 and 9.67 104 m/s the four dispersion systems registered their greatest transport of nano-TiO2 the column. The percentage transport recovery of No Surfactant, 50% and 100% CMC Triton X-100, and 100% CMC SDBS were 48.2%, 76.2%, 75.5% and 63.4%, respectively.
At pH 7, the addition of Triton X-100, a non-ionic surfactant, into aqueous dispersions in 50% and 100% CMC changed the
1.12E-03 1.26E-03 2.02E-03 2.25E-03 2.31E-03 2.68E-03 2.56E-03 2.39E-03 9
253 223 122 103 102 86.8 90.4 95.4 No Surfactant 50% CMC Triton X-100 100% CMC Triton X-100 100% CMC SDBS No Surfactant 50% CMC Triton X-100 100% CMC Triton X-100 100% CMC SDBS 7
Particle Radius, ap (nm)
Diffusion, hD
845
4.4. Influence of pH, flow velocity and non-ionic surfactant concentration on transport of nano-TiO2
Dispersion System pH
Table 4 e Experimental parameters from BTC experiments calculated with filtration theory for a flow velocity of 0.0967 cm/s.
Maximum Transport Distance, Lmax (m)
Deposition Rate Coefficient, kd (h1)
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 3 9 e8 5 1
Fig. 2 e Sedimentation of nano-TiO2 in Dispersion 1 and Dispersion 2 systems.
846
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 3 9 e8 5 1
Fig. 3 e Nano-TiO2 BTCs without surfactant and with nonionic surfactant (Triton X-100) and anionic surfactant (SDBS) at pH 7 and flow velocity of 0.0483 cm/s. BTC for a BrL tracer is also included. Left y-axis for nano-TiO2 C/C0, right y-axis for tracer (BrL) C/C0.
average zeta potential of the nano-TiO2 aggregates from 20.0 mV for aqueous dispersion with no surfactant to 13.6 and 21.2 mV, respectively (Table 2). The higher CMC permitted nano-TiO2 at pH 7 to attain better stability in solution that resulted in higher transport of nano-TiO2 through porous media (Table 5). As the pH increased from 7 to 9, the average zeta potential of the 50% and 100% CMC Triton X-100 dispersion systems became 17.9 and 17.6 mV, respectively. The stability of nano-TiO2 at pH 9 was higher than at pH 7, allowing the nano-TiO2 aggregates to repel one another for a longer period of time due to repulsive double layer interactions as also corroborated by the size of the nano-TiO2 aggregates under different dispersion systems. The pH 9 provided better transport conditions for nano-TiO2 than pH 7. At pH 9, the addition of non-ionic surfactant in different CMC did not affect the transport of nano-TiO2 significantly. Figs. 3e6 provide a comparison of the BTCs of the 50% and 100% CMC Triton X-100 dispersion systems as a function of pH and varied flow velocity. The effect of flow velocity was
investigated by interpreting the filtration theory with the experimental transport data. The computational analyses are presented in Tables 3 and 4. In the case of dispersion systems of 50% CMC Triton X-100 at pH 7 and pH 9 and 4.83 104 m/s, the single-collector contact efficiency, h0, and the attachment efficiency, a, were 2.69 103, 4.60 and 4.51 103, 0.688, respectively. By doubling the flow velocity, h0 at pH 7 and pH 9 decreased by 63.4% and 63.7% whereas a increased by 112% at pH 7 and decreased by 62.6% at pH 9. At the lower flow velocity, the 100% CMC Triton X-100 dispersion system attained values of h0 and a of 3.53 103 and 2.94 at pH 7 while at pH 9 h0 and a were 4.33 103 and 0.899. The increase in flow velocity translated to an increase in the single-collector efficiency of approximately 63% for both pH values. The attachment efficiency did not change significantly at pH 7, but at pH 9 the increased flow velocity induced a reduction in attachment efficiency of 115%. In terms of the percentage transport recovery of nano-TiO2 through the porous medium, the change in pH had a positive effect in the 50% and 100% CMC Triton X100 dispersion systems (Table 5). On the other hand, the increase in CMC from 50 to 100% had 45.2% and 83.3% increase in percentage transport recovery of nano-TiO2 at pH 7 and 4.83 104 m/s and pH 7 and 9.67 104 m/s, respectively. The increase in CMC from 50 to 100% at pH 9 did not have significant impact at either flow velocity since the percentage transport recovery of nano-TiO2 in the effluent for the 50% CMC Triton X-100 and 100% CMC Triton X-100 dispersion systems was very close at the corresponding flow velocities.
4.5. Influence of pH, flow velocity and anionic surfactant on transport nano-TiO2 The increase in flow velocity from 0.0483 cm/s (Figs. 3 and 5) to 9.67 104 m/s (Figs. 4 and 6) as well as the increase in pH from 7 to 9 in connection with the presence of anionic surfactant at 100% CMC enhanced the transport of nano-TiO2 aggregates (Table 5). By doubling the flow velocity, the transport of nano-TiO2 monitored in the effluent increased by 283% at pH 7 and only 39.7% at pH 9. The variation of pH from 7 to 9 had a greater impact in the mobility nano-TiO2 through the porous medium than the increase of flow velocity. At a flow velocity of 4.83 104 m/s, the increase in pH resulted in 487%
Table 5 e Nano-TiO2 transport recovery: total transport recovery and transport recovery from secondary energy minimum. pH
Dispersion System
Flow Velocity 4
4.83 10 Total Transport Recovery (%) 7
9
No Surfactant 50% CMC Triton X-100 100% CMC Triton X-100 100% CMC SDBS No Surfactant 50% CMC Triton X-100 100% CMC Triton X-100 100% CMC SDBS
3.80 5.92 8.59 7.72 42.5 45.9 41.1 45.4
9.67 104 m/s
m/s
Transport Recovery from Total Transport Transport Recovery from Secondary Energy Minimum Recovery (%) Secondary Energy Minimum (% of Total Transport Recovery) (% of Total Transport Recovery) 28.0 40.5 30.6 22.5 8.91 17.8 28.6 17.4
4.98 11. 6 21.2 29.6 48.2 76.2 75.5 63.4
18.8 16.4 7.59 6.79 11.2 13.6 8.40 15.2
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 3 9 e8 5 1
Fig. 4 e Nano-TiO2 BTCs without surfactant and with nonionic surfactant (Triton X-100) and anionic surfactant (SDBS) at pH 7 and flow velocity of 0.0967 cm/s. BTC for a BrL tracer is also included. Left y-axis for nano-TiO2 C/C0, right y-axis for tracer (BrL) C/C0.
increase of nano-TiO2 aggregates in the effluent as observed through the percentage transport recovery. At the higher flow velocity, the change in pH increased the percentage transport recovery of nano-TiO2 by 114%. Therefore, the addition of anionic surfactant enhanced the effects of pH variation and flow velocity. According to filtration theory, at 4.83 104 m/s, the single-collector contact efficiency and attachment efficiency at pH 7 were 3.84 103 and 2.67 whereas at pH 9 these values were 4.06 103 and 0.862, respectively. The change in pH had a significant reduction in attachment efficiency while the single-collector contact efficiency increased slightly due to the reduction size of the nano-TiO2 aggregates at pH 9. By doubling the flow velocity, the change in pH produced a reduction in attachment efficiency of 134% but the singlecollector contact efficiency remained close. At pH 7, the
Fig. 5 e Nano-TiO2 BTCs without surfactant and with nonionic surfactant (Triton X-100) and anionic surfactant (SDBS) at pH 9 and flow velocity of 0.0483 cm/s. BTC for a BrL tracer is also included.
847
Fig. 6 e Nano-TiO2 BTCs without surfactant and with nonionic surfactant (Triton X-100) and anionic surfactant (SDBS) at pH 9 and flow velocity of 0.0967 cm/s. BTC for a BrL tracer is also included.
increase in flow velocity decreased the attachment efficiency by 25.1% while at pH 9 the attachment efficiency did not have significant changes. In the case of the single-collector contact efficiency, the increase in flow velocity produced a decrease in single-collector contact efficiency of approximately 63% under both pH values.
4.6. Influence of non-ionic vs. anionic surfactant in the transport of nano-TiO2 To investigate the effectiveness of the type of surfactant on the transport of nano-TiO2 through saturated porous media, the BTCs for the 100% CMC Triton X-100 and SDBS where compared based on pH and flow velocity variation (Figs. 3e6). At a flow velocity of 4.83 104 m/s, there is no significant difference in the BTCs of the non-ionic and anionic surfactant dispersion systems at pH 7. Based on the filtration theory, the maximum transport distance, Lmax, of nano-TiO2 through saturated porous media in the presence of 100% CMC Triton X-100 and SDBS at pH 7 was estimated to 0.244 and 0.246 m, respectively (Tables 3 and 4). If the flow velocity was doubled, Lmax for these systems under pH 7 was predicted to be 0.422 and 0.503 m, respectively. Under the later conditions, the 100% CMC SDBS system has a smaller attachment efficiency leading to an increase in the transport of nano-TiO2 through the porous medium. As the pH of 100% CMC Triton X-100 and SDBS dispersion systems was increased to 9, the maximum transport distances for nano-TiO2 at 4.83 104 m/s and 9.67 104 m/s were estimated to be 0.649, 0.721 m and 2.29, 1.12 m, respectively. At the higher flow velocity and pH 9, the 100% CMC Triton X-100 system had the smallest attachment efficiency allowing the nano-TiO2 aggregates in this dispersion to transport the furthest (2.29 m) compared to all the other dispersion systems. The maximum transport distance for nano-TiO2 was closely related to the single-collector contact efficiency and attachment efficiency that are a function of nano-TiO2 aggregates size, flow velocity and pH.
848
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 3 9 e8 5 1
5.
Discussion
5.1.
Nano-TiO2 aggregation
The ability of nanoparticles to remain stable or dispersed is essential for the nano-TiO2 to be mobile and transported in saturated porous media. The repulsive forces required to stabilize particles dispersion against van der Waals attractive forces are derived from the Coulombic repulsion forces deduced from electric particle surface charge or electrostatic double layer repulsions and the steric repulsion forces generated from the adsorption of long-chain-charged polymers on the surface of particles (Hirtzel and Rajagopalan, 1985). Steric repulsions between two particles result from volume restriction and osmotic effects. The former takes place from the configuration reduction in the zone between particle surfaces; the later arises from the high concentration of adsorbed charged macromolecules in the region between the particles as they come near (Hirtzel and Rajagopalan, 1985). Electrostatic and steric repulsion forces inhibit aggregation and attachment of particles to grain surfaces (Kretzschmar and Sticher, 1997; Saleh et al., 2008). In Dispersion 1 and 2 systems consisting of nano-TiO2 in aqueous solutions at pH 7 and pH 9, respectively, we observed that as the pH of the nano-TiO2 dispersions came near the pHpzc nano-TiO2 (6.7e7.0), the nano-TiO2 aggregates increased in size. At the pHpzc, the surface ionization of nanoparticles is suppressed, limiting the repulsive forces among nanoparticles allowing for aggregate formation. The increase in pH provided a better stability condition for nano-TiO2 aggregates since it was two pH units apart from the nano-TiO2 pHpzc. The incorporation of Triton X-100, a non-ionic surfactant, and SDBS, an anionic surfactant, contributed to the steric repulsion forces and inhibited the uncontrollable aggregation of nano-TiO2. In Dispersion 1 (pH 7) systems, the particle size of the nano-TiO2 aggregates was directly related to the presence or absence of surfactants. The presence of non-ionic surfactant induced a size reduction which was dependent on the CMC (Tables 3 and 4). Non-ionic surfactants adsorb on surfaces of particles in the form of micellar structures (Romero-Cano et al., 2002; Sharma et al., 2010) resulting from the initial adsorption of surfactant molecules on the surfaces, and their subsequent reorganization into micellar structures (Levitz et al., 1984). The stability of nanoparticles in non-ionic surfactant solutions also depends on the extent of surface coverage by surfactant micelles and nanoparticle dispersions are stable as long as there is at least one layer of surfactant micelles that separates the nanoparticles according to Alexeev et al. (1996). As surfactant micelles adsorbed on the particle surface, these micelles provide a steric repulsion between the particles that stabilizes them against aggregation. The incorporation of anionic surfactant in 100% CMC produced the smallest nano-TiO2 aggregates in Dispersion 1. The negatively charged SDBS molecules can easily react with positively charged metal ions by complexation effect, electron attraction and charge neutralization (Shao et al., 2010) leading to the generation of steric repulsion. Note that in this case nano-TiO2 without surfactant attained a positive zeta potential of 20.0 mV. This facilitated the interaction between the
anionic surfactant and the metal oxide nanoparticles. In Dispersion 2, the stability provided by the pH had a tremendous effect in the nano-TiO2 aggregates size; the addition of surfactants did impact the hydrodynamic diameter of the nano-TiO2 aggregates but in less significance compared to the systems in Dispersion 1. Fig. 2 illustrates that at pH 9 very little sedimentation takes place in the No Surfactant system (less than 8% after 180 min of testing). Under the same conditions the 50% and 100% CMC Triton X-100 and 100% SDBS registered less than 29%, 23% and 15% sedimentation at the conclusion 3-h testing. The electrostatic and steric repulsion forces in connection to nano-TiO2 aggregates diameter and flow velocity had a direct impact in the single-collector efficiency and attachment efficiency which dictated the maximum transport distance of the Dispersion 1 and 2 systems.
5.2.
Nano-TiO2 mobility and transport
Electrostatic and steric repulsion forces generated by solution chemistry combined with favorable hydrodynamic conditions can further enhance the transport distances of nanoTiO2. Doubling the flow velocity from 4.83 104 m/s to 9.67 104 m/s increased the transport of nano-TiO2 aggregates without surfactant at pH 7 and pH 9. However, this increment in transport was not significant. If we combined the effect of increasing flow velocity with the presence of surfactants as well as increasing the pH from 7 to 9, i.e. 2 units away from pHpzc, a slight increase in transport was observed in some of the dispersion systems. From Table 4, we observe that by doubling the flow velocity at pH 9, the No Surfactant, 50% CMC Triton X-100, 100% CMC Triton X-100 and SDBS attained maximum transport of nano-TiO2 distances of 0.898, 2.166, 2.288 and 1.115 m, respectively, among all experiments. The 18.9 nm nano-TiO2 particle size produced nano-TiO2 aggregates in aqueous solutions that were significantly larger than the original particle size. The hydrophobic characteristics and aggregation tendencies of some nanoparticles, such as nano-TiO2, will either prevent their dispersal in natural environments or increase their deposition onto porous media thus limiting their transport. This factor in connection with pH variations and the presence of surfactants and NOM in subsurface environments may provide favorable conditions that can significantly reduce the attachment efficiency of the nanoaggregates thus increasing the transport of nano-TiO2 through the soil matrix.
5.3. Nano-TiO2 deposition and secondary energy minimum Some authors have suggested that existing transport theory may not be adequate to predict the mobility and deposition behavior of nanoparticles or nanoaggregates (Tufenkji and Elimelech, 2004a, 2005; Kuznar and Elimelech, 2007). Some of the discrepancies can be attributed to interaction energies between nanoparticles and collector surfaces, size difference between matrix pores and nanoaggregates, and shear forces (Guzman et al., 2006). Filtration theory assumes that the particle deposition rate kd is constant. Nonetheless, changes in collectoreparticle interaction can lead to variations in the
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 3 9 e8 5 1
deposition rate. In the presence of the repulsive electrostatic double layer, particles in solution may demonstrate a dual deposition approach, that is, some portion of the particles experience a fast deposition rate whereas the rest of the particles experience a slow deposition rate (Tufenkji and Elimelech, 2004b). Favorable and unfavorable interactions between solution chemistry and colloids can be credited to the variation in deposition kinetics (Tufenkji and Elimelech, 2004b, 2005). In the absence of repulsive interaction energies, particle deposition is favorable, i.e. increased deposition rates take place due to the occurrence of a deep secondary energy minimum well. In contrast, when repulsive colloidal interactions occur, unfavorable or slow rate deposition takes place because of the deep primary energy well (Tufenkji and Elimelech, 2004b, 2005). Franchi and O’Melia (2003) and Hahn and O’Melia (2004) established the reversibility of particle deposition in the secondary energy minimum. In addition, particles deposited in the secondary energy minimum well may be released by vanquishing the energy barrier with electrolyte solution, that is, without colloids at a very low salt concentration (Franchi and O’Melia, 2003; Hahn and O’Melia, 2004). In our study, nano-TiO2 aggregates deposited onto surfaces of porous media due to secondary energy minimum were eventually released during the flushing period of the column with DI water. The reversible nano-TiO2 deposition was depicted by the flushing peaks. It is our understanding, that this is the first time in which a research study reported the release of manufactured nanoaggregates due to the elimination of the secondary energy minimum well. At pH 7 and pH 9, the reversible deposition of nano-TiO2 was more pronounced at the lower flow velocity of 4.83 104 m/s and was correlated to the presence and concentration of surfactants (Table 5), which may indicate that changes in solution chemistry may have more impact on the release of nano-TiO2 from secondary energy minima than hydrodynamic forces. From the BTCs it is observed that the systems containing nonionic surfactant were more effective in conducting reversible depositions upon the elimination of the secondary energy barrier. The deposition of nano-TiO2 in primary and secondary energy minima, the reversibility of their deposition in secondary energy minima should be addressed when characterizing the fate and transport of nanoparticles in porous media when assessing the risk of nanoparticles to public health and the environment.
6.
Conclusion
The effects of solution chemistry on the mobility and transport of nano-TiO2 in saturated porous media were investigated and the following inferences can be drawn: Deposition process is a key retention mechanism of nanoTiO2 in saturated porous media. As the solution pH approached the pHpzc of nano-TiO2, the mobility and transport of nano-TiO2 were limited due to the reduction of electrostatic interaction forces leading to the increase in the deposition rate coefficients. The presence and concentration increase of surfactant enhanced the transport of nano-TiO2 in saturated porous
849
media regardless of the pH as the impacts of surfactant on the steric repulsion forces allowed the nano-TiO2 to remain stable or dispersed, which resulted in an increase in mobility. Secondary energy minima played a critical role in the deposition mechanisms of nano-TiO2, and the reversibility of the deposition of nano-TiO2 was observed due to changes in solution chemistry.
Acknowledgements This research was supported by the National Science Foundation (NSF) e University of Illinois at Chicago (UIC) Bridge to the Doctorate Fellowship, the UIC Abraham Lincoln Fellowship and the Department of Civil and Materials Engineering at UIC, IL, U.S.A. We thank Professor Indachochea for his assistance in the X-Ray Diffraction analysis, Professor Khodadoust for the use of the zeta potential analysis and Professor Rockne for the use of the IC Chromatography System. We thank the three anonymous reviewers for their thoughtful and constructive comments to improve our manuscript.
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Low-cost struvite production using source-separated urine in Nepal B. Etter, E. Tilley, R. Khadka, K.M. Udert* Eawag: Swiss Federal Institute of Aquatic Science and Technology, U¨berlandstrasse 133, 8600 Du¨bendorf, Switzerland
article info
abstract
Article history:
This research investigated the possibility of transferring phosphorus from human urine
Received 5 July 2010
into a concentrated form that can be used as fertilizer in agriculture. The community of
Received in revised form
Siddhipur in Nepal was chosen as a research site, because there is a strong presence and
8 October 2010
acceptance of the urine-diverting dry toilets needed to collect urine separately at the
Accepted 9 October 2010
source. Furthermore, because the mainly agricultural country is landlocked and depends
Available online 16 October 2010
on expensive, imported fertilizers, the need for nutrient security is high. We found that struvite (MgNH4PO4$6H2O) precipitation from urine is an efficient and simple approach to
Keywords:
produce a granulated phosphorus fertilizer. Bittern, a waste stream from salt production, is
Magnesium ammonium phosphate
a practical magnesium source for struvite production, but it has to be imported from India.
(MAP)
Calculations show that magnesium oxide produced from locally available magnesite would
Nutrient recovery
be a cheaper magnesium source. A reactor with an external filtration system was capable
Phosphorus
of removing over 90% of phosphorus with a low magnesium dosage (1.1 mol Mg mol P),
Sustainable sanitation
with coarse nylon filters (pore width up to 160 50 mm) and with only one hour total
Struvite precipitation
treatment time. A second reactor setup based on sedimentation only achieved 50% phos-
Urine separation
phate removal, even when flocculants were added. Given the current fertilizer prices, high volumes of urine must be processed, if struvite recovery should be financially sustainable. Therefore, it is important to optimize the process. Our calculations showed that collecting the struvite and calcium phosphate precipitated spontaneously due to urea hydrolysis could increase the overall phosphate recovery by at least 40%. The magnesium dosage can be optimized by estimating the phosphate concentration by measuring electrical conductivity. An important source of additional revenue could be the effluent of the struvite reactor. Further research should be aimed at finding methods and technologies to recover the nutrients from the effluent. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Extensive sewer networks with large centralized wastewater treatment plants have been the common civil engineering approach to provide sanitation in urban and peri-urban areas. However, the high investment and operating costs, dependency on increasingly scarce resources (e.g. water and fossil fuels),
and intensive maintenance make this system not only unattainable, but unsustainable for most areas in the developing world. To reverse the trend of reproducing inappropriate designs, there is a growing demand for new technologies, which better suit the needs of the local population and their environment (Larsen et al., 2009; Guest et al., 2009; Tilley et al., 2008a) and which emphasize the value of wastewater as a resource
* Corresponding author. Tel.: þ41 44 823 53 60; fax: þ41 44 823 53 89. E-mail addresses:
[email protected] (B. Etter),
[email protected] (E. Tilley),
[email protected] (R. Khadka), kai.
[email protected] (K.M. Udert). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.10.007
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 5 2 e8 6 2
from which nutrients, water or energy can be recovered. The goal of extracting value from waste is to maximize the social, environmental and health benefits while minimizing the investment and operation costs (Schuen et al., 2009). Recovering resources from wastewater might allow the free market to play a role in the sanitation in developing countries by supporting small businesses that collect the waste, treat it and sell the value-added products. The present study focuses on the recovery of phosphorus from urine. Worldwide, approximately 50e60% of the phosphorus fertilizer demand is covered by mineral fertilizer (Smil, 2000). However, mineral phosphorus, like oil, is a finite resource and supplies are expected to peak around 2030 (Cordell et al., 2009). To prevent food shortages, additional phosphorus sources must be exploited, such as human and animal excreta (ibid.). In fact, humans typically excrete 1.6e1.7 g phosphorus per day, most of which (about 60%) is found in urine (Schouw et al., 2002). Alternative sanitation concepts, which are based on the separation and collection of excreta at the source, facilitate the recycling of nutrients from faeces and urine to agriculture (Larsen et al., 2009; Jo¨nsson et al., 2004). However, preliminary treatment of urine is needed to prevent nutrient loss by ammonia volatilization, to reduce weight (caused by the water content of urine) and to remove pathogens. Composting is a common and effective treatment for faeces (Niwagaba et al., 2009) and several treatment methods have been proposed for urine (Maurer et al., 2006), but few have been tested as thoroughly as struvite precipitation (see Ronteltap et al., 2010; Tilley et al., 2008b for an overview). Through a basic precipitation reaction, the majority of phosphorus in urine can be crystallized into a white, odourless powder called struvite or magnesium ammonium phosphate hexahydrate (MAP, MgNH4PO4$6H2O). Struvite is an effective phosphorus fertilizer (Johnston and Richards, 2004; Ro¨mer, 2006), it is compact and can be stored and transported easily. Full-scale struvite reactors have been used for several years to recover phosphate from different solutions such as WWTP digester supernatant, swine manure or agro-industry wastewater (Forrest et al., 2008; Bowers and Westerman, 2005; Moerman et al., 2009). However, these reactors are too large and too complex for urine treatment on a small scale in developing countries. Recently, a stainless steel reactor has been manufactured commercially to produce struvite from urine in decentralized settings (Paris et al., 2007; Abegglen, 2008; Antonini et al., 2009). The reactor is equipped with a spiral pump (precipitant dosage), magnetic valves and a process unit to allow for automated operation. Antonini et al. (2009) reported that they used magnesium oxide as the precipitant and dosed it at a molar ratio of 1.5 mol Mg mol P1. After the magnesium dose, the mixture was stirred for 30 min and then left to settle for 3 h. The precipitate was later collected in a filter bag attached to the outflow of the reactor. The phosphate removal was as high as 98%. Abegglen (2008) dosed magnesium at a molar ratio of 1.8 mol Mg mol P1 and observed phosphate removal efficiencies higher than 95%. The reactor type used in the studies by Abegglen (2008) and Antonini et al. (2009) has proven to be very suitable for pilot studies with a reliable power supply, but the investment costs are still rather high. In our project, we wanted to build struvite
853
reactors, which conform to the requirements of low-cost sanitation systems in Nepal, i.e. where the struvite process uses only locally available inputs, without in-depth technical knowledge, and without continuous electricity supply. Siddhipur, a village close to Kathmandu, was chosen as the project site. The opportunity and need to implement alternative sanitation in Nepal is large, as only 27% of the population has access to improved sanitation (of which, 3% are connected to sewers) (WHO/UNICEF, 2010). Although urinediverting dry toilets (UDDT) have been promoted heavily in recent years, the use of urine directly as a fertilizer is not common in Nepal (Water Aid, 2007). We conducted a comprehensive study to assess the technical and economic feasibility of producing struvite from source-separated urine in Nepal. Specifically, the determination of phosphate content in the influent (urine), the economics of magnesium sources for the precipitation process, the most efficient technology for crystal recovery, the potential of flocculants to improve phosphate recovery, the role of the filter cake on the recovery of struvite, and the production of a userfriendly granulated struvite fertilizer, were investigated. This study emphasized the technology’s reproducibility by making maximum use of locally available resources.
2.
Materials and methods
2.1.
Location and community
The village of Siddhipur was chosen as pilot test site because of the high number of operating UDDTs (100þ) (Water Aid, 2007), the institutional strength of the Drinking Water and Sanitation User Committee, and the well-established contacts to development agencies. The majority of Siddhipur’s population engages in agriculture and the predominant ethic group in the settlement (Newar) has traditionally used human and animal excreta as fertilizers (Water Aid, 2007).
2.2.
Urine sources, quantity and quality
A bicycle, modified with a steel rack to carry two 20 L plastic jerry cans, was used to collect the urine from a dozen households, where the urine had been stored in 100 L tanks connected to the UDDTs. Due to the occasional cleaning of the UDDTs with water, the urine was slightly diluted. Approximately 250 L of urine were collected during one day. This quantity was sufficient for one week of experiments. Fresh urine samples from 14 individuals (8 male, 6 female, aged between 6 and 64) were collected to determine typical nutrient concentrations before storage.
2.3.
Analytical methods
Prior to analysis, urine samples were filtered with membrane filters (Whatman, Maidstone, UK) to a final pore size of 0.45 mm. The phosphate concentration in the stored urine samples was measured using a portable Hach DR 2000 photospectrometer (molybdovanadate method, Hach, Denver (CO), USA). To determine total phosphate, phosphate mineral particles were dissolved by adding 1 M hydrochloric acid.
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Electrical conductivity and pH values were measured with a portable conductivity meter (LF 340, WTW, Weilheim, Germany) and a portable pH meter (pH 330, WTW, Weilheim, Germany), respectively. The temperature effect was compensated for 25 C by a linear function of 1.91% K1 (Standard Methods, 2006). Chloride, sulphate and phosphate (only fresh urine) were analyzed with ion chromatography (IC, Metrohm, Herisau, Switzerland), while cations (potassium, sodium and magnesium) were determined with inductively coupled plasma optical emission spectrometry (ICPeOES, Ciros, Spectro Analytical Instruments, Kleve, Germany). Ammonium was measured photometrically on a flow injection analyzer (Application Note 5520, FOSS, Hillerød, Denmark). Urea was calculated as the difference between ammonia after digestion with urease and the initial ammonia in the sample (Eawag standard operation procedure). Chemical oxygen demand (COD) was measured with Dr. Lange cuvet tests (LCK 614, Lange, Berlin, Germany). Magnesium sulphate was powdered and dissolved in distilled water prior to analysis. The magnesite samples were dissolved using aqua regia digestion and microwaved before analyzing the magnesium content with ICPeOES. The pore size of the filter bag was characterized by the maximum pore width (average of thirty measurements) measured under a light microscope.
2.4.
Magnesium sources
Three different types of magnesium sources were investigated to determine the most cost effective and chemically appropriate precipitant for struvite production in Siddhipur: magnesium sulphate, bittern and magnesite rock from Nepal. Magnesium sulphate heptahydrate granulate (MgSO4$7H2O, Rohit Chemicals, Birganj imported to Nepal from India) was bought on the fertilizer market. Bittern, the waste brine remaining after salt (NaCl) extraction from seawater, was directly obtained from the Jakhau Salt Company in Gujarat, India. Bittern was used as a magnesium source in all our field and lab experiments. Magnesite (MgCO3) samples were collected from a former mine in Kharidhunga, approximately 80 km from Kathmandu.
2.5.
Reactors
2.5.1.
Sedimentation reactor
A first reactor design was developed using a common 50 L polypropylene tank, which is widely available and popular throughout Nepal. To keep the necessary modifications to a strict minimum, the tank was only fitted with two plastic outlet taps at the bottom. The top of the tank was fitted with a metal stirring mechanism with two stirrer paddles and a metal crank (see Fig. 1). After the addition of the appropriate magnesium dose (molar ratio 1.1 0.1 mol Mg mol P1), the urine-magnesium solution was stirred for 10 min (previous work (Etter, 2009) indicated that this was the optimal reaction time). Since Nepal suffers from acute electricity shortages (up to 16 h daily), the struvite reactors were designed to be operable without electricity. After approximately 6 h, the precipitated struvite had settled on the reactor’s bottom, where it was retained in a flat tray fitted with a cloth filter. Following the sedimentation period, the higher of the two outlet taps
Fig. 1 e The two reactor setups tested for struvite production from urine: sedimentation reactor (left) and filtration reactor (right).
was opened to drain the supernatant. The filter tray was then lifted out of the reactor and the struvite cake was dried at ambient temperature to form a powder.
2.5.2.
Filtration reactor
Because of low phosphorus recovery, the sedimentation reactor was shown to be inadequate and after several months of testing, an improved reactor was developed. Because sheet metal workshops can be found all through the Kathmandu Valley, a slightly more sophisticated reactor was produced from galvanized sheet metal instead of plastic (plastic processing for larger installations is not accessible in Nepal). The filtration reactor consisted of a cylindrical drum (50 L) with a conical bottom. From the lower central outlet, a polypropylene (PP) pipe led through a sturdy ball valve (Nepatop) to a filter bag with a filtering surface of approximately 0.2 m2 (Fig. 1). Just like the sedimentation reactor, the filtration reactor was also fitted with a welded metal stirring mechanism. Nylon (pore size 160 50 mm) was selected as the filter material, for reasons of resistance and longevity. Following the addition of magnesium (molar ratio 1.1 0.1 mol Mg mol P1) and subsequent precipitation (during 10 min of continuous stirring), the crystals were separated from the liquid by opening the valve at the bottom of the reactor tank and allowing the struvite-urine solution to pass into the hanging filter bag. The retained filter cake was dried at ambient temperature.
2.6.
Flocculation experiments
To determine the effect of flocculants on struvite recovery, 10 mL of bittern (275 mg of dissolved magnesium) were added to identical glass columns filled with 1 L of stored urine and mixed continuously for 10 min using a magnetic stirrer. Fivedifferent flocculants were tested according to the manufacturer’s recommended concentrations or according to literature values: alum (Al2(SO4)3$10H2O, 500 mg L1), lime (Ca(OH)2, 100
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mg$L1), two synthetic polyelectrolytes (cationic polyamine, Ensola 2001, 10 mg L1; anionic polyacrylamide, Ensola 2051, 10 mg L1) and a natural product: moringa oleifera (prepared at two concentrations from dried seeds in a food blender, 50 mg L1 and 500 mg L1). The flocculants were added to the magnesium-dosed urine and, to prevent the destruction of crystals, while ensuring the incorporation of flocculants, the stirring was continued at a lower speed for another 2 min. In addition to the columns where the flocculants were added, a blank sample of urine with only a bittern addition (i.e. without a flocculent addition) was prepared. The pipette method, as is common in soil science, was used to compare the sedimentation rates: 25 mL aliquots were taken at a 10 cm depth (Burt, 2004) at the start of the experiment, and then after 10 min, 1 h, and 1 day. The samples were dried at 105 C for 24 h prior to weighing, in order to determine the total solids content. Heating struvite above 55 C causes a gradual mass loss (Bhuiyan et al., 2008) due to ammonia and water dissociation. Therefore, the final mass after drying does not correspond to the potentially recoverable struvite mass. In the experiment with moringa seeds, a control column containing only stored urine without a bittern addition was also dosed with moringa to determine the effect, if any, on urine without induced struvite formation.
2.7.
Filtration
As the urine passes through the filter bag, the struvite is retained and forms a filter cake. The influence of the struvite cake on the removal efficiency was investigated: outflow samples from the filter were taken 1, 2, 5, 10 and 20 min after the urine was mixed with magnesium. The nylon filter bag provided approximately 2000 cm2 of filter surface (40 cm2 per litre of reactor volume). The total phosphate was measured and the total outflow volume was monitored.
2.8.
Granulation
Granulation of struvite from the filtration reactor was tested at laboratory scale using a stainless steel rotating drum with
a diameter of 0.2 m. Besides water, no other binding agents were added. Initially, 50 g of struvite powder produced from urine in the filtration reactor was added to the revolving drum, and water was injected gradually using a 50 mL syringe, until sufficient granulation was attained. The drum was operated at a constant speed of 60 rpm. To make maximum use of locally available resources, the rotating drum was assembled using a common steel pot and an electric fan motor.
3.
Results and discussion
3.1.
Urine quantity and quality
The phosphorus concentration in the stored samples (195 65 mg P L1) was considerably lower than in fresh urine (388 251 mg P L1) (refer to Table 1 for a comparison). The measured values are at the lower end of the range of literature data (e.g. 370e740 mg P L1, Tilley et al., 2008b; Udert et al., 2003a). The low phosphate concentration in stored urine can be explained by precipitation. A valuable part of the phosphate e in undiluted urine about 30% (Udert et al., 2003b; Tilley et al., 2008c) e precipitates spontaneously as calcium phosphate and struvite when urea is hydrolyzed by urease during storage. To validate the difference between fresh and stored urine, the residual phosphate in stored urine was estimated assuming that all magnesium and all calcium in fresh urine (Table 1) precipitated spontaneously as struvite or octacalcium phosphate (OCP, Ca8H2(PO4)6$5H2O) as suggested by Udert et al. (2003a). The phosphate incorporated in the minerals was then subtracted from the amount of dissolved phosphate. Based on this calculation, at least 28% of the initial phosphate precipitated (13% as OCP, 15% as struvite) leaving a residual concentration of 279 mg P L1. The difference between the calculated phosphate concentration and the measured value (195 mg P L1) is likely due to the addition of flushing water, which not only dilutes the urine but also induces additional precipitation by adding magnesium and
Table 1 e Average concentration of selected compounds in fresh urine (14 samples, 8 male, 6 female, ages between 6 and 64) and stored urine from UDDT tanks (10 samples) in Siddhipur, Nepal. Fresh urine
NH4-N Urea PO4-P Cl SO4 Total carbonate COD Mg Ca K Na pH El. Cond.a
1
[mg L ] [mg L1] [mg L1] [mg L1] [mg L1] [mg L1] [mg L1] [mg L1] [mg L1] [mg L1] [mg L1] [e] [mS cm1]
Stored urine
Average
Standard deviation
Median
438 4450 388 6620 878 8.53 7660 45.4 89.2 1870 3240 5.6 22.6
207 1730 251 2390 379 2.16 4630 22.9 56.6 976 1230 0.4 6.3
418 4420 383 6410 822 7.60 6340 36.9 74.4 1940 3270 5.5 23.5
a Electrical conductivity is temperature corrected.
Average
195
9.0 25.9
Standard deviation
65
0.1 3.9
Median
170
8.9 25.8
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calcium (Udert et al., 2003b). The rather small difference between the measured and calculated phosphate concentrations suggests that the dilution with flushing water was low. With the stored urine collected in our study, 1 kg struvite could have been produced from 640 L urine. However, the yield of solid phosphate could have been increased by at least 40%, if the spontaneously precipitated struvite and OCP were completely recovered as well. Since struvite and calcium phosphate are both good phosphate fertilizers (UNIDO, 1998), collecting spontaneously precipitated phosphate increases the overall fertilizer recovery from urine.
3.2. urine
Estimation of phosphate concentration in stored
In struvite production, the phosphate concentration in the process influent (i.e. urine) must be known as exactly as possible, in order to facilitate an optimal magnesium dosage. Estimating the phosphate concentration is also needed for accurate financial compensation for urine deliveries. Since electrical conductivity measurements have proven to be a rapid and cost-efficient tool for online phosphate monitoring in biological processes (Wylie, 2009; Maurer and Gujer, 1995), we adapted the method for the use with urine. The temperature compensated electrical conductivity (25 C) and phosphate concentration were determined in ten samples of stored urine and plotted against each other. The samples originated from the storage tanks of different UDDTs in Siddhipur and were taken during the 3 months field research. The results (Fig. 2) show a very good correlation (r2 ¼ 0.94). Within the measured range, the phosphorus content can be estimated using Equation (1): ½PO4 -P ¼ 15$EC25 C 200
(1)
phosphate concentration [mg P· L-1]
where the temperature compensated electrical conductivity EC25 C is measured in mS cm1 and the phosphate concentration [PO4-P] is given in mg P L1. For the samples analyzed (phosphate concentrations were between 119 and 315 mg P L1), the standard deviation for the
350
300
250
200
measured estimated 150
100 20
25
30
35
electrical conductivity [mS·cm ] -1
Fig. 2 e Correlation between electrical conductivity (compensated for 25 C) and phosphate concentration. The error bars depict standard deviations of the phosphate measurements by photo-spectrometry.
determination of phosphate via electrical conductivity was 22 mg P L1 with respect to the photo-spectrometric analysis. This standard deviation is considerably lower than the standard deviation determined for the average phosphate concentration without linear correlation (65 mg P L1, see Table 1), which means that estimating the phosphate concentration with Equation (1) results in a significantly more accurate value than just using the average phosphate concentration. Estimating the phosphate concentration via electrical conductivity has important advantages over the direct chemical determination of phosphate: it delivers an instantaneous result, it is much cheaper and it does not require careful handling of chemicals. However, the correlation between phosphate and electrical conductivity should be validated for every community anew to account for differences in the urine collection (e.g. composition changes due to ammonia volatilization).
3.3.
Magnesium sources
Table 2 summarizes the estimated costs of the three magnesium sources magnesium sulphate fertilizer, bittern and pretreated magnesite rock. The most common precipitants for laboratory experiments, magnesium chloride (Liu et al., 2008; Ronteltap et al., 2007) and magnesium oxide (Wilsenach et al., 2007), were not considered because both were only available through laboratory suppliers. Our estimation shows that magnesium oxide produced from locally available magnesite would incur the least costs (12 NRs kg struvite1). Bittern (22 NRs kg struvite1) and magnesium sulphate (37 NRs kg struvite1) are two- and three-times as expensive, respectively. Bittern is the waste brine remaining after salt (NaCl) extraction from seawater. It has been suggested as a suitable magnesium source due to its high residual magnesium content, which ranges from 31 g L1 to 64 g L1 (Lozano et al., 1999; El Diwani et al., 2007; Lee et al., 2003). Our sample was directly obtained from the Jakhau Salt Company in Gujarat, India, and contained about 27.5 g L1 magnesium (though magnesium concentrations of up 85 g L1 have been achieved at the Jakhau Salt Company (Venkataraman, 2010)). Although bittern is a waste product and can be obtained for free at the source, transport costs are high. One important consideration for imported magnesium sources (bittern and magnesium sulphate) is that of trade restrictions, which make locally produced magnesium oxide an interesting alternative. Although magnesium oxide is not currently produced in the vicinity of Kathmandu, high quality magnesite (MgCO3) deposits are available in Kharidhunga, approximately 80 km from Kathmandu. Until 1990, a mine was operated in Kharidhunga and dead burnt magnesite with a MgO content of 88e96% was produced (Wu, 1994). Since magnesite hardly dissolves in water, it must be converted into a reactive magnesium compound such as magnesium oxide before it can be used for struvite precipitation. Magnesium oxide can be produced by calcining the mined magnesite rock at elevated temperatures (Shand, 2006). Based on fuel consumption and efficiency data for conventional kilns (burning efficiency 35%; UN Habitat, 1993) the calcination of 1 kg MgCO3 would require about 0.2 L diesel.
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Table 2 e A comparison of the magnesium sources tested for struvite production in Nepal. Prices are given in Nepalese Rupees [NRs]. 100 NRs are about 1 Euro. Magnesium sulphate
Magnesite
Bittern
Description Origin Magnesium compound Magnesium content [g kg1] Required input [kg kg struvite1] Required pre-treatment Appr. raw cost [NRs$kg Mg1] Processing cost [NRs$kg Mg1] Transportation cost [NRs$kg Mg1]e Total cost [NRs$kg Mg1] Total input cost [NRs kg struvite1]f Major advantages
Fertilizer product India MgSO4$7H2O 96a 1.10 e 320c 0 22 342 37 Product available on the market
Rock mineral Nepal MgCO3 244a 0.41 Calcination 32c 76d 3 111 12 Local resource
Major drawbacks
Long transport
Requires treatment, possibly air pollution
Reject brine India Mg2þ 85b 1.43 e 0 0 205 205 22 Easy dosage, waste recycling Long transport
a Measured by the authors (n ¼ 1; method standard deviation ¼ 5%). b Highest achieved concentration by the Jakhau Salt Company (Venkataraman, 2010). c As quoted by Everest Yeti International P. Ltd., Kathmandu, Nepal (August 2008). d Calculated with: theoretical energy demand for MgO production 2415 kJ/kg MgCO3 (Shand, 2006), burning efficiency 35% (conventional shaft kiln; UN Habitat, 1993), net calorific value of heating oil 42.6 MJ/kg (DIN 51603-1), density of heating oil at 15 C 0.86 g/L (DIN 51603-1), diesel price 70 NRs/L (October 2008) 60% of total costs are for fuel (UN Habitat, 1993). e Calculated with the rates charged for transport between Birganj and Kathmandu (2.14 NRs$kg1 for 270 km; November 2008). Magnesium sulphate is transported from the border (Birganj) to Kathmandu (270 km), magnesite from the mine (Kharidhunga, 80 km) and bittern from Gujarat/India to Kathmandu (2200 km). f Assuming a magnesium dosage of 1.1 mol Mg mol P.
When choosing a magnesium source, one must also consider the addition of compounds other than magnesium. We calculated the change of the major compounds in the urine matrix for bittern and magnesium sulphate additions. For the calculation, we used the matrix of fresh urine, the measured composition of bittern (Table 3) and the theoretical MgSO4 stoichiometry. The calculations showed that the magnesium sulphate addition (1.1 mol Mg mol P1) changes the urine matrix drastically. A MgSO4 addition would result in a sulphate concentration increase of 76%, while bittern would cause only slight increases of chloride (1.0%), sulphate (1.7%), potassium (2.6%) and sodium (13%). The calculation was not done for MgO, because the product was not available yet. Pure MgO, however, contains only oxygen as an additional compound and therefore would hardly change the composition of urine. The final MgO sources (as well as bittern and MgSO4) also have to be analyzed for heavy metals, before choosing one of the magnesium sources for large scale struvite production.
3.4.
Reactor efficiencies
The phosphate removal efficiency was determined by comparing the phosphate concentrations in the influent and effluent of the reactor. The sedimentation reactor (Fig. 1) achieved only about 50% phosphate removal. Slow crystallization was not the reason for the low removal, as lab experiments showed that a reaction time of 10 min was sufficient to precipitate 95% of phosphate as struvite. The low removal was mainly due to the inefficient solids recovery caused by an inadequate filtering process: small particles were easily lost
through the gap between the reactor wall and the filter tray or when the tray was lifted. Since the retention of solids through the liquidesolid separating mechanism of the sedimentation reactor was insufficient, the filtration reactor was constructed. Successive field tests with the external filter bag demonstrated a phosphate removal efficiency of up to 91%. This efficiency was much higher than in the sedimentation reactor, but slightly lower than in lab experiments with the same magnesium:phosphate ratio, probably due to some loss of phosphate through the nylon bag (see the discussion on filtration). The removal efficiency could be increased further with larger molar Mg:P ratios. Abegglen (2008) reported that in lab experiments with stored urine and magnesium oxide as precipitant, a minimum molar ratio of 1.2 mol Mg mol P1 was required to remove at least 95% of the phosphate. Abegglen (2008) and Antonini et al. (2009) achieved phosphate removal efficiencies with filter bags of more than 95%, but they added 1.8 mol Mg mol P1 and 1.5 mol Mg mol P1, respectively, and the filter bags probably had much smaller pore widths. A reactor of the same type currently installed at the headquarters of the Deutsche Gesellschaft fu¨r Technische Zusammenarbeit (GTZ) in Eschborn, Germany is operated with filter bags that have pore widths of 5e10 mm (Heynemann, 2010).
Table 3 e Measured composition of bittern from the Jakhau Salt Company, Gujarat, India. Ion
Cl
SO2 4
Mg2þ
Kþ
Naþ
Concentration [g L1]
17.4
3.3
27.5
1.9
3.2
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The time to process 50 L of urine could also be reduced from more than 6 h (required for the sedimentation method) to about 1 h in the filtration reactor. The total construction costs was 3500 NRs (35 V) for the sedimentation reactor, and 6000 NRs (60 V) for the filtration reactor (including labour). For a comparison of the two reactor models refer to Table 4. Several research groups identified scaling inside the struvite reactor as one of the major problems for struvite production (Wilsenach et al., 2007; Abegglen, 2008; Ronteltap et al., 2010). In our experiments, scaling mainly occurred in the sedimentation reactor, where a small layer of struvite on the reactor wall was detected soon after the experiments started. However, in the filtration reactor, hardly any scaling was observed after more than 50 batches (2500 L urine). The reduced scaling in the filtration reactor is probably due to the much lower retention time of the urine in the reactor vessel: the overall treatment time including stirring and filtration was one hour. Abegglen (2008) reported that scaling problems (especially clogging of the effluent valve) could be prevented if the sedimentation time was shorter than four hours.
3.5.
Flocculation experiments
The addition of flocculants was tested as a means to improve the solids retention of the sedimentation reactor. Flocculants are commonly used to improve settleability of organic and inorganic solids in wastewater treatment (Tchobanoglous et al., 2004). The most common flocculants are inorganic compounds or synthetic polyelectrolytes. In the case of struvite, the cationic polyacrylamine (Ensola 2001, Ensola GmbH, Wetzikon, Switzerland) was assumed to be the most effective, because struvite particles have a strong negative surface charge (Le Corre et al., 2007). Ground moringa oleifera seeds are a novel flocculant, which have been tested for COD (Bhuptawat et al., 2007) and turbidity removal (Katayon et al., 2006) in water or wastewater treatment. We do not know of work which has addressed the removal of nutrients, such as nitrogen or phosphorus, with the addition of moringa oleifera seeds. As anticipated, the absolute total solids content in the 1 L columns increased with the addition of flocculants (data not shown). To compare the solids removal, the total solids concentration was measured at several times at a depth of 10 cm (Fig. 3). After a 10 min sedimentation period, only alum
Table 4 e Comparison of the struvite reactors. Design parameter
Volume Installation costs Phosphate removal efficiency Average treatment cycle duration Daily treatment capacity Filter surface Reactor tank material
Liquidesolid separation mechanism Sedimentation
Filtration
50 L 3500 NRs (35 V) 50%
50 L 6000 NRs (60 V) 90%
12 h
1h
50 L
400 L
1000 cm2 Polypropylene
2000 cm2 Galvanized steel
had achieved a higher total solids decrease than the blank sample. Initially, the cationic flocculant (Ensola 2001) did not affect sedimentation, whereas the anionic flocculant (Ensola 2051) actually slowed down settling. After 1 h, and again after 1 day of sedimentation time, all the total solid concentrations at a depth of 10 cm were similar: between 10.18 and 10.43 g L1 (the blank sample’s total solid concentration was 10.28 g L1). Thus, it was concluded that the addition of flocculants is not a suitable method for improving struvite recovery in the sedimentation reactor.
3.6.
Filtration
The filter efficiency of the nylon bag on the filtration reactor was measured as a function of filtration time. The struvite had been produced by adding bittern in a molar ratio of 1.1 mol Mg mol P1, and by stirring for 10 min before opening the valve. The results showed that the phosphate concentration in the struvite effluent decreased with an increase in filtered volume (Fig. 4). As a control, the individual phosphate concentrations of each sample (corresponding to the indicated time) were integrated over the percolated liquid volume (Equation (2)). ½PO4 -P ¼
X ½PO4 -Pi $DVi =Vtotal
(2)
where [PO4-P]i is the instantaneous phosphate concentration measured in the partial volume DVi at time ti. The calculated phosphate concentration agrees well with the final phosphate concentration measured in the mixed effluent tank (90% reduction of phosphate concentration from process influent to effluent). Measurements by Ronteltap et al. (2010) using a batch reactor with a propeller stirrer, showed that the average struvite particle diameter was 92 mm. This is actually smaller than the maximum pore width of the nylon filter (160 50 mm). The good phosphate removal (more than 90%) must be due to the build-up of a filter cake. With accumulating particles, the permeability of the filter cake decreases, smaller particles are retained, and particles of considerably lower diameters than the filter’s pore size are therefore held on the filter (Cheremisinoff, 1998). Our experiments show that using filter bags with large pore widths can achieve high struvite recoveries, while keeping the filtration time short.
3.7.
Granulation
Most fertilizers are marketed in a granulated form because a powder is not user-friendly, there is a risk of caking if exposed to moisture, and powder does not withstand aeolian transport once applied to agricultural surfaces. We used struvite powder produced in the filtration reactor to test whether granulation could be achieved in a simple rotating drum. After an initial nucleation phase with water as a binding agent, the particles grew quickly by assimilating remaining particles of lower diameter, as was described by Adetayo et al. (1993). If the water was injected into the drum gradually over five minutes, a higher number of small diameter granules (<2 mm) formed.
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Fig. 3 e Total solids concentration from the urine-struvite suspension at a depth of 10 cm versus time for various flocculants.
After more than 6 months of storage in a closed container, the formed granules still maintained their size and shape. When processing greater volumes of struvite slurry, a sieving mechanism would be required to ensure a uniform particle size distribution of the final product.
3.8.
Economic efficiency of struvite production
50
50
40
40
30
30
20
20
10
10
0
0 0
5
10
15
phosphate concentration [mg P· L-1]
cumulative volume [L]
One important idea behind struvite recovery is the use of the financial value of the nutrients in urine for the promotion of sanitation. Ideally, the sale of the struvite produced would cover all the costs or even generate profit. Based on the data collected in this project, we did a first estimate of the financial efficiency of struvite production (Table 5). We chose a reactor of 500 L assuming that one person should still be able to operate the reactor alone. The magnesium dosage was set to 1.1 mol Mg per mol P and the struvite recovery efficiency was assumed to be 90%, which would yield 1 kg struvite from 720 L stored urine in Siddhipur. As cost factors, we considered the amortization and capital costs for the reactor and the expenses for the magnesium sources. The revenue was solely based on the sale of struvite and did not take into account the public benefits related to health or the environment. The
20
time [min] Fig. 4 e The decreasing phosphate concentration at the reactor’s outlet at an increasing cumulative filtrate volume (50 L treated urine) illustrates the importance of the filter cake in the filtration process.
market value of the nutrients was taken from the work presented by Tilley et al. (2009), which is based on a market survey of 16 different fertilizer products in Siddhipur. Based on a regression analysis, the struvite market value is between 25 and 57 NRs$kg1 with a best estimate of 41 NRs$kg1. Our estimate shows that treating 4000 L stored urine per day yields a maximum net profit of 32,000 NRs$a1 if magnesite is used
Table 5 e Economic breakdown for struvite production based on experience and without transportation costs for urine. Operation parameters Reactor size Cycles per day Struvite recovery efficiency Yearly struvite production (250 workdays) Molar Mg:P ratio Yearly magnesium requirement Installation costs Steel tank for reactor Additional tanks, fittings, pipes etc. Building Total Operation duration Amortization Capital costs (12%) Expenses Amortization and capital costs MgSO4 price Bittern price Magnesite price Revenue Struvite market price Profit Option ‘MgSO4’ Option ‘bittern’ Option ‘magnesite’
500 8 90
L d1 %
Etter (2009)
1400
kg struvite$a1
1.1 170
mol Mg mol P1 kg Mg a1
15,000 28,000
NRs NRs
Etter (2009) Etter (2009)
30,000 73,000 10 7300 880
NRs NRs years NRs$a1 NRs$a1
Estimated
6
NRs$kg struvite1
37 22 12
NRs$kg struvite1 NRs$kg struvite1 NRs$kg struvite1
Table 2 Table 2 Table 2
41
NRs$kg struvite1
Tilley et al. (2009)
2600 18,200 32,100
NRs$a1 NRs$a1 NRs$a1
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S 293
N 5436
Struvite precipitation reactor
N 5421
all units in g
K 1869
S 293
S 293
N 5308 Mg 44
Mg 44
P 28
P 28
P 278
N 128 P 360 Mg 242
N 113 P 250 spontaneous precipitation
K 101
S 18
S 18
N 316
N 316
P 62 Mg 8
N 323
Struvite precipitation reactor
Mg 8
P 44
all units in NRs
Mg 8
P4
P4
N7 P 40
N7 P 58
spontaneous precipitation
Mg 42
solids 107
N 324
effluent 447
K 101
S 18
filtration
K 101
S 18
storage
512 NRs
K 101
struvite production from urine also helps to prevent environmental pollution by phosphorus.
3.9.
Fig. 5 e Mass [g] and monetary flow [NRs] (100 NRs w 1 V) through the struvite precipitation process, assuming 90% precipitate recovery, no dilution with flushing water, no ammonia volatilization, 1.1 mol Mg mol PL1 magnesium dosage. Fertilizer market values are based on Tilley et al. (2009). Concentrations for fresh urine are taken from Table 1, total ammonia was assumed to be 11% higher than the sum of ammonium and urea (Udert et al., 2005).
Effluent treatment
Although stripped of phosphorus, effluent is still rich in ammonium, potassium and sulfur. From the balance in Fig. 5 it can be seen that the effluent has about four times the monetary value of struvite, mainly due to nitrogen and potassium. To maximize the value of urine, and mitigate the potentially harmful effects of discharging a liquid with a high eutrophication potential into water bodies, these nutrients should be recovered and used productively. The easiest way to recover the nutrients from the effluent is through direct application to agricultural soils. Because direct application of urine (or ‘process effluent’) using buckets or watering cans results in high losses of ammonia to volatilization, we applied the process effluent to crops through a drip irrigation system, which not only prevented ammonia loss but also clogged less than using urine, since all of the minerals with a precipitation potential, had been removed (Kashekya, 2009). However, direct use of the effluent is probably restricted to very few locations, where high population densities and large areas of agricultural land coincide. As an alternative to drip irrigation, the effluent may be treated using additional physical, chemical and/or biological processes, such as a combination of nitrification and evaporation (Udert and Wa¨chter, 2010). The recovery of remaining nutrients, particularly nitrogen, potassium and sulfur, will be important if the true value of the nutrients contained in urine is to be maximized. This work requires further attention and is underway in both the lab and in the field.
4.
Mg 42
P 388 Mg 45
N 5308
effluent 1m3
S 293
K 1869
solids 3kg
K 1869
filtration
K 1869
storage
1m3 urine
Mg 240
as the magnesium source. MgSO4 is too expensive to generate a net profit and the use of bittern results in about half the profit generated by using magnesite. This comparison clearly shows the importance of finding a cheap and efficient magnesium source. However, with cheap, locally produced MgO, the profit of struvite sales would be too low to cover a basic wage 400 NRs$d1 (100,000 NRs$a1); three times more urine would have to be treated, which is about 12 m3 or the urine excreted by 10,000 people per day. The struvite recovery scheme has to be improved to become financially sustainable. Collecting spontaneously precipitated struvite and calcium phosphate from the urine collection tanks would increase the phosphate recovery by at least 40% (see urine quality and quantity). Therefore, the UDDTs should be constructed such that the pipe to the tank is as short as possible to ensure that spontaneous precipitation occurs in the tank and not in the pipe. The choice of location will also have a strong influence on whether struvite recovery will be economically feasible. To prevent additional costs for transportation, urine treatment reactors should be installed at locations where large volumes of urine are generated (e.g. markets, bus stations, public buildings etc.). In public toilets, additional revenues can be created if the users pay a small fee. Struvite production could also be promoted directly by the state, considering that
Conclusions
With this project, we could show that an efficient and reliable struvite reactor can be built in Nepal with locally available materials and at a low cost (60 V for a 50 L reactor). Filtration resulted in much higher phosphate recoveries than sedimentation; more than 90% of phosphate could be recovered, using only little magnesium (dosage ratio 1.1 mol Mg mol P1) and a simple nylon fabric filter (pore width 160 50 mm). The accumulation of a filter cake helped to recover most of the struvite, while the large pore width still allowed for a short treatment time (one hour per cycle). The short retention time in the reactor was probably the reason why scaling on the reactor walls and the stirrer paddles was negligible. After the drying of the filter bag and granulation in a rotating drum, the end-product was an easy-to-handle, granulated, phosphate fertilizer. Currently, we are investigating the hygienic properties of struvite from urine; the high fertilizer value of struvite has been proven before. An estimation of the financial profitability revealed that it is not possible to make struvite production self-sustaining given the current fertilizer prices, even if transport costs are kept low. While the costs for building the reactor are low, the magnesium source is expensive. The cheapest magnesium source is probably magnesium oxide produced from locally minable magnesite (ca. 12 NRs$kg struvite1). Bittern, a waste
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stream from salt production, has a negligible cost, but transporting it from the coast to Nepal is expensive. Magnesium sulphate fertilizer, another magnesium source available in Nepal, is too expensive for struvite production. There are several other possibilities to reduce the cost of struvite production. First, the magnesium dosage should be as accurate as possible, for example, by using electrical conductivity to estimate the phosphate concentration. Second, the recovery of spontaneous precipitates from the collection systems can increase the overall phosphate recovery significantly; good care should be taken that the precipitates are not lost during urine collection. Third, state sanitation programs which take into account and promote the societal and environmental benefits of phosphate removal from urine may help to increase sanitation coverage. Last but not least, struvite precipitation can only recover about 20% of the fertilizer market value from urine. A lot of the value is in ammonium and potassium, which remains in the effluent or volatilizes as ammonia. Reuse or treatment of the effluent is probably the most effective measure to increase the economic efficiency of nutrient recovery from source-separated urine.
Acknowledgements The project was supported by Eawag discretionary funds, The Angel Fund of the Gemeinnu¨tzige Stiftung SYMPHASIS, Zu¨rich, the Swiss Agency for Development and Cooperation (SDC) and Inge´nieurs du Monde at the Swiss Federal Institute of Technology Lausanne (EPFL). The funding sources were neither involved in the study design, nor the collection, analysis or interpretation of the data. Much of this work would not have been possible without contributions from Basil Gantenbein, Edmund John Kashekya and Mingma Sherpa, and the logistical support from UN HABITAT Nepal. The authors would like to thank Jiban Maharjan and his family for providing the land for installing the field experiments. The authors also thank Hermann Mo¨nch, Claudia Ba¨nninger and Karin Rottermann at Eawag for chemical analysis, Loı¨c Decrey and Brian Sinnet at Eawag for the filter analysis, Subodh Sharma and the team from Kathmandu University for their support, Willem Jan Oosterkamp, and Paul Olivier for the information about using bittern and magnesite for struvite production.
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INRES-Plant Nutrition, University of Bonn, Germany, ISBN 3-937941-14-2 SANSED e PROJECT, Final Report. Bhuiyan, M.I.H., Mavinic, D.S., Koch, F.A., 2008. Phosphorus recovery from wastewater through struvite formation in fluidized bed reactors: a sustainable approach. Water Science and Technology 57 (2), 175e181. Bhuptawat, H., Folkard, G.K., Chaudhari, S., 2007. Innovative physico-chemical treatment of wastewater incorporating Moringa oleifera seed coagulant. Journal of Hazardous Materials 142, 477e482. Bowers, K.E., Westerman, P.W., 2005. Performance of coneshaped fluidized bed struvite crystallizers in removing phosphorus from wastewater. Transactions of the American Society of Agricultural Engineers 48 (3), 1227e1234. Soil Survey Investigations Report No. 42, Version 4.0. In: Burt, R. (Ed.), Soil Survey Laboratory Methods Manual. USDA e United States Department of Agriculture, USA. Cheremisinoff, N.P., 1998. Liquid Filtration. ButterworthHeinemann, Woburn, MA, USA, pp. 59e75. Cordell, D., Drangert, J.O., White, S., 2009. The story of phosphorus: global food security and food for thought. Journal of Global Environmental Change 19, 292e305. DIN 51603-1: Flu¨ssige Brennstoffe e Heizo¨le e Teil 1: Heizo¨l EL, Mindestanforderungen (Liquid fuels e fuel oils e Part 1: fuel oils EL, specifications). Deutsches Institut fu¨r Normung (DIN, German Institute for Standardization), Version 2008-8, BeuthVerlag, Berlin, Germany. El Diwani, G., El Rafie, Sh., El Ibiari, N.N., El Aila, H.I., 2007. Recovery of ammonia nitrogen from industrial wastewater treatment as struvite slow releasing fertilizer. Desalination 214, 200e214. Etter, B., 2009. Process optimization of low-cost struvite recovery. MSc thesis, EPFL: Swiss Federal Institute of Technology, Lausanne, Switzerland. Forrest, A.L., Fattah, K.P., Mavinic, D.S., Koch, F.A., 2008. Optimizing struvite production for phosphate recovery in WWTP. Journal of Environmental Engineering 134 (5), 395e402. Guest, S.G., Barnard, J.L., Beck, M.B., Daigger, G.T., Hilger, H., Jackson, S.J., Karvazy, K., Kelly, L., MacPherson, L., Mihelcic, J. R., Pramanik, A., Raskin, L., van Loosdrecht, M.C.M., Yeh, D., Love, N.G., 2009. A new planning and design paradigm to achieve sustainable resource recovery from wastewater. Environmental Science and Technology 43 (16), 6126e6130. Heynemann, J., 2010. Personal communication. Fachhochschule Giessen. Supervisor of a struvite reactor at GTZ in Eschborn/ Germany. Johnston, A.E., Richards, I.R., 2004. Effectiveness of different precipitated phosphates as phosphorus sources for plants. Phosphorus Research Bulletin 15, 52e59. Jo¨nsson, H., Richert Stinzing, A., Vinnera˚s, B., Salomon, E., 2004. Guidelines on the use of urine and faeces in crop production. In: EcoSanRes Publication Series. Stockholm Environment Institute, Sweden. Kashekya, E.J., 2009. Struvite production from source separated urine in Nepal: the reuse potential of the effluent. Master’s thesis, Unesco-IHE, Delft, The Netherlands and Swiss Federal Institute of Aquatic Science and Technology (Eawag), Du¨bendorf, Switzerland. Katayon, S., Megat Mohd Noor, M.J., Asma, M., Abdul Ghani, L.A., Thamer, A.M., Azni, I., Ahmad, J., Khor, B.C., Suleyman, A.M., 2006. Effects of storage conditions of Moringa oleifera seeds on its performance in coagulation. Bioresource Technology 97, 1455e1460. Larsen, T.A., Alder, A.C., Eggen, R.I.L., Maurer, M., Lienert, J., 2009. Source separation: will we see a paradigm shift in wastewater handling? Environmental Science and Technology 43 (16), 6121e6125.
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Le Corre, K.S., Valsami-Jones, E., Hobbs, P., Jefferson, B., Parsons, S.A., 2007. Agglomeration of struvite crystals. Water Research 41, 419e425. Lee, S.I., Weon, S.Y., Lee, C.W., Koopman, B., 2003. Removal of nitrogen and phosphate from wastewater by addition of bittern. Chemosphere 51, 265e271. Liu, Z., Zhao, Q., Lee, D.J., Yang, N., 2008. Enhancing phosphorus recovery by a new internal recycle seeding MAP reactor. Bioresource Technology 99, 6488e6493. Lozano, J.A.F., Colmenares, A.R., Rosas, D., 1999. A novel process for the production of multinutrient phosphatic base fertilizers from seawater bittern and phosphoric acid. Interciencia 24, 317e320. Maurer, M., Gujer, W., 1995. Monitoring of microbial phosphorus release in batch experiments using electric conductivity. Water Research 29, 2613e2617. Maurer, M., Pronk, W., Larsen, T.A., 2006. Treatment processes for source-separated urine. Water Research 40 (17), 3151e3166. Moerman, W., Carballa, M., Vandekerckhove, A., Derycke, D., Verstraete, W., 2009. Phosphate removal in agro-industry: pilotand full-scale operational considerations of struvite crystallization. Water Research 43 (7), 1887e1892. Niwagaba, C., Nalubega, M., Vinnera˚s, B., Sundberg, C., Jo¨nsson, H., 2009. Bench-scale composting of sourceseparated human faeces for sanitation. Waste Management 29 (2), 585e589. Paris, S., Schlapp, C., Netter, T., 2007. A contribution to sustainable growth by research and development. In: Proceedings of the International Symposium on ‘Water Supply and Sanitation for All’, 27e28 Sep 2007. ISBN: 13978-1-84339514-0. Ro¨mer, W., 2006. Plant availability of P from recycling products and phosphate fertilizers in a growth-chamber trial with rye seedlings. Journal of Plant Nutrition and Soil Science 169 (6), 826e832. Ronteltap, M., Maurer, M., Gujer, W., 2007. The behaviour of pharmaceuticals and heavy metals during struvite precipitation in urine. Water Research 41, 1859e1868. Ronteltap, M., Maurer, M., Hausherr, R., Gujer, W., 2010. Struvite precipitation from urine e influencing factors on particle size. Water Research 44, 2038e2046. Schouw, N.L., Danteravanich, S., Mosbaek, H., Tjell, J.C., 2002. Composition of human excreta e a case study from Southern Thailand. Science of the Total Environment 286 (1e3), 155e166. Schuen, R., Parkinson, J., Knapp, A., 2009. Study for Financial and Economic Analysis of Ecological Sanitation in Sub-Saharan Africa. Water and Sanitation Program-Africa. The World Bank, Nairobi, Kenya. Shand, M.A., 2006. The Chemistry and Technology of Magnesia. John Wiley & Sons, Hoboken NJ, USA, pp. 83e86. Smil, V., 2000. Phosphorus in the environment: natural flows and human interferences. Annual Review of Energy and the Environment 25, 53e88. Standard Methods, 2006. Standard Methods for the Examination of Water & Wastewater. APHA e American Public Health Association, AWWA e American Water Works Association, WEF e Water Environment Federation.
Tchobanoglous, G., Burton, F.L., Stensel, H.D., 2004. Metcalf & Eddy Wastewater Engineering. Treatment and Reuse, fourth ed. McGraw-Hill, Boston. Tilley, E., Lu¨thi, C., Morel, A., Zurbru¨gg, C., Schertenleib, R., 2008a. Compendium of Sanitation Systems and Technologies. Swiss Federal Institute of Aquatic Science and Technology (Eawag), Du¨bendorf, Switzerland. Tilley, E., Atwater, J., Mavinic, D., 2008b. Recovery of struvite from stored human urine. Environmental Technology 29 (7), 797e806. Tilley, E., Atwater, J., Mavinic, D., 2008c. Effects of storage on phosphorus recovery from urine. Environmental Technology 29 (7), 807e816. Tilley, E., Gantenbein, B., Khadka, R., Zurbru¨gg, C., Udert, K.M., 2009. Social and economic feasibility of struvite recovery from urine at the community level in Nepal. In: Ashley, K., Mavinic, D., Koch, F. (Eds.), International Conference on Nutrient Recovery from Wastewater Streams. IWA Publishing, London, pp. 169e178. Udert, K.M., Wa¨chter, M., 2010. Complete nutrient recovery from source-separated urine. In: Proceedings of the 7th IWA Leading-Edge Conference on Water and Wastewater Technologies, 2e4 June 2010, Phoenix, AZ, USA. Udert, K.M., Larsen, T.A., Biebow, M., Gujer, W., 2003a. Urea hydrolysis and precipitation dynamics in a urine-collecting system. Water Research 37 (11), 2571e2582. Udert, K.M., Larsen, T.A., Biebow, M., Gujer, W., 2003b. Estimating the precipitation potential in urine-collecting systems. Water Research 37 (11), 2667e2677. Udert, K.M., Larsen, T.A., Gujer, W., 2005. Fate of major compounds in source-separated urine. Water Science and Technology 54 (11e12), 413e420. UN Habitat, 1993. Vertical Shaft Limekiln Technology. United Nations Centre for Human Settlements (Habitat), Nairobi, Kenya, ISBN 92-1-131225-6. UNIDO, 1998. Fertilizer Manual, third ed. United Nations Industrial Development Organization, International Fertilizer Development Center, Kluwer Academic Publishers, Dordrecht. Venkataraman, V.R., 2010. Personal communication. Chief Executive, Marine Chemicals, The Archean Group of Companies, 5th Floor, Tower 2, Beliciaa Towers, 94 MRC Nagar, 600028 Chennai, India. Water Aid Nepal and Environmental and Public Health Organization (ENPHO), 2007. Assessment of Urine Diverting EcoSan Toilets in Nepal. ENPHO, Kathmandu, Nepal. WHO/UNICEF, 2010. Estimates for the Use of Improved Sanitation Facilities: Nepal. Joint Monitoring Programme for Water Supply and Sanitation. WHO/UNICEF, Geneva, Switzerland. Wilsenach, J.A., Schuurbiers, C.A.H., van Loosdrecht, M.C.M., 2007. Phosphate and potassium recovery from source separated urine through struvite precipitation. Water research 41, 458e466. Wu, J.C., 1994. The Mineral Industry of Nepal. USGS, Mineral Resources Program, Reston, VA, USA. Wylie, A.C., 2009. Investigation of electrical conductivity as a control parameter for enhanced biological phosphorus removal in a pilot scale sequencing batch reactor. Master thesis, University of British Columbia, Vancouver, Canada.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 6 3 e8 7 1
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Critical flux and chemical cleaning-in-place during the longterm operation of a pilot-scale submerged membrane bioreactor for municipal wastewater treatment Chun-Hai Wei a,b, Xia Huang a,*, Roger Ben Aim c, Kazuo Yamamoto d, Gary Amy b a
State Key Joint Laboratory of Environment Simulation and Pollution Control, Department of Environmental Science and Engineering, Tsinghua University, Beijing 100084, China b Water Desalination and Reuse Center, 4700 King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia c Universite´ de Toulouse; INSA, LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France d Environmental Science Center, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo 113-0033, Japan
article info
abstract
Article history:
The critical flux and chemical cleaning-in-place (CIP) in a long-term operation of a pilot-scale
Received 27 June 2010
submerged membrane bioreactor for municipal wastewater treatment were investigated.
Received in revised form
Steady filtration under high flux (30 L/(m2 h)) was successfully achieved due to effective
14 September 2010
membrane fouling control by sub-critical flux operation and chemical CIP with sodium
Accepted 15 September 2010
hypochlorite (NaClO) in both trans-membrane pressure (TMP) controlling mode (cleaning
Available online 1 October 2010
with high concentration NaClO of 2000e3000 mg/L in terms of effective chorine was performed when TMP rose to 15 kPa) and time controlling mode (cleanings were performed
Keywords:
weekly and monthly respectively with low concentration NaClO (500e1000 mg/L) and high
Chemical cleaning-in-place
concentration NaClO (3000 mg/L)). Microscopic analysis on membrane fibers before and after
Membrane fouling
high concentration NaClO was also conducted. Images of scanning electron microscopy (SEM)
Municipal wastewater
and atomic force microscopy (AFM) showed that NaClO CIP could effectively remove gel layer,
Critical flux
the dominant fouling under sub-critical flux operation. Porosity measurements indicated that
Submerged membrane bioreactor
NaClO CIP could partially remove pore blockage fouling. The analyses from fourier transform infrared spectrometry (FTIR) with attenuated total reflectance accessory (ATR) and energy dispersive spectrometer (EDS) demonstrated that protein-like macromolecular organics and inorganics were the important components of the fouling layer. The analysis of effluent quality before and after NaClO CIP showed no obvious effect on effluent quality. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
The membrane bioreactor (MBR), especially the submerged membrane bioreactor (SMBR), has been extensively investigated and applied for municipal and industrial wastewater treatment and reuse worldwide in recent years due to its advantages (i.e., excellent effluent, small footprint and less excess sludge) over conventional activated sludge (Yang et al.,
2006; Itokawa et al., 2008; Huang et al., 2010). However membrane fouling, the major factor limiting the wide application of SMBR, reduces permeate production and increases operational cost in long-term operation. Thus the mechanisms of membrane fouling and control strategies have become the focus areas in SMBR studies. In general, membrane fouling can be classified as pore blockage, gel layer and cake layer according to fouling formation mechanisms. Cake layer fouling,
* Corresponding author. Tel.: þ86 10 62772324; fax: þ86 10 62771472. E-mail address:
[email protected] (X. Huang). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.021
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mainly caused by sludge flocs, can usually be prevented by subcritical flux operation and removed by enhanced air scouring (Bouhabila et al., 1998; Chang and Judd, 2002; Pollice et al., 2005; Wang et al., 2008) in SMBR. From the viewpoint of a real application, one hypothesis is generally accepted that for SMBR under steady conditions (including membrane, sludge characteristics and operational parameters), there is a critical flux, above which membrane permeability will deteriorate too rapidly to realize steady long-term filtration. However the common method of “flux stepwise increment” for critical flux measurement is based on a short-term constant-flux filtration test in terms of minutes or hours (Bouhabila et al., 1998; Cho and Fane, 2002; Yu et al., 2003; Le Clech et al., 2003; Pollice et al., 2005). For real SMBR operation in terms of days or months, its applicability should be further investigated. Gel layer and pore blockage fouling, mainly caused by colloidal and soluble organic fractions (such as extracellular polymeric substances and soluble microbial products), and inorganic substances (such as calcium carbonate), are inevitable in long-term operation of SMBR even under sub-critical flux operation because there are significant amounts of potential foulants contained in activated sludge mixed liquor (Cho and Fane, 2002; Guglielmi et al., 2007; Wang et al., 2008). Therefore cleaning techniques are necessary for eliminating these inevitable foulants and achieving long-term steady operation. Physical cleaning techniques (i.e. water and/or air scouring/backwashing, ultrasonic cleaning and mechanical scouring) have been proven effective only for removing cake layer fouling (Chang et al., 2002; Lim and Bai, 2003; Adham et al., 2004; Huang et al., 2008). Chemical cleaning techniques with different reagents, such as an oxidant, acid, alkali and metalchelator, appear to be more effective for removing gel layer and pore blockage fouling (Liu et al., 2000; Lim and Bai, 2003; Xing et al., 2003; Adham et al., 2004; You et al., 2006; Brepols et al., 2008; Grelot et al., 2010). But these results on chemical cleaning were mainly obtained from off-line cleaning (i.e. membrane modules are taken out of bioreactor and immersed in a tank of cleaning reagent, or membrane modules are immersed directly in membrane tank full of cleaning agent after draining off sludge). Compared with off-line chemical cleaning, chemical cleaning-in-place (CIP) (i.e. cleaning reagent is injected into the membrane in reverse to normal filtration while membrane modules are still submerged in bioreactor) is simpler and cheaper. Thus the other hypothesis, that membrane fouling under sub-critical flux can be controlled by chemical cleaning-in-place and thus long-term steady filtration can be achieved, appears to be feasible.
Although chemical cleaning is used in most full-scale SMBR plants (Fatone et al., 2007; Lyko et al., 2008; Itokawa et al., 2008; Kraume et al., 2009), little information about chemical CIP is available in literature, especially in terms of performance and mechanisms in long-term operation. In our previous study (Wei et al., 2006), steady filtration under high flux of 30 L/(m2 h) for 190 d was successfully achieved with sub-critical flux operation and chemical CIP in a pilot-scale SMBR for municipal wastewater treatment. In order to address the former two hypotheses, during the following continuous operation of this SMBR (up to 750 d), different fluxes were adopted to check the applicability of critical flux derived from short-term constant-flux filtration tests (Wei et al., 2006). At the same time, chemical CIP, including its macroscopic performance for membrane fouling control with different operational parameters (cleaning reagent and cleaning frequency) and microscopic mechanism on removing membrane fouling, was investigated in detail.
2.
Materials and methods
2.1.
Experimental set-up
A pilot-scale SMBR with an effective volume of 2.14 m3 was used in this study. (Fig. 1) A hollow fiber membrane module made of polyvinylidene fluoride (PVDF) (Mitsubishi Rayon Co. Ltd, Japan) with a nominal pore size of 0.4 mm and filtration area of 29 m2 was submerged in the riser of the bioreactor. The membrane effluent was intermittently (13 min on, 2 min off) extracted with a suction pump at a constant-flux mode. The suction pump could also function as the cleaning pump during chemical CIP. Municipal wastewater after primary sedimentation from Qinghe wastewater treatment plant located in Beijing was pumped into the bioreactor automatically by level sensor control. Trans-membrane pressure (TMP) was monitored through two U-type mercury manometers as an indicator of membrane fouling evolution. A perforated pipe was located below the membrane module for aeration, both for oxygen supply and membrane fouling control through crossflow scouring.
2.2.
Method of chemical CIP
Sodium hypochlorite (NaClO) with effective chlorine concentration of 500e3000 mg/L was used as the dominant cleaning reagent due to its widespread application in MBR for
Membrane module Level sensor
U-type mercury manometer
Influent
Timer
Valve Flow meter
Waste sludge
Raw wastewater Feed pump
Bioreactor
Air blower
Effluent
Cleaning reagent Suction or backwashing pump
Fig. 1 e Diagram of the pilot-scale SMBR system.
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membrane cleaning. Hydrochloric acid (HCl, pH ¼ 1) and sodium hydroxide (NaOH, pH ¼ 12) were used as supplementary cleaning reagents. All doses were applied at 2 L per square meter membrane filtration area. This membrane is tolerant to all cleaning agents used in this study according to the manual provided by manufacturer. Two types of cleaning modes with primary emphasis on NaClO were mainly investigated. One was TMP controlling mode, i.e., cleaning with high concentration NaClO (2000e3000 mg/L) was performed when TMP rose to a pre-determined value (15 kPa). The other was time controlling mode, i.e., cleanings were weekly and monthly performed, respectively, with low concentration NaClO (500e1000 mg/L) and high concentration NaClO (3000 mg/L). HCl and NaOH were aperiodically used for the supplementary cleaning. TMP change before and after chemical CIP was regarded as the macroscopic index for chemical CIP efficiency. Each cleaning process consisted of several steps including stopping influent and effluent, stopping aeration, switching corresponding valves, pumping cleaning reagent in 30 min (flow rate of 116 L/h and corresponding flux of 4 L/(m2 h)) into membrane fibers in reverse direction of normal filtration, idle for 30 min and 90 min for low concentration NaClO cleaning and HCl/NaOH cleaning and high concentration NaClO cleaning respectively, switching valves, starting aeration, and starting influent and effluent. The whole cleaning process required about 60 min for low concentration NaClO cleaning and HCl/NaOH cleaning and about 120 min for high concentration NaClO cleaning respectively.
2.3.
Operational conditions
From our previous study (Wei et al., 2006), critical flux zone of this pilot-scale SMBR was about 30e35 L/(m2 h) under a mixed liquor suspended solid concentration (MLSS) of lower than 13 g/L and air flow of more than 13.7 m3/h, according to the adopted flux stepwise increment method with a flux step of 5 L/ (m2 h) and the criterion for identifying the critical flux zone was when the TMP increment during 120 min filtration time exceeded 133 Pa (i.e., the precision of a U-type mercury
manometer). Although steady filtration was already achieved under sub-critical flux (30 L/(m2 h)) operation (Wei et al., 2006), higher flux was attempted to investigate further the accuracy of critical flux in the following experiment because the criteria for identifying critical flux in this study was more conservative than in the literature (Pollice et al., 2005). Table 1 lists the operational parameters in all runs. During most of the time in the long-term (up to 750 d) operation, MLSS was 8e12 g/L by adjusting sludge retention time (SRT) (10e15 d) and air flow was 14e20 m3/h. Therefore, the critical flux zone during the longterm operation could be still regarded as 30e35 L/(m2 h) based on the assumption of constant membrane characteristics.
2.4.
Analytical methods
In order to characterize the microscopic effects of chemical CIP on membrane fouling, several analytical techniques, such as scanning electron microscopy (SEM, FEI QUANTA 200, FEI Company, USA), atomic force microscopy (AFM, SPA-300 HV, Seiko Instrument Inc., Japan), mercury porosimeter (AUTOPORE II 9220, Micromeritics, USA), fourier transform infrared spectrometry with attenuated total reflectance accessory (ATR-FTIR, Nicolet 560, Thermo Electron Corporation, USA) and energy dispersive spectrometer (EDS) by field emission gun-scanning electron microscopy (FEG-SEM, JSM 6301F, JEOL, Japan), were employed in this study. This hollow fiber with large wall thickness of about 0.9 mm consists of three layers e outer active layer (200e250 mm), intermediate porous connection layer and inner support layer. The fibers were cut to separate the flat-sheet active layer alone for the measurements of AFM, mercury porosimeter, ATR-FTIR and EDS. Before the analysis, membrane samples were dried under vacuum and 50 C for 8 h to eliminate the moisture. Chemical oxygen demand (CODCr), biochemical oxygen demand (BOD5), ammonia nitrogen (NHþ 4 eN), total nitrogen (TN), total phosphorus (TP) and total Escherichia coli for effluent samples before and after chemical CIP were monitored according to Standard Methods (APHA-AWWA-WEF, 1995) to investigate the effect of chemical CIP on effluent quality.
Table 1 e Operational parameters in all runs. Time (d)
Flux (L/(m2 h))
Chemical CIP
1 2 3
1e200 201e265 266e406
27.6e30.3 29.0e30.3 29.7e35.9
4 5
407e484 485e584
34.9e42.8 23.5e38.1
6
585e750
29.0e36.6
TMP controlling mode Time controlling mode Combination of TMP and time controlling modea Time controlling mode Enhanced cleaning with high concentration NaClO and acid/alkali Time controlling mode and enhanced cleaning with acid/alkali
Run No.
Note Sub-critical flux operation Sub-critical flux operation Critical flux operation Super-critical flux operation Variable flux operation for restoring membrane permeability Critical flux operation
a Time controlling mode (weekly cleaning with low concentration NaClO of 500e1000 mg/L and monthly cleaning with high concentration NaClO of 2000e3000 mg/L) was conducted when TMP was less than 20 kPa. TMP controlling mode, i.e. cleaning with high concentration NaClO of 2000e3000 mg/L, was conducted once TMP exceeded 20 kPa.
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Fig. 2 e TMP and flux changes of the pilot SMBR during the long-term operation.
3.
Results and discussion
3.1. Membrane performance under different fluxes and chemical CIP modes Fig. 2 shows the evolution of TMP and flux in the long-term operation. Table 2 lists the membrane performance in terms of flux, TMP and its rising rate between two cleanings in all runs. In Run 1 of 200 d, TMP and its rising rate were in the low range of 4.6e17.6 kPa and 0.15e2.09 kPa/d respectively, indicating steady filtration was successfully achieved in the longterm operation. This depressed TMP evolution was mainly attributed to both synergistic effectiveness of chemical CIP, with a TMP controlling mode, and sub-critical flux operation. Sub-critical flux operation could reduce the cake layer caused by sludge flocs, resulting in slow TMP rise (such as the average of 0.79 kPa/d in Run 1 in this study) with time (Pollice et al., 2005). After every chemical CIP, TMP decreased almost down to the original value (5e7 kPa), indicating that chemical CIP could effectively remove the fouling in terms of membrane pore blockage and gel layer caused by colloids and soluble organic substances. Cleaning intervals were from 2 to 3 weeks to nearly 3 months due to fluctuation of sludge characteristics. In Run 2, TMP and its rising rate were also in the low range of 5.1e18.2 kPa and 0.50e1.48 kPa/d respectively under effective membrane fouling control by sub-critical flux operation and chemical CIP, but with a time controlling mode. Although TMP did not decrease to the original value after weekly cleaning with low concentration
NaClO and rose, to some extent, in one month, TMP decreased to the original value after monthly cleaning with high concentration NaClO. From the low average TMP and its rising rate in Run 1 and Run 2 with the similar flux, steady filtration in the long-term operation could be achieved under sub-critical flux operation and chemical CIP (with TMP or time controlling mode). Compared with Run 1 and Run 2, TMP (5.3e26.6 kPa) and its rising rate (0.39e3.05 kPa/d) between two cleanings in Run 3 were higher due to critical flux operation. Although filtration stability was not as good as in Run 1 and Run 2, TMP could be kept below 30 kPa in Run 3, showing a possible steady longterm filtration under critical flux operation and chemical CIP with combined TMP and time controlling modes. Especially TMP increase in the second half of Run 3 with flux of 29.7e35.9 L/(m2 h) was comparable to that in initial Run 1 with flux of 27.6e30.3 L/(m2 h), indicating the possible seasonal effects on membrane fouling. Both initial Run 1 and the second half of Run 3 were in summer with good sludge filterability. High temperature not only decreased the sludge viscosity but also affected microbial activity especially under low SRT, thus resulting in less fouling than low temperature. Similar results have been reported by several researchers (AlHalbouni et al., 2008; Lyko et al., 2008; Miyoshi et al., 2009). In addition, the relative steady filtration in Run 3 also showed that the criterion for identifying critical flux in this study was a little conservative. In Run 4, under super-critical flux operation and chemical CIP with time controlling mode, both TMP and its rising rate were in the higher range of 6.7e43.2 kPa and 0.61e4.69 kPa/ d compared to Run 1e3. Especially at the second month of Run 4, TMP rising rate was higher than 3 kPa/d, showing characteristics of cake layer fouling. After weekly cleaning with low concentration NaClO, virtually no TMP decrease was observed. Although TMP could be depressed after monthly cleaning with high concentration NaClO, TMP rose up to higher than 40 kPa at the end of Run 4 and flux decreased with increased TMP due to the limit of suction pump. This indicated that steady filtration under super-critical flux operation could not be achieved even adopting chemical CIP. Adham et al. (2004) also got similar results from different flux operation of a pilot-scale US Filter MBR for municipal wastewater treatment. In Run 5, variable flux operation was conducted in order to restore membrane permeability. Although enhanced chemical CIP (NaClO and HCl or NaOH) was performed, low TMP (less than 20 kPa) was only adequate under low flux (about 25 L/(m2 h)) due to accumulative fouling in Run 4. In addition,
Table 2 e Membrane performance in all runs. Run No.
Time (d)
Flux (L/(m2 h))
1 2 3 4 5 6
1e200 201e265 266e406 407e484 485e584 585e750
27.6e30.3 29.0e30.3 29.7e35.9 34.9e42.8 23.5e38.1 29.0e36.6
Values in parentheses are the average values.
(30.1) (30.0) (34.2) (40.1) (30.4) (33.3)
TMP (kPa) 4.6e17.6 (9.7) 5.1e18.2 (11.0) 5.3e26.6 (12.1) 6.7e43.2 (17.2) 10.1e61.3 (27.3) 5.8e61.3 (20.3)
TMP rising rate between two cleanings (kPa/d) 0.15e2.09 0.50e1.48 0.39e3.05 0.61e4.69 0.99e9.95 0.30e5.26
(0.79) (0.84) (1.22) (2.11) (3.89) (1.63)
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the relative poor filterability of activated sludge under winter temperature (10e15 C) in Run 5 might be another factor for low permeability recovery (Al-Halbouni et al., 2008; Lyko et al., 2008; Miyoshi et al., 2009). In Run 6 under critical flux operation, TMP showed a gradual decrease followed by a gradual increase due to enhanced chemical CIP and fluctuation of sludge characteristics. In the second half, TMP was obviously lower than in Run 4 and Run 5 and was close to that in Run 3, indicating membrane permeability could be recovered from serious fouling, caused by super-critical flux operation in Run 4, to some extent by chemical CIP. From all runs, long-term steady filtration was successfully achieved under sub-critical flux operation (30 L/(m2 h)) and chemical CIP with both cleaning modes. Critical flux operation (30e35 L/(m2 h)) appeared to be feasible for achieving long-term steady filtration under chemical CIP with combined TMP and time controlling mode because this combined cleaning mode could provide the more intensive protection for membrane. However super-critical flux operation (35e42 L/(m2 h)) was not possible for achieving long-term steady filtration even adopting chemical CIP. The results from the long-term operation of the pilot-scale SMBR in this study also demonstrated that the critical flux should be a key parameter for realizing long-term steady filtration for real SMBR application and the method for critical flux measurement through short-term constant-flux filtration test used in this study was feasible for long-term operation.
3.2. Chemical CIP performance of different cleaning reagents Fig. 3 and Table 3 show chemical CIP performance of various cleaning reagents in terms of TMP decrease after cleaning under the same flux, i.e. the simple index for permeability recovery. From Table 3, average TMP decrease after cleaning for high concentration NaClO (2000e3000 mg/L), low concentration NaClO (500e1000 mg/L), HCl (pH ¼ 1) and NaOH (pH ¼ 12) was 12.7 kPa, 5.1 kPa, 9.8 kPa and 6.2 kPa respectively, indicating high concentration NaClO was the best among all of the reagents. Grelot et al. (2008) also found that
Table 3 e Cleaning performance of different reagents. Reagent
TMP decrease after cleaning (kPa)
NaClO (2000e3000 mg/L) NaClO (500e1000 mg/L) HCl (pH ¼ 1) NaOH (pH ¼ 12) HCl (pH ¼ 1) followed by NaClO (3000 mg/L) Mixture of NaClO (3000 mg/L) and NaOH (pH ¼ 12)
1.8e31.9 (12.7), n ¼ 31 1.4e13.0 (5.1), n ¼ 25 5.0e12.1 (9.8), n ¼ 4 4.7e7.6 (6.2), n ¼ 2 7.4e49.2 (22.4), n ¼ 3 14.6e28.4 (21.7), n ¼ 3
Values in parentheses are the average values. n is the number of cleaning.
high concentration NaClO (2000 mg/L) was better than other chemicals (such as H2O2, NaOH, HCl, citric acid and enzymes) used in their study. The recovery of membrane permeability by NaClO cleaning has also been reported in many publications (Liu et al., 2000; Xing et al., 2003; Adham et al., 2004; Trussell et al., 2005; Le-Clech et al., 2006; Fatone et al., 2007; Qin et al., 2009). HCl and NaOH showed results similar to that of low concentration NaClO. The combination of NaClO and HCl or NaOH appeared to be better than NaClO only, especially when serious fouling occurred. Similar results have been reported by Xing et al. (2003), Adham et al. (2004), Zhang et al. (2005), Brepols et al. (2008) and Matosic et al. (2009). In addition, for the performance of each cleaning in terms of TMP decrease, it depended not only on cleaning reagent but also on fouling condition before cleaning. Under the same cleaning reagent, generally the higher TMP before cleaning was, the higher TMP decrease after cleaning was. Serious fouling occurred at the second half of Run 4, Run 5 and initial Run 6, was partially the reason for the large fluctuation of cleaning performance. Finally, it should be noted that HCl, NaOH, combined NaClO and HCl, and combined NaClO and NaOH cleaning were performed only 4, 2, 3 and 3 times, respectively, in this study. Therefore, further investigation is necessary to optimize CIP strategies for MBR.
3.3. Changes of membrane surface morphology and properties by chemical CIP
50 NaClO (2000-3000 mg/L)
45
In order to characterize the microscopic changes of a membrane by chemical CIP, membrane fibers before and after cleaning with high concentration NaClO (3000 mg/L) on day 623 in Run 6 were cut off for various analyses including membrane surface morphology, membrane material characteristics and foulants. TMP values under a flux of 30 L/(m2 h) before and after cleaning were 31 kPa and 20 kPa, respectively, indicating the limited performance for fouling removal because TMP for a new membrane was only about 5 kPa under the same flux. This large difference of membrane permeability was mainly caused by accumulative fouling in Run 4 under super-critical flux operation.
NaClO (500-1000 mg/L) HCl (pH=1)
40
NaOH (pH=12) HCl (pH=1) followed by NaClO (3000 mg/L)
35 ΔTMP (kPa)
Mixture of NaClO (3000 mg/L) and NaOH (pH=12)
30 25 20 15 10 5 0 0
75
150
225
300
375 450 Time (d)
525
600
675
Fig. 3 e TMP decrease after chemical CIP with different cleaning reagents.
750
3.3.1.
Membrane surface morphology
From the appearance, new membrane fibers were white, fouled membrane fibers appeared dark and cleaned membrane fibers
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Fig. 4 e SEM images of membrane fibers (30003) (A: new membrane; B: fouled membrane; C: cleaned membrane).
looked like mixed yellowewhite more close to new membrane than fouled membrane, indicating the macroscopic effect of removing membrane fouling by chemical CIP. Fig. 4 shows the SEM images of membrane fibers. There was a widespread gel layer and no obvious cake layer on the fouled membrane surface, indicating that deposition of flocs could be avoided by sub-critical flux operation. Gel layer disappeared virtually after cleaning, indicating that chemical CIP could remove gel layer fouling effectively. Compared with a new membrane, fouling (mainly pore blockage foulants) still existed after cleaning. This explanation was supported by the measurement results of mercury porosimeter in the next section. From AFM analysis (image no shown), for a new membrane, fouled membrane and cleaned membrane, Ra (the average roughness) and RMS (the square mean roughness) of the AFM images, were 37.39 nm, 126.4 nm, 83.74 nm and 49.69 nm, 156.8 nm, 113.9 nm, respectively. The sequence of both Ra and RMS was fouled membrane > cleaned membrane > new membrane, indicating that the height of fouling layer decreased after cleaning. This was in agreement with the SEM images.
3.3.2.
decrease or even increased because partial blocked big pores would be measured as small pores.
3.3.3.
Analysis of membrane foulants
Fig. 6 shows the measured ATR-FTIR spectra of new, fouled and cleaned membrane fibers. Two characteristic peaks (1070/1180/ 1280 cm1, CeF str.; 1400 cm1, CH2 def.) of membrane material (e[eCF2eCH2e]en) were clearly seen for all membrane fibers. Compared with the new membrane, the peaks of amide I/II (1643 cm1, C]O str.; 1546 cm1, NeH def.) and the peak (3300 cm1, NeH str.) on the fouled membrane demonstrated the existence of protein-like substances in the fouling layer. In addition, the peak at 1000e1100 cm1 was obviously broadened and strengthened on the fouled membrane, indicating that some foulants (possibly polysaccharide-like substances or inorganics) had overlapping peaks with membrane material. Kimura et al. (2005) also found that protein-like and polysaccharide-like substances were the main foulants in SMBR for municipal wastewater treatment through FTIR analysis of offline alkali cleaning solution of the fouled membrane modules. In comparison with the fouled membrane, the peak at 1000e1100 cm1 was obviously narrowed and weakened and the peaks of amide I/II were also weakened after cleaning,
Membrane material characteristics
Fig. 5 shows the changes of porosity in terms of pore volume per unit mass of membrane material with pore size. The mean pore diameter of new membrane was about 0.4 mm, in agreement with the nominal pore size from membrane manufacturer. In comparison with a new membrane, the porosity of a fouled membrane and a chemically cleaned membrane decreased noticeably, and pore diameter distribution also narrowed to some extent. This demonstrated the existence of pore blockage fouling. Compared with a fouled membrane, the porosity increased slightly after cleaning, indicating that chemical CIP could remove pore blockage fouling to a certain extent. There was apparently no difference at pore size less than 0.2 mm between new and fouled/cleaned membrane from Fig. 5. However pore blockage should also occur in smaller pores because the potential pollutants with smaller size in activated sludge could enter the smaller pore and block it. Partial pore blockage might be the possible reason. Generally membrane pore is not blocked completely. The partial blocked pore can be regarded as a smaller pore during measurement. Thus for fouled/cleaned membrane, smaller pores were really blocked but the apparent porosity at smaller pores did not
Fig. 5 e Porosity changes of new, fouled and cleaned membrane fibers.
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Table 4 e Effluent quality before and after the CIP with NaClO (3000 mg/L) on day 471.
Fig. 6 e ATR-FTIR spectrums of new, fouled and cleaned membrane fibers.
indicating that NaClO CIP could remove some foulants including protein-like substances to some extent. From the qualitative EDS spectrum (data not shown), metal elements such as Ca, Fe, Mg, Al, Na and Mn were detected, indicating that inorganic substances also contributed to membrane fouling. Metal ions or clusters exist widely in municipal wastewater and activated sludge could be the origin of inorganic foulants. These cations (Ca2þ, Fe3þ, Mg2þ, Al3þ) could be easily precipitated by the biopolymers with 2 3 anion groups (SO2 4 , CO3 , PO4 , OH ), thus enhancing membrane fouling (Seidel and Elimelech, 2002). Inorganics were generally the major foulants in anaerobic MBR (Yoon et al., 1999; Kang et al., 2002; You et al., 2006) and were also detected as the important foulants in aerobic MBR (Ognier et al., 2002; Adham et al., 2004; Meng et al., 2007; Wang et al., 2008). In addition, no obvious change was found for metal elements before and after NaClO CIP. This indicated that NaClO CIP was not effective in removing inorganic foulants. In general acid (Yoon et al., 1999; Adham et al., 2004; Lee and Kim, 2009) and metal-chelator (You et al., 2006) cleaning agents were more effective in removing inorganic fouling. Considering the former analysis, the residual fouling in terms of pore blocking and partial gel layer after NaClO CIP was likely related to inorganics. The existence of inorganic foulants also demonstrated the necessity of acid cleaning for restoring membrane permeability completely. According to the above analysis, a preliminary explanation on membrane fouling in Run 1 and Run 2 under sub-critical flux operation and NaClO CIP could be concluded. Sub-critical flux operation prevented sludge flocs from depositing on the membrane surface and avoided the formation of cake layer fouling. Thus gel layer and pore blockage, formed mainly by macromolecular organics and inorganics, became the dominant membrane fouling. With the accumulation of gel layer and pore blockage fouling, membrane resistance and TMP increased under constant-flux operation. NaClO CIP could remove most of the gel layer and partial pore blockage fouling formed mainly by macromolecular organics. Therefore TMP clearly decreased due to the restoration of membrane filterability after NaClO CIP. Steady filtration in Run 1e3 without acid cleaning was possibly attributed to negligible inorganic fouling caused by low concentrations of metal ions in municipal wastewater and/or short time
Item
Background value before cleaning
Initial value after cleaning
COD (mg/L) BOD5 (mg/L) NHþ 4 eN (mg/L) TN (mg/L) TP (mg/L) Total E. coli (CFU/ml)
40.2 1.0 3.4 13.5 1.5 Not detected
42.3 1.4 3.5 7.5 4.1 Not detected
operation. Chemical CIP with NaClO as a primary agent and supplementary HCl should be adopted in order to achieve steady filtration for much longer operation (at least 1 year).
3.4.
Effects of chemical CIP on effluent quality
Table 4 shows the effluent quality before and after one chemical CIP performed with high concentration NaClO (3000 mg/L) on day 471. CODCr, BOD5 and NHþ 4 eN exhibited no obvious changes before and after cleaning. TN decreased and TP increased in the initial time after cleaning. This was mainly attributed by the non-aerated period in the bioreactor during cleaning, providing the anoxic and anaerobic environment for denitrification and phosphorus releasing. Subsequently, TN and TP returned to a normal value after about 1 h operation. In general, chemical CIP had no obvious effect on effluent quality.
4.
Conclusions
During the long-term (750 d) operation of a pilot-scale submerged membrane bioreactor for municipal wastewater treatment, steady filtration for at least 265 d under high flux (30 L/(m2 h)) was successfully achieved due to effective membrane fouling control by sub-critical flux operation and chemical CIP with NaClO. Sub-critical flux operation prevented rapid fouling caused by cake layer formation. NaClO CIP, in both a TMP controlling mode (cleaning with high concentration NaClO (2000e3000 mg/L) was performed when TMP rose to 15 kPa) and a time controlling mode (cleanings were performed weekly and monthly respectively with low concentration NaClO (500e1000 mg/L) and high concentration NaClO (3000 mg/L)), effectively controlled slow fouling caused by pore blockage and gel layer. Critical flux operation (30e35 L/ (m2 h)) under chemical CIP with combined TMP and time controlling mode achieved steady filtration up to 140 d and appeared promising for achieving longer steady filtration although it was less stable than sub-critical flux operation. However super-critical flux operation (35e42 L/(m2 h)) was not attainable for achieving long-term steady filtration even adopting chemical CIP. In addition, the results also demonstrated that the method for critical flux measurement through short-term constant-flux filtration test used in this study was feasible for long-term operation.
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From macroscopic cleaning performance, high concentration NaClO was the best. HCl (pH ¼ 1) and NaOH (pH ¼ 12) showed results similar to low concentration NaClO. The combination of NaClO and HCl or NaOH appeared to be better than NaClO alone, especially when serious fouling occurred. Microscopic analysis on membrane fibers before and after high concentration NaClO CIP was conducted in order to investigate the mechanism of NaClO CIP for removing membrane fouling. SEM and AFM images showed that NaClO cleaning could effectively remove the gel layer, the dominant fouling under sub-critical flux operation. Porosity measurements indicated that NaClO cleaning could partially remove pore blockage fouling. ATR-FTIR and EDS analyses demonstrated that protein-like macromolecular organics and inorganics were the important components of the fouling layer. The analysis of effluent quality before and after NaClO CIP showed no obvious negative effect on effluent quality.
Acknowledgements This work was supported by the National Science Fund for Distinguished Young Scholars (No. 50725827) and 863 program (No. 2009AA062901).
references
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Available at www.sciencedirect.com
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Lack of correlation between enterococcal counts and the presence of human specific fecal markers in Mississippi creek and coastal waters C. Flood a,1, J. Ufnar c,3, S. Wang a,1, J. Johnson b,2, M. Carr a,1, R. Ellender a,* a
Department of Biological Sciences, University of Southern Mississippi, 118 College Drive #5018, Hattiesburg, MS 39406-001, USA Center for Research Support, 118 College Drive #5116, University of Southern Mississippi, Hattiesburg, MS 39406-001, USA c Center for Science Outreach, Vanderbilt University Medical Center, 806 Light Hall, Nashville, TN 37232-0670, USA b
article info
abstract
Article history:
The objective of this study was to determine whether statistically valid correlations could
Received 22 March 2010
be shown between enterococcal counts of samples from creek and coastal sites and the
Received in revised form
presence of two molecular, library-independent markers that specify human and/or
13 September 2010
sewage pollution. Four hundred ninety samples were collected between August 2007 and
Accepted 17 September 2010
April 2009 to determine enterococcal counts and the presence of genetic markers for the
Available online 8 October 2010
sewage indicator organisms Methanobrevibacter smithii and Bacteroidales. The presence of human/sewage markers and enterococcal counts were higher in creek samples than
Keywords:
coastal samples, but the higher creek levels did not statistically correlate with the either
Enterococci
enterococcal count or the presence of the markers present in coastal samples. Further-
Human markers
more, there was no correlation between enterococcal counts in coastal samples and either
Methanobrevibacter
marker at any of the beach sites tested. The results of this investigation in Mississippi
Bacteroidales
coastal waters suggest that human/sewage markers are unlikely to correlate with
MultiNA
enterococci counts in the nearshore environment and that enterococcal counts may be indicative of other animal or environmental sources. Additionally, a study comparing conventional gel electrophoresis with capillary electrophoresis did not convincingly establish that one method was better than the other in regard to the results obtained. The capillary method does allow reproducibility of results and the ability to analyze multiple samples in a short period of time; however, the operational expenditures exceed the cost of traditional gel electrophoresis. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
The safety and the quality of life along the world’s beaches are highly dependent upon successful stewardship of coastal waters, whose safety with respect to human health is threatened by extensive development and other anthropogenic
activities. Along the coasts of the United States, water quality is routinely tested by the enumeration of the indicator bacteria. Coastal beach waters are monitored using Enterococcus (EN) species and fecal coliforms are used to test for fecal pollution in freshwater creeks and shellfish beds (USEPA, 2002, 2004, 2005). Recently, the public value of microbial
* Corresponding author. Tel.: þ1 601 266 4720/601 266 4752; fax: þ1 601 266 5797. E-mail address:
[email protected] (R. Ellender). 1 Tel.: þ1 601 266 4752. 2 Tel.: þ1 601 266 5040. 3 Tel.: þ1 615 612 8901. 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.026
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 7 2 e8 7 8
indicators to determine the presence of water pollution has been questioned and the governmental agencies responsible for implementing the elements of clean water legislation are pressed to discover a suitable alternate to the quantitation of the standard indicator species (USEPA 2009). Enterococci and fecal coliforms are associated with a wide variety of fecal inputs from humans and animals, but offer no information about the source(s) of pollution that can degrade water quality in coastal areas. This failure impedes the ability of regulatory agencies and managers to protect public health and remediate sources of pollution. Microbial source tracking (MST) methods have been developed and tested over the past several decades, showing promise for discriminating between animal and human fecal pollution sources (Scott et al., 2002; Simpson et al., 2002; Field et al., 2003; Meays et al., 2004; Rochelle and De Leon, 2006). Of the MST methods available, those that focus on the detection of a single gene or group of host specific genes have emerged as creditable measures for reasons of specificity, sensitivity economy, speed and transferability. Bacteroidales (Bernhard and Field, 2000a; Dick et al., 2005) was the first bacterial human marker to be used in source tracking applications. Additional microbial human markers have been developed including Bacteroides thetaiotamicron (Carson et al., 2005), Methanobrevibacter smithii (Ufnar et al., 2006), human polyomavirus (Bofil-Mas et al., 2000; McQuaig et al., 2006) and Faecalibacterium (Zheng et al., 2009). As the variety of human and animal markers has increased, researchers have begun to blend the technologies into predictive models (Balleste et al., 2010). The application of these methods in coastal waters of the United States has not been frequently published, but reports of the use of the Bacteroidales marker in coastal waters have stressed the value of these unique and specific tests (Bernhard and Field, 2000b; Bernhard et al., 2003; Shanks et al., 2006). These studies differ from those which inject human sewage into various environmental waters and evaluate recovery (Ahmed et al., 2009; Harwood et al., 2009). Needed are additional research statistics that determine the value of human and animal markers in real world environments, comparing the presence of one marker with the presence of other markers, and including valuation of marker presence or absence and EN counts in coastal waters, as well as other significant assessments. In this study, enterococcal counts and the presence or absence of the Bacteroidales (HF183F and Bac708R) and the M. smithii marker (Mnif142f and 363r) products were analyzed in identical samples from Mississippi coastal waters. Statistical analyses were conducted to determine the correlation between EN levels and the percentage of each human marker at each sampling site and to examine whether nearshore beach sampling sites with tidal creek influx were significantly different from those sites with no associated creek.
2.
Materials and methods
2.1.
Sampling sites
Six Mississippi Department of Environmental Quality (MDEQ) coastal beach sites were chosen for analysis based on the
873
frequency of beach closure events. They included one site with a moderate number of beach closures (1e2/yr) (7A: 30 200 48500 N 89 09.6210 W), and five with high number of beach closures (3e5/yr) (9: 30 22.2010 N 89 04.7830 W, 10: 30 22.5590 N 89 03.1610 W, 10A: 30 220 45500 N 89 020 76300 W, 11: 30 22.9380 N 89 01.5780 W, and 12A: 30 23.5860 N 88 56.2910 W). Six tidal creek sites that flow into the Mississippi Sound affecting MDEQ sites 7A, 10 and 11, were also tested (7ACC: 30 200 51800 N 89 90 25600 W; 7ACT: 30 200 33700 N 89 090 37700 W; CC1: 30 220 46.8900 N 89 030 22.0000 W, CC2: 30 220 40.8800 N 89 030 18.4700 W; AOC: 30 230 14.4200 N 89 010 08.2500 W, and Condo: 30 230 1.5500 N89 10 30.4400 W, respectively) (Ufnar et al., 2007) (Fig. 1). The coastal stations represented here are the dominant beach recreational sites along the Mississippi coast.
2.2.
Sample collection
Samples of creek and beach water were collected in 500 mL sterile plastic bottles, labeled, transported on ice to the laboratory and processed within 6 h of collection. Forty one sampling trips were conducted between August 2007 and April 2009. Data set A (Table 1) represents samples collected between August 2007 and August 2008; the presence or absence of the human markers was analyzed by traditional gel electrophoresis. A data subset (B) of samples collected between September 2008 and April 2009 were analyzed by traditional gel electrophoresis and microchip capillary electrophoresis.
2.3.
Enterococcus isolation/characterization
Enterococci were cultured and enumerated following the United States Environmental Protection Agency (USEPA) Standard Method 1600 (Messer and Dufour, 1998; USEPA, 2006). Briefly, water samples were diluted (100e106), filtered through a 0.45 mm, 47 mm cellulose acetate membrane (Pall Corporation, Ann Arbor, MI), placed on 60 mm Petri dishes containing mEI agar (BD Bioscience, Sparks, MD), and incubated at 41 C for 24 h. Colonies (0.5 mm in diameter) characterized by blue halos on mEI were designated enterococci. Counts were expressed as CFU/100 ml.
2.4.
DNA extraction and PCR analysis
Sample volumes (500 ml) were filtered through 3.0 mm and 0.45 mm cellulose acetate membranes (Pall Corporation, Ann Arbor, MI) and the 0.45 mm filter extracted using the PowerSoil DNA kit (MoBio Laboratories, Inc., Carlbad, CA). DNA concentrations were measured in ng/ml using a Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies, Wilmington, DE) and frozen (20 C) pending analysis, generally in 2 weeks of collection. The M. smithii (MS) (Mnif142f and 363r) primer sequences (Ufnar et al., 2006) and the Bacteroidales (BA) (HF183F and Bac708R) (Bernhard and Field, 2000a) were purchased from Integrated DNA Technologies, San Jose, CA. The cycling conditions for both markers included an initial denaturation for 3.5 min at 94.0 C, followed by a step down procedure (94 C for 45 s, 65e62 C at 1 decrease per 2 cycles, and 62e55 C at 1 decrease per one cycle) and elongation at 72 C for 30 s. The latter step was followed by 30 cycles at 94 C
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 7 2 e8 7 8
Fig. 1 e Coastal Mississippi e Harrison County. Black arrows represent coastal sampling sites. White arrows represent creek sampling sites. ª Google 2010.
for 45 s, 55 C for 45 s, 72 C for 30 s and a final extension of 72 C for 5 min (Harwood et al., 2009).
2.5.
Electrophoretic analysis
PCR products from Data Set A (August 2007 to August 2008) were electrophoresed in the traditional manner using 1.5% agarose gels and 72 V for 1.75 h. PCR products from Data Set B (September 2008 to April 2009) were separated using 1.5% agarose gels (G), and using the microchip electrophoretic system (M) (MCE-202 MultiNA, Shimadzu Corporation Kyoto, Japan) as per the manufacturer’s specifications.
2.6.
Statistics
Categorical and continuous statistics encompassing correlating and contingency effects were performed using SPSS v.17.1.
3.
Results
For all samples from each site, the average enterococcal count was calculated and the frequency (%) of a positive PCR result for M. smithii (%MS) and Bacteriodales (%BA) was tabulated (Table 1, Part A). The highest average EN counts were recorded in creek samples as shown by the measurements for sites 7ACT, 7ACC, CC1, CC2, AOC and Condo. Coastal sample (sites 7A, 9, 10, 10A, 11 and 12A) averages were lower with site 10 having the highest count in this group of samples and station 10A the lowest count. With rare exception, the %BA at each site was higher than the %MS; however, certain sites contained virtually equal percentages of each marker (sites 7A, CC1, 10A, AOC and 11). Not all creek-associated coastal sites
showed the trend shown for EN. For example, the percentages of each marker in the creek leading to the coastal 7A site (15/ 32; 15/32; %MS/%BA, respectively) were in the same general range as the coastal site (24/27; %MS/%BA, respectively). The same basic outcome was found at the Turkey creek stations (AOC and Condo) exiting to the coast at site 11. In contrast, the CC1 and CC2 creek stations yielded high percentages of each marker but the concentrations of the markers dropped in the coastal water site (10) by a factor of 3. Station 9 is approximately 1 mile from station 10. Since the prevailing winds are from the southeast, it is possible that the EN and human markers drift with the water from site 10 to site 9, but there is no direct evidence for this conclusion. Site 12A is approximately 10 miles east of site 9 and there is no known source for the human markers at this site. To determine whether there was a significant relationship between EN counts at the different sampling sites, one way ANOVA was performed (using the data set shown in Table 1, Part A) and demonstrated that all sites were significantly different {F(11,314) ¼ 18.34, p < 0.001}. Multiple comparisons of all sites tested showed significant differences between creek and coastal sites, with the exception of sites 11 and 12A. These sites were significantly different from CC1, CC2, AOC and Condo. Table 1, part B demonstrates the comparison of EN counts and the percentage of each human marker from samples collected between September 2008 and April 2009 and representing 11 of the 41 sampling dates. The one way ANOVA comparison of EN data between sampling sites during this time interval again demonstrated that all sites were significantly different {F(11,112) ¼ 5.292, p < 0.001}. An examination of the relationship between EN and the presence or absence of BA and MS at each sampling site showed significant differences between EN counts and
17 45 27 45 45 27 659 1364 9 9 45 0 2136 1516 9 9 9 9 3503 2158 27 9 64 9 25 37 18 18 36 9 93 131 27 18 45 18 2839 32000 36 36 64 36 61 97 9 0 55 0 1556 1236 9 0 45 9 EN (N ¼ 11) %MS %BA %MS %BA G G M M B.
2770 2577 18 27 64 27
254 390 27 0 64 0
3397 2530 27 55 64 36
718 1428 15 38 644 1679 22 22 2916 2317 14 29 3640 2918 29 26 221 335 27 29 257 552 10 33 3612 2997 53 82 385 863 17 24 1781 1832 15 32 EN (N ¼ 30) %MS %BA A. G G
2024 1535 15 32
189 466 24 27
4248 2845 61 66
12A 11 Condo AOC 10A 10 CC2 CC1 9 7A 7ACC 7ACT
Table 1 e Average enterococcal count (EN), number of sampling dates, and the percent positive reactions for each human marker (MS, BA) at each of the 12 study sites. (A). Values for each site from August 2007 to August 2008 (Average enterococcal per 100 ml; Percentage of each human marker detected by gel electrophoresis {G}); (B). Values for each coastal site from September 2008 to April 2009; (Average enterococcal count per 100 ml; Percentage of each human marker detected by gel electrophoresis {G}) and by MultiNA analysis (M).
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 7 2 e8 7 8
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presence or absence of the markers (BA: [t(323) ¼ 2.41, p ¼ 0.016]; MS: [t(324) ¼ 2.79, p ¼ 0.006]). A MS to BA crosstabulation (Table 2, A) demonstrated that when MS was not present in a sample, BA was not present 73% of the time. When MS was present in a sample, BA was present 69% of the time. The BA to MS crosstabulation showed that when BA was not present in a sample, MS was not present 90% of the time; when BA was present in a sample, MS was also present 40% of the time. These data seem to imply that each measure is testing for the same parameter, i.e. fecal pollution of water. Taking into account all samples, both BA and MS were negative 54% of the time, positive 20% of the time, and disagreed 26% of the time. A comparison of the percentages of each marker as determined by gel electrophoresis and MultiNA analysis is shown in Table 2, parts B and C. These data show that at 11 of the 12 locations, the percentage of the MS marker was higher than the BA marker when tested by the MultiNA method. Gel electrophoresis showed that the MS percentage was higher at 4 sampling sites, BA higher at 3 sites and the remaining sites were equivalent. MultiNA-gel electrophoresis crosstabulations demonstrated that when the MultiNA did not indicate the presence of the MS marker in a sample, the gel method did not show the marker 94% of the time. On only one occasion did the MultiNA present a negative result when the gel method was positive. When the MultiNA analysis indicated the presence of the MS marker, the gel method showed 61% dissimilarity with the capillary method. Both methods agreed on the presence of the MS marker 39% of the time. Crosstabulation of the BA marker analyzed by MultiNA and gel electrophoresis showed that when the MultiNA result did not display this marker, the gel method did not show the marker 92% of the time. When the BA marker was found by the MultiNA analysis, the gel electrophoresis method found the marker 80% of the time. Taking into account all measurements, the MultiNA data agreed with the gel electrophoresis data 68% of the time for MS and 90% of the time for BA.
4.
Discussion
For the present, enterococcal measurements are the standard measure of human risk from contact with enteric pathogens in coastal waters; however, recent studies have indicated that there are factors that mitigate the value of these analyses. For example, enterococci are known to exist in many animal species, and to reproduce in the coastal environment (Signoretto et al., 2004). Furthermore, sediments and beach sand have been shown to harbor enterococci and allow them to persist in the environment (Scott et al., 2002; Hartz et al., 2008). In partial response to the problems experienced by users of the enterococcal standard, researchers developed human and animal markers to identify sources of coastal pollution and allow remediation efforts to occur. The question is: In natural samples, are enterococci a reliable indicator of human fecal pollution, and do human markers correlate with the levels of enterococci observed in coastal samples? For this geographical area, the answer is no.
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 7 2 e8 7 8
Table 2 (continued) Table 2 e Crosstabulation (A) analysis of MS± vs. BA±. The % within MS is a row percentage (202/277) whereas the % within BA is a column percentage (202/225). That is, 72.9% of the “no MS” are also “no BA” but 89.8% of the “no MS” are also “no BA”. The numbers total 100%. The % within MS total across whereas the % within BA total down. Crosstabulations of the MultiNA vs. gel electrophoresis for MS and BA are found in B and C. No
Yes
9
92
8
96
36
78
7
4
16
20
80
4
64
3
12
107
25
81
19
100
100
81
19
100
No, % within G BA
Total
75
72.9
27.1
85
89.8
60.0
54.4
20.2
23
50
31.5
68.5
10.2
40.0
6.2
13.5
225
125
60.6
33.7
100
100
60.6
33.7
60
1
94
2
59
4
46
1
41
26
61
39
40
96
31
20
102
27
77
21
100
100
77
21
No, % within BA
73
Yes, % within MS
100
Yes, % within BA
19.7
% of total
19.7
Count
100
Yes, % within G BA
15
% of total
74.7
Count
20
Yes, % within M BA
74.7
% of Total
85
Count
100
Totals
Total
112 103
No, % within M BA
277 202
No, % within MS
15
Count
132
Yes, % within M BA
100
Yes, % within G BA
100
% of total
100
371
% within MS
100
% within BA
100
% of total
100
Gel MS Count
64
No, % within M MS
100
No, % within G MS
49
% of total
49
Count
67
Yes, % within M MS
100
Yes, % within G MS
51
% of total
Totals
Gel BA Count
BA Count
B MultiNA MS
C MultiNA BA
Yes
% of total
A MS
Totals
No
51
Count
132
Yes, % within M MS
100
Yes, % within G MS
100
% of total
100
During this study, enterococcal counts at 12 coastal sampling sites were not positively correlated. Unquestionably, creek waters contain substantial enterococcal levels and frequently show the presence of the human markers; however, these measurements did not statistically translate into associated beach water counts of enterococci or the presence of the human markers. During the same period from August 2008 to April 2009, there were 131 enterococcal exceedances (Mississippi uses a single sample count of 104/100 ml to designate a polluted beach) associated with the six coastal sites tested. Forty eight exceedances occurred at station 10, followed by 26 at site 10A, 22 at site 9, 17 at site 11, 13 at site 12A and 5 at site 7A. These data imply that a statistical correlation should occur at site 10 which is influenced by sampling sites CC1 and CC2, but it does not exist. Therefore it must indicate that other factors are at play to create this disparity. Differences do exist between the creek and the beach environments including such variables as fresh vs. salt water, the levels of ultraviolet light, the dilution effect as creek water enters the estuary, and water transport at beach sites, as well as differences in turbidity and sediment disturbance. All or a portion of these factors may account for the lack of correlations observed (Ufnar et al., 2007). Similarly, there was a significant difference between EN counts and the presence or absence of the BA or MS marker in either the creek or coastal samples. This is not unexpected since one measurement is a quantifiable bacterial count and MS and BA are measures of presence or absence and represent other microbial genera. The crosstabulations indicated that higher percentages were recorded when neither of the markers was present in a sample. In fact, the BA and MS markers agreed more frequently than they disagreed. Differences between capillary
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 7 2 e8 7 8
electrophoresis and gel electrophoresis were negligible when neither of the markers was present, but agreement between the methods was higher (80%) when the BA marker was analyzed. The MultiNA and the gel procedure were in agreement when the two markers showed different results (61%). In general, these results demonstrate that either marker can be used to evaluate the presence of human coastal water pollution and that either method can be used to generate the results. The advantage of the automated MultiNA method is its sensitivity to small concentrations of DNA in a sample and its ability to evaluate a large number of samples in a short time period. Further, gel staining is not required, avoiding the use of ethidium bromide. The digital gel picture which the instrument presents is a very high resolution image; typically, bands will appear during a MultiNA analysis where none can be seen on an agarose gel. The capillary method has the added advantage of presenting data on the base pair units for each band and the amount (ng/ml) of each DNA fragment in the sample. The instrument requires careful management during the analysis of environmental samples and chip cleaning is often necessary and time consuming. However, if the objective of analysis is to process numerous samples in an abbreviated timeframe, requiring minimal operator attention and inexpensive results, the capillary electrophoresis method would be an appropriate technology. Despite the fact that these data were part of a local sample population, the conclusion that EN levels did not correlate from sampling site to sampling site nor was their correlation with the levels of two human markers is troubling. Marker analysis has been persistently studied by a variety of international researchers for at least a decade and was considered a complement to enterococcal analysis. However, the random nature of the isolation of both the BA and the MS markers points to the fact that marker presence can be influenced by such factors as dilution, the salt water environment, tidal movements, the presence of sediment in the water column, or other coastal features. This randomness suggests that the analysis of human markers and their relationship to the EN count cannot be used to identify and control pollution on coastal beaches. In the future, a substitute for the measurement of indicator bacterial levels in coastal waters may be the dependable detection of specific microbial pathogens. Currently, several viral pathogens can be detected by qPCR (McQuaig et al., 2006) and other bacterial and protozoal pathogens can be detected with molecular methods. For the time being, the use of the enterococcal count or the qPRC analysis of the level of this organism in coastal waters will continue, almost certainly in concert with data on one or more of the human markers.
5.
Conclusions
No correlation was found between enterococcal counts and the presence or absence of two human markers at 12 coastal sampling sites. A higher occurrence of human markers was found in creek compared to coastal waters. Enterococcal counts at coastal stations did not statistically correlate with counts at other coastal sampling stations, nor
877
did the enterococcal counts at coastal stations correlate with enterococcal counts at creek sampling sites. The presence of the human markers in the freshwater creeks indicates that they are a source of pollution for the coastal environment. The MultiNA method of DNA analysis is favored when many samples are to be analyzed in a short time period or when a sample has a very low concentrations of a target amplicon; the method is not favored when low cost and ease to use are significant priorities. The lack of correlation between enterococcal counts and the presence of human specific fecal markers reported here is for coastal waters along the Northern Gulf of Mexico where the water is generally warm and rich in organic material. However, additional research in other types of habitats and geographic areas in both the United States and in other countries is needed to develop a more comprehensive understanding of the types of environments in which correlation between these two assays of environmental water quality can be expected.
Acknowledgements This project was funded by grants from the U.S. Environmental Protection Agency, Gulf of Mexico Program, USEPAGOMP MX964495 and the National Oceanic and Atmospheric Administration, Northern Gulf Institute, 191001 363558-02. The study sponsors did not participate in the design, the collection, analysis, the interpretation of data, the writing of the report, or in the decision to submit the paper for publication.
references
Ahmed, W., Goonetilleke, A., Powell, D., Chauhan, K., Gardner, T., 2009. Comparison of molecular markers to detect fresh sewage in environmental waters. Water Res. 43 (19), 4908e4917. Balleste, E., Bonjoch, X., Belanche, L., Blanch, A., 2010. Molecular indicators used in the development of predictive models for microbial source tracking. Appl. Environ. Microbiol. 76 (6), 1789e1795. Bernhard, A.E., Field, K.G., 2000a. PCR assay to discriminate human and ruminant feces on the basis of host differences in BacteroidesePrevotella genes encoding 16S rRNA. Appl. Environ. Microbiol. 66 (10), 4571e4574. Bernhard, A.E., Field, K.G., 2000b. Identification of nonpoint sources of fecal pollution in coastal waters by using host specific 16S ribosomal DNA genetic markers from fecal anaerobes. Appl. Environ. Microbiol. 66 (4), 1587e1594. Bernhard, A.E., Goyard, T., Simonich, M.T., Field, K.G., 2003. Application of a rapid method for identifying fecal pollution sources in a multi-use estuary. Water Res. 37 (4), 909e913. Bofil-Mas, S., Pina, S., Girones, R., 2000. Documenting the epidemiologic patterns of polyomaviruses in human populations by studying their presence in urban sewage. Appl. Environ. Microbiol. 66 (1), 238e245. Carson, C.A., Christiansen, J.M., Yampara-Iquise, H., Benson, V.W., Baffaut, C., Davis, J.V., Broz, R.R., Kurtz, W.B., Rogers, W.M.,
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Fales, W.H., 2005. Specificity of a Bacteroides thetaiotamicron marker for human feces. Appl. Environ. Microbiol. 71 (8), 4945e4949. Dick, L.R., Bernhard, A.E., Brodeur, T.J., Santo Domingo, J.W., Simpson, J.M., Walters, S.P., Field, K.G., 2005. Host distribution of uncultivated fecal Bacteroidales bacteria reveal genetic markers for fecal source identification. Appl. Environ. Microbiol. 71 (6), 3184e3191. Field, K.G., Bernhard, A.E., Brodeur, T.J., 2003. Molecular approaches to microbiological monitoring: fecal source detection. Environ. Monit. Assess. 81 (1e3), 313e326. Harwood, V.J., Brownell, M., Wang, S., Lepo, J., Ellender, R., Ajidahun, A., Hellein, K., Kennedy, E., Ye, X., Flood, C., 2009. Validation and field testing of library-independent microbial source tracking methods in the Gulf of Mexico. Water Res. 43 (19), 4812e4819. Hartz, A., Cuvelier, M., Nowosielski, K., Bonilla, T.D., Green, M., Esiobu, N., McCorquodale, D.S., Rogerson, A., 2008. Survival potential of Escherichia coli and Enterococci in subtropical beach sand: implications for water quality managers. J. Environ. Qual. 37, 898e905. doi:10.2134/jeq2007.0312. McQuaig, S., Scott, T., Harwood, V., Farrah, S., Lukasik, J., 2006. Detection of human derived fecal pollution in environmental waters by use of a PCR based human polyomavirus assay. Appl. Environ. Microbiol. 72 (12), 7567e7574. Meays, C.L., Broersma, K., Nordin, R., Mazumder, A., 2004. Source tracking fecal bacteria in water: a critical review of current methods. J. Environ. Manage. 73, 71e79. Messer, J.W., Dufour, A.P., 1998. A rapid, specific membrane filtration procedure for enumeration of enterococci in recreational water. Appl. Environ. Microbiol. 64 (2), 678e680. Rochelle, P.A., De Leon, R., 2006. The Status of Microbial Source Tracking Methods. WERF Project 03-HHE-3. Metropolitan Water District of Southern California, Microbial Source Tracking Workshop, San Antonio, TX. February 16e18, 2005. Scott, T.M., Rose, J.B., Jenkins, T.M., Farrah, S.R., Lukasik, J., 2002. Microbial source tracking: current methodology and future directions. Appl. Environ. Microbiol. 68 (12), 5796e5803. Minireview.
Shanks, O.C., Nietch, C., Simonich, M., Younger, M., Reynolds, D., Fields, K.G., 2006. Basin-wide analysis of the dynamics of fecal contamination and fecal source identification in Tillamook Bay, Oregon. Appl. Environ. Microbiol. 72 (8), 5537e5546. Signoretto, C., Burlacchini, G., Mar Lleo`, M., Pruzzo, C., Zampini, M., Pane, L., Franzini, G., Canepari, P., 2004. Adhesion of Enterococcus faecalis in the nonculturable state to plankton is the main mechanism responsible for persistence of this bacterium in both lake and seawater. Appl. Environ. Microbiol. 70 (11), 6892e6896. Simpson, J.M., Santo Domingo, J.W., Reasoner, D.J., 2002. Microbial source tracking: state of the science. Environ. Sci. Technol. 36 (24), 5279e5288. Ufnar, J.A., Wang, S., Christiansen, J.M., Yampara-Iquise, H., Carson, C.A., Ellender, R.D., 2006. Detection of the nifH gene of Methanobrevibacter smithii: a potential tool to identify sewage pollution in recreational waters. J. Appl. Microbiol. 101 (1), 44e52. Ufnar, D., Ufnar, J., White, L., Rebarchik, D., Ellender, R., 2007. Environmental influences on fecal pollution in the Mississippi Sound. Trans. Gulf Coast Assoc. Geol. Sci. 55, 835e843. USEPA, 2002. National Beach Guidance and Required Performance Criteria for Grants EPA-823-B-02-004, Washington, D.C. USEPA, 2004. Water Quality Standards for Coastal and Great Lakes Recreation Waters Final Rule (40 CFR Part 131), Washington, D.C. USEPA, 2005. The EMPACT Beaches Project: Results from a Study on Microbiological Monitoring in Recreational Waters EPA600/R-04/023, Cincinnati, OH. USEPA, 2006. Method 1600: Enterococci in Water by Membrane Filtration Using Membrane-Enterococcus Indoxyl-b-glucoside Agar (mEI), EPA 821-R-06e009, pp. 1e23. USEPA, 2009. Differences in the Use of Indicator Organisms and Select Pathogens for Assessing Health Risks Associated with Primary Contact with Inland Waters EPA Contract EP-C07e036 to Clancy Environmental Consultants. Zheng, G., Yampara-Iquise, H., Jones, J.E., Carson, C.A., 2009. Development of Faecalibacterium 16S rRNA gene marker for identification of human feces. J. Appl. Microbiol. 106 (2), 634e641.
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Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Characterisation of aquatic humic and non-humic matter with size-exclusion chromatography e organic carbon detection e organic nitrogen detection (LC-OCD-OND) Stefan A. Huber a,*, Andreas Balz a, Michael Abert a, Wouter Pronk b a b
DOC-LABOR Dr. Huber, Eisenbahnstr. 6, 76229 Karlsruhe, Germany Eawag, Swiss Federal Institute of Aquatic Science and Technology, P.O. Box 611, 8600 Du¨bendorf, Switzerland
article info
abstract
Article history:
Size-exclusion chromatography in combination with organic carbon detection (SEC-OCD) is
Received 5 February 2010
an established method to separate the pool of NOM into major fractions of different sizes
Received in revised form
and chemical functions and to quantify these on the basis of organic carbon. One specific
6 September 2010
approach, also known as LC-OCD-OND, is based on the Gra¨ntzel thin-film UV-reactor. This
Accepted 17 September 2010
approach is described with recent improvements in fraction assignation (humic
Available online 29 September 2010
substances, biopolymers, building blocks, low molecular weight organic acids and neutrals, hydrophobic organic carbon), the coupling of a novel organic nitrogen detector (OND), and
Keywords:
an improved diagram for the characterisation of aquatic humic substances (HS-diagram).
Humic substances
The diagram replaces the operational distinction between humic and fulvic acids by
Fulvic acids
a continuum ranging from aquagenic fulvic acids to pedogenic humic acids. ª 2010 Elsevier Ltd. All rights reserved.
Humic acids HS-diagram Organic carbon detector SEC Nitrogen detector Biopolymers EfOM
1.
Introduction
The concept of organic carbon detection (OCD) for chromatography is not new. The first OCD was described by Axt (1969), who coupled a SEC with a customised thermal combustion DOC-analyser. Ten years later a similar design was used to study NOM in Swiss lakes (Gloor and Leidner, 1979). In 1985, a SEC-OCD based on the Gra¨ntzel thin-film reactor with vacuum-UV oxidation was described (Fuchs, 1985). This design was improved in the following years and the detection limit was lowered from the low-ppm range to
the low-ppb range for individual fractions of NOM (Huber and Frimmel, 1991). In 2002, an OCD based on a modified commercial DOC-analyser with a detection limit of 100 ppb was described (Her et al., 2002a) and in 2007 an OCD based on UV oxidation in a quartz coil (Allpike et al., 2007). A detection limit was not given but was found to be sufficient for most natural waters. The SEC-OCD design based on the Gra¨ntzel thin-film reactor was applied to study NOM in drinking waters (Gruenheid et al., 2005; Cornelissen et al., 2008), waste waters (Amy and Her, 2004) and marine waters (Huber and Frimmel, 1994; Dittmar
* Corresponding author. E-mail address:
[email protected] (S.A. Huber). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.023
880
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 7 9 e8 8 5
and Kattner, 2003). More recent applications of SEC-OCD focus on membrane fouling issues, probably caused by biopolymers (Huber, 1998; Jacquemet et al., 2005; Lesjean et al., 2005; Rosenberger et al., 2005; Laabs et al., 2006; Haberkamp et al., 2007; Zheng et al., 2009, 2010). SEC-OCD was found to be equally interesting for quality control of technical waters which are supposed to be NOM-free, like waters used for turbine steam (Heijboer et al., 2006), water for injection (Woiwode and Huber, 2000) or in the semiconductor industry (Huber, 2003, 2005). With this paper we describe the SEC-OCD technique based on Gra¨ntzel, called LC-OCD-OND, covering all improvements of past years. This includes refined fraction assignation, the implementation of an organic nitrogen detector and an improved diagram for the characterisation of aquatic humic matter.
2.
Materials and methods
2.1. Physical description of the LC-OCD-OND system (Fig. 1)
DOC in Bypass 0.45μm in-line filter
Gräntzel Thin-film Reactor
Column Capillary UV-Reactor
Anular UV-Reactor
Mobile Phase
Acid
On-line purified mobile phase (a phosphate buffer exposed to UV-irradiation in an annular UV-reactor) is delivered with an HPLC pump (S-100, Knauer, Berlin, Germany) at a flow rate of 1.1 mL/min to an autosampler (MLE, Dresden, Germany, 1 mL injection volume) and the chromatographic column (250 mm 20 mm, TSK HW 50S, 3000 theoretical plates, Toso, Japan). The chromatographic column is a weak cation exchange column on polymethacrylate basis. Prior to chromatographic separation, samples are made particle-free by passing a 0.45 mm PES-filter (Sartorius, Germany, # 16537). The first detector after chromatographic separation is nondestructive, fixed wavelength UV-detection (UVD 254 nm, type S-200, Knauer, Berlin, Germany) and thereafter the organic carbon detector (OCD, Huber and Frimmel, 1991). At the inlet of the OCD, the solution is acidified at a flow rate of 0.2 mL/ min (gravity-driven) to convert carbonates to carbonic acid. The column is bypassed with a restricted flow (flow rate 0.1 mL/min, back pressure-driven) to obtain a DOC value at the dead volume time of each chromatographic run. For nitrogen detection (Section 2.2) a side stream is diverted after UVD with
OCD
a restricted flow rate 0.1 mL/min (back pressure-driven) for nitrogen analysis (see Section 2.3). OCD and UVD calibration was based on potassium hydrogen phthalate. Its carbon mass was used to calibrate the OCD and its extinction coefficient (e) at 254 nm was used to calibrate the UVD. The extinction coefficient (e ¼ 1.683 103 L mol1 cm1) was determined with a UV-spectrometer following the law of Lambert Beer. OND calibration was based on potassium nitrate. For data acquisition and data processing a customised software program was used (ChromCALC, DOC-LABOR, Karlsruhe, Germany).
2.2.
The diverted side stream after UVD enters a helical, fused silica capillary of about 4 m length and 1 mm inner diameter. The helix is fused into the electric discharge arc of a lowpressure mercury lamp (80 cm length, 18 mm diameter) emitting at 185 nm and 254 nm (DOC-LABOR, Karlsruhe, Germany). In this UV-reactor, organic carbon (OC) is converted to carbonic acid (which remains unstripped in the aqueous phase) while organically bound nitrogen (e.g. bound to humic substances or biopolymers) and inorganically bound nitrogen (ammonium, nitrite and urea) is converted to nitrate while primary nitrate remains unaltered. Nitrate absorbs strongly in the deep UV-range. This property was used to quantify nitrate in a UV-detector at 220 nm (K-2001, Knauer, Berlin, Germany).
2.3.
WASTE
Fig. 1 e Flow scheme of the liquid chromatographic SECOCD system.
Chemicals and samples
Mobile phase is a phosphate buffer of pH of 6.85 (2.5 g KH2PO4 þ 1.5 g Na2HPO4 2H2O to 1 L, Fluka, #30407 þ #30412). The acidification solution was prepared by adding 4 mL o-phosphoric acid (85%, Fluka #79620) and 0.5 g potassium peroxodisulfate (Fluka, #60489) to 1 L of demineralised water. For calibration of detectors, potassium hydrogen phthalate (Fluka, # 60359) and potassium nitrate (Fluka, #31263) was used. For calibration of HS molecular weights Suwannee river Standard II humic (HA) and fulvic (FA) acids from the International Humic Substances Society (IHSS) were used (Perdue, 2008). Nominal average molecular weights (Mn-values) for IHSS-FA and IHSS-HA were determined to be 711 and 1066 Da based on published data (Aiken et al., 1989). A river water sample was taken from the River Pfinz near Karlsruhe, Germany.
3. Results and discussion (see also Supplementary materials) 3.1.
Autosampler
Novel organic nitrogen detector (OND, Fig. 1)
LC-OCD-OND fingerprint and fraction assignation
Meaningful interpretation of SEC-OCD fingerprints requires specific software because a full separation of individual peaks of NOM cannot be achieved. Here we introduce our approach which is based on an HS peak fit. As an example for fraction assignation we show first the fingerprints of a surface water (river Pfinz) for all three detectors OCD, UVD and OND (Fig. 2) and later the same figure with fitted fractions (Fig. 5). Peaks up to 60 min show responses in all detectors, except for peak “A” which does not appear in
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 7 9 e8 8 5
B
A
Bypass
C D
F
G
Signal Res pons e ( Ar bitr ar y Units )
6 5
OND
4 3 UVD
2 E
1 OCD
0
0
10
20
30
40
50
60
70
80
90
100
Retention Time in Minutes
Fig. 2 e SEC-OCD chromatogram of a surface water (River Pfinz, Karlsruhe, Germany) with responses for organic carbon detection (OCD), UV-detection at 254 nm (UVD) and organic nitrogen detection (OND). Arbitrary assignment of fractions AeG prior to data processing.
UVD. The OCD responds to organic carbon (OC), UVD to UVabsorbing components at 254 nm (aromatic and unsaturated structures) and OND to organic nitrogen, e.g. bound to NOM, but also to inorganic species (“F”: nitrate; “G”: ammonium) as confirmed by injecting solutions of these salts. The late elution of inorganic salts is typical in SEC and allows quantification of nitrogen in biopolymers and NOM in the presence of inorganic nitrogen, as well as analysis of NOM in waters with difficult matrices.
3.2.
Fraction A: Biopolymers
The left boundary of this fraction is defined by the exclusion limit of the column, the right boundary is defined by the left slope of the HS-fraction. The fraction elutes close to the exclusion volume of the SEC column which indicates that it must be a hydrophilic fraction (no hydrophobic interaction with the column) with a high molecular weight. It is essentially non-ionic as it is not removed by cation and anion exchange resins (see supplementary material). As the column has a separation range of 0.1e10 kDa, the molecular weight of this fraction should be 10 kDa or higher. The fraction is in most cases not responding in the UVD (no unsaturated structures) but shows some response in the OND. This points to the presence of polysaccharides with some contribution from nitrogen-containing material such as proteins or amino sugars. Polysaccharides are considered to be the dominating material of Extracellular Polymeric Substances (EPS) (Flemming et al., 2007). EPS also contains proteinic matter (Wingender et al., 1999). As EPS is also a generic term the term biopolymers was chosen to characterise this fraction.
3.3.
Fraction B: humic substances (HS)
identical. This is specific for humic substances and shows that UVD is not suited to quantify and characterise HS properly (Her et al., 2002b). The OCD-chromatograms of IHSS-FA and IHSS-HA are shown in Fig. 3. It is found that HS elute in agreement with theory, with humic acids eluting before fulvic acids (43.4 min versus 46.7 min). This shows that SEC-OCD can be used to determine the molecular weight of HS in natural waters, provided that the relation between retention time and molecular mass is known and a suitable method for fitting the HS peak is available. According to the theory of SEC, the elution volume decreases linearly with the logarithm of the molecular hydrodynamic diameter or molecular mass (Mori, 1999) and thus a logarithmic relationship between molecular mass and retention time can be applied. For peak fitting a Gaussian distribution cannot be used because the number of theoretical plates of the column (3000) is not sufficient (Her et al., 2002b). Therefore, a Poisson distribution is used in a bimodal fashion with independent parameters for the right and left slope of the HS peak.
3.3.1.
Improved humic substances diagram (HS-diagram)
The correlation between HS aromaticity and HS molecular weight was first described in 1994 using HS isolates with SECUVD and off-line DOC-analysis (Chin et al., 1994). Two years later the correlation was refined by also including HS from natural waters instead of HS isolates alone, but data were scarce, not very precise and difficult to interpret (Huber and Frimmel, 1996). The improved HS-diagram presented here (Fig. 4) shows curved instead of straight boundaries for the HS-area. The HS-diagram plots the SAC/OC ratio of the HS-fraction against its nominal average molecular weight (Mn-value). SAC is the Spectral Absorption Coefficient obtained with the UVD. SAC/ OC is the specific UV absorption of the HS peak, and a measure for HS aromaticity, considering that the response in UVD reflects aromatic and unsaturated structures. For the range of water samples measured, the SAC/OC ratio was plotted against Mnvalues to produce a diagram for which we introduced in 1996 the term Humic Substances diagram or HS-diagram (Huber and Frimmel, 1996). As shown, a close correlation exists between HS aromaticity and HS-molecularity.
7 tR = 43.3 min
Bypass
6
Signal Res pons e ( Ar bitr ar y Units )
7
5 4
IHSS-HA tR = 46.7 min
3 2 1 IHSS-FA
0
The dominating peak in UVD and OCD elutes at around 45 min (“B”). This peak shows good agreement with HS with respect to retention time, peak shape and detector ratios. Therefore, the name Humic Substances (HS) is proposed for this fraction. Retention times of peak maxima for OCD and UVD are not
0
10
20
30
40
50
60
70
80
90
100
Retention Time in Minutes
Fig. 3 e SEC-OCD chromatograms of IHSS fulvic and humic acid (IHSS-HA, IHSS-FA) with fitted peaks (bimodal Poisson distribution).
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w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 7 9 e8 8 5
8
IHSS-HA
Aromaticity
7
S AC/OC(HS) i n L/(m g· m )
6
pedogeni c HA
Derwent Res.
Lady Bower Res. River Westend
Molecularity
BM12-FA BM10-FA BM4-FA IHSS-FA pedogeni c NOR1-FA HO6 orig. FA HO3-FA CA2-FA mixing Steinbach KR1-FA zone HO8 aged Kleine Kinzig BO1-FA aquagenic HO8 orig. FA river Pfinz
5
4
3
Main Lake Galilee sewage Seine FA Carst Wasser Greenland sea water Sosa
2
1
Caspian sea
Ems Rhine
Humic Acid - Isolate Fulvic Acid - Isolate Surface Water I (aquagenic) Surface Water II (pedogenic)
Siberian sea water
marine FA 0 350
450
550
650
750
850
950
1050
1150
Mn in g/mol
Fig. 4 e Humic substances diagram (HS-diagram): The SAC/ OC ratio (aromaticity) of aquatic humic substances is plotted against Mn-values (nominal molecular weights), shortly expressed as molecularity.
HS standards of the IHSS, which were used for calibration, originate from a dystrophic brown water lake, Suwannee River. These HS are of pedogenic origin. In the HS-diagram the positions for IHSS-FA and IHSS-HA are in the upper centre and upper right of the diagram (full dots). As both species were isolated from the same water it is clear that the position of the non-isolated, original IHSS-HS must be between the positions for IHSS-FA and IHSS-HA. Therefore, HS in the upper centre and upper right of the HS-diagram are of pedogenic origin. This is confirmed by the positions of other HS isolates of
7
B
A
Bypass
C D
F
G
nitrate
Signal Res pons e ( Ar bitr ar y Units )
133 46
5
OND
653
ammonium
285
6
3.4.
4 3 UVD
2
375 4015
45 HOC = 387
1
E
297 2435 476
OCD
0
0
10
20
30
40
50
60
70
pedogenic origin (e.g. BM-10 FA, NOR-1 FA) and HS from brown water rivers whose catchment areas are bogs (e.g. river Westend, Lady Bower reservoir). At the lower left of the position for IHSS-FA, HS of other isolates are found, but also HS from surface waters (e.g. Steinbach, Kleine Kinzig). This area reflects small rivers and creeks whose HS originate from areas with strong slopes. It is assumed that the high hydraulic energy allows relatively high molecular weight FA from soils to be washed into the water body. Often the aromaticity of these FA is lower in comparison to FA from brown water lakes. This is presumably due to sunlight induced bleaching of HS in the photic zone. Although in brown water lakes HS in the upper layer may also be subject to photobleaching, deeper layers are not accessible and the impact will be relatively low. At the left side of the HS-diagram FA derived from lakes are found (e.g. Lake Sosa, Lake Galilee, Caspian Sea). It is known that in lakes FA are also produced in-situ by microbial activity and are lower in molecular weight than soil-derived FA (Her et al., 2002b). HS from marine environments are also found here. Thus, this area reflects aquagenic, autochthonous FA in contrast to pedogenic, allochthonous FA. In between the area of aquagenic and pedogenic FA, HS of large rivers are found (e.g. River Rhine, River Seine). To understand their position in the HS-diagram we have to consider that a large proportion of NOM in many rivers originate from treated and untreated sewage, or Effluent Organic Matter (EfOM). HS in EfOM are a mix of town water HS which are typically of pedogenic FA type. These FA are superimposed by aquagenic FA produced either in the sewage plants or in the river itself by microbial activity. Superposition of both species defines this area in the HS-diagram: a mixing zone containing both aquagenic and pedogenic FA. Thus, it can be concluded that the HS-diagram gives information about the origin of HS. The specificity of HS is remarkable and “hallmarks” natural water bodies. The HS-diagram also can be used to assess the reactivity of HS in treatment processes (see also Section 3.8).
80
90
100
Retention Time in Minutes
Fig. 5 e SEC-OCD chromatogram of a surface water (River Pfinz, Karlsruhe, Germany), based on Fig. 2 with fitted peaks for HS and fitted boundaries for non-humic matter. A [ Biopolymers; B [ Humic substances; C [ Building blocks; D [ low molecular weight acids; E [ low molecular weight neutrals; F, G [ nitrate, ammonium (only OND). HOC [ calculated difference between bypass and sum of chromatographic fractions. Values in OCD chromatogram are concentrations in mg/L C. Values in OND chromatogram are concentrations in mg/L N.
Fraction C: building blocks
The left boundary of this fraction is defined by the right slope of the HS-fraction, the right boundary is defined by the low molecular weight acids fraction. HS from the IHSS are almost free of this fraction while it is present in most natural waters. A low energy input, i.e. ultrasonification, leads to formation of this fraction at the expense of HS. Other experiments showed that, in contrast to HS, building blocks cannot be removed by flocculation. The UVD response can be highly variable. When the resolution of SEC is increased (e.g. by coupling two columns in series) several sub-fractions can be observed within this fraction. From these data it can be concluded that this fraction reflects breakdown products of HS and therefore, the term Building Blocks was used to reflect HS-like material of lower molecular weight.
3.5.
Fraction D: low molecular-weight acids
The left boundary of this fraction is defined by the right slope of the building blocks fraction, the right boundary is defined by a vertical line to the baseline at the maximum curvature of
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 7 9 e8 8 5
the right slope. As can be seen in Fig. 2 the broad peak for HS is followed by a small peak. This peak is due to an ion chromatographic effect: the mobile phase is a buffer, the stationary phase is a weak cation exchange resin and nonbuffered samples are injected. In this situation, the fact that no buffer is present in the sample induces anionic repulsion forces between low-molecular weight (LMW) anions and the resin. LMW-acids, which are anions at the neutral pH of the buffer, elute as a compressed peak ahead of the missing buffer in the sample. The peak was described in literature as the salt boundary peak (Swift and Posner, 2006). Considering this ion chromatographic effect, we conclude that all LMW-acids are forced to elute here. This is confirmed by a study in which organic acids in SEC-factions were analysed: almost all LMWacids found were concentrated in the salt boundary zone. However, small amounts of LMW-HS co-elute with LMWacids. An empirical formula allows the quantification of LMWacids in the presence of LMW-HS.
3.6.
Fraction E: low molecular-weight neutrals
The left boundary of this fraction is defined by the vertical line to the baseline at the maximum curvature of the LMW-acid peak, the right boundary is defined as the baseline once it is reached after completion of analysis. This fraction was denoted LMW-neutrals, reflecting the most characteristic properties of the fraction: low molecular weight and low ion density: if it were anionic it would elute with the salt boundary zone or before, If it were cationic it would elute later. In addition the material is hydrophilic to amphiphilic as it elutes close to or slightly after to the permeation volume of the column. The material shows no or very little response in UVD. In summary the properties point to LMW alcohols, aldehydes, ketones, sugars, but also amino acids. In surface waters distinct peaks may overlap, while in ground waters the fraction elutes as an asymptotic steady line. This could be an indication for a highly complex composition.
3.7.
Hydrophobic organic carbon (HOC)
For many natural water samples, the DOC value measured in the column bypass exceeds the value obtained for the hull curve of the chromatogram. The difference is organic matter which remains on the column. The reason for strong retention is considered to be hydrophobic interaction and therefore, the term hydrophobic organic carbon (HOC) was chosen for this fraction.
3.8. Interpretation of processed River Pfinz LC-OCDOND chromatograms Fig. 5 is identical to Fig. 2 (River Pfinz) but includes fraction designations and quantitative data for OCD (in ppb C) and OND (in ppb N). The DOC value is 4015 ppb. The N/C ratio of the biopolymer fraction is 0.15. Assuming that all N is bound in proteinic matter and assuming a typical C:N mass ratio of 3 for proteins, a protein content of 136 ppb as C, or 46% relative to the total C in the biopolymer fraction, would result. HS are in the mixing zone (see Fig. 5). The N-content of HS is 133 ppb
883
or 5.4% relative. The HOC content is 387 ppb C or 9.6% relative. Nitrate is 285 ppb N and ammonium is 653 ppb N. The River Pfinz is a small river with many sewage plant inputs upstream. The DOC value and the presence of ammonium reflect the strong input of EfOM: All tributaries have DOC values of less than 1000 ppb and ground water exfiltration waters should also be low in DOC. Thus, more than 50% of DOC should be attributed to EfOM. HS in the mixing zone are typical for strong input of EfOM. The high protein content in the biopolymer fraction is also an indication for EfOM. The N-content of HS is high for natural HS but, as we have found, not unusual for HS derived from EfOM. This is probably a new important aspect which should be considered in studies on N-cycling. The impact of EfOM is also reflected in the HOC content; values around 10% are typical for EfOM, surface waters typically have values between 1% and 5%. Based on the composition of NOM, the following conclusions can be drawn with regard to water treatment. Flocculation will not be very efficient in DOC-reduction because HS are in the mixing zone and hence too low in average molecular weight. The fouling potential for UF and RO membranes is high due to the high amount in biopolymers including proteins. The biodegradability of NOM is relatively high due to the presence of LMWacids and LMW-neutrals, and therefore biological treatment processes (slow sand filtration, biologic activated carbon filtration) should result in a considerable reduction of NOM.
3.9.
Oxidation yields for OCD and OND
Understanding factors influencing the oxidation yields of detection (OCD and OND) is important because it influences the reliability of detectors and interpretation of analytical results. For humic substances (Lankes et al., 2009) and low molecular weight compounds (Specht et al., 2000), oxidation yields of an LC-OCD system similar to this study were quantitative when compared with high-temperature catalytic oxidation techniques (Shimazdu 5000). For Soluble Microbial Products (SMP) isolated from municipal waste water by ultrafiltration and dialysed to remove inorganic N a similar LC-OCD-OND system showed good agreement with carbon and nitrogen elemental analysis (Metzger, 2010). For aquatic biopolymers no commercially available reference materials exist. For pullulanes and polystyrenesulfonates we found high oxidation yields compared to weighted-in masses (Table 1). Also for low molecular weight compounds we found high oxidation yields, both for OCD and OND (Table 2). The Achilles’ heel of OCD based on UV oxidation is not dissolved high molecular weight organic matter but triazines. The triazine ring is stable toward OH radicals in aqueous solutions (Minero et al., 1997). This includes the two main compounds melamine and cyanuric acid, whose presence in NOM is negligible. Urea is fully decomposed which suggests that the ring structure plays an important role in UV-stability. For other N-heterocyclic compounds, oxidation yields are between 60% and 90%, depending on the amount of N and the position in the ring. It is concluded that the OCD and OND described here give quantitative or close to quantitative data for all organic
884
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Table 1 e Recoveries for selected high molecular weight compounds. High molecular mass compounds Recoverya/%
Molar mass/Da Pullulane (PSS Mainz/Germany) 380000 200000 106000 45900 22000 11200 5600
108.6 91.9 95.7 119.4 109.5 123.4 125.2
Dextran (Fluka # 31392) 500000
103.0
Poly(styrene sulfonate) sodium salt (PSS Mainz/Germany) 356000 79.3 82800 92.2 35700 93.1 13400 106.3 6710 90.1 4480 90.3 a Recovery based on weighed compound mass and organic carbon.
compounds in the dissolved state, except triazines and some N-heterocyclic compounds. The Gra¨ntzel thin-film reactor is suitable for quantitative organic carbon detection as it provides sufficient exposure to UV-light and sufficient residence time. If these conditions are not fulfilled, oxidation may not be quantitative, which would make interpretation of analytical results highly speculative. Summarising, LC-OCD-OND provides a highly sensitive and reproducible method (see Supplementary materials) for NOM characterisation. It can be argued that the subdivision of NOM into 6 classes of compounds is arbitrary and still far away from the real situation, while NOM in fact comprises an immense pool of different compounds. However, such a full
Table 2 e Oxidation yields for selected low molecular weight compounds. Substance Sodium acetate Glycolic acid Oxalic acid Malonic acid Hydroquinone Orcin Vanilic acid Citric acid Urea Glucosamine EDTA Alanine Asparagine Glutamine Proline Tryptophan Histidine Adenine
characterisation is not feasible at the moment, and is certainly not suitable for routine analysis. LC-OCD-OND can provide a good compromise between practicability, user-friendliness and degree of detail.
Oxidation efficiency C/%
Oxidation efficiency N/%
98.7 94.2 98.4 99.1 100.1 103.3 97.2 92.7 101.0 99.3 98.7 100.1 100.6 101.5 99.2 100.4 91.2 75.6
e e e e e e e e 101.1 91.9 94.4 100.0 100.0 100.6 98.0 102.9 90.7 79.5
4.
Conclusions
Size-exclusion chromatography coupled to three detectors (organic carbon, organic nitrogen and UV-absorbance), was applied to subdivide the pool of organic matter in a natural water sample into 6 major sub-fractions which could be assigned to specific classes of compounds: Biopolymers, Humic Substances, Building Blocks, Low Molecular-weight Acids, Low Molecular-weight neutrals, and Hydrophobic Organic Carbon. The nitrogen content of the Biopolymer and the Humic Substances fractions can be estimated. Humic substances can be further characterised using the HS-diagram which plots the aromaticity of HS against its nominal molecular weight. This diagram gives information about the origin of the water, replacing the operational distinction between humic and fulvic acids by a continuum. For biopolymers, the amount of bound nitrogen can be determined, which represents a measure of protein content of this fraction. LC-OCDOND is a robust and sensitive method. The sensitivity is sufficient to measure low NOM-waters directly, such as demineralised waters or RO permeates.
Acknowledgement The development of the organic nitrogen detector (OND) was part of the IntegTa project (Integrative management of multipurpose drinking water reservoirs) funded by the German Federal Ministry of Education and Research (BMBF, Bundesministerium fu¨r Bildung und Forschung, Grant No. 02WT0724)
Appendix. List of abbreviations
DOC dissolved organic carbon EfOM effluent organic matter EPS extracellular polymeric substances FA fulvic acid HA humic acid HOC hydrophobic organic carbon HS humic substances ICP-IDMS inductively coupled plasma e isotope dilution mass spectrometry IHSS International Humic Substances Society NOM natural organic matter OC organic carbon OCD organic carbon detector OND organic nitrogen detector SAC spectral absorption coefficient SEC size-exclusion chromatography SMP soluble microbial products TOC total organic carbon
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 7 9 e8 8 5
UV UVD
ultraviolet UV-detector
Supplementary data Supplementary data related to this article can be found online at doi:10.1016/j.watres.2010.09.023.
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Huber, S.A., Frimmel, F.H., 1994. Characterization and quantification of marine dissolved organic carbon with a direct chromatographic method. Environ. Sci. Technol. 28, 1194e1197. Huber, S.A., Frimmel, F.H., 1996. Gelchromatographie mit Kohlenstoffdetektion (LC-OCD): Ein rasches und aussagekra¨ftiges Verfahren zur Charakterisierung hydrophiler organischer Wasserinhaltsstoffe. Vom Wasser 86, 277e290. Huber, S.A., 1998. Evidence for membrane fouling by specific TOC-constituents. Desalination 119, 229e234. Huber, S.A., 2003. Characterization of organics in UPW with LCOCD (Liquid Chromatography e Organic Carbon Detection) using sample pre-concentration: preliminary results. In: Proceedings, Semiconductor Pure Water and Chemicals 22nd Annual Conference, pp. 16e32. Huber, S.A., 2005. Determination of urea, TMA, IPA and other water-soluble organic compounds in UPW at the PPT-level using LC-OCD-OND. In: Proceedings, Semiconductor Pure Water and Chemicals 24th Annual Conference, pp. 75e89. Jacquemet, V., Gaval, G., Rosenberger, S., Lesjean, B., Schrotter, J.-C. , 2005. Towards a better characterisation and understanding of membrane fouling in water treatment. Desalination 178, 13e20. Laabs, C.N., Amy, G., Jekel, M., 2006. Understanding the size and character of fouling-causing substances from effluent organic matter (EfOM) in low-pressure membrane filtration. Environ. Sci. Technol. 40, 4495e4499. Lankes, U., Mueller, M.B., Weber, M., Frimmel, F.H., 2009. Reconsidering the quantitative analysis of organic carbon concentrations in size exclusion chromatography. Water Res. 43, 915e924. Lesjean, B., Rosenberger, S., Laabs, C., Jekel, M., Gnirss, R., Amy, G., 2005. Correlation between membrane fouling and soluble/ colloidal organic substances in membrane bioreactors for municipal wastewater treatment. Water Sci. Technol. 51, 1e8. Metzger, U. 2010. Dissertation University of Karlsruhe, Germany. Minero, C., Maurino, V., Pellizetti, E., 1997. Heterogeneous photocatalytic transformations of s-triazine derivatives. Res. Chem. Intermed. 23, 291e310. Mori, S., 1999. Size Exclusion Chromatography. Springer, Heidelberg, pp. 95e97 and 168e169. Perdue, M. Downloaded from the web on Dec. 2nd 2008 at http:// www.ihss.gatech.edu/. Rosenberger, S., Evenblij, H., TePoele, S., Wintgens, T., Laabs, C., 2005. The importance of liquid phase analyses to understand fouling in membrane assisted activated sludge processes e six case studies of different European research groups. J. Memb. Sci. 263, 113e126. Specht, C.H., Kumke, M.U., Frimmel, F.H., 2000. Characterisation of NOM adsorption to clay minerals by size exclusion chromatography. Water Res. 34, 4063e4069. Swift, R.S., Posner, A.M., 2006. Gel chromatography of humic acid. J. Soil Sci. 22, 237e249. Wingender, J., Ja¨ger, K.-E., Flemming, H.-C., 1999. Interactions between extracellular enzymes and polysaccharides. In: Wingender, J., Neu, T., Flemming, H.-C. (Eds.), Microbial Extracellular Polymeric Substances. Springer, Heidelberg, Berlin, pp. 231e251. Woiwode, W., Huber, S.A., 2000. Differenzierende TOCBestimmung zur Charakterisierung von Reinstwasser und Ru¨ckstandspru¨fung im Verlauf der Reinigungsvalidierung. Pharm. Ind. 62, 377e381. Zheng, X., Ernst, M., Jekel, M., 2009. Identification and quantification of major organic foulants in treated domestic wastewater affecting filterability in dead-end ultrafiltration. Water Res. 43, 238e244. Zheng, X., Ernst, M., Jekel, M., 2010. Pilot-scale investigation on the removal of organic foulants in secondary effluent by slow sand filtration prior to ultrafiltration. Water Res. 44, 3203e3213.
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Removal of Chromium (VI) from wastewater using bentonite-supported nanoscale zero-valent iron Li-na Shi a, Xin Zhang b, Zu-liang Chen a,* a b
School of Chemistry and Material Sciences, Fujian Normal University, Fuzhou 350007, Fujian Province, China School of Medicine, Shanxi University of Chinese Medicine, Xianyang 712000, Shanxi Province, China
article info
abstract
Article history:
Bentonite-supported nanoscale zero-valent iron (B-nZVI) was synthesized using liquid-
Received 8 February 2010
phase reduction. The orthogonal method was used to evaluate the factors impacting Cr(VI)
Received in revised form
removal and this showed that the initial concentration of Cr(VI), pH, temperature, and
14 September 2010
B-nZVI loading were all importance factors. Characterization with scanning electron
Accepted 18 September 2010
microscopy (SEM) validated the hypothesis that the presence of bentonite led to a decrease
Available online 1 October 2010
in aggregation of iron nanoparticles and a corresponding increase in the specific surface area
Keywords:
g, while the SSA of nZVI and bentonite was 54.04 and 6.03 m2/g, respectively. X-ray
Bentonite
diffraction (XRD) confirmed the existence of Fe0 before the reaction and the presence of Fe
Nanoscale zero-valent iron
(II), Fe(III) and Cr(III) after the reaction. Batch experiments revealed that the removal of Cr
Cr(VI)
(VI) using B-nZVI was consistent with pseudo first-order reaction kinetics. Finally, B-nZVI
Wastewater
was used to remediate electroplating wastewater with removal efficiencies for Cr, Pb and Cu
(SSA) of the iron particles. B-nZVI with a 50% bentonite mass fraction had a SSA of 39.94 m2/
> 90%. Reuse of B-nZVI after washing with ethylenediaminetetraacetic acid (EDTA) solution was possible but the capacity of B-nZVI for Cr(VI) removal decreased by approximately 70%. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Chromium (VI) is an industrial contaminant in both soil and groundwater and is also a well-known human carcinogen (Katz and Salem, 1994). Due to its toxicity, Cr(VI) must be removed from wastewaters prior to discharge into aquatic environments (Ju-Nam and Lead, 2008). Conventional remediation techniques typically involve reduction of Cr(VI) to Cr(III) which precipitates as chromium hydroxide or chromium iron hydroxide at high pH, followed by disposal of the resulting dewatered sludge (Ngomsik et al., 2005). Other treatments, including phytoextraction, reverse osmosis, electrodialysis, ion exchange, membrane filtration and adsorption, have also been developed to remove metals from industrial wastewaters. While these methods are useful in removing Cr(VI)
from aqueous solution, they have some limitations and it is still necessary to develop new and effective remediation techniques (Mohan and Pittman, 2006). In recent years, nanoscale zero-valent iron (nZVI) has been used to remove various groundwater contaminants. The advantages of nZVI over zerovalent iron (ZVI) include higher reactive surface area, faster and more complete reactions, and better injectability into aquifers (Li et al., 2006). However, there are still some technical challenges associated with practical applications, such as the aggregation of nZVI particles and limitations imposed by high reactivity and low stability (Liu et al., 2007). Furthermore, the agglomeration of iron particles is often unavoidable due to the extremely high-pressure drops occurring in conventional systems, which along with its lack of durability and mechanical strength limits the application of nZVI (Cumbal et al., 2003).
* Corresponding author. Tel./fax: þ86 591 83465689. E-mail address:
[email protected] (Z.-l. Chen). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.025
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 8 6 e8 9 2
Recently, technologies have been developed using porous materials as mechanical supports to enhance the dis¨ zu¨m et al., 2009). Bentonite is persibility of nZVI particles (U a traditional low-cost efficient adsorbent, which has high potential for heavy metal removal from wastewaters due to its abundance, chemical and mechanical stability, high adsorption capability and unique structural properties (Bhattacharyya and Gupta, 2008). Removal of metal ions using bentonite is based on ion exchange and adsorption mechanisms due to the materials relatively high cation exchange capacity (CEC) and specific surface area (Bhattacharyya and Gupta, 2008). In this paper, bentonite was used as a porousbased support material for synthesized nZVI. More recently, nZVI supported by zeolite (Li et al., 2007) and stabilized by chitosan (Geng et al., 2009) has been reported to increase the durability and mechanical strength of nZVI. However, only a few studies have reported using natural clays as support ¨ zu¨m et al., 2009). materials for nZVI (U In this paper the removal of Cr(VI) from an aqueous solution was investigated using B-nZVI and the objectives were: (1) synthesis of bentonite-supported nanoscale zero-valent (B-nZVI) by reduction of Fe3þ ions with NaBH4, and characterization of the produced material with SEM, XRD and BET-N2 technology; (2) evaluation of the factors impacting on Cr(VI) removal using an orthogonal method; nZVI and bentonite were used for Cr(VI) removal individually as a control, and the kinetics of Cr(VI) reduction by B-nZVI were also evaluated; and (3) remediation of electroplating wastewater including some heavy metal ions using B-nZVI and evaluation of reuse.
2.
Materials and methods
2.1.
Materials and chemicals
Bentonite was provided by Fenghong Co. Ltd, Anji, Zhejiang, China, primarily as Na-Mt montmorillonite (>90%), the chemical composition was 62.5% SiO2, 18.5% Al2O3, 1.75% Fe2O3, 4.25% MgO, 0.95% CaO, and 2.75% Na2O. The cation exchange capacity (CEC) was 75.4 meq/100g. After drying overnight at 80 C, the raw bentonite was ground and sieved through a 200 mesh screen prior to use in experiments. All the reagents were analytical grade (Shanghai Nanxiang Reagent Co., Ltd., China) and distilled water was used in all preparations. A stock solution containing potassium dichromate (K2Cr2O7) was prepared by dissolving K2Cr2O7 with deionized water and a series of solutions used during the experiment were prepared by diluting the stock to the desired concentrations.
2.2.
Synthesis of nZVI and supported nZVI
The nZVI and B-nZVI were prepared using conventional liquid-phase methods via the reduction of ferric iron by borohydride without or with bentonite as a support material (Celebi et al., 2007). Bentonite (2.00 g) was initially placed into a three-necked open flask, and a ferric solution produced by dissolving ferric chloride hexa-hydrate (9.66 g) in an ethanolwater solution (50 mL, 4:1 v/v) was added and stirred for 10 min. Subsequently, a freshly prepared NaBH4 solution
887
(3.54 g of NaBH4 in 100 mL) was added drop-wise into the mixture with constant stirring for 20 min after addition. The whole process described above was performed under a N2 atmosphere with vigorous stirring to avoid the oxidization of B-nZVI. The formed suspension was filtered and the black nanoscale precipitate was washed three times with pure ethanol and dried overnight at 75 C under vacuum (Celebi ¨ zu¨m et al., 2009). The theoretical mass fraction et al., 2007; U of bentonite in synthesized B-nZVI was 50%, and nZVI was prepared under identical conditions but with bentonite omitted. The nZVI and supported nZVI samples were stored in brown, sealed bottles under dry conditions and were not acidified prior to use.
2.3.
Characterizations and measurements
Scanning electron microscopy (SEM) was performed using a Philips-FEI XL30 ESEM-TMP (Philips Electronics Co., Eindhoven, The Netherlands). Images of various materials were obtained at an operating voltage of 30 kV. The SSA of nZVI, B-nZVI, and bentonite was measured via the BET adsorption ¨ zu¨m et al., 2009) using Micromeritics’ ASAP 2020 method (U Accelerated Surface Area and Porosimetry Analyzer (Micromeritics Instrument Corp.,USA). The specific surface areas of nZVI, B-nZVI and bentonite were 54.04, 39.94 and 6.03 m2/g, respectively. X-ray diffraction (XRD) patterns of B-nZVI before and after contacting Cr(VI) were performed using a PhilipsX’Pert Pro MPD (Netherlands) with a high-power Cu- Ka radioactive source (l ¼ 0.154 nm) at 40 kV/40 mA. The concentration of total Cr in solution and the concentrations of different heavy metal ions in the wastewater were determined using a flame atomic absorbance spectrometer (VARIAN AA 240FS, USA), and the Cr(VI) concentration was determined using the 1,5-diphenylcarbazide method (Geng et al., 2009) on a 722N visible spectrophotometer (Shanghai Precision & Scientific Instrument Co., Ltd, China).
2.4.
Batch experiments
The orthogonal method was used to test the effects of various factors on the reaction, and to optimize the conditions for Cr(VI) removal using B-nZVI. The experimental design was developed with the aid of the Orthogonal Design Assistant, where the initial concentration of Cr(VI), B-nZVI loading, temperature and pH were chosen as variables. Cr(VI) solutions (25 mL) with a known mass of B-nZVI were sealed in 50 mL centrifuge tubes and mixed for 4h before being centrifuged prior to analysis of the aqueous phase for residual Cr(VI). In order to investigate the role that bentonite and Fe0 played in the B-nZVI system, nZVI, B-nZVI and bentonite were all used in batch experiments examining Cr(VI) removal from aqueous solutions at an initial concentration of 50 mg/L at 35 C and 250 r/min. As the mass ratio of Fe0:bentonite was 1:1 in the B-nZVI system, the dosages of nZVI and bentonite were both set at 1.5 g/L, which was half the B-nZVI dosage. The mixtures were filtered through 0.45 mm mixed cellulose ester (MCE) membranes prior to determining the residual concentrations of Cr(VI) after contacting for 3 h. In order to investigate the effects of the different factors mentioned in
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the orthogonal experiments further, the mixtures of Cr(VI) solution and B-nZVI were mixed in 50 mL centrifugal tubes in a rotary shaker for determined periods of time, the conditions of which were initially set at 25 mL of 50 mg/L Cr(VI) solution, 3 g/L of B-nZVI, 35 C and 250 r/min. At selected timed intervals, the suspension was filtered through 0.45 mm MCE membranes, and the concentration of Cr(VI) in the filtrate was determined. To explore the feasibility of removing heavy metal ions from wastewater, B-nZVI was used to remediate electroplating wastewater collected from an electroplating factory’s sewage outfall (Fuzhou, China). The wastewater was centrifuged at 3000 r/min for 10 min to remove any insoluble impurities, prior to determining the initial pH and concentrations of total Cu, Cr, Pb and Zn. A batch of 50 mL bottles containing wastewater (10 mL) and B-nZVI (0.10 g) were mixed on a rotary shaker at 35 C and 250 r/min for 4 h. Then the mixtures were centrifuged at 3000 r/min for 10 min and the upper aliquot collected to determine pH and the concentration of each heavy metal ion. The potential to reuse B-nZVI for removing Cr(VI) from aqueous solution was also evaluated. B-nZVI (0.1 g) was added to 50 mg/L Cr(VI) solution (25 mL) and the mixture was shaken on a rotary shaker (35 C and 250 r/min). After 3 h, centrifugations at 3000 r/min were performed for 10 min to obtain solid-liquid separation. The supernatant was decanted carefully and used to determine the concentration of Cr(VI) remaining in solution while the used B-nZVI was mixed with different concentrations of EDTA. The residual B-nZVI-EDTA solution was washed with distilled water three times and shaken for another 3 h under identical conditions. The B-nZVI treated with 50 mg/L and 10 mg/L of EDTA was used to remove Cr(VI) for 3 times in succession to test the efficacy of reuse. In order to ascertain the accuracy, reliability and reproducibility of the data, orthogonal experiments were conducted in quadruplicate (n ¼ 4) and other batch experiments were carried out in triplicate (n ¼ 3) to minimize any experimental errors. The average values of the parallel measurements were used in all analysis and together with the standard deviations of these means were listed in Tables 1 and 3.
3.
Results and discussion
3.1.
Characterization
Table 1 e Orthogonal experimental design and the results obtained from the full 24 factorial experiment matrix. Removal T Cr(VI)ina pHina B-nZVIina Cr(VI)res (mg/L) (mg/L) efficiency (%) ( C) (mg/L) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
25 25 25 25 30 30 30 30 35 35 35 35 40 40 40 40
20 50 70 100 20 50 70 100 20 50 70 100 20 50 70 100
2.0 5.0 8.0 10 5.0 2.0 10.0 8.0 8.0 10.0 2.0 5.0 10.0 8.0 5.0 2.0
2 3 4 5 4 5 2 3 5 4 3 2 3 2 5 4
0.11a 29 1 45.0 0.6 73 2 0a 0.06a 55.3 0.9 75 2 0.04a 31.1 0.7 0.04a 71 2 0.10a 6.75a 32.0 0.9 0.24a
99.5 42.0 35.7 26.6 100 99.9 21.0 25.3 99.8 55.6 99.9 29.4 99.5 86.5 54.3 99.8
ina - initial; res - residual; a means the standard deviations are too low to be listed.
presence of bentonite (Fig. 1). The synthesized nZVI without bentonite as a support material showed that nZVI particles were roughly globular and aggregated into a chain-like conformation (Fig. 1a). The diameters of the nanoscale zerovalent iron particles were in the range of 20e90 nm when bentonite was introduced as a support material. Compared with Fig. 1a, the aggregation of nZVI particles seemed to decrease and their dispersity increase in Fig. 1b, where the mass fraction of bentonite was 50%. A similar conclusion has been drawn using kaolin as a support material to synthesize kaolin supported nZVI, which was used to remove Cu(II) and Co ¨ zu¨m et al., 2009). As indicated in (II) from an aqueous solution (U Fig. 1c, the sizes of iron nanoparticles increase prominently after reacting with Cr(VI). This phenomenon could be attributed to the co-precipitation of Cr(III) and Fe(III) on the surface of the nanoparticles (Ponder et al., 2000; Manning et al., 2007), which occurs due to a redox reaction between Cr(VI) and Fe0 (Ponder et al., 2000; Manning et al., 2007). The XRD patterns of synthesized materials were compared with the XRD patterns obtained from standard materials, to identify the apparent peaks attributable to different iron and chromium compounds. The XRD patterns of B-nZVI before reaction (Fig. 2a) with Cr(VI) showed an apparent peak of Fe0 (2q z 44.90), which weakened significantly after the reaction
The SEM images of nZVI and B-nZVI showed the morphology and nanoparticle distribution of nZVI in the absence or
Table 2 e Range analysis and variance analysis of the orthogonal test. Factors
Temperature ( C) Cr(VI)ina (mg/L) pHina B-nZVIina (mg/L)
Range Analysis
Variance Analysis
k1
k2
k3
k4
Ranges
SSE
DOF
F-value
F critical values
50.9 99.7 99.7 59.1
61.6 71.0 56.4 66.7
71.2 52.7 61.8 72.8
85.0 45.3 50.7 70.1
34.1 54.4 49.1 13.7
2518 7039 5912 422
3 3 3 3
0.63 1.77 1.49 0.11
3.49 3.49 3.49 3.49
ina - initial; SSE - the Square Sum of Errors; DOF - Degree of Freedom.
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optimal conditions for chromium removal were 40 C, 20 mg/L of initial Cr(VI) concentration, 4 g/L B-nZVI loading and pH 2.0.
Table 3 e Remediation of actual electroplating wastewater by B-nZVI. Wastewater
Total Cr
Pb2þ
Cu2þ
c0 (mg/L) 73 2 13.1 0.6 33 1 a 2.4 0.1 c’ (mg/L) after reaction a Removal amount 7.3 1.3 3.0 (mg/g B-nZVI) Removal percentage (%) 100 100 92.7
Zn2þ
pH
284 4 1.9 115 3 4.5 16.8 e 59.4
e
a means the concentration of the heavy metal was under the limit of detection.
¨ zu¨m et al., 2009). The XRD patterns of B-nZVI after (Fig. 2b) (U reaction (Fig. 2b) indicated the presence of g-Fe2O3 (2q ¼ 35.68), Fe3O4 (2q ¼ 35.45) and Cr2FeO4 (2q ¼ 35.50), which were not detected in the sample before reaction (Chen et al., 2008). The appearance of Fe(II), Fe(III) and Cr(III) in B-nZVI after reaction demonstrated the occurrence of redox reactions between Fe0 and Cr(VI) where nZVI particles were acting as reductants, which was consistent with previous literature (Ponder et al., 2000).
3.2.
889
Orthogonal test
The designed complex conditions and the final removal efficiencies of Cr(VI) from aqueous solution by B-nZVI were listed in Table 1. Results were processed using the Orthogonal Design Assistant software (El Hajjouji et al., 2008) in Range Analysis and Variance Analysis (Table 2). The higher the range and the F-value, the more significant the factor was and the greater the influence of the factor on Cr(VI) removal Comparing the ranges and F-values in Table 2, factors influencing Cr(VI) removal were (in order of decreasing influencing): initial Cr(VI) concentration > pH > temperature > BnZVI loading. The K values from K1 to K4 represented each level of each factor, from the lowest to the highest. The higher the K value, the higher the removal efficiency, and the better the level of the factor. Take temperature for example, the highest K was K4, which represented the level 40 C, and this made 40 C the optimum temperature. The change in K values indicated that chromium removal increased with temperature and decreased as initial Cr(VI) concentration and pH rose. The
3.3.
Conditions affecting Cr(VI) removal
After contacting for 3 h under identical conditions, the removal efficiencies of Cr(VI) were 5.5, 60.0 and 100.0% respectively when bentonite, nZVI and B-nZVI were added individually. Bentonite generally has poor adsorption of Cr(VI) due to its negatively charged surface and the predominant existence of Cr(VI) as anions (Bhattacharyya and Gupta, 2008). The activity of nanoscale zero-valent iron particles was enhanced significantly when bentonite was introduced as a support material, which confirmed the role bentonite played as a dispersant and stabilizer in B-nZVI (Ponder et al., 2000). Kinetics studies of Cr(VI) reduction using B-nZVI suggested that the reactivity of nZVI particles supported on bentonite were enhanced significantly due to an increase in SSA and a decrease in aggregation. Reduction kinetics of Cr(VI) by B-nZVI were described by a pseudo first-order reaction (Ponder et al., 2000): dc v ¼ ¼ kSA as rm c dt
(1)
Where c was the concentration (mg/L) of contaminant, kSA was the specific reaction rate constant associated with the SSA of the materials (L/h m2), as was the specific surface area (m2/g), and rm was the mass concentration (g/L). For kSA, as, and rm are constant for a specific reaction, the product of the three can be expressed with one parameter kobs, which is called the observed rate constant of a pseudo first-order reaction (h1). Therefore Eq. (1) can be integrated into: ln
c ¼ kobs t c0
(2)
The kobs values under different conditions are equal to the slope of the line achieved by plotting lnðc=co Þ versus time under various conditions. In this study, the plots of lnðc=co Þ versus time produced linear plots with correlation coefficients (R2) higher than 0.9 (Fig. 3). This indicated that the rate of Cr(VI) reduction by B-nZVI fitted well the pseudo first-order model under various conditions. Additionally, the reduction of Cr(VI) by B-nZVI represented a solid-liquid inter-phase reaction, which agreed with the pseudo first-order kinetics model.
Fig. 1 e SEM images of laboratory synthesized iron particles with and without a support material. a. NZVI; b. B-nZVI before reaction with Cr(VI) solution; c. B-nZVI after reaction with Cr(VI) solution. The scale bar in the figure is 500 nm.
890
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3.3.1.
Fig. 2 e X-ray diffractogram of B-nZVI. a. before the reaction with Cr(VI) solution; b. after the reaction with Cr (VI) solution.
Effect of initial Cr(VI) concentration
The effect of initial Cr(VI) concentration on removal efficiency was investigated in the range 20e100 mg/L. The plot fitted the pseudo first-order model well (Fig. 3a), where the observed rate constant decreased significantly as the initial Cr(VI) concentration increased, which agreed with the orthogonal test results. The equilibrium time became longer and the final removal efficiency of Cr(VI) decreased as the initial Cr(VI) concentration increased, so that while the percentage of Cr(VI) removed within 20 min at a Cr(VI) concentration of 20 mg/L was nearly 100%, it was only 30.4% within 60 min at a Cr(VI) concentration of 100 mg/L. Generally, the slower rate and lower efficiency of Cr(VI) removal from aqueous solution were found at higher concentrations of Cr(VI). Based on the SEM analysis and previous research, Cr(VI) reduction by nZVI could be defined as a surface-mediated process (Ponder et al., 2000; Rivero-Huguet and Marshall, 2009). The more the Cr(VI) ion approached the surface of nZVI dispersed on the bentonite, the faster Fe0 was oxidized into Fe(III) and the faster the coprecipitation of Cr(III) and Fe(III) oxides/hydroxides occurred. This reduced the reactivity of nZVI and subsequently resulted
Fig. 3 e Effects of various factors on Cr(VI) removal by fitting to the pseudo first-order model. a. initial Cr(VI) concentration; b. B-nZVI loading; c. pH value; d. temperature.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 8 6 e8 9 2
in a decrease in kobs (Geng et al., 2009; Yuan et al., 2009). On the other hand, the highest removal amount was obtained at an initial Cr(VI) concentration of 70 mg/L, which could be ascribed to the limited capacity of B-nZVI for Cr(VI) removal determined by SSA.
3.3.2.
Effect of B-nZVI loading
The initial loadings of B-nZVI in Cr(VI) solution were 1, 2, 3 and 4 g/L. The Cr(VI) removal percentage increased as the B-nZVI concentration increased (Fig. 3b). The removal percentage of Cr(VI) was 54.6% using B-nZVI at 1 g/L for 120 min, but was nearly 100% when the B-nZVI loading was over 3 g/L. Meanwhile, kobs increased as the B-nZVI loading increased. These phenomena can be attributed to the increase in the available active sites resulting from the elevation in B-nZVI loading, where the reduction of Cr(VI) occurred (Geng et al., 2009; Yuan et al., 2009). However, the concentration of Cr(VI) decreased dramatically in the initial 10 min, then slightly declined in the later reduction. A few researchers (Ponder et al., 2000; Manning et al., 2007) reported a sorption phase during the reaction which could also be supported by our SEM images, suggesting that the overall mechanism was more complicated than a simple chemical reaction.
3.3.3.
Effect of the pH value
The two dominant forms of Cr(VI) in aqueous solution were 2 HCrO 4 , between pH 1.0 to 6.0 and CrO4 above pH 6.0 (Mohan and Pittman, 2006). The dependence of the reaction rate constant on pH was investigated by adjusting the solution pH to 2.0, 4.0, 6.0 and 8.0 with either 0.1 M HCl or NaOH (Yuan et al., 2009). Except for pH 2.0, the reduction of Cr(VI) can be described using the pseudo first-order model well (Fig. 3c). A remarkable increase in the removal rate occurred at pH 2.0, where equilibrium was achieved within 1 min and the residual Cr(VI) was below the detection limit, which made kinetic fitting infeasible. The Cr(VI) removal percentage decreased significantly with increases in the initial pH, so that only 27.2% Cr(VI) was reduced at pH 8.0 in 20 min while nearly 100% Cr(VI) was removed in 1 min at pH 2.0. In addition, kobs was respectively 0.0275, 0.0163, and 0.0083/min, when the initial pH value was 4.0, 6.0 and 8.0, indicating that the reduction rate increased as pH decreased, which had also been reported in other studies (Geng et al., 2009; Yuan et al., 2009). These results demonstrated that a lower pH favored Cr(VI) reduction, since at lower pH corrosion of nZVI was accelerated and the precipitation of Cr(III) and Fe(III) hydroxides on the surface of iron was consequently not as favorable, which led to an increase in the reaction rate (Lee et al., 2003). Furthermore, the increase in Hþ concentration left the surface of bentonite less negatively charged, which reduced the electrostatic repulsion between bentonite and Cr(VI) anions. This consequently promoted the electron transfer between zero-valent iron and Cr(VI) (Yuan et al., 2009).
3.3.4.
891
ambient temperature. Fig. 3d highlights the relationship between lnðc=co Þ and time, where the linearity suggested that the reduction of Cr(VI) at different temperatures in the presence of B-nZVI fitted pseudo first-order dynamics (Ponder et al., 2000; Manning et al., 2007). The kobs was 0.020, 0.023, 0.026 and 0.030/min at four temperatures (25, 30, 35 and 40 C), showing that an increase in the reaction temperature resulted in an improved reaction rate. The apparent activation energy (Ea) of Cr(VI) reduction by B-nZVI was 24.9 kJ/mol, demonstrating that it is a chemically controlled adsorption process having an Ea value higher than 21 kJ/mol (Geng et al., 2009).
3.4. B-nZVI used to remove Cr(VI) from electroplating wastewater and B-nZVI reuse The data obtained from batch experiments where B-nZVI was used to remove Cr(VI) and other metals from electroplating wastewater are presented in Table 3, which indicated that BnZVI had the capacity to remove various heavy metals and was a potential promising candidate for applications to in situ environmental remediation. After reacting 10 mL of the wastewater with 0.1 g of B-nZVI for 4 h, the residual concentration of each metal ion showed that 100% total Cr, 100% Pb (II), 92.7% Cu(II), and 59.4% Zn(II) were removed, following treatment with B-nZVI. Total Cr, Pb(II) and Cu(II) received higher removal percentages due to their higher standard reduction potentials compared to Fe(II) (fqFeðIIÞ=Fe0 ¼ 0:44V). In contrast, a lower removal percentage of Zn(II) was obtained because the standard reduction potential of Zn(II) (fqZnðIIÞ=Zn0 ¼ 0:762V) was more negative than Fe(II) (Ladd, 2004). The amount of Cr(VI) removed when using B-nZVI treated with different concentrations of EDTA after being used four times was calculated and it was shown that B-nZVI’s ability to remove Cr(VI) was dramatically reduced after being used only once with an initial Cr(VI) concentration of 50 mg/L (Fig. 4). The rapid deterioration of B-nZVI was ascribed to the inability of the redox reaction between Cr(VI) and Fe0 to proceed
Effect of temperature
To assess the effect of different temperatures, batch experiments were conducted at 25, 30, 35 and 40 C. The results showed that 82.4% Cr(VI) was removed at 40 C while only 73.4% Cr(VI) was reduced at 25 C in 60 min. Thus zero-valent iron could have a positive effect on Cr(VI) reduction even at
Fig. 4 e The variation of Cr(VI) removal amount by B-nZVI after reusing four different times. The solutions of EDTA used for treatment of B-nZVI were 50 mg/L and 10 mg/L respectively as marked in the figure.
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further since this was a chemical controlled and irreversible process. As confirmed by XRD analysis (Fig. 2), reaction products were deposited on the surface of B-nZVI in the form of oxide-hydroxide co-precipitation of Fe(II), Fe(III) and Cr(III), which consequently decreased the activity of Fe0 (Chen et al., 2008). This phenomenon confirmed that the active ingredient of B-nZVI was Fe0 which acted as a reductant, while bentonite only played a role as a dispersant and stabilizer.
4.
Conclusions
In this study, nZVI particles became more effective when bentonite was introduced as a support material due to reduction of aggregation and increased SSA. Batch experiments indicated that the removal rate increased as the temperature and B-nZVI loading (4 g/L) increased, and fell as the initial Cr(VI) concentration and pH increased, which agreed with the result obtained from the orthogonal test. Under the various operational conditions considered, reduction of Cr(VI) using B-nZVI was in accordance with a pseudo first-order model. B-nZVI was effective in removing Cr(VI) and other heavy metals, including Pb(II), Cu(II) and Zn(II) from electroplating wastewater. Since bentonite is a stable and lowcost clay mineral, B-nZVI could be an efficient and promising remediation material to remove Cr(VI) and other metals from wastewater. However, further research must be carried out to slow and control the degree of nZVI oxidation in the atmosphere and as a consequence a more effective regeneration method may emerge from such studies.
Acknowledgements This work is supported by the Fujian “Minjiang Fellowship” from Fujian Normal University (gs1). The authors also gratefully acknowledge the significant contributions of Dr Gary Owens in editing and improving the many revisions of this manuscript, his suggestions and corrections have undoubtedly significantly improved the quality of the final manuscript.
references
Bhattacharyya, K.G., Gupta, S.S., 2008. Adsorption of a few heavy metals on natural and modified kaolinite and montmorillonite: a review. Adv. Colloid Interface Sci. 140, 114e131. ¨ zu¨m, C¸., Shahwan, T., Erten, H.N., 2007. A radiotracer Celebi, O., U study of the adsorption behavior of aqueous Ba2þ ions on
nanoparticles of zero-valent iron. J. Hazard. Mater. 148, 761e767. Chen, S.S., Hsu, B.C., Hung, L.W., 2008. Chromate reduction by waste iron from electroplating wastewater using plug flow reactor. J. Hazard. Mater. 152, 1092e1097. Cumbal, L., Greenleaf, J., Leun, D., SenGupta, A.K., 2003. Polymer supported inorganic nanoparticles: characterization and environmental applications. React. Funct. Polym. 54, 167e180. El Hajjouji, H., Ait Baddi, G., Yaacoubi, A., Hamdi, H., Winterton, P., Revel, J.C., Afidi, M., 2008. Optimisation of biodegradation conditions for the treatment of olive mill wastewater. Bioresour. Technol. 99, 5505e5510. Geng, B., Jin, Z., Li, T., Qi, X., 2009. Kinetics of hexavalent chromium removal from water by chitosan-Fe0 nanoparticles. Chemosphere 75, 825e830. Ju-Nam, Y., Lead, J., 2008. Manufactured nanoparticles: an overview of their chemistry, interactions and potential environmental implications. Sci. Total Environ. 400, 396e414. Katz, S., Salem, H., 1994. The Biological and Environmental Chemistry of Chromium. VCH Publishers, New York. Ladd, M.F.C., 2004. Introduction to Physical Chemistry, third ed. Cambridge University Press, Cambridge. Lee, T., Lim, H., Lee, Y., Park, J.W., 2003. Use of waste iron metal for removal of Cr(VI) from water. Chemosphere 53, 479e485. Li, X., Elliott, D.W., Zhang, W.X., 2006. Zero-valent iron nanoparticles for abatement of environmental pollutants: materials and engineering aspects. Crit. Rev. Solid State Mater. Sci. 31, 111e122. Li, Z., Kirk Jones, H., Zhang, P., Bowman, R.S., 2007. Chromate transport through columns packed with surfactant-modified zeolite/zero valent iron pellets. Chemosphere 68, 1861e1866. Liu, Y., Phenrat, T., Lowry, G.V., 2007. Effect of TCE concentration and dissolved groundwater solutes on NZVI-promoted TCE dechlorination and H2 evolution. Environ. Sci. Technol. 41, 7881e7887. Manning, B.A., Kiser, J.R., Kwon, H., Kanels, S.R., 2007. Spectroscopic investigation of Cr (III)-and Cr (VI)-treated nanoscale zerovalent iron. Environ. Sci. Technol. 41, 86e592. Mohan, D., Pittman, C.U., 2006. Activated carbons and low cost adsorbents for remediation of tri-and hexavalent chromium from water. J. Hazard. Mater. 137, 62e811. Ngomsik, A., Bee, A., Draye, M., Cote, G., Cabuil, V., 2005. Magnetic nano-and microparticles for metal removal and environmental applications: a review. Comptes RendusChimie 8, 963e970. Ponder, S.M., Darab, J.G., Mallouk, T.E., 2000. Remediation of Cr (VI) and Pb (II) aqueous solutions using supported, nanoscale zero-valent iron. Environ. Sci. Technol. 4, 2564e2569. Rivero-Huguet, M., Marshall, W.D., 2009. Reduction of hexavalent chromium mediated by micron-and nano-scale zero-valent metallic particles. J. Environ. Monit. 11, 1072e1079. ¨ zu¨m, C¸., Shahwan, T., Ero lu, A.E., Hallam, K.R., Scott, T.B., U Lieberwirth, I., 2009. Synthesis and characterization of kaolinite-supported zero-valent iron nanoparticles and their application for the removal of aqueous Cu2þ and Co2þ ions. Appl. Clay Sci. 43, 172e181. Yuan, P., Fan, M., Yang, D., He, H., Liu, D., Yuan, A., Zhu, J., Chen, T., 2009. Montmorillonite supported magnetite nanoparticles for the removal of hexavalent chromium [Cr (VI)] from aqueous solutions. J. Hazard. Mater. 166, 821e882.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 9 3 e9 0 3
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Impact of an intense combined sewer overflow event on the microbiological water quality of the Seine River Julien Passerat a, Nouho Koffi Ouattara a, Jean-Marie Mouchel b, Vincent Rocher c, Pierre Servais a,* a
Ecologie des Syste`mes Aquatiques, Universite´ Libre de Bruxelles, Campus de la Plaine, CP 221, Boulevard du Triomphe, B-1050 Bruxelles, Belgium b UMR 7619 Sisyphe, Universite´ Pierre et Marie Curie, Case 105, 4 place Jussieu, 75005 Paris, France c Syndicat Interde´partemental pour l’Assainissement de l’Agglome´ration Parisienne, Direction du De´veloppement et de la Prospective, 82 avenue Kle´ber, 92700 Colombes, France
article info
abstract
Article history:
For a better understanding of the short and mid-term impacts of a combined sewer
Received 5 March 2010
overflow (CSO) on the microbiological quality of the receiving river, we studied the
Received in revised form
composition of a CSO discharge and monitored during several hours the changes in the
9 September 2010
concentration of fecal indicator bacteria (FIB) in the impacted river water mass. The CSO
Accepted 18 September 2010
occurred at the Clichy outfall (Paris agglomeration, France) in summer 2008 as a result of
Available online 29 September 2010
the most intense rainfall of the year. In 6h, 578, 705 m3 of sewage and 124 t of suspended matter (SM) were discharged into the Seine River. The CSO contained 1.5 106 E. coli and
Keywords:
4.0 105 intestinal enterococci per 100 mL on average, and 77% of the E. coli were attached
River microbiological contamination
to SM. It was estimated that 89% of the CSO discharge was contributed by surface water
Combined sewer overflow
runoff, and that resuspension of sewer sediment contributed to w75% of the SM, 10e70% of
Escherichia coli
the E. coli and 40e80% of the intestinal enterococci. Directly downstream from the CSO
Intestinal enterococci
outfall, FIB concentrations in the impacted water mass of the Seine River (2.9 105 E. coli
Attached bacteria
and 7.6 104 intestinal enterococci per 100 mL) exceeded by two orders of magnitude the
Sewer sediment
usual dry weather concentrations. After 13e14 h of transit, these concentrations had decreased by 66% for E. coli and 79% for intestinal enterococci. This decline was well accounted for by our estimations of dilution, decay resulting from mortality or loss of culturability and sedimentation of the attached fraction of FIB. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Many urban centers are drained by a unique sewer network in which wastewater is mixed with urban runoff water in wet weather. When rainfalls are intense, the transport capacity of the sewer system can be insufficient to allow all the water flow to reach the wastewater treatment plant (WWTP) or the treatment capacity of the WWTP can be insufficient to treat all the water flow. In such cases, combined sewer overflows
(CSOs) occur, resulting in the discharge without any treatment of a mixture of wastewater and runoff water, loaded with urban surface pollution, into the receiving waters. CSO impacts on aquatic environments are multiple in terms of pollution types and dynamics in time and space, and encompass: (i) oxygen depletion due to the biodegradation of the high load of organic matter brought by the untreated wastewater, (ii) turbidity increase leading to the reduction of photosynthetic primary production, (iii) increase in the concentration of some
* Corresponding author. Tel.: þ32 2 650 5995; fax: þ32 2 650 5993. E-mail address:
[email protected] (P. Servais). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.024
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organic micro-pollutants, (iv) increase in metal concentrations and (v) increase in the concentration of pathogenic and fecal indicator micro-organisms. Concerning this latter topic, a lot of studies have reported degraded microbiological water quality due to the release of stormwater runoff and CSO in various kinds of receiving natural waters as coastal zones (Hall et al., 1998; Noble et al., 2003), lakes (McLellan et al., 2007) and rivers (Rechenburg et al., 2006; Ham et al., 2009). Other researches were conducted on the microbiological quality of the CSO (Jefferies et al., 1990; Ashley and Dabrowski, 1995) but only few studies (Donovan et al., 2008) reported data on both microbiological CSO quality and its impact on the receiving water. The aim of the present work was to study in parallel the fecal microbial contamination of a large CSO in the Parisian area and its impact on the Seine River. The Paris agglomeration is equipped with a combined sewer system and the Seine River is the receiving environment of the wastewater treated in WWTPs and of CSOs in wet weather conditions. This river is a typical example of an aquatic system severely impacted by wastewaters due to the large size of the conurbation (10 million inhabitants) and the relatively low discharge of the river (328 m3 s1 on average at its entry into Paris). In the framework of the PIREN-Seine program (Meybeck et al., 1998), previous studies of CSO impacts on the Seine River had been mainly devoted to the problem of oxygen depletion. Anoxic conditions created in the river after CSO events used to cause fish mortality in the 90 s and before. Measurements were performed in CSOs (Seidl et al., 1998a; Servais et al., 1999) and downstream in the receiving Seine River (Seidl et al., 1998b) in order to improve the understanding of all the processes involved in oxygen depletion. This allowed the building of an ecological model, ProSe, able to describe and predict the impact of a CSO on the Seine River oxygen concentration (Even et al., 2004, 2007). CSO impacts on metal contamination were also investigated in the Parisian area (Estebe et al., 1998). In the last ten years, the SIAAP (Syndicat Interde´partemental pour l’Assainissement de l’Agglome´ration Parisienne), which is in charge of the management of the sewer network and the WWTPs in the Paris agglomeration, has developed significant efforts to reduce the frequency and the volume of the CSO spill events into the Seine River. Many facilities, allowing the storage or the treatment of wastewater during wet weather periods, have been built inside the sewer network (storage tunnels, detention basins) or in the WWTPs (ballasted flocculation processes). In addition, significant efforts have been made by the SIAAP operators to improve the control of the wastewater flow in the sewer system during wet weather periods, using hydrological and meteorological predictive models. Despite this progress, CSO discharges still occur in the Parisian area and one of them was studied in August 2008. Up to now, no studies were devoted to the impact of CSOs on the microbiological water quality in the Parisian area. In the present study, we monitored the fecal microbial contamination discharged during an intense CSO occurring at the Clichy outfall, and we investigated the impacts on the microbiological quality of the receiving waters of the Seine River. The fecal indicator bacteria (FIB) Escherichia coli and intestinal enterococci were used to assess fecal contamination. FIB enumeration is commonly used to control microbiological water quality, as the
search for the presence of all types of pathogens in aquatic systems is not feasible. Today, E. coli and intestinal enterococci are considered as the best FIB to predict the sanitary risk associated with freshwaters (Edberg et al., 2000; Kay et al., 2004).
2.
Materials and methods
2.1.
Study site
2.1.1. The combined sewer system of the Paris agglomeration and the Clichy CSO outfall More than 75% of the sewage water from Paris and its suburb are collected and transported by gravitation towards three wastewater treatment plants (WWTPs) located in the western part of Paris agglomeration: Seine Centre (240,000 m3 d1, hereafter referred as WWTP 1), Seine Aval 1,700,000 m3 d1, WWTP 2) and Seine-Gre´sillons (1,00,000 m3 d1, WWTP 3) (Fig. 1A). Treatment process at WWTP 1 and 3 consists of primary treatment, biofiltration for carbon and nitrogen removal and physico-chemical phosphorus removal. Treatment process at WWTP 2 consists of primary treatment, activated sludge for carbon removal, biofiltration for nitrogen removal and physico-chemical phosphorus removal. In their course, due to the presence of meanders, collectors leading to these WWTPs cross the Seine River by means of siphon systems. During rainstorm events their transport capacity may be exceeded and the water overload is discharged to the river. A major wet weather outlet in this system is located on the right bank of the river, at the Clichy pretreatment plant. This plant is a major node in the sewer system: in dry weather conditions it collects and pre-treats (screening and grit removal) approximately 600,000 m3 d1 of wastewater which are transferred towards the three WWTPs. WWTP 1 receives no other water except from the Clichy pretreatment plant, located 2 km upstream. Therefore, the quality of the influent water at WWTP 1 can be considered as well representative of the quality of the sewage water transiting at the Clichy site.
2.1.2.
The Seine River
The Seine River has been canalized for more than a century and is nowadays equipped with navigation dams in order to maintain a constant water level between 4 and 5 m. Summer flow is regulated by reservoirs constructed on the upstream part of the Seine, Marne and Aube rivers. The average summer flow in Paris is 144 m3 s1 (measured at the Austerlitz Bridge during the 1974e2009 period). Positions in the Seine River are administratively identified by their kilometric point (KP), which is their distance in km from the reference bridge Pont Marie in downtown Paris. The Clichy CSO outfall is located at KP 23.4. Twice downstream from the Clichy outfall, the Seine River is divided into two arms by central longitudinal islands: first between KP 25.5 and KP 32.6 by the Iˆle St-Denis, second between KP 40.3 and KP 50.6 by a continuous succession of islands beginning with the Iˆle de Chatou and ending with the Iˆle de la Loge (Fig. 1B). At KP 25.5, because the Clichy outfall is located on the right bank and the lateral dispersion is still limited, most of the water mass impacted by a Clichy CSO flows into the right arm. At KP 40.3, the impacted water mass flows into both arms, and we chose to
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Fig. 1 e (A) Map of the study area and (B) schematic localization of the sample series in the Seine River.
follow the right arm. Because transit times are different in the two arms, their confluence results in the mixing of two water masses with distinct ages. We chose not to sample this new heterogeneous water mass, and the end of the Iˆle de la Loge marked therefore the downstream limit of our study zone.
2.2.
Sample collection
The CSO discharge was sampled with a Bu¨hler 1029 automatic sampler (Hach Lange) equipped with a cooled (4 C) sample compartment as described in Kafi et al. (2008). Successive composite samples were constituted by the assembling of subsamples collected every 6 min over 30 min into detergentwashed 1-L PE bottles. The sampling system was purged between each subsample. In parallel, the water mass of the Seine River impacted by the CSO was followed during its flow with a small motorized inflatable boat and was sampled. Before each sampling series, the impacted water mass was located by in situ measurements of conductivity and oxygen concentration. A water mass impacted by a CSO has indeed a lower oxygen concentration than the surrounding non-impacted water due the degradation of the organic matter contained in the CSO; it has a higher conductivity due to the high conductivity of a CSO in comparison to the Seine River. Three series of samples (B, C
and D) were collected, from directly downstream from the outfall to the end of the Iˆle de la Loge (Fig. 1B). For each series, a subsurface sample and a bottom sample were collected in the same water column at two to three different locations in the impacted water mass. In addition, a fourth series of samples (A) was collected directly upstream from the Clichy CSO outfall. Distances between sampling stations and the CSO outfall as well as the time of sampling are given in Table 1. Bottom samples were collected half a meter above the bottom with a length of rubber tubing connected to a pump. In order to rinse the sampling system between two samples, 2 to 3 L of the new sample were pumped and discarded before collecting 1 L in a sterile polypropylene bottle. Subsurface samples were grabbed by opening then closing the cap of a sterile 1-L polypropylene bottle 30 cm below the surface. All samples were kept cold until returned to the laboratory where they were refrigerated (4 C). River samples were processed within 4 h after collection, CSO samples within 3 h after collection of the last sample.
2.3.
E. coli and intestinal enterococci enumeration
E. coli and intestinal enterococci were enumerated by plate counting on Chromocult Coliform Agar (CCA) and Chromocult Enterococci Agar (CEA) respectively (Merck KGaA, Darmstadt,
Table 1 e Time and location of the four series of samples collected in the Seine River on the day of the CSO event (the Clichy CSO outfall is located at KP 23.4). Sample series A B C D
Date
Collection time
Kilometric point (KP)
Distance from the Clichy outfall (km)
2009-08-07 2009-08-07 2009-08-07 2009-08-08
8:55e10:10 12:10e14:45 18:35e21:50 2:00e3:35
22.7e23.0 29.0e30.2 34.5e41.7 48.8e50.5
0.66 to 0.42 5.7e6.8 11.1e18.3 25.4e27.1
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Germany). Both growth media are specific to their corresponding indicator bacteria. Plate counting on CCA has been shown to be equivalent to the reference method ISO 9308-1 recommended in the UE Directive 2006/7/EC on bathing water quality (Mavridou et al., 2010). Depending on the expected concentration, different volumes of each water sample were plated in duplicate on the agar plates after ten-fold serial dilution in sterile Ringer solution or after filtration through a 0.45-mm GN-6 membrane filter (Pall Corporation, Ann Arbor, MI, USA). CCA and CEA plates were incubated at 36 C for respectively 24 h and 48 h. Plate counts were expressed as colony-forming units (CFU) per 100 mL of sample.
2008. Most of the rain fell between 5:00 a.m. and 9:00 a.m. The resulting CSO at the Clichy site was the first since 12 days. The CSO lasted 6 h (from 5:50 a.m. to 11:50 a.m.) and resulted in the discharge of 5,78,705 m3 into the Seine River. This was the largest discharge observed at Clichy in 2008, the third largest discharge of the 2006e2008 period. The average flow rate of 26.8 m3 s1 was the highest observed for this period. The flow peaked at 7:31 a.m. at 45.4 m3 s1 (Fig. 2A). Twelve successive samples were collected automatically in the CSO, each sample being a time-proportional composite of the waters discharged during a 30-min period.
3.1.2. 2.4. Determination of the fraction of E. coli attached to suspended matter In this study, the approach proposed by Garcia-Armisen and Servais (2009) to estimate the fraction of E. coli attached to suspended matter (SM) was used. It is based on measurements of the b-D-glucuronidase (GLUase) activity (an enzymatic activity specific to E. coli) in two particle size fractions. GLUase activity measurements have been shown to be a good surrogate to E. coli enumeration by plate counts in different types of aquatic systems (Servais et al., 2005; Lebaron et al., 2005; Garcia-Armisen et al., 2005). Briefly, GLUase activity retained on a 0.2-mm poresize membrane is used to quantify the GLUase activity of the total population of E. coli in a sample while the GLUase activity retained on a 5-mm pore-size membrane is used to quantify the activity of the fraction of E. coli attached to SM. The ratio of both activities gives an estimate of the proportion of E. coli attached to SM. Measurements of GLUase activities were performed following the protocol proposed by George et al. (2000) slightly modified. A known volume of river water was filtered through a 0.2-mm or a 5-mm pore-size filter (47 mm-diameter polycarbonate membrane). Each filter was placed in a flask with 17 mL of a 67 mM phosphate buffer (pH 6.9) and incubated in a water bath at 44 C. The reaction was started by adding 3 mL of a 2.83 mM 4-methylumbelliferyl-b-D-glucuronide solution (Biosynth AG, Staad, Switzerland). Every 5 min for 30 min, a 2.9-mL aliquot was poured in a quartz cell with 110 mL of a 1 M sodium hydroxide solution to increase the pH to 10.7. The fluorescence intensity of the aliquot was measured with an SFM 25 spectrofluorometer (Kontron AG, Zu¨rich, Switzerland) at an excitation wavelength of 362 nm and an emission wavelength of 445 nm. GLUase activity is measured by the production rate of 4-methylumbelliferone (MUF), determined by a linear least squares regression of the MUF concentration on the incubation time.
3.
Results
3.1.
Characterization of the CSO
3.1.1.
The CSO event
3.1.3. The Clichy CSO event that was studied occurred on August 7, 2008. It resulted from a summer rainstorm on the Parisian area, during which 39 mm of precipitation were measured (average precipitation calculated from the measurements of 32 rain gauges located on the catchment area managed by the SIAAP). This was the highest daily rainfall measured on the area in
Conductivity
The conductivity of the 12 samples evidenced a strong variation of the CSO composition over time (Fig. 2A). The conductivity peaked during the first half-hour (518 mS cm1) and rapidly decreased to reach a minimum during the third halfhour (143 mS cm1), after which it progressively increased until the end of the CSO discharge to 327 mS cm1. During an 18-day survey of the raw wastewater quality at the entrance of WWTP 1, carried out by the SIAAP in spring 2008, a conductivity of 1175 30 mS cm1 was observed on average in dry weather conditions (SIAAP, unpublished data). This conductivity is a good estimate of the conductivity expected for dry weather wastewater at the Clichy site, located on the same collector 2 km upstream. Therefore all CSO samples displayed conductivities much lower than dry weather wastewater. These low conductivities were the consequence of the dilution of wastewater by urban stormwater runoff with a much lower conductivity. In an attempt to estimate the respective proportions of wastewater and runoff water composing the CSO, we hypothesized the mixing of an average dry weather wastewater with an average runoff water. The conductivity of the dry weather wastewater was set to 1175 mS cm1 as discussed above. Previous measurements inside the Clichy catchment had shown that the average conductivity of runoff water during a rainstorm event ranged between 56 mS cm1 and 141 mS cm1 (Kafi-Benyahia, 2006). Fig. 2B shows the mixing proportions estimated if runoff conductivity was set to the minimum, the centre (100 mS cm1) or the maximum of this range. According to these hypotheses, runoff water was estimated to represent 85%, 89% and 92% of the total CSO respectively. Since the different hypotheses resulted in minor differences, we considered in the rest of the study an average conductivity of 100 mS cm1 for the runoff water constituting the CSO discharge. The lowest proportion of runoff water was estimated at 61% for sample 1 and the highest at 96% for sample 3 (Fig. 2B). The proportion of runoff water began to decrease during the half-hour preceding the peak flow, probably as a result of an increase in the volume of wastewater arriving at the Clichy pretreatment station after 7 a.m.
Suspended matter
In 2008, the average concentration of SM in wastewater at the entrance of WWTP 1 was 264 48 mg L1 in dry weather conditions (SIAAP, unpublished data). In comparison, water discharged during the first 30 min of the CSO carried a very high load of SM (830 mg L1) (Fig. 2C). During the following 30 min, the concentration dropped rapidly to an average of 290 mg L1, and then went on decreasing at a slower pace
11:20 11:50
8:50 9:20 9:50
7:50 8:20
5:50 6:20 45 40 35 30 25 20 15 10 5 0
Conductivity Flow rate
600 500 400 300 200
µS cm -1
m³ s -1 Composition (%)
B
6:50 7:20
Tim e
A
10:20 10:50
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 9 3 e9 0 3
100 0
100 80
Runoff w ater Wastew ater
60 40 20 0
C1000
Other Wastew ater
SM (mg L -1)
800 600 400 200 0
106 CFU (100 mL) -1
D
7 6 5
Attached E. coli Free E. coli
4 3 2 1 0
106 CFU (100 mL) -1
E
1.4 1.2
Intestinal enterococci
1.0 0.8 0.6 0.4 0.2 0.0 1 2 3 4 5 6 7 8 9 10 11 12 CSO sam ple
Fig. 2 e Variation of some CSO parameters over time. (A) Flow rate (moving 5-min average, line) and conductivity of the 12 CSO samples collected during successive intervals of 30 min (bars). (B) CSO composition in wastewater and runoff water as estimated by conductivity measurements. (C) SM concentration and fraction estimated to originate from wastewater. Vertical bars represent the variation of the estimation according to the assumptions made about urban runoff conductivity, as described in the text. (D) Concentrations of E. coli attached to SM and free E. coli. (E) Intestinal enterococci concentration. towards a final concentration of 110 mg L1 approximately. Considering that wastewater was estimated to represent only 11% of the average CSO, SM carried by wastewater was highly insufficient to account for the concentrations observed in the 12 samples. Fig. 2C shows an estimation of the proportion of
897
SM that was not directly brought by wastewater, based on a calculation where wastewater carries 264 mg L1 and is mixed with runoff water in the proportions derived above. For the whole CSO, 86% of the total SM discharge (1,24,000 kg) was estimated to originate from another source than wastewater. Two sources can be proposed for it: (i) the particles washed by the stormwater runoff on urban surfaces or (ii) the resuspended sewer sediment.
3.1.4.
Fecal indicator bacteria
The concentrations of E. coli and intestinal enterococci in the CSO followed a trend similar to conductivity (Fig. 2D and E). The concentrations of both FIB were the highest during the first 30 min of discharge (6.4 106 E. coli and 1.2 106 intestinal enterococci per 100 mL), next they dropped to reach a minimum in sample 3 (3.8 105 E. coli and 1.2 105 intestinal enterococci per 100 mL), when the proportion of runoff water peaked in the CSO discharge, and then they increased progressively until the end of the CSO. The increase was more pronounced for E. coli than for intestinal enterococci, leading to a final E. coli concentration close to what was observed in the first sample. Maximal concentrations were similar to those measured in raw wastewater, since the influent at WWTP 1 contains on average 6.5 106 E. coli and 1.1 106 intestinal enterococci per 100 mL (median values of bimonthly measurements between 2003 and 2007) (Gonc¸alves et al., 2009). Minimal concentrations were at least one order of magnitude higher than concentrations usually observed in the treated effluents of the WWTPs from Paris agglomeration (Gonc¸alves et al., 2009). FIB concentrations were positively correlated to conductivity (R2 ¼ 0.79 for E. coli and R2 ¼ 0.88 for intestinal enterococci, p < 0.001), suggesting that they were primarily driven by the proportion of wastewater in the CSO. We analyzed more closely if FIB concentrations followed a strict dilution pattern. We compared them to the theoretical concentrations that would be observed if wastewater, carrying a load of FIB corresponding to dry weather conditions as measured in the WWTP 1 influent, was diluted with runoff water carrying no FIB, in the proportions derived above (Fig. 3). In six out of the twelve CSO samples for E. coli, and in all samples for intestinal enterococci, the measured concentrations were higher than those expected if wastewater was the only source of FIB. Another source of FIB can therefore be suspected, the two possibilities being again resuspended sewer sediment and runoff water. Finally, it was estimated that 77% of the E. coli discharged during the whole CSO were attached to SM. A marked difference in the proportion of attached E. coli was observed between the first half-hour of the CSO and the rest of the discharge, as it was already observed for conductivity and for the concentrations of SM and FIB. The proportion of attached E. coli was the highest in the first sample (91%) while it was on average 68 7% in the other samples (Fig. 2D).
3.2. Impact on the microbiological water quality of the Seine River 3.2.1. Characterization of the Seine upstream from the Clichy CSO outfall On the day of the rainstorm event at 6:00, the Seine flow rate was of 157 m3 s1 in downtown Paris (Austerlitz bridge), which
898
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 9 3 e9 0 3
1.4
7
E. coli
106 CFU (100 mL)-1
6
Intestinal enterococci
1.2
5
1.0
4
0.8
3
0.6
2
0.4
1
0.2
0
0.0 0
10
20
30
0
40
10
20
30
40
Wastewater in CSO (%)
Fig. 3 e Comparison of the actual concentrations of FIB in the CSO (white symbols) with their expected concentrations if wastewater was the only source of FIB. Expected concentrations were calculated on the basis of the 1st decile (“low hypothesis”, lower dashed line), the median (“medium hypothesis”, plain line) and the 9th decile (“high hypothesis”, upper dashed line) of the concentrations measured bimonthly between 2003 and 2007 in dry weather wastewater at the entry of WWTP 1 (Gonc¸alves et al., 2009).
400 350
m 3 s -1
300 250 200 150 100 50
20 08 -0 20 8-0 08 5 0 20 08- 0:0 0 0 08 5 12 -0 20 8-0 :0 0 08 6 00 -0 8 :0 20 0 08 -06 12 -0 8 : 20 00 08 07 00 -0 :0 20 80 08 07 1 -0 2 20 8-0 :0 0 08 8 00 -0 :0 20 80 08 08 12 -0 809 :00 00 :0 0
0
Fig. 4 e Seine River flow rate as measured under the Austerlitz Bridge in the City of Paris on the days around the CSO event.
rainstorm and its microbiological quality was already impaired upstream from the Clichy CSO outfall. This deterioration could result from the discharges of several minor CSOs located upstream from the Clichy outfall, from sediment resuspension from the river bed due to the increase in the flow rate or from increased runoff from rural areas due to the rain event.
5 log (CFU (100 mL) -1)
is close to its average summer flow rate of 144 m3 s1. In the following 3 h, the flow rate dramatically increased and peaked at 10:00 at 367 m3 s1 (Fig. 4). Therefore, the flow of the Seine River was already strongly impacted by the rainstorm upstream from the Clichy CSO outfall. To have an estimate of the microbiological quality of the Seine River before being impacted by the Clichy CSO, the Seine was sampled directly upstream from the Clichy outfall once the CSO had begun (sample series A). Mean concentrations of 4.0 104 E. coli and 8.6 103 intestinal enterococci per 100 mL were observed (Fig. 5). These wet weather concentrations were compared with two different estimates of the microbiological quality expected in dry weather conditions, obtained from (i) a longitudinal profile in the same river portion six days later, and (ii) a bimonthly monitoring at one location over the year 2008 (SIAAP, unpublished data). The two data sets gave very similar estimates: 2 103 E. coli and 2 102 intestinal enterococci per 100 mL (Fig. 5). The concentrations in series A were more than one order of magnitude higher. Therefore, as seen with the flow rate, the Seine River was already impacted by the
E. coli Intestinal enterococci
4 3 2 1 n=2
n=4
n = 23
Wet weather 2008-08-07 (series A)
Dry Weather 2008-08-13
Dry weather 2008
0
Fig. 5 e Comparison of the microbiological water quality of the Seine River, as observed directly upstream from the Clichy outfall on the day of the CSO event, with dry weather data. Left: FIB concentrations as measured on the day of the CSO event directly upstream from the Clichy outfall (sample series A); bars represent the arithmetic mean; error bars represent the range bewteen the two samples. Centre: FIB concentrations as measured during a longitudinal profile (four locations between KP 16.4 and KP 37.3) carried out in dry weather conditions six days after the CSO event; bars represent the arithmetic mean; error bars represent the standard deviation. Right: FIB concentrations as measured on average in 2008 at the level of the Argenteuil Bridge (KP 36.0) during dry weather conditions; bars represent the medians of bimonthly measurements; error bars represent the 1st and 9th deciles.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 9 3 e9 0 3
3.2.2. Microbiological water quality downstream from the Clichy CSO outfall Downstream from the Clichy outfall, the water mass of the Seine River impacted by the Clichy CSO was followed and successive series of samples were collected in it. As a result of the dramatic increase in the Seine flow rate, the impacted water mass reached the downstream limit of our study zone in only 20 h (see Section 2.1.2). In dry weather conditions, the transit time of the Seine River between the Clichy outfall and this limit is on average of 30 h. Therefore, only three series of samples were collected in the impacted water mass (sample series B, C and D) (Table 1). For each series an average FIB concentration was calculated for the whole impacted water mass. In series B, directly downstream from the outfall, E. coli and intestinal enterococci concentrations were almost 1 log higher than those measured directly upstream in series A (Fig. 6). They subsequently decreased in C and D, representing in D 34% and 21% of the concentrations measured in B for E. coli and intestinal enterococci respectively. Because of the limited number of samples, only general qualitative trends describing the spatial (longitudinal and vertical) distribution of the FIB within the impacted water mass can be suggested. For series D, no difference in the FIB concentration of the samples was observed at the longitudinal or the vertical level, suggesting that the impacted water mass was homogenous. In series B and C, the samples collected in the downstream part of the water mass were more contaminated than the samples collected in the central and upstream parts (data not shown). This was consistent with the temporal variation in the CSO composition, since the FIB discharge was the highest during the first half-hour. As regards the vertical distribution, very weak differences or no differences at all were observed between subsurface samples and bottom samples from the same water column. Nevertheless, when the differences were significant ( p < 0.05), the FIB concentration was higher in the bottom sample.
4.
log (CFU (100 mL) -1)
E. coli
Intestinal enterococci
5
4
n=2
n=4
n=5
n=6
A
B
C
D
3 Seine sample series Fig. 6 e FIB concentrations in the Seine River on the day of the CSO event. FIB concentrations were measured directly upstream from the Clichy outfall (sample series A) and in the downstream water mass impacted by the CSO (sample series B, C and D). Bars represent the arithmetic mean of the sample series; error bars represent the standard deviation.
Discussion
Few studies have monitored in parallel the composition of a CSO and its impacts on the receiving waters over time in order to explore the dynamics of microbial contamination. Important logistics have to be deployed at the precise day and hour of the CSO event in order to sample simultaneously the CSO and the river, to carry out the in situ monitoring of the pollutant dynamics in the river during several hours and to perform the laboratory analyses at the same time. The present study focusing on microbiological aspects was possible because it was part of a larger campaign aimed at investigating a large range of pollutants. Due to the logistical difficulties, only one single CSO event was monitored during this study. This CSO event resulted from the most intense rainfall on the Paris agglomeration in 2008. As a consequence, the volume discharged was exceptional. Therefore, the data gained from this event may not be completely representative of more ordinary CSO events and their impact on the Seine River. It is not easy to tell how the exceptional intensity of the rainstorm has affected the parameters we measured, since it may have resulted in at least two antagonistic effects: sources of SM and FIB may have been increased (higher resuspension of sewer and river bed sediments, higher duration of the discharge) but the contaminants may have been more diluted (larger amount of stormwater in the CSO discharge and in the Seine River).
4.1.
Dynamics of the CSO
Due to the intensity of the rainfall event, a very high proportion of the CSO was estimated to be constituted of runoff water (89%), and the wastewater constituting the remaining 11% was apparently insufficient to account for the observed SM and FIB concentrations. The composition of the CSO varied over time, particularly between the first half-hour and the rest of the discharge.
4.1.1.
6
899
Suspended matter
After the study of 16 rain events between 2003 and 2006, Gasperi et al. (2010) have estimated the mean contributions of wastewater, runoff and sewer deposit resuspension to the wet weather load of SM in sewage at the Clichy catchment. Depending on the sub-catchment, wastewater contributed between 22% and 44% of the total SM load, while runoff only contributed between 7% and 12%. The major contributor was the resuspension of sewer deposits that accounted for 47e69%. Additionally, it was shown that the contribution of sewer deposits tended to increase with the intensity of the rain event while wastewater contribution tended to decrease; runoff contribution appeared less related to rainfall intensity. Considering the exceptionally high intensity of the rain event we studied, our estimation that only 14% of the SM discharged during the CSO was attributable to wastewater seems realistic. If we admit a contribution of w10% for runoff water, then w75% of the SM must have originated from the resuspension of sewer sediment. A large part of the sewer sediment stock seems to have been resuspended at the beginning of the event since 32% of the total SM load was discharged during the first half-hour of the CSO.
900
4.1.2.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 9 3 e9 0 3
Fecal indicator bacteria
As regards FIB, it was estimated that 8.7 1015 E. coli and 2.3 1015 intestinal enterococci were discharged during the whole CSO event (Table 2). These fluxes can be compared with the amount of FIB discharged during the same time by the three WWTPs that represent the main impact on the Seine microbiological quality in dry weather conditions downstream from the city of Paris. It was estimated that the CSO discharged 79 times more E. coli and 100 times more intestinal enterococci than the WWTPs (Table 2). Although very large, the Clichy CSO is only one of the CSOs in the combined sewer system of Paris. Accordingly, these factors of 79 or 100 do underestimate the total load of FIB the CSOs discharged to the Seine River during this rainstorm event. Among the discharged FIB, 9e71% E. coli and 43e83% intestinal enterococci was estimated to originate from another source than wastewater, depending on the assumptions made on the dry weather load of wastewater (low hypothesis: 1st decile; high hypothesis: 9th decile). A review of numerous measurements in separated stormwater sewers has shown that typical E. coli concentrations in runoff water are in the order of magnitude of 103e104 CFU per 100 mL (Marsalek and Rochfort, 2004). E. coli concentration in the dry weather wastewater at Clichy is in the order of magnitude of 106e107 CFU per 100 mL. As runoff water constituted w90% of the CSO, its contribution to the load of E. coli was therefore at the very least ten times lower than wastewater. Resuspension of FIB from sewer deposits was therefore the most plausible additional source. It is consistent with: (i) the large contribution of sewer deposits to the SM load (w75%), (ii) the previous observation in sewers from the Clichy catchment that the resuspended fraction of the deposits consisted mainly of the organic layer found at the interface between water and the gross bed sediment (Gasperi et al., 2010), (iii) the fact that this layer is favorable for the survival of fecal bacteria (Ellis and Yu, 1995) and (iv) the observation that the proportion of attached E. coli was notably higher during the first half-hour of the discharge. Indeed, since the major part of the very high SM load of the first half-hour was estimated to originate from the resuspension of sewer sediment and since E. coli is expected to be in an attached form in this sediment, an increase in the ratio of attached to free E. coli would be expected. If the medium hypothesis for the dry weather load of wastewater (median FIB concentrations) is retained, sewer deposits are estimated to have contributed
w45% and w65% to the discharge of E. coli and intestinal enterococci respectively.
4.2.
Dynamics of the discharged FIB in the Seine River
In the Seine River water mass impacted by the CSO, a decrease in concentration of 66% for E. coli and 79% for intestinal enterococci was observed between the sample series B and D, collected at an interval of 13e14 h. Three in-stream processes could account for this decline, namely dilution in the waters of the Seine River not impacted by the Clichy CSO, sedimentation and decay (meaning here mortality or loss of culturability). In order to explore the respective contribution of these processes, we first estimated for each river sample to what level the CSO waters were diluted in the Seine River waters. For that purpose, an average non-impacted Seine River water and an average CSO water were hypothesized. The parameters of the average Seine River water (conductivity, SM concentration, FIB concentrations and the proportion of attached E. coli) were estimated by the arithmetic mean of their values measured directly upstream from the Clichy outfall (samples A). The parameters of the average CSO water were estimated by the weighted mean of their values in the twelve successive samples of the discharge; the sample weight represented the proportion of the total CSO volume that was discharged during the 30 min of its collection. Then we calculated for each river sample the proportions of average Seine River water and average CSO water that, when mixed together, would have resulted in its conductivity. Conductivity was used for this calculation as it can be considered as a conservative tracer. That way, the proportion of average Seine River water was an estimate of the dilution rate of the CSO in the samples: a value of 0 signifies the sample is only composed of average CSO water and a value of 1 signifies it is only composed of average Seine River water. On the basis of these dilution rates and the FIB concentrations in both average waters, it was possible to calculate the FIB concentrations that would have resulted from their mixing. These values were thus estimates of the FIB concentrations that would have been observed in the river samples if dilution was the only decline process (“dilution-only” concentrations). They were systematically higher than the actual concentrations, for both FIB (Fig. 7). Therefore dilution alone was not sufficient to account for the decrease in the FIB concentrations.
Table 2 e Comparison of the FIB loads discharged into the Seine River downstream from the City of Paris by the studied CSO and the three WWTPs of the area. Discharge (m3 per 6 h)
E. coli b
Concentration (CFU (100 mL)1) WWTP WWTP WWTP WWTP CSO
1a 2 3 1þ2þ3
60,000 425,000 25,000 510,000 578,705
1.7 104 2.4 104 3.4 103 1.5 106
Intestinal enterococci Load (CFU per 6 h) 1.0 1.0 8.5 1.1 8.7
1013 1014 1011 1014 1015
Concentration (CFU/100 mL) 9.9 102 5.2 103 2.5 102 4.0 105
a For the identification of the three WWTPs and the description of their treatment process, see Section 2.1.1. b For WWTPs, median concentrations measured in the effluent in 2008. WWTP 1: n ¼ 25, WWTP 2: n ¼ 50, WWTP 3: n ¼ 26.
Load (CFU per 6 h) 5.9 2.2 6.3 2.3 2.3
1011 1013 1010 1013 1015
901
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 9 3 e9 0 3
We subsequently explored the possible contribution of decay. The decay of culturable fecal bacteria in aquatic environments results from the combined actions of various biological and physico-chemical processes (grazing by protozoa, virus-induced cell lysis or autolysis, stress due to nutrient depletion, solar radiation or low temperature, all of them inducing mortality or loss of culturability). This decay is usually modeled by a first-order kinetics (Kashefipour et al., 2002; Tian et al., 2002; Menon et al., 2003; Collins and Rutherford, 2004). In our calculation, we set the decay constant to 0.045 h1 for E. coli, as proposed by Servais et al. (2007a,b), and to 0.032 h1 for intestinal enterococci on the basis of previous experimental measurements (Passerat, unpublished data). The time during which decay was considered was the interval between 8:50 a.m. (time at the half of the CSO duration) and the collection time of the river samples. The new estimates, incorporating both dilution and decay contributions (“dilution þ decay” concentrations), matched better the actual FIB concentrations but were nevertheless generally slightly higher than them (Fig. 7). This could suggest that dilution and decay are not sufficient to account for the observed decrease in the FIB concentrations. Finally, we explored the role of sedimentation. For that purpose, we compared the dynamics of free and attached E. coli. Indeed, Garcia-Armisen and Servais (2009) have shown
that only E. coli attached to SM is settleable. First we compared the actual concentrations with the “dilution þ decay” concentrations. Free E. coli concentrations were rather well accounted for or even slightly underestimated, but attached E. coli concentrations were systematically overestimated (Fig. 7). An additional process could therefore contribute specifically to the decline of attached E. coli, and sedimentation was the most relevant candidate. For each river sample, we then tried to estimate the sedimentation that had occurred since the discharge of the CSO. It was done on the basis of the difference between the sample’s actual SM concentration and its “dilution-only” estimate: the higher the discrepancy, the more SM was expected to have settled since its discharge. A sedimentation factor was calculated as the ratio of this difference to the “dilution-only” estimate: a factor of 0 meant no sedimentation and a factor of 1 meant a complete sedimentation of SM. By deducting the proportion equal to this factor from the “dilution þ decay” concentration of attached E. coli, a “dilution þ decay þ sedimentation” estimate was obtained. The new estimates matched much better the actual concentrations (Fig. 7). Therefore sedimentation could be a major driver of the fate of attached FIB in the waters impacted by the CSO. Nevertheless, new calculated concentrations of total E. coli, incorporating dilution, decay and, for its attached fraction
6
2.0
5 1.5
5
-1
Calculated abundance (10 CFU (100 mL) )
4 3
1.0
2 0.5 1
Total E. coli
0 0
1
2
3
4
5
6
0.0
5
5
4
4
3
3
2
2
1
1
Free E. coli
0 0
1
2
3
4
Intestinal enterococci
0.0
0.5
0 5
1.5
2.0
Attached E. coli
0 5
1.0
1
2
3
4
5
-1
Measured abundance (10 CFU (100 mL) ) Fig. 7 e Scatter plot of calculated vs. measured concentrations of FIB in the Seine River impacted water mass. For each river sample, calculated concentrations were estimated on the basis of a modeling of dilution only (black symbols), dilution D decay (white symbols) or dilution D decay D sedimentation (grey symbols). Sample origin: series B (triangles), C (squares) and D (diamonds).
902
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 8 9 3 e9 0 3
only, sedimentation, were on majority slightly inferior to the actual concentrations (data not shown), suggesting that the way we modeled FIB decline by combining the three processes lead to a slight overestimation.
5.
Conclusion
The CSO we monitored was a major discharge event, due to very intense rainfall. Its study revealed that: the FIB discharged during the CSO represented 80e100 times the dry weather discharge that the Seine River receives in this area due to WWTPs, by their resuspension, sewer sediments were estimated to contribute to w75% of the SM, 10e70% of the E. coli and 40e80% of the intestinal enterococci that were discharged, 77% of the discharged E. coli were attached to SM, directly downstream from the CSO outfall, the FIB concentrations in the impacted water mass of the Seine River were 7e9 times higher than directly upstream, these concentrations had decreased by 66% for E. coli and 79% for intestinal enterococci after 13e14 h of transit in the Seine River. These results stress that a CSO at the Clichy site can have considerable impacts, although limited in time, on the microbiological quality of the Seine River. It justifies, if needed, the efforts undertaken by the SIAAP in the past decade to reduce them, and is an incentive to pursue these efforts for better understanding and management. By combining the analyses on both the CSO and the receiving river, it was possible to compare what was actually measured in the impacted water mass with what was expected on the basis of the CSO parameters and our knowledge of the in-stream processes affecting the contaminants. Regarding fecal bacteria, the processes of dilution, decay and sedimentation of the attached fraction, as modeled in this study, gave a reasonably good explanation for the in situ observations.
Acknowledgements This work was supported by the PIREN-Seine program of the Centre National de la Recherche Scientifique (France). During the course of this study, Nouho Koffi Ouattara benefited of a grant from the Ivory Coast Government. The authors are grateful to the SIAAP for providing helpful technical facilities during the fieldwork and the water quality data used in this paper, and to the OPUR program for providing and installing the automatic samplers. The authors wish to thank the PIREN-Seine people that contributed to the extensive fieldwork.
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Rechenburg, A., Koch, C., Classen, T., Kistemann, T., 2006. Impact of sewage treatment plants and combined sewer overflow basins on the microbiological quality of surface water. Water Science and Technology 54, 95e99. Seidl, M., Servais, P., Martaud, M., Gandouin, C., Mouchel, J.M., 1998a. Organic carbon biodegradability and heterotrophic bacteria along a combined sewer catchment during rain events. Water Science and Technology 37, 25e33. Seidl, M., Servais, P., Mouchel, J.M., 1998b. Organic matter transport and degradation in the river Seine (France) after a combined sewer overflow. Water Research 32, 3569e3580. Servais, P., Billen, G., Goncalves, A., Garcia-Armisen, T., 2007a. Modelling microbiological water quality in the Seine river drainage network: past, present and future situations. Hydrology and Earth System Sciences 11, 1581e1592. Servais, P., Garcia-Armisen, T., George, I., Billen, G., 2007b. Fecal bacteria in the rivers of the Seine drainage network (France): sources, fate and modelling. Science of the Total Environment 375, 152e167. Servais, P., Garcia-Armisen, T., Lepeuple, A.S., Lebaron, P., 2005. An early warning method to detect faecal contamination of river waters. Annals of Microbiology 55, 151e156. Servais, P., Seidl, M., Mouchel, J.M., 1999. Comparison of parameters characterizing organic matter in a combined sewer during rainfall events and dry weather. Water Environment Research 71, 408e417. Tian, Y.Q., Gong, P., Radke, J.D., Scarborough, J., 2002. Spatial and temporal modeling of microbial contaminants on grazing farmlands. Journal of Environmental Quality 31, 860e869.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 9 0 4 e9 1 2
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Cork industry wastewater partition by ultra/nanofiltration: A biodegradation and valorisation study Marisa Bernardo a, Ana Santos a, Paula Cantinho a,*, Miguel Minhalma a,b a
Chemical Engineering Department, High Institute of Engineering of Lisbon, R. Conselheiro Emı´dio Navarro, 1, 1949-014 Lisboa, Portugal Universidade Te´cnica de Lisboa, Instituto Superior Te´cnico, Instituto de Cieˆncia e Engenharia de Materiais e Superfı´cies (UTL/IST/ICEMS), Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal b
article info
abstract
Article history:
Wastewater from cork processing industry present high levels of organic and phenolic
Received 28 June 2010
compounds, such as tannins, with a low biodegradability and a significant toxicity. These
Received in revised form
compounds are not readily removed by conventional municipal wastewater treatment,
15 September 2010
which is largely based on primary sedimentation followed by biological treatment. The
Accepted 19 September 2010
purpose of this work is to study the biodegradability of different cork wastewater fractions,
Available online 29 September 2010
obtained through membrane separation, in order to assess its potential for biological treatment and having in view its valorisation through tannins recovery, which could be
Keywords:
applied in other industries. Various ultrafiltration and nanofiltration membranes where
Biodegradability index
used, with molecular weight cut-offs (MWCO) ranging from 0.125 to 91 kDa. The waste-
Cork wastewater partition
water and the different permeated fractions were analyzed in terms of Total Organic
Ultrafiltration
Carbon (TOC), Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), Total
Nanofiltration
Phenols (TP), Tannins, Color, pH and Conductivity. Results for the wastewater shown that it is characterized by a high organic content (670.5e1056.8 mg TOC/L, 2285e2604 mg COD/L, 1000e1225 mg BOD/L), a relatively low biodegradability (0.35e0.38 for BOD5/COD and 0.44e0.47 for BOD20/COD) and a high content of phenols (360e410 mg tannic acid/L) and tannins (250e270 mg tannic acid/L). The results for the wastewater fractions shown a general decrease on the pollutant content of permeates, and an increase of its biodegradability, with the decrease of the membrane MWCO applied. Particularly, the permeated fraction from the membrane MWCO of 3.8 kDa, presented a favourable index of biodegradability (0.8) and a minimized phenols toxicity that enables it to undergo a biological treatment and so, to be treated in a municipal wastewater treatment plant. Also, within the perspective of valorisation, the rejected fraction obtained through this membrane MWCO may have a significant potential for tannins recovery. Permeated fractions from membranes with MWCO lower than 3.8 kDa, presented a particularly significant decline of organic matter and phenols, enabling this permeates to be reused in the cork processing and so, representing an interesting perspective of zero discharge for the cork industry, with evident environmental and economic advantages. ª 2010 Elsevier Ltd. All rights reserved.
* Corresponding author. Tel.: þ351 218317178. E-mail address:
[email protected] (P. Cantinho). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.027
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1.
Introduction
Wastewater from cork industry results from the boiling of cork planks (the outer bark of Quercus suber L.), which is the main stage of cork processing, and so, largely used by the industry of the sector in the Mediterranean countries, such as Spain and Portugal (Benitez et al., 2003). This wastewater is typically characterized by high levels of organic and phenolic compounds, such as tannins, that must be degraded before discharge into the municipal sewer or into public water courses. In fact, these components present a low biodegradability and a significant toxicity, and so, are not readily removed by conventional municipal wastewater treatment, which is largely based on primary sedimentation followed by a biological treatment. Biodegradability is an important feature to consider when choosing an appropriate wastewater treatment, once the efficiency of biological treatments is significantly influenced by the readiness of biodegradability of the present compounds (Brenes et al., 2000; Reushchenbach et al., 2003). Several studies report that biological processes used for the treatment of cork processing wastewater, particularly activated sludge, promote insufficient chemical oxygen demand (COD) reductions (13e37%) due to the low biodegradability of the organic content, particularly of phenolic compounds which also present a significant toxicity (Guedes et al., 2003; Machado et al., 2006; Santos et al., 2010). Various physicochemical methods have been employed in order to improve COD removal and/or to increase its biodegradability. Studies have mainly focused on advanced oxidation processes and membrane separation processes (Minhalma et al., 2000; Guedes et al., 2003; Benitez et al., 2003, 2006). Membrane processes have been proven to be extremely efficient when applied to the cork wastewater treatment, not only because they selectively reduce the pollutant content, allowing the use of a biological process to treat the permeate stream, or eventually its reuse, but also because they offer the opportunity to recover some compounds of the rejected stream, which could be applied in other industries, namely in the leather industry (Geraldes et al., 2009; Teixeira et al., 2009). However, cork wastewater is composed by organic matter of different molecular weights and no studies report information about the characterization of different cork wastewater fractions, in terms of composition and biodegradability, which we think must be a crucial issue in order to establish the most
adequate conditions to achieve the goal of obtaining an wastewater more readily biodegradable, or reusable in the process, in simultaneous with the recovery of some of its components. The purpose of this work was to study the biodegradability of different cork wastewater fractions, obtained through ultrafiltration (UF) and nanofiltration (NF) membranes with different molecular weight cut-off (MWCO), in order to establish the most convenient MWCO in regard to both the permeated fractions biodegradability and the valorisation of the rejected fractions through tannins recovery.
2.
Materials and methods
2.1.
Membranes
Permeation experiments were carried out with six laboratorymade UF membranes, prepared as described below, and one commercial NF membrane of polysulfone-polyamide, DS5eDK, of 0.125 kDa supplied by GE water technology.
2.2.
Preparation of UF membranes
Six membranes of cellulose acetate (CA0eCA5), with various MWCO, were prepared in the laboratory by the phase inversion method (Kunst and Sourirjan, 1974), using cellulose acetate with 40% of acetyl content. Table 1 displays the casting solutions composition of the CA0eCA5 membranes. All chemicals used were of reagent grade and supplied by Merck.
2.3.
Experimental set-up
UF and NF permeation essays were carried out on two membranes units. UF unit (Fig. 1, a) consisted of a feed tank, a high pressure pump (with flow regulator), a permeation cell, a back-pressure valve, two manometers (before and after the membrane cell), and a rotameter. The permeation cell held two membranes, each one with a surface area of 0.147 m2. The temperature of the feed tank (5 L) was kept constant by a cooling system. The NF unit (Fig. 1, b) consisted of a spiral wound membrane module DK 20/40 GE (membrane surface area of 2.09 m2), a feed pump and a recirculation pump, two manometers, a rotameter and a microfiltration unit. The
Table 1 e UF membranes: casting solutions composition and film casting conditions. Membrane Casting solution (wt%) Cellulose Acetate Acetone Formamide Casting conditions Temperature of casting solution ( C) Evaporation time (min) Gelation medium
CA0
CA1
CA2
CA3
17.0 64.0 19.0
17.0 61.0 22.0
17.0 56.0 27.0
17.0 55.0 28.0
20e25 0.5
20e25 0.5
20e25 20e25 0.5 0.5 Water between 0 and 3 C
CA4
CA5
17.0 48.0 35.0
17.0 43.0 40.0
20e25 0.5
20e25 0.5
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Fig. 1 e Schematic outline of experimental units, UF (a) and NF (b).
temperature of the feed tank (36 L) was kept constant by a cooling system.
2.4.
UF and NF membranes characterization
Membranes characterization was carried out, in total recirculation mode (permeate and concentrate recirculation to the feed tank), by the determination of the following parameters: 1) hydraulic permeability (Lp), 2) apparent rejection coefficients ( f ) to salts and to reference organic solutes and 3) molecular weigh cut-off (MWCO). The Lp of the various UF membranes was determined by sequent water permeation experiments, with transmembrane pressures respectively of 1.0, 1.5, 2.0, 2.5 and 3.0 bar, a feed circulation flowrate of 200 L/h, a surface area of membrane of 0.294 m2 and a feed temperature of 25 C. In the case of the NF membrane the transmembrane pressure varied between 2.5 and 15 bar, the feed circulation flowrate was of 500 L/h and the feed temperature of 25 C. The f coefficient to salts was determined for a monovalent salt, NaCl, and for a divalent salt, Na2SO4. The concentration of each salt was determined through a calibration curve of concentration vs conductivity. Conductivity was measured with a WTW Multi 340i/set apparatus and the f coefficient was calculated according to f ¼ (Cf Cp)/Cf, where Cf is the salt concentration for the feed and Cp the salt concentration for the permeate. The MWCO characterization involved the determination of the f coefficient to different reference organic solutes. The quantification of the organic solutes was performed by analysis of the Total Organic Carbon (TOC) according to the High-Temperature Combustion Method described in Standard Methods (APHA, 1998), in an O.I. Analytical Aurora 1030 TOC Analyser. The f coefficient was
Table 2 e UF and NF membranes characterization.
Lp (L/h/m2/bar) f NaCl f Na2SO4 MWCO (kDa)
DS-5 CA0 CA1
CA2
CA3
CA4
CA5
5.2 60 99 0.125
34.79 2.0 6.8 13.6
37.83 0.8 4.8 25
56.03 0.4 6.7 45
106.01 0.1 1.2 91
1.43 40 62.1 1.2
2.55 30.6 50.2 3.8
calculated according to f ¼ (Cf Cp)/Cf, where Cf is the TOC concentration for the feed and Cp the TOC concentration for permeate. Glucose and sucrose were used in the case of NF membrane. In the case of UF membranes, polyethylene glycol (PEG) of various molecular weights (600, 1000, 1500, 3000, 6000, 10000, 20000 Da) and dextrans (40000 and 70000 Da) were used. UF experiments were performed at a transmembrane pressure of 1 bar, a feed temperature of 25 C and a feed circulation flowrate of 200 L/h. NF experiments were performed at a transmembrane pressure of 5 bar, a feed temperature of 25 C and a feed circulation flowrate of 500e550 L/h. Solute concentration for the feed was, in any case of 600 ppm. MWCO was determined by the Michaels method (Michaels, 1993 cited by Minhalma, 2002). The operating conditions, namely feed circulation flowrate and transmembrane pressure, were set in a way that polarization concentration was minimized, i.e. the feed flowrate was set for the maximal value (allowed in each installation) and the transmembrane pressure was kept as low as possible.
2.5.
Cork wastewater samples
The wastewater investigated in this study was taken from a Portuguese cork processing industry (Fabricor-Indu´stria,
Table 3 e Cork wastewater characterization. Wastewater sample parameter
pH Conductivity (mS/cm) TOC (mgC/L) Color (Hazen units) BOD5 (mgO2/L) BOD20 (mgO2/L) COD (mgO2/L) CODb (mgO2/L) TP (g tannic acid/L) Tannins (g tannic acid/L) BOD5/COD BOD20/COD
1
2
20 t of cork boiled
22 t of cork boiled
5.14 0.934 670.5 5700 875 1000 2285 1093 0.36 0.25 0.38 0.44
4.70 0.935 1056.8 7100 900 1225 2604 1339 0.41 0.27 0.35 0.47
6.5e8.4 2500 NEb NDc 40 NEb 150 NEb NEb 2 106 e e
LDLa 91 kDa
5.13e4.90 0.984e0.862 411.6* 2200e1650 550e550 733e691.7 979e1550 801e756 0.19e0.20 0.13e0.11 0.56e0.35 0.75e0.46
45 kDa
5.23e4,90 0.825e0.852 339.8e448.5 422e554 550e517 683e708 734e1469 747e774 0.11e0.14 0.08e0.10 0.75e0.35 0.93e0.48
907
Preparac¸a˜o e Transformac¸a˜o de Cortic¸a SA.) located in Alcochete. Two samples were collected, one after 20 t of cork boiled (sample 1) and other after 22 t of cork boiled (sample 2), in a boiling tank with a capacity of 16 m3.
2.6.
UF and NF experiments with cork wastewater
Essays with the wastewater samples were conducted in total recirculation mode (permeate and concentrate recirculation to the feed tank). UF experiments were performed with 5 L of wastewater, at a transmembrane pressure of 3 bar, a feed circulation flowrate of 200 L/h and a feed temperature of 25 C. NF experiments were performed with 20 L of wastewater, at a transmembrane pressure of 10 bar, a feed circulation flowrate of 500 L/h and a feed temperature of 25 C.
4.98** 0.843** 422.6** 485** 517** 746** 979** 815** 0.13** 0.09** 0.53** 0.76**
The wastewater samples and the permeated fractions were characterized in terms of the following parameters: pH, conductivity, biochemical oxygen demand after five days of incubation (BOD5), ultimate biochemical oxygen demand (BOD20), TOC, COD, color, total phenols (TP) and tannins. Conductivity and pH were determined in a WTW Multi 340i/Set apparatus, at 25 C. Color determination was carried out in an HACH DR/2000 spectrophotometer, by a photometric method referenced to a PteCo standard solution. TOC was determined by the High-Temperature Combustion Method (5310 B), COD was determined by the titrimetric method (5220 B) and BOD was determined by the respirometric method (5210 D), according to Standard Methods (APHA et al., 1998). Total phenols were determined by the Folin-Ciocalteou method described by Ainsworth and Gilliespie (2007). Tannins were determined by the gravimetric method described by Makkar et al. (1993). All analyses were performed in triplicate. The ratios of BOD5/COD and BOD20/COD were calculated and used as biodegradability indices.
a Legal discharge limit (EU directive 91/271/CEE). b Not specified. c Not detected.
5.25e5.01 0.714e0.814 231.15e369.3 171e446 400e450 440e605 571e898 481e661 0.07e0.11 0.05e0.07 0.70e0.50 0.77e0.67 5.38e5.19 0.558e0.576 152.3e192.2 105e228 190e330 240e448 588** 489** 0.05e0.08 0.03e0.05 0.56** 0.76** 5.50e5.23 0.463e0.517 94.6e101.6 52e55 100e195 130e195 237e212 142* 0.03e0.04 0.01e0.02 0.42e0.92 0.55e0.92 5.52e5.34 0.460e0.418 35.3e37.2 3e10 60e118 80e123 90e137 87e134 0.02e0.04 0.01e0.01 0.67e0.86 0.89e0.90 pH Conductivity (mS/cm) (20 C) TOC (mgC/L) Color (Hazen units) BOD5 (mgO2/L) BOD20 (mgO2/L) COD (mgO2/L) CODb (mgO2/L) TP (g Tannic acid/L) Tannins (g Tannic acid/L) BOD5/COD BOD20/COD
3.8 kDa 0.125 kDa
1.2 kDa
13.6 kDa
25 kDa
2.7. Wastewater and permeated fractions characterization
MWCO (kDa)
Table 4 e Permeated fractions characterization. (Values refer to: permeate from sample 1 (20 t) e permeate from sample 2 (22 t); * permeate from sample 1; ** permeate from sample 2).
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3.
Results and discussion
3.1.
Membranes characterization
Table 2 presents the results for the characterization of the membranes used in this study, showing a wide range of membranes MWCO with salt rejections that are typical of NF and UF. Typical values for NF membranes are a MWCO of 200e1000 Da, monovalent salts rejections of approximately 50% and divalent salts rejections higher than 90%. Typical values for UF membranes are a MWCO of 1e100 kDa and low salts rejections (Mulder, 1996).
3.2.
Cork wastewater characterization
Table 3 presents the results for the cork wastewater characterization. As expected, accordingly to previous studies (Santos et al., 2010), the results shown an acid wastewater, with a dark brown color, a high organic content (BOD, COD, TOC) and a high content of polyphenols and tannins,
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Fig. 2 e TOC, COD, BOD20, TP, tannins, color and their f coefficients vs applied MWCO.
exceeding, in any case, the wastewater discharge regulations of several European countries. Results also corroborate the relatively low biodegradability of the organic content, expressed by the ratios of BOD5/COD (0.35e0.38) and BOD20/ COD (0.44e0.47). It is important to note that, despite the fact that the BOD5 is the parameter usually considered in discharges regulation, and so the only one that is mentioned
in the literature review, the characterization of a wastewater in terms of both of the parameters, BOD5 and BOD20, is an important issue as BOD5 can be understood as the rapidly biodegradable fraction (BODrb) of the total biodegradable organic matter present. Our results corroborate the results we obtained in previous studies with other samples of this wastewater (Santos et al., 2010), showing a BOD5/BOD20 ratio
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Fig. 3 e Correlations between parameters. BOD5 vs BOD20, COD vs TOC, COD vs TP and CODb vs TP.
of about 75% and so, of the same magnitude of that usually found in urban wastewater (Metcalf and Eddy, 2003). According to Metcalf and Eddy (2003), the characterization of wastewater biodegradability in terms of its COD fractions, biodegradable (CODb) and not biodegradable (CODnb), is an important issue concerning the application of wastewater treatment simulation models, and may be determined by the following equation: CODb BOD20 =BOD5 ¼ 1 1:42fd ðYH Þ BOD5
(1)
where the coefficients fd and YH are, respectively, the fraction of cell mass remaining as cell debris (g/g) and the synthesis yield coefficient for heterotrophic bacteria (g VSS/g COD used). From our results, when using the referenced values for an urban wastewater fd ¼ 0.15 and YH ¼ 0.40 (Metcalf and Eddy, 2003), we obtained CODb fractions of 48% and 51%, respectively for samples 1 and 2. TOC, COD and BOD20 present higher values for the wastewater from 22 t of cork boiled than for wastewater from 20 t, reflecting the greatest quantity of cork processed and eventually some differences in the composition of cork. However, the value of BOD5 in both samples is similar, suggesting that the increase of organic matter in sample 2 does not specially
concern the BODrb fraction. Despite the fact that the difference between the two samples concerning phenols concentrations are not so significant, a more relevant inhibitor effect of these components on the biodegradability of organic matter in sample 2, must also be considered.
3.3.
Permeated fractions characterization
Table 4 presents the results for the permeated fractions characterization. Results showed a general decrease in the organic and phenolic/tannin concentrations with the decrease of the MWCO of the applied membranes. However, parameters concentrations are not low enough to comply with its legal discharge limits (Table 4, LDL), which means that, in any case, permeates would require some additional treatment for that goal.
3.4. Evolution of parameters and their f coefficients with wastewater partition Fig. 2 presents the rejection coefficients ( f ¼ (Cf Cp)/Cf, where Cf is the parameter concentration for the feed and Cp the parameter concentration for permeate) and the permeated
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Fig. 4 e Molecular weight distribution of the cork wastewater organic/phenolic content.
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fractions concentrations for TOC, COD, BOD20, TP, tannins and color, as a function of the membrane MWCO applied. Permeated fractions from membranes with MWCO equal or greater than 13.6 kDa present significant differences for the two wastewater samples (20 t and 22 t of cork boiled) in which concerns to TOC and COD concentrations, but not concerning to BOD (Fig. 2, aec), particularly to BOD5 (Table 4), that remain in the same magnitude for all these permeates; f coefficients for TOC and COD are similar for both samples, suggesting that those differences mainly reflect the greatest quantity of cork processed in sample 2, as was observed for the raw wastewater. In the permeated fractions from membranes with MWCO smaller than 3.8 kDa, TOC, COD and BOD present much more similar concentrations for the two wastewater samples, suggesting that the additional organic matter present in sample 2 is mostly rejected by the MWCO of 3.8 kDa; BOD is apparently more variable, with lower values for sample 1, but, due to the sensitivity of the applied BOD method, these differences may not be particularly meaningful; f coefficients show an increase with the decreasing MWCO, particularly pronounced for MWCO lower than 3.8 kDa. In the permeated fractions from sample 2, the biodegradability indices, expressed by the ratios of BOD5/COD or BOD20/ COD (Table 4), clearly tend to increase with the decrease of the MWCO of the applied membrane, with particularly high values in the permeated fractions from MWCO lower than 3.8 kDa. The indices are more variable in permeates from sample 1, with a much more significant increase in the permeated fractions from the higher MWCO membranes but not showing a conclusive trend for the lower MWCO membranes. Higher biodegradability indices correspond to higher proportions of biodegradable organic matter in the total amount of organic matter present (COD) in permeated fractions and so, higher rejections of resistant COD. Fig. 3 (a) presents the significant correlation found between BOD5 and BOD20 in permeates from both samples, with the rapidly biodegradable fraction (BOD5) representing about 75% of the total biodegradable matter present (BOD20). This value is of the same magnitude of that found for the wastewater samples in this study, as well as in other studies concerning this wastewater (Santos et al., 2010). These results allow us to conclude that, despite the fact that the biodegradability indices (BOD5/COD or BOD20/COD) clearly improve with partition for both of the wastewater, the proportion of the rapidly biodegradable fraction (BOD5, BODrb) in the total of biodegradable organic matter present remains similar. For the lowest MWCO applied (1.2 and 0.125 kDa) permeated fractions from sample 2 present particularly higher values, but more studies have to be conducted in order to confirm this trend. The characterization of the COD/TOC ratio for a wastewater is an important task in order to estimate one of the parameters from the other. According to the stoichiometry of the COD oxidation, COD/TOC ratio is usually of 2.66, although some deviations of this value may occur depending on the nature of the wastewater organic content (Metcalf and Eddy, 2003). Fig. 3 (b) shows the significant correlation found between COD and TOC in the permeated fractions of both samples, enabling us to define a COD/TOC ratio around 2.52. The rejection coefficients for TP and tannins (Fig. 2, dee) are quite significant in the range of the higher MWCO
911
membranes, but particularly for membranes with MWCO equal or lower than 13.6 kDa, where they are in the order of 80%. Permeated fractions from membranes with MWCO higher than 13.6 kDa present significant differences for the two wastewater samples, in accordance with what we observed for TOC and COD. Fig. 3 (c) shows the significant correlation between COD and TP. The less significant correlation that is observed when we consider the biodegradable fraction of the COD (CODb) vs TP (Fig. 3 (d)) is most probably due to the fact that a part of the present TP does not concern to that COD fraction. In fact, particularly for sample 2, the increase in the biodegradability indices of the permeated fractions with the decrease of the MWCO of the applied membrane should be related to the decrease of their phenolic content. Fig. 2 (f) shows that most of the organic colored compounds in the wastewater samples are rejected in the range of MWCO of 45e91 kDa.
3.5. Molecular weight distribution of the cork wastewater organic/phenolic content Fig. 4 presents the distribution of the wastewater total content of each parameter through different ranges of molecular weight, according to the MWCO of the applied membranes. This was done by expressing (Fi Fiþ1)/Sx in percentage for each parameter, where Fi and Fiþ1 are the concentrations for the parameter in two successive fractions and Sx is the concentration of the parameter in the sample feed. The analysis of the figure shows that most of the wastewater organic content corresponds to organic matter in a range of molecular weight higher than 91 kDa and that less than 20% of the organic matter is in a range lower than 3.8 kDa, most of it being biodegradable matter (Fig. 4, aec). Most of the phenols and tannins are in a range of molecular weight higher than 13.6 kDa and no more than 10% are in the range lower than 3.8 kDa (Fig. 4, dee). Most of the colored compounds are in the range higher than 91 kDa and they are less than 5% in the range of 3.8e45 kDa and quite negligible in the range lower than 3.8 kDa (Fig. 4 (f)).
4.
Conclusions
1. Cork wastewater is characterized by a high organic content (670.5e1056.8 mg TOC/L, 2285e2604 mg COD/L, 1000e1225 mg BOD/L), a relatively low biodegradability (0.35e0.38 for BOD5/COD, 0.44e0.47 for BOD20/COD) and a high content of phenols (0.36e0.41 g tannic acid/L) and tannins (0.25e0.27 g tannic acid/L). 2. Wastewater fractions, obtained through UF and NF membranes, shown a general decrease on the organic pollutant content with the decrease of the membranes MWCO. In the permeated fractions from sample 2 (22 t of cork boiled) a clear trend of an increased biodegradability with the decrease of the membranes MWCO was observed. In this sample, the obtained index of biodegradability (0.8) in the permeated fraction from the membrane MWCO of 3.8 kDa is clearly more favourable than it was in the raw wastewater.
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3. In the permeated fraction from the membrane MWCO of 3.8 kDa the toxicity that may be associated to the phenols concentrations is minimized, meaning that this permeate presents viability to undergo a biological treatment and so, to be treated in a municipal wastewater treatment plant. On the other hand, within the perspective of valorisation, the rejected fraction obtained through this membrane MWCO may have a significant potential for tannins recovery, which could be applied in other industries. 4. In the permeated fractions from membranes MWCO lower than 3.8 kDa, the decline of organic matter and phenols was particularly significant, enabling this permeates to be reused in the cork processing and so, representing an interesting perspective of zero discharge for the cork industry, with evident environmental and economic advantages.
Acknowledgments The authors would like to acknowledge Fundac¸a˜o para a Cieˆncia e Tecnologia for the financial support to the project PTDC/EQU-EQU/68424/2006 and the company Fabricor, Indu´stria, Preparac¸a˜o e Tranformac¸a˜o de Cortic¸as, S.A. for the wastewater samples.
references
Ainsworth, E.A., Gilliespie, K., 2007. Estimation of total phenolic content and other oxidation substrates in plant tissues using Folin-Ciocalteu reagent. Nature Protocols 2, 875e877. APHA, AWWA, WPCF, 1998. Standard Methods for the Examination of Water and Wastewater, twentieth ed. American Public Health Association, Washington D.C. Benitez, F.J., Acero, J.L., Garcia, J., Leal, A.I., 2003. Purification of cork processing wastewaters by ozone, by activated sludge, and by their two sequential applications. Water Research 37 (17), 4081e4090. Benitez, F., Acero, J., Leal, A., 2006. Application of microfiltration and ultrafiltration processes to cork processing wastewaters
and assessment of the membrane fouling. Separation Purification Technology 50 (3), 354e364. Brenes, M., Garcia, P., Romero, C., Garrido, A., 2000. Treatment of green table olive wastewaters by an activated sludge process. Journal of Chemical Technology Biotechnology 75 (6), 63e75. Geraldes, V., Minhalma, M., de Pinho, N.M., Anil, A., Ozgunay, H., Bitlisli, O.B., Sari, O., 2009. Nanofiltration of cork wastewaters and their possible use in leather industry as tanning agents. Polish Journal of Environmental Studies 18 (3), 353e357. Guedes, A., Madeira, L., Boaventura, R., Costa, C., 2003. Fenton oxidationof cork cooking wastewater e overall kinetic analysis. Water Research 37, 3061e3069. Kunst, B., Sourirjan, S., 1974. An approach to the development of cellulose acetate ultrafiltration membranes. Journal of Applied Polymeric Science 18 (11), 3423e3434. Machado, M.D., Madeira, L.M., Nogales, B., Nunes, O.C., Manaia, C.M., 2006. Treatment of cork boiling wastewater using chemical oxidation and biodegradation. Chemosphere 64 (3), 455e461. Makkar, H.P.S., Blummel, M., Borowy, M., Becker, N.K., 1993. Gravimetric determination of tannins and their correlations with chemical and protein precipitation methods. Journal of the Science of Food and Agriculture 61 (2), 161e165. Metcalf and Eddy, Inc., 2003. Wastewater Engineering. Treatment and Reuse, fourth ed. McGraw Hill, Inc. International Editions. Minhalma, M., Dias, C.R., de Pinho, N.M., 2000. Membrane fouling in ultrafiltration of cork processing wastewaters. Advances in Environmental Research 3, 539e549. Minhalma, M., 2002. Synthesis and optimization of processes for the recovery of industrial wastewaters with ultrafiltration and nanofiltration (Sı´ntese e optimizac¸a˜o de processos de recuperac¸a˜o de a´guas residuais industriais com integrac¸a˜o de ultrafiltrac¸a˜o e nanofiltrac¸a˜o). PhD Thesis, Instituto Superior Te´cnico, Universidade Te´cnica de Lisboa, Portugal. Mulder, M., 1996. Basic Principles of Membrane Technology. Kluwer Academic Publishers. Reushchenbach, P., Pagga, U., Strotmann, U., 2003. A critical comparison of respirometric biodegradation tests based on OECD 301 and related methods. Water Research 37 (7), 1571e1582. Santos, A., Vespeira, C., Cantinho, P., Minhalma, M., 2010. Assessment of treatability and sub-products recovery from cork processing wastewater. In: Michael Theophanides and Theophile Theophanides (Ed.), Biodiversity Science for Humanity. ATINER, ISBN 978-960-6672-41-5, pp. 163e170. Teixeira, A., Santos, J., Crespo, J., 2009. Sustainable membranebased process for valorisation of cork. Separation and Purification Technology 66 (1), 35e44.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 9 1 3 e9 2 5
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Partition of pollution between dissolved and particulate phases: What about emerging substances in urban stormwater catchments? Sally Zgheib a,*, Re´gis Moilleron b, Mohamed Saad a, Ghassan Chebbo a,c a
Leesu (ex-Cereve), Universite´ Paris-Est, AgroParisTech, 6-8 avenue Blaise Pascal, Cite´ Descartes, Champs sur Marne, 77455 Marne la Valle´e Cedex 2, France b Leesu (ex-Cereve), Universite´ Paris-Est, AgroParisTech, 61 Avenue du Ge´ne´ral de Gaulle, 94010 Cre´teil Cedex, France c Faculte´ de Ge´nie, Universite´ Libanaise, Beirut, Lebanon
article info
abstract
Article history:
This paper presents results about the occurrence, the concentrations of urban priority
Received 20 May 2010
substances on both the dissolved and the particulate phases in stormwater. Samples were
Received in revised form
collected at the outlet of a dense urban catchment in Paris suburb (2.30 km2). 13 chemical
12 August 2010
groups were investigated including 88 individual substances. Results showed that storm-
Accepted 23 September 2010
water discharges contained 45 substances among them some metals, organotins, PAHs,
Available online 21 October 2010
PCBs, alkylphenols, pesticides, phthalates, cholorophenols and one volatile organic compound, i.e. methylene chloride. With respect to the European Water Framework
Keywords:
Directive, these substances included 47% of the priority hazardous substances (n ¼ 8), 38%
Priority substances
of the priority substances (n ¼ 10). The remaining substances (n ¼ 27) belong to a list of
Stormwater
others specific urban substances not included in the Water Framework Directive but
Environmental quality standards
monitored during this work. Finally, stormwater quality was evaluated by comparing the
Sediment quality guidelines
substance concentrations to environmental quality standards (EQS) and the particulate
Water quality
content to Canadian sediment quality guidelines. This showed that stormwater was highly contaminated and should be treated before being discharged to receiving waters in order to avoid any adverse impact on the river quality. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
The Water Framework Directive (WFD) of the European Union (2000/60/EC) (EC 2000) requires that specific measures shall be adopted to prevent and to control pollution of water bodies to achieve the objective of “good status”. The WFD sets also the prevention of any further deterioration of water bodies and the protection and enhancement of the status of aquatic ecosystems as its primary objectives (Vignati et al., 2009). A list of 33 priority substances has been regulated in the Decision
2455/2001/EC and a number of specific “daughter” directives by defining emission limit values and quality objectives in water bodies, namely surface and coastal waters, for member states. The choice of these substances was relevant with respect to their specific industrial sources since they were identified using the COMMPS approach (Combined Monitoring-based and Modelling-based Priority Setting). Meanwhile, researchers have shown an increased interest in the monitoring of priority and/or emerging substances to generate information about their occurrence and effects in the
* Corresponding author. Leesu e ENPC, 6e8 avenue Blaise Pascal, Champs sur Marne, 7455 Marne la Valle´e Cedex 2, France. Tel.: þ33 1 64 15 37 58; fax: þ33 1 64 15 37 64. E-mail address:
[email protected] (S. Zgheib). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.032
914
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environment (Gasperi et al., 2009, 2008; The´venot, 2008; Eriksson et al., 2007; Rule et al., 2006a, 2006b; Allen Burton and Pitt, 2001). However, to date there has been little agreement in the international researcher community on a list of substances to be monitored, especially for stormwater. For instance, Eriksson et al. (2007) proposed a list of substances to give valuable support for stormwater managers regarding the comparison of various stormwater management strategies. Their approach was based on a theoretically assessment of the stormwater substances presence called the CHIAT (Chemical Hazard Identification and Assessment Tool) methodology. Nevertheless, the stormwater priority substances (SPS) of this list have not been yet experimentally screened on stormwater samples. Besides, a number of studies have linked specific substances in stormwater runoff with their sources (Allen Burton and Pitt, 2001). As a consequence, in order to improve the characterization of urban stormwater, based on both a literature review on monitoring programs and on an inventory of the sources of substances in the urban environment (i.e., atmospheric deposits, traffic, gardens and buildings), we compiled the EU list of priority substances with the one deriving from the CHIAT approach for establishing a new list of 88 SPS (i.e., 65 organic substances, 8 metals and 15 volatile organic compounds), along with ordinary water quality parameters (Zgheib et al., 2008). Therefore, an accurate knowledge of these substances in the water cycle is definitely needed from the atmosphere to receiving waters. Indeed, priority pollutants can enter aquatic ecosystems from different sources such as wastewater treatment plant (WWTP) effluents, combined sewer overflows (CSOs), discharges of more or less treated waters from storm sewers and, lastly, dry and wet atmospheric deposition. As a consequence, a number of studies focused on identifying the concentration of priority pollutants in runoff, wastewater in both separate and combined sewer systems (Gasperi et al., 2009, 2008, Rule et al., 2006a, Rule et al., 2006b). However, considering that only limited information is available regarding most priority substances in stormwater (BertrandKrajewski et al., 2008), our research project aims at contributing to the identification, evaluation and characterisation of priority substances transported by stormwater, in separate sewer systems. The chemical features of stormwater are dependent on the types of impervious surfaces (roads, roofs etc.), on which runoff occurs, along with natural processes and anthropogenic activities in the watershed. Recent studies on SPS show that analyses of priority substances are often carried out on whole water samples (Gasperi et al., 2009, 2008, Rule et al., 2006a, Rule et al., 2006b). More recently (Vignati et al., 2009), emphasized that the WFD gives only marginal attention to the partitioning of priority substances, which is a crucial phenomenon in aquatic ecosystems and (Lepom et al., 2009) noted that more attention needs to be paid to the distribution of chemical substances between suspended particulate matter (SPM) and the liquid phase. In addition, analyses carried out on unfiltered samples will provide poorquality data with respect to the representativeness of the sample contamination and also poor comparability between data from different laboratories (Coquery et al., 2005). As a matter of fact, extraction on whole water samples with substantial amount of suspended matter will be much less
efficient for those substances than if performed on SPM itself, using suitable methods for solid phases, such as sediments or soils (Lepom et al., 2009; Coquery et al., 2005). As a consequence, SPS should be monitored on both the dissolved phase and the particulate phase for management strategy purposes. Due to the lack of available data on SPS in stormwater, the outlet of a storm sewer network in an urban watershed in Paris suburb was equipped for sampling stormwater before its discharge to the Marne River. A whole methodology procedure was applied from sampling to analysis to avoid any contamination of water samples. This study is one of the firsts to report information, in stormwater, about (i) the occurrence of 88 SPS, (ii) their partitioning among dissolved and particulate phases, (iii) their concentrations (ng/L) and, finally, (iv) the impact of the discharge of untreated stormwater in the receiving waters has been assessed with respect to SPS concentrations.
2.
Material and methods
2.1.
Sampling site
Located in the East suburb of Paris (Noisy-Le-Grand), the urban watershed we considered for this study covers a surface of 2.30 km2 and its impervious surface coefficient was estimated at 0.65. The watershed is a very dense urban area (commercial centre, apartments, buildings.) with a population of 59 000 inhabitants (i.e. approximately 258 inhabitants/ha) (Fig. 1). The watershed is served by separate sanitary and storm sewage infrastructure. Wastewater is treated at the Marne Aval treatment plant, while stormwater is directly discharged to local stream, i.e. the Marne River, without treatment. Our sampling point was located at the storm sewer outlet just before discharge to the receiving waters.
2.2.
Sampling strategy
Two refrigerated (4 C) automatic samplers (Buhler 1029, vacuum-based sampler) were used. Each sampler was programmed to fill 12 1 L bottles during the storm duration. It allowed us to collect flow-proportional samples. Based on the list established by Zgheib et al. 2008), different metals and organic compounds were investigated, accounting for 88 individual substances. To avoid any risk of contamination during the sampling procedure (adsorption or release of contaminants), the samplers were configured differently. For organic substances, samples were collected in Pyrex amber glass bottles with Teflon (Tygon SE 200) sampling tubes. Before sampling, glassware was thoroughly washed with TFD4 (Franklab) to remove any trace of organic contamination, then rinsed with deionized water and finally heated at 500 C prior to use. For metals and ordinary water quality parameters, samples were collected in polyethylene (PE) bottles with PVC tubes. To avoid any metallic contamination, bottles were cleaned according to the following method: 24 h in a detergent bath (5%, Extran), then 24 h in a nitric acid (5%, Normatom) bath and finally 24 h in another nitric acid (5%, Normatom) bath. Between each step, bottles were rinsed with ultra-pure water (MilliQ system, Millipore).
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Fig. 1 e Sampling site location in Noisy-Le-Grand (Paris suburb).
phase (D). 0.7 mm glass fibre filters were used rather than 0.45 mm filter membranes, because prior to use they were combusted at 550 C in a furnace to eliminate any traces of organic contamination. Finally, the dissolved phase was analyzed within 24 h, while filters with SPM were deep-frozen at 28 C, then lyophilized and analyzed after 48 h.
Flow measurements were made using ultrasonic transit time flow meter capable of accurate measurement of depth and velocity in the sewer. Six storms were monitored and analyzed for metals and organic substances. They were collected between February 2008 and March 2008. The characteristics for each storm are summarized in Table 1.
2.4. 2.3.
Water quality parameters
Sample pre-treatment For each sample, water quality parameters, such as pH, conductivity, suspended particulate matter (SPM), chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN) and total phosphorus (Ptot), were assessed on the whole water sample or total phase (T). These parameters were measured in accordance with French standard methods as reported in Table 2.
Once the samples were fetched after a storm, the first step consisted in mixing the 12 1 L bottles from each automatic sampler to get two event-mean samples of about 12 L, one for the analysis of organic substances, whereas the other was used to analyze metals and ordinary water quality parameters. For the purpose of metal analysis, the dissolved phase (D) was obtained after filtration of the suitable event-mean sample through a 0.45 mm filter membrane as recommended by the Directive 2008/105/EC on environmental quality standards in the field of water policy. Conversely, the event-mean sample devoted to the analysis of organic substances was filtered with a succession of 90-mm pre-combusted glass filters from 2.7 mm (GF/D, Whatman) to 0.7 mm (GF/F, Whatman), in a glass filtration unit in order to get the dissolved
2.5.
Emerging urban substances
As previously mentioned (Zgheib et al., 2008), have established a list of substances that can be found in stormwater on the basis of the WFD and other literature reviews (Creaud-Hoveman et al., 2008; Eriksson et al., 2007). 12 chemical groups were identified accounting for 88 individual substances. A total of 8 metals (Cd,
Table 1 e Characteristics of the six storms sampled between February and March 2008. Rain event 01/02/2008 09/03/2008 10/03/2008 16/03/2008 14/04/2008 21/04/2008
Height (mm)
Mean intensity (mm/h)
Max intensity (mm/h)
Rain duration (hh:min)
Antecedent dry weather duration (hh:min)
30.4 4.4 4 2 8.6 5.4
3.2 2.8 0.9 0.8 1.1 1.3
9.6 9.6 8 8 4.8 7.2
09:11 03:05 03:36 02:10 07:18 04:02
05:25 131:25 19:09 09:06 144:08 19:33
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Table 2 e Analytical methods for water quality parameters. Parameter
Analytical method
pH Conductivity TKN Ptot SPM COD
NF NF NF NF NF NF
T90-008 EN 27888 EN 25663 EN ISO 11885 EN 872 T90-101
Cr, Cu, Hg, Ni, Pb, Pt, and Zn) and 80 organic compounds: organotins (n ¼ 3), polycyclic aromatic hydrocarbons PAHs (n ¼ 16), polychlorinated biphenyls PCBs (n ¼ 8), volatile organic compounds VOCs (n ¼ 12), chlorobenzenes (n ¼ 5), chlorophenols (n ¼ 2), alkylphenols (n ¼ 5), polybrominated diphenyl ethers PBDEs (n ¼ 3), pesticides (n ¼ 24), chloroalkanes (sum of C10eC13, n ¼ 1), phthalate (n ¼ 1). Analyses were performed by a laboratory certified by the French Ministry of the Environment, i.e. COFRAC (French Accreditation Committee). For international quality control purposes, the COFRAC calibration certificate is recognized by other European calibration services (EA d European Cooperation for Accreditation). Analyses were then performed according to the French (AFNOR) or International (ISO) standard methods. When no standard method was available, the laboratory developed and validated its own methods. Organic contaminants were analyzed on both the particulate (P) and the dissolved (D) phases of each sample. Contaminants linked to particles were extracted by ASE (accelerate solvent extractor) technique. Then, they were analyzed according to the analytical standard methods summarized in Tables 2 and 3, either by gas or liquid chromatography coupled with a mass spectrometer (GCeMS and LC-MS) or with an electron caption
detector (GC-ECD) or even with fluometric detection (LC-fluo). Ultra Performance Liquid Chromatography/Tandem Mass Spectrometry (UPLC/MS/MS) was used to analyze pesticides, whereas trace metals were measured by inductively coupled plasma atomic emission spectroscopy (ICP-AES) and mercury by atomic absorption spectrometry (AAS). For analytical matters, VOCs and ordinary water quality parameters were analyzed on the whole water sample, i.e. the total phase (T). In addition, field blanks were carried out on the whole analytical procedure from sampling to analysis.
3.
Results and discussion
For all compounds, the frequency of detection (occurrence) is given in Table 4, whereas, for both phases, the limit of quantification (LOQ), as well as the minimum, average and maximum values, is summarized in Table 5. As previously mentioned, for each determinand we assessed its concentrations in both the dissolved phase (D, expressed as ng/L) and the particulate phase (P, expressed either as ng/L, with respect to the whole water sample or ng/g.dw (P*) when SPM was considered). Hence, the total concentration refers to the sum of dissolved (D) and particulate (P) concentrations, namely (D þ P). It was then calculated, by considering SPM concentration, as follows: (D þ P) (ng/L) ¼ D (ng/L) þ P (ng/L) with P (ng/L) ¼ P* (ng/ g.dw) SPM (g/L). However, when a substance was observed in only one of the two phases, we calculate (D þ P) in a way to maximize its concentration, as follows:
Table 3 e Analytical methods used for the determination of stormwater priority substances (SPS). Group Metals Hg Organotins PAHs Alkylphenols Alkylphenol (4-ter-butylphenol) VOC Pesticides Phthalate (DEHP) PCBs Chlorobenzenes Chlorophenols PBDEs Chloroalkane C10eC13
Priority substancesa (n ¼ 26)
Priority hazardous substancesa (n ¼ 17)
Other urban substancesb (n ¼ 45)
Analytical methods
2
4
3
1 1 1 5
1
2
1 1
ICP AES AAS GCeMS GC-MSMS & LC-Fluo/UV GC-MSMS GC-MSMS
8
1
6
GCeMS
10
2
12
2 8
1 8
Standard NF EN ISO 11885 NF EN 1483 ISO 17353 ISO 17993 ISO 18857-1 ISO 18857-2
GCeMS UPLC-MSMS GCeMS GC-MSMS
ISO 10301 & 11423-1 EN 12918 ISO 11369 & 21458 Internal method ISO6468
GC-MSMS GC-ECD GC-ECD
NF EN 12673 ISO 22032 Internal method
2 1 1 1
a According to the classification of the EU Water Framework Directive (2000/60/EC). b According to Zgheib et al. (2008).
1 2
-
Table 4 e Occurrence of the substances observed in stormwater (in percentage, n [ 6). Never
Rarely
0
[0e25] a
4-chloro-3-methylphenol Pentabromodiphenylethera Octabromodiphenylether Decabromodiphenylether Alachlorb Aldrin Endrin Dieldrin DDT-2,40 DDT-4,40 Isodrin Endosulfan alphab Endosulfan beta b Lindanea alpha Hexachlorocyclohexanea Chlorfenvinphos b Chlorpyrifosb Trifluralinb Atrazineb Desethylsimazine Simazineb
Pentachlorophenol 4-(para)-nonylphenola 4-n-octylphenol Metaldehyde
Often
[25e50]
[50e75]
Chromium Desethylatrazine
Isoproturon Glyphosate
Reccurent [75e100] b
a
Tributyltin Dibutyltin Monobutyltin [MBT] Leadb Copper Zinc Naphthaleneb Acenaphthene Acenaphthylene Fluorene Phenanthrene Anthraceneb Fluorantheneb Pyrene Benzo[a]anthracene Chrysene Benzo[a]pyrenea Benzo [k]fluoranthenea Benzo [b]fluoranthenea Dibenzo[a,h]anthracene
Benzo[g,h,i]perylenea Indeno[1,2,3-cd]pyrenea PCB28 PCB52 PCB101 PCB118 PCB138 PCB153 PCB180 methylene chlorideb Nonylphenolsa para-tert-octylphenolb 4-ter-butylphenol Diuronb Aminotriazole AMPA Di(2-ethylhexyl)phtalateb
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 9 1 3 e9 2 5
Cadmium Mercurya Nickelb Platinum PCB194 Hexachlorobenzenea Pentachlorobenzenea 1,2,4-trichlorobenzeneb 1,2,3-trichlorobenzeneb 1,3,5-trichlorobenzeneb Benzeneb Ethylbenzene Isopropylbenzene Toluene Xylenes (Sum o,m,p) 1,2-dichloroethane b Hexachlorobutadiene a Chloroformb Carbon tetrachlorideb Tetrachloroethyleneb Trichloroethyleneb C10eC13 chloroalkanesa
b
Fairly
a Priority hazardous substance. b Priority substance.
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Table 5 e Concentration (minimal-maximal [average]) in dissolved and particulate phases of urban substances in stormwater. (n ¼ 6)
LOQ-P*
P*
P
D
DþP
AA-EQS
ng/L
ng/g.dw
ng/g.dw
ng/L
ng/L
ng/L
ng/L
Dilution factor (F)
10
1000e51000 [15000]
15000e119000 [56000]
25000e129000 [66000] 10000e45000 [25000] 50000e220000 [118000] 130000e520000 [305000] 50e78 [60]
7200 3400a 1400 (D)a 7800 (D)a 0.2
50
10
70e320 [170]
23e41 [29]
74e93 [80]
e
50
10
150e510 [290]
36e81 [52]
91e[120]
e
50 10 20 10 10 10 10 10 10 10 10 10 10 10
10 10 10 10 10 10 10 10 10 10 10 10 10 10
150e370 [260] 30e80 [60] 80e200 [130] 70e200 [140] 820e1700 [1140] 90e200 [160] 1200e2400 [1600] 1300e3100 [2200] 330e770 [510] 670e1800 [1210] 440e830 [580] 280e310 [410] 850e1800 [1220] 160e260 [210]
20e125 [59] 30e34 [14] 7e60 [30] 9e86 [34] 65e602 [275] 6e86 [39] 85e817 [387] 92e1204 [555] 27e288 [125] 78e645 288] 31e305 [139] 23e211 [98] 67e645 [294] 11e86 [48]
50e94 [66] 0e10 [8] 0e70 [34] 10e20 [16] 25e110 [73]
88e175 [131] 13e44 [26] 27e126 [76] 19e106 [55] 90e712 [370] 16e96 [50] 98e832 [425] 100e1223 [575] 37e298 [147] 88e655 [331] 41e315 [159] 33e221 [117] 77e655 [328] 21e96 [56]
2400 700a 400a 300a 110a 300 100 24a 5a 6a 50 30b 30b 0.006
0.1 0.1 0.3 0.3 7 0.3 8 51 60 109 6 7 22 1600
10 10
10 10
850e1400 [1070] 580e830 [680]
61e559 [254] 43e344 [159]
71e569 [279] 53e354 [176]
2c 2c
285 177
30 30 30 30 30 30 30 200 100 100 100 100 50 20 50 20 50
10 10 10 10 10 10 10 100 100 100 100 100 50 50 50 50 50
20e30 [20]
1e9 [5]
32e39 [35]
1a 1a 1a 1a 1a 1a 1a 300 e e e 100
39 39 43 43 52 52 43 31 e e e 0.2 e 3 0.3 e e
10 10 0.02 0.02 50
200 300 e e
18 13 143 65 390
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 9 1 3 e9 2 5
Lead Chromium Copper Zinc Tributyltin [TBT] Dibutyltin [DBT] Monobutyltin [MBT] Naphthalene Acenaphthene Acenaphthylene Fluorene Phenanthrene Anthracene Fluoranthene Pyrene Benzo[a]anthracene Chrysene Benzo[a]pyrene Benzo[k]fluoranthene Benzo[b]fluoranthene Dibenzo[a, h]anthracene Benzo[g,h,i]perylene Indeno[1,2, 3-cd]pyrene PCB28 PCB52 PCB101 PCB118 PCB138 PCB153 PCB180 Nonylphenols 4-(para)-nonylphenol para-tert-octylphenol 4-tert-butylphenol 4-n-octylphenol Metaldehyde Diuron Isoproturon Aminotriazole AMPA
LOQ-D
P*: concentration (ng/g.dw) in particulate fraction, P: concentration (ng/L) in particulate fraction, D: concentration (ng/L) in dissolved fraction, (D þ P): total concentration in both fractions, AA-EQS: annual average Environmental quality standard defined for inland surfaces according to the European Directive 2008/105/EC. a FR AA-EQS defined within the French guidelines (circular 2007/23). b 0.03 is defined for the sum of BkF & BbF but we have compared each concentration for each compound to this value. c 0.002 is defined for the sum BP & IP but we have compared each concentration for each compound to this value.
Glyphosate DEHP Methylene Chloride Pentachlorophenol
50 5000 2500 (total) 100
50 5000 e 200
1300 20000 400
e 47 0.7 0.7
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 9 1 3 e9 2 5
919
- When a substance was one hundred percent particle-bond, then (D þ P, ng/L) ¼ P (ng/L) þ LOQD of the substance. - When the substance was one hundred percent in the dissolved phase, then (D þ P, ng/L) ¼ D (ng/L) þ LOQP of the substance. Maximizing concentrations was investigated so as to be in the worse case and to ensure a better consideration of the impact due to the release of untreated stormwater to receiving waters. To assess this impact and to put into perspective the importance of the reported concentrations, values were compared with pertinent guidelines, regulations, and levels that have been reported to cause possible adverse impacts.
3.1.
Stormwater quality characteristics
The ordinary water quality parameters provided key information on the stormwater quality (Table 6). Hence, SPM ranged from 58 to 430 mg/L with respect to the characteristics of the storm. In addition, conductivity varied between 288 and 1316 mS cm1 with a mean pH value of 7.21. COD ranged between 48 and 230 mg/L, this latter value was similar to that observed for runoff samples collected on the Marais urban catchment (Gromaire-Mertz, 1998) but it represented half the value of wastewater from combined sewer networks (Gasperi, 2006; Gromaire-Mertz et al., 1999). So, stormwater contained rather low concentration for COD, SPM, TKN and Ptot when compared to wet weather flows in combined sewer. Moreover, the frequency and the variability of sampled events ensure a good assessment of SPS.
3.2.
Occurrence of stormwater priority substances
Of the 88 stormwater priority substances monitored in both the particulate and the dissolved phases, a total of 45 different determinands including 4 metals (Cu, Cr, Pb and Zn), 1 VOC (methylene chloride), 7 PCBs (congeners 28, 52, 101, 118, 138, 153, 180), 16 PAHs, 3 organotins (DBT, MBT, and TBT), 1 chlorophenol (pentachlorophenol), 5 alkylphenols (nonylphneols, 4-para-nonylphenol, 4-n-octylphenol, para-tertoctylphenol and 4-tert-butylphenol), 1 phthalate (DEHP) and 7 pesticides (aminotriazole, AMPA, desethylatrazine, diuron, glyphosate, isoproturon, metaldehyde) were quantified in the 6 samples collected at the outlet of the urban stormwater catchment. They were distributed between the two phases as follows: -
22 substances were quantified only on the particulate phase; 6 substances were only quantified on the dissolved phase; 16 substances were quantified on both phases; 1 VOC was quantified on the whole water sample.
With respect to their frequencies of occurrence (in %), which are summarized in Table 4, SPS were classified in 5 groups. A total of 37 determinands displayed a frequency of occurrence of almost 100%. Some other substances, 8 of the 45 quantified SPS, were found at a lowest frequency ranging from 0 to 25%. This was the case for pentachlorophenol, 4 para-nonylphenol, 4-n-octylphenol, chromium and four pesticides, namely metaldehyde, isoproturon, glyphosate and desethylatrazine (a metabolite of
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Table 6 e Water quality parameters. Parameter pH Conductivity Total NTK Total Phosphorus SPM COD
Unit
Stormwater (this study)
pH mS.cm1 mgN.l1 mgP.l1 mg/L mg/L
7.05e7.35 [7.21] 288e1316[547] 1.55e5.94 [3.10] 0.47e3.52[1.52] 58e430[193] 48e230[125]
Runoff water at Marais catchmenta
Wastewater of dry weather OPURa
Combined wet weather flows OPURa
e e <4 e 30e75 [36] 43e113 [56]
e e 30e43 e 157e243 [298] 315e528 [388]
e e 15e35 [25] e 174e403 [279] 286e633 [432]
MinimumeMaximum [average]. a d10ed90ed50 (Gasperi, 2006).
atrazine). Consequently, 43 molecules were never quantified in any sample. This latter case was observed for PBDEs, chloroalkanes, PCB194, 4-chloro-3-methylphenol, most of the VOCs, some metals (Cd, Hg, Ni, Pt), and some pesticides. In fact, 24 pesticides were systematically monitored: alachlor, aldrin, endrin, dieldrin, DDT-2,40 , DDT-4,40 , isodrin, endosulfan (alpha & beta), lindane, alpha hexachlorocyclohexane, chlorfen-vinphos, chlorpyrifos, trifluralin, atrazine, desethylatrazine, desethylsimazine, simazine, diuron, isoproturon, metaldehyde, aminotriazole, glyphosate and AMPA. However, only diuron, aminotriazole, and AMPA (glyphosate major’s metabolite) were measured in all samples while desethylatrazine and metaldehyde were quantified at a lesser extent, as previously mentioned. Furthermore, some pesticides, such as atrazine, simazine and its metabolite desethylsimazine, were detected in the dissolved phase for all the samples but not quantified because their concentrations were below LOQ. The use of many of these pesticides is now banned in France. As a consequence their presence would no longer be expected in environmental samples. This, along with their seasonal use, might explain their weak occurrence in stormwater. Another reason could be that they occur at lower level in stormwater samples than the respective LOQ. Methylene chloride was the sole VOC quantified in all samples; the remaining 15 VOC were never detected. This might be related to our sampling procedure that could not be suitable for VOC analysis. These latter should have been analyzed after a punctual sampling that would have limited losses. However, such result is in accordance with those reported by Gasperi et al. (2009) in surface waters for the Seine Basin in that VOC were never quantified. Besides, our results showed that stormwater samples contained 47% of priority hazardous substances (8 out of 17 substances), 38% of priority substances (10 out of 26) and 60% of other substances (27 out of 45 substances). These substances belong to 9 chemical groups: metal, organotin, PAH, PCB, alkylphenol, pesticide, phthalate, chlorophenol and VOC. Substances form the remaining 4 chemical groups, namely chlorobenzene, BTEX, PBDE and chloroalkane, were always below LOQ. These chemicals are mainly used in a variety of industrial applications (Rule et al., 2006a). As a result the probability of being discharged into storm sewer in an urban catchment remains limited.
3.3.
g.dw), P (ng/L), the dissolved phase D (ng/L) and the sum D þ P, expressed in ng/L, for quantified substances.
3.3.1.
Metals
Heavy metals are of particular interest in stormwater runoff due to their toxicity, ubiquitousness and the fact that they cannot be chemically transformed or destroyed. A total of 8 metals (Pb, Cu, Cr, Cd, Pt, Hg, Ni, Zn) were analyzed. Whereas Pt (<0.02 mg/L), Hg (<1 mg/L), Ni (<20 mg/L) and Cd (<2 mg/L) exhibited concentrations lower than LOQ, Cu, Cr, Pb and Zn were detected and quantified in all samples. Only Zn was observed in the dissolved phase. The concentration in this latter phase represented about 12% of the total concentration. The distribution of metals between dissolved and particulate phase has been previously assessed for samples collected in street runoff (Gromaire-Mertz et al., 1999), similar trends were reported, i.e. metals were mainly particle-bound, since 97, 83, 67 and 52% of total Pb, Cd, Cu, and Zn, respectively, were associated with suspended solids. However, being less associated to suspended solids, the proportion of Zn in the dissolved phase was the highest among the four metals. Besides, the (D þ P) concentrations observed in stormwater follow that order: Zn (130e520 mg/L) > Cu (50e220 mg/L) > Pb (25e129 mg/L) > Cr (10e45 mg/L). It is difficult to compare the concentrations reported in the literature from one site to another, due to the influence of local sources, like roofing material, land use and traffic, etc. on the observed levels. However, these four metals were previously detected in runoff samples collected within a small and highly impervious urban catchment in Los Angeles with similar concentrations for Cr (2.1e20 mg/L) and Zn (32e320 mg/L), while Cu (5.9e37 mg/L) and Pb (1.2e16 mg/L) presented concentrations 10 times smaller (Sabin et al., 2006). In runoff, sampled in a small urban catchment in Paris (GromaireMertz et al., 1999), the concentrations were reported at higher levels for Zn (1024e3343 mg/L), linked to the erosion of the zinc roofing material and to a lesser extent to the zinc gutters, at the same levels as those observed in this study for Cu (58e208 mg/L) and Pb (132e377 mg/L), Cr was not analyzed. The presence of these metals in stormwater is due to vehicle brake emissions for Cu and tire wear for Zn (Davis et al., 2001), atmospheric deposition is also an important source for Cu and Pb (Davis et al., 2001; Legret and Pagotto, 1999; Sorme and Lagerkvist, 2002).
Concentrations of stormwater priority substances 3.3.2.
Table 5 summarizes the minimum, average and maximum concentrations in each phase, i.e. the particulate phase P*(ng/
Organotins
Three organotin compounds, namely the monobutyl- (MBT), the dibutyl- (DBT) and the tributyl- (TBT), were monitored. As
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 9 1 3 e9 2 5
illustrated in Table 5, the three compounds were quantified in 100% of the samples, mainly in the particulate phase, at the following levels: 91e139, 74e93 and 50e78 ng/L for MBT, DBT and TBT, respectively. Scarce are the studies illustrating organotin concentrations in urban waters and even more in stormwater. (Cornelissen et al., 2008), for runoff in two harbors in Norway, observed quite similar organotin concentrations, since they mentioned levels in the 9e85, 8e140, 9e185 ng/L range for MBT, DBT and TBT, respectively. (Gasperi et al., 2008) reported organotin concentrations in wastewater during dry as well as wet weather periods for the combined sewer network of Paris city. The levels observed during wet weather periods were slightly higher than dry period observations. Indeed, during dry weather periods, the medians were, 20, 15, <5 ng/L for MBT, DBT and TBT, respectively, while they became 28, 16, <5 ng/L for MBT, DBT and TBT during wet weather periods. When compared to the levels found in stormwater during this study, it can be noticed than, whatever the compound considered, our levels in stormwater were significantly higher than those for the Parisian combined sewer network, since the mean concentrations were 120, 80, 60 ng/L for MBT, DBT and TBT, respectively. However, it is important to mention that (Gasperi et al., 2008) have analyzed organotins on whole water samples of wastewater, this might explain the relatively low levels they observed, since organotins have very low water solubility and present a strong tendency to absorb to particulate matter. Concentrations might have been underestimated (Zgheib et al., 2009). The majority of organotin uses are comprised of major commercial applications: PVC, heat stabilizers, biocides, catalysts, glass coatings, surface disinfectants and preservatives for wood, paints etc. (Fromme et al., 2005). Thus they are present in water pipes, food packing materials, glass coatings, polyurethane foams and many other consumer products (Cornelissen et al., 2008; Fromme et al., 2005). The ratio between the two main TBT degradation products and TBT itself, (MBT þ DBT)/TBT, was used to get information about sources and degradation, knowing that TBT half-lives are usually in the order of years to decade (Cornelissen et al., 2008). In our samples, the (MBT þ DBT)/TBT ratio was about 2.0. We observed levels of MBT and DBT in stormwater that exceed TBT levels. Since there is no accumulation of sediment in the storm sewer pipe that could be re-suspended during a storm, TBT degradation was neglected. Accordingly, this can be related to local emissions. The most likely source of MBT and DBT emission is organotin-stabilized PVC (known to contain MBT and DBT at 5e20 g kg1) used in e.g. packaging material, foils, piping, window frames and coating materials (Hoch, 2001).
3.3.3.
PAHs
A total of 16 PAH, i.e. naphthalene (N), acenaphthene (Ace), acenaphthylene (Acyl), fluorene (F), phenanthrene (P), anthracene (A), fluoranthene (Fluo), pyrene (Pyr), benzo[a] anthracene (BaA), chrysene (Chry), benzo[a]pyrene (BaP), benzo[b]fluoranthene (BbF), benzo[k]fluoranthene (BkF), dibenz[ah]anthracene (DahA), benzo[ghi]perylene (BP), indeno [cd]pyrene (IP), were analyzed for all the samples. Each individual PAH was quantified in all stormwater samples. The mean concentration of carcinogenic PAH expressed by sum of
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P 6 PAH ( 6 PAHs ¼ BaA, BbF, BkF, BaP, DahA, IP), predefined by the International Agency for Research on Cancer (IARC) and the World Health Organization (WHO) recommendation total, in stormwater was 0.98 mg/L (0.26e1.94 mg/L). The minimum, average and maximum concentrations of the sum of the 16 P PAH ( 16 PAHs) defined by the US environmental protection agency (USEPA) were determined as 0.88, 3.30 and 6.48 mg/L respectively. The main compound contributing to the total PAH load was Pyr (16%), followed by Fluo (13%), P (12%), Chry (10%), BbF (10%), BP (8%). These high molecular weight PAHs (from 4 to 6 aromatic rings) indicate inputs from pyrolytic origins linked to the high density of combustion sources within this urban dense catchment in Paris suburb, such as gasoline-powered vehicles, residential heating (Gasperi et al., 2008, 2006). PAHs are formed primarily from incomplete combustion of organic substances such as wood, carbon, or mineral oil. The most common anthropogenic sources of PAHs are vehicle traffic, power stations in urban area (Rule et al., 2006a), but also discharges of certain petroleum products from garages, fuel stations and vehicle washing and maintenance. Moreover, the occurrence of N in runoff is due to the use of degreasing petroleum solvents (Rule et al., 2006a). These PAH levels in stormwater are higher than those found in the combined wet weather flows (2.12 mg/L, median) or in street runoff (0.76 mg/L, median) in previous studies at the Marais watershed in Paris (Gasperi et al., 2008). Finally, PAHs were quantified in all samples in the particulate phase while most of them, namely A, BaA, Chry, BaP, BbF, BkF, DahA, BP and IP, were never been observed in the dissolved phase. Actually, a Log Kow greater than 3 (as it is the case for PAHs) indicates a strong tendency to partition in SPM rather than in dissolved phase. To ensure a complete extraction of compounds sorbed on particles, i.e. with Log Kow greater than 3, a suitable extraction method is required (Zgheib et al., 2009).
3.3.4.
PCBs
Eight PCB congeners were investigated in this study (28, 52, 101, 118, 138, 153, 180 and 194). Despite their ban in France since 1987, 7 congeners (i.e. PCB28, 52, 101, 118, 138, 153 and 180, designated in European legislation) were quantified in stormwater samples. PCBs are toxic substances characterized by their persistence in the atmosphere and their ability to be transported over long distances (Chevreuil et al., 1996; Rossi et al., 2004). PCBs were found to be particle-bound at 100%. As a consequence, they were observed only in the particulate phase, being always below LOQ (<30 ng/L) in the dissolved phase. The observed concentrations were in the 27e42 ng/L range for the mean concentrations. This is in accordance with (Hwang and Foster, 2008), who noticed that PCBs in storm flow were significantly enriched in the particle phase (90%). There are only a very few studies concerning PCB in stormwater. However, an investigation about the loadings, solid-water partitioning, transport dynamics, and sources of PCBs in urban stormwater runoff from southern Washington (USA), reported elevated levels of PCBs (9.82e211 ng/L), higher than base flow by up to 80-fold (Hwang and Foster, 2008). These levels are in the same range as ours. PCB congener patterns found in stormwater samples clearly explain stormwater runoff is a major transport pathway of PCBs as also observed in Switzerland (Rossi et al., 2004).
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3.3.5.
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Alkylphenols
For the alkylphenols chemical group, five substances were investigated: nonylphenols, 4-(para)-nonylphenol, para-tertoctylphenol, 4-ter-butylphenol and 4-n-octylphenol. All these substances were quantified in stormwater: nonylphenols (1.59e9.17 mg/L) > OP (0.11e0.27 mg/L) > 4-ter-butylphenol (0.13e0.20 mg/L) > NP (
4-n-octylphenol (
3.3.6.
Phthalates
For this group of chemicals only the diethylhexyl phthalate (DEHP), the main plasticizer, was analyzed. It was observed at 100% in stormwater samples in both the particulate and the dissolved phases. The total concentrations ranged between 15 and 61 mg/L (30 mg/L, mean value). This concentration was higher than those found in the literature, i.e., 5 mg/L as median value in runoff from three catchment areas in Sweden (Bjo¨rklund et al., 2009), 0.75e1.25 mg/L at London (Rule et al., 2006a) but comparable to those measured in wastewater at Paris: 16e57 mg/L (Gasperi et al., 2008). These high concentrations of DEHP were expected since this phthalate is the main plasticizer used. Moreover, the DEHP proportion in the particulate phase was higher (75% in average) than those for the dissolved phase. These data confirm that surface runoff contribute to the DEHP pollution during a storm at the outlet of urban catchments.
3.3.7.
Pesticides
Among the 24 pesticides analyzed in the stormwater samples, only 6 were quantified above the limits of quantification (Table 5). Most of these substances are herbicides, while metaldehyde is a molluscicide. The undetected pesticides were the same as those reported previously by Rule et al. (2006a) for runoff and wastewater in London and by Gasperi et al. (2008) for wastewater in Paris. For many of them, their use is now banned. Hence, their presence would not be expected anymore. The pesticide concentrations followed this order: diuron (0.39e0.65 mg/L) > glyphosate ( AMPA (0.48e0.73 mg/L) > aminotriazole (0.14e0.53 mg/ L) > isoproturon (0.004e0.082 mg/L) > metaldehyde (
3.3.8.
3.4.
In order to identify substances that could represent an important risk to the environment, an environmental risk evaluation was carried out using two different approaches: firstly (D þ P), concentrations were compared to EQS. This approach was applied to substances having an EQS fixed by either the European Commission (directive 2008/105/CE) or the French government (circular 2007/23) (MEEDDAT, 2007). EQS, in the Directive 2008/105/EC, refers to the concentration in the whole water sample, with the exception for metals, in that particular case it refers to dissolved concentration (at 0.45 mm). As explained in the introduction, analyses were performed on both the dissolved and the particulate fractions to assess the partition of contamination. As there is no available EQS for both fractions, the (D þ P) concentrations were compared to the EQS when existing, (D þ P). The purpose of this comparison of SPS concentrations with EQS was to give a more or less realistic picture of the threat to aquatic environment, due to the release of untreated runoff to waterways. This was a simple and easy approach that gave an indicative rate of dilution needed in the receiving environment to prevent any pollution impact. The dilution factor (F) was therefore calculated as follows: F ¼ ½D þ P=EQS. However, this method needs to be considered with some precaution when either the dissolved or the particulate concentration was below LOQ. In this case, we maximised the concentration of the contaminant, and also its impact, by attributing LOQ as a concentration. In addition, this approach implies that stormwater was discharged in receiving waters containing none of the 88 monitored substances. As a result, SPS were then classified in 5 groups according to their dilution factor as shown in Table 7. 15 substances needed a dilution factor comprised between 10 and 100, whereas 6 others needed a dilution
Table 7 e Dilution factor (F) classes. Dilution factor (F)
Substances
Total
0
Priority: naphthalene, anthracene, pentachlorophenol, methylene chloride, isoproturon Others: acenaphthene, acenaphthylene, fluorine, 4-n-octylphenol Priority: fluoranthene, diuron Priority hazardous: benzo[a]pyrene, benzo[k]fluoranthene Others: Phenanthrene Priority: DEHP, Pb Priority hazardous: benzo[b]fluoranthene, nonylphenols others: pyrene, Zn, benzo[a]anthracene, PCB28, PCB52, PCB101, PCB118, PCB138, PCB153, PCB180, Cr Priority: Priority hazardous: TBT, benzo[g,h,i]perylene, indeno[1,2, 3-cd]pyrene Others: Cu, chrysene, dibenzo[a, h]anthracene
9
1 < F 10
10 < F 100
Other chemical compounds
In stormwater, methylene chloride was the unique volatile organic compound quantified in 100% of samples with a concentration ranging from 1.5 to 13 mg/L. Among the chlorophenol chemical family, only pentachlorophenol was quantified once at 0.2 mg/L. This latter was used as a biocide, particularly in the timber and textile industries (Rule et al., 2006a). Due to its phasing out of use, we did not expect its detection in all samples.
Environmental risk evaluation
F > 100
5
15
6
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Table 8 e Comparison between particulate levels of stormwater particles with the Canadian sediment guidelines (SQG and PEL).
Lead Chromium Copper Zinc Naphthalene Acenaphthene Acenaphthylene Fluorene Phenanthrene Anthracene Fluoranthene Pyrene Benzo[a]anthracene Chrysene Benzo[a]pyrene Dibenzo[a,h]anthracene SPCBs
P*
Settleable particles
SQG
This study (mg/g.dw)
(Gasperi et al., 2009) (mg/g.dw)
(mg/g.dw)
15 4.4 26 69 0.26 0.06 0.13 0.14 1.14 0.16 1.6 2.2 0.51 1.21 0.58 0.21 0.22
66 56 73 354 <0.05 <0.03 <0.05 0.05 0.34 0.10 0.76 064 0,37 0,39 0.47 0.08 0.11
35.0 37.9 35.7 123 0.03 0.01 0.01 0.02 0.04 0.05 0.11 0.05 0.03 0.06 0.03 0.01 0.03
P*/SQG
PEL
P*/PEL
(mg/g.dw) 0.4 0.1 0.7 0.6 9 6 13 7 29 3 15 44 17 20 19 21 7
91.3 90 197 315 0.9 0.9 0.13 0.14 0.52 0.25 2.36 0.88 0.39 0.86 0.78 0.14 0.28
0.2 0.0 0.1 0.2 0.7 0.1 1.0 1.0 2.2 0.6 0.7 2.5 1.3 1.4 0.7 1.5 0,8
SQG: Sediment Quality Guideline and PEL: Probable Effect Level, according to the Canadian Sediment Quality Guidelines for the Protection of Aquatic Life (Canadian Council of Ministers of the Environment, 1999).
factor greater than 100. For those, 3 belongs to the priority hazardous substances, namely TBT, benzo[g,h,i]perylene, indeno[1,2,3-cd]pyrene, and 3 to the other urban substances, i.e., Cu, chrysene, dibenzo[a,h]anthracene. Secondly, in the absence of acute sediment quality criteria defined in the WFD, particulate content of SPM (P*) were compared with the Canadian Sediment Quality Guidelines for the Protection of Aquatic Life (Canadian Council of Ministers of the Environment, 1999). These guidelines set forth two types of standards: the Sediment Quality Guideline (SQG), and the Probable Effect Level (PEL). With respect to those guidelines, for a given substance in sediment, a concentration below SQG is not expected to be correlated with any adverse biological effects, whereas a concentration above PEL is expected to be frequently associated with such adverse effects. As can be seen from Table 8, SPM appeared to be heavily contaminated since most substances (13 out 17) exhibited particulate levels above the SQG values. As a matter of fact, P* below SQG were observed only for metals. Hence, the level for SPCBs was 7 times higher than the recommended value, while PAH levels were in the range of 3e44 times higher. Similar results were reported by Gasperi et al. (2009), the metal and SPCBs levels were roughly 1e3 times higher than the recommended value, whereas PAH levels were 2e15 times higher. These results underlined that SPM from stormwater are more contaminated than settleable particles of the Seine River. Additionally, considering PEL values, 5 PAHs (namely, phenanthrene, pyrene, benzo[a]anthracene, chrysene and dibenzo[a,h]anthracene) exceeded the guideline threshold, thus implying potential adverse biological effects on freshwater organisms. For all PAHs, the exceedances equal about 1.3e2.5 times higher than the PEL guideline. From time to time, depending on the storm, it appears that PCBs also exhibited particulate content in the same order of magnitude than PEL. In conclusion, these findings point out that the
levels of PAHs and PCBs in stormwater particles constitute a potential risk to freshwater organisms.
4.
Conclusions
As previously stated (Eriksson et al., 2007; The´venot, 2008), a practical application of monitoring stormwater priority substances is its role in an approach to support watershed managers in evaluating the use of stormwater best management practices (BMPs) with specific regard to the removal of the key pollutants. Our results provide field data for SPS that often lack to really evaluate the fate of SPS during their passage through BMPs. As a consequence, the combination of a theoretical approach, such as the one proposed by Eriksson et al. (2007), with field measurements of the partition of substances between both the particulate and the dissolved phases, as advised in this study, would offer a relevant decision support tool to urban stormwater practitioners. Indeed, in stormwater, there are a wide variety of substances, with very different physicochemical properties, the choice of any BMP will be a compromise, none of them will be efficient for all the substances. Besides, for already implemented BMPs, knowing such partition for SPS in influent and effluent will be helpful to evaluate the efficiency of a given BMP and to ensure that it still achieves the water quality goal it was designed for. Therefore, this paper consists in one of the first investigations for describing the presence of priority substances in stormwater in an urban dense area in Paris suburb. Our observations of the level of selected urban substances in stormwater allows for the following conclusions: 1- Within the 88 urban substances, 45 substances were quantified including contaminants belonging to the following chemical
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groups: metals, organotins, PAH, PCB, alkylphenols, pesticides, phthalates, cholorophenols and VOC. However, chemicals from the last 4 remaining groups (chlorobenzene, BTEX, PBDE and chloroalkane) were always below LOQ and LOD in all samples. 2- The concentrations for these substances in both the dissolved (mg/L) and the particulate phases (mg/g.dw) provide a new set of information about pollution of stormwater. These data can be used afterwards to determine the most suitable treatment for stormwater before it reaches watercourses. 3- The methodology used for SPS analysis illustrated that some substances were quantified only in the particulate phase, while their concentrations in the dissolved phase were below the limit of quantification, as for PCBs, some PAHs, organotins. Analyses must be performed on both the dissolved and the particulate phases in order to allow for a better assessment of ecological risks for priority substances. Most priority substances in stormwater were linked to particles, suggesting that a removal of those particles using the most suitable best management practices (BMPs) would likely decrease the impact of stormwater on watercourses. 4- The concentrations observed for most of the emerging substances were higher than their EQS when existing. With respect to the receiving waters, contaminants exhibiting a dilution factor greater than 1 should be considered with more attention by managers. With respect to Canadian sediment quality guidelines, the levels of PAHs and PCBs in stormwater particles constitute also a potential risk to freshwater organisms.
Acknowledgments The authors gratefully acknowledge the Seine-Normandy Water Agency (AESN), the Interdepartmental Association for Sewage Disposal in Paris Conurbation (SIAAP), the Municipality of Paris, the Water and Sewage Disposal Departmental Administrations of Seine Saint Denis (DEA93), the Water and Sewage Disposal Departmental Administration of Val de Marne (DSEA94) and Regional Council of “Ile de France” (CRIF) for their financial support within the framework of the OPUR programme research.
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European Water Framework Directive. Journal of Chromatography A 1216 (3), 302e315. MEEDDAT, 2007. Circulaire 2007/23 de´finissant les normes de qualite´ environnementale provisoires (NQEp) des 41 substances implique´es dans l’e´valuation de l’e´tat chimique des masses d’eau ainsi que des substances pertinentes du programme national de re´duction des substances dangereuses dans l’eau. JO-RF: 1e13. Rossi, L., de Alencastro, L., Kupper, T., Tarradellas, J., 2004. Urban stormwater contamination by polychlorinated biphenyls (PCBs) and its importance for urban water systems in Switzerland. Science of the Total Environment 322 (1e3), 179e189. Rule, K.L., Comber, S.D.W., Ross, D., Thornton, A., Makropoulos, C.K., Rautiu, R., 2006a. Sources of priority substances entering an urban wastewater catchmentetrace organic chemicals. Chemosphere 63 (4), 581e591. Rule, K.L., Comber, S.D.W., Ross, D., Thornton, A., Markropoulos, C.K., Rautiu, R., 2006b. Survey of priority substances entering thirty English wastewater treatment works. Water and Environment Journal 20, 177e184.
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Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Formation of disinfection by-products in indoor swimming pool water: The contribution from filling water natural organic matter and swimmer body fluids Amer Kanan, Tanju Karanfil* Department of Environmental Engineering and Earth Sciences, Clemson University, 342 Computer Court, Anderson, SC 29625, USA
article info
abstract
Article history:
The contribution and role of different precursors in the formation of three class of disin-
Received 4 July 2010
fection by-products (DBPs) [trihalomethanes (THMs), haloacetic acids (HAAs), and haloni-
Received in revised form
tromethanes (HNMs)] in swimming pool waters were examined using filling waters
6 September 2010
obtained from five drinking water treatment plant (WTP) effluents and three body fluid
Accepted 23 September 2010
analogs (BFAs). BFAs exerted higher chlorine demands as compared to natural organic
Available online 1 October 2010
matter (NOM) in filling waters. BFAs exhibited higher HAA formation potentials than THM formation potentials, while the opposite was observed for the filling water NOM. There was
Keywords:
no appreciable difference in the HNM formation potentials of BFAs and filling water NOM.
Disinfection by-products (DBPs)
Different components in the BFAs tested exhibited different degree and type of DBP
Body fluid analogs (BFAs)
formation. Citric acid had significantly higher THM and HAA yields than other BFA
Natural organic matter (NOM)
components. The effect of temperature was greater on THM formation, whereas the effect
Trihalomethanes (THMs)
of contact time had more impact on HAA formation. Experiments with filling waters
Haloacetic acid (HAAs)
collected from WTP effluents at three different times showed more variability in HAA than
Halonitromethanes (HNMs)
THM formation at the WTPs studied. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Disinfection is critical for controlling the microbial activity in swimming pools; however it has also important unintended consequences. Chlorine, the most commonly used disinfectant in swimming pools, reacts with the organic matter in swimming pool water producing reaction by-products known as disinfection by-products (DBPs). DBPs have been classified as probable or possible carcinogen, and associated with reproductive and developmental effects. As a result, they are currently regulated in drinking waters around the world (Karanfil et al., 2008), and increasingly stringent regulations have been imposed for them in the United States (US) under the US EPA’s D/DBP Rule (US EPA, 2006). DBPs in swimming
pools can be ingested, inhaled or absorbed through the skin. It has been shown that there is more risk of DBPs exposure from inhalation and dermal pathways during swimming, showering and bathing than ingestion drinking water (Caro and Gallego, 2007; Villanueva et al., 2007; Kanan, 2010). Precursors of DBPs in pool water are (i) natural organic matter (NOM) that comes from filling water that is also used as dilution and make-up water, and (ii) human body fluids (BF) that are added to the pool water from swimmers. The filling water is commonly obtained from a drinking water distribution system or a groundwater source. These two types of organics have very different characteristics; therefore, it is hypothesized that they will exhibit different reactivity toward DBP formation and speciation. NOM is present in all natural
* Corresponding author. Tel.: þ1 864 656 1005; fax: þ1 864 656 0672. E-mail address: [email protected] (T. Karanfil). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.031
927
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waters, and it is a heterogeneous mixture of various organic molecules originating from allochthonous (i.e., biota in a water body) and autochthonous (i.e., soil and terrestrial vegetation) sources. If the filling water is obtained from a water distribution system, it will contain the NOM fraction remaining in water after treatment operations at the drinking water treatment plant. If the filling water is obtained from a groundwater and used without any treatment, it consists of the NOM in the groundwater. In the US, water is disinfected at the effluents of water treatment plants, and the maintenance of a disinfectant residual is required in the distribution systems. As a result, some amount of DBPs is already present in the filling water of swimming pools. However, the DBP formation potential of NOM is not completely exhausted in distribution system because some DBPs are regulated, and chlorine is applied only at sufficient amounts in order to comply with the US EPA’s microbial and DBP regulations. Given the fact that high levels of free chlorine residuals are continuously maintained in public swimming pools in US (NSPF, 2006), NOM components will continue to form DBPs in the swimming pool water. A recent survey of twenty-three indoor public pools in the US showed that the median chlorine residual concentration was 3.0 mg/L (Kanan, 2010). The human body excretions (HBE) are mainly composed of urine, sweat, dirt, saliva, body cells (skin cells, hair), and lotions (synthetic chemicals such as sunscreen, cosmetics, soap residues, etc.). Specifically urine and sweat constituents such as ammonia, urea, various amino acids (e.g., arginine), creatinine, citric acid, uric acid, gluconic acid, and sodium chloride are the major components that are released to water by swimmers (Barbot and Moulin, 2008; Anipsitakis et al., 2008). It is estimated that a bather releases a mixture of 50 mL urine and 200 mL sweat in an average swimming event (Judd and Bullock, 2003). These HBE compounds continuously accumulate and increase in swimming pools with time due to semi-batch nature of swimming pool operations. The survey of twenty-three indoor public pools in the US showed that the total organic carbon (TOC) concentrations ranged from 3.0 to 23.6 mg/L with a median of 7.1 mg/L, which was significantly
higher than the TOC levels in the filling waters (Kanan, 2010). In eight indoor swimming pools in London, the dissolved organic carbon (DOC) significantly increased with the number of swimmers, ranging from 3.3 to 12.9 mg/L (Chu and Nieuwenhuijsen, 2002). Thacker and Nitnaware (2003) reported a range of 0.094 to 16 mg/L TOC, which was related to the number of swimmers. Kim et al. (2002) chlorinated materials of human origin (i.e., hair, saliva, skin and urine) separately and together as a mixture in two water sources (surface and groundwater). The formation of five different DBPs (chloroform, bromodichloromethane, chloral hydrate, dichloroacetonitrile, and trichloropropane) from these materials was shown in addition to the amounts formed from the background water used in the experiments. Since it is not feasible to collect and preserve large amounts of HBEs, different body fluid analog (BFA) recipes have been proposed in the literature to simulate swimmer body excretions (Table 1) (Borgmann-Strahsen, 2003; Judd and Bullock, 2003; Goeres et al., 2004). These recipes were prepared to simulate the release of 50 mL urine and 200 mL sweat during an average swim for one person. They have been used in previous swimming pool research to investigate: (i) the formation and accumulation of THMs (Judd and Bullock, 2003), (ii) the efficiency of disinfection against biofilm formation (Goeres et al., 2004), and (iii) biocidal efficacy of different disinfectants (Borgmann-Strahsen, 2003). To date, no study has compared the formation and speciation of DBPs from these BFAs despite some differences in their compositions. The objective of this work was to examine the roles and contributions of the two main types of organic precursors (i.e., filling water (NOM) vs. body fluids (BF) components of swimmers) to the formation of three class of DBPs [trihalomethanes (THM4), haloacetic acids (HAA9), and halonitromethanes (HNM9)] in swimming pools. Specifically, we examined and compared the reactivity of (i) three BFAs proposed in literature and some of their components, and (ii) the filling water NOM obtained from distribution systems in the US. Understanding the formation of DBPs from different types of precursors is important for developing strategies to control DBP formation
Table 1 e Body fluid analogs components. BFA(G) Ingredient urea creatinine uric acid lactic acid albumin glucuronic acid ammonium chloride sodium chloride sodium sulfate sodium bicarbonate potassium phosphate potassium sulfate
BFA(J) mg/L 62.6 4.3 1.5 3.3 9.7 1.2 7.0 22.1 35.3 6.7 11.4 10.1
BFA(B)
Ingredient
mg/L
Ingredient
mg/L
urea creatinine uric acid citric acid histidine hippuric acid ammonium chloride sodium phosphate
14800 1800 490 640 1210 1710 2000 4300
urea creatinine glutamic acid aspartic acid glycine histidine Lysine
23000 1250 300 830 450 200 75
BFA(G): BFA proposed by Goeres et al. (2004); BFA(J): BFA proposed by Judd and Bullock (2003); and BFA(B): BFA proposed by Borgmann-Strahsen (2003). The table shows the recipes used in the preparation of BFA stock solutions. The solutions used in the experiments were diluted to the target TOC concentration (1 mg/L) of the experiments.
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and speciation in swimming pools and reduce the exposure of swimmers and pool attendants to DBPs, especially in indoor public pools. Furthermore, knowing the differences in the DBP formation and speciation from the proposed BFAs is important to assess their use for studying DBPs in swimming pools.
2.
Materials and methods
2.1.
BFAs
to eliminate the particles and biological activity, and stored in a dark constant temperature room (4 C) until the experiments, which were usually performed within 2e4 days of storage. For each sample, the TOC, total nitrogen (TN), ultraviolet absorbance at 254 nm (UV254) and bromide concentrations were measured, and specific ultraviolet absorbance (SUVA254 ¼ UV254/DOC) was determined.
2.3.
Three BFAs [BFA(G), BFA(J), and BFA(B)] were prepared using distilled and deionized water (DDW) following their recipes in literature (Judd and Bullock, 2003; Goeres et al., 2004; Borgmann-Strahsen, 2003). Their compositions are provided in Table 1. The BFA solutions used in the experiments had the TOC concentration of 1 mg/L, which were prepared by diluting the stock solution of each BFA to the target TOC concentration. Additional information about their preparation can be found in the Supporting Information and elsewhere (Kanan, 2010).
2.2.
Filling water NOM
Filling water NOM samples were collected from five drinking water treatment plants after conventional clarification processes (i.e., after coagulation, flocculation, and sedimentation before any oxidant or disinfectant addition) in South Carolina, US: Spartanburg (SP), StartexeJacksoneWellforde Duncan (SJWD), Greenville (GV), Myrtle Beach (MB), and Charleston (CH). The only exception was the use of dissolved air flotation at the GV plant instead of sedimentation. Samples were collected before any oxidant and disinfectant addition to determine the overall DBP formation potentials of filling water NOM and compare with BFAs. They were collected at three different times at each location during the study (Table 2). They were filtered with pre-washed 0.2 mm Supor membrane filters
Formation potential (FP) tests
The DBP reactivity of the precursors in the filling water NOM and BFA samples was investigated by conducting FP tests. These tests, originally developed for drinking water samples, are conducted at excess chlorine concentrations for a long period of time (e.g., 5 and 10 days). Each sample, BFAs or filling water NOM, was initially diluted to bring the organic matter concentration to a constant level of 1 mg/L TOC, except GV samples for which the source water TOC concentrations were consistently slightly lower than 1 mg TOC/L (Table 2). Free chlorine was spiked to each sample at the constant initial dose of 50 mg/L. Therefore, Cl2/TOC levels were constant for all samples, except slightly higher in GV due to its lower TOC at the source. Chlorinated samples were incubated in headspace free amber glass bottles for 5 and 10 days at 26 and 40 C using a water bath. The selected temperatures are representative of minimum and maximum water pool temperatures in the US (Kanan, 2010).
2.4.
Analytical methods
Analytical methods used in the study and the minimum reporting levels (MRLs) are summarized in Supplemental Information (Table S1) and detailed information can be found elsewhere (Kanan, 2010). In brief, THM4 and HNM9 were quantified by liquid/liquid extraction with MtBE followed by gas chromatography with electron capture detection (GC/ECD)
Table 2 e Filling water NOMs collected for the experiments. TOC mg/L
TN mg/L
UV254 cm1
SUVA L/mg-m
November-08 March-09 September-09
1.7 1.4 1.7
0.3 0.3 0.3
0.0300 0.0237 0.0287
1.74 1.73 1.71
31 19 19
SP
November-08 March-09 September-09
1.6 1.7 1.7
0.2 0.2 0.2
0.0181 0.0209 0.0204
1.11 1.25 1.17
<MRL <MRL <MRL
GV
November-08 March-09 September-09
0.7 0.8 0.8
0.1 0.1 0.1
0.0119 0.0138 0.0075
1.63 1.71 0.96
<MRL 10 <MRL
CH
November-08 March-09 September-09
2.5 3.1 3.1
0.3 0.3 0.3
0.0535 0.0586 0.0583
2.10 1.88 1.86
110 79 75
MB
November-08 March-09 September-09
6.7 6.1 5.4
0.3 0.3 0.3
0.1365 0.1212 0.1127
2.05 1.98 2.08
37 29 45
WTP
Sampling Date
SJWD
Br mg/L
WTP: Water Treatment Plant, TOC (total organic carbon), TN (total nitrogen), SJWD (StartexeJacksoneWellfordeDuncan), SP (Spartanburg), GV (Greenville), MB (Myrtle Beach), CH (Charleston), MRL (minimum reporting level: 10 mg/L).
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according to US EPA Method 551.1 with some modifications (Kanan, 2010). HAA9 were analyzed by liquid/liquid extraction with MtBE followed by derivatization with diazomethane and analysis by GC/ECD. The relative standard deviation of DBP measurements were less than 10%.
3.
Results and discussion
3.1.
DBP formation potentials of BFAs
The chlorine demands (17e25 mg Cl2/mg TOC) of BFAs were relatively high, which was attributed to chlorine demands of the nitrogenous and carbonaceous compounds present in their compositions. These demands are consistent with those reported during previous chlorination studies of some of the same BFA components (Hureiki et al., 1994; Li and Blatchley, 2007; Hong et al., 2009). Since there was no detectable bromide present in the DDW used to prepare BFA solutions, formation of only chlorinated DBP species [TCM (chloroform) for THM4, DCAA and TCAA for HAA9 and TCNM (chloropicrin) for HNM9] were observed. The formation of TCM was always lower than the formation of total HAA (i.e., sum of DCAA and TCAA) during the FP tests (Table 3, Fig. S1). BFA(B) and BFA(J) exhibited comparable but higher total HAA formation potentials than that of BFA(G), mainly due to significantly higher formation of DCAA than TCAA (Fig. S2). DCAA to TCAA mass ratio ranged from 2.5 to 8.0 for BFA(J) and BFA(B), but it was nearly 1:1 for BFA(G). These differences were attributed to the presence of citric acid and two free amino acids (histidine and aspartic acids) in the compositions of BFA(B) and BFA(J). These two amino acids have been reported to exhibit high HAA formation potentials with preferential formation of DCAA in a previous study (Table 4). TCNM formation was at the trace levels from all three BFAs in the range of 0.8e2 mg/L (Table 3). TCM yields increased with contact time and temperature; however, the majority of 10-day yield (55e88%) formed during five days of reaction time. The effect of temperature was more pronounced on TCM than DCAA and TCAA formation.
In order to further examine the reactivity of BFAs, FP tests were conducted with the individual organic components in their mixtures. The results showed that citric acid exhibited significantly higher TCM and DCAA formation than all other individual components despite its lower chlorine consumption than other components except hippuric acid (Table 4). The occurrence of citric acid in tap waters (28e35 mg/L) and natural waters (44e85 mg/L) has been reported (Afghan et al., 1974; Bjork, 1975; Larson and Rockwell, 1979). Citric acid is also introduced continuously to pool waters from metabolic activity of both human body (sweat and urine) and microbial cells. Chlorination of citric acid in a previous study produced a considerable amount of TCM at a very fast rate (within 2 h) at pH 7 (Larson and Rockwell, 1979). After citric acid, albumin showed the second highest TCM yield (Table 4). TCM formation potential from 5 mg/L albumin at 20 C and pH 7 was previously reported to be 97 mg/L (i.e., 19 mg/mg) (Scully et al., 1988), which was comparable to the yield obtained in this study (23 mg/mg). Other BFA components urea, creatinine, hippuric acid, glucuronic acid, lactic acid and uric acid showed comparable TCM yields. The formation of TCM from the amino acids in the BFA composition was shown to be minimal in previous studies (Hong et al., 2009). The DCAA and TCAA yields from BFA components other than amino acids were similar to the pattern observed for TCM with two major differences: (i) formation of DCAA and TCAA from histidine and aspartic acid were higher or comparable to the yield of albumin, and (ii) DCAA and TCAA yields of urea, creatinine, hippuric acid, glucuronic acid, lactic acid, uric acid were very small. The formation of TCNM was always at the MRL levels of the measurement and did not show any compound specific pattern.
3.2.
DBP formation potentials of filling water NOM
The chlorine demands (2e8 mg Cl2/mg TOC) of filling water NOM samples were much lower than those of the BFAs under the same experimental conditions. Filling water NOM exhibited significantly higher reactivity toward producing THMs than HAAs at 26 C (Table 5 and Fig. 1) and at 40 C
Table 3 e THM, HAA and HNM formation from BFAs at pH 7, TOC of 1 mg/L, and initial chlorine dose of 50 mg/L. T ( C)
Incubation (days)
FAC residual (mg/L)
TCM (mg/L)
DCAA (mg/L)
TCAA (mg/L)
THAA (mg/L)
TCNM (mg/L)
BFA(G)
26 26 40 40
5 10 5 10
31 30 28 25
21 0.7 28 0.9 29 0.9 35 0.6
15 22 20 18
19 24 19 18
34 1.5 46 0.7 39 2.4 36 3.0
1.5 0.2 1.3 0.2 1.1 0.3 0.8 0.0
BFA(J)
26 26 40 40
5 10 5 10
33 32 29 28
30 1.1 38 1.1 50 0.8 77 2.8
51 54 66 68
19 21 15 27
70 2.7 75 6.4 81 0.6 95 5.1
1.6 0.1 1.1 0.4 1.1 0.4 0.9 0.0
BFA(B)
26 26 40 40
5 10 5 10
28 27 26 24
16 0.4 18 1.0 18 0.6 33 0.7
63 70 63 66
11 12 8 14
74 4.3 82 3.3 71 1.4 80 1.0
2.0 0.3 1.0 0.1 1.1 0.1 1.0 0.1
BFA
T (temperature), FAC (free available chlorine), TCM (chloroform), DCAA (dichloroacetic acid), TCAA (trichloroacetic acid), THAA (total HAA: sum of DCAA and TCAA), TCNM (trichloronitromethane).
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Table 4 e Disinfection By-Products formation from 1 mg/L BFA components at 22 C, pH 7, initial chlorine dose 50 mg/L, and 5-days contact time. BFA Component Urea Albumin Creatinine Citric acid Hippuric acid Glucuronic acid Lactic acid Uric acid Histidinea Aspartic acida Glycinea Lysinea
FAC mg/L
TCM mg/L
DCAA mg/L
TCAA mg/L
THAA mg/L
TCNM mg/L
37 43 37 45 46 41 41 39
13 23 12 307 14 13 14 15 1.55 0.5 1.68 0.5 ND 1.09 0.011
4 16 2 173 2 2 2 2 32.5 0.031 26.9 4.97 ND 3.52 0.12
6 23 4 8 4 4 5 4 14 0.2 3.17 2.12 ND 0.52 0.05
10 39 6 181 6 6 7 6 46.5 30
0.7 0.8 0.7 0.8 0.9 0.8 0.8 <MRL NM NM NM NM
b b b b
4
FAC (free available chlorine), TCM (chloroform), DCAA (dichloroacetic acid), TCAA (trichloroacetic acid), THAA (total HAA), TCNM (trichloronitromethane). ND: Not Detected; NM: Not Measured); MRL: Minimum Reporting Level. a Obtained from Hong et al. (2009). b Experimental condition of the study was Cl2/DOC ¼ 10, 4- days contact time at 20 C.
Table 5 e THM4, HAA9 and HNM9 formation potentials of filling water NOM samples at 26 C, pH 7, TOCa of 1 mg/L, and initial chlorine dose 50 mg/L. WTP
Date
Incubation days
TCM mg/L
BDCM mg/L
THM4 mg/L
DCAA mg/L
TCAA mg/L
BDCAA mg/L
HAA9 mg/L
SJWD SP GV CH MB SJWD SP GV CH MB
November-08
5 5 5 5 5 10 10 10 10 10
64 70 44 64 80 83 94 58 82 100
5 5 3 12 4 7 5 4 14 5
70 0.8 75 1.9 48 1.6 77 1.4 85 0.9 91 1.4 99 0.6 63 0.0 98 0.7 106 2.6
13 13 7 13 13 9 10 10 14 13
20 22 12 23 27 18 21 16 26 30
3 2 1 8 3 3 2 2 9 3
SJWD SP GV CH MB SJWD SP GV CH MB
March-09
5 5 5 5 5 10 10 10 10 10
69 79 70 71 78 95 99 87 90 94
5 4 4 10 3 6 5 5 11 12
75 3.0 84 0.2 75 1.8 82 3.3 81 0.1 103 0.9 104 0.1 92 0.9 104 1.2 108 1.3
NR NR NR NR NR 39 52 29 36 27
14 22 11 14 16 48 59 33 45 43
3 2 2 5 1 7 5 4 12 12
NR NR NR NR NR 97 3.8 119 2.3 68 3.1 97 2.0 86 4.5
1.1 0.1 1.3 0.0 1.1 0.0 1.0 0.2 1.1 0.0 0.8 0.0 1.0 0.0 <MRL <MRL 0.7 0.2
SJWD SP GV CH MB SJWD SP GV CH MB
September-09
5 5 5 5 5 10 10 10 10 10
85 90 101 83 97 103 110 121 102 117
5 4 3 10 4 6 4 4 12 5
91 3.7 95 2.3 106 2.0 96 0.7 103 2.9 110 3.7 115 1.2 126 2.0 117 1.8 123 2.4
NR NR NR NR NR 25 32 48 21 20
18 20 31 29 24 31 49 49 33 39
5 3 5 17 5 8 6 6 18 6
NR NR NR NR NR 65 3.7 89 2.9 105 2.3 73 1.1 66 2.3
<MRL 0.7 0.0 1.0 0.1 0.7 0.0 <MRL 0.7 0.1 0.8 0.1 1.0 0.1 0.7 0.0 0.7 0.1
37 39 20 47 43 31 33 28 50 46
1.2 1.3 0.2 1.7 0.2 0.8 1.3 4.4 2.4 1.3
TCNM mg/L 1.5 1.7 1.1 1.3 1.1 1.1 1.2 1.1 1.3 1.0
0.3 0.2 0.0 0.3 0.2 0.1 0.0 0.1 0.3 0.0
WTP: Water Treatment Plant. TCM (chloroform), BDCM (bromdichloromethane), DCAA (dichloroacetic acid), TCAA (trichloroacetic acid), BDCAA (bromodichloroacetic acid), TCNM (trichloronitromethane). The concentrations of dibrochloromethane, bromochloromethanes and dibromochloroacetic acid were lower than 3 mg/L; they were not individually listed in table. NR: Not Reported; MRL: Minimum Reporting Level. a GV had TOC concentrations 0.7e0.8 mg/L (Table 2); they were used in the experiments without any dilutions. Other filling water NOMs were diluted to 1 mg/TOC/L.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 9 2 6 e9 3 2
THM4, µg/L
120
a
5-days
10-days
80
40
0 BFA(G) BFA(J) BFA(B) SJWD
HAA9, µg/L
120
SP
GV
CH
MB
SP
GV
CH
MB
b
80
40
0 BFA(G) BFA(J) BFA(B) SJWD
Fig. 1 e THM4 (a) and HAA9 (b) yields from BFAs and five filling water NOMs (November 08 samples) after 5 and 10 day FP tests (error bars are standard deviation).
(Table S2). THM yields were twice and sometimes higher than those of HAAs. This trend is opposite to the observations with BFAs that produced higher amounts of HAAs than THMs (Table 3). The higher THM formation potential of NOM than proteins and free amino acids was also observed in early DBP studies when proteins, free amino acids and humic acid were chlorinated and compared under the same conditions (Morris et al., 1980; Scully et al., 1988). These opposing trends of THM and HAA formation from filling water NOMs and BFAs can explain the occurrence of very high HAA concentrations in US indoor swimming pools and their potential precursors. In a recent monitoring study of 23 indoor pools in the US showed that the ranges and average of THM and HAA concentrations were 26e213 mg/L (ave. 80 mg/L) and 173e9005 mg/L (ave. 1541 mg/L), respectively (Kanan, 2010). Therefore, very high concentrations of HAAs in US indoor pool waters are likely due to a combination of factors: (i) the BF components that are continuously added from bathers to swimming pools waters and reacting with chlorine, (ii) the pool waters in the US are not diluted systematically and not periodically replaced, resulting in very long water ages (>1e2 years), and (iii) HAAs are highly soluble in water and do not degrade in the presence of high chlorine residual. In terms of DBP speciation, TCAA concentrations were always higher than those of DCAA which was attributed to high chlorine to TOC ratios of the FP tests (Miller and Uden, 1983; Zhuo et al., 2001; Hua and Reckhow, 2008). Hua and Reckhow (2008) explained the decrease in DCAA formation at high Cl2/NOM ratios by the differences in the reactivity of the precursors as a function of chlorine doses and different pathways of DCAA and TCAA formation. Few brominated
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THM and HAA species were observed due to low bromide levels in filling water tested and high Cl2/Br ratio of the FP experiments. Increasing the contact time and the temperature increased both THM and HAA formation. The time effect was more apparent on HAA than THM formation; THM yields at 26 C ranged from 70 to 123 mg/L and from 91 to 103 mg/L for 5- and 10-days reaction periods, respectively, while the HAA yields were 18e50 mg/L and 31e119 mg/L during the same periods. The increase in temperature resulted in higher THM than HAA yields. TCNM concentrations, most of the time, ranged between 0.7 and 1.7 mg/L. The lower concentrations and narrow ranges of TCNM measured indicate low reactivity of the filling water NOMs to produce HNMs, as compared to THMs and HAAs. This was consistent with low degree of HNM formation potentials reported in chlorinated drinking waters (Hu et al., 2010). THM, HAA, and HNM formation of the filling water NOMs were tested at three different times and under the same experimental conditions (Table 5 and SI Table S2). For THM, although samples were obtained after conventional clarification processes from five water treatment plants using source waters with significantly different characteristics (Table 2), THM FP yields at the treatment plant effluents showed relatively low range of variability (10-days yield from 91 to 123 mg/L) independent of the time and location, except GV water sample. However, HAAs FPs exhibited more variability as function of time and location. This suggested that the removal of HAA precursors during conventional treatment processes was more variable than THM precursors in the treatment plants monitored in this study. The temporal variability of HAAs formation was also reported by others when chlorination of different water types was carried out at the same conditions but at different sampling dates or seasons (Rodriguez et al., 2007).
4.
Conclusions
The following conclusions were obtained from the comparative analysis of DBP formation potentials of BFAs and filling water NOM in this study: BFAs were more reactive toward chlorine than filling water NOM. BFAs formed more HAAs than THMs, while filling water NOM produced more THMs than HAAs. On the other hand, both NOMs and BFAs produced a similar amount of HNMs, which was significantly lower as compared to THM and HAA formation. Analysis of individual BFA components demonstrated that citric acid had significantly higher reactivity toward THM and HAA formation than other components. Individual reactivity of BFA components is important to consider and will determine the DBP formation and speciation of a particular BFA mixture. Filling waters collected from five different treatment plants in three different sampling events during a one year period exhibited comparable THM formation relatively independent of time and location, whereas HAA yields exhibited more time and spatial dependent variability.
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Increasing temperature and incubation time increased DBP formation. Higher THM and HAA yields were observed at 40 C than at 26 C. The effect of temperature was more pronounced on TCM than DCAA and TCAA formation, while incubation time had more impact on HAA than THM formation.
Acknowledgment During this research, Amer Kanan was sponsored by Ford Foundation International Fellowships Program and received a partial financial support from Al-Quds University. The manuscript has not been subjected to the peer and policy review of these agencies and therefore does not necessarily reflect their views.
Appendix. Supplementary material Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.watres.2010.09.031.
references
Afghan, B.K., Leung, R., Ryan, J.F., 1974. Automated fluorometric method for determination of citric acid in sewage and sewage effluents. Water Research 8, 789e795. Anipsitakis, G.P., Tufano, T.P., Dionysiou, D.D., 2008. Chemical and microbial decontamination of pool water using activated potassium peroxymonosulfate. Water Research 42, 2899e2910. Barbot, E., Moulin, P., 2008. Swimming pool water treatment by ultrafiltrationeadsorption process. Journal of Membrane Science 314, 50e57. Bjork, R.G., 1975. GLC determination of PPB levels of citrate by conversion to bromoform. Analytical Biochemistry 63, 80e86. Borgmann-Strahsen, R., 2003. Comparative assessment of different biocides in swimming pool water. International Biodeterioration & Biodegradation 51, 291e297. Caro, J., Gallego, M., 2007. Assessment of exposure of workers and swimmers to trihalomethanes in an indoor swimming pool. Environmental Science and Technology 41, 4793e4798. Chu, H., Nieuwenhuijsen, M.J., 2002. Distribution and determinants of trihalomethanes concentrations in indoor swimming pools. Occupational and Environmental Medicine 59, 243e247. Goeres, D.M., Palys, T., Sandel, B.B., Geiger, J., 2004. Evaluation of disinfectant efficacy against biofilm and suspended bacteria in a laboratory swimming pool model. Water Research 38, 3103e3109. Hong, H.C., Wong, M.H., Liang, Y., 2009. Amino acids as precursors of trihalomethanes and haloacetic acids formation during chlorination. Archives of Environmental Contamination and Toxicology 56 (4), 638e645. Hu, J., Song, H., Karanfil, T., 2010. Comparative analysis of halonitromethane and trihalomethane formation and speciation in drinking water: the effects of disinfectants, pH, bromide, and nitrite. Environmental Science and Technology 44 (2), 794e799.
Hua, G., Reckhow, D.A., 2008. DBP formation during chlorination: effect of reaction time, pH, dosage, and temperature. Journal of the American Water Works Association 100 (8), 82e89. Hureiki, L., Croue, J.P., Legube, B., 1994. Chlorination studies of free and combined amino acids. Water Research 28 (12), 2521e2531. Judd, S.J., Bullock, G., 2003. The fate of chlorine and organic materials in swimming pools. Chemosphere 5 (19), 869e879. Kanan, A., 2010. Occurrence and Formation of Disinfection ByProducts in Indoor Swimming Pools Water. Clemson University, Clemson, SC, USA, PhD dissertation. Karanfil, T., Krasner, S.W., Westerhoff, P., Xie, Y., 2008. Recent advances in disinfection byproduct formation, occurrence, control, health effects, and regulations. In: Karanfil, T., Krasner, S.W., Westerhoff, P., Xie, Y. (Eds.), Disinfection By-Products in Drinking Water: Occurrence, Formation, Health Effects, and Control. American Chemical Society, Washington, D.C. Kim, H., Shim, J., Lee, S., 2002. Formation of disinfection byproducts in chlorinated swimming pool water. Chemosphere 46, 123e130. Larson, R.A., Rockwell, A.L., 1979. Chloroform and chlorophenol production by decarboxylation of natural acids during aqueous chlorination. Environmental Science and Technology 13 (3), 325e329. Li, J., Blatchley III, E.R., 2007. Volatile disinfection byproduct formation resulting from chlorination of organic-nitrogen precursors in swimming pools. Environmental Science and Technology 41 (19), 6732e6739. Miller, J.W., Uden, P.C., 1983. Characterization of nonvolatile aqueous chlorination products of humic substances. Environmental Science and Technology 17 (3), 150e157. Morris, J.C., Ram, N.M., Baum, B., Wajon, E., 1980. Formation and Significance of N-chloro Compounds in Water Supplies. U.S. Government Printing Office, Washington, DC. EPA 600/2-80031. 1980. NSPF, 2006. Certified Pool-Spa Operator Handbook. National swimming pool Foundation. Colorado Springs, CO. Rodriguez, M.J., Serodesb, J., Royc, D., 2007. Formation and fate of haloacetic acids (HAAs) within the water treatment plant. Water Research 41, 4222e4232. Scully, F.E., Howell, G.D., Kravltz, R., Jewel, J.T., 1988. Proteins in natural waters and their relation to the formation of chlorinated organics during water disinfection. Environmental Science and Technology 22 (5), 537e542. Thacker, N.P., Nitnaware, V., 2003. Factors influencing formation of trihalomethanes in swimming pool water. Bulletin of Environmental Contamination and Toxicology 71 (3), 633e640. US EPA, 2006. National Primary Drinking Water Regulations: Stage 2 Disinfectants and Disinfection Byproducts Rule; Final Rule. http://www.epa.gov/fedrgstr/EPA-WATER/2006/January/Day04/w03.pdf. Villanueva, C.M., Cantor, K.P., Grimalt, J.O., Malats, N., Silverman, D., Tardon, A., Garcia, C.R., Serra, C., Carrato, A., Castano-Vinyals, G., Marcos, R., Rothman, N., Real, F.X., Dosemeci, M., Kogevinas, M., 2007. Bladder cancer and exposure to water disinfection by products through ingestion, bathing, showering and swimming in pools. American Journal of Epidemiology 165 (2), 148e156. Zhuo, C., Chengyong, Y., Junhe, L., Huixian, Z., Jinqi, Z., 2001. Factors on the formation by-products MX, DCA and TCA by chlorination of fulvic acid from lake sediments. Chemosphere 45, 379e385.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 9 3 3 e9 4 3
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
What affects public acceptance of recycled and desalinated water? Sara Dolnicar a,*, Anna Hurlimann b,1, Bettina Gru¨n a,c,2 a
Institute for Innovation in Business and Social Research, School of Management & Marketing, University of Wollongong, Northfields Ave, NSW 2522 Wollongong, Australia b Faculty of Architecture Building and Planning, The University of Melbourne, Melbourne 3010, Australia c Institute for Statistics and Mathematics, WU Wirtschaftsuniversita¨t Wien, Augasse 2-6, A-1090 Vienna, Austria
article info
abstract
Article history:
This paper identifies factors that are associated with higher levels of public acceptance for
Received 4 March 2010
recycled and desalinated water. For the first time, a wide range of hypothesized factors,
Received in revised form
both of socio-demographic and psychographic nature, are included simultaneously. The
21 September 2010
key results, based on a survey study of about 3000 respondents are that: (1) drivers of the
Accepted 23 September 2010
stated likelihood of using desalinated water differ somewhat from drivers of the stated
Available online 1 October 2010
likelihood of using recycled water; (2) positive perceptions of, and knowledge about, the respective water source are key drivers for the stated likelihood of usage; and (3) awareness
Keywords:
of water scarcity, as well as prior experience with using water from alternative sources,
Recycled water
increases the stated likelihood of use. Practical recommendations for public policy makers,
Desalinated water
such as key messages to be communicated to the public, are derived.
Public acceptance
1.
Introduction
Many countries endure water supplies that are insufficient to meet their present and future demands. Escalating pressure from increased population, along with the uncertainty of water supply conditions due to climate change, amounts to a burgeoning water crisis. While technologies are available to alleviate water shortage, many countries have experienced public resistance to the adoption of much needed water augmentation projects. To address the world’s water crisis it is essential that engineers and social scientists work together. Engineers can provide the best, safest and most energy-efficient solutions to augment water supplies, whereas social scientists can facilitate better understanding of the reasons
ª 2010 Elsevier Ltd. All rights reserved.
for public resistance to the adoption of water from alternative sources. Social scientists can also suggest ways in which public policy makers may be able to increase acceptance of alternative water sources and find solutions which are most acceptable for the community. The present study represents a social science contribution to this field. To date a significant amount of empirical work has been conducted to investigate the level of stated public acceptance for recycled water e Bruvold and Ward (1970); Bruvold (1972); Kasperson et al. (1974); Sims and Baumann (1974); Stone and Kahle (1974); Olson et al. (1979); Bruvold et al. (1981); Milliken and Lohman (1985); and Po et al. (2004). Recently, similar studies have been conducted in the context of desalinated water: Dolnicar and Scha¨fer (2006); Dolnicar and Scha¨fer
* Corresponding author. Tel.: þ61 2 4221 3862. E-mail addresses: [email protected] (S. Dolnicar), [email protected] (A. Hurlimann), [email protected] (B. Gru¨ n). 1 Tel.: þ61 3 8344 6976; fax: þ61 3 8344 5532. 2 Tel.: þ43 1 31336 5032; fax: þ43 1 31336 734. 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.030
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(2009); and Dolnicar and Hurlimann (2010). Each of these studies has provided an interesting snapshot of the public’s sentiments toward alternative water sources at the time of survey. Additionally, a number of other studies identified correlates of high acceptance levels e Hanke and Athanasiou (1970); Gallup (1973); Kasperson et al. (1974); Sims and Baumann (1974); Johnson (1979); Olson et al. (1979); Alhumoud et al. (2003); and Hurlimann and McKay (2004). However to date, a limited number of studies have attempted to include a comprehensive set of potential explanatory variables, and to simultaneously test the effect they have on the acceptance levels of water from alternative sources. The aim of this paper is to fill this gap, both for recycled and desalinated water. Specifically, we investigate which of the hypothesized personal characteristics are in fact associated with higher or lower levels of acceptance of recycled and desalinated water. Testing is conducted simultaneously for a wide range of independent variables, thus avoiding the overinterpretation of single factors. From the empirical findings we derive key insights and recommendations for public policy makers.
2.
Literature review
Since the 1970s a significant body of knowledge has developed around the topic of public acceptance of recycled water, providing useful information about general acceptance levels for various uses of recycled water. Most studies investigating public acceptance of recycled water come to the same conclusion e that people are very open to using recycled water for uses with low personal contact, such as watering trees and shrubs in their garden, but are reluctant to adopt recycled water for uses with high personal contact, such as drinking or bathing one’s baby. Although it could be argued that recycled water has now been used for many decades, recent studies have shown that the same pattern is still valid e Marks et al. (2006); Dolnicar and Scha¨fer (2006); Hurlimann (2006); and Hurlimann (2007a,b,c). For example, Dolnicar and Hurlimann (2010) found that 92% of Australian respondents would use recycled water for garden watering, but only 36% for drinking. Despite the significant research attention that public acceptance of recycled water has attracted, very little social science research has focused on water from other alternative sources. Only recently have comparative studies of acceptance across different kinds of water been undertaken, such as Dolnicar and Scha¨fer (2006), and Dolnicar and Scha¨fer (2009). Both conclude that people e in this case the Australian population e clearly discriminate between recycled and desalinated water. Desalinated water was preferred over recycled water for close-to-body uses such as drinking (49% compared to 20% acceptance respectively). Recycled water was preferred over desalinated water, however, for some uses with little body contact, for example, for watering gardens (89% compared to 68% acceptance respectively). Respondents understood that water recycling is more environmentally friendly than desalination which, in turn, was perceived by respondents as less risky from a public health perspective. More recently, Dolnicar and Hurlimann (2010) conducted a similar comparison, finding that Australians now generally
prefer desalinated water: 53% were willing to drink it (as compared to only 36% who were willing to drink recycled water) and 84% were willing to water their garden with it (compared to 86% who were willing to water their garden with recycled water). It is likely that developments since the 2006 study have significantly impacted people’s perceptions. Most importantly, Australians in a Queensland country town, Toowoomba, voted against the development of a water recycling plant. Public opposition led by the community group ‘Citizens Against Drinking Sewage’ dominated national media (for a detailed case study see Hurlimann and Dolnicar, 2010). Possibly as a consequence of the Toowoomba case, many Australian state governments have chosen desalination as the preferred path, thus communicating to the public the benefits of this alternative method of securing Australia’s water for the future. It is likely that these developments have led to the shift in public perception observed between the 2006 and the 2009 studies. While a significant amount of survey research has been conducted to ask respondents directly about their willingness to use different kinds of water from alternative sources, only a small amount of work has attempted to identify which personal characteristics are associated with a high or low level of acceptance towards alternative water sources. An overview of these studies is provided in Table 1. As can be seen, key explanatory factors include trust (in the water provider or public policy makers); knowledge and information; past experience with alternative water sources; and perception of risk. Demographic variables have been explored, but consensus on the nature of the association is low, particularly for age. The main limitation of this body of work is that most studies investigate factors hypothesized to be associated with acceptance of water from alternative sources in isolation from one another, thus risking that the association is over-interpreted. The possible interaction effects of multiple factors have mostly been ignored to date. To the authors’ knowledge only one study, Po et al. (2005), attempted this in the context of the general public’s acceptance of indirect potable reuse of wastewater. Statements of intended use were found to be significantly related to positive attitudes towards indirect potable reuse, which, in turn, were influenced by a number of factors: subjective norms, emotions, trust in the authorities, risk perceptions, sense of obligation to protect the environment, and their perceived control over the source of their drinking water. However, this study focused mainly on complex psychological constructs which are hard to assess and are thus of limited value to public policy makers who need to be able to easily target certain segments of the population with educational messages about water from alternative sources.
3.
Methodology
3.1.
Fieldwork administration
Data was collected online in January 2009 using an Australian permission-based research-only internet panel. 13,884 invitations were sent out to panel members. The final total sample size amounted to 3094 respondents (a 22% response rate); 1495
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Table 1 e Factors found to influence community acceptance of recycled water. Factor positively influencing attitudes to recycled water
Study
Attitudes and experiences Trust in authorities associated with recycled water use
Knowledge/information
Risk perception (negative)
Past experience with alternative water source
Health concern (negative)
Perception of good water quality
Lohman and Milliken (1985) Jeffrey and Jefferson (2003) Hurlimann and McKay (2004) Po et al. (2005) Hurlimann (2007b) Hurlimann (2007c) Lohman and Milliken (1985) Flack and Greenberg (1987) Jeffrey and Jefferson (2003) Tsagarakis and Georgantzis (2003) Hurlimann et al. (2008) Po et al. (2005) Hurlimann (2008) Hurlimann et al. (2008) Sims and Baumann (1974) Olson et al. (1979) Lohman and Milliken (1985) Flack and Greenberg (1987) Dishman et al. (1989) Hurlimann (2007a) Olson et al. (1979) Dishman et al. (1989) Marks et al. (2006) Baggett et al. (2006) Higgins et al. (2002) Po et al. (2005) Baggett et al. (2006) Hurlimann et al. (2008)
Demographic variables Age e older Age e younger
Gender e being male
Education level e having a higher education degree
wanting to participate in the survey are rejected because they do not qualify as the kind of respondents still required to ensure representativeness. Quotas were set for gender, age, state and education level. Census data from the Australian Bureau of Statistics was used to specify the quota requirements numerically. Note, however, that the present study does not require the sample to be representative because we are interested in assessing which factors affect public acceptance of recycled and desalinated water. It is more important to ensure that there is sufficient variety in those variables which are hypothesized to play a role. This is ensured by the way the sample was drawn. The remaining 1599 were collected from specific locations which differ in their local water situations (Adelaide, Sydney, Brisbane, Melbourne, Perth, Darwin, The Mallee and Toowoomba). The online data collection allowed controlling for nonresponse. The questionnaire administration ensured that respondents could not proceed without having completed all questions on a page. As a consequence, missing values due to oversight or unwillingness to answer, as experienced in paper-and-pencil data collections, were not a factor.
3.2.
of the respondents were representative of the Australian public. Representativeness was ensured by using a quota sampling procedure. This is achieved by online fieldwork companies who send out invitations to a large group of panel members representative of the population and then monitoring, for all quota criteria, frequency of responses. Toward the end of the process it may be that some respondents
Questionnaire
Respondents were asked to answer a number of questions which are related to their behaviour, attitudes and sociodemographic characteristics. They are discussed below under the headings of Dependent variables and Independent variables, reflecting the hypothesized relationship in the model.
3.2.1. Hurlimann (2007a) Dolnicar and Scha¨fer (2009) Stone and Kahle (1974) Lohman and Milliken (1985) McKay and Hurlimann (2003) Baumann and Kasperson (1974) Lohman and Milliken (1985) Tsagarakis et al. (2007) Hurlimann (2007a) Nancarrow et al. (2008) Dolnicar and Scha¨fer (2009) Bruvold (1972) Stone and Kahle (1974) Flack and Greenberg (1987) Lohman and Milliken (1985) Alhumoud et al. (2003) Menegaki et al. (2006) Hurlimann (2007a) Dolnicar and Scha¨fer (2009) Robinson et al. (2005)
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Dependent variables
Stated likelihood of using recycled/desalinated water is the dependent variable in this model. One such variable was computed for recycled water, one for desalinated water. The variables aim to measure the attitude of the respondents towards recycled and desalinated water by determining the likelihood of using this kind of water for different purposes. The final value of the stated likelihood of using recycled/ desalinated water is computed as the sum of 10 items, each of which represents one particular water use. The 10 alternative uses were: watering the garden (flowers, trees, shrubs), washing clothes/doing laundry, cooking, showering/taking a bath, drinking, brushing teeth, toilet flushing, cleaning (the house, windows, driveways), watering the garden (vegetables, herbs to be eaten raw), and washing the car. In order to ensure that the data would not be biased by respondents who differed in their understanding of what recycled/desalinated water meant, each were provided with the following definitions before they were asked to state the likelihood of use: “For the following questions we will use the term ‘recycled water’ to describe ‘purified wastewater or sewage,’ and we will use the term ‘desalinated water’ to describe ‘purified seawater,’ and we will assume that both recycled and desalinated water are treated to the same level of water quality.” For each item the respondents had to place a cross on a line. The endpoints were assigned the values 1 and 100 and all intermediate values were equidistantly determined. Respondents also had the option not to answer a question by ticking
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a box labelled not applicable. However, since no information was available for such items, the summated score cannot be determined. For each item of the likelihood to use recycled water variable, between 0.7% and 5.9% of the questions were answered not applicable. The average of not applicable answers for each item was 2.3%, with 11.6% of respondents answering not applicable to at least one of the items measuring this variable. For the likelihood to use desalinated water variable the situation was similar, with between 0.8% and 5.7% of the answers being not applicable, with an average of 2.2% for each item. Respondents who had chosen not applicable in any part of the survey were removed, leading to an exclusion of 12.9% of the respondents, a method which was preferred to that of coding each answer as zero. Substituting zero for these answers would suggest that the respondents do not use any kind of water for certain purposes, however, this would distort the data to suggest a negative attitude towards recycled and/or desalinated water. The final sample size therefore was 2694 which lead to a precision level under the worst care scenario (for binary questions with maximum variance and a confidence level of 95%) of 2%. A comparison of the state distribution as well as the size of the city distribution between the retained and excluded respondents indicated no significant differences (state: c2 ¼ 11.3, df ¼ 7, pvalue ¼ 0.13; size: c2 ¼ 8.7, df ¼ 10, p-value ¼ 0.56). Thus the composition of the sample with respect to location and size of city was not significantly altered by the omission.
3.2.2.
Independent variables
The following independent variables were included in the model: Environmental attitudes were measured using the New Ecological Paradigm (NEP) scale designed by Dunlap et al. (2000), which e according to Bragg (1996) e is the most widely used instrument for measuring environmental attitudes. The scale consists of 15 items covering five dimensions: reality of limits to growth, anti-anthropocentrism, fragility of nature’s balance, rejection of exemptionalism, and possibility of ecocrisis. Respondents were offered five answer options to indicate their level of agreement. The item labels with corresponding scores were Strongly agree (2), Mildly agree (1), Unsure (0), Mildly disagree (1) and Strongly disagree (2). Item-level responses were added to the total NEP score. Environmental concern was measured using the items developed by Berenguer et al. (2005) for general environmental concern. A sample item is: To what extent are you concerned about the situation of the environment in general? Respondents were asked to record their answer using a five-point agreement scale identical to the scale used for the environmental attitudes. The values of the six concern items were added to form the overall value for environmental concern. Altruism was measured using Clark et al.’s (2003) nine item altruism scale, which is based on Schwartz’s (1970,1977) norm-activation model. Three items measure personal norms, three measure awareness of consequences, and three measure ascription of responsibility. Respondents expressed their beliefs on a five-point agreement scale identical to the scale used for the environmental attitudes. The total altruism value was computed as the sum over all nine altruism items. Moral obligation to behave in an environmentally friendly way has been shown to be a good predictor of pro-environmental
behaviour. For example, Berenguer et al. (2005) find moral obligation to be the best predictor of pro-environmental behaviour. Dolnicar and Leisch (2008) found moral obligation to be a useful segmentation base to identify subgroups of the population with distinctively different levels of pro-environmental behaviour. We used the following wording for the single item measure: Do you consider yourself morally obliged to carry out environmentally friendly behaviours? Respondents had to respond by ticking either Yes (1) or No (0). Pro-environmental behaviour was a summated value across respondents’ answers to the following question: You will now see a list of behaviours. Please indicate how frequently you carried out each of these behaviours at home in the last year. Response options were Always (4), Often (3), Rarely (1) and Never (0) and Not applicable (0). A total of thirty behaviours were included. Active involvement in searching for information about water was measured using a single item measure by asking respondents: How much effort have you made this year to look for information on water-related issues (water recycling, desalination, water conservation, rain water etc.)? Respondents had four response options: Absolutely no effort (0), A small effort (1), A big effort (2) and A huge effort (3). Previous use of recycled/desalinated water was measured using a single item measure, worded as follows: Have you ever used recycled water/desalinated water? Answer options were Yes (1) and No (0). Experience with water restrictions was measured by asking respondents Have you ever experienced water restrictions? Answer options were Yes (1) and No (0). Perception of being limited by water restrictions was measured asking To what extent do you feel limited by water restrictions? Answer options were Not at all (0), Slightly (1) and Strongly (2). For analysis we used a collapsed variable with the categories Not at all (0) and Slightly/Strongly (1). Attitude towards water conservation consisted of the sum over nine items about water conservation which were developed specifically for this study based on results from the qualitative fieldwork stage. One example is: Water conservation is necessary because of water scarcity. Response options were I agree (1) and I disagree (1). Water conservation behaviour was also computed as a sum over 17 items indicating different means of water conservation behaviour, such as I make sure that taps do not drip. Answer options were Yes (1) and No (0). Extent of influence of other people on people’s water-related behaviour and attitudes was computed as the sum over 14 items which listed different social sources of influence, for example, friends, partner, the media. Answer options were Yes (1) and No (0) for each listed social source. Knowledge about recycled and desalinated water, as well as perceptions of recycled and desalinated water, respectively, were measured with knowledge and perception items developed by Dolnicar and Scha¨fer (2006). The sum across all items was used to arrive at separate overall measures of knowledge about recycled and desalinated water. Note that the knowledge and perception questions were asked before the definition of recycled and desalinated water was provided and respondents were asked to state their likelihood of use. Once respondents were provided with the definition and the
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statement that both recycled and desalinated water were treated to the same level of water quality, respondents were not able to click back anymore. This was done to ensure they would not retrospectively change their answers to the perceptions and knowledge questions. Finally, a number of socio-demographic questions were asked covering age, gender, education, size of city, feeling of belonging to the region, importance of religion, media use and whether or not respondents had read something about recycled or desalinated water recently. These variables were chosen because they emerged as predictive in a number of studies trying to explain pro-environmental behaviour of different kinds, namely pro-environmental behaviour in general (Berenguer et al., 2005), intentions to undertake pro-environmental behaviour (Cordano et al., 2003) as well as specific kinds of pro-environmental behaviour such as subscribing to green electricity programs (Clark et al., 2003), willingness to pay for species protection (Kotchen and Reiling, 2000), for environmentally sound products (Laroche et al., 2001) and environmental protection in general (Stern et al., 1993). We deliberately included a wide range of criteria which were found to be associated with proenvironmental behaviour more generally because we felt that limiting our selection of variables to those studied in waterrelated research may lead to the omission of key factors.
4.
Analysis
The numeric independent variables (such as environmental attitudes, environmental concern or altruism) were standardized to have comparable coefficient estimates. For variables with answers Yes or No the baseline category are the No answers, which are therefore included in the intercept, and the estimated coefficient indicates the change in likelihood if this question was answered with Yes. All of the proposed independent variables are assumed to be correlated with the likelihood of using recycled or desalinated
water and hence, might be used to predict this likelihood. Separate multivariate linear regression models were fitted for the two dependent variables. Variables which are specific to recycling water e such as experience with recycling water or the perception of recycled water e were only employed in the regression, using the likelihood to use recycled water as the dependent variable; the same approach was taken for desalinated water. Variable selection was made using stepwise forward selection by adding the variable with the smallest pvalue and utilising the F-test to compare the model with this variable added against the model without this variable added. Candidates for terms which could be added in the model were all variables and all pairwise interactions between the variables already included in the model. The selection process was stopped when all p-values were larger than 0.05. Variables which are not included in the final model therefore do not significantly increase the explained variance if added to the model. The final model is analysed with respect to: (1) the variables included; and (2) the estimated coefficients for each of the variables.
5.
Results
The empirical distributions for both dependent variables are provided in Fig. 1. Both dependent variables range from 10 to 1000, because each respondent provided responses for 10 items, each of which was assessed on a 100-point scale. Overall, public acceptance for desalinated water is higher, supporting the results of previous studies as discussed in Section 2.
5.1.
Explaining the likelihood of use of recycled water
Results for recycled water are provided in Table 2. The table gives the parameter estimates together with the standard errors and the p-values of the corresponding t-tests. For numeric variables, negative estimates indicate that an increase
Percent of Total
Recycled water
Desalinated water
20
20
15
15
10
10
5
5
0
0
0
200
400
600
800
1000
0
200
400
600
800
1000
Likelihood of use
Fig. 1 e Empirical distribution of the dependent variables (stated likelihood of using recycled/desalinated water).
938
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 9 3 3 e9 4 3
Table 2 e Regression coefficients e recycled water.
Intercept Perception of recycled water (positive) Knowledge (more) Age (older) Extent of influence of others (higher) Environmental attitudes (positive) Experience with water restrictions e Yes Feeling limited by water restrictions e Slightly or strongly TV (commercial) e State e Don’t watch Religious e Yes e Not sure or did not say Interactions Knowledge (more): extent of influence of others (higher) Perception (positive): extent of influence of others (higher) Extent of influence of others (higher): TV (commercial) e State e Don’t watch Environmental attitudes (positive): TV (commercial) e State e Don’t watch
Estimate
Std. Error
p-value
666.81 102.05 18.57 20.13 10.90 5.89
10.53 3.07 3.06 3.11 3.54 3.53
<0.001 <0.001 <0.001 <0.001 0.002 0.095
39.22
11.85
<0.001
17.18
7.24.
0.018
20.22 5.18
7.25 29.98
0.005 0.863
14.48 17.53
6.88 8.13
0.035 0.031
8.28 9.00
2.84 3.12
0.004 0.004
17.86 36.43
7.20 32.53
0.013 0.263
5.25 61.92
6.89 24.06
0.447 0.010
R2 ¼ 0.398.
in the variable leads to a decrease in the likelihood of using recycled water; for categorical variables, the likelihood of using recycled water is decreased compared to the base level of the variable which is accounted for in the intercept. The order of estimates is in the sequence each entered the model. The R2 value of 0.398 indicates that the model was able to account for a substantial amount of the variance. Nine factors hypothesized to increase the level of likelihood that respondents would use recycled water are significant: (1) previous experience with water restrictions; (2) not feeling limited by water restrictions; (3) greater knowledge about recycled water; (4) more positive perceptions of recycled water; (5) a high extent of other people influencing one’s water-related behaviours; (6) pro-environmental attitudes; (7) older age (note that the underlying model is assuming a linear relationship, so the regression results indicate that higher age is associated significantly with a higher stated likelihood of using recycled water); (8) religion not being an important life factor; and (9) watching State (non-commercial) TV channels. This information contained in the Estimate column in Table 2 provides information about how sensitive the dependent variable (likelihood of use of recycled water) is to each of the factors in the regression model. This number is interpreted as follows: if the independent variable is increased by one unit the dependent variable increases with Estimate units, i.e., if the Estimate is negative the dependent variable decreases. The Standard Error indicates the precision of the Estimate, i.e., the 95% confidence interval for the estimate is approximately given by Estimate 2 Standard Error. For ease of interpretation we also provide a graph with standardized estimates in Fig. 2. In this graph all factors
that positively affect the likelihood of use plot to the right of the vertical axis and all factors with negative effects plot to the left. The length of each bar indicates the extent of the effect. In addition to the individual effects, there are significant interaction effects between variables. Between two numeric variables this indicates that their combined effect is different from their separate effects. For example, the interaction effect between higher knowledge and the greater influence of others is negative, indicating that while these two variables separately have a positive effect on the likelihood of using recycled water, the effect levels off if both are increased. This observation also holds for the combination of more positive perception and the greater influence of others. For the combination of a numeric and a categorical variable, this can be interpreted as different slopes for the different levels of the categorical variable. The fitted model implies that the higher the influence of others, and the more positive the attitudes towards the environment, the better is the attitude towards recycled water. However, this effect is strongest for those who do not watch TV, followed by respondents preferring State TV channels. The fact that not feeling limited by water restrictions increases the stated likelihood of using recycled water appears counter-intuitive at first. A proposed explanation is that people with higher pro-environmental attitudes have more understanding for the need for water restrictions and are therefore more tolerant of them. Consequently, this would lead them to express less frustration about water restrictions.
939
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 9 3 3 e9 4 3
Recycled water Perception of recycled water (positive) Age (older) Knowledge (more) Experience with water restriction (yes) Extent of influence of others (higher) Watching state TV Interaction of extent of influence of others (higher) and watching state TV Interaction of environmental attitudes (positive) and don't watch TV Environmental attitudes (positive) Interaction of extent of influence of others (higher) and don't watch TV Interaction of environmental attitudes (positive) and watching state TV Don't watch TV Religious (yes) Religious (not sure or not say) Feeling limited by water restrictions (slightly or strongly) Interaction of perception (positive) and extent of influence of others (higher) Interaction of knowledge (more) and extent of influence of others (higher) 0.0
0.1
0.2
0.3
0.4
0.5
Standardized estimates
Fig. 2 e Standardized regression coefficients for recycled water.
5.2.
Explaining the likelihood of use of desalinated water
Results for desalinated water are presented in Table 3 and in Fig. 3. The number of variables contributing significantly to the stated likelihood of using desalinated water across a range of household uses is higher than it is for recycled water, with ten explanatory variables being significant. The explained variance of the model, which is equal to 31.2%, is slightly lower than that for the recycled water model.
The overlap between the results for recycled water and desalinated water is great, with eight explanatory variables having the same significant influence for the likelihood of using either water source. Watching TV is the only explanatory variable which is significant for the likelihood of using recycled water, but not significant for desalinated water use. Additional variables which significantly influence the likelihood of using desalinated water are the previous use of desalinated water and the respondent’s attitude towards
Table 3 e Regression coefficients e desalinated water.
Intercept Perception of desalinated water (positive) Knowledge (more) Attitude towards conservation (positive) Environmental attitudes (positive) Previous use of desalinated water e Yes Age (older) Religious e Yes e Not sure or not say Extent of influence of others (higher) Experience with water restrictions e Yes Feeling limited by water restrictions e Slightly or strongly Interactions Perception (positive): attitude towards conservation (positive) Perception (positive): extent of influence of others (higher) Knowledge (more): attitude towards conservation (positive) Age (older): extent of influence of others (higher) Environmental attitudes (positive): age (older) Previous use of desalinated water (yes): religious e Yes e Not sure or not say Environmental attitudes (positive): previous use of desalinated water (yes) Environmental attitudes (positive): experience with water restrictions (yes) Perception (positive): feeling limited by water restrictions (slightly or strongly) R2 ¼ 0.312.
Estimate
Std. Error
p-value
752.39 92.87 15.18 9.89 38.09
12.55 6.68 3.53 3.80 12.07
<0.001 <0.001 <0.001 0.009 0.002
38.67 10.81
13.12 3.70
0.003 0.004
8.60 26.54 9.29
8.72 10.29 3.67
0.324 0.010 0.012
38.85
14.08
0.006
21.07
8.50
0.013
10.77 9.09 7.19 9.33 7.02
3.51 3.40 3.37 3.66 3.56
0.002 0.008 0.033 0.011 0.049
60.88 4.56 21.24 26.61 16.28
22.60 27.23 9.51 12.49 7.71
0.007 0.867 0.026 0.033 0.035
940
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Desalinated water Perception of desalinated water (positive) Environmental attitudes (positive) Knowledge (more) Interaction of perception (positive) and perception of being limited by water restrcitions (slightly or strongly) Previous use of desalinated water (yes) Experience with water restriction (yes) Age (older) Attitude towards conservation (positive) Extent of influence of others (higher) Interaction of age (older) and extent of influence of others (higher) Interaction of previous use of desalinated water (yes) and religious (not sure or not say) Religious (yes) Interaction of environmental attitudes (positive) and age (older) Interaction of knowledge (more) and attitude towards conservation (positive) Interaction of environmental attitudes (positive) and previous use of desalinated water (yes) Interaction of perception (positive) and extent of influence of others (higher) Perception of being limited by water restrictions (slightly or strongly) Religious (not sure or not say) Interaction of perception (positive) and attitude towards conservation (positive) Interaction of previous use of desalinated water (yes) and religious (yes) Interaction of environmental attitudes (positive) and experience with water restriction (yes) -0.1
0.0
0.1
0.2
0.3
0.4
Standardized estimates
Fig. 3 e Standardized regression coefficients for desalinated water.
conservation, where previous use and the higher valuation of conservation both increase the likelihood of use. The interaction effects of two numeric variables compensating their effect are observed for positive perception and attitude towards conservation, positive perception and the influence of others, knowledge and attitude towards conservation, as well as for environmental attitudes and age. By contrast, for age and the influence of others, the combined effect is even more emphasized. The influence of positive attitudes towards the environment on the likelihood of using desalinated water is smaller for respondents who have previously used desalinated water and who have experienced water restrictions in the past. Furthermore, the influence of positive perceptions of desalinated water is enforced if respondents perceive themselves as limited by water restrictions. Religion only impacts on the use of desalinated water if respondents have used this type of water before. One possible explanation for the finding that positive environmental attitudes increase the likelihood of using desalinated water, is that the knowledge level about desalination within the Australian population is relatively low (Dolnicar and Hurlimann, 2009). The environmental disadvantages of water desalination are not commonly understood, which may lead to (uninformed) support for desalinated water from people generally concerned about the environment. If people have previous experience with the use of desalinated water they are likely to know more about the negative environmental impacts of desalination and therefore become more reluctant to embrace it. These findings and explanation are in line with previous findings that people opposed to desalinated water are often opposed for environmental reasons (Dolnicar and Scha¨fer, 2009).
6.
Discussion and conclusions
The following key findings emerged from the study: First, some of the factors identified previously as being associated with higher levels of public acceptance of recycled water (e.g.,
gender and education) do not appear to be the main drivers, but may possibly be correlated with them. Our results provide support for previous research which has found favourable attitudes to recycled water use from: (1) older respondents (Hurlimann, 2007a; and Dolnicar and Scha¨fer, 2009); and (2) knowledge (Lohman and Milliken, 1985; Flack and Greenberg, 1987; Jeffrey and Jefferson, 2003; Tsagarakis and Georgantzis, 2003; and Hurlimann et al., 2008). Our results also provide evidence for the impact of environmental attitudes, positive perceptions of recycled water, the influence of other people, religion, experience of water restrictions, the perception of being limited by water restrictions, and watching State TV channels, on the stated likelihood of using recycled water. We believe that the predictive value of watching State TV may be due to the fact that State TV (non-commercial) channels have a number of current affairs programs and news shows which provide in-depth analyses on the topics covered. With respect to recycled water, for example, they not only discuss people’s fear of health risks, but also provide information about the environmental advantages of recycled water. We think that it is this additional insight which is associated with the increased stated likelihood of use. Second, drivers of the stated likelihood of using desalinated water were found to be similar to those for recycled water. Only watching State TV channels did not emerge as an influential factor. In addition, respondents who have previously used desalinated water and who indicated a positive attitude towards conservation, were reportedly more likely to use desalinated water than those who have not. The fact that people in Australia know relatively little about desalinated water and how it is produced seems to work in favour of acceptance because the negative environmental effects are not commonly known. But the perception in terms of public
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 9 3 3 e9 4 3
health is more positive than for recycled water (for example, 38% perceive recycled water as “disgusting” but only 25% perceive desalinated water as such; 48% perceive recycled water as not tasting good, whereas only 41% feel the same way towards desalinated water). These findings have significant practical implications, particularly for public campaigns designed to promote the use of desalinated and/or recycled water. Too much information regarding desalinated water may have the effect of decreasing public acceptance due to the environmental concerns usage might raise. In the case of recycled water it is likely that public campaigns will potentially increase public acceptance and usage since public perceptions play a major role in acceptance. Key drivers for the acceptance of both water sources are the possession of positive perceptions about alternative water sources, and the extent to which other people might influence a person’s decisions about alternative water sources. Positive messages about recycled and/or desalinated water, particularly from personal communication channels such as family, friends and colleagues, are important to the uptake of these water sources. Since knowledge was a significant influencing factor for both water sources, it follows that public information and marketing have a major contribution to make in the context of introducing water from alternative sources. Marketing strategies that make water from alternative sources a positive conversation topic may be particularly valuable. Finally, previous experience with water restrictions, in addition to previous experience with these water sources, evidently increases the likelihood of use. Again, this is key information for public policy makers as it informs the nature of the communication message that is likely to be effective. In this particular instance it has to be concluded that messages emphasizing the real problem of water scarcity, e.g. by showing examples of current water scarcity in the near geographical proximity of where people live, will have a higher likelihood of positively impacting acceptance. These findings have important practical implications as they provide guidance to water providers and public policy makers about interventions that are likely to increase public acceptance of water augmentation projects, especially information and communication campaigns: 1. It is essential that people understand that water from alternative sources is not an option, but a necessity; and 2. Suggesting non-threatening ways for people to be able to experience recycled and desalinated water may be a useful strategy to increase public acceptance and usage. Nonthreatening ways include voluntary opportunities, such as tasting recycled and desalinated water, filling public swimming pools with recycled and desalinated water. These techniques are likely to be far more effective than public announcements stating that recycled or desalinated water would be added to water supplied to households. Such announcements have proven to be very threatening and have resulted in public rejection of water augmentation schemes in the past (Hurlimann and Dolnicar, 2010). The above findings support a barely enacted recommendation made more than three decades ago by Baumann and Kasperson (1974), namely, to “put the reclaimed water in an
941
attractive setting and invite the public to look at it, sniff it, picnic around it, fish in it, and swim in it” (p. 670). This study is limited in three ways, providing opportunities for future researchers to further extend our understanding of why the public rejects or accepts water from alternative sources. First, this study was conducted in Australia only. Although it could be argued that the drivers for resisting acceptance are universal, there is some evidence that critical events in the history of certain Australian locations e such as the Toowoomba referendum e are likely to have an impact on results. Secondly, this study did not include a comprehensive list of every factor that can be expected to affect people’s acceptance of water from alternative sources. In future work it would be valuable to include measures for trust, risk perception, health concerns, or perceptions of quality, and include those into the model as independent variables. Finally, respondents were not asked about frequency or volume of water use for different purposes, which could be used to assess the extent to which dam water could easily be substituted with water from augmented sources without raising public health or environmental concerns among the population. Such a study, or studies, would be of great value in future, especially in countries which do not currently use water from augmented sources and where, as a consequence, the population may be reluctant to accept large-scale water augmentation projects.
Acknowledgements This study was funded by the Australian Research Council (ARC) through the Discovery Grant (DP0878338) and the Linkage International Grant scheme (LX0881890) and the Austrian Science Fund (FWF) Hertha-Firnberg Grant T351-N18.
references
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Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Demonstration of 20 pharmaceuticals and personal care products (PPCPs) as nitrosamine precursors during chloramine disinfection Ruqiao Shen, Susan A. Andrews* Department of Civil Engineering, University of Toronto, 35 St. George St., Toronto, Ontario, Canada M5S 1A4
article info
abstract
Article history:
The worldwide detection of pharmaceuticals and personal care products (PPCPs) in the
Received 6 July 2010
aquatic environment and drinking water has been a cause for concern in recent years. The
Received in revised form
possibility for concurrent formation of nitrosamine DBPs (disinfection by-products) during
18 September 2010
chloramine disinfection has become another significant concern for delivered drinking
Accepted 25 September 2010
water quality because of their potent carcinogenicity. This study demonstrates that a group
Available online 13 October 2010
of PPCPs containing amine groups can serve as nitrosamine precursors during chloramine disinfection. Molar yields higher than 1% are observed for eight pharmaceuticals, with
Keywords:
ranitidine showing the strongest potential to form N-nitrosodimethylamine (NDMA). The
PPCPs
molar conversion increases with the Cl2:N mass ratio, suggesting that dichloramine is
Nitrosamine
relevant to the formation of NDMA from these precursors. Although the trace level of
NDMA
PPCPs in the environment suggests that they may not account for the majority of nitro-
Chloramine
samine precursors during the disinfection process, this study demonstrates a connection
Disinfection
between the transformation of PPCPs and the formation of nitrosamines during chlora-
Precursor
mine disinfection. This both expands the pool of potential nitrosamine precursors, and provides a possible link between the presence of trace levels of certain PPCPs in drinking water sources and potential adverse health effects. ª 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
Pharmaceuticals and personal care products (PPCPs) are a group of compounds including pharmaceutical drugs, cosmetic ingredients, food supplements, and ingredients in other consumer products such as shampoos and lotions. They have gained much attention in recent years with the worldwide increasing consumption of these substances and their frequent detection in the aquatic and terrestrial environment, ranging from ng/L to lower mg/L (Calamari et al., 2003; Conley et al., 2008; Godfrey et al., 2007; Jasim et al., 2006; KasprzykHordern et al., 2008; Kolpin et al., 2002, 2004; Metcalfe et al.,
2003; Servos et al., 2007; Zuccato et al., 2005). However, many of the compounds that have been detected and studied only comprise a small subset of the whole PPCP family. Most research projects have been focused on the removal of PPCPs using different treatment processes, but data in terms of their degradation or transformation products during these processes are largely lacking. In particular, there is quite limited information on the transformation of PPCPs upon drinking water disinfection. Concurrently, formation of nitrosamines during chloramine disinfection has become a significant issue for delivered drinking water quality because of their potential
* Corresponding author. Tel.: þ1 4169460908; fax: þ1 4169785054. E-mail addresses: [email protected] (R. Shen), [email protected] (S.A. Andrews). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.036
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carcinogenicity, especially with the switch of secondary disinfectant from free chlorine to chloramine gaining much popularity in recent years. Compared with free chlorine, chloramine can maintain a more stable residual in the distribution system and can form less regulated disinfection by-products (DBPs) such as trihalomethans (THMs) and haloacetic acids (HAAs). However, chloramine has been more commonly associated with the formation of emerging nitrosamine DBPs than free chlorine. Up to now, there are no federal regulations about nitrosamines in North America, but the USEPA has placed six nitrosamines on the Unregulated Contaminant Monitoring Rule List 2 (USEPA, 2006). The Ontario Ministry of the Environment (MOE) has established an interim maximum acceptable concentration of 9 ng/L for NNitrosodimethylamine (NDMA) in drinking water (MOE, 2003). The California Department of Health Services (CDHS) has implemented a notification limit of 10 ng/L of NDMA, and the California Office of Environmental Health Hazard Assessment (OEHHA) has set up a public health goal for NDMA at 3 ng/L (OEHHA, 2006). A considerable amount of research has been conducted to investigate the potential precursors of nitrosamines, especially NDMA. The most well-known NDMA precursors related to water and wastewater treatment include dimethylamine (DMA; Mitch et al., 2003), tertiary and quaternary amines containing DMA groups (Kemper et al., 2010; Lee et al., 2007), natural organic matter (NOM) (Chen and Valentine, 2007; Dotson et al., 2007; Gerecke and Sedlak, 2003; Krasner et al., 2008; Mitch and Sedlak, 2004), polyelectrolytes and resins used in water and wastewater treatment plants (Kohut and Andrews, 2003; Mitch and Sedlak, 2004; Najm and Trussell, 2001; Wilczak et al., 2003), and some agriculturally related fungicides and herbicides (Chen and Young, 2008; Graham et al., 1995; Schmidt and Brauch, 2008). However, current research regarding the potential precursors cannot account for all the nitrosamines detected, based on their yields during drinking water disinfection, indicating the possibility of other as yet unknown precursors. Ranitidine, one of the most prescribed drugs in the world, has been demonstrated to render a high conversion rate to NDMA upon chloramination (Sacher et al., 2008; Schmidt et al., 2006). Some early studies have also reported the formation of nitrosamines via amine drugs in the stomach (Lijinsky and Taylor, 1977; Andrews et al., 1980). Therefore, it is possible that drugs with tertiary or quaternary amine groups might contribute to the formation of nitrosamines during drinking water disinfection. So far, very limited information is available regarding the formation of nitrosamines via PPCPs. Krasner (2009) has suggested the possibility of amine-based pharmaceuticals and their breakdown products to be part of the NDMA precursor pool in wastewater effluent organic matter (EfOM), but no further results have been reported so far. The current study demonstrates the transformation of 20 selected PPCPs to form nitrosamines. PPCPs containing DMA or diethylamine (DEA) in their structures were selected as potential precursors for NDMA and N-Nitrosodiethylamine (NDEA), respectively. Selection of target compounds was also based on their prevalence in the North American pharmaceutical markets and/or their frequent detection in the environment.
2.
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Materials and methods
Experiments were conducted using both Milli-Q water produced from an Ultra Pure Water System (MilliPore, Bioprocess Division, Etobicoke, Ontario) and tap water from Toronto, Ontario. Nineteen pharmaceutical compounds and one personal care product (i.e., N, N-diethyltoluamide (DEET)) were tested in terms of their potential to form nitrosamines during chloramination.
2.1.
Materials
Chemical structures of the selected PPCPs are summarized in Fig. 1. Stock solutions of PPCPs were prepared in methanol and stored at 4 C until use. NDMA and NDEA (reagent grade) were used as standards, and deuterated NDMA (d6-NDMA, 98 atom % D) was used as the internal standard for both compounds. All of these chemicals were purchased from SigmaeAldrich Canada (Oakville, Ontario). Phosphate buffer (pH ¼ 7.0) was made by dissolving 62 g KH2PO4 and 78 g Na2HPO4 into 1 L of Milli-Q water. Chlorine stock solution (4000e6000 mg/L as Cl2) was prepared by diluting 10 times of the 4e6% bleach and stored at 4 C. Monochloramine dosing solution was prepared fresh daily by mixing an ammonium chloride solution and a chlorine stock solution at a desired Cl2:N mass ratio, and equilibrating for at least 1 h before use. The actual concentrations of chlorine stock solution, monochloramine dosing solution, and the final test solutions were determined using DPD colorimetry (DR2010 HACH-Kit). L-ascorbic acid was used to quench chloramine and stop the reaction following each test period.
2.2.
Nitrosamine formation tests
Nitrosamine formation potential (FP) tests were conducted in 1 L amber bottles with LDPE (low-density polyethylene) caps, adopted from standard operating procedures for DBP yields under uniform formation conditions (Summers et al., 1996). FP tests usually apply high doses of disinfectants to predict the ultimate formation potential; while the Simulated Distribution System (SDS) tests (Koch et al., 1991) simulate the conditions common to water treatment plants and distribution systems. The two sets of general experimental conditions applied in the current study are summarized in Table 1, only differing in the concentrations of chloramine applied: 28.4 mg/L for the modified FP (MFP) tests was adopted from Schmidt et al. (2006), while 2.5 mg/L for the SDS tests met the requirement for chloraminated distribution system allowed by the Ontario Drinking Water Quality Standards (MOE, 2006). Sample pH was controlled by the addition of 2 mL/L of phosphate buffer solution. Reactions were halted after 24 h by the addition of excess ascorbic acid powder (approximately 300 mg per 1 L water sample).
2.3.
Nitrosamine analysis
The procedure for extraction and concentration of nitrosamines (NDMA and NDEA) in water samples was adopted from that reported by Taguchi et al. (1994). An aliquot of 500 mL
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Fig. 1 e Structures of selected PPCPs.
sample was transferred to a clean 1 L amber bottle with an LDPE cap. After the addition of internal standard d6-NDMA (50 ng/L in 500 mL sample) and 200 mg of Lewatit AF 5 beads (conditioned at 320 C for 3 h before use), the bottle was swirled at 250 rpm for 1 h on an orbit shaker (Thermolyne Bigger Bill M49235, Barnstead International, Asheville, N.C., USA). The beads were then collected by filtration, air dried for 20 min, transferred to a 2.0 mL autosampler vial, and further air dried for at least 1 h. Finally, 500 mL of dichloromethane (DCM, 99.9%) was added to extract nitrosamines from the beads. The cap for the GC vial was Teflon-lined and contained no rubber. Extracted nitrosamines in DCM could be stored in amber vials at 15 C or less for up to 28 days after sample extraction (Munch and Bassett, 2004). The extracted samples were analyzed via a Varian 3800 GC coupled with a Varian 4000 ion trap mass spectrometer and CombiPAL autosampler. The injector was fitted with a Carbofrit liner (Chromatographic Specialties; 3.4 mm ID and 5.0 mm OD; 54 mm length) and a programmed temperature vaporizer (PTV), and a DB 1701 column (30 m 0.25 mm 0.25 mm) was employed. Chemical ionization (CI) was applied with methanol as the reagent liquid. 8 mL of sample was injected into the GC through the PTV inlet, with the initial temperature of 25 C
held for 0.8 min, increased by 200 C/min to 240 C, and held for 24 min. Coolant was enabled at 200 C after the run to bring the injector back to the initial temperature. Column flow was 1.2 mL/min, with a pressure pulse of 19 psi held for 4 min. Oven temperature was initially held at 35 C for 5.5 min, increased by 15 C/min to 155 C, and further increased by 40 C/min to 240 C which was held for 10 min. Filament delay was 8.2 min. CI parameters were as follow: 3 mScan; emission current of 50 mAmps; electron multiplier offset of þ300 V. NDMA and d6-NDMA were both eluted at a retention time of 8.6 min, with indicating ions monitored at 75 and 81 amu, respectively. NDEA was eluted at a retention time of 10.5 min with the major indicating ion monitored at 103 amu.
2.4.
QA/QC
Quantification of nitrosamines was attained through internal calibration using deuterated internal standard (d6-NDMA). The calibration standards were subjected to the same extraction process as water samples in order to account for recovery. A calibration curve was prepared together with each set of water samples. Interference from the background of water samples was accounted for using blank control sample.
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Table 1 e Nitrosamine-FP experimental conditions. Experimental conditions pH Temperature Incubation time Cl2:N mass ratio Chloramine dosage
MFP
SDS
7.0 0.1 21 C 24 h 4.2:1 28.4 0.2 mg/L
7.0 0.1 21 C 24 h 4.2:1 2.5 0.2 mg/L
All samples and blanks were prepared in triplicate. Error bars in all the graphs demonstrate the variability due to multiple formation potential tests (n ¼ 3) under the same reaction conditions.
3.
Results and discussion
3.1.
Nitrosamine-FP under MFP conditions
Nitrosamine-FP upon chloramination was determined for all twenty selected PPCPs under the MFP conditions, in both MilliQ and tap water. Results are summarized in Fig. 2. Among the tested PPCPs, eight pharmaceuticals showed molar conversions higher than 1% (i.e., 18.5 ng/L of NDMA or 25.5 ng/L of NDEA formed). Ranitidine rendered the highest conversion (89.9e94.2%), followed by doxylamine (8.0e9.7%), sumatriptan (6.1%), chlorphenamine (5.2e5.5%), nizatidine (4.5e4.8%), diltiazem (2.1e2.6%), carbinoxamine (1.0e1.4%) and then tetracycline (0.8e1.2%). In both types of water, the nitrosamine-FP varied generally within 25% for most compounds, with somewhat higher variability observed for limited tests in MilliQ water with the four macrolide antibiotics (azithromycin, clarithromycin, erythromycin, and roxithromycin; 30e60%).
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Additional MFP tests that were performed with ranitidine indicated that the overall NDMA-FP from ranitidine varied within 5% (n ¼ 9). The selected PPCPs can be treated as tertiary amines containing DMA/DEA functional groups. Mitch and Schreiber (2008) have discussed the degradation pathways for model tertiary amines to form nitrosamines during chloramination, involving a chlorine transfer from the chloramine to the nitrogen atom in the tertiary amines as the rate-limiting step. Because it is an electrophilic chlorine substitution, the nature of moieties close to the DMA/DEA group can influence the reaction rate and thus affect the molar conversions. Generally, an electron-donating group (EDG) close to the DMA/DEA group can increase the electron density on the nitrogen atom and thus help attract chlorine; while an electron-withdrawing group (EWG) can decrease the electron density and slow down the reaction. Moreover, the reactivity may also be affected by the steric hindrance between the EDG/EWG and the electrophile (i.e., chlorine). In this study, the eight pharmaceuticals showing high NDMA-FPs all have the DMA group bound to an electron-rich moiety. Ranitidine and nizatidine, two H2-antihistamines, both have the DMA group bound to the C2 site of a fiveelement heterocyclic ring; but ranitidine has a much higher molar conversion than nizatidine. This is because C2 on the furan ring of ranitidine is a strong electrophilic site due to the electron-donating effect of the oxygen heteroatom, while C2 on the thiazole ring of nizatidine is a slight nucleophilic site because of the combined effects from the nitrogen and the sulfur atoms. Sumatriptan and diltiazem have the DMA bound to an electron-donating indole and benzothiazepine structure, respectively; however, there are two carbons between the EDG and the DMA group, weakening the electron-donating effects. In terms of the three structurally similar H1-antihistamines
Fig. 2 e Nitrosamine-FPs for selected PPCPs under the MFP conditions (Initial concentration of PPCPs [ 25 nM).
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Table 2 e Comparisons with literature (MFP conditions: pH [ 7, chloramine [ 28.4 mg/L, PPCPs [ 25 nM, room temperature). Compound Schmidt et al., 2006 (Drinking water, 7d) Present data (Milli-Q water, 24 h)
Ranitidine Nizatidine Tetracycline
NDMA (ng/L)
Molar conversion (%)
1200 91 23
62.9 4.9 1.2
NDMA (ng/L) Molar conversion (%) NDMA (ng/L) Molar conversion (%) 1665 6 88.0 1.3 23.0 1.6
(carbinoxamine, chlorphenamine, and doxylamine), they all have the electron-rich bulky aromatic system in their structures, but the distance between the aromatic structure and the DMA group is farther than that of ranitidine, resulting in the overall lower molar conversions. For the rest of the PPCPs showing low molar conversions, lidocaine and DEET have the DEA bound to an electron-withdrawing carbonyl group, and thus both showed very low yields of NDEA. A similar structure is observed for metformin, which has the DMA group bound to an electron-withdrawing biguanide. Amitriptyline and escitalopram have a long alkyl carbon chain between the DMA group and the bulky aromatic system, and this weakens the electron-donating effect. In the case of tramadol, venlafaxine, and the four macrolide antibiotics (azithromycin, clarithromycin, erythromycin, and roxithromycin), they have complicated steric structures close the DMA group, which may hinder the chlorine transfer reaction. The results were generally in good agreement with a previous study by Schmidt et al. (2006), as summarized in Table 2. Ranitidine gave a much higher yield of NDMA in the present study than reported in the literature, even with a shorter reaction time. However, there was not enough information available on the characteristics of the drinking water matrix used in the literature, and thus it was difficult to further compare the results and explain the discrepancy. Further studies would be needed to determine the potential impact from various water matrices relevant to drinking water.
3.2.
Present data (Tap water, 24 h)
Nitrosamine-FP under SDS conditions
SDS conditions are usually applied to mimic practical disinfection conditions common to water treatment plants and distribution systems. Nitrosamine-FP upon chloramination under the SDS conditions was determined for the eight pharmaceuticals which rendered an NDMA molar conversion higher than 1% under MFP conditions. The results are summarized and compared with MFP results in Fig. 3. No significant difference was observed regarding the NDMA molar conversion between the two sets of conditions (paired t-test, 95% confidence level). Under either condition, the relative amount of chloramine was in large excess relative to that of the pharmaceuticals (mg/L vs. mg/L), suggesting that the reaction is not limited by the availability of chloramine and that essentially complete reactions can be achieved under the SDS conditions. It also indicates that the eight pharmaceuticals are capable of forming NDMA under the practical chloramine disinfection conditions. SDS tests for the eight target pharmaceuticals were also performed using a series of initial PPCP concentrations (Fig. 4). The NDMA molar conversion varied as the initial PPCP
89.9 0.3 4.8 0.1 1.2 0.1
1744 82 82.7 6.9 14.9 1.0
94.2 4.4 4.5 0.4 0.8 0.1
concentration changed, but no common pattern was observed. For ranitidine, chlorphenamine and doxylamine, the molar conversion was generally consistent, varying within 30%; while for nizatidine, carbinoxamine, diltiazem and tetracycline, the molar conversion decreased slightly with increasing initial PPCP concentration; sumatriptan was the only compound showing consistently a slightly increasing molar conversion as the initial PPCP concentration increased. Under all the concentrations tested, chloramine was always in large excess, so the different trends in response to the change of initial PPCP concentrations might be related to their reaction kinetics. Thus, further kinetic studies are needed and may require case-by-case investigation. These eight pharmaceuticals have been largely consumed in the market and some have been detected in surface waters. For example, ranitidine and nizatidine are widely used in North America to treat and prevent peptic ulcer disease and gastroesophageal reflux disease. Specifically, ranitidine has been detected in surface waters at various locations with concentrations in the tens of ng/L range (Kasprzyk-Hordern et al., 2008; Kolpin et al., 2002, 2004; Zuccato et al., 2005). Diltiazem is used to treat hypertension and some types of arrhythmia, with concentrations up to several hundred ng/L detected in some US and UK streams (Kasprzyk-Hordern et al., 2008; Kolpin et al., 2002, 2004). Tetracycline is a broad-spectrum antibiotic used against various bacterial infections, and has been frequently detected in many US and Canadian sites with concentrations up to 300 ng/L (Kolpin et al., 2002, 2004; Miao et al., 2004). In the present study, the lowest concentration tested was 100 ng/L for ranitidine, nizatidine, chlorphenamine, and doxylamine (Fig. 4). Ranitidine shows the
Fig. 3 e Comparison of NDMA-FPs for selected PPCPs between the MFP and the SDS conditions (Tap water; initial concentration of PPCPs [ 25 nM).
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Fig. 4 e NDMA-FPs and molar conversions for selected PPCPs at different initial concentrations (SDS conditions; tap water).
strongest potential to form NDMA, with molar conversion higher than 77% at all the concentrations tested. Even at 100 ng/L, ranitidine can form 18.2 1.2 ng/L of NDMA, which is beyond the current Ontario regulation of 9 ng/L and the California regulation of 10 ng/L. NDMA formed via the other three compounds at 100 ng/L were low, but still within the quantifiable range. As well, even though the majority of the selected PPCPs form low levels of NDMA, added together they may still pose a concern in terms of the overall formation of nitrosamines. In real environments, PPCPs are usually present in the form of mixtures rather than as single compounds. A test was conducted to examine the potential effect of mixtures on the formation of NDMA via PPCPs. The eight pharmaceuticals
were prepared in a mixture and subjected to chloramination under the SDS conditions. Although a slight antagonistic effect was observed in the mixture of pharmaceuticals, the NDMA-FP was reduced by less than 10e15% compared with the sum of NDMA concentrations produced from single compounds at the same concentration (Fig. 5). Furthermore, it is worth noting that to better evaluate the NDMA-FP of pharmaceuticals, it will be necessary to take into consideration all the PPCP-derived species containing the DMA groups that may enter the drinking water treatment scheme. Pharmaceutical substances usually undergo metabolism within the human body and thus are excreted as a mixture of parent compounds together with the metabolites; also some pharmaceuticals are subjected to transformations
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the parent compounds but have not detected or reported the likely substantial amounts of their metabolites and transformation products. As a result, even though the concentration of the parent compound in the environment is low (ng/L level), altogether with its metabolites and transformation products, the overall nitrosamine formation potential may still be high and should be taken into consideration.
3.3.
Fig. 5 e NDMA-FPs for single PPCP vs. PPCP-mixture (Eight PPCPs: ranitidine, nizatidine, carbinoxamine, chlorphenamine, doxylamine, diltiazem, sumatriptan, and tetracycline) (SDS conditions; tap water).
in the environment, resulting in the formation of different transformation products. However, as long as the DMA functional groups are components of the metabolites and/or transformation products, they may still contribute to the formation of NDMA when reacting with chloramines. Take ranitidine as an example, earlier pharmacokinetics and pharmacodynamics studies have indicated that 30e70% of ranitidine is excreted as the parent form (Jjemba, 2006), and its major metabolites in human body include N-oxide, S-oxide, and desmethylranitidine (Carey et al., 1981). In the aquatic environment, ranitidine is transformed into two major products under solar irradiation (Isidori et al., 2009). The S-oxide metabolite and both the solar irradiation products maintain the DMA groups in their structures. Moreover, removal of parent ranitidine in conventional wastewater treatment plants has been reported to vary between 0 and 89% in different seasons (Castiglioni et al., 2006), but no data is available in terms of the removal of its major metabolites. Currently, most occurrence studies have been only focused on
Impact of Cl2:N mass ratio
Chlorine to ammonia nitrogen (Cl2:N) mass ratio is an important factor for chloramine disinfection. It can determine the dominant chloramine species along with pH (6.5e8.5) typically encountered in drinking water disinfection (USEPA, 1999). Monochloramine is predominately formed when the applied ratio is less than 5:1; dichloramine starts to form as the ratio increases, yielding a mixture of monochloramine and dichloramine; breakpoint reaction occurs when the ratio is above 7.6:1, resulting in the formation of free chlorine and nitrogen trichloride. Mitch et al. (2005) have reported that the occurrence of dichloramine can significantly enhance the NDMA formation via tertiary amines, regardless of its relatively minor fraction. In the present study, the potential impact from the Cl2:N mass ratio was studied by exposing a mixture of eight pharmaceuticals to chloramines prepared at different Cl2:N mass ratios. It was observed that the NDMA molar conversion increased as the ratio increased from 3:1 to 6.3:1, corresponding to an increasing fraction of dichloramine from approximately 10e40% in the dosed samples (Fig. 6). For utilities applying chloramine disinfection, it is generally recommended to maintain the Cl2:N mass ratio near to but below 5:1 to achieve the required residual and to avoid breakpoint reactions (USEPA, 1999). However, it is difficult to maintain a stable operating ratio, and a slight shift of the ratio above 5:1 might cause a spike of NDMA formation. Furthermore, monochloramine undergoes disproportionation to form some dichloramine over a period of a day or so (USEPA, 1999). Therefore, the chloramine residual towards the further end of the distribution system very likely includes a portion of
Fig. 6 e Impact of Cl2:N mass ratio (left) or the fraction of NHCl2 (right) on the NDMA formation via selected PPCPs (Eight PPCPs in the mixture: ranitidine, nizatidine, carbinoxamine, chlorphenamine, doxylamine, diltiazem, sumatriptan, and tetracycline; SDS conditions; tap water).
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dichloramine. If there are any potential nitrosamine precursors present in the finished drinking water, such as trace level PPCPs, the prolonged formation of nitrosamines in the distribution system may cause a concern especially when water needs to be delivered over a long distance.
4.
Conclusions
All of the 20 selected PPCPs were able to form corresponding nitrosamines upon chloramine disinfection. Eight pharmaceuticals rendered molar conversions higher than 1%, showing the potential to form NDMA under practical disinfection conditions. Ranitidine showed a particularly strong potential to form NDMA, even at environmentally relevant concentrations. The molar yields of NDMA via ranitidine (0.1e15.7 mg/L) were higher than 77%. NDMA molar conversion increased with the Cl2:N mass ratio, indicating an enhancement effect of dichloramine on the formation of NDMA via selected PPCPs. This may cause potential concern in the distribution system. Although the majority of these compounds gave yields of less than 1% molar conversion, when added together they may still contribute significantly to the formation of nitrosamines during chloramine disinfection. Overall, results from the present study have suggested that PPCPs with substituted amine groups can serve as potential nitrosamine precursors during chloramine disinfection. Due to their trace level in source waters, it is not likely that PPCPs will account for the majority of nitrosamine precursors in drinking water. However, this study proves the possible connection between the transformation of PPCPs and the formation of nitrosamines during chloramination process. Further research would be needed to determine the possible impact from different water matrices. Kinetic studies are also required to investigate the possible reaction mechanisms involved. Moreover, metabolites and transformation products of some PPCPs may also pose the potential to form nitrosamines, thus the overall nitrosamine formation potential of PPCPs should consider the parent compounds, their metabolites, as well as the possible transformation products.
Acknowledgement This research was supported by the Canadian Water Network, the Natural Sciences and Engineering Research Council of Canada, and the Ontario Research Fund.
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Krasner, S.W., Garcia, E.A., Dale, M.S., Labernik, S.M., Yun, T.I., 2008. Source and Removal of NDMA precursors. In: Proceedings of the AWWA Annual Conference and Exposition, Atlanta, G.A., June 8e12, 2008. Lee, C., Schimidt, C., Yoon, J., von Gunten, U., 2007. Oxidation of N-nitrosodimethylamine (NDMA) precursors with ozone and chlorine dioxide: kinetics and effect on NDMA formation potential. Environ. Sci. Technol. 41, 2056e2063. Lijinsky, W., Taylor, H.W., 1977. Feeding tests in rats on mixtures of nitrite with secondary and tertiary amines of environmental importance. Food Chem. Toxicol. 15, 269e274. Metcalfe, C.D., Miao, X.S., Koenig, B.G., Struger, J., 2003. Distribution of acidic and neutral drugs in surface waters near sewage treatment plants in the lower Great Lakes, Canada. Environ. Toxicol. Chem. 22 (12), 2881e2889. Miao, X.S., Bishay, F., Chen, M., Metcalfe, C.D., 2004. Occurrence of antimicrobials in the final effluents of wastewater treatment plants in Canada. Environ. Sci. Technol. 38, 3533e3541. Mitch, W.A., Oelker, G.L., Hawley, E.L., Deeb, R.A., Sedlak, D.L., 2005. Minimization of NDMA formation during chlorine disinfection of municipal wastewater by application of preformed chloramines. Environ. Eng. Sci. 22 (6), 882e890. Mitch, W.A., Schreiber, I.M., 2008. Degradation of tertiary alkylamines during chlorination/chloramination: implications for formation of aldehydes, nitriles, halonitroalkanes, and nitrosamines. Environ. Sci. Technol. 42, 4811e4817. Mitch, W.A., Sedlak, D.L., 2004. Characterization and fate of N-nitrosodimethylamine precursors in municipal wastewater treatment plants. Environ. Sci. Technol. 38, 1445e1454. Mitch, W.A., Sharp, J.O., Trussell, R.R., Valentine, R.L., AlvarezCohen, L., Sedlak, D.L., 2003. N-nitrosodimethylamine (NDMA) as a drinking water contaminant: a review. Environ. Eng. Sci. 20 (5), 389e404. MOE, 2003. Ontario regulation 268/03 made under the safe drinking water act, 2002. June 25, 2003. MOE, 2006. Procedure for disinfection of drinking water in Ontario. June 4, 2006. Munch, J.W., Bassett, M.V., 2004. USEPA Method 521: Determination of Nitrosamines in Drinking Water by Solid Phase Extraction and Capillary Column Gas Chromatography with Large Volume Injection and Chemical Ionization Tandem Mass Spectrometry (MS/MS). Version 1.0. National Exposure
Research Laboratory, Cincinnati, O.H. September 2004. EPA 600-R-05-054. Najm, I., Trussell, R.R., 2001. NDMA formation in water and wastewater. J. Am. Water Works Assoc. 93 (2), 92e99. OEHHA, 2006. Public Health Goal for N-nitrosodimethylamine and cadmium in drinking water. . Sacher, F., Schmidt, C.K., Lee, C., von Gunten, U., 2008. Strategies for Minimizing Nitrosamine Formation during Disinfection. AwwaRF Report 91209. Water Research Foundation, Denver, C. O. August 15, 2008. Schmidt, C.K., Brauch, H.J., 2008. N, N-dimethylsulfamide as precursor for N-nitrosodimethylamine (NDMA) formation upon ozonation and its fate during drinking water treatment. Environ. Sci. Technol. 42, 6340e6346. Schmidt, C.K., Sacher, F., Brauch, H.J., 2006. Strategies for minimizing formation of NDMA and other nitrosamines during disinfection of drinking water. In: Proceedings of the AWWA Water Quality Technology Conference, Denvor, C.O., November 5e9, 2006. Servos, M.R., Smith, M., McInnis, R., Burnison, B.K., Lee, B.H., Seto, P., Backus, S., 2007. The presence of selected pharmaceuticals and the antimicrobial triclosan in drinking water in Ontario, Canada. Water Qual. Res. J. Can. 42 (2), 130e137. Summers, R.S., Hooper, S.M., Shukairy, H.M., Solarik, G., Owen, D., 1996. Assessing DBP yield: uniform formation conditions. J. Am. Water Works Assoc. 88 (6), 80e93. Taguchi, V., Jenkins, S.D.W., Wang, D.T., Palmentier, J.P.F.P., Reiner, E.J., 1994. Determination of N-nitrosodimethylamine by isotope dilution, high-resolution mass spectrometry. Can. J. Appl. Spectrosc. 39 (3), 87e93. USEPA, 1999. Alternative disinfectants and Oxidants Guidance Manual, Chapter 6: chloramines. . USEPA, 2006. Unregulated contaminant monitoring Rule 2 (UCMR2). . Wilczak, A., Assadi-Rad, A., Lai, H.H., Hoover, L.L., Smith, J.F., Berger, R., Rodigari, F., Beland, J.W., Lazzelle, L.J., Kincannon, E.G., Baker, H., Heaney, C.T., 2003. Formation of NDMA in chloraminated water coagulated with DADMAC cationic polymer. J. Am. Water Works Assoc. 95 (9), 94e106. Zuccato, E., Castiglioni, S., Fanelli, R., 2005. Identification of the pharmaceuticals for human use contaminating the Italian aquatic environment. J. Hazard. Mater. 122, 205e209.
w a t e r r e s e a r c h 4 5 ( 2 0 1 1 ) 9 5 3 e9 5 9
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/watres
Temporal and spatial inhibitory effects of zinc and copper on wastewater biofilms from oxygen concentration profiles determined by microelectrodes Zhou Xiao-Hong a, Yu Tong b, Shi Han-Chang a,*, Shi Hui-Ming a a b
Department of Environmental Science and Engineering, Tsinghua University, Beijing 100084, China Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta, Canada T6G 2W2
article info
abstract
Article history:
To understand the temporal and spatial toxic effect of heavy metals on the microbial
Received 13 April 2010
activities of biofilms, microelectrodes were used to measure the inhibitory oxygen (O2)
Received in revised form
concentration profiles resulted from the effects of zinc (Zn2þ) and copper (Cu2þ). Using the
26 September 2010
O2 microprofiles as bases, the spatial distributions of net specific O2 respiration were
Accepted 27 September 2010
determined in biofilms with and without treatment of 5 mg/L Zn2þ or 1 mg/L Cu2þ. Results
Available online 13 October 2010
show that microbial activities were inhibited only in the outer layer (w400 mm) of the biofilms and bacteria present in the deeper sections of the biofilms became even more
Keywords:
active. The inhibition caused by the heavy metals was evaluated by two methods. One was
Zinc
derived from the oxygen influx at the interface and the other was based on the integral of
Copper
the oxygen consumption calculated from the entire O2 profile. The two methods yielded
Biofilm
significantly different results. We argue that the integral method results in more accurate
Toxicity
assessment of toxicity than the surface flux determination. ª 2010 Elsevier Ltd. All rights reserved.
Inhibitory effect Microelectrode
1.
Introduction
Because microorganisms are key components in the decomposition of organic substances, considerable attention has been drawn to metal toxicity towards microorganisms in recent years. The growing metal toxicity problem is not only due to the heavy metal discharges that end up in bodies of waters but also because the discharges lead to an observed reduction in biological wastewater treatment efficiency (Battistoni et al., 1993). The existence of heavy metals also reduces microbial diversity and abundance in wastewater treatment systems (Tsai et al., 2005).
Although selected heavy metals stimulate biological reactions at low concentrations, all heavy metals are toxic to microorganisms at moderate or high concentrations and ¨ zbelge et al., 2007). A can inhibit biological processes (O number of methods have been proposed for the assessment of metal toxicity in microbial systems by measuring indicative targets, such as respiratory rate, enzymatic activity, cell growth parameters, and cell viability via plate counting and bioluminescent analyses (Gikas, 2008). Generally speaking, the inhibitory effects of heavy metals on microbial activities are expressed as a reduction in the indicative target. The respirometric method, which measures the
* Corresponding author. Tel.: þ86 10 62773095; fax: þ86 10 62771472. E-mail address: [email protected] (S. Han-Chang). 0043-1354/$ e see front matter ª 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2010.09.035
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toxicant- or inhibitor-induced decrease in the oxygen (O2) consumption rate of activated sludge, is one of the most widely used methods in the assessment of toxicity and ¨ zbelge et al., 2007; Madoni inhibition (Battistoni et al., 1993; O et al., 1999; Cokgor et al., 2007; Juliastuti et al., 2003; Albek et al., 1997). Apart from activated sludge, biofilm is a major topic in wastewater treatment. Because of abundant extracellular polymeric substances (EPS), the biofilm system is considered a highly promising approach to the treatment of wastewater containing low concentrations of metal ions (Quintelas et al., 2008). The presence of binding sites enables EPS not only to sequester minerals and nutrients for microbial growth, but also remove toxic metals in the biological treatment of wastewater (Liu and Fang, 2002). Also, the EPS matrix functions as a barrier that protects bacteria inside microbial flocs against chemicals (Henriques and Love, 2007). In a study by Bae et al., intact granules were found more resistant to toxicity than the disintegrated granules in the upflow anaerobic sludge blanket reactor when metal ions and organic toxic chemicals were added to synthetic wastewater (2002). In the past decades, knowledge on microbial diversity in biofilms exposed to heavy metals or other toxins has been significantly explored with the development of molecular biology techniques (Jacek and Jan, 2000; Boivin et al., 2006). Moreover, highly complex structures containing voids, voidconnecting channels, and microbial clusters or layers with nonuniform spatial distributions of biofilm properties (e.g., density, porosity, effective diffusivity, and biokinetic parameters) were found in multi-species wastewater biofilms (Beyenal and Tanyolac¸, 1996; Zhang et al., 1995a,b; Zhou et al., 2009). Microelectrodes are effective research tools for measuring internal chemical distribution in biofilms. Through microelectrode technology, non-destructive measurements can be carried out in situ to reflect microbial biodegradation activity in thin biofilms. Over the last several decades, microbial kinetic reaction and mass transfer have been investigated in biofilms. Also, the inhibitory effects of inhibitors on microorganisms in biofilms could potentially be investigated using microelectrodes (Satoh et al., 2005). Most work has investigated the toxic effect of Zn and Cu on activated sludge systems, where zinc (Zn2þ) is revealed less toxic than copper (Cu2þ) (Juliastuti et al., 2003; Carbero et al., 1998), while the effects towards microbial activities in biofilms are less investigated. Furthermore, a deeper knowledge of the temporal and spatial inhibitory effects in the presence of metals is still needed to achieve a better understanding of metal toxicity on biofilms. For the above-mentioned reasons, the study aims to develop methods for investigating the temporal and spatial inhibitory effects of heavy metals on the O2 respiration activities of wastewater biofilms. Using microelectrodes, we sequentially monitored the O2 concentration microprofiles in municipal wastewater biofilms exposed to Zn2þ and Cu2þ. The spatial distributions of microbial activities in the inhibitortreated and untreated biofilm were obtained from the measured O2 microprofiles. The inhibitory effects of Cu2þ and Zn2þ on the biofilms were also evaluated using two methods, whose reliability in terms of interpreting toxicant inhibition were compared and discussed.
2.
Materials and methods
2.1.
Biofilms
Mature biofilms were taken from a four-stage rotating biological contactor (RBC) system treating municipal wastewater in Devon, Alberta, Canada. A detailed discussion on the RBC system can be found in literature (Lu, 2001). Biofilm samples with intact substratum (made of acrylic with a depth of 1 mm) were removed from the RBC plates and brought to the laboratory for measurement within 2 h while keeping the biofilms wet. The biofilm samples were cut into a size of approximately 2 2 cm2, from which the biofilm grown on one side of the substratum was wiped off. The non-biofilm side was fixed onto a testing chamber (made of transparent acrylic with a reaction area of 7.5 3.2 cm2 and a height of 1 cm) with a double-sided tape.
2.2.
Microelectrode measurement
For microelectrode measurement, a testing chamber (Fig. 1) was used to simulate the growth conditions in the wastewater treatment plant. The chamber was supplied with influent at a constant flow rate. To simulate the flow over the biofilm in the RBCs, the effluent was recycled to maintain 0.5 mm/s over the biofilm. The O2 concentration in the testing chamber was kept at 6e7 mg/L by aeration. All measurements were conducted at 20 1 C. The combined O2 microelectrodes with tip diameters of approximately 30 mm were fabricated as described by Revsbech (1989). The sensor signals were monitored using a picoamperometer (PA2000, Unisense, Denmark). In most cases, the microelectrode exhibited a quick response time of approximately 3 s. Each microelectrode was calibrated before mounting onto the three-dimensional micromanipulator (WPI, USA) for measurement. The system, including the microelectrode, three-dimensional micromanipulator, and testing chamber, was placed on a high-performance vibration isolation table with a Faraday cage. The entire system had a clean grounding line that was connected to the central ground grid of the Natural Resources Engineering Facility of the University of Alberta.
2.3.
O2 profiles in the biofilm
To evaluate the effect of heavy metals on biofilms, two experiments with Zn2þ and Cu2þ were conducted. First, wastewater
Fig. 1 e Schematic of the measuring system. 1. Testing chamber 2. Adjusting chamber 3. Pump for recycling 4. Pump for influent. 5. Air blower 6. Aeration head 7. Outlet for recycling 8. Biofilm 9. Influent 10. Effluent.
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samples taken from the Devon plant were filtered through a 0.45-mm membrane and used as the influent for the testing chamber. The mean chemical oxygen demand (COD) concentration was 89 mg/L for the filtered solution and 149 mg/L for the original wastewater. Several O2 profiles were measured at different times and locations in the biofilms that were not exposed to heavy metals; these measurements served as reference. In the second phase, influent was replaced by wastewater treated with Zn2þ (in ZnSO4 form) or Cu2þ (in CuSO4 form) to a final concentration of 5 mg Zn2þ/L or 1 mg Cu2þ/L. The influent with heavy metals was kept at a constant flow rate of 7 mL/20 min. With exposure to heavy metals, O2 profiles were sequentially and spatially re-measured to evaluate the effect of the heavy metals on the biofilm.
2.4. Zn2- and Cu2þ-induced local activity change in the biofilm In accordance with the mass balance in the biofilm, derived using diffusion reaction kinetics, the net specific O2 respiratory rate is calculated using Fick’s second law of diffusion with a consumption term. vCðz; tÞ=vt ¼ De $v2 Cðz; tÞ=vz2 RðzÞ
(1)
where C(z, t) is the concentration at time t and depth z, De is the effective diffusion coefficient in the biofilm, and R(z) denotes the net specific metabolic rate at depth z. Assuming that the left expression is zero because the O2 microprofiles selected for analysis changed slightly for reasonably long intervals, the following equation can be obtained as RðzÞ ¼ De $d2 C=dz2
(2)
The differential solver supplied by the Origin software was used to obtain the second-order derivative from the O2 profiles, enabling depiction of the net specific metabolic rates that varied with depth. The effective diffusion coefficient used for the calculations was 40% of the value in pure water (8.36 106 cm2/s for O2 at 20 C) (Beyenal and Tanyolac¸, 1996).
2.5.
The oxygen consumption generally follows zero-order kinetics because of the small O2 half saturation coefficient. Thus, the general solution to Eq. (2) can be obtained as C¼
k 2 z þ bz þ c 2De
(5)
where k is the zero-order reaction rate, b and c are constants. The boundary conditions to Eq. (5) are given as follows: z ¼ 0; C ¼ Co ; z ¼ Z; dC=dz ¼ 0 and C ¼ 0;
(6)
where Co is the O2 concentration at the interface of biofilm/ bulk solution and Z is the biofilm depth where O2 is depleted. Using the boundary conditions in Eq. (6), the analytical solution to Eq. (5) can be obtained, resulting in the quantification of k by least square fitting with the measured O2 profile. The inhibition ratio of O2 respiratory activities in the biofilms is calculated from the following equation. Ik ¼ 1 ðkT ZT =kU ZU Þ
(7)
where kT and ZT are the zero-order reaction rate and oxygen penetration depth in the treated biofilms, respectively, and kU and ZU are the zero-order reaction rate and oxygen penetration depth in the untreated biofilm, respectively.
3.
Results
3.1.
Effect of Zn2þ on change in biofilm local activity
Three O2 concentration profiles were measured at the same location to de-emphasize the change caused by spatial chemical heterogeneity in the Zn2þ-treated and untreated biofilms (Fig. 2). The O2 concentration in the bulk solution ranged from 6 to 7 mg O2/L. To clearly observe the O2 gradients that occurred in the untreated and treated biofilms, the O2 concentration was normalized by dividing it with the O2 concentration in the bulk solution (applicable in Cu2þ inhibition). The results showed a deeper penetration of O2 in the
Measurement and calculation of inhibition Untreated Treated for 1 h Treated for 24 h
Two methods for measuring the decrease in total metabolic rate of the biofilm were used to evaluate the inhibition caused by Zn2þ and Cu2þ. The total metabolic rate of the biofilm, flux (J, mg/(cm2 s)), is calculated according to Fick’s first law of diffusion. J ¼ Dw
dC dz
(3)
where dC/dz is the measured concentration gradient in the boundary layer at the biofilmeliquid interface. Dw is the O2 molecular diffusion coefficient in water (2.09 105 cm2/s for O2 at 20 C) (Xu and Long, 2000). Toxicity IJ is quantified as the reduction in the flux of J before and after exposure to an inhibitor (Cu2þ or Zn2þ). IJ ¼ 1 ðJT =JU Þ
(4)
where JT and JU are the total metabolic rates of the biofilms treated and untreated with the metals.
Standardized concentration
1.0 0.8 0.6 0.4
Biofilm
Bulk solution
0.2 0.0 -0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
z [mm] Fig. 2 e O2 profiles measured at the same location in the untreated biofilm and biofilm treated with 5 mg/L Zn2D for 1 and 24 h.
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treated biofilm than in the untreated biofilm, indicating that the microbial activities decreased in the former. Furthermore, the profiles measured after treatment with Zn2þ for 1 and 24 h were almost identical, clearly demonstrating that Zn2þ inhibited O2 respiration within 1 h. Moreover, the oxygen distribution showed a slight change with time after 1 h of exposure, indicating that a pseudo steady state was achieved. Therefore, assuming that the left expression of Eq. (1) is zero is reasonable. The average O2 concentration profiles in the Zn2þ-treated and untreated biofilms (measured after 1 h of exposure) are shown in Fig. 3A. The concentration profiles were measured four times in the untreated biofilm and five times in the treated biofilm. The measurements were conducted in different, randomly chosen locations. Error bars represent the standard deviations (S.D.). Similar to the results depicted in
A 8 Untreated treated
Concentration [mg/L]
6
4
2
Biofilm
Bulk solution
0
-0.6
-0.3
0.0
0.3
0.6
0.9
z [mm]
2
Second-order derivative of oxygen profiles [mg/L/cm ]
B
Fig. 2, O2 penetration was deeper in the treated biofilm than in the untreated biofilm. To investigate the inhibitory effects of Zn2þ on spatial O2 respiratory activities in the biofilm, the net specific O2 respiration rates at different depths were calculated. Their spatial distributions are shown in Fig. 3B. O2 respiratory activity was found with a maximum rate of 240 mg/(L h) at the biofilm surface to a depth of 300 mm in the untreated biofilm. After the addition of Zn2þ, the rates decreased in the outer 400 mm of the biofilm, whereas the rates increased below a depth of 450 mm. Therefore, the maximum O2 respiratory activity shifted to the deeper sections of the treated biofilm because of deeper O2 penetration.
Fig. 4 shows the O2 concentration profiles measured at the same location in the biofilm with and without treatment of 1 mg Cu2þ/L. The O2 concentration profiles were measured twice in the untreated biofilm, and depicted as the mean and S.D. in Fig. 4. After 24 h of exposure, the O2 penetration observed in the treated biofilm was deeper than that in the untreated biofilm, indicating the decreasing microbial activities in the former. No inhibitory influence was found in the biofilm within 150 min of exposure to Cu2þ. It was concluded that the acute toxicity on the biofilm resulting from exposure to Zn2þ at 5 mg/L was stronger than that from Cu2þ at 1 mg/L. The average O2 concentration profiles in the untreated biofilms and biofilms treated with Cu2þ for more than 24 h are shown in Fig. 5A. The concentration profiles were measured five times in the untreated biofilm and three times in the treated biofilm at random and varying locations. Error bars represent S.D. Similar to the results in Fig. 4, a deeper O2 penetration was observed in the treated biofilm than in the untreated biofilm. To investigate the inhibitory effects of Cu2þ on spatial O2 respiratory activities in the biofilm, the net specific O2 respiratory rates were calculated. Their spatial distributions are shown in Fig. 5B. In the untreated biofilm, the O2 respiratory activity was found at a depth of 300 mm
Untreated Treated
30
0
Biofilm
-30
Untreated Treated for 1 h Treated for 110 min Treated for 150 min Treated for 24 h
1.0
Standardized concentration
60
Effect of Cu2þ on change in biofilm local activity
3.2.
Bulk solution
0.8 0.6 0.4 0.2
Biofilm
Bulk solution
0.0 -0.3
0.0
0.3
0.6
0.9
z [mm]
Fig. 3 e A. Average O2 profiles in the untreated biofilm and biofilm treated with 5 mg/L Zn2D for more than 1 h B. Second-order derivatives from the measured O2 profiles in the biofilm.
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
z [mm] Fig. 4 e O2 profiles measured at the same location in the untreated biofilm and biofilm treated with 1 mg/L Cu2D for 1 h, 110 min, 150 min, and 24 h.
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A 8 Untreated Treated
Concentration [mg/L]
6
4
Biofilm 2
Bulk solution 0 -0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
z [mm]
2
Second-order derivative of oxygen profiles [mg/L/cm ]
B 150 Untreated Treated
(kT and kU) by integrating the activities along the biofilm depth resulted in an inhibition ratio Ik of 17%. A discrepancy between the IJ and Ik values was also observed in the biofilm exposed to Cu2þ. As shown in Fig. 5A, IJ was calculated to be 11% after exposure to 1 mg Cu2þ/L for more than 24 h, whereas Ik derived from kT and kU was 37%. To compare and discuss the reliability of using IJ and Ik in determining the effect of inhibitors on the biofilms based on the microelectrode measurement, the authors investigated the changes in the total metabolic rate calculated from the interfacial influx and zero-order reaction rate in biofilms after exposure to Cu2þ at different periods (data shown in Fig. 4); IJ and Ik are presented in Fig. 6. Note that the O2 concentrations in the bulk solution varied in the range of 1.5 mg/L mainly because of the change of fresh water, which may also influence the total O2 flux in the biofilm. As shown also in Fig. 6, the profiles of inhibition ratios IJ and Ik show similar trends, indicating that the two indexes reasonably represent the inhibition of the toxicants. However, for the two relatively identical O2 profiles measured at a pseudo steady state in the untreated biofilm, inhibition ratio IJ was calculated to be 0 0.43, whereas the S.D. of Ik was almost negligible (0.01) because of a stable k value.
100
4. 50
Bulk solution 0
-50
Biofilm
-100 -0.3
0.0
0.3
0.6
z [mm]
Discussion
With the help of O2 microelectrodes, the inhibitory effects of heavy metals may be determined based on microbial respiration in the biofilm. From the results of this study, the presence of Zn2þ (5 mg/L) and Cu2þ (1 mg/L) are found to be toxic to the microorganisms in the biofilm, which is consistent with the reported results in an activated sludge system (Carbero et al., 1998). Different procedures (such as chemical and microscopic analyses, measurement of the inhibition of growth and viability of bacterial cells, respirometric procedures) for screening the presence and effects of toxicants and inhibitors in wastewater have been applied. The common feature among
Fig. 5 e A. Average O2 profiles in the untreated biofilm and biofilm treated with 1 mg/L Cu2D for more than 24 h B. Second-order derivatives from the measured O2 profiles.
IJ
1.0
Ik
with a maximum rate of 750 mg/(L h). After the addition of Cu2þ, the rates decreased in the outer 350 mm of the biofilm, whereas the rates increased below a depth of 400 mm. With the addition of Cu2þ, the maximum O2 respiratory activity shifted to the deeper sections of the biofilm because of deeper O2 penetration.
IJ , Ik
0.5
0.0
-0.5 3.3. Comparison of Zn (IJ and Ik)
2þ
and Cu
2þ
inhibition ratios
-1.0 As shown in Fig. 3A, calculating the O2 interfacial flux to measure the changes in the total O2 respiratory rates (JT and JU) resulted in an inhibition ratio IJ of 29% after exposure to 5 mg Zn2þ/L for more than 1 h. Under the same conditions, however, using the changes in the zero-order reaction rates
0
5
10
15
20
Exposure time [h] Fig. 6 e Calculated Cu2D inhibition ratios IJ and Ik at different exposure times.
25
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all these procedures is that each one yields a markedly different index (Cokgor et al., 2007). The inconsistency makes for more challenging quantitative comparisons. Despite the lack of quantitative comparison, the spatial distributions of net specific O2 respiration in biofilms treated with Zn2þ and Cu2þ indicate that the microbial activities were inhibited only in the outer layer (w400 mm) of the biofilms, and the activities of bacteria present in the deeper sections of the biofilms increased. This proves that the biofilm functions as a protective barrier for the bacteria inside against chemicals. These results agree with those from previous studies (Henriques and Love, 2007; Bae et al., 2002). The same phenomenon was also found in a study by Satoh et al., in which microelectrodes were used to investigate the effects of 2-chlorophenol on microbial activities in biofilms (2005). Theoretically, the inhibitory effects resulting from the O2 influx and the simulated zero-order reaction rate in biofilms treated with heavy metals should match. In this study, however, the biofilm treated with inhibitors yielded different IJ and Ik values. Despite this discrepancy, IJ and Ik provided comparable results at the different exposure times to Cu2þ (Fig. 6). Moreover, the Ik derived from two relatively identical O2 profiles exhibited a much lower error bar compared with IJ. The authors attribute these to the following reasons: although the O2 flux at the interface of the biofilm and the bulk solution described by Fick’s first law of diffusion is commonly considered to be equal to the total respiration rate of the biofilm at steady state (Satoh et al., 2005; Zhang et al., 1995a,b), the accuracy of O2 flux depends only on the O2 concentrations measured at the biofilm/bulk solution interface. This introduces large errors, especially when there are several ways to determine the biofilm/bulk solution interface (Wȁsche et al., 2002). Furthermore, the interfacial flux may be overestimated as microsensor insertion compresses the boundary layer (BL) (Lorenzen et al., 1995). Zhang and Bishop (1994) attribute the compression of BL to the velocity and concentration fluctuations due to the nonuniform nature of biofilm structure. Besides diffusion, convection is observed and important for the overall mass transport toward the biofilm (Lewandowski et al., 1995; Horn et al., 1998). Nevertheless, it is still understandable that the interfacial mass transfer process is more influenced by hydraulics in the bulk solution as the deeper biofilm was protected by the outside part, so that it is easy to deviate from the ideal Fick’s diffusion law. However, the inhibition resulting from the zero-order reaction rate was derived by curve fitting the entire O2 profile in the biofilm, thereby, generating smaller errors. Also, the smeared signal from the deeper biofilms could be less influenced by the biofilm heterogeneity than the interfacial flux. Therefore, the results more accurately reflect changes in microbial activities in biofilms being exposed to heavy metals. Microelectrodes are effective tools for studying the effects of inhibitors on microbial activities in biofilms. However, further studies are needed to establish a more reliable and accurate inhibitory index to interpret the O2 profiles measured by microelectrodes. Inhibitors at various concentrations also require further investigation. Nevertheless, microelectrodes remain promising tools for the study on how microorganisms in biofilms respond to inhibitors.
5.
Conclusion
Studies on the toxic effect of heavy metals on wastewater biofilms are far from complete. In this work, the oxygen microprofiles were investigated in untreated biofilms and biofilms treated with Zn2þ and Cu2þ. The conclusions drawn are as follows: (1) With the help of microelectrodes, the spatial microbial activities treated with Zn2þ and Cu2þ were revealed and found inhibited only in the outer layer (w400 mm) of the biofilm; the bacteria present in the deeper sections of the biofilm became even more active. (2) The microelectrode measurement demonstrates that Zn2þ inhibited O2 respiration within 1 h. However, pronounced inhibition was observed after 24-h Cu2þ treatment. (3) Two comparable methods, Fick’s first law of diffusion and the zero-order reaction rate, were presented to evaluate the inhibitory effect of the heavy metals on biofilms. Inhibition ratios of IJ and Ik were 29% and 17%, respectively, after exposure to 5 mg Zn2þ/L for more than 1 h. Inhibition ratios of IJ and Ik were 11% and 37%, respectively, after exposure to 1 mg Cu2þ/L for more than 24 h. The Ik derived from two relatively identical O2 profiles at a pseudo steady state exhibited a considerably smaller error bar than did IJ, indicating that Ik more reasonably reflects the microbial activities change in the biofilm being exposed to heavy metals.
Acknowledgements We thank two anonymous reviewers for their helpful comments on the manuscript. This research work was supported by the China-Canadian cooperation project (2008CB41720), China Postdoctoral Science Foundation funded project (20080430046, 200801093).
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water research 45 (2011) 960
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Erratum
Erratum to “The fate of Transparent Exopolymer Particles (TEP) in integrated membrane systems: Removal through pre-treatment processes and deposition on reverse osmosis membranes” [Water Research 43 (2009) 5039e5052] Loreen O. Villacorte a,*, Maria D. Kennedy a, Gary L. Amy a,b, Jan C. Schippers a a b
UNESCO-IHE, Institute for Water Education, Westvest 7, 2611 AX Delft, The Netherlands Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands
article info Article history: Available online 18 June 2010
The authors regret that some errors have occurred in this paper. The equation in Section 2.3.1 was mistakenly typed as CAB ¼
CCu 0:0384
should be replaced by CAB ¼
CCu 0:0489
DOI of original article: 10.1016/j.watres.2009.08.030. * Corresponding author. Tel.: þ31 15 2151715; fax: þ31 15 2122921. E-mail address: [email protected] (L.O. Villacorte). 0043-1354/$ e see front matter ª 2010 Published by Elsevier Ltd. doi:10.1016/j.watres.2010.06.001
In Section 3.5 and in Section 4 (Conclusion #5), the statement “.around 30e70% of TEP from the feedwater were deposited.” should be written as “.around 30e70% of TEP from the recovered portion of feedwater were deposited.”. In Table 2 (legend c) was written as “calibration factor fx ¼ 114 Xeq$ L1” should be replaced with “calibration factor fx ¼ 114 mg Xeq”. The authors apologize for these errors.