Aerosol Measurement: Principles, Techniques, and Applications

EXAMPLE 8-2 Particles of 15 urn aerodynamic diameter must be sampled representatively from still air at 1.013 x 105Pa (latm) and 293 K [200C] by an instrument with a sampling flow rate of 8.3 x 10~5m3/s [5L/min].What is the inlet diameter required to meet the Davies criterion, the Agarwal and Liu criterion, and the criterion in Eq. 8-44? Answer: The Davies criterion consists of an inertial condition (Eq. 8-38) and a gravitational-settling condition (Eq. 8-39), which can be expressed in terms of volumetric flow and solved to give the following conditions for the inlet diameter: inertial condition gravitational settling condition For the conditions given in the example, T= 6.8 x 10"4S, Vts = 0.0067 m/s [0.67 cm/s], and Q = S3 x 10~5m3/s [5L/min].This gives 0.0165m < d < 0.025m for the inlet size range that will meet the Davies criterion for representative sampling. The Agarwal and Liu criterion is a single condition based on the inlet size, particle relaxation time, and settling velocity (Eq. 7-15). The condition for inlet diameter is

which gives d > 9.0 x 10"5m [0.009 cm] for the inlet diameter to meet the Agarwal and Liu criterion for representative sampling. For the specified flow of Q = 8.3 x 10"5m3/s [5L/min], Eq. 8-44 can be used to directly determine the inlet diameter:

which gives d > 1.63 x 10"2m [1.63cm] for the inlet diameter to meet the Eq. 8-44 criterion for representative sampling. Note that this is a reasonable inlet diameter, and the criterion is applicable to all particle sizes. Use Eq. 8-42 to calculate the actual sampling efficiency.

Sampling from Low-Velocity Gas Flow

The previous sections deal with sampling from a moving gas in which the gas flow velocities are large compared with the particle settling velocity and with sampling from calm air when the particle settling velocity and gravitational effects are important. This section deals with sampling from low-velocity gas flows in which particle settling velocity can influence the aspiration efficiency into a sharp-edged nozzle. Grinshpun et al. (1993,1994) have examined the available literature and offer a correction factor for the effects of particle gravitational settling on aspiration efficiency that can be applied to the aspiration efficiency correlations given above. They also provide an interpolation method to employ the corrected aspiration effi-

Still air sampling criteria

Stokes Number, SfZc7=T U/d

Agarwal & Liu (1980) representative sampling criterion

SfZrV5'= 0.05

Stk V3' < 0.05 Efficiency > 90%

Grinshpun, Willeke &Kalatoor(1993) efficiency > 95% SIK= 0.016

Davies (1968) V8' = 0.04 Region of perfect sampling

Relative Velocity, V5' = Vts/U Fig. 8-11. Plot of the Stokes number (based on the inlet velocity, U, and the inlet diameter, d) with respect to the relative velocity, V8, (the ratio of particle-settling velocity, Vte, to inlet velocity, U), showing the regions of representative sampling for a tube from still air as given by the sampling criteria of Davies (1968), Agarwal and Liu (1980), and Grinshpun et al. (1993).

ciencies at free-stream gas velocities down to calm air conditions. This has had the effect to extended the aspiration efficiency equations of Hangal and Willeke (1990a,b) for sharp-edged inlets to calm air sampling conditions. The overall aspiration efficiency is calculated as a combination of calm air and moving air efficiencies: (8-45) where 7]asp is the appropriate correlation from the above section "Sampling from Flowing Gas with a Thin-Walled Nozzle." The correction factor for gravitational settling is given by (1 + 5)m where 8 is defined by (8-46) where the angle 0 is the aspiration angle defined as the angle from the sampling gas velocity vector to the free-stream velocity vector and the angle

= 0°) to horizontal (

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The interpolation weighting factors are (8-47) and (8-48) Equation 8-42 is used for 77asp5Calmair. Equation 8-8 (Belyaev and Levin, 1974,1974) is used for isoaxial sampling, 0 = 0°. Equation 8-20 (Durham and Lundgren, 1980; Hangal and Willeke, 1990b) is used for 0° < 0 < 60°. Equation 8-22 (Laktionov, 1973; Hangal and Willeke, 1990b) is used for 60° < 0 < 90°. Values of parameters must be within the range of applicability for Stokes number and velocity ratios indicated for each correlation. These equations give good agreement with experimental data over a wide range of experimental results. The range of conditions for which these equations apply are generally given as 0° < 6 < 90°; 0° < cp < 90°; 0 < U0SU < 10; 10~3 < Stkx < 102; 10"3 < Vs/U < 1, with the stipulation that each correlation used in Eq. 8-45 not be used outside its range of applicability. Note that downward sampling is not considered, primarily because of a lack of data under calm air conditions. Chen and Baron (1996) found that aspiration efficiencies agreed well for the asbestos sampling inlet (25 mm diameter, 50 mm long) over most sampling conditions. However, at (p = 60°, U
GENERAL PRINCIPLES OF FILTER SAMPLING The essential components of a filter sampling system for aerosol measurement are shown in Figure 9-1. Typically, aerosol-laden air is drawn through a sampling probe for isokinetic sampling from a flow stream into a filter holder containing an appropriately selected filter medium. Here, the aerosol is separated from the flow stream to the extent dictated by the characteristics of the filter, the air velocity through the filter, and other factors such as the particulate loading on the filter. The air drawn through the filter flows to an optional flow measurement device, such as a rotameter, mass flow meter, or dry test meter, and then into a flow-regulating mechanism, such as an orifice or valve coupled with an air-moving device or pump. Selecting the appropriate components and the optimum order for the flow progression through the system is crucial in achieving a representative sample of the aerosol on the filter. The sampling probe upstream of the filter holder is required when sampling from moving air streams. In such applications, the volumetric sampling rate and the cross-sectional area of the sampling probe nozzle determine the air velocity through the probe inlet. The combination of an air inlet velocity equal to that of the air stream in the vicinity of the inlet and the alignment of the probe parallel to the gas streamlines ensures isokinetic sampling of the aerosol. The importance of isokinetic sampling in ensuring that a representative aerosol sample is extracted from the air stream is discussed in greater detail in Chapter 8. The effectiveness with which the sampling probe extracts the aerosol from a moving air stream is termed the aspiration efficiency of the inlet. An excellent treatment of the various factors affecting inlet aspiration efficiency is provided by Davies (1968). In addition, Vincent (1989:86-137) presents a detailed discussion of the aspiration efficiencies of thin-walled and blunt samplers in moving air streams. Chapter 8 presents an updated overview of these issues. Sampling from still air using an inlet probe also introduces possibilities of sampling biases, which are addressed by Davies (1968), Agarwal and Liu (1980), and Vincent (1989:144-164). In addition to the limitations imposed by aerosol dynamic behavior on inlet aspiration efficiency, particle losses due to temperature gradients between the probe inlet surfaces and the

Sampling probe (optional)

Filter holder (with filter, support screen and O-ring/gasket) Flow regulator Flow measurement device

Pump

Pressure gauge Fig. 9-1. Schematic example of a filter collection system typically used for aerosol measurement.

air stream, and electrostatic deposition losses due to charged probe inlet surfaces, particularly in the case of those made with plastic materials, must also be considered. In many instances, the geometry of the sampling environment necessitates a length of connecting tubing between the inlet probe and the filter holder. The passage of the aerosol from the inlet to the filter holder is characterized by the transport efficiency and is influenced by a number of factors such as gravitational, diffusional, and inertial deposition onto the wall surfaces, as well as temperature gradient-related and electrostatic wall loss effects. These issues are discussed in Chapter 8 on the sampling and transport of aerosols. It is critical that the aspiration and transport efficiencies of a filter sampling system be determined in each application to adequately characterize any distortion in the aerosol size distribution or concentration obtained on the sampling filter. Maximizing these efficiencies is particularly critical due to their inherent particle size dependence, making corrections for sampling biases extremely difficult if the aerosol size distribution is unknown. In general, sampling losses are minimized by locating the filter holder as close as possible to the sampling inlet probe and, if possible, sampling with no inlet probe or connecting tubing upstream of the filter. Filter holders provide the means by which the filtration media can be supported, typically on a coarse wire screen or backup medium, and held in a positive seal to constrain the air drawn by the sampling pump to pass through the filter. This seal is generally obtained through the use of an O-ring or a gasket of appropriate form and material that does not damage the filter material. Teflon gaskets are increasingly popular for this application due to their inherently low adhesivity to filter surfaces. Filter holders are available for all common filter sizes in the range 13 to 47 mm in diameter, as well as in 0.2 x 0.25 m [8 x 10 in] sizes for high-volume ambient air quality sampling applications. A compilation of the wide spectrum of commercially available filter holders is presented by Lippmann (1995). Filter holders may be open-faced, in-line, or of a cassette variety, the latter commonly used in industrial hygiene sampling applications, as shown in Figure 9-2. In general, open-faced filter holders provide a greater assurance of uniform filter deposition and lower sampling losses in the inlet. However, closed-face or in-line filter holders are necessary for probe-based sampling applications and also protect filter media from possible mechanical damage and rupture during sampling. In-line filter holders generally use a gradual expansion from the inlet to the filter surface, as well as downstream to the outlet, so as to ensure uniform air velocities over the cross section of the filter. This enables the collection of a uniform aerosol deposit on the filter surface, which is critical for subsequent analyses that may utilize only a fraction of the filter. The use of a filter holder for aerosol collection on a filter can introduce the potential for electrostatic and diffusional aerosol deposition losses onto filter holder inlet surfaces, as well

Fig. 9-2. Examples of filter holders available commercially for aerosol collection. From left: in-line, GEL, 47 mm; open face, MIL, 47 mm; and cassette style, GJSL, 25 mm (closed-face shown; can also be used as open-faced). (Photograph coutesy of H. T. Kim, Kwangju Institute of Science and Technology.)

as for aerosol transformations from condensation or evaporation induced by temperature gradients present between the air stream and the filter holder. These factors must be considered when designing aerosol measurement filtration systems for specific applications. The techniques utilized in the field to overcome these potential problems include the use of a controlled-temperature enclosure for housing the filter holder, thereby minimizing temperature gradients. The most critical function of a filter holder—that of ensuring a positive seal around the circumference of the filter—is typically the most frequent source of errors in filter sampling. Such sampling errors result from failures in the outer seal, thus permitting air to flow around the filter. Seal integrity and leak testing procedures must be employed to ensure that the sampled air is drawn only through the filter in the filter holder. Among the techniques that may be applied for this purpose are (1) presampling positive pressurization tests conducted with the filter holder assembly capped-off appropriately; (2) the use of a pressure gauge to monitor the vacuum pressure in the sampling line connecting the filter holder to the pump during sampling—leaks and ruptures in the filter are then easily detected from any drop in the vacuum, with normal operation indicated by a steady or gradually increasing vacuum pressure; and (3) measurement of submicrometer aerosol (e.g., ambient aerosol) penetration through the filter assembly—if the filter collects these particles with high efficiency, any particles dectected downstream of the filter are an indication of bypass leakage around the filter. A wide spectrum of filters is available commercially for aerosol measurement applications, providing the user with a selection of filter materials, pore sizes, and collection characteristics, as well as a variety of shapes such as disks and sheets sized to fit commonly available filter holders. The parameters of importance when selecting a particular type of filter for an application generally include collection efficiency of the filter for the aerosol size distribution to be sampled, pressure drop across the filter in relation to the air volumetric throughput required, compatibility of the filter with the sampling conditions, use and handling procedures, and analytical method to be employed. This includes issues of potential artifact formation on the filter surfaces from chemical reactions, interferences such as would occur from the use of hygroscopic filters in the gravimetric analysis of aerosols, and cost constraints relating to the size of the sampling effort and the number of filters required. A more detailed discussion of the various types of filters and their collection efficiencies and pressure drop characteristics is presented subsequently. Also discussed are several examples of compatibility problems typically encountered during aerosol sampling and alternatives developed to circumvent these potential problems. A comprehensive catalog of commercially available filters is provided by Lippmann (1995), including a compilation of filter manufacturer trade names and corresponding filter characteristics such as pore size, thickness, pressure drop, ash content, weight per unit area, maximum operating temperature, tensile strength, and refractive index. In a subsequent section of this chapter, a compilation of commonly used filter types for various applications is presented, together with the relative advantages and limitations of each category of filters. The use of appropriate filter handling procedures is important to ensure the collection of a representative aerosol sample. The procedures range from filter seal and surface integrity testing to minimizing interferences and artifacts from such factors as chemical reactions/transformations on the filter surface, electrostatic charging of filters, and moisture uptake in filter materials and hygroscopic aerosols. These and other procedures are discussed subsequently in connection with the requirements of filter analysis methods. Finally, the measurement and control of the air flow rate through the filter is as important as the collection of a representative aerosol sample on the filter because the air flow rate or cumulative volume through the filter is necessary to calculate the aerosol concentration from the sample. A detailed discussion of the various alternatives available for use as flow regulating and/or measuring devices and as pumps or air movers is presented by Rubow and

Furtado (1989). In addition, established scientific procedures are available for the calibration of various types of flow measurement devices as outlined, for example, by Cheng (1995). A well-designed filter collection system for aerosol measurement thus involves the collection of a representative aerosol sample on a suitable filter, combined with an accurate knowledge of the air flow rate or cumulative air volume transported through the filter. AEROSOL MEASUREMENT FILTERS A logical way to classify aerosol measurement filters is by their characteristic structure. Accordingly, filters used for aerosol sampling may be classified as fibrous filters, porousmembrane filters, straight-through pore membrane filters, and granular-bed filters. The terms porous-membrane and straight-through pore membrane are very similar to those suggested by Hinds (1999), and discussions here closely follow his classification and the discussion provided by Lippmann (1995). A summary of the salient characteristics of each type of filter is also provided in Table 9-1, complementing the discussion that follows. Fibrous Filters

Fibrous filters consist of a mat of individual fibers. Generally, filter porosity is relatively high, ranging from about 0.6 to 0.999. Porosities of less than 0.6 are not typically found in fibrous filters because of the difficulties in effectively compressing the component fibers into a smaller thin layer. Fiber sizes range from less than 1 urn to several hundred micrometers. The range of fiber diameters for a given filter is usually very diverse, although some types of fibrous filters may consist of fibers of a uniform size. Often, the filters are fabricated using a binder material to hold individual fibers together. The mass for the binder material can be as high as 10% of the filter material. Binder-free filters are generally selected for aerosol measurement because of the artifacts and interferences caused by the presence of the organic binder in the filter. The materials used for fibrous filters include cellulose, glass, quartz, and plastic fibers. Sometimes, mixed fibers of cellulose, asbestos, and glass are also used as filters for certain low-cost qualitative sampling applications. Figure 9-3 shows the microstructure of a glass fiber filter and reveals the fibrous, mat-like nature of the filter material. Cellulose fiber (paper) filters were used once very widely for general-purpose air sampling. Whatman (WHA)* filters are one of the most representative filters in this category. The filters are inexpensive, come in various sizes, and have good mechanical strength and low pressure drop characteristics. Some of the critical limitations of cellulose fiber filters are their moisture sensitivity and relatively low filtration efficiency for submicrometer particles. Glass fiber filters typically have a higher pressure drop than paper filters and often provide filtration efficiencies of greater than 99% for particles >0.3um. The filters are more expensive than paper filters. However, glass fiber filters are less affected by moisture than are cellulose fiber filters. Glass fiber filters are widely used as the standard filter media for highvolume air sampling. Teflon-coated glass fiber filters overcome some of the inherent inadequacies of glass fiber filters by being inert to catalyzing chemical transformations as well as by being less moisture sensitive. Quartz fiber filters are commonly used in high-volume air sampling applications involving subsequent chemical analyses such as atomic absorption, ion chromatography, and carbon analysis due to their low trace contamination levels as well as to their relative inertness and ability to be baked at high temperatures to remove trace organic contaminants. Polystyrene fiber filters have been used for sampling purposes to a limited extent. These filters have less mechanical strength than cellulose filters. However, their filtration efficiency * See Appendix I for full manufacturer addresses referenced by the italicized three-letter codes.

TABLE 9-1. Summary of the Salient Characteristics of the Various Types of Filters Commonly Utilized for Aerosol Measurement

Characteristics

Filter Type Fibrous filters

Porous-membrane filters

Straight-through pore filters

Granular-bed filters

Porous-foam filters

Mat/weave of fibers with diameters of 0.1-100 um. Cellulose or wood (paper), glass, quartz, and polymer fiber filters are available Porosities of 60-99%, thicknesses of 0.15-0.5mm Particle collection is throughout the depth of the filter from interception, impaction, and diffusion onto fibers High particle collection efficiencies require low air velocities Pressure drops are the lowest among all filters under comparable conditions Microporous membranes with tortuous pores throughout the structure Polymer, sintered metal, and ceramic microporous filters available Pore sizes (determined from liquid filtration) in the range 0.02-10 um Porosities of <85% and thicknesses of 0.05-0.2 mm Particle collection through attachment to microstructure elements High collection efficiencies, but highest pressure drop among all filters Thin polycarbonate films (lOum) with cylindrical pores perpendicular to film surface, with diameters in the range 0.1-8 urn Porosities are low, in the range of 5-10% Particle collection through impaction and interception near the pores and diffusion to tube walls of pores Collection efficiencies are intermediate between fibrous and microporous membrane Pressure drops are significantly higher than fibrous filters and comparable with or higher than microporous membrane filters for equivalent collection efficiency For special sampling, granules of specialty chemicals, sugar, naphthalene, sand, metal, and glass beads are used Samples are recovered by washing or volatilization Granular bead sizes range from 200 urn to a few millimeters Filtration is achieved by impaction, interception, diffusion, and gravitation Filter porosities of 40-60% for stationary beds Low collection efficiency due to large granule size. To enhance diffusion, low flow is used; bed depth is increased, or smaller granules are used Porosities of <97% Pore diameters from 10 to 50fim are common Collection efficiencies are low

is comparable with that of glass fiber filters. Other plastic materials used in filters include polyvinyl chloride and dacron. For special applications involving high temperatures and corrosive environments, filters made of stainless steel fibers have also been recently introduced. Porous-Membrane Filters A variety of membrane filters made of cellulose esters, polyvinyl chloride, Teflon, and sintered metals are commercially available. Membrane filters are gels formed from a colloidal solution and have a very complicated and uniform microstructure providing a tortuous or irregular air flow path. Often, the complex filter structure consists of a series of layers formed by different processes, depending on the manufacturing technique. The pore sizes provided by manufacturers often do not match any of the physical filter pores or structural characteristics and are defined from liquid filtration. In general, the pressure drop and the particle

Fig. 9-3. Electron micrograph showing the typical microstructure of a glass fiber filter (GEL Type AfE). Scale bar shown on micrograph.

Fig. 9-4. Electron micrograph of a microporous-membrane filter {MIL, 0.22 urn pore size) showing the tortuous flow path and structural elements in the filter. Scale bar shown on micrograph.

collection efficiency are very high, even for particles significantly smaller than the characteristic pore size. Particles are captured by the surfaces provided by the filter structure, principally by Brownian motion and inertial impaction mechanisms. An example of a microporous membrane filter structure is shown in Figure 9-4. Straight-Through Pore Membrane Filters

Straight-through pore membrane filters consist of a polycarbonate membrane with straightthrough pores of a uniform size. They are very often called Nuclepore filters after their original manufacturer, Nuclepore Corp. (Pleasanton, CA), although they are also currently manufactured by Poretics Corp. (POR). The filters are manufactured by subjecting polycarbonate membranes to neutron bombardment, followed by an etching process that produces uniform-sized pores in the membrane. The number of pores is controlled by the bombardment time, and the pore size is determined by the etching time. Capillary pore membrane filters have a very different and simpler structure compared with porous-membrane filters.

Fig. 9-5. Electron micrograph of a straight-through pore membrane filter (Nuclepore, 0.22 um pore size) showing the smooth surface and uniform pores in the filter. Scale bar shown on micrograph.

They consist of a very smooth and translucent surface with straight-through capillary holes across the membrane structure. Figure 9-5 shows the microstructure of a Nuclepore filter and reveals the flat surface and uniform pores in the filter. Straight-through pore membrane filters are widely used for particle analysis using surface analytical techniques such as light and electron microscopy. Mori et al. (1991) examined various membrane-type filters and proposed to classify both porous-membrane and straight-through pore filters into five new groups according to the appearance of the filter structure. The five groups proposed are (1) random directional fibershaped filters, (2) unidirectional fiber-shaped filters, (3) net-shaped filters, (4) agglomerateshaped filters, and (5) pore-shaped filters. This scheme may have some utility when cataloguing the spectrum of membrane filters available. Granular-Bed Filters

For specialized applications, aerosol sampling may be performed using a granular- or packedbed filter. Filtration is accomplished by passing particle-laden air through a bed consisting of granules and recovering aerosols afterwards by extraction procedures. A major advantage of aerosol sampling by granular beds is that, by selecting the proper bed media, both particulate and gaseous pollutants can be simultaneously collected (Kogan et al., 1991). Possibilities for using this method at high temperature and pressures also make this filter type attractive. Granules of activated charcoal, XAD-2, Florisil,Tenax, sugar, naphthalene, glass, sand, quartz, and metal beads have been used. XAD-2 and Tenax are polymeric adsorbent resins made of spherical granules, and Florisil is a magnesia-silica material. Figure 9-6 shows two different types of granular aerosol collection media, Florisil and Tenax. Aerosols are usually recovered from the granular media for chemical analysis by washing, volatilization, or the use of solvents. Porous-Foam Filters

Particle size-dependent penetration characteristics can be achieved by the use of porousfoam filters in an instrument that is very compact, simple, and cost effective. This type of filter is usually formed from reticulated polyurethane or polyethylene with a structure consisting of a matrix of bubbles that are pierced at their points of contact and create an open and three-dimensional lattice of connected short elements of approximately triangular cross

Fig. 9-6. Optical microscope photograph of two different types of granular-bed filtration material: Florisil (left) and Tenax (right). Millimeter scale also shown.

Fig. 9-7. Electron micrograph of a porous-foam filter. Scale bar shown on micrograph.

section. The foam structure is characterized in terms of the following geometrical parameter: The nominal porosity expressed conventionally by the foam manufacturers in terms of "pores per inch"; the volume fraction; and the effective diameter of fiber width (Vincent et al., 1993). Porosities up to 0.97 are possible, and pore diameters from 10 to 50 um are common. Efficiencies are lower than the best fibrous filters. Figure 9-7 shows an electron micrograph of a typical foam filter that is made up of a large number of bubbles. The geometric arrangement of the bubbles is not clear in this figure. A wide selection of fibrous and membrane filters are available commercially for use in aerosol measurement. Tables 9-2 to 9-4 list a number of different filter manufacturers for each type of filter discussed as well as a general price range indicating the relative differences in cost between the various filters. FILTRATION THEORY

The filtration of particles by both fibrous and membrane filters has been the subject of numerous analytical, numerical, and experimental studies during recent years. As a result, the

TABLE 9-2. Commercial Sources and Typical Cost Ranges for Fibrous Aerosol Measurement Filters Filter Material(s)

Variety of Characteristics Available

Cellulose fiber

Fine, medium, coarse void sizes

Borosilicate glass fiber

With organic binder Without organic binder With Teflon coating With cellulose content

Quartz fiber

Pure With small percentage borosilicate glass Polypropylene, 0.45,0.8, 1.2 um uniform pore sizes

Polymer fiber

Commercial Sources"

Typical Cost Range*

WHA MSI MFS S&S WHA MSA GEL MIL H&V PAL NUC MFS, MSl S&S WHA MFS GEL MSI

$10-20/100

$10-50/100 (Teflon-coated)

$50-100/100 $40/100

" See Appendix I for full manufacturer addresses referenced to the italicized three-letter codes. b Costs are for 47 mm diameter disks and are taken from manufacturers' 1998-1999 catalogs.

TABLE 9-3. Commercial Sources and Typical Cost Ranges for Microporous-Membrane Aerosol Measurement Filters Filter Material(s)

Variety of Characteristics Available

Commercial Sources

Typical Cost Range" $70-130/100

Cellulose membrane

Cellulose nitrate Mixed ester Cellulose acetate, 0.02-8 um pore size

Teflon/PTFE membrane

Pure

MIL GEL SAR NUC MFS, MSI S&S, WHA NAL Same as above

Polypropylene reinforced, 0.2-1.0 jam pore size 0.02-15 urn pore sizes

Same as above

$50-200/100

OSM MIL NUC GEL MSI SAR

$150-450/25

GEL NUC

$100-150/100

Polyester/ polycarbonate/ polypropylene membranes Silver membrane

Nylon membrane

Polyvinyl chloride membrane

Pure metallic silver, 0.2-5 um Pure nylon Laminated or impregnated with polypropylene for support, 0.1-20 um pore size Pure PVC PVC with acrylonitrile, 0.45, 0.8, 5.0um pore sizes

"Costs are for 47mm diameter disks and are taken from manufacturers' 1998-1999 catalogs.

$170-250/50

$90-110/100

TABLE 9-4. Commercial Sources and Typical Cost Ranges for Straight-Through Pore Membrane Aerosol Measurement Filters Filter Material(s) Polycarbonate

Polyester

Variety of Characteristics Available

Commercial Sources

Typical Cost Range0

0.01-14 um pore size (uniform with ±15%) 6-1 Oum thickness 5-10% porosity 0.1-12.0 Jim pore sizes

NUC OSM

$75-185/100

NUC

$75-100/100

"Costs are for 47mm diameter disks and are taken from manufacturers' 1998-1999 catalogs.

Fig. 9-8. Definition of single-fiber efficiency.

dependence of filtration efficiency on the particle size, the filter media characteristics, and the flow velocity is now well established, both qualitatively and quantitatively. In this section, filtration mechanisms are discussed and some useful predictive equations for filter collection efficiency and the pressure drop across a filter are introduced for practical application. Filtration theory is discussed for fibrous filters, and its applicability to membrane filters is described subsequently. The starting point in characterizing fiber filtration is to consider the capture of particles by a single fiber. The single-fiber efficiency, 77, is defined as the ratio of the number of particles striking the fiber to the number that would strike if the streamlines were not diverted around the fiber. If a fiber of radius Rf removes all the particles contained in a layer of thickness Y as shown in Figure 9-8, the single fiber efficiency, 77, is then defined as Y/R{. The overall efficiency, or the total efficiency, E, of a filter composed of many fibers in a mat can be related to the single-fiber efficiency as follows: (9-1) where a is the solidity or packing density of the filter (1 - porosity), L the filter depth or thickness, and df the fiber diameter. Equation 9-1 relates the total efficiency of a filter to the single-fiber efficiency as previously defined. Usually, Eq. 9-1 is used to calculate the singlefiber efficiency from the total filter efficiency, E, which can be measured experimentally. The advantage of using the single-fiber efficiency is that it is independent of the filter thickness, L. While it is not meaningful to compare the total efficiencies of two filters of two different thicknesses, different filters can be compared on the basis of their single-fiber efficiencies. This is an important point to consider when comparing the overall filtration efficiencies of filters because a filter with a lower single-fiber efficiency may have a higher total efficiency by virtue of being thicker.

Filtration Mechanisms

As air penetrates a filter, the trajectories of particles deviate from the streamlines due to several mechanisms. As a result, particles may collide with the fiber surface and become deposited on them. The important mechanisms causing particle deposition are diffusion, inertial impaction, interception, and gravitational settling. The single-fiber efficiency, 77, can then be assumed in the first approximation to be composed of the arithmetic sum of the individual efficiencies from diffusion, T7diff, interception, 7]inter, inertial impaction, 7|imp, and gravitational-settling, 77grav> mechanisms. In addition, dendrite formation from particles collected on fibers can provide additional particle collection in filters. Brownian Diffusion. Under normal conditions, aerosol particles undergo Brownian motion. Small particles generally do not follow the streamlines but continuously diffuse away from them. Once a particle is collected on a surface, it would adhere to it due to the van der Waals force. The particle concentration at the surface can, thus, be assumed to be zero, and the resulting concentration gradient normal to the media surface can be considered as the driving force for the diffusion of particles. Because Brownian motion generally increases with decreasing particle size, the diffusive deposition of particles is increased when the particle size is reduced. This phenomenon is illustrated in Figure 9-9. Similarly at low flow velocities, particles can spend more time in the vicinity of the fiber surfaces, thus enhancing diffusional collection. From the convective diffusion equation describing this process, a dimensionless parameter called the Peclet number can be defined as (9-2) where Pe is the Peclet number, dc the characteristic length of collecting media, U the average air velocity inside the filter medium, and D the diffusion coefficient of the particle. For pure molecular diffusion, D can be written as

Efficiency

(9-3)

Diffusion regime

Diffusion and interception regime

Inertial impaction and interception regime

Particle diameter, /im Fig. 9-9. Schematic of filter efficiency versus particle size illustrating the different filtration regimes.

where k is the Boltzmann constant, T the absolute temperature, 77 the air viscosity, dp the particle diameter, and Cc, the Cunningham slip correction. From the above discussion, particle collection by diffusion is expected to decrease with increasing Peclet number. Cc is written as (9-4) where X is the mean free path of the gas molecules. The approach taken to quantify the single-fiber diffusional collection efficiency, T]diff, by investigators such as Friedlander (1957), Natanson (1957), and Lee and Liu (1982a,b) is a boundary layer model that is commonly used in heat and mass transfer analysis. In early models by Friedlander and Natanson, a flow field that is an isolated cylinder model was utilized. Recent theories, such as that of Lee and Liu (1982), utilize a multiple-cylinder model that takes into account the flow interference effects of neighboring fibers. Thus, these models provide a better representation of the actual flow profile in the filters. The Lee and Liu (1982) theory yields (9-5) where K is the hydrodynamic factor, (9-6) Interception. Even if the trajectory of a particle does not depart from the streamline, a particle may still be collected if the streamline brings the particle center to within one particle radius from the fiber surface. The fact that particles with a finite size can be collected even in the absence of Brownian motion and inertial impaction indicates the importance of the interception effect. One would expect the interception to be relatively independent of flow velocity for a given fiber, and this characteristic can be contrasted to the flow-dependent characteristics of diffusion and inertial impaction. The dimensionless parameter describing the interception effect is the interception parameter, R, defined as the ratio of particle diameter to fiber diameter: (9-7) If the Kuwabara flow is used, one can obtain the following expression: (9-8) where 77inter is the single-fiber efficiency due to interception. Although Eq. 9-8 is a complete expression for the interception efficiency, the form of the equation is somewhat long, and it can be shown to be approximated into the following simpler form: (9-9) Enhanced collection of particles by a fiber can also occur from the interception of diffusing particles and has been proposed by Stechkina et al. (1969). The magnitude of this additional efficiency term is of the same order as that of the errors involved in the approximation

method used in the analysis. Spielman and Goren (1968) also indicate that the term is not theoretically consistent and is, consequently, not introduced here. Inertial Impaction. The streamlines of a fluid around the fiber are curved. Particles with a finite mass and moving with the flow may not follow the streamlines exactly due to their inertia. If the curvature of a streamline is sufficiently large and the mass of a particle sufficiently high, the particle may deviate far enough from the streamline to collide with the media surface. The importance of this inertial impaction mechanism increases with increasing particle size and increasing air velocity, as shown in Figure 9-9. Therefore, the effect of increasing air velocity on the inertial impaction of particles is contrary to that for the diffusive deposition. The inertial impaction mechanism can be studied by the use of the dimensionless Stokes number, defined as (9-10) where pp is the density of the particle. The Stokes number is the basic parameter describing the inertial impaction mechanism for particle collection in a filter. A large Stokes number implies a higher probability of collection by impaction, whereas a small Stokes number indicates a low probability of collection by impaction. Stechkina et al. (1969) calculated the inertial impaction filtration efficiency for particles, using the Kuwabara flow field. Their expression for the filtration efficiency due to inertial impaction, T7imp, is as follows: (9-11) Equation 9-11 has been used extensively for calculating the contribution by the inertial impaction mechanism. Gravitational Settling. Particles will settle with a finite velocity in a gravitational force field. When the settling velocity is sufficiently large, the particles may deviate from the streamline. Under downward filtration conditions, this would cause an increased collection due to gravity. When flow is upward, this mechanism causes particles to move away from the collector, resulting in a negative contribution to filtration. This mechanism is important only for particles larger than at least a few micrometers in diameter and at low flow velocities. The dimensionless parameter governing the gravitational sedimentation mechanism is (9-12) where U is the flow velocity and Vg is the settling velocity of the particle. It can be shown that the single-fiber filtration efficiency due to gravity, 77grav, can be approximated (Davies, 1973) as (9-13) In filtration theories, it is common to assume that the individual filtration mechanisms discussed above are independent of each other and additive. Therefore, 77, the overall single-fiber collection efficiency in Eq. 9-1, can be written as the sum of individual singlefilter efficiencies contributed by the different mechanisms. This approximation has been found to serve adequately for predicting the overall collection efficiencies of fibrous filters, owing to the different ranges in particle sizes and face velocities in which the different filtration mechanisms predominate, as illustrated in Figure 9-9.

EXAMPLE 9-1 Calculate the single-fiber efficiencies for a 0.5 urn diameter particle at 293 K [200C] and 101.3 kPa [latm] due to (1) Brownian diffusion, (2) interception, (3) inertial impaction, and (4) gravitational settling for a fibrous filter having a fiber diameter of 5 um and a solidity of 0.2 and operating at an air flow velocity of 0.15 m/s [15cm/s]. Assume the particle density is 1000kg/m3 [lg/cm3]. Answer: (1) Brownian diffusion: Using Eq. 9-3, the diffusion coefficient is calculated as

where Cc is calculated using Eq. 9-4:

From Eq. 9-2, the Peclet number is

Using Eq. 9-6, the hydrodynamic factor is

Using Eq. 9-5, the single-fiber efficiency 7]diff is

(2) Interception (from Eq. 9-7):

Therefore,

from Eq. 9-9. (3) Inertial impaction: The Stokes number is obtained using Eq. 9-10:

Continued

The single-fiber efficiency due to inertial impaction is obtained using Eq. 9-11:

(4) Gravitational settling: The settling velocity of a 0.5 um particle is

Then

Therefore,

Thus, the collection efficiency for a 0.5 um particle has the greatest contribution from inertial impaction, a bit less from diffusion, and little from settling.

Loading Effects. It is well recognized and observed that the particulate collection and the pressure drop increase if high particulate concentrations are filtered for an extended period of time. This takes place because particulates accumulate on filter media and the deposited particles provide additional surfaces for collecting incoming particles. This mechanism is inherently time dependent because the size, shape, and morphology of particle dendrites change continuously. Payatakes (1976) treated this filtration mechanism by numerically solving sets of differential equations. Kanaoka (1989) has also developed a simple method for accounting for the dendrite filtration mechanism. According to his theory, the total particle collection efficiency, loaded, including the effects of dendrite formation, is (9-14) where (9-15) Y is the efficiency increase factor, c the particulate mass concentration, U the air velocity, t the time, and L the filter mat thickness. His theory assumes that the filtration efficiency of a single fiber increases in the following manner:

(9-16) where loaded is the filtration efficiency for the fiber that accumulated the particle mass m, and riinitiai is the filtration efficiency of the clean fiber, y in Eq. 9-16 was recommended by Kanaoka to be determined from experimental data. Most Penetrating Particle Size and Minimum Efficiency

As discussed, an increase in the particle size causes increased filtration by interception and inertial impaction mechanisms, whereas a decrease in particle size enhances collection by Brownian diffusion. As a consequence, there is an intermediate particle size region where two or more mechanisms are simultaneously operating, yet none is dominant. This is the region where the particle penetration through the filter is a maximum and the filter efficiency a minimum. This is schematically illustrated in Figure 9-9. The particle size at which the minimum efficiency occurs is termed the most penetrating particle size. This size was previously assumed to be «0.3 um and is the basis for the so-called DOP (dioctyl phthalate) test method for high-efficiency particulate air (HEPA) filters. As fibrous filtration theory has been improved in recent years, the most penetrating particle size and the corresponding minimum efficiency have been observed to vary with the type of filter and the filtration velocity. Lee and Liu (1980) derived the following equation for predicting the most penetrating particle diameter: (9-17) Figure 9-10 is a comparison of Eq. 9-17 with experimental data. The most penetrating particle diameter decreases with increasing flow velocity and increasing filter solidity (1 - porosity). As the filter medium size increases, the most penetrating particle size increases. The corresponding minimum efficiency is given as

Most penetrating particle size, /um

Uo 1 cm/sec

Predicted

Solidity, <x

FIg. 9-10. Comparison of theory and experiment for the most penetrating particle diameter.

Minimum efficiency, 77min

Solidity, a Fig. 9-11. Comparison of theory and experiment for the minimum single-fiber efficiency.

(9-18) A comparison of the equation with experimental data for minimum efficiency is shown in Figure 9-11.

EXAMPLE 9-2 What is the particle diameter that will give minimum efficiency for the filter given in Example 9-1? Answer: From Eq. 9-17,

Membrane Filters

The predictive theory presented in this section was developed originally for fibrous filters. Studies of porous-membrane filters conducted by Rubow (1981) have indicated that the filtration mechanisms for porous-membrane filters are equivalent to those for fibrous filters. Furthermore, fibrous filtration theories were found to be applicable with the use of actual

thickness and solidity, the only modification necessary being the use of an effective fiber diameter to represent the structural elements in the membrane. Similarly, a most penetrating particle size has also been shown to exist experimentally for porous-membrane filters. Thus, Eqs. 9-17 and 9-18 can also be expected to be applicable for such membrane filters with the appropriate choice of the effective fiber diameter value. Particle collection in straight-through pore membrane filters can be estimated using of tube diffusion theory for diffusional collection in pore walls and impaction and interception theory for collection near pore inlet surfaces (see, e.g., Spurny et al., 1969). Typically, the efficiency of the filters is low for particle sizes less than the pore size and greater in the size range where diffusional collection is significant (i.e., dp > 0.1 jum). Particles greater than the pore size are collected with high efficiency. Due to their unique, somewhat "impactor-like" cutoff filtration characteristics, Nuclepore filters have been utilized as size-selective aerosol samplers in sequential collection stages using different pore size filters (e.g., Cahill et al., 1977; Parker et al., 1977). Heidam (1981) reviews aerosol fractionation by straight-through pore filters and concludes that particle bounce can be a potential problem in using such filters in these applications. Pressure Drop of Filters

As air passes through filter media, the filter structure causes a resistance that is a measure of air permeability or the pressure drop. A consideration of the pressure drop across filter media is important in choosing a specific filter type in a particular application. The pressure drop is easily measurable and can be used as a check on the flow fields on which deposition mechanisms are based. More importantly, the measurement of the pressure drop across filter media plays a central role in the practical estimation of filtration efficiency. Ideally, filters that exhibit a high filtration efficiency at a low pressure drop are the most desirable ones. Due to the diversity of filter types and the complicated nature of filter structure, it is difficult to describe precisely the media geometry and the corresponding flow patterns. Therefore, a prediction of the pressure drop for real filters, such as porous-membrane filters, is not straightforward. In fact, a comparison of the calculated pressure drop based on an idealized flow model with the actual measured pressure drop is used conveniently as an indication of how uniformly the media structure elements, such as fibers and pores, are arranged. A factor utilizing this concept is called the pressure drop factor, p, and is written as (9-19) where APex and APih are the pressure drop measured experimentally and predicted by the model, respectively. This pressure drop factor is then applied to the theoretically calculated filtration efficiency value as (9-20) where Eex and Eth are the filter efficiencies measured experimentally and predicted by the model, respectively (Davies, 1973; Lee et al., 1978; Schlichting, 1968). The pressure drop for fibrous filters is given by the following theoretical equation: (9-21) The measured pressure drop across a filter has been found to provide an adequate approximation, through Eq. 9-21, for the effective fiber diameter, df, for use in the filtration efficiency theory described previously. This is particularly useful for fibrous filters composed of a highly polydisperse range of fiber filters.

EXAMPLE 9-3 What is the filter efficiency for the filter given in Example 9-1 with a mat thickness of 0.1cm? Suppose the measured pressure drop for this filter is 7.37 cm [= 2.9 in] of water. What is the expected filter efficiency, considering that the filter structure is not ideal? Answer: Assuming the individual mechanisms are independent and additive, the singlefiber efficiency is

The filter efficiency is calculated using Eq. 9-1:

From Eq. 9-21, the theoretical pressure drop becomes

The expected filter efficiency becomes

Filter Testing Method

Filter testing is important for characterizing the operational characteristics of a filter medium. Traditionally, filters were tested using the conventional DOP test method. This method is described in the U.S. Army Military Standard (MIL-STD-282, replaced by ISO standard, see Chapter 33) and uses a 0.3 Jim aerosol as test particles and a photometer. As discussed previously, however, particle collection efficiency depends on the particle size and the filtration velocity. Thus, a comprehensive particle collection efficiency test needs the ability to vary particle size. Furthermore, a number of highly efficient commercially available membrane filters exhibit considerable high pressure drops across the filter, and this needs to be accommodated when the efficiency testing is performed. To address these requirements, an improved filter testing method was introduced by Liu and Lee (1976). Subsequently, the method was used extensively to test various types of filters (Liu et al., 1983). Figure 9-12 is a schematic of the experimental setup for measuring the filtration efficiency as a function of particle size and flow velocity. The system consists of an aerosol source that is capable of providing a series of monodisperse aerosols of known size, a filter testing section, and an aerosol detector.

Kr-85 NEUTRALIZER MONODISPERSE EXCESS AEROSOL GENERATOR AEROSOL

(I)

DCVOLTAGE

PINCH CLAMPS (2)

CONDENSER

ANGLE VALVE (3) DIFFERENTIAL PRESSURE GAUGE (5)

EXPANSION CHAMBER

FILTER HOLDER

ELECTRICAL AEROSOL OETECTOR NEEDLE VALVE (4) TO VACUUM PUMP Fig. 9-12. An example of an experimental system for measuring filter efficiencies.

For the aerosol source, an atomization-condensation technique separately described by Liu and Lee (1975) is used. The technique initially atomizes a DOP solution dissolved in alcohol to produce initially polydisperse particles. Subsequently, the particles become monodisperse by the vaporization-condensation method. The particle size can be varied between about 0.03 and 1.3 Jim in diameter this way. The geometric standard deviation of the test particles is found to be about 1.3. These test particles are allowed to pass through a Kr85 electrical neutralizer to avoid possible electrostatic attraction effects on filter testing results. By passing the test aerosol through the filter to be tested and then through the reference line, the amounts of the particles penetrating the paths can be compared to compute the efficiency. For testing a filter of high pressure drop, it is necessary to expand the aerosol into a low pressure in the reference line using an expansion system. In this way, the flow stream condition of both the filter tested and the reference line can be maintained the same. This can be important to ensure that the performance of the particle-detecting instrument is not affected by the different pressure condition. Consideration must also be given to particle loss in the reference sampling line, and measurement for this purpose can be performed using the electrostatic charge characteristics of particles (Liu and Lee, 1976) or by calibration using chemical analysis. For measuring aerosol concentrations in the reference line

and downstream of the filter, any aerosol instrument suitable for detecting the aerosols in the size range of interest can be used. In the aforementioned study, an electrical aerosol detector was used. Liu et al. (1983) performed extensive filter testing for 75 different filter media and compiled the results for each filter; collection efficiencies were measured for 0.035,0.01,0.3, and 1.0 um particles at four different pressure drops. Table 9-5 is an adaptation of the results of Liu et al. (1983) and subdivides the filters tested into various categories described previously. Note that filter manufacturing technology has improved since this study was performed, and some of the filter types reported in this reference currently have greater collection efficiencies. Manufacturers should be consulted for a better estimate of overall filter collection efficiencies. TABLE 9-5. List of Filters Tested and Principal Results Type

Manufacturer

Fibrous filter

Whatman

Gelman

MSA Pallflex

Porous membrane filters

Filter

No.l No. 2 No. 3 No. 4 No. 5 No. 40 No. 41 No. 42 Type A Type A/E Spectrograde Microquartz 1106B 2500 QAO E70/2075W T60A20

Another lot T60A25 TX40H 120 Another lot Reeve Angel 934AH acid-treated GF/A Whatman GF/B GF/C EPM 1000 MicrosorbanDelbag 98 MF-VS Millipore MF-VC MF-PH MF-HA MF-AA MF-RA

Material

Pore Size (um)

Cellulose fiber

NA

Glass fiber

NA

NA Quartz fiber NA Teflon-coated glass fiber

Glass fiber

NA

Glass fiber

Polystyrene Cellulose acetate/ nitrate

NA

Filter Permeability Velocity (cm/s) (AP = lcmHg)

Filter Efficiency Range (%)*

6.1 3.8 2.9 20.6 0.86 3.7 16.9 0.83 11.2 15.5 15.8 14.1 15.8 41 36.5 49.3

49-99.96 63-99.97 89.3-99.98 33-99.5 93.1-99.99 77-99.99 43-99.5 92.0-99.992 99.92 to >99.99 99.6 to >99.99 99.6 to >99.99 98.5 to >99.99 99.5 to >99.99 84-99.9 84-99.95 55-98.8

40.6 36.5 15.1 9.0 12.5 20 14.5 5.5 12.8 13.9 13.4

52-99.5 65-99.3 92.6-99.96 98.9 to >99.99 98.9 to >99.99 95.0-99.96 99.0 to >99.99 >99.99 99.6 to >99.99 99.0 to >99.99 98.2 to >99.99

0.025

0.028

99.999 to >99.999

0.1 0.3 0.45 0.8 1.2

0.16 0.86 1.3 4.2 6.2

99.999 to >99.999 99.999 to >99.999 99.999 to >99.999 99.999 to >99.999 99.9 to >99.999

Type

Manufacturer

Filter

MF-SS MF-SM MF-SC Polyvic-BD Polyvic-VS PVC-5 Celotate-EG Celotate-EH Celolate-EA Mitex-LS Mitex-LC

Metricel

Fluoropore FG FH FA FS GM-6

VM-I DM-800 Gelman

Gelman

Ghia

S2 37PL 02 S2 37PJ 02 S2 37PK 02 P5PJ03750 P5PI03750 75-F 75-M 75-C FM0.45 FM0.8 FM 1.2 FM5.0 NOlO N030 N040 N060 NlOO N200 N300 N500 N800 N1000 N1200

Zefluor Chemplast

Selas Flotronics

Straightthrough membrane filter

Nuclepore

Material

Polyvinyl chloride

Cellulose acetate Teflon PTFEpolyethylene reinforced

Cellulose acetate/ nitrate Polyvinyl chloride PVC/ acrylonitrile Teflon Teflon Teflon

Teflon Teflon

Silver

Polycarbonate

Filter Pore Size Permeability Velocity (Mm) (cm/s) (AP = lcmHg)

Filter Efficiency Range

(%y

3.0 5.0 8.0 0.6

7.5 10.0 14.1 0.86

98.5 to >99.999 98.1 to >99.9 92.0 to >99.9 99.94 to >99.99

2.0 5.0 0.2

5.07 11 0.31

88 to >99.99 96.7 to >99.99 >99.95 to >99.99

0.5 1.0 5.0 10.0

1.07 1.98 4.94 7.4

99.989 to >99.999 >99.99 84 to >99.99 62 to >99.99

0.2 0.5 1.0 3.0

1.31 2.32 7.3 23.5

>99.90 to >99.99 >99.99 >99.99 98.2-99.98

0.45

1.45

>99.8 to >99.99

5.0

51.0

0.8

2.7

5.0

56.8

1.0 2.0 3.0 2.0 3.0 1.5 1.0 1.0 0.45 0.8 1.2 5.0 0.1 0.3 0.4 0.6 1.0 2.0 3.0 5.0 8.0 10.0 12.0

12.9 23.4 24.2 32.5 31.6 3 6.6 32 1.8 6.2 9.2 19.0 0.602 3.6 2.9 2.1 8.8 7.63 12 30.7 21.2 95 161

49-98.8 >99.96 to >99.99 85-99.90 >99.97 to >99.99 99.89 to >99.99 92-99.98 94.6-99.96 88-99.9 83-99.99 54 to >99.99 26-999.8 93.6-99.98 90-99.96 73-99.7 25-99.2 99.9 to >99.99 93.9 to >99.99 78 to >99.99 53-99.5 28-98.1 9-94.1 9-90.4 6-90.7 1-90.5 1-46 1-66

"Note that current versions of many of these filters have higher efficiency. b Filter efficiency values generally correspond to a particle diameter of 0.035-1 um, a pressure drop range of 1-30 cmHg, and a face velocity of 1-100 cm/s. Source: Adapted from Liu et al. (1983).

FILTER SELECTION

The factors influencing the selection of a filter medium for a specific application can be numerous and varied. As mentioned earlier, the important considerations include particle collection efficiency, pressure drop through the filter at the flow required, compatibility with the analytical method to be employed, and cost constraints. In addition, constraints originating from the mechanical strength of the filter medium and compatibility with environmental sampling conditions such as temperature, pressure, humidity, and corrosiveness can also influence filter selection. The nature and requirements of the analytical technique(s) employed to study the aerosol, following its collection on the filter, greatly influence the selection of the most appropriate filter medium. Filter analysis techniques for deposited aerosols can be divided into three general categories: gravimetric, microscopic, and microchemical. A review of these categories demonstrates a number of the filter selection factors involved, as well as the potential for errors or biases and possible corrective techniques. Gravimetric Analysis

The measurement of the increase in weight of a filter following a well-defined sampling period is the most common way to determine aerosol mass concentration. The technique requires that the filter collect the aerosol with a high efficiency (close to 100%) and that the weight increase following sampling be entirely attributable to the collected aerosol, that is, filter weight must be independent of age and the temperature and humidity exposure conditions. Gravimetric filter analysis has been found to be most sensitive to the effects of moisture/ relative humidity and static charge buildup on filter materials. Moisture effects arise from the uptake of water vapor by the filter material and from the hygroscopicity of the aerosol sample. Filters of cellulose fibers are the most affected by water vapor uptake, with glass and cellulose quartz fiber filters being less susceptible. Water vapor uptake is lowest in Teflon membrane filters, closely followed by polycarbonate and some polyvinyl chloride (PVC) membrane filters (Mark, 1974; Demuynck, 1975; Charell and Hawley, 1981). Table 9-6 shows the weight stabilities and sensitivities to water vapor uptake of a selection of common filter media and is taken from the results of Lowry and Tillery (1979). A standard means of minimizing relative humidity interferences in gravimetric analysis involves equilibrating the filters at a constant temperature and humidity (e.g., 20, 50% RH) for 24h before and after sampling. Overcoming the complicating effects of moisture uptake by hygroscopic aerosols collected on a filter is more difficult, with few approaches available other than to calibrate the weight gained under different humidity conditions using a control sample or to minimize the time lag between sampling and weight measurements. The accumulation of static charges on a filter can result in handling difficulties, enhanced or diminished particle collection, and weighing errors in electrobalances (Engelbrecht et al., 1980). Depending on the filter material and the manufacturing process, certain types of filters such as polycarbonate and PVC membranes can become significantly charged and result in both sampling and measurement errors. A common approach to minimize these effects is to expose the filter to a source producing bipolar ions such as Po-210 or Am-241 before sampling and before gravimetric analysis. Sampling of fibers (e.g., asbestos) has also been found to be biased by charge accumulation on plastic, nonconducting filter holders—the use of conductive filter holders has been found to alleviate this problem.

TABLE 9-6. Weight Sensitivity and Stability of a Selection of Common Filter Media to Water Vapor Uptake Filter Type

Gelman glass filter type AfE (without organic binder) MSA #457193; 5um pore size PVC membrane with fibrous backup filter Millipore AA; 0.8 |im pore size, cellulose ester membrane Millipore Teflon; 5 um pore size, Teflon membrane Selas Flotronics FM-37; 0.8 um pore size, silver membrane (no longer available)

Average Weight 37 mm Diameter (mg)

Weight Stability Under Standard Conditions" (mg)

Average Weight Change Following 24 h in a Desiccator* (mg)

Average Weight Change Following 24 h at 80% RH" (mg)

86

0.14

-0.02

+0.01

243

0.04

-0.04

0

53

0.13

0

+0.35

108

0.02

+0.01

460

0.03

0

0 +0.01

"95% confidence interval based on three measurements a day for six filters of each type over 30 days. b Filters were equilibrated under room conditions for 24 h, weighed, desiccated, or humidified for 24 h, equilibrated under room conditions for 24 h, and reweighed. RH, relative humidity. Source: Lowry and Tillery (1979).

EXAMPLE 9-4 Ambient air is to be sampled to collect a particulate mass of at least lOmg for gravimetric analysis. A flow velocity of 0.305 m/s [= lft/s] will be adopted using an 0.20 m x 0.25 m [8 x 10 in] sheet quartz filter with an effective filtration area of 0.18 m x 0.230 m (= 0.0414 m2). Calculate the minimum required sampling time assuming that the particle collection efficiency is 100%. The ambient average particle concentration is estimated tobe«20|ig/m 3 . Answer: The flow rate through the filter is

The particle mass collected per unit time at this flow rate is

The required sampling time for collecting 10 mg is

Microscopic Analysis

Particle analysis by light or electron microscopy is frequently used to obtain information on the size, morphology, and compositional characteristics of aerosol samples. Microscopic analysis requires that the collection of the particles occurs on, and as closely to, a flat filter surface as is the case with microporous and straight-through pore membrane filters.

Polycarbonate, straight-through pore membrane filters are particularly well suited to microscopic applications due to their smooth, flat surface and near complete surface collection characteristics (provided that pore sizes are selected appropriately). Other surface analysis techniques that impose similar constraints on aerosol collection media include X-ray fluorescence (XRF), X-ray diffraction (XRD), and proton-induced Xray emission (PIXE) analyses for elemental and chemical species concentration measurement and aerosol radioactivity measurement techniques. These techniques also benefit from the collection of the aerosol on or close to the surface of the filter and impose additional constraints of minimizing both the aerosol collection surface area and the background concentration or response of the blank filter material in the analysis. Generally, microporous and straight-through pore membrane filters are both well suited to these other filter analysis techniques. Teflon membrane filters are most commonly used for XRF analysis of filter-sampled aerosols owing to their inertness and low blank value concentrations (e.g., Chow et al., 1990). Quartz and glass fiber filters can be useful for XRD analyses in cases of high particulate loading, while Teflon filters are superior for low loadings (Davis and Johnson, 1982). Silver membranes composed of metallic silver are useful for the analysis of crystalline silica using XRD techniques due to their extremely low interference in the quartz region of the diffraction spectrum. Aerosol radioactivity analysis using oc- or Irradiation detectors usually requires a high flow rate, high collection efficiency, and the collection of particles as close to the surface as possible to minimize the absorption of radiation. Microporous cellulose ester membranes of pore sizes between 0.45 and 0.8 urn (e.g., Type AA, MIL) are commonly employed to meet these objectives. Busigin et al. (1980) reviewed the collection characteristics of radon progeny radioactive aerosols by a variety of different filters. The selection of filters for sampling airborne microorganisms (viruses, bacteria, and fungi) is also governed by the need to microscopically count the number of viable microorganisms or colony-forming units collected on the filter. In these applications, the loss of some viable microorganisms may result from the desiccation induced by their collection on the filter surface. Thus, collection on filter surfaces is limited to hardy microorganisms that can withstand desiccation and can be transferred to suitable growth media following collection. More detailed discussions of sampling airborne bioorganisms are presented in Chapter 24, and by Chatigny et al. (1989). Microchemical Analysis

The chemical analysis of particles collected on filter media is becoming increasingly routine in applications such as air quality monitoring. The most important factors for consideration in selecting filter media for microchemical analysis are the quantity of particulate matter required for the analysis and the minimization of (1) interferences arising from the background response of the blank filters and (2) artifact formation from chemical transformations occurring on the filter during and after sampling. The magnitude and variability of the blank filter or background trace element/chemical species concentrations of different filters can be significant when determining the sensitivity and the limits of detection of the analytical technique. Maenhaut (1989) reviews a number of different analytical techniques for trace atmospheric elements and discusses the blank concentration ranges for different types of filters in various analyses. A number of other important aspects in aerosol sampling for microchemical analyses are also presented by Hopke (1985). Most chemical analyses require the extraction of the particles collected on the filter following sampling in a manner suitable for input into analytical instrumentation. Filters of cellulose (Whatman-type paper, WHA), glass, Teflon-coated glass, and quartz fibers are all commonly used in aerosol sampling for microchemical analysis due to their low pressure drop characteristics that permit high-volume sampling. Cellulose paper filters are conveniently

processed for aerosol extraction using incineration, ashing, or digesting in acid solution. However, cellulose paper filters suffer from low particle collection efficiency characteristics at low particulate filter loadings (see Table 9-7). Glass, Teflon-coated glass, and quartz fiber filters have significantly higher particle collection efficiencies but must be acid leached for recovery/extraction of the aerosols. Glass fiber filters suffer from a positive artifact mass in ambient air sampling due to their slight alkalinity that results in the in situ conversion of sulfur dioxide to sulfate (e.g., Coutant, 1977; Rodes and Evans, 1977; Stevens et al., 1978). Artifact particulate nitrate can also be formed on glass fiber filters depending on the gaseous nitric acid concentrations (Appel and Tokiwa, 1981). Quartz and paper fibers do not suffer from significant sulfate artifacts in ambient air sampling, as reported by Pierson et al. (1980). Quartz fiber filters are particularly useful in aerosol sampling for microchemical analysis due to their low water vapor uptake characteristics and low background/blank elemental concentrations. Hence, they are commonly used in ion chromatographic analysis for species such as chloride, nitrate, sulfate, potassium, and ammonium ions (e.g., Chow et al., 1990). In

TABLE 9-7. Compilation of Common Applications, Advantages, and Disadvantages for Various l>pes of Fibrous Filters Filter Type

Typical Applications

Advantages

Limitations

Low pressure drop at high-volume sampling operation Low cost High particulate loading capacity Inexpensive Convenient extraction of particulates

Lower collection efficiencies for submicrometer particles Particle collection occurs throughout the depth of the filter Highly moisture-sensitive Limited temperature range Low particle collection efficiency Low chemical resistance Sulfate artifact formation due to alkalinity of fibers Water vapor uptake can occur and must be equilibrated appropriately Artifact nitrate

Fibrous filters (general)

Air quality sampling

Cellulose fiber

Typically used in limited/qualitative applications in air quality sampling

Borosilicate glass fiber

Wide scope in air quality sampling. Used without organic binder

Temperature resistance to «500°C Chemically resistant to some extent

Teflon-coated glass fiber

Wide range of air sampling applications—emissions analysis, gravimetric analysis, biological and mutogenic analysis Air sampling for chemical analysis of particulates—ion chromatography, atomic absorption, carbon analysis, PAH analysis, etc.

Low moisture uptake Minimizes chemical transformation artifacts

Quartz fiber

Low moisture uptake Stable to temperatures up to 8000C Low trace contaminant levels Can be baked to remove trace organics before sampling Low artifact formation

Friable Artifact nitrate formation has been observed

TABLE 9-8. Compilation of Common Applications, Advantages, and Disadvantages for Various Types of Microporous Membrane Filters Filter Type

Typical Applications

Advantages

Membrane filters (general) (apply to all below)

Used in air sampling for surface analytical techniques, submicrometer particle collection

Cellulose Mixed esters Nitrate Acetate, etc. PVC membranes

Sampling of metals, Inexpensive among cotton dust, asbestos, membrane filters etc., in NIOSH Low chemical standard methods resistance

Teflon membranes

Gravimetric analysis, neutron activities analysis, XRF, XRD (see text)

Inert to chemical transformations Extremely low moisture sensitivity Low trace/background concentrations Chemical resistant

Organic particulate collection and analysis, e.g., benzo[a]pyrene, PAH, etc.; XRD analysis of silica

Chemical resistant Low background interferences High maximum operating temperature of 5500C

High collection efficiency High mechanical strength

Limitations High pressure drop Low particulate loading capacity, rapid clogging Limited temperature range, typically Susceptible to water vapor uptake Operating temperature limited to 75°-130°C Electrostatic charge buildup observed in PVC membranes Loss of nitrates observed Temperature range limited to «150°C for supported membranes and 2600C for pure

PTFE Silver membranes (no longer available)

membranes Most expensive among common membrane filters

TABLE 9-9. Compilation of Common Applications, Advantages, and Disadvantages for StraightThrough Pore Membrane Filters Filter Type Polycarbonate membranes

Typical Applications

Advantages

Limitations

Ideal for aerosol collection for subsequent surface analytical techniques, e.g., microscopy, PIXE (see text)

Flat, uniform surface Nonhygroscopic Low background/blank concentration Surface capture characteristics Semitransparent surface

High pressure drop Low particulate loading capacity Particle size range of low collection efficiency usually exists Susceptible to static charge buildup

addition, quartz fiber filters can be baked at high temperatures to lower blank organic concentration values, allowing them to be used for collecting particulate samples that can be extracted for organic species, polycyclic aromatic hydrocarbons, alklyating aspects, and mutagenic activity (e.g., Lioy and Daisey, 1983). Quartz fiber filters are also utilized in organic and elemental carbon analyses of particulate deposits on filters by a combination of flash and

rapid heating of the filters and conversion of evolved carbon to CO2 that can then be measured (e.g., Tanner et al., 1982). Membrane filters can also be used for microchemical analysis, although they suffer from problems of limited particulate loading ability and the possibility of losing coarse particles during handling and transport following sampling (Dzubay and Barbour, 1983). Teflon membrane filters have the advantage of low blank chemical species concentrations, as well as chemical inertness that permits sampling with minimal sulfate artifact formation. However, ammonium nitrate (nitrate salts) and nitric acid can be lost from "inert" Teflon, as well as quartz, filters through volatilization (Rodes and Evans, 1977) and as a result of reactions with acidic species on the filter (Harker et al., 1977). Nylon filters have been utilized as an alternative for nitrate collection (Grosjean, 1982; Spicer et al., 1982), although they are susceptible to sulfate artifact formation (Chan et al., 1986). Artifact formation in the sampling of organic aerosols on filters has also been reported in a number of studies. As discussed by Pitts and Pitts (1986), these include negative artifacts from volatilization of the more volatile particulate organics (Van Vaeck et al., 1984), positive artifacts from adsorption of gaseous organics on the filter (Stevens et al., 1980; Cadle et al., 1983), and transformations/reactions occurring with species such as O3 and NO2 sampled through the filter. De Raat et al. (1990) compared glass fiber, Teflon-coated glass fiber, and Teflon membrane filters for sampling of mutagens and polycyclic aromatic hydrocarbons (PAH) in ambient airborne particles and found: (1) a slightly greater mutagenicity when sampling with glass fiber filters, probably due to adsorption of gaseous PAH on the glass fibers followed by conversion to mutagens; (2) higher adsorption of volatile PAH on glass fiber filters and greater volatilization on the Teflon-coated glass fiber and Teflon membrane filters; and (3) lower concentrations of the more reactive PAH species on the glass fiber filters. The preceding discussions indicate that the requirements of filter analysis techniques and the need to minimize interferences and artifacts are often inherently conflicting and can rarely be perfectly satisfied. However, an adequate filter medium can generally be selected to meet the needs of most applications through a careful consideration of the important issues. Tables 9-7 to 9-9 are compilations of the common applications, advantages, and limitations of the various types of filters discussed in previous sections. The information in the tables is derived from a review of the literature and illustrates the availability of an adequate selection of filter media for the wide spectrum of applications that utilize filter sampling.

REFERENCES Agarwal, X K. and B. Y. H. Liu. 1980. A criterion for accurate aerosol sampling in calm air. Am. Ind. Hyg. Assoc. J. 41:191-197. Appel, B. R. and Y.Tokiwa. 1981. Atmospheric particulate nitrate sampling errors due to reactions with particulate and gaseous strong acids. Atmos. Environ. 15:1087. Brown, R. C. 1993. Air Filtration. Oxford: Pergamon Press. Busigin, A., A. W. van der Vooren, and C. R. Phillips. 1980. Collection of radon daughters on filter media. Environ. Sci. Technol 14:533-536. Cadle, S. H., P. J. Groblicki, and P. A. Mulaya. 1983. Problems in the sampling and analysis of carbon particulate. Atmos. Environ. 17:593. Cahill, T. A., L. L. Ashbauch, and J. B. Barone. 1977. Analysis of respirable fractions of atmospheric particulates via sequential filtration. /. Air Pollut. Control Assoc. 27:675. Chan, W. H., D. B. Orr, and D. H. S. Chung. 1986. An evaluation of artifact SO4 formation on nylon filters under field conditions. Atmos. Environ. 20:2397. Charell, P. R. and R. G. Hawley. 1981. Characteristics of water adsorption on air sampling filters. Am. Ind. Hyg. Assoc. J. 42:353-360.

Chatigny, M. A., J. M. Macher, H. A. Burge, and W. R. Soloman. 1989. Sampling airborne microorganisms and aeroallergens. In Air Sampling Instruments for Evaluation of Atmospheric Contaminants, 7th Ed. Cincinnati, OH: American Council of Governmental Industrial Hygienists, pp. 199-220. Cheng, Y. S. 1995. Aerosol sampler calibration. In Air Sampling Instruments for Evaluation of Atmospheric Contaminants, 8th Ed. Cincinnati, OH: American Conference of Governmental Industrial Hygienists. Chow, J. C, J. G. Watson, R. T. Egami, C. A. Frazier, Z. Lu, A. Goodrich, and A. Bird. 1990. Evaluation of regenerative-air vacuum street sweeping on geological contributions to PM10. /. Air Waste Manag. Assoc. 40:1134. Coutant, R. W. 1977. Effect of environmental variables on collection of atmospheric sulfate. Environ. ScL Technol 11:873. Davies, C. N. 1968. The entry of aerosols into sampling tubes and heads. Br. J. Appl Phys. Ser. 21:921. Davies, C. N. 1973. Air Filtration. London: Academic Press. Davis, B. L. and L. R. Johnson. 1982. On the use of various filter substrates for quantitative particulate analysis by X-ray diffraction. Atmos. Environ. 16:273. Demuynck, M. 1975. Determination of irreversible absorption of water on air sampling filters. Atmos. Environ. 9:523-528. de Raat, W. K., G. L. Bakker, and F. A. de Meijere. 1990. Comparison of filter materials used for sampling of mutagens and polycyclic aromatic hydrocarbons in ambient airborne particles. Atm. Environ. 24A:2875-2887. Dzubay, T. G. and R. K. Baybour. 1983. A method to improve adhesion of aerosol particles on Teflon filters. /. Air Pollut. Control Assoc. 33:692. Engelbrecht, D. R., T. A. Cahill, and P. J. Feeney. 1980. Electrostatic effects on gravimetric analysis of membrane filters. /. Air Pollut. Control Assoc. 30:391. Friedlander, S. K. 1957. Mass and heat transfer to single spheres and cylinders at low Reynolds numbers. AJ.Ch.KJ. 3:43-48. Grosjean, D. 1982. Quantitative collection of total inorganic atmospheric nitrate on nylon filters. Anal. Lett. 15(A9):785. Harker, A., L. Richards, and W. Clark. 1977. Effect of atmospheric SO2 photochemistry upon observed nitrate concentrations. Atmos. Environ. 11:87. Heidam, N. Z. 1981. Review: Aerosol fractionation by sequential filtration with Nuclepore filters. Atmos. Environ. 15:891. Hinds, W. D. 1999. Aerosol Technology. New York: John Wiley & Sons. Hopke, P. K. 1985. Receptor Modeling in Environmental Chemistry. New York: John Wiley & Sons. Kanaoka, C. 1989. Time dependency of air filter performance. /. Aerosol Res. Jpn. 4:256-264 [in Japanese]. Kogan, V., M. R. Kuhlman, R. W. Coutant, and R. G. Lewis. 1991. Aerosol filtration by sorbent beds. In JAWMA 43:1367-1373, press. Lee, K. W, L. D. Reed, and J. A. Gieseke. 1978. Pressure drop across packed beds in the low Knudsen number regime. /. Aerosol ScL 9:557-566. Lee, K. W. and B. Y. H. Liu. 1980. On the minimum efficiency and the most penetrating particle size for fibrous filters. /. Air Pollut. Control Assoc. 30:377-381. Lee, K. W. and B. Y. H. Liu. 1982a. Experimental study of aerosol filtration by fibrous filters. Aerosol ScL Technol. 1:35-46. Lee, K. W. and B. Y. H. Liu. 1982b. Theoretical study of aerosol filtration by fibrous filters. Aerosol ScL Technol. 1:147-161. Lioy, P. J. and J. M. Daisey. 1983. The New Jersey project on airborne toxic elements and organic substances (ATEOS): A summary of the 1981 summer and 1981 winter studies. / Air Pollut. Control Assoc. 33:649.

Lippmann, M. 1995. Filters and filter holders. In Air Sampling Instruments for Evaluation of Atmospheric Contaminants, 8th Ed. Cincinnati, OH: American Conference of Governmental Industrial Hygienists. Liu, B. Y. H. and K. W. Lee, 1975. An aerosol generator of high stability. Am. Ind. Hyg. Assoc. J. 36:861865. Liu, B. Y. H. and K. W. Lee. 1976. Efficiency of membrane and Nuclepore filters for submicrometer aerosols. Environ. Sci. Technol. 10:345-350. Liu, B. Y. H., D. Y. H. Pui, and K. L. Rubow. 1983. Characteristics of air sampling filter media. In Aerosols in the Mining and Industrial Work Environments. Ann Arbor, MI: Ann Arbor Science. Lowry, P. L. and M. 1. Tillery. 1979. Filter Weight Stability Evaluation. Los Alamos Scientific Laboratory, Report No. LA-8061-MS. Maenhaut, W. 1989. Analytical techniques for atmospheric trace elements. In Control and Fate of Atmospheric Trace Metals, eds. J. M. Pacyna and B. Ottar. Dordrecht: Kluwer, pp. 259-301. Mark, D. 1974. Problems associated with the use of membrane filters for dust sampling when compositional analysis is required. Ann. Occup. Hyg. 17:35. Mori, I, H. Emi, and Y. Otani. 1991. Classification of membrane gas filters and their performance evaluation. J. Aerosol Res. Jpn. 6:149-156 [in Japanese]. Natanson, G. L. 1957. Proc. Acad. Sci. USSR. Phys. Chem. Sec. 112:21-25. Parker, R. D., G. H. Buzzard, T. G. Dzubay, and J. P. Bell, 1977. A two-style respirable aerosol sampler using Nuclepore filters in series. Atmos. Environ. 11:617. Payatakes, A. C. 1976. Model of transient aerosol particle deposition in fibrous media with dendritic pattern. A.I.Ch.E. J. 23:192-202. Pierson, W. R., W. W. Brachaczek, T. J. Korniski, T. J. Truer, and J. W. Butler, 1980. Artifact formation of sulfate, nitrate and hydrogen ion on backup filters: Allegheny mountain experiment. /. Air Pollut. Control Assoc. 30:34. Pitts, B. J. F. and J. R. Pitts, Jr. 1986. Atmospheric Chemistry: Fundamentals and Experimental Techniques. New York: John Wiley & Sons. Rodes, C. E. and G. F. Evans. 1977. Summary of LACS Integrated Measurements. EPA-600/4-77-034. Research Triangle Park, NC: U.S. Environmental Protection Agency. Rubow, K. L. 1981. Submicrometer Aerosol Filtration Characteristics of Membrane Filters. Ph.D. thesis, University of Minnesota, Minneapolis, Minnesota. Rubow, K. L. and V. C. Furtado. 1989. Air movers and samplers. In Air Sampling Instruments for Evaluation of Atmospheric Contaminants, 7th Ed. Cincinnati, OH: American Conference of Governmental Industrial Hygienists. Schlichting, H. 1968. Boundary Layer Theory, 6th ed. New York: McGraw Hill. Spicer, C. W., J. E. Howes, T. A. Bishop, L. H. Arnold, and R. K. Stevens. 1982. Nitric acid measurement methods: an intercomparison. Atm. Environ. 16:1478-1500. Spielman, L. and S. L. Goren. 1968. Model for predicting pressure drop and filtration efficiency in fibrous media. Environ. Sci. Technol. 2:279-287. Spurny, K. R. 1998. Advances in Aerosol Filtration. Boca Raton, FL: Lewis Publishers. Spurny, K. R., J. P. Lodge, Jr., E. R. Frank, and D. C. Sheesley. 1969. Aerosol filtration by means of Nuclepore filters: Structural and filtration properties. Environ. Sci. Technol. 3:453. Stechkina, I. B., A. A. Kirsch, and N. A. Fuchs. 1969. Studies in fibrous aerosol filters—IV. Calculation of aerosol deposition in model filters in the range of maximum penetration. Ann. Occup. Hyg. 12:1-8. Stevens, R. K., T. G. Dzubay, G. Russwurm, and D. Rickel. 1978. Sampling and analysis of atmospheric sulfates and related species. In Sulfur in the Atmosphere, Proc. International Symposium, United Nations, Dubrovnik, Yugoslavia, September 7-14,1977. Atmos. Environ. 12:55. Stevens, R. K., T. G. Dzubay, R. W. Shaw, Jr., W. A. McClenny, C. W. Lewis, and W. E. Silson. 1980. Characterization of the aerosol in the Great Smoky Mountains. Environ. Sci. Technol. 14:1491. Tanner, R. L., T. S. Gaffney, and M. F. Phillips. 1982. Determination of organic and elemental carbon in atmospheric aerosol samples by thermal evolution. Anal Chem. 54:1627.

Van Vaeck, L., K. Van Cauwenberghe, and J. Janssens. 1984. The gas-particle distribution of organic aerosol constituents: Measurement of the volatilization artifact in Hi-VoI cascade impactor sampling. Atmos. Environ. 17:900. Vincent, J. H. 1989. Aerosol Sampling Science and Practice. New York: John Wiley & Sons. Vincent, J. H., R. J. Aitken, and D. Mark. 1993. Porous plastic foam filtration media: Penetration characteristics and applications in particle size-selective sampling. /. Aerosol ScL 24:929-944.

daily impactors) are the most widely used of the classifiers, the major emphasis of this chapter is on these classifiers. INERTIAL CLASSIFIERS Numerous inertial classifiers have been designed and reported in the literature, with many of them being commercially available. Tables 10-1 to 10-3 list most of the commercially available impactors and cyclones by type and manufacturer. The following sections discuss the classifiers in general terms, providing information on specific devices only to illustrate specific points. Principle of Inertial Classification The principle of inertial classification of particles is quite simple in that the particles' inertia is used in their classification. Classification is achieved in these instruments by turning the gas flow and capturing the particles with sufficient inertia to cross gas streamlines and escape the flow. Particles with less inertia will remain in the gas flow. The simplest type of inertial classifier is a body collector, which is a body (usually cylinder or ribbon) passing through particle-laden gas. As this body moves through the gas, the gas is deflected around the body. Large particles, however, due to their inertia, are not deflected as much as small particles and will strike the surface of the body. An excellent example of a body collector is the automobile. As the automobile passes through the air, large particles in the air will impact on the automobile, and, as passengers, we are in an excellent position to observe impaction on the windshield. Probably the best observations are during a snowstorm when the path of the snowflakes can be observed. If the car is moving rather slowly, the snowflakes will approach the car and pass over the windshield without impaction. As the automobile increases in speed, the snowflakes will impact on the windshield. The two determining factors as to whether or not the snowflakes will impact on the windshield are the speed of the automobile and the size of each snowflake. A similar observation is experienced when the automobile encounters flying insects. A large insect will have a trajectory undeflected by the airstream around the car and impact dramatically on the windshield. Smaller insects will follow the airstream and not impact. However, if the body is smaller, the smaller insects will be collected. This can be seen on smaller cross-sectional area bodies, such as the radio antenna. Inspection of the insects on the antenna will indicate that they are smaller than those collected on the automobile windshield. The above example indicates that the velocity of the air, U, the size of the particle, dp, and the size of the body, db, arc three important parameters in determining whether or not a particle will be collected on the body. A dimensionless parameter, the Stokes number, defined as the ratio of the particle's stopping distance to the physical dimension of the body collector, is the governing relationship as to whether or not a particle will strike the body. If the Stokes number, Stk, defined as (10-1) where PP = particle density Cc = slip correction U = relative velocity of body to air (or gas) dp - particle diameter r\ = air (or gas) viscosity dh = body diameter is larger than approximately unity, the particle will impact on the body.

TABLE 10-1. Selected List of Commercially Available Impactors Manufacturer0

Sampler Name

Cascade Impactors for Ambient Air Sampling AND Sierra/Marple Model 210 AND Low Pressure Impactor AND, GMW One ACFM Ambient Impactor DEK ELPI (Electrical Low Pressure Impactor) DEK Low Pressure Impactor DEK PM-10 Impactor HAU Berner Impactor INT Mercer 7-Stage Impactor (02-100) INT Mercer 7-Stage Impactor (02-130) INT Mercer 7-Stage Impactor (02-150) INT Mercer 7-Stage Impactor (02-170) INT Multijet CI (02-200,02-220, 02-240) INT Multijet CI (02-260) MSP MOUDI (micro-orifice impactor) MSP Nano-MOUDI MSP Cleanroom Cascade Impactor MSP Airborne Cascade Impactor CMI QCM Real-Time Impactor, PC-2 CMI QCM Real-Time Impactor, PC-2H CMI QCM Real-Time Impactor, PC-6H Cascade Impactors for Pharmaceutical Applications Eight Stage Non-Viable Cascade Impactor AND, GMW COP "Andersen-Type" Cascade Impactor COP Multistage Liquid Impinger (MSLI) MSP Marple-Miller Pharmaceutical Impactor MSP

Next Generation Pharmaceutical Impactor

No. of Stages

Cut Points (Range, um)

Comments6

12 [7] 5 [3] 47 [28] 17,33,50 [10,20,30] 17, 50 [10, 30] 17,33,50 [10,20, 30] 50 [30] 0.17 [0.1] 1.7 [1] 3.3 [2] 8.3 [5] 17,25,33 [10,15,20] 47 [28] 50 [30] 17 [10] 5 [3] 170 [100] 0.42 [0.25] 3.3 [2] 3.3 [2]

10 12 8 13 13 3 9 7 7 7 7 7 7 10 3 6 5 10 10 6

0.16-18 0.08-35 0.4-10 0.03-10 0.03-10 10,2.5,1.0 0.063-16.7 0.33-3.1 0.32-4.5 0.25-5.0 0.5-5.0 0.5-8.0 0.5-9.0 0.056-10 0.01-0.032 0.05-10 0.25-2.5 0.05-25 0.05-10 0.05-6.0

1 3

47 [28] 47 [28] 50 [30] 8.2,20,50,100 [4.9,12, 30, or 60] 50-170 [30-100]

8 8 4 5

0.4-10 0.4-10 1.7-13 0.63-10

7

0.23-11

Flow Rate (XlO"5 m3/s [L/min])

4

5

6 6 7 7 7

{continued)

TABLE 10-1. Continued Manufacturer0

Sampler Name

Impactors for Ambient HiVoI Samplers AND, GMW HiVoI Impactor, Series 230 AND, GMW HiVoI Impactor, Series 230

Flow Rate (XlO"5 m3/s [L/min])

No. of Stages

Cut Points (Range, um)

Comments6

1880 [1130] 942 [565]

4 6

0.49-7.2 0.41-10

9 9

Single-Stage Impactors for Ambient Samplers ADE MS&T Area Sampler ADE MS&T Area Sampler ADE MS&T Area Sampler MSP Micro-Environmental Monitor URG Portable Size Selective Impactor

38 [23] 6.7,17,33 [4,10, or 20] 6.7,17,33 [4,10, or 20] 17 [10] 6.7 [4]

1 1 1 1 1

1.0 2.5 10 2.5 or 10 2.5

10 10 10

Personal Samplers AND, GMW MSP MSP SKC SKC URG URG URG

3.3 [2] 3.3,6.7,17 [2,4, or 10] 3.3,6.7,17 [2,4, or 10] 4.2 [2.5] 3.3 [2.0] 6.7 [4] 6.7 [4] 3.3 [2]

8 1 1 1 1 1 1 1

0.5-20 2.5 10 4.0 2.5 1,2.5, or 10 1,2.5, or 10 2.5

12 [7] 5-35 [3-21] 1.7 [1] 120 [70] 16 [16] 47 [28] 4.7 [2.8] 47 [28] 47 [28]

9 8 21 4 7 13 7 3 14

Marple Personal Sampler (Model 290) Personal Environmental Monitor Personal Environmental Monitor Spiral Sampler (respirable) Spiral Sampler (PM-2.5) Personal PUF Pesticide Sampler Personal Impactor Filter Pack Personal Impactor Filter Pack

Source Test Impactors AND In-Stack Air Sampler, Series 220 AND Stack Sampling Head (Mark III, IV) AND Impactor Preseparator DEK Automotive Mass Impactor INT High Temp, High Pres. Impactor PCS UW (Pilat) Mark V Cascade Impactor PCS UW (Pilat) Mark III Cascade Impactor PCS Mark 8 High Grain Loading Impactor PCS UW (Piiat) Low Pressure Source Test Impactor

0.16-18 0.4-11 10 0.2-2.5 0.62-8.8 0.2-20 0.2-20 1.5-10.8 0.05-20

1

1

PM Inlets AND, GMW AND, GMW AND, GMW AND, WED BGI URG

Hi-Volume PM-IO Inlet Medium Flow PM-10 Inlet Dichotomous Sampler Inlet Hi-Volume PM-10 Inlet Low Flow PM-10, PM-2.5 Inlet Low Flow PM-10 Inlet

Viable and Biological Impaction Samplers Single Stage Bioaerosol Sampler AND Microbial Air Sampler AND Particle Fractionating Viable Sampler AND Slit-to-Agar Biological Sampler NBS BioStage-lBioaerosol Impactor SKC BioSampler SKC SAS Portable Sampler SSI

1880 [1130] 187 [112] 17.8 [16.7] 1880 [1130] 17.8 [16.7] 17.8 or 53 [16.7 or 32]

1 1 1 1 1 1

10 10 10 10 2.5 or 10 10

47 [28] 47 [28] 47 [28] 92 [55] 47 [28.3] 10.8 [12.5] 150, 300 [90,180]

1 2 6 1 1 1 1

0.65 0.65, 3.5 0.65-7 Not stated Not stated Not stated Not stated

"See Appendix I for full manufacturer addresses referenced to the italicized three-letter code. b Comments: 1. Radial slot design. 2. Circular jets, interchangeable nozzles. 3. Four low-pressure stages. 4. Uses electrometers connected to each impactor stage for counting charges on particles for real-time measurement. 5. One round jet per stage. 6. Micro-orifice plates of 2000 jets on bottom stages. 7. Uses vibrating quartz crystal collection surfaces as mass-to-frequency transducers for real-time measurement. 8. Fits on HiVoI, round jets. 9. Fits on HiVoI, rectangular jets. 10. Also referred to as the "Harvard Impactor." 11. Collection directly onto agar plates. 12. Slot impactor with rotating turntable for agar plates.

11 11 11 12

TABLE 10-2. Selected List of Commercially Available Virtual Impactors Manufacturer

Sampler Name

Flow Rate (XlO"5 nr7s [L/min])

No. of Stages

Cut Points (Range, um)

AND, GMW BGI, INT INT MSP MSP MSP MSP URG

Dichotomous Sampler Cascade Centripeter Virtual Impactor High Volume Virtual Impactor Microcontaminant Particle Sampler Universal Air Sampler Bioconcentrator VAPS

27.8 [16.7] 50 [30] 1.7-8.3 [1-5] 1880 [1130] 50 [30] 500 [300] 550 [330] 53 [32]

1 3 2 1 1 2 3 2

2.5 1.2,4,14 0.5-10 1.0,2.5 1.0 10,1.0, or 2.5 10,2.0,2.0 10,2.5

TABLE 10-3. Selected List of Commercially Available Cyclones Manufacturer AND AND, INT AND, INT AND, INT AND, INT AND, INT BGI BGI BGI BGI BGI BGI, GMW DEK MSA, SEN, SKC INT SEN SEN SKC SKC URG URG URG URG

Cyclone Name

Flow Rate Range (XlO"5 m3/s [L/min])

D50 Range (um)

Sharp Cut Cyclone SRIV SRIIV SRI III AIHL SRIII SRII Respirable PM 1.0 Sharp Cut PM 2.5 Sharp Cut GK 2.05 (KTL) Triplex (SCC 1.062) Aerotec 2 Aerotec 3/4 Dekati Cyclone 10 mm Cyclone (also called Dorr-Oliver) STR 1/2" HASL 1" HASL BK 76 BK-152 Aluminum GS Sharp Cut-point Sharp Cut-point Sharp Cut-point Sharp Cut-point

27.8 [16.7] 12-47 [7-28] 12-47 [7-28] 23-47 [14-28] 13-45 [8-27] 23-47 [14-28] 23-47 [14-28] 3.67,7.00 [2.2,4.2] 27.8 [16.7] 27.8 [16.7] 6.67 [4.0] 5.83,2.5,1.75 [3.5,1.5,1.05] 583-833 [350-500] 37-92 [22-55] 17 [10] 1.5-8.3 [0.9-5]

2.5 0.3-2.0 0.5-3.0 1.4-2.4 2.0-7.0 2.1-3.5 5.4-8.4 4.0 1.0 2.5 2.5 1.0,1.5,4.0 2.5-4.0 1.0-5.0 10 1.8-7.0

1.7-100 [1-60] 13_17 [8-10] 108-583 [65-350] 670-1830 [400-1100] 192O-4500 [1150-2700] 4.17 [2.5] 4.58 [2.75] 27.8 [16.7] 5,17,27.8 [3,10,16.7] 47.2 [28] 27.8,47.2 [16.7,28.3]

0.3-10 2-5 1.0-5.0 1.0-3.0 2.0-5.0 4.0 4.0 1.0 2.5 3.5 10

Note that in the Stokes number, the three parameters discussed in the above example (U, dp, and db) are included as well as properties of the gas and particles (77, Cc, and pp).The Stokes number is important in all types of inertial collectors and not just body collectors. The Stokes number for a conventional impactor or a virtual impactor is the ratio of the stopping distance to the radius of a circular nozzle, or half width of a rectangular nozzle, and is defined as

(10-2) where U = average air (or gas) velocity at the nozzle exit = Q/7c(W/2)2 (round nozzle impactor) or = QILW (rectangular nozzle impactor) W = nozzle diameter (circular impactor) or nozzle width (rectangular impactor) Q = volumetric flow rate through the nozzle L = rectangular nozzle length The Stokes number is a dimensionless parameter that can be used to predict whether or not a particle will impact on a body, an impaction plate or in the collection probe of a virtual impactor, or will follow the air streamlines out of the impaction region and remain airborne. Actually, the square root of Stokes number, ^IStk, is more commonly used because it is a dimensionless particle size. A critical value of 4Sik, often used to characterize inertial classifiers, is ^StIc50. This is the value of «JStk corresponding to d50, the value of dp at which particles are collected with 50% efficiency. Thus, if the value of ^StJc50 is known, the value of d50, corresponding to the cut size of the impactor, can be found from (10-3) for body impactors, and (10-4) for conventional and virtual impactors. General Description

Inertial classifiers have been widely used for the separation of particles by their aerodynamic diameters where the aerodynamic diameter is the diameter of a standard density (1000 kg/m3 [1 g/cm3]) sphere that has the same gravitational settling velocity as the particle in question. Four types of inertial classifiers in common use, as shown in Figure 10-1, are body impactors, conventional impactors, virtual impactors, and cyclones. The first of these, the body impactor, is the simplest in that it consists of only a body in a moving aerosol stream onto which particles impact. The latter three of the inertial classifiers all consist of a jet of gas impinging on a target. A conventional impactor, in its simplest form, consists of a jet of particle-laden gas impinging on a flat plate with particles impacting on the plate. Variations of the impactor include the use of either round or rectangular nozzles, single or multiple nozzles, and flat or cylindrical impaction plates. In a virtual impactor, the impaction plate is replaced by a collection probe slightly larger than the nozzle, with the classified particles penetrating into the collection probe. A small fraction of the gas passes through the collection probe to transport the classified particles out the lower end of the probe. The remainder of the gas, the major portion, reverses direction in the collection probe and escapes at the upper edge. In the cyclone, the aerosol stream is drawn through the inlet and impinges tangentially on the inner surface of a cylinder, flows in a spiral pattern down the inside of the cylinder and cone walls, reverses direction, spirals upward around the cyclone axis, and exits through a centrally located tube at the upper end of the cylinder. Particles are collected on the

Total Flow Body Acceleration Nozzle lmpaction Plate

a) Body Impactor

b) Conventional Impactor

Total Flow Acceleration Nozzle Major Flow

Total Flow Inlet

Collection Probe

Minor Flow c) Virtual lmpactor

d) Cyclone Fig. 10-1. Four types of inertial classifiers.

cylindrical and conical walls by inertial forces on the particles. Clumps of impacted particles that are knocked off or dropped from the cyclone walls tend to fall to the apex of the cone where they are collected in a cup, sometimes called a dust cap or grit pot. Another inertial device, although not a particle classifier in the traditional sense, is the aerodynamic focusing lens, which consists of a series of small sharp-edged orifices on one axis (de Juan and Fernandez de Ia Mora, 1998; Liu et al., 1995a,b; Fernandez de Ia Mora and Riesco-Chueca, 1988). The focusing lens operates under the principle that the flow streamlines are radially inward as the air approaches the inlet to a sharp-edged orifice. The inertia of the particles will cause the particles to cross the streamlines and be closer to the center line of the flow when the flow exits the orifice than when it entered. By passing the flow through a series of these orifices of decreasing size, the particles will exit the lens with the particles focused near the center line. These lenses are described in detail in Chapter 13. Conventional Impactors. The most common type of impactor consists of a single jet of particle-laden gas (aerosol) impinging on a flat plate, as shown in Figure 10-2. Particles larger than the cut size of the impactor will slip across the streamlines and impact on the plate, while smaller particles will follow the streamlines and not be collected. The most important

W Acceleration Nozzle

Streamlines

Impaction Plate Trajectory of Impacted Particle

Trajectory of Particle too Small to Impact

a) Conventional Impactor

Efficiency

Actual

Ideal Vstk b) Efficiency Curve Fig. 10-2. Schematic diagram of a conventional impactor and corresponding particle collection efficiency curve.

impactor characteristic is the collection efficiency curve, also shown in Figure 10-2. The collection efficiency (as a function of particle size) is defined as the fraction of particles passing through the nozzle that are collected on the impaction plate. The ideal impactor has a perfectly sharp efficiency curve, that is, all particles larger than the cut size of the impactor are collected on the plate, while all smaller particles follow the gas flow out of the impaction region. A nozzle and an impaction plate constitute a single-stage impactor that is useful when classifying particles into two size fractions. For example, this is the case for analyzing particles that are less than 10 urn (PM-IO) or less than 2.5 urn (PM-2.5) (see Chapter 29). In these types of impactors, the particles larger than the cut size are removed from the air stream, while the smaller particles penetrate the impactor stage to be either collected on a filter, where they can be analyzed (e.g., for mass concentration or elemental composition), or passed into some other instrument for real-time mass or number concentration measurement. Often, it is desirable to determine the entire size distribution of the aerosol and not just the quantity less than a certain size. In this case, a series of impactor stages are used in a cascaded fashion such that the gas passes from one stage to the next, as shown in Figure 10-3,

Nozzle Stage 1 Impaction Plate

Stage 2

Stage N

After Filter

Filter

To Vacuum Pump Fig. 10-3. Schematic diagram of a cascade impactor.

to remove particles in discrete size ranges (Lodge and Chan, 1986). This is known as a cascade impactor and is widely used for determining size distributions of aerosols. A cascade impactor makes use of the fact that particle collection is governed by the Stokes number. The velocity of the particle-laden gas stream is increased in successive stages, resulting in the collection of successively smaller particles in the subsequent stages. For example, if a four-stage cascade impactor has cut-sizes of 10, 5, 2.5, and 1.25 urn, the first stage will collect particles larger than 10 urn, the second stage will collect particles between 5 and 10 um, the third stage between 2.5 and 5jim, and the fourth stage between 1.25 and 2.5 urn. Particles less than 1.25 urn penetrate the final stage of the impactor and can be collected on an after-filter. The particle deposits on the impaction plates can be evaluated by a variety of methods. A few of the more common methods are (1) the particles are collected on glass plates, membrane filters, or foils and are inspected or counted under a microscope; (2) the particles are collected on foils and weighed to determine the mass of particles at each stage; (3) the particles are collected on quartz crystals, and the mass of particles is determined by the change in the natural frequency of the crystals (Fairchild and Wheat, 1984; see also Chapter 14); or (4) the particles are charged before passing through the impactor, and the current is measured at each impaction plate to determine the number of particles being collected

(Keskinen et al, 1992; see Chapter 14). The first two methods provide size distribution data integrated over time, while the latter two methods provide size distribution data in near real time. The uncertainties in the particle size distribution are the size of the largest particles collected on the first stage and the size of the smallest particles collected on the after-filter. These sizes may be estimated, or, better, an impactor may be selected with a sufficient number of stages to span the entire size distribution of interest so that the mass collected on the first stage and the after-filter is minimized. As stated above, many cascade impactor designs have been built and tested. The calibration curves for no two designs will be exactly alike due to differences in design parameters (e.g., nozzle diameters, number of nozzles, sampling flow rates) and to small influences of the boundary conditions on particle collection. Figure 10-4 shows a set of typical cascade impactor efficiency curves. Some impactors may have sharper cut-off characteristics and some not as sharp, but the general shape will be very similar to those shown. Analysis of Eq. 10-4 reveals that the particle size range of an impactor can be lowered to very small sizes by either increasing the value of the slip correction, Cc, (i.e., by going to low pressures in the impactor), or by decreasing the nozzle diameter, W. Impactors utilizing these techniques are known as either low-pressure impactors or micro-orifice impactors. Both types have been developed and used successfully in sampling particles down to approximately 0.05 jam diameter (Berner et al., 1979; Hering et al., 1978,1979; Hering and Marple, 1986; Hillamo and Kauppinen, 1991; Marple et al., 1991). There are several differences that must be considered when selecting either low-pressure or micro-orifice impactors for a particle sampling program. In a low-pressure impactor, the particles are collected by increasing the value of the slip correction at highly reduced pres-

Collection Efficiency, %

Nano-MOUD! Stages

Stage:

Inlet

Aerodynamic Particle Diameter, fim Fig. 1(M. Particle collection efficiency curves for the micro-orifice uniform deposit impactor (MOUDI), including nano-MOUDI stages.

sures down to approximately 3 x 103Pa [0.03 atm].This means that the vacuum pump drawing the flow through the impactor must be rather large or the flow rate rather small. In addition, particles that are sensitive to evaporation at low pressures experience a reduction in size during the collection process (Biswas et al., 1987). However, the low-pressure impactor is relatively simple to construct because the nozzle diameters are similar in size to those used in conventional impactors. In a micro-orifice impactor, the pressures are substantially larger than in low-pressure impactors, and conventional vacuum pumps can be used to obtain moderate flow rates (Marple et al., 1991). Volatile particles can be more easily collected in this impactor because the pressure drop through the entire impactor is only about 4 x 104Pa [0.4 atm] (Fang et al., 1991). The difficulty with the micro-orifice impactor is in its construction because the nozzle diameters are very small (approximately 50 urn for the final stages), and the number of nozzles is large to obtain an adequate flow rate (as many as 2000 for the final stages). However, these are manufacturing problems and not problems in the use of the impactor. A special class of impactors is one that makes use of both low pressures and microorifices to achieve smaller cut sizes than can be obtained by either low pressures or microorifices alone. For example, by using micro-orifices, the lower limit of the cut size is about 0.05 urn. However, by using a combination of micro-orifices and low pressure, the cut size of the impactor can be pushed to even smaller sizes. The lower three efficiency curves shown in Figure 10-4 are from three such stages. Because the pressure is dropping at each stage of a cascade impactor, the volumetric flow rate increases at each stage. Therefore, at some point in the cascade impactor, it may be necessary to reduce the flow. In the example shown in Figure 10-4, the flow in the three lower stages, called nanostages, has been reduced to 1.7 x 4 3 IQT4WL3ZS [lOL/min] from 5 x 10" m /s [30L/min] in the upper stages (Marple and Olson, 1999). This is accomplished by bleeding 3.3 x IQT4WL3ZS [20L/min] from the cascade impactor at the stage where the flow is reduced. Impactors are normally designed to have sharp cut-off characteristics (steep efficiency curves). However, in some cases, it is desirable to have a cut-off curve (efficiency curve) that is not sharp but rather one that follows a particular retention curve. Such is the case in designing impactors with penetration curves matching the American Conference of Governmental Industrial Hygienists (ACGIH) or the British Medical Research Council (BMRC) respirable mass criteria curves (Lippman, 1989) (see Chapter 25). A special class of impactors, called respirable impactors, has been designed to emulate penetration characteristics similar to these respirable curves (Marple, 1978; Marple and McCormack, 1983).This is accomplished by multiple-nozzle, single-stage impactors with the nozzles having different diameters. The respirable penetration curve is approximated by a series of steps corresponding to the number of different diameters used in the impactor stage. The fraction of the flow passing through each set of nozzles of a particular diameter is proportional to the total cross-sectional area of the nozzles of that particular diameter. With this technology, it is possible to design impactors for nearly any flow rate, with penetration characteristics that approximate any monotonically decreasing penetration curve. Inertial impactors can be used over a wide range of conditions and have been designed for cut sizes of 0.005 Jim (Fernandez de Ia Mora et al., 1990) up to approximately 50 urn (Vanderpool et al., 1987) and flow rates from a few cmVmin to several thousand nrVmin.There are, however, limitations to impactors when considering their use. Three major areas of concern are particle bounce from the collection surface, overloading of collected particle deposits on the impaction plates, and interstage losses (collection of particles on internal surfaces of the impactor other than the impaction plate). Of the three limitations, particle bounce has received the most attention because particles bouncing from an impaction plate will be collected on a subsequent stage that has a smaller cut point or on the after-filter and, thus, bias the measured size distribution or become lodged

in and clog smaller nozzles of subsequent stages. The most logical approach to solving a particle bounce problem, and the technique used by many researchers, is to provide a sticky surface on the impaction plate. The mass stability and chemical composition, purity, and stability of the sticky surface are all factors in selecting the appropriate substance. Numerous types of greases and oils, including petroleum jelly, Apiezon greases (AP/)* and silicone oils and sprays, have been used to coat the impaction plates; however, these coatings must be used correctly in order for the results to be satisfactory (Hering, 1995; Rao and Whitby, 1978a,b). For example, if the impaction plate has just a sticky surface, once a monolayer of collected particles forms on the surface, additional incoming particles will impact on the particles that have already been collected, and particle bounce is again possible. Therefore, if a sticky surface is to be used and a large quantity of particles collected, the sticky surface will not be adequate; a sticky substance must be used that will wick up through the particle deposit by capillary action and continually provide incoming particles with a sticky surface. As described in a later section, silicone oil works well. The techniques that will be used for particle analysis must be considered when selecting an impaction surface. If the particles collected on the impaction plate are to be discarded, the impaction plate can be a porous material saturated with a light oil (Reischl and John, 1978). This oil will wick up through the deposit and continually provide the incoming particles with a sticky impaction surface. Because these particles are to be discarded, the weight stability of the oil is not important. If the mass size distribution for solid particles is to be determined from the deposits collected on the stages of a cascade impactor, great care must be taken in providing a sticky impaction plate surface. Because the deposits must be weighed, particle collection substrates such as metal foils, plastic films, or filters are placed on the impaction plates so that they can be easily removed for weighing. Grease, oil, or other sticky substances are applied to the substrates. The sticky substance must have mass stability because treated substrates will be weighed on a balance before and after sampling. In most cases, the sticky substance will be dissolved into a solvent and applied in a light film to the substrates. The treated substrates are then baked in an oven to drive off the volatiles, thereby leaving weight-stable sticky substrates onto which the particles can be collected. The problem of deposit overloading on an impaction plate is easier to control because the quantity of particles collected is a function of sampling time. It is normal practice to sample from several minutes to several hours to obtain an initial size distribution. The deposits on the impaction plates are then inspected, and, if any stage is overloaded, the next sampling period is shortened. Conversely, if the deposits are too light, the sampling period is extended. The major difficulty in determining the appropriate sampling time is dictated by the total quantity of particles that can be collected on an impaction plate (before overloading occurs). This quantity varies from impactor to impactor, and only experience or data from the manufacturer can provide this information. The final area of concern is the collection of particles on surfaces other than the impaction plate. These particles are deposited on the interior surfaces of the impactor (interstage loss) and are a function of particle size, again distorting the measured size distribution. In the upper stages, where the collected particles are rather large, particles can be lost by impaction or turbulent deposition. However, because each stage removes particles larger than the cut size of that stage, the interstage losses rapidly decrease as particles penetrate the upper stages. In the lower stages, particles can be lost by diffusion (a problem only for very small particles). Most of the diffusional losses occur in the final stages, where the nozzles are small and particles can diffuse to the nozzle walls. In a properly designed cascade impactor that covers a wide range of particle sizes, one would expect to find interstage losses in the initial stages and maybe in the final stages of the * See Appendix I for full manufacturer addresses referenced to the italicized three-letter codes.

impactor (if there are cut sizes below 0.1 um) with the losses being a minimum through the intermediate stages. Typically, particle loss varies but may be as high as 30% to 40% for large particles in the inlet and first stages. If experiments have been performed to determine the interstage loss as a function of particle size, the particle size distribution data of the impactor can be corrected. One difficulty in estimating what this correction should be is that the losses will vary as a function of the nature of the particles. For example, if the particles are liquid or sticky, they will adhere to any surface with which they come in contact. A dry particle, however, may rebound when it hits a surface and remain airborne until it reaches the next stage. Thus, interstage losses are less for a particle that bounces easily than for a sticky particle. In summary, the three major concerns for an impactor, that is, particle bounce, overloading, and interstage losses, are a function of both the particle properties and the impactor. If the particles are sticky, particle bounce is minimized, interstage losses are maximized, and overloading is of less concern. If the particles are solid and rebound easily from a surface, particle bounce is a problem, interstage losses are decreased, and overloading is of concern. Normally a cascade impactor consisting of several stages is used to determine particle size distributions. The inertial spectrometer (INSPEC) and high-flow spectrometer (LASPEC) were developed to provide particle size distribution information on one stage (Belosi and Prodi, 1987; Prodi et al, 1979, 1983, 1984). The aerosol sample flow rates are up to 3.3 x 10"6m3/s [0.2L/min] and 1.7 x MTW/s [lOL/min] in the INSPEC and LASPEC, respectively. These devices employ a rectangular filter instead of an impaction plate and introduce the particles through a nozzle near one edge of the filter, as shown in Figure 10-5. The particles are introduced at the center of this nozzle with a sheath of clean air on both sides. The inertial properties of the particles distributes the particles in the sheath air at distances that are a function of the particles' aerodynamic diameters. Subsequently, as the particles are removed

Clean Air Flow

Aerosol Flow

Nozzle

Filter Filter Support

To Vacuum Pump

Fig. 10-5. Schematic diagram of an inertial spectrometer.

from the gas stream at the combination filter-impaction plate, the position of each particle on the filter is a function of the aerodynamic diameter of the particle. This inertial classifier provides the entire size distribution differentiated by size on the filter. It is also possible to design a spectrometer with the nozzle being at the center of a circular filter. The radial deposition of particles on the filter is a function of particle size, with the larger particles being deposited closer to the center than the smaller particles. This has been developed in a personal sampler, called a personal inertial spectrometer (PERSPEC), operating at a flow rate of 3.3 x 10~5m3/s [2L/min] (Prodi et al, 1988). Virtual Impactors. A virtual impactor is a particle inertial classification device that is very similar to the conventional inertial impactor, with the primary difference being that the impaction plate has been replaced by a collection probe as shown in Figure 10-6. Hie jet of particle-laden gas exiting the nozzle penetrates into the collection probe where classification occurs. Large particles penetrate further into the collection probe than do small particles. A small fraction of the flow, the minor flow, is allowed to penetrate through the collection probe carrying with it the particles larger than the cut size of the virtual impactor. Most of

Total Flow

Streamlines

Acceleration Nozzle

Major Flow Trajectory of Particle too Small to be Collected

R Collection Probe

Trajectory of Collected Particle

Minor Flow

a) Virtual Impactor

Actual /ouep!jj3 Ideal Vstk b) Efficiency Curve Fig. 10-6. Schematic diagram of a virtual impactor and corresponding particle collection efficiency curve.

the flow, the major flow, reverses in the collection probe and exits at the top of the collection probe, carrying with it the particles that are smaller than the cut size. Thus, both size fractions remain suspended in the gas. These two gas streams can then be directed to collection filters, into another inertial classification device, into another impactor stage, or into an instrument that automatically measures the concentration of particles in the gas streams. Conner (1966) described the first device to incorporate a virtual impactor with a controlled minor flow. The particle collection efficiency curves for virtual impactors, as for conventional impactors, are quite sharp, as was shown in the theoretical study by Marple and Chien (1980). The major difference between the efficiency curves of virtual and conventional impactors is that particles less than the cut size of the virtual impactor remain in both the major and minor flows. Therefore, the collection efficiency curve of particles in the collection probe asymptotically approaches the percentage of total flow that is penetrating the collection probe. For example, if the minor flow is 10% of the total flow, 10% of the particles smaller than the cut size will remain in the minor flow and "contaminate" the large-particle fraction. One method of eliminating the small particles in the large-particle fraction is to provide a central core of clean filtered air in the nozzle of the virtual impactor (Masuda et al., 1978; Chen et al., 1986). Because the air flow in the central portion of the nozzle is the air that constitutes the minor flow, the small particles will not be present in the minor flow. The major difficulty with this solution is that the virtual impactor becomes more complicated with the addition of the clean airflow. If some small particles in the large-particle fraction are not of concern, the contamination can be minimized by reducing the minor flow to the lowest value possible. In some cases this has been reduced to as low as 0.1% of the total flow (Xu, 1991). Another method for determining the quantity of contamination by the small particles is their direct measurement. This is accomplished by extracting a sample from the major flow at the same flow rate as the minor flow (Marple and Olson, 1995). The minor flow sample is analyzed in the exact same manner as the minor flow sample and the results subtracted from the results of the minor flow sample. In this manner, contribution of the small-particle contamination in the minor flow can be quantified. For example, if the mass of particles in the minor flow is determined by filter analysis, the mass of particles taken from the major flow at the same flow rate and on the same filter material will indicate the mass of particles on the minor flow filter attributable to small-particle contamination. Besides being an inertial classifier, a virtual impactor can also be considered a particle concentrator for particles larger than the cut size. This is very useful when sampling particles that are of low concentration or sampling particles into an instrument that requires concentrations greater than are normally present (Keskinen et al., 1987; Liebhaber et al., 1991; Marple et al., 1989; Wu et al., 1989). Because the particles larger than the cut size of the impactor are concentrated in the minor flow, the concentration factor is equal to the ratio of the total flow rate to the minor flow rate. For example, if the minor flow is 5% of the total flow, the concentrating factor is 20. Virtual impactors have also been used to concentrate ambient particles for exposure studies. One such impactor uses rectangular nozzle and receiving tube geometry to concentrate particles in the size range of 0.15 to 2.5 um by a factor of about 25 at a total inlet flow rate of 0.083 m3/s [5000L/min] (Sioutas et al., 1997). To design a virtual impactor that concentrates particles at an even smaller size, particles can be grown by condensing a vapor on the particles before passing them through the virtual impactor. After virtual impaction, the liquid is evaporated from the particles, restoring them to their original small sizes. This has been employed to concentrate ultrafine particles with a concentration factor of approximately 25 at an inlet flow rate of 1.77 x 10"3m3/s [106L/min] (Sioutas et al., 1999). Interstage particle loss is a major area of concern with the virtual impactor. These losses normally occur at the upper edge of the collection probe or on the backside of the nozzle

plate. The losses are a maximum for particle sizes corresponding to the cut point of the impactor and can reach values as high as 60% if the virtual impactor is improperly designed or operated (Marple and Chien, 1980). Major factors that influence these losses include the ratio of the collection probe diameter to the nozzle diameter, the shape of the collection probe inlet, the alignment of the axes of the nozzle and collection probe, the shape of the nozzle protruding through the nozzle plate, the jet Reynolds number, and the minor flow percentage. Several investigators have studied these parameters, and the optimum values are still being refined (Jaenicke and Blifford, 1974; McFarland et al., 1978; Loo, 1981; Chen et al., 1985,1986; Chen and Yeh, 1987; Loo and Cork, 1988; Xu, 1991). In general, however, the axes of the nozzle and collection probe must be aligned as close as possible, the collection probe diameter should be from 30% to 40% larger than the nozzle diameter, the inlet to the collection probe should be a smooth radius, the nozzle should protrude through the nozzle plate approximately two to three nozzle diameters, and the minor flow should be 5% to 15% of the total flow. Initially, nearly all virtual impactors use round nozzles and collection probes. However, attempts to use rectangular configurations have been studied (Forney, 1976; Forney et al., 1982) and successfully applied to particle concentration of atmospheric particles (Sioutas et al., 1997). Most virtual impactor designs have been single-stage units with one nozzle and one collection probe. However, some virtual impactor samplers have been designed with multiple nozzles and collection probes in a single stage. The purpose for this design feature is to reduce the size of the sampler and to reduce the pressure drop through the sampler (Marple et al., 1990; Szymanski and Liu, 1989). Body Impactors. The major problem in designing a conventional impactor for classifying large particles is that of drawing large particles into a sampling inlet. However, large particles can be classified inertially by the use of body impactors that do not involve drawing particles through an inlet. As shown in Figure 10-7, the body impactor functions by simply sweeping an impaction surface (body) through the air or, conversely, drawing air past the body. In either case, the problems associated with the sampling of large particles is reduced. The collection efficiency of the body impactor is defined as the fraction of particles that are impacted on the body from a volume of air swept out by the body (Fig. 10-7). Particle collection curves are presented in Figure 10-7 for particle deposition on ribbons, spheres, and cylinders. Golovin and Putnam (1962) and May and Clifford (1967) summarize particle impaction on bodies of various shapes. The cut size is a function of the body size (dimension normal to the flow direction) and the relative velocity between the air and body. The cut size is governed by the Stokes number equation (Eq. 10-1) where dh is the body size and U is the velocity difference between the air and the body. Examples of two body impactors that have been developed are the rotorod sampler and the Noll impactor. The rotorod sampler (MUL) is used for sampling particles larger than 15 um, and the most common application is the sampling of pollen levels in outdoor air. The Noll impactor is designed for sampling coarse atmospheric particles. It has four rotating plates of various dimensions to collect particles from 6 to 29 um (Noll, 1970; Noll et al., 1985). Cyclones. In a cyclone, a jet of air impinges tangentially on the inner surface of a cylinder and then swirls downward in a cyclonic fashion inside the cylinder and into a conical section. In the conical section the air reverses direction and spirals upward around the cyclone axis to an exit tube at the upper end of the cylinder. Particles larger than the cut size are deposited on the inner surface of the cylinder and in the cone. The particles fall downward into the apex of the cone and into a dust collection cavity (grit pot). Many cyclones have been designed in a variety of sizes for numerous applications and are very popular for collecting dust in industrial process lines. These units are normally large

Streamlines

Trajectory of Particle too Small to Impact

Trajectory of Impacted Particle

Flat Plate

Cylinder or Sphere

Efficiency

a) Body Impactors

U CYLN I DER OR SPHERE U RIBBON

VstiT b) Efficiency Curves Fig. 10-7. Schematic diagrams of body impactors and corresponding particle collection efficiency curves from Golovin and Putnam (1962).

and have high flow rates. Cyclones are also popular for aerosol sampling. While a variety of cyclones have been developed for respirable dust samplers, the most popular cyclone used in the United States is the 10 mm nylon cyclone, which has penetration characteristics that simulate the ACGIH respirable mass criteria (Lippmann, 1995). The cut size of the cyclone is governed by the flow rate, the sizes of the inlet and outlet tubes, and the size of the cylinder (e.g., Hering, 1995). A rigorous theoretical analysis of a cyclone is more difficult than that of an impactor because the flow is three-dimensional and must be analyzed using a three-dimensional numerical program. Although some numerical work has been performed by the authors and others, it is not common practice to apply this technique in the design of cyclones. Most cyclones are used as a single stage; however, there have been cascaded versions developed (Smith et al., 1979; Liu and Rubow, 1984).The unit developed by Smith et al. (1979) for stack sampling consists of five stages with cut sizes ranging from 0.32 to 5.4 urn at a sampling flow rate of 4.7 x 10"4InVs [28.3L/min].

Particle deposition in a cyclone is caused by the cyclonic action of the fluid in the cylinder. Most cyclones achieve this by the jet of gas impinging tangentially on the inner surface of a cylinder. However, it is possible to achieve the cyclonic spiral flow by turning vanes at the inlet of a tube. A cascade version of this type of cyclone has been developed for collecting dust particles in a size range from 1 to 12 urn (Liu and Rubow, 1984). Although the construction of this cyclone is more complicated than the conventional cyclone, it does lend itself to a compact cascade design. A simple model of axial cyclone performance was developed that approximately predicts the cyclone cutpoint (Maynard, 2000). The conventional cyclone configuration, where the air enters tangentially and exits along its axis, makes it difficult to design a compact cascade cyclone sampler. Measurement Strategies

Before selecting an inertial classifier, one must first decide the purpose for which the particles are being collected. If the purpose is to determine the characteristics of aerosols as a function of size, such as obtaining the mass size distribution or the chemical composition of the aerosol at various particle sizes, it is most convenient to use a cascade sampler. If only the quantity of particles less than a specified size is desired, such as is often the case for compliance sampling, then a two-stage sampler consisting of an inertial classifier followed by a filtration stage is most convenient. A decision must also be made as to whether or not the sample needs be time resolved or can be an integrated sample. Normally, when samples are taken with an inertial classifier, the sample is integrated over the time period. Some impactors provide time-resolved samples on rotating impaction plates, for example, the Lundgren cascade impactor (Lundgren, 1971) and the Davis Rotating-drum Universal-size-cut Monitoring (DRUM) impactor (Raabe et al., 1988). Another important factor is the particle size range over which one needs to operate. Although inertial classifiers can classify particles in the size range of about 0.005 to 50 um, a more precise size range must be known in order to select the device best suited for the test. In some cases, the size distribution of the aerosol may not be known until after the first sample has been taken. If little is known about the size distribution before the first test, a wide-range cascade sampler provides the most information. Finally, the analysis technique to be employed for inspection of the deposits will influence the type of sampler used for particle collection. For example, if the mass size distribution is to be determined by gravimetric analysis of the collected samples, an impactor with substrates coated with a sticky surface can be used. However, the substrates and sticky surfaces must have good mass stability, if a particle sample is to be analyzed with scanning electron microscopy (SEM), then a sticky surface is not desirable. In this case, a method must be devised whereby the particles can be collected on a dry surface. When sticky coatings cannot be used to reduce particle bounce, it is desirable to have the cut sizes of a cascade impactor as close together as conveniently possible so that the inertia of particles impacting on the impaction plates are kept as small as possible. It has also been found that submicrometer particles do not appear to bounce as easily as supermicrometer particles. Therefore, if a sticky coating cannot be used, a greased 1 um cut stage should follow an ungreased 1 (xm stage to collect any supermicrometer particles that may have bounced to this point in the impactor. Thus, particles penetrating the lum cut stage (the submicrometer particles) will not be contaminated with any supermicrometer particles that may have bounced through the upper stages. Design Considerations

Of the various types of inertial classifiers discussed in the previous sections, impactors are most likely to be designed for specific applications. References have been provided in previ-

ous sections for the basic designs of other inertial samplers. In some cases the sampling criteria may be such that a suitable commercially available impactor does not exist. This may be for reasons of size, configuration, number of stages, or the particular cut sizes desired. For these situations it is recommended that a special impactor be designed and built for the study following a few simple guidelines (Marple and Rubow, 1986). Inertial impactors have been studied extensively through theoretical analysis by numerical methods (Marple, 1970; Marple and Liu, 1974; Rader and Marple, 1985). From these studies, numerous particle collection efficiency curves have been calculated and reported in the literature. In most cases, the theories have been compared with experimental results with good agreement. For the most part, an impactor's efficiency curve can be determined with as much accuracy from theoretical analysis as it can from experimental calibration. To operate correctly, two simple design guidelines must be followed. These guidelines involve two dimensionless parameters: jet-to-plate distance divided by the nozzle diameter (SAV) and jet Reynolds number (Re1) (Marple, 1970; Marple and Liu, 1974; Marple and Willeke, 1976a,b; Rader and Marple, 1985). These guidelines were obtained from a theoretical, numerical analysis of the Navier-Stokes equations to determine the flow field and the subsequent numerical integration of particle trajectories. This process was used to determine guidelines necessary to obtain sharp collection efficiency curves. These theoretical guidelines have been used in the design of impactors and have been shown to result in impactors with collection efficiency curves that have sharp cut-off characteristics (Marple et al., 1988). Figure 10-8 shows the theoretically determined efficiency curves as a function of jet-toplate distance divided by the nozzle diameter (S/W) and jet Reynolds number (Rej) of the flow. Both of these terms are important in that they will influence the efficiency curve and the 50% cut point of the impactor. For example, the position of the efficiency curve as a function of the SIW ratio is shown to be sensitive for small values of S/W. The values of StJc50 are relatively constant for S/W values larger than 0.5 and 1.0 for round and rectangular impactors, respectively. The SIW value should not be less than this value because small variations in SAV will then change the cut size of the impactor. To provide a margin of safety, it is recommended that the SIW value be greater than 1.0 for round impactors and 1.5 for rectangular impactors. The upper value of SIW that can be used in an impactor is not as well known. However, impactors have operated well with SIW values as high as 5 or 10. If values larger than these are to be used, a calibration is recommended to ensure that the jet has not dissipated before impinging on the plate. The importance of the jet Reynolds number is more related to the sharpness of cut than to the cut size of the impactor. If the Reynolds number is low, the gas viscous forces will be large and the velocity profile will be parabolic at the nozzle exit and in the air jet as it approaches the impaction plate. This will enhance the collection of particles near the center line, where the smallest particles will be collected. The low velocity near the nozzle wall will require the particles in this portion of the flow to be larger in order for collection to occur. Therefore, the result of an impactor operating at a low Reynolds number is a less sharp collection efficiency curve. Theoretical analysis has shown, and experiments have verified, that the efficiency curve will be its sharpest if the Reynolds number is kept in the 500 to 3000 range for both round and rectangular impactors. The Re1 is expressed as (round) (rectangular)

(10-5) (10-6)

where pg is the gas density. Example 10-1 shows the calculation of the nozzle diameter and Reynolds number for an impactor.

EXAMPLE 10-1 Calculate the nozzle diameter (W) and the jet Reynolds number (Re^) for a 10 urn cut point impactor. The impactor has one round nozzle, and the flow rate is 5 x 10"4In3Zs [30L/min]. Assume normal temperature and pressure. Answer: The first step is to determine the required nozzle diameter for a cut point of 10 urn. Substituting the flow rate for the average jet velocity

into the Stokes equation (Eq. 10-2) for a round jet impactor gives

where PP dp Q Cc

= = = =

particle density (1000 kg/m3 [lg/cm3]) cut point particle diameter (10 x 10"6m [10 x 10"4Cm]) volumetric flow rate (m3/s [cm3/s]) Cunningham's slip correction factor (approximately 1)

Solving the Stokes equation for the nozzle diameter gives

The square root of the Stokes number (^IStk) corresponding to the 50% collection efficiency (-JStIc50) can be estimated from Figure 10-8, which shows the theoretical impactor efficiency curves for different values of the jet Reynolds number. Assume a jet Reynolds number of 3000; then the corresponding ^StIc50 is 0.47 and

Knowing the nozzle diameter, the jet Reynolds number is calculated from Eq. 10-5 by substituting the flow rate for the jet velocity:

The jet Reynolds number is outside of, but close to, the upper limit of the suggested range of 500 to 3000 and, therefore should provide sharp cut off characteristics.

EFFICIENCY

RECTANGULAR Rej(SZW=i.5tTZW=l) ROUND (SZW=I1TZW=O Re

Vstk EFFECT OF JET REYNOLDS NUMBER ROUND S/W(T/W=I)

EFFICIENCY

ReOOOO

RECTANGULAR SZW(TZW=I)

Vstk EFFECT OF JET TO PLATE OISTANCE (Re^ 3000) Fig. 10-8. Theoretical impactor efficiency curves for rectangular and round jet impactors showing the effect of jet-to-plate distance ratio (SAV) and jet Reynolds number (Re^) (Rader and Marple, 1985). (Reproduced with permission of the Elsevier Sciences Publishing Co., Inc.)

Example of Impactor Application

Impactors have been used to sample a wide variety of aerosols in a variety of studies. Because it is impossible to discuss all of the situations, the next few sections describe the use of a generic impactor to solve a hypothetical problem. In each step of the process, remarks are made as to how the step applies to the general sampling of aerosols with impactors. Examples are given in these sections to demonstrate the type of calculations that must be made when using impactors. The problem in this example was to determine the size distribution and mass concentration of dust particles in an industrial environment. In the initial phase of the program, it was suspected that the particle size distribution contained particles as small as 0.1 urn. The cut size of the upper stages of the impactor had to be large enough to cover the respirable range, with the first stage of the impactor having a cut point of at least 10 um. The impactor selected for this analysis had nine stages and an after-filter. The cut sizes of these stages are given. The flow rate through the impactor was 5 x 10"4ITrVs [30L/min].

Substrate Preparation. Because the purpose of sampling the particles was to determine the mass size distribution, the substrate had to have a stable weight and, therefore, aluminum foil substrates were selected. Filter material could also have been used, although some of these materials have a tendency to change weight with different humidity conditions. In addition, because the dust particles were solid, the application of some type of sticky substance to the substrates to reduce particle bounce was necessary. Therefore, the material used for the substrate had to be impervious to the oil so that it did not migrate through the substrate and be lost on the support of the substrate. For this application it was decided to use a silicone oil spray. A mask was prepared by cutting a hole in a clear plastic sheet just large enough to accommodate the deposited particles and placed over the foil and oil applied. The substrates were then placed in an oven at a temperature of 65°C for 90min to evaporate the volatiles. The substrates were weighed on a microbalance and then placed on the impaction plates for use in the sampling program. Sampling Time Estimations. To determine the appropriate sampling period, an estimate of the aerosol mass concentration was made to prevent overloading and bounce on the substrates. The size distribution of the aerosol was not known, so it was assumed that the aerosol was spread uniformly over the number of stages used in the impactor. The regulatory limit for the particle mass concentration in this work environment was 2mg/m3. Thus, it was assumed that the mass concentration was no more than this value. The nine stages of the impactor covered a size range from 0.1 to 18 um, and each stage was assumed to hold 1 mg of material. The sampling period was estimated to be 2.5 h at a sampling flow rate of 5 x 10"4m3/s [30L/min]. After the samples were collected, the substrates were brought back to the laboratory for analysis. For this particular problem, it was necessary to determine the mass size distribution. There were several other types of analysis that could have been performed with the substrates at this point, for example, X-ray diffraction analysis to determine the elemental composition, optical microscopy for inspection of the deposit, and SEM to investigate shape and elemental composition of individual particles. The analysis of these samples with SEM would have been difficult because of the presence of the sticky oil surface; an ungreased substrate on one of the stages would have allowed this type of analysis. If particle bounce had been too severe, this would not have resulted in good data. If grease coatings were necessary, the particles could have been washed from the substrates with a solvent and the particles separated from the solvent by filtration. These particles would then have been available for SEM analysis. The mass concentration during one sampling period was 2.0mg/m3 (Example 10-2). The size distribution results are tabulated.

EXAMPLE 10-2 The nine-stage cascade impactor was used to sample an aerosol for 167 min operating at a flow rate of 5 x lO^mVs [30L/min]. Table 10-4 lists the results of the gravimetric analysis. From Table 10-6, determine the total mass concentration. Plot a histogram of relative mass (i.e., Ara/Alog[da]), versus aerodynamic diameter on semilog graph paper. Plot the cumulative mass versus aerodynamic diameter on lognormal probability graph paper. Determine the mass median diameter and the geometric standard deviation (<7g). continued

TABLE 10-4. Sample Size Distribution Data Impactor Cutpoint (Mm)

Am(ug)

Cumulative Mass Percent Less Than Indicated Size

18

10

99.9

10

100

98.9

5.6

590

93.0

3.2

1,800

75.0

1.8

3,100

44.0

1.0

2,810

15.9

0.56

1,240

3.50

0.32

305

0.45

0.18

42

0.03

Filter

3

0.00

AM Alog(da) 392 2,340 7,410 12,400 11,000 4,920 1,250 168

Answer: The total mass concentration is found by summing the Am column and dividing that sum by the product of the flow rate and the sampling time. The total mass is 10.0 mg; therefore, total mass concentration

Figure 10-9 shows the relative mass histogram. The curve superimposed on the histogram shows that the mass distribution is lognormal. A plot of cumulative mass as a function of aerodynamic diameter is shown in Figure 10-10. From this plot the mass median diameter (MMD) and geometric standard deviation can be obtained. The mass median diameter is the diameter where 50% of the mass is collected and, from Figure 10-10, is 2.0 urn. The geometric standard deviation is given by

Size Distribution Data Analysis

dm/dlog (Aerodynamic Diameter, d ), ug

Particle size distribution data are normally represented as number, surface area, or volume (mass) size distributions using histograms or cumulative graphs. Because an impactor collects particles that are weighed on a balance to provide the mass of the particles in a particular size range, the mass distribution is the most common method for presenting impactor data. The data from the previous example are shown as the mass of particles in each of the size ranges represented by the stages of the impactor. It is possible to estimate the surface area and number of particles in each of these size fractions by assuming the particles are spheres. To remove the bias of the width of the size fraction, the mass of particles is divided by the classification width. Furthermore, because the particle size is normally represented on a log scale, it is better to have the histogram height as Ara/Alog da. When the histogram is plotted as shown in Figure 10-9, the area under the curve is representative of the percentage of mass in a particular size range. For this particular example, the mass size distribution is of interest, and Figure 10-9 shows the resulting mass size distribution. A cumulative distribution can also be calculated. This has been performed, and the results are shown in Figure 10-10. Because the size distribution is made up of one distinct class of particles with a log-normal size distribution, the data lie along one straight line in the cumulative distribution. In the above analysis it was assumed that the impactor had ideally sharp cuts at the 50% cut points of the stages. Because the true efficiency curve is probably S shaped but fairly sharp, this is a good assumption in that some particles are included from the size class above the stage and some are collected on the subsequent stage. In a rather broad distribution, these two errors tend to cancel each other, and the data are a fairly good representation of the actual size distribution. However, there are techniques by which the actual shape of the efficiency curve can be incorporated into the data analysis and a more accurate description of the particle size distribution obtained. In recent years a number of these data inversion techniques have been developed (e.g., Crump and Seinfeld, 1982; Markowski, 1987; Rader et al., 1991; Wolfenbarger and Seinfeld, 1990). Additional information on data analysis is presented in Chapter 8.

Aerodynamic Particle Diameter, d , urn Fig. 10-9. Relative mass histogram for Example 10-2.

Cumulative Mass Percent Less Than Indicated Size

84.1 t h percentile 50 t h percentile = MMD 15.9 th peicentile

Aerodynamic Particle Diameter, d , j i m Fig. 10-10. Log-normal probability plot for Example 10-2.

SETTLING DEVICES AND CENTRIFUGES The definition of a particle's aerodynamic diameter is the equivalent diameter of a unit density sphere that has the same gravitational settling velocity as the particle in question. Therefore, a device that measures the settling velocity directly, such as a settling chamber, is a natural selection for a device to make direct measurements of a particle's aerodynamic diameter. Settling chambers do not operate well for small particles because of their low settling speeds (e.g., the settling velocity of a lum particle is 0.035mm/s). In addition, Brownian motion interferes with settling of small particles and sets a lower limit at about 0.6 urn (Orr and Keng, 1976). However, settling chambers can be used for larger particles (the settling speed of 10 um particles is 3.05 mm/s). Furthermore, because of the low settling velocities, great care must be taken to eliminate any convective air currents in the chamber. John and Wall (1983) constructed a device to measure the settling speeds of particles in the 10 to 20 jam size range. This was achieved by illuminating the particles with a laser beam directed up a tube in which the particles were settling and measuring the time taken to fall a predetermined distance. A special form of a settling chamber is the horizontal elutriator where the aerosol is passed slowly along a horizontal channel. Particles settle onto the bottom of the flow channel at locations dependent on the particle size, particle density, gas velocity, and channel height. The larger aerodynamic diameter particles settle near the entrance while the smaller particles are deposited near the exit. Two devices that incorporate this technique for particle classification are the horizontal elutriator used for respirable dust sampling and the Timbrell aerosol spectrometer. Horizontal elutriators are used in two-stage samplers for respirable dust measurement. The nonrespirable particles deposit on horizontal, parallel plates in the elutriator while the respirable particles penetrate through the elutriator to be either collected for subsequent gravimetric analysis or passed into a detector such as a photometer. The particle penetration characteristics of the ideal horizontal elutriator, by definition, are equivalent to the BMRC respirable dust criteria. The MRE gravimetric dust sampler was developed for respirable dust measurement (Wright, 1954; Dunmore et al., 1964).

EXIT FILTER ASS'Y SPIRAL CHANNEL CHANNEL HOUSING

CLEAN AIR LAMINATOR

AEROSOL INLET LARGE PARTICLES

SMALL PARTICLES

COLLECTION FOIL Fig. 10-11. Schematic diagram of a centrifuge (Cheng et al., 1988). (Reproduced with permission of the Elsevier Sciences Publishing Co., Inc.)

The physical description and operation of the Timbrell aerosol spectrometer are detailed by Timbrell (1954,1972). This spectrometer achieves accurate particle size classification by winnowing sampled particles in a laminar stream of clean air where they settle along the bottom of a horizontal sedimentation chamber according to their aerodynamic diameter. The chamber is a wedge-shaped channel with microscope slides recessed into the horizontal floor. Particles ranging from 1.5 to 25 um aerodynamic diameter can be classified at a winnowing air flow rate of 1.7 x 10"6HrVs [0.1L/min]. The instrument is calibrated in terms of aerodynamic particle diameter by means of spherical particles of known density. This spectrometer has been used to determine the aerodynamic size classification of spheres, particles of irregular shape, fibers, and aggregates (e.g., Griffiths and Vaughan, 1986). The difficulty in using a settling chamber or horizontal elutriators is that the force on the particles is quite small. One method to greatly increase the forces and increase the settling speed is to use a centrifuge. In a centrifuge, the air and particles are rotated at a high rotational speed, and the centrifugal force is used to deposit the particles on the outer edge of an aerosol chamber. Although several centrifuges have been developed, the one that has seen the greatest application and is currently in use today is the spiral centrifuge (Hoover et al., 1983; Stober, 1976; Kotrappa and Light, 1972; Stober and Flachsbart, 1969). One commercially available centrifuge is the Lovelace aerosol particle separator (LAPS, INT). This classifier is actually a spectrometer in that the particles are introduced at the inner wall of a flow entering a spiral flow passage that is being rotated at a high rate of speed, as shown in Figure 10-11. At the entrance to the spiral, the particles are near the inner wall, and clean sheath air is adjacent to the outer wall. As the particles flow along the passage they are forced by centrifugal forces through the clean air and are deposited on the outer edge of the spiral. The forces on the larger particles are greater, and, therefore, they are deposited closer to the inlet of the spiral than are the smaller particles. Thus, the particles are deposited along the spiral passage as a function of decreasing particle size. Before sampling, a foil is placed in the outer wall of the spiral passage that is removed after sampling, and the particles are analyzed. The centrifuge can be calibrated with particles of known size and density such that the aerodynamic diameter as a function of distance from the inlet is known. Once calibrated, a centrifuge can be used to obtain the aerodynamic size distribution of irregular shaped particles, aggregate particles, and fibers (e.g., Martonen and Johnson, 1990).

THERMAL PRECIPITATORS Thermal precipitators are a class of instruments that make use of thermophoretic forces on particles to collect particles onto a sampling surface. The thermal precipitator has been used for sampling respirable-sized particles in mines in Great Britain and South Africa. The principle of operation is quite simple (Waldmann and Schmitt, 1966). When a particle passes through a temperature gradient in the air, the air molecules from the warmer side of the particle strike the particle with higher energy than the air molecules on the cold side. This provides a net force in the direction of the cold surface, and the particle migrates to the cold surface. The results of this phenomenon can be experienced in everyday life. For example, where a hot water or steam radiator is adjacent to a wall, the wall behind the radiator becomes dirty. Also, in cold climates, the inside surfaces of windows in an automobile collect a film of contamination. Both of these phenomena are due to the particles migrating to a cold surface when a temperature gradient exists near the surface. By making use of the thermophoretic forces, the thermal precipitator can be simple in design. AU that is necessary is to place a hot wire or filament close to a cooler surface with particles passing between the filament and the surface. The particles then migrate to the cool surface, which is most often a microscope slide, and are collected. The particles can subsequently be analyzed microscopically. Characteristics of thermal precipitators are that the flow rates are rather low (on the order of a few cm3/min) and the collection efficiencies are good for particles in the submicrometer range (i.e., for particles down to 0.01 um). The upper size limit of particles collected by this technique is on the order of 5 to 10 urn. With the thermal precipitator being efficient at collecting particles in this size range, it has been used most extensively for collecting respirablesized particles in industrial atmospheres, especially in the mining industry. One configuration of the thermal precipitator is shown in Figure 10-12, including the heated filament and the cooler surface that contains the microscope slide. The standard thermal precipitator was rather bulky and required a fair degree of expertise to operate, and samples had to be counted under a microscope, a labor intensive task. In the early 1950s, a modified thermal precipitator was developed that employed a photoelectric detector to replace the optical microscope counting of the particles (Kitto and Beadle, Aerosol Flow

Heated Wire Deposition Zone Collection Surface Heat Sink

Fig. 10-12. Cross-sectional view of a heated wire and plate thermal precipitator.

1952; Beadle, 1954).The modified thermal precipitator operated at 1.7 x 10"7m3/s [10cm3/min] for sampling periods from 1 to lOmin. A horizontal elutriator was used at the inlet to remove particles that were nonrespirable and the particles deposited on a slide. The slide would be advanced between tests, and up to 11 samples were collected on one slide. The samples in the slide were then conditioned in a desiccator and placed in a photoelectric assessor that compared the light transmission through the deposit with the light transmission through the clean portion of the slide. The amounts of light transmitted through the deposit and slide were measured by photoelectric cells, and the numbers were quoted as dimensionless photoelectric readings that could be related to the mass of particles collected.

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and Industrial Work Environments, eds. V. A. Marple and B. Y. H. Liu. Ann Arbor: Ann Arbor Science Publications, Vol. 3, pp. 931-949. Prodi, V., C. Melandri, G. Tarroni, T. De Zaiacomo, M. Formignani, and D. Hochrainer. 1979. An inertial spectrometer for aerosol particles. /. Aerosol ScL 10:411-419. Raabe, O. G., D. A. Braaten, R. L. Axelbaum, S. V. Teague, and T. A. Cahill. 1988. Calibration studies of the drum impactor. /. Aerosol ScL 19:183-195. Rader, D. X and V. A. Marple. 1985. Effect of ultra-stokesian drag and particle interception on impaction characteristics. Aerosol Sci. Technol 4:141-156. Rader, D. X, L. A. Mondy, X E. Brockmann, D. A. Lucero, and K. L. Rubow. 1991. Stage response calibration of the mark III and Marple personal cascade impactors. Aerosol ScL Technol. 14:365-379. Rao, A. K. and K. T. Whitby. 1978a. Non-ideal collection characteristics of inertial impactors—Single stage impactors and solid particles. /. Aerosol ScL 9:77-86. Rao, A. K. and K. T. Whitby. 1978b. Non-ideal collection characteristics of inertial impactors—Cascade impactors. /. Aerosol ScL 9:87-100. Reischl, G. P. and W. John. 1978. The collection efficiency of impaction surfaces: A new impaction surface. Staub-Reinhalt Luft 38:55. Smith, W. R., R. R.Wilson, and D. B. Harris. 1979. A five stage cyclone system for in situ sampling. Environ. ScL Technol. 13:1387-1392. Sioutas, C, S. Kim, and M. Chang. 1999. Development and evaluation of a prototype ultrafine particle concentrator. /. Aerosol ScL 8:1001-1017. Sioutas, C, P. Koutrakis, X X Godleski, S. T. Ferguson, C. S. Kim, and B. M. Burton. 1997. Fine particle concentrators for inhalation exposures—Effect of particle size and composition. /. Aerosol ScL 6:1057-1071. Stober, W. 1976. Design performance and applications of spiral duct aerosol centrifuges. In Fine Particles, Aerosol Generation, Measurement Sampling and Analysis, ed. B. Y. H. Liu, New York: Academic Press, pp. 351-398. Stober, W. and H. Flachsbart. 1969. Size-separating precipitation of aerosols in a spinning spiral duct. Environ. ScL Technol. 3:1280-1296. Szymanski, W. S. and B. Y. H. Liu. 1989. An airborne particle sampler for the space shuttle. /. Aerosol ScL 20:1569-1572. Timbrell, V. 1954. The terminal velocity and size of airborne dust particles. Br. J. Appl Phys. 5:S86. Timbrell, V. 1972. An aerosol spectrometer and its application. In Assessment of Airborne Particles, eds. T. T. Mercer, P. E. Morrow, and W. Stober. Springfield, IL: Charles C. Thomas, pp. 290-330. Vanderpool, R. W, D. A. Lundgren, V. A. Marple, and K. L. Rubow. 1987. Cocalibration of four large-particle impactors. Aerosol ScL Technol. 7:177-185. Waldmann, L. and K. H. Schmitt. 1966. Thermophoresis and diffusiophoresis of aerosols. In Aerosol Science, ed. C. N. Davies. New York: Academic Press, pp. 137-162. Wolfenbarger, X K. and X H. Seinfeld. 1990. Inversion of aerosol size distribution data. /. Aerosol ScL 21:227-247. Wright, B. M. 1954. A size-selecting sampler for airborne dust. Br. J. Ind. Med. 11:284. Wu, X X, D. W. Cooper, and R. X Miller. 1989. Virtual impactor aerosol concentrator for cleanroom monitoring. / Environ. ScL 5:52-56. Xu, X. 1991. A Study of Virtual Impactor. Ph.D. Thesis, University of Minnesota, Minneapolis, MN.

cause visibility degradation, participate in acid deposition, result in material and crop damage, impact global climate change, and cause general soiling of property (U.S. EPA, 2001). The chemical components of atmospheric particulate matter provide clues to the sources of particles, as different sources can produce particles of different chemical composition. Source identification is then determined by advanced statistical techniques (e.g., receptor modeling, an advanced statistical analysis method for linking pollution observed at an ambient location back to its source) or by the use of advanced mathematical simulation models (e.g., grid based) that integrate source emissions, atmospheric chemistry, and meteorology to trace the fate of source emissions through the atmosphere. Driven by relationships observed between ambient fine particulate matter (PM-2.5, i.e., particles less than 2.5 Jim in diameter) concentrations and adverse health effects (U.S. EPA, 1996a,b, 2001; Albritton and Greenbaum 1998; U.S. EPA 1996a,b; U.S. EPA 2001), the U.S. Environmental Protection Agency (EPA) has established a series of ambient particulate matter monitoring networks to better characterize the spatial and temporal distributions of PM-2.5 mass and its composition throughout the United States. Data from these networks will help link particles observed at ambient receptor location species to their sources, resulting in a means for developing effective emissions control strategies for primary particles and secondary particle precursor species. Three interlaced networks are being established by the EPA to provide different levels of information in space, time, and aerosol composition (U.S. EPA, 1998a,b). The base network consists of about 1100 monitors, distributed based on population, for measuring PM-2.5 mass using the Federal Reference Method (FRM; U.S. CFR 1997, Parts 50,53, and 58). The second network, the EPA National PM-2.5 Chemical Speciation Network (referred in this chapter as the Chemical Speciation Network), consists of up to 300 chemical speciation monitors, 54 of which are part of the U.S. EPA's long-term SLAMS (State and Local Air Monitoring Network) and NAMS (National Air Monitoring Network) networks for measuring trends in atmospheric pollutants (e.g., ozone, CO, SO2, PM-10) (U.S. EPA, 1997). The chemical speciation monitors provide 24 h integrated chemical composition data, typically on a 1 in 3 or 1 in 6 day schedule. This network is not used for compliance, but to assist states with the development of equitable and efficient State Implementation Plans. The components measured include PM-2.5 mass, nitrate, sulfate, ammonium, chloride, sodium, and potassium ions; organic carbon (OC); elemental carbon (EC); and trace elements obtained by X-ray fluorescence (XRF). Size-selective inlets, particle fractionators, and denuder/filter-based methods are used in these samplers (Solomon et al., 2000a). Methods for collecting samples for monitoring networks in general are described in Chapter 27, and the methods used in the Chemical Speciation Network are described elsewhere (U.S. EPA, 1999a). The third network consists of eight Supersites projects that together comprise the EPA's Supersites Program (Albritton and Greenbaum, 1998; U.S. EPA, 1998a). Each project consists of one or more highly instrumented sites that are coordinated with active air quality monitoring and health effects-related studies. The Supersites Program is designed to provide (1) detailed information on the spatial and temporal nature of particulate matter and on atmospheric processes and thus to provide states with additional data for developing costeffective emissions management strategies for lowering concentrations of PM-2.5 in ambient air; (2) support to health effects-related programs; and (3) evaluation of advanced monitoring methods for their potential transition to routine monitoring networks. The Chemical Speciation Network measurements are obtained by collecting PM-2.5 on several filter types followed by chemical analysis in the laboratory (U.S. EPA, 1999a; Solomon et al., 2000a,b). The primary chemical analysis methods discussed in this chapter relate to those used in the Chemical Speciation Network. These include gravimetric analysis for mass, ion chromatography for anions and cation species, thermal optical transmission for OC and EC, and energy dispersive X-ray fluorescence (EDXRF) for minor and trace elements.

Although these are well-established methods, they may not be suitable to all species desired for receptor modeling and understanding atmospheric processes and for obtaining improved relationships between particulate matter and its sources and adverse health effects (see Albritton and Greenbaum [1998] for 11 hypotheses for potential causal factors); and these methods may not be readily available to everyone. For example, detailed organic speciation of the collected aerosol is important for improved receptor modeling (Schauer, 1998; Schauer et al., 1996; Schauer and Cass, 2000), while acidity, soluble transition metals, and organic compounds are potential causal factors associated with adverse health effects (Albritton and Greenbaum, 1998). Finally, for example, laboratories may not have an XRF available to them, but may have other elemental measurement instrumentation, or they may prefer to analyze cation species by colorimetry (e.g., ammonium ion) or graphite furnace atomic absorption (GFAA; e.g., K+, Na+, Mg2+). Therefore, other analysis methods are discussed below and summarized along with the primary methods in Table 11-1. More detailed descriptions of most of the methods described in this chapter can be found in Chow and Watson (1999) and Lodge (1989).

SCOPE AND OBJECTIVES This chapter surveys the analytical techniques used to determine the concentrations of aerosol mass and its chemical components. The techniques surveyed are listed in Table 11-1. Table 11-2 provides information on the major components and trace inorganic components of the atmospheric particulate matter collected at several locations in the United States, including two urban and two rural location species. The urban location species represent periods of highest pollution in the east (Atlanta, GA), typically observed in the summer, and in the west (Rubidoux, CA), typically observed during the late fall and winter months. The rural sites represent an annual average during the last available year of sampling. The information presented is similar to that being collected in the Chemical Speciation Network (U.S. EPA, 1999). As can be seen, there are wide variations in the chemical compositions of the aerosols and their concentrations, thus, possibly requiring application of several analytical methods to obtain valid data. Over the last decade, determination of organic aerosol species in ambient particulate matter has become feasible. Organic species marker compounds used in source apportionment modeling of particulate matter are given in Table 11-3 (Schauer, 1998). The availability of this type of detailed organic aerosol data has greatly enhanced the ability of receptor models to identify sources (Schauer et al., 1996; Schauer and Cass, 2000). Thus, analytical methods to determine organic aerosol species are described in this chapter. Finally, semicontinuous species-specific methods are rapidly emerging that could eliminate the need to collect aerosols on filters with retrospective chemical analysis in the laboratory. These methods are becoming available for sulfate, nitrate, other anions and cation species, OC, EC, and trace elements. These methods are mentioned briefly at the end of this chapter. The analysis method is only one aspect involved in determining the concentration of species in atmospheric particulate matter after collection of the sample on a filter or other substrate. Other issues include sample storage, where applicable, sample extraction from the filter, and quality control and quality assurance. Sample storage includes stabilizing the collected sample from the end of sampling through sample analysis. It also may include longterm storage to allow for reanalysis of the filter later. Finally, precision and accuracy of the measurements are needed to define uncertainty in data. These issues are also briefly mentioned in this chapter.

TABLE 11-1. Analysis Methods Suitable and Most Commonly Used for Determining the Chemical Compositions of Atmospheric Particulate Matter

Species PM-2.5 mass

Anions and cation species

Particulate carbon

Method

Sample Preparation

Comments

Gravimetric"

Equilibrate at fixed relative humidity, temperature, and neutralized charge

(3-Attenuation semicontinuous method Inertial microbalance (TEOM) Semicontinuous method (TEOM) CAMM semicontinuous method Ion chromatography0

None

This is the Federal Reference Method as described in 40 CFR, Part 50, Appendix J for PM-IO and in 40 CFR, Part 50, Appendix L for PM-2.5 (U.S. CFR, 1997) Lillienfeld and Dulchinos (1972), Macias and Husar (1976)

Equilibrate at 500C;

Standard commercial method, potential loss of volatile species

Equilibrate to 300C with a Nafion dryer None

Ion selective electrode

Liquid extraction in water

Colorimetry X-ray fluorescence Fourier transform infrared spectroscopy Steam condensation/ion chromatography Automated Nitrate Monitor Thermal optical reflectance (TOR)

Liquid extraction in water None None

Nonroutine in research mode, reduces volatilization of labile species (Patashnick and Rupprecht, 1991) Research method. Relates pressure drop across filter to mass loading (Koutrakis et al., 1995) EPA method for PM-2.5 Chemical Speciation Network for anion and cation species (Mulik et al., 1978; Mulik et al. and Sawicki, 1979; U.S. EPA, 1999b) Most often used for NH4+, but also for sulfate and nitrate (Appel et al, 1988; Lodge, 1989) Most often used for NH4+ (Bolleter et al., 1961; Lodge, 1989) Only for sulfate as sulfur times 3 (Chow, 1995) Semiquantitative (Ingle and Crouch, 1988; McClenny et al., 1985; Allen et al., 1994) Semicontinuous for anion and cation species (Dasgupta, 1993; Ito and Thurston, 1996) Semicontinuous for nitrate (sulfur and carbon under development) (Hering and Stolzenburg, 1998) Provides data on OC, EC, CC, and TC. Method used to measure particulate carbon concentrations with the Interagency Monitoring of Protected Visual Environments (IMPROVE) protocol (Chow et al, 1993) Provides data on OC, EC, CC, and TC. Method used to measure particulate carbon concentrations with the NIOSH Method 5040 protocol (Birch and Cary, 1996)

Thermal optical transmittance* (TOT)

Liquid extraction in water or other aqueous solvent

None Condensation onto metal surface followed by flash volatilization Bake quartz filters at temperatures greater than 773 K for several hours or as high as 1173 K for 10,800 s (3 h) Bake quartzfiltersat temperatures greater than 773 K for several hours or as high as 1173 K for 10,800 s (3h)

Temperatureprogrammed volatilization (TPV) Aethalometer

Organic aerosol speciation

Elements— nondestructive

Elements— destructive

In situ thermal/optical carbon analyzer GC-MS*6

X-ray fluorescence" (XRF)

Bake quartz filters at temperatures greater than 773 K for several hours or as high as 1173 K for 10,800 s (3h) None None Bake quartz-fiber filters at temperatures greater than 773 K for several hours or as high as 1173K for 10,800s (3h), followed by solvent extraction after sample collection None

Proton-induced X-ray emission (PIXE)

None

Instrumental neutron activation analysis (INAA) Inductively coupled plasma mass spectrometry (ICP-MS) Semicontinuous metals monitor

Filters are folded or made into pellets and sealed in polypropylene bags or vials Liquid extraction in water or acid solution

Single-particle mass spectrometry

Aerosols are collected by steam condensation and analyzed by GFAAS None

"Indicates methods Employed in the EPA's National PM-2.5 Chemical Speciation Network. b Limited number of analyses in PM-2.5 Chemical Speciation Network.

Research method. Provides data on OC, EC, and TC on quartz filters and OC from carbon-impregnated filters (Eatough et al., 1995) Provides semicontinuous measurement of optical absorption due to elemental or light absorbing carbon (Rosen et al., 1978; Hansen and Novakov, 1990) Semicontinuous OC and EC by the TOT method (Turpin et al., 1990) Provides a large number of nonpolar and a few polar organic compounds that are extremely useful as tracers for receptor modeling and understanding atmospheric chemical processing (Rogge et al., 1993; Schauer et al., 1996; Schauer and Cass, 2000) Primarily provides data on crustal-related species (Si, Ca, Fe) and S, Zn, and Pb. Many other atmospheric species are usually below the limit of detection. See Watson et al. (1999) Differs from XRF only in the method used to generate the fluorescence, i.e., high-energy protons versus X-rays (U.S. EPA Method IO-3.6) (Cahill et al., 1987) With the exception of C, Si, Ni, Sn, and Pb, INAA provides quantitative results for many trace elements typically observed in ambient aerosols (Lodge, 1989) ICP-MS provides detection limits on the order of 1 to 100 parts per trillion for approximately 65 elements with a linear dynamic range in excess of eight orders of magnitude (Grohse, 1999) A semicontinuous method that allows for determination of up to six elements simultaneously (Kidwell and Ondov, 2000) In this mostly qualitative method, the chemical composition of single particles are measured in real time (Prather et al., 1994; Murphy and Thomson, 1995; Johnston and Wexler, 1995; Hinz et al., 1996; Noble and Prather, 1996)

TABLE 11-2. PM-10 Mass and PM-2.5 Mass and Chemical Compositions at Selected Locations Throughout the United States Species0

Atlanta, GA, August 1999* (n = 15)

Rubidoux, CA, Jan-Feb 1999C (n = 14)

Shenandoah National Park, VA, May 1998-May 1999rf (n = 102)

Meadview, AZ, May 1998-May 1999* (n = 76)

Concentrations (Average ± Standard Deviation) in fig/m3 PM-10 mass PM-2.5 mass Sulfate Nitrate" Ammonium OC EC

NA 32.9 ± 9.0 10.8 ± 4.1 0.61 ± 0.11 3.7 ± 1.4 9.3 ± 2.4 0.86 ± 0.30

38.4 ± 24.6 26.4 ± 22.5 1.6 ± 1.6 10.0 ± 9.6 3.5 ± 3.7 6.6 ± 3.3 2.7 ± 1.7

13.9 ± 9.2 11.4 ± 8.1 3.4 ± 3.6 0.3 ± 0.3 NA 1.3 ± 0.9 0.8 ± 0.5

9.2 ± 32.7 3.1 ± 4.2 0.7 ± 0.5 0.1 ± 0.1 NA 0.5 ± 0.3 0.3 ± 0.1

Concentrations (Average ± Standard Deviation) in ng/m3 S Si K Ca Mn Fe Cu Zn Pb As

4100 ± 1500 180 ± 75 58 ±15 71 ±42 3.2 ± 1.9 130 ± 42 3.9 ± 3.2 14.7 ± 8.9 5.1 ± 3.3 2.1 ± 0.8

640 ± 620 160 ± 120 86 ±44 160 ± 120 3.7 ± 2.7 180 ± 120 5.0 ± 3.5 60 ±77 11 ±10 0.4 ± 0.6

1500 ± 1300 120 ± 90 40 ±20 34 ±25 2.5 ± 10.2 31 ±20 1.5 ± 10.1 6.5 ± 10.0 3.1 ± 10.0 1.3 ± 10.1

20 ± 180 250 ± 1100 37 ± 110 54 ± 170 1.9 ± 9.0 42 ± 150 1.3 ± 8.7 2.2 ± 8.7 1.6 ± 8.7 1.2 ± 8.8

"This is a partial list of species that are usually observable above limits of detection (LOD). Other species often measured in ambient aerosols by filter methods are given with their LOD in Chow (1995). b Solomon et al. (2000b). c Tolocka et al. (2000). d Calculated from publicly available IMPROVE data located at http://alta_yista.cira.colostate.edu/; go to FTP site, data, IMPROVE, aerosol, SHENl, and MEVEl. e Obtained using denuders and reactive filters. This value is typically higher than nitrate concentrations obtained from a Teflon filter.

MASS MEASUREMENTS The U.S. Environmental Protection Agency has promulgated new primary and secondary National Ambient Air Quality Standards (NAAQS) for respirable particles or fine particulate matter (PM-2.5), defined as particles with an aerodynamic diameter less than 2.5 um. The Code of Federal Regulations (40 CFR, Part 53, Sub-part E) specifies sampler design, performance characteristics, and operational requirements of the FRM for fine particulate matter. The new PM-2.5 standards, as well as the revised standards for inhalable particles (PM-IO, defined as particles with an aerodynamic diameter of less than 10 um), are based on gravimetric analysis of aerosols collected on a filter over a sampling period of 24 h (PM-10: 40 CFR, Part 50, Appendix J; PM-2.5:40 CFR, Part 50, Appendix L) (U.S. CFR, 1997). Gravimetric analysis was selected because it has been used historically with past particle standards, that is, total suspended particulate (TSP) and PM-10 (TSP: 40 CFR, Part 50, Appendix B and J) (U.S. CFR, 1997). Also, epidemiological studies have found associations between PM mass determined gravimetrically on filters and mortality and morbidity (U.S. EPA, 1996a,b; U.S. EPA,2001;40CFR, Part 50, Subpart II [U.S. CFR, 1997];Dockery et al., 1993;Pope et al., 1995;

TABLE 11-3. Organic Species Molecular Markers and Associated Emissions Source Used for Source Apportionment of Airborne Particulate Matter

Molecular Marker w-Pentacosane n-Hexacosane /r-Heptacosane n-Octacosane n-Nonacosane H-Triacontane n-Hentriacontane «-Dotriacontane n-Tritriacontane rc-Tetratriacontane w-Pentatriacontane anteiso-Triacontane iso-Hentriacontane anteiso-Hentriacontane iso-Dotriacontane anteiso-Dotriacontane iso-Tritriacontane 20S&R-5a(H),14p(H),17p(H)-Cholestanes 20R-5a(H),14a(H),17|3(H)-Cholestanes 20S&R-5a(H),14p(H),17p(H)-Ergostanes 20S&R-5a(H),14P(H),17p(H)-Sitostanes 22,29,30-Trisnorneohopane 17a(H),21p(H)-29-Norhopane 17a(H),21p(H)-Hopane 22S-17a(H),2ip(H)-30-Homohopane 22R-17a(H),2ip(H)-30-Homohopane 22S-17a(H),2ip(H)-30-Bishomohopane 22R-17a(H),2ip(H)-30-Bishomohopane Particle-phase nonanal /i-9-Hexadecanoic acid Cholesterol Levoglucosan 8,15-Pimaradien-18-oic acid Pimaric acid Isopimaric acid Propionylsyringol Butyrylsyringol Benzo [kjfl uoranthene Benzojbjfluoranthene Benzo[e]pyrene Indeno[l ,2,3-cd]fluoranthene Indeno[l,2,3-cd]pyrene Benzo [ghijperylene Coronene Elemental carbon Aluminum Silicon Source: Schauer (1998).

Major Urban Sources Gasoline vehicles, diesel vehicles Gasoline vehicles, diesel vehicles Gasoline vehicles, diesel vehicles Gasoline vehicles, diesel vehicles Vegetative detritus Motor vehicle Vegetative detritus, cigarette smoke Variety Vegetative detritus, cigarette smoke Tire wear debris Tire wear debris Cigarette smoke Cigarette smoke Cigarette smoke Cigarette smoke Cigarette smoke Cigarette smoke Gasoline vehicles, diesel vehicles Gasoline vehicles, diesel vehicles Gasoline vehicles, diesel vehicles Gasoline vehicles, diesel vehicles Gasoline vehicles, diesel vehicles Gasoline vehicles, diesel vehicles Gasoline vehicles, diesel vehicles Gasoline vehicles, diesel vehicles Gasoline vehicles, diesel vehicles Gasoline vehicles, diesel vehicles Gasoline vehicles, diesel vehicles Meat cooking Meat cooking Meat cooking Hardwood burning, softwood burning Softwood burning Softwood burning Softwood burning Hardwood burning Hardwood burning Gasoline vehicles, natural gas comb. Gasoline vehicles, natural gas comb. Gasoline vehicles, natural gas comb. Gasoline vehicles, natural gas comb. Gasoline vehicles, natural gas comb. Gasoline vehicles, natural gas comb. Noncatalyst cars Diesel exhaust Road dust Road dust

Schwartz et al., 1994,1996; Thurston et al., 1994). Gravimetric mass also will be measured in the Chemical Speciation Network at up to 300 sites nationwide (U.S. EPA, 1996a,b; U.S. EPA, 2001). Gravimetric analysis of aerosols collected on filters is a difference measurement, where filters are weighed before and after sampling under controlled conditions. The most widely used sampling media are Teflon, quartz fiber, Teflon-coated quartz fiber, or glass fiber. Properties of these filter media and others are described by Chow (1995) and in Chapters 9 and 27. A detailed protocol has been established for the gravimetric analysis of aerosols collected on filters (U.S. CFR 1997, Part 50 Appendices J and L). For PM-2.5, this protocol requires equilibration of filters within a narrow range of temperature (293 to 296 K [20° to 23°C] and not to vary by more than ±2K [2°C] over 24h) and relative humidity (30 to 40% RH and not to vary by more than ±5% RH over 24h). These controls provide reproducible and standard conditions to account for possible variations in water associated with the filter and the collected aerosols during pre- and postweighing periods. The latter varies among location, species, seasons, and aerosol compositions and cannot be accounted for directly by other methods. Affinity to water or changes in relative humidity are the primary reason hygroscopic filter media (e.g., cellulose and cellulose ester membrane) are not recommended for gravimetric analysis of particulate matter in ambient air (Demuynck, 1978; Tierney and Conner, 1967; Chow, 1995). Mass concentrations of aerosols in ambient air are obtained by weighing filters before and after sampling using an analytical balance with a sensitivity of ±100 jig for PM-10 collected on standard 0.20 by 0.25 m2 [8 by 10 in2] filters and ±1 jig for fine particles collected on 37 mm or 47 mm diameter Teflon filters. These analytical limits of detection (LOD) for the balance correspond to atmospheric limits of detection of 0.06|ng/m3 for PM-10 and 0.04ug/m3 for PM2.5. The LOD for PM-10 assumes the sample was collected over a 24 h sampling period, on a standard filter (0.18 by 0.23 m2 [7 by 9 in2] sampling area), and operated at a flow rate of 0.0189 m3/s [40ft3/min]. The LOD for PM-2.5 assumes the sample was collected over a 24 h sampling period on a 47 mm filter and operated at a flow rate of 2.78 x 10"4HrVs [16.7L/min or ImVh]. To achieve high accuracy and precision, the weighing area must be free of vibrations and the balance must be shielded from air currents. Static charge must be removed from the filters before weighing to minimize electrostatic attraction to balance surfaces (PM-10:40 CFR Part 50, Part 50, Appendix J [US. CFR, 1997]; PM-2.5: Part 40, Part 50, Appendix L [US. CFR, 1997]; Lawless and Rodes, 1999). Potential errors due to electrostatic charge are especially significant with Teflon and other membrane-type filters used in low-volume samplers. Other factors influencing the accuracy and precision of aerosol mass measurements include sampling artifacts (see Chapter 27) and sampling, transport, and storage procedures. Precision of the mass measurement is usually obtained by reweighing filters and reporting their reproducibility or from collocated measurements. Precision values within 5% are typically obtained (40 CFR Part 58, Appendix A [U.S. CFR, 1997]; US. EPA, 1998).

WATER-EXTRACTABLE ANION AND CATION ANALYSIS METHODS Overview Ionic species can comprise the bulk of the mass of secondary atmospheric particulate matter (Tolocka et al., 2001; Solomon et al., 1988; Chow and Watson, 1999; U.S. EPA, 2001). Furthermore, the ionic content of the aerosol can be used to estimate source attributions at a receptor (Chow et al., 1992; Watson et al., 1994; Magliano et al., 1999). Ion chromatography is a widely used multispecies technique employed for anion and cation analysis and is the

method of choice for the Chemical Speciation Network (U.S. EPA, 1999a). Concentrations in water of both simple ions, such as chloride, potassium, and sodium, and polyatomic ions, including sulfate, nitrate, and organic acids, can be determined by ion chromatography. CoIorimetry and ion-selective electrodes have been developed to measure a few ions, such as ammonium and hydrogen ions, in aqueous extracts of aerosol samples collected on filters. Finally, there is the possibility of sample loss and degradation during extraction, so methods have been developed or applied to measure a few species directly on the filters. These include, for example, Fourier transform infrared (FTIR) spectroscopy, an optical technique, and XRF, which have been used to determine ions such as sulfate or bisulfate directly in the aerosol sample collected on the filter media. Aqueous Extraction The extraction of atmospheric particulate matter from glass fiber and quartz fiber filters for the analysis of anions and cation species (e.g., NO3", SO42", Cl", K+, Na+, and NH4+) presents few problems (Chow, 1995; Chow, and Watson, 1999). Filters that are hydrophilic, such as paper, glass fiber, or quartz fiber filters, typically have extraction efficiencies for water-soluble species approaching 100% (Appel et al., 1980b, 1981; Chow and Watson, 1999). Teflon filters, which are hydrophobic, however, have much lower extraction efficiencies and require special treatment before extracting water-soluble compounds with an aqueous solution. For example, based on the work of Derrick and Moyers (1981), many groups "wet" Teflon filters with 50ul of ethanol just before extraction. The U.S. EPA (1989) suggests 100 ul of ethanol followed by extraction in 10"4 N HClO4. After filters are wet, extraction usually is performed by shaking or using an ultrasonic bath for several hours to overnight (U.S. EPA, 1989; Chow and Watson, 1999). Maintaining the sample at reduced temperatures minimizes the potential loss of volatile species such as nitrate or ammonium nitrate during the extraction process. Nylon filters are often used to efficiently collect aerosol nitrate and/or nitric acid. Nitrate bound to Nylon is not efficiently recovered as NO3" with water extraction (Sickles et al., 1986); however, an alkaline solvent (e.g., the dilute CO32--HCO3" buffer employed as eluent in ion chromatography) provides efficient extraction (Hering et al., 1988; Solomon et al., 1988), with the possible exception of very lightly loaded samples (Appel et al., 1988). Total strong acidity, H+ or H3O+, can be measured by extracting particles collected on a Teflon filter with water (Appel et al., 1980a; Appel, 1993; Koutrakis et al., 1992; Chow, 1995). However, analysis requires the elimination of dissolved CO2 and a technique to eliminate or allow for the dissociation of weak acids. Strong acidity, as well as strong acid anions in particles, can be obtained using Teflon filters (47 mm) wet with 100 ± 5ul of ethanol followed by ultrasonic extraction in 10"4 N HClO4 (U.S. EPA, 1989). The use of a pH 4-extraction solvent represses the dissociation of weak acids and minimizes the concentration of dissolved CO2. The hydrogen ion concentration is then measured using a combination pH meter equipped with a semimicroelectrode. Although the accuracy of hydrogen ion measurements (about 10%) is less than that of the related ionic species (e.g., sulfate and ammonium at about 5%), their accuracy is considerably better than measurements of carbonaceous aerosols and nitrate (about 15%) (Koutrakis et al., 1988). Sampling Requirements The filter material from which water-soluble ions will be extracted must be hydrophilic in nature, allowing the aqueous solution to penetrate the filter. As discussed above, Teflon filters must be "wet" before extraction, while a weak base must be used to efficiently extract Nylon filters for nitrate.

Ion Chromatography Ion chromatography is a widely used method to rapidly determine the concentrations of anions and cation species in aqueous solutions (Mulik et al., 1978; Appel, 1993; Chow and Watson, 1999; U.S. EPA, 1999a; U.S. EPA 2001). An excellent basic description of ion chromatography and related methods is given by Weiss and Johnson (1986). In ion chromatography, an aliquot of the extracted sample is injected into the ion chromatography sampling port. The eluent, in most cases, for strong acid anions is a dilute carbonate-bicarbonate solution; a dilute solution of boric acid is used for weak acid anions and a dilute solution of HCl for cation species. Depending on the species to be analyzed, an anion or cation guard column is used to remove organic compounds and other interfering components from the sample. Anions are separated using a basic ion exchange resin, while cation species are separated using an acidic ion exchange resin (Weiss and Johnson, 1986; Chow and Watson, 1999). The ion exchange column is followed by the eluent suppressor column, which converts the ions in the solvent (e.g., carbonate or bicarbonate for anions) into less conductive forms (e.g., H2CO3) while converting the analytes into their acidic forms that are strongly conductive. This increases the signal-to-background ratio in the detector, allowing low, often sub-ppb concentrations, of the analyte to be detected (Mulik et al., 1978; Small, 1981; Colenutt and Trenchard, 1985; Lodge, 1989; Chow and Watson, 1999). Ions are detected using an electroconductivity detector. The determination of SO42", NO3", and NH4+ can be made over a range of at least three orders of magnitude using this technique, called chemically suppressed anion chromatography. The instrument detection limit (IDL) values vary with the system configuration. Other species routinely determined by ion chromatography in atmospheric particles include Cl", F", formic acid, acetic acid, K+, Na+, NH4+, and many others (Weiss and Johnson, 1986; Mulik et al., 1978; Chow and Watson, 1999). Ion Selective Electrodes Ion selective electrodes (ISEs) have been used to determine concentrations of sulfate, nitrate, and ammonium ions in aqueous extracts of atmospheric particulate matter collected on filters (Clarke et al., 1999; Saleh et al., 1999; Appel et al., 1988). ISEs are membrane-type electrodes that produce a potential when immersed in a solution containing the appropriate ion. The potentiometric response is obtained by selectively extracting the ion of interest into the membrane by the appropriate counterion. The counterion is constrained within the membrane because the organic matrix is hydrophobic. The potential across the membrane is measured using a pH meter with an expanded scale in contact with a reference electrode on either side of the membrane. ISEs are easy to deploy and are inexpensive. However, they are prone to interferences from other ions in solution. The method for ammonium is described by Appel (1993). For ammonium, ISEs have an operating range from about 0.04 to 1700jig/ml. Single laboratory precision values of 2% to 4% have been obtained for ammonium, with recoveries on the order of >90% from Teflon filters. Colorimetry Colorimetry, as the name implies, refers to measuring the color change of a solution by absorption spectroscopy. Today, several manufactures sell automated colorimetric systems for the determination of a variety of species. The most common ions measured are SO42", NO3", Cl", and NH4+ (Butler et al, 1978; Mueller et al, 1979; Fung et al, 1979; Pyen and Fishman, 1979). A sensitive, selective, and rapid (up to 50 samples per hour) automated method is often employed to measure NH4+ based on the indophenol blue method (Bolleter et al, 1961). Sulfate is measured using the methylthymol-blue method, while nitrate is reacted

with sulfanilamide to form a diazo compound. In each case, the ion in question is transformed quantitatively into a species that absorbs light at a particular wavelength. The amount of light absorbed is proportional to the amount of analyte in the sample extract. The Beer Lambert law describes the proportionality. Comparison studies between ion chromatography and automated colorimetry have found excellent agreement between the two methods for sulfate, nitrate, and ammonium (Butler et al., 1978; Fung et al., 1979). X-Ray Fluorescence

XRF is used routinely to determine sulfur concentrations in atmospheric particles collected on Teflon filters as described in this chapter (see below for a description of the technique). Experience indicates that sulfur concentration times 3 (ratio of the molecular weights of S and SO42") is a reasonable estimate of the water-soluble sulfate concentration for most ambient aerosol samples (Dzubay and Stevens, 1975; Chow, 1995; Solomon et al., 2000a). Comparison of sulfur times 3 to sulfate also is used routinely as a quality assurance check of the data. Other alkali metals can be obtained by XRF; however, these values represent total metal content and not water-soluble content, which may be quite different. For example, water-soluble K+ is a good tracer for wood smoke burning; however, it is not correlated with nonwater-soluble K, which derives from soil and sea salt emissions. Fourier Transform Infrared Spectroscopy

Most polyatomic ions found in aerosols absorb infrared radiation. Nitrate, for example, has strong vibrational transitions that occur at 1400 cm"1 (X = 7.14 x 10"6m) while ammonium has medium-strength vibrational transitions in the same region. Infrared spectra are obtained using an FTIR spectrometer. A Michelson Interferometer, located inside the spectrophotometer, is used to measure and separate or deconvolute the composite signal (Ingle and Crouch, 1988). The interferometer splits the light beam from the sample in two, varying the path length taken by the separate beams. This introduces a phase difference in the signal between the two paths, and they recombine at the detector either destructively or constructively depending on the path length difference and the frequency of the absorption signal. The resulting intensity observed at a detector is an interferogram, a time-dependent signal that contains the frequencies allowed to reach the detector (i.e., constructive recombination). This time-dependent signal is transformed into a frequency signal using a Fourier transform, a mathematical conversion. Species are detected by passing the infrared light through the filter substrate (McClenny et al., 1985) or impaction onto a crystal (Johnson et al., 1983). Background subtraction is used to remove absorption features due to the C-C stretching and bending modes found in Teflon filters. FTIR can provide mass sensitivities in the high picogram to low nanogram range (Allen and Palen, 1989). However, the method is not directly quantitative and suffers from interferences. Concentration calibrations have been obtained relative to filter samples that have been analyzed by another method (Allen et al., 1994). Determination of sulfate, nitrate, ammonium, and organic species by functional groups on low-pressure impactor stages has resulted in unique aerosol size distribution information important for understanding aerosol chemistry (Allen et al., 1994). Many other methods exist for determining the concentration of anion and cation species in aqueous solutions. Those presented here are some of the most common methods, including ion chromatography and the FTIR method, the latter of which is still under development. Ion chromatography is the method of choice for the Chemical Speciation Network. Other methods discussed here, such as colorimetry and XRF, are useful tools but report only a single water-soluble anion or cation of interest per analysis and are not necessarily quantitative. For

example, ammonium ion is determined by colorimetry from aqueous extracts and sulfur by XRF, where the sulfate concentration is estimated from the XRF value by multiplying XRF sulfur concentration by three. The FTIR method is still under development for its application to atmospheric aerosols. PARTICULATE CARBON Particulate carbon is a major component of PM-2.5, typically accounting for 25% to over 50% of the mass for samples collected in the eastern and western United States (U.S. EPA, 1996a, 2001). Recent measurements in the eastern United States during the winter exceeded 70% OC (Tolocka et al., 2001). Particulate carbon is operationally classified into elemental or black carbon (EC), organic carbon (OC), and carbonate carbon (CC). Total carbon (TC) represents the sum of EC, OC, and CC. EC is emitted primarily from anthropogenic sources, such as diesel vehicle exhaust, especially in urban areas, due to incomplete combustion. OC is emitted from anthropogenic sources (primary aerosols) and is formed in the atmosphere from gaseous precursors (secondary aerosols). CC is typically associated with soil-related sources (Appel et al., 1983) and usually constitutes less than 5% of the TC (Chow et al, 1993). A number of techniques have been used to measure particulate carbon collected on filters. Carbon analyses are destructive except for the indirect measurement of black carbon by laser transmission or adsorption (Novakov, 1982; Hansen and Novakov, 1990; Chow, 1995). Direct measurements of carbon are obtained by heating a filter containing collected aerosol and measuring the amount of carbon evolved from the particulate matter (thermal analysis). During the heating process, some of the particulate OC is pyrolized or converted to EC, which may or may not be accounted for by the different methods. Carbon analysis methods differ by the use of direct or indirect measurement of carbon, heating temperatures, length of analysis time at each temperature, rate of temperature increase, atmospheres used for oxidizing the organic compounds, and the method used to adjust for pyrolysis (i.e., light transmittance or reflectance) (Hering et al, 1990; Cadle and Groblicki, 1982; Birch, 1998; Chow et al, 1993b). Direct measurement methods for particulate carbon provide similar total carbon results (Chow, 1995); however, differences in the operational aspects of the methods result in variations between the reported EC and OC concentrations. Direct methods only provide estimates of EC, although multiwavelength light-absorbing methods are now commercially available and provide additional information on the type of carbon (functional groups) present in the sample. Sampling Requirements

The filter medium used for carbon analysis by thermal methods cannot itself contain carbon, thus eliminating the use of Teflon filters. The most widely used filter media are quartz fiber and glass fiber. Quartz fiber filters provide a lower carbon blank value (<1 |ig/cm2) because they can be pre-fired at temperatures up to 1173 K (9000C) (Chow et al, 1993b) compared with <723K (4500C) for glass fiber filters. Heating the filter to reduce initial carbon blank levels is an important and essential step. Quartz filters are required for the commonly used thermal optical protocols because the highest temperature in the protocols is 1073 K (8000C) or higher. The most common quartz fiber filters used for carbon measurements include 2500 QAT- Ultra Pure (UT) type 7201 (PAL)* for 37 mm and 2500 QAT- Ultra Pure (UT) type 7202 (PAL) for 47 mm. After samples are collected, sample transport and storage should be at reduced temperatures (Chow, 1995). The effects of storage and shipping temperatures have not been investigated thoroughly; however, recommended temperatures range from * See Appendix I for full manufacturer addresses referenced to the italicized three-letter codes.

refrigeration for OC and EC determination (<4°C; Chow, 1995) to temperatures from -25°C (Rogge et al., 1991) to -800C for determination of organic species (Sverdrup et al., 1990). Sampling requirements needed for optical methods are discussed below. Calibration, Precision, Limits of Detection, and Interferences

A carbon analyzer, using the thermal analysis approach, is calibrated by spiking a filter punch with a solid containing a known amount of carbon, usually sucrose, disodium salt of ethylenediaminetetraacetic acid (EDTA), or potassium hydrogen phthalate (KHP) (Chow et al., 1993b; Birch, 1998). These standards provide both OC and TC standards. An ambient particulate EC standard is presently not available from the National Institute of Standards and Technology (NIST), although EC standards have been prepared by collecting particles from a butane flame (Appel et al., 1983). Injections of CO2 and CH4 also can be used to calibrate the response from a nondispersive infrared (NDIR) detector (Cadle and Mulawa, 1990) or a flame ionization detector, respectively. Carbon dioxide also can be used to verify the operation of the methanator, which converts CO2 to methane. Interferences are minimal in thermal analysis methods, expect for the pyrolysis correction, which has become part of the method itself. Precision and limits of detection are described below within the method descriptions. The following sections describe several methods for determining OC and/or EC collected on filters. The first, thermal optical with correction for pyrolyzed carbon using light transmittance, is the method currently used in the Chemical Speciation Network for determining atmospheric concentrations of OC, EC, CC, and TC. Thermal Optical and Thermal Techniques

The two most widely used thermal optical methods are thermal optical transmittance (TOT) (Birch, 1998; Birch and Cary, 1996) and thermal optical reflectance (TOR) (Huntzicker et al., 1982; Chow et al., 1993). The TOT and TOR methods are primarily used with the National Institute for Occupational Safety and Health (NIOSH) Method 5040 (Eller and Cassinelli, 1996; Birch, 1998) and the Interagency Monitoring of Protected Visual Environments (IMPROVE) (Chow et al., 1993a) protocols, respectively. A third, earlier method was developed by Novakov (1982) and is briefly described below. The three methods differ in the way they correct for the amount of OC pyrolyzed to EC during the He heating step, as well as the temperature ramp (temperature and length of time at each step) each employs. The TOT and TOR methods evolve carbon in four temperature steps from a quartz fiber filter in an He atmosphere to determine OC and in an He/O2 atmosphere in three thermal steps to evolve EC. Evolved carbon is catalytically first oxidized to CO2, reduced to CH4, and quantified with a flame ionization detector (Johnson et al., 1981; Stevens et al., 1982; Huntzicker et al., 1982; Grosjean, 1984; Hering et al., 1990). The split between OC and EC, and thus determination of the pyrolysis correction, in the TOT and TOR methods is based on either transmittance (Birch and Cary, 1996) or reflectance (Chow et al., 1993b). CC is determined by acidifying the sample (Chow et al., 1993b). The acidification procedure requires thermal analysis of two identical portions of the sample, one before and one after acidification. The difference between the TC results obtained for the two punches gives an estimate of CC. Alternatively, the carbonate can be estimated by integrating the carbonate peak at 1093 K (8200C) as described below (Birch and Cary, 1996). The latter approach applies only when the carbonate is removed as a single peak during the fourth temperature step in He (e.g., calcium carbonate). The analytical limit of detection for TOR and TOT is about 0.2 fig/cm2 carbon (Chow et al., 1993b; Birch and Cary, 1996). Assuming a sample collected at 2.78 x 10~4m3/s [16.7L/min or Im2Vh] for 24 h and a punch of 100 mm2 (lcm2) from a 47 mm filter (deposit area = 1200mm2 [12.0cm2]), the atmospheric limit of detection is O.ljxg/m3. A semicontinuous TOT method has been evaluated using thermal optical transmission by

Turpin et al. (1990) and operated in Atlanta, GA, as part of an advanced methods intercomparison study during the summer of 1999 (Turpin et al., 2001). Novakov (1982) measured TC by linearly heating a sample in an O2 atmosphere and measuring the CO2 with NDIR. The transmittance of a 633 nm HeNe laser is monitored as the temperature is increased at 10°C/min. The split between EC and TC is determined when the transmittance starts to decrease, indicating removal of EC (around 743 K [4700C]). A modification of this method, temperature-programmed volatilization (TPV) (Eatough et al., 1995), is well suited for determining OC from carbon-impregnated filters used as backup filters behind a quartz fiber filter to estimate carbon volatilized from the first filter (Tang et al., 1994). In the modified method, N2 is used as the carrier gas to prevent oxidation of the EC and OC is determined by heating the filter to 623 K (3500C). Thermal methods use a combination of temperature and an inert gas with oxidizing agents to distinguish between EC and OC. Separation of OC and EC has been obtained by heating a sample in an inert atmosphere with MnO2 (Stevens et al., 1982; Mueller et al., 1982; Grosjean, 1984). OC was determined at 623 K (3500C) and then at temperatures greater than 1123 K (9000C) (Fung, 1990); the MnO2 provides the O2 required to oxidize the EC to CO2. Carbon dioxide is then measured with an NDIR detector or an electrochemical cell (Countess, 1990). Other methods heat the samples in He to determine OC and use an He/O2 atmosphere to measure EC. Evolved carbon is either measured as CO2 or catalytically reduced to CH4. Methane is quantified with a flame ionization detector. R&P manufacturers and sells a continuous carbon analyzer (Series 5400) that heats samples at 613 K (3400C) and 1023 K (9000C) to quantify OC and EC. No optical measurements are made to quantify pyrolysis. Samples are collected for Ih at a flow rate of 2.78 x lO^nrVs [16.7L/min or Im3/h] and have an atmospheric limit of detection of about 0.2jig/m3. Optical Techniques

Particulate matter collected on filters appears gray to black due to the presence of EC in the particulate matter (Rosen et al., 1978), with higher loadings producing darker colors. Organic material, on the other hand is typically transparent to visible light. Thus, optical methods only can be used to estimate concentrations of EC or black carbon in atmospheric particulate matter samples. Light absorption by particles collected on a filter is linearly related to the EC concentration in the collected particles by Beers Law (Novakov, 1982,1984; Gundel et al., 1984). The relationship between the amount of light absorbed and the EC concentration (i.e., the amount on the filter)—the absorption coefficient—is determined experimentally and can vary depending on the source of EC and the collection media (Appel et al., 1983). Gundel et al. (1984) did not find differences depending on the source of EC and estimated a typical absorbance coefficient of 25.4 m2/g at 632nm for particles collected on quartz fiber filters from a variety of combustion sources (Hansen and Novakov, 1990). An aethalometer uses filter transmission to continuously measure the EC collected on a quartz fiber filter (Rosen et al., 1978; Hansen and Novakov, 1990). A commercial Aethalometer (MAG) uses a continuous quartz fiber filter tape. The instrument wavelength for a Model AE16 is 880 nm, and the absorbance coefficient is 16.7 m2/g. The limit of detection for an older Aethalometer that used an incandescent lamp as its light source (AE- 9) was 100ng/m3 black carbon for a Ih measurement (Allen et al., 1999). Recently, MAG has developed dual and multi wavelength aethalometers. These provide an improved estimate of EC concentrations as well as insight into other organic categories in the collected particulate matter. Comparison of Methods

Cadle and Groblicki (1982) evaluated the measurement of EC on filters by comparing several methods: organic extractions, nitric acid digestion, vacuum stripping, thermal methods, and

the integrating plate method. Systematic and correlated differences were found between the methods. Hering and colleagues (1990) compared seven variations of thermal carbon analysis methods. Similar results were found for OC and TC, but the EC concentrations varied between the methods. Cadle and Mulawa (1990) conducted a round-robin methods comparison study with 11 laboratories that used both thermal and thermal optical techniques. EC concentrations had the largest difference between methods, and differences were observed for OC, EC, and TC for diesel and automobile exhaust source samples. Birch (1998) conducted a study that compared TOC (NIOSH Method 5040) (Eller and Cassinelli, 1996; Birch, 1998), TOR (Chow et al., 1993), and thermal techniques with coulometric detection of CO2. Similar TC results were found in the study, but the EC concentrations for the coulometric techniques were 2.5 to over 800 times higher than the TOT method, depending on the sample type. EC concentrations by TOR were 1.25 to 10 times higher than the EC concentrations obtained from TOT. Two recent comparison studies have indicated the primary difference between TOR (IMPROVE protocol) and TOT (NIOSH protocol) is due to their different temperature programs (Chow et al., 2001; Norris et al., 2001). EC typically varied by a factor of 2 (TOR > TOT) and OC by about 10% to 20% (TOT > TOR). The various carbon analysis methods showed more consistent agreement for TC; however, the measurement of EC is operationally defined, adding to its variability among the methods. Organic Speciation Analysis of Aerosols

OC represents a large fraction of fine-particle mass (e.g., Gray et al., 1986; Solomon et al., 1989; Chow et al., 1993b, Tolocka et al., 2001) and is composed of many compounds, most of which partition between the gas and aerosol phases at ambient conditions and are referred to as semivolatile organic compounds (SVOC). As well, many of the compounds exist only as aerosols or just in the gas phase (volatile organic compounds [VOC]). It is the SVOC compounds that make filter sampling for organic aerosols a challenge, in a similar fashion to using filters to collect ammonium nitrate (Russell et al., 1983). In addition, gas phase organic compounds (VOC or SVOC) can be adsorbed onto the filter (known as a positive artifact), or SVOC compounds in aerosols collected on the filter can volatilize back into the gas phase during sampling (known as a negative artifact) (Tang et al., 1994; Eatough et al., 1990; Turpin et al., 1994). The debate on the magnitude of organic artifacts, positive and negative, is far from being concluded and appears to depend on many yet to be identified variables. Knowing more about the individual components of the organic aerosol in conjunction with the gas phase semivolatile species and VOC will help to resolve these issues. Organic aerosol compound speciation also can provide a great deal of information regarding the sources and formation processes of carbonaceous particles (Schauer et al., 1996). A very limited number of samples collected in the Chemical Speciation Network are being analyzed for a series of individual organic compounds. Although speciation is desirable, it is not easy to perform because there is no single analytical method that can be used to analyze all classes of organic compounds. Usually, nonpolar organic compounds are analyzed using thermal desorption or solvent extraction followed by gas chromatography (GC) and mass spectrometry (MS) (Mazurek et al., 1987,1993; Rogge, 1993; Rogge et al., 1993; Schauer et al., 1996). Analysis of polar organic compounds is more challenging because special sample preparation is required (e.g., derivatization) for the different compound classes (Blando et al., 1998; Hawthorne et al., 1992; J. O. Allen et al., 1997). Comprehensive determination of organic speciation is expensive and yields a large data set that may be time consuming to process and incorporate into air quality models. It may be more cost effective to develop inexpensive methods that allow the measurement of specific characteristic groups of organic compounds (Turpin, 1998). Similar lumping approaches for VOC species have been used successfully in ozone modeling. The most common method for identifying and quantifying individual organic compounds

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found in ambient aerosol samples is gas chromatography coupled with a mass spectrometer as the detector (GC-MS) (Simoneit, 1986; Mazurek et al., 1987; Rogge et al., 1993; Rogge, 1993; Schauer et al., 1996). Samples are collected on preheated quartz fiber filters and extracted with various solvents depending on the species of interest. An aliquot of the extract is injected into the front end of the GC column. The organic compounds are then separated in the GC column based on the differential distribution of their components between the stationary and mobile phases as the sample passes through columns containing both phases. The distribution of the components, and thus the time they take to reach the end of the column or the detector, is determined by the chemical or physical attributes of the analyte and/or stationary phase. Peak areas are integrated to determine the concentration of that compound in the sample aliquot. The theory of separation by chromatography and discussion of important variables are found in Karger et al. (1973) and Grob (1995). After separation, the samples associated with individual peaks are injected into an MS for identification. In the laboratory, samples are extracted via extraction protocols (e.g., Soxhlet, sonication, supercritical fluid) that are custom designed to optimize the removal of nonpolar and polar organic compounds from the quartz fiber filters (Paputa-Peck et al., 1983; Nielsen et al., 1984; Kamens et al., 1985; Bayona et al., 1988; Rogge et al., 1993; Rogge, 1993; Hildemann et al., 1994; Turpin et al., 1994; J. O. Allen et al., 1997). Typically, sample extracts can be concentrated using rotary evaporation. This is normally followed by further solvent evaporation with a high-purity stream of N2 or other inert gas such as argon. The concentrated extract can be analyzed by chromatography using silica gel or alumina coated columns, divided into aliquots for derivitazation, or directly analyzed. Extracts from the fine carbon particle samples can be analyzed using liquid or gas chromatographic methods. Some compounds of interest used in receptor modeling (Schauer et al., 1996) are listed in detail in Table 11-3. ELEMENTAL ANALYSIS BY NONDESTRUCTIVE TECHNIQUES This section describes three multielemental analysis techniques that are nondestructive for the elements of interest; however, other species in the sample may be lost due to volatilization from sample heating or being placed in a vacuum (Solomon et al., 2000a). The Chemical Speciation Network uses XRF analysis for determining concentrations of elements in ambient aerosols collected on Teflon filters. The IMPROVE visibility network (Eldred et al., 1998) uses proton-induced X-ray emission (PIXE), and that method is described in this section as well. Finally, instrumental neutron activation analysis is described, a sensitive multielement method that complements XRF and PIXE. X-Ray Fluorescence Analysis XRF methods are common analytical techniques for quantifying a broad range of minor and trace elements in ambient aerosol collected on filters. Several comprehensive reviews of the theory and application of XRF methods have been published (Dzubay and Stevens, 1975; Lodge, 1989; Watson et al., 1999). XRF methods are convenient for elemental analysis because filters require no preparation, and the method is applicable to about 45 elements between Na and U (Stevens, 1984; Lodge, 1989; Chow, 1995; U.S. EPA, 2001; Chow and Watson, 1999). XRF analysis can be qualitative by comparing sample X-ray spectra to a library of known element spectra or quantitative by calculating spectral peak areas and relating them to calibration standards with corrections for X-ray attenuation (Dzubay and Stevens, 1991). Atmospheric concentrations are obtained by dividing the mass of the element calculated per filter by the volume of air sampled through the filter. Summary of Technique. In general, XRF analysis involves irradiating a sample with a beam of X-ray radiation. The elements in the sample are excited by the X-rays and upon return to the ground state emit characteristic X-rays that are subsequently measured by an X-ray

source primary radiation

secondary radiation

(ens

spectrometer specimen

detector

Fig. 12-1. Schematic representation of generic microanalysis showing excitation and emission radiation. The incident radiation can be electrons, photons, or ions. The emissions are photons in the infrared, ultraviolet, or visible; ions; or X-rays.

by masking a portion of the secondary radiation (aperturing) or by employing a spatially sensitive detector. Some specific examples of instruments using electron beam excitation are electron probes, scanning electron microscopes, and analytical electron microscopes. The emitted radiation, backscattered electrons or X-rays, is used for single-particle analysis. Examples of instruments that use photon radiation are the Raman microprobe (or micro-Raman), where the detected beam is frequency-shifted photons; the laser microprobe, which detects ions in a time-of-flight mass spectrometer; and the Fourier transform infrared microscope, which is based on photon absorption. Examples of instruments that use ion beams are the ion microprobe and microscope based on secondary ion mass spectrometry (SIMS). In SIMS, an argon, oxygen, or cesium ion beam is used to bombard the sample, and either a time-of-flight mass spectrometer or more usually a magnetic sector or quadrupole mass spectrometer is used to detect the secondary ions generated in the interaction process. Often in an analysis, complementary capabilities of two or more instruments can be used in sequence on an individual particle to provide more complete particle characterization (phase, chemical, and morphological information) about that particle (Steel et al., 1984; Fletcher et al., 1990). This chapter describes microscopes and microprobes used for analysis of collected, individual particles. The instruments discussed are the light microscope, electron microscopes (both scanning and transmission), electron microprobes, laser, optical, scanning probe, and ion microprobes. The principles of operation and the instrumental capabilities are presented. The chapter also contains some basic information about sample preparation, useful for the aerosol scientist. Several excellent references that should be consulted for more detailed information include the books by Spurny (1986,1999), the publication by Heinrich (1980), the article by Newbury (1990), and the review chapter by Grasserbauer (1978). For quick reference, Table 12-1 presents a comparison of the microanalytical techniques capable of single-particle analysis.

TABLE 12-1. Comparison of Typical Microbeam Analytical Instrument Capabilities Analytical Method Excitation Emission Quantity measured Lateral resolution

SEM

TEM

LMMS

EPMA

Electrons Electrons, X-rays Electrons, X-rays

Electrons Electrons, X-rays Electrons, X-rays

Electrons Electrons X-rays Electrons, X-rays

5nm

0.3 nm

0.05 um

a

Photons Ions m/z lum

SIMS

Micro-Raman

LM

FT-IR

AFM

Ions Ions

Photons Photons

Photons Photons

Photons Photons

N.A. N.A.

m/z

Frequency shift

Light intensity

Intensity at given wavelength 10-50 inm

Distance, force, other interactions

2um

0.5 um

N.A. N.A. Organic and inorganic

N.A. N.A. Inorganic

N.A. N.A. Organic and inorganic

N.A. N.A. N.A.

10"12g

N.A.

5 x l(T12g

N.A.

Detectable elements Isotopic detection Detectability of molecular or chemical compounds Absolute detection sensitivity Relative sensitivity Sample vacuum mounted Destructive method Surface sensitive Imaging capability

>Carbon No None

>Carbon No Inorganic

>Carbon No None

AU Yes Organic and inorganic

10"16g

io-2Og

io-16g

10"18 to 10-20g

0.1 um (probe) AU Yes Organic and inorganic io-19g

0.1% Yes

0.1% Yes

0.1% Yes

1-100 ppm Yes

1-1000 ppm Yes

1% No

N.A. No

1% No

N.A. No

No No Yes

No No Yes

No No Yes

Yes Yes Yes

No No Yes

No No Yes

No No Yes

No Yes Yes

Automation demonstrated Quantitative

Yes

Yes

Yes

Yes Unknown Demonstrated, not widely available No

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No

No

No

No

Semi

N.A.

a

Photons in the UV to IR range. Source: Adapted from Wieser et al. (1980).

LIGHT MICROSCOPY Underlying Principle

Optical microscopy or light microscopy (LM) is one of the more familiar microanalytical techniques. The operating principle of light microscopes is well known. In the simplest form, the light microscope utilizes light refraction via a lens system to form enlarged images of microscopic objects. The image is focused on the detector, which can be the human eye or a camera. An object must absorb approximately 0.3% of the incident light to be visible to the eye (Dovichi and Burgi, 1987). Instrumentation

A schematic of a light microscope is presented in Figure 12-2. The important components of a light microscope are a light source, an objective lens, and an ocular. The light source can be diffuse or bright and serves to illuminate the sample. The objective lens collects light that passes through the sample or is reflected from the sample surface and projects an image near the ocular. The ocular magnifies the image that is projected by the objective for the eye. Normally, the virtual image seen resides below the sample plane. There are a number of optical accessories used in conjunction with the light microscope to characterize a sample physically. Some of these capabilities are discussed later. Some considerations in LM are depth of field, referring to the distance beyond the plane of focus that the object remains in focus; magnification, quantifying the image enlargement; numerical aperture, relating the maximum light-gathering capability of the microscope objective; and resolving power, indicating the size of the smallest feature that can be discriminated. Table 12-2 presents characteristics of various common microscope objectives (Steel, 1980).

Eye or Camera

Ocular Lens Image

Objective Lens Object Virtual Image Fig. 12-2. Schematic of a light microscope. Light is transmitted through the sample and focused by an objective lens. The intermediate image is then enlarged and transmitted to the eye or the detector. (Adapted from McCrone and Delly, 1973.)

TABLE 12-2. Characteristics of Some Common Light Microscope Objectives Magnification 3 10 50 100

Numerical Aperture

Depth of Field (M-m)

Diameter 0 of Field (mm)

Resolving* Power (M-m)

0.08 0.25 0.85 1.3

50 8 1 0.4

9 2 0.5 0.4

5 1.3 0.4 0.25

"Approximate value with xlO oculars. Wide field is now common; values may be 1.5 to 2 times larger. b Approximate value in green light. Source: Steel (1980).

Capabilities and Applications

LM is often the first microanalytical technique used to examine a sample because it is a nondestructive approach. It is relatively easy to use as an imaging tool for many applications, but identifying a material through its optical properties can be difficult. A skilled microscopist can use the physical and optical properties of a particle (such as the size, shape, surface texture, color, refractive indices, crystallographic properties, and birefringence) to help identify a given particle and thus possibly its source (Grasserbauer, 1978). Two additional references containing information on analysis of particles by LM are Steel (1980) and Friedrichs (1986). A general detailed reference for LM is given by Chamot and Mason (1958). The Particle Atlas (McCrone and Delly, 1973) is regarded as a principal reference for identification of particles by LM. Size and Shape Analysis. Determination of shape and size often represents the first step in single-particle analysis. Sometimes the shape will provide information about the particle type and thus the most probable formation mechanism of a particle. For example, fly ash normally appears under a light microscope as spheres, and dark, fractal-like (complex branched-chain) structures are usually formed from a combustion source. Fibers may be asbestos or glass or may come from a wide variety of other natural or man-made sources. The Particle Atlas (McCrone and Delly, 1973) can be used to compare the image of a sample particle to reference micrographs. Although particles can be resolved and thus observed at the 0.25 to 0.5 um size level, shape and size determination is most reliably done on particles larger than several micrometers in diameter (Steel, 1980). Fibers are an exception to the above statement because even with a lateral dimension as small as 0.5 um, size and shape can be determined. Related to particle size, particle magnification in the light microscope is limited due to the wavelength of light used (diffraction limit). A good working rule of thumb is that the maximum magnification of a light microscope is 1000 times the numerical aperture of the objective (McCrone and Delly, 1973). The shape and size are useful for identifying particle origin, but some additional physical properties help to provide more definitive information about a particle's makeup. Identification by Light Microscopy. Using the physical properties of particle shape and size and the optical properties of color, refractive index, and birefringence, the identity of an unknown particle can often be determined. Normally, optical characterization requires particles to be greater than 5 to 10 um in lateral dimension (Steel, 1980). Particles that have an index of refraction different from the substrate-mounting material are most easily viewed. It is the contrast between the background support and the particle that is most often important when trying to view an object by LM. The contrast can be

a

c

b

d

Fig. 12-3. Set of four light micrographs illustrating various LM techniques that help to increase particle visibility by increasing contrast and, in the last case (d), help to identify birefringent materials in the sample, a, Straight transmitted light; b, transmitted light using phase contrast; c, differential interference; d, effect of slightly uncrossed polarizers. The last frame (d) brings out the birefringent material present in the sample as the apparent luminous objects. (Courtesy of E. Steel, NIST.)

improved by a number of techniques. Figure 12-3 shows the same field of view of a collected airborne particle sample using different contrast enhancers. The filter material is mixed esters of cellulose, and the sample is prepared by treating with acetone vapor to make the filter transparent (Baron and Pickford, 1986). The micrographs are taken in transmitted light. Figure 12-3a illustrates the problem with viewing the filter in direct (unaided) transmission. Two large objects in the central part of the micrograph are barely visible under these conditions. When phase contrast microscopy is applied (Fig. 12-3b), the particles become quite visible; the phase shift of the light transmitted through the particles is used to enhance the contrast. Another way to increase the visibility of the particle is by differential interference contrast, shown in Figure 12-3c. This is considered a complementary technique to phase contrast, but in this case the objects take on a three-dimensional appearance. Finally, Figure 12-3d shows the effect of slightly uncrossed polarizers. The advantage of uncrossed polarizers is that the particles are, for the most part, still visible in the field of view and the particles made of anisotropic material stand out as illuminated objects. Crossed polarizers, on the other hand, would make all of the isotropic materials (such as glass

or amorphous plastics) "invisible" and show only those particles (as luminous objects) that rotate the polarized transmitted light. Detailed discussion of these techniques is not possible here, but is given in McCrone and Delly (1973). Several additional techniques are useful in conjunction with LM. Some microscopes are equipped with monochromatic or near-monochromatic light sources that can be used to excite fluorescence, if present, in a particle. Often the observation of fluorescence, usually excited in the ultraviolet, provides information for identifying the particle's composition. Refractive index is another parameter that can be determined and used as a powerful tool for particle identification. The refractive index measurement, with accuracy to one part in one thousand (Grasserbauer, 1978), is accomplished by immersing the particle in a series of index matching fluids to find the matching refractive index that causes the particle to "disappear." Microchemical reactions can be used to help identify the particle composition (Seeley, 1952; Chamot and Mason, 1940). Analyses of these kinds require considerable experience to be useful for identifying particle composition. Fiber Analysis. Baron (1993) presents an extensive discussion of microscopic techniques for fibers, and the following contributions relating to fibers have been extracted from his work. Phase contrast microscopy (PCM) is an interferometic technique that enables particles with low contrast (transparent particles) to be viewed. Light that is transmitted through the object is phase shifted relative to that transmitted through the substrate only. The phase shift is not detectable by the eye, but intensity differences are detected. The PCM transforms the phase difference to an intensity difference by forming an interference pattern using the phase shifted and unshifted light. PCM is primarily used for fiber counting to provide an index of asbestos fiber exposure in workplaces where asbestos is known to be present. It cannot be used to detect fibers thinner than about 0.25 |im. Although the morphology observed under PCM allows some discrimination between fiber types, it is not specific enough to allow positive identification of asbestos or other fibers. PCM also is used for measurement of man-made mineral fibers (MMMF), for example, mineral wool and fibrous glass (NIOSH, 1977). It can be used with other analytical techniques, such as polarized light microscopy (PLM) and scanning (SEM) and transmission (TEM) electron microscopy, for specific fiber types. Measurement Accuracy of Fibers by PCM. The accuracy of various fiber-counting techniques is poor when compared with other analytical methods. For instance, most analytical methods in the NIOSH Manual of Analytical Methods state an overall uncertainty (combined variability and bias) of better than 10% (NIOSH, 1984). Under optimum analysis conditions (uniform sample deposit, no background dust interference, optimum loading) a relative standard deviation of 0.10 (or 10%) is expected. Thus, the accuracy (including bias and variability) can be no better than this level. In fact, other sources of variability and bias can occur. Some of these are due to the small sample size observed as part of the measurement procedure. For instance, fibers may be nonuniformly distributed in the sample due to inertial, electrostatic, or other sampling influences. Because the analysis assumes a uniform distribution on the filter, taking a small portion for microscopic analysis can therefore result in significant variations in the reported concentration. In addition, one microscopist may introduce biases relative to other microscopists due to decisions about which particles to count as fibers. When comparisons are made between groups of microscopists, these biases may appear as increased variability in the overall results. The use of established analytical procedures for fiber count analysis is extremely important. This is the only way that results from one laboratory can be compared reliably with those of other laboratories. Microscopist training, proper equipment, and established quality control procedures are all-important components of proper laboratory practice. To ensure

uniformity of application of these analytical procedures, both within-laboratory and interlaboratory sample exchanges are necessary (Abell et al., 1989; Ogden et al., 1986). The usual technique for determining analytical biases, that is, the comparison with a reference method, does not work because an alternate fiber-counting technique that measures the "true" fiber concentration does not exist. Thus, the final test of fiber counting accuracy is the comparison of one's results with those of a group of competent laboratories. Several formal programs of sample exchange have been established for PCM. These include the American Industrial Hygiene Association's (AIHA) Proficiency Analytical Testing (PAT) program (Groff et al., 1991), the U.K.'s Regular Inter-Laboratory Counting Exchange (RICE) program (Crawford and Cowie, 1984), and the International Asbestos Fibre Regular Interchange Counting Arrangement (AFRICA) program (Institute of Occupational Medicine, Edinburgh). For a laboratory performing PCM analyses to establish compliance with U.S. Occupational Safety and Health Administration (OSHA) asbestos fiber exposure level regulations, regular sample exchanges with other laboratories are required. Measurement of fiber concentrations for comparative use within a single study may not need all the components of a complete quality assurance scheme. For instance, if a study is intended only to provide relative fiber concentrations to show differences with time or location, interlaboratory exchange of samples may not be necessary. However, the use of published counting and sample preparation procedures, as well as performance of blind repeat analyses, are important for establishing analytical confidence limits. The Particle Atlas can be consulted to classify a particle on the basis of its physical and optical properties. This reference contains over 600 color photomicrographs of particles from various sources and of known composition (McCrone and Delly, 1973). In this reference, the types of particles are broken down into four categories: (1) wind-blown particles such as fibers and minerals; (2) industrial particles such as abrasives, polymers, fertilizers, and cleaners; (3) combustion particles such as auto, coal-fired, and oil-fired soots; and (4) miscellaneous particles. The authors provide a step-by-step characterization procedure for classifying the particles into one of the four categories. The same reference contains scanning electron micrographs of the same 600 particles shown in the photomicrographs. Sample Preparation and Practical Applications. In a light microscope, either transmitted or reflected light can be used. For transmitted light, the particles are mounted on a glass or quartz slide. An index or immersion oil may be applied to the sample to improve viewing under transmitted light. An aliquot of the particles should be tested with the oil to ensure that no reaction or dissolution takes place. The oil will contaminate the particles prepared in this manner, so subsequent microanalysis on the particles is unlikely using other techniques. Reflected light can be used to view particles collected on an aerosol filter surface, but the particles usually must be >1 urn. Opaque particles and particles with large indices of refraction are most easily viewed in this manner. To overcome this size limitation, some filters can be made transparent or removed entirely to allow viewing with transmitted light. Friedrichs (1986) mentions three ways to transform the filter. Each has its own advantage and disadvantage. In the first method, filters can be treated with index matching fluid. Particles remain on the filter, but liquid particles or particles soluble in the fluid may be dissolved or possibly removed, and particles with a refractive index matching the immersion oil will be difficult to see. Some "filter clearing" agents are given in Table 12-3 (Friedrichs, 1986; LeGuen and Galvin, 1981; Baron and Pickford, 1986). A second method is to dissolve the filter using an appropriate solvent (e.g., polycarbonate filters dissolve in chloroform). In the third method, the filter is ashed, leaving the refractory particles behind. In the last two approaches, the particles no longer have a filter support and must be remounted on a transparent substrate. The number of particles collected on a substrate can be determined and related to the particle concentration in an aerosol. The number of particles is normally determined per

Next Page TABLE 12-3. Membrane Filter Clearing Agents for Light Microscopy

Filter Type Mixed esters of cellulose

Polycarbonate

Clearing Agent

Refractive Index

Acetone vapor/triacetin (AIA method) Dimethyl formamide/Euparal method Acetone vapor/Euparal method Immersion oils Chloroform-dissolved materials

1.44-1.48 1.48 1.48 1.584 or 1.625

Sources: Friedrichs (1986), Le Guen and Galvin (1981), and Baron and Pickford (1986).

viewing area, and, when a number of randomly selected viewing areas are taken together, an estimate can be made of the number of particles on the entire filter surface. This estimate can be related to the airborne particle number concentration (based on sample air volume) as in the case for asbestos number concentration (Asbestos International Association, 1979; Carter et al., 1989). The relevance of depth of field, especially for particle counting, is illustrated by the set of micrographs in Figure 12-4. In these micrographs, certain particles are visible and in focus, while others are difficult to see. Figure 12-4 is an example of a typical LM application that might be employed by an aerosol scientist for examining a filter surface in reflectance. The particles in Figure 12-4 were collected on a filter consisting of mixed esters of cellulose. The filter is slightly bowed in the center as a result of air flow through the filter cassette. This bowing causes a distorted planar surface for the microscopy, leading to poor particle detection due to limited depth of focus. In Figure 12-4a, the upper left of the field of view is in focus, but the lower right is out of focus. As the microscope focus is altered, the lower right regions shown in Figure 12-4b become clear images, and particles in the upper left gradually become fuzzy and indistinguishable. Clearly a shallow depth of field is problematic. The recent development of the confocal microscope provides a different approach by limiting the image formation strictly to those photons scattered within the depth of field. By changing the objective to specimen distance, a series of images can be obtained as "optical sections" of a three-dimensional object. Depth of field usually encountered in LM is not as large as that found in the SEM. Figure 12-5 contains two micrographs of the same field of view of amosite asbestos. The top is a light micrograph and the bottom is an electron micrograph, both with approximately the same magnification. Note that only some of the asbestos fibers are in focus in the light micrograph while the entire electron-generated image is in focus. ELECTRON BEAM ANALYSIS OF PARTICLES Principle of Electron Beam Excitation A schematic of a typical electron beam instrument and some of its analytical functions is shown in Figure 12-6. The source of the electron beam is an emitter such as a heated tungsten filament shaped into a fine tip. The electrons emitted from the filament are formed into a beam and focused by an ion lens system onto the specimen. The electron beam interacts with the atoms of the sample, resulting in the scattering of the beam electrons and the ejection of both electrons and X-ray photons from the specimen. Capabilities

Electron Imaging. Electron imaging that provides the analyst with particle size and morphology is accomplished by two different methods. One of the imaging methods is used in

Inlet: Focus Particles into a Beam Size the Particles Source Region: Ablate/Ionize Particles to Form Ions

Thermal or Radiative Energy Source

Mass Spectrometer: Analyze Ion Masses Computer: Digitize and Store Spectra Fig. 13-1. Block diagram of the typical components in on-line single-particle analysis instruments.

detail below. Likewise, compact high-flux ultraviolet lasers are now available due to improved material quality in mirrors, lenses, crystals, and electrodes. Most current on-line single-particle analyzers share a number of components (Fig. 13-1). First, the aerosol passes through an inlet where the gas is removed and a particle beam is formed. Second, the particles pass through one or more continuous laser beams where they are detected and sized by the light that they scatter. Third, one or more high-energy, pulsed lasers are fired in synchrony with a particle's arrival to ablate and ionize chemical constituents. Fourth, the ions are mass analyzed and the spectrum is stored. Several reviews of real-time single-particle mass spectrometers are available in the literature (Thomson and Murphy, 1994; Johnston and Wexler, 1995; Peter, 1996; Noble and Prather, 2000; Johnston, 2000). A special double issue on on-line single-particle analysis was published in Aerosol Science and Technology in July 2000 (Vol. 33[l-2]). Particle Beam Formation

Inlets to on-line single-particle analyzers serve two purposes: They form a particle beam and drop the pressure from near-atmospheric to the nPa pressures required for operation of a mass spectrometer—a pressure drop of about eight orders of magnitude. The goal of a well-designed inlet is transmitting as many particles to the source region as possible, and the way to accomplish this is to process as much gas as possible. The amount of gas that can practically be processed is constrained by vacuum-pumping costs—the greater the gas load, the greater the energy consumption, and the larger and heavier the pumps that are required. Most inlets are composed of a number of pressure reduction stages, as illustrated in Figure 13-2. Initially there is a primary orifice or capillary that restricts the flow, followed by one or usually more skimmer stages that reduce the pressure. Vacuum pumps remove gas and reduce the pressure between the initial orifice and the first skimmer and then between each subsequent skimmer. The orifices in each stage should be as small as possible to permit the minimum gas flow to the next stage. The orifice sizes cannot be too small, however, or they will block the passage of particles to the source region of the mass spectrometer. Also, the orifices must be precisely aligned to permit passage of the particle beam—the smaller the orifice, the more difficult this alignment becomes.

Stage 0

Stage 1

Stage 2 Particle "Beam Out

Aerosol In

To Vacuum Pumps Fig. 13-2. Typical inlet configuration.

Fluid Mechanical Considerations in Inlet Design

Effective inlets minimize pumping costs while maximizing the particle transmission rate. The pumping costs are minimized by efficiently removing gas and reducing pressure at each skimmer stage. The transmission rate is maximized by directing particles into a beam that has low divergence and is fully contained within the ionization volume of the mass spectrometer (see "Particle Vaporization and Ionization," below). As mentioned earlier, smaller skimmer orifices permit less gas transmission to subsequent stages but also constrain the particle beam size and make alignment more difficult. Let us now use these fluid mechanics principles to estimate the pressure in a skimmer stage. We will use subscript "0" to denote the previous stage, " 1 " to denote the stage we are analyzing, and "2" to denote the next stage (see Fig. 13-2). If we assume that the pressure drop from one stage to the next is large, two simplifications arise. First, the flow at the incoming orifice is choked so that the velocity is close to the speed of sound, Usonic. In rapid contractions such as these, the gas undergoes a near-isentropic expansion resulting in a temperature and pressure at the orifice of 0.83T and 0.53p, where T and p are the temperature and pressure upstream, respectively. Therefore, the mass flow rate into stage 1 is A0p(0.53/7o,0.83To)t/Sonic(0.83r0), where A0 is the area of the orifice and p is the density at a pressure of 0.53/?0 and a temperature of 0.83T0. This flow enters stage 1 and expands to a much larger volume due to the substantially lower pressure. Most of the air entering stage 1 leaves through the vacuum pumps drawing on this stage, which leads to the second assumption. In a well-designed inlet, the aerosol volume transmitted to the next stage should be negligible compared with that being pumped away. Let us assume that the pump draws a constant volume flow rate, Vpump, that the pressure change in the tubing between the pump and the stage is negligible, and that due to heat transfer through transfer tubing the air is at the ambient temperature, near 300K, at the pump. Then the mass flow rate drawn by the pump is p(pi,ramb)VpUmp. Equating these two to conserve mass and employing the ideal gas equation of state gives an expression for the pressure ratio between stage 0 and 1:

(13-1) We desire a large pressure drop from stage 0 to 1, but the first two terms are on the order of 1 so do not help much. The last term is the ratio of the volume flow rate into stage 1 divided by the volume flow rate withdrawn. A small orifice and a large pump yield a large pressure decrease. Particle Beam Transmission Considerations in Inlet Design

It is difficult to efficiently form and transmit a particle beam through inlets with large pressure drops. Particle transmission goals include minimizing clogging and accurately sizing particles, both of which must be accomplished while removing the carrier gas. Some inlet designs do not place much emphasis on aerodynamic particle sizing. This makes the design easier because there is one less consideration, but then less accurate sizing techniques must be employed, such as light-scattering intensity (see Chapter 15). The light scattered from particles is a function of the wavelength of the light, the size of the particle, and its shape and composition, but is limited to particles greater than about 200 nm. With conventional light intensity measurements, the morphology and composition confound accurate sizing. In singleparticle analysis, the composition is obtained, so it may be possible to perform accurate sizing solely with light scattering if composition information is employed too. Currently, scattered light sizing is employed in some instruments to obtain an indication of particle size (e.g., Murphy and Thomson, 1995). Aerodynamic sizing is used for more accurate particle sizing but imposes constraints on the inlet design. Two approaches have been taken to aerodynamic sizing in inlets: (1) transmitting a wide particle size range and imparting a size-dependent velocity to the particles and (2) transmitting a narrow particle size range that can be tuned so that a known particle size is transmitted. Transmitting a Wide Particle Size Range—Capillaries and Aerodynamic Lens Arrays. Two techniques are used for transmitting a wide particle size range—capillaries and aerodynamic lens arrays. Capillaries are long, thin tubes that are effective at forming particle beams. Aerodynamic lenses are contractions in the flow that focus a range of particle sizes. An array of these lenses in series is able to focus a wide particle size range (Liu et al., 1995a,b). Capillaries were used in some of the earlier inlets but are becoming less common because they have two disadvantages relative to aerodynamic lens arrays—they clog, and they impart similar velocities to the particles transmitted. Let us examine these issues more carefully. Consider a capillary inlet. Longer capillaries impose more friction on the flow, resulting in a lower volume flow rate through the inlet. For instance, the volume flow rate entering a capillary 160 urn in diameter and 12 mm long is about lOOml/min, giving a velocity of about 80m/s, whereas the velocity through a sharp orifice is sonic—340m/s (Mallina et al., 1999). The lower velocity yields a lower volume flow rate into subsequent stages and consequently lower pumping costs and a lower hit rate. There are two Stokes numbers that are important to characterizing the performance of the capillary. The Stokes number at the entrance to the capillary determines the particle sizes that are deposited on the capillary walls, while the Stokes number based on the capillary length determines the capillary's aerodynamic sizing capabilities. Table 13-1 contains the particle stokes numbers for a range of particle sizes. If we assume that the inlet is much wider than 160 urn before the capillary, then the gas velocity changes from near zero to 80 m/s. Likewise, the flow accelerates from a low value to 80 m/s over about the diameter of the capillary because the velocity is much lower just a few capillary diameters away from the capillary entrance. Using Stkradia\ - SId = Urp/d, where

TABLE 13-1. Particle Radial and Axial Stokes Numbers for a Typical Capillary dp (|Lim)

Stkiadial

&£axial

0.1 1 10

0.05 1.8 120

0.002 0.076 5.0

U= 80m/s and d = 160 um, gives the Stokes number for radial acceleration as a function of diameter. Stokes numbers much larger than one indicate that the particles travel in straight lines and are not focused into beams. As a result, particles larger than about 1 um do not make it through the capillary and, more importantly, many hit the walls of the capillary, resulting in clogging (see Table 13-1). The capillary entrance also focuses particles that have a Stokes number near 1. Simulations of this capillary show that particle diameters about 0.75 um are optimally focused to a beam (Mallina et al., 1999). Particles with a Stokes number much larger deposit on the capillary walls. Particles with a Stokes number somewhat smaller are focused less well, while particles much smaller follow the fluid streamlines. At the exit of the capillary, the pressure drops dramatically, leading to much larger mean free paths for the gas, and the particles that are transmitted exit in straight-line trajectories. In fact, this is how capillaries are able to transmit a wide particle size range. Particles with a Stokes number near and below 1 at the entrance conditions flow down the capillary. Because it has a small diameter, the pressure drop along the capillary is significant. The lower exit pressure means less drag on the particles due to the pressure dependence in the Cunningham correction factor. Thus particles with a Stokes number greater than 1, evaluated at the capillary exit conditions, maintain a linear trajectory whereas particles whose exit Stokes numbers are less than 1 act like the gas and diverge. The difference between the entrance and exit conditions of the capillary dictate which particle size range is focused into a beam. As particles traverse the capillary, they are also accelerated axially down the capillary, but now the flow accelerates over the whole length of the capillary, not just near the entrance, so the length scale is much larger than the radial acceleration at the entrance. Stokes numbers for axial acceleration in this capillary are given in Table 13-1. The goal of many inlets is to impart an aerodynamic diameter-dependent velocity to the particle so that the particle size can be inferred from this velocity. In capillaries, the particle accelerates over the length of the capillary, a relatively long time. Particles whose axial Stokes number is significantly less than 1 are accelerated to the speed of the gas. Particles larger than this limit have a size-dependent velocity. Note that very short capillaries, that is, orifices, are the best for imparting a size-dependent velocity over the largest particle size range, which is why this configuration is used in aerodynamic particle sizing instruments. This issue will be alluded to in the next section. As a result of clogging problems and limitations on aerodynamic sizing, capillaries are being used less frequently in favor of aerodynamic lens arrays. Aerodynamic lenses consist of holes in plates and in many ways can be thought of as short capillaries. They focus particles with a Stokes number near 1. Lenses have the additional advantages that (1) they are relatively sharp compared with capillaries so that little deposition may occur, (2) they are usually relatively large so that deposition does not lead to significant clogging, and (3) their short length axially accelerates particles to different velocities. Single lenses only focus a narrow range of particle sizes, so transmitting a wide range can be accomplished with a series of matched lenses, as illustrated in Figure 13-3. The lenses must

FIg. 13-3. Aerodynamic lenses focusing particles to the center line (From Liu et al., 1995a, Fig. 8, with permission from Aerosol Science and Technology.)

operate at low velocities so that (1) the pressure drop across the lens is not too great and (2) the deceleration of the flow after the lens does not cause turbulence that would mix the particles back into the flow. Keeping eddies from forming in the flow is only possible at low Reynolds numbers, but this requirement is in conflict with particle focusing because large velocities are needed to accelerate the particles to the center line. The solution to both problems lies in the pressure. The Reynolds number, Re = Udp/jj,, is proportional to pressure, so low pressure gives a lower Reynolds number for the same velocity and orifice diameter. The viscosity, fx, is nearly independent of pressure. Lower pressure also enables smaller particles to be focused at the same velocity and orifice diameter because the Cunningham correction factor in the Stokes number is larger. That is, the lower pressure reduces the drag on the particles because the noncontinuum effect becomes more important. In the aerodynamic lens array of Liu and coworkers (1995a,b), a pressure of about 0.26 kPa (2torr) was employed, which at standard temperature gives an air mean free path of about 20 um. By using a series of well-matched lenses, a wide range of particle sizes can be focused to the center line. The pressure, velocity, and size of the first lens focuses particles of a given size, say, dpU close to the center line, and a range of particles around this size are also moved closer to the center line. The next lens is somewhat smaller, focusing smaller particles, say, dP2 < dpi. The larger particles that were brought very close to the center line by the first lens stay there. After a series of such lenses, carefully matched to each other, a relatively wide range of particle sizes can be focused to the center line (see, e.g., lens configuration e in Table 1 in Liu et al., 1995b).

Once the particles are focused to the center line, they pass through a choked orifice and the skimmer stages are traversed. Because the particles are close to the center line, they have a narrow divergence as they pass through the skimmers, but have a range of velocities due to their acceleration through the first choked orifice. Thus the particles have a range of velocities that can then be used to assess their size. This is discussed in more detail below. Transmitting a Narrow Particle Size Range—Sharp Orifices. Whereas capillaries and lens arrays can be configured to transmit a range of particle sizes, single sharp orifices can be used to select only a narrow particle size range. As we have already discussed, passing an aerosol through a sharp orifice where the flow is choked focuses certain particles (Dahneke et al., 1982; Fernandez de Ia Mora and Riesco-Chueca, 1988; and references therein). The focused particles have a Stokes number around 1, the exact value depending on the nozzle geometry and the distance from the nozzle to the focal point. This principle can be employed to focus only a narrow range of particles, which has a few advantages and disadvantages relative to nozzles that transmit a wide size range. The primary disadvantage is that the instrument must then be operated in a scanning mode. That is, the operating conditions must be adjusted to focus particles of different sizes, one size at a time. This limits the particle sampling rate because only a narrow particle size range has been selected by the inlet. The primary advantage is that it enables particles to be sized that are quite small—10 nm or smaller. The orifice focuses only a narrow range of particle sizes to the source region of the mass spectrometer, but this size range can be selected by adjusting the pressure upstream of the nozzle. Let us term the Stokes number that is focused Stk{ and approximate the Cunningham correction factor, Cc = 1 + 1.66(2A/dp), to give (13-2) m

where dp is the particle diameter that is focused, dp,max = (lSjjdnStk{/ppUson\c) is the maximum particle size that the orifice can focus, A is the gas mean free path, pp is the particle density, the velocity through the orifice, t/sonic, is sonic since the flow is choked, n is the viscosity of air, and dn is the orifice diameter. The mean free path and the viscosity must be evaluated at the pressure and temperature in the orifice, which are 0.53p and 0.83 T, where p and T are the upstream pressure and temperature, respectively. Therefore, the size that is focused can be adjusted by altering dp,max or A. Most of the parameters that comprise dpmax are not adjustable, such as the viscosity and speed of sound in air and the particle density. The focused Stokes number, Stkf, can be adjusted somewhat by changing the geometry of the orifice and the distance of the focus from the orifice, but substantial departures from Stkf = 1 are difficult to achieve. The orifice diameter can be changed because a smaller orifice leads to a smaller dp,max and consequently a smaller dp, but a smaller dn also means that the volume of air brought through the orifice is lower. dp,max changes linearly with the square root of dn, but the volume flow rate varies with its square. Thus, reducing the orifice diameter leads to sampling of smaller particles but at a much reduced volume flow rate. The only remaining parameter than can be adjusted is the gas mean free path, which may be varied over many orders of magnitude by suitably controlling the upstream pressure, p (see Chapter 4 for more on this dependence).

PARTICLE DETECTION Particles transmitted through the inlet enter a mass spectrometer where they undergo chemical analysis. Because it is not known ahead of time when a particle will arrive, a particle

detection step is normally required to achieve a high analysis rate. In principle, particle detection could be performed with a variety of techniques, which are discussed in Chapters 15 to 20. Light scattering is the most common method. A continuous laser beam intercepts the particle beam emerging from the inlet. The scattered radiation from a single particle indicates the arrival of that particle in the mass spectrometer. Particle detection by an independent method such as light scattering has two important advantages. First, the arrival of a particle can be synchronized with the chemical analysis sequence. Without synchronization, many or most particles may pass through the system without being analyzed. Second, the detection step provides an opportunity to size the particle before analysis (see "Particle Sizing," below). The main disadvantage of light scattering is that particles smaller than the wavelength of light are difficult to detect. For this reason, instruments that incorporate particle detection by light scattering (with a visible laser beam) are generally limited to particles greater than about 200 nm in diameter. A fundamentally different detection strategy is to simply use the chemical analysis step as a means of particle detection. When a particle is vaporized and ionized, a burst of ions is produced over a short period of time. If the ion signal rises above a background or threshold level, the particle is detected because its mass spectrum is detected. The spectrum detection approach has the advantage that individual particles down to 10 nm diameter, and possibly smaller, can be analyzed (Reents et al., 1995; Carson et al., 1997a; Ge et al., 1998). However, this approach has the potential disadvantage that the probability of detecting very small particles may be composition dependent. That is, particles containing material that is easy to ionize may give larger ion signals and be more easily detected than particles containing material that is difficult to ionize (Kane and Johnston, 2000). In a particle stream containing a range of particle compositions, caution must be used when interpreting the number of particles detected by this approach. Depending on the ionization method used, this approach may also have the disadvantage of poor synchronization between the arrival of a particle and initiation of the chemical analysis sequence. For ionization methods that operate in a continuous mode (see "Particle Vaporization and Ionization," below), synchronization is not required. However, for pulsed ionization methods such as laser ablation, the laser is "on" for only a short fraction of the time. If there is no synchronization between the arrival of a particle and firing of the laser, most particles pass through the mass spectrometer while the laser is "off" and no spectrum is detected. In principle, it should be possible to overcome the synchronization problem by trapping one or more particles temporarily in an electrodynamic trap and injecting them into the mass spectrometer when the ablation laser fires (Frankevich et al., 1998).

PARTICLE SIZING

As with particle detection, sizing is often performed using similar techniques employed in other instruments. Particle sizing by the amplitude of the light scattered is discussed in Chapter 15. The amount of light scattered by a particle may depend on its shape and composition. In conventional instruments, this information is not available, which leads to substantial uncertainty in the sizing. In single-particle analysis instruments, the composition is available, and from the composition the morphology can often be estimated, so it may be possible to use the amplitude of the scattered light along with the composition to obtain a more accurate assessment of size. Such approaches have not yet been attempted. Aerodynamic sizing instruments are discussed in Chapters 16 and 17. Some singleparticle analysis instruments accelerate the particle in the inlet to velocities that are aerodynamic diameter dependent. Detection and sizing are done using one or two lasers. A number of design trade-offs must be chosen to employ this technique in single-particle analysis. If the particles are not accelerated rapidly, they may have similar velocities, so the

size may be difficult to distinguish. Small differences in velocity can be ascertained by using two detection lasers spaced substantial distances apart from each other and then accurately timing the arrival of the two scatter signals. Unfortunately, particle beams invariably diverge so that a significant fraction of the detected particles may not be analyzed. The TSI Aerodynamic Particle Sizer and Aerosizer use a rapid acceleration to impart a wide range of velocities to the particles and then are able to position the two detection lasers close together. These instruments do not, however, need to form a well-focused particle beam because the detection occurs very close to the nozzle, something not possible in the inlets discussed here because of the intervening skimmer stages. Sizing by selectively aerodynamically focusing particles of a known size to the source region of the mass spectrometer was discussed in "Introduction" under "Particle Beam Transmission Considerations in Inlet Design" (Mallina et al., 2000). Aerodynamic focusing is able to size particles over a wide size range, but is limited by the ability to detect, usually by spectrum detection, the particles that are focused. Aerodynamic particle selection also forces the instrument to be operated in a scanning mode whereby particle sizes must be analyzed in sequence. Another sizing technique employed in single-particle analysis is particle beam chopping (Jayne et al., 2000). Here particles are aerodynamically accelerated to velocities that are size dependent, and a beam is formed. Then the particles pass through a chopper that only transmits a narrow packet of particles in time. The particles are then analyzed with a continuous ionization method, and the time delay between chopping of the beam and the detection of ions is used to infer the particle velocity and concomitant size. Similarly, two choppers could be used to transmit only particles of a given velocity for subsequent analysis. PARTICLE VAPORIZATION AND IONIZATION Chemical analysis is performed by vaporizing a particle and ionizing the atomic and molecular products. The vaporization and ionization processes can be performed simultaneously in a single step or sequentially in two steps, and in a continuous or pulsed manner. Continuous Ionization Methods

Continuous methods involve directing the particle beam onto a heated surface (filament) where atomic and molecular species are vaporized. For single-particle work, the filament is heated to a high enough temperature to flash vaporize the particle in a short period of time (Stoffels and Allen, 1986). If the filament is set to a temperature that is too low, vaporization occurs over a long time scale, causing the signals from multiple particles to overlap. Ionization can be performed by either surface ionization or electron ionization. In the surface ionization mode, ions produced directly from particle vaporization at the filament are detected. The ionization efficiency is determined by the difference between the work function of the filament surface and the ionization energy of the atomic or molecular species vaporized from the particle. Typically, only atomic and molecular species having ionization energies less than about 8eV are efficiently ionized. For this reason, most applications of surface ionization involve the detection of alkali metals in particles. Alternatively, neutral species vaporized from a particle can be ionized with an electron beam. Electron ionization is more broadly applicable because most chemical components can be ionized with reasonable efficiency. For example, ammonium salts give ions such as NH2+ and NH3+; sulfate salts give ions such as S+, SO+, and so forth. Organic molecules give positively charged molecular ions (the original molecule minus an electron) and fragment ions. The molecular ion gives a direct measure of the molecular mass, while fragment ions indicate molecular structure. When many chemical components are present in the same particle, the mass spectra of the individual components overlap, making interpretation of the

spectrum of the entire particle difficult. In these cases, it may be possible to replace electron ionization with a soft ionization method such as chemical ionization or photoionization. The soft ionization methods induce less fragmentation of the molecular ions, making the spectrum easier to interpret. An advantage of the thermal vaporization electron ionization approach is that individual particles can be completely vaporized and analyzed. Thus, the absolute ion signal intensity can give a measure of particle mass, and both surface and interior chemical components can be detected. Surface ionization and thermal desorption electron ionization produce strong ion signals from materials that can be vaporized upon heating to several hundred degrees. Refractory compounds and other nonvolatile materials are vaporized only slightly or not at all and therefore give only weak ion signals. Extensive reviews of continuous ionization methods for particle analysis (Stoffels and Allen, 1986) and organic molecular characterization by electron ionization (McLafferty andTurecek, 1993) are available. One-Step Laser Ablation and Ionization

Because the arrival of each particle in the mass spectrometer is a discrete event, pulsed ionization methods can be used to create much larger ion signals from individual particles than continuous methods. However, the pulsed methods must be synchronized to the arrival of a particle to achieve a high efficiency for particle analysis. Pulsed ionization can be performed in a single step where a single laser beam ablates the particle and ionizes vaporized material or in two steps where one laser beam ablates the particle and a second laser beam ionizes the vaporized material. Wavelengths from the infrared to the vacuum ultraviolet have been investigated for single-step ablation and ionization (Thomson and Murphy, 1993; Thomson et al., 1997). The threshold irradiance for ion production strongly depends on laser wavelength. With infrared radiation, the threshold irradiance is prohibitively high for analysis of individual micrometer and submicrometer sized particles. With ultraviolet radiation, intense ion signals can be obtained by ablating a single particle with a single laser pulse. The threshold irradiance for ion production depends on chemical composition. For example, aqueous particles generally have much higher threshold irradiances than dry particles (Neubauer et al., 1997,1998). The ions produced by one-step laser ablation and ionization correlate with chemical composition in a similar manner to those in a laser microprobe mass spectrometer (see Chapter 12; see also Kaufmann, 1986; Wieser and Wurster, 1986). Laser ablation is a robust ionization method. Strong ion signals can be obtained from most types of materials: refractory and semivolatile, organic and inorganic. The main difference between off-line and on-line laser ablation is that on-line analysis is able to detect semivolatile components such as particulate phase water (Neubauer et al., 1997) and methanesulfonic acid (Neubauer et al., 1996). In a laser microprobe experiment, semivolatile components evaporate from particles as they are mounted on a substrate and inserted into the mass spectrometer. In real-time mass spectrometry, particles experience a rapidly changing environment in both temperature and pressure as they pass through the inlet. The net effect is usually condensation induced by the gas expansion from the inlet (Mallina et al., 1997). Although re-evaporation could occur in the vacuum downstream of the inlet, the temperature decrease induced by the expansion and the short transit time to analysis inhibit evaporative losses. Several decades of laser microprobe research have provided a general understanding of the characteristics of laser ablation mass spectra for real-time analysis. First, positive ion spectra primarily give information on cationic materials and organic compounds, while negative ion spectra primarily give information on anionic materials. Second, metals are generally detected with high sensitivity, while nonmetals are generally detected with low sensitivity. Third, absolute ion signal intensities are irreproducible and vary greatly from pulse to pulse.

TABLE 13-2. Common Ions in Laser Ablation Mass Spectra Positive Ion Spectrum (m/z)

Negative Ion Spectrum (m/z)

Elemental carbon Organic carbon Oxygenated carbon NH4+ H2O NaCl NO3" SO 4 2 Metal oxides (Ca, Fe, etc.) a

Low intensity. ^ Very low intensity.

Finally, chemical components located at or near the particle surface tend to exhibit enhanced intensities in laser ablation mass spectra (Carson et al., 1997b). This enhancement is due to the fact that the particle core is incompletely ablated (Weiss et al., 1997). Table 13-2 gives examples of common ions observed in laser ablation mass spectra of ambient particles. Two important principles are reflected in this table. First is the complementary nature of positive and negative ion spectra. Because different types of ions are prominent in each spectrum, the acquisition of both polarity spectra from a single particle gives a more complete representation of chemical composition, particularly for inorganic components, than either polarity alone. Whereas it is difficult or impossible to acquire both spectra from the same particle in a laser microprobe experiment, it is relatively easy to do with real-time mass spectrometry. Second is the difficulty of identifying organic components in particles. Elemental carbon produces carbon cluster ions (Cn+'') in both positive and negative ion spectra. In contrast, organic compounds can produce carbon-hydrogen clusters in positive ion spectra (CxHy+). Thus, elemental and organic carbon can often be distinguished. However, the laser ablation process normally causes extensive bond breaking in organic molecules. Molecular ions are rarely observed, and the specific organic compounds present in the particle are difficult or impossible to identify. The only exceptions are certain aromatic molecules (particularly PAHs) that have low ionization energies and high fragmentation energy thresholds. Figure 13-4 shows representative positive and negative ion spectra patterns of different types of ambient particles (Hinz et al., 1999). Two-Step Laser Ablation and Ionization

Some but not all of the drawbacks of one-step laser ablation and ionization can be overcome through the use of two separate lasers, one to vaporize and the other to ionize (Morrical et al., 1998; Zelenyuk et al., 1999). Vaporization is typically performed with an infrared laser having an irradiance below the threshold for ion formation. A second laser is fired approximately 1 jis later to photoionize vaporized species. The second laser operates typically in the ultraviolet region, and molecular species are ionized upon absorption of two or three photons. It is also possible to use coherent vacuum ultraviolet radiation to photoionize molecular species with a single photon (Van Bramer and Johnston, 1992).

secondary aerosol (1)

soot with secondary components (2)

salt (3)

biogenic soot (4)

mineral dust (5)

Fig. 13-4. Representative spectra patterns of individual ambient particles.

A direct comparison of one-step and two-step ionization of single particles shows that indeed aromatic molecular ions are enhanced by the latter method (Morrical et al., 1998). In addition, the large optical penetration depth of infrared radiation allows micrometersized particles to be completely ablated, suggesting that artifacts due to an inhomogeneous chemical composition will be reduced (Zelenyuk et al., 1999). These advantages are offset by the fact that the two-step method, under conditions optimized for aromatic molecular ion formation, does not produce strong ion signals from many inorganic species or aliphatic organic molecules. Maintaining optical alignment is also more difficult with the two step method.

MASS ANALYSIS

Four types of mass analyzers have been used to obtain single-particle mass spectra: magnetic sector, quadrupole, time of flight, and quadrupole ion trap. Magnetic sector instruments have found only limited use and are not discussed. Quadrupole Mass Analyzers

Quadrupole instruments were used almost exclusively in early single-particle work. The analyzer consists of an entrance aperture, four hyperbolic rods, and an exit aperture (March, 1989). As the name suggests, a combination of time-varying radio frequency (rf) and constant potentials are applied to the rods to generate a quadrupole electric field. The rf amplitude and constant potential are selected to allow only one m/z to pass through the exit aperture at a given time. A mass spectrum is obtained by scanning the rf amplitude and constant potential to allow different m/z ions to sequentially pass through the exit aperture and strike the detector. Quadrupole mass analyzers are rugged, inexpensive, and easy to operate, making them ideal for many types of field measurements. When applied to real-time particle analysis, however, they are limited in that the spectrum cannot be scanned on the time scale that a burst of ions is produced from a single particle. If single-particle analysis is desired, then the analyzer must be tuned to transmit a specific m/z ion. Therefore, only one chemical component can be monitored from any given particle. Because of this limitation, other types of analyzers are more advantageous for single-particle analysis. Time-Of-Flight Mass Analyzers

The most common analyzer used in current single-particle instruments is the time-offlight mass analyzer because it is inexpensive, rugged, and easy to construct in-house (Cotter, 1997). Time-of-flight analyzers were not used in the early years of real-time single-particle analysis because of limitations in spectral acquisition. Recent advances in the performance of high-speed digitizers and computers has made this the analyzer of choice for many applications. A schematic of a simple linear time-of-flight analyzer is shown in Figure 13-5. The analyzer consists of a source region, a secondary acceleration region, a field free drift region, and a microchannel plate detector. Ions produced at a specific point in time and space are extracted from the source region into the field free drift region where they are separated by time of flight. Because all ions are accelerated to the same nominal kinetic energy, different m/z ions will achieve different velocities and reach the detector at different times. Low m/z

Source region

Drift tube

Detector

Fig. 13-5. Ion paths in a time-of-flight mass spectrometer.

ions will achieve the highest velocities and reach the detector first. High m/z ions will achieve the lowest velocities and reach the detector last. A mass spectrum is recorded by simply digitizing the ion signal from the detector as a function of time after the laser pulse. In practice, all ions of a given m/z do not have the same kinetic energy and therefore reach the detector at slightly different times, causing peak broadening. The main contributors to peak broadening are spatial and velocity broadening. An effective approach to reduce broadening is to incorporate a reflecting field into the flight path (Fig. 13-6). In the reflector, ions are decelerated and turned back in the direction from which they came. Ions having a high kinetic energy penetrate further into the reflecting field than ions having a low kinetic energy. The additional time that high kinetic energy ions spend in the reflecting field compensates for the shorter time they spend in the field free regions. In this manner, all ions of a given m/z regardless of kinetic energy can be made to strike the detector at nearly the same time. For real-time single-particle analysis, the particle beam traverses the source region perpendicular to the drift tube. Ionization is typically performed with a pulsed laser timed to coincide with arrival of the particle and triggering of the digitizer. This approach has three important advantages for single-particle analysis. First, the time-of-flight analyzer is relatively simple in design and easy to construct in-house for specialized instrumental configurations. Second, the time-of-flight analyzer is able to separate and detect all mass to charge ratios in the burst of ions produced from a single particle. Thus, all chemical components encompassed by the spectrum can be detected simultaneously. Third, positive and negative ions from a single particle can be simultaneously analyzed by putting dual drift tubes on either side of the source region. Positive ions are accelerated into one tube, while negative ions are accelerated into the opposite tube. This arrangement produces two separate spectra for each particle and allows a wider range of chemical components to be characterized. The main difficulty with time-of-flight mass spectrometry is dynamic range. Digitizing boards and oscilloscopes are able to digitize the microchannel plate current at rates of 500MHz and higher with nominal 8-bit resolution. However, only 6- or 7-bit accuracy is achieved in practice (1 part in 64 to 128). As a result, the digitization error for small signals can be a substantial fraction of the total. To partially alleviate this problem, logarithmic amplifiers can be interposed between the microchannel plate and digitizer. Logarithmic amplification can give a higher dynamic range and spread the digitization error evenly over that range (Murphy and Thomson, 1995). This is a significant advantage for ion formation by laser ablation because particle to particle variations of the absolute signal intensity can be an order of magnitude or more. However, these amplifiers have a 30MHz bandwidth, which can result in a loss of mass resolution.

Resolution Enhancement of a Reflectron Time-of-Flight Mass Spectrometer

At minimized for same m/z ions having different initial velocities Fig. 13-6. Ion paths in a reflecting-field time-of-flight mass spectrometer. • , O = ions, all with the same m/z, having higher and lower initial velocity, respectively.

Ion Trap Mass Analyzers

Another method capable of obtaining a complete mass spectrum from a single particle is the quadrupole ion trap (March, 1997). Like the time-of-flight mass analyzer, the quadrupole ion trap can obtain a complete mass spectrum from the burst of ions from a single particle. The analyzer consists of three hyperbolic electrodes: two end caps and a ring electrode. Ions are trapped in a potential well created by applying a sinusoidal potential to the ring electrode. Ions are trapped across a broad m/z range, and each m/z is successively ejected from the trap to the detector by application of a second sinusoidal potential to the end caps. Quadrupole ion trap analyzers are compact, robust, and well suited for field measurements. They have not been as popular as time-of-flight analyzers because they are more difficult to construct inhouse (commercial models are available), and it is more difficult to implement simultaneous positive and negative ion detection. Quadrupole ion traps have a significant advantage over time-of-flight analyzers in their ability to perform tandem mass spectrometry. This capability is most useful for identifying organic molecules in complex samples. A specific m/z can be isolated in the ion trap by ejecting all other m/z ions by application of the various resonant frequencies. Once isolated, ions at the selected m/z can then be dissociated by applying a low-amplitude potential at the resonant frequency to the end caps. The amplitude is too low to eject the ion from the trap, but it is sufficient to increase the ion kinetic energy. Ions gain internal energy through collisions with background gas molecules in the trap and eventually dissociate. The dissociation products remain trapped in the potential well and are subsequently analyzed by resonant ejection. An example of the power of this approach is given by Gieray et al. (1997), who used laser ablation tandem mass spectrometry to characterize individual bacteria sampled in real time.

DATA HANDLING AND INTERPRETATION The preceeding section discusses how chemical components in an aerosol particle can be determined from its mass spectrum. How can composition data for individual particles be transformed into useful information about the atmosphere? The simplest approach is to identify individual components or combinations of components that indicate the source of the particle and/or the chemical transformations it has undergone. For example, sea salt particles have large signal intensities of ions indicating sodium, potassium, and chloride; crustal matter has large signal intensities of ions indicating calcium and/or iron; secondary aerosols have large signal intensities of ions indicating ammonium, nitrate, and/or organic components; soot has large signal intensities of carbon cluster ions (Cn+); and so forth. In early experiments, particles were grouped according to their chemical composition (and size) by manual inspection of the spectra. This can be a daunting task because tens of thousands of particle mass spectra might be obtained in a single experiment. Therefore, automated methods of particle classification are attractive. Single-particle spectra can be classified into groups using methods such as principal components analysis, fuzzy cluster analysis, and neural network analysis. Indeed, similar approaches have been used for over a decade to interpret single-particle compositions obtained by electron and laser microprobe methods (see Chapter 12). Typically, these algorithms group particles without any a priori assumptions about their possible identities. Computers have become sufficiently fast that classification of individual particle spectra can be performed in near real time (Hinz et al., 1999; Song et al., 1999). Once a sufficient number of particles have been grouped, each group is interpreted by manual inspection of the centroid mass spectrum for that group. By counting the number of particles in each group, the relative loading of each type of particle in the aerosol can be

determined. This loading can then be followed as a function of particle size, time of day, or location. It should be emphasized that the chemical information derived from a single-particle mass spectrometer is fundamentally related to particle number rather than particle mass. In essence, single-particle mass spectrometers count particles of a given composition type and size. If the aerosol is externally mixed, then the mass of each chemical constituent in the aerosol can be estimated from the number and size distributions of particles in the group containing this constituent. If the aerosol is internally mixed, then mass composition cannot be obtained. PUTTEVG IT ALL TOGETHER—SELECTED INSTRUMENTS

As discussed above, many possibilities exist for particle detection, sizing, vaporization, ionization, and mass analysis. The specific techniques that are incorporated into an instrumental design depend on which characteristics are most important for the intended application. For this reason, many types of instruments have been reported. Table 13-3 lists an instrument classification scheme based on how coupled size/composition measurements are made for single particles. For each measurement (size and chemical composition), particles can be analyzed in a sequential or parallel manner. Sequential analysis means that only one type of particle (i.e., one particular size and/or composition) can be analyzed at a time. In this case, the instrument must be "scanned" in some way to obtain information on all possible combinations of particle size and composition. Parallel analysis means that all particle sizes and/or compositions can be analyzed simultaneously. From a data throughput perspective, the least desirable instrument is one that must sequentially scan through both particle size and composition. It is not surprising that no such instrument has been reported. In principle, the most desirable instrument is one in which the full range of particle sizes and compositions can be determined in parallel. Indeed, several field instruments are based on this approach. In some cases, instruments that perform one measurement—either particle size or composition—sequentially and the other in parallel can equal or surpass the performance of a fully parallel instrument. If the sequential method allows particles to be transmitted and/or detected with greater efficiency than the parallel alternative, then the higher analysis rate can compensate for the inherent inefficiency of a sequential measurement. Table 13-3 gives examples of instruments in each category. Several of these instruments are discussed below.

TABLE 13-3. Classification of Real-Time Single-Particle Mass Spectrometers Size

Chemical Composition

Examples

Parallel

Parallel

Murphy and Thomson (1995), Mie Scatter/LA-TOFMS Gard et al. (1997), laser velocimetry/LA-TOFMS Yang et al. (1996), laser velocimetry/LA-ITMS

Parallel

Sequential

Jayne et al. (2000), velocimetry/TD-EIMS

Sequential

Parallel

Mallina et al. (2000), dynamic focusing/LA-TOFMS Hinz et al. (1996), laser velocimetry/LA-TOFMS Weiss et al. (1997), laser velocimetry/LA-TOFMS

Sequential

Sequential

None

ppQ^Q-Hinrlo mnnitnr Beam splitter

Energy meter

Inlet

Pulse Steering region plates

Grid detector

Micro-channel plate detector

Avalanche photo-diode Backing plate

Focusing lens

HeNe \aser

Baffle

Extraction plate & mirror

Beam combiner Focusing lens

Exc\roer \aser

Mirror

Altenuators

Fig. 13-7. The NOAA Aeronomy Laboratory mass spectrometer.

National Oceanic and Atmospheric Administration (NOAA) Aeronomy Laboratory

Figure 13-7 shows the first single-particle mass spectrometer used for ambient measurements (Murphy and Thomson, 1995). A similar design was used by the same group to perform airborne measurements (Murphy et al., 1998). Particles enter the mass spectrometer through a capillary inlet. In the source region, individual particles pass through a continuous wave helium-neon laser beam and are detected by light scattering. The sensitivity of particle detection is maximized with an elliptical mirror that focuses scattered radiation over a wide solid angle through a spatial filter (i.e., small aperture) onto an avalanche photodiode. The elliptical mirror also serves as the first acceleration plate of the time-of-flight mass spectrometer. The height of the scatter pulse provides a crude measure of particle size. The scatter pulse also triggers an excimer laser that fires promptly (within 1 jus) after receiving the trigger pulse. During this time period, the particle barely moves, so only a slight vertical offset between the helium-neon and excimer laser beams is needed for the excimer pulse to "hit" the particle. Ions produced by laser ablation of the particle are accelerated into a drift tube and detected with a grid detector/microchannel plate detector combination that provides a dynamic range of over four orders of magnitude in signal intensity. The advantage of the experimental configuration in Figure 13-7 is the compact inlet and optical design. Virtually all particles detected by light scattering are "hit" by the ablation laser. Both particle size and composition are determined in parallel, allowing for fast characterization of the aerosol. Even though less than 1 in 1000 particles entering the inlet reaches the source region, those particles traversing the laser beams are detected and ablated with very high efficiency. In an urban environment, many particles per second may be analyzed. University of California, Riverside

Figure 13-8 shows an alternative design (Gard et al., 1997) that is commercially available (TSI). Particles enter the mass spectrometer through a conical inlet terminating in a short capillary. Particles along the center line of the inlet pass through two differentially pumped stages and intercept two continuous wave laser beams (Prather et al., 1994). A scatter pulse

Inset 1 Nozzle close-up

Lifting mechanism

Particle trajectory Inset 2 Cross sectional view Ball valve

Beam probe

Reflectron adjustment rod

MSP Detectors

Light horn

PMT

Elliptical mirror

Ion source

Flight tube

Reflectron

Linear MSP detector

Nd: YAG Laser Fig. 13-8. The University of California, Riverside, mass spectrometer.

is detected as the particle passes through each laser beam. The time delay between the two pulses gives the velocity of the particle from which the aerodynamic diameter can be determined. After leaving the light-scattering region, the particle enters the source region of the mass spectrometer where it is "hit" by the ablation laser. The continuous laser beams are configured orthogonal to each other, and the time delay between the two scatter pulses is used to synchronize the arrival of the particle in the source region with the firing of the ablation laser. In this way, a majority of the particles detected by light scattering are ablated and analyzed. Dual time-of-flight mass analyzers simultaneously obtain positive and negative ion spectra for each particle that is ablated. The advantages of this configuration are precise aerodynamic sizing and greater opportunities for chemical characterization because both polarity mass spectra are obtained. As with the previous design, most particles entering the inlet are lost. However, the small fraction of particles that intercept the three laser beams are detected and analyzed with high efficiency, yielding hit rates of several per second in urban environments. University of Delaware

Figure 13-9 shows a third type of instrument design (Mallina et al., 2000). Here, the aerosol is sampled through a dynamic focusing inlet that collimates only a narrow size range of particles for a given set of conditions. The size range selected passes through several stages of differential pumping into the mass spectrometer, where laser ablation occurs. The ablation laser is aligned collinear with the particle beam to maximize overlap. A continuous wave Nd:YAG laser beam intercepts the particle beam in the source region to detect "large" par-

Sample Inlet To Mechanical Vacuum Pumps

Nd:YAG Laser 532 nm (CW) Backing Plate

TOF Mass Spectrometer

Computer with Data Acquisition Board PMTs with Amplifiers and Coincidence Checking

Excimer Laser 193 nm (pulsed) Trigger Signal Pulses

MicroChannel Plate Signal

Fig. 13-9. The University of Delaware mass spectrometer.

tides by light scattering. In this way, the ablation laser can be fired synchronously with the arrival of the particle. For small particles that cannot be detected by light scattering, the ablation laser is free-fired at a high repetition rate to maximize the probability that a particle is "hit." One advantage of this approach is the high transmission efficiency of the inlet. Particles having the proper aerodynamic diameter are transmitted with near unit efficiency into the mass spectrometer source region. The high transmission efficiency provides high data throughput even though different sized particles must be analyzed sequentially. Another advantage is the ability to select and analyze particle sizes below the limit to light scattering, roughly 200 nm. Ambient particles as small as 20 nm diameter have been detected. Future Developments The instruments described in this chapter have proven particularly useful for ambient measurements. Many other instrument designs are possible. Several are listed in Table 13-3; others have been developed and are in the testing stage; still others are in the conceptual stage. Given the dramatic growth in the field over the past few years, we expect that singleparticle mass spectrometer design and operation will continue to evolve at a rapid pace. REFERENCES Carson, P. G., M. V. Johnston, and A. S. Wexler. 1997a. Laser desorption/ionization of ultrafine aerosol particles. Rapid Commun. Mass Spectrom. 11:993-996. Carson, P. G., M. V. Johnston, and A. S. Wexler. 1997b. Real-time monitoring of the surface and total composition of aerosol particles. Aerosol ScL Technol 26:291-300. Cotter, R. J. 1997. Time-of-Flight Mass Spectrometry: Instrumentation and Applications in Biological Research. Washington, DC: American Chemical Society. Dahneke, B., J. Hoover, and Y. S. Cheng. 1982. Similarity theory for aerosol beams. /. Colloid Interface ScI 87:167-179.

Fernandez de Ia Mora, J. and P. Riesco-Chueca. 1988. Aerodynamic focusing of particles in a carrier gas. /. Fluid Mech. 195:1-21. Flagan, R. 1993. Probing the chemical dynamics of aerosols. In Measurement Challenges in Atmospheric Chemistry, ed. L. Newman. Washington, DC: American Chemical Society. Frankevich, V., B. Oktem, and M. Johnston. 1998. Electrodynamic trapping of ultrafine particles for MS characterization. Proceedings of the 46th American Society for Mass Spectrometry Conference on Mass Spectrometry and Allied Topics, Orlando Fl, May 31-June 4. Santa Fe, NM: ASMS p. 519. Friedlander, S. K. 1971. The characterization of aerosols distributed with respect to size and chemical composition—II. Classification and design of aerosol measuring devices. /. Aerosol ScL 2:331-340. Gard, E., J. E. Mayer, B. D. Morrical, T. Dienes, D. P. Fergenson, and K. A. Prather. 1997. Real-time analysis of individual atmospheric aerosol particles: Design and performance of a portable ATOFMS. Anal Chem. 69:4083-4091. Ge, Z, A. S. Wexler, and M. V. Johnston. 1998. Laser desorption/ionization of single ultrafine multicomponent aerosols. Environ. ScL Technol 32:3218-3223. Gieray, R. A., P. T. A. Reilly, M. Yang, W. B. Whitten, and J. M. Ramsey. 1997. Real-time detection of individual airborne bacteria. /. Microbiol Meth. 29:191-199. Hinz, K.-R, M. Greweling, F. Drews, and B. Spengler. 1999. Data processing in on-line laser mass spectrometry of inorganic, organic or biological airborne particles. /. Am. Soc. Mass Spectrom 10:648-660. Jayne, J. T, D. C. Leard, X. Zhang, P Davidovits, K. A. Smith, C. E. KoIb, and D. R. Worsnop. 2000. Development of an aerosol mass spectrometer for size and composition analysis of submicron particles. In press. Aerosol ScL Technol. 33:49-70. Johnston, M. V. 2000. Sampling and analysis of individual particles by aerosol mass spectrometry. /. Mass Spectrom. 35:585-595. Johnston, M. V. and A. S. Wexler. 1995. Mass spectrometry of individual aerosol particles. Anal. Chem. 67:721A-726A. Kane, D. B. and M. V. Johnston. 2000. Size and composition biases on the detection of individual ultrafine particles by aerosol mass spectrometry. Environ. ScL Technol 34:4887-4893. Kaufmann, R. L. 1986. Laser-microprobe mass spectroscopy of particulate matter. In Physical and Chemical Characterization of Individual Airborne Particles, ed. K. R. Spurny. New York: John Wiley & Sons. Liu, P., P J. Ziemann, D. B. Kittleson, and P. H. McMurry. 1995a. Generating particle beams of controlled dimensions and divergence: I. Theory of particle motion in aerodynamic lenses and nozzle expansions. Aerosol ScL Technol. 22:293-313. Liu, P., P J. Ziemann, D. B. Kittleson, and P H. McMurry. 1995b. Generating particle beams of controlled dimensions and divergence: I. Experimental evaluation of particle motion in aerodynamic lenses and nozzle expansions. Aerosol ScL Technol. 22:314-324. Mallina, R. V., A. S. Wexler, and M. V. Johnston. 1997. Particle growth in high-speed particle beam inlets. J. Aerosol ScL 28:223-238.

Mallina, R. V., A. S. Wexler, and M. V. Johnston. 1999. High-speed particle beam generation: Simple focusing mechanisms. /. Aerosol ScL 30:719-738. Mallina, R. V, A. S., Wexler, K. P. Rhoads, and M. V. Johnston. 2001. High speed particle beam generation: A dynamic focusing mechanism for selecting ultrafine particles. Aerosol ScL Technol. 33:87-104. March, R. E. 1989. Quadrupole Storage Mass Spectrometry. New York: John Wiley & Sons. March, R. E. 1997. An introduction to quadrupole ion trap mass spectrometry. /. Mass Spectrom. 32:351-369. McLafferty, F. W. and F. Turecek. 1993. Interpretation of Mass Spectra, 4th Ed. Mill Valley, CA: University Science Books. Morrical, B. D., D. P Fergenson, and K. A. Prather. 1998. Coupling two-step laser desorption/ionization with aerosol time-of-flight mass spectrometry for the analysis of individual organic particles. /. Am. Soc. Mass Spectrom. 9:1068-1073.

Murphy, D. M. and D. S. Thomson. 1995. Laser ionization mass spectroscopy of single aerosol particles. Aerosol ScL Technol 22:237-249.

Murphy, D. M. and D. S. Thompson. 1997. Chemical composition of single aerosol particles at Iolaho Hills: Negative ion measurements. /. Geophys. Res. 120:6353-6368. Murphy, D. M., D. S. Thomson, and M. J. Mahoney. 1998. In situ measurements of organics, meteoric material, mercury and other elements in aerosols at 5 to 19 kilometers. Science 282:1664-1669. Neubauer, K. R., M. V. Johnston, and A. S. Wexler. 1997. On-line analysis of aqueous aerosols by laser desorption ionization. Int. J. Mass Spectrom Ion Processes 163:29-37. Neubauer, K. R., M. V. Johnston, and A. S. Wexler. 1998. Humidity effects on the mass spectra of single aerosol particles. Atmos. Environ. 32:2521-2529. Neubauer, K. R., S. T. Sum, M. V. Johnston, and A. S. Wexler. 1996. Sulfur speciation in individual aerosol particles. /. Geophys. Res. 101:18701-18707. Noble, C. A. and K. A. Prather. 2000. Real time single particle mass spectrometry: A historical review of a quarter century of the chemical analysis of aerosols.. Mass Spectrom. Rev. 19:248-274. Peter, T. 1996. Airborne particle analysis for climate studies. Science 273:1352-1353. Prather, K. A., T. Nordmeyer, and K. Salt. 1994. Real-time characterization of individual aerosol particles using time-of-flight mass spectrometry. Anal. Chem. 66:1403-1407. Reents, W. D. Jr., S. W. Downey, A. B. Emerson, A. M. Mujsce, A. J. Muller, D. J. Siconolfi, J. D. Sinclair, and A. G. Swanson. 1995. Single particle characterization by time-of-flight mass spectrometry. Aerosol ScL Technol. 23:263-270. Song, X.-H., P. K. Hopke, D. P. Fergenson, and K. A. Prather. 1999. Classification of single particles analyzed by ATOFMS using an artificial neural network, ART-2A. Anal. Chem. 71:860-865. Stoffels, J. J. and J. Allen. 1986. Mass spectrometry of single particles in situ. In Physical and Chemical Characterization of Individual Airborne Particles, ed. K. R. Spurny. New York: John Wiley & Sons.

Thomson, D. S., A. M. Middlebrook, and D. M. Murphy. 1997. Threshold for laser-induced ion formation from aerosols in a vacuum using ultraviolet and vacuum-ultraviolet laser wavelengths. Aerosol ScL Technol. 26:544-559. Thomson, D. S. and D. M. Murphy. 1993. Laser-induced ion formation threshold of aerosol particles in a vacuum. Appl. Optics 32:6818-6826. Thomson, D. S. and D. M. Murphy. 1994. Analyzing single aerosol particles in real time. Chemtech 24:30-35. Van Bramer, S. E. and M. V. Johnston. 1992. Tunable, coherent vacuum ultraviolet radiation for photoionization mass spectrometry. Appl. Spectrosc. 46:255-261 Wieser, P. and R. Wurster. 1986. Application of laser-microprobe mass analysis to particle collections. In Physical and Chemical Characterization of Individual Airborne Particles, ed. K. R. Spurny. New York: John Wiley & Sons. Weiss, M., P. J. T. Verheijen, J. C. M. Marijnissen, and B. Scarlett. 1997. On the performance of an on-line time-of-flight mass spectrometer for aerosols. /. Aerosol ScL 28:159-171. Yang, M., P. T. A. Reilly, K. B. Boraas, W. B. Whitten, and J. M. Ramsey. 1996. Real-time chemical analysis of aerosol particles using an ion trap mass spectrometer. Rapid Commun. Mass Spectrom. 10:347-351. Zelenyuk, A., J. Cabalo, T. Baer, and R. E. Miller. 1999. Mass spectrometry of liquid aniline aerosol particles by IR/UV laser irradiation. Anal. Chem. 71:1802-1808.

In this type of instrument, particles are deposited by inertial impaction or electrostatic precipitation onto the surface of an oscillating piezoelectric quartz crystal disk. An electrode is attached to the center of both sides of the crystal, and the particles are deposited onto one of the electrodes. The natural resonant frequency of the crystal decreases as particle mass accumulates. The changing frequency of the sampling crystal is electronically compared with that of a clean reference crystal, generating a signal that is proportional to the collected mass. Quartz crystals have sensitivities of several hundred hertz per microgram. This sensitivity results in the ability to measure the mass concentration of about 10ug/m3 in less than one minute (Olin and Sem, 1971). However, the sensitivity over the crystal surface is nonuniform and depends on the excitation mode of the crystal. The highest sensitivity occurs near the center of the electrode. Each collection device is calibrated to compensate for the particle mass deposition pattern and the crystal sensitivity. A reference crystal is typically used to compensate for temperature and humidity deviations. The collection electrode surface is often coated with grease to improve particle coupling to the vibrating surface. Potential Biases

The advantage of the technique is that it measures mass directly with high sensitivity and, under the proper conditions, high accuracy. The main drawback of this technique is that the relatively high frequency of the electrode surface (5 to 10 MHz) can result in poor coupling between the vibrating plate and collected particles. These coupling problems result in reduced sensitivity and appear to be present with agglomerates, with fibers, and with compact particles, increasing with particle size. Furthermore, saturation occurs at relatively low deposited mass, especially for nonsticky particles, a situation that requires frequent cleaning or regreasing of the collection surface. An investigation of a cascade impactor microbalance for drug inhaler aerosol size distributions indicated that the substrate crystals had to be cleaned and re-greased between each measurement to prevent modification of the measured size distribution by particle bounce (Tzou, 1999). However, the high sensitivity and high time resolution of quartz crystal sensors make this technique suitable for some applications. Instruments

Three versions of a cascade impactor quartz crystal microbalance instrument are available from California Measurements (CMI)* A low flow version (PC-2,4 x 10"6m3/s [0.24L/min]) has been used for higher concentration aerosols, including pharmaceutical aerosols, and exposure monitoring; two higher flow versions have been used for lower concentration aerosols, such as indoor and ambient aerosol (PC-2H and PC-6H, 3.3 x 10~5m3/s [2L/min]) (see Chapter 10 for cut points). An early version of this instrument was evaluated by Fairchild et al. (1980). Some improvements have been made to these instruments since the previous edition of this book (for details of the basic design, Williams, Fairchild, and Jaklevic, 1993, first edition), including lower dead volume in the sensing chambers for reduced internal losses and improved response time, and an isokinetic sampling inlet for more accurate high concentration measurements. An automated crystal cleaning system is in development that should alleviate some difficulties in the routine use of this instrument. A portable self-contained Respirable Aerosol Mass Monitor (Model 3511, KAN), capable of measurement after either a 24 s or a 120 s sampling period, has also been available since * See to Appendix I for full manufacturer addresses referenced to the italicized three-letter codes.

the 1970s. A manually operated cleaning system in this instrument alleviates crystal loading problems. A new version of a quartz microbalance monitor is being manufactured by Booker Systems Ltd. (BKR). BETA GAUGE METHOD Measurement Principles The beta gauge method of mass determination depends on the near-exponential decrease in the number of beta particles transmitted through a thin sample as the deposit thickness is increased. The beta particles are emitted as a continuum energy distribution by a radioisotope source, and a suitable electron counter measures their intensity. The method has the advantages of instrumental simplicity and ease of automation for large-scale applications. The dynamic range of sensitivity is well matched to the mass range normally of interest in aerosol monitoring in which various filters are the substrates. However, a detailed understanding of the parameters that affect the measurements is necessary to ensure optimal instrumental implementation and correct interpretation of results. Figure 14-1 is a schematic diagram of a beta gauge instrument using a two-beam compensating method. It consists basically of the radioactive source, detectors, and sample. The total flux in a continuous beta particle spectrum emitted by the radioisotopic source is determined in a reference section, as well as through the sample after transmission. This new development

Fig. 14-1. Schematic diagram of a beta gauge. The aerosol enters the instrument at position 1 and is fed into a chamber (3) where the particles are deposited on a filter (9). The beta source (5) is surrounded by vacuum chambers (4, 6), which are connected to a flow controller and vacuum pump (10). In this setup, high measurement stability is achieved with two beta detectors (2, 8), which measure the signal (4, 3, 2) and the reference beam (6, 7, 8) to compensate for fluctuations in temperature and pressure.

increases the sensitivity of this instrument (ESM). Under proper experimental conditions, the transmitted flux (/) is related to the sample mass through the relationship (Evans, 1955) (14-1) where I0 is the incident flux, \i is the mass absorption coefficient for (3-radiation absorption (cm2/g), and x is the mass thickness of the sample (g/cm2). The mass absorption coefficient is normally determined through a calibration procedure involving the measurement of a series of known standards, which bracket the mass range of interest (Jaklevic et al, 1981). The incident flux, I0, can be either derived during the same calibration procedure or made to cancel the value of I0 by calculating the ratio between transmitted fluxes measured with and without the particle deposit, if the interval between successive measurements is short. The latter case applies to certain beta gauge designs where continuous particle deposition is monitored (Macias and Husar, 1974). Instrument Design

The optimal choice of source and detector depends on many factors. The radioisotope source must have beta particle emission as the dominant mode of decay and exhibit a half-life sufficient for large decay corrections or frequent source replacements to be unnecessary. The source strength should provide adequate precision in counting statistics within the limitations of the detector rate-handling capabilities. Finally, as discussed in more detail below, the energy of the beta spectrum must be chosen to produce a mass absorption coefficient matched to the range of thicknesses to be measured. Table 14-1 lists some radioisotopes appropriate for aerosol beta gauge applications together with relevant parameters for each source (Lilienfeld, 1975). The detector must be sensitive to the beta particles (i.e., electrons) in the energy range of interest and capable of operation at a counting rate sufficient to perform measurements in the required time interval. While other detectors have been used in the past, the most recent beta gauge systems employ semiconductor diode detectors and solid-state pulse processing electronics because of their simplicity and stability of operation. The following discussion is oriented toward this type of system, although most of the comments also apply to other types of detectors. The source-detector geometry must be maintained in a stable mechanical configuration to minimize spurious counting-rate variations. Also, it is important that the spacing be as close as possible so that changes in atmospheric density within the gap are not interpreted as mass variations in the sample (Courtney et al., 1982). The lower limit to the spacing is normally determined by the thickness of sample holders and the associated handling mechanisms used with automated instrumentation.

TABLE 14-1. Commonly Available Sources Suitable for Beta Attenuation Measurements Isotope 64

Ni C 147 Pm 85 Kr 36 Cl 204 Tl 14

Half-Life (years)

Emax (MeV)

Range (mg/cm 2 ) in Carbon at Emax

Range (mg/cm 2 ) in Carbon at Ema = 0.4 Emax

92 5730 2.62 10.76 3.1-10 4 3.8

0.067 0.156 0.225 0.67 0.712 0.765

7.7 32 60 290 320 340

1.6 6.6 13 77 84 94

Theoretical Considerations

Studies have shown that the functional dependence of beta particle transmission expressed in Eq. 14-1 is not valid for precise experimental measurements (Jaklevic et al., 1981; Heintzenberg and Winkler, 1984).This result is not unexpected because the exponential behavior is not a reflection of fundamental mechanisms associated with beta particle attenuation in matter. Electrons with kinetic energies less than 1 MeV lose energy primarily through collisions with atomic electrons present in the sample. As a consequence, an electron with a well-defined initial energy distribution will slow down through a series of discrete energy losses as it traverses the sample. An incident electron beam with a well-defined initial energy and direction will experience a gradual decrease in the average energy, accompanied by a spreading in the distribution of the beta ray's energy and angle of incidence on the target material. The radioisotopic beta particle emission process results in a continuum of electron energies. Figure 14-2 is an idealized representation of a measured beta spectrum from a typical source. The energy distribution extends from a minimum energy, determined by the source window thickness, to a maximum endpoint energy, Emax, which is the total energy available for the radioactive decay process. An electronic discriminator level, £disc, has been indicated above a low-energy electronic noise tail. As the mass between source and detector is increased, the counting rate observed above this discriminator level in the beta gauge detector represents those electrons in the original continuum spectrum that have not been totally stopped in the sample and are still incident on the detector. This rate reflects a complex energy loss process, which depends on several variables, including the average energy of the electrons in the beta spectrum and the amount of material traversed. When averaged over all these effects, the observed dependence of the counting rate on the thickness traversed is approximately exponential. Repeated measurements in a carefully defined experimental geometry using aluminum absorbers have established an approximate relationship between the mass attenuation coefficient in Eq. 14-1 and the beta spectrum endpoint energy (Gleason et al., 1951): (14-2)

Number of Electrons

However, there is considerable variation among investigators regarding the values of the coefficients, which reflects the empirical nature of these parameters. Because the beta particle energy-loss process involves scattering from atomic electrons rather than nuclei, there is an additional dependence of the absorption coefficient on the average atomic number of the samples. Various authors have studied this effect, and an empirical relationship has been derived (Klein et al., 1984):

E disc

E max

Electron Energy FIg. 14-2. Idealized beta particle spectrum emitted from a radioisotope source.

(i«) where Z is the atomic number and A is the atomic weight. However, the validity of this relationship seems to depend on the specific geometry of the beta gauge. A less dramatic dependence of attenuation on Z/A has been observed by Jaklevic et al. (1981) and attributed to the relative importance of the particle angular distribution in the particular source-detector arrangement employed in that study. Regardless of the details of functional dependence, it should be noted that variations associated with this effect are normally tolerable because the range of Z/A is small for all elements, with the exception of hydrogen. The practical consequences of these theoretical observations are that, although estimates of beta gauge sensitivity can be obtained using generalized expressions, precise mass measurements require that each specific instrument be calibrated using known gravimetric standards. It is also necessary to limit the dynamic range over which one attempts to apply a given calibration using a strictly exponential approximation. To the extent that the deposited mass is normally a small fraction of the tare weight of the filter medium, this is not a severe limitation in aerosol applications. A more complete understanding of precision and accuracy requires a detailed error analysis. If one assumes that the value of JJL has been carefully determined and that the counting interval is known with complete certainty, then the precision of the mass measurement is determined by experimental variations in the determination of I0 and /. Using Eq. 14-1, the root mean square error, c2(x), in the calculated concentration, x, can be derived as (Cooper, 1975) (14-4) where /0 and / are the incident and transmitted fluxes integrated for a fixed time interval. When the errors associated with / and I0 are the result of Poisson counting statistics only, that is, o(7) = (f)m, and if the counting intervals of the two measurements are equal, the precision o{x) for a difference measurement varies as the inverse of the mass absorption coefficient and the inverse square root of measurement interval, as might be expected. The inverse dependence of precision on the mass absorption coefficient supports the intuitive observation that lower energy beta spectra will provide a more sensitive indicator of small mass changes in the sample. On the other hand, if the energy is too low, the exponential approximation is no longer valid because an increasing fraction of beta particles is totally stopped in the sample. Referring to Table 14-1, a useful rule of thumb is to select a beta spectral average energy corresponding to a range that is several times the maximum thickness to be measured. For this reason, most beta gauges designed for aerosol monitoring employ either 85 Kr, 14C, or 147Pm as reasonable choices for use with substrates in the range of 0.1 to 1 mg/mm2 [10-100 mg/cm2] and deposits in the range of 0.2 to 5 jig/mm2 [20-500 jig/cm2] (Klein et al., 1984). The derivation of Eq. 14-4 assumed that the value of the mass absorption coefficient was known from a previous calibration procedure. However, the value of the mass absorption coefficient is normally calculated from transmission measurements performed on a series of mass standards that bracket the anticipated range of operation of the instrument. A more complete error analysis, including uncertainties in the fitting procedure, is discussed by Jaklevic et al. (1981). A general conclusion of that analysis is that, although the absolute accuracy of a mass measurement is affected by such calibration errors, the precision is dominated by the variability in the determinations of / and I0. In principle, the precision of a given mass determination can be improved by increasing the counting interval to reduce the relative error associated with Poisson counting statistics. However, one eventually reaches a limit where the variability in the measured counting rate is dominated by other effects, which are

not easily controlled. Sources of systematic errors can include fluctuations in atmospheric density; changes in laboratory relative humidity, which can affect the substrate mass for hygroscopic media; instabilities in the mechanical design; and variability in the placement of the sample in the instrument (Courtney et al., 1982). A major source of instrumental instability is the result of long- and short-term drift in the detector and analogue pulse processing electronics. Because of the difficulty in controlling such sources of errors, it should be requirements in all beta gauge measurement protocols that recalibration of the instrument be performed before each series of measurements and that replicate samples be repeatedly analyzed at the same time as the unknowns in order to monitor instrumental stability. Potential Biases

The near-exponential behavior of the beta absorption process and the variations discussed above can result in several potential measurement artifacts that should be understood. Principal among these are particle size effects, substrate inhomogeneity, and atomic number dependence. Particle size effects result from the fact that the beta gauge transmission measurements represent an average of the absorption experienced by an aggregate of particles deposited on the filter substrate. When this deposition is a homogeneous layer of small particles whose average diameter is much less than the layer thickness, the interpretation of the results in terms of exponential absorption by a uniform deposit is valid. On the other hand, one can imagine a deposit of equivalent mass, but consisting of only a few, very massive particles. In an extreme manifestation of this latter case, there could exist total absorption within a given particle. The transmission measurement in this limiting case would then reflect the fractional area covered by the particles, and the interpretation in terms of an exponential absorption by a uniform deposit would be invalid. A detailed discussion of this problem for the general case of exponential absorption is given by Cooper (1976). A similar treatment using a simplified model applied to the case of aerosol particles is given by Jaklevic et al. (1981). Their results indicate that one must either limit the size distribution to particles below lOjmn diameter or, if larger particles are to be analyzed, ensure that the average deposit thickness is sufficient for a statistically meaningful number of particles to be present. Similarly, for impaction samples, the deposit thickness must be uniform over the measurement area to a degree where the average of the exponential is equal to the exponential of the average. An effect that can be explained using similar logic has to do with discrepancies caused by filter and source inhomogeneities. A microscopic examination of most membrane filter media shows that the substrate consists of a nonuniform distribution of fibers or flocculated material having relatively open spaces in between. Similarly, a radioisotope source is normally fabricated by methods that result in local inhomogeneities in radioactivity across the face of the source. Although a point source can, in principle, alleviate this problem, there is a practical limit to the specific activity that can be concentrated in a small volume. Variations in the apparent mass of the substrate can result from random alignments between the respective inhomogeneities causing spurious high or low mass readings. The problem is exacerbated by the large mass of the substrate relative to the deposit. Because neither the source nor the substrate can be made to be perfectly spatially uniform, it is important to constrain the measurement protocol to position the filter in the instrument identically for both the initial and final weight determinations. This is most easily implemented in the case of large-scale automated systems and is necessary for mass measurements, which aspire to achieve the limits of instrumental precision (Courtney et al., 1982). The atomic number dependence of the mass absorption coefficient requires that certain precautions be taken regarding the choice of calibration standards and in the interpretation of results from discrete pollution sources. Table 14-2 shows the Z/A values compounds commonly observed in ambient aerosol sampling. The mass absorption coefficients are calculated

TABLE 14-2. Effect of Atomic Number Dependence on the Measured Mass of Several Compounds Compound

ZIA

\i (cm2/mg)a

\i (cm2/mg)6

(NH4)2SO4 NH 4 HSO 4 CaSO 4 -2H 2 O SiO2 CaCO 3 Carbon Fe2O3 NaCl PbSO 4 PbCl2 PbBrCl

0.530 0.521 0.511 0.499 0.500 0.500 0.476 0.478 0.429 0.417 0.415

0.153 0.152 0.152 0.154 0.154 0.154 0.163 0.172 0.193 0.204 0.206

0.166 0.163 0.159 0.154 0.154 0.154 0.144 0.145 0.126 0.121 0.120

"From Jaklevic et al. (1981). b Z/A dependence calculated from Eq. 14-3. Values normalized to 0.154 for carbon to account for instrumental differences.

using both the ZJA dependences observed by Jaklevic et al. (1981) and those calculated from Eq. 14-3. The mass absorption coefficient for a mixture of compounds would be the weighted sum of the respective coefficients. It is obvious that an inappropriate choice of calibration foils can affect the accuracy of the measurements if not corrected for in the data analysis. Similarly, measurements of a set of samples in which the relative contribution of diverging Z/A compounds varies widely will need special consideration in the interpretation. Although a complete correction requires that the sample composition be known, some estimate of the probable error can be obtained by observing the range of values of jn for the compounds listed in Table 14-2 and incorporating an error analysis based on Eq. 14-1. It should be noted that the errors associated with ZfA variations affect the accuracy of the measurements but not the precision and, as a consequence, have little effect on the lower limit of sensitivity of the beta gauge method. Volatilization Losses

A general problem with particulate mass measurements is that the sampling temperature affects measured mass concentrations. An example of this problem is the water vapor content in the atmosphere, which affects the reading of a beta gauge instrument. At high relative humidity, the aerosol particles, as well as the filter substrate, will adsorb water, which may result in a significant increase in the indicated particle mass (Tsai and Cheng, 1996). In some beta gauge instruments the inlet system can be heated to reduce relative humidity in the sample, thus minimizing the contribution of water to the aerosol mass. However, this might result in substantial losses of semivolatile material and thus a negative bias of the particle mass (see also next section). Results and Applications

Commercial sources and specifications of several beta attenuation instruments are given in Table 14-3. In recent years the monitoring of low aerosol concentration levels has been made possible through the use of the dual beam compensation method, which compensates for fluctuations in temperature, pressure, and supply voltages. According to the manufacturers, with these instruments, detection limits down to 3 and ~1 ug/m3 can be achieved at a temporal resolution of 30min and 24 h, respectively. There are a number of criteria other than precision

TABLE 14-3. List of Commercially Available Beta Gauges with Specifications Producer

Device name

ESM ATVD MET KIM OPS VER

FH62I-R GBAM-1020 BAM1020 SPM-611 ADAM F 701

HOR

APDA-360

Source

Detection limit

Mode

Kr C 14 C 147 Pm 14 C 14 C

3 (1/2 h)

Contin. Step Step Contin. Step Step

C

10(Ih)

85

fcg/m3)

14

14

10

Step

and individual designs that need to be evaluated in terms of specific applications. These include speed, convenience, cost, operating environment, and automated operation. Beta gauge mass measurements have been incorporated into a number of studies. Hoek et al. (1997) compared different ambient mass measurement methods and found a good correlation between beta monitors and a PM-10 sampler. Speer et al. (1997) used the beta gauge method to measure the liquid water content of aerosol particles on a polytetrafluoroethylene (PTFE) membrane filter as a function of relative humidity. Other applications using continuous mass monitors have been described by Heintzenberg and Winkler (1984). A number of older applications are additionally found in the first edition of this book. One such instrument, the Atmospheric Dust Automatic Monitor, ADAM (OPS) system, collects atmospheric particles on standard 47 mm membrane filters and determines the amount of collected particle mass using the (3-ray attenuation method. In addition, the residual beta activity on the sampled particles due to the presence of short-lived radon daughters (natural radioactivity) is measured and taken into account. The dual filter geometry permits the performance of the beta measurement on one filter while the next filter is sampled. The instrument automatically performs up to 40 unattended sequential measurements. The loaded filters are then available for the possible gravimetric determination of the collected mass and for subsequent chemical analysis of the particulate matter. The temperature of the airflow is kept as close as possible to ambient temperature (no heating) to minimize sampling artifacts due to the loss of volatile compounds. TAPERED-ELEMENT OSCILLATING MICROBALANCE METHOD Measurement Principles

In Tapered-Element Oscillating Microbalance (TEOM) devices, aerosol mass is collected on a vibrating collection substrate and measured through a change in oscillation frequency. Figure 14-3 shows a typical arrangement for a TEOM instrument. The active element of any TEOM system is a specially tapered hollow tube constructed of an elastic, glass-like material. The wide end of the tube is firmly mounted on a relatively massive base plate. The narrow end supports a replaceable collection medium, such as a filter or impaction plate, and is made to oscillate. Particle-laden gas streams are drawn through the collection medium, where particles are deposited. The filtered gas is then drawn through the hollow tube, typically controlled by an automatic mass flow controller. An electronic feedback system initiates and maintains the oscillation of the tapered element. In 1983, the U.S. Bureau of Mines (BOM) and the National Institute of Occupational Safety and Health (NIOSH) funded the development of a prototype TEOM dust

Field plates

LED

Photetransistor

Fig. 14-3. Typical arrangement for the TEOM.

monitor for mining applications (Patashnick and Rupprecht, 1983). In that particular device, a light-emitting diode (LED )-pho to transistor pair aligned perpendicular to the plane of oscillation of the tapered element detects the frequency of oscillation. The light-blocking effect of the oscillating element, positioned between the phototransistor and the LED, modulates the output signal of the phototransistor, which is then amplified. Part of the amplified signal is applied to a conductive coating on the outside of the tapered element. In the presence of constant electric field plates, this signal provides sufficient force to keep the tapered element in oscillation. In other words, part of the amplified signal from the LED-phototransistor pair is used in an electrical feedback loop to overcome any amplitude damping of the tapered element oscillation. The other part of the amplified signal from the LED-phototransistor pair is sent to a counter and data processing stage. Here, the oscillation frequency of the tapered element is calculated and stored in the memory. The manufacturer (R&P) has made several proprietary improvements to the feedback system since the early BOM/NIOSH prototype (Patashnick and Rupprecht, 1991). The equation that describes the behavior of the TEOM system derives from the equations of motion for a simple harmonic oscillator: (14-5) where Am is the mass of the collected sample, / b is the frequency of the oscillating element after sample collection, /a is the frequency before sample collection, and K0 is a constant (spring constant) unique to each tapered element. As the collection medium collects aerosol, the mass increases, thereby decreasing the frequency of oscillation. By measuring only the change in frequency, one can determine the gain in the aerosol mass on the collection medium. Although this expression for Am is nonlinear, it is monotonic (single valued), independent of m, and depends only on the constant K0. For subsequent measurements, / b becomes /a, a new initial frequency that reflects the total mass of the system. The new / b after sampling will differ from /a only because of the new mass uptake, Am, collected during sampling. Instrument Design

A TEOM instrument can be tailored for a particular application. To do so, the manufacturer must know the minimum mass concentration that the instrument must measure, how quickly each measurement must be made, and what sampling air flow rate will be used. To employ the highest sensitivity for a particular application, the manufacturer must consider the

total mass of the tapered-element load. This mass, which in most applications is primarily the filter cartridge, must be held to a minimum to effect the maximum frequency change with a sample mass deposit. The reduction of the filter mass has practical limits that are related to both flow rate and filter life. A filter cartridge must have reasonable dimensions to sustain the desired flow before loading to a point where the flow drops to an unacceptable level. In addition to the filter mass, the tapered element has a certain amount of mass that also contributes to the total mass of the oscillating system. The limits on this mass depend on the dimensions of the tapered element. The element must have a sufficiently wide bore to allow the desired flow with minimum pressure drop and also have sufficient wall thickness to support the filter cartridge. Temperatures are maintained at a constant value, typically 303 or 323 K [30° or 500C], to minimize thermal expansion of the tapered element and to reduce relative humidity. Potential Biases

Calibration. Calibration of TEOM instruments is equivalent to determining the spring constant ^0- Because K0 is determined by the physical characteristics of the tapered element, calibration is not likely to change over a period of time. The manufacturer provides a value of K0, but the user can easily check the value of ^ 0 by adding a known mass to the tapered element, measuring the change in oscillation frequency, and using Eq. 14-5 to determine K0. Using this method, Shore and Cuthbertson (1985) found that the manufacturer-supplied value for ^ 0 was correct within the experimental error. However, they also checked the calibration by injecting known masses of dioctyl phthalate (DOP) over the surface of the collection filter. This method suggested that the TEOM instrument measures particulate masses up to 10% lower (on an average) when the K0 value supplied by the manufacturer was used. However, the masses of DOP used were much higher than those expected during particle collection. They also observed that limiting aerosol collection to the center of the filter influenced the measurements. These results suggest that if one elects to verify the manufacturer-supplied .K0, aerosol deposits similar to those expected during sampling should be used. Particle Size Effects. Because TEOM devices typically use a filter collection medium, collection efficiency will not be significantly affected by particle size. Particles not collected by the filter medium would not represent significant mass. Particle size is likely to be more important to inlet bias of the sampling head; however, these problems are common to all sampling devices and not just to TEOM measurement technology. Adherence Effects. Tapered elements typically oscillate at several hundred Hertz, and the collection medium is typically a filter. This reduces coupling problems between the filter and agglomerates, that could occur at higher frequencies. Theoretically, if sufficient mass were collected on the TEOM filter, particles would begin to flake off. In practice, however, the filters clog before collecting particle loadings become large enough to cause flaking. In fact, most TEOM instruments provide a warning to replace the filter at high loading. Overloading. If the collection filter became sufficiently loaded, the added mass could conceivably damp the oscillations beyond the ability of the feedback system to sustain them. This situation, called saturation, could introduce serious error.The dynamic range of TEOM instruments, however, is several orders of magnitude. As discussed in the previous section, filters will clog before particle loadings become large enough to cause saturation. Volatilization Losses. The volatilization problem mentioned for the beta gauges applies also to the TEOM. To reduce relative humidity, this instrument is often operated at 323 K [500C],

which is high enough to vaporize semivolatile aerosol material such as ammonium nitrate and certain organic compounds. Mignacca and Stubbs (1999) reported a 22% higher signal at an equilibration temperature of 303 K [300C] compared with 323 K [500C]. As a consequence, lower TEOM concentrations are generally measured when compared with reference methods, a situation especially encountered during the colder seasons (Allen et al., 1997; Salter and Parsons, 1999; Soutar et al., 1999; Muir, 2000; King et al., 2000). Brook et al. (1999) also compared PM-2.5 data from TEOM and dichotomous samplers and found a relatively small difference in the warmer months, while during the colder months TEOM values were lower by 23% on average. Ayers et al. (1999) compared 24 h PM-2.5 aerosol loadings determined by a TEOM and manual gravimetric samplers in Australian cities. They found systematically lower results from the TEOM by an average of >30%, indicating that aerosol material is lost from the heated sample filter employed in the TEOM. The problem may be minimized by using a diffusion dryer, which allows the temperature in the system to be reduced (Eatough et al., 1999). See Chapters 5,9, and 27 for discussions of particle volatilization. Damping. If the mass of the support structure for the element is sufficiently small, the element may induce vibrations in the structure itself. The oscillation of the tapered element will then be slightly damped. This phenomenon was observed when the prototype TEOM personal sampler was tested for miners (Williams and Vinson, 1986). Such damping will not be significant if the mass of the support structure is much larger than the mass of the element or if the support structure is properly clamped to a large mass. Tests of the prototype TEOM personal sampler for miners represent a special case; damping of this sort has not been reported for the commercial TEOM instruments. Results and Applications

The TEOM technology had its beginnings at Dudley Observatory in the 1960s in conjunction with micrometeorite research (Patashnick and Hemenway, 1969). A microbalance, at that time consisting of a thin quartz fiber, was designed to measure particle masses in the range of 10"5 to 10"11 g. Since then, its inventors have made many improvements to the quartz fiber approach and formed Rupprecht & Patashnick Co. (R&P) to market the device. R&P is currently the only manufacturer and vendor of TEOM instruments. According to the manufacturer, the detection limit of these instruments is less than 2figm"3 for a 24 h integrated sample. A variety of applications utilizing the real-time mass-measuring capability of the TEOM have been investigated. Internal combustion engine emission measurements with the TEOM have allowed designers to identify conditions contributing to particulate emissions, and even transient vehicle-to-vehicle emissions have been detected (Shore and Cuthbertson, 1985). Egan and Litton (1986) monitored the mass concentrations of smoke from wood. Newman and Steciak (1987) used the TEOM instrument to help characterize smoke properties for fire modeling, fire detector evaluation, and assessment of the nuclear winter problem. In a field study, two TEOM instruments were used at the entrance and exit of a road tunnel to measure the emission factors of fine particles emitted from individual vehicles (Weingartner et al., 1997). Patterson and Eatough (2000) compared indoor and outdoor PM-2.5 concentrations measured with two TEOM monitors. Morawska et al. (1999) used a TEOM in conjunction with volume size distribution measurements to determine the average density of aerosol particles. The U.S. Environmental Protection Agency (1989) has certified R&P models 1200 and 1400a for PM-IO measurements of ambient air quality. The TEOM instruments are often used to monitor urban PM-2.5 and PM-IO levels (Meyer et al., 1992; Deacon et al., 1997; Harrison et al., 1997,1999; Keary et al., 1998; Olcese and Toselli, 1998). As already mentioned, care must be taken to properly account for volatile species.

ELECTRICAL LOW-PRESSURE IMPACTOR Measurement Principles

The Electrical Low-Pressure Impactor (ELPI) was developed at Tampere University of Technology (TUT-ELPI) in the early 1990s (Keskinen, 1992; Keskinen et al., 1992). A modified version of the TUT-ELPI manufactured by Dekati, Ltd. (DEK) has been commercially available since 1995 (ELPI models 95 and 97). Unless otherwise specified, the following addresses the commercial models. The instrument is based on the charging of particles, which enter a low-pressure cascade impactor, and subsequently measuring the electric current carried by the particles onto the impactor stages. Real-time operation is essentially achieved by measuring the current with a time constant of a few seconds. Figure 14-4 shows a schematic diagram of the ELPI. It consists of three main components: a cascade impactor, a unipolar diode charger, and a multichannel electrometer. The charger is used to charge sampled particles to a well-defined charge level. The charged particles are then introduced into the cascade impactor that classifies the aerosols into 12 size fractions according to their inertia and consequently their aerodynamic diameter. The particles collected on a specific impactor stage produce an electrical current that is recorded, in real time, by the appropriate electrometer channel. A PC is required to run the operating software and record the measurement results. Instrument Design

Impactor design is always a compromise. In the ELPI, the main design criteria were sharpness of cut, low losses of fine particles, and small stage and interstage volumes. Relatively

Corona charger

High voltage source ion trap voltage source

Electrometers

impactor with insulators and contact needles

A/©

RS-323 serial

Fig. 14-4. Schematic diagram of the Electrical Low-Pressure Impactor.

high jet speeds and high-pressure ratios between stages are employed. To achieve an adequate size resolution, the impactor uses a relatively high number of stages. There are 12 stages equipped with current detection, plus an extra impactor stage providing the upper cut diameter of lOum. The lowest cut diameter (stage 1) of the instrument is 30nm. Jet orifices are drilled symmetrically in rings around the center of each stage. Stage 1 serves as a critical orifice and controls the flow rate through the impactor. An external valve is used to obtain the correct operating pressure at the impactor outlet. To charge particles, the ELPI uses a simple point-tube geometry corona charger. A high positive voltage (approximately 5 kV) is applied to a tungsten needle electrode in the center of a cylindrical tube. The particle stream is then introduced through the same region in the direction perpendicular to the applied electric field. Particles are thus exposed to a unipolar positive ion environment and electrically charged. After the charging zone, an electrical mobility analyzer of Oth order ("trap") is used to remove ions and those charged particles that have a smaller size than the measuring range of the instrument. This type of analyzer is characterized by the critical mobility (i.e., the particle mobility with zero penetration). The critical mobility of the trap depends on the physical dimensions, flow rate, and voltage. In the commercial unit, the cut size is approximately 20 nm. The advantages of this charger configuration are its simple construction and high charging efficiency. One disadvantage is the relatively high particle loss for the charger. Theoretical Considerations

In the case of pure diffusion charging, the measured current can be used as a direct measure of the active surface area of the particles, as described in the surface area measurement section below (Eqs. 14-21 and 14-24). Therefore, the primary output of the instrument is related to the aerodynamic size distribution of the active surface area concentration. The desired output is, however, normally either particle number or mass distribution. To obtain these, the measured current signal is converted to a number concentration as a function of aerodynamic diameter, da, using the charger efficiency function £ch(da).The resulting response function relates the measured current to the number concentration at different particle sizes as follows: (14-6) where N is the number concentration (m"3), / is the measured current (A), P is the penetration through the charger, n is the average number of charges per particle, e is the charge of an electron (1.602-10"19 C), and Q is the flow rate of the instrument (m3/s). The charger efficiency function can be defined as Ech(dp) = PneQ. In practice, the charger efficiency function is determined experimentally. The function includes particle losses inside the charger, the fraction of particles charged, and the average particle charge. The overall response function of an ELPI channel is the product of the charger efficiency function and the collection efficiency function of one of the impactor stages. The impactor stage response functions are approximated by ideal rectangular functions. They have a value of one between the cut size of the stage and the cut size of the one preceding it. The channel response function then equals the charger efficiency function evaluated at the stage i geometric midpoint diameter, which is defined as (dx dk)1/2, where dx is the cut point of the stage in question and dk is the cut point of the next upstream stage. The number concentration at stage i is acquired by dividing the measured current of channel / with the charger efficiency calculated at the stage geometric midpoint dx\ (14-7)

In an electric field, particle acceleration is measured by electrical mobility, which is used to define an electrical mobility diameter dx. The particles are classified in the impactor according to their aerodynamic diameter, dai. To evaluate the number concentration as a function, the effective density peff of the particles needs to be known or estimated (Moisio et al., 1997; Ahlvik et al., 1998). Using a standard density (1000kg/m3, p0), (14-8) where Cc is the Cunningham slip correction factor. The mass concentration my at channel i is calculated as (14-9) Actual impactor collection efficiency curves differ from the ideal rectangular ones. Losses, primarily by diffusion and space charge forces, cause the curves to display a tail of increased collection on particle diameters much smaller than the actual impaction cut-off diameter (Juan et al., 1997; Keskinen et al., 1999). On gravimetric measurements made by cascade impactors, fine particle losses to the upper stages are not critical, as the mass introduced by diffusion deposition is insignificant when compared with the mass of particles actually impacting the stage. When electrical detection is used instead of the gravimetric method, the current carried by the fine particles can be significant compared with the signal caused by particles impacting the stage. To correct for the effect, a simple noniterative calculation algorithm is used, solving the one-sided cross-correlation problem (Moisio, 1999). Calibration

The concentration measurement is affected by the precision of the values describing charging efficiency, collection characteristics of the impactor, flow rate, and current measurement. The calibration of the flow rate of the instrument is straightforward and can be performed following the principles discussed in Chapter 21. The current measurement is affected by the whole amplification-A/D conversion train. This can be calibrated using a current standard. For the calibration of charging efficiency and impactor cut values, aerosol techniques are needed. It is advantageous to employ the electrical current measurement system of the ELPI to calibrate the impactor. Indeed, the measurement principle can be used to calibrate any cascade impactor (Keskinen et al., 1999). Marjamaki et al. (2000) measured collection efficiency curves of all the impactor stages using monodisperse aerosols. The average value of the square root of the Stokes number at 50% collection efficiency was found to be 0.456, with a standard deviation of 0.017, so the charger efficiency can be determined using monodisperse aerosols. To approximate the charging efficiency, a power function is used to fit the experimental data in three different particle size ranges. (14-10) It is impossible to produce absolutely identical cascade impactor units, as very small differences in the jet dimensions may cause changes in the cut values. In principle, therefore, every instrument should be individually calibrated. In practice, this is unnecessary as the procedure described by Hillamo and Kauppinen (1991) can be followed: First, the impactor jet sizes, flow rate, and operation pressures are measured. Second, the impactor cut values are

calculated using Eq. 10-4, and the gas properties are evaluated for each stage at the upstream stagnation conditions (Flagan, 1982; Hering, 1987). For the Stokes number, the value mentioned above is used. Good agreement has been found between cut values calculated in this manner and the calibrated ones (Hillamo and Kauppinen, 1991). For minor changes in the flow rate, the Pn values of Eq. 14-10 can be used without correction. Potential Biases

It is important to stress that the number and mass size distributions are obtained indirectly by calculation using the instrument software or a spreadsheet application. The relative importance of different nonideal phenomena depends on which of the two size distributions are being measured. To obtain an accurate mass distribution, it is necessary to accurately determine the effective density of the measured aerosol particles. As described above, to obtain accurate results, the effective density of the particles should be known. This is possible by conducting simultaneous measurements with a DMA and an ELPI (e.g., Ahlvik et al., 1998). Without any a priori knowledge, there can be substantial error in the estimated particle density. This causes a potential source of bias. The size of bias in measured concentration depends on particle size through the size dependence of slip correction and charging efficiency. Moisio (1999) reported calculations of the effect on the TUT prototype ELPL Rather against intuition, the effect of density on the instrument reading is higher in the number concentration measurement. In the mass measurement, the effect of particle density is small for coarse particles, but becomes quite significant for fine particles having a large slip correction factor. The ELPI is based on impaction and, therefore, has some of the problems associated with impactor measurements, namely, particle bounce and blow-off (see Chapter 10). These effects are further complicated by possible contact charging during particle bounce. Contact charge transfer is difficult to predict without knowledge of the particle material (John, 1995; Horton et al., 1992). As in normal impactor use, preparation of the collection substrates is extremely important. Moisio (1999) reported severe distortion of the number size distribution of solid NaCl particles when ungreased aluminum foil substrates were used. Even careful greasing of the substrates did not completely solve the problem. Replacing the foil substrates with sintered metal plates covered with light oil solved the problem (Moisio, 1999). These substrates, however, also change the cut characteristics of the impactor, requiring a new calibration. Problems encountered trying to calibrate the instrument with solid polystyrene latex (PSL) particles (Maricq et al., 1999) can probably be attributed to particle bounce and contact charging. The correction algorithm mentioned above assumes that all particles are collected. If there is a significant fraction of the aerosol distribution below the lowest cut size, this assumption is not met. This causes an overestimate in the measured concentration of particles with diameters that are much larger than the mode of the distribution. A similar bias is encountered if the calibrated loss values are too low, as described by Virtanen et al. (2001). They showed that for high-concentration measurements the space charge force has to be taken into account. Results and Applications

The ELPI offers an indirect but rapid means of measuring particle size distribution. The most appealing applications are those where rapid response is necessary. As an example, Kymalainen et al. (1996) used a TUT-ELPI to measure the time-dependent fume release rate during black liquor pyrolysis. In this application, the fuel is heated in a controlled atmosphere in a grid heater, where particle release takes place during short, 5 to 60s periods. With real-

TEOM [mg m"3]

ELPI [mg m"3l

Fig. 14-5. Comparison of instantaneous mass concentration readings of ELPI and TEOM (in mg/m3). Different markers indicate different power plants. Both instruments sampled through a 1/100 dilution system; the readings are the calculated stack concentrations (Moisio, 1999).

time measurement, it was possible to identify two successive particle production stages even during this short period. For the total particle mass integrated over the release period, good agreement was reported with gravimetric filter measurements. The first application of the TUT-ELPI was a real-time measurement of combustion aerosol size distributions. Measurements were made at several full-scale power plants (Moisio et al., 1995; Moisio, 1999; Latva-Somppi et al., 1998). Among other things, instantaneous mass concentration values calculated over the measured size range were compared with TEOM results. These results are shown in Figure 14-5 (Moisio, 1999). There is clearly a very good correlation over a wide range of concentrations. The actual concentrations measured by the instruments were a factor of 100 lower than those shown in Figure 14-5 because both instruments sampled through a dilution system. Therefore, the large differences at low concentrations are probably influenced by instrumental noise. The most important application of the ELPI is in measuring particles emitted by diesel and gasoline engines (Ahlvik et al., 1998; Klein et al., 1998; Pattas et al., 1998; Maricq et al., 1998, 1999). To evaluate emissions, the engine is typically operated according to a standardized test cycle, where the speed and load of the engine vary rapidly (for an overview of the cycles, see, e.g., http://www.dieselnet.com/). Ahlvik and co-workers (1998) used the ELPI to measure size distributions of diesel emissions. Light- and heavy-duty engines were tested for both steady-state and transient driving cycles. Relatively good agreement in total number concentration with the scanning mobility particle sizer (SMPS) was reported assuming standard density, and good agreement was found when using measured effective density values. The main limitation was found to be the lower size limit of 30 nm because a large number of particles had a smaller diameter. Maricq et al. (1999) used both the ELPI and the SMPS in measuring size-resolved particle emissions of gasoline vehicles during transient drive cycles. Good agreement was reported between ELPI and SMPS size distributions, except for the lowest ELPI channel, which tended to systematically overestimate the particle concentration.

SURFACE AREA MEASUREMENT Introduction and Theoretical Background The attachment of molecules or atoms to aerosols is a method that allows surface-related information to be obtained by on-line techniques. These techniques do not yield the geometric surface, through use of a Brunauer-Emmett-Teller (BET) analysis for example, but an "active surface," which is defined below. To detect attached atoms or molecules, they have to be labeled. Two techniques are therefore used: radioactive labeling or electrical charging. Instruments based on these two principles are described in the following sections. Attachment is determined by two probabilities: the collision probability and the sticking probability or sticking coefficient. Only the first depends on geometry and is of importance here. As only the product of both can be measured, the sticking coefficient has to be unity. This is the case for ions, detected by an electrical measurement. Even if the "carrier molecule" does not stick, a charge transfer will occur. The condition is also fulfilled for large molecules or heavy atoms such as lead. When a sticking coefficient of unity is assumed, the attachment probability equals the collision probability. For ions, image and Coulomb potentials may influence the collision probability in addition to geometry. To avoid Coulomb repulsion, the ion concentration has to be kept low enough to avoid multiple charging. The image force can be neglected if the particle diameter is larger than about 10nm (Filippov et al., 1993). If these conditions are fulfilled, the integral collision cross section, or attachment cross section, can be measured. This is an important quantity by itself, as it determines adsorption kinetics and, thereby, has significant influence on chemical reactions between particles and the surrounding gas phase or gas phase reactions when the particles serve as a catalyst. It also determines particle growth by attachment of material from the gas phase. It could be described as the fraction of geometrical surface, which is directly accessible from outside. For this reason the term active surface is used. Another related designation, introduced in connection with the Epiphaniometer (Pandis et al., 1991), is the Fuchs surface. In addition, it can be shown that for a certain particle mobility b, mobility and active surface A are inversely proportional within an uncertainty of some percent independent of particle size, shape, or material (Siegmann and Siegmann, 2000). BA = const.

(14-11)

This is plausible because the drag force, defining B, is also due to collisions with carrier gas molecules. The collisions determine the momentum transfer and, thereby, also the drag force. This means that A will scale with the mobility diameter d2 in the free molecular regime and with d in the continuum regime. In a more rigorous calculation, size and mass of the adsorbing species have to be considered, as is taken into account in the definition of the Fuchs surface by Pandis et al. (1991). In this discussion, it is assumed that dmoiecuie « dparticie. This makes the calculation more straightforward, and hence the resulting error is small compared with the experimental errors. Figure 14-6 (adapted from Gaggeler et al., 1989) shows the attachment rate as a function of the mobility diameter. Both regimes (<* cfi and <*d) are clearly seen. In the free molecular range, the relation between mobility B and mobility diameter d is given by (Fuchs, 1989) (14-12) where A is the mean free path in the gas, 7] is the viscosity, and 8 is a scattering parameter, depending on whether the scattering is diffuse or specular, which is close to unity. In this size range the active surface is defined by the geometric surface of a sphere

Activity per Particle Concentration [cm s" ]

Particle Diameter [um] Fig. 14-6. Attachment rate as a function of the mobility diameter. (Adapted from Gaggeler et al, 1989.)

(14-13) The relation between A and B then becomes (14-14) As this relation is valid for the whole size range according to Eq. 14-11, it is used as a definition of the active surface of a particle of mobility B. The total active surface then becomes (14-15) What is really measured is the attachment rate dn/dt. For particles of a certain size, this is (14-16) where n is the atom/ion concentration and dn/dt their decrease in concentration due to attachment to particles, N is the concentration of monodisperse particles of diameter d or mobility B, respectively, and K is the appropriate coefficient. In the free molecular regime K is (Siegmann and Siegmann, 2000) (14-17) where v is the average velocity of the atom/ion and m its mass (14-18) Replacing d2 by A according to Eq. 14-13 yields

(14-19) The attachment rate then becomes (14-20) For poly disperse aerosols A has to be replaced by the total active surface ^4tot (Eq. 14-15). Atot can now be calculated from the measured dn/dt by (14-21)

Again, no assumptions on particle shape or size are required. EPIPHANIOMETER Measurement Principles The Epiphaniometer (EPI) was developed at the Paul Scherrer Institute (PSI), Switzerland, in the 1980s (Gaggeler et al., 1989). The EPI is based on the attachment of lead atoms (211Pb), produced by the radioactive decay of a long-lived 227Ac source:

The number of attached events of its daughter, 211Bi.

211

Pb lead atoms is then determined by counting the oc-decay

Instrument Design Figure 14-7 shows the instrumental configuration. The aerosol sample is first introduced into a 0.002 m3 [2L] exposure chamber. The actinium source at the bottom of this container provides a constant supply of 219Rn gas. The radon decays with a half-life of 3.96 s to 211Pb, which deposits on the particles. Next, the stream of particles and unattached lead flows through a 1 m tube. The unattached lead diffuses to the walls of the tube while the aerosol continues on to a total particle filter located directly beneath an a-detector. The attached 211Pb concentration is measured via the cc-decay of its daughter 211Bi. The geometry of the detection unit is optimized to ensure correct measurement of oc-particles. A Nuclepore filter collects particles on its smooth surface; thus the a-particles do not have to penetrate out of the filter material. The tubing is fed through the hole of the detector for optimized geometry. The ocdetector is an annular PIPS-detector (Planar Ion-implanted Passivated Silicon, Canberra Semiconductor, NV) with a central 5 mm hole. This detector produces a signal proportional to the energy of the incident a-particle. The signals from the detector are classified into a 1024 channel histogram. This design offers the ability to simultaneously measure the activity concentration of 212Pb, the daughter nuclide of the naturally occurring 220Rn, and 211Bi from the 227Ac source. Due to overlapping oc-energies, the measurement of the better-characterized 214Pb (daughter nuclide of natural 222Rn) is only possible if the 227Ac source is eliminated from the system.

aerosol in

capillary a-detector

filter

227

Ac source

Fig. 14-7. Instrumental setup of the Epiphaniometer.

Flow control (typically 1.7 x 10~5m3/s [lL/min]) is achieved with a mass flow controller, which directly regulates the pump. Both an alternating current version and a batteryoperated version are available. The battery version has a very low energy consumption of only 8 W. Improved versions of these instruments will soon be commercially available by Matter Engineering (MAT). Principally, the detection limit of the EPI depends on the background of the detector signal, which is negligible. For low aerosol concentration the uncertainty of the measurement is given by counting statistics. An average Poisson error of 10% translates into a surface area concentration error of 0.2 and 0.003 urn2 cm"3 for integration times of 30min and 24 h, respectively. Under steady-state conditions (i.e., a constant aerosol input) the decay rate of 211Bi equals the rate of lead deposition on the filter. For nonsteady conditions, the instantaneous rate of 211 Pb deposition can be deconvoluted from the EPI signal using the known radioactive decay rates as follows. The EPI accumulates alpha counts for time intervals At, typically from 5 to 30min.The total number of counts in this interval Y, depends on the amount of lead deposited during the count interval and also on previously deposited lead. The average rate of lead deposition over the count interval / + 1 is, therefore, approximately (Rogak et al., 1991) (14-22) where X = In 2/36.1 min 1 is the 211Pb decay constant. This inversion procedure may be applied for time intervals of lOmin and higher. For shorter time intervals, a rigorous inversion giving a time resolution of a few minutes has been developed by Pandis et al. (1991). A higher time resolution is not advisable because of the inherent damping by the half-life of the 211Pb. Calibration of the instrument is best performed with a monodisperse aerosol obtained with a Differential Mobility Analyzer (DMA), where the number concentration is simultaneously determined with a Condensation Particle Counter. Potential Biases

The EPI signal is proportional to the active surface area concentration only when the lead concentration in the chamber is unaffected by the aerosol. This is the case when most of the

lead atoms are deposited on the walls of the instrument rather than on the particles. For large surface area concentrations, most of the lead produced in the chamber becomes attached to aerosol particles resulting in saturation. When saturated, the detector count rate reaches a maximum, ,smax. It was experimentally shown that for count rates less than 45% of S103x the EPI signal is linearly related to the surface area (Rogak et al., 1991). For higher count rates the EPI response becomes significantly nonlinear, and a correction factor F was determined experimentally (Rogak et al., 1991) as (1^23) where x = s/smax is the measured activity normalized by the saturation value. This correction is not necessary as long as the measurements are not made too close to sources. Even though the attachment rate scales with the lead atom concentration in the container, this saturation limit may not be increased by decreasing the 227Ac activity because it is determined by the relative change of the lead atom concentration. It can, in principle, be increased either by decreasing the container volume or increasing the flow rate, both resulting in a decreased residence time of the aerosol in the container. However, a complete decay of the gaseous 219 Rn has to be ensured, which otherwise penetrates to the filter and contributes to an increased background count rate. This takes at least 10 half-lives (40 s). Another potential bias may result if the sticking coefficient is lower than unity because the attachment probability will then not equal the collision probability (see introduction to the discussion of surface area measurements, above). Tests using a variety of different aerosols, however, have confirmed that the sticking coefficient does not depend on the chemistry of the aerosol particles and is equal to unity in all cases (Rogak et al., 1991). Results and Applications

Because of its low detection limit and robust design, the EPI is a suitable instrument to be operated at remote locations. Due to its low energy consumption, the instrument has been deployed on a glacier using a solar panel power source (Baltensperger et al., 1991; Lugauer et al., 1998). In 1988, EPI measurements were started at the high-alpine research station Jungfraujoch, 3454 m above sea level (Baltensperger et al., 1997). As an example, Figure 14-8 shows EPI measurements from the Jungfraujoch. Since July 1995 continuous measurements of other aerosol parameters have additionally been conducted, and the station has been incorporated into the European baseline station of the Global Atmosphere Watch Program (GAW) of the World Meteorological Organization (WMO), also comprising the Zugspitze (Germany) and the Sonnblick (Austria). The highest aerosol concentrations have been found from June to August and the lowest from December to February. A pronounced diurnal variation, having a maximum in the late afternoon, was observed in summer. This feature is due to thermal transport processes of air masses from the polluted planetary boundary layer into the free troposphere. A detailed meteorological analysis (Lugauer et al., 1998) has shown that seasonality and diurnal variations mainly depend on synoptic weather conditions, as does the vertical transport. This is most evident in the difference between anticyclonic and cyclonic weather conditions. The EPI has also been deployed on a cruising ship in the Atlantic and southern oceans (Davison et al., 1996), in a tunnel study (Weingartner et al., 1997), and for measurements in an urban area (Harrison et al., 1999). DIFFUSION CHARGER Measurement Principles and Instrument Design Diffusion charging has already been used for a long time mainly in connection with size analyzers. A unipolar diffusion charger is used, for example, in the TSI Electrical Aerosol

Aerosol Surface Area (u.m2 cm"3)

Epi Signal (counts s"1)

summer, anticyclonic summer, indifferent summer, cyclonic winter, anticyclonic winter, indifferent winter, cyclonic

Time (LST) Rg. 14-8. Median diurnal variation of aerosol concentration at the high-alpine site Jungfraujoch (3454 masl) for anticyclonic, indifferent, and cyclonic conditions of the convective weather types in summer (June, July, August) and in winter (December, January, February). Data from 1991 to 1997 (Lugauer et al, 1998). Left axis: Epiphaniometer signal; right axis: aerosol surface area concentration, deduced from Epiphaniometer signal by assuming a constant shape of the number size distribution.

Analyzer (EAA) and in the ELPI, described above. Here, its use in combination with an "aerosol electrometer" (i.e., a device to measure the current corresponding to the flow of charged particles, termed DC sensor) will be discussed. In a similar manner to the ELPI, the diffusion charger uses positive or negative air ions to measure the attachment coefficient of particles. Figure 14-9 illustrates the working principle of this instrument. Ions are created by an electrical corona discharge from a corona tip operated at several kilovolts. These ions may attach to the particles by Brownian diffusion. After the charging section, remaining ions are removed by an ion trap electrode to which a low voltage is applied. The particles are then precipitated onto an electrically insulated filter, and the filter current is measured using a sensitive current amplifier. This current / directly yields the ion attachment rate (14-24) which is required to calculate the active surface according to Eq. 14-21. The simple design described above has some drawbacks. In particular, small particles are lost by precipitation in the high electric field in the vicinity of the corona electrode. A number of other charger designs have been developed. To our knowledge, only one commercial instru-

aerosol in

HV

ion trap

current amplifier Fig. 14-9. Working principle of the diffusion charger. HV indicates the high voltage corona power supply.

measuring filter

corona wire

aerosol in

out

high voltage grid voltage

ion trap current amplifier

ion current Fig. 14-10. Configuration of the LQl-DC by Matter Engineering (MAT).

ment is available that allows charging plus current measurement in one unit. This is the LQlDC (MAT). It has the configuration shown in Figure 14-10, where the charging section is similar to the configuration used by TSI in the EAA. The corona discharge is not concentrated on a tip, as in Figure 14-9, but occurs along a thin Pt wire. This has the main advantage that it is less sensitive to contamination. To reduce particle losses due to the high-voltage field, the charging section and the corona discharge are separated by a screen. The electrical field is small in the charging section. Only several volts are applied between the screen and the outer tube. This field is required to make the ions penetrate the screen and enter into the charging section. The ion concentration may be monitored by measuring the current between the outer tube and ground. This current can be used to keep the ion concentration constant by a feedback control, which controls the high-voltage supply. This feedback control is not yet included in the LQl-DC, which uses a constant corona voltage. This design offers efficient charging and relatively low particle losses except for very small particles of only a few nanometers in diameter, which have an electrical mobility high enough to also be precipitated in the weak field. Again, ions have to be prevented from reaching the measuring filter through use of an ion trap, as described above. The positively charged particles are then deposited onto the measuring filter, which has a high insulation to ground. The filter is

connected to the input of an ultralow current charge amplifier (resolution 1 fA = 10 15A). The voltage signal from the charge amplifier is converted into an analog signal, calibrated to yield the active surface. Other charger designs described in the literature are, for example, designed to minimize ultrafine particle losses. Buscher et al. (1994) described a charger using a square-wave AC field instead of a DC field in the charging section. This reduced small particle losses. Potential Biases

As mentioned, several conditions have to be fulfilled to obtain a value that corresponds to the collision probability of neutral atoms or molecules onto the active surface, respectively. 1. The particle concentration or, more precisely, the total active surface has to be low enough to avoid depletion of ions due to attachment to particles; otherwise, saturation occurs. From this point of view, the time available for charging should be short. 2. Multiple charging should be avoided because Coulomb repulsion decreases the attachment probability. This condition is usually fulfilled for very small particles. For larger particles problems occur if the n-t product is high. This means that avoiding multiple charging requires a small charging efficiency or low n-t product. 3. For particles <10nm the image potential will increase the effective collision cross section; the attachment rate of ions will be higher than that for neutral species. 4. The detection limit is determined by the noise and zero stability of the current amplifier. For the LQl-DC, the detection limit is about 1 fA (10"15A). To obtain a low detection limit in terms of the active surface with available amplifier characteristics, efficient charging is required. This can be obtained by means of higher n-t values, resulting in a measurable concentration range of 10 to 2000 um2 cm"3 in the active surface area concentration. 5. Ion attachment is not material dependent as long as it is dominated by Brownian diffusion. As soon as electrical forces become important, material dependence has to be considered. This is the case for ultrafine particles smaller than 10 nm where, as mentioned above, the image potential becomes important. The image force depends on the polarizability, which is material dependent. These considerations show that the requirements on the charging efficiency are contradictory. A compromise has to be found to obtain optimal performance. For maximum flexibility, the ion concentration needs to be adjustable, to be adapted to the specific measurements. The ion concentration should be high if a low detection limit is required and the particles are small and low if larger particles have to be measured. Results and Applications

In principle, the EPI and DC sensor yield the same information, which is the active surface as defined above. However, when regarding possible applications, there are significant differences. The EPI is a very sensitive instrument, as it is based on counting single events (radioactive decays). It is, therefore, especially suited to measure very low concentrations, whereas at higher concentrations saturation occurs due to the long residence time. The diffusion charger, on the other hand, operates up to a much higher concentration, as the residence time is short. However, due to current amplifier limitations, its detection limit is significantly higher than that of the EPI. The response time of the diffusion charger is short; a time resolution of less than 1 s is possible. This makes this instrument suitable for

monitoring transient processes such as engine acceleration. Examples are given after the next section.

PHOTOELECTRIC AEROSOL SENSOR Measurement Principle

Photoemission has long been used as an analysis method in surface physics, mostly under vacuum conditions. Applied to aerosols, aerosol photoemission is a sensitive method for the characterization of fine particles. The method was developed at the ETH in Zurich (Schmidt-Ott and Federer, 1981), and the working principle of aerosol photoemission is described in detail by Burtscher (1992). If particles are irradiated with ultraviolet light of a photon energy higher than the ionization threshold of the particles, electrons are emitted from the particles. Depending on the carrier gas, these electrons will rapidly attach to a gas molecule and form an ion, diffusing in the vicinity of the particle. If the particle is small compared with the mean free path of the electron, the probability of back diffusion is negligibly small. For larger particles this probability increases, which means that only small particles can be charged efficiently by photoelectron emission. In practice, particles with diameters smaller than ljim can be charged. A quantitative treatment of this process was developed by Filippov et al. (1993). As mentioned, the photon energy has to be higher than the threshold; on the other hand, it should be lower than the excitation energies of the carrier gas, and it has to be lower than the ionization threshold of the carrier gas. In air, this leaves a relatively small window in the range from 4 to 7 eV. At 7 eV, O2 becomes excited; however, the efficiency is low enough to keep O3 formation and other undesired photochemical reactions at a tolerable level. Electron emission probability, also termed photoelectric yield, depends on light absorption leading to an excited electron and on the subsequent escape probability of this electron. Because the mean free path of electrons is on the order of several monolayers, only electrons excited close to the surface can be emitted. This method, therefore, is extremely sensitive to the state of the surface, for example, to material adsorbed on the particle surface. A number of studies have shown that ambient particles, originating from incomplete combustion of hydrocarbons, exhibit the highest photoelectric yield and may, therefore, be selectively detected. A special role is ascribed to adsorbed polycyclic aromatic hydrocarbons (PAHs), which usually dominate the photoelectric yield in the case of urban ambient aerosols. On the other hand, diesel emissions may contain so few PAHs that photoemission is determined by elemental carbon, even though it has a significantly lower photoelectric yield. Instrument Design

Figure 14-11 shows the basic components of a sensor to measure aerosol photoelectron emission. Sampled air is introduced into the system through a quartz glass tube where it is irradiated by an external ultraviolet light source. The resultant photoelectrons (or negative ions) are separated from the positively charged particles in an ion trap that follows the ionization chamber. The charged sample particles are finally collected from the sample stream in an electrically insulated filter that is placed in a Faraday cup. This filter is connected to a current amplifier where the current of positively charged particles is measured. The design of the photoelectric charger is similar to that of the diffusion charger. The major difference is in the way the particles are charged. The EcoChem PAS2000 (ECO) uses an excimer lamp surrounding the sample air tube. This design feature allows a compact construction. In addition, excimer lamps have a long lifetime and a very short warm-up time. This means that the sensor can be operated in a

quartz tube

uv lamp measuring filter

out

ion trap current amplifier Fig. 14-11. Instrumental setup of a photoemission sensor.

pulsed mode by continuously switching the lamp on and off. This allows continuous offset compensation and increased sensitivity by measuring the difference signal between having the lamp on or off. Relatively small battery-operated units are available that can be used in personal monitors. Potential Biases

The chamber wall, which is irradiated with ultraviolet light, should be made from a material with a high work function and that is transparent to ultraviolet light. However, such materials can readily achieve high static charge levels, and the resulting electric field can cause significant small particle losses. This is usually no problem for particles with d > lOnm. A solution for particles in the nanometer range is presented by Jung et al. (1988). The authors used two concentric metal grids placed on the inside of a chamber. Rosatzin and Burtscher (1988) presented a similar design, which can be used for high-temperature sample streams. In the case of PAHs that are adsorbed on carbon particles, photoemission increases sharply for surface coverage less than a monolayer (Burtscher and Schmidt-Ott, 1986). Different PAHs have a different influence on the photoelectric yield. Some even cause a decrease compared with pure carbon particles (Niessner and Wilbrig, 1989). The major increase of photoelectric activity is observed for large planar PAHs. As long as the coating is thin, the increase in photoelectric activity is directly related to the degree of surface coating or amount of PAH on the particle. This relationship is no longer true for thick coatings. In this case photoemission only occurs at the top layer. Results and Applications

Particles from incomplete combustion were found to have a high photoelectric yield. This was attributed to the activity of adsorbed PAH that partially covered the surface of the particles. The photoelectric yield of combustion particles has been shown to be linearly related to the total mass concentration of particle-bound PAHs (PPAH) in ambient air samples (McDow et al., 1990; Hart et al., 1993; Wilson and Barbour, 1994). For an urban aerosol, PPAH can be detected with these sensors in the concentration range of 1 to 5000 ngm"3. Experimental results and theoretical considerations show that the size dependence of photoelectric yield is very similar to that of atom attachment, as measured by an EPI or a DC sensor (Siegmann and Siegmann, 2000). Normalizing the signal of the Phototoelectric Aerosol Sensor to that of the EPI or the DC sensor therefore eliminates the influence of particle size and concentration. What remains is information on the chemical nature of the surface and

photoelectric charging [a.u.]

candle diesel paper cigarette

diffusion charging [a.u.] Fig. 14-12. Photoemission versus diffusion charging for particles from different types of combustion. (From Siegmann et al, 1999.)

the bulk of the particle. This method was used by Ammann et al. (1992,1993) to characterize volcanic aerosol and by Matter et al. (1999) to investigate the source attribution of different combustion aerosols. As an example, Figure 14-12 shows the signal of the photoemission sensor versus the signal of the DC sensor for exhaust from different kinds of combustion. The slopes contain the above-mentioned chemical information, while the linear relation shows that the size and concentration do not influence the slope (= normalized signal). Photoelectric aerosol sensors are commercially available (ECO and MAT) and are often used for ambient air quality monitoring.

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an instrument, characteristic features of an OPC-like permissible range of number concentration, sensitivity, sampling flow rate, classification accuracy, and size resolution are discussed on the basis of experimental results. Aerosol photometers are useful as real-time dust monitors in industrial hygiene, for concentration measurements of atmospheric aerosols, and in aerosol inhalation studies. The linearity range of light intensity versus aerosol concentration is limited at high concentrations by multiple scattering and at low concentrations by the stray-light background. The noise level of sensitive instruments is determined by Rayleigh scattering from the air molecules. Whereas theoretical and experimental calibration curves of optical systems are usually based on particles of spherical shape, most real aerosol systems consist of particles of irregular shape. Therefore, the final section deals with the influence of particle shape on scattering and extinction of light. LIGHT SCATTERBSG AND EXTINCTION BY A SINGLE SPHERE Rigorous Electromagnetic Theory Electromagnetic theory (Mie, 1908) yields the rigorous solution of light scattering and extinction by a spherical particle. The mathematical expressions have been published in extensive textbooks, such as those by van de Hulst (1957) and Kerker (1969). A list of programs available from several institutions to calculate these expressions is provided on the Internet at diogenes.iwt.uni-bremen.de/~wriedt/Mie_Type_Codes/body_mie_type_codes.html. Light Scattering. Let a spherical particle of diameter dp, made up of a material of refractive index m, be situated at the origin of a system of spherical coordinates and be illuminated from the negative z-direction by a plane, linearly polarized, monochromatic wave with electric vector in the x-direction (Fig. 15-1). The spatial distribution of the light scattered by the particle per unit solid angle can then be described by a scattering function / that depends on the scattering angle 0, the polarization angle <j>, the particle diameter dp, the wavelength A, and the refractive index m- n -jk of the particle material («, real part; k, imaginary part): (15-1) Particle diameter dp and wavelength A are connected by the dimensionless size parameter (15-2) The power of light scattered by a particle per unit solid angle in direction 0 is given by

Fig. 15-1. Spatial parameters for light-scattering calculations.

(15-3) where I0 is the illumination intensity (power of light per unit area). Various optical arrangements used for making light-scattering measurements differ in the mean scattering angle 0O, the receiver aperture AQ, and the kind of illumination. Using linearly polarized light of a plane monochromatic wave for the illumination, the power of scattered light collected by a certain optical arrangement can be calculated by the integral (15-4) For the dependency of the scattering function i on the azimuthal (polarization) angle 0, one obtains (15-5) where I1 (0, a, m) and i2 (0, a, m) are the two components of the scattering function in the plane perpendicular and parallel to the polarization vector E of the incident light (Fig. 15-1), For unpolarized light, one gets i = l/2(/i+ i2), and the scattering pattern is symmetrical in rotation with respect to the axis of the illuminating beam. White light illumination additionally requires an integration over the wavelength, whereby the spectral radiation density of the light source and the spectral sensitivity of the photodetector have to be taken into account (Heyder and Gebhart, 1979). If the particles are illuminated with noncollimated light, a further integration over the illumination aperture has to be carried out. To characterize single-particle light scattering, two further quantities are commonly used: (1) partial scattering cross section: (15-6) and (2) angular scattering coefficient: (15-7) In this definition Qs is the power of scattered light per unit solid angle in relation to the power /0(7i/4)dp striking the projected area of the particle. Light Extinction. Light extinction is the attenuation of a parallel beam of light due to absorption and scattering by the particles. For a single sphere with size parameter a and refractive index m, light extinction can be described by its extinction coefficient Ex(a, m), which can be calculated by electromagnetic theory. The power of light -APx, which is removed by a particle from a parallel beam of light with intensity /0, is then given by (15-8) Approximations

The mathematical formalism of the rigorous electromagnetic theory does not allow a good physical understanding and interpretation of the phenomena of light scattering and extinction by small particles. This understanding can be improved using two approximations.

Light Scattering. When the particle size is much smaller than the wavelength (a« 1), the particle is subjected to an almost uniform field. The particle then oscillates like a dipole with a polarization proportional to the electrical field of the incident wave. The scattering properties of such a particle can be expressed by its polarizability, p, which is generally a tensor and reduces to a scalar for a homogeneous sphere. In the dipole or Rayleigh approximation and for unpolarized monochromatic light, the flux of light scattered by a spherical particle per unit solid angle is given by (15-9) Equation 15-9 contains the polarizability ps of a sphere (van de Hulst, 1957) so that (15-10) where ps = 3l(ra2 - l)/(m2 + 2)1 V and V is the volume of the sphere. In the limiting case of a » 1, the scattered light can be considered as consisting of three parts, which are due to the physical effects of diffraction, reflection, and refraction (Fig. 15-2). For monochromatic light, the angular scattering coefficient Qs can be expressed as the sum of its components according to (15-11) Higher order internal reflections Qx, i > 2 (rainbows) are not contained in Eq. 15-11. Qo(O, a) is the diffracted part of scattered light. It is independent of the optical constants of the particle material. Its angular distribution, however, depends on the size parameter a. Q1 (Q, m) is the fraction of light scattered by reflection on the surface of the particle. Its angular distribution is independent of the particle diameter but is influenced by the optical constants of the material. Qi(Q, m) is the component scattered by two refractions by the particle surface. Its angular distribution again depends on the optical constants but not on the particle diameter.

REFLECTION, Q1

REFRACTION, Q2 Q3

DIFFRACTION, Q0 Fig. 15-2. Decomposition of scattered light into components according to ray optics.

For low scattering angles (0 < 10°), the diffracted part of scattered light can be approximated by the Fraunhofer formula according to (15-12) where I1 (a sin 0) is a first-order Bessel function. Some angular distributions of the three fractions of scattered light are shown in Figure 15-3. The calculations are based on formulae published by Hodkinson (1962). For the reflected and the refracted light, only the components polarized perpendicular to the plane of observation are drawn in the diagram. Because the diffraction part changes with particle diameter, two a values have been considered. The forward lobe of diffraction is limited to the angular range 0 < 0min, where (15-13) While the reflected component Qx (0, m) covers the whole angular range 0 < 0 < 180°, the refracted part is confined to the range 0 < 0max, where (15-14) Light Extinction. The extinction coefficient in the dipole or Rayleigh approximation can be expressed by two terms, according to (15-15) where Im is the imaginary part. The first term represents the absorption component of extinction and the second term the scattering component.

SCATTERING COEFFICIENT, Q

DIFFRACTION,Q0

REFRACTION, Q2 REFLECTION, Q1 M=

SCATTERING ANGLE, DEGREE Fig. 15-3. Angular distribution of the components of scattered light. (From Gebhart, 1991.)

For classic optics (a » 1) the extinction coefficient has the value 2, including equal contributions of unity from light undergoing reflection, refraction, or absorption and from light being diffracted on the particle contour. In practice, the extinction coefficient is measured to be less than 2 because some of the scattered light is collected due to the finite aperture angle # of the detection unit (15-16)

Theoretical Response Functions

Although it is advisable to calibrate an optical instrument experimentally by means of test aerosols of known size and refractive index, theoretical response functions give a general survey of the characteristics of an optical system. With electromagnetic theory, the optical system response functions, which describe the power of light scattered through the collecting aperture, can be calculated as a function of the diameter of a spherical particle. The refractive index of the particle material is the principal parameter determining the behavior of these functions. Detailed calculations of this kind have been performed by Hodkinson and Greenfield (1965), Brossmann (1966), Gucker and Tuma (1968), Quenzel (1969), Oeseburg (1972), Cooke and Kerker (1975), and Heyder and Gebhart (1979). The response characteristics of coherent laser aerosol spectrometers have been calculated by Garvey and Pinnick (1983), Pettit and Peterson (1984), Soderholm and Salzman (1984), and Hinds and Kraske (1986). Commercial optical systems can be divided roughly into instruments using low-angle scattering (diffraction lobe), those collecting scattered light in the forward direction (0 < 90°), and those employing right-angle scattering. It is also important to distinguish between instruments using a polychromatic incandescent light source and those using a monochromatic laser light source. The influence of monochromatic and polychromatic light on the response function is demonstrated in Figure 15-4, where the partial scattering cross section Cs is plotted against the particle diameter for monochromatic and white light and a mean scattering angle of O0 = 45°. For particle diameters larger than the illuminating light wavelength, light scattering can be considered as a surface effect, and outside the diffraction lobe the mean flux of scattered light depends on the particle diameter squared. For monochromatic light, the scattering cross section curves show typical oscillations. These fluctuations can be smoothed by using white light and a large collecting aperture; however, the smoothing by white light by itself is more effective. In the diameter range smaller than the wavelength, the response functions are monotonic even for monochromatic light. In Figure 15-5, the partial scattering cross section of submicrometer particles is shown for monochromatic light of an HeNe laser at a mean scattering angle of B0 = 40° and a receiver aperture of # = 20°. For dp < A/2, all curves are monotonic, and their slope indicates a d\ dependency that is typical for dipole scattering, as expressed by Eq. 15-9. Response functions for different real parts of refractive index tend to run parallel in a log-log plot, which means that they differ by an approximately constant factor, which is a function of the refractive index. For absorbing materials with a complex refractive index, m-n- jk, the influence of the k value is reduced considerably in the size range below the wavelength. A value of k = 0.25, for instance, represents the absorption properties of coal (Olaf and Robock, 1961). All response curves having the same real part, but different imaginary parts of the refractive index, cross over for particle diameters below the wavelength. High-sensitivity laser aerosol spectrometers can make use of this submicrometer part of the response function to achieve more accurate particle size analysis.

rel. intensity

d, um

d, urn

Fig. 15-4. Theoretical response functions of a forward-scattering system using monochromatic (left) and polychromatic (right) illumination. (Adapted from Gehhart et al., 1976.)

C8, cm2

m =2 m = 1.5 m = 1.45 m = 1.33

d, |im

m = 1.5 m = 1.5-0.125j m = 1.5 -0.25Oj m = 1.5-0.50Oj

d, ^m

Fig. 15-5. Theoretical response functions in the submicrometer size range of a forward-scattering laser spectrometer. Wavelength A = 0.6328 um.

According to classic optics, light entering the particle is scattered due to refraction. As can be seen from Figure 15-3, this component dominates in the angular range up to about 100° except within the forward diffraction lobe. For particles of opaque materials, the fraction of light entering the particle is absorbed. Instruments that mainly collect light scattered by refraction, therefore, show great differences in the response function between transparent and absorbing materials. Theoretical response functions, which include light refracted in the forward direction, are given in Figure 15-4. Experimental calibrations of optical particle counters with light-absorbing particles have been carried out by Whitby and Vomela (1967) with particles of India ink, by Willeke and Liu (1976) with coal particles, and by Szymanski and Liu (1986) with methylene blue. The experimental findings confirm the theoretical predictions of Figure 15-4 that opaque particles in the micrometer size range scatter a factor of 6 to 10 less light in the forward direction than the transparent particles of the same size. Better conditions for particle size analysis can be realized if the light scattered within the forward lobe of diffraction (0< 6°) is collected. Within this angular range, the diffraction part Qo(O, a) dominates, and the flux of scattered light is independent of the optical properties of the particles. Theoretical response curves for low-angle scattering using an HeNe laser are given in Figure 15-6. The response functions belonging to particles with different absorption now coincide, and those of transparent spheres show periodic oscillations rather than a sys-

Cs, cm2

d, |xm Fig. 15-6. Theoretical response functions of a low-angle scattering instrument. (Adapted from Gebhart et al., 1976.)

m = 1.33; 1.40; 1.44; 1.486; 1.50

Ex

2oc(m-1)

Fig. 15-7. Extinction coefficients of transparent spheres.

tematic deviation from the response calculated for absorbing materials. Because these oscillations originate from interference between diffracted and refracted light, they change with the real part of the refractive index and are smoothed by using polychromatic light or when detecting particles of irregular shape. For opaque materials, the refracted component is absorbed inside the particle, and no interference occurs. A laboratory instrument that uses the polychromatic light from a mercury lamp and collects light scattered within the forward lobe of diffraction has been calibrated by Gebhart et al. (1976) with particles of different optical properties. The results confirm that light scattering within the forward lobe of diffraction is nearly independent of the optical properties of the particle material. Similar experimental findings have been reported by Szymanski and Liu (1986), who calibrated a low-angle laser counter (ASAS-300X, PMS)* with particles of carbon black. Light extinction of a particle can be best described by its extinction coefficient Ex(a, m), which is obtained from electromagnetic theory. In Figure 15-7, extinction coefficients of nonabsorbing spheres of different refractive indices are plotted versus the normalized size parameter a(m - 1 ) . For particle diameters of many wavelengths, the extinction coefficient is seen to approach a limiting value of 2. Another striking feature of the extinction curve is its oscillation, which is caused by constructive and destructive interference of diffracted and refracted light in the forward direction (see also Fig. 15-6). As the absorption inside the particle increases, the oscillations decline in importance and settle more quickly to a value of 2. The successive cycles in the extinction curve also depend on the high symmetry of a sphere and vanish for irregular particles. LIGHT SCATTERING AND EXTINCTION BY AN ASSEMBLY OF PARTICLES Ensemble Characteristics Light Scattering. We define /\(d p , A, m) as the flux of monochromatic light scattered by a single particle into the receiver aperture of an optical system. In the presence of many particles inside the sensing volume of the photometer the resulting light flux R collected by the detector is given by (15-17) * See Appendix I for full manufactuer addresses referenced to the italicized three-letter codes.

where Cn is the number concentration of the particles and f(dp) the probability density function of the particle size distribution. Equation 15-17 clearly demonstrates the problem of photometric measurements of aerosol concentrations: If the photometer response R varies, one cannot distinguish whether the number concentration cn, the size distribution f(dp), or the optical properties m of the material have changed. Using a test aerosol of a homogeneous chemical composition, the influence of the refractive index m can be eliminated so that only Cn and f(dp) remain as variables. In this case a linear relationship R = const, x cn can be realized if either the function f(dp) can be replaced by one particle size (monodisperse aerosol) or the function f(dp) is constant during the measurements. In these cases the photometer response is linearly correlated with the number or mass concentration of an aerosol, and a simple gravimetric calibration procedure can be carried out. The simple proportionality of the scattered light flux to the number of particles inside the sensitive volume only holds if independent scattering by separate particles occurs and multiple scattering can be neglected. Light Extinction. Let a parallel beam of monochromatic light with intensity I0 and wavelength X traverse a homogeneous mixture of particles with number concentration cn and size distribution function f(dp). According to the Lambert-Beer law, the transmittance T of the light beam can then be expressed by

(15-18) where s is the extinction length according to Figure 15-8. The term (15-19) is the total extinction coefficient (= scattering coefficient as + absorption coefficient cra) of the particle assembly also known as turbidity. The expression sa = T represents the "optical depth." As can be seen from Eq. 15-19, the turbidity remains a unique function of the number concentration only if the size distribution f(dp) and the refractive index m of the particles do not change during the measurement.

Io

I

Fig. 15-8. Schematic of light extinction measurement system.

Sensor

When the particle concentration becomes very high, the particles may no longer scatter independently and the coefficient Ex(a, m) of the single particle may change. Usually, this happens at concentrations much larger than those for which the Lambert-Beer law breaks down due to multiple scattering. EXAMPLE 15-1 An aerosol generator based on controlled condensation of vapor produces uniform droplets of 3um diameter at a concentration of 2 x 1012m"3.The output of the generator passes through a glass tube of 30 mm inner diameter. Its constancy is monitored by a narrow-angle extinction technique that uses a collimated laser beam. Calculate the expected transmission of the measuring system. Answer: The relevant parameters for the transmission T according to Eq. 15-18 are the total extinction coefficient a of the particle assembly and the extinction length 5. The extinction coefficient Ex of a 3 um droplet in a parallel laser beam approaches 2.

The integral optical properties of atmospheric aerosols are usually summarized in terms of light interaction coefficients o|. If the number size distribution f(dp) of a chemically homogeneous aerosol consisting of spherical particles is known, the scattering coefficient CJS of an assembly of particles can be calculated as (15-20) where Es (a, m) is the total scattering coefficient of a single particle, that is, the integral of the angular scattering coefficient Qs over the whole solid angle. In this definition, as, with the dimension [length"1], describes the contribution of atmospheric aerosols to overall light scattering. The angular distribution of light scattered by an assembly of particles is usually described by a so-called phase function /5(0) defined as 4/r times the ratio of the angular scattering cross section to the extinction cross section of the assembly. The phase function of an assembly of particles for unpolarized light is given by (Hodkinson, 1962)

(15-21)

The main characteristic of /}(0) can be expressed by an asymmetry parameter g according to

(15-22)

For isotropic scattering (e.g., Rayleigh scattering) the asymmetry parameter is zero; for strongly forward scattering aerosols it approaches one. In the case of nonabsorbing aerosols, light extinction occurs only due to light scattering, and the attenuation of a beam of light passing through such an aerosol is given by (15-23) where cm is the aerosol mass concentration and bs =
Usually light scattering by an assembly of particles is evaluated by adding the intensities scattered by each particle as if it were present alone (Eq. 15-17). This kind of treatment of ensemble scattering, however, is only correct if the particles are randomly distributed in space (incoherent superposition of waves) and the aerosol is sufficiently dilute. When the particles are less than a few wavelengths apart from each other, their fields interact and violate the boundary conditions used in Mie theory (van de Hulst, 1957). The scattering behavior changes relative to that for an isolated sphere. Even if the particles are several wavelengths apart so that mutual polarizations can be neglected, light scattered by one particle is likely to be incident on another one, which then scatters it again. This effect is known as multiple scattering and can be avoided only by diluting the aerosol. A criterion for independent and isolated scattering is a linear relationship between the flux of scattered light R and the number concentration Cn of the aerosol. In practice, the onset of multiple scattering is found by diluting a dense aerosol until scattering is a linear function of particle number concentration. Multiple scattering generally depends on number concentration Cn, particle diameter dp, and sensing volume Vm. Hodkinson (1962) performed extinction measurements on suspensions of 1.8 urn polystyrene spheres in water and showed that within the transmittance (T) range 0.37 < T < 1, the results obeyed the Lambert-Beer law. With narrow-angle extinction techniques, where only a negligible amount of scattered light can reach the detector, the validity range of the Lambert-Beer law may be extended even to T < 0.1. Comprehensive investigations of multiple scattering effects have been carried out by Szymanski (1992,1996) using an apparatus described by Preining et al. (1981). With this experimental setup, light extinction, particle size, and number concentration of a monodisperse aerosol were measured simultaneously by independent methods. Transmittance through a medium undergoing light extinction in association with multiple scattering was described as function of the optical depth T = s a by (Szymanski, 1992): (15-24) The correction term X represents the radiation additionally reaching the detector unit due to multiple scattering. In the limit of diluted aerosols, the expression reduces to the LambertBeer law (Eq. 15-18). Gravimetry and Photometry

Air quality standards for particulate matter in the environment are usually based on gravimetry. It is, therefore, of general interest to correlate photometer signals with the mass (volume) concentration of aerosols. The mass concentration cm of a poly disperse aerosol is given by (15-25) where pp is the density of the particle material.

P*(d) 3 P7t d 6

Particle diameter Fig. 15-9. Schematic diagram illustrating the relationship between photometry and gravimetry.

From the photometer response as expressed by Eq. 15-17, it follows that a linear relationship R = const, x cm is only obtained if the flux of scattered light Px (dp, m, X) collected by an optical system is a function of the particle volume. Using the electromagnetic theory of light scattering on spheres, a specific scattering function Pxs (dp, m, X) can be defined as (15-26) which is the flux of scattered light per unit mass (volume) concentration of aerosol. The general behavior of Pxs for a fixed m and X is illustrated in Figure 15-9. In the micrometer size range, light scattering is a surface effect and Pxs decreases proportional to dp1. For particle diameters dp < 0.5A, light scattering can be approximated by Eq. 15-9 (Sx ~ dp), and the specific scattering function increases proportional to dp. At about dp ~ X, the specific scattering function reaches a maximum, which, for dielectric spheres, has additional resonances superimposed. Only within this narrow plateau, light scattering is nearly proportional to particle volume. Armbruster et al. (1984) calibrated a respirable dust photometer that measures infrared light (X = 0.94 (xm) scattered by airborne particles at a mean angle of 70°, with monodisperse test aerosols having different optical properties. Their results confirmed the general relationship of the specific scattering function as indicated in Figure 15-9. It is interesting to note that for atmospheric aerosols and visible light, particles within the accumulation mode (0.1 to 1 Jim) nearly coincide with the maximum of the specific scattering function so that particles within this size range exhibit the greatest sensitivity when detected with optical means (Willeke and Brockmann, 1977). Light extinction, expressed by the extinction cross section {id4) d\ Ex(a, m) of a particle, generally shows characteristics similar to light scattering. Only for absorbing particles in the Rayleigh regime, where the first term of Eq. 15-15 dominates, does light extinction become proportional to the volume of the particles regardless of their size. Light Scattering by Gas Molecules

For dielectric particles much smaller than the wavelength of light, dipole theory can be applied so that the power of scattered light per unit solid angle, Sx (9, dp, A, m), can be expressed by Eq. 15-10. For randomly oriented gas molecules, the mean polarizability,/?m, of

a single molecule is connected with the macroscopic refractive index rag of the gas according to Born (1965): (15-27) where cg is the number concentration of the molecules. Substituting Eq. 15-27 into Eq. 15-10 (with pm = ps) yields the contribution of a gas volume Vg to light scattering: (15-28) In Figure 15-10, Rayleigh scattering of air under normal pressure is compared with light scattering by single spherical particles of refractive index m = 1.5. Macroscopic refractive indices of some gases at A = 0.546 urn and the corresponding fluxes of scattered light in relation to air are listed in Table 15-1.

cm3

Air volume

Sair <| Spart.

Particle diameter d, \im Fig. 15-10. Rayleigh scattering of air in relation to spheres with refractive index m = 1.5.

SINGLE-PARTICLE OPTICAL COUNTERS General Remarks

An optical particle counter (OPC) measures the size and number concentration of aerosol particles in a limited size range by means of light scattering by single particles. For this purpose, a stream of aerosol is drawn through a condensed light beam. Light flashes scattered from single particles are received by a photodetector and converted into electrical pulses. From the count rate of the pulses, the number concentration, and from the pulse height, the size of the particles is derived. The light power that an individual particle scatters is a function of its size, refractive index, and shape. Particle sizing based on this principle has been known for more than 50 years. Meanwhile, the technique has been steadily developed, and for about 40 years OPCs using white light illumination have been commercially available (Lieberman, 1986). The invention of the laser has allowed the successful replacement of the white light illumination by coherent and monochromatic laser light. Important characteristic features of an OPC are its permissible range of number concentration, its sampling flow rate, its sensitivity (lower detection limit), and its size measurement accuracy. OPCs are now widely used in basic aerosol research, air pollution studies, and clean-room monitoring. Because the requirements for an OPC change for these different kinds of applications, the specifications of an instrument have to be adjusted to the specific measurement problem. OPCs that cover these various kinds of applications are offered, for instance, by CL/, PAC, and PMS. Trends in development are handheld, battery-operated counters with a laser diode as the light source.

Calibration Procedures

In recent decades, many theoretical response functions of commercial OPCs have been published. Meanwhile, menu-driven programs for PCs are available to carry out light-scattering calculations for spherical particles (Reist, 1990). Despite these theoretical calculations, OPCs are usually calibrated with monodisperse test aerosols of known size and refractive index. Instruments using incandescent lamps for particle illumination were calibrated by Whitby and Vomela (1967), Liu et al. (1974a,b), Willeke and Liu (1976), and Fissan et al. (1984). Experimental calibrations of white light as well as laser light counters with various test aerosols have been carried out by Gebhart et al. (1983), Chen et al. (1984), and Liu et al. (1985). More recent papers published by Liu and Szymanski (1986), Plomb et al. (1986), and Szymanski and Liu (1986) report experimental work on laser particle counters. To produce monodisperse test aerosols, several generation techniques can be used. Aerosols with a high degree of monodispersity are obtained if aqueous suspensions of polystyrene latex (PSL) are nebulized, dried, and drawn into the OPC (Gebhart et al., 1980). Another technique is the vibrating-orifice generator, which allows the calculation of the particle diameter to an accuracy of about 1% from the operating parameters of the generator (Berglund and Liu, 1973). By varying the frequency of vibration, the liquid feed through the orifice, and the concentration of the aerosol material in the volatile solvent, monodisperse aerosols from about 0.3 to 30jim in diameter can be produced for calibration purposes. For the generation of submicrometer aerosol standards, the principle of electrostatic classification can be used (Liu and Pui, 1974c). The aerosol material is either dissolved in a liquid or prepared as a colloidal suspension and then atomized through a jet nebulizer. After the mist is dried, a polydisperse aerosol consisting of solid particles or of low-volatility droplets remains. By passing the polydisperse aerosol through a differential-mobility analyzer, particles within a narrow size range are extracted according to their electrical mobility. Instrument calibration techniques are presented in Chapter 21 in greater detail. When applying an experimentally calibrated OPC to an unknown aerosol, one has to keep in mind that light-scattering signals generally depend on the optical properties of the test

aerosol. To overcome these problems, direct field calibration of OPCs has been proposed. A direct aerodynamic particle size calibration of OPCs by means of inertial impactors was performed by Marple and Rubow (1976). To obtain a single calibration point, two runs are made with the OPC. During one run, an impactor is attached to the inlet of the OPC, and during the second run the impactor is removed. An analysis of the data from the two runs yields a calibration point corresponding to the 50% cut point size of the impactor. By changing the size of the impactor nozzle, different calibration points can be obtained. Similar aerodynamic calibration procedures have been applied to a white-light 90° counter by Biittner (1983) using a sampling cyclone and by Friehmelt and Heidenreich (1999) using an aerodynamic particle sizer (TSI). Optical Systems

In the following discussion, typical optical arrangements used in laboratory and commercial OPCs are presented. The optical setup of a forward-scattering instrument with converging illumination from an incandescent light source and a coaxial collecting aperture is shown in Figure 15-11. This configuration was used, for instance, in the former models PC 215, PC 245, PC 247, and PC 2102 (HIA) and is typical for an optical arrangement using forward scattering. Light from an incandescent lamp is concentrated in the sensing volume by a system of condenser lenses forming an illumination cone of 5° semiangle. After passing the sensing volume, the primary light beam is absorbed in a concentric light trap with a half-angle of 16°. Light scattered by individual particles is collected through a coaxial aperture of 25° half-angle and directed onto the cathode of a photomultiplier. Considering the illumination cone, scattered light covering an angular range from about 10° to 30° is collected by the system. Experimental response curves of the models PC 215 and PC 245 (HIA) measured with PSL spheres and droplets of dioctyl phthalate (DOP) were reported by Willeke and Liu (1976). A characteristic feature of this forward-scattering sensor is a multivalued region in the response curve between 0.7 and about 1.2 urn. The counter model Cl-208 (CLI) shown in Figure 15-12 utilizes an elliptical mirror in its optical system. In the detector, the particle sensing zone is located at the primary focal point of the elliptical mirror. High-intensity light from a quartz halogen lamp is focused on the sensing zone, where it illuminates each traversing particle. Light scattered from each particle is collected over an angular range from 15° to 105° and is directed to a photodetector located at the secondary focal point of the ellipsoid. The sample air is surrounded by a sheath of clean filtered air, allowing the precise placement of the particles at the primary focal point. At a sample flow rate of 1.2 x 10~4 m3/s [7 L/min], a size sensitivity of 0.3 um is quoted by the manufacturer. Experimental calibrations of the Climet instrument were carried out by Clark and Avol (1979), Makynen et al. (1982), and by Chen

AEROSOL LAMP

DETECTOR

OUTLET Fig. 15-11. Optical system of a forward-scattering instrument using an incandescent light source.

INLET DETECTOR LAMP CALIBR.

OUTLET

Fig. 15-12. Optical system of the particle counter CI-208 (CL/). (From Gebhart, 1991.)

et al. (1984). A monotonically increasing response function for PSL and DOP aerosols were found over the entire size range from 0.3 to 10|im. With laser light illumination, intensities can be realized in the sensing volume several orders of magnitude higher than those obtained with incandescent light sources. Furthermore, a collimated laser beam needs fewer optical elements, such as stops and lenses, reducing the stray-light background in the chamber considerably. Three kinds of particle illumination exist with laser light: (1) the output of a laser can be focused into the sensing volume like a common incandescent light source; (2) according to Knollenberg and Luehr (1976), high illumination intensities with a low-power laser can be achieved if the sensing volume is positioned inside the laser resonator (active scattering); and (3) in the passive cavity system, the original laser output is multiply reflected to create high-intensity illumination. A high-resolution laser aerosol spectrometer for laboratory purposes was developed by Roth and Gebhart (1978) for the size range between 0.06 and 0.6 um. An argon ion laser (X = 0.5145 urn) with an output of 2 W serves as the external light source. The laser light is focused by an astigmatic system of lenses into the sensing volume. The light scattered by the particles is collected by a microscope objective at a mean scattering angle of 40°. An example of an active cavity sensor is shown in Figure 15-13. It is contained in models LAS-X, MS-LAS, LPC101, and MicroLPC0710 (PMS) and in the models 226/236 (HIA). This sensor employs a high-Q laser cavity to achieve a high illumination intensity (~ 500 W/cm2). The primary collector of the scattered light is a parabolic reflector. Particles in the sample stream intersect the laser beam within the cavity at the focus of the paraboloid, which focuses the scattered light onto a 45° flat mirror. The light reflected from this mirror is refocused onto a photodetector by an aspheric lens. The whole system collects scattered light from 35° to 120°, providing a 2.2 steradian solid angle. The aerosol stream is aerodynamically focused. The theoretical response functions of this sensor have been published by Hinds and Kraske (1986), and experimental calibration data have been reported by Szymanski and Liu (1986). The LASAIR series (PMS) are instruments with a passive cavity design. They are available at minimum sizing thresholds of 0.1,0.2,0.3, and 0.5 urn and with sample flow rates from 9.44 x 10"7 to 4.72 x 10~4 m7s (0.002 to Ift3/min). Most OPCs utilize an aerosol nozzle to transport the particles through the beam of illuminating light. In this case, the sensing volume is limited by the cross section of the aerosol stream and the width of the light beam. Using an aerosol nozzle has the advantage that, under stable flow conditions, the sensing volume and the sample flow rate are well defined. In most instruments, the aerosol stream is surrounded by a clean air sheath, which establishes a stable flow toward the outlet of the measuring chamber and thus avoids stray particles in the optical system. The clean air sheath can additionally be used to focus the aerosol stream aerodynamically down to a filament of less than 0.1mm diameter. The problem that arises in con-

PHOTODETECTOR OUTLET JET ASPHERIC COLLECTOR CURVED MIRROR

11

O" RING SEAL

11 11

O RINGSEAL

FLAT MIRROR HE-NE-LASER REFERENCE BREWSTERS ' WINDOW DETECTOR

AERODYNAMICALLY FOCUSING INLET SHEATH AIR

PARABOLIC MIRROR

SAMPLE AIR Fig. 15-13. Setup of an active scattering laser spectrometer used in the models LAS-X, HS-LAS, and LPC 101 (PMS) and in the models 226 and 236 (HIA). (From Gebhart, 1991.)

Observation

PM Stop Il

Flow direction

Illumination

Lens Il Sensing volume

Lens I

Stop I Lamp

Fig. 15-14. Arrangement of Counter HC 15 (PLY), which forms the sensing volume by optical means only. (From Gebhart, 1991.)

nection with an aerosol nozzle is that a sample representative of all particle sizes has to be taken out of the sampled aerosol. Therefore, systems also have been developed that can count individual aerosol particles directly in the main stream of an aerosol. Such an instrument is, for instance, the particle counter HC 15 (PLY), which forms its sensing volume by optical means only (Umhauer, 1980). The optical system, which is schematically drawn in Figure 15-14, consists of two optical pathways, one for illumination and one for the collection of scattered light.

LIGHT BEAM STOP 2 MIRROR STOP1 Fig. 15-15. Elimination of particles traversing the periphery of the sensing volume by means of a twobeam system and masked apertures.

The lenses I and II project miniaturized images of square masks (stops I and II) into the measuring chamber, where the two optical axes cross at right angles. The images of stops I and II, which coincide with the crossing point of the two axes, form the sensing volume. Particles passing this optically defined sensitive area are illuminated with incandescent light, and the light scattered by individual particles is collected at a mean scattering angle of 90°. Because an optically defined sensing volume can be made very small (about 100 um across), high concentrations can be measured with such an instrument. The advantage of such a system is that all difficulties associated with aerosol sampling through inlets and small nozzles are avoided. There exist, however, problems in exactly defining the sensing volume and the sample flow rate. Whereas small particles are only counted when they pass the central region of the stop, larger particles can also give rise to countable pulses at the periphery of the imaging. Because these pulses are smaller than if the particle passed through the center of the detection volume, they are sensed as coming from smaller particles. Thus, the effective sample flow rate is particle size dependant. The bigger the particles, the more the peripheral detection contributes to the measured cumulative number distribution. This has been shown in an experimental analysis of the HC 15 counter by Helsper and Fissan (1980). The authors passed monodisperse 9um droplets through the sensing volume and found that about 50% of the count pulses were attenuated and simulated smaller droplets down to a detection limit of 0.3 um. A method to eliminate the effect of the border zone on the measured particle size distribution was reported by Knollenberg and Luehr (1976) and Umhauer (1983). Its principle of operation is illustrated in Figure 15-15. A beam splitter behind the collecting optics directs the scattered light through masked apertures on two independent detectors. The signals from the two detectors, in conjunction with double pulse height analysis, lead to a system that determines the position of a particle in the light beam and that rejects all counts originating from particles in the border zone. The model HP-LPC (PMS) contains a sensor of this kind that can be directly installed in a conduit for gases. Although the sensing area of this model covers only a fraction of a percent of the conduit's cross section, a unique linear relationship between count rate and number concentration is obtained for all particle sizes above the detection limit. Umhauer (1983) constructed two independent rectangular collector and detector units for the model HC 15 (PLY), which overlapped in the sensing volume and which differed only in the size of the masks. The two different masks, in combination with double-pulse analysis, allowed identifi-

cation and rejection of those particles that passed the sensing volume in the border zone. An improved version of this two-channel detector device was investigated by Sachweh et al. (1998). A modified type of this instrument is commercially available (PCS-2000; PAS). Pulse Processing The light flashes originating from individual particles in the sensing volume are converted into photocurrent pulses. Due to the high time resolution of most photodetectors, the pulse shape follows the light intensity profile inside the sensing volume. The intensity profile is approximately a Gaussian one and has a pulse width from several to about 40 (is, depending upon the OPC used. The output pulses of the photodetector are usually fed to a current-tovoltage transducer, the main part of which consists of a high-frequency amplifier. Then a decision has to be made whether the pulse amplitude or its total charge (area) will be used as a measure of the scattered light. The charge integration method improves the resolution and the sensitivity of an OPC considerably, provided that the pulse duration is very constant for all particles. A clean air jacket in conjunction with aerodynamic focusing of the aerosol stream results in a constant pulse duration necessary for charge integration. In general, a multichannel analyzer requires a constant pulse height of a few microseconds duration in order to read the signal height. Therefore, the amplifier output signal—which is proportional to either the amplitude or the area of the original pulse—has to be stretched in a pulse converter. The pulse stretching introduces an electronic dead time during which no additional signals are processed. Additionally, the converter removes low-frequency components and dc offset from the incoming signal. A further improvement of the signal evaluation is obtained, if the photomultiplier signals are digitized directly after the current-to-voltage transducer (Sachweh et al., 1989,1998). By this procedure the raw signal containing electronic noise ripples is converted into nearly a square wave, the base line of which is given by the transit time of the particle through the sensing zone. This allows a well-defined determination of the mean pulse height. Most OPCs have an analog output that can be connected directly to an oscilloscope. Observations of the pulse shape show that in some instruments many stray particles pass the sensing volume, producing pulses up to 10 times longer than the normal ones. Because pulse times of this length exceed the recovery time of the electronics, multiple counts can originate from a single stray particle that are classified as small particles (Gebhart et al., 1983). The existence of the stray particles themselves may be explained by unstable flow conditions that prevent particles from being completely removed from the measuring chamber after their initial passage through the light beam. Range of Number Concentration Number concentration C0 of the aerosol, sample flow rate Q, and counting rate dN/dt of a particle counter are connected by the relationship (15-29) Eq. 15-29 assumes that each particle contained in the sample flow produces a single count. In practice, however, counting losses due to coincidences occur. Less than 10% loss in particle counts approximately requires (van der Meulen et al., 1980) (15-30) where tT is the recovery time of the electronics between successive count events, including the transit time of the particle through the light beam and the pulse processing time of the multichannel analyzer. From Eq. 15-30, it can be seen that for a given recovery time tr, mea-

TABLE 15-2. Maximum Permissible Number Concentration in an OPC in Relation to the Volumetric Flow Rate Q(m 3 /s) Cmax(nr3)

4.72XlO" 4 1.1 XlO 7

4.72 x 10"5 1.1 xlO 8

4.72 x 10"6 1.1 xlO 9

4.72 x 1(T7 1.1 xlO 1 0

4.72 x IO"8 1.1 XlO 11

"Evaluated from Eq. 15-30 for an electronic recovery time of 20us.

surements at high number concentrations are only possible at the expense of the sample flow rate Q and vice versa. Some maximum permissible number concentrations derived from Eq. 15-30 for a typical recovery time of tx = 20 us are shown in Table 15-2. As indicated, cmax never falls below 3.5 x 106/m3, which corresponds to ISO Class 8 (particles per m3 > 0.5 um) as defined in ISO 14644-1 (see Table 33-1). Coincidence errors, however, are important if OPCs are applied to laboratory or environmental aerosols.

EXAMPLE 15-2 A filter with an expected penetration of 0.01 for 0.5 um particles is checked with an OPC. The OPC samples aerosol at a flow rate of 47.16 x 10"6 m3/s [2.83 L/min] and has an electronic recovery time of 12 us. Monodisperse 0.5 um particles with a true number concentration of C0 = 4 x 108m~3 serve as the test aerosol. Calculate the experimental error due to coincidence if no dilution steps are used. Answer: The experimental penetration is C2Icx, where C1 and C2 are the measured concentrations upstream and downstream of the filter. Using Eq. 15-30, the count loss due to coincidence is

The error is:

The lower limit cmin of the detectable number concentration depends on the background noise according to (15-31) where (dN/dt)ns is the rate of noise pulses and cns the apparent number concentration produced by noise. Count noise can originate from internal particle sources, the electronics, ionizing radiation, and instabilities of the power supply. There is a general agreement that reliable concentration measurements with an OPC should start only at levels more than one order of magnitude above the background, that is, cmin > 10 cns. For the determination of the noise level of an OPC, filtered air is drawn through the instrument for several hours. Measurements of this kind have been reported by Gebhart and Roth (1986), Wen and Kasper (1986), Liu and Szymanski (1987), and Gebhart (1989a). Some results of background noise are presented in Table 15-3.

TABLE 15-3. Specifications and Count Noise of a Selection of Commercial OPCs Light Source Incandescent light Laser light

Model HIAC/Royco 4102 HIAC/Royco 5000 CLIMET CI 8060 PMS LAS-X PMS LPC-110 HIAC/Royco 5100 TSI 3755 CLIMET Cl 6300

Receiver Optics 10°-30° 50° range 15°-150° 35°-120° 35°-120° 60°-120° 15°-88° 45°-135°

Noise Counts per m3

Q 3

(m /s) 4.72 1.67 4.72 5.00 4.72 4.72 4.72 4.72

x x x x x x x x

5

10" 10-5 10"4 10"6 10~5 10"4 10-5 IQ-4

Pump On

Pump Off

530 > 21 84.8 0.95 10,840 > 210 <17.6 4236 > 28 <35.3 <70.6

Wash-out ~0 ~0 Wash-out ~0 Wash-out

Sources: Gebhart and Roth (1986) and Gebhart (1989a).

In most cases, particles originating from the internal flow system have been identified as being responsible for the count noise. This can be concluded from the wash-out effect of the instruments and their different behavior in the positions "pump on" and "pump off." Washout here means that the rate of noise counts decreases with time when the instrument is supplied with particle-free air. After an exposure to environmental aerosols, the instruments needed up to 15 h to obtain a stable count rate at a low level (Gebhart and Roth, 1986). In general, OPCs with incandescent light sources have somewhat lower noise levels. After completion of the wash-out most instruments with a sensitivity of 0.5 urn are prepared to monitor ISO Class 4 or 3 (see Table 33-1). For the detection of ISO Class 3, for instance, instruments with a sensitivity of 0.5 jxm must produce less than 3.5 noise counts per m3, whereas laser counters with a sensitivity of 0.1 um need a background level smaller than 100 counts per m3 (see Chapter 33). In one model the count rate of noise in the first channel (0.1 to 0.3 um) increased with time of operation, indicating that heating during the course of operation may have affected electronic noise. The high background in another instrument was caused by a leak in the housing and vanished when the instrument was put in a filtered laminar flow box (Gebhart and Roth, 1986). From these experimental findings it is obvious that a certain model does not necessarily guarantee a low noise level. It is rather the status of an individual device and its history (exposure to concentrated aerosols) that determines the background noise. Therefore, instruments used for monitoring clean room Class 100 or lower should be carefully checked before the measurements. Sensitivity and Sample Flow Rate

Manufacturers usually express the sensitivity of an OPC as the smallest particle diameter dmin detectable with the instrument. According to a convention of the German standard setting organization (VDI 3491, number 3), however, sensitivity should be associated with counting efficiency and the particle size with 50% counting efficiency taken as the lower detection limit dm[n. For the determination of the counting efficiency r\ near the lower detection limit, the measured concentration c has to be related to the concentration C0 obtained by independent reference methods according to (15-32) During an experimental run, instruments to be checked simultaneously sample a monodisperse test aerosol from a common reservoir together with a reference instrument. With

PMS/LAS-X(0.12|im;0.01 cfm) C/Co

DEHS PSL

C0, cm 3 Fig. 15-16. Experimental coincidence curve of the laser spectrometer LAS-X (PMS).

C0

PMS-ULPC(0.1 (xm; 0.1 cfm)

PSL

C0, cm-3 Fig. 15-17. Experimental coincidence curve of the laser counter ULPC (PMS).

this procedure, counting efficiencies of OPCs were measured by van der Meulen et al. (1980), Gebhart et al. (1983), Gebhart and Roth (1986), Wen and Kasper (1986), Liu and Szymanski (1987), and Gebhart (1989a). A difference between c and c0 can originate from (1) coincidence losses, (2) an incorrect sample flow rate Q, and (3) a decreasing sensitivity of the instrument. The effects of these parameters on counting efficiency can be separated by means of the coincidence curve of an instrument, where the ratio c/cQ is plotted versus the reference concentration C0. Coincidence curves of two instruments measured by Gebhart (1989a) are shown in Figures 15-16 and 15-17. Starting with test aerosols far above threshold, a single curve is obtained for all particle sizes in a semilogarithmic plot. If the extrapolation of this curve to zero concentration (c0 —> 0 ) deviates from one, the sample flow rate of the instrument is not correct. Deviations from this curve for particle sizes approaching the lower detection limit indicate a decreasing sensitivity of the instrument. If counting efficiencies are generally evaluated by extrapolating the measured concentration ratio c/c0 back to zero concentration, coincidence losses are eliminated and the counting efficiency reflects a specific property of an instrument.

Counting efficiencies of different OPCs obtained by this extrapolation technique are summarized in Table 15-4 (Gebhart, 1989a). In the submicrometer size range, the counting efficiency decreases with decreasing particle size so that the lower detection limit is a gradual transition rather than a sharp step. The data confirm previous findings by van der Meulen et al. (1980) and Gebhart et al. (1983) that instruments using incandescent light count 0.32 um particles with an efficiency of less than 20%. For most laser instruments, on the other hand, counting efficiencies near 100% are achieved down to particle diameters of 0.1 um. With the highly sensitive model MS-LAS (PMS), even particles of 0.07 um can be counted with an efficiency of about 30% at a reduced sample flow rate of 300 cmVmin.

EXAMPLE 15-3 The sensitivity of a laser particle counter (LPC) has been determined with PSL spheres to be dmin = 0.1 um. The LPC shall now be applied to droplets of di-2-ethylhexyl sebacate (DEHS). Calculate the shift in the sensitivity of the LPC for DEHS droplets. Answer: One has to calculate the diameter of a DEHS droplet that scatters the same flux of light as a PSL sphere of 0.1 um in diameter. In the size range of 0.1 um, the Rayleigh approximation of Eq. 15-9 can be applied, which for identical optical arrangements, reduces to

PSL: m = 1.59, DEHS:m = 1.45

d = 0.1 um

In the supermicrometer size range, satisfactory results are obtained. However, for particle diameters above 2 jam, inlet losses in the sampling system may again affect counting efficiency, especially if instruments with lower sampling rates are used in connection with plastic tubes. The sampling efficiency of the particle counter Royco 245 was investigated by Willeke and Liu (1976). Comprehensive studies of inlet characteristics and their effect on the counting efficiency of an optical sensor have been carried out by Tufto and Willeke (1982) and by Okazaki and Willeke (1987). Fissan and Schwientek (1987) performed a review of aeerosol sampling and transport. Further information on sampling efficiency and aerosol transport is given in Chapter 8. Table 15-4 indicates that the sensitivity of an OPC can be increased by using laser light illumination. On the other hand, it can be seen from this table that there exists a close

TABLE 15-4. Specifications and Counting Efficiencies of a Selection of Commercial OPCs Incandescent Light Model HIAC/Royco 227 HIAC/Royco 4102 HIAC/Royco 5000 CLIMET CI 8060 Laser Light Model PMS LAS-X PMS LAS-X PMS LPC-110 PMS MS-LAS HIAC/Royco 5120 HIAC/Royco 236

dmin (Jim)

Counting Efficiencies at Diameter dp (um)

Q (mVs) 0.163

0.3 0.3 0.3 0.3 dmin (|xm)

4.72 4.72 1.67 4.72

x 10" x 10~5 x 10"5 x 10"4

Q (m3/s)

5.00 1.00 4.72 5.00 4.72 5.00

x x x x x x

10"6 10"6 10"4 10-6 10"4 10"6

0.32

0.47

0.12 0.18 0.18 0.15

0.92 1.01 0.89 1.02

0.72

0.95

2.02

1.05 0.98 0.91 0.98

Counting Efficiencies at Diameter d? (um) 0.068

0.12 0.9 0.1 0.065 0.2 0.12

0.22

6

0.27

0.082

1.03

0.109

0.12

0.163

0.98

0.46 0.98 0.47

0.98 0.99 0.98 1.04 0.99 1.03

0.97 0.24

0.94

0.22

0.32 1.02

1.0 1.0 0.99

1.0

Sources: Gebhart and Roth (1986) and Gebhart (1989a).

connection between the sampling flow rate Q of a laser particle counter and its smallest detectable particle size dmin. The lower the sampling flow rate, the more sensitive is the instrument. This is because above a certain illumination intensity, light scattering by the air molecules within the sensing volume becomes the limiting factor for the sensitivity. Under these circumstances, the signal-to-noise ratio of an OPC can only be further improved by a reduction in the number of molecules that scatter light into the detector; in other words, an increase in sensitivity is only possible at the expense of the sensing volume and, in consequence, at the expense of the sampling flow rate. From Figure 15-10 it can be seen that 1 mm3 of air scatters about the same amount of light as a 0.22 Jim particle with refractive index m - 1.5. This principal limitation of the sensitivity of an OPC exists regardless of the kind and intensity of illumination. The alternatives in clean-room technology, therefore, seem to be either to use an instrument with high sensitivity (0.1 jum diameter particles) but a lower sampling rate or a less sensitive device with a higher sampling rate (4.72 x 10"4InVs [28.3L/min]). In a more recent development by Particle Measuring System Inc. (PMS), successful attempts have been made to overcome the problem of Rayleigh scattering on gas molecules. In these models an elongated sensing volume is imaged onto a photodetector array consisting of independent elements. Owing to the facet structure of the detector unit, each element views only part of the gas molecules of the sampling flow but records the total flux of light scattered by a single traversing particle. With this segmentation technique, laser particle counters can be constructed that combine a high sensitivity (0.1 urn) with a high sampling rate of 28.3L/min. For instance, the model LPC 110 (PMS) in Table 15-4 is an instrument based on this technique. Sizing Accuracy and Resolving Power

The sizing accuracy of an OPC describes its ability to measure the geometrical diameter of aerosol particles by classifying their photoelectric pulse heights into channels of given size intervals. For the evaluation of the sizing accuracy, cumulative size distributions of wellcharacterized test aerosols are compared with those measured with the OPC (Gebhart et al., 1983; Liu et al., 1985; Szymanski and Liu, 1986). The count median diameter derived from

Cumulative distribution

Royco 226

Royco 227 Particle diameter d/um Fig. 15-18. Cumulative size distributions of different PSL test aerosols (above) compared with measurements with different OPCs. (Gebhart et al., 1983.)

these measured size spectra are compared with the true size of the test aerosol to provide a measure of the sizing accuracy. Examples of cumulative size spectra measured with different OPCs by Gebhart et al. (1983) are shown in Figure 15-18. The PSL test aerosols have been analyzed by electron microscopy. Regarding the sizing accuracy, one has to distinguish between avoidable and unavoidable errors. Avoidable errors are systematic shifts on the size scale. They can be corrected by changing the linear amplification factor of the electronics. Unavoidable errors exist as long as the calibration curve has an ambiguous part or the calibration standard differs in refractive index from the aerosol to be investigated. Because of a multivalued part in the response function of the forward-scattering sensor shown in Figure 15-11, PSL spheres of 0.95 urn in diameter are classified as 0.77 um particles (see Fig. 15-18). An extreme example of the effect of refractive index are measurements of opaque particles (coal dust, India ink) in an OPC that applies forward scattering and has been calibrated with transparent spheres (Whitby and Vomela, 1967; Willeke and Liu, 1976). In this case, particles of the same geometrical size differ by about one order of magnitude in the flux of scattered light, which reduces the measured size of the particles by about a factor of three. The resolution power of an OPC describes its ability to distinguish two monodisperse aerosols of different mean particle size. It depends on the slope of the calibration curve and on the ability of an OPC to produce uniform pulses upon exposure to a monodisperse aerosol. The main factors influencing the resolution power are the homogeneity of illumination in the sensing volume and optoelectronic noise. Because the homogeneity of illumination is affected by the alignment of the optics, instruments of the same model may not have the same resolution due to normal manufacturing tolerances. To measure the resolution power, a monodisperse aerosol of known size and standard deviation is sampled by the OPC, and the instrumental broadening of the size distribution is determined. This can be done directly either by recording differential spectra (Liu et al., 1974a; Roth and Gebhart, 1978) or by evaluating the slope of a cumulative size distribution (see Fig. 15-18).

A typical indication of nonuniform illumination of the particles is the recorded spectra having a sharp cutoff with respect to bigger particles and a gradual cutoff with respect to smaller sizes. Optoelectronic effects on the resolution power of an OPC become important only near the lower particle size detection threshold, where the noise ripples are comparable with the particle signals. The modulation of the particle signal by quantum-statistical processes in the photodetector reduces the resolution power near threshold considerably, but has no effect for bigger particles that contain enough photons in their scattered light pulses (Liu et al., 1974a). For instruments with an optically defined sensing volume, the resolution power is additionally diminished by the effect of the border zone (Helsper and Fissan, 1980). With well-designed laser aerosol spectrometers, size resolutions comparable with that of electron microscopy can be achieved, especially in the size range below the wavelength of light (Roth and Gebhart, 1978; Knollenberg, 1989). A Combined DMPS/LAS System A differential mobility particle sizer (DMPS; see Chapter 18) and a laser aerosol spectrometer (model LAS-X, PMS) were combined by Gebhart et al. (1990) for automated "in situ" evaluation of number concentration and size distribution of environmental aerosols in the diameter range between 0.01 and 3 urn. In the particle size range of 0.01 to 0.4 urn, analysis was performed using the DMPS. The particles were classified according to their electrical mobility using a differential mobility analyzer (DMA; model 3071, TSI) and counted using a condensation nucleus counter (CNC; model 3760, TSI). To determine the mobility size of the original distribution, a numerical algorithm was applied to take into account the charge distribution of the particles and the transfer function of the DMA (Keady et al. 1983). In the size range 0.09 to 3um, the LAS-X was used to size particles with laser illumination at a wavelength of 0.63 um. To eliminate the effect of the refractive index on the classification accuracy of the LAS-X, the DMA was used to separate particle fractions of uniform electrical mobility from the unknown aerosol to be investigated. After sizing by the LAS-X, each of these fractions was split into several subfractions of uniform size but different charge. The peak of particles with one charge was suitable for a direct field calibration of the LASX in terms of mobility diameters up to about 1.2 um. Using this procedure between 0.01 and about 1.2 urn, a uniform diameter calibration was obtained. Another advantage of the combined system is that the numerical algorithm for the data inversion in the DMA can be validated using absolute measurements of the LAS-X particle number concentration in the range where both sensors overlapped (0.09 to 0.4 um). This system was successfully applied by Brand et al. (1992) during a 1 month field study of environmental aerosols in the city of Frankfurt/Main. Single-Particle Shape Analyzer Illumination of a spherical particle with unpolarized or circularly polarized light results in a scattering pattern that is rotationally symmetrical relative to the axis of illumination. Deviations from spherical shape, on the other hand, lead to a variability in azimuthal light scattering. This principle can be used in an OPC to characterize the shape of a particle. An instrument that detects scattered light under a constant scattering angle of 6 = 55° at eight azimuthal positions equally distributed between 0° < 0b < 360° was developed by Sachweh et al. (1995,1999) to analyze the shape of various kinds of aerosol particles. For this purpose 1 minus the relative standard deviation of all eight detector responses was introduced as a sphericity index, SPX. The SPX is 1 for perfect spheres and decreases with increasing deviations from the spherical shape. The instrument had a lower detection limit of dmin = 0.5 um and could clearly distinguish between PSL spheres and quartz particles. In a modified version,

it could also identify fibers among an assembly of isometric particles (Sachweh et al., 1999). An apparatus based on the same principle is the Aspect Size and Shape Analyser (BIR). This instrument includes four detectors and covers the size range between 0.5 and 20 urn. A similar instrument, but mounted on an aircraft wing and using six detectors, was designed to discriminate between supercooled cloud droplets and ice crystals (Hirst et al., 2001).

MULTIPLE-PARTICLE OPTICAL TECHNIQUES General Considerations

Multiple-particle detection instruments (photometers) based on scattered light intensity are useful for concentration measurements of aerosols if certain requirements are met. For the determination of concentration ratios or relative concentrations, the composition of the aerosol (particle size distribution, refractive index) must be constant during a series of experimental runs. For absolute measurements of mass concentration, the photometer must be calibrated with the aerosol to be investigated. In both cases, the instrument must be operated in its linear range, where the number of particles in the sensing volume is linearly correlated with the photometer signal. This range of linearity is limited at high concentrations by multiple scattering and at low concentrations by the stray-light background in the chamber. Stray light originates from optical elements such as lenses, glass windows, and optical stops. Well-designed instruments are limited by Rayleigh scattering from the air molecules, resulting in nearly steady-state noise levels. By replacing air with a gas of known scattering properties, it is possible to calibrate a photometer in terms of scattering cross section. A measure of the stray-light background is the photometer response in the presence of particle-free air. Concentration ratios evaluated from photometer responses R{ are measured correctly only if the contribution of the background Rm is either negligible or subtracted from the photometer reading: (15-33) In all cases where the analog signal is subjected to electronic data processing, the photometer should be adjusted to a zero response for particle-free air. From the lower detection limit to onset of multiple scattering, the linearity range of a typical photometer can span at least three orders of magnitude in number concentration. Aerosol photometers in combination with inert aerosols are currently applied in filter testing and in aerosol medicine. They are also useful as real-time dust monitors in industrial hygiene (see Chapter 25) and for continuous recordings of aerosol concentrations in the atmosphere (see Chapter 27). In general, instruments based on light scattering are much more sensitive than light extinction systems. Applications

Aerosol Inhalation Studies. Since the pioneering work of Altshuler et al. (1957), photometers in combination with monodisperse test aerosols have been used in aerosol research to measure the total deposition of aerosol particles in the human respiratory tract as well as to study gas mixing processes and deposition mechanisms inside the airways (Gebhart et al., 1988; Gebhart, 1989b). An integral photometer developed for aerosol inhalation studies is shown in Figure 15-19. Its cylindrical aerosol channel can pass sample flow rates up to 1 nrVmin. An expanded parallel beam (3 mm diameter) of an HeNe laser traverses a system of diaphragms, crosses

PM

L2

k Laser

Fig. 15-19. Photometer for aerosol inhalation studies.

the aerosol channel, and is absorbed in a light trap. Light scattered by the particles within the angular range of 90° ± 14° is collected by a lens L1, which images the illuminated particles onto a slit stop. The slit stop confines the sensitive volume to about 100 mm3 and keeps stray light from reaching the photomultiplier. A second field lens L2 images the aperture of lens L1 onto the cathode of the photomultiplier. The instantaneous aerosol concentration is represented by the anode current of the photomultiplier, which, after amplification, can be used for data processing. The photometer technique permits continuous recording of the aerosol concentration close to the mouth during the entire course of a breathing cycle. From such records and simultaneous measurements of respiratory volumes and flow rates the amount of aerosol in successive fractions of inspired and expired air can be evaluated. Data of this kind provide new possibilities for lung function tests (Gebhart et al., 1988; Heyder, 1994). An improved version of this inhalation measurement technique is the Respiratory Aerosol Probe (PAR). In this device, the analog signals of aerosol number concentration and of respiratory flow rate are measured simultaneously at the entrance of the respiratory tract and fed to a personal computer for automatic data collection and processing (Westenberger et al., 1992). In the photometer unit, the beam of a 3 mW laser diode (X = 0.78 um) is collimated to illuminate about 70% of the cross section of an elliptically shaped respiratory channel. Light scattered by inspired particles within an angular range 90 ± 32° is collected and projected onto a miniaturized photomultiplier. The respiratory channel of the photometer is immediately connected with the mouthpiece and can be supplied with either clean air or aerosol by switching a set of pneumatic valves. The aerosol can be applied as a continuous flow or in boluses with volumes down to 2 x 104mm3 [20 cm3]. The respiratory flow rate is measured with a pneumotachograph, which yields the inspired air volumes after integration. Industrial and Environmental Hygiene. The TM digital uP (HUN) is an instantaneously reading respirable dust photometer constructed for concentration levels in workplaces. The

portable, battery-supplied instrument measures the infrared light (0.94 um) scattered by airborne dust particles at a mean scattering angle of 70°. Its open measuring chamber, which is insensitive to daylight, is filled by natural convection and includes coarse particles (total dust). The photometer reading, however, is calibrated against the mass concentration of respirable dust by means of a gravimetric respirable dust sampler (see Chapter 25). Experiments carried out by Armbruster et al. (1984) using monodisperse test aerosols and industrial dusts confirm the instrument's principle. The photometer values obtained for industrial dusts can be linearly converted into mass values of respirable dust as long as ultracoarse particles are excluded. A respirable aerosol monitor (RAM; MIE) allows real-time assessment of respirable dust by light scattering after aerodynamic separation of this fraction in a cyclone (Lilienfeld, 1983). A miniature real-time aerosol monitor (model MiniRAM, MIE) incorporates a pulsed nearinfrared light-emitting diode, a silicon detector, and various collimating and filtering optics, collecting light scattered over a range of 45° to 95°. Air surrounding the instrument passes freely through its open sensing chamber, and its measurement ranges cover mass concentrations from 0.01 to 100mg/m3. A further step of development of real-time aerosol monitoring is the personal/Data RAM (MIE). This instrument is also available in a model (pDR-1200, MIE) using active sampling of aerosol through a size-selective device to allow monitoring of respirable, thoracic, and PM-2.5 fractions of environmental aerosols (see Chapters 25,27, and 29). Filter efficiencies can be checked within the linearity range of a photometer by measuring the concentrations of uniform test aerosols upstream and downstream of a filter by means of light scattering on an assembly of particles. In the case of high-efficiency filters, however, integral photometry requires a relatively high concentration upstream of the filter so that the use of an OPC or DMA is more reasonable. Atmospheric Aerosol Measurement. Common mass concentration levels for respirable as well as total dusts in workplaces are in the range of 0.1 to 100mg/m3, whereas typical aerosol mass concentrations in the atmosphere cover the range from 10 to about 200|ig/m3 (Charlson et al., 1967).Therefore, particulate pollution monitoring in the atmosphere requires nephelometers of high sensitivity. The integrating nephelometer was modified by Charlson et al. (1967) for air pollution and visibility studies. Its main component is a 0.15 m diameter pipe 2 m long containing all optical and sampling elements. The light source is a xenon flash lamp mounted on the pipe wall and powered at a rate of 1.2 flashes/s. An opal glass in front of the lamp provides a cosine characteristic of the light source. At one end of the pipe, a photomultiplier is mounted rigidly behind a series of collimating disks. The opposite end serves as a light trap, which is also constructed with collimating disks. Sample air is drawn through the central chamber at a flow rate of about 1.7 x 10"3m3/s [100L/min]. The output from the phototube, a pulse of about 20 us, is amplified and averaged over time. The device has sufficient sensitivity to distinguish gases on the basis of their Rayleigh scattering (see Table 15-1) so that different particle-free gases can be used to calibrate the instrument in terms of scattering cross sections. The results indicate that for aged atmospheric aerosols (accumulation mode), light scattering is proportional to the mass concentration of suspended particles (Charlson et al., 1967). A newer generation of integrating nephelometers based on the same principle as described by Charlson et al. (1967) was developed by TSI. Model 3551 is a high-sensitivity, one-wavelength (A = 0.55 jam) instrument that determines the scattering coefficient crs of atmospheric aerosol particles (Eq. 15-20). Because of its high sensitivity, the nephelometer can measure scattering coefficients as low as 2 x 10"7In"1 . In addition to model 3551, TSI offers another model (model 3563) using three wavelengths and a backscatter option. It is designed for short- and long-term measurements of the light-scattering coefficient O5 of atmospheric aerosols, including visibility and air quality studies.

LIGHT SCATTERING BY IRREGULAR PARTICLES General Remarks Theoretical and experimental calibration curves of optical techniques for particle characterization are usually confined to particles of spherical shape, whereas most aerosol systems consist mainly of particles having irregular shape. In this section, approximations valid for particle diameters dp « a and dp » a are used to derive some general predictions about the influence of the particle shape on light scattering. The predictions derived from these approximations are compared with experimental results with optical particle counters using irregularly shaped particles (Gebhart, 1991). The experimental findings are correlated with angular scattering patterns of nonspherical particles obtained from microwave analog measurements (Zerull et al., 1977) and with light-scattering observations on single particles suspended in an electrodynamic balance (Bottlinger and Umhauer, 1989). Particles Smaller than the Wavelength When the particle size is much smaller than the wavelength, the particle is subjected to an almost uniform optical field. In this case, light scattering is mainly a volume effect, and the light-scattering diameter dsc of such a small nonspherical particle approaches its volumeequivalent diameter, dy. This can be shown theoretically by applying dipole theory (polarizability) to spheroids that are smaller than the wavelength (van de Hulst, 1957; Kerker, 1969). As has been shown by Gebhart (1991), a ratio of the semiaxes of a/b = 5 results in a variation of the scattering intensity by a factor of 1.27. Because of the d6 dependence of the scattered light on the particle diameter, however, the light-scattering diameter dsc of such a spheroid deviates only about 4% from that of a volume-equivalent sphere. For experimental investigations, agglomerates of uniform polystyrene spheres are useful objects to study the response of an OPC to nonspherical particles. Let dscj be the light-scattering diameter of an agglomerate formed by; uniform spheres with diameter dx and refractive index ra.Then a relative light-scattering diameter, Fj, of the aggregates can be defined as (15-34) Relative light-scattering diameters Fj have been measured with a laser aerosol spectrometer by Gebhart et al. (1976) and Gebhart (1991). Owing to the high resolving power of the instrument, agglomerates consisting of up to six spheres could be distinguished. It was found that Fj =j\ so that, within an experimental error of 3%, dsc] was identical to the light-scattering diameter of a sphere of equal volume. Chen and Cheng (1984) investigated agglomerates of PSL spheres with the Royco 236 laser counter (see Fig. 15-13) and also measured a volumeequivalent response for PSL particles below 0.5 um. See Chapter 23 for further discussion of agglomerate measurements. Particles Larger than the Wavelength In the limiting case of dp » X, the scattered light can be considered as consisting of three components, which are due to the physical effects of diffraction, reflection, and refraction. The light-scattering components are expressed by Eq. 15-11 and illustrated in Figure 15-3. In accordance with the Fraunhofer diffraction formula, the light scattered within the forward diffraction lobe is a function of the projected area of a particle. Outside the diffraction lobe, however, light scattering is affected by reflection and refraction on the particle surface. Therefore, specular and internal reflections can produce angular distributions that

deviate considerably from the scattering diagram of a sphere of equal projected area. Consequently, the effect of the particle shape is highest if light scattered outside the diffraction part is collected through a relatively small aperture. For irregularly shaped particles with dimensions above the wavelength of light, the effect of particle shape on light scattering is lowest if the flux of scattered light is a function of the projected area of the particle, regardless of its shape. There are two possibilities for building instruments with a projected area response: either collect only diffracted light (low-angle scattering) or collect all light scattered by reflection and refraction (4^-geometry scattering). The response of a low-angle scattering instrument to agglomerates of polystyrene spheres was investigated by Gebhart et al. (1976) and Gebhart (1991). It was found that F1 = /i so that, within an experimental error of about 3%, dscj was identical to the diameter of a sphere of equal projected area. It should be mentioned in this connection that the response of a projected-area instrument to a nonspherical particle, of course, depends on particle orientation in the sensing volume. It can be shown, however, by statistical considerations that even for randomly oriented agglomerates of spheres the more frequent orientations are those that exhibit projected areas that are multiples of the cross section of a single sphere. An instrument with a relatively large receiver aperture is the CI208 counter (CLf) shown in Figure 15-12. The response of this instrument to doublet aggregates (j = 2) of polystyrene spheres was investigated by Chen and Cheng (1984). Their results demonstrated that for dx > 0.6 urn, the light-scattering diameter dscj of such doublet aggregates is identical to the diameter of a sphere that has the same projected area as the cluster. Results obtained from microwave analog measurements (Zerull et al., 1977) showed that the scattering patterns of a nonspherical particle and of a sphere of equal projected area agreed quite well in the angular range where diffraction dominated. Outside the diffraction lobe, however, the intensity of light scattered by a nonspherical particle was, on average, higher than that predicted by Mie theory, and the intensity of light scattered in a fixed direction (small aperture) varies considerably with particle orientation. The experimental findings of Biittner (1983), who measured irregularly shaped particles of quartz and limestone with the right-angle scattering instrument shown in Figure 15-14, confirmed the microwave measurements. From Biittner's results, it is apparent that the particles consisting of quartz and limestone scatter much more light in the 90° range than spherical particles of polystyrene and glycerine having the same Stokes diameter, although the optical constants of the materials are comparable. Angular distributions of scattered laser light measured by Coletti (1984) on assemblies of isometric nonspherical particles support the experimental findings of Zerull et al. (1977) and Biittner (1983). In a recent study, monodisperse NaCI crystals produced from a vibrating-orifice generator were classified in the counting mode of a two-mode laser aerosol photometer that collected light at 90° ± 11° (Gebhart et al., 1988). Whereas a low-angle scattering instrument indicated a monodisperse NaCI aerosol at the generator outlet, the 90° aerosol photometer classifies the NaCl crystals as polydisperse. Converting the same NaCl crystals into saturated NaCl-H2O droplets, however, lead to a size distribution in the 90° aerosol photometer that could be characterized as monodisperse (Gebhart, 1991). With a modified Millikan device using the electrodynamic balance (see Chapter 20), Bottlinger and Umhauer (1989) suspended single irregular particles in an optical sensor similar to that in Figure 15-14 and studied the signal variation for all possible orientations of the particle. Based on signal spectra reflecting all orientations of a particle, the authors developed a concept to eliminate the effect of particle shape on size analysis by singleparticle light scattering. When considering a random assembly of large irregular particles, the angular distribution of light scattered by external reflection should be approximately equal to that of large spheres because there exists, on average, a similar probability for angles of reflection. Considering

light refraction by the surfaces of randomly oriented nonspherical particles, however, there is a greater probability of high internal reflection angles inside irregular particles, resulting in more total internal reflections at the expense of the refracted component Q2 realized for spherical particles in the forward direction (see Fig. 15-3).

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As a second limitation, we generally consider only real-time or direct-reading equipment. This is not a serious restriction, as most in situ techniques readily lend themselves to on-line analysis and presentation. It should be noted, of course, that subsequent (and more sophisticated) data analysis is often essential to ensure data integrity. One exception is that holography is considered. Although holograms must be first developed and later reconstructed for analysis, automated techniques are beginning to appear, and so this powerful technique has been included. Finally, we restrict discussion to techniques that provide size distribution measurements; excluded by this restriction are techniques that provide only limited information about the particle cloud. One example is a transmissometer, which is capable of measuring either particle volume concentration or Sauter mean diameter (Holve and Self, 1980). In short, this chapter reviews commercially available, direct-reading, optically based, in situ aerosol measurement instruments. Among them, capabilities exist for measurement of individual particle sizes from about 0.1 to above 1000 urn, concentrations as high as 106/cm3, and speeds in the kilometer per second range. Ensemble techniques can measure mean diameters as low as 1 nm. While in situ instruments overcome many of the limitations encountered with extractive methods, they do suffer (as a class and individually) from a wide range of new limitations. To describe these limitations, the next section provides an overview of in situ optical particle-sizing systems. A brief section introducing light scattering theory follows. With this background in place, the chapter continues with a review of the instruments that are currently commercially available to the researcher. The individual reviews are by necessity short, but sufficient references are provided to help the reader to explore each method further. Although every effort has been made to include all of the available equipment, some manufacturers may have been overlooked. The chapter concludes with a section on the crucial topic of performance verification, including the issues of standards, calibration generators, and instrument comparisons.

OVERVIEW

The in situ measurement of particles by optical methods has been an area of active research. Thus, many excellent reviews are available on the topic (Black et al., 1996; Koo and Hirleman, 1996; Tayali and Bates, 1990; Lefebvre, 1989; Hirleman, 1983, 1984, 1988a; Hovenae, 1987). Several sets of proceedings contain current applications and discussions of in situ techniques (Kuo, 1996; Hirleman et al., 1990; Hirleman, 1990; Gouesbet and Grehan, 1988). It is helpful to divide optical in situ techniques into two general classes, based on whether they analyze single-particle events or aggregate cloud properties. Single-particle counters (SPC) generally make a size determination on one particle at a time by analyzing its scattering behavior while it passes through a well-defined (usually small) volume of highintensity (usually laser) light. Intensity, phase, or image information in the scattered light have all been used for particle sizing. A size distribution is obtained by sizing a number of particles sufficient to ensure statistical accuracy. This class of instruments is similar in principle to the extractive, optical techniques covered in the previous chapter except that the measurement volume is now located external to the instrument. SPCs generally provide a wealth of information on the counted particles, providing correlations among particle properties such as size, velocity, and time of arrival and allowing spatial characterization of the particle field. At high number concentrations, however, single-particle counting techniques suffer from coincidence errors, which occur when more than one particle occupies the sensing volume at the same instant. The second class of in situ systems, collectively called ensemble techniques, generally operates by illuminating a volume containing a large number of particles and analyzing the col-

lective scattering. An illustrative example of an ensemble technique would be a photographic snapshot (or a hologram in three dimensions), which captures the state of a particle distribution at one instant in time. A drawback of photographic systems is that it is difficult to obtain real-time readout of results. Real-time ensemble techniques are available that remedy this limitation, but require a mathematical inversion of the data to determine the size distribution. Ensemble techniques are well suited for measurements at high particle concentration, but become ineffective at low concentration. Generally, ensemble techniques do not provide as detailed information as SPCs because individual particle information is lost in the averaging. Real-time ensemble techniques provide only limited spatial resolution of the particle field. Generally, ensemble techniques measure particle concentration (number/volume), while SPC systems measure particle flux (number/area/time) (Hirleman, 1988a). That is, ensemble techniques report the number (and sizes) of particles present in the sampling volume over the measurement time (spatial averaging), whereas SPCs report the number (and sizes) of particles passing through the sampling volume during the measurement time (temporal averaging). To obtain aerosol concentration, SPCs require additional particle velocity information. As Hirleman (1988a) points out, the distributions measured by concentrationor flux-based techniques will differ if a systematic correlation exists between particle size and velocity, as demonstrated in Example 16-1. As each SPC or ensemble particle sizing technique offers distinct strengths and weaknesses, an ideal instrument can only be defined in terms of measuring a specific set of properties for a specific aerosol in a specific environment. In this vein, Hovenac (1987) and Hirleman (1988a) outline an approach to in situ optical sizing in terms of instrument operating envelopes. The central idea is that the choice of instrument must be a two-step process: First, identify the particle properties that need to be measured and the conditions under which the measurement must be made, and, second, establish that these conditions fall within the instrument's operating envelope. The final step is critical. As Hirleman (1988a) points out, many instruments will continue to "merrily report erroneous data and not notify the user." An instrument operating envelope will be defined by the ability of the instrument to measure the desired property over an appropriate range to an acceptable accuracy. Hirleman (1988a) groups the parameters that comprise the operating envelope into three domains: particle, instrument, and environmental properties. Based on Hirleman's scheme, a general overview of the operating envelopes of in situ methods follows. Particle Properties

A variety of particle properties can be of interest, including size, shape, concentration, velocity, and index of refraction. Each of these properties can be distributed among a population of particles, and the problem becomes one of measuring the related distributions. With nonspherical particles, an ambiguity arises in selecting a dimension to characterize particle size. Moreover, most measurement techniques actually infer particle size indirectly from an observation of some particle behavior (settling speed, light scattering intensity, and so forth). Thus, size distribution measurements must be reported in terms of equivalent diameters: Optical, aerodynamic, hydrodynamic, or electric mobility equivalent diameters are commonly reported. Care must be taken even when comparing among optical techniques, as the scattering behavior of the same particle will depend greatly on the details of the measurement technique used to observe it. Besides size, the particle concentration (number, surface area, or mass of particles per unit volume of gas) is also frequently of interest. A further complication arises as all particle properties can show spatial or temporal variation. Measurement of particle size distributions demands that both particle sizing and counting be accomplished with great accuracy. High spectral resolution is required when the size distribution is itself of fundamental importance. For example, an exact description of the size

EXAMPLE 16-1 A fluid containing a bimodal droplet distribution is rapidly accelerated before a measurement access window. The smaller mode consists of monodisperse droplets with diameter dpl = 10 um and concentration C1 = 100 particles/cm3 that are moving with the fluid at velocity V1 = lOm/s. The larger mode consists of monodisperse droplets (dp2 = 100 um, C2 - 20 particles/cm3) that are lagging the flow at V2 = 2m/s.lliis aerosol is measured simultaneously by an ensemble diffraction technique (sensing volume v = 1 cm3) and an SPC (sensing area A = I cm2 normal to the flow, sample time At = 1 s). What is the number mean diameter measured as (1) a spatial average by the ensemble diffraction system, (2) a temporal average by the SPC (no correction for particle velocity), and (3) a spatial average by the SPC? Assume perfect measurement by both techniques, that is, neglect trajectory ambiguity, edge effects, and so forth. Answer: 1. The ensemble technique responds to the number of particles of each size in the measurement volume. Thus, it would give a number mean of

2. If no correction is made for the discrepancy in droplet velocities, the SPC would weight the mean according to the number of counts during the sample time, nx = A At V1 Cx. During the sample time, the SPC would record nx = 100,000 counts and n2 = 4000 counts. To find the mean,

3. An SPC can be used to measure a spatial average concentration if the particle velocities are measured. To do this, the observed number of counts, nx, is divided by an effective sample volume given by A At Vx. This calculation gives back the true concentrations (C1 = 100/cm3 and C2 = 20/cm3) given in the problem statement and therefore gives the same mean diameter as in case 1.

distribution can be essential in understanding or predicting physical processes or in identifying origin or formation mechanisms. In some settings, however, accuracy may be less important than reproducibility. Typically, the mean size, the spread, and the shape of the distribution are all of interest. Ideally, the selected instrument's sizing range should suitably span the actual particle size range. This can complicate the characterization of wide distributions, as particle sizing over more than one order of magnitude in size is difficult to cover with one instrument in one configuration. The distribution's behavior at its tails can be important, particularly when transforming from a frequency to a mass weighted distribution.

A second property of interest is particle concentration: Aerosol mass, area, and number per volume of gas are each of interest in some context. The variety of concentrations that are encountered in particle measurement is impressive, ranging from a few particles per cubic meter in ultraclean areas to trillions of particles per cubic meter in some industrial settings. Obviously, one technique cannot be expected to cover this entire range. In most situations, it is impractical (or impossible) to characterize every particle present; thus, it becomes necessary to infer the true aerosol properties from a measurement of some subset. Difficulties arise when the particles are present only in small numbers, as is typical at larger sizes or in clean environments. Instrument noise (phantom counts) can become important under this condition, and an effort must be made to ensure statistical significance. Instrument limitations also become evident at high concentrations, as is discussed below. Concentration measurement errors can be amplified when extrapolating volume or mass distributions from measured frequency distributions. The particle velocity distribution can be important in understanding dispersal, transport, or flux. In some applications, the correlation between particle size and velocity is desired. Even when particle velocity is not of interest itself, it may be a limiting factor in system performance. For example, particles moving at high speeds can pose signal-processing and response-time difficulties in SPCs. If the electronics are not fast enough, the signal from a high-speed particle will broaden and its intensity peak will diminish; the result is that the particle is undersized. Size-velocity correlations can also adversely influence system performance. Generally, SPCs provide particle velocity information while ensemble systems do not (a notable exception being pulsed photography or holography). For SPCs, a velocity measurement is typically required to infer particle concentration from the measured distribution of particle size: Otherwise, faster particles will be counted preferentially in a flux-based measurement. Lower limits for particle velocity in SPCs are discussed by Ho venae (1987); upper limits are typically about 300 m/s, but some systems can operate in the kilometer per second range. Particle shape and index of refraction are less commonly of interest to the researcher, but are always important through their role in determining a particle's scattering characteristics. Note that some of the imaging systems discussed below are capable of recording particle shape. Instrument Properties

An accurate determination of a particle size distribution requires that the instrument both size and count particles accurately. Ho venae (1987) describes factors that adversely affect SPC sizing and counting performance. Although both size and count sensitivity are crucial for ensemble techniques as well, the discussion is complicated by the averaging nature of the measurement. The discussion below focuses on the measurement limits imposed by instrument features. Perhaps the most difficult aspect of making an accurate in situ measurement is in defining the sample volume, as particle velocities and trajectories cannot be controlled as in sampling-type instruments (Holve, 1980). This difficulty applies to both ensemble and SPC techniques and can lead to both sizing and counting errors. For most in situ systems, the sample volume is determined by the intensity profile of the illuminating beam and by the geometry and characteristics of the receiving optics (apertures, stops, lenses, filters, and so forth). Laser beam intensity nonuniformities within the sampling volume (in either the axial or radial direction) result in trajectory-dependent scattered intensity profiles for even monodisperse particles. For the common case of a laser beam with a Gaussian intensity profile, a particle passing through the axis of a laser beam will scatter more light than if it passed through the edge of the beam. Thus, a small particle passing through the beam axis and a large particle passing through the beam edge could give comparable scattering ampli-

tudes ("trajectory ambiguity"; Grehan and Gouesbet, 1986). For intensity-based SPC techniques, such multivalued response degrades instrument accuracy. Moreover, the combination of a nonuniform beam profile and photodetector sensitivity creates the situation where the effective sample volume becomes size dependent; for example, small particles are detected only by passing through the central portion of the beam, whereas large particles are detected over a much larger cross section. Both ensemble and SPC in situ techniques can suffer this counting bias, and all SPCs require some form of sample volume correction (e.g., Holve and Self, 1979a,b; Holve, 1980). One of the key parameters of interest is particle size. Several issues arise with regard to particle sizing with in situ techniques: precision (repeatability), accuracy (resolution), sensitivity (lowest detectable size), and dynamic range. One requirement for sizing accuracy is a monotonic response curve (intensity or phase versus size); unfortunately, light-scattering techniques are frequently multivalued due to Lorenz-Mie scattering effects (see below). Variations in particle shape and refractive index effects can dramatically affect the shape of the response curve and will limit system accuracy unless calibrations or calculations are performed with similar particles. Many in situ optical systems are based on near-forward scattering techniques, which minimize (but do not eliminate) shape and refractive index effects. The trajectory ambiguity discussed above also degrades accuracy for intensity-based techniques. All optical in situ techniques require that the laser beam waist be four to five times the size of the largest particle to ensure nominally uniform illumination over the particle's surface (Holve, 1980). For example, Hovenac (1987) has shown for SPCs that a single large particle in a small beam could be counted as two smaller ones. Making the linear dimensions of the measurement volume much larger than the largest particles also reduces the fraction of particles that suffer edge effects (Holve and Self, 1979a). Note that enlarging the measurement volume can increase coincidence errors, and so trade-offs must be made. Lens imperfections, misalignment, electronic and photodetector nonlinearities, and other nonidealities can significantly degrade all aspects of system performance (Holve and Davis, 1985). Beam intensity fluctuations and system misalignment transients can impair both instrument precision and accuracy. As a rule of thumb, optical and signal-processing limitations generally limit the dynamic size range that can be measured (with one instrument at one setting) to about a factor of 30. Instrument noise is frequently a limiting factor in determining dynamic range and can also influence precision, accuracy, and sensitivity. There is always a desire for improved instrument sensitivity. For in situ SPCs, a lower detection limit of about 0.3 urn is typical, although sampling-type SPCs can currently detect particles to about 0.05 urn. Knollenberg (1985) describes theoretical detection limits for SPCs and shows that the limit is dominated by background scattering from stray light or gas molecules present in the sampling volume. Interestingly, operating SPCs in vacuum can improve instrument sensitivity by a factor of two to six (Knollenberg, 1985). In summary, Knollenberg places the theoretical limit for SPCs operating in air or vacuum at around 0.02 Jim. Some ensemble techniques offer much lower detection limits, for example, 0.01 |im for dynamic light scattering. High particle concentrations can also limit system performance. For example, in SPCs this can lead to coincidence, dead time, and intensity attenuation errors. Coincidence occurs when two particles occupy the measuring volume at the same time, which may be counted as a single large particle, resulting in both sizing and counting errors and consequently skewing the size distribution to larger sizes. Coincidence places an upper limit on the number concentration that can be measured without significant interference for a given system configuration. This upper limit has been shown to be proportional to the probability of interference and inversely proportional to the effective measurement volume (Holve, 1980). Dead time occurs when the electronics are not ready when an event occurs because a previous event is still being analyzed; dead time effects can reduce or skew the measured size distribution.

High particle concentrations between the sample volume and the receiving optics can reduce the intensity of light scattered by the particle. The resulting error in intensity-based techniques would be to undersize all particles. In ensemble systems, multiple particle scattering occurs at high concentrations. In this case, measurements of the size distribution become concentration dependent. Corrections for coincidence, dead time, or multiple scattering can often be made using either hardware or software. All of the techniques discussed in this review require sophisticated data analysis, and most require a full inversion or deconvolution to finally resolve the desired size and number distributions. Real-time ensemble instruments demonstrate the classic case of inverting a finite set of measured responses to infer an unknown distribution (Hirleman, 1988a). For intensitybased SPC techniques, Holve (1980) has discussed the need to deconvolve the resulting intensity histograms to account for trajectory ambiguity. Although beam intensity variations have a minimal effect on particle sizing with phase-Doppler techniques, corrections still need to be made to account for size-velocity correlations and size-dependent sample volumes when concentration is required. The importance of proper data analysis or inversion cannot be overemphasized. Some of the problems and pitfalls of data analysis and inversion are presented in Chapter 22. The use of exposed laser illumination by most of these systems poses eye safety concerns. Most of the systems use low power lasers, but focusing can generate dangerous intensity levels. Proper laser safety operations are essential and should be considered in instrument selection, location, and operation. Environmental Properties Refractive index gradients along the optical path can cause beam steering, with a resulting change in optical collection angles. The length of the optical path and medium temperature and pressure gradients determine the extent of beam steering. Gas conditions (temperature, pressure, composition) also affect the gas refractive index. Laser systems are readily adaptable to high temperature environments, as they can mitigate the influence of high thermal radiation background. There are also practical issues like optical access and window contamination that must be considered. Also, application of optical techniques in environments with high ambient light levels can lead to spurious measurements unless suitably filtered. LIGHT SCATTERING The field of light scattering by particles is very broad and dynamic, and a thorough presentation is well beyond the scope of this brief introduction. Instead, this section provides a limited introduction to key concepts in light scattering that are used in this chapter. For further background, the reader is referred to van de Hulst (1981), Kerker (1969,1988), and Bohr en and Huffman (1983). Recent reviews of light scattering for particle characterization by Jones (1998) and Black et al. (1996) are of particular interest (see also Chapter 15). Particle Light-Scattering Properties When an electromagnetic beam is projected through a particle field, some portion is transmitted through the field, while some is absorbed and some is scattered by particles in the field. Figure 16-1 is a schematic representation of light scattering from a single particle. When the scattered light has the same wavelength as the incident beam the scattering is called elastic scattering, while inelastic scattering describes scattering with a shift in wavelength, as is seen

SPHERICAL PARTICLE INTENSITY I(a,m,6)

FORWARD SCATTERED LOBE (DIFFRACTION) INCIDENT LIGHT

REFLECTION AND REFRACTION

Fig. 16-1. Single-particle light scattering.

in Raman scattering or Doppler-shifted scattering. In general, the distribution of light scattered by a particle is a function of the particle size and shape, the incident wavelength, and the refractive indices of the particle and the surrounding medium. Both scattering and absorption characteristics of a particle can be included by describing the particle's optical properties with a complex refractive index m = n - in', where the real part n describes scattered light characteristics and the imaginary part n' describes absorption. Therefore, the complex refractive index for relatively transparent particles has a very small imaginary part (absorption coefficient), while for strongly absorbing particles the imaginary part is larger, for example, nf < 10~8 for water (Bohren and Huffman, 1983) and n' « 1 for metals in the visible range (van de Hulst, 1981). van de Hulst presents characteristic complex refractive indices for a number of different materials. Most handbook values of refractive index provide only the real part n, which is sufficient to calculate purely refractive properties. Solution of the complete electromagnetic theory describing light-scattering processes (Lorenz-Mie theory) is sometimes either difficult or invalid, so a number of approximations have been developed that are valid within certain ranges. These ranges are determined using a dimensionless size parameter, which for a sphere is given as a = (7tdp)/X, where dv is the diameter and A is the incident wavelength. Note that the symbol x is also commonly used to represent the size parameter. Rayleigh Scattering

Rayleigh scattering is typically used to describe light scattering characteristics from particles with very small values of the size parameter (diameters much smaller than the incident wavelength). Rayleigh theory is typically valid for a « 1, for example, for A = 0.6328 urn (HeNe laser), dp« 0.2 urn. In the Rayleigh scattering regime, the oscillating electric field of the light wave induces an oscillating dipole in the particle, causing symmetrical scattering (in the forward and backward directions) about the particle. The intensity for Rayleigh scattering is proportional to the sixth power of the particle diameter (/
SCATTERED INTENSITY (arbitrary units)

SIZEPARAMETER

a=

^ ~

FIg. 16-2. Lorenz-Mie scattering response curve.

Lorenz-Mie Theory

Particles with diameters of the same order as the incident light (say, 0.1 to 1 um) are too large for Rayleigh scattering because the local electric field is no longer nearly uniform at any instant. Thus, different parts of the particle scatter portions of the incident beam with different properties. In this size range, the appropriate light-scattering theory for homogeneous spheres illuminated by a plane wave is the more complex Lorenz-Mie scattering (van de Hulst, 1981), which in fact provides an exact solution to Maxwell's equations of electromagnetic propagation. In this regime there is a strong interaction between the particle and the incident beam. There is no simple relation between scattered intensity / and particle diameter dp; the plot of / as a function of dp (Fig. 16-2) is multivalued, so particles of several sizes can scatter with the same intensity. Strong dependence of scattered intensity on refractive index further complicates measurements in this regime. A number of Lorenz-Mie scattering computer programs are available (e.g., Bohren and Huffman, 1983). Lorenz-Mie theory breaks down when the particle size becomes of the same order as the focused beam diameter or for nonspherical or inhomogenous particles. To account for the effects of arbitrary incident illuminating beams on scattering by spheres, the Generalized Lorenz-Mie Theory was introduced (e.g., Gouesbet, 1994). Light scattering by nonspherical, inhomogeneous particles is discussed below.

Geometric Optics Approximation

Scattering from large particles, where a » 1, can be treated using simple ray optics by considering the light wave incident on the particle to be made up of a collection of individual rays. The rays hitting the particle lead to reflection, refraction, and absorption, while those passing along the edge of the particle give rise to diffraction. The effects of each of these phenomena can be evaluated individually for particle sizes larger than the incident wavelength, unless the particle refractive index is very close to one (van de Hulst, 1981). The scattered intensity in this regime is proportional to the particle cross-sectional area (/ ^ dp2) and not strongly dependent on shape or composition for aspect ratios close to one. Most geometrical optics approaches assume that the particle is illuminated by plane wave of uniform intensity and so are only valid for particle diameters much smaller than the beam

diameter. As the particle size approaches or exceeds the incident beam diameter, corrections to standard geometrical scattering must be applied. Diffraction (Fraunhofer and Fresnel)

For dp > A, diffraction begins to dominate refraction. For dp > 4 to 5A, Lorenz-Mie theory reduces to Fraunhofer diffraction theory when limited to the near-forward direction. Diffraction from a single point particle will, in general, produce a Fresnel diffraction pattern. However, when the far-field condition is satisfied (i.e., z » dp2/X, where z is the object to observation plane distance), the Fraunhofer diffraction pattern will be seen at the observation plane. The intensity of Fraunhofer diffraction is independent of particle refractive index, a fact that is often useful in particle-sizing applications. Diffraction is dominant at nearforward scattering angles, with an intensity proportional to the particle cross-sectional area. The angular distribution of scattered light intensity is determined by the particle shape and is inversely proportional to the diameter for spherical particles. Fraunhofer diffraction can be described theoretically using the Airy function, with the amplitude of the scattered light described by a Bessel function of the first kind. Refraction and Reflection

Refraction in a particle can be described by Snell's law, similar to refraction at a plane refractive index interface. Reflection from a particle can be described using standard laws of reflection, with specular and diffuse reflection acting the same as for plane surfaces. The angular distribution of reflected and refracted light is independent of particle size, but depends on complex refractive index, with a higher refractive index spreading the light more strongly, and can depend strongly on particle shape (Gebhart and Anselm, 1988). Laser Optics

Coherent light interferes, while incoherent light does not. Most light sources (except lasers) generate incoherent or partially coherent light. Incoherent light is sometimes preferred for optical systems in order to prevent formation of interference fringes in the image of interest, usually caused by edge diffraction. However, most optical particle-sizing techniques use coherent laser light because of its intensity, monochromaticity, and coherence, needed to provide the interference key to such techniques as laser Doppler velocimetry, phase-Doppler particle sizing, and holography. Most systems use lasers with a Gaussian intensity profile (TEM00). Propagation of a Gaussian beam can be described using diffraction theory (Dickson, 1970), including the size and location of the focused waist, and the intensity distribution along the optical axis. When a Gaussian beam is used for light-scattering measurements, the illumination field is not uniform, and, depending on the relative size of the beam and particle, the incident light intensity may vary over the particle's surface (Jones, 1999; Black et al., 1996). Generalized Lorenz-Mie Theory was developed to account for light scattering by a single spherical particle in an arbitrary incident illuminating beam (Gouesbet, 1994). The theory also can accommodate scattering by particles located off beam center line. Multiple Scattering

The Lorenz-Mie scattering theory, and the approximations used for light-scattering predictions in different size regimes, assume (among other things) single-particle light scattering. In a concentrated aerosol sample or over long path lengths, light scattered by a single particle can be rescattered by other particles in the illuminated field. The scattered light intensity measured by a photodetector will be reduced by multiple scattering. Clearly, multiple

scattering must be taken into consideration in absolute intensity-based measurements and must also be considered for diffraction-based measurements. Nonspherical Particles

Many particles of interest in aerosol measurements are not spherical, so the detailed lightscattering models must be modified to account for the particle shape. The light-scattering behavior of nonspherical, inhomogeneous particles remains an active area of research (e.g., Damaschke et al., 1998b; Jones, 1999). For sufficiently large particles, where the geometric optics approximation is valid, near-forward diffraction measurements are useful because they are independent of particle shape, as mentioned above. Gebhart and Anselm (1988) discuss approximations to light-scattering theory to include scattering by nonspherical particles for dp « X and dp » X and provide experimental comparisons using agglomerate particles and NaCl crystals. They conclude that the scattered light intensity from small nonspherical particles is within about 4% of that of an equivalent volume sphere. The light-scattering characteristics of large nonspherical particles are similar to equivalent projected area spheres only when collected in the forward diffraction lobe or when essentially all of the reflected and refracted light is collected. Killinger and Zerull (1988) discuss scattering from nonspherical particles in the intermediate size range. They show that scattering from a 3 urn diameter rough sphere is quite close to that predicted by Lorenz-Mie theory but that scattering from a rough, slightly nonspherical 50 um diameter glass bead varies significantly from scattering by an equivalent sphere. They also found up to one order of magnitude variation in scattered intensity depending on nonspherical particle orientation. See also Chapter 23 for discussion of nonspherical particle properties. Optical Imaging

In situ systems use optical configurations consisting of lenses, mirrors, filters, and apertures. All components have practical limits. An ideal lens would transform a collimated input beam to a point at the focal distance. Real lenses can cause image aberrations due to chromatic effects, astigmatism, and other effects. Physical optics shows that diffraction places limits on image resolution so that the image of a point is a small Airy disk rather than a point. The highest achievable resolution of an optical imaging system is therefore the diffraction-limited resolution 5 given by 8= 122XI/D, where / is the image distance and D is the limiting aperture of the system. Two points lying within 5 of each other will be indistinguishable in the image, as they will lie within the same Airy disk. Resolution typically limits imaging systems to examination of particles with diameters of the order 5 um and above. Note that the resolution decreases (poorer resolving ability) linearly with measurement distance from the object. The depth of focus Af is given by Af= 2S2/X, so high resolution (small 6) leads to short depth of field, making it difficult to define the boundaries of the sampled volume, that is, to determine whether an observed particle lies inside or outside the sample volume. Because the depth of field is proportional to the square of the resolution, large particles remain in focus over a much greater distance than do smaller particles. Thompson (1984) reviews imaging techniques for particle sizing, including both coherent and incoherent light, and one- and two-lens systems, along with applications in droplet measurements. SINGLE-PARTICLE COUNTERS: INTENSITY BASED The first class of instrument sizes and counts individual particles as they pass through an illuminated sample volume. As the particles pass through this region, they scatter light that is collected over some solid angle by the receiving optics and focused onto a photodetector

SAMPLE VOLUME LASER ' BEAM

DETECTOR, VIEWAREA TRANSMITTING LENS BEAM STOP

LASER

RECEIVER LENS SPATIAL FILTER

DETECTOR

Fig. 16-3. Example of a single-particle light-scattering system.

(Fig. 16-3). The particle size is determined by the peak intensity of the scattered light. A variety of such techniques are now available, and many reviews of the topic are available (Holve et al, 1981; Knollenberg, 1979,1981; Hovenac, 1987;Tayali and Bates, 1990; Koo and Hirleman, 1996; Black et al., 1996). All of the limitations and concerns reported for SPCs in the "Overview" apply to this class of techniques, including counting statistics at low concentrations and coincidence and dead time effects at high concentrations. In particular, nonuniformities in the illuminating beam can result in both sizing and counting errors for this class of equipment, and some form of correction (either hardware or analytic deconvolution) is required. Intensity-based techniques are particularly sensitive to environmental features that alter either illuminating beam or scattered light intensities, such as window contamination or high particle densities between the sample volume and collection optics. Forward-Scattering Spectrometer Probe

The forward-scattering spectrometer probe (FSSP) models (PMS)* are ground-based or aircraft-mountable probes that size particles based on the intensity of forward-scattered light as they pass through a laser-illuminated sensing volume. The newer model FSSP-300 provides better sensitivity (down to 0.3 um) and higher resolution (31 channels) over its range (0.3 to 20 Jim) than the mechanically identical FSSP-100 (15 channels over several size ranges, such as 0.5 to 8.0 and 5.0 to 95 um). The velocity operating range for these instruments is from about 10 to 125 m/s. In the typical configuration, a particle velocity distribution is not measured by the FSSP. However, an option is available that converts the running mean particle transit time (measured to correct for edge effects) into a mean velocity. The system has been used extensively in atmospheric aerosol studies (e.g., Knollenberg, 1981). The operating principles for the FSSP-100 have been described by Knollenberg (1981) and for the FSSP-300 by Baumgardner et al. (1992). A patented dual-detector arrangement is used to size only those particles passing through a prescribed sampling volume. Briefly, the * See Appendix I for full manufacturer addresses referenced to the italicized three-letter codes.

sampling volume between two probe tips is illuminated by an HeNe laser from one of the tips. When a particle enters the volume, it scatters light that is collected by optics located in the other probe tip. While a dump spot blocks the main beam, the forward-scattered light enters a beam-splitting prism and is focused onto two photodetectors. The signal photodetector is unmasked and reports an intensity maximum used to size the particle, while the annulus detector is masked to eliminate light from in-focus, centered particles. A comparison between the two signals for each particle is used as an acceptance criterion: particles passing far from the focal plane scatter a larger proportion of light into the annular detector and are rejected. A transit time test is also performed to eliminate particles traversing the beam near an edge. This test can bias the size distribution, particularly for broad distributions with size-dependent particle velocities (Baumgardner et al., 1990). Several authors have reviewed the optical and electronic limitations of the FSSP technique (Baumgardner et al., 1990,1992; Wendisch et al., 1996). Issues addressed include sample volume, sizing, and counting uncertainties. Concentration measurement uncertainties can be quite large (>50%) (e.g., Knollenberg, 1981), although correction algorithms can be applied to improve accuracy (Baumgardner et al., 1990). Size calibration studies using latex and glass spheres (Pinnick et al., 1981) and water droplets (Wendisch et al., 1996) have been reported. Later, Hovenac and Hirleman (1991) developed a rotating pinhole calibrator for use with the FSSP. Investigators have shown that experimental FSSP calibration curves are in reasonably good agreement with Mie calculations if particle refractive index and shape (Jaenicke and Hanusch, 1993) and beam nonuniformities (Hovenac and Lock, 1993) are considered. 90° White-Light-Scattering Analyzers

A description of an in situ SPC based on white-light illumination and detection at 90° scattering angle was given by Umhauer (1983). The choice of white-light illumination is intended to maximize monotonicity of the scattering intensity versus diameter response curve and to reduce (though not eliminate) index of refraction effects. These white-light systems are well suited to filter efficiency testing, especially at high pressures or temperatures, and have also been used widely in pharmaceutical spray sizing. The velocity operating range is typically from 0.1 to lOm/s, although particle velocities are not measured. Commercial systems based on the Umhauer design have been marketed as the HC series particle sizers by PLY (now discontinued) and as the Model PCS-2000 series by PAS. Both manufacturers offered several models, differing in optical geometry and, hence, in nominal size and concentration ranges. The discontinued HC series PLY provided configurations with particle size ranges from 0.4 to 22 um (model HC-2015) to 1.5 to 100 urn (model HC-2470). The larger measurement volume required for the latter size range made the HC-2470 more susceptible to coincidence errors, but the system was less susceptible to edge errors (Borho, 1970). Maximum concentrations achievable with the HC series is in the range of 105 particles/cm3. Performance of the HC series has been investigated in several calibration studies: Mitchell et al. (1989) (according to British Standard BS3406; British Standards Institution, 1988), Fissan and Helsper (1981), Sachweh et al. (1998), and Friehmelt and Heidenreich (1999) (nonspherical particles). These authors generally report smooth, monotonic instrument response functions in good agreement with Lorenz-Mie calculations for spheres, although a strong index of refraction effect was observed. Sachweh et al. (1998) modified an Umhauer-type system to achieve a smaller optical measurement volume, allowing measurements at concentrations up to ~107 particles/cm3 with a lower size detection limit of about 0.2 um. Depending on the configuration of the PCS-2000 system (PAL), the particle size range can be chosen within 0.15 and 100 um for particle concentrations up to 106 particles/cm3. PCS2000 systems offer two independent photomultipliers to minimize border zone errors (i.e.,

trajectory ambiguity). Only a few studies of the PCS-2000 system have appeared in the literature. Stier and Quinten (1998) report on an index of refraction correction, and Maus and Umhauer (1996) used a PCS-like instrument in a filter study. Performance of PCS-2000 systems is expected to be generally similar to the HC series (PLY), based on their shared ancestry (Umhauer, 1983). Particle Counter Sizer Velocimeter

The particle counter sizer velocimeter (PCSV) system (PRM) is an SPC that measures particle size based on the peak intensity of HeNe laser light scattered in the near-forward direction (Holve and Self, 1979a,b) (see Fig. 16-3). Using near-forward-scattered (predominately diffracted) light helps reduce particle shape and refractive index effects; thus, instrument response is mainly dependent on particle cross-sectional area. Particle speed is determined from the widths of the scattered light pulses (Holve, 1982). Only average speed is reported, although the entire speed distribution is recorded in the data set. Depending on the specific system configuration, the manufacturer gives the instrument's operating envelope as particle size between about 0.2 and 200 jam, concentration up to 107 particles/cm3 for submicrometer and up to 100 ppm by volume for supermicrometer particles, and particle velocity between 0.1 and 400 m/s. A maximum particle count rate of 50OkHz is claimed for the system. To cover the wide range of sizes, two separate laser beams are used to form two independent measurement volumes; the narrower beam (nominal diameter of 20 urn) is used for sizing smaller particles, while the wider beam (nominal diameter of 200 jim) is used for sizing larger particles. Process Metrix (PRM) claims an accuracy of ±5% and a precision of ±3% of the indicated size. The system is available in both bench-top (PCSV-E) and probe (PCSV-P) fiber-coupled models. The water-cooled probe version is designed for operation in hostile environments (temperatures to 14000C and pressures from vacuum to lOatm) and provides gas purging to minimize window contamination. The bench-top version consists of two towers supported by a common bridge about Im apart, with the measurement volume centrally located. Both systems contain in situ alignment systems to correct for beam steering in hostile environments (Holve and Annen, 1984). Alignment sensitivity was explored analytically by Holve and Davis (1985). A major feature of the PCSV system is the use of a deconvolution of the measured scattered intensity histogram to infer the size distribution (Holve and Self, 1979a,b; Holve and Annen, 1984; Holve and Davis, 1985). The deconvolution is required due to the trajectory ambiguity. Generalized Lorenz-Mie scattering theory is used to predict the scattering response function (scattered intensity versus particle size) for the desired geometry and has been experimentally confirmed (Holve and Self, 1979b). Although early work characterized the intensity profile of the sample volume experimentally (Holve, 1980), the current technique relies on an analytic description (Holve and Davis, 1985) to generate the profile and a single-point calibration to bring predicted and observed instrument responses into agreement. The validity of the sample volume analytic model was experimentally checked with monodisperse latex spheres (Holve and Davis, 1985), and the accuracy of the deconvolution algorithm was established using a mixture of monodisperse oleic acid droplets (Holve and Self, 1979b). PRM provides a rotating chrome on glass reference reticle for instrument calibration in the 2 to 80 um size range. PCSV systems have been used to measure particle size distributions in a variety of combustion environments (Bonin and Queiroz, 1991,1996; Holve and Annen, 1984; Holve, 1980). Measurements of soda-lime glass beads in both cold and hot flows (Holve and Self, 1979b) gave self-consistent results, showing the PCSVs ability to size particles in flames at temperatures of 1600K. More recently, the PCSV has been used to study plating processes (Bonin et al., 1995).

Dual-Beam and Top-Hat Beam Systems Several techniques have been developed that attempt to provide intensity-based particle sizing that does not depend on the particle trajectory through the beam, thus avoiding deconvolution algorithms. To reduce trajectory ambiguity, investigators have either used two-beam systems or have tried to modify the intensity profile of a single incident beam. Dual-beam, two-color, or pointer-beam techniques typically employ one beam that has a relatively wide diameter and a second, coincident beam focused to a smaller waist coaxial with the larger beam. The light-scattering signal from the smaller (pointer) beam is used to trigger an intensity measurement from the larger beam, which is used for particle sizing. For example, Hess (1984) describes and demonstrates measurements made with a two-color system using a small pointing beam inside a larger Gaussian laser beam of a different wavelength. The pointing beam defines a relatively uniform portion of the large Gaussian beam. Particles are only measured when detected by the pointing beam, indicating that they are in the uniform portion of the outer beam, so the scattered pedestal intensity can be recorded and the size calculated free of trajectory ambiguity. The MetroLaser PAS-100 and PAS-200 (MEL) instruments use a similar technique to provide a uniform measurement region for combined LDV, particle sizing, and concentration measurements. The system can be configured to collect diffracted light in cases where the particles are of unknown shape or refractive index and is designed to operate with no dead time by collecting groups of particle-scattering events before processing the raw data. The manufacturer-stated operating envelope includes a 0.4 to 6000 um diameter range, with 2% typical resolution and a 30:1 dynamic range and a velocity range up to lOOOm/s. Data rates up to 3 x 106 particles/s can be measured. Grehan and Gouesbet (1986) describe a laboratory system in which a particle-sizing laser beam was modified to a uniform (top-hat) profile using a Gaussian absorption filter. The tophat concept reduces trajectory ambiguity by creating a beam of nearly constant laser intensity across most of the beam width. Of course, the realized intensity distribution of a top-hat beam is not uniform across the entire beam width, but is characterized by a constant intensity region in the center surrounded by Gaussian-like (intensity decaying) tails toward the beam edge. Thus, some trajectory ambiguity is still present because of the tails. Black et al. (1996) provide additional discussion of the top-hat beam technique. Note that the present authors are not aware of a commercially available top-hat beam system.

SINGLE-PARTICLE COUNTERS: LDV VISIBILITY BASED Laser Doppler velocimetry (LDV) is a well-established and documented technique for noninvasive measurement of particle velocities, made by measuring the Doppler-shifted frequency of light scattered by individual particles passing through a laser beam-defined measurement volume (Durst et al., 1981). The most common LDV configuration uses crossed laser beams to define a measurement volume with typical dimensions on the order 1 mm or less. Particles passing through the measurement volume scatter light with a Doppler shift proportional to the particle speed. Speeds as high as several hundred meters/s can be measured using conventional detection electronics. The scattered light intensity signal from each particle passage ("Doppler burst") consists of a high-frequency Doppler component superimposed on a low-frequency "pedestal" component due to the Gaussian intensity distribution of the illuminating beams. After filtering out the low-frequency pedestal, the remaining component (Fig. 16-4) is the Doppler frequency, directly proportional to the particle velocity. The extent of modulation of the Doppler signal (ratio of the Doppler signal to the pedestal intensity) is called the signal visibility.

INTENSITY

DETECTOR 1

Phase Difference

INTENSITY

DETECTOR 2

Fig. 16-4. Characteristic Doppler burst signals.

A number of particle measurement techniques have been developed based on the pedestal intensity or visibility of LDV signals. Jackson (1990) reviews interferometry-based droplet sizing techniques, including on-axis LDV visibility measurements (diffraction dominated), off-axis LDV visibility measurements (refraction dominated), and phase Doppler techniques. Such techniques are attractive because they can provide simultaneous measurement of singleparticle size and velocity. Ideally, the peak intensity of the Doppler burst would be directly related to particle size. However, the Gaussian nature of the illuminating beams complicates matters due to trajectory ambiguity. Particle size determination based on signal visibility was theoretically established by Farmer (1972), and the technique was attractive as a particle's size could be measured independently of its trajectory through the beam crossing. Recent reviews of the method are available (Jones, 1999; Black et al., 1996). One drawback of the technique is that the particle size-visibility relationship becomes oscillatory for larger sizes, thus limiting the available dynamic range (see Bachalo, 1980; Durst et al., 1981;Takeo and Hattori, 1990). For example, Jackson and Samuelsen (1987) compared phase Doppler and visibility-based interferometers in a spray system and found that the phase-based systems offered broader size and velocity ranges. Consequently, phase-based interferometers have found wider application than visibility-based systems, and no commercially available visibility systems are known to the authors. SINGLE-PARTICLE COUNTERS: PHASE BASED The phase Doppler technique is an LDV-based method for simultaneous measurement of single-particle size and velocity (see Hirleman, 1996, for a historical review). This technique is not intensity dependent like the previous group of SPC techniques and can therefore offer superior performance by minimizing effects such as beam attenuation or window fouling. A phase Doppler system measures the spatial and temporal frequencies of the Doppler-shifted light scattered by individual particles passing through a laser beam-crossing measurement volume. Phase Doppler systems use multiple photodetectors to sample slightly different spatial portions of the light scattered by individual particles. Figure 16-4 demonstrates high-

BEAM SPLITTER LASER

TRANSMITTING MEASUREMENT LENS VOLUME

BEAM1

BEAM 2

COLLIMATING LENS RELAY LENSES SPATIAL FILTER

"DETECTOR 1

r D ETECTOR2 DETECTOR 3

Fig. 16-5. Layout of a phase Doppler system.

pass-filtered Doppler bursts measured by two such detectors. The phase shift between the two signals is a measure of the scattered light spatial frequency, which can be directly related to the particle diameter, refractive index, and receiver geometry. The linear relationship between the spatial frequency of the scattered light and the particle diameter for fixed refractive index and receiver geometry can be shown using Lorenz-Mie scattering theory (Saffman et al., 1984), geometrical optics (Bachalo and Houser, 1984), or Generalized Lorenz-Mie Theory (Grehan et al., 1994). Particle velocity is related to the temporal frequency in the same manner as in conventional LDV. Figure 16-5 is a schematic layout of a generic phase Doppler system. Particle sphericity is required because the phase shift is calculated for either rays refracted through spherical particles of known, constant refractive index or reflected off the surface of reflective particles. Preliminary work on measurement of nonspherical particles is presented below. Most theoretical analyses of the Doppler phase shift have been performed using the geometric optics approximation and neglecting diffraction because scattered light is collected at sufficiently large off-axis angles (typically 30° or more). One of the key assumptions in phase Doppler analysis is that a single component of scattering, either refraction or reflection, dominates. Many authors have examined the effect of this assumption when it is violated by trajectory ambiguity, that is, when scattering is first dominated by reflection when a particle's external surface enters the laser beam and then refraction as it passes through the beam center (e.g., Qiu et al., 2000; Strakey et al., 1998; Hardalupas and Liu, 1997); these investigators and others have proposed and tested several postprocessing trajectory correction routines. Phase Doppler Particle Analyzer

The fundamentals of the phase Doppler particle analyzer (PDPA) are described by Bachalo and Houser (1984). The PDPA (AER, TSI) consists of a laser, transmitting optics, receiver optics package, signal processors, and data collection and analysis software, with all operations, data collection, and analysis computer controlled. The TSI system can be supplied to measure one, two, or three velocity components in addition to particle size. The manufacturer-stated operating envelope includes a 0.5 to 10,000 um diameter range, with 1% typical accuracy and a 50:1 dynamic range, and a velocity range to over 500 m/s, with 0.2%

typical accuracy. This system also calculates number density based on the number of particles passing through a calculated size-dependent measurement volume (to correct for trajectory ambiguity effects). The maximum measurable number density is 106/cm3. Particle Dynamics Analyzer

The Particle Dynamics Analyzer (PDA; DAN) is described theoretically by Saffman (1987) and with applications by Saffman et al. (1984). This system is similar to the TSI/Aerometrics PDPA except that signal phase is measured using a cross-correlation technique instead of a Fourier transform technique. The manufacturer-stated operating envelope for the PDA system includes a 0.5 to 10,000 um size range, with 1% typical accuracy and a 40:1 dynamic range, and a velocity range to greater than 500 m/s, with 1% typical accuracy. The number density is so highly dependent on the specifics of the application that customers are advised to refer to the open literature for specific concentration and mass flux limits for their application. DAN offers a dual PDA system that makes two simultaneous particle measurements in perpendicular planes in order to minimize trajectory ambiguity and slit effect (Xu and Tropea, 1994) errors (see next section). Phase Doppler Performance and Applications

There has been an abundance of recent work with these instruments in both instrument performance characterization and applications in industrial and research settings. Note that many of the performance issues pertain to older instruments, many of which are still routinely used in industry and research; the newer generations of commercial instruments have been improved to address such concerns (e.g., frequency shift technique, photomultiplier gain optimization), Jackson (1990) reviews the older Aerometrics PDPA instrument and discusses the effect of the rotating diffraction grating (formerly used as a combined beam splitter and frequency shifter, replaced on later models with a beam splitter and Bragg cell frequency shifter) on PDPA concentration and velocity measurements. Dodge et al. (1987) performed liquid droplet measurements in sprays with the PDPA and noted that the PDPA was very sensitive to optical alignment. McDonell and Samuelsen (1990) evaluated the sensitivity of PDPA measurements to operator input parameters, specifically the photomultiplier tube (PMT) gain voltage and the frequency shift level. They found that mean velocity values were insensitive to PMT voltage and frequency shift but that fluctuating velocities were strongly dependent on PMT voltage and somewhat dependent on frequency shift because of wobble of the rotating diffraction grating. Volume flux measurement errors were caused by inconsistencies in determination of probe volume size and by the strong influence of PMT voltage and the weak influence of frequency shift on particle sizing. They conclude that PDPA operations must be made with a carefully chosen standard operating procedure and that a need exists for a reference calibration standard to guide operator selections of setup parameters for the PDPA instrument. Ceman (1990) and Bever and Hovenac (1999) have developed two different embodiments of such a standard calibration device (see "Performance Verification," below). Dressier and Kraemer (1990) calibrated an older PDPA using a multijet droplet generator. They found PDPA measurement accuracy of about 5%. In addition, up to 5% measurement error could be caused by lens aberrations. They concluded that the PDPA system should be refocused for each different beam spacing to minimize lens spherical aberration errors. They also found that measurements were quite sensitive to the PMT gain voltage in that a 50 V difference in gain voltage could lead to spurious data validations and artificially broadened distributions. They concluded that the lowest usable gain setting should be employed for measurements. Determination of this value can be difficult in measurements of unknown, transient particle distributions.

Ceman (1990) and Ceman et al. (1993) report detailed investigations into PDPA oleic acid droplet sizing performance in the 2.3 to 25 um range using a vibrating orifice aerosol generator and several different PDPA receiver geometries. They found that there were nonlinearities in PDPA measurements of particles below some critical diameter, in agreement with previous experiments (O'Hern et al., 1989; Ceman et al., 1990) and calculations (Al-Chalabi et al., 1988) using both Lorenz-Mie theory and geometrical optics. Saffman et al. (1984) found that a large receiver lens collection solid angle tended to damp these oscillations. Sankar et al. (1990) indicate that reflections from the surface of the droplet may also contribute to the oscillations in the phase versus diameter curve in the smaller diameter regime. Their work suggests that reflections can be minimized and linearity attained by collecting light at an angle close to the droplet Brewster angle; however, Ceman's results show that droplet sizing oscillations can be substantially reduced by using a larger collection solid angle (shorter focal length receiver lens) but that working near the droplet Brewster angle provided little or no additional improvement. Gobel et al. (1998) examined the accuracy of phase Doppler particle sizing at 1 um and below and concluded that ambiguities exist but can be accounted for when sufficient information is available on the particles. Alexander et al. (1985) used the PDPA for measurement of nonspherical methanol droplets produced by a vibrating orifice aerosol generator. The mean droplet diameters were 98 um, with aspect ratios ranging from 0.7 to 1.4. The PDPA diameter measurements were very sensitive to aspect ratio, with the greatest error (45% oversizing) for elongated ellipsoids oriented perpendicular to the horizontal fringes in the PDPA measurement volume. Brefia de Ia Rosa et al. (1989) have shown that the PDPA can be used to examine the shape of large nonspherical bubbles in water (1200 to 1800 um diameter) by making separate measurements of the bubble major and minor diameters. This technique could possibly be extended to small particle measurements, although such an extension does not appear to be straightforward, especially for particles with rough or angular surfaces. Damaschke et al. (1998a) experimentally and numerically analyzed the effects of nonspherical particles on phase measurement by the phase Doppler technique, demonstrating that nonspherical droplets can be a significant error source, especially for the standard three-detector systems. They suggested that alternative detector arrangements and larger measurement volumes may help minimize such errors. The PDPA has been employed in a large number of applications. Strakey et al. (1998) examined use of the PDPA for measuring sprays from high-pressure injectors. Their work included a careful analysis of probe volume dimensions smaller than the droplet diameters and showed that such a device could perform well even in dense sprays provided that phase and intensity validation and probe volume correction algorithms were implemented. Van Den Moortel et al. (1997) applied PDPA to examine turbulent gas-solid flow in a circulating fluidized bed and developed a postprocessing routine to correct for artificially high count rates measured from noisy Doppler bursts. Hardalupas et al. (1988) used both Aerometrics and custom-made phase Doppler devices to measure solid particles transported in a gas-solid dusty jet flow and found that asphericity of the glass beads used as seed particles in the flow limited the measurement accuracy. Bachalo et al. (1990) presented details of PDPA operations including data analysis routines and applied the PDPA to measurements of liquid spray droplets in both cold and combusting regimes. Droplet velocity time records showed significant spray fluctuations, leading to droplet cluster formation. Several other recent PDPA applications are discussed below in "Performance Verification." SINGLE-PARTICLE COUNTERS: IMAGING Determining a particle's properties by direct imaging is among the earliest techniques used in particle measurement: Consider the optical (and subsequently the electron) microscope.

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A significant advantage is that the shape and index of refraction issues that complicate singleparticle light-scattering measurements are avoided. In fact, imaging techniques provide one of the few avenues for investigating particle shape. The accuracy of single-particle imaging systems is limited by Fresnel diffraction and depth of field effects (Hovenae, 1987). Fresnel diffraction blurs image edges and complicates sizing. The depth of field effect arises from its dependence on particle size, with the result that large particles remain in focus over a greater axial distance than smaller ones. Knollenberg (1970, 1979, 1981) designed an automated, in situ, single-particle, optical imaging system that is commercially available (PMS) as the Optical Array Imaging Probe (OAP). In this family of probes, a collimated laser beam defines a measurement volume located between two sensing tips that extend forward from the main body of the system. Receiving optics direct the beam to illuminate a linear array of photodiodes. A particle passing through the measurement volume casts a shadow on the array, resulting in a decreased signal from the individual elements that lie in the shadow. Three methods are used to analyze the resulting data from the array: one-dimensional, two-dimensional, and greylevel processing. In a one-dimensional OAP system, the array elements are read and latched during the particle transit in a way that only provides particle size information. In a standard two-dimensional OAP system, the entire two-dimensional image of the particle is stored in high-speed memory as a series of "snapshots" of the particle during its transit. For each image, the status of each of the 32 elements in the photodiode array is recorded by one bit, indicating if the element is being shadowed or not. The great advantage of acquiring twodimensional particle images becomes apparent when measuring nonspherical particles. In the Grey Probe two-dimensional OAP system, a 64-element array is used where each element reports one of four shadow levels. The increased sophistication of the Grey Probe provides twice the resolution (twice as many elements) of the standard two-dimensional system, as well as depth-of-field information. For all of these imaging systems, both instrument resolution and size range depend on physical spacing of the array elements, magnification, and particle velocity. Consequently, the user must identify the expected velocity range in order to properly configure the system. OAP probes also suffer from depth-of-field limitations, leading to erroneous interpretation of out-of-focus (blurred) particle images. To characterize OAP performance, Hovenac and Hirleman (1991) and Reuter and Bakan (1998) used a rotating reticle calibrator; the latter authors proposed a depth-of-field correction that allowed accurate sizing (rms uncertainty less than 6%) of particles from 50 to 500 urn. In addition, OAP probes reject particles (resulting in counting bias) that shadow elements at either edge of the array, as the fraction of the particle falling outside the array cannot be determined and thus precludes correct sizing. Rugged, pylon-mountable airborne versions of the one-dimensional, two-dimensional, or grey-probe OAP configurations are available for either cloud droplet or precipitation measurements. Particle sizing ranges and resolution depend on the particular models, which differ in the number of array elements and optical configuration used. The cloud-droplet models are suited to sizing smaller particles, with ranges such as 10 to 620 um (with 10 um resolution) or 200 to 6000 um (with 200 um resolution). The precipitation models are suited to sizing larger particles, with ranges such as 50 to 3100 um (with 50 um resolution) or 150 to 9300 um (with 150 Jim resolution). The resolution limits given above assume instrument operation at aircraft speeds (100 m/s); significant improvement in instrument resolution can be achieved at lower velocities. The lower limit for OAP sizing (somewhere between 1 and lOum according to Knollenberg [1979]) results from the vanishingly small depth of field at these sizes. Particle velocities are not measured explicitly, but could be recovered by later analysis of the image sequences using the known imaging frequency. A ground-based precipitation OAP also is available. The system is contained in a weatherized package, with the two sensing tips extending above the top. The distance between probe sensing tips is 50 cm, providing a large sampling area for measuring the droplets in freefall.

While the aerodynamic diameter describes the inertial properties, the electrostatic charge influences the electrodynamic behavior of the particle in transport processes. Both aerodynamic diameter and electrostatic charge measurements on individual particles are needed in many electrodynamic processes; some examples are electrophotography and laser printing, electrostatic powder coating, electrostatic precipitation, electrostatically enhanced fabric filtration, and electrostatic beneficiation of minerals and coal. The E-SPART is capable of measuring particle charge as well as aerodynamic diameter. Airborne asbestos fiber measurements went through a similar progression over time in that, originally, relatively crude measurements of concentration were made by collection with midget impingers and microscope counting of all large particles. Filter collection with microscopic analysis was developed so that only fibers were detected. Finally, the development of the Fibrous Aerosol Monitor (model FAM-I, MIE) allowed continuous, real-time detection of airborne fibers. The FAM-I was designed to give results close to those of the phase contrast light microscopic method (see Chapters 12 and 26). These sophisticated instruments provide more specific data about aerosols; however, because of the complexity of their detection and analysis systems, they may also have various limitations and subtle problems associated with the interpretation of the data. The following sections present a discussion of these instruments.

ELECTRIC-SBVGLE PARTICLE AERODYNAMIC RELAXATION TIME ANALYZER Measurement Principles

The Electric Single Particle Aerodynamic Relaxation Time (E-SPART) analyzer can be operated in several modes, the first of which is the original SPART mode (Mazumder and Kirsch, 1977; Mazumder et al., 1979).The SPART analyzer determines aerodynamic diameter by subjecting particles to an acoustic field of frequency/and measuring the response of these particles to the acoustic excitation. In its typical sampling configuration, the aerosol is sampled in a laminar air flow moving vertically downward through the SPART's sensing volume (Fig. 17-1). The acoustic field induces an oscillatory velocity component to the particle motion in the horizontal direction. The inertia of each particle causes a phase lag 0 in the particle's periodic motion with respect to the acoustic field. This phase lag <j> is related to the relaxation time of the particle rp, which is a function of the aerodynamic diameter da of the particle. The SPART analyzer employs a differential laser Doppler velocimeter (LDV) to measure the oscillatory velocity component (in the horizontal direction) of the particle and a microphone to measure the acoustic field. The phase lag of the particle motion with respect to the acoustic field driving the particle is converted to aerodynamic diameter using a microcomputer. The microcomputer stores the aerodynamic size data for the particles sampled and provides the measured size distribution. Although the response of the SPART can be calculated theoretically, it is calibrated with monodisperse latex particles because some instrumental parameters are difficult to measure. In the E-SPART analyzer, an electrical particle acceleration field is used. There are two configurations: (1) a dc electric field superimposed on the acoustic field (Mazumder et al., 1983) and (2) an ac electric field replacing the acoustic field (Renninger et al., 1981). In the first configuration (i.e., an acoustic E-SPART analyzer), the horizontal motion of a charged particle is caused by the superposition of two fields: (1) the acoustic field forcing the particle in an oscillatory motion and (2) a dc electric field inducing a migration velocity component that depends on the polarity and magnitude of the electrical charge q of the particle and the field strength. In this configuration, da is measured for either electrically charged or uncharged particles by determining the phase lag <j>. Electrical charge q is determined from

Photomultiplier Tube Beam Dump Screen Electrode

Acoustic Transducer Interference Fringes

> Particle

Acoustic Drive Phase Shift Network Laser Beams Fig. 17-1. Aerosol relaxation chamber showing the geometrical configuration of acoustic transducers and electrodes for applying acoustic and electric fields in the Electric-Single Particle Aerodynamic Relaxation Time (E-SPART) analyzer. One of the illuminating laser beams has been shifted by 40 MHz.

the measured electrical migration velocity Vc and the aerodynamic diameter da. The direction of V6, which is also measured, provides the polarity of q. In the second E-SPART configuration, no acoustic field is used, and the particles are subjected to an ac electric field. Therefore, the measurement process is applicable only to electrically charged particles. If a particle is charged, it experiences an oscillatory motion caused by the applied ac electric field. This oscillatory velocity component of the particle will have a phase lag 0 with respect to the applied electric field, and measurement of (j> again allows determination of da. The amplitude of the oscillatory velocity component of the particle is directly proportional to the electric charge on the particle. Thus, from the measurement of the velocity amplitude Vp and the phase lag 0, the electrostatic charge for individual particles can be calculated. There is a phase shift of 180° for particles of opposite polarity, and this 180° phase shift is detected to determine the polarity of the electrical charge. Both the phase lag and the amplitude information are obtained by the LDV and the associated signal processing electronics. More recently, applications of acoustic and electric drives to the measurement of particle size, charge, and density have been described (Cole, 1999; Cole and Tennal, 1993). Particle Motion: External Oscillating Force

The oscillation of a particle experiencing a sinusoidal force Fe in a gaseous medium was derived by Stokes and can be represented in the Stokes regime by the following equation (Fuchs, 1964:80): (17-1)

where mT is the effective mass of the particle, Bx is the effective mobility of the particle, vp is the time-dependent (instantaneous) particle velocity, and F6 is the external force. Because particle velocity is not constant, Stokes showed that mr and Bx can be replaced by (17-2) (17-3) where rap is the particle mass, r\ is the gas viscosity, Cc is the Cunningham slip correction factor, m' is the mass of air displaced by the particle, co is the angular frequency of oscillation (2nf), and (17-4) where v is the kinematic viscosity (rj/pg), and pg is the gas density. Equation 17-1 is a simplified version of the original Stokes equation for rap » m'. When the frequency approaches zero, that is, the velocity approaches a constant, £ approaches zero and the effective particle mass can be replaced by mp, Bx is reduced to the mobility B, and (17-5) where Vv is the particle velocity and Ug is the steady-state gas velocity. This is the more familiar form of the Stokes equation. Particle Motion: Acoustic Field. Equation 17-1 describes the motion of a particle under the influence of an external field, for example, a charged particle in an ac electric field. When there is no external field, but the medium itself is oscillating, for example, the particle experiences an acoustic excitation, the time-dependent gas velocity ug can be expressed as (17-6) where Ug is the maximum gas velocity and t is time. The parameters measured by the instrument are the velocity amplitude ratio

(17-7)

and the phase lag of the particle behind the air motion, given by S (17-8)

where (17-9)

and (17-10) The right-hand sides of Eqs. 17-7 and 17-8 are described by the terms a, £, and v, which are functions of the particle and gas properties. Figures 17-2 and 17-3 show (0 - 6) and VpIUg, respectively, plotted as a function of aerodynamic diameter for several acoustic drive frequencies. If the inertial terms caused by the acceleration of the particle in the medium are neglected, then particle motion can be represented using the particle relaxation time TP (17-11)

Phase Lag (degrees)

where po is standard density (1000kg/m3 [1 g/cm3]). The phase lag of the particle behind the air motion in the acoustic field is given by

Acoustic Field ($- Q) Electric Field (<|>)

4

Excitation Frequency

Aerodynamic Diameter (|im)

Amplitude Ratio (percent)

Fig. 17-2. Phase lag of the particle motion with respect to an acoustic excitation field (Eq. 17-8) and an electrostatic excitation field (Eq. 17-12) plotted as a function of aerodynamic diameter for several drive frequencies.

Acoustic or Electric Field Excitation Frequency

Aerodynamic Diameter (p.m) Fig. 17-3. Amplitude ratio of the particle motion with respect to an electrostatic field drive plotted as a function of aerodynamic diameter for several drive frequencies for the E-SPART (Eq. 17-7). The simpler equation (Eq. 17-13) gives similar curves throughout the entire range of sizes.

(17-12) where CO/2TU is the acoustic frequency. The phase lag calculated using this equation is also plotted in Figure 17-2. Under this condition, the ratio of the amplitude of particle velocity Vp to the amplitude of the gas motion Ug due to the acoustic field is (17-13) Equations 17-11 and 17-12 can also be applied when the particle motion is induced by an external field, such as with a charged particle in an ac electric field. These equations, as well as Eqs. 17-7 and 17-8, indicate that the measurement of either the phase lag of the particle motion relative to the gas motion or the velocity amplitude ratio of the particle in the acoustic field is sufficient to determine rp or da. In the case of acoustic excitation, there are two major forces acting on the particle besides gravitational field: (1) the viscous drag force and (2) the force caused by the pressure gradient in the medium. The first one is caused by the fluid resistance due to the viscosity of the medium, and the second is due to the inertial resistance. The effective fluid resistance will depend on the product corp. For small values of this product, the fluid resistance is primarily viscous, and for large values it is inertial. For sizing aerodynamic diameter, the product COTP can vary from 0.01 to 100. In the range 0.01 to 2, the resistance can be approximated by the viscous drag (Eq. 17-12), and, when corp > 2, both viscous and inertial resistance need to be considered (Eq. 17-8). Note that for da < 100 urn, particle Reynolds number Rep is less than 0.1. The value of <mp = 2 corresponds to 0 = 63.5°. For < 63.5°, Eq. 17-12 gives results for an acoustic excitation within 15% of Eq. 17-8. For larger values of phase lag, the two curves are quite different (Fig. 17-2). However, the amplitude ratio curves given by Eqs. 17-13 and 17-7 are nearly identical (within less than 1%, Fig. 17-3), and, therefore, the simplified Eq. 17-13 holds over the entire range of corp. To operate the analyzer over a wide size range, it is necessary to use two frequencies of excitation either in tandem inside a single relaxation chamber or simultaneously in two relaxation chambers connected in series. For example, a prototype E-SPART analyzer has been operated at two frequencies, 24kHz (for 0.3 to 4.0um) and 1.0kHz (for 2.0 to 20.0um), using two chambers connected in series. Integration of the experimental data can be accomplished with the appropriate software. The aerosol sampling system with only one relaxation chamber used in the E-SPART analyzer, is shown in Figure 17-4. Particle Motion: dc Electric Field. When placed in a constant electric field E, the electrostatic force on a charged particle can be expressed as Fe = qE, where q is the particle charge. A particle of diameter da with n elementary charges will move with an electrical migration velocity Ve given by (see Chapter 3) (17-14) where e is the elementary charge. As shown in Figure 17-3, the acoustic velocity component Vpsin((o t - 0 + 0), is superimposed on this electrical migration velocity V6. Figure 17-1 shows the geometrical configuration of transducers and electrodes for applying acoustic and electric fields. The field E is calculated from the voltage applied across the electrodes divided by the distance between them. Hence, a measurement of Ve can be used to calculate n, the number of elementary charges, once the aerodynamic diameter of the particle has been deter-

Aerosol Relaxation Chamber Crossed Laser Beam Sensing Volume

Exhaust

Filter

Needle Flow Valve Controller

Vacuum Pump

Rotameter Fig. 17-4. A schematic of the aerosol sampling system used in the E-SPART analyzer.

mined. The software performing the charge measurement reads the voltage (which is adjustable) applied across the electrodes and computes the field E for determining the magnitude of the charge q(ne) for each particle. The analyzer also recognizes the direction of Ve, which depends on the polarity of the charge q of the particle. Thus, from the direction and magnitude of Ve, the computer can record both polarity and magnitude of particle charge. Particle Motion: ac Electric Field. The phase lag measurement technique can also be used on a charged particle in an ac E-SPART analyzer with the same electrode configuration shown in Figure 17-1 with no acoustic field applied. An electrical sinusoidal voltage V0sin(cot) is applied across the two electrodes. In this process, it is necessary that the particles be electrically charged in order to make size and charge measurements. When a charged particle transits the LDV sensing volume, the particle will experience an oscillatory electric field, E0sin(cot), and a zero gas velocity. When the time t» rp, 0 has the same expression as Eq. 17-12, and the amplitude ratio is (17-15) Equations 17-12 and 17-15 indicate that, for a charged particle, da can be determined from the measured value of the phase lag or the amplitude ratio as with the acoustical SPART analyzer. In the case of electric excitation, there are two major forces acting on the particle besides the gravitational field: (1) the coulombic force and (2) the viscous drag force. For Rep < 0.1, which is the case for many practical applications, the inertial resistance of the fluid can be neglected, and the phase lag is given by Eq. 17-12. Unlike acoustic excitation, there is no pressure gradient force, and hence there is no foldover in the phase lag relationship (Fig. 17-2). The amplitude ratio, in the case of either electric or acoustic excitation, is given by Eq. 17-13 without significant error. When Rep > 1, appropriate correction will be needed to compute the viscous and inertial resistance forces acting on the particle for accurate size and charge measurements. In the E-SPART analyzer, the measurement of da is independent of the driving field amplitude E0 and the magnitude of the particle charge q. Once da is determined from 0, the analyzer then calculates the electrical mobility (q • B) or the electrostatic charge q of the particle from Eq. 17-15. The phase lag measurement technique is independent of the amplitude of

the driving force as long as the particle amplitude is sufficiently large for accurate measurement of 0. E-SPART Analyzer The E-SPART analyzer consists of four components: (1) a dual-beam, frequency-biased laser Doppler velocimeter; (2) a relaxation cell; (3) an electronic signal and data processing system; and (4) a personal computer. The LDV measures the particle velocity. The sensing volume of the LDV is formed by the intersection of the two laser beams and is located between the electrodes as shown in Figure 17-1. As a particle passes through the sensing volume in the direction normal to the plane containing the two converging laser beams, the particle experiences an acoustic and/or an electric field. The LDV detects only the horizontal velocity component of the particle. It does not detect the vertically downward sampling velocity. However, the duration of the LDV signal burst is the residence time of the particle within the sensing volume, and it is inversely proportional to the sampling velocity. The residence time must be long enough to measure (0 - 6) and Ve or 0 and Vp/£o.The residence time is discussed further in "Aerosol Sampling." A helium-neon laser (632 nm) or an argon-ion laser (488 nm) is used as the monochromatic light source for the LDV. The choice of laser (HeNe or Ar+) and the output power (10 mW for HeNe or 50 to 50OmW for Ar+) depends on the application. Two output laser beams are derived by passing the laser beam through an acousto-optic cell (Bragg cell) modulator. The output beams have nearly equal power, but one of the beams is shifted in frequency by 40MHz by the modulator (Figure 17-1). The two beams intersect at the focal volume within the relaxation cell. Light scattered from aerosol particles passing through the sensing volume is collected by the receiving lenses and focused to a pinhole directly in front of a photomultiplier tube. The output of the photomultiplier is an electrical signal that represents the Doppler burst containing the particle motion information. Aerosol Sampling Figure 17-4 shows a schematic of the flow control system used for the E-SPART analyzer. The aerosol sample is drawn into the relaxation chamber by using a vacuum pump. A differential flow controller is used to maintain a constant rate of sampling flow approximately 0.5L/min through the relaxation chamber. The sampling rate through the LDV sensing volume is a few milliliters per minute, depending on the specific optical configuration and the diameter of the particle. If the length of the sensing volume is L and the sampling velocity of the aerosol particles is V2, then the maximum residence time of the particle in the sensing volume will be L/Vz.This residence time is set equal toN-t, where Af is the number of acoustic cycles and t is the time period of the acoustic or electric excitation. Under this condition, an aerosol particle passing vertically downward through the center of the sensing volume will undergo periodic motion for N excitation cycles. Phase lag (j> measurements on each individual cycle for this particle can be performed by the E-SPART analyzer, and the average value of (j) over N cycles is used to determine da. The choice of N depends on three factors: (1) the size resolution desired, (2) the response time of the signal-processing electronics, and (3) the particle concentration. Typically, N is set between 3 and 8 by adjusting the sampling velocity Vz. Because the time period t depends on the frequency / of the acoustic or electric excitation drive, the residence time for particles is varied depending on/and the size measurements. All the particles may not pass through the center of the sensing volume, resulting in a shorter sensing time; however, the particle must stay in the volume for at least one cycle to be measured. It is essential that a laminar flow field is maintained as the aerosol sample passes through and around the LDV sensing volume. Velocity components of the particle in the x direction

in the absence of any acoustic and electrical excitation should be less than the particle's Brownian motion. Signal and Data Processing Electronics

The E-SPART signal-processing electronics are organized into five functional sections: (1) a receiver containing the RF amplifier, mixer, and demodulator; (2) the signal conditioning circuitry; (3) the size and charge measurement circuitry; (4) a direct memory access board for interfacing with the computer; and (5) a personal computer. The instantaneous Doppler signal frequency generated by a particle traversing the sensing volume in the absence of any excitation is given by /o, which is the LDV bias frequency (40MHz) as determined by the Bragg cell. When a particle experiences an acoustic excitation of frequency / and a dc electric field, the Doppler frequency / D is (17-16) where O is the intersection angle of the two laser beams, X is the laser radiation wavelength, and co is 2jtf. It is assumed 0 is <63.5° in Eq. 17-16. A similar equation can be used when ac electric field excitation is used. The carrier frequency shift Af is (17-17) where n is the number of electronic charges on the particles and E is the dc electric field. In an acoustic E-SPART, the phase lag of the particle motion is determined from the time interval between the zero crossings of the acoustic field and the resultant particle motion (Fig. 17-5). The da for the particle is computed from/as follows: (17-18) and from Eq. 17-17, (17-19)

Velocity

Acoustic Velocity (ug) Particle Velocity (vp) ,U9 sin (cot) ^Ve + VpSin(cot - 4> + 6)

Electrical Migration Velocity

Phase Lag (4> - 6)

Time Fig. 17-5. Wave forms of the motion of charged particles within the sensing volume of the E-SPART analyzer.

In the case of the ac E-SPART, an ac electric field E0sin(cot) replaces the dc electric field and the acoustic excitation. da is determined from 0, and q is calculated from the ratio of the amplitude of the particle motion (Vp) to the amplitude of the ac electric field E0 as shown in Eq. 17-15. The maximum value of the frequency deviation is (17-20) and (17-21) The frequency deviation Af0, with respect to /o, can be either positive or negative depending on the polarity of the charge. The magnitude of the charge is determined from \Afo\. Table 17-1 shows a comparative analysis of two drive systems for the E-SPART analyzer: (1) Acoustic and DC Electric Drives and (2) AC Electric Drive. For such powder applica-

TABLE 17-1. Advantages and Disadvantages of Different Excitation Methods in the E-SPART Analyzer Operational Features

AC Drive

Acoustic and DC Drive Measurement of da

Range of operation

From measurement in the range 0° to 70° (da can be measured from amplitude

Need for corrections

Stokes law does not remain valid when > 70° (no corrections are needed if amplitude ratio measurements are used) Applicable to both charged and uncharged particles

Measurement of q

Measurement

ofda

Measurement of q

From VVE measurement in the range 0 to ± qmax

From <j> measurement in the range 0° to 90°

From VpIE0 measurement in the range 0 to ± qmax

Particle Reynolds number (Re) may exceed 1 for highly charged large particles

Stokes law can be applied without significant error

Particle Reynolds number (Re) does not exceed 1, even for the highly charged large particles

Highly charged particles may be deflected away from the sensing volume Flow turbulence and acoustically generated flow field affect q/m measurement

Applicable only to charged particles

No sampling loss caused by excitation

Range of operation can be changed continuously by changing frequency of the ac drive

No acoustically generated flow field noise. Measurement of q is insensitive to flow turbulence

ratio [VJUJ)

Counting efficiency and sampling error Change of size range/noise immunity

Change of acoustic drive frequency to change size range may need adjustments in electrode spacing

tions as toners and powder coatings, the AC Drive System is more convenient for operating the instrument over a wide particle size range (Mazumder et al., 1999). Instrument Operation

d Number/d Log d a

A data summary can be obtained that will provide the total number of particles counted with the average charge tabulated for a given size channel for both positive and negative charged particles. The software can provide plots of the size distribution of the aerosol in terms of number (Fig. 17-6), cumulative number, volume, cumulative volume, as well as statistics including count median diameter, mass median diameter, and geometric standard deviation. For each particle, the aerodynamic diameter (da) and the charge (q) are determined in the E-SPART analyzer and the average value of the charge-to-mass ratio computed. For a spherical particle of diameter dp and specific gravity pp, we can write an approximate relationship:

d Number/d Logd a

Aerodynamic Diameter (jam)

Aerodynamic Diameter {\im) Fig. 17-6. a, Size frequency distribution d N/dlogda for a mixture of polystyrene latex (PSL) spheres with diameters of 0.6,1.2, and 2.1 um (acoustic E-SPART). b, Size frequency distribution d N/d\ogda for monodisperse bis-ethyl hexyl sebacate droplet aerosols of different sizes (ac E-SPART). The data were obtained to give equal peak concentrations.

(17-22) The mass rap of the particle can be computed from the measured value of da, if po is known. Thus, (17-23) For each size channel (da);, from i = 1 to i = 32, the particle count is stored as nx\ mp for each channel is approximately n nx po3/2 da3/(6 pp1/2). The total mass of the particle sample is given by summing over / channels: (17-24) For each size channel, (da)j, the total count nx is also stored in the charge channels. The sums are performed over all 32 channels. The number of charged particles, nx, is equal to n° + nx+ + nx~ where n°, rf, and rf represent the total number of particles with zero, positive, and negative charges with diameter (da)x, respectively. The software provides computations and plots of /Ij+, n°, nf, versus charge-to-mass (q/m) ratio for any channel (da)i, as shown in Figure 17-7. A three-dimensional plot of number versus charge-to-mass ratio and da is also available (Fig. 17-8). Size Resolution. Particle size is measured by determining the phase lag 0 between the particle motion and the driving force (acoustic or electric). In practice, a time interval At is measured, and the relationship between the phase lag 0 and At is given by At = $1(0. The signal-processing electronics determines At by generating a phase comparator pulse with the duration At and then counting the time period of that pulse using a counter of frequency /c. To obtain good resolution, the counter frequency / c is made many times larger than the excitation frequency /. The number of counts nc for a given time interval At can be written as nc = Atfc. Because the maximum value of phase shift is 90°, the maximum count will occur for a 90° phase shift. As shown in Figure 17-2, the variation of phase shift with respect to

Particle Number

5.25 \im Diameter Blue Toner Particles 15 Minute Agitation Time

Charge-to-Mass Ratio (uC/g) Fig. 17-7. Electrostatic charge distribution of a sample of blue toner showing the variation of charge density for a selected aerodynamic diameter.

Number Frequency

Number Frequency

Fig. 17-8. A three-dimensional representation of the variation of particle count and charge density as a function of aerodynamic diameter.

aerodynamic diameter d/d(da) depends on the frequency of operation. For particles with the Cunningham correction factor equal to 1, that is, da > 2um, maximum resolution (Renninger et al., 1981) is obtained when d/d(da) is maximum, which gives the phase angle

Charge-to-Mass Ratio (uC/g)

Gaussian Limit Measured Charge

Aerodynamic Diameter (jim) Fig. 17-9. The charge distribution of a tribo-charged toner sample as measured by an E-SPART analyzer. The solid line shows the saturation charge calculated from the Gaussian limit. The experimental data show that the E-SPART analyzer can measure particle charge near the saturation limit.

The desired range of measurement of electrostatic charge on each particle is from zero charge to its saturation value with positive or negative polarity. Figure 17-9 shows the average values of the charge-to-mass ratio of a toner sample experimentally measured as a function of da and the calculated Gaussian limit for maximum qlm as a function of diameter (Mazumder et al., 1991). The particles were tribo-charged. The saturation charge-to-mass ratio for tribo-charged, dielectric solid particles varies inversely with particle diameter. The data show that the analyzer can measure particles with saturation charge. For highly charged particles, the AC drive should be used to minimize sampling losses. Precision and Accuracy. The basic principle applied in the E-SPART analyzer can provide absolute measurements of particle size and electric charge if the physical parameters involved are accurately known. For example, aerodynamic diameter depends on the viscosity of the gas in which the particles are suspended. Because viscosity is independent of pressure, the size measurement can be performed at different ambient pressures. However, if the temperature or the constituents of the gas change, the viscosity will change, and, therefore, the measured value of the aerodynamic diameter will be related to the properties of the gas in which the particles are suspended. This is an advantage of the E-SPART analyzer for in situ measurements. However, if there are uncertain variations in the ambient conditions from sample to sample, such as changes in temperature, the instrument's operation will be affected adversely. When the instrument is operated at a relatively high acoustic frequency (25 kHz), it is important to maintain a constant temperature in the relaxation cell so that the phase offset value does not change. This constraint is less severe when the acoustic E-SPART is operated at a frequency of 1 kHz or lower or is absent when an ac excitation is used. AERODYNAMIC PARTICLE SIZER The development of the Aerodynamic Particle Sizer (APS; TSI) was based on a particle acceleration nozzle and laser Doppler detection system constructed by Wilson and Liu (1980). In this study particles were introduced into the center of an accelerating nozzle. Small particles followed the motion of the air closely while larger particles lagged behind, causing

an increase in relative velocity between air and particle. This increase in relative velocity is analogous to the increase in settling velocity with particle da. Wilson and Liu (1980) indicated that the particle velocity is a function of da as long as Rev stays small (within the Stokes regime, Rep < 0.1). As Rep increases, apparent particle size becomes a function of particle density and shape as well as da. In addition, there is a trade-off in size resolution and nozzle velocity. At high nozzle velocities, particle motion is more non-Stokesian (less accurate aerodynamic sizing) but particle sizing is more rapid. Artifacts in the observed aerodynamic distribution may also occur because the light scattering used for detection of particles results in incomplete detection or particle coincidence. Based on similar principles, TSI developed the APS with support from the National Institute for Occupational Safety and Health (NIOSH) (Agarwal and Remiarz, 1981). The APS sizes particles by measuring their velocity relative to the air velocity within an acceleration nozzle. This velocity is compared with a calibration curve established using monodisperse spheres. Several commercial models have been available over the past two decades: Model 3300 Model 3310 Model 3320

Model 3312

Based on prototype design; used an Apple II computer for data analysis. HeNe laser light source (commercially discontinued) Updated version of 3300 using an IBM PC-compatible computer for data analysis. Improved data display software, (commercially discontinued) Particle acceleration system identical to other APS models, but using a solid-state laser and redesigned sensor package with integrated particle spectrum readout. Further data analysis and logging possible using external computer. Lower size resolution and better coincidence rejection than 3310. Light-scattering intensity data available Particle sizing capability identical to Model 3320 (UV-APS), but with ultraviolet fluorescence and optical scattering signal information also available. Designed specifically for biologically based aerosols (also termed Fluorescence APS or FLAPS)

Instrument Description

The APS 3310 consists of a sensor unit containing the sampling system, detector, preliminary processing electronics and internal flow rate indicator, and a computer. The computer receives the data from the sensor unit about once per second and updates the calculated aerodynamic size distribution. One version of the software that collects and displays the data comes with the instrument; more sophisticated software providing near-real-time display is provided. While the entire unit is sufficiently portable and rugged that it can be used for field measurements (Baron, 1986; Baron and Willeke, 1986; Szewczyk et al., 1992), it is generally more suited to laboratory environments. The APS 3320 sensing unit is more compact and can be used as a stand-alone unit, having a direct-reading display of the size distribution. The more compact sensor unit size and more stable flow system make it more amenable to field measurements than the 3310. For additional data analysis and recording of the data, a computer must be attached. The inlet system and nozzle in all four APS models are identical. Aerosol is introduced to the inlet at a flow rate of 5L/min. Four liters per minute of this flow is removed, passed through a filter, and reintroduced upstream of the acceleration nozzle as sheath air. The remaining 1 L/min aerosol flow is fed through a focusing nozzle, recombined with the sheath air, and accelerated through the final nozzle (Fig. 17-10). The pressure below the nozzle is approximately 100 mm Hg (Chen et al., 1985). The sheath and total flow are controlled by valves and monitored with thermal mass flow meters (3300 and 3310). The 3320 has microprocessor volumetric flow controllers for total and sheath flows.

At the exit of the acceleration nozzle, each particle passes through two light beams. The light comes from an HeNe laser in the 3310, while a laser diode (LD) provides illumination in the 3312 and 3320. The light scattered from the particle causes two pulses to be detected by a photomultiplier, and the time lag between the two pulses, representing time of flight (TOF) of the particle between the two beams, is recorded. Because larger particles have not accelerated to the air velocity in the sensing zone, they are represented by larger time lags. The TOF data are stored in an accumulator in bins representing equal time intervals. In the 3310, two sets of data are stored: one by the small particle processor (SPP) in 4 ns bin intervals and the other by the large particle processor (LPP) in bin intervals of 66.7 ns. The software gives the option of using just the SPP for particle distributions in the range 0.5 to 15.9 urn, while the LPP can be used to extend that range up to 30 um. The LPP has anticoincidence circuitry that virtually eliminates excess counts due to coincidence. When both

Aerosol In (5 L/min) a

Filter Sheath Flow Valve 4UmIn

Sheath Mass Flow Meter

1 L/min

Acceleration Nozzle

Pressure Transducer

Light Beam Detection Region Damping Chambers Filter Total Mass Flow Meter

Totar Flow Pump

Fig. 17-10. Schematic of Aerodynamic Particle Sizer nozzle and laser velocimeter for models APS 3300 and 3310 (a) and for models APS 3320 and 3312 (b).

Aerosol In (5 L/min) b

Sheath Flow JPumoj

Filters Orifice Sheath Flow Pressure Transducer

4UmIn 1 L/min

Acceleration Nozzle Light Beam

Absolute Pressure Transducer

Total Flow Pressure

Transducer Detection Region

Filters , Total Flow Pump Fig. 17-10. Continued

processors are used, the two sets of data are linearly combined in the range from 5.7 to 15.9 |xm. In the 3320 and 3312, the LD illumination beams are relatively broad, and the light pulses overlap for each particle. The pulse shape is differentiated, and the inflection point of each peak defines the center point or mode of that peak. The light-scattering from each peak pair is measured and can be correlated to the TOF, allowing post process rejection of peak pairs whose heights do not correspond to the measured TOF. The light-scattering data are also available as a separate spectrum in the recorded data. The final aerodynamic size is determined from a calibration of the accumulator spectrum using monodisperse spherical standard density (1000 kg/m3) reference particles. Sample Inlet

The 20 mm diameter inlet of the APS is located at the top of the instrument and is not conveniently located for sampling moving air directly. Thus, aerosols are typically ducted to the

APS with external tubing. Particle losses and aerosol nonuniformity within this tubing must be determined separately. Within the inlet, the air is split between the inner inlet (measured aerosol flow) and the sheath flow as indicated in Figure 17-10. The gas velocity at the measured flow inlet is higher than the velocity at the APS inlet (i.e., superisokinetic). This sampling arrangement produces some oversampling of larger particles to compensate for losses within the inner nozzle tube. Aerosol entering the inlet is assumed to be uniform because only 20% of the aerosol is taken from the inlet stream and measured. Upstream manipulation of the aerosol stream, such as inertial stratification due to bends or cyclones, can bias the concentration at the measurement inlet. Careful mixing of the aerosol upstream of the measurement inlet may be necessary to reduce these effects with minimal losses. Sample dilution systems are available as optional equipment for the APS to reduce problems with particle coincidence in the sensor. Penetration curves for these dilutors are measured by the manufacturer and are provided as part of the software to correct size distributions. At 15 urn the loss within the dilutor is near 50% and increases rapidly with increasing da; corrections of this magnitude indicate that larger particle channels provide data of questionable accuracy. At the bottom of the inner inlet, a nozzle constricts the flow and focuses the aerosol in the center of the acceleration nozzle. The inner walls of the focusing nozzle form a 60° angle with the direction of flow. Impaction may produce particle accumulation on this nozzle surface, further restricting the penetration of the inner inlet to 50% for about 8 urn oil droplets (Kinney, 1990). Kinney et al. (1989) also evaluated modifications to this nozzle and found that a smaller nozzle angle (2° or 8°) produces less internal loss but decreases the resolution of the APS. The amount of particle loss for the 60° inlet nozzle can be approximated using an equation developed for particle deposition efficiency 77 in a tube with a 90° contraction. (17-25) where (17-26) Stk is the Stokes number in the inlet tube, D1 is the diameter of the inlet tube, and Dn is the diameter of the nozzle (Ye and Pui, 1990). Impaction of liquid particles may follow the deposition efficiency described by Eq. 17-25, while solid particles may bounce and exhibit lower deposition. Laser Velocimeter Sensor

Aerosol passing through the inner nozzle is combined with the sheath flow and focused into the center of the acceleration nozzle. The airflow conditions in the nozzle region have been modeled and agree well with experimental measurements (Ananth and Wilson, 1988). The air velocity reaches approximately 150m/s at the exit of the acceleration nozzle. The flow conditions affecting the particle acceleration depend on the nozzle dimensions as well as the spacing of the nozzles. In the 3300 and 3310, the laser beam is split into two parallel, flattened beams that intersect the particle path 200 to 500 urn from the acceleration nozzle. The distance of these beams from the acceleration nozzle also affects the measured particle velocity. These various dimensions are difficult to control precisely during instrument manufacture. Thus, the calibration of each of these APS instruments is slightly different. In the APS 3320 and 3312, the distance of the LD beam from the nozzle is more tightly

controlled during manufacture and can be reset without extensive recalibration. The sensor region is thus more accurately positioned, and the calibration from instrument to instrument should be more consistent. Detection and Data Analysis As each particle passes through the two laser beams, the pulses are detected by a photomultiplier and the TOF is recorded. Because the light scattered from particles changes rapidly with particle size, two high-speed data accumulator systems are used in the detector module of the APS 3300 and 3310: a small particle processor (SPP) and a large particle processor (LPP). The SPP collects the TOF data in an accumulator in increments of 4 ns, and the LPP collects data in increments of 66.7 ns. These data are passed from the detection module to a personal computer for transformation to size distribution according to a stored calibration curve, as well as for any further manipulation or storage as desired. The SPP covers the aerodynamic diameter range of 0.5 to 15.9 um, and the LPP covers 5 to 30 urn. In the overlap range, the data from the two processors are blended together, proportionately increasing the LPP contribution with increasing size. The treatment of the overlap range is discussed further below in the section on coincidence. The APS 3320 uses a single processor for particle detection. It can also correlate the particle velocity and light-scattering signal for post process rejection of particles whose velocities do not indicate particle sizes that match the observed light-scattering signal. For example, a particle with a low velocity (indicating a large particle) that produced a small scattering signal could be rejected as not being physically reasonable. This correlation changes for particles with different refractive indices. Once a number distribution has been measured, various other differential and cumulative distributions can be calculated in a similar fashion to those described for the E-SPART in the previous section. Calibration

Monodisperse latex spheres are typically used for calibration of the full size range of the APS if it is to be used for measuring solid particles. Latex spheres smaller than about 5 um can readily be generated by nebulizing a water suspension of the spheres. Note that while isopropyl alcohol suspensions of latex spheres may be easier to generate and dry, the alcohol slowly dissolves in the spheres and will cause a slight increase in size after a period of time. Larger calibration particles can be generated dry from a surface by suction, as with the Small Scale Powder Disperser (Model 3433, TSf) or by gently brushing the calibration particles from a clean surface, such as a glass slide. Because latex particles are only available in specific sizes, the calibration curve is completed using a spline or polynomial function to fit the calibration points. The calibration of each APS 3300 and 3310 instrument is unique due to variations in the nozzle sizes, spacing, and laser beam locations. However, once the calibration of the APS has been completed in air at ambient pressure, calibration for other gas viscosities and pressures can be achieved as described by Rader et al. (1990). The gas velocity Ug in the nozzle can be calculated from the Bernoulli equation for compressible flow: (17-27) where R is the universal gas constant, T is the absolute temperature, M is the gas molecular weight* P is the ambient pressure, and AP is the pressure drop across the nozzle. AP is measured by the flow transducer in the APS. The particle velocity V9 can be determined from

(17-28) where t is the transit time of the particle between the laser beams and tmin is the minimum transit time for small particles observable in the APS accumulator. Plotting the ratio V1JUg as a function of Stokes number results in a universal response curve. This means that the check on the APS size response under the same or new pressure or viscosity conditions can be achieved always setting Ug to the same value. The design of the APS 3320 detection system is more compact and precise, resulting in reproducible spacing of the particle acceleration and illumination components. These components can be replaced by the user without the extensive factory recalibration procedure required with the earlier models. The APS also has a second pump that helps regulate the sheath flow. This results in less sensitivity to external pressure changes. The flow system in the APS 3300 and 3310 is not as carefully controlled as in the APS 3320. Slight changes in pressure at the inlet of the earlier APSs could cause significant shifts in the apparent size distribution of submicrometer particles. The pressure drop change produced a slight shift in the flow through the acceleration nozzle, resulting in a slight change in the calibration curve as noted above. The channels in the submicrometer range were especially sensitive to slight changes in the calibration. The increased pressure drop shifted the calibration, decreasing the measured concentration of the smallest particles. This would become especially apparent when comparing size distributions upstream and downstream of a classifier (e.g., cyclone or impactor) that had small but measurable pressure drops.The ratio of the downstream to upstream concentrations could drop as low as 50% in the submicrometer range. The improved flow control in the APS 3320 sheath flow reduces the likelihood of such a calibration shift with small inlet pressure changes. Other monodisperse particles, such as those generated from the vibrating orifice monodisperse aerosol generator (model VOMAG, TSI) can also be used for calibration. However, it was found that oil droplets generated in this fashion distorted into oblate spheroids due to the high acceleration (see below) and therefore exhibited a smaller aerodynamic diameter than predicted for a spherical shape (Baron, 1986). Unless the calibration is used for measuring droplets of the same oil, only solid particles should be used for the particle size calibration of the APS. Non-Stokesian Corrections

The acceleration in the nozzle produces Reynolds numbers outside the Stokes regime, as indicated in Table 17-2 for particles in the APS size range. Thus, the measured size depends on

TABLE 17-2. Particle Properties in the APS Nozzle Particle Diameter (um) 0.5 1.0 3.0 10.0 15.0 20.0

Relative Velocity (cm/s)

Particle Reynolds Number

Weber Number (Oil Droplets)"

40 1,750 6,490 10,600 11,500 12,300

0.013 1.16 12.9 69.6 114.0 163.0

2.9XlO"6 0.0113 0.468 4.13 7.36 11.2

"These represent either oleic acid or di-octyl phthalate, both of which have a surface tension of about 0.033N/m [33 dyne/cm]. Source: Baron (1986).

other factors besides the aerodynamic size, including gas density, viscosity, particle density, and particle shape. Using the approach of Wang and John (1987), correction factors for the measured size of compact particles can be calculated if the particle density, gas density, and gas viscosity are known. These calculations have been validated by the Navier-Stokes calculation of the flow field in the APS nozzle by Ananth and Wilson (1988). The following equations (Rader et al., 1990) are iterated until no further significant change occurs. (17-29) (17-30) (17-31) where subscript 1 refers to calibration conditions with unit density spheres, subscript 2 refers to measurement conditions, Stk is the Stokes number, and S (= Ugtmin) is the distance between the laser beams. Measurements were made in argon and N2O to confirm that this approach improved the accuracy of aerodynamic size measurement (Lee et al, 1990; Rader et al, 1990). The largest error in da (12%) was noted when these corrections were applied to large (30jnm) particles in argon. The slip correction factor must also be modified in the above equation because of the reduced pressure in the nozzle, and computer code is available to perform these corrections (Wang and John, 1989). The high acceleration in the APS nozzle may also cause inaccuracies in measuring the da of nonspherical particles. Cheng et al. (1990) found that the measured size decreased with increasing shape factor. The above iterative correction was further modified to include shape factor. For more extreme shapes such as fibers, this approach may not be adequate. Identical-diameter fibers with different lengths gave the same measured size. Fibers, as well as other nonspherical particles, tend to orient themselves with their maximum cross section oriented perpendicular to the flow (Clift et al., 1978:142). However, larger fibers (on the order of 10 um diameter) may not have sufficient time to orient in the flow field and may produce a measured size intermediate between the perpendicular and parallel orientation. Thus, the initial conditions of the particle (e.g., orientation, location in the flow field) during acceleration can affect the measured aerodynamic size. The APS allows the rapid, precise measurement of aerodynamic size of most particles. Due to non-Stokesian flow in the acceleration nozzle, various factors bias that measurement. As described above, the biases caused by particle density, particle shape factor, gas viscosity, and gas density are sufficiently well understood that corrections to measured size can be made. The size of these biases is often on the order of 25% or less. Thus for many purposes, an estimated value of the particle density can yield sufficient accuracy in the corrected aerodynamic size. An exception may occur when the dynamic shape factor of the particle is large, as with fibers. Chen and co-workers (1990) suggested that the correction factor for density is sufficiently well characterized that it can be used to provide estimates of aerosol particle density. Brockmann and Rader (1990) also used the APS response to measure shape factors for several types of particles. Droplet Deformation As indicated above, the high acceleration field in the APS will distort droplets into oblate spheroids with the maximum cross section perpendicular to the direction of motion, increasing the drag and causing them to be recorded as smaller particles. Figure 17-11 shows two

Fig. 17-11. Picture of droplets in the high-velocity air jet just beyond the APS nozzle taken with a highspeed laser-imaging system showing a droplet flattened by the drag force. The nozzle tip is about 200 um to the right of droplet A, with the air and the droplets moving to the left. The scale markers are approximately 5 um apart. The larger droplet A has an extreme 10 by 60 um spheroidal shape, while the smaller droplet B is about 8 by 10 um and is only slightly flattened.

dioctyl phthalate droplets detected with a laser imaging system (LAS) just past the tip of the acceleration nozzle. Air motion is directed from right to left, causing the deformation of the larger droplet into an extreme oblate shape and increasing the droplet drag. The flattened droplets in Figure 17-11 indicate that an oblate spheroidal shape is produced. The shape is not a true ellipsoid because the surface tension limits the curvature at the rim of the distorted droplet. The distortion of a droplet in the sensing zone will depend on droplet size, the liquid surface tension, and viscosity. The Weber number, We = u2pdp/% where u is the particle velocity relative to the air and yis the droplet surface tension, represents the ratio of the air pressure force to the surface tension force. Droplets will eventually break up when experiencing Weber numbers between 12 and 20. Distortion increases with droplet size because the force on the droplet increases with size. While the Weber number indicates the maximum distortion that the droplet can undergo, the droplet viscosity determines the rate at which the droplet distorts. The rapid acceleration in the APS nozzle usually precludes droplet break up before reaching the sensing zone. Because the acceleration is high, viscosity is the controlling factor for distortion of many liquids (Griffiths et al., 1986). The degree of distortion has been calculated and agrees well with experimental measurements of the droplet undersizing for several oils with different viscosities (Bartley et al., 2000). Water droplets, which have a low viscosity but relatively high surface tension, distort less in the acceleration field (Baron, 1986; Bartley et al., 2000). In addition, the degree of distortion depends on the precise acceleration history and therefore can vary from instrument to instrument. Coincidence Effects (APS 3310)

Accurate detection of a particle in the TOF detection system of the APS requires full detection of two pulses from the same particle. The coincidence effects resulting from such a system are more complex than those of a standard optical particle counter, where two coincident particles produce one somewhat larger measured particle. For the APS 3300 and 3310, several coincidence scenarios are presented in Figure 17-12. Coincidence between two particles can result in two smaller particles (Fig. 17-12a), one smaller particle, one randomly sized particle (Fig. 17-12b), or no particles (detected). The relative frequency of these possible

a: Phantom particle coincidence A

A

i

B

2

B

1

2

Detection Threshold Particle B

Particle A

Measured Transit Time b: Overlap coincidence Ai

B,

A2

B2

Detection Threshold Particle A Particle B

Measured Transit Time

Measured Transit Time

c: Large particle processor anti-coincidence logic Detection Threshold = 3.0 V

A2

*1

Interference Threshold = 0.5 V Measured Transit Time

If an interfering peak larger than 0.5 V occurs during this time period, no particle is recorded. Fig. 17-12. Coincidence scenarios in the small particle processor (SPP) and the large particle processor (LPP) in the APS. a, A single detected pulse triggers the timer, while a pulse from a second particle produces a measured transit time that may indicate a particle of random size, b, Overlap of the pulses from two coincident particles produces two smaller detected particles, c, A particle detected by the LPP must have pulses larger than 3 V. The LPP anti-coincidence circuitry prevents detection of a particle when an interfering pulse occurs within 8400 ns before or after the evaluated pulse pair. (Adapted from Heitbrink et al., 1991.)

coincidence results and the effect on the measured distribution depends on the shape of the size distribution, the particle concentration, and which of the two signal processors (SPP or LPP) is used to detect the particles. The LPP is designed to completely eliminate coincidence counts (Fig. 17-12c) in the large particle range where coincidence can produce relatively large changes in apparent concentration. The coincidence effects in the APS 3310 have been modeled mathematically as well as with a Monte Carlo calculation (Heitbrink et al, 1991). Particle-counting systems are prone to detection problems when more than one particle is present, or coincident, in the detection volume at the same time. The number of coincidence events in a measured distribution can be estimated from the difference between the actual concentration C3 and the measured concentration Cm: (17-33) where Q is the flow rate through the detection volume and t is the residence time of the particle in the detection volume (Willeke and Liu, 1976). This equation can be used to estimate the APS coincidence loss to the peak of a monodisperse size distribution. Obtaining an accurate coincidence level for most size distributions is more difficult because it depends on particle size. For instance, the concentration giving 1% coincidence in the SPP for 0.8, 3, and 10 urn particles is approximately 560, 390, and 230 particles/cm3, respectively. For the LPP, a 1 % coincidence level is predicted for 10, 20, and 29 um particles at concentrations of 55,48, and 43 particles/cm3 (TSI Inc., 1987). By using Eq. 17-33 with several particle sizes, the upper limit to the number of coincidence events can be estimated for broader size distributions. Ca may also be difficult to estimate for many distributions where many of the particles detected by the SPP are smaller than 1 um. These particles may be only partially detected, resulting in single detected pulses that contribute to coincidence events (Fig. 17-12a), but not to the observed small particle concentration. These coincidence events result in a randomly sized "phantom" particle. The result is a nearly constant background of these coincidenceinduced phantom particles (Heitbrink et al., 1991). The phantom particle background produced by the SPP is therefore dependent on the number of particles near the pulse detection limit of the sensor as well as the concentration of fully detected particles. This background becomes important in size regions where relatively few real particles are detected. Thus, when particle number distributions are converted to mass distributions, a few large phantom particles can bias the calculated mass and unrealistically skew the distribution (Baron, 1986). Another situation arises when two particle distributions are being compared, such as before and after a filter to measure penetration efficiency (Wake, 1989). In the size range where phantom particle concentration is more than a few percent of the real particle concentration, the ratio of the upstream and downstream distributions will be inaccurate. The SPP thus tends to produce overestimates of particle concentration near the tail of a distribution, such as often occurs at large particle sizes. On the other hand, the LPP is designed to completely eliminate phantom particles (Fig. 17-12c). Therefore, coincidence results solely in a loss of LPP-detected particles. The difference between the LPP and the SPP concentration in the overlap range can give a hint of the magnitude of coincidence effects. The SPP coincidence can sometimes be reduced by lowering the photomultiplier gain. For size distributions skewed to small particle sizes, this reduces the number of small particles detected, thus reducing the phantom particle creation. If a region of the size spectrum is known not to contain any real particles, the detected particles can be assumed to be phantom particles created by coincidence. The average detected particle number per channel in this region can be subtracted from the entire distribution to obtain a more accurate distribution.

Coincidence Effects (APS 3312,3320) In the APS 3312 and 3320, the detection circuitry rejects all particles that may have experienced coincidence, much the same as with the APS 3310 LPP described above. This causes a reduction in observed particle concentrations that is likely to increase with particle size, because the detection time for larger particles is increased. The number of coincidence events is recorded and provided to the user. The number of single pulses is also recorded and used to indicate the bin below the lowest fully measured particles. These reported values should aid in estimating the importance of coincidence events. Further research is needed to allow a quantitative estimate of the change in the measured size distribution due to particle coincidence. Resolution and Accuracy As noted above, the APS measures particles largely outside the Stokes regime and requires corrections to the data to provide an accurate da. As with any complex instrument, frequent size calibration provides additional confidence in the accuracy of the results (see Chapter 21). Measurement of spherical particles can be corrected largely by taking into account particle density and, if necessary, changes in sampled gas density and viscosity. Liquid particles can also be accurately measured if calibrated with the same liquid. The resolution for spherical particles is high. For example, Remiarz et al. (1983) found geometric standard deviations in the range of 1.0058 (6.8 urn oil) to 1.025 (0.8 urn latex) for monodisperse particles using the APS prototype instrument. The particle size resolution of the APS 3320 is lower than in the previous models due to the poorer velocity measurement precision with the relatively broad LD light beams. Due to non-Stokesian behavior and variations in the acceleration flow field experienced by particles approaching the detection region, resolution and accuracy may be diminished for nonspherical particles. If these are not corrected, the measurement accuracy will decrease. Marshall et al. (1991) found that the da of particles with a shape factor of 1.19 was underestimated by 25% in the APS. Applications The APS 3300,3310, and 3320 can be used to measure size distributions in a variety of applications. These instruments have been combined with an electrical sizing instrument (see Chapter 18) to obtain size distribution measurements over a wide size range (0.02 to 30 um). Sioutas et al. (1999) found reasonably good agreement between calculations of mass concentration from APS spectra and direct mass measurements in the size range of 0.5 to 9.2 Jim. Peters et al. (1993) found good agreement between APS size distribution measurements and low pressure impactor measurements (see Chapter 10). The APS has been used for bioaerosol measurement (Baron and Willeke, 1986). Specific detection of biologically based particles has been enhanced using the APS 3312 where the fluorescence signal can be combined with the aerodynamic size to obtain characteristic size distributions of bioaerosols (Ho et al., 1999). Biological particle growth has been observed (Madelin and Johnson, 1992). Several biological species were measured using the APS 3312 to evaluate the response (Brosseau et al., 2000). Differences in the fluorescence signals of biological particles versus non-fluorescent control particles were seen, but no relationship to culturability of the bacteria or fungi was found, and the differences in fluorescence signal could not be related to known bacterical species. The APS has been applied to the measurement of penetration curves of several types of aerodynamic classification devices, such as impactors (Baron, 1983), cyclones (Kenny and Gussman, 1997; Chen et al., 1999; Maynard, 1999), and open-pore foams (Fabries et al., 1998;

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Chen et al., 1999). Systems optimized for measurement of penetration curves have been developed (John and Kreisberg, 1999; Maynard et al., 1999). The challenge aerosol for this type of measurement preferably consists of spherical particles with a density of approximately 1000 kg/m3 [lg/cm3] and with a distribution centered near the 50% cutpoint of the classification device. There should be an adequate concentration of particles throughout the size range of measurement, with a minimal concentration in the 0.5 urn range, to reduce the effects of coincidence. The challenge aerosol concentration is generally optimal in the 2 x IO"5 to 1 x 10"4 particles/m3 (20 to 100 particles/cm3) range. AEROSIZER Principles of Measurement The Aerosizer (TSI) is based on the acceleration of particles and TOF principles, although at higher particle acceleration than in either the E-SPART or APS. The idea of accelerating particles in a sonic expansion flow and measuring the terminal velocity was first proposed and demonstrated in laboratory prototype instruments by Dahneke and co-workers

Aerosol Flow

Sheath Flow

Sonic Nozzle Laser Beams

Photomultiplier Prism Photomultiplier

Fig. 17-13. Schematic of the detection system of the Aerosizer. The two laser beams are perpendicular to both the aerosol flow and detection direction.

Electrostatic forces are also used in sampling systems, in other technologies that are employed for aerosol particle characterization, and in particle levitation apparatuses that facilitate extended study of a single charged particle without interferences created by a substrate. The latter systems are discussed in Chapter 20. This chapter addresses ways that electrostatic forces are employed for sampling and measurement of aerosol particles. BEHAVIOR OF CHARGED PARTICLES A particle with charge q in an electric field of strength E experiences a force of strength F = qE* Because of the low ion densities and slow charge transfer kinetics in ambient temperature gases, aerosol particles usually carry only a small number of elementary charges. The charge is, therefore, conveniently represented as q = ne, where e = 1.609 x 10"19 is the magnitude of the elementary unit of charge and n is the number of charges that a particle carries (n < 0 for negatively charged particles, and n > 0 for positively charged particles). For times that are long compared with the aerodynamic relaxation time, t a = mpB, or frequencies sufficiently low compared with TJ1, a charged particle will migrate at a steady-state migration velocity relative to the gas motion of (18-1) where B and Zp are the mechanical and electrical mobilities of the particle, respectively. For motion of spherical particles with diameter dp in the Stokes regime, this mobility is (18-2) where T] is the gas viscosity, Cc is the slip correction factor that accounts for noncontinuum effects when the particle size becomes comparable with or smaller than the mean free path of the gas molecules, A, or in dimensionless terms, when the Knudsen number, (18-3) becomes large (see Chapter 4). The slip correction factor is given by (18-4) where the empirically determined coefficients are a = 1.142, P = 0.558, and 7= 0.999 (Allen and Raabe, 1985). Figure 18-1 shows the variation of the electrical mobility with particle size under normal ambient atmospheric conditions for particles carrying different numbers of elementary units of charge. For particles with a given charge in the continuum size regime, Zp « dp\ while in the free molecular regime, Zp °c dp2. Because of this strong dependence of the mobility on particle size, the range of electric field strengths required to classify particles throughout the

* Caution must be exercised to avoid unit conversion problems in calculations of the forces and energies where electrostatics are involved. The force produced by a charge of IC by an electric field of strength E = 1V m"1 is IN. The force between two charges q± and q2 separated by a distance r is F = q^lATte^e, where S0 = 8.542 x 10~12 C2 N"1 m"2, and e is the dielectric constant of the medium (eair = 1.00054).

EXAMPLE 18-1: CALCULATION OF MIGRATION VELOCITY OF A CHARGED PARTICLE IN AN ELECTRIC HELD Consider a singly charged 1 urn diameter particle. What is its migration velocity in an electric field of strength E = 106Vm"1? Answer: To evaluate the mobility of the particle using Eq. 18-2, the slip correction must be known. The particle Knudsen number is

The corresponding slip correction factor is

The viscosity of air at ambient conditions is m = 1.81 x 10 5 kgm 1S \ and the charge on the electron is e = 1.61 x 10"19C, so the particle mobility becomes

The migration velocity is the product of the mobility and the electric field strength, that is,

i=1 i=2 i=3 i=4 i=5

Z (Cm2V1S"1)

Dp (nm) Fig. 18-1. Variation of particle mobility with size.

submicrometer size range exceeds the capabilities of a single instrument operating at fixed flow rates. RELATIONSHIP BETWEEN MIGRATION AND DIFFUSION Mobility techniques are generally applied to small particles that migrate at relatively high velocities in attainable electric fields. These same particles diffuse rapidly. To compare these two effects, consider particles that migrate a distance b under the action of an applied electric field, E, produced by applying a voltage difference V over the distance b. The time required to migrate that distance is T = blZvE. The particle diffusivity is related to the mobility by the Einstein relation D = BkT, where k is the Boltzmann constant and T is the temperature. The ratio of the migration distance to the root mean square displacement due to Brownian diffusion during the migration time, (x2)* = V2Df (Eq. 4-15), is simply (18-5)

This measure of the resolution of mobility-based sizing techniques is, thus, proportional to the square root of the ratio of the electrostatic potential energy of the charged particle in the applied electric field to its thermal energy. For large voltages, migration will dominate, while for small applied voltages diffusion will distort the response of any mobility-based particle measurement. At ambient temperatures, kTte « 26 mV. The voltage that is required for a given measurement depends on the desired mobility or size resolution. Electrostatic breakdown within the apparatus limits the maximum operating voltage and, therefore, the highest attainable resolution. AEROSOL CHARGE CONDITIONING Aerosols generally include substantial numbers of charged particles. The charged accumulation-mode aerosol particles were initially called large ions by Langevin (1903). Mobility characterization of aerosols requires a known charge on those particles that are classified—ideally a single elementary charge in most applications. Known charge on the classified particle allows accurate determination of the particle size from measurements of the electrical mobility. Measurement of the size distribution also requires that the fraction of particles of a given size that carry a given number of charges be accurately known. Hence, a critical facet of electrical mobility characterization of an aerosol is the production of a known charge distribution. A number of processes contribute to the charging of aerosol particles: static electrification, photoemission, thermionic emission, charging by small gas ions, and self-charging of radioactive aerosols. Static electrification involves transfer of charge to a particle as it is separated from bulk material. This mechanism causes particles to acquire charge as they are generated from the bulk material and can contribute to charging when particles strike and rebound from surfaces. Several physical mechanisms contribute to this electrolytic charging. These include (1) contact charging resulting from differences in the electrochemical potential between a particle and a contacting surface; (2) induced charging resulting from charge transfer when a particle separates from a surface in the presence of an electric field; (3) spray charging due to disruption of liquid surfaces (often in combination with induced charging); and (4) electrolytic charging that occurs when highly dielectric liquids are separated from

solid surfaces. The particle charge acquired during static electrification is difficult to predict, so it is rarely employed in size distribution measurements. When surfaces are heated to sufficiently high temperature, they may undergo thermionic emission. This mechanism causes particles processed in flames or other high-temperature systems to acquire similar charge, that is, all positive at high temperature, and may lead to differences in polarity of charge in different regions of the flame. Gas ions are generally present in high concentrations in such environments, so ion-particle reactions can lead to rapid charge redistribution as the particles leave the hot region. Radioactive particles may become charged due to loss of a or p particles. Self-charging may also occur due to ejection of valence electrons or the release of charged fragments during a, p, or y radiation (Yeh, 1976; Yeh et al., 1978). Only rarely can the charge distribution of the sampled aerosol be used directly for mobility measurements. Because the initial charge state of the aerosol is rarely known with any precision, the aerosol must be conditioned to produce a known charge state. In an ideal system, one might seek to produce an equilibrium charge distribution because that distribution can be determined from well-established thermodynamic considerations. The process of attaining that distribution by charge transfer reactions with an electrically neutral cloud of positive and negative ions is called bipolar diffusion charging when it is used to produce the charge state for aerosol classification and aerosol neutralization when applied to dissipating a nonequilibrium charge state on the aerosol. Although, as will be shown below, the method can produce a well-characterized charge distribution, at the small end of the electrical mobility sizing range only a tiny fraction of particles acquire any change. Moreover, the charge distribution generally differs from that at thermodynamic equilibrium. Numerous studies have attempted to resolve the charge state of the aerosol produced in aerosol neutralizers, while others report on attempts to design new aerosol chargers that produce a known charge distribution with much higher probabilities of charging the ultrafine particles. Most approaches to bringing the charge on aerosol particles to a known distribution involve reactions with small gas ions. Depending on whether the gas contains only ions of a single polarity or contains a mix of positive and negative ions, the aerosol may undergo unipolar or bipolar charging, respectively. The migration of the charges to the particle may be by Brownian diffusion alone, in which case the aerosol is said to undergo diffusion charging, or it may be augmented by an imposed, macroscopic electric field, in which case the aerosol undergoes field charging. The electric field strengths required for field charging to be significant are large enough that the particles undergo significant migration during the charging process. This introduces considerable uncertainty in mobility analysis of particle properties and leads to substantial deposition. Because of these complications to mobility analysis, field charging is rarely employed for aerosol measurements, although it is often important in collection of aerosol samples by electrostatic precipitation, as it is in gas cleaning. A number of chargers do, however, impose weak electric fields to manipulate the gas ions and achieve an additional degree of control over the diffusion charging process. Most mobility analyses rely on some form of diffusional ion-particle reactions, taking advantage of the high degree of control of the ultimate charge state of the aerosol that diffusion charging enables. One additional approach to particle charging is used in some special cases, that is, photoelectron emission charging of particles due to illumination with short wavelength radiation. Photoelectron emission is highly dependent on the composition of the surface being illuminated, so it affords an opportunity to infer particle composition from its charging behavior. Equilibrium Charge Distribution

Although the minimum charge on an aerosol particle is, in principle, zero, this condition is rarely achieved because ions present in the gas attach to some particles. Pairs of positive and

negative ions are produced by collisions of cosmic rays and energetic nuclear particles produced by radioactive decay with the gas molecules. Ambient ion concentrations typically amount to about 103 cm"3, although this number can vary widely as gas ions are lost to the surfaces of aerosol particles and undergo ion-ion recombination reactions. It has often been hypothesized that, after long exposure to such bipolar ion mixtures, frequent ion-particle collisions will bring the particles to a state of charge equilibrium with the ionic atmosphere. In this equilibrium state, the fraction of particles of diameter dp that carry n charges is described by the Boltzmann charge distribution, that is,

(18-6)

(18-7)

While the simplicity of this distribution is attractive, and some investigators report that measured charge distributions have been found to be in reasonable agreement with it (Liu and Pui, 1974), its use would only be rigorously justified if charge detachment were to occur at appreciable rates (Fuchs, 1963). The forces between an ion and a particle to which it has become attached are, however, so strong that such ion emissions do not occur spontaneously at ambient temperatures unless the total charge on the particle is extremely high. Nonetheless, measured charge distributions are often similar in form to the Boltzmann distribution. These similarities result from reactions of particles with positive and negative ions in a bipolar ion mixture. Reactions of ions with particles carrying opposite charges are favored over reactions when the charges have the same polarity. After exposure to a bipolar ion mixture, the aerosol charge distributions asymptotically approaches a steady state. While similar to the equilibrium distribution, the steady-state charge distribution is generally asymmetrical due to differences in the properties of positive and negative ions. When the concentrations of positive and negative ions differ, this asymmetry increases and no true steady-state exists. The extreme limit of unipolar charging occurs when the aerosol is exposed only to ions of one polarity. To understand real charge distributions anywhere in this continuum of diffusional charging, we must examine the kinetics of aerosol particle charging.

Diffusion Charging: Charge Transfer Kinetics

The rate of ion attachment to particles in the absence of an external electric field is determined by the coupled rates of diffusion of the ions and ion migration in the electric field that is induced by the interactions between their respective charges. Coulombic forces lead to attraction or repulsion at long distances; image forces lead to attractive forces at small separations. For particles large compared with the ionic mean free path, (18-8)

The rate of ion arrival at the surface of the particle is given by the convective diffusion equation, taking into account the ion migration induced by the electric field created by the charge already present on the particle and Brownian diffusion (mx and mg are the molecular masses of the ion and background gas, respectively, and Zx = ^8kBT/7wi^ is the mean thermal speed of the ions). For smaller particles, Fuchs (1963) developed the so-called limiting sphere approach: Outside a region of radius Sp, the ion transport is described by the continuum convective diffusion equation; inside that radius a kinetic (free-molecular) transport model is applied. Sp can be calculated in terms of the particle ion Knudsen number, Kn1 = 2XxIdp, that is, (18-9) The number of ions striking the particle per unit time is the product of the number that would strike the particle in the absence of electrostatic forces, J0 = 7IdIn(Sp)C\(dp/2Sp)2, and an attachment coefficient a. Here, n(Sp) is the ion concentration at the limiting sphere, 4KdIn(Sp) c J4 is the number of ions emerging from the limiting sphere, and (dp/2 Sp)2 is the fraction of those ions that reaches the particle. The attachment coefficient is the fraction of ions that enters the limiting sphere and ultimately collides with the particle; a > (dp/2Sp)2 when the forces are attractive, and a < (dp/2Sp)2 for repulsive forces. In the presence of electrical forces, the fraction a is determined from the radius of closest approach, called the apsoidal distance, ra. Without image forces, the ion would only collide with the particle when ra < dp/2. The short-range image forces can produce particle trajectories that have no apse and spiral in toward the particle. The minimum apse divides particle trajectories that escape capture from those that spiral into the particle (Hoppel and Frick, 1986). Hoppel and Frick (1986) note that, in the original development of this model, Fuchs (1963) incorrectly assumed that a = 1 for all attractive encounters. Hoppel and Frick (1986) corrected this error and also accounted for three-body trapping to produce the ion attachment coefficients shown in Figure 18-2. With these attachment coefficients, it is now possible to evaluate the charge distributions resulting from various aerosol charging systems. Given a, the frequency of ion attachment to particles carrying charge k is (18-10) where p = ± denotes the polarity of the ion. Using the attachment coefficients, the charge state of the aerosol can be determined by solving a system of balance equations for the particle concentration Nk at each charge level k. The rates of loss of positive and negative gas ions, concentrations n+ and n_, are (18-11)

(18-12) where px is the rate of production of ion pairs and p± is the ion-ion recombination rate coefficient. The rate equation for Nk is

(18-13)

ATTACHMENT COEF CM3 SEC1

RADIUS [ocm] Fig. 18-2. Ion attachment coefficients calculated using the modified Fuchs (1963) limiting sphere approach of Hoppel and Frick (1986). The dashed lines show the attachment coefficients that result when the Fuchs a coefficients are not corrected. (From Hoppel and Frick, 1986, with permission.)

The total particle concentration (18-14) is constant, leading to a redundancy that is usually eliminated by replacing the equation Eq. 18-13 for k = 0 with Eq. 18-14. Bipolar Diffusion Charging

When a mixture of positive and negative ions is present, the aerosol is said to undergo bipolar diffusion charging. This is a process in which the magnitude of the charge on the particle may increase due to the attachment of charges of like polarity or may decrease when charges of opposite polarity attach to the particle. If the ion populations are initially balanced, that is, n+(t = 0) = nJj = 0), the charge state of the aerosol will eventually reach a quasi steady state at which the rate of attachment of positive ions exactly balances the rate of attachment of

negative ions. Assuming that the ratio of the concentrations of positive and negative gas ions is fixed, the aerosol balance equations decouple from the ion balance equations. Detailed balancing then requires that in equilibrium (18-15) and the ratio of the number of particles carrying k charges to the number of uncharged particles, N0, becomes (18-16) where p = k/\k\ accounts for the polarity of the charge on the particle. The fraction of particles carrying k charges thus becomes (18-17) and (18-18) Gas ions have different mobilities due to differences in their molecular weights and, in atmospheric measurements, due to differences in the propensity to form water clusters around the different types of ions. Hoppel and Frick (1986) estimated that M+ = 150 amu, M_ = 90amu, Z1+ = 1.20Cm2C-1S"1, and Zx. = 1.35Cm2V-1S"1. The resulting charge state of the aerosol has a distribution of positive and negative charges that is not perfectly balanced due to differences in the ion attachment rates due to differences in the ion mobilities. Figure 18-3 shows the variation of the fraction of particles carrying a given charge with particle size. Calculations are presented for the Boltzmann charge distribution (dots), the uncorrected Fuchs model (dashed lines), and the Hoppel and Frick model including three-body stabilization of ion attachment reactions (solid lines). Several features are apparent in this plot: (1) At the ultrafme end of the size spectrum, the charging probability, while larger than that predicted at equilibrium, is small; (2) for particles larger than about 100 nm diameter, the probability that a particle will acquire more than one charge becomes appreciable; and (3) for particles larger than about 200 nm, several charge states will be present. The low charging probability for ultrafine particles often leads to poor counting statistics in mobility measurements. Multiple charging of large particles causes several sizes of particles to have the same mobility, degrading instrumental resolution and complicating data analysis. Calculation of the steady-state bipolar charge distribution using the corrected Fuchs model is quite involved. Wiedensohler (1988) developed an approximate representation of the corrected Fuchs charge distribution that is more readily applied. The fraction of particles carrying k charges is estimated to be (18-19) where dnm is the particle diameter in nanometers; the coefficients aiX are given in Table 18-1. While the charge distribution deviates from the equilibrium Boltzmann distribution and is asymmetrical, the stability of the distribution and insensitivity to such parameters as flow

FRACTION WITH P CHARGES

RADIUS ifim) FIg. 18-3. Variation of the steady-state charge fraction with particle size (Hoppel and Frick, 1986). Solid lines are the predictions using the corrected Fuchs model. The dashed lines are the predictions using the Fuchs a coefficients. The dots show the boltzman charge distribution. (From Hoppel and Frick, 1986, with permission.)

TABLE 18-1. Coefficients aiM for the Wiedensohler (1988) Approximation to the Corrected Fuchs Charge Distribution £ O0x aiX a2)k a3X a4X a5X

-

2 -26.3328 35.9044 -21.4608 7.0867 -1.3088 0.1051

-

1 -2.3197 0.6175 0.6201 -0.1105 -0.1260 0.0297

0 -0.0003 -0.1014 0.3073 -0.3372 0.1023 -0.0105

1

2 -2.3484 0.6044 0.4800 0.0013 -0.1553 0.0320

-44.4756 79.3772 -62.8900 26.4492 -5.7480 0.5049

Note that coefficients a4>1 and a52 differ from those originally published.

rates makes it useful for mobility-based measurements of the particle size distribution. Unfortunately, not all bipolar diffusion chargers attain this ideal distribution. Hoppel and Frick (1990) demonstrated that the imbalance in ion concentrations due to the unequal diffusion of ions to aerosol particles and apparatus walls is magnified in the ionic ratio because ionic recombination rapidly depletes both polarities equally. Hence, the charge distribution changes from the steady state that may (or may not) be present in the region where ion generation takes place in response to the changing environment.

Finally, it is important to note that the large molecular weight of the gas ions, and the correspondingly low mobilities, reflect ions comprising molecular clusters surrounding a core molecular ion. In atmospheric measurements, the primary clustering species is water vapor due to its abundance and high polarity. Observations of such clustering date back to the earliest measurements of gas ions (Nolan, 1926), yet the relationship between the ion properties and atmospheric parameters such as relative humidity remain poorly understood. Most investigations employ published values of the ionic properties and do not consider the possibility that they may vary. Unipolar Diffusion Charging

Unipolar diffusion charging occurs when only ions of one polarity are present. As charge accumulates on the particle, the repulsive forces cause the rate of charging to slow. Because the accumulated charge is not neutralized by attachment of ions of opposite polarity, higher charge levels on large particles and higher charging probabilities for small ones can be attained than with bipolar diffusion charging. Unipolar diffusion charging was employed in the electrical aerosol analyzer, the first commercial mobility sizer for aerosol particles, to measure the concentrations of transmitted particles as small as 50 nm diameter with an electrometer. Many theoretical and experimental studies of unipolar diffusion charging have been reported (e.g., Arendt and Kallmann, 1926; Fuchs, 1947; Bricard, 1948; White, 1951; Liu and Pui, 1977; Marlow and Brock, 1975; Davidson and Gentry, 1985; Pui and Liu, 1988). Pui and Liu (1988) showed that the theory of Marlow and Brock (1975) best describes charging of ultrafine particles (Kn1 = 2Xjdv « 1), while Fuchs and Bricard's theories are best for much larger particles. The kinetic treatment of Fuchs (1963) as extended by Hoppel and Frick (1986) is readily extended to unipolar charging, although closed-form solutions are not obtained and numerical integration is needed. An estimate of the order of magnitude of the mean number of charges, k, acquired by a particle of diameter dp can be obtained using White's equation (1951), that is, (18-20) where t is the charging time in seconds. It should be noted that White's equation only describes the mean charge state; there will be a distribution of charge levels for any particle size for any value of the Nj product. For large Nf, the charge level varies with particle size as oc (J^. In the continuum limit, the electrical mobility of the particle varies as Zp = e/3KjLidp oc dp. Hence, in the large particle limit at high N\t, the particle mobility increases linearly with particle diameter (assuming the particles are spherical). Mirme (1994) has taken advantage of this variation to use a DMA to measure size distributions well beyond the limits of single charging imposed by bipolar diffusion charging. Even in unipolar diffusion charging, charged particles below some size will carry only one charge, so the mobility varies with size as Zp °c dp2. Thus, there is a minimum in the mobility of those particles that do acquire charge. To correct for the contribution of small particles to his large particle size distribution measurements, Mirme simultaneously measured the size distribution of the small particles with a DMA equipped with a bipolar diffusion charger. He then subtracted the signal contributed by the small particles from the large particle measurements. Field Charging

When a dielectric particle carrying charge q is placed in a uniform electric field E00, the distortion of the electric field by the particle causes ions migrating along field lines in the ambient electric field to be deflected toward the particle, causing its charge to increase as

(18-21) where qs is the saturation charge level for a particle of relative permittivity K (18-22) The permittivity ranges from 1 for a vacuum to °o for a conductor. The time required to attain the saturation charge is often quite short, so the saturation charge can be used as a reasonable estimate of the charge level attained under field-charging conditions. When both field and diffusion charging take place, the saturation charge provides an initial charge state for the diffusion-charging process. Ion Generation

Controlled particle charging is essential to electrical mobility size distribution measurements. From the discussion of charging mechanisms above, it is apparent that production of a welldefined charge distribution can be attained only if the gas contains ions at known, or at least consistent, concentrations. Gas ions can be produced at ambient temperatures by highvoltage corona discharge (White, 1951), radioactive decay (Liu and Pui, 1974), photoelectron emission (Schmidt-Ott et al., 1980), and droplet formation in the presence of an electric field. An extreme form of droplet charging, the electrospray has been used to generate specific ions from solute molecules in atomized solutions and coupled with differential mobility analysis for the characterization of large molecules in solution (Gamero-Castano and de Ia Mora, 2000). All of the other methods have been used to charge aerosol particles for mobility analysis. A gas usually contains a few free electrons and a comparable number of positive ions. At sufficiently high electric fields, the free electrons can be accelerated to sufficiently high velocities that their collisions with gas molecules lead to the ejection of additional electrons. A cascade of such events, called an electron avalanche, creates the corona and generates large numbers of positive ions and free electrons in the gas. The corona may be operated with the discharge electrode (a needle or wire) held at a high positive potential, in which case the free electrons are attracted toward the wire, or with the discharge electrode at a negative potential to repel the electrons. The negative corona is generally more stable than the positive corona, but it requires that the gas contain a species that can absorb free electrons to be effective. Moreover, it produces O3. The positive corona does not require an electron-absorbing gas. It is frequently used to charge aerosol particles for measurement and was used in numerous electrostatic precipitators and the original electrical aerosol analyzer charger, which is shown in Figure 18-4. In that charger, the corona is operated with a potential between a central wire and a coaxial screen. The aerosol passes between that screen, which is maintained at a small positive voltage, and a coaxial electrode so that charging takes place in a region of weak electric field and charged particle losses due to migration to the wall are minimized. By monitoring and controlling the ion current arriving at the outer electrode, the Nj product can be determined. In an earlier version of that charger, Hewitt (1957) introduced an alternating potential in the outer region to reverse the migration of charged particles and further reduce their deposition. This approach has been employed more recently in efforts to improve the efficiency of unipolar charging of particles in the low nanometer size range (Buscher et al., 1994) The original chargers used in the differential mobility analyzer and in aerosol neutralizers were based on ion generation by radioactive decay. In a typical aerosol device, the gas

AEROSOL

CHARGER SHEATH FLOW

CORONA VOLTAGE CHARGER CURRENT SCREEN CORONA WIRE

Fig. 18-4. Unipolar diffusion charger used in the TSI electrical aerosol analyzer.

Aerosol flow

85

Kr Sealed Source

Fig. 18-5. Bipolar diffusion charger based on a sealed 85Kr /3-particle source.

flows through a chamber that contains a small radioactive source, such as that shown in Figure 18-5. The design of the charger is a compromise between safety, that is, providing adequate shielding and preventing line-of-sight access to the radioactive material, and factors that determine the charging efficiency, notably the residence time and flow geometry. A number of different isotopes have been used in aerosol neutralizers and chargers. Table 18-2 summarizes some of their properties. Cooper and Reist (1973) provide a detailed discussion of the issues involved in charge generation by radioactive decay. Heavy a particles (He nucleus) produce short straight tracks with a range in air Ra = 0.56E (cm), where E is the energy of the emitted particle in MeV. Ions are generated at a rate of about 1 ion pair/35.5 eV, with a slight increase in ionization occurring at the end of the trajectory as the ion slows down. The light P particle (electron) produces a long irregular path. These particles are emitted with a continuous spectrum. The energy flux decays with distance as (18-23)

TABLE 18-2. Properties of Radionuclides That Have Been Used in Gas Ion Generation Source 63 90

Ni Sr

90y 85

Kr

241

210

Am

Po

i

Radiation Type

Energy (MeV)

100 yr 27.7 yr 2.7 d 10.76 yr

P P P P

458.6 yr

Y a

138.4 d

a

0.065 0.546 (max) 2.18 (max) 0.67 (max) 0.514 (0.41%) 5.49 (85%) 5.44 (13%) 5.30 (100%)

T

(cm)

(cm"1)

k (ion pairs/cm)

0.449 0.044 0.0089 0.035

230 196 220

4.0 4.0 3.8

where the decay rate is \i = 17(cm4g"1)p£'m1ax4. p Particles generate ion pairs at a rate of about 1 ion pair/34 eV. Cooper and Reist address the geometry of ion generation, providing guidance in the design of aerosol chargers and neutralizers that will produce well-known ion densities, thereby enabling reproducible particle charging. Photoelectron Emission Charging

Photoelectron emission is a well-known method for probing the basic properties of condensed matter. Schmidt-Ott et al. (1980) demonstrated that aerosol particles emit photoelectrons when illuminated with photons with energies above the intrinsic photoemission threshold of the material, $. The threshold for electron emission is enhanced by the attractive Coulombic force that will lead to recapture of the emitted electron if the photon energy, hv, is less than (18-24) where k is the charge on the particle. For a given photon energy, hv, the maximum number of charges that a particle can acquire by photoemission is, therefore, (18-25) Because photoemission depends on material properties, photoemission charging provides information about the composition of the particles being probed, at least at their surfaces. Particles charged by photoemission may be measured either by mobility analysis or directly. Matter et al. (1995) have demonstrated that the negative charges produced by photoemission can be removed by a weak electric field, enabling the current carried by the positively charged particles to be measured with a Faraday cup aerosol electrometer. See Chapter 14 for use of this technique to measure particle surface area. Nanoparticle Charging

In the steady-state charge distribution produced by bipolar diffusion charging, only a very small fraction of particles in the low nanometer size range acquire any charge. Combined with high diffusive losses, this leads to low sensitivity of mobility analyzers to nanoparticles. Development of charging systems for this important size range is ongoing. Unipolar diffu-

sion chargers, such as that used in the early electrical aerosol analyzer, produce higher charge levels, although losses due to charged particle migration can be substantial for ultrafine particles. However, as the charging efficiency for ultrafine particles is increased, the size of particles that acquire multiple charges decreases. Modulation of the electric field in the charging region can reduce migration-induced losses (Hewitt, 1957; Buscher et al., 1994). If, however, an electric field is applied in the flow direction, charged particles can be induced to migrate in the direction of the gas flow (Yun et al., 1997; Fomichev et al., 1997; Chen and Pui, 1999). This reduces the losses of the charged particles and decreases the probability that the particle will acquire multiple charges. PARTICLE SAMPLING The drift velocity of charged aerosol particles in strong electric fields can be high enough to make the electrostatic precipitator much more efficient than gravity or inertia for particle separations, particularly for ultrafine particles. Moreover, carefully designed systems based on electrophoretic migration can be used to measure the charge and size of aerosol particles. Electrostatic Precipitation

Electrostatic precipitators (ESPs) have long been used to collect samples of aerosol particles (Tolman et al., 1919). Two basic steps are involved in electrostatic precipitation: (1) charging the particles and (2) subjecting them to a strong electric field to cause them to migrate toward and deposit on a substrate that is suitable for subsequent analysis. Tolman et al. (1919) collected aerosol samples to determine the mass and chemical composition of smokes, an application that has generally been replaced by filters today. More commonly, electrostatic precipitation is now used to collect samples for analysis by microscopy. Field charging is usually employed to charge the particles quickly. The high unipolar ion concentrations produced by corona discharge and the high field strengths lead to rapid charge saturation. The point-to-plane ESP is widely used to deposit aerosol particles onto electron microscope grids or platens. Figure 18-6 illustrates a point-to-plane ESP (Morrow and Mercer, 1964). The corona and electric field are formed between the sharp point of a corona needle and a flat deposition substrate that is located a few millimeters from the needle. A voltage of several kilovolts is applied to the needle, with the plane grounded. Typical ion currents are a few microamperes. Aerosol particles passing between the needle and the substrate are charged and then deposited on the substrate. Because the mobility of a particle is a function of particle size, the efficiency of collection in the short transit time of the collector illustrated in Figure 18-6 may be a function of size and samples may be biased (Cheng et al., 1983). Liu et al. (1967) developed a two-stage, pulsed electrostatic precipitator for aerosol sampling to minimize this bias. The particles are first charged and then introduced into the collection region where the electric field is pulsed for 1.5 s with 3 s intervals during which the electric field is turned off. Because only those particles that are in the region between electrodes at the beginning of a voltage pulse are collected, the deposition is relatively uniform. PARTICLE SIZE DISTRIBUTION MEASUREMENT Measurements of the distribution of particles with respect to their electrical mobilities have been made since 1902, when Langevin used a simple coaxial cylinder ion condenser in his

CORONA DISCHARGE

AEROSOL

ELECTRON MICROSCOPE GRID (PLANE ELECTRODE)

Fig. 18-6. Point-to-plane electrostatic precipitator for depositing aerosol particles onto electron microscope grids.

discovery of "large ions" in the atmosphere. He observed that these ions had mobilities that were 3000 times smaller than gas ions, placing them in the accumulation mode of the atmospheric aerosol. His measurement involved drawing a sample of gas containing ions and charged particles through the annular region between two electrodes. The current collected on the central electrode when a voltage was applied to the outer one provided a measure of charged particles with mobilities greater than a threshold value. This technique was used by many researchers over the following decades (see Flagan, 1998, for a detailed survey of these pioneering investigations). Efforts to improve the resolution of measurements of the distributions of ion and particle mobilities predated Langevin's discovery with the introduction of a segmented central electrode and culminated with the development of a differential electrical mobility classifier for gas ion measurements by Erikson in 1921 and for particles by Rohrmann in 1923. Although it saw little use for several decades, this instrument reappeared in a paper by Hewitt (1957) that introduced a number of innovations that would finally enter common usage years later. Electrical mobility methods did not see much use until commercial instruments were introduced by TSI* in the 1960s and 1970s. The first, called the Whitby Aerosol Analyzer (WAA), modified the traditional condenser analyzer by introducing the aerosol near the outer electrode and particle-free sheath air in the gap between the electrodes (Whitby and Clark, 1966). In the WAA the potential was applied to the inner electrode, and the electrical current carried in the flow exiting the downstream end of the condenser was measured. Dif* See Appendix I for full manufacturer addresses referenced to the italicized three-letter codes.

ferences in that current conveyed as the applied voltage was varied were used to infer the particle size distribution. A more compact and robust version of that instrument, known as the electrical aerosol analyzer (EAA), was introduced by Liu and Pui in 1974 and commercialized by TSI, making the technique widely available to researchers around the world. That instrument has, however, been largely displaced by a commercial version of the differential electrical mobility classifier of Erikson (1921), Rohrmann (1923), and Hewitt (1957) that was introduced at the same time as the EAA. Differential Mobility Analyzer

The differential mobility classifier, more commonly called the differential mobility analyzer (DMA), was introduced by Knutson and Whitby (1975) as a source of monodisperse submicrometer particles for use in calibrating other instruments. Although Knutson and Whitby understood the potential of their instrument for measurement of aerosol size distributions, it had to await the development of suitable detectors before that application became practical. The cylindrical differential mobility classifier, illustrated schematically in Figure 18-7a, is a coaxial flow condenser. Aerosol particles enter the condenser through a narrow slot in the outer electrode. A particle-free sheath flow separates them from the high-voltage inner electrode. Downstream from the aerosol injection slot, a classified aerosol sample is extracted through a port in the inner electrode. Particles that migrate across the gap between the electrodes during the time that they are carried that length through the classifier are extracted. Particles with higher mobilities migrate across the annulus, depositing on the central electrode upstream of the sample extraction port. Particles with too low mobility deposit farther downstream or are discharged with the excess sheath flow. Thus, only particles having mobilities within a narrow range are transmitted. To determine the size of particles transmitted through the DMA, we consider a simplistic model of the DMA that yields the same results as more complex and rigorous models. We first limit the discussion to classification in the absence of particle diffusion. The electric field in the common cylindrical DMA (CDMA) is (18-26)

where V is the potential applied to the inner electrode, and the radii of the inner and outer electrodes are R^ and .R2, respectively. The particles migrate in the radial direction at velocity, v(r) = ZV/(rlnR2/R\), while they are advected in the axial direction at the gas velocity, u(r, x). Assuming that the gas flow is parallel to the electrodes throughout the volume, that is, u = u(r), the particle trajectory is described by (18-27)

Rearranging, multiplying each side by 2 n, and integrating, yields (18-28)

CLEAN SHEATH AIRIN AEROSOL IN

ANNULAR AEROSOL ENTRANCE CHANNEL CLASSIFIED AEROSOL OUTLET

CLEAN SHEATH AIR INLET TANGENTIAL AEROSOL INLET CLASSIFIED AEROSOL OUTLET PORT

EXCESS FLOW OUTLET (b)

EXCESS AIR OUTLET (a) Fig. 18-7. Schematic diagram of a cylindrical DMA designed by Knutson and Whitby (1975) (a) and the radial DMA of Zhang et al. (1995) (b) Trajectories for particles with mobilities Z-Z* and for particles with higher and lower mobilities are illustrated.

The characteristic mobility of the particles classified at given flow rates and applied voltage, Z*, is that corresponding to particles entering at the middle of the incoming aerosol flow rate, that is, gin = Gsh + Ga/2 = (Ge + Qsh + Qs)/2, and exits at the middle of the classified aerosol outlet flow rate, Qout = QJl. The characteristic mobility is, thus,

(18-29) By a similar derivation, the characteristic mobility is (18-30)

for a radial-flow DMA (RDMA; Fig. 18-7b) with a gap between the two electrodes of b and in which the aerosol enters at a radial position R2 and flows inward to a radial position R^ where the classified aerosol is discharged. If the particles are large enough (i.e., the classifi-

EXAMPLE 18-2: DMA OPERATION The TSI long column cylindrical DMA (based on the Knutson and Whitby design) is to be used to measure particles as large as 1 urn diameter at a flow rate ratio of /5 = (Qa + Qs)/(Qsh = Qe) = 0.1, and balanced flows, that is, Qa = Qs. Figure 18-1 shows that the resolution of the DMA falls to about one-half of its ideal value of p~l = 10 at about 10 V. What flow rates are needed, and what is the smallest particle size that can be measured with reasonable resolution? (The dimensions of the DMA are R1 = 9.38 mm, R2 = 19.58 mm, and L = 444 mm.) Answer: 1. Calculate the flow rates needed to classify lum particles at 10,000 V. From Example 18-1, the mobility of a singly charged, 1 urn particle is Zp = 1.09 x 10^m 2 V 1 S 1 . Note that <2sh + Qa = Ge + Qs, and balanced flows implies Qe = Qsh. From Eq. 18-30,

2. The maximum mobility classified with reasonable resolution is that transmitted at a classification voltage of about 10 V, that is,

From Figure 18-1 it is apparent that this corresponds to approximately 15 nm diameter. Thus, the size range that can be classified at given flow rates extends over a factor of about 65 in diameter. 3. Smaller particles can be classified if the flow rate is increased, although the maximum particle size is then reduced as well. Examination of Figure 18-1 reveals that the size range that can be classified with reasonable resolution narrows for smaller particles. Thus, a DMA designed to classify particles as small as 3 nm diameter is limited to particles smaller than about 120 nm by the breakdown voltage, covering only a factor of about 40 in diameter. 4. A given DMA can cover a wider sizing range by changing flow rates or by accepting lower resolution at the high mobility (small particle) end of the distribution. Collins et al. (2000b) have demonstrated that, by scanning the DMA flows simultaneously with the voltage scan, the sizing range can be increased by a factor of 4 or more while maximizing the particle size resolution over the entire size range.

cation voltage is high enough) that diffusion is unimportant, particles included in the classified aerosol flow will span a range of mobilities of Z% - AZ < Zp< Z* + AZ, where (18-31) a n d £ = ( e a + Qc)/(Qsh + (2e). The probability that particles with mobility Z that enter the classification region of the DMA will be included in the classified aerosol flow when the flow rates and applied voltage are for a characteristic mobility of Z% is £2(ZP, Zf), which is called the classifier transfer function. Knutson and Whitby (1975) derived an expression for the transfer function for particle

classification when diffusion is unimportant. Most uses of the differential mobility classifier extend to sufficiently small particles that diffusion cannot be neglected. A number of investigators have modeled the effect of diffusion on the transfer function. Stolzenburg (1988) derived a diffusional transfer function of the cylindrical DMA that can be expressed as

(18-32) where (18-33) erf (v) is the error function, and (18-34)

(18-35) where the Peclet number for particle migration across the gap between the electrodes is /(geometry)

(18-36)

and G is a parameter that depends weakly on the DMA geometry and the flow rate ratio, /3. This result applies to both radial and cylindrical DMAs (Zhang and Flagan, 1996). The factor /(geometry) accounts for nonuniformities in the electric field along the migration path. Because the electric field in the RDMA is uniform,/RDMA = 1. For the CDMA,/ C D MA = № R1)I(R2InR2IR1).

Hie resolution of a differential mobility classifier can be expressed conveniently as the ratio of Z% to the observed range of mobilities of the transmitted particles. Following the approach taken in a wide range of spectroscopies, we use those mobilities corresponding to the full width of the DMA transfer function evaluated where the transmission efficiency is one-half the maximum value, z*Zfwhm.The resolution is bounded by a value that is determined by the ratio of flow rates, /3, in the nondiffusive limit for balanced flows, that is, Qa = g s ; the value is (Flagan, 1999) (18-37) At low particle migration Peclet numbers, diffusion dominates and the resolution becomes (18-38)

Figure 18-8 shows the variation of the resolution of several DMA designs for a flow rate ratio of p = 0.1 and a commercial cylindrical DMA (TSI long column) for several values of

TSI long & short Reischl long Reischl short Rossell-Llompart RDMA SMEC

3x10°

J?

V (Volts) Fig. 18-8. Ideal resolution of several DMA designs as a function of applied voltage and flow rate ratio (Flagan, 1999).

p. For balanced flows within the DMA, that is, S=O, the two limiting expressions intersect when (18-39) providing an estimate of the transition from nearly ideal operation where diffusion plays only a minor role to the low-voltage region where resolution decreases as Vm. Because the maximum voltage of any DMA design is limited to about 10 kV by electrostatic breakdown (arcing) for dry gas at atmospheric pressure, the range of particle mobilities that can be analyzed with resolution near that determined by the flow rate ratio decreases with decreasing p. Lower pressures or high humidity may lower the breakdown potential and further constrain the operating range. Differential Mobility Analyzer Designs

Most of the measurements made with differential mobility analyzers before the mid-1990s were obtained either with the commercial version of the Knutson and Whitby (1975) cylin-

drical DMA (KW-CDMA) that was produced by TSI (St. Paul, MN) or with similar instruments. That device is illustrated in Figure 18-7. The coaxial cylinder instrument performed well for particles between about 10 nm and 1 urn diameter. A shortened version of that instrument was used to measure smaller particles, but diffusive losses of ultrafine aerosol particles are high due to the narrow annular entrance region seen at the top of the instrument in Figure 18-7a. Reischl and coworkers at the University of Vienna (see Winklmayr et al., 1991) developed a new DMA, commercially produced by HAU, that was better suited to measurement of nanoparticles. A tangential aerosol inlet greatly reduced the particle losses. The instrument was initially used with a built-in high-sensitivity electrometer to measure size distributions extending down to about 2.5 nm diameter. That design has been the basis of a number of new designs. Rossell-Llompart et al. (1996) constructed a version of the Vienna DMA with a very short analysis region to optimize its performance in the low nanometer size regime. DMA development continues at too rapid a pace to allow an exhaustive listing of all of the developments, so only a few key innovations are summarized below. Zhang et al. (1995) and Pourprix and co-workers (see Fissan et al., 1996) applied the tangential inlet to the development of a new type of DMA in which the classification takes place in an inward radial flow between parallel disk electrodes (illustrated in Fig. 18-7b). These instruments have been called the Radial DMA (RDMA) and the Spectrometre de Mobilite Elitrique Circulaire (SMEC), respectively. The design of the KW-CDMA was modified by Pui and co-workers (see Chen et al., 1998) to extend its operation into the nanometer size range. This instrument, called the nanoDMA, passes a large aerosol flow through the annular entrance region of the KW design to reduce losses. A small portion of the flow conveys aerosol into the classification region of the DMA, with the remainder being discharged. The aerosol flow rate into the classification region, Qa, is determined as the difference between the much larger flows that enter and are discharged from the annulus. The ideal performance of all of these depends on the DMA geometry. Table 18-3 summarizes these geometry parameters for several DMAs. As seen in the inset in Figure 18-8, the theoretical performance does not vary significantly with the geometry of the DMA. Real instruments vary in how closely they approach the ideal. Degraded performance may result from imperfections in the construction of the instrument, maldistribution of the aerosol or sheath flows around the axis of the classifier, geometrical distortions of the electric field, turbulence, or imprecise control of the flow rates and voltages. Measurement of the transfer function in the high-voltage limit where diffusion plays only a minor role provides a clear test for these nonidealities. DMA Operation DMA classification of an aerosol requires precise control of four flow rates: the incoming aerosol and sheath flows, Qa and Qsh, and the outgoing classified aerosol and exhaust flows, Q5 and Q6, as illustrated schematically in Figure 18-9. Typically, the sheath and exhaust flow rates are much larger than either the aerosol or classified sample flow rates. These flows combine in the classification region, which, from a flow-balancing point of view, can be treated as a plenum with no pressure drops. The metering and precision control of the four flows is particularly challenging because of the need to process the two aerosol flows with minimal particle losses and, furthermore, because the flows must be measured and controlled with very low pressure drops. The large particle limit of the resolution of the DMA is equal to /J"1. The differences in the resolving power of different DMA designs (assuming no imperfections that will degrade their performance) are small as illustrated by the magnified inset. The DMA can be operated in underpressure (illustrated in Fig. 18-9) or overpressure mode. In the underpressure operation, the flows are pulled through the DMA by pumps. If the system being sampled is at higher pressure than ambient, that pressure can be used to

TABLE 18-3. Geometry Parameters for Various DMAs for 8= 0.0 R2 (mm)

b (mm)

P

G

/

(mm) 9.37 9.37 9.37 9.37 9.37 25.0

19.58 19.58 19.58 19.58 19.58 33.0

444.4 444.4 444.4 444.4 111.1 110

0.01 0.05 0.1 0.2 0.1 0.1

1.87 1.99 2.14 2.42 2.15 2.48

0.707 0.707 0.707 0.707 0.707 0.837

29.0

37.0

600

0.1

2.51

0.867

25.0

33.0

16

0.1

2.80

0.867

(mm)

R2 (mm)

b (mm)

24

50.4

10

0.1

2.92

1.0

4

0.1

2.81

1.0

DMAs Cylindrical TSI long TSI long TSI long TSI long TSI short Vienna short Vienna long Rossellblompart Radial Caltech RDMA SMEC

~5

65

P

G

/

The University of Vienna DMA (produced by Hauke) and the short column nanoparticle version of it are described by Winklmayr et al. (1991), and RossellLlompart et al. (1996), respectively. The RDMA and the Spectrometre de Mobilite Electrique Circulaire (SMEC) are described by Zhang et al. (1995) and Fissan et al. (1996), respectively. Geometry parameters are calculated assuming fully developed laminar flow within the classification regions of the instruments.

OPTIONAL RECYCLE LOOP

MATCHING RESISTANCE

DETECTOR

CRITICAL ORIFICE

VARIABLE SPEED PUMP

TO VACUUM Fig. 18-9. Schematic diagram of flow control system for differential mobility analyzer operation. The system shown includes laminar flow metering of all four DMA flows. Using a variable-speed pump, the excess flow can either be discharged or recycled as indicated by the dashed lines. The DMA illustrated is the design of Winklmayr et al. (1991).

force the flow through the DMA, producing the overpressure mode of operation. In either case, balancing of the flows is critical. Because the pressure drop through the filter that removes particles from the sheath flow is often much larger than the pressure drops in the sample flows, an additional pressure drop in the form of small-bore tubing of appropriate diameter is sometimes added to the sample flow line to aid in balancing the flows. If the pressure drops are not balanced, it can be quite difficult to maintain stable flows through the DMA, leading to uncertainty in the sizes of particles that are classified. The first generation of commercial DMAs used mass flow meters and manual control valves on the aerosol, sheath flows, and excess flows. Passage of either of the aerosol flows through a conventional valve is undesirable because particle losses within the valve may vary with valve setting. One approach to improving precision in the control of the flow rate ratio is to filter and recycle the exhaust flow (Rogak et al., 1991), allowing the aerosol and classified sample flows to be matched easily and to be metered downstream of the particle detection system as illustrated by the dashed line in Figure 18-9. It should be noted, however, that small leaks in the recycle system can lead to substantial errors in the much smaller aerosol flow rates. Diaphragm pumps reduce, but do not eliminate, the likelihood of such leaks. Moreover, they introduce pressure pulsations that may affect the measurement. These are generally minimized by the addition of large plenums on the flows entering and leaving the recirculation pump. Rotary blowers or pumps can also be used, but extreme care is required to ensure that leaks do not compromise the measurement. Recycle systems also heat the sheath gas, introducing the possibility that particles may decrease in size due to evaporation as they migrate through the DMA. Ideally, each of the flows would be measured and controlled. Replacing the mass flow meters with laminar pressure drop elements and high-sensitivity, electronic differential pressure transducers facilitates design of flow meters with low particle losses. Such meters make it possible to monitor all four flows as illustrated in Figure 18-9. Combined with active control of the sheath and exhaust flows, highly stable flow control can be achieved (Russell et al., 1996). The active control of the critical flow rate ratios enables robust and precise operation of the DMA even in variable-pressure environments such as those encountered in airborne measurements. Differential Mobility Particle Sizer

Routine mobility-based aerosol size distribution measurements were facilitated by the EAA, which employed a Faraday-cup electrometer to measure the current carried by those particles that escaped collection within the classification column. Unipolar diffusion charging was employed in that instrument to increase the charging probabilities to sufficiently high levels that ambient particle concentrations would produce electrical currents of a small fraction of a picoammeter. Multiple charging became significant at particle sizes as small as 50 nm diameter in that operating mode. Knutson and Whitby (1975) recognized the potential of the differential mobility classifier for measurements of the particle size distribution, but the technique had to await the development of a suitable detector before it saw significant use as a measurement instrument. The high resolution of the DMA resulted from the low charging probabilities achieved with bipolar diffusion charging. The lower charge levels made the use of the electrometer difficult at ambient concentrations, although Winklmayr et al. (1991) eventually produced an electrometer-based size distribution measurement system. The introduction of the continuousflow, single-particle-counting condensation nucleus counter (CNC) (Agarwal and Sem, 1978) first enabled routine size distribution measurements with the DMA. Coupled with computercontrolled stepping of the DMA voltage, the TSI 3020 CNC made it possible to take full advantage of the resolution of the DMA in making size distribution measurements (Helsper et al., 1982; TenBrink et al., 1983). This computerized size distribution measurement system

was later commercialized by TSI as the Differential Mobility Particle Sizer (DMPS). This automated DMA system soon displaced the EAA as the primary system for mobility-based size distribution measurements because the newer technique determined the concentration of particles within a narrow mobility interval instead of measuring all particles with mobilities less than a threshold value. In the DMPS measurement, the classification voltage is stepped and, after a delay to allow the aerosol signal to reach steady state, the particle concentration is measured. The voltage is then stepped again, and the process is repeated. Computer control allowed the instrument to step through the many voltages required to determine the size distribution relatively rapidly, although the measurement time of lOmin or more was too long for the DMPS to be used in the study of rapidly varying aerosols. Scanning Mobility Particle Sizer

While the DMA afforded much greater resolution than its predecessor, the measurement was too slow for many applications. Wang and Flagan (1990) observed that the critical aspect of mobility measurements was the dependence of the particle migration velocity on the strength of the electric field, but noted that separation could be achieved if the electric field were ramped continuously and particle counts were continuously recorded in time bins. Moreover, they showed that, in the nondiffusive limit, the same transfer function would be achieved in the system they called the Scanning Electrical Mobility Spectrometer (SEMS) as in the DMPS provided that the mean field strength during the particle migration time were used in the data analysis. Differential mobility analysis of particle size distributions over the full DMA classification range was reduced to less than 1 min with the SEMS. This scanning mode of DMA operation was later commercialized by TSI as the Scanning Mobility Particle Sizer (SMPS). To determine the relationship between counts in a time bin and particle mobility, one must take delays in the transmission of particles from the DMA outlet to the point of measurement in the detector into account. Wang and Flagan (1990) took into account a time offset due to the residence time of the particles in the plumbing and detector. Russell et al. (1995) later showed that this fixed offset model was oversimplified. The SEMS/SMPS systems require a time response for which the available detectors (either CNCs or electrometers) were not designed. Mixing within the flow passages of the CNC produced a distribution of delay times that smear the SEMS/SMPS response function by allowing particle counts to appear in later time bins than expected. Russell et al. (1995) took this residence and distortion into account by deriving a transfer function based on a model of the flow between the DMA outlet as a plug flow region in series with a perfectly mixed volume. A much simpler approach was later used by Collins et al. (2000), who developed a deconvolution algorithm that was sufficiently fast to allow mobility spectra to be "desmeared" immediately upon measurement. Data Analysis/Inversion

Efforts to deduce particle size distributions from DMA data predated the development of the automated instruments. The particle concentration signal obtained during steady-state operation of the DMA is the integral over particles transmitted over the entire range of mobilities sampled, that is, (18-40) where T]iran%(dp) is the transmission probability for particles of diameter dp and charge i, and Scount(dp) is the mean signal (counts in the case of a CNC detector or electrical current in the case of an electrometer) produced by the detector in response to a particle of diameter dp

and charge L The transfer function, Q for particles of diameter dv and charge i depends on the mobility of that particle compared with the nominal classification mobility for channel y, Zl(V,p,8). In the limit of singly charged particles, this reduces to (18-41) Assuming that the size distribution, n(dp), varies little over the interval of transmitted mobilities, that is, n(dp) « n(dPj), and that the transmission efficiency, detector response function, and charging probability are slowly varying functions of dp, the size distribution becomes

(18-42) When evaluated using the nondiffusive transfer function for the DMA, Eq. 18-42 represents the zeroth order approximation to the particle size distribution. In that limit, and for balanced flows (5= 0), JoX2(Zp,Z*)dZp = AZP = /3Z*(dp,l). More generally, the diffusive transfer function must be used in the data inversion. A number of higher order data inversion algorithms have been applied to inference of particle size distributions, for example, non-negative least squares matrix inversion (Lawson and Hansen, 1974) and constrained regularization (Wolfenbarger and Seinfeld, 1990). One of the most widely used inversion algorithms is the very simple one proposed by Twomey (1975); see also Markowski (1987). In this approach, the general Fredholm integral equation for the instrument response, Eq. 18-40, is approximated by a summation, (18-43) where the summation is taken over the m sizes of particles used to represent the particle size distribution, n(dk)Adk = Nk, and (18-44) Starting with a reasonable guess of the particle size distribution, №k,fc= 1,2,..., ra, the solution is calculated by iteration. At any iteration, /, a new trial solution is estimated as (18-45) where X\k = S^^K-^Nl is the ratio of the actual instrument response to the calculated instrument response. X[k may be calculated once for each channel / in a given iteration /. The iteration, Eq. 18-45, is then done for each measurement channel; and particle size k, producing a cycle of m x n iterations. This process is repeated until adequate convergence is achieved. The entire data inversion can be performed in a single operation, although partial inversion to assign counts to the appropriate mobility bins often precedes inversion to account for the classifier response function. Such a stepwise inversion might involve a time deconvolution to correct for the mixing-induced distribution of delays in counting particles, followed by an iterative correction for multiple charging. Approaches for carrying out these partial inversions are described below. Residence Time Deconvolution Particle counts appear in a range of channels when the DMA voltage is scanned or stepped rapidly, distorting the inferred particle size distribution function. Collins et al. (2001) devel-

oped a simple and efficient deconvolution algorithm that can correct for this distortion. To a first approximation, the fraction of particles that exit the DMA at time f that are detected at later time t can be expressed as the sum of a fixed "plumbing" delay, rp, and an exponentially decaying function, that is, (18-46) where rs is the mixing-induced smearing time. Note that an electrometer produces similar response smearing due to the inherent electronic time constant of the electrometer circuit. The counts appearing in time bin m due to particles that exited the DMA at times corresponding to earlier counting bins, n, can be expressed as a summation, (18-47) For the exponential distribution of delay times, the estimated instrument response in the absence of smearing can be computed recursively, (18-48)

where u = etdx, pn = / ^ 1 iH LL + fn_r The value of Tv1 will have been calculated for the previous bin. It equals

ulnu

(18-49) For a continuous stream of data, Eq. 18-48 can be used directly after a brief start-up transient. This recursive relationship enables real-time display of DMA size distributions that have been corrected for any smearing that may occur. This calculation attributes particle counts to the proper time bin. It should be performed before the correction for multiple charging and inversion. Multiple Charging Correction Some particles at the upper end of the DMA sizing range (Dp < 0.1 urn) acquire multiple charges in bipolar diffusion charging. Multiple charging can occur for even smaller particles when unipolar chargers are employed. To transform the measured counts versus nominal mobility to a particle size distribution, the presence of large, multiply-charged particles in a measurement channel (counting bin) that nominally corresponds to smaller particle sizes must be taken into account. Obviously, knowledge of the charge distribution as a function of particle size is essential for this part of the data inversion, but this is not enough. The channel corresponding to the highest classification voltage may include larger particles that are outside the nominal size range of the DMA, but that have sufficiently high mobilities for classification due to their charge states. To avoid this problem, the DMA is often used with a precut impactor that removes particles larger than that transmitted when singly charged at the highest operating voltage. A recursive procedure can then be used in conjunction with the known charge distribution to assign counts to the mobility channels where they would have appeared if singly charged. Because no multiply charged particles are present at the largest classified particle size, the number of particles corresponding to that mobility is

directly measured. Any particles of that size that are multiply charged will appear in bins with smaller, more mobile particles. Using the known charge distribution, the number counts in the respective bins are reduced accordingly. By sequentially making these corrections to the succession of channels from the lowest to highest mobility, detected particles are attributed to the counting bins for mobilities corresponding to single charging. A similar procedure can be employed when no large particle precut is used provided that the distribution of larger particles is known, either through auxiliary data from optical particle counters, aerodynamic particle sizers, or related instruments, or because no particles are present in the lowest mobility channels. Tandem Differential Mobility Analyzer

The DMA can be used to study changes in aerosol particles due to condensation, evaporation, or other processes that will change the particle size or structure. One DMA is used to classify the aerosol particles at fixed voltage, selecting particles from a narrow interval of particle sizes. The classified aerosol is then processed to change the particle size, for example, by drying or humidification.The size distribution of the modified particles is then measured using a second DMA that is stepped or scanned through voltages. The size shift observed in this tandem DMA (TDMA; McMurry et al., 1983) can be used to infer such properties as vapor pressures or the influence of humidity on aerosol optical properties. Hme-of-Flight Mobility Analyzers

Mobility analysis can also be accomplished by measuring the time of flight of charged particles in an electric field. This method, called ion mobility spectrometry (Karasek, 1970; Eiceman and Karpas, 1994), is ideally suited for measurements of very high mobility particles or ions. Figure 18-10 illustrates a typical ion mobility spectrometer. A pulse of charged particles is injected through a shutter grid into a drift region. The current carried to a collector electrode at the opposite end of the drift region determines the number of particles transmitted within intervals of transit time. Typical drift times are 10 to 50 ms, and drift region lengths are several centimeters. The mobility resolution of the ion mobility spectrometer is determined by the ratio of the transit time to the time that the shutter is open. Brownian diffusion within the REACTION REGION HIGH VOLTAGE

ELECTROMETER CARRIER GAS + SAMPLE

DRIFT REGION

DRIFT GAS

GAS EXIT Fig. 18-10. Ion mobility spectrometer.

ion mobility spectrometer is generally relatively minor because of the high migration Peclet number, Pemig = eV/kBT. Nonspherical Particles

The discussion above has assumed that the particles being analyzed are spherical, but solid aerosols frequently contain nonspherical particles. The particle shape can influence mobility analysis in two ways: (1) the drag on the particle will differ from that of a sphere of equal volume; and (2) the charging probability may also differ. In the absence of detailed knowledge about the structures of particles, mobility analyzer data can be reported as mobility equivalent diameter, that is, the size of the spherical particle that has the same electrical mobility as the particle being measured. Studies of particle shape effects have appeared in the literature for small clusters of spheres (Kousaka et al, 1996), aggregate particles (Schmidt-Ott, 1988; Rogak and Flagan, 1992; Rogak et al., 1993), and fibers (Han et al., 1994; Baron et al., 1994). Schmidt-Ott (1988) and Rogak et al. (1993) found that the mobilities of agglomerate particles scale with their projected areas. When the fractal dimension of the particle is small so that little of the internal surface is shadowed by outer portions of the agglomerate, then the mobility is proportional to the total area of the particle. Similarly, Rogak and Flagan (1992) found that bipolar diffusion charging of low fractal dimension aggregates was similar to that for spheres of the same surface area. See Chapter 23 for further discussion of nonspherical particles. Fibers

Mobility analysis of fiber particles has been the subject of a number of investigations. Laframboise and Chang (1977) developed a diffusion charging model for particles of arbitrary geometry. Han and Gentry (1993a,b) modeled unipolar diffusion charging of fibers in the free molecular regime and combined diffusion and field charging in the continuum regime. Their calculations and subsequent experiments showed that fiber separation on the basis of length can be achieved by unipolar charging followed by electrostatic precipitation. The high rate of charging of long, thin fibers produced sufficient differences in mobility to enable effective length separations. Lipowicz and Yeh (1989) demonstrated that macroscopic (0.5 to 5 mm long) neutral aluminum wires can be separated by an electric field gradient in a liquid. This separation, known as dielectrophoresis, results from charge separation within the particles that is induced by the electric field gradient.The terminal dielectrophoretic migration velocity is (Baron et al., 1994) (18-50) where Km is the dielectric constant of the medium, a is the ratio of the dielectric constant of the fiber to that of the medium, and p = Lf/df is the fiber aspect ratio, the ratio of the fiber length to its diameter. The functions of the aspect ratio,/(/?) and g(j3), are given by (18-51)

(18-52)

For high-aspect-ratio (P < 5) conductive fibers, the velocity is a function of Ll but is only a weak function of the fiber diameter, so the length of the fiber can be determined with high resolution. High resolution of fiber length is also possible for low conductivity fibers, but the migration velocity of fibers of intermediate conductivity depends on both length and diameter. However, small fibers can be considered to be conductive because the distances that charges must migrate are small. Baron et al. (1994) constructed a coaxial flow fiber classifier in which the classifier surfaces were coated with a high dielectric strength material to prevent sparks or corona discharge. With a 500Hzac potential of 12 to 14 kV peak to peak, asbestos fibers were effectively classified, and size distributions were determined. Glass fibers have lower conductivity and are, therefore, more difficult to classify, but they can be made more conductive by raising the humidity before classification.

REFERENCES Agarwal, J. K. and G. J. Sem. 1978. Generating submicron monodisperse aerosol for instrument calibration. TSI Quarterly IV(2):3-8. Allen, M. D. and O. G. Raabe. 1985. Slip correction measurements of spherical solid aerosol particles in an Millikan apparatus. Aerosol ScL Technol 4:269-286. Arendt, P. and H. Kallmann. 1926. The mechanism of charging dust particles. Z. Physik. 35:421-441. Baron, P. A., G. J. Deye, and J. Fernback. 1994. Length separation of fibers. Aerosol ScL Technol. 21:179-192. Bricard, J. 1948. Sur l'equilibre ionique de Ia basse atmosphere. C. R. Acad. ScL (Paris) 226:1536-1538. Buscher, P., A. Schmidt-Ott, and A. Wiedensohler. 1994. Performance of a unipolar square-wave diffusion charger with variable Nt product. /. Aerosol ScL 25:651-663. Chen, D. R., D. Y. H. Pui, D. Hummes, H. Fissan, F. R. Quant, and G. J. Sem. 1998. Design and evaluation of a nanometer aerosol differential mobility analyzer (Nano-DMA). / Aerosol ScL 29:497-509. Chen, D. R., and D. Y. H. Pui. 1999. A high-efficiency, high-throughput unipolar aerosol charger for nanoparticles. /. Nanoparticle Res. 1:115-126. Cheng, Y. S., H. C. Yeh, and G. M. Kanapilly. 1983. Collection efficiencies of a point-to-plane electrostatic precipitator. AIHAJ 42:605-610. Collins, D. R., R. C. Flagan, and J. H. Seinfeld. 2001. Improved inversion of scanning DMA data. Aerosol ScL Technol. (in press). Collins, D. R., R. C. Flagan, and J. H. Seinfeld. 2000. Scanning flow DMA. /. Aerosol ScL 31:1129-1144. Cooper, D. W. and P. C. Reist. 1973. Neutralizing charged aerosols with radioactive sources. /. Colloid Interface ScL 45:17-26. Davidson, S. W. and J. W. Gentry. 1985. Differences in diffusion charging of dielectric and conducting ultrafine aerosols. Aerosol ScL Technol. 4:157-163. Eiceman, G. A. and Z. Karpas. 1994. Ion mobility spectrometry. Boca Raton, FL: CRC Press. Erikson, H. A. 1921. The change of mobility of the positive ions in air with age. Phys. Rev. 18:100-101. Fissan, H., D. Hummes, F. Stratmann, P. Buscher, S. Neumann, D. Y. H. Pui, and D. Chen. 1996. Experimental comparison of four differential mobility analyzers for nanometer aerosol measurements. Aerosol ScL Technol. 24:1-13. Flagan, R. C. 1998. History of Electrical Aerosol Measurements. Aerosol ScL Technol. 28:301-380. Flagan, R. C. 1999. On differential mobility analyzer resolution. Aerosol ScL Technol. 30:556-570. Fomichev, S. V., N. M. Trotsenko, and A. V. Zagnitko. 1997. Aerosol chargers using ionizing radiation and electric field collinear to flow: Simulation and experiment for fine particle charging in electronegative air and electropositive nitrogen. Aerosol ScL Technol. 26:21-42. Fuchs, N A. 1947. Izvestiya AN SSSR (Ser. Geophysicheskaya) 2:341. Fuchs, N. A. 1963. On the stationary charge distribution on aerosol particles in a bipolar ionic atmosphere. Geofts. PuraAppl. 56:185-193.

Gamero-Castano, M. and J. F. de Ia Mora. 2000. Kinetics of small ion evaporation from the charge and mass distribution of multiply charged clusters in electrosprays. /. Mass Spectrom. 35:790-803. Han, R. J. and J. W. Gentry. 1993a. Field and combined diffusional and field charging of fibrous aerosols. Aerosol Sci. Technol 18:165-179. Han, R. J. and J. W. Gentry. 1993b. Unipolar diffusional charging of fibrous aerosols—Theory and experiment. /. Aerosol Sci. 24:211-226. Han, R. I, O. R. Moss, and B. A. Wong. 1994. Airborne fiber separation by electrophoresis and dielectrophoresis—Theory and design considerations. Aerosol Sci. Technol. 21:241-258. Helsper, C, H. Fissan, A. Kapadia, and B. Y. H. Liu. 1982. Data inversion by simplex minimization for the electrical aerosol analyzer. Aerosol Sci. Technol. 1:135-146. Hewitt, G. W. 1957. The charging of small particles for electrostatic precipitation. Trans. Am. Inst. Elect. Engr. 76:300-306. Hoppel, W. A. and G. M. Frick. 1986. Ion attachment coefficients and the steady state charge distribution on aerosols in a bipolar ion environment. Aerosol Sci. Technol. 5:1-21. Hoppel, W. A. and G. M. Frick. 1990. The nonequilibrium character of the aerosol charge distributions produced by neutralizers. Aerosol Sci. Technol. 12:471-496. Karasek, F. W. 1970. The plasma chromatograph. Res. Dev. 21:34. Knutson, E. O. and K. T. Whitby. 1975. Aerosol Classification by electric mobility: Apparatus, theory, and applications. /. Aerosol Sci. 6:443-451. Kousaka, Y., Y. Endo, H. Ichitsubo, and M. Alonso. 1996. Orientation specific dynamic shape factors for doublets and triplets of spheres in the transition regime. Aerosol Sci. Technol. 24:36-44. Laframboise, J. G. and J.-S. Chang. 1977. Theory of charge deposition on charged aerosol particles of arbitrary shape. /. Aerosol Sci. 8:331-338. Langevin, P. 1903. L'lonization des gaz. Ann. Chim. Phys. 28:289-384. Lawson, C. L. and R. S. Hansen. 1974. Solving Least-Squares Problems. Englewood Cliffs, NJ: PrenticeHall. Lipowicz, P. J. and H. C. Yeh. 1989. Fiber dielectrophoresis. Aerosol Sci. Technol. 11:206-212. Liu, B. Y. H. and D. Y. H. Pui. 1974. Equilibrium bipolar charge distribution of aerosols. /. Colloid Interface Sci. 49:305-312. Liu, B. Y. H. and D. Y. H. Pui. 1977. On unipolar diffusion charging of aerosols in the continuum regime. /. Colloid Interface Sci. 58:142-149. Liu, B. Y. H., K. T. Whitby, and H. S. Yu. 1967. Electrostatic sampler for light and electron microscopy. Rev. Sci. Instrum. 38:100-102. Markowski, G. R. 1987. Improving Twomey algorithm for inversion of aerosol measurement data. Aerosol Sci. Technol. 7:127-141. Matter, D., M. Mohr, W Fendel, A. Schmidt-Ott, and H. Burtscher. 1995. Multiple wavelength aerosol photoemission by excimer lamps. /. Aerosol Sci. 26:1101-1115. McMurry, P. H., H. Takano, and G. R. Anderson. 1983. Study of the ammonia (gas)-sulfuric acid (aerosol) reaction rate. Environ. Sci. Technol. 17:347. Mirme, A. 1994. Electrical Aerosol Spectrometry. Ph.D. thesis, Universitatis Tartuensis, Tartu, Estonia. Morrow, P. E. and T. T. Mercer. 1964. A point-to-plane electrostatic precipitator for particle size sampling. AIHAJ 25:8-14. Nolan, J. J. 1926. The characteristics of the ionization produced by spraying water. Phil. Mag. 1:417428. Pui, D. Y. H. and B. Y. H. Liu. 1988. Advances in instrumentation for atmospheric aerosol measurement. Physica Scripta 37:252-269. Rogak, S. N., U. Baltensperger, and R. C. Flagan. 1991. Measurement of mass transfer to agglomerate aerosols. Aerosol Sci. Technol. 14:447-458. Rogak, S. N. and R. C. Flagan. 1992. Bipolar diffusion charging of spheres and agglomerate aerosol particles. /. Aerosol Sci. 23:693-710. Rogak, S. N., R. C. Flagan, and H. V. Nguyen. 1993. The mobility and structure of aerosol agglomerates. Aerosol Sci. Technol. 18:25^7.

Rohrmann, H. 1923. Methode sur Messung der Grosse von Schwebelteilchen. Z. Phys. 17:253-265. Rossell-Llompart, J., I. G. Loscertales, D. Bingham, and J. F. de Ia Mora. 1996. Sizing nanoparticles and ions with a short differential mobility analyzer. / Aerosol ScL 27:695-719. Russell, L. M., R. C. Flagan, and J. H. Seinfeld. 1995. Asymmetric instrument response resulting from mixing effects in accelerated DMA-CPC measurements. Aerosol ScL Technol 23:491-509. Russell, L. M., M. R. Stolzenburg, S. H. Zhang, R. Caldow, R. C. Flagan, and J. H. Seinfeld. 1996. Radially classified aerosol detector for aircraft based submicron aerosol measurements. /. Atmos. Oceanic Technol 13:598-609. Schmidt-Ott, A. 1988. In situ measurements of the fractal dimensionality of ultrafine aerosol particles. Appl Phys. Lett. 52:954-956. Schmidt-Ott, A., P. Schurtenberger, and H. C. Siegmann. 1980. Enormous yield of photoelectrons from small particles. Phys. Rev. Lett. 45:1284-1287. Stolzenburg, M. R. 1988. An ultrafine aerosol size distribution measuring system. Ph.D. Thesis. University of Minnesota, Minneapolis MN. TenBrink, H. M., A. Plomp, H. Spoelstra, and J. F. van de Vate. 1983. A high resolution electrical mobility aerosol spectrometer (MAS). /. Aerosol Sci. 14:589-597. Tolman, R. C, L. H. Reyerson, A. P. Brooks, and H. D. Smith. 1919. An electrical precipitator for analyzing smokes./ Am. Chem. Soc. 41:587-589. Twomey, S. 1975. Comparison of constrained linear inversion and an iterative nonlinear algorithm applied to the indirect estimation of particle size distributions. /. Comput. Phys. 18:188-200. Wang, S. C. and R. C. Flagan. 1990. Scanning electrical mobility spectrometer. Aerosol Sci. Technol. 13:230-240. Whitby, K. T. and W. E. Clark. 1966. Electrical aerosol particle counting and size distribution measuring system for the 0.015 to 1 [i size range. Tellus 18:573-586. White, H. J. 1951. AIEE Trans. 70:1186-1191. Wiedensohler, A. 1988. An approximation of the bipolar charge distribution for particles in the submicron size range. /. Aerosol Sci. 19:387-389. Winklmayr, W, G. P. Reischl, A. O. Lindner, and A. Berner. 1991. A new electromobility spectrometer for the measurement of aerosol size distributions in the size range from 1 to 1000 nm. /. Aerosol Sci. 22:289-296. Wolfenbarger, J. K. and J. H. Seinfeld. 1990. Inversion of aerosol size distribution data. /. Aerosol Sci. 21:227-247. Yeh, H. C. 1976. A theoretical study of electrical discharging of self-charging aerosols. /. Aerosol Sci. 7:343-349. Yeh, H. C, G. J. Newton, and S. V. Teague. 1978. Charge distribution on plutonium-containing aerosols produced in mixed-oxide reactor fuel fabrication and the laboratory. Health Phys. 35:500-503. Yun, C. M., Y. Otani, and H. Emi. 1997. Development of unipolar ion generator—Separation of ions in axial direction of flow. Aerosol Sci. Technol. 26:389-397. Zhang, S. H., Y. Akutsu, L. M. Russell, and R. C. Flagan. 1995. Radial differential mobility analyzer. Aerosol Sci. Technol. 23:357-372. Zhang, S. H. and R. C. Flagan. 1996. Resolution of the radial differential mobility analyzer for ultrafine particles. /. Aerosol ScL 27:1179-1200.

Both diffusion and condensation devices are used for the measurement of ultrafine aerosol particles. A diffusion battery is often used with a CNC in a sampling train to determine both the concentration and particle size distribution. This chapter describes the operating principle, theory, design and applications, and data analyses of the CNCs, diffusion batteries, and diffusion denuders. CONDENSATION THEORY A CNC is an instrument for detecting ultrafine particles. The principle upon which the condensation technique is based involves three processes: (1) supersaturation of water or other working fluids, (2) growth of particles by condensation of vapors, and (3) detection of particles. Supersaturation

Ultrafine particles including Aitken nuclei are too small to be detected by an optical microscope, which has a detection limit of about 0.1 urn. In a CNC, the aerosol is first saturated with a vapor of water or alcohol and subsequently cooled to induce a supersaturated condition, that is, the vapor pressure is higher than the saturation pressure at a given temperature (see Chapter 5). Vapor molecules condense on particles to form liquid droplets of larger sizes. For bulk liquids, the vapor pressure at the saturation or equilibrium condition (ps in Pa) as a function of temperature (T, in degrees Kelvin) is shown by the following equation: (19-1) For some compounds, a slightly different equation gives the best fit to the data: (19-2) Table 19-1 lists the constants (a, b, and c) for some working fluids used in the CNCs, including water and alcohols. The saturation vapor pressure (p$) is defined as the equilibrium partial vapor pressure for a flat liquid surface. For liquid droplets in an aerosol system, the partial pressure required to maintain the equilibrium is greater than that for a flat surface. This is called the Kelvin effect. The relationship between the saturation vapor pressure (pd) on the droplet surface and particle diameter (dp) can be expressed as the following function (see, for example, Hinds, 1999): (19-3)

TABLE 19-1. Vapor Pressures of CNC Working Fluids

Fluid Water Methanol Ethanol N-butanol Dibutyl phthalate

Equation

a

b

c

19-1 19-1 19-1 19-2 19-1

10.23 10.00 10.16

1750 1233 1554 46.78 5099

38 45 50.2 11.26 109

18.39

Saturation Ratio

Pure Water

Aqueous Solution

Particle Diameter, (\xm) Fig. 19-1. Saturated ratio of water as a function of droplet size at 293 K [200C]. The solid line is the pure water droplet, and the dashed line is the solution particle.

where 7is the surface tension, v is the molar volume of the liquid, and R is the gas constant. Figure 19-1 plots the saturation ratio at equilibrium (SR = pjps) as a function of the particle size for a water droplet. As the diameter becomes smaller, the saturation ratio needed to induce condensation becomes larger. At a given level of saturation ratio in a CNC (SR between 1.5 and 3), the Kelvin diameter, d*, as defined by Eq. 19-3 by setting d* = dp, is the minimum size to initiate the condensation. Particles larger than the Kelvin diameter will grow, while those smaller than d* will be too small for vapor condensation. Note that Eq. 19-3 is derived for vapor condensed on liquid droplets of the same material or on insoluble particles with wettable surface properties for the working fluid. For aerosol systems consisting of nonvolatile solute particles and a volatile solvent, such as hygroscopic particles in existence with water vapor, the saturation vapor pressure is lowered because there are fewer solvent molecules in the surface layers than in the case of a pure solvent. For an ideal solution, the reduction of vapor pressure should be proportional to concentration (Raoult's law). In a solution droplet, the vapor pressure can be expressed in a form similar to the Kelvin equation (Eq. 19-3) (Tang, 1976; Friedlander, 1977): (19-4) where 5, vu and mf are the activity coefficients, partial molar volume of the solvent, and mole fraction of the solute, respectively. Thus, there are two competing effects for solution droplets. The Kelvin effect tends to increase the vapor pressure, whereas the solute tends to reduce it. For an ideal solution (8 = 1), Eq. 19^4 can be approximated in the following form (Friedlander, 1977):

(19-5) where n2 is the number of moles of the solute. Figure 19-1 compares vapor pressure for a pure water droplet to that of a solution particle, showing that the latter may be stable even at vapor pressures below saturation. In a CNC using water as the working fluid, hygroscopic particles, such as sodium chlorides and other salts, may have lower detection limits because they are soluble in water. If a particle carries an electrostatic charge, the vapor pressure at the surface is also reduced, as well as the saturation ratio required for the condensation (Scheibel and Porstendorfer, 1986):

(19-6)

where q is the electrostatic charge and e is the dielectric constant of the droplet. Droplet Growth

The droplet growth by condensation can be calculated by the following equation (Sutugin and Fuchs, 1965; Zhang and Liu, 1990): (19-7) where Da is the diffusion coefficient of the condensing vapor; p and T are the vapor pressure and temperature in the surrounding gas far away from the particle, respectively; and v is the molar volume. The Fuchs correction, f(Kn), is important for particles smaller than 0.1 urn: (19-8) where Kn (=2A/dp) is the Knudsen number and X is the mean free path of the gas medium. Furthermore, pd and Td are the vapor pressure and temperature at the surface, respectively. For the Kelvin effect, pd can be calculated from Eq. 19-3. The temperature on the droplet surface, r d , includes the effect of the droplet temperature increase due to the latent heat of condensation and can be estimated as follows (Hinds, 1999): (19-9) where L is the latent heat of the working fluid and kv is the thermal conductivity of the carrier gas. Equation 19-7 describes the growth of a homogeneous liquid droplet or the growth of insoluble particles with wettable surfaces. Friedlander (1977) has described the growth of soluble particles. Droplet Detection In a CNC, particles grow to a near uniform size between 5 and 15jim, large enough to be detected by optical means. Aitken (1888) used an optical microscope to count particles col-

lected on slides for determining the concentration. Later, Pollak and others (Pollak and Daly, 1958; Jaenicke and Kanter, 1976) mounted a camera on the CNC to photograph the particles. The Aitken and photographic CNCs are sometimes referred to as the absolute CNCs because the number concentration is determined by direct counting. However, these CNCs are manually operated, and they have been used primarily as calibration references for newer types of CNCs in which the particles are detected by photoelectrical means. Early photoelectric CNCs, such as the Pollak counter (Pollak and Metnieks, 1958), use the lightextinction method, whereas recent models use light-scattering techniques. The detection system consists of a light source and a photodetector. In a light-extinction device, the transmission of light is detected; whereas in newer CNCs, the scattered light from the aerosol is detected in the photodetector. The signals from individual particles can be identified and counted, or the intensity of the scattered light is used as an indication of the particle concentration. The response curve of a photoelectric CNC must be calibrated. Early calibrations were performed with the photographic or Aitken CNCs. In recent studies, positively charged, monodisperse aerosol particles generated from an electrostatic classifier have been used; the concentration of a charged aerosol is determined with an electrometer. The aerosol concentration of the CNC is number concentration, conventionally expressed as particles/cm3. CONDENSATION NUCLEI COUNTERS CNCs can be classified by the technique used to activate condensation and growth of particles or by the aerosol detection method. Three techniques have been used to induce supersaturation conditions: (1) adiabatic expansion, (2) conductive cooling, and (3) mixing of hot and cold air streams. The aerosol detection systems include photography, light extinction, and light scattering as described in the section on theory. Both photographic and light-extinction methods are associated exclusively with CNCs having expansion chambers, whereas the lightscattering detection method is incorporated into more recent CNCs—with condensation provided by thermal systems. This section describes the CNCs according to the supersaturation technique used, with emphasis on the commercially available instruments (also listed in Table 19-2). TABLE 19-2. Commercial CNCs and Diffusion Batteries Company CNCs BGI

Model

Type

Type of Detection

Detection Limit (nm)

Pollak Counter"

Light extinction

2.8

Light scattering

3.8

Environment One

Rich 200"

TSI TSI TSI TSI TSI Diffusion batteries INT INT

3007" 3010 3022A 3025A 3760A/3762

Expansion/ overpressure Expansion/ overpressure Expansion/ underpressure Conductive Conductive Conductive Conductive Conductive

02-2000 02-1900

In-line flow Parallel flow

Gardner

"No longer commercially available. ''Portable instrument.

Light scattering Light Light Light Light Light

scattering scattering scattering scattering scattering

10 10 7 3 10

Description of Condensation Nuclei Counter

Three types of CNCs based on the technique used to induce supersaturation conditions are described here. Expansion-Type CNCs. An expansion-type CNC consists of a humidifier, an expansion chamber, and a detector. Water is the working fluid for this type of CNC. The aerosol stream is first humidified to reach the saturation of water vapor at room temperature, and then the aerosol in an expansion chamber is suddenly cooled by volume expansion or pressure release. At lower temperatures the chamber becomes supersaturated with water vapor, which then condenses on particles. The expansion-type CNC was first designed by Aitken (1888) and modified later by others. Several commercial instruments are based on these modifications dating back to the 1950s and 1960s, including the Pollak counter (Pollak and Metnieks, 1958) and the Rich counters (Rich, 1955, 1961), the Environment One and Gardner counters (Hogan and Gardner, 1968), and the GE counters (Skala, 1963; Haberl, 1979). EXAMPLE 19-1 An expansion-type CNC with water as the working fluid has a pressure expansion ratio of 1.21. If the CNC is operated at a room temperature of 293 K [200C] and 98.6 kPa pressure, what is the minimum particle diameter that can grow in the CNC (Kelvin diameter)? Answer. The following are steps to solve the problem: 1. Estimate the temperature after expansion, Th from Eq. 19-13:

2. Calculate the saturated water pressure, ps(T{) and/?s(Tf), from Eq. 19-1:

Similarly,ps(rf) = 850Pa. 3. Estimate the supersaturation ratio using Eq. 19-25:

4. Calculate the Kelvin diameter using Eq. 19-3 and surface tension of 75 dyne/cm for water:

In the original Aitken counter, the air volume was expanded by using a piston; in other models, the pressure inside the expansion chamber was reduced to cause the expansion. In an overpressure system, the chamber was pressurized by pumping air into the chamber using a hand pump (Pollak counter; Pollak and Metnieks, 1958) or bellows (Gardner counter;

Hogan and Gardner, 1968), and then a valve was opened to release the chamber pressure to the ambient value. In the Environment One Model 200 CNC (Rich, 1961; Skala, 1963), underpressure systems were used in which air in the chamber under ambient pressure was released to an evacuated chamber. The volume or pressure expansion ratios as denned in the following equations were determined by the geometry of the expansion chamber: p pressure expansion ratio = -^Pt Vf volume expansion ratio = —

(19-10)

(19-11)

where P{ and P{ are pressures at the beginning and the end stage of expansion and v{ and vf denote volumes at the beginning and end stage of the expansion. The expansion ratios in a CNC determine the supersaturation ratio of vapors. Usually the dry adiabatic expansion of ideal gases is assumed for air and water vapor because, in the short time of expansion, there is little time for heat transfer. Under these assumptions, the two expansion ratios are related: (19-12) where / i s the specific heat ratio and is 1.4 for air. Because both the expansion ratios are greater than 1, Eq. 19-12 shows that the pressure expansion ratio is larger than the volume expansion ratio. The pressure expansion ratio of CNCs ranges from 1.1 to 1.5 (Miller and Bodhaine, 1982). After expansion, the chamber temperature at the onset of condensation (T{) can be related to the initial temperature (TJ) before expansion and the expansion ratios (Miller and Bodhaine, 1982): (19-13) At this point, the water vapor in the system becomes supersaturated, and the saturation ratio is defined as the ratio of the partial pressure of water,p(T{), to the saturated water pressure, Ps(Tf):

(19-14) Assuming that the air is initially saturated with water, the partial pressure of water after expansion can then be calculated similar to Eq. 19-12: (19-15) Substituting Eq. 19-15 into Eq. 19-14, we obtain the saturation ratio in terms of saturated vapor pressures and the pressure expansion ratio: (19-16)

An expansion-type CNC is normally operated in a cyclic fashion. The chamber is first filled with aerosol and vapor, the expansion occurs, the aerosol particles grow, and the concentration is determined. In photographic CNCs (Pollak and Daly, 1958; Jaenicke and Kanter, 1976; Scheibel and Porstendorfer, 1986), additional steps are necessary to develop photographs and to count the particles for concentration measurements. Some of the early CNCs, including the Pollak counter and other photographic CNCs, used an overpressure system with slow manual operation. They were primary standards for other CNCs because the counting method was considered a direct measurement of particle concentration. The Environment One and Gardner CNCs (Skala, 1963) use underpressure systems, and the expansion cycles are controlled by rotary valves. The sampling rates are 1 to 5 cycles per second, providing near continuous operation. Figure 19-2 shows a schematic diagram of a Pollak counter (Pollak and Metnieks, 1958). The counter includes a vertical expansion chamber or fog-tube (0.025 m inner diameter and 0.60 m long) with a water-saturated ceramic lining, a light source at the top, a photodetector at the bottom, and a hand pump. Thus, the fog-tube combines the functions of humidification and expansion and also provides the light path for photoelectrical detection. The aerosol is first allowed to flush through the chamber, and then the inlet and the exit to the chamber are closed. Next, the fog-tube is pressurized to 21.3 kPa [160 mm Hg] over the ambient pressure by operating the hand pump. After a delay to allow the air to become saturated, the initial

Condensation Nuclei Counter Light Source Condenser Lens Aerosol Out Ceramic Lining Manual Pump

Pressure

Aerosol In Window Photocell

Signal Out Fig. 19-2. Schematic of the Pollak counter.

light intensity is taken. Then the expansion valve is opened for air to expand to the ambient pressure. A second reading is taken, and the ratio of the final reading to the initial reading is the light transmission. It is calibrated against the aerosol concentration measured simultaneously from a photographic CNC as listed in the tables of Pollak and Metnieks (1958).These tables have been checked more recently using a monodisperse aerosol generated from electrical aerosol analyzers (Liu et al., 1975; Sinclair, 1984). Conductive Cooling-Type CNCs. A major disadvantage of the expansion-type CNC is that the flow is cyclic and therefore incompatible with the requirement of steady-state flow when the CNC is used to measure concentration from a diffusion battery or electrical mobility analyzer. Continuous-flow CNCs are based on the principle of thermal cooling to induce the supersaturation of a working fluid with a steady flow in the system. Sinclair and Hoopes (1975b), Bricard et al. (1976), and Agarwal and Sem (1980) first designed the conductive cooling-type CNC. As shown in Figure 19-3, the conductive cooling CNC consists of a saturator, condenser, and particle detector. The aerosol passes through an alcohol reservoir kept at an elevated temperature. The residence time is such that the aerosol will be saturated with the working fluid at the set temperature. Then the aerosol enters a condenser tube, which is kept at a lower temperature by cooling the wall. In the condenser, gas cooling takes place by conduction and convection, which leads to supersaturation in the cooled aerosol stream. Particles then grow by condensation to form droplets. The concentrations of alcohol vapor and temperature are functions of the location in the condenser tube and are determined by numerically solving the heat and mass transfer equations (Ahn and Liu, 1990; Zhang and Liu, 1990). Figure 19-3 shows a continuous-flow CNC (Model 3022, TSI*) described by Agarwal and Sem (1980). The saturator containing butanol is kept at 35°C, and the condenser tube is maintained at 100C. The aerosol flow rate is 5 x 10"6In3S"1 [300mL/min]. Under normal operating conditions at ambient pressure, the saturation ratio is calculated as a function of location as shown in Figure 19-3 (Zhang and Liu, 1990). The highest saturation ratio is at the center of the tube mainly because of the high vapor concentration. The vapor concentration and saturation ratio decrease near the tube wall. Because of the distribution of the vapor concentration, not all particles are activated for growth, resulting in a decreased counting efficiency for particles smaller than 10 nm (Su et al., 1990; McDermott et al., 1991; Kesten et al., 1991). Particles that grow to a size of about 12 urn are detected with a light-scattering system. The photodetector is operated as a single-particle counter for any concentration lower than 109 particles/m3 [1000 particles/cm3] and as a photometer for higher concentrations. Decreasing counting efficiency for nanometer-sized particles can be attributed to diffusional particle loss in the flow passage and incomplete activation due to inhomogeneous vapor concentration distribution in the condenser (Egilmez and Davies, 1984). To increase the counting efficiency of nanometer-sized particles, Wilson et al. (1983) modified the condenser tube so that for stratospheric aerosol measurements the aerosol is introduced in the center surrounded with clean sheath air saturated with alcohol for a low-pressure CNC. Based on similar concepts, Stolzenburg and McMurry (1991) designed continuous-flow CNCs (TSI) with lower detection limits (over 70% at a particle size of 3nm) as shown in Figure 19-4. Mixing-Type CNCs. The mixing-type CNC combines an aerosol stream at room temperature and a hot air stream with a saturated vapor of dibutyphthalate or dioctyl sebacate with a high boiling point. The CNC consists of a saturator with a reservoir of working fluid, a mixing chamber, and a particle detector. The aerosol flow is mixed rapidly with the hot stream in a nozzle assembly. The resultant mixed air is cooled down adiabatically. The temperature and vapor concentration can then be estimated from the initial temperatures and flow rates * See Appendix I for full manufacturer addresses referenced to the italicized three-letter codes.

Ultrafine Condensation Particle Counter Filter

Filter Press Flow Meter

Valve

Optics

Dilution Air (Optional) Condenser (Cooled)

Bypass Flow (Optional)

Saturator (Heated)

Inlet Liquid Reservoir Fig. 19-3. Schematic of a TSI model 3022 CNC

AP

Filter

Vacuum Pump

Fixed Orifice

Filter

Flowmeter Optics Cooled Condenser

Heated Saturator

Aerosol Flow

LiquidSoaked Felt I Sheath i Ar

Bypass Flow

IHEPA Filter Inlet Flow

Variable Orifice

Dryer Filter

Variable Orifice

Filter Make-up Air

3-way Valve

Fig. 19-^1. Schematic of a TSI model 3025 CNC.

of the two air streams. Vapor condenses on particles at a steady-state continuous flow. The particle size can be estimated from the operating conditions and the number concentration (Okuyama et al., 1984). This mixing device was first used in Russia (Sutugin and Fuchs, 1965) primarily as an ultrafine particle generator called a particle size magnifier. It has been used as a CNC by incorporating a particle detection system (Okuyama et al., 1984). This type of CNC has two advantages over the thermal-cooling CNC: (1) higher aerosol flow rates (0.83 to 3.33 x 10"5InV1 [0.5 to 2L/min]) can be used, and (2) minimum diffusional loss of aerosol particles occurs because of the short aerosol delivery distance since the aerosol stream does not pass through the saturator. THEORIES OF THE DIFFUSION MEASUREMENT TECHNIQUE These mathematical expressions relate collection or penetration of vapors and particles through cylindrical and rectangular tubes and screens. These expressions can be used to calculate diffusion coefficients or particle sizes from experimental measurements through diffusion samplers. These mathematical expressions were derived from the convective diffusion equation describing the concentration (c) in various geometries and flow profiles: (19-17) where D is the particle diffusion coefficient, r is the radial direction, z is the axial direction, and u(r) is the velocity profile in the axial direction. Several assumptions were made in the derivation of Eq. 19-17: (1) the concentration is in a steady-state condition; (2) the flow field in the device is a fully developed laminar flow; (3) the effect of diffusion in the direction of flow is neglected; (4) no production or reaction of the gas or aerosol occurs in the device; and (5) the sticking coefficient of the gas or particle is 100% on the collection surface (walls or screens). Diffusion devices can be classified as tube (channel) type or screen type, and each type has different flow profiles. Solutions to Eq. 19-17 for different types of diffusion samplers are summarized in the following section. Tube (Channel) Type

Penetration (P) of particles or gases due to the diffusion mechanism has been derived for channels of different geometries, including cylindrical, rectangular, disk, and annular shapes. The general solution of Eq. 19-15 can be expressed as a series of exponential functions: (19-18) where \i is the dimensionless argument relating the diffusion coefficient, channel length, and flow rate, and pn is the eigenvalue. Convergence of Eq. 19-18 depends on the magnitude of \i. For larger values of fi (low penetration), few terms are needed for convergence, whereas at smaller values of JJL (high penetration), many terms are required. For low penetration, alternative equations have been derived. Specific equations for each channel type are described below. Cylindrical Tubes, Penetration through a circular tube (Fig. 19-5) at a flow rate, Q, for particles with a diffusion coefficient of D has been derived by several investigators as a function of the parameter jn (Gormley and Kennedy, 1949; Sideman et al., 1965; Davis and Parkins, 1970; Tan and Hsu, 1971; Lekhtmakher, 1971; Bowen et al., 1976).

Flow

Flow Flow Flow

Flow

Flow

Flow Fig. 19-5. Schematics of different shapes of tubes.

(19-19) The numerical solution obtained by Bowen et al. (1976) for JLL between 1 x 10 7 and 1 is the most accurate. Results obtained by Davis and Parkins (1970), Tan and Hsu (1971), Sideman et al. (1965), and Lekhtmakher (1971) agree substantially with those of Bowen et al. (1976). By comparison of various expressions, the following analytical solutions have the accuracy of four significant figures as compared to Bowen's result in the entire range of ji:

(19-20) (19-21) The formula for the small values of \i is taken from Gormley and Kennedy (1949), Newman (1969), and Ingham (1975). Rectangular Channels and Parallel Circular Plates. Particle penetration through a parallel narrow rectangular tube (Fig. 19-5) of width W and separation H, where H « W, has been derived as a function of fi defined as SDLW/3QH (Gormley, cited by Nolan and Nolan, 1938; DeMarcus and Thomas, 1952; Sideman et al., 1965; Mercer and Mercer, 1970;Tan and Thomas, 1972; Bowen et al., 1976). The same equation can be used to calculate penetration through parallel circular plates (Fig. 19-5), where the diffusion parameter ji is defined as 8nD(r22 r^2)/3QH, where r2 and rx are outer and inner radii of the disks (Mercer and Mercer, 1970). Tan and Thomas (1972) and Bowen et al. (1976) gave the most accurate solution; other investigators agreed substantially with their results. Using the first four terms of the solution given

by Tan and Thomas, the penetration can be calculated with the accuracy of four significant numbers as compared with the numerical solution of Bowen et al. (1976):

(19-22) (19-23) Ingham (1976) gave the formula for the small values of fi. Kennedy (quoted by Nolan and Kennedy, 1953) derived a similar formula with different coefficients, but the results are different by only 1%. Annular Tubes. The theoretical calculation of diffusional losses through an annular tube (Fig. 19-6) has been derived by solving Eq. 19-17 using a fully developed flow in the annular tube (Winiwarter, 1989). The penetration is then a function of the inner to outer radius of the denuder, k, and the diffusional parameter, fi = nDL(d2 + di)/2Q(d2 - di)]:

(19-24) where d2 and dx are outer and inner diameters, respectively. Values of An(Jc) and pn(k) for selected values of k are given in Table 19-3. For k = 0 (cylindrical tube) and k = 1 (parallel plate), Eqs. 19-20 and 19-22 are the asymptotic solutions (Kerouanton et al., 1996). A numerical solution of the diffusional equation in an annular tube has been determined by solving the velocity profile instead of using the fully developed flow (Fan et al., 1996). From the numerical solution, the fitted equation is (19-25) where Pe (= um d2 IADL) is the Peclet number and um is the mean velocity. These equations are in general agreement with experimental data (Possanzini et al., 1983). Screen Ttype

The aerosol penetration through a stack of fine-mesh screens with circular fibers of uniform diameter and arrangement has been derived (Cheng and Yeh, 1980; Cheng et al., 1980; Yeh

Coating on Walls

Gases and Particles

Stripped Gas and Particles

Gases Deposited on Walls

Stand Off Fig. 19-6. Schematic diagram of an annular denuder. (From Stevens [1986] with permission of Lewis Publishers.)

TABLE 19-3. A n and A. for Selected Values of A in Eq. 19-24 k Po P1 p2 p3 A0 A1 A2 A3

0.05

0.1

0.25

0.5

0.75

0.85

0.95

3.493 7.906 12.31 16.71 0.879 0.036 0.044 0.006

3.610 8.086 12.56 17.02 0.889 0.026 0.046 0.005

3.765 8.315 12.87 17.42 0.900 0.012 0.05 0.002

3.851 8.436 13.03 17.63 0.908 0.003 0.052 0.001

3.878 8.473 13.08 17.70 0.910 0.001 0.053 0.000

3.882 8.479 13.09 17.71 0.911 0.000 0.053 0.000

3.884 8.481 13.09 17.71 0.911 0.000 0.053 0.000

et al., 1982; Cheng et al., 1991). A stack of fine-mesh screens simulates a fan model filter (Kirsch and Fuchs, 1968; Kirsch and Stechkina, 1978) in terms of flow resistance and aerosol deposition characteristics (Cheng et al., 1985). The theoretical penetration was derived based on the aerosol filtration in the fan model filter: (19-26) (19-27) where n is the number of screens, d{ is the fiber diameter, h is the thickness of a single screen, a is the solid volume fraction of the screen, and k is the hydrodynamic factor of the screen: (19-28) where R = dvld{ is the interception parameter and Pe is the Peclet number: (19-29) In addition, U is the velocity entering the screen. Equation 19-26 includes the diffusional and interceptional losses of aerosol on screens and is valid for particles up to 1 urn in size (Cheng et al., 1985). For particles larger than 1 urn, inertial impaction becomes an important mechanism, and Eq. 19-26 may not be adequate. For smaller particles (dp < 0.01 urn), diffusional deposition is the dominant mechanism, and Eq. 19-26 is simplified to (19-30) This equation is valid for a Reynolds number smaller than 1 (Cheng et al., 1992). DIFFUSION DENUDERS Gas or vapor molecules diffuse rapidly to the wall of a diffusion sampler and adsorb onto the wall coated with material suitable for collecting the gas. Diffusion tubes have been used to measure diffusion coefficients of several gases in the air (Thomas, 1955; Fish and Durham,

1971; Ferm, 1979; Durham et al., 1987). Since 1980, diffusion denuders followed by a filter pack have been developed to sample atmospheric nitric acid vapors and nitrate particulate aerosols. Using this sampling technique, called the denuder difference method, one can separate gaseous species, such as HNO3 and NH3, from particulate nitrates and thus minimize sampling artifacts due to the presence of these gases (Stevens et al., 1978; Appel et al., 1981; Shaw et al., 1982; Forrest et al., 1982; Ferm, 1986; Stevens, 1986). Diffusion denuders are also used to monitor vapors, such as formaldehyde, chlorinated organics, and tetra alkyl lead, in the ambient air or work environments (Johnson et al., 1985; Cecchini et al., 1985; Febo et al., 1986). Some personal samplers have also been developed for industrial hygiene use (DeSantis and Perrino, 1986; Gunderson and Anderson, 1987). Description of Diffusion Denuders Two types of diffusion denuders have been designed: the cylindrical tube and annular tube. Cylindrical Denuders. In cylindrical denuders, a single cylindrical glass or Teflon tube is often used for collecting gases or vapors. The diameter and length of the tube and the sampling flow rate are designed to have greater than 99% collection efficiency. For example, a glass tube of 3 mm inner diameter (ID) and 0.35 m [35 cm] long would have over 99% efficiency for ammonia (Da = 2.47 x 10"5m2s"1) at 5 x 10"5 m3 s"1 [3 L/min] (Ferm, 1979). For higher sampling flow rates, parallel tube assemblies have been designed (Stevens et al., 1978; Forrest et al., 1982), consisting of 16 glass tubes 5 mm ID and 0.30 m [30 cm] long. The sampling flow rate was 8.33 x 10"4In3S"1 [50L/min], and the collection efficiency for ammonia was over 99% (Stevens et al., 1978). A two-stage diffusion denuder consisting of 212 glass honeycomb tubes, each with a height of 25.4 mm [2.54 cm] and an ID of 2 mm [0.2 cm] has been designed to removes gases for collection of ambient particles (Sioutas et al., 1994). Penetration through the tube-type denuders can be estimated by taking the first term of Eq. 19-20 only: (19-31) This simplified equation is accurate at higher values of JJL (>0.4) and at a lower penetration (P < 0.190). The error of the estimated penetration from Eq. 19-31 increases with the decreasing value of m (0.25% error for ft = 0.2 and P = 0.395, and -2.7% for \i = 0.1 and P = 0.628). Equation 19-31 is applicable for the fully developed laminar flow region in the tube. The flow Reynolds number in the tube should be less than 2300 for laminar flow: (19-32) In the entrance of the tube, the flow is in a transition region from plug flow to developed flow. The length of entrance, L6, is defined by the following equation and should be minimized: (19-33) Annular Denuders. Higher sampling flow rates are desirable, especially for sampling trains consisting of denuders and filters or dichotomous samplers (Shaw et al., 1982). An annular tube denuder has been designed for this purpose (Possanzini et al., 1983). This denuder consists of two coaxial cylinders with the inner one sealed at both ends so that air is forced to pass through the annular space (Fig. 19-6). The collection efficiency of the annular tube can be estimated from Eq. 19-24 for lower Reynolds numbers (Re < 2300) defined as:

(19-34) Comparing the performance of the cylindrical and annular denuders in removing a gas from an air stream, the flow rate of a typical annular denuder (d2 = 33 mm and dx = 30 mm) can be determined by equating Eqs. 19-24 and 19-31. It can be shown that (19-35) This relationship shows that for equal sampling time and tube length, the annular denuder can operate at 30 times the flow rate of the cylindrical denuder and still have the same removal efficiency. Also, the Reynolds number still indicates laminar flow conditions for the annular tube system. A multichannel annular diffusion denuder has been tested and used in ambient air sampling (Johnson et al., 1985). Compact Coil Denuder. Pui et al. (1990) designed a compact coil denuder consisting of a 10 mm [1.0 cm] ID and 0.95 m [95 cm] long (L) glass tube bent into a three-turn helical coil with a 0.1m [10cm] diameter (Fig. 19-7). The heat and mass transfer rates to the tube wall in a curved tube are much higher than those in a straight tube operated at the same conditions (Mori and Nakayama, 1967a,b). This denuder is operated at 1.67 x lO^ms"1 [lOL/min] (Q) with a Reynolds number of 1400. The penetration through the denuder can be expressed as (Pui et al., 1990) (19-36)

(19-37) where Sh is the Sherwood number; De is the Dean number, De = (Ref/[coi\ diameter/tube diameter]172), the flow Reynolds number divided by the square root of the curvature; and S

Fig. 19-7. Schematic of a compact coil denuder.

is the thickness ratio of the concentration boundary layer to the momentum boundary layer. This unit has 99.3% collection efficiency for SO2 with less than 6% particle losses for particles between 0.015 to 2.5 urn in diameter. It is compact and easy to operate. Transition-Flow Denuder. Both cylindrical and annular denuders are operated under laminar flow conditions, and they are designed to remove all gases of interest from the aerosol stream. In passing through such denuders, particles may evaporate or decompose and increase the concentration of some gases, especially in the case of the decomposition of NH4NO3 into HNO3 and NH3 gases. Biases in sampling due to evaporation of particles can be avoided by collecting only a known fraction of gases in the denuder and then calculating the gas concentration. Durham et al. (1986) designed a transition-flow denuder, which permits a higher sampling flow. The cylindrical denuder has an ID of 9.5 mm with a 60 mm distance of the first active surface to allow for development of a stable flow profile. The denuder section is lined with a 32 mm long nylon sheet. Assuming a complete mixing in the active section, the penetration can be expressed as (19-38)

(19-39) where L is the length of the active surface; Q is the flow rate; r is radius of the pipe; and 8 is the boundary thickness, which is a function of the flow Reynolds number. The penetration must be determined empirically. Operating the denuder at 2.68 x 10"4In3S"1 [16.1L/min] (Re = 2500), Durham et al. (1986) obtained a penetration of 0.911 for HNO3. Scrubber-Type Diffusion Denuders, Most diffusion denuders have a solid coating on the wall to collect gaseous species, and the coating substrates are washed after sampling for analysis. For continuous analysis of gas species, diffusion scrubbers are used, where the absorbent or solvent in the liquid form is continuously flowing along the tube wall, and the analyte can be analyzed in real time (Dasgupta, 1984; Dasgupta et al., 1988). A tubular scrubber is made by inserting a membrane tube into the glass tube to form a jacket between the glass wall and the membrane (Fig. 19-8). Porous membranes, such as polytetrafluoroethylene and polypropylene, allow gases but not particles to permeate and dissolve in a solution that flows continuously through the jacket. The collection characteristics of this type of diffusion scrubber should be similar to the tube diffusion denuder. Another type of diffusion denuder consists of a tube with a membrane tube at the center in which air flows through the annular space while the solvent passes through the membrane tube. Coating Substrates

Absorbent material can be coated onto the tube wall of a denuder to collect the gas of interest from the air stream. Table 19-4 lists substrates for removal of some gases as reported in the literature. Some materials absorb more than one gaseous species. For example, sodium carbonate can absorb acidic gaseous species found in the ambient air, including HCl, HNO2, HNO3, and SO2. How the material is applied to the tube wall depends largely on the nature of the material. Most materials are first dissolved and then applied to the tube wall. Solvents are allowed to evaporate, leaving the absorbent on the glass tube wall. In some cases, the glass denuder wall has been etched by sand blasting the surface to increase the capacity of walls to support the denuding chemical substrate (Possanzini et al., 1983). Absorbent paper

Solution In

Air In

Air In

Solution Out Membrane

Solution In

Air Out

Air Out

To Analytical Instrument

Membrane Solution Out

Fig. 19-8. Schematic of diffusion scrubbers.

TABLE 19-4. Materials for Absorbing Gases in the Diffusion Denuder Coating Material Oxalic acid Oleic acid UH3PO3 K 2 CO 3 Na 2 CO 3 CuSO 4 PbO 2 WO 3 MgO NaF NaOH and guaiacol Bisulfite-triethanolamine Nylon sheet Tenax powder Silica gel ICI

Gas Absorbed NH 3 , aniline SO 3 NH 3 SO2, H2S SO 2 , HCI, HNO 3 , HNO 2 NH 3 SO 2 , H2S NH 3 , HNO 3 HNO 3 HNO 3 NO 2 Formaldehyde SO 2 , HNO 3 Chlorinated organics Aniline Tetra alkyl lead

Reference Ferm (1979), DeSantis and Perrino (1986) Thomas (1955) Stevens et al. (1978) Durham et al. (1978) Forrest et al. (1982) Thomas (1955) Durham et al. (1978) Braman et al. (1982) Stevens et al. (1978) Slanina et al. (1981) Buttini et al. (1987) Cecchini et al. (1985) Durham et al. (1986) Johnson et al. (1985) Gunderson and Anderson (1987) Febo et al. (1986)

impregnated with liquid or solution substrate, such as oleic acid, has been used to line the inside of the denuder wall (Thomas, 1955). A nylon sheet has also been used as a liner (Durham et al., 1986). Anodized aluminum surfaces have been found to be a good absorbing surface for nitric acid. Annular denuders made of anodized aluminum do not need a coating (W. John, personal communication, 1987). Tenax or silica gel powder is more difficult to apply; however, these materials adhere to the glass wall coated with silicon grease (Johnson et al., 1985; Gunderson and Anderson, 1987). Sampling Trains

When sampling ambient or working atmospheres, it is sometimes necessary to collect gas species and particulate materials separately. In this case, a sampling train consisting of diffu-

Pheumatic Flow Controller

Nylon Filter Teflon Filter

Denuder #2

Total Flow Adjuster Pump

Connector Glycerine Coating Denuder#1

Teflon Cyclone 15liter/min Fig. 19-9. An ambient acidic aerosol sampler consisting of a precutter, two annular denuders, and a filter pack. (From Stevens [1986] with permission of Lewis Publishers.)

sion denuders and a filter pack have been used. A more complex system (Fig. 19-9) includes a cyclone preclassifier, two Na2CO3-coated annular denuders, and a filter pack with a Teflon and a nylon filter. This system has been used to collect acidic gases (HNO3, HNO2, SO2, and HCl) separately from nitrate and sulfate particles (Stevens, 1986). The first denuder removes gases quantitatively, whereas the second accounts for the interference from particulate material deposited on the wall assuming that particle deposition on each denuder is the same (Febo et al., 1986). The denuders are placed vertically to avoid particle deposition on the walls by sedimentation. A diffusion scrubber can be connected to an ion chromatograph or other analytical instruments for real-time analysis of gaseous species (Lindgren and Dasgupta, 1988; Dasgupta et al., 1988).

DIFFUSION BATTERIES Diffusion batteries were originally developed to measure the diffusion coefficient of particles less than 0.1 um diameter. They have since been used for determination of particle size distributions by converting the diffusion coefficient to the particle size. Diffusion batteries are one type of only a few instruments that are applicable in measuring ultrafine particles

between 0.1 (xm and about 0.8 nm, corresponding to the size of molecular clusters. In this section, various designs of the instrument, detection of particles, and methods of data analysis are discussed. Description of Diffusion Batteries Several types of diffusion batteries have been designed. Those based on rectangular channels and parallel circular plates are single-stage diffusion batteries. Cylindrical-tube and screen-type diffusion batteries usually have several stages. Rectangular Channel. Rectangular channel diffusion batteries usually consist of many rectangular plates forming parallel channels of equal width. These plates are separated by spacers and glued to a container with an airtight seal. For example, Thomas (1955) designed a diffusion battery (0.1mm wide, 0.127 m high, and 0.473 m long) consisting of 19 parallel channels made from graphite plates for a 1.67 x 10"5In3S"1 [1 L/min] sampling flow rate. Other instruments have been made from aluminum or glass plates with similar construction (Nolan and Nolan, 1938; Nolan and Doherty, 1950; Pollak et al., 1956; Megaw and Wiffen, 1963; Rich, 1966). Each channel should be parallel and have the same width. Deviation of channel width results in a nonuniform flow rate through each channel, which in turn causes the deviation of penetration from the theoretical prediction of Eqs. 19-22 and 19-23. Pollak and Metnieks (1958) designed a diffusion battery of 10 single channels, each separately housed in a box. A single-stage diffusion battery can be used to measure the diffusion coefficient of monodisperse aerosols at one flow rate. When it is used to measure polydisperse aerosols, such as those found in ambient air, several measurements must be taken at different flow rates to determine the distribution of diffusion coefficients. Parallel Disks. Kotrappa et al. (1975) designed a diffusion sampler. It is based on the diffusional losses of particles from a fluid flowing radially inward between two coaxial, parallel, or circular plates as originally proposed by Mercer and Mercer (1970). Stainless steel plates (37.7 mm diameter) with a central hole of 2 mm diameter in the upper plate are the collecting substrate. Separation between the plates is 2.25 mm. An absolute filter is used to collect material penetrating the device. This sampler has been used to determine the diffusion coefficient of radon decay products, which have diffusion coefficients on the order of 5 x 10"6In2 s"1 [0.05cm2/s]. The amount of radioactivity collected at the plates and absolute filter was determined and the diffusion coefficient calculated from Eq. 19-22, simplified to contain only the first term: (19-40)

Cylindrical Tubes. Tube-type diffusion batteries made of cylindrical tubes usually consist of a cluster of thin-walled tubes with IDs less than 1 mm. Large equivalent length (actual length x number of tubes) is required for measurement of particle size, because particles have much smaller diffusion coefficients than do gas molecules. Sinclair (1969), Breslin et al. (1971), and Scheibel and Porstendorfer (1984) have designed several cluster tube diffusion batteries. Figure 19-10 shows the schematic of a tube-type diffusion battery (Scheibel and Porstendorfer, 1984). Three diffusion batteries with 100,484, and 1000 single tubes were used with lengths of 0.05,0.093, and 0.3903 m, respectively. Tube-type diffusion batteries use materials that are commercially available and are also easier to construct than the parallel-plate diffusion battery. A lightweight material such as aluminum is often used; however, this type of diffusion battery is still heavy, bulky, and expensive. Most cluster tube diffusion batteries

Fig. 19-10. Schematic of a cluster-tube diffusion battery (From H. G. Scheibel and J. Porstendorfer / Aerosol ScL 15:673-682,1984, with permission.)

Fig. 19-11. A stainless steel collimated hole structure disk.

consist of one to three stages (Breslin et al., 1971; Scheibel and Porstendorfer, 1984), although an eight-stage diffusion battery was constructed (Sinclair, 1969). Compact diffusion batteries with many stages have been designed by using collimated hole structures (CHSs). The CHSs are disks containing a large number of near circular holes. Figure 19-11 shows a 44.5 mm diameter CHS disk made from stainless steel containing 14,500 holes 0.23 mm in diameter (Brunswick Co., Chicago, IL). With a thickness of 3.2 to 25.4 mm, the equivalent length ranged from 46 to 369 m. A portable 11-stage diffusion battery was designed with CHS elements (Sinclair, 1972). The total length is 0.60 m, and the equivalent length is 5094 m. Figure 19-12 shows the schematic of a five-stage diffusion battery made from CHS elements. A multiple-stage diffusion battery is required to measure the size distribution of a polydisperse aerosol. The development of a multiple-stage CHS diffusion battery opens up the possibility of routine measurements of submicrometer aerosols. Other CHS disks made from glass capillary tubes of 25 or 50 mm in diameter and a thickness of 0.5 to 2.0 mm are also available (GLL). A six-stage CHS diffusion battery made from glass has also been designed (Brown et al., 1984).

C0

Tube Length: (Disk Thickness)

C1

1/8" (3.1mm)

C2

1/4" (6.4 mm )

C3

1/4" (6.4 mm >

C4

1/2" (12.7 mm)

c5

1/2" (12.7 mm)

Fig. 19-12. Schematic of a five-stage diffusion battery consisting of a stainless steel collimated hole structure.

CQ

C1

C2

C3

C4

C5

Cg

C7

CQ

C9 C1Q

Fig. 19-13. Schematic of a 10-stage screen-type diffusion battery.

Screen Type. Sinclair and Hinchliffe (1972) and Twomey and Zalabsky (1981) have used diffusion batteries with stacks of filters as the cell material. This material is lightweight and inexpensive to build. However, commercial fiber or membrane filters are not ideal materials because of the nonunif orm fiber diameter and packing. Aerosol penetration through the filter may not be consistent and could not be predicted accurately by filtration theory. Sinclair and Hoopes (1975a) designed a 10-stage unit using stainless steel mesh 635 screens of uniform diameter, opening, and thickness (Fig. 19-13). The designed flow rate ranged from 6.66 to 10 x 10"5IIi3S"1 [4 to 6L/min]. Stacks of these well-defined screens simulate a fan model in both geometry and flow resistance (Cheng et al., 1985). Penetration through screens can be predicted by the fan model filtration theory (Eq. 19-25) (Cheng and Yeh, 1980; Cheng et al., 1980). Subsequently, this unit has become commercially available (Model 3040, TSl). Other types of screens have also been tested and found useful (Yeh et al., 1982; Cheng et al., 1985). Table 19-5 lists characteristics of the different screens shown in Figure 19-14. Screen-type diffusion batteries are compact in size and simple to construct. Screens can be cleaned and replaced easily when contaminated or worn out. Most multistage diffusion batteries described here are arranged in a series so that the aerosol concentration decreases continuously through the cells. Aerosol penetration is usually detected by a CNC. Based on parallel flow and mass collection principles, Cheng et al. (1984) designed a parallel flow diffusion battery (PFDB).This unit measures the penetration by mass or radioactivity without a particle-detecting unit. It is also more useful to detect unstable

TABLE 19-5. Characteristic Dimensions and Constants for Various Types of Screens in Screen-Type Diffusion Batteries Weave Screen diameter (mm) Screen thickness (mm) Solid volume fraction Bf k

Square 145

Square 200

Square 400

Twill 400

Twill 635

55.9 122 0.244 0.8969 0.330

40.6 96.3 0.230 0.9021 0.352

25.4 57.1 0.292 0.1180 0.269

25.4 63.5 0.313 1.450 0.246

20.0 50.0 0.345 1.677 0.216

SS145

SS400

SS200

SS635

Fig. 19-14. Photomicrographs of stainless mesh screens.

aerosols with fluctuating size and concentration. A schematic diagram of the PFDB is shown in Figure 19-15. It consists of a conical cap and a collection section containing seven cells. Each diffusion cell contains a different number of stainless steel 200-mesh screens followed by a 25 mm Zefluor filter (GEL). The seven cells typically contain 0 through 35 screens. Critical orifices provide a 3.33 x 10"5In3S"1 [2L/min] flow rate through each cell, resulting in a total flow rate of 2.33 x 10"4In3S"1 [14L/min]. Gravimetric determination of collected filter samples from each cell provides the direct mass penetration as a function of screen number for the determination of aerosol size distribution, thereby eliminating the sometimesinaccurate conversion of number to mass. Other Diffusion Batteries. Screen diffusion batteries have been used routinely to determine the activity size distribution of radon progeny. A single screen and a filter have been used to

Parallel Flow Diffusion Battery

Filter

Number of Screens Per Cell

Diffusion Cell Screens Critical Orifice Fig. 19-15. Schematic of a parallel flow diffusion battery.

estimate the unattached fraction. The screen and flow rate with a 50% penetration in the screen of about 4 to 10 nm are usually used. The radioactivity collected on both the screen and the filter are counted, and the amount of activity collected on the screen is assumed to be the unattached radon progeny. The activity size distribution of the radon progeny can be measured with either graded screen diffusion batteries (Holub et al., 1988; Cheng et al., 1992; Knutson et al., 1997) or PFDBs (Reineking and Porstendorfer, 1986; Strong, 1988; Ramamurthi and Hopke, 1991;Wasiolek and Cheng, 1995). A graded diffusion battery (GDB) consists of several stages, each with a different type of screen. The screens with a lower mesh number are used to collect particles in the nanometer size range, whereas screens of a larger mesh number are used to collect larger particles. Figure 19-16 shows a schematic of a GDB including five stages of screens and a backup filter (Cheng et al., 1992). After a brief sampling time (5 to lOmin), the screens and filters are counted directly for radioactivity. Because of the geometry of the screen, the efficiency of the alpha counting is not 100%, and corrections should be made to allow for a lower counting efficiency on screens (Solomon and Ren, 1992). PFDBs with much higher flow rates (over 5.0 x 10"4InV1 [30L/min]) are used to collect indoor radon progeny, which often have very low concentrations. Usually, only the backup filters for each stage of the PFDB are counted for radioactivity with a detector mounted near each back filter. Thus, this method eliminates possible errors due to the attenuation of oc rays by the screen and also provides near-real-time measurement of the activity size distribution. Wire screen-type diffusion batteries are most commonly used for the activity size distribution, but other types have also been reported. Holub and Knutson (1987) designed a parallel-wire GDB consisting of four screens of parallel wires having uniform diameters and spaces and a filter. A theoretical equation developed for the model filter of parallel staggered array cylinders has been used for data analysis (Cheng et al., 2000). Sinclair (1986) designed a PFDB made of carbonized open-pore foam called reticulated vitreous carbon.

G R A D E D D I F F U S I O N B A T T E R Y (ITRI) 635 MESH

FILTER

24 MESH

50 MESH

200 MESH

35 MESH

Fig. 19-16. Schematic of a five-stage graded diffusion battery.

Boulaud and Diouri (1988) report a five-stage PFDB with glass beads of different sizes. A working equation for the configuration has been developed. Recently, a new diffusion sampler consisting of an annular tube with a solid-state alpha-track detector placed in the inner cylinder tube has been designed for indoor radon measurement (Tymen et al., 1999). The activity size distribution of 218Po can be determined after exposure to radon progeny for a relatively long sampling period (a few hours compared with 5 to lOmin for GDBs). Use and Data Analysis Aerosol penetration through a diffusion battery provides data for the determination of particle size distribution. Aerosol penetration through a diffusion cell is obtained by measuring the number, mass, or activity concentrations at the inlet and outlet of each cell. A CNC is used to measure the number concentration. Figure 19-17 shows the schematic diagram of a system including a diffusion battery, automatic switching valve, and a CNC. With the automatic sampling system, it takes 3min to complete an 11-channel measurement. For radioactive aerosols, penetration based on activity can be obtained by collecting samples at the diffusion cell and a backup filter at the end of the diffusion battery. The singlestage parallel disk diffusion sampler (Kotrappa et al., 1975) and screen diffusion batteries have been used for this purpose. Screens can be counted directly for radioactivity (Reineking and Porstendorfer, 1986). Penetration based on the mass can be obtained by using a PFDB. Data Analysis Monodisperse Aerosol Particle size distributions are calculated from penetration data obtained from the diffusion battery measurement. For a monodisperse aerosol, the diffusion coefficient, Z)p, can be calculated directly from the corresponding Eqs. 19-20 to 19-25. The particle size is then calculated from the following relationship: (19-41) (19^2)

Aerosol Flow " to CNC = 300 cm 3 /min

CNC

Switching Valve Aerosol Flow to Switching Valve Screen DB

Aerosol Entry to DB Flow Rate = 4-6 L/min Fig. 19-17. Schematic of a diffusion battery, automatic switch valve, and condensation nuclei counter.

EXAMPLE 19-2 What is the aerosol penetration through a single-stage tube diffusion battery (0.10 m long, 100 tubes, and 6.67 x 10"5Hi3S"1 flow) for a 0.01 Jim particle diameter, under an ambient pressure of 101.3 kPa and 23°C? Answer: 1. Calculate the diffusion coefficient under the ambient condition using Eq. 19-41 by first calculating the slip correction factor (Cc [Eq. 19-42]):

2. Calculate the penetration through a circular tube using Eqs. 9-19 and 9-20:

where k is the Boltzmann constant (1.38 x 10"23JK"1), T is the absolute temperature (K), Cc is the Cunningham slip correction factor, and X is the mean free path of air (0.0673 urn at 293 K [23°C] and 101.3 kPa). With monodisperse particles, measurements from a single-stage diffusion battery are sufficient, and measurements from multiple-stage devices should improve the accuracy. Polydisperse Aerosols. Most aerosols in ambient environments and workplaces have polydisperse size distributions, and the method described in the previous section does not apply. Three penetration data points are the minimum required, but more will improve the accuracy of the size determination. Both graphical and numerical inversion methods have been developed for the size determination from penetration data. Fuchs et al. (1962) generated a family of penetration curves for rectangular channel diffusion batteries assuming that the aerosol size distribution is lognormally distributed. Mercer and Greene (1974) provided curves representing the penetration of aerosols in both cylindrical and rectangular channels as functions of the diffusion parameter, |i, and the geometrical standard deviation from 1 to 5. Once the data are properly aligned with one of the curves, this method gives a rough estimate of the mean and geometric standard deviation of the diffusion coefficient. Similar curves have been derived for screen-type diffusion batteries (Lee et al., 1981). This method does not apply to aerosols that do not follow lognormal size distributions. Sinclair (1969) used a graphical "stripping" method to estimate the particle size distribution from penetration data through a multistage cylindrical-type diffusion battery. A family of penetration curves has been calculated for monodisperse particles over a range of equivalent length. The experimental penetration data are plotted on a different paper of the same scale. The experimental curve is matched against the theoretical curves, and the one having the best fit at the right hand of the experimental curves (i.e., where penetration is least) is subtracted, leaving a new experimental curve. The process is repeated until the original experimental curve is entirely eliminated. Particle size and fractions of each size in the original aerosol are indicated by the matched theoretical curves and their intercepts with the ordinate of the graph. A similar method has been applied to the screen-type diffusion battery (Sinclair et al., 1979). This method does not assume a certain size distribution and thus is more useful. However, results for both graphical methods depend on judgment in matching curves. More consistent results can be obtained by using numerical inversion methods. In a diffusion battery, aerosol penetration through stage /, /*{, can be expressed mathematically as the integration of the aerosol penetration equation for monodisperse aerosol Px(x): (19-43) where f(x) is the size distribution and P\(x) is the aerosol penetration of size x in stage /. Each observed penetration for stage i = 1 through n can be expressed in the form of Eq. 19^-3. Several numerical inversion methods have been developed to obtain the aerosol size distribution,/^). Raabe (1978) developed a nonlinear least-square regression to solve Eq. 19-43 under the assumption of a lognormal distribution for f(x). Soderholm (1979) used a similar method for diffusion battery data analysis. Twomey (1975) proposed a nonlinear iterative method that Knutson and Sinclair (1979) applied to diffusion batteries. A modification of Twomey's method is used for data analysis of screen-type diffusion batteries (Kapadia, 1980; Cheng and Yeh, 1984). Other commonly used data inversion techniques include the simplex method (Nelder and Mead, 1965) and an expectation-maximization algorithm that have been developed for the screen-type diffusion battery (Maher and Laird, 1985). Knutson (1999) has summarized additional techniques. Based on tests performed by Wu et al. (1989), both

simplex and expectation-maximization techniques performed satisfactorily in the data analysis of diffusion batteries. See Chapter 22 for further discussion of data inversion techniques. CONCLUSIONS Diffusion batteries and CNCs are useful instruments in measuring the aerosol concentration and size distribution of ultrafine particles. The lower limit of detection in a CNC is about 2 to 5 nm, thus limiting the usefulness of a CNC to large molecular clusters. However, diffusion denuders and diffusion batteries can still be used to collect and separate particles and gases smaller than a few nanometers. Other techniques, including counting radioactivity and analytical chemical techniques, can be used to identify and quantitate materials collected on diffusion devices. These techniques are important in studying particles between the size range of gases and aerosols, a region where interesting phenomena can occur.

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Wu, J. J., D. W. Cooper, and R. J. Miller. 1989. Evaluation of aerosol deconvolution algorithms for determining submicron particle size distributions with diffusion battery and condensation nucleus counter. / Aerosol ScL 20:477-482. Yeh, H. C, Y. S. Cheng, and M. M. Orman. 1982. Evaluation of various types of wire screens as diffusion battery cells. /. Colloid Interface ScL 86:12-16. Zhang, Z. Q. and B. Y. H. Liu. 1990. Dependence of the performance of TSI 3020 condensation nucleus counter on pressure, flow rate and temperature. Aerosol ScL Technol. 13:493-504.

THE MILLIKAN CONDENSER

STRAUBEL1S THREE ELECTRODE EDB

as SOURCE Fig. 20-1. The Millikan condenser and Straubel's (1959) three electrode EDB.

cell, and an ac potential was applied to the center electrode. The ac field exerts periodic radial and vertical forces that, near the center of the balance, are proportional to the distance from the nullpoint (center) and vanish at the nullpoint. These oscillatory forces trap the particle. When the dc field balances the resultant of the gravitational force and any other vertical forces on the particle, the ac field exerts no time-average force on the particle. If the only vertical forces are the electrostatic force and gravity, the EDB can be used as an analytical balance, for in this case force balance yields (20-1) where C0 is a geometrical constant that is unity for a Millikan condenser with infinitely large electodes, q is the coulombic charge on the particle of mass m, Vdc# is the potential on the upper electrode when the lower electrode is at potential -Vdc,o» 2zo is the distance between

EXAMPLE 20-1 An electrodynamic balance with C0 = 0.8 and Zo = 5 mm is used to trap a 1.0 urn diameter water droplet. The dc potential on the top electrode is 8.0 V. What is the number of elementary charges on the droplet? Answer: If the droplet density, pp, is 1000 kg/m3 and dp is the diameter, the droplet mass is given by

Solving for q using Eq. 20-1, one obtains

Now the unit charge is 1.6022 x 10 19C, so the number of charges is Z = |-4.013 x 10-18 C|/(1.6O2 x 10~19 C/electron) = 25 elementary charges

the dc electrodes, and g is the acceleration of gravity. For finite electrodes and when an ac electrode is in place, C0 < 1.0. The uniform field of the Millikan condenser makes it impossible to position a particle in the center of the chamber when perturbations in position occur unless radial focusing and some type of vertical feedback control are applied. Arnold (1979) adapted a Millikan condenser to measure particle mass and charge by providing a radial electric field and an electrooptic feedback control system. Many different electrode configurations have been used for electrodynamic trapping, and Hartung and Avedisian (1992) discussed nine of the dozen or more (Davis, 1997) that have been proposed. Three of the most frequently encountered configurations are presented in Figure 20-2. Figure 20-2a shows the classic bihyperboloidal EDB introduced by Wuerker et al. (1959), Figure 20-2b shows the double ring balance that is based on superposition of the ac and dc potentials on the ring electrodes, and Figure 20-2c shows the spherical void EDB of Arnold and Folan (1987). The latter device was developed to maximize fluorescence signals by coating the interior surface with a reflective material. LEVITATION PRINCIPLES Many applications of the EDB are based on the stability characteristics of a trapped particle, so it is appropriate here to outline the theory needed to interpret experimental measurements. It is sufficient to consider only the vertical motion of a particle, for the vertical component of the ac field is larger than the radial component for all of the electrode configurations used. In the absence of a vertical gas flow through the chamber, the equation of vertical motion for the bihyperboloidal EDB is (20-2) in which z is the vertical distance from the midplane of the balance, dv is the "effective" particle diameter if the particle is nonspherical, and K is a correction factor for Stokes's law for a nonspherical particle. The gas viscosity is ji, Fz is a vertical force such as the radiation pressure force of a laser beam or the thermophoretic force, Vac is the amplitude of the ac potential, and co = 2nf, in which /is the ac frequency. The dc and ac balance constants, C0 and C1, depend on the electrode geometry. We shall take Fz = 0 in the analysis of particle stability. The equation of motion can be written in a more convenient form by introducing dimensionless variables defined by Z = zlb and T= cot/2 in which b = Z0CoZC1. The parameter b can be obtained by a calibration procedure outlined below. The nondimensional equation of motion becomes (20-3) where 8 is the drag parameter, /5 is the ac field strength parameter, and cr is a dc offset parameter defined by (2(M) Here VdCt0 satisfies Eq. 20-1 in the absence of force F2. The three dimensionless parameters S, P, and a govern the particle stability.

OBSERVATION PORT OSCILLATOR BALANCE CHAMBER DRIVE OSCILLATOR 0.02-20 kHz

GRIDS POWDER RESERVOIR

(a)

TORAMAN !SPECTROMETER ELECTRICAL FEEDTHROUGH DRYN2

LASER BEAM

MIRROR (b)

GLASS LIGHT PIPE

MYLAR VIEW PORT

FLUORESCENCE TO PHOTOTUBE

BEAM FROM DYE LASER (C) Fig. 20-2. Three frequently used EDB electrode configurations.

CC LU

UNSTABLE

I

(D

UNSTABLE

LJJ

I

MULLER1S EQUATION

Q

LlJ

STABLE

C

ca

8, DRAGPARAMETER FIg. 20-3. The lower stability envelopes determined by solution of the equation of motion compared with Muller's approximation for the lowest springpoint.

When the dc potential is adjusted to balance the gravitational force, Vdc = Vdc$ and
pcTit.

EXAMPLE 20-2 A 20 Jim diameter glass sphere having a density of 2200 kg/m3 (2.20 g/cm3) is levitated in air at 300 K (,u = 1.846 x 10"5 kg/m - s) using a dc voltage of 23 V. The spring point is reached by increasing the ac voltage to 1500V at a frequency of 100 Hz. Determine the geometrical constant, b, for this EDB. Answer: Using the definitions of P and S and the spring point data, we obtain

and

Because S< 1.5, we can use Miiller's equation to relate pCTit and S. Thus, with Eq. 20-5, the result is

Then pCTit = 2.890. Equating this with pCTit calculated above from the spring point data, the geometrical constant is b = 0.8 x 10"4. Once b is known, spring point measurements can be used to size a microsphere of known density, for the drag parameter corresponding to pCTit yields information about the diameter. PARTICLE SIZING The determination of the particle size by spring point measurements can be complemented by several other techniques. These include a variety of methods based on particle motion in an EDB. For a sphere, light-scattering techniques provide a highly accurate determination of the size, but we shall examine sizing procedures based on the particle dynamics first. Particle Dynamics Equation 20-3 is the basis not only for spring point measurements but also for three other approaches related to the particle dynamics. When the dc field does not balance the other vertical forces, that is, when G^ 0, the particle oscillates at the ac frequency and is, in general, out of phase with the ac source. Gobel et al. (1997) used phase lag measurements to size droplets in a four-ring EDB. Frickel and his co-workers recognized that it should be possible to determine the size of a particle by measuring either the amplitude of its oscillation or the shift of the center of the oscillation from the nullpoint. Zheng and Davis (1999) and Zheng et al. (2000) explored these methods with a double-ring EDB of the type shown in Figure 20-2b. They mounted a two-dimensional CD array on one port of the chamber and recorded the image of the oscillating particle with a PC equipped with Labview software to process the data. Figure 20-4 shows the line scan image and the resulting particle trajectory from which the amplitude, shift, and phase lag were determined. By comparing the solution of Eq. 20-3 with the measured amplitude, shift, or phase lag, the particle size can be determined. Figure 20-5 shows a comparison between theory and

LINE IMAGE POSITION, Ji{m

TIME, ms

Vdp. DIMENSIONLESS OFFSET

FIg. 20-4. A line scan image and particle trajectory for a GeO2 particle. (Source: Zheng et al., 2000).

DATA THEORY

V d c /V d c , 0 -1 Fig. 20-5. A comparison between the measured dc offset and stability theory for a 10.6 Jim GeO2 particle. (Source: Zheng and Davis, 1999).

experiment as a function of (Fdc/Fdc,0 - 1) for a GeO2 particle with an effective diameter of 10.6 um based on spring point measurements. Zheng and Davis found the oscillation methods to be within 3% of the light-scattering size for glass microspheres and within 4% for polystyrene latex (PSL) microspheres. Simpler methods of sizing a particle in an EDB were demonstrated by Davis and Periasamy (1985) and by Sageev et al. (1986). Both methods are applications of Stokes's law

for the drag force on a sphere. Davis and Periasamy levitated a particle in a laminar jet passing upward through the balance chamber and measured the force on the particle as a function of the center line velocity of the jet, which was determined by calibration. When no dc field was required to balance the gravitational force, it was balanced by the aerodynamic drag. Sageev and his co-workers momentarily turned off the ac electric field in an EDB and changed the dc voltage from VdCi0 to Vdc, where Vdc$ is the levitation voltage required to balance the gravitational force. The particle then moved under the influence of gravity and the dc field. They measured the time required for a particle to fall a given distance in their EDB chamber. They assumed that the particle fell at its terminal velocity, justifying this assumption by letting the particle fall 100 to 200 ms before measuring the velocity. The distance of fall was 0.9 mm. When both the dc field and gravity act on the particle, the size is given by (20-6) in which U is the velocity, pp is the particle density, pg is the gas density, and the Cunningham correction factor has been neglected. Sageev and colleagues (1986) also determined the particle size of their PSL spheres using the spring point method and found that it gave diameters 2% to 3% larger than the terminal velocity measurement method. Equation 20-6 applies to the experiments of Davis and Periasamy (1985) with Vdc = 0. EXAMPLE 20-3 A polystyrene latex (PSL) sphere having a density of 1055 kg/m3 (1.055 g/cm3) is found to fall 1.0 mm in 0.85 s in air at 300K and atmospheric pressure (/i = 1.846 x 10"5kg/m-s and Pg = 1.1774 kg/m3) when no dc field is applied. What is the diameter of a PSL particle? Answer: In this case, Vdc = 0 in Eq. 20-6, and the particle velocity is

The diameter is given by

Note: The Cunningham correction for slip has been neglected in this calculation.

Light Scattering

In the nineteenth century, Lord Rayleigh derived expressions for the intensity of light scattered by spheres much smaller than the wavelength of light, and at the beginning of the twentieth century Mie (1908) applied electromagnetic theory to analyze light scattering from spheres of any size. Mie theory predicts that the intensity of the scattered light depends on the polarization of the incident light (usually from a laser beam now), the size of the sphere, the position of the observer (the detector), and the refractive index ratio, m, of the sphere

dc SOURCE 2000-5000 V

ROTATING PERISCOPE

DROPLET INJECTION NEEDLE

MICROSCOPE

LASER BEAM

PMT RECORDER Fig. 20-6. A double-ring EDB equipped with light scattering peripherals for phase function measurements.

and surrounding medium. Measurements of the intensity of the scattered light can be compared with Mie theory to determine the size and/or refractive index of a sphere to approximately 2 parts in 105 (Ray et al., 1991). The light-scattering size is usually written in dimensionless form as x = ndvIX, where A is the wavelength of the incident light. A graph of the intensity versus scattering angle, 0, is often called a phase function. One measurement technique for obtaining phase functions is illustrated in Figure 20-6. A rotating periscope/photomultiplier tube (PMT) driven by a stepper motor detects light scattered from the particle through a window in the EDB. This type of device was used by Ray et al. (1991) to obtain the results shown in Figure 20-7 for a levitated droplet of dioctyl phthalate (DOP). Also shown in Figure 20-7 is the "best fit" of Mie theory. The excellent agreement between theory and experiment yields a light-scattering size of x = 95.13 ± 0.02. An alternate approach for measuring angular light scattering, which is used in the author's laboratory (Davis, 1987), consists of an EDB with a linear photodiode array mounted on the chamber wall. This permits rapid acquisition of phase functions and is particularly well-suited to study droplet evaporation and condensation processes. Figure 20-8 presents a set of phase functions obtained by Davis et al. (1987) for an evaporating droplet of hexadecane compared with calculated phase functions. Two features are particularly relevant; the number of peaks in a given range of scattering angles decreases as the size decreases, and the angle-average intensity of the scattered light decreases as the size decreases. Although the fine structure of the phase functions depends on the refractive index of the particle and its size, the number of fringes (peaks) in a given range of angles can be used to obtain an estimate of the lightscattering size. Thus, if Np is the number of peaks per degree, a rough approximation based on Mie theory yields (20-9)

RELATIVE INTENSITY

EXPERIMENT

MIE THEORY x = 95.13

9. SCATTERING ANGLE. DEGREES

INTENSITY, ARBITRARY UNITS

PHOTODIODE ARRAY OUTPUT

Fig. 20-7. A comparison between experimental and calculated phase functions for a droplet of DOP. (Source: Ray et al., 1991 with permission from Optical Society of America).

SCATTERING ANGLE, DEGREES

SCATTERING ANGLE, DEGREES

Fig. 20-8. Phase functions obtained with a linear photodiode array for an evaporating droplet of hexadecane. (Source: Davis et al., 1987 with permission from American Association for Aerosol Research).

Ragucci et al. (1990) verified the linear relationship between x and Np using a calibrated Berglund and Liu (1973) droplet generator to produce water droplets of uniform size, and they used a vertically polarized pulsed Nd-YAG laser with X = 532 nm to obtain phase functions. Their results agree with Eq. 20-9 if the constant is 197. They also showed that the number of fringes per degree is the same for horizontal and vertical polarized incident light. EXAMPLE 20-4 Estimate the size of the DOP droplet whose phase function is shown in Figure 20-7 by peak counting. Answer: The angle range presented in Figure 20-7 is AO = 40°. If we include the small peak at 86° and consider the fringe at the far left as one-half peak, we obtain 19.5 peaks. Using Eq. 20-9, the dimensionless size is

This is within 2% of the value obtained by direct comparison of the measured phase function with Mie theory (x = 95.13). If the laser light source was an HeNe with a wavelength of 632.8 nm, the droplet diameter is

Spherical particles and spheroids have unique light-scattering features that can be used to size them. These are morphology-dependent resonances (MDRs). They have also been called whispering gallery modes because of their similarity to the modes associated with acoustic waves in domed structures (e.g., the dome of the cathedral of Florence). For certain combinations of microsphere diameter, particle refractive index, and wavelength of light, the scattered intensity is very large compared with off-resonance conditions. The MDRs are so sensitive to size that they can be used to determine the size with high precision. Barber and Chang (1988) edited a book that deals with such optical effects in considerable detail. If the size (or refractive index or wavelength) is changed in a systematic way and if the intensity of the scattered light is measured at a single angle, typically at 90° to the incident laser beam, the graph of intensity versus time has been called a resonance spectrum. Figure 20-9 shows a comparison between a resonance spectrum obtained by Richardson et al. (1986) for an evaporating droplet of sulfuric acid and a calculated spectrum based on Mie theory. Note the sharp peaks corresponding to MDRs. The measured spectrum is seen to be in very good agreement with the theoretical result that predicts resonance peaks at x = 22.88,21.38, 19.89, and 18.37. The droplet was levitated in vacuum in an EDB and was illuminated with an HeNe laser (A = 632.8nm). FORCE MEASUREMENT The forces that can be exerted on an aerosol particle include the radiation pressure force, thermophoretic and diffusiophoretic forces, photophoretic forces, electrical forces, and, possibly, magnetic forces, in addition to aerodynamic drag and gravity. The thermophoretic force arises when a particle is placed in a surrounding gas having a nonuniform temperature.

DETECTOR OUTPUT INTENSITY

EXPERIMENT T=284 K

TIME THEORY

x = dpraa, DIMENSIONLESS SIZE Fig. 20-9. Experimental and theoretical resonance spectra for an evaporating droplet of sulfuric acid. (Source: Richardson et al., 1986 with permission from American Association for Aerosol Research).

Molecules colliding with the particle from hotter regions transfer more momentum to the particle than those coming from colder regions. The somewhat related phenomenon of photophoresis is the result of a temperature gradient generated within the particle by electromagnetic heating. Diffusiophoresis results when a particle exists in a gas-vapor mixture having a concentration gradient of vapor. If Fz represents one of these vertical forces, the dc potential required to balance gravity and force Fx is given by (20-10) Using Eq. 20-1 to eliminate Coq/zo, the ratio of force Fz to the gravitational force is given by (20-11) Thus, the ratio FJmg is obtained from two voltage measurements, Vdcfi in the absence of the force and Vdc with the force exerted. Li and Davis (1995) used this principle to measure the thermophoretic force on particles in the Knudsen regime. They mounted two heat exchangers inside an EDB to establish a temperature gradient in the gas phase, and they measured the dc potential required for levitation with and without the temperature gradient. The balance chamber could be evacuated to vary the mean free path, A, of the gas molecules, thereby changing the Knudsen number, Kn = 2AJd, in which d is the particle diameter. Figure 20-10 presents representative results for a PSL microsphere in air obtained by Li (1995). The thermophoretic force is seen to increase with Knudsen number until a maximum is reached that corresponds to the freemolecule regime. The force decreases for Kn > 4 because of edge effects associated with the isothermal chamber walls. In a similar way, Lin and Campillo (1985) measured the photophoretic force on crystalline ammonium sulfate particles, and Allen et al. (1990) measured the radiation pressure force on microspheres.

Fth/mg, FORCE RATIO

Kn, KNUDSEN NUMBER Fig. 20-10. The ratio of the thermophoretic force to the particle weight for a PSL sphere in air. (Source: Li, 1995).

MASS AND CHARGE MEASUREMENT Although the mass of a spherical particle of known density can be determined by measuring its diameter using the methods described above, an alternative method must be applied for particles of irregular shape and/or unknown density. Arnold (1979) proposed such an alternative in which he changed the charge on a particle of constant mass by photoemission of electrons. Charge loss can occur due to ionizing radiation (Ward and Davis, 1989), photoemission due to ultraviolet (UV) radiation (Arnold and Hessel, 1985), and thermionic emission at elevated temperatures (Bar-Ziv et al., 1989). Arnold's method involves changing the charge by one electron at a time. Equation 20-1 relates the dc voltage and the charge q. Let Vn be the levitation voltage when the charge is given by qn = ne, where n is the number of elementary charges on the particle and e the charge on the electron (e = 1.6022 x 10"19C). If the charge is qn^ after one electron is ejected, then the dc levitation voltages before and after the loss of a unit charge are related by (20-12)

Consequently, the absolute mass can be obtained from two voltage measurements. Arnold applied this method repeatedly to yield a series of steps when plotted as levitation voltage versus time of exposure to UV light. A step involving more than one electron emission can be clearly distinguished from a single emission in this manner. Furthermore, once the mass has been determined, the charge on the particle is obtained using Eq. 20-1.

EXAMPLE 20-5 An electrodynamic balance with Zo = 6 mm and C0 = 0.80 is used to measure the absolute mass by the one-electron emission method. If the dc voltage before illumination with an ultraviolet source is 9.850 V and after a short period of time the voltage must be changed to 9.796 V, what is the mass of the particle, its initial charge, and the initial number of elementary charges? Answer: Using Eq. 20-12, one obtains

From Eq. 20-1 the initial charge is

The number of elementary charges is

The charge on a liquid droplet can be determined by levitating it in a flowing gas. Taflin et al. (1989) developed the method to determine the Rayleigh limit of charge. When the surface tension of a droplet is balanced by the repulsive force associated with the surface charge, a droplet explodes. Taflin and his coworkers let a drop evaporate until the surface charge density reached the point of explosion. For upward flow in the Stokes regime, Eq. 20-10 becomes (20-13) This equation can be rearranged to give (20-14) Thus, if the droplet diameter is measured by light scattering, a graph of d\ versus VJdp should yield a straight line with slope -6Coq/nppgZo. Taflin et al. (1989) and Bridges (1990) used this method to determine the droplet charge, and Figure 20-11 shows evaporation rate data obtained by Bridges (1990) for a dodecanol drop evaporating in air at room temperature.

dp2, M^2

BEFORE RUPTURE AFTER RUPTURE

Vdc/dp, V/pm Fig. 20-11. Evaporation data for a dodecanol droplet before and after explosion upon reaching the Rayleigh limit. (Source: Bridges, 1990).

EXAMPLE 20-6 For the EDB used by Bridges Z0 = 15.4 mm and C0 = 0.64. The density of dodecanol at room temperature is 820kg/m3. Based on the data in Figure 20-11 after droplet explosion, what was the coulombic charge on the droplet? How many elementary charges correspond to that coulombic charge? Answer: The slope of the lower line in Figure 20-11 is estimated to be S = 1700 jrni3/V. Based on Eq. 20-14 the slope is

Solving for q, one obtains

The number of elementary charges is n = \q\/e = (1.72 x 10"13 C)/(l.6O22 x 10"19 C/electron) = 1.08 x 106 elementary charges

EVAPORATION/CONDENSATION Davis and Ray (1977) and Ray et al. (1979) showed that gas phase diffusion coefficients and vapor pressures can be determined from droplet evaporation experiments in the continuum regime where the evaporation is diffusion controlled. For sufficiently slow rates of singlecomponent droplet evaporation into a gas containing no vapor, the droplet diameter, d, satisfies the equation (20-15) in which d0 is the diameter at time t0, M1 is the molecular weight of the evaporating species, p%Ta) is the vapor pressure at surface temperature Ta, D^ is the diffusion coefficient of vapor i in surrounding gas /, and R is the ideal gas constant. If the evaporation rate is sufficiently low, the surface temperature is very close to the surrounding gas temperature, but rapid evaporation lowers the surface temperature because the heat of vaporization must be supplied by heat transfer from the gas phase. Note that, based on Eq. 20-15, a graph of a2 versus time should yield a straight line with slope S = -^Mfl^T^lp^RT^ Figure 20-12 shows some of the data of Ray and coworkers (1979) for dibutyl sebacate (DBS) evaporating in nitrogen at various temperatures. The slopes of the d2 versus time plots become more negative as the temperature increases due primarily to the increase in vapor pressure and to a lesser extent to the increase in the diffusion coefficient with temperature. If the vapor pressure is known, the diffusion coefficient can be determined from the slope. Conversely, if the diffusion coefficient is known, the vapor pressure can be determined.

EXAMPLE 20-7 Ravindran et al. (1979) reported the diffusion coefficient of DBS in N2 at 307 K to be 3.07 x 10"6In2S"1 (0.0307 cm2/s). Based on the data in Figure 20-12, determine the vapor pressure of DBS at 307 K. The molecular weight of DBS is 314.47, and its density is 940.5 kg/m3. Answer: Using Figure 20-12, the slope of the line for 307 K is estimated to be

Using the definition of S, one obtains

and, solving for the vapor pressure,

This result is in reasonable agreement with the vapor pressure equation proposed by Ray et al. (1979), which yields $ (307 K) = 1.19 x 10"5 mmHg.

a2, jim2

TIME, s Fig. 20-12. Evaporation rate data for droplets of DBS evaporating in N2 at various temperatures. {Source: Ray et al., 1979 with permission from American Institute of Physics).

EXAMPLE 20-8 Richardson et al. (1986) fitted their vapor pressure results to the equation

Using data extracted from Figure 20-9, estimate the evaporation coefficient for sulfuric acid at 284 K. Answer. The time between the resonance peak at X0 = 22.88 and the peak at xx = 18.37 is estimated to be 560 s. The corresponding droplet radii are d0 = (22.88) (0.6328 urn)/ K= 4.608 um and dx = (18.37) (0.6328 um)/7r = 3.700 um. The molecular weight of sulfuric acid is 98.08, and the density is 1834 kg/m3. The mean molecular speed is

The vapor pressure at 284 K is given by

Based on Eq. 20-16

Thus, considering the accuracy of the vapor pressure equation, the evaporation coefficient is of order unity.

Tang and Munkelwitz (1991) performed evaporation experiments in the continuum regime as well as in the free-molecule regime (Kn » 1) to determine vapor pressures, and the sulfuric acid evaporation rate data of Richardson et al. (1987) shown in Figure 20-9 were obtained by operating in the free-molecule regime. For isothermal evaporation in the free-molecule regime, the time-dependent radius satisfies (20-16) where e is the evaporation coefficient and C1 is the molecular speed given by (20-17)

CHEMICAL REACTIONS The electrodynamic balance can serve as a platform for studying chemical reactions. Rubel and Gentry (1984) and Straubel and Straubel (1984) were among the first to use electrodynamic levitation to follow chemical reactions gravimetrically (using the dc levitation voltage). The former explored the reaction between a phosphoric acid droplet and ammonia: (20-18) Rubel and Gentry reported the extent of the reaction as a function of time for various droplet sizes and NH3 gas pressure. They observed an initially rapid change in the extent of the reaction followed by a slower diffusion-controlled process. Straubel and Straubel made gravimetric measurements for HCl reactions with crystals of ZnCO3-3Zn(OH)2, with Na2S2O3 • 5H2O, and with CaCO3 in the presence of water, but no attempts were made to interpret the data to obtain reaction velocity constants or other kinetics information. The Straubels photographed the diffraction patterns from particles levitated in a three-plate balance shown in Figure 20-1 and illuminated from above by a laser beam. The photographic film was placed below the lower electrode, which had a window in it to permit observation of the diffraction rings. They presented the dc voltage as a function of time for the reaction between CaCO3 and HCl vapor, which is (20-19) The levitation voltage changed from -150 V at the outset of the reaction to -600 V at the end, which corresponds to a mass increase of 300%, assuming no charge loss. From the stoichiometry of the reaction (MCaco3 = 100.09, MCaa2 = 110.99, MH2co3 = 62.03) one would expect the mass increase to be 135%. It appears that there was significant water uptake. This is consistent with the observation that the final state was a droplet. Gravimetric analysis is not sufficient to understand the chemistry involved with reactions such as described by Eq. 20-19 because of the uptake of water and the possible decomposition of H2CO3. To chemically characterize such reactions Davis and his co-workers (Davis and Buehler, 1990; Buehler et al., 1991; Rassat and Davis, 1992; Aardahl and Davis, 1996; Aardahl et al., 1998) coupled the EDB with a Raman spectrometer, and Figure 20-13 shows one configuration of the system used. In this case, Rassat and Davis (1994) used an Argonion laser to illuminate the particle from below, and a second CO2 laser was used to heat the particle (infrared (IR) heating). The incident beam for Raman measurements typically had

BEAM STOP

TOGAS SCRUBBER He-Ne ALIGNMENT LASER CO2 HEATING LASER

GAS

EDB

MONOCHROMATOR

BEAM SPLITTER

ARGON-ION LASER Fig. 20-13. The EDB/Raman spectrometer system. (Source: Rassat and Davis, 1994 with permission from Society of Applied Spectroscopy.)

a power of 0.75 W (an irradiance of 2300 W/cm2) at a wavelength of 514.8 nm, and at this power the radiation pressure force on the particle was significant. The IR beam was split to heat the particle from two sides, thereby balancing the radiation pressure force in the horizontal direction. The inelastically scattered light was collected at 90° to the incident beam and was focused on the slit of a monochromator. An optical channel analyzer and a single channel detector mounted on the monochromator (not shown in Figure 20-13) were used to record the Raman spectra. Rassat and Davis (1992) used the EDB/Raman system in Figure 20-13 to study the chemical reaction between a sorbent particle, CaO, and a humidified stream of SO2 in air. This reaction constitutes a "dry scrubbing" process used for stack gas desulfurization. At elevated temperatures (T > 800K) the product of the reaction is CaSO4, but at low temperatures the overall reaction is (20-20) Rassat and Davis levitated a CaO particle and then introduced a polluted gas stream into the balance chamber and followed the reaction by recording Raman spectra during the course of the reaction. Figure 20-14 shows a sequence of Raman spectra reported by Davis et al. (1992) for the reaction at room temperature. The sequence shows peaks associated with the gases SO2 (at 1152 cm"1) and N2 (at 2328 cm"1) as indicated in Figure 20-14. The peak at approximately 3620 cm"1 corresponds to the OH stretching mode of Ca(OH)2, and the peak at approximately 1000 cm"1 is associated with the SO bond in the product. The broad peak around 3380 cm"1 is identified as that for the water of hydration in the calcium sulfite product. The presence of Ca(OH)2 in the spectra indicates that the reaction (20-21) occurred either simultaneously with the reaction in Eq. 20-20 or that the reaction in Eq. 20-21 was followed by the reaction

INTENSITY

RAMAN SHIFT, cm"1 Fig. 20-14. A sequence of Raman spectra for the reaction between a CaO particle and SO2 in a humid sir stream. (Source: Davis et al., 1992 with permission from Elsevier Science).

(20-22)

Gravimetric analysis cannot help elucidate the mechanism. The overall reaction did not go to completion, for it generally ceased at less than 40% conversion. This can be attributed to either pore-plugging or coating of the reacting particle by the product. CONCLUDING REMARKS The applications considered here represent only a few of the many applications of electrodynamic levitation of aerosol particles that have been investigated, and it is likely that new uses will continue to be found. The simplicity of the measurements makes the technique attractive, and the coupling of the EDB to elastic and inelastic light-scattering equipment greatly extends the capabilities.

EXAMPLE 20-9 Table 20-1 presents the dc levitation voltage data for two different CaO particles suspended in a humid air stream (without SO2). Because there is no SO2 present the reaction should be represented by Eq. 20-21. What can you conclude about the extent of the reaction from the data of Figure 20-15? TABLE 20-1. Levitation Voltage Data for CaO Particles Reacting with Water Vapor Particle 1 Time (min) 0 95 150 250 380 440 540 650

Particle 2 Voltage

Time (min)

Voltage

20.7 21.3 22.3 24.1 26.5 28.1 29.7 29.7

0 110 160 200 300 340 400

15.8 16.9 18.5 19.6 23.1 23.7 23.7

Answer. The molecular weights of CaO, H2O, and Ca(OH)2 are 56.08,18.01, and 74.09, respectively. If complete conversion to Ca(OH)2 occurred, the ratio of the final voltage to the initial voltage would be 74.09/56.08 = 1.321, which is also the mass ratio. However, for Particle 1 the ratio is 29.7/20.7 = 1.435, and for Particle 2 the ratio is 23.7/15.8 = 1.500. In both cases the final mass is too large for the particle to be only the hydroxide. It is likely that there was an uptake of water from the humid air in both experiments. For Particle 1 the excess mass is (1.435 - 1.321)/(1.321) = 0.0863 or 8.63% of the mass of pure Ca(OH)2, and for Particle 2 the excess mass is (1.500 - 1.321)/(1.321) = 0.136 or 13.6% of the mass of pure Ca(OH)2. REFERENCES Aardahl, C. L. and E. J. Davis. 1996. Gas/aerosol chemical reactions in the NaOH-SO 2 -H 2 O system. Appl. Spectrosc. 50:71-77. Aardahl, C. L., J. F. Widmann, and E. J. Davis. 1998. Raman analysis of chemical reactions resulting from the collision of micrometer-size particles. Appl. Spectrosc. 52:47-53. Allen,T. M., M. F. Buehler, and E. J. Davis. 1990. Radiometric effects on absorbing microspheres. /. Colloid Interface ScL 145:343-356. Arnold, S. 1979. Determination of particle mass and charge by one electron differentials. /. Aerosol ScL 10:49-53. Arnold, S. and L. M. Folan. 1987. A spherical void electrodynamic levitator. Rev. ScL Instrum. 58:1732-1736. Arnold, S. and N. Hessel. 1985. Photoemission from single electrodynamically levitated microparticles. Rev. ScL Instrum. 56:2066-2069. Barber, P. W. and R. K. Chang. 1988. Optical Effects Associated with Small Particles. Singpore: World Scientific. Bar-Ziv, E., D. B. Jones, R. E. Spjut, D. R. Dudek, A. F. Sarofim, and J. P. Longwell. 1989. Measurement of combustion kinetics of a single char particle in an electrodynamic thermogravimetric analyzer. Combustion Flame 75:81-106.

Berglund R. N. and B. Y. H. Liu. 1973. Generation of monodisperse aerosol standards. Environ. ScL Technol 7:147-153. Bohren, C. F. and D. R. Huffman. 1983. Absorption and Scattering of Light by Small Particles. New York: Wiley. Bridges, M. A. 1990. Measurement of Surface Tension Using an Electrodynamic Balance. M.S. thesis, University of Washington. Buehler, M. E, T. M. Allen, and E. J. Davis. 1991. Microparticle Raman spectroscopy of multicomponent aerosols. /. Colloid Interface ScL 146:79-89. Chan, C. K., R. C. Flagan, and J. H. Seinfeld. 1992. Water activities of NH4NO3Z(NH4)ZSO4 solutions. Atmos. Environ. 26A:1661-1673. Chan, C. K., Z. Liang, J. Zheng, S. L. Clegg, and P. Brimblecombe. 1997. Thermodynamic properties of aqueous aerosols to high supersaturation: I—Measurements of water activity of the system Na+-Cr-NO3--SO42--H2O at -298.15 K. Aerosol ScL Technol. 27:324-344. Cohen, M. D., R. C. Flagan, and J. H. Seinfeld. 1987a. Studies of concentrated electrolyte solutions using the electrodynamic balance. 1. Water activities for single-electrolyte solutions. /. Phys. Chem. 91:4563-4574. Cohen, M. D., R. C. Flagan, and J. H. Seinfeld. 1987b. Studies of concentrated electrolyte solutions using the electrodynamic balance. 2. Water activities for mixed-electrolyte solutions. /. Phys. Chem. 91:4575-4582. Davis, E. J. 1985. Electrodynamic balance stability characteristics and applications to the study of aerocolloidal particles. Langmuir 1:379-387. Davis, E. J. 1987. The picobalance for single microparticle measurements. ISA Trans. 26:1-5. Davis, E. J. 1997. A history of single aerosol particle levitation. Aerosol ScL Technol. 26:212-254. Davis, E. J. and M. F. Buehler. 1990. Chemical reactions with single microparticles. MRS Bull. 15:26-33. Davis, E. J. and R. Periasamy. 1985. Light scattering and aerodynamic size measurements for homogeneous and inhomogeneous microspheres. Langmuir 1:373-379. Davis, E. X, S. D. Rassat, and W. Foss. 1992. Measurement of aerosol/gas reaction rates by microparticle Raman spectroscopy. /. Aerosol ScL 24:S429-S432. Davis, E. J. and A. K. Ray. 1977. Determination of diffusion coefficients by submicron droplet evaporation. /. Chem. Phys. 67:414-419. Davis, E. X, S. H. Zhang, X H. Fulton, and R. Periasamy. 1987. Measurement of the aerodynamic drag force on single aerosol particles. Aerosol ScL Technol. 6:273-287. Frickel, R. H., R. E. Shaffer, and X B. Stamatoff. 1978. Report No. ARCSL-TR-77041, Chemical Systems Laboratory, Aberdeen Proving Ground, MD. Gobel, G, T. Wriedt, and K. Bauckhage. 1997. Periodic drag force and particle size measurement in a double ring electrodynamic trap. Rev. ScL Instrum. 68:3046-3052. Hartung, W. H. and C. T. Avedisian. 1992. On the electrodynamic balance. Proc. R. Soc. Lond. A 437:237-266. Kim Y. P., B. Pun, C. K. Chan, R. C. Flagan, and X H. Seinfeld. 1994. Determination of water activity in ammonium sulfate and sulfuric acid mixtures using levitated single particles. Aerosol ScL Technol. 20:275-284. Kurtz, C. A. and C. B. Richardson. 1984. Measurement of phase changes in a microscopic lithium iodide particle levitated in water vapor. Chem. Phys. Lett. 109:190-194. Li, W. 1995. Experimental Study of the Thermophoretic Force and Evaporation Rates for Single Microparticles in the Knudsen Regime. Ph.D. Dissertation, University of Washington. Li, W. and E. X Davis. 1995. Measurement of the thermophoretic force by electrodynamic levitation: Microspheres in air. /. Aerosol ScL 26:1063-1083. Liang, Z. and C. K. Chan. 1997. A fast technique for measuring water activity of atmospheric aerosols. Aerosol ScL Technol. 26:255-268. Lin, H.-B. and A. J. Campillo. 1985. Photothermal aerosol absorption spectroscopy. Appl. Opt. 24:422433.

Mie, G. 1908. Beitrage zur Optik truber Medien, speziell kolloidaler Metallosungen. Ann. Physik. 25:377. Millikan, R. A. 1911. The isolation of an ion, a precision measurement of its charge, and the correction of Stokes's law. Phys. Rev. 32:349-397. Miiller, A. 1960. Theoretische untersuchungen iiber das verhalten geladener teilchen in sattelpunkten electrischer wechselfelder. Ann. Phys. 6:206-220. Ragucci, R., A. Cavaliere, and P. Massoli. 1990. Drop sizing by laser light scattering exploiting intensity angular oscillation in the Mie regime. Part. Part. Syst. Charact. 7:221-225. Rassat, S. D. and E. J. Davis. 1992. Chemical reaction between sulfur dioxide and a calcium oxide aerosol particle. /. Aerosol Sci. 23:765-780. Rassat, S. D. and E. J. Davis. 1994. Temperature measurement of single levitated microparticles using Stokes/anti-Stokes Raman intensity ratios. Appl Spectrosc. 48:1498-1505. Ravindran, P., E. J. Davis, and A. K. Ray. 1979. Diffusivities of low-volatility species in light gases. AIChE J. 25:966-975. Ray, A. K., E. J. Davis, and P. Ravindran. 1979. Determination of ultra-low vapor pressures by submicron droplet evaporation. /. Chem. Phys. 71:582-587. Ray, A. K., A. Souryi, E. J. Davis, and T. M. Allen. 1991. The precision of light scattering techniques for measuring optical parameters of microspheres. Appl. Opt. 30:3974-3983. Richardson, C. B., R. L. Hightower, and A. L. Pigg. 1986. Optical measurement of the evaporation of sulfuric acid droplets. Appl. Opt. 25:1226-1229. Richardson, C. B. and C. A. Kurtz. 1984. A novel isopiestic measurement of water activity in concentrated and supersaturated lithium halide solutions. /. Am. Chem. Soc. 106:6615-6618. Richardson, C. B. and J. F. Spann. 1984. Measurement of the water cycle in a levitated ammonium sulfate particle. /. Aerosol Sci. 15:563-571. Rubel G. O. and J. W. Gentry. 1984. Investigation of the reaction between single aerosol acid droplets and ammonia Gas. /. Aerosol Sci. 15:661-671. Sageev, G, J. H. Seinfeld, and R. C. Flagan. 1986. Particle sizing in the electrodynamic balance. Rev. Sci. Instrum. 57:933-936. Straubel, H. 1959. Verdampfunsgeschwindigkeit und Ladungsanderung von Flussigkeitstropfen. DECHEMA Monogr. 32:153-159. Straubel, E. and H. Straubel. 1984. Investigations of chemical reactions on aerosols. / Aerosol Sci. 15:301-305. Taflin, D. C, T. L. Ward, and E. J. Davis. 1989. Electrified droplet fission and the Rayleigh limit. Langmuir 5:376-384. Tang, I. N. and H. R. Munkelwitz. 1991. Determination of vapor pressure from droplet evaporation kinetics./ Colloid Interface Sci. 141:109-118. Tang, I. N. and H. R. Munkelwitz. 1994. Water activities, densities, and refractive indices of aqueous sulphates and sodium nitrate droplets of atmospheric importance. /. Geophys. Res. 99:18801-18808. Tang, I. N., H. R. Munkelwitz, and N. Wang. 1986. Water activity measurements with single suspended droplets: The NaCl-H2O and KCl-H2O systems. /. Colloid Interface Sci. 114:409-415. Ward, T. L. and E. J. Davis. 1989. Electrodynamic radioactivity detector for microparticles. Rev. Sci. Instrum. 60:414-421. Wuerker, R. F., H. Shelton, and R. V. Langmuir. 1959. Electrodynamic containment of charged particles. J. Appl. Phys. 30:342-349. Zheng, F. and E. J. Davis. 1999. Size determination of single crystalline particles by electrodynamic oscillation measurements. Paper 6C5, AAAR 1999, Tacoma, WA, October 13,1999. Zheng, E, M. L. Laucks, and E. J. Davis. 2000. Aerodynamic particle size measurement by electrodynamic oscillation techniques. /. Aerosol Sci. 31:1173-1185.

In the last two decades, developments in aerosol generation and classification, progress in electron microscopy and imaging analysis, and improvement of test facilities have made instrument calibration easier and the results more reproducible. This chapter reviews calibration techniques relevant to aerosol measurement devices, such as sizing instruments, condensation nuclei counters, and mass monitors. The generation methods for test aerosols and important parameters in instrument calibration are emphasized. Also reviewed are the calibration and use of flow monitoring devices, which play an integral role in aerosol sampling and instrument calibration. MEASUREMENT METHODS AND CALIBRATION STANDARDS Aerosol instruments can be categorized according to the particle properties characterized (inertial, gravitational, optical, diffusional, thermal, or electrical) or the measuring techniques (real time or sample collection, personal or area, passive or active). The measured parameters are usually particle size, number concentration, or mass concentration and distributions of these parameters. The calibration of an aerosol instrument implies that the instrument response will be related to a particle standard; for example, for particle sizing, a suitable standard may be latex particles that have been independently sized by methods traceable to the usual laboratory standards. For concentration, the sampled volume is needed, necessitating calibration of the instrument's flow rate. This can be accomplished by the use of various flowmeters that have been calibrated by traceable standards. In practice, high accuracy is rarely needed in aerosol measurements. For example, the Environmental Protection Agency requires that the cut point of a PM-IO (particulate matter smaller than a 10urn cut size) sampler be determined to within ±0.5 urn, or ±5%, and the mass concentration must be within 10% of that of an ideal sampler (Federal Register, 1987). Usually the nature of the aerosol source and the conditions under which the sampling is done result in values of precision that do not justify high accuracy. This does not mean that care is not needed in the calibration procedure, but that the National Institute of Standards and Technology (NIST) standards are not normally needed. The calibration may be done by comparison to an instrument calibrated by the manufacturer, for example, an Aerodynamic Particle Sizer (APS). It is advisable, however, to check the sizing of such a device for a few particle sizes using commercial samples of calibrated latex particles and to check the flow rate of the APS with a calibrated flowmeter. Some aerosol parameters can be measured absolutely in the laboratory, that is, by determining the parameter through combined measurements of length, mass, and time (LMT). For example, the aerodynamic diameter of large particles can be determined by measuring the time required to fall a given distance (Wall et al., 1985).The particle diameter calculated from the operating parameters of the vibrating-orifice generator can be considered absolute because all of the involved quantities can be reduced to LMT (see discussion of the vibratingorifice generator, below). Geometric particle diameters can be measured in an electron microscope. However, attention must be paid to possible effects of beam heating and exposure to vacuum. Liquid particles are sometimes sized by deposition on a plate and optical microscopy. This requires a correction for droplet distortion (Liu et al., 1982b; John and Wall, 1983; Cheng et al., 1986). Manual measurements by microscopy have limited accuracy because of the small sample size. This can be overcome by using image analysis under computer control. Monodisperse, submicrometer particles of known size can be obtained by using an electrostatic classifier where the particles are selected by their electrical mobility (Mulholland et al., 1999). For the calibration of filter samplers, the collected aerosol mass concentration can be obtained directly by weighing the filters on a microbalance. Other mass samplers can be calibrated by comparison to a filter sampler. The air volume is calculated from the flow rate and

the sampling duration. Flow rates can be calibrated with a variety of flowmeters. Some afford an absolute measurement as in the case of a bubble meter, where the volume swept out by the bubble is measured for a given time. Because instrument calibration is time consuming, there is a trend toward using wellcalibrated, real-time laboratory instruments with direct readouts to characterize the test aerosol and to measure the aerosol penetrating the instrument under test. It is important that the laboratory instrument be operated according to the manufacturer's specifications and to perform checks on its functioning.

GENERAL CONSIDERATIONS Instrument calibration is essential to a successful measurement of aerosol properties in a sampling environment. However, before engaging in a rigorous process of instrument calibration, one should decide why the calibration is needed, where the instrument is to be used, which important parameters are to be measured, what levels of efforts are to be made, and how the task is to be conducted and the data be processed. AU these issues are considered and discussed here. Scientific knowledge and technical experience play an important role in making the right decisions, facilitating the calibration process, and obtaining defendable results. Rationale for Instrument Calibration

Aerosol sampling is often employed within the context of a general survey, investigating a specific complaint, regulatory compliance, or simply for scientific research purposes. The measurement data are used to characterize emission sources, to assess human exposures, as well as to evaluate control devices. To obtain reliable data, the sampling instrument must be calibrated. For some applications, the instrument must perform according to performance criteria recommended by different organizations, such as the National Institute for Occupational Safety and Health (NIOSH), American Industrial Hygiene Association (AIHA), American Conference of Governmental Industrial Hygienists (ACGIH), American National Standards Institute (ANSI), International Standards Organization (ISO), and the European Standards Commission (CEN), or regulatory standards established by government agencies, such as Occupational Safety and Health Administration (OSHA), Mine Safety and Health Administration (MSHA), and the Environmental Protection Agency (EPA). Table 21-1 summarizes some existing performance criteria and regulatory standards for aerosol sampling instruments. As an example, according to 40 CFR Part 53, Ambient Air Monitoring

TABLE 21-1. Performance Criteria for Aerosol Sampling Instrumentation

Type of Instrumentation

PM-IO inlet sampler Workplace sampling instrument Portable field instrument Respirable dust sampler Personal sampling pump

Performance Criteria/Guidelines Sampling effectiveness, 50% cut point, precision, flow-rate stability Inhalable, thoracic, and respirable fractions; Sampling efficiency; 50% cut point; sampling precision Portability, reliability, calibration, interference, etc. Entry, penetration, and sampling efficiencies Interferences

Source: Kenoyer and Leong (1995).

Regulatory Agency or Organization EPA ACGIH, ISO, CEN NIOSH ISO, CEN OSHA, ANSI

Reference and Equivalent Methods (Federal Register, 1987), a PM-IO sampler is to be calibrated in a wind tunnel using 10 different sizes of solid or liquid particles ranging from 3 to 25 um at wind speeds of 2, 8, and 24km/h (0.56, 2.2, and 6.7 m/s, respectively).

Environment to be Surveyed

The type of aerosol instrument selected and the manner in which it is calibrated may strongly depend on the environment in which the aerosol is to be sampled. In general, one should first attempt to identify the aerosol sources in the environment and decide what information is needed and for what purpose before selecting the parameters and the test aerosol for calibration. Depending on the wind speed in the environment, the sampling can be classified as still (or calm) air sampling or sampling in a moving air stream (Vincent, 1989,1995; Hinds, 1999; see also Chapter 8). Still-air sampling generally refers to a wind speed less than 0.5 m/s and applies to indoor environments, including residential homes, offices, schools, and factories. Moving air stream sampling refers to environments with higher wind speed, such as ambient atmosphere or inside ventilation ducts and stacks. Settling chambers with uniform, low flow rates are suitable for testing instruments under still-air conditions (Kenny et al., 1999), while wind tunnels are more appropriate for testing instruments under moving air stream conditions.

Parameters to be Investigated

It is necessary to select a set of parameters to be investigated during the instrument calibration. These parameters should be chosen depending on the type of instrument as well as the aerosol properties of interest in the sampling environment. For example, volumetric flow rate, pressure drop, and light source intensity are operating parameters, while particle size and composition and the nature of the suspending gas medium are aerosol parameters. The parameters selected can be different between two instruments. To calibrate an aerodynamic sizing device, the effects of particle density, velocity, and ambient pressure on the instrument response are important while in an optical particle counter (OPC) the particle refractive index, wavelength of light source, and collection angles of scattered light are the important parameters.

Design of the Calibration Program

The level of effort to be undertaken in the calibration should be considered. A full-scale calibration examining the instrument response over its full operational range requires elaborate test facilities and extensive effort in terms of time and labor. However, when a calibration curve with the full-range response is available, it may be sufficient to perform a single or twopoint calibration. It is not unusual for different components of the same instrument to be calibrated separately. For example, when one is interested in the performance of the sampling inlet (inlet efficiency), transport line (transport loss), and detection (counting efficiency) or collection section (collection efficiency) of a high-volume aerosol sampling system, a series of calibrations can be made, one for each individual component (Chen et al., 1999). Different parameters might be selected for investigation in the different components, and, consequently, different test facilities with different test aerosols are often employed during the component calibrations. In contrast, several instruments are sometimes arranged serially and calibrated as an integrated unit. For example, a two-stage virtual impactor and an electrical classifier were combined in series to investigate their integrated performance in fiber classification (Chen et al., 1996).

Selection of Test Aerosols Proper selection of test aerosols is essential to instrument calibration. Because most instruments have responses strongly dependent on the physical and chemical properties of the aerosol particles, the calibration curve of an instrument is strictly valid only for the test aerosol. For an aerosol whose physical and chemical properties are significantly different from those of the test aerosol, data interpretation based on the calibration could be misleading (Willeke and Baron, 1990). For example, one would underestimate the size distribution of a carbon black aerosol by using the calibration curve of an OPC obtained from polystyrene latex spheres because the carbon particles cannot scatter as much light as the polystyrene latex spheres. Ideally, an aerosol that has similar physical and chemical properties (e.g., size, shape, density, refractive index, dielectric constant, and thermal conductivity) to the aerosol to be measured should be selected as the test aerosol during calibration. Sampler performance can be investigated by use of selected test aerosols. Liquid particles can be used to simulate sticky particles that suffer wall losses. Solid, bouncy particles can be used to test for particle bounce and/or re-entrainment. Data Analysis A calibration curve contains the relationship between the instrument response and the values of a certain aerosol property to be measured. In the case of a direct-reading instrument, the calibration provides an adjustment (or a correction factor) to the indicated value. In addition, the resolution and sensitivity of the instrument should be examined and analyzed. After collecting calibration data, it is desirable to express that data in a generalized mathematical equation, relating the instrument response to a single parameter (Chen et al., 1985; Zhang and Liu, 1990). For data analysis, instrument manufacturers sometimes provide a built-in algorithm whose properties, accuracy, and limitations are often unknown to the user. It may be desirable to base the analysis only on the raw calibration data without manipulation by the built-in algorithm. Safety Precautions When generating aerosols, it should always be borne in mind that a respiratory health hazard may be created. A primary consideration is containment of the aerosol. A chemistry hood is a good location for an aerosol generator. Even if the exhaust from the generator is vented, there are usually times when the apparatus is open or there may be leaks. A walk-in hood is especially convenient to accommodate an auxiliary apparatus. If a hood cannot be used, the exhaust should be vented or filtered. Hazardous substances require more stringent containment measures. Care should be exercised in the choice of aerosol materials. For example, in the past dioctyl phthalate (DOP) was commonly used as a test aerosol because it has nearly unit density and is an oil with low volatility. However, animal tests have implicated DOP as a possible carcinogen. A good substitute is oleic acid, also a nonvolatile oil, which is available in food grade. A side benefit is that uranine, which is frequently added as a fluorescent tracer, is soluble in oleic acid, whereas it is insoluble in DOP. This means that the uranine is uniformly dispersed in the oleic acid droplets. Uranine is commonly used to trace waterways and is presumably harmless. Of course, even when the aerosol material is believed to be safe, it is prudent to avoid exposure. Another hazard is associated with the use of radioactive sources to "neutralize" the electrical charges on aerosols resulting from the generation process. 85Kr, a P (high-energy

electron) emitter, is commonly used in source strengths up to 1OmCi. Unfortunately, 85Kr also emits y rays. Whereas the p rays are absorbed by the walls of the container, the y rays penetrate. It is recommended that a qualified health physicist check the radiation level to evaluate the adequacy of the shielding, a particle sources, such as 210Po, represent a hazard when ingested and must be handled with care. CALIBRATION APPARATUS AND PROCEDURES Figure 21-1 a is a schematic of a typical calibration apparatus for aerosol instruments. It includes an aerosol generator, aerosol conditioning devices (e.g., diffusion dryer, charge neutralizer, aerosol classifier, aerosol concentrator, and dilution air supply), a mixing chamber, pressure and air flow monitoring equipment, the instrument to be calibrated, and a calibration standard. The aerosol produced from the generator can be monodisperse or polydisperse, solid or liquid, wet or dry, charged or uncharged, or spherical or nonspherical (described later). Generally, this aerosol requires several steps of conditioning before use. For an aerosol containing volatile vapors or water droplets, a diffusion dryer with desiccant and/or charcoal is commonly used to produce a dry aerosol. In some cases, a heat treatment using a high temperature furnace is required for the production of a test aerosol (Kanapilly et al., 1970; Chen et al., 1990). The heat treatment involves either sintering or fusing the particles to reach the desired particle morphology and chemical form or initiating particle evaporation and subsequent condensation to produce monodisperse particles. Because aerosol particles are usually charged by static electrification during formation, a neutralizer containing a bipolar ion source (e.g.,63Ni,85Kr, and 241Am) is often used in the aerosol treatment. This reduces the number of charges on particles and results in an aerosol with charge equilibrium (John, 1980). In addition, a size-classifying device is often used in the aerosol treatment to segregate particles of similar size or of desired size fraction (Liu and Pui, 1974; Chen et al., 1988; Romay-Novas and Pui, 1988). In addition, a concentrator or a dilutor is often used to adjust the aerosol concentration (Barr et al., 1983; Yeh and Carpenter, 1983). The desired test aerosol can be used to calibrate instruments in several ways. The simplest way, as shown in Figure 21-1 a, is to introduce the test aerosol into a mixing chamber in which the aerosol is uniformly distributed and sampled by both the instrument to be calibrated and the calibration standard. Pressure in the chamber and flow rate through the instrument are monitored. A sampling device, such as a filter sampler or an electrostatic precipitator, is often used to collect reference samples for the calibration standard. To ensure that both the calibration device and the instrument to be calibrated have comparable aerosol samples, an aerosol divider is used as a common sampling port for calibrating a mass monitor (Marple and Rubow, 1978). In the aerosol divider, the flow is split isokinetically into two streams: One passes directly into the instrument to be calibrated, and the other flows through the calibration standard (Fig. 21-la). This mixing chamber setup is inexpensive, easy to use, and does not require a large working area. It is most widely used for instrument calibrations that require particle sizes less than 5 urn, such as those for obtaining the response curve of an OPC or the collection efficiency of an impactor. For calibrations requiring particles larger than 5 or 10 urn, it is relatively difficult to provide a stable aerosol with sufficiently high concentration. Because the instrument is placed outside the mixing chamber, the setup is not adequate for testing the aspiration efficiency of the instrument inlet. Another way of calibrating an instrument is to introduce the aerosol particles into an aerosol test chamber (Fig. 21-lb) that contains the subject instrument and the test standard (Marple and Rubow, 1983; Chen et al., 1999). This chamber usually has a large test section to provide a quiescent atmosphere in which the entire instrument can be exposed to the aerosol as in the real sampling environment. The test aerosol is introduced at the top of the

LIQUID OR POWDER FEED

COMPRESSED AIR

AEROSOL GENERATOR

FLOW AND PRESSURE MONITORING SYSTEM

DUMPAND FORMATION OF DROPLET DILUTION SYSTEM OR POWDER AEROSOL DIFFUSION DRYING AEROSOL CHARGE NEUTRALIZING TREATMENT HEATING OR FUSING SIZE CLASSIFYING, ETC. FORMATION OF TEST AEROSOL

(a) MIXING CHAMBER (b) AEROSOL TEST CHAMBER (c) WIND TUNNEL FACILITY AEROSOL IN AEROSOL IN AEROSOL IN EXHAUST GRID GRID I.C. CS.

FLOW STRAIGHTENER

OVERFLOW (a') AEROSOL DIVIDER ISOKINETIC SAMPLING PROBE

AIRIN

BLOWER

TEST SECTION

I.C CS.

FILTER

I.C.| CS. I

ROTATING TABLE

EXHAUST

CS. I.C

I.C : INSTRUMENTTO BE CALIBRATED CS.: CALIBRATION STANDARD

AEROSOL SIZE DISTRIBUTION, NUMBER CONCENTRATION, OR MASS CONCENTRATION PAN BE MONITOREDy

Fig. 21-1. Schematic diagrams of a setup for instrument calibration using (a) a mixing chamber, (a) an aerosol divider, (b) a test aerosol chamber, and (c) a wind tunnel facility.

chamber and uniformly distributed in the section where the instrument is set on a rotating table. Rotation provides a means to reduce any effects due to possible temporal and spatial variations in aerosol concentration. This test chamber setup can provide uniform concentrations of aerosol particles as large as 90 um for instrument calibration (Maynard and Kenny, 1995; Aitken et al., 1999; Kenny et al., 1999). Several instruments can be placed inside the chamber for side-by-side comparison, including the sampling inlet. The flow rate and

turbulence intensity in the chamber are low, simulating still-air sampling conditions. It should be noted that the air is exhausted from the chamber (i.e., the air is not completely static). A test chamber with static air is not recommended because it is very difficult to avoid convection currents that can affect the measurements. Both the mixing chamber and the aerosol test chamber are used when the instrument to be calibrated is operated in a low or zero ambient wind velocity. To evaluate a sampler that will be operated in moving air, a wind tunnel facility (Fig. 21-1 c) is needed (Prandtl, 1952). The sampler is located inside the tunnel and should not occupy more than 10% to 15% of the cross-sectional area of the tunnel's test section to avoid blockage effects. Personal samplers are often mounted on the upper torso of a full-sized manikin placed within the test section of the wind tunnel (Vincent and Mark, 1982; Kenny et al., 1997). The wind tunnel provides a wide range of wind speeds (0.5 to lOm/s) to simulate different atmospheric conditions. The wind velocity, flow uniformity, and turbulence are monitored using flow-monitoring devices (described later). During calibration of the test sampler, an isokinetic sampler is generally used to collect reference samples. Two types of wind tunnels are commonly used: an open circuit tunnel (Vincent and Mark, 1982) and a closed circuit tunnel (Ranade et al., 1990). The open circuit tunnel operates by drawing filtered ambient air into the system and exhausting the air into the ambient downstream of the test section; the closed circuit tunnel circulates the air in a continuous path. Each type of tunnel has advantages and disadvantages. For example, the open tunnel occupies a smaller space with less installation cost, while the closed tunnel is less noisy and requires less energy consumption. Before any calibration, a standard operating protocol should be prepared. First, the manual for the instrument to be calibrated should be read carefully to learn as much as possible about the operating principles of the instrument, the construction, and the recommended operating procedures. However, the manual may not cover all aspects relevant to the application. For example, a laser-operated OPC tends to produce oscillatory responses when the particles are larger than the wave length of the laser beam; however, the calibration curve provided by the manufacturer seldom shows this phenomenon (Chen et al., 1984). The condition of the instrument should be checked before calibration. The integrity of the flow system can be quantified by a series of pressure measurements on a sealed system that has initially been brought to a pressure slightly above or below ambient pressure (Mokler and White, 1983). Any leakage can be discovered by various methods. The simplest one is to pressurize the system slightly and then put soapy water on the surface of the system to detect the leakage. A tracer gas can also be injected into the system to detect the leak location. It may be advisable to check the electronics by observing the signals on an oscilloscope, especially if the instrument outputs a signal to be processed by other instruments. General Approaches to Instrument Calibration

Calibration methods can be characterized by whether the test aerosol is monodisperse or polydisperse and how the aerosol is measured. Monodisperse aerosol is either produced directly by a generator or classified after generation by auxiliary apparatus. The use of polydisperse test aerosol implies that particle sizing will be done on the airborne particles or by analysis of collected samples. In one approach to calibration, the particles collected or deposited within the instrument or sampler is analyzed (e.g., gravimetrically or by analysis of tracers). An alternative approach is to measure the aerosol entering and leaving the sampler. This may involve testing the sampler without a final filter. The choice of the calibration method is made in consideration of the type of instrument to be calibrated, the kind of information needed, and available resources. The various methods have distinct advantages and disadvantages. The use of monodisperse particles necessitates many repetitions with different particle sizes. However, it may yield unambigu-

ous information, for example, on whether 20 um particles penetrate a size selector for PM10. Similarly, the measurement of deposited particles may involve tedious extractions and quantitations, but may determine where wall losses occur. The use of polydisperse aerosol with a sizing instrument that produces a real-time size distribution makes possible rapid calibration measurements. This can be important if a parameter such as sampler flow rate is to be varied between calibrations or variations in the sampler's configuration are to be explored (John and Kreisberg, 1999). TEST AEROSOL GENERATION Test aerosols contain either monodisperse or polydisperse, spherical or nonspherical, and solid or liquid particles (Mercer, 1973; Raabe, 1976; Hinds, 1999; Cheng and Chen, 1995). The characteristics of an ideal generator are a constant and reproducible output of stable aerosol particles whose size and concentration can be easily controlled. For general instrument calibration, the test aerosol often contains monodisperse, spherical particles. Table 21-2 lists the test aerosols frequently used for instrument calibration. Monodisperse aerosols containing spherical particles are frequently used. Particles with nonspherical shapes are sometimes used in calibration to study the possible effect of shape on the instrument response. Polydisperse dust particles have also been used in calibrating dust monitors. This is important because most real aerosols contain nonspherical particles of different sizes. The size distribution and concentration of a test aerosol depend on the characteristics of both the generator and the feed material. The information given in this section is intended as a guide for selection of appropriate generation techniques. The actual size distribution in each application should always be measured directly with the appropriate instruments. Monodisperse Aerosols with Spherical Particles

The methods for producing monodisperse aerosols with spherical particles have been reviewed by Fuchs and Sutugin (1966), Mercer (1973), and Raabe (1976). These methods include the atomization of a suspension of monodisperse particles, the formation of uniform droplets by dispersion of liquid jets with periodic vibration or a spinning disk, and the growth of uniform particles or droplets by controlled condensation. Atomization of Suspensions of Monodisperse Particles. A common way of producing monodisperse aerosols is by nebulizing a dilute liquid suspension containing monodisperse polystyrene (PSL) or polyvinyltoluene (PVT) latex spheres. These spheres are commercially available in sizes from 0.01 to over 100 um (BAN, DUK, DYNJSR, MMM, POL, SER)* PSL particles of different sizes have also been concurrently produced in an aerosol to obtain more than one data point per experimental run. Monodisperse latex particles containing fluorescent dye or radiolabeled isotopes are also used in calibrations when quantitative measurements by fluorometric or radiometric techniques are needed (Newton et al., 1980; Chen et al., 1999). Two problems arise in the generation of these latex particles: formation of agglomerates and existence of residual particles. Agglomerates are formed when more than one latex particle is in a nebulized droplet. The percentage of agglomerates can be reduced by diluting the suspension. Assuming that the probability of the number of particles in an atomized droplet can be described by Poisson statistics and that the droplet-size distribution can be approximated by a lognormal distribution, Raabe (1968) derived the following equation to calculate the latex dilution factor, Y, necessary to give a desired singlet ratio, R, which is the number of droplets containing single particles relative to the total number of droplets containing particles: * See Appendix I for full manufacturer addresses referenced to the italicized three-letter codes.

TABLE 21-2. Test Aerosols and Generation Methods Used for Instrument Calibration Test Aerosol0

Particle Morphology

Size Range6 VMD (urn)

Density (kg/m3)

Refractive Index

Generation Method Nebulization Dry powder dispersion Dry powder dispersion Vibrating-orifice atomization Vibrating-orifice atomization Spinning-disk atomization Spinning-disk atomization Spinning-disk atomization Evaporation/ condensation Evaporation/ condensation Dry powder dispersion Dry powder dispersion

PSL (PVT) Fluorescent PSL

Spherical, solid Spherical, solid

0.01 to >100 6 to >100<*

<1.02c 1.08-1.17

1,050 (1,027) 1,050

1.59 1.59

Soda lime glass (borosilicate glass) Oleic acid

Spherical, solid

1.1 to >100

1.07-1.3

Spherical, liquid

0.5-40

<1.1

2,460 (2,500-2,550) 890

1.51 (1.56) 1.46

Ammonium fluorescein Fused ferric oxide

Spherical, solid

0.5-50

<1.1

1,350

Spherical, solid

0.2-10

<1.1

2,300

Fused aluminosilicate

Spherical, solid

0.2-10

<1.1

3,500

Fused cerium oxide

Spherical, solid

0.2-10

<1.1

4,330

Sodium chloride

Irregular, solid

0.002-0.3

<1.2

2,170

1.54

Silver

Irregular, solid

0.002-0.3

<1.2

10,500

0.54

Coal dust

Irregular, solid

-3.3

-3.2

1,450

1.54-0.5i

Arizona road dust

Irregular, solid

-3.8

-3.0

2,610

a

Aerosol Output (particles/m3) <1010 e

<10n <10n <1013 <1013 <1013 <1012 <1012 <30mg/m3 <30mg/m3

Standard particles, such as the PSL,fluorescentPSL, glass spheres, and Arizona road dust, are commercially available from companies such as BAN, DUK, IDC, and POL. Aerosol treatment of drying, charge neutralization, and size classification is generally used. c For VMD less than 0.1 urn, ag is between 1.03 and 1.14. d This size range is forfluorescentparticles in dry powder form; particles of submicrometer sizes are available in suspension. e The aerosol output for these dry particles depends on the particle size, bulk concentration, and generation parameters. Normally, particles of a larger size have a smaller concentration. b

(21-1)

EXAMPLE 21-1 A bottle of lum PSL suspension containing a 10% solid is being used to produce a test aerosol containing at least 95% singlets. What is the dilution factor required in this suspension if the Retec X-70/N nebulizer is used and operated at 20 psig? Answer: Using eq. 21-1:

Based on size distribution data given in Table 21-3:

A dilution factor of at least 515 is needed to produce an aerosol containing 95% of singlet PSL particles.

where F is the volumetric fraction of individual particles of diameter dp in the original latex suspension, and VMD and ag are the volume median diameter and the geometric standard deviation of the droplet size distribution, respectively. The values of VMD and <7g of commonly used air-blast atomizers are listed in Table 21-3. This equation is limited to values of (Tg < 2.1 and R > 0.9. The second problem arises when nonlatex residual particles are present in the aerosol as a result of the surfactant usually present in the liquid suspension to prevent coagulation. Because most of the atomized droplets contain no latex particles, the nonlatex particles form a large background of small particles. If this background interferes with the measurements, the surfactant may be removed from the suspension before use by diluting, centrifuging, and discarding the supernate. In recent years, latex suspensions containing no surfactant have become available. These suspensions are stabilized by surface coatings of functional groups (IDQ. Vibrating-Orifice and Spinning-Disk Aerosol Generators. The vibrating-oriflce aerosol generator can produce highly monodisperse aerosols in the approximate size range from 0.5 to 50 um (Fulwyler et al., 1969; Raabe and Newton, 1970; Berglund and Liu, 1973).

TABLE 21-3. Operating Parameters of Air-Blast and Ultrasonic Nebulizers Nebulizer

Air-blast type Collison

Operating Conditions Orifice Diameter (mm)

Air Pressure (kPa [psig])

0.35

100 [15] 170 [25] 100 [15] 200 [30] 100 [15] 200 [30] 140 [20] 350 [50] 140 [20] 350 [50]

DeVilbiss* D-40 DeVilbiss D-45 Lovelace

0.84

Retec

0.46

0.76 0.26

X-IOfN

Ultrasonic type DeVilbiss 880 Sono-Tek a

Output per orifice. Vent closed. c Power settings. b

(2)c (4)c

Frequency (mHz)

Flow* Rate (xlO-5 m3/s [L/min]) 3.3 [2.0] 4.5 [2.7] 20.7 [12.4] 34.8 [20.9] 15.7 [9.4] 24.2 [14.5] 2.5 [1.5] 3.8 [2.3] 8.3 [5.0] 16.2 [9.7]

1.35 1.35 0.025-0.12

68.3 [41.0] 68.3 [41.0] 10"6-0.73 [lO-M)^]

Aerosol Output (uL/L)

Droplet Size Distribution

Commercial Source

VMD (urn) 8.8 7.7 15.5 12.1 23.2 22.9 40 27 46 47

54 150

2.5-3.0 1.9-2.0 4.2 2.8 4.0 3.4 5.8 2.6 5.7 3.2

3.0 2.0 1.8 1.9 1.8 2.3 1.8 2.2

INT

5.7 6.9 18-80

1.5 1.6

DEV

BGI DEV DEV

INT

SON

Rotameter Absolute filter Pressure regulator High Control pressure valve air

Aerosol Aerosol generator Signal Porous_ generator plate

Absolute Variable B j o w e r filter transformer Differential pressure gauge

Dispersed droplets Cover

Membrane Holderfilter Infusion pump

' Dispersion orifice

,Porous plate

Electrical Liquid signal Dispersion air

Fig. 21-2. Diagram of a Vibrating-Orifice Aerosol Generator of the Berglund-Liu Design. (Reprinted from Liu (1974) with the Permission of Air and Waste Management Assoc).

The particle diameter can be calculated from the generator's operating conditions so that the aerosol can be considered a primary particle size standard. Also, the aerosol concentration is inherently stable. In the vibrating-orifice generator (Fig. 21-2), a liquid is forced through an orifice. The resulting liquid jet is made to break up into uniform droplets by subjecting the jet to a mechanical disturbance of constant frequency. The droplet diameter, dd, is then given by (21-2) where QL is the liquid feed rate in m3/s and / is the vibrating (disturbing) frequency in Hz. The droplet diameter is typically some tens of micrometers. To generate smaller particles, a nonvolatile solute can be dissolved in a volatile solvent. After evaporation of the solvent, the particle diameter is related to the volumetric concentration of the solute, Cv, by (21-3) Liquid particles can be produced, for example, from a solution of oleic acid in isopropyl alcohol, or solid particles of sodium chloride can be produced from an aqueous solution. The minimum particle size attainable, in practice, depends on the purity of the solvent. The maximum practicable particle size is not well defined. Particles larger than about 20 urn diameter become more difficult to generate as the diameter is increased. It also becomes more difficult to avoid particle losses in transport. Therefore, the generation of particles larger than about 50 Jim requires special effort. Referring to Figure 21-2, the solution is forced through the orifice by a syringe pump. An alternating voltage from a signal generator is applied to a piezoelectric crystal, which then vibrates the assembly holding the orifice plate. A turbulent jet of air issuing from the hole in the cover above the orifice disperses the droplets before they can coagulate. Filtered, dry dilution air is introduced to dry the droplets and transport the aerosol from the generator. There is a range of about a factor of two in the frequency producing uniform droplets. Within this range, certain frequencies may produce satellite droplets (i.e., droplets much smaller than the main drops that are being produced). They can be eliminated by adjustments of the vibrating frequency. Another undesirable characteristic is the production of multiplets

EXAMPLE 21-2 Oleic acid aerosol is produced by a vibrating-orifice atomizer from a solution of oleic acid in isopropyl alcohol, with a volume concentration of 1.48 x 10~2. The liquid feed rate is 3 x 10"9m3/s [0.18cm3/min], and the vibrating frequency is 5.5 x 104Hz. The dilution air flow rate is 3.33 x 10"4m3/s [20L/min]. What are the diameters of the droplets and the oleic acid particles? What is the particle number concentration? Answer: Using Eqs. 21-2 and 21-3:

substituting Q L ,/, and C, we find the droplet diameter dd = 106 [6 (3 x 10-9)/55,000^]1/3 = 47.1 um and the particle diameter dp = (1.48 x 10"2)1/3(47.1) = 11.6 jam The rate of particle production is the same as the vibrating frequency, 55,000 s"1. Dividing this by the flow rate of the dilution air, we find that the particle number concentration is 1.7 x 108nT3.

because coagulation of the droplets cannot be completely suppressed. In practice, the operating conditions are adjusted to minimize the multiplets, and corrections are applied to the data taken with the aerosol, if necessary. Particles from the vibrating orifice typically carry several thousand elementary charges. Because the presence of a high electrical charge may affect subsequent processes involving the aerosol, it is common practice to "neutralize" the charge by exposure of the aerosol to a radioactive source. The source creates charged ions in the gas that are attracted to charges of the opposite sign on the particles, reducing the particle charge distribution to a Boltzmann equilibrium. The radioactive sources commonly used include (3 emitters such as 85Kr or tritium and a emitters such as 210Po or 241Am. In general, solid particles dry with the formation of voids, resulting in a density less than that of the bulk material. To some extent, the drying process can be controlled by varying the volatility of the solvent, for example, by varying the proportions of water and alcohol and by controlling the amount of dilution air. If the drying is too rapid, the particles tend to have more voids. The average density of the particles, including voids, can be determined by the following method: (21-4) where ppav is the average density of the particles (in kg/m3), dg is the particle geometric diameter (in Jim) determined with a microscope, and Cm is the mass concentration (in g/L) of the solute (John and Wall, 1983). One example of a solid particle aerosol, namely, ammonium fluorescein, deserves special mention because of its useful particle properties and the requirement for special generation procedures. Ammonium fluorescein particles are very smooth and have essentially bulk density (1350kg/m3 [1.35 g/cm3]). The material has low hygroscopicity, and the fluorescence can be used for detection with high sensitivity. The particles are useful for sampler calibration and for checking for particle bounce. The solution is prepared by dissolving fluorescein in ammonium hydroxide. A reaction takes place with an ammonium group replacing a

hydrogen atom on the fluorescein molecule. As a result, the molecular weight increases by 5%; thus, 12.8g fluorescein per liter of solution is a 1% volume concentration. When particles of ammonium fluorescein larger than about 10 urn are generated, the droplets tend to dry too fast. To produce smooth particles, it is necessary to humidify the dilution air to slow down the drying. By this method, particles as large as 70 um have been generated (Vanderpool and Rubow, 1988). The vibrating-orifice aerosol generator can be used to produce uniformly charged, monodisperse aerosols (Reischl et al., 1977). The technique involves insulating the cap over the orifice assembly and applying a dc voltage to the cap. Charges are induced onto the top of the jet and are trapped on the droplets when they separate from the jet. The monodispersity of the droplets leads to uniform charging. The application of modest voltages, that is, up to ±10 V, produces particles with ±104 elementary charges. Reischl et al. (1977) present the theory and data demonstrating the method, which is useful for experimentation with charged aerosols. Another method of producing monodisperse droplets is by the spinning disk, in which a liquid jet is fed at a constant rate onto the center of a rotating disk. The liquid spreads over the disk's surface in a thin film, accumulating at the rim until the centrifugal force exceeds the capillary force acting to hold it together, and a droplet is thrown off. Droplet size dd depends on disk diameter, ds (um), and rotating speed, ca* (rpm), as follows: (21-5) where ft and pL are the surface tension and the density of the liquid and W is a constant. Spinning disks have been investigated by Walton and Prewett (1949) and May (1949) using an air-driven spinning top and by Whitby et al. (1965) and Lippmann and Albert (1967) using a motor-driven spinning disk. Unlike a vibrating-orifice atomizer, aqueous suspensions as well as solutions can be used. The spinning disk produces an order of magnitude higher aerosol concentrations compared with the vibrating orifice. However, the monodispersity is not as high; the ag values are approximately 1.02 and 1.1 for the vibrating orifices and spinning disks, respectively. A disadvantage of the spinning disk is that undesired satellite droplets are formed and must be removed from the useful aerosol. In addition, the constant W (Eq. 21-5) varies with the instrument and the feed material used so that the particle size cannot be easily calculated as for the vibrating-orifice atomizer. Controlled Condensation Techniques. Condensation is also a method that produces monodisperse aerosols for calibration purposes. In this method, the heated vapor of a substance is mixed with nuclei on which it subsequently condenses when it passes in laminar flow through a cooling zone. If the condensation process is diffusion controlled, the surface area of the growing droplet will increase at a constant rate, producing a particle having a diameter, du at time t related to the initial diameter, do, of the nucleus, by (21-6) where b is a constant related to the concentration, the diffusivity of the vapor, and the temperature. If bt is the same for all particles and much larger than dl, an aerosol containing monodisperse particles is produced. In practice, a uniform temperature profile, sufficient vapor concentration, and sufficient residence time in the condensation region are the key controls, and a constant nuclei concentration provides a stable aerosol concentration (Sinclair and LaMer, 1949; Rapaport and Weinstock, 1955; Prodi, 1972; Liu and Lee, 1975; Tu, 1982). Particle sizes from 0.03 to greater than 2um with crg of 1.2 to 1.3 can be produced this way. The number concentration can be as high as 1013 particles/m3 [107 particles/cm3]. An example of a condensation aerosol generator is shown in Figure 21-3.

1

Ze" ORIFICE MADE UP FROM STANDARD COPPER FITTING

DRY COMPRESSED AIR, 35 PSI

40 MESH SCREEN

FLOW METER

HEATING TAPE , I " X 2' (192 WATTS WITH LINE VOLTAOE)

PRESSURE GAGE

HOV AC ABSOLUTE FILTER VARIABLE TRANSFORMER PLASTIC TUBE 3 6 V I. D. FOR THERMAL SHIELDING 19 MM I. D. PYREX GLASS TUBE 5KV

ONE ORIFICE COLLISON ATOMIZER

FLOW DIVIDER MADE UP FROM STANDARD COPPER FITTING

NO. 47 DRILL HOLE

2.70 L/MIN.

FLOW BALANCE METER

5KV

0.28 L/M1N. IONIZER

NO. 74 DRILL HOLE CRITICAL VALVES ORIFICES

NEUTRALIZA TION CHAMBER

ELECTROSTATIC PRECIPITATOR FOR EXCESS AEROSOL

DJLUTING AIR EL. CONDENSER AEROSOL OUT Fig. 21-3. Condensation-Type Monodisperse Aerosol Generator. (Reprinted from Tomaides, Liu, and Whitby (1971) with the Permission of Pergamon Press, Inc.).

Electrospray Techniques (Generation of Monodisperse Nanoparticles). Another method for producing monodisperse aerosols is to use an electrostatic atomizer or electrospray device (Hayati et al., 1987a,b; Fernandez de Ia Mora et al, 1990; Meesters et al., 1992; Grace and Marijnissen, 1994). Electrospraying refers to the generation of liquid droplets by feeding semiconducting fluid through a capillary tube and applying an electric field. There are several modes by which the liquid can break up into droplets, depending on flow rate, field strength, and other parameters. For certain conditions, when the field is strong enough, the liquid meniscus at the capillary outlet forms a cone from whose tip a very thin liquid jet emerges in the cone-jet mode (Cloupeau and Prunet-Foch, 1989,1994). The microjet breaks by varicose wave instabilities into a stream of charged droplets, having diameters roughly twice as

large as the jet diameter but much smaller than the capillary diameter (Rosell-Llompart and Fernandez de Ia Mora, 1994; Tang and Gomez, 1994). A system using this technique can generate very small droplets without the clogging problems associated with a very small orifice. The mean droplet size is usually in the range of 0.3 to 50 urn, but can be as small as 10 nm. The size is a function of the nozzle diameter, liquid feed rate, field strength, and properties of the liquid, including surface tension, electrical conductivity, and viscosity (Smith, 1986). The droplets are charged up to an appreciable fraction of the Rayleigh limit and initially repel each other until they are neutralized. Tang and Gomez (1994) have demonstrated a generation system producing monodisperse droplets in the size range from 2 to 12 urn with a ag of 1.15 for small droplets and 1.05 for large droplets. Monodisperse droplets of 0.3 to 4 urn have been produced with a crg of 1.1 (Rosell-Llompart and Fernandez de Ia Mora, 1994). By using volatile solvents, particles down to nanometer diameters can be produced. More recently, a practical system has been developed using the electrospray technique to produce monodisperse particles with a mean diameter from 4nm to 1.8 um and a ag of 1.1. The operating ranges of the important parameters such as liquid feed rate, electrical conductivity, and concentration of the solution were determined (D. R. Chen et al., 1995). Unfortunately, the main group of particles is accompanied by a second group having diameters approximately eight times smaller. For some applications it will be necessary to remove the second group by using a size-selective device. The electrospray technique produces supermicrometer aerosol less monodisperse and with lower concentration than that from a vibrating-orifice generator. However, for submicrometer particles and especially for nanoparticles, electrosprays offer unique advantages. Monodisperse Aerosols with Nonspherical Particles

The effects of particle shape on instrument response are important, especially for sizing instruments. The effects of shape on instrument response can be investigated by using monodisperse aerosols of nonspherical particles during calibration. One way of generating these aerosols is to nebulize a liquid suspension containing monodisperse nonspherical particles. Various techniques have been used to produce monodisperse particles of highly uniform particle size and shape. Matijevic (1985) produced inorganic and polymer colloid particles of cubic, spindle, and rhombohedral shapes by chemical reactions. Fiber-like particles of a narrow size range were also produced using different methods (Esman et al., 1980; Loo et al., 1982; Vaughan, 1990; Hoover et al., 1990; Baron et al., 1994; Chen et al., 1996; Deye et al., 1999). The vibrating-orifice and spinning-disk aerosol generators described above can also be used to generate nonspherical particles, such as crystalline sodium chloride particles. Although the generators produce spherical droplets, the crystal form of the solid particles determines the shape of the final aerosol after drying the liquid. In addition, naturally occurring materials, such as fungal spores, pollens, and bacteria or multiplets of spheres, have been used as test aerosols of nonspherical particles (Corn and Esmen, 1976; Adams et al., 1985). The aerosols of fungal spores, bacteria, and pollens are commonly generated using either the wet dispersion or the dry powder dispersion technique (described later). Depending on the needs when using these test particles of biological origin, the concerns of viability and culturability might need to be considered (Henningson and Ahlberg, 1994; Griffiths et al., 1996; Reponen et al., 1996; Ulevicius et al., 1997). The details of instrument calibration such as the generation, collection, and assay methods for microorganisms and other bioaerosols are described in Chapter 24. Size Classification of Polydisperse Aerosols

An aerosol with a narrow size range can be produced from a polydisperse aerosol by passing the aerosol through a size classifier. For particles smaller than 0.1 um, Liu and Pui (1974)

developed a differential electrical mobility analyzer to classify aerosol particles of the same electrical mobility. Because most of the classified particles are singly charged, most of the aerosol produced is monodisperse, but there is a smaller amount of doubly charged particles with the same electrical mobility but different particle size. This classification technique has been used to produce a submicrometer aerosol for calibrating CNCs and diffusion batteries and for determining particle deposition in human nasal and oral casts (Liu et al., 1975; Scheibel and Porstendorfer, 1984; Cheng et al., 1990). For particles greater than 1 urn, a sizeclassifying technique based on particle inertia is generally used. Two virtual impactors can be placed in a series to segregate the desired fraction of the input aerosol for use in instrument calibration (Chen et al., 1988; Pilacinski et al., 1990). To classify aerosols in the 0.1 to 1.0 urn range, a technique involving both the mobility analyzer and a single-stage micro-orifice impactor has been used (Romay-Novas and Pui, 1988). The above techniques are also used for reducing undesired particles, such as PSL agglomerates from an air nebulizer or satellite particles from a spinning disk generator. All the devices and techniques described above classify aerosol particles while the particles are airborne. Other instruments, such as elutriators, spectrometers, cascade impactors, and cascade cyclones, can be used to classify particles by collecting size-classified particles on a substrate that can then be resuspended. For example, a spiral centrifuge can collect aerodynamically classified particles on aluminum foil; resuspension of the particles caught on a narrow segment of the foil can be used to produce monodisperse aerosols (Kotrappa and Moss, 1971). The disadvantage of most size-classifying techniques is that only a small quantity of particles is produced. Polydisperse Aerosols

Polydisperse aerosols can be used as test aerosols to calibrate instruments and samplers when used with an auxiliary sizing instrument such as the APS. Because the entire size distribution typically can be obtained in a minute, this method has significant advantages. Some polydisperse aerosols, such as aluminum oxide, coal dust, and Arizona road dust, are used in calibrating dust monitors, including samplers for respirable dust. There are two common ways to generate polydisperse aerosols: wet droplet dispersion and dry powder dispersion. Wet Dispersion. The simplest way to disperse a droplet aerosol is by nebulization. Two types of nebulizers are commonly used to produce aerosols. Air-blast nebulizers (Mercer et al., 1968) use compressed air (15 to 50psig; 1 psig = 6.87 x 104 dyne/cm2) to draw bulk liquid from a reservoir as a result of Bernoulli effect (Fig. 21-4). The high-velocity air breaks up the liquid into droplets and then suspends the droplets to form an aerosol. Droplets produced from this method have a VMD of 1 to 10 um and crg of 1.4 to 2.5 (Table 21-3). The particle size distribution can be modified by varying the pressure of the compressed air or the dilution ratio in the solution. A problem arises when the bulk liquid contains a volatile solvent that evaporates rapidly after droplet formation. The continuous loss of solvent increases the solute concentration in the reservoir and causes particle size to increase gradually with time. This problem can be circumvented by circulating the solution through a large reservoir (DeFord et al., 1981), delivering the solution at a constant rate (Liu and Lee, 1975), and presaturating the supply air and cooling the nebulizer. In the ultrasonic nebulizer, the mechanical energy necessary to atomize a liquid comes from a piezoelectric crystal vibrating under the influence of an alternating electric field. The vibrations are transmitted through a coupling fluid to a cup containing the solution to be aerosolized. At a certain frequency (1.3-1.7MHz), a heavy mist appears above the liquid surface of the cup. The diameter of the droplets making up the mist is related to the wavelength of the capillary waves, which decreases with increasing frequency of the ultrasonic vibrations. Normally the VMD is 5 to lOum, with a ag of 1.4 to 2.0 (Table 21-3).

Aerosol out

Vent (closed)

Coarse spray droplets impact on wall and drain to reservoir

Liquid feed tube Liquid reservoir

Compressed air inlet Fig. 21-4. Drawing of a DeVilbiss Model 40 Glass Nebulizer. (Reprinted from Hinds (1999) with the permission of John Wiley & Sons, Inc.).

Aerosol particles with chemical properties different from those of the liquid feed material can be produced through wet dispersion by using suitable gas phase reactions, such as polymerization or oxidation. Production of spherical particles of insoluble oxides and aluminosilicate particles with entrapped radionuclides has been described by Kanapilly et al. (1970) and Newton et al. (1980). Dry Dispersion. The dry dispersion of powders can produce aerosols that have physical and chemical characteristics that are the same as or similar to those that will be sampled by the instrument under calibration. Numerous techniques have been described by Hinds (1980) for dispersing dust or fiber particles (Table 21-4). Basically, the techniques consist of two steps: (1) a means of delivering powder into the dispenser at a constant rate and (2) a means of dispersing the powder to form an aerosol. However, the dispersibility of a powder depends on the powder material, particle size, particle shape, and moisture content. Two common methods for generating aerosols from powders are the Wright Dust Feed (Fig. 21-5) and the fluidized bed (Fig. 21-6). Aerosol generators using fluidized beds as the dispersing mechanism have the ability to thoroughly deagglomerate powdered samples. When equipped with a suitable dust feed mechanism, fluidized beds can operate stably over long periods of time. Fluidized beds can be scaled over a wide range of sizes, from very small to extremely large, producing aerosol concentrations from milligrams to tens of grams per cubic meter. A fluidized bed consists of relatively large bed particles, typically on the order of 100 um diameter, in a cylindrical container. The floor of the bed is of a porous material, such as a fine

TABLE 21-4. Operating Parameters of Dry Powder Dispersers

Type of operation

Wright Dust Feed

Fluidized Bed

NBSII Dust Generator

Small Scale Powder Disperser

Jet-O-Mizer Model 00

Scraping the packed plug and dispersing it with air

Feeding the powder to the bed on a conveyor and air fluidizing it

Using metering gear to deliver the powder and air dispersing it

Using rotating plate to deliver the powder and dispersing it with Venturi suction

Using Venturi suction to feed the powder into a fluid energy mill in which centrifugal force and air velocity are used to break up the agglomerate and to disperse the powder

Air flow rate (XlO"5 m3/s [L/min]) Feedflowrate (mnvVmin) Output mass concentration, g/m3 (p= 1000 kg/m3) Recommended size range (urn)

0.24-210 0.012-11.5

1.2-36 0.13^.0

0.2-100

0.5-100

Source

BGI

14-67 [8.5-40]

8-33 [5-20]

TSI

80-140 [50-85] 1,200-50,000 15-100

20-35 [12-21] 0.9-2.5 0.0003-0.04

1-100 BGI

1-50 TSI

23-188 [14-113] 2,000-30,000 10-1,500 0.2-30 FLU

Dust cylinder (rotating)

Compacted dust Scraper head; \ (stationary) : Scraper blade;

Direction of cylinder advance

Differential gear train

Threaded spindle Spindle gear (powered by gear drive) Compressed air inlet Base plate Partial impactor

Aerosol outlet Fig. 21-5. Wright Dust Feed. (Reprinted from Hinds (1999) with the Permission of John Wiley & Sons, Inc.).

screen or a filter, which can retain the bed particles but allow an upward flow of air. In operation, a fluidized bed resembles a boiling liquid, but the best indicator of fluidization is the pressure drop across the bed. As the upward airflow is increased from zero, the pressure drop initially rises linearly with flow rate. Eventually, a condition is reached where the air drag on the particles is equal to the weight of the bed. The pressure drop curve then levels off. The flow velocity at the break in the curve is called the minimum fluidization velocity (MFV) (Carpenter and Yerkes, 1980). The MFV is correlated with the bed particle Reynolds number. For a typical fluidized-bed aerosol generator, the MFV is on the order of lOcm/s. In the two-component fluidized bed initially described by Guichard (1976), a relatively fine powder of the material to be aerosolized is added to a fluidized bed containing larger

DUST AEROSOL

RADIOACTIVE SOURCE

ELUTRIATION CHAMBER

FLUIDIZED BED

OPTIONAL 1/2" HASL CYCLONE ATTACHMENT POWDER HOLDING CHAMBER CHAIN DRIVE SPROCKET

SUPPORT SCREEN

POWDER FILTERED AIR GASPLENUMCHAMBER

RAKE CHAIN

PLASTIC SLEEVE

Fig. 21-6. Two-Component Fluidized-Bed Aerosol Generator. (Reprinted from Marple, Liu, and Rubow (1978) with the Permission of American Industrial Hygiene Assoc).

bed particles. An airflow velocity sufficient to fluidize the bed will exceed the elutriation velocity of the powder particles. The details of the aerosolization process are not known, but a plausible scenario is that in the collisions between bed particles, adhering powder or dust particles are knocked off the bed particles. The constant action of the bed promotes uniform coating of the bed particles with a layer of dust particles, accounting for the deagglomeration that is observed. Fresh surfaces are constantly generated by the grinding action in the bed. Therefore, the use of dry air is recommended because moisture promotes oxidation of particle surfaces. Strong electrical charging is usually produced from contact and triboelectrification. This indicates that the aerosol should be neutralized before use. Some fluidized-bed generators incorporate strong sources of sound or vibration. Vibration can improve the performance in several ways. There is a tendency for channeling to occur in the bed, whereby the air flows at a higher rate in localized areas. Vibration can suppress the channeling, promoting a more uniform flow. Dust may collect on the walls above the bed. Periodically, the accumulation breaks loose, producing a burst in concentration. Vibration inhibits such an accumulation of dust. Vibration also improves the feeding of dust into the bed from a screw or chain. An example of a two-component fluidized bed aerosol generator (Marple et al., 1978) is shown in Figure 21-6. The fluidized bed has a 1.4 cm thick layer of lOOurn brass beads

(stainless steel is also frequently used) in a 5.1cm diameter chamber. This type of generator is commercially available (Model 3400, TSf). While such a generator is useful for many purposes, there are some nonideal aspects that require attention. When the generator is turned on with fresh metal bed particles, an aerosol is produced from the bed particles themselves. Initially, the source of particles is the fraction of small particles present in the bed particle sample. These small particles can be rapidly cleared from the bed by operating first at a higher than normal flow velocity. Even after the small particles are removed, a fine aerosol persists for a long period of time due to the grinding action that removes asperities from the bed particles. This type of background is not observed with glass beads, which are smoother and have less violent collisions because of their lower mass. After operation with a dust sample, a fluidized bed cannot be cleaned up effectively. If the same type of aerosol is needed again, the bed can be emptied and the material stored for future use. The properties of some dusts may be altered in a generator having large metal bed particles. For example, aluminum particles can be flattened, and aluminum oxide particles can be broken up in the bed. Such alterations of particle properties could be significant for the subsequent use of the aerosol. The problem can be alleviated by using smaller bed particles or bed particles of lower density. John and Wall (1983) developed a sonic fluidized bed, which avoids some of the problems of the large beds and which is useful for some applications. The bed's main feature is its small size, 25 mm diameter at the base, requiring less than Ig of bed particles. The bed is funnel shaped so that the fluidizing velocity is higher than the exit velocity, favoring control of the elutriation velocity. For bed particles, 200 um glass beads of the type used in gas chromatographic columns can be used. Such beads are highly uniform and clean. Because of the small amount of glass beads required, they can be discarded when dust samples are changed. A refinement is the addition of sonic energy to the bed. The sonic fluidized bed (Fig. 21-7) is vibrated by inexpensive piezoelectric crystals driven by an electronic oscillator at approximately 9 kHz. Because the bed lacks a feed system, it can only be used in a batch mode. It has been used successfully to generate aerosols of glass beads, A/C test dust, and soil for the testing of aerosol samplers. The soil was simply passed through a coarse screen and placed in the bed without bed particles, the coarsest soil particles functioning as bed particles. A feed system for a fluidized bed generator was designed by Sussman et al. (1985) that allowed more constant and controllable output over time. The powder and bed beads were mixed, placed in a hopper, and pneumatically fed in small amounts at selected time increments into the fluidized bed. The overflow from the fluidized bed was allowed to fall into an overflow chamber to keep the bed height constant. Spurny et al. (1975) developed a fluidized bed for the generation of aerosols of asbestos fibers. A special feature of this generator is a mechanical vibrator with adjustable amplitude and frequency. The effect of these vibration parameters on the aerosol concentration, fiber diameter, and fiber length was explored for several varieties of asbestos. It was found that the aerosol characteristics can be controlled to some extent by adjusting the vibration parameters. The generator was found capable of producing useful asbestos aerosols. In a similar approach, Weyel et al. (1984) used a low-frequency sonically fluidized bed to generate cotton fibers. Besides the Wright Dust Feed and the fluidized bed, several dry powder generators are commercially available (Table 21-4). The TSI small-scale powder disperser is used to produce a small quantity of powder aerosols primarily for laboratory testing (B. T Chen et al., 1995), and the Jet-O-Mizer is able to produce a large quantity of powder aerosols for inhalation studies (Cheng et al., 1985). A simple technique for generating brief bursts of latex particles is to place a small quantity of the suspension on a glass slide or other clean surface, allow the suspension to dry, and gently brush the deposit off the surface toward the inlet of the instrument to be calibrated. In the approximate range 2 to 20|Lim and larger, this approach is useful for size calibration

Next Page

DILUTION AIR CLEAN AIR

Po-210 SOURCES (3) 10 ^m MESH NYLON SCREEN

PIEOZOELECTRIC CRYSTALS

MYLAR LUCITE

FLUIDIZING AIR AEROSOL Fig. 21-7. Sonic Fluidized-Bed Aerosol Generator. (Reprinted from John and Wall (1983) with the Permission of Pergamon Press, Inc.)

of high-resolution instruments. When generated in this fashion, particles are more likely to be agglomerated, especially at the low end of the indicated size range. Some generation techniques for fibrous aerosols are described in Chapter 23. Test Aerosols with Tagging Materials

For some applications, particle detection is facilitated by incorporating fluorescent dye or radioisotope tags in the particles during their production. A fluorescent tagging material such as fluorescein can be analyzed in solution with nanogram sensitivity. Colored substances such as methylene blue can be analyzed with microgram sensitivity. The tagged aerosol may be extracted from a filter or a surface, enabling the quantitation of collection efficiency and wall losses within a sampler. Radiolabeling techniques have been used with the capability of detection of extremely low concentrations (Newton et al., 1980). CALIBRATION OF FLOW, PRESSURE, AND VELOCITY Accurate measurement of gas flow rate, pressure, and velocity is an integral part of instrument calibration (Mercer, 1973; Lippmann, 1995; Hinds, 1999). Various instruments

Types of Particle Size There are different kinds of geometric particle size used to characterize nonspherical particles. A nonspherical particle's size might be its longest dimension, shortest dimension, or some combination of the two. One might use the diameter of a sphere with the same volume or the same surface area. For spherical and nonspherical particles measured with a technique that does not give geometric size, other "equivalent" diameters are often used: aerodynamic, optical, diffusion, electrical mobility, and so forth. These are described in various chapters throughout this book. Particle Shape Particle shape is also very important, and different shapes can produce rather different behaviors for particles of the same "equivalent size." There is a substantial body of literature concerning characterizing particle shape, much of which has been contributed by J. K. Beddow and co-workers. A valuable starting place for accessing this information is Beddow's excellent book, Paniculate Science and Technology (1980), and more information is available in a second text of his (Beddow, 1997). With a narrower focus, a fine review of spray-pyrolysis particle morphology is presented by Jain et al. (1997). Further information about particle shape as it relates to aerosol measurement is described in Chapter 23. PARTICLE SIZE DISTRIBUTIONS Having decided on an appropriate measure of the particle size, which here we label d, one is still faced with not a single size, but a wide range of sizes, in most aerosols. Although summary statistics, such as the mean and standard deviation of the particle diameter, can be used to advantage, one may want to give a more detailed, albeit approximate, description of particle sizes. To do so, we often use either a frequency distribution or a cumulative distribution. Cumulative Versus Frequency Size Distributions A cumulative distribution gives the fraction of particles (by some measure) smaller than the particle size (by some measure). Table 22-1 shows a cumulative distribution F(d), in this case a normal (also called Gaussian) distribution, with a mean of fi = 10 and a standard deviation of (J= 1. The first column shows the particle size at the upper end of each sizing interval. The next column shows the fraction of the number of particles smaller than the size indicated in the column. This is a cumulative distribution. Figure 22-1 shows the full cumulative size distribution, abbreviated in Table 22-1, plotted on linear axes. The distribution is S shaped, "sigmoidal."

TABLE 22-1. Normal (Gaussian) Distribution, with a Mean of H = 10Jim and a Standard Deviation of a = l|im Particle Size (|im) 7 = jn - 3CT 8 = n - 2(7 9 = ji - I a 10 = Ai 11 = / i + lcr 12 = \i + 2(7 13 = pL + 3cr

Percentile (Percent Smaller) 0.135 2.27 15.9 50.0 84.1 97.73 99.865

Percentile

Particle size, \im Fig. 22-1. Cumulative distribution, particle size: Gaussian (normal) distribution on linear axes. (Mean is H = 10, and standard deviation is a = 1.)

The frequency distribution is determined from the change in the cumulative distribution divided by the change in particle size. The data in Table 22-1 can be analyzed to create an approximate frequency distribution, the fraction of the aerosol in various size intervals. The most common approach is to take the cumulative distribution value at one diameter, F(d2), and subtract from it the cumulative distribution value at a smaller diameter, F(dx), and divide by the difference between the two diameters: (22-1) The value of d to which f(d) corresponds is usually chosen to be the mean of dx and d2: (22-2) Such an approximate frequency distribution might best be plotted as a histogram, a bar graph with the bars filling each size interval and having a height representing the frequency. The choice of the size intervals will somewhat affect the shape of the distribution, however. This process of finding the frequency distribution numerically is an approximation of the definition of the relationship between the frequency and cumulative distributions: (22-3) The term dd is a bit strange in appearance, but it denotes an infinitesimal change in a variable, in this case d. The frequency distribution is the derivative of the cumulative distribution. The frequency distribution and the cumulative distribution contain the same information, in different forms. Figure 22-2 shows the frequency distribution that would be calculated in the limit of infinitesimal size intervals. The frequency distribution is the Gaussian (or normal) distribution, discussed next. The Normal (Gaussian) Distribution

Most scientists and engineers are familiar with the Gaussian (normal) frequency distribution, the "bell-shaped curve" that describes many phenomena. For particles with a Gaussian size

Frequency

Particle size, um Fig. 22-2. Frequency distribution, particle size: Gaussian (normal) distribution on linear axes. (Mean is ji = 10, and standard deviation is a= 1.)

distribution, the fraction of the total (by count) within an infinitesimal size interval, dd, centered on size d is (22-4) in which a is the population standard deviation and fx is the population mean. The mean is a measure of the central tendency of the sizes, their sum divided by the number measured. The standard deviation is a measure of their spread, the square root of the mean squared difference between the sizes and the mean size. Normal distributions tend to arise when a multitude of small additive (+) factors influence a variable that would otherwise have a single value. The normal distribution is found in aerosol science in cases where the particles are all nearly of one size, nearly monodisperse, such as polystyrene latex spheres used for calibration of particle measurement equipment or the droplets produced by the vibrating orifice or spinning disk generators. The Lognormal Distribution

Lognormal particle size distributions are more common than normal distributions in aerosol science and technology. Particle size distributions often have standard deviations that are large in comparison with their mean sizes, which cannot occur for the inherently nonnegative normal distribution. Such distributions are often described mathematically by the lognormal distribution. Lognormal distributions arise when a multitude of multiplicative factors (both greater and less than 1) act on a variable that would otherwise have a single magnitude. The lognormal distribution (Aitchison and Brown, 1957; Fuchs, 1964; Hinds, 1982; Crow and Shimizu, 1988; Heintzenberg, 1994) is the distribution that results when the distribution of log(x) is Gaussian ("normal"). Lognormal distributions can be shown to result from the proportional breakup of large objects into smaller ones (Kolmogorov, 1941; Epstein, 1947) or from certain types of agglomeration of small objects into larger ones (Friedlander, 1977). The lognormal distribution will result from the growth or breakup process dx(i)/dt = k(i)x(i) when the growth constants k(j) are normally distributed. The x(i) are variables having to do with the sizes of the species i, typically the volumes of the species. Many aerosol particle size distributions have been well approximated by the lognormal distribution. The estimation of its parameters from truncated measurements was analyzed by Gentry (1977).

TABLE 22-2. Percentile Values for the Lognormal Distribution Size

Percentile 3

d50/<7g dso/cJg2 dso/cTg d50

0.135 2.27 15.9 50.0

d50 O8

84.1

^50 C7g2 J 5 0 CTg3

97.73 99.865

"The median is d50, and the geometric standard deviation is crg. Note the similarity to the results in Table 22-1.

Fiber length distributions and length-weighted diameter distributions have also been found to be approximately lognormal (Fogel et al., 1999). Some counter-examples were presented by Christensen et al. (1993). Using the assumption of lognormal length distribution and Monte Carlo simulations of fiber interactions with a rectangular filter grid (see also Cooper et al., 1978), Myojo (1999) showed that penetration through the grid depended on the ratio of the length of the fibers to the spacing of the grid elements; he also gave an extensive list of references on fibrous aerosols. The lognormal distribution is actually a normal distribution of logarithms of particle size, \og(d/do). The reference size, do, is usually ljim. The lognormal distribution's measure of central tendency is the geometric mean for a lognormal distribution, and the measure of spread is the geometric standard deviation, labeled O8. The geometric mean is the nth root of the product of n values; it is the antilog of the mean of the logarithm of the n values. The median is the 50th percentile particle size and equals the geometric mean for the lognormal. Common medians are the number median, area median, activity (radioactivity) median, volume median, and mass median. The geometric standard deviation (c7g) for a lognormal distribution is the ratio of the 50th percentile size to the 16th percentile size, which for a lognormal distribution is also equal to the ratio of the 84th percentile size to the 50th percentile size. The geometric standard deviation is related to the standard deviation of the logarithms of size. As the geometric standard deviation approaches <jg = 1.0, the lognormal distribution approaches the normal distribution having a relative standard deviation of a- ag - 1. Table 22-2 shows the percentiles for the lognormal distribution in terms of the median and the factors of the geometric standard deviation,
The power-law frequency distribution is given by (22-5) where b is typically a power less than 0. The cumulative power-law distribution is (22-6) In the atmospheric sciences, the power-law distribution is often called the "Junge" distribution after C. Junge, an important contributor to the field. The cumulative size distribution is proportional to the reciprocal of the particle size approximately cubed, thus b = -4.

Percentile

In the contamination field, a power-law distribution is used in Federal Standard 209E (U.S. General Services Administration, 1992) shown in Figure 22-4 to characterize the typical particle size distribution in a clean room. The cumulative count size distribution is proportional to d~22. The cleanroom class in U.S. customary units is determined by the number of particles larger than 0.5 urn in the distribution per cubic foot of air. "Class 100" describes a clean room that has been shown to have fewer than 100 particles 0.5 urn or larger in diameter per cubic foot. The metric version, uses concentrations in terms of per cubic meter and steps of powers (M) of ten and the square root of ten, 1005 = 3.16. The power-law distribution is just a useful approximation, allowing one to predict easily the approximate behavior of the entire aerosol rather than just of individual particles. For example, if the deposition velocity of the aerosol is approximated by a power law, then the flux (deposition velocity times concentration) can be readily determined as a power-law function, too. A power-law distribution can be created by a process that subdivides material with breakage rates proportional to the size of the material. This mechanism may explain why certain aerosols from wear processes are well described by this distribution. Power-law distributions

Particle size, jim

Cumulative number concentration, ft~3

Fig. 22-3. Cumulative particle size distribution: lognormal on linear axes. (Median size is d5Q = 1, and geometric standard deviation is ag = 2.)

Particle size, urn Fig. 22-4. Cumulative particle size distribution: power law on log-log axes (Federal Standard 209E Classes 10,100, and 1000).

also arise in the study of fractal materials, some of which are formed by agglomeration. Here again, the agglomeration rate is proportional to a power of the particle size. A fine survey of fractal geometries is presented by Mandelbrot (1977). In Kaye (1989) are many illustrative examples of fractals in particle contexts, and there is even a section devoted to "fractal geometry and aerosol physics." Kaye also contributed two valuable chapters on sizing and on shape characterization in the Fayed and Otten (1997) handbook on powders. One, two, or three different fractal dimensions were identified by Xie et al. (1994) from images of different types of particles, using "multifractal" analysis, essentially fitting straight lines to "Richardson" plots, log[P (perimeter)] versus log[L (step size)]. The effects of repetitive condensation/evaporation on agglomerate shapes were studied by Ramachandran and Reist (1995) using fractal descriptive techniques; the size at the slope transition in the Richardson plots was a good indicator of the size of the primary particles in the agglomerates. The surface fractal dimension was used to characterize powders of industrial significance, following the measurement of their nitrogen adsorption isotherms, and found to be a useful approach, though one of relatively low resolution (Wu, 1996). Multifractal analysis has also been used on the time series data from particle concentration measurements to get fractal time "lengths." See Chapter 23 for further discussion of fractal particles. Other Distributions Other distributions have been useful in various contexts, such as the Rosin-Rammler for coarse dusts and sprays (Lefebvre, 1989), the exponential for powders, the NukiyamaTanasawa for sprays, the exponential for some powders, and the Khrgian-Mazin for cloud droplets. One should consult texts such as those by Fuchs (1964) and by Hinds (1999) for more information. For light scattering from atmospheric aerosols ("haze"), McCartney (1976) described in detail the modified y function, the product of the size to a power times the negative exponential of the size to a separate power, with four adjustable constants. In some cases, there may be value in using the four-parameter hyperbolic distribution described by Christiansen and Hartmann in Syvitski (1991). This distribution includes the normal, lognormal, Laplace, and exponential distributions as special cases; its four adjustable parameters allow very flexible fitting to data. The log (3 distribution was used to characterize aerosol size distributions by Bunz et al. (1987). The distributions listed here are all unimodal distributions, but multimodal distributions can be formed from the addition of unimodal distributions. CONCENTRATION DISTRIBUTIONS Aerosol concentrations are rarely uniform and constant. Instead, one finds distributions of concentration values. Depending on how the data are accumulated over time, such changes may add uncertainty to the determination of the particle size distribution. Concentration distribution information can often be summarized with the same methods as those used for particle size distributions. For example, the sample mean and the sample standard deviation are obtained for the particle count just as for the particle size. Three such distributions are discussed next: normal, lognormal, and Poisson. The Normal Distribution Normal distributions of concentrations are unusual. As noted above, the normal distribution arises from a large number of (positive and negative) additive effects. Sometimes, instruments that are themselves variable will be used to measure a relatively constant aerosol concentration, and the resulting data will be approximately normally distributed.

The Lognormal Distribution

Lognormal distributions of concentrations are not unusual. The lognormal distribution arises from a large number of multiplicative effects. It can arise from rate equations that have variable rates, as well. The geometric mean concentration and the geometric standard deviation of the concentration values fully describe such lognormal concentration distributions. The Poisson Distribution

The Poisson distribution, discussed next, is a very useful approximation. It describes the particle count data that one would obtain from sampling an aerosol that has a constant mean number concentration. Even though the concentration is constant, the entrance of particles into the sampler is probabilistic, leading to some variation, the smallest amount of variation encountered in practice. Denote the particle count in the ith time interval as n(i). The mean count in an interval is /x, and the standard deviation of the count is a. For the Poisson distribution the mean equals the variance, fx = a 2 , so that the count standard deviation can be obtained from the square root of the mean count. If one has accumulated counts over many intervals to form one long-duration sample, then the estimated standard deviation for this long-duration sample is the square root of the total count. The total count is the best estimate of the mean total count. The ratio of the standard deviation to the mean will be inversely proportional to the square root of the total count, indicating that the percentage uncertainty in the total count decreases inversely with the square root of the total count. For the Poisson distribution, the probability of getting n counts in an interval from a population that has a mean fi in the interval is (22-7) This is the distribution one would expect when using an optical particle counter to obtain particle counts over many time intervals from a constant concentration aerosol or when inspecting a large number of equal surface areas that have been exposed to a constant concentration aerosol for the same duration. A simple application of the Poisson distribution is the estimation of an upper limit for the true count when the interval that was sampled gave no counts. One uses (22-8) (22-9) and notes that if the concentration had been as high as \i = 2.3, there is only a 10% probability, P(OI2.3) = 0.1, that n = 0 particles would have been found. The hypothesis that /i = 4.6 yields a 1% probability that n = 0 would be found, and so forth. One has 90% confidence that the true mean count for the interval is 2.3 or less and 99% confidence that it is 4.6 or less, and so forth. The Poisson distribution can be used as the expected or hypothetical distribution when testing the hypothesis that the counts came from a constant, uniform count concentration distribution, using the chi-square analysis described below, for example. Another use of the Poisson distribution is for modeling coincidence, the arrival of two or more particles (or their signals) in an interval (of space or time), discussed below. For means much larger than 10, the Poisson distribution is very similar to the normal distribution (evaluated only at integer values). The use of the square root of the count as the

variable transforms a Poisson distribution into a more nearly normal distribution (at integer count values) (See Box et al., 1978.) Transforming count data by taking the square root not only stabilizes the standard deviation (to 0.5) for a Poisson distribution but has the general advantage of allowing convenient graphing of counts that have a large range that includes 0, which would make use of the logarithmic transformation awkward. With the square root transformation, the range of 0 to 10,000 would use the same extent as would 0 to 100 without the transformation.

SUMMARIZING DATA WITH A FEW PARAMETERS Data for Individual Particle Sizes One kind of data to summarize with a few parameters is particle size data obtained by sizing and counting individual particles. Assume that each particle has a measured diameter, d(i), and that the number of particles measured is N. The sample mean particle diameter is then the sum of all the diameters divided by the number measured: (22-10) in which we have used M to denote the sample mean. The sample standard deviation, s, of the particle diameter is the square root of the sum of the squared differences between the particle diameters and the mean, divided by N - 1, one less than the number measured: (22-11) The sample variance is s2, the square of the sample standard deviation. The sample mean, M, is an estimate of the population mean, JJ,, and the sample standard deviation, s, is an estimate of the population standard deviation, <x The estimates become increasingly accurate as the number TV in the sample increases. If the population has a Gaussian (normal) distribution, then it is completely described by these two parameters. Data for the Fraction of the Aerosol in Each Interval

If the data are such that each datum represents the fraction of the aerosol (e.g., by count or by mass),/(d[/]), in each size interval, Ad(i), then these equations for the sample mean diameter and the sample standard deviation become (22-12) (22-13) Elsewhere in this book is an example of hypothetical count data for some size intervals and the f{d[i\) calculated from these data. Functional Form of Distribution

From the functional form of the distribution, /(^), one can calculate the population mean from

(22-14) and the population standard deviation from (22-15) Often, the expected value notation is used for population means: (22-16) where E(g[d]) is the expected value (= population mean) of g(d),f(d) is the frequency distribution, and g(d) is a function of diameter, such as area, volume, and so forth. If f(d) is based on count, then E(g[d]) would be the count mean of g(d). If f(d) is based on mass, then E(g[d]) would be the mass mean of g(d). Other possibilities exist, such as the surface mean of g(d) or the mass mean of g(d), and so forth. The most common means are the count (number) mean diameter and the mass mean diameter, which use g(d) = d and use the count or the mass frequency distributions for f{d). Parameters for the Normal Distribution

As can be seen from Eq. 22-^, the normal distribution is completely determined once the mean and standard deviation are specified. Parameters for the Lognormal Distribution

The two parameters that totally describe the lognormal distribution are the geometric mean (which, for the lognormal, equals the median) and the geometric standard deviation. These are usually obtained from count or mass distributions. The median diameters are then labeled "count median diameter" or "mass median diameter." Sometimes, one uses the surface median or activity median diameter. In all these cases, the definition of the median diameter is that half (50%) of the count or mass or surface or activity, as appropriate, is contained in particles that are smaller than the median diameter. Of course, other percentiles (such as the 90th) could be used as the basis of a descriptive diameter, and occasionally are. The geometric standard deviation for a lognormal distribution is independent of the power of the diameter being measured. Thus, O8 will be the same whether particles are counted or weighed. The various diameters for a lognormal distribution are related to each other (see Example 22-3). These "Hatch-Choate" relationships for various means and medians for the lognormal distribution are presented in the text by Hinds (1999), from which we extracted the following equations, in which MMD is the mass median diameter, SMD is the surface median, LMD is the length median diameter, and CMD is the count median diameter: (22-17a) (22-17b) (22-17c) (22-17d) (22-17e) Often, MMD is much larger than CMD. For example, if cxg = 2, MMD/CMD = exp[31n(2)2] = 4.23. CTg = 2 may be a typical geometric standard deviation for a poydisperse aerosol. If org = 3, MMD/CMD would be 37.4, and so forth.

TABLE 22-3. Various Means and Medians for a Lognormal Distribution having a Count Median of 1 and a Geometric Standard Deviation of 2

Count mode diameter (frequency peak) Count median diameter (CMD) Count mean diameter Diameter of particle of average area Diameter of particle of average mass Area median diameter Area mean diameter Mass (volume) median diameter (MMD) Mass (volume) mean diameter

0.62 1.00 1.27 1.62 2.06 2.61 3.32 4.23 5.37

Based on Reist (1984).

Another good reference on the Hatch-Choate relations for lognormal distributions is the book by Reist (1984). Heintzenberg (1994) gave not only the familiar relationships for the lognormal distribution but also equations for integrals involving the lognormal function. Table 22-3 shows the various weighted means and medians for a lognormal frequency distribution (CMD = 1, ag = 2) based on the values given by Reist (1984). To get the area mean diameter, for example, one must transform the count or mass distribution to an area distribution and then integrate the area frequency distribution function times the particle diameter. For lognormal distributions, the Hatch-Choate relationships include such transformations. Unfortunately, it is rare for any aerosol to be so nearly lognormal that measurements by count of the count median diameter and the geometric standard deviation can be used to estimate accurately the mass median diameter, or vice versa, except for cases of nearly monodisperse aerosols (
A linear regression equation is the least-squares best fit of the linear equation y = mx + b to sets of x(i), y(i) pairs. The slope (dy/dx) of the line is m, and the intercept (y at x = 0) is b. The least-squares best fit produces the estimates for m and b that minimize Z(y[i] - mx[i] b)2. The equations for estimating m and b from the data are given in most statistics texts, such as Hays and Winkler (1970) or Draper and Smith (1981), and form the basis for many different computer programs, which is how virtually all regression analyses are done now. The computer programs generally give not only the best estimates of m and b but also the uncertainty in the estimates, the standard errors of m and of b, SE(ra) and SE(b). These

standard errors are analogous to the standard errors of the mean for a variable x. If the data were to match the assumptions of linear regression, then in 68% of the instances the confidence interval of m ± l.OSE(m) would include the true slope, and in 95% of the instances the confidence interval of m ± 1.96SE(ra) would include the true slope. The same statements can be made about b ± SE(b) and b ± 1.96SE(6) and the true intercept. The more data, and the smaller the error in the data, the narrower will be the confidence limits for the slope and the intercept. Note that the assumptions underlying linear regression are rarely fulfilled exactly. These assumptions are (1) the relationship between y and x is truly linear; (2) the measurements of the independent variable, x(i), contain no errors; (3) the measurements of the dependent variable, y(i), contain only an additive error that is Gaussian with a mean of zero and a standard deviation that does not change. Because these assumptions are rarely fulfilled exactly, the probabilities associated with the confidence intervals are at best approximations. If the measurements of the independent variable, x, do contain an error, then the slope will be erroneously biased toward zero. The intercept will be biased in this case, too. For a power-law distribution, the relationship becomes linear if logarithms are used: (22-19a) (22-19b) (22-2Oa) (22-2Ob) It is easy to use a linear regression program on the logarithm of the cumulative size distribution versus the logarithm of the size. One can also simply plot logF(d) versus logd, or plot F(d) versus d on log-log axes, and draw the apparent best-fit line through the data. The use of a linear regression package is preferable, not so much for improved accuracy (recall that the regression assumptions are rarely met) as for being objective rather than subjective and, thus, being wholly reproducible by others. Figure 22-5 shows hypothetical data and the best-fit straight line obtained from linear regression. Note that the assumption that the standard deviation of y is independent of x is clearly violated because the spread in the values of y gets bigger as y gets smaller. It would be advisable to transform y to log(y) or to ^y" and try regression again. Log(y) versus log(jt) should also be tried.

Y

9 READINGS EACH X. VARIANCE DEPENDS ON X; SHOULD TRANSFORM Y.

X Fig. 22-5. Example of data and a least-squares linear regression line of best fit.

Often the correlation coefficient (-1 < r < 1) is used as a measure of the degree to which y is linearly dependent on x. This can be misleading. The smaller the errors in y in comparison to the change in y across the range of x, the better is the correlation coefficient and the better is the fit of the line to the data. Note that data taken with a very large range in y can have large errors (in y) and still give impressive correlations (IrI close to 1). Also, seemingly high correlation values can be obtained from small numbers of samples even if there is no underlying relationship between the variables. The reader is encouraged to consult statisticians or statistics texts about such fine points as hypothesis testing with correlation coefficients, carrying out regressions when there are errors in the independent variable (x), and accounting for variation in the error of y over the range of x by using regression with weights adjusted to the estimated errors in y (Draper and Smith, 1981). SUMMARIZING SIZE DISTRIBUTIONS GRAPHICALLY Although equations or tables are more useful for further analysis of the data, graphs can quickly convey the essential features of particle size distributions. General Advice An excellent book on the topic of graphing data is the work by Cleveland (1985). Some of his valuable suggestions include the following: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

Make the legend describing the graph complete and succinct. Choose symbols for the data that are easily distinguished. Check carefully for errors: labels, tick marks, captions, data. Make sure that the elements are large enough and clear enough for reproduction and any planned size reduction. Make the data prominent. Keep necessary elements only. Surround the region with the scale lines, putting the tick marks outside the rectangular "data region." Keep labels, captions, and so forth, outside the data region if possible. Explain any error bars (e.g., standard deviation, standard error). Choose axis scales that help minimize empty space. Zero need not always be shown, for example. Choose axis scales that help readers draw conclusions (e.g., log-log where a power law, y = axb, is being investigated).

We add that when two or more graphs are to be compared, their axes should be as similar as possible. Cumulative Versus Frequency Representations

The familiar bell-shaped curve of the normal frequency distribution shows readily the location and width of that distribution. This frequency presentation is also convenient and clear when the distribution is made up of the sum of several distributions, for example, in multimodal distributions, having more than one local maximum. A frequency distribution having more than one component may become a cumulative distribution with a shape that is not so readily interpreted by eye. To determine the fraction of the distribution that lies between two sizes, for example, the frequency distribution format is awkward because one must integrate

the area under the curve. Shifting the interval widths or changing from linear to logarithmic progression in the independent variable can produce rather dramatic changes in the appearance of a frequency distribution. The cumulative distribution format contains the same information as the frequency distribution. The central location can be estimated from the median, the 50th percentile, and the spread can be estimated from the difference between two percentiles, such as the 16th to the 84th percentiles or the 25th to the 75th, called the interquartile range. The percentage between two sizes is readily obtained by subtraction using the cumulative distribution rather than by the more difficult process of integration (estimation of the area) using the frequency distribution. There are transformations that convert some cumulative distributions into easily fitted straight lines. Thus, it seems generally better to use the cumulative size distribution rather than the frequency size distribution. Plotting Data and a Fitted Curve

For plotting one or a few distributions, the presentation of both the data and the best-fit curve(s) on the same graph has the advantages of completeness and clarity. Each curve helps to summarize, and the data are shown to allow others to decide on how good a summary each curve provides or to help them take the analysis further. The data should be made available in a tabulated form, too. The easiest way to plot the best-fit curve is to use axes that transform the cumulative distribution relationship it into a straight line. This is discussed next. Plotting with Linear Axes

The size data usually start as counts (or mass) in particle size intervals. By dividing by the total count (or mass), the data can be converted to segments dF(d[i]) of the cumulative distribution in the intervals around d(i). By accumulating the segments dF, one can create the useful cumulative distribution function, F(d). If F(d) plotted against d using linear axes produces a rather symmetrical S-shaped curve, then it is likely that the distribution is normal or nearly so. If the curve is somewhat S shaped but with the large-diameter part much more extended than the small-diameter part, then the distribution may well be lognormal or nearly so. If the F(d) curve is a straight line or if it has only one curved region rather than two, then the power-law distribution is a candidate. Plotting each of these is discussed next. Plotting with Transformed Axes

A cumulative normal size distribution will plot as a straight line on axes that have their percentile values spaced in equal intervals of probits as the ordinate scale and micrometers (or other units of length) as the abscissa scale. Probits indicate how many standard deviations from the mean the percentile value represents for a truly normal distribution. The probit value for the 50th percentile is 0, and the probit values for the 16th and 84th percentile values are -1 and 1, respectively, and so forth. Seventeen probit-percentile pairs are given in Table 22-4, with more values readily obtained in most statistical texts and from some pocket calculators or from computer programs. Other probit values can be obtained in this range from the approximation formula (22-21) Plotting data from a cumulative normal distribution on a probit axis versus the size on a linear axis will produce a straight line. However, the data will rarely be perfectly normal, so

TABLE 22-4. Percentile Values and Corresponding Probit Values Percentile

Probit Value

Cumulative mass fraction, F(d), probits

00.003 00.023 00.135 00.621 02.275 06.681 15.866 30.854 50.000 69.146 84.134 93.319 97.725 99.379 99.865 99.977 99.997

-4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 +0.0 +0.5 +1.0 +1.5 +2.0 +2.5 +3.0 +3.5 +4.0

LABAIR

WOOD DUST

Particle diameter, d, jim Fig. 22-6. Cumulative size distribution data (Shen and Ring, 1986) plotted as mass fraction measured in probits versus the logarithm of the apparent diameter. Open symbols indicate laboratory air; closed symbols indicate wood dust. Impactor 296A data are denoted with triangles, 296B data with circles. Lognormal distributions would plot as straight lines.

some deviation from a straight line can be expected. In plotting the best-fit straight line to data that are approximately normal, it is better to calculate the mean (M) and the standard deviation (s) directly from the formulas given above than to use linear regression to obtain the slope and the intercept of the curve. The probit value for points on the cumulative normal probability curve will be probits = (d - M)/s

(22-22)

Figure 22-6 shows an example of cumulative distributions plotted on a probit scale after the particle size d has been transformed by \og(d/d0), a transformation that creates a normal distribution in logd from a lognormal distribution in d. The data were taken from impactor measurements reported by Shen and Ring (1986), discussed more fully below.

The lognormal distribution independent variable is generally referred to as the logarithm of the particle size. Because logarithms take dimensionless numbers for their arguments, the variable is actually \og(d/d0), where d0 is often in micrometers. Finally, the cumulative distribution for a power-law aerosol is readily plotted either as log F versus log(d/d0) on linear axes or F versus d/d0 on logarithmic axes. CONFTOENCE INTERVALS AND ERROR ANALYSIS Having made measurements, one would like an estimate of their precision, an estimate of where future measurements of the same type would fall if they were made. One can also calculate a mean from the measurements, and one would like to know how precise an estimate the sample mean is of the true mean (unknown). The measured values may be used in other equations to obtain derived values, and one would like to know how precise these derived values are. These aspects are discussed in this section. Confidence Intervals The confidence intervals described here are of the following type: If the data have only random errors that are nearly normal in their distribution with a standard deviation of a, then one can estimate a by using the sample standard deviation, s, and can estimate a confidence interval in which P% of future measurements of the same kind would fall. One can also estimate a confidence interval for the measured mean, Af, which would include the real mean, /*, in P% of the instances if repeated sets of the same kind of measurement were made. Confidence Intervals for Single Readings. If one has a large number of estimates of d, N > 30, from a distribution that is approximately normal, then one can calculate the sample mean and the sample standard deviation and estimate that the readings come from a normal distribution with that mean and that standard deviation. Thus, a 95% confidence interval would be the measured mean plus or minus two measured standard deviations: M ± 2s. The prediction that the next measured value would be within M±2s would be correct in about 95% of the cases. Looking at the tabulation of percentiles and probit values in Table 22-4, it is clear that M ± 3s (-3 probits to +3 probits) would cover 99.865 - 0.135 = 99.73% of the instances, and so forth. With a smaller number of readings, the estimate of the mean and the estimate of the standard deviation would have more uncertainty, and the ability to predict future readings would be less. Figure 22-7 shows 100 simulated observations, taken at random from a normal distribution with mean = 10 and standard deviation = 1. Lines have been drawn at ±3 standard deviations from the mean. About 68% of the readings are within 1 standard deviation, about 95% within 2 standard deviations, and none are outside 3 standard deviations. This kind of chart is a "control chart" and can be used to identify when a change in a process has occurred by noting an unusual number of values lying outside the three standard-deviation limits. More often, the means of many readings are plotted rather than individual readings, and their limits are in terms of standard errors. When long-term records are not available to set control chart limits, the counts in one interval can be compared with those in another by the use of chi-square methods that assume Poisson statistics, a test of the constancy of the concentration (Box et al, 1978). The statistic (22-23) for counts from one Poisson distribution will itself be normal with a mean of E(u) = 0 and a standard deviation of 1. This can be used to test whether the count, x, from one counting

Y

Run Fig. 22-7. "Data" drawn from a Gaussian (normal) distribution; \i = 10, and (J= 1, with lines drawn for 3 standard deviations from the mean, JJ, ± 2>o.

interval is likely to have come from the same concentration as the count, v, from another counting interval (Cooper, 1991a). A value IwI > 2 provides strong evidence (95% confidence) that the concentrations are not the same. Confidence Intervals for Means. If the measurements follow a normal distribution, then the means of the measurements will have a distribution that will be normal and will have a standard deviation equal to l/^N times the standard deviation of the individual readings. TV is the number of readings used to calculate an individual mean. Even if the measurements are not quite normal, the means will be given approximately by the t distribution (often called Student's t after the pseudonym of its discoverer). For samples drawn from a normal distribution, the confidence interval for the true mean, JJ,, is given by the inequalities (22-24) where t(N - 1, a/2) is obtained from tables of the Student's t distribution, available in statistics texts. N - 1 is the number of "degrees of freedom." The value of a/2 is half of the fraction of instances that will not occur within the confidence interval. If the confidence interval is a 90% confidence interval, then a/2 is 5%. Asymmetrical confidence intervals can also be obtained, such as (22-25) which would cover 95% of the instances. This formula was used in the clean-room classification methodology used for Federal Standard 209E (U.S. General Services Administration, 1992). Although the t distribution is more complicated than the normal distribution, its values are tabulated widely. Furthermore, as N becomes much larger than 10, t becomes distributed as nearly a normal distribution, and the t values become the corresponding probit values. For example, for such large N values, (22-26) would be a 95% confidence interval for the mean.

The value of multiple measurements (large N) in decreasing the uncertainty (improving the precision) of the mean is evident. Regardless of the distribution that the readings come from, the means of N readings will have a distribution that has a standard deviation ("standard error of the mean") that is lA/w times the standard deviation of the individual readings. The lAfiv" dependence implies that halving the uncertainty in the mean can be achieved by going from N = 1 to N = 4 or from N = 4 to N = 16 or from N =16 to N = 64, giving diminishing improvements as N increases by one. Confidence Intervals for Standard Deviations and Variances. A very similar approach, but using the chi-square distribution rather than the Student's t distribution, can be made for generating confidence intervals for the standard deviation or its square, the variance (Crow et al., 1960). Error Analysis: Propagation of Error "Propagation of error" concerns how the errors in measurements affect calculations based on these measurements. To carry out the analysis, it is more convenient to work with the variance, s2, rather than the standard deviation. The general formula for the propagation of error makes use of partial derivatives: the variance of g(jc[i]), a function of one or more variables x(i)9 is the sum of the products of the squares of the partial derivatives of g with respect to x(i) and the variances of x(i). Often, one uses just the particle diameter d as a variable (so x[i] = d) and calculates the variance of a function g(d). The variance in g(d), s(g)2, is obtained from the variance in d, s{d)2, as follows: (22-27) If g(d) = ad, then s(g)2 = a2s(d)2, a simple result. If g(d) = adb, then the manipulation becomes somewhat more complicated, but can be simplified by using (22-28a) (22-28b) where the value of d to be used is the mean. If the volume is predicted from the diameter, then the relative standard deviation of the volume (approx. ,s[
Analyzing Data from a Normal Distribution: Student's t

If a particle counter has been used, then the mean particle size, M1, from one distribution can be compared with a hypothetical mean, M, by the Student's t test, the statistic (22-29) in which S1 is the sample standard deviation and N1 is the number of particles sized. This value of t is to be compared against the critical values of t at very low or very high percentiles, such as 1%, 5%, 10%, 90%, 95% and 99%. What is being sought is the probability that a t value of this size would have occurred if there were no difference between the means. For example, if one is testing whether M1 = M, then an unusually negative t value (e.g., t« -1) or an unusually positive t value (e.g., t » +1) would be a strong evidence against the hypothesis. Tables of the critical values of t for various N are available in most statistics texts. When N » 10, the t statistic can be evaluated from the normal distribution, with t < 0 expected in 50% of the instances, t < 1 expected in 84% of the instances, t < 2 expected in 97.7% of the instances, and so forth. Usually, IfI > 2 would be taken as strong evidence that M was not equal to M (this is the 95% significance level). If the first distribution of mean M1 is to be compared not against a theoretical mean, //, but against a measured mean, M2, then there are other formulas for Student's t test available in the literature (e.g., Hays and Winkler, 1970). One must decide whether to assume that the true underlying population distributions have equal standard deviations or not. If, as is often done, one assumes that the population standard deviations are equal (variances are equal) for the N1 and N2 measurements, one has (22-30) (22-31) the latter being the "pooled estimate of variance" (Meyer, 1975). The denominator of the expression for s2, Nx + N2- 2, is the number of degrees of freedom. If the denominator is much larger than 10, t can be obtained from the cumulative normal distribution, as above. If the denominator is not much larger than 10, then tables of Student's t distribution should be used with the appropriate number of degrees of freedom. If the observations under two different conditions to be compared can be analyzed as pairs, then the differences, Zx= Xx-y ^ can be used as the data to be analyzed. The mean of z can be tested against zero to show some effect or against another value to show an effect larger than that value. In most instances, paired comparisons are preferred, as each member serves as the "control" for the other. Correlation and Regression

There are three commonly used measures of correlation: (1) the Pearson product-moment correlation coefficient, which is the most common; (2) the Spearman rank correlation coefficient; and (3) the Kendall tau coefficient. Each has a somewhat different set of assumptions (see, e.g., Hays and Winkler, 1970). Analysis of Variance

Analysis of variance (ANOVA) has some dependence on the form of the underlying data distribution, but it is rather robust (Hays and Winkler, 1970). The data need to come from a balanced statistical experimental design, which can be awkward to obtain (see standard

statistics texts, e.g., Hays and Winkler, 1970.) Transformations can be used to make the data more nearly normal. Examples are the use of the logarithm of the particle size or the square root of the count. Multiple measurements of the distribution allow a statistical comparison because the variability can be estimated from the replications. Distinguishing Among Count Distributions: Chi-Square

Comparing count distributions is simpler than comparing mass distributions because the uncertainty in the count, its standard deviation, can often be assumed to be the square root of the count (Poisson statistics). By using as the dependent variable the square root of the count, a Poisson distribution can be transformed to be more nearly normal. Furthermore, this square root transformation makes the variance for Poisson statistics approximately constant (0.25), which helps satisfy the assumptions in linear regression (Box et al., 1978). Without making assumptions about the size distribution, one can use a chi-square (^2) analysis, after apportioning the counts into matching size intervals. Chi-square is the sum of squares of the differences between the counts, C(J), in the ith interval or category, and the expected counts, E(i), divided by the expected counts: (22-32) The values for the expected counts come from the hypothesis being tested. The x2 value is to be compared with percentile values in statistical tables of %2 distributions for the appropriate number of degrees of freedom. Details are available in standard statistical texts. An example of the application of x2 to compare size distributions is a test to see whether one size distribution of particles contaminating a surface is different from another. The data (sample A and sample B) are count data, subdivided into particle counts for diameters larger or smaller than 5 um: Sample A

Sample B

Totals

d<5\im d>5um

160 75

105 105

265 180

Totals

235

210

445

The expected values are obtained by using the subtotals to divide the total particle count proportionally into the four cells so that the upper-left-hand cell would have an expected count of E(i) = (445) (265/445) (235/445) = 140.0, and so forth. The value of %2 here computes to 15, a value expected in much less than 1% of the cases if the two samples were drawn from the same population. The fraction of particles larger than 5 um in sample A is larger than that of sample B, and the difference is statistically significant. Chi-square analysis is also often used in "goodness of fit" comparisons with hypothesized distributions, where the expected values come from the hypothesized distribution. Distinguishing Count or Mass Distributions: Kolmogorov-Smirnov

If the data can be put into a large number of intervals (N » 10 generally) that have the same boundaries, then the Kolmogorov-Smirnov (K-S) test can be quite effective. Although many aerosol sizing instruments do not have a large number of sizing channels, Heitbrink et al. (1991) noted that the Aerodynamic Particle Sizer (TSl)* does have many such intervals. They * See Appendix I for full manufacturer addresses referenced by the italicized three-letter codes.

used the K-S test successfully to analyze particle count data from this instrument. In other cases, the exact sizing of a large number of particles (N » 10) could be used; in that case, one could select the size intervals to analyze, with the interval end points for the two size distributions chosen so as to provide a large number of common boundaries and comparisons. The K-S test involves comparing the cumulative distributions at the common end points of each interval and calculating the maximum absolute difference between them. The maximum difference is compared with the tabulated values for various levels of statistical significance and various numbers of intervals tested. The maximum difference values needed for statistical significance of 90% or more are differences on the order of magnitude of 1A//V~. For more information, see standard statistical texts, such as Hays and Winkler (1970). Detailed Example of Size Distribution Comparison

This example demonstrates some methods for comparing size distributions using data from the literature. It assumes that the reader has substantial familiarity with statistical techniques. Table 22-5 shows the weight data obtained from an article by Shen and Ring (1986). "WA" is a wood dust aerosol sampled by Sierra impactor 296A; "LB" is a laboratory air sampled by Sierra impactor 296B, and so forth. The weights were in milligrams and had estimated standard deviations of about 0.1 mg for the wood aerosol and 0.01 mg for the laboratory air, according to the authors. Figure 22-6 is plotted from the data. The axes are similar to log-probability axes, with cumulative mass fraction measured in probits. The log of the apparent diameter (effective cut diameter for the impactor) is the other axis. A cumulative lognormal distribution would plot as a straight line on these axes. The data certainly seem to indicate (1) the two distributions for the wood dust (WA and WB) are similar to each other, (2) the two distributions for the laboratory air (LA and LB) are similar to each other, and (3) the distributions for the wood dust are different from the distributions for the laboratory air. For both aerosols, sampler 296B reported a somewhat larger mass median diameter and a somewhat larger geometric standard deviation than did sampler 296A, which may indicate a problem of bias. We analyzed the data using ANOVA. The factors tested for were the effects of the samplers (A and B) and the effects of the aerosols (wood dust and laboratory air). Analysis of variance tests whether assuming there is a contribution to the mean that is associated with a factor reduces the variance by a statistically significant degree. The values that Shen and Ring inferred for the mass median diameters were used as inputs. First, the logarithms of the mass medians were used (shown in the top part of Table 22-6). This transformation was made to TABLE 22-5. Weights (mg) in each Impactor Size Interval for Impactor A Sampling Laboratory Air (LA), Impactor B Sampling Wood Dust Aerosol (WB), and so Forth Diameter (^m)

WA

WB

LA

LB

>10 6-10 3.5-6 2.0-3.5 0.22-2.0 0.6-0.9 0.0-0.6

0.68 0.47 0.49 0.36 0.13 0.09 0.03

0.86 0.60 0.60 0.35 0.13 0.09 0.09

0.066 0.021 0.001 0.009 0.021 0.049 0.090

0.068 0.015 0.010 0.018 0.010 0.040 0.090

Source: Shen and Ring (1986).

TABLE 22-6. Data Used in Analysis of Variance Mass Median Diameter (MMD) Sampler

Dust

MMD

In(MMD)

A B A B

Wood Wood Lab Lab

6.16 6.65 1.22 1.33

1.818 1.895 0.199 0.285

Geometric Standard Deviation (o~g) Sampler

Dust

A B A B

Wood Wood Lab Lab

In(CT8)

2.87 3.53 16.4 21.5

1.054 1.261 2.797 3.068

have values more closely approximating what would come from a normal distribution. A similar analysis on the logarithms of the inferred geometric standard deviations, shown in the bottom part of Table 22-6, was also performed. The ANOVA procedure was from the statistical library provided by SAS, Inc. (SAS). There were two readings at each of two levels for each of two factors: aerosol, sampler. This is a balanced design. As applied to the logarithm of the mass median diameter, the procedure indicated that there was a difference between the two dusts at a better than 99.9% level of confidence and a difference between the two samplers at a better than 95% level of confidence. As applied to the log of the mass geometric standard deviation, the ANOVA procedure indicated there was a difference between the two dusts at more than a 98% level of confidence and a difference between the two samplers at a better than 90% level of confidence. When ANOVA was applied to the untransformed mass median diameters and to the geometric standard deviations, there was no statistical significance to the differences between the samplers. Thus, using the logarithmic transformation, which better matched the assumptions of ANOVA, produced a more sensitive test for discriminating between the samplers. ANOVA applied to the untransformed mass median diameters and geometric standard deviations indicated that the dusts were different at the 98% confidence level (median diameters) and at the 90% confidence level (geometric standard deviations). We performed the Student's t test on the mass median diameters, the geometric standard deviations, and their logarithms (using Nx = N2 = 2). The degrees of freedom are calculated not from the number of sizing intervals, but from the number 2, for the two samples being compared. Multiple determinations of the distribution with a mass sampler do allow statistical comparison, because the variability can be estimated from the replications. The estimates of the mass median diameter can be used as the data being analyzed. The logarithms of the mass median diameters give the mean logarithms, and these means can be compared: 1.818 and 1.895 for the wood dust; 0.199 and 0.285 for the laboratory air. The difference between the two estimates for the mean logarithm for the same aerosol is an estimate of the standard deviation of the estimate of the mean logarithm. The differences are 0.08 for the wood dust and 0.09 for the laboratory aerosol, suggesting that we can pool the variances. Using Eqs. 22-30 and 22-31 for comparing means by the t test, with Nx = 2, N2 = 2 for the number of samples for each mean, we obtained t = 27.9, significant at a better than 99% level. The t test is appropriate because the logarithms were distributed almost normally. It is not so clear that

the t test is appropriate to analyze the logarithms of the geometric standard deviations, but the test is often not very sensitive to the form of the underlying distribution. Applying it to the logarithms of the geometric standard deviations gave us t = 10.4, also significant at a 99% level. The t test applied to the untransformed median diameters gave t - 20.2, a difference between the wood dust and the laboratory air at greater than the 99% confidence level. Applied to the untransformed geometric standard deviations, the t test gave t = 6.1, significant at between 95% and 99%, thus somewhat less definitive. Our statistical analyses confirmed what "common sense" indicates when looking at the graphs of the cumulative size distributions: The wood dust and the laboratory aerosol size distribution were different in median size and in slope. COINCIDENCE ERRORS When two or more particles are present simultaneously in the sensing zone of an instrument, they may be counted as 1 particle (type 1 coincidence), perhaps of a different size, or as 0 particle (type 0 coincidence), depending on the instrument. Coincidence is similar to "saturation," where the pulses from the sensing zone come too rapidly for the electronics to separate. In general, coincidence causes a loss of particles, underestimating concentration, and a shift in the particle size distribution to larger sizes. Coincidence has been treated by many authors, including Jaenicke (1972); Bader et al. (1972); Julanov et al. (1984, 1986); Raasch and Umhauer (1984), and Cooper and Miller (1987). A series of useful articles on the topic are those of Knapp and Abramson (1994,1996) and Knapp et al. (1994). When the particles arrive in the zone at random, the probability of having n particles in the zone can be described by the Poisson distribution. The probability that the zone is empty is exp(-cV), in which c is the concentration (number per extent) and V is the zone extent (volume, area, time, for example). The mean number will be cV. The apparent concentration (assuming coincidence type zero) is (c)exp(-cV), which is nearly c for cV < 1, and reaches a maximum at cV = 1; the concentration will appear to decrease if cV becomes > 1. If the coincidence causes a count in a larger size interval, then this type 1 coincidence causes less loss of count but does create spurious counts of larger particles, further distorting the size distribution (see, e.g., Raasch and Umhauer, 1984). For type 1 coincidence, the counting efficiency becomes [1 - &xp(-cV)]/cV These formulas can be adapted to correct the counts for coincidence losses. The statistics of coincidence and time of arrival was explored in a pair of papers by Julanov et al. (1984,1986), where methods for obtaining concentration estimates using the behavior of multiple nearly simultaneous arrivals were presented. Counting errors of various types for surface scanners were analyzed by Pecen et al. (1987) for scanning-beam types and by Cooper and Miller (1987) and Cooper and Rottmann (1988) for vidicon-based (pixel) types. Special precautions with design and data analysis have to be taken for scanning instruments not to overcount particles on surfaces due to multiple intersections of the scan with the same particle (Galbraith and Neukermans, 1987). Such surface monitors can be calibrated with patterned surfaces, with surfaces having a known number of particles, or—by repeated counting—with surfaces having an unknown number of particles of the same size (Cooper and Neukermans, 1991). More complicated particle-counting instruments, such as the Aerodynamic Particle Sizer and the Aerosizer, have more complicated coincidence effects (Heitbrink et al., 1991; see Chapter 17 for more information). Measurement of particles in liquids and on surfaces has been covered extensively in a handbook edited by Knapp et al. (1996). Dahneke (1983) covered the use of the variation over time of light scattered from particles in fluids ("quasi-elastic light scattering") to infer particle size from correlations.

Detection proability

IDEAL SIGMOIDAL

MULTI-VALUED

Particle size, jim Fig. 22-8. Some hypothetical response functions for a particle-sizing instrument: step function, sigmoidal function, and multivalued function.

CHOOSING SIZING INTERVAL DEMARCATIONS Particle-sizing techniques that classify particles into size intervals have boundaries between the size intervals that are not sharp step functions, but are generally sigmoidal (Fig. 22-8). For example, the instrument may go from having zero probability of including a particle in the interval to having a probability of 1 of including the particle (absent other sizing stages), as the particle size increases. The common approach to ascribing a characteristic size ("cut size") to one end of the interval is to use the size corresponding to a 0.5 probability of detection. A somewhat more precise choice is the size that divides the response function into equal regions of underestimation and overestimation (Cooper and Guttrich, 1981). Calibration of multistage classifiers that takes into account the interaction among the response functions for various stages has been modeled by Lu et al. (1993) and demonstrated experimentally by Lu et al. (1995). References to some earlier work are available therein. If the precision obtained from such cut-diameter approximations is inadequate, one will be tempted to improve it by techniques of data inversion (sometimes called deconvolution or unfolding), as discussed next. DATA INVERSION If one cannot obtain exact measurements, then perhaps one can use knowledge of the behavior of the measurement instrument to correct the data. This is the goal of various data inversion methods that have been presented in the literature of aerosol science and technology and in the literature of many other fields as well (some of the early examples are cited by Cooper and Davis, 1972; Cooper, 1976; Yu et al., 1983; Yu and Gentry, 1984). There is even a journal (Inverse Problems) dedicated to this topic. Although there are instances—such as computerized axial tomography—where data inversion works very well, there have been many alternatives published in the scientific literature, even just in the literature on aerosol science, criticizing previous methods and presenting a new method. There has yet to emerge a clear-cut best-choice method for analyzing particle size distribution data, which should be a warning to those who hope to salvage ambiguous data using mathematical magic. An excellent review article with more than 50 references on the subject is that of Kandlikar and Ramachandran (1999). They describe the problem, many contexts, and various

approaches. They provide a useful tabulation of the various approaches on the basis of technique, references, constraints, or a priori information needed, relative computational effort, and comments. The techniques were subdivided into (A) Linear: 1, least squares; 2, constrained least squares (Philips, 1962; Twomey, 1963, 1977); 3, Tikhonov regularization (Tikhonov and Arsenin, 1977); 4, synthesis of basis functions (Twomey, 1963). (B) Nonlinear: 1, the Chahine (1968) method; 2, the Twomey (1975) method. (C) Extreme value estimation (Paatero, 1991). (D) Bayesian (Ramachandran and Kandlikar, 1996). Kandlikar and Ramachandran (1999) concluded that "there is no single algorithm that can be considered to be universally superior to the others...." Typically, the "exact" solution to the input that created the output is unrealistic due to small errors in the data, in the instrument description, or in the problem definition. Various treatments of the data, the instrument description, or the description of the unknown are available to obtain more realistic results (e.g., smoother or non-negative) at the cost of reduced agreement with the measurements. An ideal instrument would have a probability of 1 of responding to particles within the nominal size boundaries of the sizing channel and a probability of 0 of responding to particles outside the nominal size interval boundaries. Figure 22-8 shows some hypothetical response functions for one particle size boundary of one channel of an instrument. An ideal instrument would have a step-function response. Typically, one has a sigmoidal boundary. In some cases, the boundary is multivalued rather than monotonic, complicating the picture still further. The instrument would have a set of such response functions to make up the channel responses. An ideal instrument would not need to have its data subjected to data inversion. Typical instruments may profitably have their data inverted, depending on (1) the size of the channel widths, (2) the size interval over which the response goes from nearly 0 to nearly 1, and (3) the size interval over which the particle size distribution changes appreciably. Instruments that have multivalued responses are not good candidates for data inversion unless the sizing intervals are chosen to segregate completely the regions of multivaluedness within distinct intervals. This section outlines the topic of data inversion. As noted, much has been written on the topic. Tikhonov and Arsenin (1977), as cited by Wolfenbarger and Seinfeld (1991), summarize the difficulties of these often ill-posed problems: "Solutions will become unstable when the number of data is large. Solutions are not unique. Exact solutions often do not exist." Illposed, or "ill-conditioned," problems typically show large changes in the inferred solutions due to small changes, or errors, in the data. Before discussing data inversion below, we emphasize the advice from Noble (1969): The best way to deal with ill-conditioning, the sensitivity of the results to small changes in problem formulation or in the data, is to avoid it through the choice of measuring instrument and conditions of measurement. This advice was echoed by Cooper and Wu (1990) some 20 years later. More details are given below. What follows in this section is intended for readers strong in mathematics. The Problem: An Integral Equation

Assume that a multichannel instrument produces a set of data that gives the fraction of the aerosol (by count or mass, typically) in each of n channels. The response F(i)' can be described by the integral equation (22-33) where f(d) is the particle size frequency distribution and K(i, d) is the probability that a particle of size d will be counted (or weighed) in the /th channel of the instrument. The data will

usually have at least some error, e(i), as well, error being the degree to which F(J)' does not exactly match the integral. Although K(U d) is known, generally through calibration and perhaps through theory,/(J) is unknown. The ^-channel instrument, i = 1 to n, gives only n data points from which one tries to infer /(J), which is a continuous function. The best we can expect to do is to get n elements of information on f(d). "Elements" is deliberately ambiguous. One kind of element is the value of/(J) at n values of ~d(i) throughout the range of the instrument. Another kind of element is the first n moments of the f(d) distribution: the mean diameter, the mean squared diameter, the mean cubed diameter, or the mean, the variance, the kurtosis, and so forth. Another kind of element might be the k(i) coefficients of a polynomial expansion,/(J) = A;(0) + Zr(I)J1 + Ie(I)J1 + Yet another kind of element would be the Fourier expansion coefficients. The list seems boundless. The most common choice is to obtain estimates of/(J) or F(J) at n or fewer values of J. With only n measurements, at most only n values of /(J) (or other parameters) can be determined, and an infinite number of different curves can be drawn through a finite number of points, so the unknown function is never fully determined by the data alone. Solving by Converting to a Set of Linear Equations

A simple method to convert the integral equation into a set of linear equations in coefficients /[J(Z)] =/(/) is to evaluate the integral with numerical quadrature, which means to evaluate the integral by evaluating the function at a set of J values. For example, the midpoint quadrature evaluates f(d) at the midpoint of the sizing intervals H o r n and produces the approximation (22-34) for j = 1 , . . . , m. Ad(j) is the width of the size interval for which f(d(j)) is at the midpoint in this formulation. The values of K(U J) are known, so the quadrature produces a set of n equations in m unknowns. These equations can be written compactly: (22-35) The "approximately equals" sign is used to emphasize that not only is there error in the data but also there is error in going from the integral equation to the set of linear equations. For n equations in m unknowns: (1) we may have a solution if the number of equations equals the number of unknowns, m = n; (2) we do not have a solution if the number of equations is less than the number of unknowns, n < m; and (3) we may have many possible solutions if the number of equations is greater than the number of unknowns, n>m. Where n > m, more data (equations) than unknowns, we need to choose one of the possible solutions, generally by requiring that the chosen solution satisfy another constraint, such as being the smoothest of the alternative solutions or being the least-squares fit to the data. The unknown values are often labeled as JC(/), and the known coefficients on the righthand side are labeled A(I,/). This avoids making the decision whether the user is solving for /(/) or/(/) Ad(j). The known values are often labeled as b(i), and they may be part of a cumulative distribution description or part of a frequency distribution description. Regardless of the details of the description, we want to solve the set of linear equations (22-36) which in vector-matrix notation becomes

b=Ax + e

(22-37)

Some definitions are needed: A(U j)T = transpose of A; A(U j)T = A(j, i). HA = inverse of A; (A)(IIA) = J, the identity matrix. 1(Uj) = 1 for i = j ; 1(Uj) = 0 otherwise. Solving the Equations when n Equations Equal m Unknowns If we have n equations in m unknowns and if n = m and if all the equations are linearly independent of each other (i.e., none is simply equal to a constant times another), then the solution is straightforward: (22-38) the x vector (our solution) is the inverse matrix operating on the data vector. In fact, we know b, but not e, so that we calculate an x = (IM)Z? that is in error by about (VA)e. An example from Cooper and Wu (1990) shows a simple two-by-two matrix from a hypothetical instrument with rather good resolution, which one can see by noting that the matrix is nearly the identity matrix (which would need no correction). (22-39) The inverse matrix is (22-40) The solutions from data inversion become *(1) = 1.056 b(l) - 0.056 b(2) x(2) = -0.056 b(l) + 1.056 b(2) The corrections are of the same magnitude as the off-diagonal elements of A (/,/). These are rather small corrections and do not change x(i) from the data b(i) by very much nor would they multiply the error term by any large factor. Examples where much larger correction factors need be applied, having greater error magnification, were given in the same article. Solving the Equations when n Equations Exceed m Unknowns Where we have more equations than unknowns, we need an added constraint to select one solution from the possible solutions. Generally, the constraint chosen is to minimize the sum of the squared differences between what is calculated from Ax and what was measured, b. The least-squares criterion can be shown to lead to the following equation: (22-41) where, again, the error term cannot be calculated because e is unknown. The approximate solution for x is obtained by finding the matrix that is the product of AT and A, and then taking its inverse and using this inverse to operate on the vector ATb. Perhaps surprisingly, this is a relatively convenient approach because it comes down to using the readily available programs for multiple linear regression to solve the equations

Ax - b. Simply use the multiple regression programs, giving the data, b(i), and the A(i,j) coefficients. The solution will be the least-squares fit, x(j). The difficulties arise when the answers are unrealistic: negative values for some of the size distribution components or components that oscillate as particle size increases. To get around this problem, one can try to solve for fewer unknowns by reformulating the equations. For example, x(k) and x{k +1) can be combined to form a new x(k'), for two or more of the adjacent intervals and the set of equations solved again. Inversion as Regression

Once it is recognized that the solution of the linear equations is simply linear regression, widely studied by statisticians and mathematicians, then a deep and broad literature opens up. There are many books on regression, and various computer programs are available, such as those from SAS. An excellent source is the book by Draper and Smith (1981). Many candidate methods are revealed; for example, (1) one can choose to select only those x(i) values that are statistically significantly different from zero (Student's t test on the regression coefficients); (2) one can carry out stepwise regression, starting by keeping only the most statistically significant component or starting by dropping the least significant component; and (3) to the regression equations can also be added confidence intervals on the true means for the coefficients (the x[i\) and on individual values. For example, Liley (1992) used a generalized linear model (GLM) on optical particle counter data along with assumed size distributions of (1) the lognormal, (2) the power-law (Junge), and (3) the modified Gamma forms to obtain fits to the particle count data. Liley also showed the advantage of the square root transformation of the counts, as it made the variance almost independent of particle size, better matching the conditions under which such regression is valid. A measure of the sensitivity of the inferred solutions to the errors in measurement and definition is the condition number (Forsythe and Moler, 1967). In unfortunate cases, this number multiplies the error e. One definition of the condition number, cond(A), shows that it can be as large as the largest matrix element of A divided by the smallest matrix element of (l/A).The condition number can be orders of magnitude larger than 1. Most multiple linear regression programs will not indicate what cond(A) is. To estimate the error magnification, simply make a small change in the data b(j) for each channel, one after another, and look at the change in the computed results for x(i). Some Guidelines on Reducing Sensitivity to Errors

Designing an experiment to reduce problems with data inversion can be done by calculating the condition number for various options (Cooper, 1974; Jochum et al., 1981; Yu, 1983; Farzanah et al., 1985; Kaplan and Gentry, 1987) or inspecting the inversion matrix, (VATA)AT, for particularly large coefficients (Cooper and Wu, 1990). Even without calculating an inverse, one can examine the original matrix. The best matrices to invert are those that are most nearly like the identity matrix, /(/,/), which has 1 on its upper left to lower right diagonal and 0 elsewhere. Methods that Change the Response Function: Smoothing, Regularization

Wolfenbarger and Seinfeld (1990,1991) favored the technique of "regularization," associated withTikhonov (1963). Related books are those by Tikhonov and Arsenin (1977) and Groetsch (1984). Regularization solves a family of problems that are almost the same as the original problem, with the degree of difference specified by the investigator to reflect the uncertainty in the data. The modified problems are chosen to have stable inverses. Phillips (1962) and Twomey (1963) applied this approach to problems in which the solution was not constrained

to be non-negative but was constrained in other ways, such as the smoothest solution fitting the data within a specified degree of precision. Wolfenbarger and Seinfeld applied regularization and the constraint that the solutions be nonnegative. Wolfenbarger and Seinfeld noted, as others have, that the results can be quite misleading if the device has poor sensitivity or poor resolution in size ranges where the size distribution is still significant. This is one advantage for combining the results of more than one type of instrument (Wu et al., 1989). Defining a level of smoothness to rule out very unsmooth (highly oscillatory) solutions has its complications. Wahba (1985) discussed this in the context of the generalized crossvalidation (GCV) inversion method. Wahba (1977) presented suggestions for handling the difficulties caused by noisy data. The Phillips-Twomey smoothing approach was used by Amato et al. (1995) on atmospheric optical depths measured with a grating spectrometer; a chi-squared criterion was used to select the optimal value of the smoothing parameter. Methods that Change the Data

As with any signal, the data can be decomposed by Fourier analysis into a sample from a weighted sum of sine waves, sin(2jrfd), where / i s a frequency in units of reciprocal length, such as urn"1. High-frequency components are those that vary greatly with small changes in particle diameter. High-frequency components in the data become high-frequency components in the results. By averaging the data over several runs and then carrying out the inversion on the averages, some reduction in these components can be achieved. Fitting curves to the data and then inverting the values interpolated by the curves is a variation on this theme. There should be some improvement, and one is not requiring that the answer look plausible so that if the data are still poor an implausible answer can result and warn the user of the method. Markowski (1987) used smoothing the data to improve the Twomey (1975) nonlinear method. Methods that Constrain the Answer

One approach to preventing unrealistic solutions is to constrain the solutions. A simple constraint is normalization: The sum of the size distribution contributions must be the total number or mass or add to 1.0, and so forth. Another constraint is non-negativity, not allowing a negative solution. These two were applied, using nonlinear programming (simplex minimization; after Nelder and Mead, 1965) by Cooper and Spielman (1976) for the variable-slit impactor and by Kapadia (1980) and Helsper et al. (1982) for the electrical aerosol analyzer. Removing the high-frequency solution components can be used to produce smoother solutions: Twomey (1965) set lower limits on eigenvalues and used these to drop eigenvectors that contributed to the solution vector, a method related to smoothing to avoid highly oscillatory solutions that are clearly nonphysical. Similar work was presented by Baker et al. (1964), but neither they nor Twomey seems to have been aware of each other's publications. Twomey (1975) achieved non-negativity in the solutions with his nonlinear method of inversion (see the criticism by Cooper and Wu, 1990, however). Crump and Seinfeld (1982) demonstrated shortcomings in Twomey's approach and presented their own, involving constraints and "crossvalidation," later found to work in some cases and not in others. Wolfenbarger and Seinfeld pointed out the weaknesses in other approaches and incorporated non-negativity and smoothness in their regularization methods. Constraining the solution helps to reject implausible solutions, but may also hide the fact that the solutions are still wrong (e.g., good and bad solutions in Fig. 1 in Wolfenbarger and Seinfeld, 1991). Ramachandran and Leith (1992) used a deconvolution technique on their multispectral light extinction data. Instead of using a set of orthogonal functions for the unknown function, they used such a set for the second derivative of the unknown function,

using cross-validation to determine how much smoothing to use (the smallest eigenvalues to retain). Their approach was robust in the presence of as much as 10% noise. They studied the effects of increasing the number of measurements and found that more measurements did not necessarily provide better results. Subsequently Ramachandran and Vincent (1997) compared this method with one constraining the answer to be lognormal and found that the latter approach was somewhat better for the impactor data they studied (see following paragraph, also). A particularly constrained method is to choose to fit a preselected functional form to the data, such as a set of normal distributions (Jaenicke, 1972) or one or more lognormal distributions (Kubie, 1971; Puttock, 1981). Such approaches give plausible results, sometimes with answers extending beyond the range of the data, which can be misleading. Chang et al. (1995) assumed lognormality for the components and employed light scattering and extinction data to determine size distributions in multicomponent aerosols. The assumption of lognormality was used by Morawska and Jamriska (1997) to obtain the parameters of radon progeny as measured with wire screen diffusion batteries; they reported that this procedure performed better than did the Twomey (1975) deconvolution algorithm. Another form of constrained solution is the "apparent diameter" method, as used by Yu et al. (1983) and Yu and Gentry (1984). The apparent diameter is that which gives the measured response when substituted into the kernel. By assuming a power-law form for the kernel (collection efficiency here) and a lognormal form for the aerosol distribution, they extended this to obtain the geometric standard deviation, the second parameter for the lognormal distribution. Extreme value estimation (EVE) tries to put bounds on the upper and lower values of the size distribution elements that are consistent with the data. Some success has been had with this approach (Paatero and Raunemaa, 1989), originally set forth by Replogle, Holcomb, and Burrus at a time when the computations were much less convenient than they are now, which may explain why it was not widely used immediately thereafter. Other Methods

The expectation-maximization (EM) algorithm has had some success (Maher and Laird, 1985) in inverting diffusion battery data, a challenging task. The method takes advantage of relations among columns as well as rows in the matrices to be inverted and of the Poisson nature of particle counts, with the errors proportional to the square root of the counts. Used to infer particle size distribution values from diffusion experiments involving graded-screenarray diffusion batteries, the EM algorithm also did well (Knutson et al., 1997), although some arbitrary decisions regarding smoothing and the number of iterations to use still had to be made. An application of deconvolution was the extraction of size distribution values from photon correlation data (Bertero et al., 1989), the response of particles undergoing Brownian motion to illumination with coherent (laser) light. The inversion is equivalent to the difficult problem of inverting the Laplace transform numerically, known to be highly sensitive to errors in the data. Some authors have focused on rewriting the equations to estimate the various integral moments of the size distributions, the mean, variance, kurtosis, and so forth. Unfortunately, as shown by White (1990), some distribution types cannot be recovered from these moments. Rather different distributions can have the same values of the mean, variance, and kurtosis, for example. For diffusion battery inversion, Yu and Gentry (1984) compared their apparent size method with nonlinear inversion and linear inversion with cross-validation. The apparent size method depends on tractable choices of the particle size distribution and the kernel of the integral equation.

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23 Nonspherical Particle Measurements: Shape Factors, Fractals, and Fibers PAUL A. BARON Centers for Disease Control and Prevention,* National Institute for Occupational Safety and Health, Cincinnati, OH

CHRISTOPHER M. SORENSEN Department of Physics and Program for Complex Fluid Flows, Kansas State University, Manhattan, KS

JOHN E. BROCKMANN Sandia National Laboratories,1 Albuquerque, NM

INTRODUCTION Many aerosol measurement techniques are based on the behavior of ideal aerosol particles, that is, spherical particles with a density close to 1000 kg/m3 [lg/cm3]. In dealing with most real-world particles, allowances must be made for nonspherical particle behavior and other characteristics. In many situations, nonideal particle behavior can be considered as a modification of ideal particle behavior by using a correction factor, commonly called the shape factor. Two types of particles, agglomerates (or clusters of particles) and fibers, have been dealt with extensively in the literature and are discussed in more detail in this chapter. As well as being important for predicting particle behavior, particle shape can provide clues to the particle formation mechanisms. SHAPE FACTOR The dynamic shape factor relates the drag on an irregular particle to the drag on a sphere with the same particle mass and composed of the same material. The dynamic shape factor facilitates the transformation between the mass (or volume) equivalent diameter and the aerodynamic equivalent diameter of nonspherical and agglomerated particles (Brockmann and Rader, 1990).This transformation is necessary in aerosol dynamics codes (Gelbard, 1982), which describe the aerodynamic behavior of aerosol particle distributions while conserving mass. Helton et al. (1986) have shown that the results of aerosol behavior calculations can * Mention of product or company name does not constitute endorsement by the Centers for Disease Control and Prevention. f Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy under contract DE-AC04-94AL85000. Aerosol Measurement: Principles, Techniques, and Applications, Second Edition, Edited by Paul A. Baron and Klaus Willeke. ISBN 0-471-35636-0 Copyright © 2001 Wiley-InterScience, Inc.

be highly sensitive to the dynamic shape factor. Brockmann (1985,1987) reviewed the wide range of dynamic shape factors reported in the literature. The dynamic shape factor, %, is defined as the ratio of the drag force on a particle, FD, to the drag force on that particle's mass equivalent sphere, FDM, at the same velocity, V. (23-1) The dynamic shape factor has been developed as a correction to Stokes law drag for nonspherical particles (Hinds, 1999), that is, a Stokes law drag ratio (Clift et al., 1978). This definition expresses the drag on a nonspherical particle in the Stokes law regime as (23-2) where fj, is gas absolute viscosity, V is gas velocity past the particle, dm is mass equivalent diameter of the particle, and C(dm) is slip correction factor for the mass equivalent diameter. The choice of particle diameter used in the expression for slip correction has been a subject of concern (Cheng et al., 1988). Here, the use of the same equivalent diameter in the argument of the slip correction as is used in the expression for drag simplifies the presentation. This treatment will introduce a slight particle size dependence on dynamic shape factor in the transition regime. In the case of spherical nonporous particles, this treatment reduces to the correct usage. Several equivalent particle diameters are defined in the literature. The mass equivalent diameter, dm, of a particle is the diameter of a nonporous sphere composed of the bulk particle material that has the same mass as the particle. The envelope equivalent diameter, de, of a particle is the diameter of a sphere, which is composed of the bulk particle material and included void spaces (or voids), that has the same mass as the particle. When there are no included voids in the particle, these two diameters are identical. When there are included voids in the particle, these two diameters are different and de > dm. The mobility equivalent diameter, dB, of a particle is the diameter of a sphere with the same dynamic mobility as the particle. The aerodynamic equivalent diameter, or aerodynamic diameter, dae, of a particle is the diameter of a sphere of unit specific gravity that settles at the same terminal velocity as the particle. These equivalent diameters are related by the dynamic shape factor x, the particle material density pp, and the unit specific gravity density p0 = 1000 g/m3 [1 gm/cm3], and by the effective particle density pe of a particle containing voids. (23-3) (23^) (23-5) When the dynamic shape factor is defined with the particle material bulk density, it is comprised of two components: (23-6) The first component K is due solely to the shape of the particle envelope (Fuchs, 1964). For oblate and prolate spheroids (Fuchs, 1964) and rectangular prisms (Johnson et al., 1987), K may be calculated. These calculated values compare well with experimentally determined values (Johnson et al., 1987). The second component 8is due to particle porosity. This second

component is defined in terms of the particle bulk material density and the particle effective density as (23-7) The void fraction of a particle can be defined as (1 - l/£3), and the envelope and mass equivalent diameters are related as (23-8) Both K and S are potentially important for aggregate or agglomerate particles. Agglomerate particles with two basic morphologies are seen in the data of Kops et al. (1975), van de Vate et al. (1980), Allen and Briant (1978), Allen et al. (1978,1979), Kasper and Shaw (1983), and Stober et al. (1970). These morphologies are (1) a branched chain structure and (2) a compact aggregate with a more discernible enveloping shape. The dynamic shape factors of each of these two morphologies are also distinct. For the branched chain aggregates, the dynamic shape factor increases with the addition of primary spheres to the aggregate and is directly proportional to the cube root of the total number of primary particles (Stober, 1971; Kops et al., 1975; Allen and Briant, 1978; Allen et al., 1979). This behavior is consistent with particles' shape determining their dynamic shape factor. In compact agglomerates, the porosity component dominates the dynamic shape factor. Gieseke et al. (1977) define this component as the dynamic shape factor and employ it to characterize sodium oxide aerosols. The porosity component is also employed by Wegrzyn and Shaw (1979). For compact agglomerates, the shape factor is relatively independent of the number of primary particles but depends rather on the packing density of the primary particles in the aggregate (Kops et al., 1975; van de Vate et al., 1980). This behavior is consistent with the porosity of the agglomerate determining its dynamic shape factor.

FRACTAL PARTICLES Introduction

Fractals are scale invariant objects. This means that they have dilation symmetry, that is, they look the same on all scales. Mathematical examples include the Koch curve, which has bumps upon bumps upon bumps, or the Sierpinski gasket, which has a descending series of holes, or the "dog chow" symbol shown in Figure 23-1, which has some similarity to the aggregates to be described here. Mathematical fractals are scale invariant over all scales, yet Nature provides many examples of objects that are fractals over a finite range of scales. Examples include coastlines that display inlets and peninsulas on a large scale (e.g., the western coastline of the island of Great Britain) and similar bumps and wiggles at much finer scale (e.g., a local map of a few miles of coastline). This scale invariance is not present in simple geometric objects. Thus, changing the scale of a circle makes it flatter or rounder; similarly, a square's corners get farther or closer apart. Fractal and geometric objects differ in another very important manner and that is in their dimensionality, a quantifiable parameter. Geometric objects have integer dimensions, while fractals have noninteger, fractal dimensions, D{. An excellent introductory description of fractals is given by Family (1991). The mathematical background of fractals and their relevance to natural objects put forward by Mandelbrot (1977,1983) allowed Forrest and Witten (1979) to experimentally establish that random aggregates of metal smoke particles over a finite range of scales were fractals with a noninteger dimension. This seminal work caused an explosion of subsequent interest involving both simulation of aggregation mechanisms and experimental work on

Fig. 23-1. "Dog chow" fractal with Df = In5/ln3 = 1.465.... The pattern of five boxes in a larger box with three times the side length continues to all scales as implied by the box in the lower right.

aggregation in colloids and aerosols that continues today. The purpose of this chapter is to describe methods whereby the size and morphological parameters of fractal aggregates (i.e., aggregates composed of simple particles that display a fractal morphology) can be determined. Fractal Aggregates

A fractal aggregate is an aggregate or cluster of particles that displays fractal scaling over the length scales from the primary or monomer particle size to the overall size of the aggregate. Fractal aggregates occur in aerosols and colloids as a result of random aggregation. Examples of both soot and titania fractal aggregates are given in Figures 23-2 and 23-3. The motion can be diffusive, ballistic (straight line), or in the crossover between these, and the resulting aggregate will be a fractal within the scale limits monomer to overall aggregate size. Both computer simulation and experiments have been important in establishing our knowledge of fractal aggregates (Family and Landau, 1984; Meakin, 1988; Viscek, 1992) because aggregation can be readily simulated on the computer. An example of a computer-generated fractal aggregate is given in Figure 23-4, and the similarity to the real-world examples given in Figures 23-2 and 23-3 is apparent. We now know that the aggregates can be classified into two major categories: 1. Particle-cluster aggregation, or diffusion-limited aggregation (DLA), which occurs when single monomers diffuse to and stick to a stationary, growing cluster (Witten and Sander, 1981). In three dimensions the clusters that result have a fractal dimension of Df — 2.5. It has recently been shown that those aggregates are not fractal over the entire range of their length scales (Oh and Sorensen, 1998). 2. Cluster-cluster aggregation, or diffusion-limited cluster aggregation (DLCA or DLCCA), which occurs when all clusters diffuse and then stick when they randomly touch (KoIb et al., 1983; Meakin, 1983). In three dimensions the clusters that result have a fractal dimension of D{ ~ 1.75 when the cluster motion is diffusive. Ballistic motion

Fig. 23-2. Soot fractal aggregates from a premixed methane flame. The fractal dimension is Dt« 1.8.

Fig. 23-3. TEM picture of titania (TiO2) fractal aggregates with Df « 1.8 produced by pyrolysis of titanium isopropoxide.

causes Df ~ 1.9 (Meakin, 1984). If the sticking probability is significantly less than one, the reaction-limited cluster aggregation (RLCA) regime is entered for which Df = 2.15, a situation important for many colloids (Lin et al., 1990). It is now known that only cluster-cluster aggregates occur in aerosols and colloids, with the DLA morphology finding application in other areas. The meaning of the fractal dimension is in the relation between linear and volumetric size, the latter of which is linearly related to the mass or number of primary particles per aggre-

Fig. 23-4. A simulated DLCA aggregate with Df = 1.79. gate N. If R is a linear size of the aggregate, then N ©c RD*. We say that "the mass" Af scales with linear size to the fractal dimension Df. Geometric objects scale as well; for example, the volume of a sphere increases by a factor of eight when the diameter is doubled because its dimensionality is three, but fractals have a noninteger dimension D{ less than the embedding spatial dimension d, that is, Z>f < d. The beauty of the fractal dimension is that it allows a quantitative description of the degree of openness, or ramification, of the random aggregate. The smaller the D{ relative to the spatial dimension d, the more quickly the aggregate fills space as R increases. The fractal dimension plays a part in determining the aggregate density, the optical properties, the way in which it diffuses, and the kinetics of its further growth. The relation between "mass" and linear size for a fractal aggregate can be quantified in three ways, all of which will form a basis for measurement of Df to be describe below. We let a be the monomer (primary particle) radius and Rg be the radius of gyration (a root mean square radius; see below) of the aggregate. Then (23-9) In Eq. 23-9 ko is a constant of order unity (Wu and Friedlander, 1993), perhaps best described by ko~ 1.3 to 1.4 (Cai et al., 1995a; Oh and Sorensen, 1997; Sorensen, 1997) but see also Koylu and Faeth (1992). This is perhaps the most important defining relation for a practical description of a fractal aggregate. Another important relation describes the spatial correlation function of the density g(r) given by (23-10) where g(r) is a conditional probability relating on average the density at two points separated by a distance r. The function h(x) is a cut-off function for the power law, h(x < 1) « 1, but h(x) decreases more rapidly than any power law for x> 1. This implies that the length £ is on the order of the size of the aggregate. For DLCA the Gaussian h(x) ~ exp(-*2) is fairly accurate (Sorensen et al., 1992b; Cai et al., 1995a; Sorensen and Wang, 1999). A third valuable relation involves the amount of material within regions of side length s centered on the aggregates. For a fractal particle

(23-11) AU three of these equations will be used for the particle analysis described below.

Real Space Analysis Collection Methods Thermophoretic Collection. Often aerosols are hot, for example, soot in a flame or metal oxides from a reactor, and insertion of a colder probe will cause thermophoresis of the hot aerosol particles down the thermal gradient to the colder probe. The great advantage of thermophoresis is that all particle sizes move at the same rate; hence the sampling is unbiased (Rosner et al., 1991) (at least for submicrometer particles; see Sorensen and Feke, 1996). A "frog tongue" probe device designed after Dobbins and Megaridis (1987) has been used to sample flame soot and TiO2 and SiO2 aerosols with apparent success (Cai et al., 1993). This device was built from a modified disk hard drive and a carbon arrow shaft. The essential quality is the ability to inject quickly a probe into the aerosol, hold it there for a residence time determined by the operator, and then quickly remove the probe. The device can move the probe 5 cm in 3 ms and has a selectable residence time of 15 to 150 ms. The "frog tongue" part of the probe is a thin "knife-blade" of metal. Transmission electron microscope (TEM) copper grids are mounted on the blade with polystyrene cement. This cement is easily broken so that the grid can be removed. The grids consist of copper mesh, having either a carbon or a Formvar coating. The blade is inserted with its face parallel to the aerosol flow to avoid perturbation of the flow and impaction of particles. Imp action Collection. Placing a probe with its face perpendicular to the aerosol flow will allow impaction of aggregates onto the probe. This approach was used to collect soot particles ljim sized or larger (Sorensen and Feke, 1996; Sorensen et al., 1998). Impaction relies on inertia, so there is a bias toward collecting a larger fraction of the bigger aggregates. The Stokes number quantifies the likelihood of impaction and is discussed in Chapters 4 and 10. Collection by Settling. Often the aerosol settles out to form a powder at the bottom or on the sides of the chamber, container, room, or region of aerosol study. This powder can be carefully collected and redispersed to create samples for microscopic examination. Collection should proceed with some care because the fractals may be fragile, as implied by their tenuous nature. Little is known about the fragility of fractal aggregates. Experiments exist in which aggregates have been stretched and then snapped back (Friedlander et al., 1998). These imply considerable resiliency. Despite this, any reasonable caution when handling would not be wasted. Redispersion has been accomplished using volatile liquids, perhaps with surfactants, and then drying. Water, ethanol, and acetone have been used to study carbonaceous soot and TiO2. Water has a large surface tension, and this tends to crush the aggregate as it dries. Aerosol methods have been used for redispersion of soot (Prenni et al., 2000), though it has not been established that individual particles retain their original structure. Each type of material has its own peculiarities, so experimentation is warranted. In all cases collection densities on the microscope substrate (e.g., TEM grid) should not be great, the aggregates occupying 10% of the area or less (Cai et al., 1993). This is necessary to avoid significant "artificial" aggregations on the substrate. If two aggregates overlap

on the substrate, there is no way to distinguish the resulting cluster from one that formed in the aerosol phase. Analysis of Projected Images Visualization. An electron microscope is necessary to study nearly all conceivable fractal aggregates because the primary particles (or monomers) are smaller than optical wavelengths ("big" particles do not stick together readily). Standard carbon- or Formvar-coated copper grids are sufficient to hold the sample. The magnification should be large enough to allow an accurate measurement of the monomer. For example, soot is usually composed of aggregates of monomers with radii a ~ 10 nm. To magnify these to lmm images requires 100,000 magnification. Photographs can be digitized with a digital scanner for computer analysis. The image data will be in the form of a two-dimensional array with a magnitude representing the gray level at a given pixel. A computer analysis routine can be written to identify individual clusters in the digitized array, or the operator can look at the image on the monitor, pick out the images visually, and store them as separate gray level arrays, one for each cluster. Often it is useful to subtract a background from this cluster gray level. The background is the average gray level of the pixels near the circumference of cluster. With this subtraction, all the nonaggregate pixels are set to zero ("white"). Once a gray level array for each cluster is achieved, analysis for morphological parameters can begin. A major problem in the analysis of the cluster morphology is that the three-dimensional structures are viewed as two-dimensional projections as a consequence of the microphotography. One way to overcome this is to view the clusters in at least two different projections and with this stereo technique regenerate the true three-dimensional structure. This has been done in the past (Sampson et al., 1987; Koylii et al., 1995) but the method is laborious and, as will be shown, largely unnecessary. If the analysis is limited to one projection, and if the density of this projection can give accurate information regarding the total mass along a given projection through the cluster, then a viable analysis of the three-dimensional morphology can be obtained. Such a mass-preserving image is difficult to achieve, however, because the attenuation of the electrons producing the projected image is not linearly related to the total mass of material through which the electrons passed. Furthermore, the response of the photographic film that captures the image is linear only over a small range before it saturates and becomes insensitive to the mass of the cluster above it. In previous work involving small soot clusters (Cai et al., 1993), some success was achieved with masspreserving projections largely because the clusters were small enough to keep the gray level-to-projected mass relationship approximately linear. In general, however, masspreserving projection is uncertain, so we are left with projection of the cluster onto the twodimensional plane in a binary format, that is, a shadow, in which any part of the cluster is the same degree of gray (black) as any other and the background is white. The advantage of this method is that it eliminates the response of the detector. The conversion to a binary format appears not only easier to apply, but also more reliable and accurate. What is needed is a quantitative method to convert two-dimensional information into three-dimensional information, and such a method is presented below. The bulk of the discussion applies to DLCA aggregates with Z)f« 1.75, which are very typical. When Df>2, the problem of determining the size parameters of aggregates is much less explored, but a possible direction will be suggested. Binary Projection Analysis THE RADIUS OF GYRATION.

body as given by

We first consider the radius of gyration Rg of a three-dimensional

(23-12) where p(r) is the density. A reasonable assumption is made that an ensemble of clusters on a TEM grid when viewed from one direction will yield an average spherical symmetry. Then, because r2= x2 + y2 + z2, and because a projection onto a plane eliminates one of the dimensions, it follows from Eq. 23-12 that (Cai et al., 1993) (23-13) In Eq. 23-13, Rg3 is the true, three-dimensional radius of gyration of the cluster and Rg,pToi is that observed for the mass-preserving projected image. The factor 3/2 results from the elimination of one of the three dimensions. Furthermore, Eq. 23-13 applies to a mass-preserving projection. Equation 23-13 is verified by the computer simulations of Koylu et al. (1995), who found the empirical factor relating the two radii to be 1.24 ± 0.01, in good agreement with V3/2 = 1.225. The previous discussion shows the difficulty in achieving an accurate mass-preserving projection, so Eq. 23-13, while informative, is of questionable utility. The purpose here is to show that a better measurement can be obtained with a two-dimensional binary representation of the cluster. It is well established that the fractal dimension of d = 3 DLCA clusters is less than 2; typically, D{ is in the range 1.7 to 1.8. Thus, it might be expected that the image of a cluster projected onto a plane in a binary format would be mass preserving (i.e., no significant screening or occultation between monomers would occur). This would imply that the number of monomers in the aggregate would be proportional to the projected area of the cluster (i.e., N <* A0). It must be stressed that this expectation is only correct for asymptotically large (N —> °°) clusters. For finite size clusters, however, screening occurs, and it is found empirically that (23-14) where a = 1.1 (for references and a complete discussion, see "Determination of Af," below). Thus, the effective fractal dimension in the two-dimensional plane of the binary projection should be different from the fractal dimension of the real, three-dimensional cluster. In the immediately following argument, we will call these fractal dimensions D12 and D13, respectively. Consider how the three-dimensional cluster is projected onto the two-dimensional plane. With spherical or circular symmetry, we assume the density profile of either the three-dimensional fractal cluster or its projection is given by (23-15a) (23-15b) where R is the perimeter radius and D{ = D12 or D t3 , depending on the spatial dimension of d = 2 or 3 for the binary projected or real cluster, respectively. Then Eq. 23-12 yields (23-16)

(23-17)

Thus, a relation between the true radius of gyration Rg3 and the measured, binary projection radius of gyration jRg,binary can be determined if we have a relation between Di3 and D12. To determine this latter relation, consider the empirical fact of Eq. 23-14 that N3 ~ A% where we now label the number of monomers per cluster with a subscript of three to designate that this is the number in three-dimensional space (i.e., the true number). We also have by Eq. 23-9 the relation Af3 « Rff. The binary projection has analogous relations such that N2« i?Dgbinary, which defines Di2, but, and here is the key, N2-A0. Furthermore, by Eqs. 23-16 and 23-17, Rg3 ~ i?g,binary AU these proportionalities together yield (23-18) This result is consistent with past work that has measured the fractal dimension of clusters in terms of both three-dimensional quantities and projectional quantities, where it was found that the projectional dimension is typically 10% less than that determined with the threedimensional quantities (Sampson et al., 1987; Zhang et al., 1988; Cai et al., 1993). It is also consistent with simulations by Jullien et al. (1994), who also found the projectional fractal dimension to be about 10% less than the fractal dimension of the unprojected clusters. Because a ~ 1.1, we believe Eq. 23-18 explains these past observations. Finally, we use Eqs. 23-16 to 23-18 to find (23-19) Now recall that Rg = Rg3 and Df = D13. Then for typical values of D{ = 1.8 and a ~ 1.1, this correction factor is 1.026. Thus, as anticipated and qualitatively explained earlier (Cai et al., 1993), the binary projection yields a remarkably accurate measure of the true, threedimensional radius of gyration. Computer analysis of the clusters begins with the total gray level defined as (23-20)

where G{x,y) = 0 or 1 is the gray level of the pixel at position (x,y). Because G(x,y) is binary, Gtot is the total number of pixels in a cluster. To determine the radius of gyration Rg of a cluster, first calculate the cluster center of mass: (23-21) and then the radius of gyration (23-22)

Correction using Eq. 23-19 to obtain Rg/Rg3 could now be made, but because the correction is only about 2%, this is hardly warranted given that other experimental errors are most likely larger. Another useful, and fairly simple, method to determine Rg is using the maximum, projected length of the aggregate image. Computer simulations indicate that half this length (hence a radius) R2 is a constant ratio to Rg independent of N. This is shown in Figure 23-5. The result is that (23-23)

Ratio

N Fig. 23-5. Ratio of the aggregate radius of gyration Rg (in d = 3 space) to half the longest length R2 of the clusters projected onto a d - 2 plane as a function of N for simulated DLCA aggregates with D1 = 1.79.

This method is simpler than calculation of Rg from the gray level, Eq. 23-22, but relies on the clusters being DLCA with Z)f« 1.79 (i.e., equivalent to the simulation that produced Fig. 23-5). DETERMINATION OF N. Determination of the number of monomers per aggregate N from the projected area of a cluster has a long and well established history for DLCA (Z)f « 1.75) (Medalia, 1967; Medalia and Heckman, 1969; Mandelbrot, 1977; Sampson et al., 1987; Megaridis and Dobbins, 1990; Koylii and Faeth, 1992; Cai et al., 1993; Koylu et al., 1995). In general it is found that

(23-24) where K and a are constants near unity and A0 and Ap are the projected areas of the cluster (aggregate) and primary particle (monomer), respectively. Medalia and Heckman (Medalia, 1967; Medalia and Heckman, 1969) first used this form and found empirically K- 1.0 and a = 1.1.This has subsequently been corroborated by a number of workers with a varying by a few hundredths. Sorensen and co-workers (Oh and Sorensen, 1997) found that fractal soot clusters with D1« 1.75 obeyed Eq. 23-24 with a = 1.09. Koylu et al. (1995) analyzed both computer-simulated and real soot clusters and found K- 1.15 - 1.16 and a = 1.09 - 1.10. Note that with these results the limit as N -> 1 is not preserved because K is not unity. In another simulation, Meakin et al. (1989) created DLCA clusters with £>f = 1.8 and N up to N = 104, larger than any in any other work that has compared N to the projected area. They fit their data with (23-25) This result is equivalent to Eq. 23-24 with K = 1.00 and a = 1.10 over the range of N = 1 to 100, K = 1.00 and a = 1.084 for N = 1 to 1000, K = 1.075 and a = 1.083 for N = 10 to 100, and K = 1-106 and a = 1.069 for N = 10 to 1000. The slope of a log N versus log (AJAp) graph is a, and Eq. 23-25 yields a slowly decreasing a with increasing N. This is consistent with the

notion that, for clusters with D1 < 2, as N -> <*>, N should be linear with AC9 that is, a asymptotically approaches 1.00 because the cluster dimension is less than the dimension of the plane onto which it is projected. In summary, all these results agree fairly well. Given this and because we desire to conclude with a recommendation for all plausible situations, we use the computer results of Meakin et al. (1989), Eq. 23-25, which are the most extensive (up to N = 104). A good procedure is to estimate the range of Af values for the clusters to be analyzed and then fit Eq. 23-24 to Eq. 23-25 for this range to determine a and ka. Then use these values to determine N from Ac and Ap. For example, if the clusters lie in the approximate range N = 10 to 1000, use ka= 1.106 and a = 1.069. Most likely other sources of experimental error will be larger than the uncertainty incurred by use of ka and a. There are three useful ways to determine the fractal dimension D{. Two methods analyze a single cluster; they are the method of nested circles or squares and the analysis of the density correlation function. The third method requires an ensemble of aggregates and compares N to Rg (or any measure of the aggregate's linear size) via Eq. 23-9. We discuss these methods sequentially below.

THE FRACTAL DIMENSION.

The Method of Nested Circles or Squares. In this method circles or squares of increasing radius or side length s are computer drawn on the aggregate centered on the aggregate's center of mass. The total binary black area within the circle or square is calculated and plotted versus s. Then from Eq. 23-11 the following scaling relation holds: (23-26) Equation 23-26 is best used as a log-log plot of G versus s, which will have a slope of D12. Df2 must be converted to the three-dimensional fractal dimension with Eq. 23-18 and a. Often a given cluster will yield a nonlinear, strangely shaped plot of N versus s. One must remember that "fractal" is a statistical concept, and not all clusters are alike. Examination of an ensemble of clusters is therefore highly recommended. While most clusters will exhibit fractal behavior, that is, have a linear log G versus logs plot, occasional "odd" clusters will occur. An example of this cluster-to-cluster variation is given by Zhang et al. (1988) and reproduced in Figure 23-6. There it is found that the sum of the black areas for three clusters is better described by Eq. 23-26 than for any individual cluster. On the other hand, if there are more "odd" clusters than "fractals," then the system is not a fractal system. The Density Correlation Method. The density correlation function is a conditional probability that, given material at one point, defines the probability that there will be material at another. It is represented by g(r) and is expressed by (23-27)

In Eq. 23-27 p(R) is the density at point R, and the brackets mean an average over all positions R. The digitized, binary images can be used to calculate g(r). Because the density is proportional to the gray level of the image, Eq. 23-20, we can write (23-28) where (23-29)

A B C

Darkness (arb. unit)

A + B+ C

Radius (arb. units) Fig. 23-6. The total gray level (Gtot) within a series of nested circles centered on the cluster center of mass for three different soot clusters A, B, and C determined from their binary projected images. The curve A + B + C is their sum, which is fairly linear to imply an average Dt = 1.72 ± 0.10.

In calculating with Eq. 23-28, u and v should be constrained to points (pixels) within the aggregate. This is not necessary, but otherwise G = O and unnecessary computation results. Note that Eq. 23-28 could also be restricted to averages over u or v separately to yield g(x) and g(y). If these differed, anisotropy would be implied. Many different x and y values yield the same r, so these could be averaged over a range r to r + dr to calculate a final g(r). Once the density correlation function is calculated, it can also be plotted on a log-log plot to display its power-law nature, as written in Eq. 23-10. If the projected image has been stored in the computer in a binary format, the projected image has pixels with gray level G = 0 or 1 only, and the effective spatial dimension is d = 2 to be used in Eq. 23-10. Also for the binary format, the fractal dimension is Df2= Di3/a (i.e., the correction of Eq. 23-18 must be made). Here again cluster-to-cluster variation is expected, but typically not as much as in the "nested" method above. Figure 23-7 gives an example for a soot fractal aggregate. The Ensemble Method. Both N and Rg can be extracted from the projected, binary images of the fractal aggregates. Given these parameters, Eq. 23-9 suggests that a log-log plot for a polydisperse ensemble of aggregates will yield D{ from the slope and ko from the intercept. This fact has been used many times in the literature, and Figure 23-8 gives an example. Note that this analysis yields D{ = D0. If ko is not needed, this analysis can be simplified by using the total gray level of the binary projected image of the aggregate versus any aggregate length (e.g., the longest, 2d projected length). The slope of such a graph would yield D{2. This method usually yields excellent results. Aggregates with Df>2. Jullien et al. (1994) modeled d = 3 random fractal aggregates on a computer with 1 < Df < 2.5 and then studied their projection onto a plane. Figure 23-9 is a reproduction of their Figure 2a, which is particularly useful. It shows the binary projected fractal dimension D11 versus the true d = 3 fractal dimension Df of the aggregate for a variety of aggregate sizes ranging from Af=16to8192.A line representing Eq. 23-18 with a = 1.1 is included. The data for Df<2 support the line, giving yet more credence to the analysis above. For Di > 2, this line fails, more so with increasing Df. However, larger values of a in Eq. 23-18

C(r)

r/Rg Fig. 23-7. Density correlation function for a soot cluster obtained from a premixed CHVO2 flame. Dashed line is a fit to the first 13 points (thereafter the cut-off function h[x] takes over) of the power law g(r) « rDf-2~d.

could describe the relationship between D{2 and Df>2. Given this, if one need analyze an aggregate with Df>2, a can be extracted from Figure 23-9, and then the analysis technique described above using Eqs. 23-18,23-19, and 23-24 can be applied. Optical Characterization of Fractal Aggregates

Light scattering is an excellent method for in situ determination of fractal aggregate size and morphology (Sorensen, 1997,2001; Oh and Sorensen, 1999). The method involves measuring the scattered intensity / as a function of the scattering wave vector q, where (23-30) X is the optical wavelength, and 6 is the scattering angle. Although 0 is the experimental parameter, it is important to both think and work in terms of q because inverse q is the length scale of the scattering experiment. Plots of / versus q will have changes of slope whenever q~x passes through a length scale change of the system. / versus q is called the optical structure factor. Figure 23-10 is a schematic diagram of the information that can be obtained from an optical structure factor measurement. At small q the scattering is constant and proportional to the cluster number density n and the number of monomers per cluster N squared. This is termed the Rayleigh regime. Note that in an aggegating system nN - nm, the monomer number density, which is ideally a conserved quantity. Then / « nN1= nmN, which will increase as the system aggregates because Af increases. This increase in scattering (in the Rayleigh regime) as the system coarsens is the Tyndall effect (see Kerker, 1969) for a fractal aggregate system.

z

Rg/a Fig. 23-8. Total number of monomers per aggregate versus radius of gyration divided by the monomer radius for an ensemble of soot clusters collected from a premixed methane/oxygen flame. The slope of this log-log plot demonstrates the power-law dependency of Eq. 23-1 and yields a fractal dimension of D1 = 1.14 ± 0.04. The intercept yields ko = 1.23 ± 0.07.

As q increases from the Rayleigh regime, the slope eventually changes, indicating an aggregate length scale. Qualitatively one need only measure q where this first bend in /versus q occurs, invert it to q~\ and identify this length as the overall average size of the aggregates. This can be quantified because this first bend is the Guinier regime where (Guinier et al., 1955) (23-31) In Eq. 23-31 Rgz is the z-average radius of gyration (23-32) n(N) is the number of aggregates of size Af per unit volume (i.e., the aggregate size distribution). After the Guinier regime comes a regime of constant slope (on this log-log plot), the so-called power-law regime where (Sorensen and Wang, 1999) (23-33)

D(2 Fig. 23-9. Fractal dimension Di2 of the projection of a fractal aggregate versus its d = 3 space fractal dimension, Dh for N= 16 (open circles), 128 (filled circles), 1024 (open squares), 8192 (filled circles) for computer-simulated aggregates after Jullien et al. (1994). Line is Eq. 23-10 with a =1.1.

In Eq. 23-33 Cp and C are constants to be described below. The important dependency is / oc q~Df, which means that the slope of the double-log plot of / versus q is the negative fractal dimension; hence, D{ can be measured. Finally, at highest q, another bend occurs indicating the monomer radius a. Thereafter, the monomer regime is entered with slope -4 (a Porod regime) if the monomers have a smooth, nonfractal surface. Optical Structure Factor Measurements—Quantitative Description. With the qualitative description of the optical structure above, we now describe how to obtain accurate values of fractal aggregate Rg and Z)f from light-scattering measurements. Measurement of Rg. An example of an optical structure factor from some of our early work (Gangopadhyay et al., 1991) is given in Figure 23-11 for a soot aerosol in a premixed methane/oxygen flame. Aggregation increases with increasing height above the burner. Inspection of Figure 23-11 shows that with increasing height, the bend in the optical structure factor (i.e., where the slope of/versus q goes from zero to negative) progresses to smaller q. The cardinal rule is that a change in slope implies a length scale. In this case the length scale is the overall aggregate size, and because R « q~\ this is a direct observation, albeit qualitative, of the aggregate size increasing with time. Notice the essentially isotropic scattering at h = 8 mm to indicate very small particles. Figure 23-12 presents a more recent example of scattering from a titania aerosol. Note the scale in q is an order of magnitude smaller than in Figure 23-11, and hence the clusters of titania are an order of magnitude larger. In Figure 23-12 a significant power-law regime is seen with a slope implying D{ ~ 1.7.

Rayleigh regime

Guinier regime

Power law regime

Slope = -D Kq)

Increasing polydispersity

Monomer regime

Slope = -4

q Fig. 23-10. Schematic representation of the scattered light intensity I(q) versus q = AnX 1Sm 0/2, where G is the scattering angle, from an ensemble of fractal aggregates of dimension Dt with Af monomers per aggregate on a log-log plot. In the ensemble n is the number density of clusters and Nm is the total number of monomers, nN = Nm.

l(q) (Arb. Units)

h(mm) 20 17 15

C/O = 0.69

10

8 q (unr 1 ) Fig. 23-11. Scattered light intensity I(q) as a function of the scattering wave vector q for five different heights above burner h for a premixed methane/oxygen flame.

run 1 run 2 run 3 run 4 run 5

l(q)

Slope = -1.7

q (^m-1) FIg. 23-12. Scattered light intensity I(q) as a function of the scattering wave vector q for a titania aerosol.

Quantitative analysis of the optical structure factor proceeds in two steps (Sorensen et al., 1992a; Sorensen, 2001). First, the Guinier regime is analyzed to yield the aggregate radius of gyration. The Guinier equation, Eq. 23-31, may be inverted to yield (23-34) This implies that a graph of I(0)/I(q) versus q2 should be linear with a slope of 1/3 Rgz. This plot is similar to the Zimm plot of biophysics (Zimm, 1948; Tanford, 1961; Kerker, 1969). The data of Figure 23-11 are so plotted in Figure 23-13. Figure 23-13 is a proper Guinier analysis in that, for the most part, for the data used, qRg< 1, which is equivalent to I(0)II(q) < 4/3. Precisely speaking, this Guinier analysis should be limited to qRg^ 1. Often, however, the data are not plentiful and precise enough within these bounds to yield an accurate Rg. It has been found (Cai et al., 1995b) through experience with both real data and numerical calculations to create simulated data, that I(0)/I(q) versus q2 remains linear well beyond these limits, and data up to about I(0)/I(q) ~ 2 can be trusted to yield accurate Rg values. Measurement ofDp The power-law regime yields the fractal dimension Df by its slope. One would like to have a good decade of linearity to get a good measure of this slope but this is rarely the case. Moreover, the true power-law character does not really show until qRg > 5. Hence one must beware when data in this regime are limited. An example of structure factor data with an ample power-law regime is given in Figure 23-12. The power-law regime is quite linear in this log-log plot with a slope that yields D{= 1.75. Polydispersity does not affect the negative fractal dimension slope of the structure factor for qR% > 5 except in extreme cases, as yet not encountered in aerosols. Martin and Ackerson (1985) discuss this theoretically. Measurement of Polydispersity. A well-endowed power-law regime, that is, one that extends out to qRg ~ 10 or more, has another advantage besides easy extraction of an accurate fractal dimension. A measure of the polydispersity can be obtained using the coefficient Cp in Eq. 23-33. In a recent study Sorensen and Wang (1999) proposed the large qRgz form in Eq. 23-33 to differentiate between single-cluster and polydispersity effects on this regime. The constant C depends on the cut-off function h(x) of the density correlation function, Eq. 23-10. It was

C/O = 0.69

h = 20 mm 17 15 10

I (O)/l (q)

8

q2(nm"2) Fig. 23-13. Flame soot aerosol data of Figure 23-11 plotted for a Guinier analysis.

concluded that C = 1.0 + 0.1 best describes cluster-cluster fractal aggregates. The constant Cp depends solely on the polydispersity of the cluster system and can be used to measure the polydispersity. For further information on this topic, see Sorensen and Wang (1999). In summary, the measurement of optical structure factor is a very useful method capable of yielding particle i?g, Df, and the polydispersity. The particle refractive index need not be known. If data beyond qRg< 5 are not available, the D{ measurement can only be considered qualitative. An excellent procedure is to determine Rg with the Guinier analysis using small q data and then Df with a log-log plot (or fit) using large q data. See Sorensen (2001) for an extensive review of fractal aggregate light scattering. Shape Factor. Sorensen and co-workers (Cai and Sorensen, 1994; Wang and Sorensen, 1999) have used light scattering to study fractal aggregate diffusivity.This is relevant to the dynamic shape factor because the diffusion coefficient D and the coefficient of the drag force / in F = fV are related by the Einstein relation (23-35) where k is the Boltzmann constant and T is the temperature. Their method used static light scattering to measure the aggregate radius of gyration, Rg, and the fractal dimension Df and dynamic light scattering to measure the diffusion coefficient. The fractal aggregates were soot in a premixed methane/oxygen flame and TiO2 aggregates in air at room temperature and pressures from 1/15 to 1 atmosphere. Wang and Sorensen (1999) also reinterpreted aerosol mobility data of Schmidt-Ott (1988) and Rogak et al. (1990) and the colloidal data of Wiltzius (1987) and combined all these with their data to achieve a general picture of the mobility of fractal aggregates at all values of the Knudsen number.

Wang and Sorensen (1999) gave their results in terms of the ratio of the mobility radius, ^mob, to the radius of gyration, Rg. To recast their results into the dynamic shape factor, recognize that Eq. 23-1 implies (23-36) where Rm is the mass equivalent radius. The mobility radius was defined by the modified Stokes relation (23-37) Wang and Sorensen's results (1999) pertain to the common situation of diffusion-limited cluster aggregates, which have a fractal dimension Z)f ~ 1.75. Under these conditions two regimes of functionality for Rmoh were found depending on the number of monomers or primary particles (N) per aggregate. When N is small (i.e., <60), then (23-38) where a is the monomer radius. This empirical result has the correct Af —> 1 limit. Because Ri = a3N, it follows from Eqs. 23-36 and 23-37 that X = N0'11, N<60

(23-39)

For large N it was found empirically that Rmob = 0JRg

(23^0)

Rg is related to N via (23-41) Where ko « 1.3. It then follows that (23-42) Before we discuss these results, we consider two other functional relationships. First, Chan and Dahneke (1981) calculated i?mob for straight chains of N individual spheres in the free molecular limit. From this we find (23^3) Second, a limiting case, often called the free draining limit in polymer science, would be when the drag on an aggregate is the sum of the drags due to the N individual spheres. Then it follows that (23-44) AU these results, Eqs. 23-39,23-42,23-43, and 23-44, are plotted in Figure 23-14. We see that the independent spheres approximation works very poorly. This implies that there is considerable interaction of the flow fields around each monomer. On the other had, the linear chain result agrees fairly well with the fractal aggregate when N is small, especially when N < 10.

Next Page

Independent particles

Shape factor (%)

Large N

Linear Chain Small N

Monomer number (N) Fig. 23-14. Calculated values of shape factor for several particle agglomerate configurations.

This implies that the geometry of the arrangement of the monomers is not important until Af is larger than 10. Figure 23-14 may be used to determine the dynamic shape factor for fractal aggregates for all values of the Knudsen number. When Kn « 0, the continuum regime, %, is given by the greater of the two solid lines, representing Eqs. 23-39 and 23-42, in Figure 23-14. For large Kn, the behavior of % is less certain. A description for this regime consistent with the conclusions of Wang and Sorensen (1999) is that % is given by the small N curve whenever Kn > 5. For N > 60 and 1 < Kn < 5, we expect % crosses over smoothly between the small and large N curves as Kn —> 0. FIBERS Introduction The term fiber has been applied to a wide variety of particles having an elongated shape (i.e., one particle dimension significantly greater than the other two). Because of this elongation, fibers can have aerodynamic and other properties quite different from more compact particles. Certain fibers have several unique properties that make them not only useful from a commercial standpoint but also important from a health standpoint. Asbestos, for instance, includes six commercial fibrous minerals that have high tensile strength, chemical resistance, and excellent thermal and acoustic insulation characteristics. These properties have made asbestos useful in a variety of products, including friction materials, high-temperature insulating materials, acoustic insulation, fire-proof cloth and rope, and floor tiles. While the bulk materials in these products may consist primarily of macroscopically sized fibers, many of them can release long, thin fibers into the air.

compromised by both environmental and sampling stresses (Cox, 1987). Some bioaerosol particles are bits and pieces of biological origin that are shed by an ecosystem. Bacteria emitted from aerated wastewater are residues of evaporated droplets. Many of the bacteria shed by humans, for example, are residues of the skin. They probably stay viable while airborne because they are adapted to the dry conditions of their environment and protected by their original substrate (i.e., the skin scale). Otherwise, most vegetative cells of bacteria are prone to damage as a result of becoming airborne. Microbial cells in an aerosol may be viable or nonviable. The definition of the viability of a cell is not explicit (Roszak and Colwell, 1987), but, generally, viable cells are able to reproduce or they have metabolic activity. Nonviable organisms are dead or unable to reproduce. The nutritional requirements of many environmental microorganisms are not known; also, not all microorganisms can be grown on laboratory media. From a natural soil or water sample, for example, it is typically possible to culture less than 1% of the microorganisms present (Atlas, 1988), and this may also be true for airborne microorganisms. Bioaerosols that are not whole cells, such as endotoxins, mycotoxins, or various allergens, may also be present. Therefore, depending on the detection method, the results are commonly expressed as colony-forming units (cfu) when viable organisms are determined or as cells, spores, or pollen grains per unit volume of air when the viability of the organisms is not determined. The results of chemical analyses (e.g., for endotoxins) are expressed in ng/m3 or mg/m3. The density of microbial cells is somewhat variable, depending on the degree of cell hydration, the reserve materials, and the lipid content of the cell (Doetsch and Cook, 1973). Microbial cells consist mainly of water, about 70% of their weight. The rest of the cell material consists of macromolecules, such as nucleic acids, proteins, lipids, carbohydrates, and their combinations. The density of a single microbial cell has been reported to be 1070 to 1090kg/m3 [1.07 to 1.09g/cm3] (Hamer, 1985), 900 to 1300kg/m3 [0.9 to 1.3g/cm3] (Orr and Gordon, 1956), 1090 to 1240kg/m3 [1.09 to 1.24g/cm3] (Bratbak and Dundas, 1984), or 1500 kg/m3 [1.5g/cm3] (May, 1966). Bioaerosol particles cover a wide size range. Viruses are the smallest potentially living particles, about 0.02 to 0.3 um in length. Bacteria and fungal spores cover a size range from about 0.3 um up to about 100 um. Pollen, algae, protozoa, and dander are several tens to hundreds of micrometers in diameter. Fungi and plants are unique in their ability to produce monodisperse spore or pollen aerosol in large quantities. When microbial cells or spores are carried by other materials or when they are present as aggregates, their migration and deposition depend on the overall size of the ensemble. Although most of the bioaerosols are harmless constituents of normal environments, some bioaerosol particles may be infectious agents or allergens or may carry toxic or irritant components or metabolites. To be infectious, an organism must be viable, but to cause allergenic or toxic effects viability is not a prerequisite. Thus, dead cells as well as cell residues may affect human health (Hirvonen et al., 1997). Host factors, including the genetic and environmental factors affecting the individual immunological response, also play an important role. Biological characteristics may be utilized in controlling bioaerosol sources. Control measures can be targeted on environmental factors that regulate the growth of microorganisms (e.g., temperature and moisture).

BIOAEROSOL TYPES Bacteria Bacteria are generally found in any soils, waters, plants, and animals. In air, bacteria may occur as vegetative cells or endospores.They may be carried by other particles, such as water droplet residues, plant materials, or the skin fragments of animals. Bacteria are single-cell microor-

ganisms that range in size from 0.5 to 30 (im. The aerodynamic size of most indoor air bacteria in relatively clean environments has been reported to be 1 to 3jim (Nevalainen, 1989; Gorny et al., 1999). In homes with high concentrations of other aerosols, such as cigarette smoke, the particle size distribution has been found to have a mean size in the 0.5 to 10 urn range, which suggests that the bacteria are aggregated with other particles (Gorny et al., 1999). The shape of bacteria varies from spherical to rod shaped, spiral or filamentous. Many spherical bacteria occur as pairs, tetrads, or clusters (e.g., Micrococcus and Staphylococcus) or as chains (e.g., Streptococcus). The rod-shaped bacteria may occur as single cells or in chains (e.g., Lactobacillus). Bacteria can be divided into two groups based on the ability of the cell wall to retain crystal violet dye. Gram-positive bacteria, such as Bacillus and Staphylococcus, retain the dye, while gram-negative bacteria, such as Pseudomonas and Legionella, do not retain it. Gram-positive cell wall consists mainly of peptidoglycan, whereas gramnegative cell wall contains relatively little peptidoglycan but contains an outer membrane composed of lipopolysaccharides (endotoxin), lipoprotein, and other complex macromolecules. Both peptidoglycans and lipopolysaccharides can cause adverse health effects, as described below. Bacterial cells that actively metabolize and divide into new cells are called vegetative cells. Endospores are formed within the vegetative cells of certain bacterial genera (e.g., Bacillus and Thermoactinomyces). Bacterial endospores are dormant forms of cells and are very resistant to cold, heat, radiation, and other environmental stresses. The sizes of bacterial spores range from 0.5 to 3 urn. They are easily carried away by air currents because gravitational settling is fairly insignificant for particles of this size range. Pathogenic bacteria are known to cause disease in humans, animals, and plants. Pathogens are often specific and cause disease only to a certain species of animal or plant. Most animal and plant pathogens are different from human pathogens. Knowledge about human pathogens is the major focus of clinical microbiology. Environmental or saprophytic (i.e., living on dead or decaying organic matter) bacteria are found everywhere, and their nutritional and temperature requirements vary. Only a fraction of environmental bacteria appear to be identified and characterized thus far. Some of the environmental bacteria are opportunistic pathogens (i.e., they may attack an individual having weakened immunological response). For instance, some species in the well-known genus Legionella are opportunistic pathogens. For reasons presently not known, some Legionella species find an ecological niche in manmade warm-water systems, where they may multiply and, if aerosolized, cause serious disease in exposed people (Keleti and Shapiro, 1987). Actinomycetes are a group of soil bacteria that can both grow in a yeast form and produce mycelium and spores like filamentous fungi. According to the modern taxonomy based on genetic sequencing, they belong to the larger class of Actinobacteria (Stackebrandt et al., 1997). Their spores may contribute significantly to occupational exposure in agricultural work situations (Lacey and Dutkiewicz, 1994). In occupational and environmental hygiene, important genera of actinomycetes are Streptomyces, which gives soil its characteristic odor, and the thermophilic genera Thermoactinomyces and Faenia. These organisms cause hypersensitivity pneumonitis. Actinomycete spores may be found in office or residential buildings that have excessive microbial growth due to moisture accumulation within the structure or inside the heating, ventilation, and air conditioning (HVAC) system. In air, bacteria may occur alone or may be carried by other particles. Bacteria tend to grow in colonies in their natural habitats, such as water and soil, and as biofilms on different surfaces. Therefore, whenever they become aerosolized, bacterial particles often occur as aggregates or microcolonies attached to other materials (Eduard et al., 1990). For example, the skin scales of mammals, which are abundant in indoor air, usually contain colonies of bacteria, such as Micrococcus and Staphylococcus (Lundholm, 1982; Noble, 1975). Bacterial endotoxins are lipopolysaccharides that are specific to the cell wall of gramnegative bacteria. Endotoxins are heat resistant and chemically stable. They maintain their

biological activity after the bacterial cell is no longer viable. Endotoxins cause acute toxic effects, including fever, malaise, and decrements in pulmonary function. They can be abundant in agricultural environments, some industries, and in humidified indoor air and may contribute to symptoms of humidifier fever (Rylander and Haglind, 1984; Rylander and Jacobs, 1994). Bacterial cell wall also contains peptidoglycan; it comprises 90% of the cell wall of grampositive bacteria and 10% of the cell wall of gram-negative bacteria. The health effects of peptidoglycan are not as well known as those of endotoxin, but it has been suggested that it has biological activities similar to endotoxin (AIHA, 1996; ACGIH, 1999). Fungi

Fungi are also omnipresent microorganisms. They are responsible for most of the aerobic decay of natural organic material (Kendrick, 1985). Fungi include yeasts, molds, and mildews, which are often called microfungi, as well as large mushrooms, puffballs, and bracket fungi (Burge, 1995). The terms mold and mildew refer to visible fungal growth on surfaces. Fungi can be unicellular (e.g., yeasts) but are usually multicellular, forming long chains of cells called hyphae, which, as an ensemble, are called mycelium. Fungi are classified into groups based on their methods of spore production. The primary form of dissemination for this organism is by the release of fungal spores, which are well adapted to airborne transport. The size range of fungal spores, 1.5 to 30 urn or sometimes even larger, allows their transport by winds to long distances. Most of the fungal spores in indoor environments have been reported to be 2 to 4 urn in aerodynamic diameter (Reponen et al., 1994; Gorny et al., 1999). Fungal spores are often resistant to various environmental stresses such as dryness, cold, heat, and ultraviolet radiation. Some fungi can utilize living plant materials and cause crop diseases of great economic importance. A few fungi (e.g., Histoplasma, Blastomyces, Coccidioides, and Candida) readily invade living animal tissue and may cause infectious disease. Others are opportunistic pathogens (e.g., Aspergillus and Cryptococcus), which mainly cause infection only in people with impaired immunity. However, most fungi are saprophytic, provided adequate moisture is present. In buildings, moisture is the main controlling factor for mold growth. The relative humidity of the air (RH) affects the water content of materials in the room. However, it is the available moisture in the substrate, not the RH of the room air, that limits the growth of microorganisms in or on the materials. Virtually no microorganisms found in indoor air are able to grow if the equilibrium RH of the material is below 65% (Flannigan and Morey, 1996). Most fungal aerosols can cause allergic reactions and diseases, such as asthma, allergic rhinitis, or, in some cases, hypersensitivity pneumonitis. Studies of these allergens usually focus on fungal spores, although metabolites and fragments of mycelium can also become airborne. Although a few genera, such as Cladosporium, Alternaria, basidiospores, and ascospores, dominate the outdoor aerosols in most of the world, there is some geographical variation. In areas of seasonal variation, the levels of fungal spores are highest in summer and fall and lowest in winter. Massive exposure to fungal spores may occur in farming and food handling occupations as well as in some industries (Rylander and Jacobs, 1994). In office and residential indoor environments, the outdoor air is an important source of fungal spores. The detection of airborne spores, resulting from growth on indoor substrates, can be difficult in the presence of normal background levels of outdoor bioaerosols. In the cell wall of plants and fungi one finds glucans. The major source of j3-(l —> 3)-£>glucans is the cell wall of fungi (Rylander and Jacobs, 1994). Glucans may play a role in various respiratory diseases and symptoms related to fungi, by themselves or by interacting with endotoxin or other environmental agents. Some fungi may produce mycotoxins, which are toxic secondary metobolites. Several species of the genus Aspergillus produce carcinogenic toxins. These toxins have been studied

primarily with respect to ingestion. There is some indication that aerosol exposure is a hazard as well (Sorenson et al., 1987). Aerosol exposure has been related to trichothecenes, which are mycotoxins produced by, for example, Fusarium and Stachybotrys. These can cause serious acute effects, including headaches, dizziness, immunosuppression, and pulmonary hemorrhage (Sorenson et al., 1987; Etzel et al., 1998). Viruses Viruses differ from other microorganisms because they can reproduce only inside a host cell. Therefore, viruses are intracellular parasites and never grow on nonliving substrates. They may infect bacteria, plants, animals, or humans and are usually targeted toward a specific subgroup. Those viruses that replicate only within bacterial cells are called bacteriophages. The smallest of all microorganisms, 0.02 to 0.3 um, they consist of only one type of nucleic acid, either RNA or DNA, and are, therefore, not able to generate genetic information without a complete host cell. Viruses are surrounded by a protein layer called a capsid. Viruses can be transmitted through air in the absence of the host cell (Gerone et al., 1966). Although individually small, they usually travel in air carried by other materials such as droplets of respiratory secretions. The size of a virus-containing particle may vary greatly, depending on many factors, such as RH of the surrounding air. Viruses can cause infectious diseases (e.g., influenza, measles, and chicken pox), and the viral sources are almost always other infected humans. The viral agent for hantavirus pulmonary syndrome spreads from infected rodent droppings or urine. There is epidemiological evidence for the transmission of viral infections inside and between buildings (Riley et al., 1978; Donaldson, 1978). There is no evidence of airborne transmission of hepatitis or human immunodeficiency viruses, but there is some concern about blood and tissue aerosols generated during general and dental surgery that might contribute to the transmission of these agents. Pollen Pollen grains are produced by plants to transmit the "male" genetic material to "female" flower structures. Many ornamental plants produce pollen grains that are transported through the air by insects. However, many trees, grasses, and weeds produce pollen grains that are dispersed in the air by wind forces without the need of a carrier. To be successfully transmitted, this kind of pollen is usually produced in large amounts. For example, one shoot of hemp may produce over 109 pollen grains (Faegri and Iversen, 1989). Airborne pollen grains are usually resistant to environmental stresses such as desiccation, temperature, and light; hence, they tend to resist sampling stresses. Pollen grains from different plants vary in size, surface structure, and, to some extent, shape. The aerodynamic diameters of pollen grains are not well known, but the physical size range is approximately 10 to 100 um, with many types of pollen grain being between 25 and 50 um. Thus, pollen grains are not in the respirable size fraction. However, many kinds of pollen contain important allergens, which may be present in the air in smaller fragments (Rantio-Lehtimaki, 1995). Patterns of prevalence for airborne pollen vary with geography and climate. For example, ragweed pollen is considered to be one of the most important allergens in North America, while birch pollen is considered the most important in Northern Europe. Cat, Dog, House Dust Mite, and Cockroach Allergens A wide variety of materials derived from mammals contain potentially allergenic material. Cats and dogs are the most common household pets. Cat allergens are found in saliva and skin dander and are carried in small particles, less than 2.5 um. Dog allergens are found in

saliva, skin dander, and urine. Dog allergens are associated mainly with large particles, >9 um (Custovic et al., 1997). Cat and dog allergens have been detected in schools and offices where these animals are not kept (ACGIH, 1999), which suggests that the allergens are carried on clothing. House dust mites (e.g., Dermatophagoides pteronyssinus and D. farinae) are common allergens in residential environments. Dust mites are most prevalent in mattresses, carpets, and upholstered furniture where human skin scales collect and serve as food. Mites also need high humidity, over 50%, for survival and reproduction. Mite allergens are carried on mite fecal particles and dried body fragments, which are 10 to 20 um in size (Burge, 1995). The two most common cockroaches inside homes in temperate climate areas are Periplaneta americana and Blatella germanica. The source for cockroach allergens is not completely clear: Potential sources include cast skins, whole bodies, egg shells, fecal particles, and saliva. Cockroach allergens are found on particles over 5um in size (ACGIH, 1999). Although the exposure route for allergens is assumed to be through air, most of the allergens are difficult to detect in air samples due to their relatively large size. Therefore, allergen samples are usually collected with a vacuum cleaner directly from the reservoir (e.g., from mattresses and carpets). Immunochemical methods are widely used for allergen analyses. The amount of dust mite allergens in a dust sample can also be determined indirectly through colorimetric indication of the amount of guanine, which is a component of dust mite excreta (von Bischoff, 1989). Specialized knowledge is needed to visually identify mites in samples. SOURCES OF BIOAEROSOLS Many indoor bioaerosols originate outdoors. The surfaces of living and dead plants are probably the most important sources of airborne fungal spores and bacteria. Therefore, the mechanical movement of plants or soil (e.g., through farming activities or construction) generates bioaerosols together with nonbiological dust. Actinomycetes may become airborne from soil (Atlas and Bartha, 1987). All natural waters as well as anthropogenic waters, such as sewage lagoons and cooling water systems, contain large numbers of microorganisms. For example, gram-negative bacteria, actinomycetes, and algae are common constituents of water ecosystems. Therefore, droplets resulting from rain, splashes, or bubbling processes may contain bioaerosols, which may remain airborne after evaporation of the liquid. Strong sources of bioaerosols may be present in various occupational environments when organic material is handled, such as plants, hay, straw, wood chips, cereal grains, tobacco, cotton, organic waste, wastewater, or metalworking fluids. In agricultural and horticultural environments, exposure to fungal and actinomycete spores may be severe (Kotimaa, 1990; Lacey and Dutkiewicz, 1994; Rylander and Jacobs, 1994). Avian and rodent droppings can be a source for viral and fungal agents. In industrial and nonindustrial situations, specific bioaerosol sources may develop due to microbial growth in a building's HVAC systems or in the building's structure itself. Generally, the prerequisite for microbial growth is excessive and accessible moisture. Standing water is always a good reservoir for microbial growth and therefore a potential source of microbial aerosols when disturbed (Keleti and Shapiro, 1987; ACGIH, 1999). In addition to water, microorganisms only need minute amounts of nutrients, which are available in the water or in building materials, such as cellulose, wood, or concrete. Therefore, a source of spores or other bioaerosol material may develop wherever water is leaking or condensing inside a building. In nonindustrial indoor environments, the most important source of airborne bacteria is usually the presence of humans. Air with a high concentration of human bacteria is not nee-

essarily a health hazard, but may indicate the presence of humans and their physical activities. It may also signal inefficient ventilation. Although pathogenic bacteria or viruses are often present in these air environments, their presence may be difficult to verify by conventional bioaerosol sampling techniques. Therefore, the concentration of normal human skin bacteria in air is sometimes used as an indicator of indoor air quality. Recently, the threat of biological warfare and terrorism has gained increased attention. Agents of concern include pathogenic microorganisms, such as Bacillus anthracis and smallpox virus, as well as microbial toxins such as botulinum toxin. GENERAL SAMPLING CONSIDERATIONS The purpose of bioaerosol sampling is usually to verify and quantify the presence of bioaerosols for exposure assessment, to identify their sources for control, or to monitor the effectiveness of control measures. Dose-response relationships are poorly known, and therefore exposure guidelines have not been established for acceptable healthful levels of any bioaerosol (ACGIH, 1999). Bioaerosol concentrations have time-dependent variations ranging over several orders of magnitude. For example, bacteria and fungal spore concentrations on the order of 10 to 103cfu/m3 can be found in residential homes or occupational environments with moderate sources (e.g., tobacco processing, sanitary landfills, or biotechnical industries; Macher et al., 1991; Martinez et al., 1988; Rahkonen et al., 1990; Verhoeff et al., 1990; Reponen et al., 1992). Lower concentrations of <102cfu/m3 can be found in wellventilated facilities without significant bioaerosol sources, such as offices, laboratories, clean rooms, and operating rooms in hospitals. High concentrations of bacteria and fungal spores, with peak concentrations from 104 to as high as 1010cfu/m3, can be found in environments such as textile mills, sawmills, some agricultural exposure situations, and in seriously contaminated homes and offices (Kotimaa, 1990; Eduard et al., 1990; Lacey and Dutkiewicz, 1994). In most of these environments, the bioaerosol concentrations vary considerably in time and space. This is partly because bioaerosol sources do not necessarily generate particles continuously. For example, spore production and spore release from fungal mycelium may occur in bursts under certain air humidity and velocity conditions. Sampling Strategy

No single sampling method can collect, identify, and quantify all of the bioaerosol components existing in a particular environment. Therefore, a source inventory is important and useful. It may include a preliminary microbiological analysis of the water reservoirs and a contact sample from a surface with assumed fungal growth. In industrial exposure situations, the type and location of sources are frequently evident. In nonindustrial environments, the sources are often less obvious, which necessitates a complex sampling strategy. Sampling Efficiency of Bioaerosol Samplers

The overall sampling efficiency of a bioaerosol sampler can be divided into three components: 1. The inlet sampling efficiency is a function of the sampler inlet's ability to extract particles from the ambient air environment; it usually depends on the size, shape, and aerodynamic behavior of the particles being sampled. 2. The removal efficiency is determined by the sampler's ability to remove the particles from the air stream of the sampling inlet and deposit them into or onto the collection medium.

3. The biological aspect of sampling efficiency depends on the sampling and removal of the biological particles without altering their viability or biological activity and on the conditions for the organisms to form colonies or to be otherwise detected as biological particles. The physical (inlet sampling and removal) and the biological factors must be separated in order to quantify their effects on the overall sampling performance. None of the presently available samplers for culturable bioaerosols can be considered as a reference method, although the all-glass liquid impinger (AGI-30; AGI, HAM, MIL*) and the six-stage Andersen impactor (AND) have been suggested for that purpose (Brachman et al., 1964). Few of the currently available samplers have been adequately characterized as to their sampling performance. The results of reported field comparison studies are not easily comparable to each other, partly because the samplers, sampling times, and sampled volumes have varied within and between studies and partly due to the different operational principles and parameters of each of the samplers. Eduard and Heederik (1998) have summarized the results of several field comparison studies performed with different bioaerosol samplers. They found that some samplers are more efficient collectors of viable bioaerosol particles than others. Bioaerosol particles must be collected from the ambient air with as little bias as possible. For minimal bias over a broad particle size range, aspiration into the inlet should occur under isokinetic conditions, that is, at the inlet air velocity equal to the ambient air velocity and the inlet is oriented facing the ambient air flow. Long sampling lines after the inlet may cause significant wall losses of large particles. Dependence of the inlet efficiency on the particle size as well as on wind and sampling conditions can cause significant over- or underestimation of the concentration. For example, theoretical calculations have shown that the Burkard Portable Air Sampler (BUR) overestimates the aerosol concentration of 10 urn particles by 2.5 times when positioned horizontally parallel to the wind direction at wind speeds of 5 m/s. If the sampler is placed vertically with all other conditions the same, particles larger than 5 um will not be able to enter the sampler at all, while the concentration of particles of about 2 to 5um is considerably underestimated (Grinshpun et al., 1994). Nonisokinetic sampling is discussed in detail in Chapter 8. The physics of particle motion in air and removal from it is common to all airborne particles. Therefore, physical principles can be applied to the sampling of bioaerosols. These principles determine the amount of sample collected and the sampling time needed for proper analysis. A bioaerosol sampler has a high biological efficiency if it retains the biological properties that are used for analyzing the biological particles. For example, the sampling should not change the culturability of microorganisms if culture analysis is subsequently used. In the case of microscopic analysis, the sampling should not change the key morphological details of the particles. Table 24-1 shows that in the selected bioaerosol samplers (utilizing impaction or impingement principles) the impact velocity U0 ranges from about 1 to about 265 m/s. High impact velocity can result in metabolic and structural injuries of the collected microorganisms. Survival of bacteria has been found to decrease when the impaction velocity increases; bacterial survival also depends on the degree of embedding of bacteria into collection media (particularly for impactors) and the sampling time (Stewart et al., 1995;Terzieva et al., 1996; Lin et al., 1999a). The effect of mechanical impaction stress is more pronounced for the higher impaction velocity; the desiccation stress increases with the sampling time increase and when embedding is insufficient. The survival of bioaerosol particles depends also on their internal characteristics. Pollen grains and microbial spores are generally more protected against environmental stresses than vegetative cells (Cox and Wathes, 1995). * See Appendix I for full manufacturer addresses referenced to the italicized three-letter codes.

TABLE 24-1. Sampling Parameters of Selected Bioaerosol Samplers

Bioaerosol Sampler0

Collection Medium

AGI-30

Q (10-4Tn3Is [L/min])

U0 (m/s)

Liquid

2.1 [12.5]

265.2

Air-O-Cell

Adhesive

2.5 [15.0]

16.5

Andersen, first stage

Nutrient

4.71 [28.3]

Andersen, sixth stage

Nutrient

Burkard Personal Sampler

Adhesive

SAS

Nutrient

MK-II

Nutrient

Nozzle

Shape

W (mm)

L (mm)

d50 (jim) 2 6

A (mm ) 0.79

Number

Calculated"

Reported

1

0.31

<03d

1

2.3

2.6e

Round

1.0

Rectangle

1.055

1.08

Round

1.18

1.09

400

6.61

4.71 [28.3]

24.02

Round

0.25

0.05

400

0.57

0.65^

1.67 [10.0]

11.90

Rectangle

1.0

2.52

2.3-2.4*

30 [180]

17.34

Round

1.0

1.9*

5 [30.0]

51.42

Rectangle

0.35

14.4

14

28

"AGI, All-Glass-Impinger; SAS, Surface Air Sampler; MK-II, Casella Airborne Bacteria Sampler. fc Area per nozzle. c Equation 10-4, Tair = 293K [200C], Pah = 1 atm, 77air = 1.81 x lO^g/cms [1.81 x 10"4 poise], pp = 1000kg/m3 [1.0g/cm3]. d Linetal.(1997). Trunov et al. (2000). Andersen (1958). 8 Aizenberg et al. (2000). / 'Lach(1985).

15.2

14 0.79

219

1.45

9.8

1

0.67

Furthermore, aggregation of bacteria has been shown to increase their survival rate (Lighthart and Shaffer, 1997). PRINCIPLES OF BIOAEROSOL COLLECTION Bioaerosol sampling involves separating the particle trajectory from the air streamline trajectory. To achieve this, different physical forces are applied as illustrated in Figure 24-1. In Figure 24-1 A, the inertia of the particle forces its impaction onto a solid or semisolid impaction surface, usually either a culture medium or an adhesive surface that can be examined microscopically. This principle is used in single-stage impactors (e.g., the Surface Air Sampler; PBI, SPI) and in cascade impactors with two or more impaction stages (e.g., the Andersen cascade impactor). If the impaction stage consists of one or more slits instead of one or more circular holes, the impactor is frequently referred to as a slit sampler (e.g., Burkard spore traps and Casella [CAS], New Brunswick [NBS], and Mattson-Garvin [/?AS]

(a)

(b)

(C)

(d)

Fig. 24-1. Comparison of collection mechanisms for different inertial aerosol samplers. A, Inertial impaction. B, Centrifugal impaction. C, Liquid impingement. D, Tangential impingement. (From Willeke et al, 1998, reproduced by permission of American Association for Aerosol Research.)

culture plate samplers; more information is given below). Inertial gravitational and centrifugal particle collection techniques are described in Chapter 10. The principle of particle separation by centrifugal force is shown in Figure 24-1B. It also uses the inertial behavior of the particle, but in a radial geometry. The Reuter centrifugal sampler (BIO) is of this type. When filtration is the collection mechanism, inertial forces are also responsible for separating particles from the air stream. However, other mechanisms, such as interception, diffusion, and electrostatic attraction, also contribute to the deposition of particles onto the filter material. In liquid impingement (Fig. 24-1C), the particles are collected primarily by inertial impaction into a liquid, but also by particle diffusion within the bubbles. Several versions of liquid impingers are currently available (e.g., the AGI-4 and AGI-30 impingers). In a multistage impinger, several impingement stages remove successively smaller particles (May, 1966). Figure 24-1D illustrates the sampling principle of a tangential impinger, which collects particles by inertial impaction, and centrifugation. This sampling principle is used in the BioSampler (SKC) (Willeke et al., 1998). Particles may also be removed from the air stream by externally applied forces, such as electrical forces on charged particles, or thermal forces on an aerosol flow, which has a thermal gradient perpendicular to its flow. An electrostatic sampler has been used for virus sampling (Gerone et al., 1966) and has also been explored for bacterial sampling (Mainelis et al, 1999). Aspiration of the airborne organisms into an inlet and subsequent collection is the preferred method for bioaerosol sampling. An exception to this is the rotating arm device, for example, the Rotorod sampler (SAM) that is commonly used to sample pollen. Settling plates rely on gravity to deposit airborne microorganisms onto a culture plate. The gravitational settling of particles is highly particle size dependent and is strongly influenced by the air motion in the room. Therefore, the colony-forming units counted on a settling plate after incubation or the spores counted on a settling slide cannot be related to the concentration of biological particles in the air. At the same time, gravitational settling methods are suitable to identify the larger microorganisms present in the air environment because they all settle during relatively short time (regardless of their size and air motion). Particles can bounce when they strike the impaction surface or other previously collected particles. The latter often occurs when the sample is overloaded. Bouncing of larger particles can lead to undersampling in single-stage impactors when the particles bounce away from the collection substrate. Particle bounce can affect the size distribution obtained with cascade impactors when large particles bounce to the next collection stage. The collection efficiency, especially for larger particles, gradually decreases as the sampling surface becomes loaded. In a bioaerosol impactor, the collection efficiency may also decrease because the initially soft agar surface dries up with time, encouraging particle bounce (Juozaitis et al, 1994). This effect has not been accurately examined for commercially available bioaerosol samplers. However, it is a common practice to apply a thin film of grease to the impaction surface to reduce the bounce effect, hence, to increase the collection efficiency. The bouncing phenomenon is further discussed in Chapter 10. In all of the sampling methods mentioned above, the bioaerosol particles are collected and analyzed in two steps: First the particles are removed from the air, and then they are analyzed and identified. Direct-reading aerosol instruments are used to study aerosols without separating them from the air. These can be used for studying bioaerosol particles in the laboratory, when other particles are eliminated (e.g., Qian et al, 1995). There is increased interest nowadays—particularly among military and antiterrorist agencies—in developing direct-reading instruments that can distinguish the particles of biological origin from other particles. One commercially available, but very expensive, device, the Ultraviolet Aerodynamic Particle Size Spectrometer (UV-APS; TSl), simultaneously measures the aerodynamic

particle size, the light-scattering intensity, and the fluorescence intensity of the particles passing through the instrument. See Chapter 17. The fluorescence is measured between 400 and 580 nm upon exitation of the particles by a UV-laser beam. This fluorescence is related to the presence of life-indicating biomolecules, such as NAD(P)H (nicotinamide adenine dinucleotide phosphate) and riboflavin (Hairston et al., 1997). Particle Removal by Inertial Impaction

Inertial impaction is the most widely used mechanism for particle removal in bioaerosol samplers. In an impactor, the particle-laden airflow is pulled or pushed through a nozzle, which accelerates the air and the particles to high speed. The air jet emerging from the nozzle is deflected by the impaction surface and makes a sharp turn. Particles with sufficient inertia continue their forward movement toward the collection surface and impact. The impaction process depends on the particles' inertial properties, such as size, density, and velocity, and on the impactor's physical parameters, such as the nozzle dimensions and airflow paths. The non-dimensional Stokes' number, Stk, is used to relate the particle trajectories to the air streamlines in an impactor (see Eqs. 4-38 and 4-39). The Stk is defined as a ratio of a particle's stopping distance (see Eq. 4-36) to the dimension of the nozzle. Stk can thus be used to predict whether a particle will impact on the collection surface. S^50 defines the numerical value of Stk for which 50% of the particles are collected and 50% pass through the impaction stage. For a round nozzle, Stk50 is approximately one fourth, and for a rectangular nozzle it is approximately one half. Similarly, the "cut-off size", d50, designates the particle diameter for 50% removal. Because most impaction stages have sharp cut-off characteristics, almost all the particles larger than that size are collected. Therefore, d50 is generally assumed to be the size above which all particles larger than that size are collected. The cut-off size d50 is an important characteristic of any bioaerosol sampler. To evaluate the sampling performances of different samplers, the cutoff size can be calculated (see Eq. 10-4 and Example 24-1). Numerical Estimation of Cut-Off Sizes

The design and performance characteristics of six commonly used bioaerosol samplers are compared in Table 24-1. These samplers have a single impactor nozzle or several in parallel. The range of their volumetric flow rate, Q, is from 1.67 x 10"4m3/s to 3 x 10~3m3/s [10 to 180L/min]. For the six-stage Andersen impactor, data are given for the first and sixth stages. As seen, particle cut-off diameters (J50) are close to the experimentally determined values. The small differences between calculated and experimental values are due to the dependence of Stk50 on the impaction nozzle geometry of each sampler and the air flow pattern in the impaction stage that does not exactly match the pattern used in the theoretical calculations. The d50 value of an impactor can be used to determine which microorganism species are likely to be collected in the impactor if the mean aerodynamic sizes of the bioaerosol particles are known. The aerodynamic diameter of a particle equals the diameter of a spherical particle with density 1000 kg/m3 [1 g/cm3] (= density of water) that has the same gravitational settling velocity as the particle in question (see also Chapter 3 and Example 3-2). For bioaerosol particles with a density close to that of water and a shape close to spherical, the physical and aerodynamic diameters are approximately equal to each other. However, for rod-shaped particles, or for particles with density other than that of water, the aerodynamic diameter predicts the particle behavior more accurately than the bioaerosol particle's geometrical dimensions. The physical sizes (length and width as measured under a microscope) of bioaerosol particles are better known than their aerodynamic sizes. Table 24-2 summa-

Example 24-1 An Andersen N-6 sampler (i.e., the sixth stage of a six-stage cascade impactor) is operated at a volumetric flow rate of 20L/min instead of the required 28.3L/min (1 cfm). This is a single-stage sampler with 400 nozzles of 0.25mm diameter each, that is, 400 jets of aerosol flow are directed toward the nutrient medium below them. (1) Calculate the cutoff diameter d50 for the reduced flow. What is the smallest particle collected? (2) The N6 is operated at the required flow rate of 28.3 L/min, but 40 of its 400 nozzles are plugged. What is the smallest particle collected? Answer: (1) The air velocity, U0, and, therefore, the approximate particle velocity in each of the n jets of cross-sectional area A} are

To determine the cut-off diameter, J50, use Eq. 10-4. At normal pressure and air temperature (latm and 200C), the viscosity of air, 77, equals 1.81 x 10"4 poise (1.81 x lO^g/cms). The density of bioaerosol particles ranges from 900 to 1500 kg/m3 [0.9 - 1.5g/cm3] and is assumed to equal 1000 kg/m3 [lg/cm3] for this example. For the round nozzles, the value of Stk50 is approximately 0.25.

Therefore, a 0.77 um particle is approximately the smallest one collected. In the above calculation, the Cunningham correction factor, Cc, is assumed to be 1. It is actually somewhat larger than 1 for the calculated d50 (see Eqs. 4-23 and 4-24, Table 4-2, and Appendix F). Through an iteration procedure, the d50 is therefore calculated to be somewhat smaller than indicated. At the recommended flow rate of 28.3 L/min, d50 is 0.57 um (see Table 24-1). (2) If 40 of the nozzles are plugged, the air velocity through the 360 remaining nozzles at a flow rate of 28.3 L/min is 2668 cm/s. For this velocity and Cc = 1, the new cut size is d50 = 0.62 um. Because Cc > 1 for this particle size, the actual cut size is somewhat smaller.

rizes data on the measured aerodynamic sizes of several fungal and bacterial species. It also shows the aspect ratios for some of the species. Aspect ratio is defined as the ratio of length to cross-sectional area diameter of a particle and can influence the collection efficiency of particles sampled onto a filter. The aerodynamic particle sizes shown in Table 24-2 can now be compared with the particle cut-off sizes in Table 24-1. As seen in Table 24-1, the cut-off sizes for the various samplers or sampler stages range from less than 0.5 um to over 5um. A particle is collected by an impactor if its aerodynamic size is higher than the cut-off size of the impactor. For example, the aerodynamic size of Aspergillus fumigatus spores has been reported to be 2.0 to 2.1 um (see Table 24-2). Thus, the Burkard sampler and the first stage of the Andersen sampler do not effectively collect these fungal spores. The differences in the cut-off sizes of the various samplers partly explain the differences between sampler performances that have been reported in the literature (e.g., Eduard and Heederik, 1998).

TABLE 24-2. Aerodynamic Sizes and Aspect Ratios of Microorganisms as Measured in Laboratory Experiments Microorganism Fungal spores Aspergillus flavus Aspergillus fumigatus Aspergillus versicolor Cladosporium cladosporoides Paecilomyces variotii Penicillium brevicompactum Penicillium chrysogenum Penicillium melinii Penicillium minioluteum Scopulariopsis brevicaulis Bacterial spores Bacillus subtilis var. niger Faenia rectivirgula Saccharomonospora viridis Streptomyces albus Thermoactinomyces vulgaris Bacterial vegetative cells Pseudomonas fluorescens Micrococcus luteus Bacillus subtilis Bacillus megatherium Mycobacterium smegmatis Mycobacterium bovis

Aerodynamic Size (jam) 3.6" 2.O 6 ,2.1"' c 2.4C 1.8c'rf, 2.4« 2.6" 2.1 C , 23d 2.8" 2.7C, 3.1rf 1.7« 5.3fl 0.9rf 1.1" 1.3" 0.8rf, 0.9c, 1.2"

O.tf 0.8* 1.0* 0.8' 1.0* 1.2* 0.9e, 0.8-1.0*

Aspect Ratio

1.9e 1.3e

i.r

1.2* 1.2e 3.0-3.1*

2.6* 3-8'

"Madelin and Johnson (1992). Tasanenet al. (1991). 'Reponen et al. (1996). d Aizenberg et al. (2000). " Reponen, Willeke, Grinshpun (unpublished data). 'Reponen et al. (1998). * Willeke et al. (1996). /? Stewart etal. (1995). 'Qianetal. (1997). '"Schafer et al. (1998).

COLLECTION TIME An essential part of the sampling strategy is to define the sample collection time. Bioaerosol concentrations vary greatly with time, which is graphically shown in Part I of Figure 24-2. Typically, periods of low concentrations (e.g., tx to t2) are followed by periods of high concentrations (e.g., t3 to ^4) and vice versa. These large fluctuations are best represented on a logarithmic concentration scale, as shown. In this time plot, the average concentration, ca, is 1000 bioaerosol particles/m3. Bioaerosol concentrations of this order of magnitude are common in outdoor air and in many indoor situations. The ambient concentrations rarely remain stable within a narrow concentration range unless the time period is relatively short (i.e., minutes) or the atmosphere is undisturbed, such as in an unventilated, nonpopulated, closed room. Sampling during periods of changing concentration must be sufficiently long, or many short samples must be combined to properly represent the average environmental concentration. Part II of Figure 24-2 reflects how the concentration varies in air volume, v, being

Concentration (rrr3)

I

Ca

Time

Il

III

pump Collection on nutrient

Q

Collection on solid surface

IV

V Counting after incubation Direct counting

Fig. 24-2. The process of bioaerosol sampling. (From Nevalainen et al., 1992, with permission of Elsevier Science.)

sampled during the sampling period from starting time, ts, through the finish, tf. The volume of air equals the product of sampling flow rate, Q, and sampling time, t: (24-1) The sampling process actually integrates the instantaneous concentrations within the sampled period with respect to time. The number of particles, N, collected on the impaction substrate, Part III of Figure 24-2, equals the product of the average particle concentration, ca, and the sampled air volume, v (Eq. 24-1): (24-2) Surface Density of Collected Particles

Part IV of the sampling process in Figure 24-2 shows the bioaerosol particles being collected on a nutrient or solid surface that moves to the left during sampling, resulting in varying numbers of particles per unit area according to the changing bioaerosol particle concentration in the sampled air volume. The number of objects on the surface per viewing area, A—

microbial colonies on a Petri dish or microscopic particles on an adhesive surface—is referred to as the surface density of a sample. This surface density, 8, is (24-3) Part V of Figure 24-2 presents the postcollection phase when the collected material is analyzed. For example, this can occur either immediately upon collection by viewing the collected particles directly with a microscope or after an incubation period when the colonies are sufficiently developed to be viewed and identified by visual inspection. Viewing, counting, and identifying the particles in a sample—either optically or otherwise-is facilitated if the surface density is optimal, 8O. If the sample surface density is very low, 8 « <5O, the sampling and counting errors may be high, and the calculated aerosol concentration may not accurately reflect the true airborne concentration. If the sample surface density on a microscopic slide is very high, 8 » 8O, the particles may be located too closely to each other and pose resolution problems in counting and identification. In addition, the bioaerosol particles may be covered and masked by dust particles. If 8 » 8O on a nutrient surface, the collected organisms may grow together or inhibit each other's growth. This is especially important for fungal spores, which often release substances that inhibit germination of adjacent spores. Nonbiological particles that impact on nutrient surfaces along with the microorganisms may not cause viewing problems, but may inhibit growth. Optimal Sampling Time for Solid Surface Samplers

As seen in Eq. 24-3, the surface density of microorganisms collected on a substrate is linearly proportional to sampling time. The other parameters in Eq. 24-3—ca, Q, and A—are normally beyond the control of the investigator. Thus, insufficiently loaded samples (8 « S0) and overloaded samples (8» 8O) can be avoided only by adjusting the sampling period. By inverting Eq. 24-3, the sampling time can be calculated for each sampler by using the desired value of surface density and assuming a value for the airborne bioaerosol concentration: (24-4) The optimal sampling time for a given bioaerosol concentration is different for each sampler, depending on the sampler's flow rate and collection surface area. For purposes of this discussion, we assume an ambient concentration of 103 bioaerosol particles/m3 and a sample collection efficiency of 100%. We also assume that 8macTO = 1 particle/cm2 is the ideal surface density for colony counting on a culture plate and that <5micro = 104 particles/cm2 is the ideal surface density for microscopic particle counting on a sample slide. The latter equals three particles per microscopic field of 200 um diameter. Using these assumptions and Eq. 24-4, we have calculated the optimal sampling times for several of the bioaerosol impactors/impaction stages listed in Table 24-1. The surface density 8 was determined as the density of countable objects on the sampling surface. The definition of the sampling surface area A is specific for each sampler design. For a slit sampler with bioaerosol particles impacting onto an adhesive surface (e.g., the Burkard sampler), A is assumed equal to the area of the slit nozzle. For slit-to-agar impactors and sieve-type impactors, such as the Andersen impactor with 400 jets or the Surface Air Sampler (SAS) with 219 jets, the sampling surface is postulated to be equal to the sum of all nozzle cross sections. In culture plate samplers, the sampled particles are allowed to develop into colonies before viewing, and, therefore, the area of the sampling surface A is assumed equal to the area of the nutrient plate. For the

Surface densities used: for colony counting W o = 1crrr2 for microscopic counting «5micro=104Cm"2 Sampling time (min)

calculated sampling times example concentration

Concentration of bioaerosol particles in air (rrr3) Fig. 24-3. Collection times for selected bioaerosol samplers. (From Nevalainen et al, 1992, with permission of Elsevier Science.)

Andersen six-stage impactor (referred to as AND below), it is assumed that bioaerosol particles impact onto each of the six stages. Therefore, the total area of all six stages is used in calculating the sampling time. This assumption is valid when the particle size distribution produces an equal number of particles deposited on each of the six stages. When the sixth stage is used as a separate sampler, annotated here as the AND-VI and often referred to as the N-6, only the surface area of one Petri dish is used. It can be seen in Figure 24-3 that for ca = 103 particles/m3 and optimal surface density, the sampling time for the SAS (utilizing colony counting) is 8 s, while the Burkard personal air sampler has a sampling time of 140min (utilizing microscopic counting). This represents a difference of three orders of magnitude. Lengthening the sampling time of a culture-plate sampler may result in a more representative air sample, but is likely to cause overloading, leading to counting errors and inhibition effects. Therefore, many culture-plate samplers are best suited for environments where a very low bioaerosol concentration is expected (e.g., < 102cfu/m3). In the latter case, the sampling periods can be longer, or several short-term samples can be taken over a long period of time. As seen in Figure 24-3, the calculated sampling time for the Burkard personal sampler is 140 min for an expected average bioaerosol concentration of 103 bioaerosol particles/m3. However, in most environments this would result in a sample that is grossly overloaded with nonbiological particles. Therefore, the assumed or expected values of c and 8 can be modified in order to obtain a sampling time more in line with realistic expectations. For instance, for a higher bioaerosol concentration of 104/m3, the sampling time would be as short as 14 min. Optimal Sampling Time for Impingers

Impinger samples are not sensitive to overloading or undersampling because the liquid sample can be either diluted or concentrated, depending on the concentration of collected

AGI-30

Sampling time, t, min

AGI-4

Initial volume of collection fluid, V|Nrr, mL FIg. 24-4. Permissible sampling parameter ranges for less than 10% change in collection efficicency with the AGI-4 and AGI-30 impingers when operated at a sampling flow rate of 12.5 L/min. (Copyright American Industrial Hygiene Association, from Lin et al., 1997.)

bioaerosol particles in the liquid. However, evaporation of the sampling liquid and reaerosolization of already collected particles limit the sampling time in most impingers (Lin et al., 1997). Figure 24-4 shows the permissible sampling times and initial collection fluid volumes for sampling with AGI-4 and AGI-30 impingers when less than about 10% change

EXAMPLE 24-2 The Surface Air System (SAS) sampler is used to collect bioaerosols at an expected concentration level of 500 particles/m3. The microbiologist who will analyze the results would like to see an average of 50 colonies on each 55 mm diameter Rodac plate sample (nutrient dish). The SAS sampler has 219 sampling jets and a flow rate of 180L/min. Calculate the optimal sampling time. Answer: The area for the 55 mm diameter Rodac plate equals 23.8cm2. Thus, the surface density for colony counting (Eq. 24-3) is

To determine the optimal sampling time, use Eq. 24-4:

This sampling time may be too short for an environment with variable concentrations of bioaerosols. Several consecutive samples should be taken, or a different sampler, which allows a longer sampling period, should be chosen (see Fig. 24-3).

in the collection efficiency is permitted. The AGI-4 is an All-Glass-Impinger with its impingement nozzle 4 mm above the bottom of the liquid; in the AGI-30, this distance is 30 mm, while the liquid height is usually about 20 mm, that is, the top surface of the liquid—when there is no airflow—is 10 mm below the impingement nozzle. If the volume of the collection fluid is over 35 mL, a significant amount of liquid splashes out of the impinger. On the other hand, a minimum volume of collection fluid is required for each sampling time so that the collection efficiency does not change by more than 10% during the sampling. The upper particle size limit of 3 Jim is due to decreasing inlet efficiency when increasing particle size in the AGI4 and the AGI-30 (Grinshpun et al., 1994). When the sampling time exceeds 65min with the AGI-4 and 75min with the AGI-30, a significant amount of already collected particles is reaerosolized.

EXAMPLE 24-3 In planning bioaerosol sampling in a hospital you are told that cleaning of the postoperative patient room will take 20min. Previous reports indicate an average bacterial aerosol concentration of about 102cfu/m3. Which sampler would you choose to sample for 20min in this patient room? Answer: In Figure 24-3, the concentration of 102 bioaerosol particles/m3 can be located on the x axis and followed up to the 20min level on the y axis. At this intersection, the MK-II sampler and the Andersen N-6 sampler are indicated as being suitable for surface density Srn^10 = 1 cm"2 when the collected bioaerosol particles are counted as colonies after incubation.

SELECTION OF SAMPLER The selection of a suitable bioaerosol sampler depends on the types and levels of bioaerosols of interest in the environment to be sampled. For example, when evaluating allergens, total rather than viable counts are important. Also, when selecting a cultural sampler one must consider the vulnerability of the bioaerosol to the sampling forces because some samplers affect viability more than others. The most commonly used instruments are discussed briefly in order to provide the user with the basis for deciding which sampler to use and how it operates. The names and manufacturers of some of the commonly used instruments are given in Table 24-3. More sampler information is given by Willeke and Macher (1999). The Andersen six-stage sampler has been used in many studies (e.g., Reponen et al., 1992; DeKoster and Thorne, 1995; Gorny et al., 1999). It allows characterization of bioaerosols in specific size ranges. Two-stage and one-stage versions are also commercially available. One usually expects that the ambient bioaerosol concentration is low enough so that by the end of the sampling period bioaerosol particles have passed through only about 10% of the 400 nozzles in each impaction stage. When the bioaerosol concentration is high, several bioaerosol particles may deposit below each impaction nozzle. Only one of the organisms may grow, preventing the development of others into visible colonies. In some cases, two or more organisms may grow together into a single colony. When a high percentage of the impaction spots below the nozzles of a multiple-nozzle impaction stage develops colonies, the actual concentration of bioaerosol particles can be statistically calculated by the positive-hole conversion method described by Andersen (1958) and Macher (1989). The masking effect has been studied theoretically and experimentally by Chang et al. (1995a,b).

TABLE 24-3. Company Information about Bioaerosol Samplers Sampler

Manufacturer/ Supplier3

Inertial impactors Air-O-Cell Allergenco Air Sampler (MK-3) Andersen Sampler, 1-, 2- or 6-stage Burkard Sampler Casella Airborne Bacteria Sampler (MK-II) Mattson-Garvin Slit Sampler Rotorod Surface Air Sampler (SAS)

ZAAISKC ALL AND BUR CAS BAR SAM PBIISPI

Centrifugal impactors BioSampler* Multistage Liquid Impinger Reuter Centrifugal Sampler (RCS)

SKC BUR BIO

Impingers AGI-4,AGI-30 BioSampler6

AGIIHAMIMIL SKC

Filter samplers 37-mm Cassette Button Sampler

CCOIMILISKC SKC

a

See key to company abbreviations in Appendix I. the BioSampler utilizes several sampling principles, including centrifugal forces and tangential impingement.

b

The SAS samplers are portable one-stage multiple-hole impactors that are commercially available in three models. These samplers utilize 55 mm diameter contact plates filled with nutrient medium and also require statistical adjustments for multiple impactions. Several models are available among the slit impactors. Some rotate a Petri dish below the inlet nozzle (e.g., the Mattson-Garvin slit sampler); others impact particles on a microscope slide or tape (e.g., the Air-O-Cell and the Burkard samplers). The rotating culture plate impactors are especially useful for determining temporal changes in viable bioaerosol concentrations. This feature can be used to implicate a specific source of bioaerosol when used before, during, and after an emission episode. The Burkard recording spore traps yield time discrimination for sampling periods from 1 hour to 1 week. The Reuter centrifugal sampler (RCS, BIO) is portable and inconspicuous and, therefore, causes minimal disturbance to a room's occupants. However, the results obtained with the earlier version, the Standard RCS, must be interpreted carefully because the d50 is about 3.8Jim (i.e., higher than for the other inertial impactors), and the device cannot be easily calibrated. The newer version, the RCS Plus, has a lower cut-off size, 0.82 um (Buttner et al., 1997), and a built-in calibration system. Filtration is an easy-to-use method to sample robust or microscopically identifiable bioaerosols in heavily contaminated environments (Palmgren et al., 1986; Eduard et al., 1990). High physical collection efficiency can be achieved when the filter sampler is used with an appropriate filter. For example, porous membrane filters with a pore size of 5 urn collect 0.3 Jim particles with an efficiency of 95% or higher, whereas capillary pore membrane filters must have a pore size of 0.6 um to reach the same efficiency (Eduard and Heederik, 1998). However, due to desiccation of bioaerosol particles by the air flow through the filter after collection, filtration is not a suitable method for evaluating the levels of vegetative cells if used in combination with culture analysis (Nasman et al., 1999; Wang et al., 2000). Gelatin

filters (Li et al., 1999) and wetted porous foams (Kenny et al., 1999) have been used to decrease the desiccation effect. Filter samplers are usually small and can mostly be used with personal sampling pumps, thus allowing the collection of personal samples. Recently, filter samplers have been adapted for the sampling of inhalable bioaerosols (Kenny et al., 1999; Aizenberg et al., 2000). The liquid of an impinger-type sampler can be used for serial dilutions and subsequent cultivation or microscopic analysis. Collection in a liquid can also be used for endotoxin determinations (Milton et al., 1990), as well as for immunological, genetic, and viral analyses. Conventional impingers, such as the AGI-30 and the AGI-4, can only be used with water-based collection fluids. These fluids evaporate quickly and are not efficient for the collection of hydrophobic particles, such as fungal and bacterial spores, due to the particle bounce and the reaerosolization of already collected particles (Grinshpun et al., 1997; Lin et al., 2000; Lin and Li, 1999). The BioSampler can be used with nonevaporative liquids, such as glycerol or mineral oil, which do not evaporate and thus permit long sampling times. The viscosities of glycerol and mineral oil are up to three decades higher than that of water, thus decreasing the potential for the reaerosolization of hydrophobic particles (Lin et al., 1999a,b). Mineral oil maintains the viability of the collected microorganisms and thus can be used with culture analysis (Lin et al., 1999; Lin et al., 2000). CALIBRATION No matter how efficient a sampler is, unless the air flow through the device is properly calibrated, reliable bioaerosol quantification will not be possible. Chapter 21 on instrument calibration discusses various flow calibration procedures. In addition to precisely knowing the volume of air sampled for the quantification of the sampled bioaerosol concentration, accurately calibrated air flow rates are especially critical for impactors because they are designed to operate at specific air flow rates. Improperly adjusted air flow rates will alter an impactor's cut-off size and, thus, its ability to collect an air sample that represents the ambient bioaerosol size distribution. When sampling with impactors, the distance from the inlet to the surface of the collection medium must also be correct. If the impactor is equipped with a movable stage, as in slit-to-agar impactors, the adjustment of the nozzle-to-medium distance is fairly easy. If the sampler is not equipped with an alignment device, the nutrient medium must be poured into the Petri dish to a predetermined depth. The performance characteristics of bioaerosol samplers can be evaluated in the laboratory using aerosolized test particles. Inert test particles simulating the aerodynamic size of the bioaerosol particles in question can be used to evaluate the physical sampling efficiency of bioaerosol samplers, whereas testing of the biological sampling efficiency requires the aerosolization of the specific biological particles of interest. Bioaerosols can be aerosolized in the laboratory using various wet or dry dispersion methods (Griffiths et al., 1996; Reponen et al., 1997). Ideally, the aerosolization method simulates the natural release of the bioaerosol particles. For example, fungal spores are released from moldy building materials as dry particles by air currents.

CONTAMINATION

To avoid contaminating and, therefore, compromising bioaerosol samples, the principles of aseptic techniques must be followed. Aseptic technique means that a systematic practice is maintained to prevent undesired microorganisms and spores from contaminating the sample. In the case of ambient air sampling, for example, both the microorganisms on the human skin and within the respiratory system are among the nondesirable particles.

All surfaces, including washed hands, contain bacteria and spores unless they are specifically sterilized. Sterilization means total destruction of both cells and spores. This can be done by autoclaving or flaming the objects or materials in question. For example, all microbial culture media are sterilized before use. Not all objects can be sterilized, and therefore disinfection is used to remove the majority of microorganisms from the surfaces and materials. Disinfection treatment with an oxidizing chemical or alcohol destroys pathogenic organisms and most other vegetative cells. Although it does not destroy all spores, disinfecting sampling equipment is usually enough to prevent significant contamination of the air sample. Most samplers are reusable and should be thoroughly cleaned before each use and decontaminated either by autoclaving or by disinfection via a chemical soak or wipe-down. Special care should be given to samplers that have convoluted inlets and pathways leading to the collection media; contaminating organisms and debris can accumulate within the sampler, making it difficult to disinfect. This creates the potential for compromised successive samples. When mounting the nutrient medium dishes or slides into samplers, touching of the nutrient surfaces by hand or other nonsterile objects should be avoided. This includes falling droplets and settling dust (e.g., from inside a ventilation system). A set of control nutrient dishes should be present during the same sampling period and be incubated with the samples in order to confirm the sterility of the medium. Once used, the Petri dishes should be sealed and transported gently with the sampling surface facing down. SAMPLE ANALYSIS The sample analysis method should be selected as part of the sampling plan. There are different ways to detect and quantify the collected bioaerosol particles. Traditional methods include microscopic counting and cultivation analysis. However, limitations of these traditional methods have led to the development of other methods, such as biochemical, immunological, and molecular biological assays. Microscopy Bioaerosol particles can be enumerated under the objective lens of the microscope after collection on a glass slide, tape, or appropriate filter. This method does not distinguish between culturable and nonculturable bioaerosol particles. Large bioaerosol particles, such as pollen grains and fungal spores, are readily enumerated under the light microscope by an experienced microscopist. Pollen grains can be identified based on their morphology, but identification of fungal species is limited with the microscopic techniques. Smaller bioaerosol particles, such as bacterial cells, are easily masked by other more numerous particles. Also, bacterial cells are not visible with a light microscope unless stained. Particles containing biological material can be detected by staining their nucleic acids with a fluorescent stain (e.g., acridine orange) and counting them with an epifluorescence microscope. Labeled antibody stains can be used to identify selected microorganisms, for example, Legionella (AIHA, 1996). Various microscopic analysis methods have been discussed in detail by Morris (1995). Culture Methods Culture-based assays are used for bacteria and fungi by collecting them directly onto a nutrient agar or transferring them onto an agar from a liquid or a filter sample. When microorganisms are cultured into countable colonies, the incubation conditions and the medium should be suitable for the organisms of interest, ideally for all viable microorganisms. In most cases, however, one cannot culture all viable microorganisms on the same medium because of the great differences in growing needs for the different organisms present. With this type of sample analysis, one cannot determine the absolute number of bacterial cells or fungal

spores in the sampled air because the bioaerosol particles usually contain aggregates of two or more cells or spores (Eduard et al., 1990). The results are given in colony-forming units per cubic meter of air (cfu/m3). For air sampling purposes, a nonselective medium is most often used. Many media provide the basic nutrients needed for the growth of the most common environmental microorganisms. These nutrients usually include carbon, nitrogen, phosphate, sulfate, iron, magnesium, sodium, potassium, and chloride ions. Many broad-spectrum media are commercially available (e.g., tryptone-glucose-yeast agar, nutrient agar, or casein-soy-peptone agar for bacteria and malt-extract agar for fungi). By varying the composition of the medium or using very specific nutrients, selected microorganisms can be cultivated or excluded. Selective media are available for many types of microorganisms (e.g., eosin-methylene blue agar for gramnegative bacteria and dichloran glycerol agar for xerophilic fungi, which grow on media with low water content). Fungi and bacteria may prevent each other's growth. Therefore, it is sometimes beneficial to use a fungicide in the bacterial medium and an antibiotic against bacteria in a fungal medium. A commonly used fungicide for this purpose is cycloheximide (~ 500mg/L). Streptomycin or chlortetracycline (~40mg/L) are used as antibiotics. Bioaerosol samples for culturable counts are usually incubated at room temperature (21° to 25°C) or at 28°C or 35°C in an incubator. These temperatures may favor the growth of some microorganisms more than others, but generally most environmental strains grow well at temperatures below 300C. A temperature of 350C is generally used when monitoring food and water. As a general rule, it is important to incubate all the samples, including the reference samples, at the same temperature. Exceptions to this are selective media, for which the incubation temperature is usually given by the manufacturer. If thermophilic organisms are of interest, they are usually differentiated from other species by using a much higher incubation temperature (e.g., 55°C). Identification of fungal colonies is based on the morphology of the colonies, spores, and hyphae as well as on different physiological tests. Although a number of handbooks are available that allow identification of fungi to the genus level (e.g., Kendrick, 1985), this remains an elaborate task that requires specific expertise in environmental mycology. Identification of bacterial colonies is based mainly on the morphology and staining properties of the cells and different physiological and biochemical tests. Easy-to-use test kits are available that identify the bacteria with a certain probability when supported by morphological characteristics. Identification of environmental bacteria requires special experience because these organisms differ from the clinically significant species. The principles of identification and classification are presented in bacteriological handbooks, such as Bergey's Manual of Systematic Bacteriology (Holt, 1984) and the handbook of Truper and Kramer (1981). In most cases, general classification of the colony types or genus identification provides enough information to draw conclusions from the sampling results. For instance, the colony type may be a gram-negative rod versus a gram-positive coccus, while the genus identification differentiates between Pseudomonas and Staphylococcus. In some cases, however, species identification is needed. For example, it may be important to know whether pathogenic Pseudomonas aeruginosa or Staphylococcus aureus is present. Other Analysis Methods

Biochemical methods measure certain biological molecules in bioaerosol particles, such as endotoxin, mycotoxins, p-glucans, and fatty acids. Depending on the agent, the analysis requires gas chromatography, mass spectrometry, high-performance liquid chromatography, or spectrophotometry. In an immunoassay, antibodies are bound to a specific target antigen, such as dust mite, cockroach, animal allergen, or fungal allergen. The major limitation of immonuassay is that specific antigens for microorganisms are difficult to define and standardize (Buttner et al., 1997). Polemerase chain reaction (PCR) is a molecular biological

assay method in which selected nucleic acid sequences contained in bioaerosol particles are replicated and detected. It is especially used for the rapid detection of organisms that are difficult or impossible to culture in the laboratory, such as Mycobacterium tuberculosis and Histoplasma capsulatum (Schafer et al., 1998). These analysis methods are usually applied to filter or liquid samples. Although many of the new methods are promising, most are still research techniques. If one of these techniques is to be used, arrangements must be made with an appropriate research laboratory. Various analysis methods are discussed in detail elsewhere (AIHA, 1996). Data Analysis and Interpretation There are no guidelines for acceptable or harmful levels of bioaerosols. Therefore, it is necessary to decide in advance the criteria that will be used to determine whether or not an environment is contaminated. For this purpose, reference data on the range of bioaerosol concentrations and the airborne microbial flora in outdoor air and in indoor air in nonproblem environments should be collected with the sampler and the analysis method to be used. For example, for fungal spores in nonindustrial indoor environments, the levels should be lower than those outdoors except during the periods of snow cover, when the outdoor concentrations are close to zero. Furthermore, the composition of the fungal species in the environment under study can be compared with that in control environments. The data interpretation is discussed in more detail elsewhere (ACGIH, 1999).

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devices such as the konimeter (Le Roux, 1970; Hewson, 1996), impinger (Greenburg and Smith, 1922), and thermal precipitator (Green and Watson, 1935; Hamilton, 1956) finding widespread use. Analysis was generally accomplished by using light microscopy, with electron microscopy finding increasing use as the instrument was developed. Although exposure to fibers is still assessed on a particle number basis, current sampling and analysis methods are dominated by the use of collection on filters and mass analysis (gravimetric or, for specific elements or compounds, by chemical analysis). Many of the methods employed are the same as or similar to those used in other areas of aerosol measurement. However, workplace aerosols, and the aims of applied measurement techniques, differ somewhat from those found in other circumstances. In most cases the bulk aerosol composition is known or can be deduced from the processes or products in use. The mass concentrations involved are typically an order of magnitude or so greater than those in the general environment. Finally, sampling is carried out specifically for assessing human exposure rather than characterizing the aerosol itself. While philosophies and approaches may differ, there is a great deal of commonality between methods used in the workplace and those used in other areas of aerosol measurement. Thus, techniques and applications described elsewhere in this book will frequently be directly relevant to workplace sampling. Chapters 7, 9,10,12, and 15 are particularly pertinent, providing detailed information on approaches to aerosol monitoring; filter collection; inertial, gravitational, centrifugal, and thermal sampling; and direct-reading techniques using optical particle detection, respectively. Chapter 26 on measurements in mines covers a subfield of industrial hygiene, while Chapters 23, 24, and 27 on nonspherical particle measurement, bioaerosol measurement, ambient aerosol sampling and aerosol exposure measurement are all relevant to the workplace. In this chapter, the emphasis is therefore on the basic sampling philosophies and methods used on a daily basis in the workplace.

AEROSOL EXPOSURE MEASUREMENT IN THE WORKPLACE Biologically Relevant Sampling Aerosol sampling in the workplace is ultimately concerned with measuring that aspect of the aerosol that leads to specific health effects. Thus, the method and metric used aim to provide biologically relevant information. Aerosol particles can cause health problems when deposited on the skin and eyes, but generally the most sensitive route of entry into the body is through the respiratory system. The biological effects resulting from deposition of an aerosol in the respiratory tract will depend on the dose received and the body's response to the deposited particles. Physiological response to an aerosol depends on the chemical and physical nature of the particles and the location of the interaction (i.e., deposition region). The ultimate goal of industrial hygiene aerosol measurement is therefore to ascertain the dose of aerosol delivered to the body and to evaluate whether the dose or potential dose is sufficient to cause adverse health effects. The respiratory system deposition region is primarily governed by particle size and shape. The health response may be a function of mass, chemical composition, or morphology and possibly particle size and surface chemistry. Ideally dose should be expressed in terms of the most appropriate metric. However, additional restraining factors on industrial hygiene aerosol measurements include the practical and economic application of measurement methods. In practice, it is simpler to measure penetration to the relevant areas of the respiratory system rather than dose, thus giving a measure of the potential dose. Mass and bulk chemical composition are easier to measure than parameters such as particle shape and surface area, and correlation between health effects and particle number and mass concentration (e.g., Bedford and Warner, 1943) indicates mass to be a suitable metric in many cases. Asbestiform fibers present an exceptional case where dose is best represented by particle number and shape, and accordingly a number and morphology-based metric is used (see Chapters 12 and 23).

Deposition Regions The respiratory system is an effective size-selective aerosol sampler in its own right, and it is fallacious to assume that all airborne particles will enter it. Large particles are excluded from entering the nose and mouth (the nasopharyngeal region) through inertial separation. Aspiration is a function of a number of parameters, including particle size, external air speed, orientation to the prevailing air movement direction, and breathing rate and volume. However, for external wind speeds of a few m/s and lower, the probability of a particle entering the mouth or nose (termed inhalable particles) may be generalized as being around 100% for particles with aerodynamic diameters of a few micrometers and below, reducing to around 50% at 100 um aerodynamic diameter (Vincent et al., 1990). Aerosol deposition in the nasopharyngeal region is dominated by inertial impaction, although interception and (for particles in the nanometer size range) diffusion also contribute. Further inertial separation and interception occurs as the particles pass into the trachea and the upper lungs (tracheobronchial region). Although population variance is high (Lippmann, 1977), penetration into the tracheobronchial region may be typified by particles smaller than approximately 1OjXm aerodynamic diameter (Lippmann, 1977; ISO, 1995). As the airways bifurcate to ever finer branches toward the alveolar region, aerosol particles are predominantly removed from the flow through a combination of impaction, interception, charge effects, and diffusion. In the preceding regions, deposited particles are cleared primarily by the action of cilia transporting them along to the upper airways. Particles depositing in the alveolar, or gas exchange, region are cleared either through the action of alveolar macrophages engulfing them and transporting them to ciliated airways (phagocytosis) or by dissolution in the lung fluid. Particle deposition is through impaction and diffusion, and penetration to the alveolar region is restricted to particles around 5 Jim and less aerodynamic diameter (Lippmann, 1977; ISO, 1995). The clearance mechanism employed in the alveolar region, together with the close proximity of the bloodstream, leads to a number of health effects specific to particle deposition within this region.

Particle Characteristics and Biological Response

Although particle aerodynamic diameter dominates deposition within the respiratory tract, the subsequent effect on health is a combination of physical particle characteristics and biological response. On deposition, the body may react to the chemical substances contained within the particle, interact with the particle surface, or be influenced by physical parameters such as size and morphology. Highly soluble particles and droplets will be rapidly assimilated by the body, particularly in the alveolar region. Local effects, such as irritation and inflammation, and systemic responses may become manifest over very short time periods. The gradual release of agents from low-solubility particles will have a much longer response time. However, low-solubility particles may also act as vectors for the transport of high-solubility solids, liquids, and gases present as thin surface layers, thus leading to a response not indicated by the bulk aerosol particle properties alone. For example, adsorption of nitrogen oxides and sulfur dioxide onto particles can lead to health effects at levels normally considered safe. Very low-solubility particles are more likely to have health effects associated with their physical characteristics. Lung overload phenomena are associated with the physical limitations of the lungs' clearance mechanisms as opposed to chemical interactions with the deposited particles (Morrow, 1994). Particle shape is a factor for fibrous aerosols (Blake et al., 1998) (see Chapter 23). It also influences available surface area, which may be related to toxicity through surface interactions (Lison et al., 1997) or increased solubility. Where open agglomerates of particles exist, including those resulting from combustion (such as diesel exhaust particulates), metal processing, welding, or fine powder production, the aerosol may have a very high specific surface area and be formed from particles able to penetrate to the

alveolar region. In some fine powders, including ultrafine titanium dioxide, carbon blacks, and fumed silicas, specific surface areas in excess of 2 x 105m2/kg [200m2/g] are achieved among particles with aerodynamic diameters less than 4|im. In comparison, an aerosol of spherical particles 4 urn in diameter and with unit density would have a specific surface area of 1.5 x 103m2/kg [1.5m2/g]. There is evidence that for some low-solubility materials toxic response may be associated with surface area or even particle number (Oberdorster et al., 1994; Lison et al., 1997; Donaldson et al., 1998). However, little is known of the role of what may be termed available surface area, which will be influenced by particle surface structure and biological mechanisms. Some of the responses observed on inhaling aerosols are reversible; some may be chronic. Some effects are cumulative; others are not. For some substances, there may be an exposure level below which no effects are observed (a "no-effect" level). For others, notably carcinogens, there may be no identifiable no-effect level. For a class of substances known as sensitizers, relatively high exposure levels may be experienced without obvious effect until a person becomes "sensitized" to the substance. Following sensitization, exposure to very low levels may result in a significant biological response. Biologically relevant exposure monitoring requires the range of interactions and responses, together with aerosol dose and particle form, to be taken into account. It can be seen that in principle there are a number of particle characteristics that will influence the toxicity of inhaled particles. Although characteristics such as size, morphology, surface area, and structure may be influential, current technology lacks the means to characterize workplace aerosols as completely as may be desirable. Fortunately, the specificity of many workplace aerosols enables successful exposure monitoring to be carried out by linking a related metric (such as mass concentration) to empirical dose-response data. The extent to which this approach is tenable where toxicity data are sparse is questionable, however. Sampling Conventions

The accurate measurement of aerosol exposure via inhalation requires sampling devices that match particle deposition to the relevant areas of the respiratory system. However, aerosol deposition is highly dependent on the individual (Lippmann, 1977) and not trivial to replicate in a sampling device. Broad standards have therefore been developed describing representative penetration characteristics of aerosol particles through the respiratory system as a function of aerodynamic diameter. These standards provide a basis for estimating the aerosol concentration potentially available to cause harm within specific areas of the respiratory system and underlie many industrial hygiene aerosol sampling methods. Early estimates of penetration into what was considered the most vulnerable part of the system—the alveolar region—were proposed in the 1950s and 1960s, resulting in the British Mines Research Council (BMRC) and the American Conference of Government Industrial Hygienists (ACGIH) conventions describing respirable aerosols (BMRC, 1952; ACGIH, 1968). More recently, the International Standards Organization (ISO, 1995) and the ACGIH (1998) arrived at convergent conventions describing the probability of particles penetrating to the nasopharyngeal, tracheobronchial, and alveolar regions. However, it was not until the early 1990s that international consensus was reached on particle penetration standards between the ISO, ACGIH, and the European Committee for Standardization (CEN). The resulting conventions describe penetration as a function of particle aerodynamic diameter into the respiratory system (inhalable aerosol), into the tracheobronchial region (thoracic aerosol), and into the alveolar region (respirable aerosol), with thoracic and respirable aerosol as subfractions of the inhalable aerosol. These particle-size-dependent fractions shown in Figure 25-1 are now widely used as the standards to which industrial hygiene aerosol samplers should conform (ISO, 1995).

Penetration

Respirable Thoracic Total Inhalable PM2.5 PM10

Dae / um Fig. 25-1. International workplace sampling conventions (ISO 1995). Environmental conventions are also shown for comparison (chapter 27).

The inhalable convention is based on particle penetration through the mouth and nose of a breathing mannequin over a range of wind speeds and orientations with respect to the wind and is defined as (25-1) for 0 < dae < 100 Jim. SI(dae) is the fraction of particle entering the system as a function of aerodynamic diameter dae. Both the thoracic and respirable conventions are expressed as subfractions of the inhalable convention and are based on lung penetration measurements. The thoracic convention is given as

(25-2) ST(dae) is the fraction of particles penetrating beyond the larynx as a function of aerodynamic diameter. F(x) is a cumulative lognormal distribution, with a mass median aerodynamic diameter (MMAD) F of 11.64 um and a geometric standard deviation (GSD) Z of 1.5. The respirable convention SI(dae) is similarly given as

(25-3)

where the cumulative lognormal distribution has an MMAD F of 4.25 [xm and a GSD Z of 1.5. A respirable convention for susceptible groups is also defined, with F= 2.5 urn, although this has not been implemented in any exposure standards as yet. Standards relating to penetration to the tracheobronchial and extrathoracic regions are defined by the difference between the respirable and thoracic conventions (tracheobronchial) and between the thoracic and inhalable conventions (extrathoracic). Further information on particle sizeselective sampling for workplace contaminants may be found in ACGIH (1998). Occupational Exposure Limits

Health-based aerosol exposure limits follow country-specific systems, but in the majority of cases follow a similar philosophy (Vincent, 1998). In the United States, the primary sources of occupational exposure limits for the workplace are (1) National Institute for Occupational Safety and Health (NIOSH) recommended exposure limits (RELs); (2) the U.S. Department of Labor (OSHA and MSHA) permissible exposure limits (PELs), and (3) the American Conference of Government Industrial Hygienists' (ACGIH) threshold limit values (TLVs). NIOSH RELs are time-weighted average (TWA) concentrations for up to a 1Oh work day during a 4Oh work week. OSHA PELs are TWA concentrations that must not be exceeded during any 8h work shift of a 40 work week. The ACGIH TLVs are 8h TWA concentrations for a normal 8h workday and a 4Oh work week, to which nearly all workers may be exposed, day after day, without adverse effects. In the United Kingdom, a two-tier system of occupational exposure standards (OES) and maximum exposure limits (MELs) is employed (HSE, 1999). Each represents an 8h TWA exposure limit. An OES is set where a no-effect level can be identified for a substance, thus giving an exposure limit below which adverse effects are not expected (as for the ACGIH TLVs). MELs are employed where there is no clear no-effect level. As there will be a degree of resultant health effects manifest whatever exposure limit is chosen (above zero), the choice of limit is in essence a political decision. Reflecting the nature of substances having MELs, there is an obligation on U.K. industries to keep exposures as low as reasonably practicable, even when this results in a target exposure significantly below the limit. Even below these various exposure limits, a small percentage of workers may experience adverse health effects due to individual susceptibility, a pre-existing medical condition, and/or a hypersensitivity (allergy). In addition, some materials may act in synergy with other substances to produce undesirable health effects, even if the occupational exposures to individual contaminants are controlled at the level set by the evaluation criteria. For example, gases such as oxides of nitrogen and sulfur dioxide may adsorb on dust particles and produce health effects at levels normally considered safe. Furthermore, some substances are absorbed by direct contact with the skin and mucous membranes and thus potentially increase the overall exposure. For substances that may potentially lead to health effects following short exposures, or high peak exposures, short-term exposure limits (STELs) are generally set to complement the 8 to 10 TWA limits. These are generally sampled over shorter time periods—typically 15min—and are collected during periods when the concentration of contaminant is likely to be highest. SAMPLING AGAINST EXPOSURE CONVENTIONS The accuracy and relevance of aerosol samples taken within the workplace predominantly rely on selection of an appropriate sampling device. However, filter selection, pump selection and use, sampling strategy, and sample handling also play a role in determining the accuracy and suitability of samples. Useful sources of information include the ACGIH (1995,1998).

Matching the Sampler to Sampling Requirements

A number of the industrial hygiene aerosol samplers introduced to the market in recent years have been developed and tested against international sampling conventions (ISO, 1995). However, many devices are still available that were brought into use before acceptance of the current conventions. Some of these agree reasonably well with the relevant convention, and others have been brought into line by altering the sampling flow rate (e.g., the SIMPEDS respirable cyclone; Bartley et al., 1994; Maynard and Kenny, 1995). Others, such as the closedface 37 mm filter cassette, show poor agreement with the current conventions (Kenny et al., 1997). The analytical development of inhalable samplers has been hampered by the complexities of how external conditions affect aspiration, together with the difficulties of making penetration measurements with particles up to 100 urn aerodynamic diameter. The IOM personal inhalable sampler was the first sampler built to match the inhalable convention and was developed following aspiration measurements with a breathing mannequin (Mark and Vincent, 1986). Although the sampler has shortcomings (e.g., it is very accessible to sample tampering, and there is evidence for significant projectile entry in some environments), it is still regarded as a benchmark sampler. More recent samplers such as the CIPlO-I (ARE)* address some of the problems inherent in the IOM inhalable sampler, but still fall short of the ideal. Samplers such as the button sampler (SKC) have been developed specifically to reduce intersampler variability and wind speed dependence common to a number of inhalable samplers (Aizenberg et al., 2000). Samplers following the thoracic and respirable conventions have been easier to engineer. The development of an empirical understanding of particle penetration through cyclones and polyurethane foams in particular has led to sampling devices that match the respirable and thoracic conventions reasonably well (Vincent et al., 1993; Kenny and Gussman, 1997; Chen et al., 1998; Maynard, 1999). In recognition that no sampler will agree with the current workplace sampling conventions at all times, performance criteria are under development to set acceptable bounds on how well a device performs (CEN, 1998). The mass fraction of a lognormal aerosol characterized by its MMAD and GSD that would be sampled by a device may be compared with the mass that would be sampled by an ideal sampler (i.e., one following the convention perfectly). The comparison gives the sampler's bias as a function of aerosol size distribution (Bartley and Breuer, 1982; Liden and Kenny, 1992; Maynard and Kenny, 1995). Incorporating errors inherent in sampler performance measurements and typical usage into calculations of bias allows the sampler's accuracy as a function of the aerosol size distribution to be estimated. Ensuring that sampler accuracy and bias lie within acceptable bounds then gives a basis for determining good and poor sampler performance. From the available samplers that lie within acceptable performance criteria, the choice of device will depend largely on the sampling requirements. Two general types of sampling are used in the workplace, fixed location sampling (also called static or area sampling) or personal sampling, where the sampler is placed on the worker. Static and personal samplers should not be interchanged, except where otherwise indicated. High flow-rate samplers should be used to increase the aerosol detection limit, for instance, during short-term sampling or when the sampled material has a low exposure limit (although the detection limit will also depend on the filter used and the analysis method). Where high air velocities are expected, samplers with a sampling efficiency that are not as prone to wind speed should be selected. Other considerations should include whether the aerosol charge is likely to affect sampling (e.g., Baron and Deye, 1990; Puskar et al., 1991), whether projectiles are likely to enter the sampling orifice and be included in the sample, and whether there is a possibility of significant sample loss during transport (see Chapter 7). Table 25-1 summarizes many of * See Appendix I for full manufacturer addresses referenced to the italicized three-letter codes.

TABLE 25-1. Summary of Industrial Hygiene Aerosol Samplers Sampler

Inhalable samplers IOM Inhalable

IOM Inhalable static CIP-10I

Flow Rate (10-5 mVs [L/min])

Deployment

Manufacturer or Distributor (see Appendix I)

Agreement with Convention

3.3 [2]

Personal

SKC

Good

5 [3] 17 [10]

Static Personal

CAS ARE

Good

Usesfiltercassette. Susceptible to large projectiles. Wind speed dependent

Rotating porous foam acts as an air mover and collection medium

GSP Inhalable Conical Inhalable Sampler Seven Hole

5.8 [3.5] 5.8 [3.5]

Personal Personal

STR CAS, BGI

Good Good

3.3 [2]

Personal

Various

Fair

Single Hole

3.3 [2]

Personal

Various

Poor

PAS-6 Button sampler

3.3 [2] 6.7 [4]

Personal Personal

KOE SKC

Fair Good

Thoracic samplers Elutriator CIP-10T CATHIA IOM Thoracic GK 2.69 Cyclone

12.3 [7.4] 12 [7] 12 [7] 3.3 [2] 2.7 [1.6]

Static Personal Static Personal Personal

GMW ARE ARE IOM BGI

Poor Fair Fair Fair Good

1.3 [0.8]

Personal

3.3 [2]

Personal

Not commercially Good available Fair SKC

Modified SIMPEDS cyclone IOM Inhalable + thoracic foam

Notes

Based on the GSP sampler Also known as the multiorifice or UKAEA sampler Used for lead aerosol sampling in the United Kingdom Perforated inlet reduces wind speed dependence and intersampler variability and leads to a uniform filterdeposit Specific to cotton dust CIP-10I with a thoracic separation stage Static version of the CIP-10T Separation based on polyurethane foam Can also be used as a respirable sampler— see below Developmental modification to the SIMPEDS cyclone IOM inhalable sampler with a size-selective polyurethane foam insert

References

Mark and Vincent (1986), Kenny et al. (1997,1999a) Mark et al. (1985) Courbon et al. (1988), Kenny et al. (1997) Kenny et al. (1997) Kenny et al. (1997) Kenny et al. (1997) Kenny et al. (1997) Kenny et al. (1997) Hauck et al. (1997), Aizenberg et al. (2000) Robert (1979) Fabries et al. (1998) Fabries et al. (1998) Maynard (1999) Maynard (1999) Maynard (1999) Maynard (1999)

Respirable samplers CIP-10R

17 [10]

Personal

ARE

Good

CIP-IOI with a respirable separation stage

SIMPEDS Cyclone

3.7 [2.2]

Personal

Various

Good

Also known as the Higgins and Dewell (HD) cyclone

SKC Cyclone GK 2.69 Cyclone

7 [4.2]

Personal Personal

SKC BGI

Good Good

2.8 [1.7]

Personal

Various

Good

4.2 [2.5]

Static

CAS

Fair

Variant of the SIMPEDS cyclone Can also be used as a thoracic sampler— see above Sampler constructed from nonconducting nylon Use limited to U.K. mines

3.3 [2]

Personal

SKC

Good

3.3 [2]

Personal

Dorr-Oliver (10 mm) Cyclone MRE 113A (Gravimetric Dust Sampler) IOM Inalable + respirable foam Foam respirable sampler

4.2 [2.5]

Personal

Not commercially Good available Not commercially Good available SKC

3.3 [2]

Personal/static

Various

37 mm Cassette (closed)

3.3 [2]

Personal/static

Various

Static sampler for "total" aerosol Passive sampler

Variable

Static

CAS

Personal/static

HSE, UK

Personal/static

Various

Personal

Virtual Cyclone Spiral sampler

Miscellaneous samplers 37 mm Cassette (open)

Cowled sampler

3.3 [2] (typical)

Courbon et al. (1988) Liden and Kenny (1993), Bartley et al. (1994), Maynard and Kenny (1995) Liden (1993) Maynard (1999) Bartley et al. (1994) Dunmore et al. (1964)

IOM inhalable sampler with a size-selective polyur ethane foam insert Cowled sampler with size-selective polyurethane foam plugs Provides a good match with the respirable convention slope Uses centrifugal particle separation

Kenny et al. (1999b)

Standardfiltercassette, worn facing down at 45° to the body. Conducting versions available Standardfiltercassette with a cap containing a 2 mm diameter inlet Open-facedfilter.Widely used in the United Kingdom Electret-based sampler relying on aerosol charge and naturally occurring air movements. Correlation is good with some size-selective samplers Used in the main forfibersampling. Size selectivity not quantified

Kenny et al. (1997)

Chen et al. (1998) Chen et al. (1999) John and Kreisberg (1999) (PM25 operation)

Kenny et al. (1997) Mark et al. (1986) Brown et al. (1994, 1995)

the workplace sampling devices currently available or in use and gives some indication as to their application. Filter and Substrate Selection

Industrial hygiene aerosol samples are generally collected onto a filter, within a polyurethane foam, or onto an impenetrable impaction substrate such as Mylar (which is usually coated with a layer of grease or oil to prevent particle bounce). Filters may be held in a cartridge within the sampler, as is the case with the IOM inhalable sampler, or may be mounted directly into the sampling head. Selection of a suitable collection substrate is governed by the sampling equipment used and by the subsequent sample analysis. Low-power lightweight pumps require filters with relatively low pressure drops at the operating flow rate. Gravimetric analysis requires a high degree of weight stability in changing environmental conditions. Chemical analysis requires that the collected material can be released from the substrate and/or that background levels of the analyte are low. Sample analysis by microscopy requires deposited particles to lie on the surface of the substrate. Chapter 9 gives further details of filter properties and selection. Table 25-2 summarizes the properties of filters, collection substrates, and filter holders commonly used within the workplace. The accuracy of gravimetric samples may be affected by water adsorption onto substrates and filter holders and by losses or gains in material during transit (see Chapter 7) (van Tongeren et al., 1994; Awan and Burgess, 1996). In particular, cellulose ester membrane filters, polyurethane foams, and conducting plastic filter cassettes are particularly prone to weight changes following water uptake (Vaughan et al., 1989; Smith et al., 1998).To combat bias from such sources, it is common practice to weigh a number of control, or blank, filters with each TABLE 25-2. Filter Selection for Industrial Hygiene Aerosol Sampling Substrate or Cassette Cellulose fiber filter Cellulose nitrate filter Glass fiber filter Quartz fiber filter Cellulose ester membrane filter PVC membrane filter Teflon membrane filter Polycarbonate filter Silver membrane filter Polyurethane foam Mylar impaction substrate Aluminum foil impaction substrate Conducting plastic cassette Aluminum cassette Stainless steel cassette

Typical Application

Weight Stability

General collection Gravimetric analysis General collection Gravimetric analysis General collection Gravimetric analysis Chemical analysis Imaging, fiber sampling Chemical analysis Gravimetric analysis Chemical analysis Particle imaging Chemical analysis Various samplers Impaction substrate Impaction substrate IOM inhalable sampler, conical inhalable sampler IOM inhalable sampler IOM inhalable sampler

" A higher star rating indicates better weight stability or lower pressure drop.

Pressure drop **

*#* ** ***

** ***

*

* *

** **

N/A

**

N/A N/A

* *

N/A N/A

set of sample filters (typically one blank per 10 samples, with a minimum of three blanks). It is advisable to condition filters in the weighing area (preferably in a temperature- and humidity-controlled environment) for up to 24 h before weighing to allow them to reach an equilibrium weight. It is generally not advisable to desiccate the filters before weighing, as weight changes after removal of the filter can be sufficiently rapid to lead to significant weight change during weighing (Smith et al., 1998). Where possible, blank filters should be transported with the sample substrates and exposed to the same conditions in order to minimize bias resulting from handling, transport, and changes in environment. Other sources of bias include electrostatic attraction, where substrates are highly charged, and buoyancy effects. Electrostatic charge build-up may be significant for substrate materials such as PVC and PTFE, particularly when working at low relative humidity. In all instances, samples should be electrically neutralized using a source of bipolar ions. A common approach is to place samples close to a radioactive antistatic source before weighing. Buoyancy corrections only become necessary when the volume of the sample exceeds around 10~7m3 [0.1 cm3]. For most substrates this is not a problem, although it may be significant when using large integral filter holders or substrate supports. Pump Selection Present-day personal sampling devices usually rely on either diaphragm or piston-type pumps to draw air through them. The pump is connected to a direct current (dc) motor, supplied by a battery pack of rechargeable nickel-cadmium cells. The achievable flow rates of pumps vary among manufacturers, but most will provide flows of 1.67 x 10"5 to 5 x 10"5m3/s [1 to 3L/min] against a pressure drop of 6.25 kPA [25 inches of H2O] for periods of up to 8h. Personal pumps are available that will achieve flow rates of up to 1.67 x IQr4Ta3Is [lOL/min], but with current technology there is always a trade-off between sampling flow rate, sampling time, sustainable pressure drop, and pump weight. Most currently available pumps regulate the selected flow to minimize the impact of changes in temperature, pressure, and filter loading on the flow rate and the total volume of air sampled. Regulation is achieved in a number of ways, including using feedback from pressure drop across the filter, atmospheric temperature and pressure, pump rotational rate, and power usage. As the performance of some sizeselective samplers is adversely affected by pulsations in the sampling flow (e.g., Bartley et al., 1984), some pumps incorporate flow dampers. Wood (1977) presents a useful review of personal sampling pumps, carried out in 1977, and, apart from limited advances in control technology, it still reflects much of the hardware available today. The volumetric flow rate of sampling pumps needs to be set with the sampling device attached (including filter) and under the same conditions of temperature and humidity as sampling will be carried out under. Although many pumps incorporate a visual indication of flow rate such as a rotameter, this should be used for indication purposes only and the sampling flow measured and set using a primary standard such as a bubble flowmeter. Typically, the set flow rate is expected to be within 5% of the target flow rate, although the most recent guidelines on sampling in the United Kingdom specify flows to be set to ±1.67 x 10~6m3/s [±0.1L/min] in all cases (HSE, 1997). Sampling Strategy While "static" or "area" sampling with fixed point samplers is still used in many situations, it is now widely accepted that representative aerosol sampling in the workplace should be carried out in the breathing zone—frequently defined as a region of the body not more than 0.3 m from the mouth and nose (Vincent, 1995). Breathing zone measurements generally give a better representation of worker exposure. However, Vincent (1995) notes that placement of sampling devices in this region does not guarantee representative sampling, and large

EXAMPLE 25-1: CALCULATION OF AN 8 H TWA EXPOSURE Three consecutive air samples for lead are collected at 3.3 x 10~5m3/s [2L/min] onto filters in the breathing zone of a worker in a brass foundry, with the results shown in Table 25-3. The shift started at 08:00 and finished at 18:00. Breaks were taken between 09:30 and 10:00,12:00 and 12:30, and 15:00 and 15:30. The work pattern was split into different tasks in the morning and the afternoon. Using Eq. 25-4, calculate the 8h TWA exposure level over the total duration of the shift (600 min). The assumption is made that during breaks exposure is zero. During the afternoon period, when no sampling was carried out, it is assumed that exposure is similar to that measured by sample 3. Table 25-4 therefore gives a complete account of the day's exposure. The 8h TWA mass concentration is therefore given as

using Eq. 25-4. TABLE 25-3. Example Gravimetric Sample Data for a Worker in a Brass Factory Sample No.

Time On

Time Off

Flow rate (10"5m3/s [L/min])

Sample Duration (min)

1 2 3

08:00 10:00 12:30

09:30 12:00 15:00

3.3 [2] 3.3 [2] 3.3 [2]

90 120 150

Sample Volume (L)

Mass Collected Qig)

Mass Concentration (|ng/m3)

20 25 5

TABLE 25-4. Complete Account of a Worker's Exposure to Lead in a Brass Factory (from Table 25-3) Sample No.

Time On

Time Off

Flow rate (10"5m3/s [L/min])

Sample Duration (min)

1 Ia (break) 2 2a (break) 3 3a (break) 4 (Est. from #3)

08:00 09:30 10:00 12:00 12:30 15:00 15:30

09:30 10:00 12:00 12:30 15:00 15:30 18:00

3.3 [2]

90 3 120 3 150 3 150

Total

3.3 [2] 3.3 [2]

600

Sample Volume (L) 0 0 0

180 — 240 — 300 — — 75

Mass Collected Qig)

Mass Concentration (ug/m3)

20

111 0 104 0 17 0 17

0 25 0 5 0 5 (est.)

variations in sampled aerosol concentration can be seen across the front of the body, depending on worker orientation, placement of the aerosol source, and local air movements (Raynor et al., 1975). As a matter of convention, exposure measurements for chronic hazards are usually taken for the duration of a single work shift. An 8h TWA mass concentration (cm) relates to the process whereby exposure occurring within a 24 h period is treated as being equivalent to a single uniform exposure over 8 h. ATWA mass concentration can be determined from a single full-shift sample, or it can be calculated from a series of consecutive samples (Leidel et al., 1977). Where sampling gaps occur over a shift, exposures during these periods should be estimated from adjacent measurements or from additional information (see Example 25-1). The TWA for a given time period (e.g., 8h, or 15min for a STEL) is calculated by

= full shift duration

(25^)

where T is the given reference period (in minutes), U is the duration of sample i in minutes, and cmi is the mass concentration of sample i. For purposes of determination of compliance with occupational exposure limits, it is generally desirable to sample the workers assumed to be at maximum risk. When the maximumrisk employees cannot be ascertained, employees should be selected at random. Leidel et al. (1977) recommend calculating the 95% one-sided lower confidence limit (LCL) and the 95% one-sided upper confidence limit (UCL). These are calculated as follows:

(25-5) where ta = 1.645 when a = 0.95, CVx is the coefficient of variation for the sampling/analytical method, and OEL is the exposure limit. If LCL and % are above unity, then the exposure is classified as noncompliant. If UCL and % are below unity, then the exposure is classified as compliant. Finally, if unity lies between LCL and %, or between UCL and ^, the exposure is classified as possible overexposure. MEASUREMENT OF SIZE DISTRIBUTION Full characterization of the size distribution of an aerosol may be carried out during nonroutine investigations using a range of available methods described in previous chapters (see Chapters 10,13, and 15-19). Although many instrument types have been used in the workplace (Mark et al., 1984), cascade impactors (see Chapter 10) are often the instrument of choice, giving an indication of the mass-weighted size distribution of an aerosol. Impactors are generally capable of giving the size distribution of an aerosol between around 0.1 and 15 urn aerodynamic diameter and above. Static cascade impactors such as the Andersen eightstage impactor (AND) and the Micro Orifice Uniform Deposit Impactor (MOUDI) (MSP) have found relatively widespread use in the workplace. The Andersen consists of eight multiorifice stages with cut points between 10 and 0.4 um when operated at 4.72 x 10"4In3Zs [28.3L/min]. Collection is usually onto aluminum foils, although other substrates are available. The use of multiorifices in the Andersen impactor allows deposits to be distributed with relative evenness onto substrates. This is taken further within the MOUDI, where many

orifices per stage, together with rotating substrates, lead to highly uniform deposits. The MOUDI is available in an 8- or 10-stage version and is capable of making aerosol size distribution measurements down to 0.056 um at 5 x lO^mVs [30L/min]. Aerosol size distributions within the breathing zone are generally of greater relevance to health than static samples, and two cascade impactors have been developed to enable personal aerosol size distribution measurements to be made. The Marple personal cascade impactor (AND) (Rubow et al., 1987) is configurable with up to eight stages and will provide information on particle size distribution down to 0.5 um at a flow rate of 3.33 x 10"5m3/s [2L/min]. The Personal Inhalable Dust Spectrometer (PIDS) is similar in concept to the Marple impactor, although the slot-shaped impactor jets of the Marple device are replaced by circular jets (Gibson et al., 1987). Cut points in the eight stages of the PIDS range from 0.9 to 19 um at 3.33 x 10"5m3/s [2L/min]. Cascade impactors are of limited use for measuring aerosol size distributions up to the limit of the inhalable convention (100 um aerodynamic diameter) due to the relatively low cut point of the upper stage in most cases. Extrapolation of measured size distributions above this cut point depends on assumptions about the sampled aerosol and the aspiration efficiency of the device and is generally not reliable. However, the PIDS was designed with an inlet designed to follow the inhalable convention (Gibson et al., 1987). It may be assumed that summing all deposits within the PIDS impactor gives a measure of the inhalable aerosol mass, and subsequent analysis of the deposits gives the size distribution as a function of the inhalable aerosol. Such an approach is advantageous to industrial hygiene measurements, where ultimately measurements need to be related to the mass of particles inhaled. When the specific health-related fractions of the aerosol are of more concern than a detailed analysis of particle size distribution, a number of samplers allow simultaneous measurement of all three fractions. The IOM personal multifraction sampler uses aerosol separation within polyurethane foams to achieve this (Vincent et al., 1993). Aerosol is sampled through a 15 mm diameter inhalable inlet at 3.33 x 10~5m3/s [2L/min].Two polyurethane foam selectors of different grades placed in series then separate the thoracic and respirable subfractions. The sampler enables the inhalable fraction to be measured by weighing deposits in both foams and the backup filter. The combined deposits on the filter and adjacent foam give the thoracic fraction, and the filter alone gives the respirable aerosol fraction. A similar approach using polyurethane foams has been developed for use with the conventional IOM inhalable sampling head (Kenny et al., 1999b). An alternative approach is used by the Personal Spectrometer (PERSPEC) (Prodi et al., 1988,1989). The inhalable aerosol fraction is introduced to a highly divergent flow of clean air and deposited onto a 47 mm filter. Deposition position depends on particle size; thus by weighing the complete filter the inhalable fraction can be determined, or by weighing specific areas of the filter (after cutting them out) different subfractions can be measured (Kenny and Bradley, 1994). The Respicon sampler (TSl) achieves separation of the three aerosol size fractions using a series of virtual impactors. A modified version has been developed (Respicon, HUN) that allows real-time monitoring of each fraction using light scattering (Koch et al., 1998).

USE OF DIRECT-READING INSTRUMENTS Instruments giving a near-instantaneous, or rapid, measure of aerosol properties (commonly referred to as real-time measurement instruments) are widely used in the workplace. Vincent (1995) and Walton and Vincent (1998) provide a broad summary of techniques commonly used in the workplace. However, it is possible to find examples of most devices described in earlier chapters being applied in the workplace. For routine measurements, aerosol photometers are widely used and available from an increasing number of manufacturers. Their use covers checking short-term, task-specific, or

instantaneous exposure levels and identifying exposure hot spots. Systems have also been developed that combine photometer measurements with simultaneous video filming of workers, allowing direct comparison between work tasks and exposure levels (Gray et al, 1992; Gressel et al., 1993; Heitbrink et al., 1993; Unwin et al., 1993). The implementation of the measurement method has various guises, from passive instruments relying on convection to bring particles into the sensing zone (as with the Mini-RAM, and the later personal data-RAM, MIE), to pumped devices such as the Microdust Pro (CAS), to instruments incorporating data loggers (e.g., the DustTrack [TST] and Data-RAM [MIE]). Most devices are compact, with most being portable and a number of them being suitable for personal sampling. Over a relatively narrow size range (approximating to the upper end of respirable size fraction) the light scattered from an aerosol is roughly proportional to the scattering volume (see Chapter 15; Baron, 1994). Thus, after correcting for density, scattered light may be used as an indirect measure of mass concentration. The method is relatively good for measuring respirable aerosol concentration, but becomes tenuous when used for the thoracic subfraction and potentially misleading when used to measure the inhalable aerosol mass concentration (the sensitivity to equivalent aerosol masses represented by 20 um particles is approximately a factor of 102 lower than the sensitivity to 2um particles). Instruments such as the Respicon TM (HUN) go some way to overcoming this size dependence of photometry by selectively concentrating larger particles through the use of virtual impaction (Koch et al., 1998). In some situations it is feasible to calibrate a photometer to the inhalable mass concentration, but only when the fine particles detected form a constant fraction of the inhalable aerosol. Optical single-particle detection and sizing instruments such as the Grimm "Work-check" (GRI) overcome some of the limitations of photometers, but their sensitivity is still restricted to a similar range of particle sizes. In all cases it is advisable to calibrate photometers before using them with different aerosols, as particle size distribution, shape, and refractive index will affect measurements (see Chapter 15). Calibration is usually performed by carrying out parallel gravimetric sampling and applying an adjustment factor to the photometer to ensure that results agree. Many photometers have the facility to collect aerosol passing through the sensing zone on a filter, thus simplifying calibration. Zero offset checks are also recommended before use by placing the photometer in a clean environment: Deposits on the optics and surfaces of the sensing zone can lead to a change in the instrument calibration. Recent developments in condensation particle counter (CPC) technology have led to a commercially available portable device with logging capabilities, suitable for semiquantitative particle number measurements. The P-Trak (TSI) is designed to provide nearinstantaneous measurements of particle concentration between 20 nm and approximately 1 um. Although it is primarily aimed at investigating aerosol number concentration levels and variations and tracking contamination sources in indoor environments, it is also being applied to measuring real-time particle number concentration measurements in the workplace. Respirator Fit Testing

Both photometers and CPCs are used extensively for measuring the fit factor of respirators. The fit of a respirator can either be measured using a qualitative fit test (QLFT) or quantitative fit test (QNFT) (OSHA, 1998). Both approaches rely on the respirator filter removing the majority of a test agent, leaving gaps in the respirator-face seal as the main route for the agent to penetrate the mask. QLFT methods generally rely on the wearer's perception of a test agent through odor or taste, the most common agents being isoamyl acetate, sodium saccharin, and irritant fume. QNFT methods, on the other hand, use quantitative measurement of the leakage rate around the mask. The first QNFT methods suitable for routine use

exposed the wearer to a controlled atmosphere of dioctyl phthalate (DOP) aerosol and measured the aerosol inside and outside the mask using forward light-scattering photometry (Burgess et al., 1961; Hyatt et al., 1972). HEPA filters were used on the mask to ensure that most of the particles detected within the mask penetrated due to leakage. This method is still used, although alternative aerosol materials such as corn oil and sodium chloride have replaced the use of DOP. The use of a specific aerosol in an enclosed system is somewhat restrictive, and in 1981 Willeke et al., investigated easily generated aerosols, including cigarette smoke, fine carbon particles from high-volume sampling pumps, and fine metal/metal oxide particles generated from the filament of a hair dryer (Willeke et al., 1981). However, the most significant aerosol investigated was the ambient aerosol found in the workplace. Using a CPC to compare the ambient particle number concentration to that inside a respirator being worn, they were able to show that this formed a basis for rapid quantitative fit tests. The ambient air-CPC method is now widely applied, using the PortaCount Respirator Fit Tester (TSf). With HEPA filters, penetration is lower than 0.03% for 0.3 um particles (representing the particle size region of highest penetration).Thus, given a sufficiently high challenge aerosol concentration, fit factors of over 3000 are measurable. Ambient concentrations of submicrometer particles are rarely lower than 109 to 1010 particles/m3 [103 to 104 particles/cm3] unless the air is highly filtered, allowing fit factors greater than 1000 to be measured under most circumstances. A similar approach has been proposed for measuring leakage around filters in filter cassettes used for workplace sampling (Baron et al., 2001). Alternative QNFT methods have been proposed and are currently used, including dynamic pressure measurements inside the respirator, large-particle penetration tests, and small-particle penetration tests. A comprehensive review of current and proposed methods may be found in Han et al. (1997). FUTURE TRENDS Perhaps the most significant change in industrial hygiene aerosol measurement over the past two decades has been the development and gradual adoption of size-selective sampling conventions. These now enable measurements that have greater biological relevance to be made in the workplace. Although the next few years are likely to see the current position being consolidated, there is scope for the present sampling conventions to be revised. The inhalable convention is limited in its scope and applicability. Its abrupt termination at 100 urn brings into question whether the ingression of large particles and projectiles into more open samplers is acceptable or leads to inaccurate measures of aerosol concentration (Aitken and Donaldson, 1996). In addition, the inability to develop inhalable samplers thus far that follow the convention over a wide range of wind speeds raises the question of whether the standard is unattainable or inappropriate. At the other end of the size spectrum, toxicological information on responses to nanometer-sized low-solubility particles are challenging the applicability of current sampling conventions and philosophies. Recent toxicology on low-toxicity insoluble materials such as titanium dioxide has indicated that a more appropriate dose metric for depositing in the alveolar region may be particle number or surface area (Oberdorster et al., 1994; Donaldson et al., 1998). These studies appear to support some epidemiological investigations of the general population, indicating correlation between inhalation of fine particles and health effects (Dockery et al., 1993). The extent to which such findings are applicable to exposure within the workplace is not apparent at present. However, to begin to understand the relevance of exposure to very fine particles and the appropriate metric to use for dose, developments in the measurement of exposure in terms of particle number and surface area are necessary. As the debate on the appropriate dose and exposure metric develops, there will

no doubt be further extensions to the manner in which aerosol exposure in the workplace is measured in the subrespirable size range. Current trends in workplace sampling practice indicate a desire within the industrial hygiene community to adopt methods that provide measurements with greater rapidity and with less effort. In particular there is a growing interest in relating exposure to specific tasks and operations, thus requiring highly sensitive or rapid-response aerosol measurement methods. Such trends are perhaps most obvious in the increased use (and abuse) of directreading photometer-based instruments. These provide a rapid indication of exposure, both allowing a more rapid response to problem situations than filter collection and analysis allows, together with a means of avoiding the expense of sample collection and analysis where it is not necessary. However, their attraction has seen their increasing but erroneous use to estimate exposure to inhalable aerosol. This clear need for direct-reading instruments that extend to the inhalable fraction is likely to lead to the development of new devices. Although it is not at all clear at present whether workable technologies will present themselves, there are a number of possible contenders: The application of optical methods may be further increased to large particle sizes through the size-selective concentration of particles and the detection of individual large particles. The development of handheld oscillating microbalance methods is underway, although it is unclear whether there will be collection and sensing problems associated with particles approaching 100 jam in diameter. Aerosol mass sensing methods based on filter pressure drop are being developed for fine particles (Dobroski et al., 1995; Sioutas et al., 1999; Volkwein et al., 1998). At present, the indications are that size-dependent effects will hinder their extension to 100 um, although size-selective concentration and aerosol-specific calibration may lead to successful applications. Passive samplers provide another route to simplified exposure measurement and have been under development for some time. They offer the simplicity of a discrete lightweight badge-type sampler with no need for a sampling pump. The passive sampler developed by Brown et al., relies on electrostatic deposition onto a pre-prepared electret material. Aerosol is carried to the deposition zone through convection; thus no pump is required (Brown et al., 1994). Although the device is not designed along size-selective lines, good correlation has been seen with size-selective samplers in some cases, and it is likely that such samplers could be developed into indicative screening devices (Brown et al., 1995). A different approach to making size-selective sampling more accessible and less expensive is seen in the increasing utilization of porous foam pre-separators. The use of foam allows relatively inexpensive size-separation devices to be constructed and provides the possibility of modifying existing samplers to different applications. Chen et al. (1998) have proposed a porous foam respirable sampler based on the cowled sampler used for asbestos sampling, and foam plugs to convert the IOM inhalable sampler to either a respirable or a thoracic sampler have been investigated (Kenny et al., 1999b). At the same time, new methods of creating size-selective samplers that are better suited to occupational aerosol sampling (e.g., by operating at higher flow rates, giving better agreement with the sampling conventions, providing a more compact, lighter sampler, and operating at lower pressure drops) are under constant development. Recent work includes the investigation of virtual, axial-flow, and tangential-flow cyclones (Chen et al., 1999; Maynard, 2000; Kenny and Gussman, 1997), the development of centrifugal personal samplers (John and Kreisberg, 1999), and the development of inhalable samplers with inlet screens (Kalatoor et al., 1995; Hauck et al., 1997; Aizenberg et al., 2000), reducing the adverse effects of wind speed and large-particle projectiles on samples. The desire for simplification also extends to standards against which exposure is measured. While the current emphasis is on monitoring worker exposure, the concept of controlling emissions at the source is gaining ground. The application of such thinking to the workplace will possibly result in the use of exposure modeling to estimate exposure risk, accounting for materials used, generation processes involved, emission control measures applied, and dis-

persion in the workplace (Maidment, 1998). The logical end point is the estimation of exposures from materials and processes within the workplace and the relegation of aerosol exposure measurement to a supportive role. However, sufficient questions surround the classification of materials in terms of their ability to form an aerosol during specific processes, together with the containment or release and transport of generated aerosols, to ensure that developments in aerosol measurement methods in the workplace will continue for a number of years to come. REFERENCES ACGIH. 1968. Threshold Limit Values of Airborne Contaminants. Cincinnati, OH: American Conference of Government Industrial Hygienists. ACGIH. 1995. Air Sampling Instruments, 8th Ed. Cincinnati: ACGIH. ACGIH. 1998. Particle Size-Selective Sampling for Paniculate Air Contaminants. Cincinnati, OH: American Conderence of Government Industrial Hygienists. Aitken, R. J. and R. Donaldson. 1996. Large Particle and Wall Deposition Effects in Inhalable Samplers. Report Number 117/1996, ISBN O 7176 1270 8. Health and Safety Executive, UK. Aizenberg, V., S. A. Grinshpun, K. Willeke, J. Smith, and P. A. Baron. 2000. Performance characteristics of the button personal inhalable sampler. AIHAJ 61:398-404. Awan, S. and G. Burgess. 1996. The effect of storage, handling and transport traumas on filter-mounted dusts. Ann. Occup. Hyg 40:525-530. Baron, P. A. 1994. Direct-reading instruments for aerosols. A review. Analyst 119:35-40. Baron, P. A. and G. J. Deye. 1990. Electrostatic effects in asbestos sampling I: Experimental measurements. Am. Ind. Hyg. Assoc. J. 51:51-62. Baron, P. A., A. Khanina, A. B. Martinez, and S. A. Grinshpun. 2001. Investigation of filter bypass leakage and a test for aerosol sampling cassettes. Aerosol ScL Technol. In submission. Bartley, D. L. and G. M. Breuer. 1982. Analysis and optimisation of the performance of the 10 mm cyclone. Am. Ind. Hyg. Assoc. J. 43:520-528. Bartley, D. L., G. M. Breuer, and P. A. Baron. 1984. Pump fluctuations and their effect on cyclone performance. Am. Ind. Hyg. Assoc. J. 45:10-18. Bartley, D. L., C. C. Chen, R. Song, and T. J. Fischbach. 1994. Respirable aerosol sampler performance testing. Am. Ind. Hyg. Assoc. J. 55:1036-1046. Bedford, T. and C. Warner. 1943. Physical studies of the dust hazard and thermal environment in certain coalmines. In Chronic pulmonary disease in South Wales coalminers. II. Environmental studies. London: British Medical Research Council, HMSO. Special report series no. 244, pp. 1-78. Blake, T, V. Castranova, D. Schwegler-Berry, P. Baron, G. J. Deye, C. H. Li, and W. Jones. 1998. Effect of fiber length on glass microfiber cytotoxicity / Toxicol. Environ. Health Part A 54:243-259. BMRC. 1952. Recommendations of the BMRC Panels Relating to Selective Sampling. From the minutes of a joint meeting of Panels 1,2 and 3, held on March 4th, British Medical Research Council. Brown, R. C, M. A. Hemingway, D. Wake, and J. Thompson. 1995. Field trials of an electret-based passive dust sampler in metal-processing industries. Ann. Occup. Hyg. 39:603-622. Brown, R. C, D. Wake, A. Thorpe, M. A. Hemingway, and M. W. Roff. 1994. Preliminary assessment of a device for passive sampling of airborne particulate. Ann. Occup. Hyg. 38:303. Burgess, W. A., L. Silverman, and F. Stein. 1961. A new technique for evaluating respirator performance. Am. Ind. Hyg. Assoc. J. 22:422^29. CEN. 1998. Workplace Atmospheres: Assessment of Performance of Instruments for Measurement of Airborne Particle Concentrations. Comite Europeen de Normalisation, CEN prEN 13205. Chen, C-C, S.-H. Huang, W. Lin, T. Shih, and F. Jeng. 1999. The virtual cyclone as a personal respirable sampler. Aerosol ScL Technol. 31:422-432. Chen, C-C, C-Y. Lai, T-S. Shih, and W-Y. Yeh. 1998. Development of respirable aerosol samplers using porous foam. Am. Ind. Hyg. Assoc. J. 59:766-773.

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Robert, K. Q. 1979. Cotton dust sampling efficiency of the vertical elutriator. Am. Ind. Hyg. Assoc. J. 40:535-545. Rubow, K. L., V. A. Marple, J. OHn, and M. A. McCawley. 1987. A personal cascade impactor: Design, evaluation and calibration. Am. Ind. Hyg. Assoc. J. 48:532-538. Sioutas, C, S. Kim, and M. Chang. 1999. Development and evaluation of a prototype ultrafine particle concentrator. /. Aerosol ScL 30:1001-1017. Smith, J. P., D. L. Bartley, and E. R. Kennedy. 1998. Laboratory investigation of the mass stability of sampling cassettes from inhalable aerosol samplers. Am. Ind. Hyg. Assoc. J. 59:582-585. Unwin, J., P. T. Walsh, and N. Worsell. 1993. Visualization of personal exposure to gases and dust using fast-response monitors and video filming. Appl Occup. Environ. Hyg. 8:348-350. van Tongeren, M. J. A., K. Gardiner, and I. A. Calvert. 1994. An assessment of the weight-loss in transit of filters loaded with carbon black. Ann. Occup. Hyg. 38:319-323. Vaughan, N. P., B. D. Milligan, and T. L. Ogden. 1989. Filter weighing reproducibility and the gravimetric detection limit. Ann. Occup. Hyg. 33:331-337. Vincent, J. H. 1995. Aerosol Science for Industrial Hygienists. Bath, UK: Pergamon.

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Vincent, J. H., R. A. Aitken, and D. Mark. 1993. Porous plastic foam filtration media: Penetration characteristics and applications in particle size-selective sampling. /. Aerosol Set 24:929. Vincent, J. H., D. Mark, B. G. Miller, L. Armbruster, and T. L. Ogden. 1990. Aerosol inhalability at higher windspeeds. /. Aerosol Sci. 21:577-586. Volkwein, J. C, A. L. Schoeneman, and S. J. Page. 1998. Laboratory Evaluation of Pressure Differential Dust Detector Tube. AIHA 1998 Conference. Washington, DC: AIHA. Walton, H. W, and J. H. Vincent. 1998. Aerosol instrumentation in occupational hygiene: An historical perspective. Aerosol Sci. Technol 28:417-438. Willeke, K., H. E. Ayer, and J. D. Blanchard. 1981. New methods for quantitative respirator fit testing with aerosols. Am. Ind. Hyg. Assoc. J. 42:121-125. Wood, J. D. 1977. A review of personal sampling pumps. Ann. Occup. Hyg. 20:3-17.

for regulatory purposes as either coal mines or other types of mines (indicated as metal and nonmetal mines). This chapter summarizes aerosol measurement technology currently used in the U.S. mining industry as it relates to regulation, research, and personal exposure monitoring. MINE AEROSOL SOURCES The choice of measurement techniques and the performance of various aerosol monitors may be affected by the type of aerosol to be sampled. Mine aerosols typically originate from comminution, re-entrainment, and combustion sources. Comminution

Mechanical crushing of the mined material by drill bits, crushers, and mining machine picks generate significant concentrations of respirable aerosol mass in the mine. In metal and nonmetal mines, a typical mining process begins with the cutting or drilling and blasting of the ore from the parent rock. The ore is then scooped or gathered and loaded into trucks or onto conveyers for transport to the surface. In some cases the ore is crushed underground to facilitate transportation. The aerosol composition generated from these activities is generally similar to the host ore; however, some selective fractionation of the composition may occur based on the cleavage and strength patterns of the host ore. After the ore is removed, underground construction activities then begin to support the roof, direct ventilation air, and extend the mine's infrastructure. Continuous mining methods are the most prevalent underground coal extraction processes, accounting for most of the underground coal mined (Organiscak, 1989). In continuous mining, a mining machine, pictured in Figure 26-1, cuts coal from the face and loads it directly into shuttle cars. The shuttle cars transport the coal from the face area to the material transfer system, usually a crusher that loads onto a conveyor belt.

Fig. 26-1. Continuous miner. (From U.S. Bureau of Mines.)

Re-entrainment Aerosols that result from comminution may settle on roadways or in return air entries and be subject to re-entrainment through the activities of mobile equipment such as shuttle cars, ventilation, or in-mine construction activities (Jankowski and Hake, 1989). In coal mining, an inert limestone dust is spread on the entryway roof, floor, and ribs to prevent coal dust explosions. This limestone dust is coarse and contributes only a small fraction to the overall mine aerosol concentrations. Combustion Combustion by-products are the other major source of aerosols in mines. Combustion aerosol particles are smaller in size and currently can contribute significantly to the overall mass of respirable aerosols. They have higher number concentrations than the coarser comminution aerosol and a greater interaction potential with the lungs. Typical mining sources include blasting agent fumes, welding fumes, and diesel engine particles and condensates. Concentrations of blasting agent fumes and welding aerosols in the mine environment are primarily controlled by dilution with the mine ventilation air. Monitoring the aerosol concentration of these agents must focus on the transient nature of the aerosol and short-term exposure monitoring. Due to the intermittent nature of the sources and the substantial ventilation volumes needed for mining, the overall concentrations of these aerosols are generally low with some exceptions in areas with poor ventilation. With the exception of coal mines, diesel engines provide the primary power source for mining equipment. In addition to being exposed to dust, a miner working in an underground mine with diesel-powered equipment is exposed to a wide array of pollutants from diesel exhaust. These include CO, CO2, NO, NO2, SO2, diesel exhaust aerosol, and a variety of aerosol-associated and gas-phase hydrocarbon compounds (Cantrell and Watts, 1997). To date, ventilation rates in diesel-equipped mines have been dictated by gas concentrations. In the future, new standards may dictate that diesel exhaust aerosol concentration is the factor that will control required ventilation volumes (U.S. Department of Labor, 1998; MSHA, 2001a,b). Of the combustion aerosols in the mining environment, diesel exhaust aerosol is of particular concern because it is almost entirely respirable in size, with more than 90% of the particles, by mass, having an aerodynamic diameter less than 1.0 urn (Cantrell, 1987). This means that the aerosol can penetrate to the deepest regions of the lungs and, if retained, cause or contribute to the development of obstructive or restrictive lung disease (Watts, 1987).

PHYSICAL CHARACTERISTICS OF MINE AEROSOL Aerosols in mines sources have particle size distributions that are determined by both the mining method and the source of the aerosol (Cantrell and Rubow, 1992a,b). A size distribution summarizing the physical characteristics of mine aerosols is shown in Figure 26-2. The shape of the aerosol size distribution is influenced by the different sources contributing to the aerosol. Figure 26-2 displays some of these sources and the physical mechanisms, such as condensation and coagulation, which transfer aerosol mass from one size to another. It should be noted that these mechanisms and the general shape of the distribution are not unique to mine aerosols. There are three distinct aerosol size ranges identifiable by features in measured mine aerosol size distributions. The smallest of these, from 0.001 to 0.08 um, is the Aitken nuclei range, which contains primary aerosol from combustion sources, such as diesel engines, and

Low Volatility Vapor

Hot Vapor

Mine minerals Rock dust

CONCENTRATION

Homogeneous Heterogeneous Nucleation Nucleation

Comminution Mineral fracture

Condensational Growth of Nuclei

Condensational Growth Coagulation

Primary particles

Chain aggregates

Re-entrainment Coarse particles

Droplets

I Sedimentation

Coagulation

PARTICLE AERODYNAMIC DIAMETER (um) Transient nuclei or, Aitken nuclei range Fine Particles.

Accumulation, range

Mechanically generated particle range .Coarse Particles

FIg. 26-2. Size distribution summarizing the general physical characteristics of mine aerosol. (From Cantrell et al, 1987.)

secondary aerosols or chain aggregates, formed by coagulation of primary aerosols. The next size range, from 0.08 to approximately 1.0 u.m, is termed the accumulation range. This size range contains direct aerosol emissions plus aerosols that grow from the Aitken nuclei range by accumulating mass through coagulation and condensation processes. The last range, 1.0|Lim*to approximately 40 um, is termed the coarse particle range. Aerosols within this size fraction generally result from mechanical processes such as rock fracture and bulk material handling. Mineral dust aerosol re-entrained by mine haulage vehicles during the load-haul-dump cycle is an example of an in-mine emission that will contribute aerosol to this size range. For convenience, the Aitken nuclei and the accumulation ranges are combined in a single "fine" particle range. A division is usually made between this range and the "coarse" particle range at 1.0 um. This distinction is possible because sources of aerosol in the two ranges are usually different, and the coarse particle range contains very little mass transferred from the accumulation range by coagulation. In each of the ranges mentioned, the size distribution of mine aerosol can exhibit a maximum, or mode, which takes its name from the size range in which it occurs. Hence, the maximum in the accumulation range is termed the accumulation mode. Figure 26-3 presents a typical size distribution of aerosol mass concentration measured in a haulage entry of a diesel-equipped coal mine (Cantrell and Rubow, 1990). Here the modal character of the size distribution is discernible even though the nuclei mode is suppressed compared with the accumulation mode. In contrast, Figure 26-4 shows a mass size distribution measured in the

Acm/Alog10(dp),mg/m3

AERODYNAMIC DIAMETER (d p )^m

Acm/Alog10(dp),mg/m3

Fig. 26-3. Mass size distribution of mine aerosol in diesel-equipped mine. (From Cantrell and Rubow, 1990.)

AERODYNAMIC DIAMETER (d p )|im Fig. 26-4. Mass size distribution of mine aerosol in an all-electric-equipped mine. (From Cantrell and Rubow, 1990.)

haulage way of an all-electric-motor-equipped coal mine. Here the accumulation mode is much smaller than the coarse particle mode. Taken together, the figures indicate that diesel aerosols can make a strong contribution to accumulation mode aerosols in a diesel engineequipped mine.

MEASUREMENT TECHNOLOGY Aerosol measurement technology used by and for the mining industry can be conveniently separated into compliance measurements in support of regulation and research measurements that aid in the development of new compliance measurement techniques, determine control effectiveness, and assist in determining the fundamental properties of mine aerosols. Compliance measurements are primarily intended for use in enforcement of regulations established by MSHA. Regulatory requirements determine the sampling strategy and instrumentation used for such measurements. Research aerosol measurements are used to evaluate engineering dust control techniques, to develop new aerosol instrumentation, to define new occupational health hazards, and to expand knowledge regarding mine aerosols. They draw on all of the technology available to the aerosol community. Consequently, compliance and research aerosol measurements are treated separately here. Also, the following discussion focuses on aerosol measurements in U.S. mines. For a discussion of measurement practices in the European Community, see Vincent (1991).

Compliance Measurements and the Regulatory Environment

Regulatory Requirements Metal and Nonmetal MSHA regulates practices affecting health and safety in metal and nonmetal mines and mills under the authority of the Federal Mine Safety and Health Act of 1977 (U.S. Congress, 1977). The specific regulations are found in the Code of Federal Regulations,Title 30 (MSHA, 1991). MSHA continues to use the 1973 recommended threshold limit values of the American Conference of Governmental Industrial Hygienists (ACGIH, 1973). Compliance with these regulations is determined by the collection of environmental samples by MSHA inspectors. Aerosol-related contaminants that are regulated include total dust, respirable dust, quartz, asbestos, silicates, radionuclides in air, diesel exhaust aerosol, and welding fumes (MSHA, 1990; 2001a,b). The following example illustrates the type of sampling and analysis procedure for a respirable dust containing more than 1% quartz. A sample is collected using the personal respirable dust sampler shown in Figure 26-5. Sample air is first passed through a Dorr-Oliver nylon cyclone pre-classifler (MSA) at a flow rate of 1.7L/min to remove the nonrespirable fraction of sampled dust. Respirable dust is then collected on a filter that is analyzed gravimetrically to determine mass concentration. The filter deposit is also analyzed for quartz content using X-ray diffraction (MESA, 1975). The measured mass concentration is compared with the PEL determined from the quartz content of the respirable dust by PEL

10mg/m3 percent respirable quartz+ 2

For a given exposure level, the magnitude of the toxicity is proportional to the quartz content (ACGIH, 1980). The factor 2 in the denominator of the PEL formula ensures that dust exposures will not be excessively high when the quartz content is less than 5%. When quartz levels are less than 1%, nuisance particles listed by the 1973 ACGIH standard are regulated to 10mg/m3 of total dust. MSHA has proposed a revision of many of the existing health regulations (MSHA, 1989a). Included in these revisions is a proposed change in the PEL for respirable quartz. The current PEL is 100|ig/m3 of respirable quartz (MSHA, 1971). Coal. Respirable coal mine dust measurements are made to determine compliance with MSHA-established dust standards (MSHA, 1989b). In 1970, a mandatory total respirable dust

Fig. 26-5. Personal Respirable Dust Monitor. (Courtesy of MSA Co.)

standard of 3.0mg/m3 was established for underground coal mines in the Federal Coal Mine Health and Safety Act of 1969 (U.S. Congress, 1969). The respirable dust standard was subsequently lowered in 1972 to 2.0mg/m3. Mandatory dust standards for surface work areas of underground coal mines and surface mines also became effective in 1972. These regulations were continued under the Federal Mine Safety and Health Act of 1977 (U.S. Congress, 1977), which amended the 1969 Coal Act and merged coal and noncoal regulations into one law. In the 1969 Act, "concentration of respirable dust" was defined as that measured using a Mining Research Establishment (MRE) parallel plate elutriator (Casella, model 113A, United Kingdom) sampling instrument (Fig. 26-6) or such equivalent concentration measured with another device. This instrument was designed to have a sampling efficiency equivalent to the respirable response curve specified by the British Medical Research Council (BMRC) (Lippman, 1989). The 1977 Act changed the definition of "concentration of respirable dust" to be the "average concentration of respirable dust measured with a device approved by the Secretary (of Labor) and the Secretary of Health Education and Welfare." The personal respirable dust sampler illustrated in Figure 26-5 is also approved for measuring respirable coal mine dust (MSHA, 1989b). The sampling rate used for coal, however, is 2L/min (Tomb and Raymond, 1970). Sample analysis for total respirable dust is gravimetric (Raymond et al., 1987). Analysis for quartz is by Fourier transform infrared spectrometry (Ainsworth et al., 1989). Measurements are converted to equivalent MRE concentrations by multiplying the measured concentrations by an accommodation factor of 1.38 (Treaftis et al., 1984). The difference between the Dorr-Oliver and BMRC sampling efficiency curves is evident from Figure 26-7 (Caplan et al., 1977a,b; Lippman, 1989). Specific regulations detailing the methods for collecting respirable dust samples are found in the Code of Federal Regulations, Title 30 (MSHA, 1991). A mine is in noncompliance with its dust standard if the arithmetic average concentration of five consecutive respirable dust samples is in excess of the applicable standard (MSHA, 1989b). If the percent quartz is less than 5%, the standard is 2.0mg/m3, calculated using Eq. 27-1. In underground coal mines, samples are collected on workers with a designated occupation, usually a mining machine operator. A mine may also not be in compliance

PENETRATION, %

Fig. 26-6. Casella, Model 113a, MRE Respirable Dust Monitor. (From Cantrell et al., 1987.)

BMRC Dorr-Oliver cyclone

PARTICLE AERODYNAMIC DIAMETER, d (^m) Fig. 26-7. Respirable aerosol sampling criteria; BMRC, Dorr-Oliver Cyclone. (From Caplan et al., 1977a,b; Lippman, 1989.)

with the law if the dust control plan is not being used. MSHA is required to inspect all underground coal mines four times a year, and the mine operators are required to sample bimonthly. Diesel Exhaust Aerosol. NIOSH (1988) has recommended that "whole diesel exhaust be regarded as a 'potential occupational carcinogen,' as defined in the Cancer Policy of the Occupational Safety and Health Administration." NIOSH further stated that "though the excess risk of cancer in diesel-exhaust-exposed workers has not been quantitatively estimated, it is logical to assume that reduction in exposure to diesel exhaust in the workplace would reduce the excess risk." The International Agency for Research on Cancer (1989) has also classified diesel exhaust as "probably carcinogenic to humans." Additionally, the Mine Safety and Health Administration (MSHA, 1988) received a recommendation from an advisory committee to establish a diesel exhaust aerosol standard and to establish regulations to minimize exposure to all diesel pollutants in underground coal mines. The ACGIH has proposed a threshold limit value (TLV) for diesel exhaust particles of 50ug/m3 (ACGIH, 2000). MSHA has proceeded to establish a diesel exhaust aerosol emission standard for diesel powered equipment in underground coal mines (MSHA, 2001a) and a PEL for diesel exhaust aerosol in metal and non-metal mines (MSHA, 2001b). The regulation for coal mines specifies a limit of 2.5 grams per hour for permissible heavy duty equipment. The regulation also specifies an interim aerosol emissions limit of 5.0 grams per hour and, after the year 2005, a limit of 2.5 grams per hour for non permissible heavy duty equipment. Light duty diesel powered equipment exhaust aerosol emissions are limited to 5.0 grams per hour. For metal/non-metal mines MSHA has proposed an interim total diesel aerosol carbon (TC) PEL of 400jng/m3 based on an 8 hour sample and, after the year 2005, a final PEL of 160ug/m3. Research Aerosol Measurements

Research measurements are used to characterize the mine aerosol's mass and specific components such as quartz, trace elements, and carbon as a function of aerosol size. Recent emphasis on the potential health hazard associated with exposure to diesel exhaust aerosols has focused attention on specific techniques to measure these aerosols. The primary technique used to collect samples in coal and noncoal mines for all of these measurements is sizeselective sampling using inertial impaction. In addition, several aerosol sensor techniques are being developed for continuous monitoring of respirable mine aerosols. These include both light-scattering and direct mass measurement. Size-Selective Sampling. A size-selective sampling technique that has been useful for in-mine measurement of both diesel and mineral dust aerosol employs the Marple personal impactor (series 290, AND) discussed in Chapter 10. The series 290 sampler was originally designed for NIOSH as a wood dust sampler by Rubow et al. (1987). More recently, it has been used in surveys of diesel-equipped mines by the U.S. Bureau of Mines (BOM) and NIOSH to measure the size distribution of mine aerosol (NIOSH, 1987). Estimates were made of average concentration levels of respirable diesel aerosol for the working shift using the sub-l.Oum portion of each sample under the assumption that this accounted for most of the diesel exhaust in the mine atmosphere. The average concentration for submicrometer aerosol generated in the mining sections was 0.7 ± 0.3 mg/m3. The micro-orifice, uniform deposit impactor (MOUDI) (model 100, MSP), also discussed in Chapter 10, has also been used to measure the size distribution of mine aerosol over the size ranges in which respirable coal dust and diesel aerosols are expected to predominate (Marple et al., 1991). In addition to laboratory studies of these aerosols (Marple et al., 1986),

the MOUDI has been used during field experiments in underground coal mines to evaluate its ability to separate diesel aerosol from coal dust aerosol on the basis of their size distributions (Rubow et al., 1990a). The field evaluations were conducted in underground mines that used only electric-powered haulage equipment and in other mines that used dieselpowered haulage equipment. Typical mass size distributions of aerosols measured in the haulage entry of the dieselequipped mines, shown in Figure 26-3, exhibit two distinct maxima: one submicrometer and the other greater than ljum. These measurements indicate that more than 90% of diesel exhaust aerosols in the diesel-equipped coal mines studied were submicrometer in size (Cantrell, 1987). The diesel-associated submicrometer aerosol accounted for approximately 40% to 60% of the respirable aerosol mass concentration. In contrast, aerosol size measurements in the all-electric coal mines, typified by Figure 26-4, exhibited a very small submicrometer maximum. Less than 10% of the measured respirable aerosol mass was in the submicrometer size range. Diesel Aerosol Sampling and Analysis. Three analytical methods have been used for measurement of diesel exhaust aerosol in underground mines. These methods are (1) its mass concentration, (2) its carbon content, and (3) the combustible fraction of the sample. The first is measured using typical filter gravimetric techniques and the second by direct analysis of the organic carbon (OC) and elemental carbon (EC) content of the aerosol through thermal-optical analysis (see Chapter 11).The third is the respirable combustible dust (RCD) method that uses gravimetric techniques combined with ashing to separate mineral from combustible matter. In all cases, size-selective sampling can be used to provide a sample that contains most of the diesel-associated portions of the sampled respirable aerosol. A personal diesel exhaust aerosol sampler based on size-selective sampling, developed for use in underground coal mines (Rubow et al., 1990b; McCartney and Cantrell, 1992), is pictured in Figure 26-8. It has three stages and employs inertial impaction for separating and collecting the diesel and mineral dust fractions of the sampled respirable aerosol. The first stage is an inertial preclassifier that separates and collects the larger, nonrespirable aerosol. The pre-classifier used in this design is a 10 mm Dorr-Oliver cyclone. Its second stage is a four-nozzle impactor with a 50% cut point of 0.8 um aerodynamic diameter. The third stage, which is a filter, collects the remaining aerosol particles with less than 0.8 Jim aerodynamic diameter. The sampler components are used with an MSA dust monitor sampling frame and operate at a sampling flow rate of 3.33 x 10"5m3/s [2L/min]. It is designed to be compatible with commercial personal sampling pumps. Gravimetric Analysis. Coupled with gravimetric analysis, the personal diesel exhaust aerosol sampler can provide measurements of diesel exhaust aerosol concentrations in coal mines under worst-case sampling conditions, which are accurate to within 25%, with a confidence of 95%, for concentration levels greater than 0.3mg/m3 (Cantrell and Rubow, 1991). During field evaluation tests, the sampler was used to make numerous aerosol concentration measurements in underground coal mines that use diesel haulage equipment. Figure 26-9 summarizes respirable aerosol concentrations in these mines, determined from area samples collected in the haulage entries, on coal shuttle cars, and in the ventilation return entries (Cantrell et al., 1992). Figure 26-10 summarizes the diesel exhaust aerosol concentrations measured using the same samples. The haulage, shuttle car, and return locations have similar distributions for the diesel exhaust aerosol portion of the respirable total. This implies that diesel exhaust aerosol concentrations are uniform regardless of where they are measured in the section. Total respirable aerosol concentration levels are different depending on where the samples are taken. The highest is in the ventilation return, and the lowest is in the haulage way. These results imply that exposure to respirable mineral dust is location and hence, is occupation dependent.

Hole For Support PIn

Substrate

0.8 urn Cut Nozzles

Filter Outlet Tube

Sampler Inlet

10 mm Dorr-Oliver Respirable Cyclone

Cumulative % < Concentration

Fig. 26-8. Personal diesel exhaust aerosol sampler. (From McCartney and Cantrell, 1993.)

Haulage Return Ramcar

Concentration, mg/m3 Fig. 26-9. Cumulative frequency plot of respirable aerosol concentrations in continuous mining operations using diesel-equipped haulage vehicles. (From Cantrell et al., 1991.)

Cumulative % < Concentration

Haulage Return Ramcar

Concentration, mg/m3 Fig. 26-10. Cumulative frequency plot of respirable diesel aerosol concentrations in continuous mining operations using diesel-equipped haulage vehicles. (From Cantrell et al., 1991.)

The median diesel exhaust aerosol concentration determined with the personal sampler at the haulage location for the three mines surveyed was 0.8 ± 0.2mg/m3. Diesel aerosol contributed 65% of the respirable aerosol at this location. Direct Carbon Analysis. The use of size-selective sampling with gravimetric analysis is only intended for measuring diesel exhaust aerosol concentrations at the levels displayed in Figure 26-10 and cannot be used to support a standard below approximately 300jig/m3. Because control technology is currently capable of reducing diesel exhaust aerosol concentrations to levels well below 300jug/m\ there is a need for an analytical technique that can be used to measure diesel exhaust aerosol at this level (Ambs et al., 1991). One approach is the thermal-optical method (Birch and Cary, 1996) published as Method 5040 by NIOSH. The method was initially described in 1996 and was recently updated (NIOSH, 1999). In the analysis, the OC and EC in a quartz fiber filter sample are determined through temperature and atmosphere control. The instrument has an optical feature that corrects for sample charring. The analytical technique on which the method is based was originally developed for atmospheric aerosols (Johnson et al., 1981; Cadel and Groblicki, 1980; Hering et al., 1990). NIOSH proposed its use for occupational monitoring of DPM. Although OC and EC are determined by the method, NIOSH researchers proposed use of EC as an exposure index because it is a selective marker of DPM in many occupational settings. In contrast, OC can originate from many nondiesel sources (e.g., drill oil mist, hydraulic oil mist, hydraulic fluids, and carbonates). The method evaluation included field studies and interlaboratory comparisons (Birch, 1998; Birch et al., 1999). Improvements in the original method and instrument design were made in collaboration with the laboratory that first offered the analysis and instrument on a commercial basis. The method can detect as little as 1 (Xg/m3 of EC. Currently, there are five commercial laboratories (four in the United States and one in Canada) that perform the analysis. In underground mines, EC and total carbon (TC = EC + OC) account for about 50% and 80% respectively, of the submicrometer particulate mass, but these percentages vary depending on the engine duty cycle, fuel quality, after-treatment

devices, and other factors. Thermal analysis techniques for OC and EC are also being used in several European countries. The simplest sampling train for EC analysis, and the one used by MSHA to obtain field samples, consists of a 10 mm Dorr-Oliver cyclone followed by a 37 mm precombusted ultrapure tissue-quartz fiber filter (PALL 2500QAT, GJEJL) mounted in a 37 mm plastic cassette. Alternatively, the Dorr-Oliver cyclone can be followed by the 0.8 urn size-selective impactor described above. In this situation, diesel exhaust aerosol particulates, which are primarily smaller than 0.8 um, are collected on the filter. This is advantageous because large, mechanically generated aerosols are not collected on the filter and cannot interfere with the carbon analysis. Respirable Combustible Dust Analysis. In the RCD method, total respirable dust (TRD) is collected from sample air on a 25 or 37 mm, 0.8 urn pore size silver membrane (SM) filter after passing through a 10 mm Dorr-Oliver cyclone pre-classifier at a flow rate of 2.83 x 10"5m3/s [1.7L/min]. Flow is controlled using a personal sampling pump. At this flow rate, the cyclone is a respirable dust pre-classifier with a 50% cut point of 4 urn. TRD is determined gravimetrically by weighing the SM filter before and after the sample is collected. The RCD fraction of TRD is determined gravimetrically from the amount of material removed from the SM by controlled combustion at 673 K [4000C] for 1 to 2 h. A correction is made for loss of mass from the SM due to combustion (Gangal et al., 1990; Gangal and Dainty, 1993). Elemental and Mineral Analysis. Another method for analyzing diesel aerosol is source apportionment. This technique has been used to evaluate measurements made in underground mines using size-selective sampling (Cantrell, 1987; Rubow et al., 1990a; Cantrell and Rubow, 1990). It relates elemental, mineral, or chemical components in an aerosol sample to those same components in the sources of the aerosol. Using this relationship, the contribution of each source to the sampled aerosol can be determined. For diesel-equipped coal mines the primary sources of aerosol are coal, rock dust used as a fire retardant on the mine walls, and diesel exhaust aerosol emissions. One specific analytical technique used to apportion collected aerosol among these sources is chemical mass balance model analysis (Watson, 1984; Henry et al., 1984; Davis, 1984). The model is expressed as (26-2) Here, cei is the mass concentration of the ith elemental, mineral, or chemical component of the sample in ug/m3; al} is the fractional amount of component i in emissions from source j \ Sj is the total contribution of source / to the sample; and p is the number of sources. Apportionment of the source is achieved by first characterizing the aerosol sources, obtaining values for fly, then analyzing the aerosol in the sample for the same components, and finally solving for the Sr A least-squares regression analysis is used to determine the S-} of the over determined system of equations expressed by Eq. 26-2. Real-Time Measurement

Real-time measurement of mine dust remains an objective for aerosol research. A report of the Secretary of Labor's Advisory Committee on the Elimination of Pneumoconiosis Among Coal Mine Workers listed several recommendations dealing with the need for continuous respirable dust monitors to help protect workers' health (U.S. Department of Labor, 1996). In addition, a NIOSH Criteria Document lists improved sampling devices as a research need

pertinent to coal miner respiratory health and prevention of disease (NIOSH, 1995). In mining, personal exposure monitoring is preferable to area monitoring (Leidel et al., 1977). Dust concentrations have been shown to change dramatically over times of a few seconds and distances of just 0.6 m in underground coal mines (Kost and Saltsman, 1977). Therefore, to meaningfully approximate a worker's dust exposure, personal real-time monitoring is the preferred strategy. Several approaches are being taken to address these needs. The more promising technologies include light-scattering dust monitors, a person-wearable tapered-element oscillating microbalance, and the Dust Dosimeter (Tsao et al., 1996; Lehocky and Williams, 1996; Volkwein et al., 2000). The principal goals of each of these efforts has been to develop an instrument that will give short-term or real-time measurements of worker dust exposure. Light Scattering. Often called photometers or nephelometers, light-scattering instruments use a light source to illuminate sample aerosol and a light sensor to measure the scattered light that can be related to the mass concentration of the dust. The theory of these monitors is summarized in Chapter 15. Several of the monitors have been characterized in the laboratory for different dusts (Kuusisto, 1983; Marple and Rubow, 1981,1984; Keeton, 1979; Rubow and Marple, 1983). The relationship of the instruments' responses to dust concentration is not simple but depends on particle size, particle composition, and instrument design and manufacturing differences. Significant changes in dust particle characteristics such as shape, size, surface properties, and density, can affect the instrument's correlation with mass concentration and require calibration of the instrument for each type of dust measured (Williams and Timko, 1984). These factors have limited the use of photometers to dust source identification and control technology evaluation. They are not useful for monitoring compliance with dust standards. Most of the mining community's experience with real-time, photometric instruments is with the RAM-I and MINIRAM devices (MIE), which were initially designed for use in the mine environment. Intrinsically safe versions of these instruments were certified by MSHA for use in the potentially explosive atmospheres of underground coal and gassy noncoal mines. They are normally operated with the Dorr-Oliver 10 mm cyclone to remove water droplets and ensure a respirable dust sample (Treaftis et al., 1984; Tomb et al., 1981). The RAM, pictured in Figure 26-11, is a real-time light-scattering aerosol monitor with a sampling pump that pulls aerosol through the sensing zone. The MINIRAM employs similar technology but typically operates in the passive sampling mode wherein size classification is based solely on light-scattering properties. An optional chamber and pump may be used to allow a cyclone classifier to physically determine the respirable fraction. McCawley and Cocalis (1986) obtained good correlations (r2 = 0.92) between in-mine MINIRAM measurements and those obtained using a gravimetric sampler fitted with an intake impactor to restrict penetration of particles larger than 1.5 um. Analysis of the collected sample was accomplished using RCD measurements. This successful comparison between diesel aerosol and the response of a photometric instrument holds promise that it might be used to monitor diesel aerosol. Tapered Element Oscillating Microbalance. The theory of the Tapered Element Oscillating Microbalance (TEOM, R&P) is summarized in Chapter 14. Despite limited application in the mining industry, this technology offers one notable advantage: direct measurement of dust mass. Because dust exposure standards are based on dust mass, this attribute of the TEOM is significant. In 1983, the BOM and NIOSH funded the development of a prototype TEOM personal dust monitor (Patashnick and Rupprecht, 1983). The prototype monitor developed is a system configured for end-of-shift measurements. It is not a real-time monitor,

Fig. 26-11. MIE RAM-I Real-Time Respirable Aerosol Monitor equipped with a Dorr-Oliver Cyclone Sample Inlet. (From Olson and Veith, 1987.)

but uses oscillating microbalance technology to "weigh" the collection filter before and after dust sampling. The BOM has evaluated this prototype system in the laboratory for both end-of-shift and near-real-time applications (Williams and Vinson, 1986). The effective standard deviation of repeated measurements was 1.6 jug. Tests at controlled temperature and humidity showed less than 20 ug of drift during an 8h shift. These early attempts to construct a personwearable form of the TEOM required a substantial mass in the base of the element to dampen the vibrations, reducing the concept's "wearability." Recent proprietary developments by R&P have solved the need for a substantial base mass electronically, and development of a person-wearable TEOM-based personal dust sampler is nearing completion under a NIOSH contract. Dust Dosimeter. An alternative to highly accurate mine dust measurement devices is a new instrument that is based on a dust detector tube (Volkwein et al., 2000). The Dust Dosimeter, shown in Figure 26-12, is an inexpensive device that consists of a small pump with integral pressure transducer and a disposable detector tube, much like a gas detector tube, that correlates pressure drop across the filter with mass accumulation. It has recently been tested and shown to provide qualitative mid-shift and end-of-shift determinations of cumulative dust exposure. Figure 26-13 shows the laboratory responses for a variety of coal types. The Dust Dosimeter is an example of a new type of tool to help improve workers' health by enabling frequent and timely knowledge of current dust levels. Field testing of the device has shown that it gives a higher response to diesel particulate than to coal dust.

Dosimetrer pressure (mm Hg)

Fig. 26-12. Dust Dosimeter. (From NIOSH, 2000.)

Personal sampler mass (mg) Fig. 26-13. Differential pressure response of Dust Dosimeter to six different laboratory coal dusts. (From Volkwein et al., 2000.)

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Henry, R. C, C. W. Lewis, P. K. Hopke, and H. J. Williamson. 1984. Review of receptor model fundamentals. Atmos. Environ. 18:1507-1515. Hering, S. V., et al. 1990. Comparison of sampling methods for carbonaceous aerosols in ambient air. Aerosol ScL Technol 12:200-213. International Agency for Research on Cancer. 1989. IARC Monographs on the Evaluation of Carcinogenic Risks to Humans—Diesel and Gasoline Engine Exhausts and Some Nitroarenes. IARC, Lyon, France, 46:458. Jankowski, R. A. and J. Hake. 1989. Dust Sources and Controls for High Production Longwall Faces. Proceedings of Longwall USA, Pittsburgh, PA. Johnson, R. L., J. J. Shaw, R. A. Cary, and J. J. Huntzicker. 1981. An automated thermal-optical method for the analysis of carbonaceous aerosol. In Atmospheric Aerosol: Source/Air Quality Relationships, eds. W. S. Macias and P. K. Hopke. Washington, DC: ACS Symposium Series, No. 167, pp. 223-233. Keeton, S. C. 1979. Carbon Paniculate Measurements in a Diesel Engine. Albuquerque: Sandia Laboratories, Publication SAND 79-8210. Kost, J. A. and R. D. Saltsman. 1977. Evaluation of the Respirable Dust Area Sampling Concept as Related to the Continuous Miner Operator. Bituminous Coal Research, Inc., Report L-792, January. Monroeville, PA: Bituminous Coal Research, Inc. Kuusisto, P. 1983. Evaluation of the direct reading instruments for the measurement of aerosols. Am. Ind. Hyg. Assoc. J. 44:863-874. Lehocky, A. H. and P. L. Williams. 1996. Comparison of respirable samplers to direct-reading real-time aerosol monitors for measuring coal dust. AIHAJ 57:1013-1018. Leidel, N. A., K. A. Busch, and J. R. Lynch. 1977. The inadequacy of general (area) monitoring for measuring employee exposures. Technical Appendix C, Occupational Exposure Sampling Strategy Manual, NIOSH, NTIS #PB 77-274752. Washington, DC: Government Printing Office. Lippman, M. 1989. Size-selective health hazard sampling. In Air Sampling Instruments, 7th Ed., S. V. Hering. Cincinnati: ACGIH, pp. 163-198. Marple, V. A., D. B. Kittleson, K. L. Rubow, and C. Fang. 1986. Methods for the Selective Sampling of Diesel Particulates in Mine Dust Aerosol. U.S. Bureau of Mines OFR 44-87, NTIS PB 88-130810. Washington, DC: U.S. Bureau of Mines. Marple, V. A. and K. L. Rubow. 1981. Instruments and Techniques for Dynamic Particle Size Measurement of Coal Dust. U.S. Bureau of Mines OFR 173-83; NTIS PB 83-262360. Washington, DC: U.S. Bureau of Mines. Marple, V. A. and K. L. Rubow. 1984. Respirable Dust Measurement. U.S. Bureau of Mines OFR 92-85, NTIS PB 85-245843. Washington, DC: U.S. Bureau of Mines. Marple, V. A., K. L. Rubow, and S. M. Behm. 1991. A micro-orifice uniform deposit impactor (MOUDI): Description, calibration, and use. Aerosol ScL Technol. 14:434-446. McCartney, T C. and B. K. Cantrell. 1992. A Cost-Effective Personal Diesel Exhaust Aerosol Sampler. U.S. Bureau of Mines IC 9324, pp. 24-30. Washington, DC: U.S. Bureau of Mines. McCawley, M. and J. Cocalis. 1986. Diesel Paniculate Measurement Techniques for Use with Ventilation Control Strategies in Underground Coal Mines. Cincinnati, OH: ACGIH, VoI 14, pp. 271-281. McFarland, W. 1927. Silicosis and tuburculosis as seen in the granite workers in Barre. Br. J. Ind. Hyg. 9:315-330. MESA. 1975. The Determination of Free Silica in Airborne Dust Collected on Membrane Filters. U.S. Dept. of the Interior, Mining Enforcement and Safety Administration, IR No. 1021. Arlington, VA: Mine Safety and Health Administration. MSHA. 1971. Code of Federal Regulations (CFR), Title 30—Mineral Resources, Chapter 1. Mine Safety and Health Administration, Department of Labor, Revision of 10 March 1971. MSHA. 1988. Report of the Mine Safety and Health Administration Advisory Committee on Standards and Regulations for Diesel-Powered Equipment in Underground Coal Mines. Report to the Secretary of Labor, July 1988. Arlington, VA: MSHA. MSHA. 1989a. Air quality, chemical substances, and respiratory protection standards; proposed rule. Federal Register 54(166):35760-35785.

MSHA. 1989b. Coal mine health inspection procedures. In MSHA Handbook Series, Handbook No. 89-V-l. Arlington, VA: MSHA. MSHA. 1990. Metal and nonmetal mine health inspection procedures. MSHA Handbook Series, Handbook No. PH 90-TV-4. Arlington, VA: MSHA. MSHA. 1991. Code of Federal Regulations (CFR), Title 30—Mineral Resources, Chapter 1. Mine Safety and Health Administration, Department of Labor, Revision of 1 July 1999, pp. 3-705. MSHA. 1996. 30 CFR Parts 7, Exhaust Gas Monitoring, and Safety Requirements for the Use of Diesel-Powered Equipment in Underground Coal Mines; Final Rule. Federal Register, October 25, 1996, pp. 55411-55534. Washington, DC: U.S. Department of Labor, Mine Safety and Health Administration. MSHA. 1998.30 CFR Part 57, Diesel Particulate Matter Exposure of Underground Metal and Nonmetal Miners; Proposed Rule. Federal Register, October 29,1998, pp. 58104-58270. Washington, DC: U.S. Department of Labor, Mine Safety and Health Administration. MSHA. 2001a. Diesel Particulate Matter Exposure of Underground Coal Miners. Federal Register 66(13):5526-5706. MSHA. 2001b. Diesel Particulate Matter Exposure of Underground Metal and Nonmetal Miners. Federal Register 66(13):5706-5910. NIOSH. 1987. Diesel Particulate Measurement Techniques Applied to Ventilation Control Strategies in Underground Coal Mines. BuMines contract No. JO 145006. For information contact Mr. M. McCawley NIOSH, Morgantown, WV. NIOSH. 1988. Carcinogenic effects of expsosure to diesel exhaust. Current Intelligence Bulletin 50, NIOSH Publ. 88-116. Cincinnati, OH: NIOSH. NIOSH. 1995. Criteria for a recommended standard—Occupational exposure to respirable coal mine dust. Washington, DC: National Institute for Occupational Safety and Health, DHHS(NIOSH) Publication No. 95-107. NIOSH. 1999. Elemental carbon (diesel exhaust). Method 5040. NIOSH Manual of Analytical Methods (NMAM), 5th Edition, January 1,1999. Cincinnati, OH: NIOSH. Olson, K. S. and D. L. Veith. 1987. Fugitive dust control for haulage roads and tailing basins. BuMines RI 9069, Washington, DC: U.S. Bureau of Mines. Organiscak, XA. 1989. Respirable Dust Generation, Comparison of Continuous and Conventional Mining Methods when Excavating Rock. RI 9233. Washington, DC: U.S. Bureau of Mines. Patashnick, H. and G. Rupprecht. 1983. Personal Dust Exposure Monitor Based on the Tapered Element Oscillating Microbalance. U.S. Bureau of Mines OFR 56-84; NTIS PB 84-173749. Washington, DC: U.S. Bureau of Mines. Raymond, L., T. F Tomb, and P. S. Parobeck. 1987. Respirable Coal Mine Dust Sample Processing. Washington, DC: Mine Safety and Health Administration, IR 1156. Rubow, K. L., B. K. Cantrell, and V. A. Marple. 1990a. Measurement of Coal Dust and Diesel Exhaust Aerosols in Underground Mines. Proceedings of the VIIth International Pneumoconioses Conference, NIOSH Publ. No. 90-108. Cincinnati: NIOSH, pp. 645-650. Rubow, K. L. and V A. Marple. 1983. An instrument evaluation chamber: Calibration of commercial photometers. In Aerosols in the Mining and Industrial Work Environment, V. A. Marple and B. Y. H. Liu, eds. Ann Arbor, MI: Ann Arbor Science Publishers, Inc., 111:777-798. Rubow, K. L., V. A. Marple, J. Olin, and M. A. McCawley. 1987. A personal cascade impactor: Design, evaluation and calibration. Am. Ind. Hyg. Assoc. J. 48:532-538. Rubow, K. L., V. A. Marple, Y. Tao, and D. Liu. 1990b. Design and Evaluation of a Personal Diesel Aerosol Sampler for Underground Coal Mines. Preprint No. 90-132. Littleton, CO: Society for Mining, Metallurgy, and Exploration, Inc. Seaton, A. 1986. Respiratory disease in miners. Proc. Ann. Am. Conf. Gov. Ind. Hyg. 14:21-25. Seaton, A., J. A. Dick, J. Dodgson, and M. Jacobsen. 1981. Quartz and pneumoconiosis in coal miners. Lancet 1:1272. Tomb, T. F. and L. D. Raymond. 1970. Evaluation of the Penetration Characteristics of a Horizontal Plate Elutriator and of a 10-mm Nylon Cyclone Elutriator. U.S. Bureau of Mines, RI7367. Washington, DC: U.S. Bureau of Mines.

Tomb, T. E, H. N. Treaftis, and A. J. Gero. 1981. Instantaneous dust exposure monitors. Environ. Int. 5:85-96. Treaftis, H. N., A. J. Gero, P. M. Kacsmar, and T. F. Tomb. 1984. Comparison of mass concentrations determined with personal respirable coal mine dust samplers operating at 1.2 liters per minute and the Casella 113a gravimetric sampler (MRE). Am. Ind. Hyg. Assoc. J. 45:826-832. Tsao. C. X, T. S. Shih, and J. D. Lin. 1996. Laboratory testing of three direct reading dust monitors. AIHA J. 57:577-563. U.S. Congress. 1969. Federal Coal Mine Heath and Safety Act of 1969,30 U.S.C. Stat. 801. U.S. Congress. 1977. Federal Mine Safety and Health Act of 1977, Public Law No. 95-164, 91 Stat. 1290 (1977), Codified at 30 U.S.C. Stat. 801. U.S. Department of Labor. 1996. Report of the Secretary of Labor's Advisory Committee on the Elimination of Pneumoconiosis Among Coal Mine Workers', recommendation 8 and 17. Washington, DC: U.S. Department of Labor. Vincent, J. H. 1991. Joint Investigations of New Generations of Sampler for Airborne Dust in Mines. Synthesis Report on a joint project carried out by six laboratories in five European member states on behalf of the Commission of European Communities during 1985-1988. Report No. EUR 13414EN. Luxembourg: Commission of European Communities. Volkwein, J. C, A. L. Schoeneman, and S. J. Page. 2000. Laboratory evaluation of a pressure differentialbased respirable dust detector tube. Appl. Occup. Environ. Hyg. 15:158-164. Watson, J. G. 1984. Overview of receptor model principles. JAPCA 34(6):619-623. Watts, W F, Jr. 1987. Industrial Hygiene Issues Arising from the Use of Diesel Equipment in Underground Mines. BuMines IC 9141. Washington, DC: U.S. Bureau of Mines, pp. 4-8. Williams, K. and R. J. Timko. 1984. Performance Evaluation of a Real-Time Aerosol Monitor. Washington, DC: U.S. Bureau of Mines, IC 8968. Williams, K. L. and R. P. Vinson. 1986. Evaluation of the TEOM Dust Monitor. Washington, DC: U.S. Bureau of Mines, IC 9119.

and other sampling systems can be used to assemble filter samplers tailored to a specific need, and the new NAAQS have stimulated substantial innovation in this area. This chapter specifies the generic requirements of ambient aerosol sampling systems, tabulates available components that can meet these requirements, and describes particle sampling systems that can be purchased or assembled to address noncompliance research objectives. Citations and references direct the reader to more detailed information on each of these topics. SAMPLING SYSTEM COMPONENTS Figure 27-1 illustrates the components of a generic particulate sampling system. Air enters the inlet where particles larger than the desired size are removed. The air stream containing the remaining particles passes through a transfer tube to a denuder that transmits more than 95% of the particles while removing gases that are particle precursors or are in equilibrium with particulate ammonium nitrate and organic compounds that have high vapor pressures. Denuder surfaces naturally retain these gases, or they are treated with gas-absorbing chemicals. Inlet and transfer tubes also remove gases to varying degrees, depending on the materials of which they are made and the gases of interest. The air stream is directed through a holder containing a collection filter that transmits the gases and retains more than 99% of the particles in an even deposit across its surface. Flat membrane or fibrous filters are selected to be compatible with the intended analysis methods. One or more backup filters that naturally absorb, or that are treated with absorbing chemicals that react with specific gases, are located behind the particle filter to capture gases in the air stream or those that volatilize from particles collected on the first filter. A flow measurement or control device behind the filter monitors the volume of air sampled, and a pump of sufficient capacity to obtain the desired flow rates draws air through the system. Although Figure 27-1 is simple in concept, its practical implementation requires a careful integration of the components specific to the sampling objectives. Denuders and backup filters are commonly omitted owing to their high maintenance and cost, as is the case for FRMs. In many situations the omission of denuders and backup filters results in minimal bias to mass measurements, but there are other conditions under which deviations from actual ambient concentrations are large. The nature of the aerosol being sampled, environmental sampling conditions (e.g., temperature and relative humidity), and the types of chemical analyses applied to the filter deposit must be evaluated before the first sample is taken. Size-Selective Inlets Inertial classifiers (see Chapter 10) are used as size-selective inlets to remove particles exceeding a specified aerodynamic diameter. Inlets are characterized by sampling effectiveness curves, showing the fraction of particles that pass through as a function of aerodynamic diameter. Sampling effectiveness is summarized by a 50% cut point (J50), the diameter at which half of the particles pass through the inlet, and a slope (or geometric standard deviation), which is the square root of the ratio of the diameter of particles with 84% removal (J84) to the diameter with a 16% removal (J^). A slope of 1 indicates a step function that is impossible to obtain in practice. Many inlets have slopes of up to 1.5; the lower the slope, the "sharper" the cut point. Sampling efficiency quantifies the mass fraction of suspended particles passing through the inlet and depends on the particle size distribution as well as the inlet sampling effectiveness. Sampling efficiency is obtained by integrating the product of sampling effectiveness and aerosol mass-size distribution across all expected particle sizes (Watson et al., 1983; Wedding and Carney, 1983). Because many ambient mass-size distributions peak near lOjim, but are

particle-laden air enters

size-selective inlet sampling surfaces

denuders

filter holder, particle filters, and backup filters

flow controller/meter

pump

Fig. 27-1. Components of an aerosol sampling system.

at a minimum near 2.5 urn, changes in PM-IO inlet effectiveness curves have a larger effect on mass concentrations than do changes in PM-2.5 inlet effectiveness curves (Lundgren and Burton, 1995). PM-IO inlets with sharper cut points collect less mass than those with broader cut points. The dependence of mass on the sampling effectiveness slope is much smaller for PM-2.5 inlets. Inlet transmission characteristics must be independent of wind speed and wind direction. The sampling efficiency of the rectangular peaked-roof inlet of the high-volume (hi-vol) sampler for TSP had a variable sampling effectiveness in response to wind direction and speed (Wedding et al., 1977; McFarland et al, 1980). PM-IO inlets have been symmetrically

constructed to minimize these changes. Wind speeds and directions do not appreciably affect PM-2.5 cut points. Chow (1995a), Hering (2001), and Chapter 10 describe a large number of size-selective inlets used for ambient, workplace, and personal exposure sampling. Table 27-1 identifies and describes several PM-IO and PM-2.5 inlets currently in use for ambient air sampling. Some of these are later models of those listed by Chow (1995a), although they are based on the same principles. Inlets of the same design are offered by different vendors under different names, and Table 27-1 attempts to classify these as a single design. Cut points and slopes of effectiveness curves are those given by the manufacturer, although different results are reported by independent tests from several of the citations. These differences result from a variety of test methods, the condition of the inlet at the time of the test, and experimental uncertainty. For PM-IO and larger size-cut inlets, large wind tunnels (McFarland et al., 1977; Wedding et al., 1977; Ranade et al., 1990) are used to examine changes over wind speeds of -2 to 24m/s. Table 27-1 entries are grouped by the size separation principles (see Chapters 9 and 10) of direct impaction, impaction/elutriation, virtual impaction, cyclonic flow, and selective filtration. Direct impaction systems consist of one or more jets positioned above an impaction plate. The impactor dimensions are selected to allow particles with diameters exceeding the desired cut point to strike and adhere to the plate. Elutriator/impactors employ a vertical barrier before or after the impactor to further remove large particles by gravitational settling. When the particle settling velocity exceeds the upward air velocity, the particle is not transmitted through the inlet. The URG* vertical elutriator precedes the impaction surface, while the other inlets in this category contain vertical transport pathways and sharp bends after the impaction surface. Virtual impactors replace the impaction surface with an opening that directs the larger particles elsewhere, sometimes to another filter; these impactors eliminate re-entrainment and separate PM-10 into PM-2.5 and coarse (PM-10 minus PM-2.5) fractions. Cyclones impart a circular motion to air entering the inlet with tangential vanes. This air enters a cylindrical or conical tube, and the centripetal force of the circular motion moves particles toward the walls of this tube and then to a collection hopper or "grit pot." Selective filtration uses filters or other porous materials that have consistent and measurable particle transmission properties. The Nuclepore etched polycarbonate filters (CCO) with 8 jim pores collect particles by interception and impaction in the vicinity of the pores to provide 50% cut points for particles between 2 and 3 urn at flow rates of -1.7 x 10"4HiVs [10L/min]. A -0.4 um pore size filter is place behind the 8 urn filter to collect the transmitted particles. Mathai et al. (1990), in evaluating differences between several samplers used in western U.S. visibility studies, concluded that most differences between mass measurements from collocated sampling systems are caused by differences in inlet characteristics. These differences caused major controversies with respect to the development of PM-10 reference methods in the early 1980s (several of the citations in Table 27-1 address these controversies). Design theories for impactors (Marple and Willeke, 1976), virtual impactors (Loo and Cork, 1988; Sioutas and Koutrakis, 1998), and cyclones (Kenny and Gussman, 1997; Kenny et al., 2000) are in good agreement with empirical tests. However, as inlets become loaded with particles, their sampling effectiveness can change. Impaction inlets require frequent cleaning and oiling or greasing to prevent impacted particles from disaggregating or becoming re-entrained in the air flow (e.g., John et al., 1991a,b). Even with the highly tactile surface used in the Well Impactor Ninety Six (WINS) PM-2.5 impactor/elutriator required in FRM samplers, Pitchford et al. (1997) observed a cone-like buildup that extended above the oil surface after 3 to 4 days of sampling. The tip of this could easily break off and become entrained in the air flow to the filter. Kenny et al. (2000) found that the WINS cut point shifted from 2.5 to 2.15 um after its well was loaded with Aloxite * Appendix I for full maunfacturer addresses referenced to the italieiced three-letter codes.

TABLE 27-1. Size-Selective Inlets and Characteristics for Ambient Aerosol Sampling Name, Manufacturer, References" Impactors Airmetrics Minivol Impactor (ARM) (Turner, 1998; Wiener and Vanderpool, 1992) Harvard Sharp Cut Impactors (ADE) (Marple et al, 1987; Turner et al., 2000)

Impactor/elutriators Andersen (GRA) hi-vol PM-IO (John and Wang, 1991; Kashdan et al., 1986; McFarland et al., 1984; Ranade et al., 1990; Wedding et al., 1985)

Inlet ID: d50 (um), Slope6, Flow (L/min) MVlO: -10, NA, 5 MV2.5: -2.5, NA, 5

MST123:1,1.22,23 MST24: 2.5,1.02, 4 MST210: 2.5,1.06,10 MST220: 2.5,1.25,20 MST104:10,l.ll,4 MSTlOlO: 10,1.09,10 MST1020:10,1.06,20 G1200: 9.7,1.4,1,133

Andersen (GRA) medvol PM-10 (OHn and Bohn 1983)

SA2541:10,1.6,113

Andersen (BGI, GRA, R&P,URG) Flat Top PM-IO (McFarland etal., 1978; Van Osdell and Chen, 1990; Wedding et al., 1980; Lai and Chen, 2000) FRM (BGI, GRA, R&P, URG) Louvered PM-IO (US. EPA, 1997a) URG, Inc. (URG) Elutriator Impactors

246B Flat Top: 10.2, 1.41,16.7

EPA (BGI, GRA, R&P, URG) Well Impactor Ninety Six (US. EPA, 1997a; Kenny et al., 2000)

WINS: 2.48,1.18,16.7

Louvered PM-10:10, NA, 16.7

25A: 1-2.5 & 10, NA, 4 22D: 2.5, NA, 1-20 30KN: 2.5, NA, 1-20

Description and Comments

Machined polymeric propylene plastic or machined aluminum. PM-10 and PM-2.5 inlets are used in series for PM-2.5 sampling. Apiezon vacuum grease dissolved in hexane is pipetted onto impaction surfaces before each sample to minimize re-entrainment Machined aluminum that can also be coated with FEP Teflon

Anodized spun aluminum with a single stage of opposing jets. The body is hinged to facilitate cleaning and re-greasing of the removable impaction plate that is sprayed with an aerosol adhesive after cleaning. The G1200 was preceded by the SA-320 single-stage PM-15 inlet and the SA321A and SA321B dual stage and SA321C single stage PM-10 inlets that are no longer sold but may still be in use. It is not entirely clear which sampling effectiveness tests apply to each of these inlets Spun aluminum with 10 impactor jets and a central elutriation tube. The inlet can be disassembled for cleaning. The SA254I was preceded by the SA254, or "Blue Head" owing to the color of its enamel coating, that was nearly impossible to disassemble for cleaning Machined aluminum with one impactor tube and three vertical elutriator tubes. Rain drops are blown into the inlet beneath the flat top and accumulate on the impaction surface. Water exits through a small drain attached to a bottle on the outside of the inlet. The top unscrews for cleaning the impactor surface Same materials and design as the SA246B but with a top that curves over the inlet bug screen to minimize the entry of windblown raindrops. Also available with a PTFE Teflon® coating FEP Teflon coated glass with an elutriator tube preceding the impactor. The impactor plate is after the jet at the top of the elutriator, facing the ground Machined aluminum well with a detachable impactor jet. The impaction surface consists of a 37 mm quartz fiber filter immersed in 1 mL of vacuum pump oil to minimize particle re-entrainment over multiple sampling days between cleaning (continued)

TABLE 27-1. Continued Name, Manufacturer, References" Virtual Impactors Andersen (GRA) Dichotomous Virtual Impactor (McFarland et al., 1978)

VAPS (URG) Virtual Impactor

Inlet ID: d50 (um), Slope6, Flow (L/min) SA241:2.5, NA, 16.7

VAPSVI: 2.5, NA, 32

H1000:2.5, NA, 1000 Harvard (R&P) Particle Concentrator (Sioutas et al., 1994a,b, 1997,1998) Cyclones Wedding (GRA, TEl) IP10 (Wedding and Weigand, 1985) Andersen (G/L4)/California Air Industrial Hygiene Laboratory Cyclone (AIHL) (John and Reischl, 1980) Sharp Cut Cyclones (BGI, GRT, R&P) (Kenny et al., 2000)

Bendix/Sensidyne 240 Medium Volume Cyclone (Chan and Lippmann, 1977; Mueller et al., 1983) URG, Inc. (URG) Cyclones (Moore and McFarland, 1993; Kenny et al., 2000)

Description and Comments

Anodized machined aluminum integrated into the dichotomous sampler body. 2.8 x 10"5 m3/s [1.7 L/min] goes through the virtual impactor to the coarse particle filter, with the remaining flow directed to the PM-2.5 filter. The unit can be disassembled for cleaning, but care must be taken to reassemble it such that the PM-10 inlet tube is over the virtual impaction tube rather than over the bypass zone to the PM-2.5 filter FEP Teflon coated aluminum. 3.3 x 10"5 m3/s [2 L/min] goes through the virtual impactor to the coarse particle filter with two streams of 2.5 x 10-4 m3/s [15 L/min] each directed to PM-2.5 denuders and filters Particles are accelerated through a 0.32 x 280 mm rectangular nozzle and impact on an oil-soaked filter. Flow rates can be adjusted with different slit lengths. Particles less than 0.1 um are diverted from the flow and not collected

IPlO: 9.6,1.37,1133

RFPS-1087-062. Spun aluminum inlet cleaning port on top of inlet

SA3.68:2.7,1.16,24 SA3.68: 2.3,1.18,28

Machined aluminum used on Andersen RAAS and IMPROVE speciation samplers

BGI1.062:1.0,1.21,3.5 Nickel-plated aluminum. The cyclone has a BGI2.229:1,1.17,16.7 horizontal design, with the hopper at one end. BGISCCA: 2.5,1.19,16.7 Particles are collected over the lower surface of GRT1.118:2.5, NA, 2 the vortex cone and in the grit pot. A cap at the GRT2.141: 2.5,1.24,6.8 end facilitates regular cleaning R&P1.829:2.5,1.23,5 B240: 2.5,1.7,113 Welded steel that can be coated with PFA Teflon. Operates in a vertical position with a hopper on the bottom

30EHB: 1,1.34,16.7 30EN: 2.5,1.32,10 30EH: 2.5,1.35,16.7 30ED: 2.5, NA, 3 30EC: 3.5, NA, 28 30ENB: 10, NA, 16.7 30EA: 10, NA, 28.3

FEP Teflon coated aluminum. Cyclones can be operated vertically and horizontally

TABLE 27-1. Continued Name, Manufacturer, References" Stacked Filters Nuclepore Filters (CCO) (Spurny et al., 1969; Cahill et al., 1977; Parker et al., 1977; John et al., 1983; Droppo et al., 1995) CIS Foam (BGl) (Vincent et al., 1993)

Inlet ID: d5Q (urn), Slope*, Flow (L/min)

Description and Comments

N8: ~2, NA, 10

Large pore (8 urn) polycarbonate filters remove particles >2 to 3 urn by interception and impaction. Small particles pass through the pores to another filter. Extensively used in U.S. visibility networks during the 1970s and early 1980s

CISlO: 10, NA, 3.5 CIS4: 4, NA, 3.5 CIS25:2.5, NA, 3.5

A polyurethane foam with different thickness transmits particles with varying degrees. Used for personal sampling

0

Refer to Appendix I for manufacturer codes and information. References cited provide more complete descriptions of the inlet and tests of its collection and transmission properties. This is a relatively complete list of inlets that are commercially available for ambient air sampling and are currently in use. It complements, rather than replaces, lists published elsewhere. b Inlet IDs have been assigned for this table to facilitate later reference. These are contractions of the manufacturer's part number, where possible. "d50" is the aerodynamic diameter at which half of the particles pass through the inlet and the other half deposit in the inlet, as determined by presentation and detection of known particle sizes. Slope = -y]d^/die , the square root of the particle diameter ratios for inlet penetration at 84% and 16%. Values given are those provided by the vendor for the specified flow rate, and these may differ from those reported in some of the citations owing to different test and inlet conditions. "NA" in the slope position indicates that this value was not available.

dust. The U.S. EPA (1998b) recommends cleaning the WINS after five 24 h samples. Kenny et al. (2000) determined that the Sharp-Cut Cyclone had a sampling effectiveness curve similar to that of the WINS with a much larger collection capacity; no shift in cut point was found with increased loadings. Table 27-1 shows that different PM-IO and PM-2.5 inlets have different cut points. The U.S. EPA (1987) required a PM-IO cut point of 10 ± 0.5 am for FRMs. The Wedding IP-10 inlet had a 9.6 |im cut point, while the original SA-321A inlet had a 10.2 fim cut point. This difference made the Wedding inlet more attractive to some users because sampling with a lower cut point decreased the PM-10 measured and lowered the probability of exceeding the NAAQS. The Andersen SA-321A (AND) was replaced by the SA-321B with a 9.7 urn cut point to meet this competition; new impactor jets were also provided for the SA-321A inlet. Although both of these were replaced with the G1200 inlet described in Table 27-1, many of the original inlets are still in use, and it is difficult to distinguish them from each other by their appearance. Inlet flow rates fall into ranges appropriate for high-volume (-0.017 m3/s [lOOOL/min]), medium-volume (~0.0017m3/s [100L/min]), low-volume (-1.7 x 10"4 to 3.3 x 10"4InVs [10 to 20L/min]), and mini-volume (<8.3 x 10"5m3/s [5L/min]) sampling systems. A massive volume (>0.17m3/s [10,000L/min]) inlet and sampler has also been developed (Fitz et al., 1983) to obtain several grams of suspended particles for health studies. The medium- and high-volume inlets are especially useful when samples are taken in parallel on several substrates because flow rates can be kept high enough to obtain an adequate deposit for analysis. Sampling Surfaces

Anodized aluminum is the most commonly used material for inlets and transfer tubes. Copper, stainless steel, conducting plastic, and glass are also used as transfer tubes in some systems. Nonconducting plastic surfaces and glass can acquire an electrical charge that might

attract suspended particles to them, though the dimensions of most ambient sampling systems are sufficiently large that this attraction is negligible (Rogers et al., 1989). Certain materials absorb or react with gases and particles, thereby removing them from the air stream (John et al., 1986; Hering et al., 1988). This is especially the case for nitric acid vapor, which sticks to nearly everything. Removal of nitric acid in an inlet or sampling duct can change the gas-particle equilibrium of particulate ammonium nitrate, causing this substance to dissociate into ammonia and nitric acid gases (Stelson and Seinfeld, 1982). This is also true for some volatile organic species. John et al. (1986) tested different materials with respect to their affinity for nitric acid and found that surfaces coated with perfluoro alkoxy (PFA) Teflon can pass nitric acid with 80% to 100% efficiency. These results were confirmed by Neuman et al. (1999), who found that aluminum, stainless steel, nylon, glass, fused silica, silane-coated glass, silica-coated steel, and stainless steel tubes removed more than 80% of nitric acid over a 0.3 m tube length. PFA Teflon surfaces should be washed with a dilute solution of nitric acid to season them before sampling. Several of the Table 27-1 inlets and their associated transfer tubes are available with a Teflon coating to minimize removal of reactive gases from the air stream. Denuders Denuders (Kitto and Colbeck, 1999) are placed in the air stream to remove more than 95% of selected gases while transmitting more than 95% of the particles (see Chapter 19). Molecules in the gas phase diffuse rapidly to the surfaces of the denuder, while particles having lower diffusion coefficients pass through the denuder to the filter. The denuder surface can be coated with chemicals that retain the gas molecules. When properly treated and handled, the denuders can be extracted in a solvent for laboratory analysis to determine average gas concentrations over the sampling period. The denuder difference method (Shaw et al., 1982) operates two samplers in parallel, one with and one without a denuder. Gases and particles are collected on filters that naturally adsorb or are impregnated with gas-absorbing chemicals and are subsequently analyzed for the desired chemical compounds; the gas concentration is the difference between the nondenuded and denuded concentrations. Tubular, parallel plate, annular, honeycomb, and cloth denuders have been used, the URG glass and Teflon-coated annular denuders being the most common. A tubular denuder (Gormley and Kennedy, 1949) is a single tube or a bundle of tubes. A bundle of tubes >0.3m long removes gases at typical flow rates; a single tube is only practical for low (less than approximately 8.3 x 10"5 [5L/min]) flow rates, depending on the diffusion coefficient of the gases. A parallel plate denuder (Eatough et al., 1993) is formed from rectangular panels with a small gap between them. An annular denuder (Possanzini et al., 1983; Allegrini et al., 1987; Koutrakis et al., 1988) consists of a solid center inside a tube of slightly larger diameter. Particles pass through the annulus between the two surfaces while the gases diffuse to them. URG annular denuders create several annuli by positioning tubes of slightly larger diameters inside one another. The honeycomb denuder (Koutrakis et al., 1993; Sioutas et al., 1994c, 1996) is a thick piece of glass or aluminum through which many tiny holes have been drilled or etched. These denuders can be as short as a few centimeters, depending on flow rate, and can be treated as part of a filter stack. Fitz and Motallebi (2000) evaluated loosely woven rayon fabrics that can be soaked in different chemicals to remove gases. Their tests show that PM-2.5 is collected with >95% efficiency through the fabric while nitric acid is quantitatively recovered from extraction and analysis of the fabric. These fabric denuders can be handled as if they were another component of a filter pack. Kitto and Colbeck (1999) describe many of the coatings applied to denuders to retain specific gases. Magnesium oxide, sodium fluoride, sodium chloride, and sodium carbonate have been used to retain nitric acid. Ammonia has been removed with oxalic acid, tungstic acid, phosphoric acid, phosphorous acid, and citric acid. Organic gases have been removed by quartz filter strips (Fitz, 1990), carbon-impregnated filters (Eatough et al., 1993), finely ground

XAD resin (Gundel et al., 1995), and potassium hydroxide (Lawrence and Koutrakis, 1994). The most common use of denuders is to obtain an accurate measurement of particulate ammonium nitrate, which often volatilizes during sampling as temperatures exceed 288 to 293 K [15° to 200C] (Hering and Cass, 1999; Stelson and Seinfeld, 1982; Watson et al., 1994). Accurate nitrate measurements are obtained by removing nitric acid from the sample stream, collecting nitrate particles on a filter, and recovering volatilized nitric acid on an absorbing backup filter. John et al. (1988), while examining methods to estimate nitric acid dry deposition, found that an anodized aluminum denuder had a large capacity for nitric acid removal and developed one for the dichotomous sampler. Fitz and Hering (1996) determined that an anodized aluminum annular denuder (Chow et al., 1993) became saturated after 3 years of monitoring at a photochemically active southern California location; re-anodizing the surfaces was sufficient to restore its capacity. Filter Holders

A filter holder consists of a frame that seals the edges of the filter so that air is forced to pass through its porous center. A porous support grid downstream of the filter keeps it from being sucked through the holder by the pump vacuum. Lippmann (2001) and Chow (1995a) describe several different filter holders. Filter holders should mate to the sampler and to the flow system without leaks, be composed of inert materials that do not absorb reactive gases, create a uniform particle deposit on the filter surface, have a low pressure drop across the empty holder, accommodate the sizes of commonly available air sampling filters (e.g., 37 or 47 mm), and be durable and reasonably priced. They should also allow for filter transport to and from the field sampler without contamination, minimize adherence or damage to the filter, and minimize removal of the substances being measured. Filter holders are configured as in line or open faced. In-line holders concentrate particles in the center of the substrate, and this biases the results when analyses are performed on portions of the filter (Chow et al., 1994; Chow, 1995b). Inhomogeneous support grids, or grids with a low porosity (<50%), may also result in nonuniform deposits. Filter holders should be open faced or preceded by a diffusion zone that disperses the particles before collection. The PM-2.5 FRM (U.S. EPA, 1997a) specifies the dimensions and materials for 47 mm diameter filter holder rings made of Delrin plastic with a stainless steel grid into which 100 Jim diameter holes are etched 100 um apart. Nuclepore (CCO) polycarbonate plastic filter holders accommodate 25,37, and 47 mm diameter filters and are adaptable to many sampling systems. Each one costs a few tens of dollars, making it possible to purchase many of them for laboratory loading and unloading. Nuclepore filter holders are modified by drilling out the outlet hole to reduce flow resistance. Multiple extender sections can be used to stack several filters in series without having them touch each other. The rubber O-ring in the Nuclepore holder has been found to raise the carbon blank levels on quartz fiber filters, an artifact that can be eliminated by replacing the rubber with a Viton O-ring (Tombach et al., 1987). Little information is available on the extent to which the filter holder material affects the quantification of reactive gases. Filter holders made of or coated with Teflon are used to minimize losses of nitric acid. Several stainless steel filter holders are available, many of them with in-line connectors. These are usually expensive and absorb volatilized nitrate (Neuman et al., 1999). The speciation samplers described below usually have specially constructed filter holders that are specific to those samplers. Filters

Filter media are judged for specific applications based on their mechanical stability, chemical stability, particle or gas sampling efficiency, flow resistance, loading capacity, blank values,

artifact formation, compatibility with analysis methods, cost, and availability. Chow (1995a), Lippmann (2001), and Chapter 9 describe various types of filters, while Chapter 11 shows how filter characteristics relate to desired chemical analyses. Some filters are also used for optical transmission or reflection to measure particle absorption (Horvath, 1997) or to distinguish organic from elemental carbon (Birch and Cary, 1996; Chow et al., 2001), filter color and internal scattering properties important for these purposes. U.S. EPA (1987) filter specifications for PM-IO compliance sampling include 0.3 um dioctyl phthalate (DOP) sampling efficiency in excess of 99.9%, weight losses or gains due to mechanical or chemical instability of less than a 5 ug/m3 equivalent, and alkalinity of less than 25uEq/g to minimize absorption of sulfur dioxide (SO2) and nitrogen oxides (NOx). These are only the minimal requirements for samples that require chemical analyses. A sample from each batch (50 to 100 filters) must always be tested for contamination before sampling with that batch; high and variable blank levels typically invalidate subsequent quantification on particle deposits. The most commonly used filter media for atmospheric particle and gas sampling are Teflon membrane, quartz fiber, nylon membrane, cellulose fiber, Teflon-coated glass fiber, etched polycarbonate membrane, and glass fiber. None of these materials is perfect for all purposes. Ringed-Teflon membrane filters {GEL, WHA) consist of a thin, porous polytetrafluoroethylene (PTFE) Teflon sheet stretched across a polymethylpentane ring; the thin membrane collapses without the ring, and the filter cannot be accurately sectioned into smaller pieces. The white membrane is nearly transparent and has been used to estimate light absorption (Campbell et al., 1995). PTFE Teflon is very stable, absorbing negligible water or gases. It has inherently low contamination levels, but chemicals have been found in some batches by acceptance testing. This filter is commonly used for mass and elemental analyses. The thin membrane is especially appropriate for X-ray and proton-induced fluorescence (XRF and PIXE) analyses that obtain elemental concentrations while leaving the filter intact. Teflon is hydrophobic, and emulsifiers (such as ethanol) need to be added for water-extraction methods for ion analysis. Carbon cannot be measured on Teflon membranes because of its high carbon content, although aerosol carbon has been inferred from hydrogen measurements (Kusko et al., 1989). Because of their high flow resistance and need for a ring support, high-volume flow rates are not attainable, though it is possible to obtain flow rates required for low-volume and medium-volume inlets. A variation on this filter consists of a PTFE Teflon membrane mounted on a woven PTFE mat instead of a support ring. This filter has a higher density, but it can be obtained in larger sizes for higher volume samples. The membrane and the mat sides are similar in appearance, and care must be taken to prevent mounting it upside down, particles being drawn through the mat rather than onto the surface of the membrane in this case. Quartz fiber filters (PAL, WHA) consist of a tightly woven mat of quartz filaments. These filters meet requirements in most categories. Quartz fiber filters adsorb hydrocarbon gases during sampling (McDow and Huntzicker, 1990), as determined by placing a clean quartz filter behind a Teflon filter (Turpin et al., 1994). The extent to which this is a positive bias due to gas adsorption or a negative bias due to the collection of volatilized particles is uncertain (Cui et al., 1997, 1998). Quartz filters are baked at -117 0K before sampling to remove adsorbed organic vapors. Blank levels are high and variable for several elements (especially aluminum and silicon), though newer formulations are cleaner than earlier formulations. These filters are widely used for ion and carbon analyses. The greatest drawback of quartz fiber filters is their fragility; they require extremely careful handling for accurate mass measurements. The Whatman QM/A quartz fiber filter contains a 5% borosilicate glass binder that minimizes its friability. This filter is often used in high-volume PM-10 samplers for mass measurements. The Pallflex 2500 QAT-UP filter is pure quartz and undergoes a distilled water washing (thus the "UP," or "ultrapure," designation).

Nylon membrane filters consist of thin sheets of porous nylon and are used almost exclusively for the collection of nitric acid. Nylon filters have high flow resistance that increases with filter loading. These filters passively absorb nitric acid, depending on how long they have been exposed to an acid-rich environment, and they should be washed in a sodium bicarbonate solution followed by distilled water before use in the field. They also absorb small amounts of ammonia (Masia et al., 1994) and sulfur dioxide (Sickles and Hodson, 1999), thereby limiting their use in front of filters intended to collect these gases. Gelman Nylasorb (GEL) is most commonly used for ambient air sampling. Cellulose fiber filters (WHA) consist of a tightly woven paper mat. These filters meet requirements in most categories with the exception of sampling efficiency and water vapor artifacts. Particle penetration for submicrometer particles have been observed, but these are highly dependent on the filter weave (Biles and Ellison, 1975). Cellulose fiber is hygroscopic and requires precise relative humidity control in the filter processing environment to obtain accurate mass measurements (Demuynck, 1975). These filters have low chemical blanks except for carbon, and they can be used for elemental and ionic analyses of the deposit. Cellulose fiber filters can be impregnated with gas-absorbing compounds, similar to those described above for denuders, and located behind more efficient particle-collecting filters. Nitric acid, ammonia, sulfur dioxide, and nitrogen dioxide gases are determined by this means. The most commonly used cellulose fiber filter is Whatman 41. Teflon-coated glass fiber filters (PAL) imbed a Teflon slurry onto a loosely woven glass fiber mat. These filters meet requirements in all categories except blank element and carbon levels. Though a small amount of nitric acid absorption has been observed (Mueller et al., 1983), it is tolerable in most situations. These filters are used for ion analyses and for specific organic compounds, but not total carbon analyses owing to their Teflon coating. The most commonly used Teflon-coated glass fiber filters are Pallflex TX40HI20 and T60A20 (PAL). Etched polycarbonate membrane filters (CCO) are constructed from a thin polycarbonate sheet through which pores of uniform diameter have been produced by radioactive particle penetration and chemical etching. Depending on pore size, these filters have variable particle collection properties, as noted in Table 27-1, that are used for size-specific measurements. These filters are nearly transparent and have been used to estimate light absorption (Horvath, 1993). Polycarbonate membrane filters have low elemental blank levels and are appropriate for elemental and ion analysis. They are the best filter media for single-particle analysis by electron microscopy because particles are easily distinguished from the flat filter surface. These filters are not used for thermal evolution carbon analysis owing to their carbon composition. The filters hold an electrostatic charge that influences mass measurements unless substantial effort is invested in discharging them with a small radioactive source (Engelbrecht et al., 1980). Electrostatic discharging is good practice for all filter media, even though others do not retain as much charge as the polycarbonate membranes. The Nuclepore 8.0 and 0.4 urn filters are most commonly used in ambient aerosol sampling. While the 0.2 um pore size filter provides a higher sampling effectiveness, its higher flow resistance requires a large pressure drop across the filter for >1.7 x lO^nrVs [lOL/min] flow rates. Glass fiber filters (WHA) are obsolete and should not be considered for use in particle sampling. These filters consist of a mat of borosilicate glass filaments. The high alkalinity of these substrates causes sulfur dioxide, nitrogen oxides, and gaseous nitric acid to be adsorbed (Coutant, 1977; Spicer and Schumacher, 1979), thereby biasing mass and chemical measurements. Blank levels for most elements of interest are extremely high and variable (Witz et al., 1983). As with quartz filters, glass fiber filters adsorb organic carbon vapors that are measured as particulate carbon during analysis. Unlike quartz filters, the sodium in glass fiber filters catalyzes the combustion of elemental carbon at lower temperatures, causing biases in the quantification of the light-absorbing carbon fraction (Lin and Friedlander, 1988). Although their cost per filter is lower than that of the other filters, the savings are not worth the expense of compromised data.

Flow Measurement and Control

The quantity of air per unit time must be precisely measured and controlled to determine particle concentrations and to maintain the size-selective properties of the inlet. Manual volumetric, automatic mass, differential pressure volumetric, pump speed volumetric, and critical orifice volumetric flow control principles have been applied to aerosol samplers. Manual control is accomplished when the operator initializes a setting, such as a valve adjustment, and then relies on the known and constant functioning of sampler components, such as pumps and tubing, to maintain flows within specifications. Flow rates that are set manually change as the collection substrate loads up and presents a higher flow resistance. For most concentrations (<200ug/m3), the flow will not change by more than 10% during sampling, and the average of flow rates taken before and after sampling provides an accurate estimate of the actual flow. Mass flow controllers use temperature sensors that measure the heat transfer between two points in the gas stream. To a first approximation, the heat transfer is proportional to the number of gas molecules passing between the two points, and hence the mass flow controller is able to sense the mass flux. An electronic feedback loop compensates for temperature and pressure variations of the gas and the sensing probe. Wedding (1985) estimated potential differences in excess of 10% between mass and volumetric measurements of the same flow rates, depending on temperature and pressure variations. Differential pressure volumetric flow control maintains constant pressure across an orifice (usually a valve that can be adjusted for a specified flow rate) by a control valve located between the filter and the orifice. When the pressure between the filter and the valve increases, as it does when filters load up, the valve control opens to allow more air to pass. The pump volumetric method changes the dc current to a variable speed pump in response to a flow sensor at the pump inlet or exhaust. As the flow rate decreases with filter loading, more current is supplied to the pump, thereby increasing its speed and throughput. A critical orifice consists of a small circular opening between the filter and the pump. When the downstream pressure at the minimum flow area downstream of the orifice is less than 53% of the upstream pressure, the air velocity attains the speed of sound and it will remain constant, regardless of increased flow resistance. Critical orifices provide stable flow rates, but they require large pumps and low flow rates (typically less than 3.3 x 10"4HrVs [20L/min] with commonly available pumps) to maintain the high pressure difference. Wedding et al. (1987) developed a "critical throat" that uses a diffuser arrangement to recover much of the energy expended in back pressure behind a critical orifice. This design allows higher flow rates to be obtained with a given pump.

Flow Movers

Air is passed through the sampling substrates by means of a vacuum created by a pump. Monteith and Rubow (2001) and Chow (1995a) describe commercially available air pumps and their capacities and operating principles. High-volume samplers typically use a radial flow fan that rotates curved blades at a high velocity to move substantial quantities of air. The original fans for the TSP hi-vol were manufactured for household vacuum cleaners and do not tolerate large pressure drops caused by complex inlets, tightly woven filters, and stacks of filters and denuders. Hinds (1999) shows that a 2OkPa [15 cm Hg] resistance across the hi-vol fan reduces the flow to zero. Diaphragm and piston pumps convert the rotary motion of a driveshaft into the movement of a flexible membrane or piston that draws air through an opening on the backside of the filter and pushes it out through another opening at the pump exit. Hinds (1999) shows diaphragm pumps capable of 8OkPa [60 cm Hg] vacuum when flow is reduced to zero. The rotary pump turns carbon vanes within a circular chamber with an offset driveshaft. The centripetal force of the vanes seals their ends against the chamber surface. Air enters when

the volume between vanes is large and exits when the volume decreases where the driveshaft is closest to the chamber walls. These pumps can create a vacuum of up to 93kPa [70 cm Hg] (Hinds, 1999) and can pull more than 0.0017 m3/s [lOOL/min] through a stack of filters with high flow resistance. Carbon vane pumps are noisy, and their exhaust must be filtered as the carbon vanes are continually abrading into small particles.

SAMPLING SYSTEMS

Monteith and Rubow (2001), Hering (2001), and Chow (1995a) describe a large number of systems for ambient aerosol sampling. Chow (1995a) cites many researchers who have assembled different components (Fig. 27-1) to achieve research objectives beyond those of compliance monitoring. Typical research needs are • Simultaneous sampling on multiple filters for different analyses: Parallel samples on Teflon membrane and quartz fiber filters are needed for simultaneous mass, elemental, ionic, and carbon analyses. • Higher sample volumes for shorter sampling intervals: Samples of 3 to 6 h duration are needed to evaluate the diurnal evolution of chemical components, and higher flow rates are often needed to obtain enough sample for laboratory quantification. • Gas and particle phase measurements of volatile species: Accurate measurements of ammonium nitrate and some volatile organic compounds require denuders and backup filters. Gas phase precursors collected by denuders are also needed when equilibrium models are used to determine which precursors limit particle formation. • Multiple sequential samples without operator intervention: It is not practical or costeffective for an operator to change samples every few hours for diurnal sampling or at midnight for everyday sampling, thereby requiring an automated sampler changer. • Several particle size fractions: Multiple inlets, in series or in parallel, are needed to separate particles into different size fractions, with PM-10, PM-2.5, and coarse (PM-10 - PM-2.5) being the most widely used fractions. Many of these systems are based on FRMs developed for PM-10 or PM-2.5 compliance monitoring. Table 27-2 specifies sampling systems that have attained FRM or federal equivalent method (FEM) status with their EPA designation numbers and Federal Register announcements. High-volume samplers designated for PM-10 compliance monitoring are being replaced by low-volume samplers that are similar for PM-10 or PM-2.5; the PM-2.5 include a WINS inlet behind the PM-10 inlet. Most of the newer units control the volume throughout sampling by measuring the mass flow and then adjusting this for ambient temperature and pressure conditions. These units are controlled by a microprocessor that also logs flow rate, temperature, and pressure over periods as short as 1 min throughout the sampling period. Chow et al. (1996) outline various strategies for applying FRMs to chemical speciation, including duplicate samplers with different filter media, collocated high volume and dichotomous samplers with quartz and Teflon filters, and supplementing a permanent FRM sampler with temporary "mini-vol" samplers using complementary filter packs and size-selective inlets. The Oregon Department of Environmental Quality PM-10 Sequential Filter Sampler (SFS) has proved itself very adaptable for a variety of purposes. The transfer tube has been filled with aluminum denuder tubes, and quartz filters have been backed with sodium chloride or nylon filters to measure nitrate accurately. Two filter packs can be sampled in parallel with Teflon membrane and quartz fiber filters for different analyses. Flow rates from

TABLE 27-2. PM-IO and PM-2.5 Federal Reference Methods and Federal Equivalent Methods"

Sampler, EPA FRM Designation, Federal Register Notice Federal Reference Methods Andersen (GRA) RAAS10-100 PM-IO Single Channel PM-10 Sampler, RFPS-0699-13064, FR 33481,06/23/99 RAAS2.5-100 PM-2.5 Sampler, RFPS-0598119,63 FR 31991,06/11/98 RAAS10-200 PM-10 Audit Sampler, RFPS0699-131,64 FR 33481,06/23/99 RAAS2.5-200 PM-2.5 Audit Sampler, RFPS0299-128,64 FR 12167,03/11/99 Andersen (GRA) RAAS10-300 PM-10 Sequential Sampler, RFPS-0699-132,64 FR 33481,06/23/99 RAAS2.5-300 PM-2.5 Sequential Sampler, RFPS-0598-120,63 FR 31991,06/11/98

Description

Louvered PM-10 2.78 x HTW/s [16.7L/min] inlet with anodized aluminum sampling surfaces. FRM Delrin 47 mm ringed filter holder. Variable speed diaphragm pump. Volumetric flow measured by dry test meter at pump outlet that modulates pump speed. PM-2.5 samplers include a WINS impactor in the sample line after the PM-10 inlet. Audit samplers are of similar design but more compactly packaged for periodic collocated sampling with a permanent unit Similar to RAAS10-100 and RAAS2.5-300, but with a circular tray into which six 47 mm ringed filter holders can be located. A timer rotates the tray at preset intervals, and a plunger compresses the holder into the sample flow stream

BGI, Inc. (BGl) PQ100 FRM PM-10 Sampler, Louvered PM-10 2.78 x HTW/s [16.7L/min] inlet RFPS-1298-124,63 FR 69625,12/17/98 with anodized aluminum sampling surfaces. FRM PQ200 or PA200A PM-10 FRM Sampler, Delrin 47 mm ringed filter holder. Variable-speed RFPS-1298-125,63 FR 69625,12/17/98 double diaphragm pump controlled by mass PQ200 or PQ200A PM-2.5 FRM Sampler, flowmeter adjusted every minute to ambient RFPS-0498-116,63 FR 18911,04/16/98 temperature and pressure for volumetric equivalent. The PM-2.5 unit is identical to the PM-10 sampler with addition of a WINS impactor after the PM-10 inlet. The audit sampler (PQ200A) is of similar design but more compactly packaged for periodic collocated sampling with a permanent unit. These units can operate for 24 h on a battery Rupprecht & Patashnick (R&P) Partisol FRM 2000 PM-10 Sampler, RFPS-1298-126, 63 FR 69625,12/17/98 Partisol FRM 2000 PM-2.5 Sampler, RFPS0498-117,63 FR 18911,04/16/98 Partisol 2000 Audit Sampler, RFPS-0499129,64 FR 19153,04/19/99

Louvered PM-10 2.78 x HTW/s [16.7L/min] inlet with anodized aluminum sampling surfaces. Delrin 47 mm ringed filter holder with a tapered edge for sequential version. Differential pressure flow control using a servo-controlled valve controlled by a mass flowmeter adjusted every minute to ambient temperature and pressure for volumetric equivalent. WINS inlet added for the PM-2.5 model. An optional insulating jacket is available for cold weather. The audit sampler is packaged for portability

Rupprecht & Patashnick (R&P) Partisol Same as R&P Partisol FRM 2000 samplers but Plus 2025 PM-10 Sequential Sampler, RFPSwith a pneumatically controlled sampler insertion 1298-127,63 FR 69625,12/17/98 mechanism. Up to 18 47-mm filter holders can be Partisol-Plus 2025 Sequential Sampler, stacked in a tube and inserted into the sample RFPS-0498-118,63 FR 18911,04/16/98 stream at preset intervals Rupprecht & Patashnick (R&P) Partisol Similar to R&P Partisol FRM 2000 samplers, but not meeting all PM-2.5 FRM specifications. The 2000 PM-10 Sampler, RFPS-0694-098,59 FR sampler consists of a hub unit with up to three 35338,7/1/94 satellite units for sequential sampling

TABLE 27-2. Continued Sampler, EPA FRM Designation, Federal Register Notice

Description

Thermo Environmental Instruments, Inc. (TET) 605 "CAPS" PM-2.5 Sampler, RFPS1098-123,63, FR 58036,10/29/98

Similar to the above FRM PM-2.5 samplers, but never made commercially available. Included here for completeness

URG Corp. (URG) URG-MASS100 Single PM-2.5 Sampler, RFPS-0400-135,65 FR 26603, 5/8/2000 URG-MASS300 Sequential PM-2.5 Sampler, RFPF-0400-136,65 FR 26603, 5/8/2000

Louvered PM-10 2.78 x lO^nrVs [16.7L/min] inlet with anodized aluminum sampling surfaces followed by WINS impactor with FRM Delrin 47 mm filter holders. Variable-speed diaphragm pump with volumetric flow measured by a dry test meter at the pump outlet that modulates pump speed. The sequential system contains a circular tray holding six filters. Filters are rotated into the sampling section at preset intervals and compressed into the flow stream with a mechanical plunger

Andersen/GMW (GRA) 1200 High-Volume PM-10 Sampler, RFPS-1287-063, 52, FR 45684,12/01/87,53 FR 1062,01/15/88 321-B High-Volume PM-10 Sampler, RFPS1287-064, 52 FR 45684,12/01/87, 53 FR 1062,01/15/88 321-C High-Volume PM-10 Sampler, RFPS1287-065, 52 FR 45684,12/01/87, 53 FR 1062,01/15/88

Andersen G1200 1.9 x 10-2m3/s [1130L/min] singlestage greased PM-10 inlet and 8 x 10 inch framed filter holder with steel screen support. Spun and sheet aluminum surfaces. Flow controlled by mass flowmeter that varies the speed of a blower motor. Also available with a critical throat, thus two designation numbers. The 321B and 321C were identical, but equipped with earlier versions of the G1200 that did not open for cleaning. These are no longer sold, but many are still in service. When the inlet is replaced with a peaked roof, this sampler is identical to the hi-vol TSP sampler that is widely used throughout the world

Andersen/GMW (GRA) SA241 and SA241M Dichotomous PM-10/PM-2.5 Sampler, RFPS-0789-073, 54 FR 31247,07/27/89

Flat top SA246B PM-10 inlet and dichotomous virtual impactor for 2.78 x 10~4m3/s [16.7L/min] flow rates. Made from machined aluminum. Flow rates are controlled by a differential pressure regulator and a single-speed diaphragm pump. Ten percent of the fine particles are sampled on the coarse particle filters, and corrections must be made to the coarse particle measurements to compensate

Oregon DEQ PM-10 Sequential Filter Sampler, RFPS-0389-071, 54 FR 12273, 03/24/89

SA245I PM-10 inlet at 1.9 x 10"3m3/s [113L/min] on top of a -0.4 m aluminum transfer tube that leads to a large removable plenum. 47 mm Nuclepore polycarbonate filter holders are located in quick disconnect fittings under the plenum. Solenoid valves beneath the plenum are switched by a timer for up to 12 sequential samples without operator intervention. Flow rates are controlled by a differential pressure regulator and a GAST 1022 or 1023 carbon vane vacuum pump with filtered exhaust

Wedding 600 (TEI) High-Volume PM-10 Sampler RFPS-1087-062,52 FR 37366, 10/06/88

Wedding IP-10 cyclonic flow inlet followed by an 8 x 10 inch filter frame with flow controlled by a critical throat before the blower fan. Larger capacity blowers are used owing to the larger pressure drop across the throat (continued)

TABLE 27-2. Continued Sampler, EPA FRM Designation, Federal Register Notice Federal Equivalent Methods (FEM) Met One (PAC) BAM 1020, GBAM 1020, BAM 1020-1, and GBAM 1020-1 PM-10 Beta Attenuation Monitors, EQPM-0798122,63 FR 41253,08/03/98

Description

Flat-top 246B style 2.78 x 10-4InVs [16.7L/min] PM-10 inlet, aluminum transfer tubes. Particles are collected on a quartz fiber filter tape each hour. The attenuation of electrons from a radioactive source is related to mass loading by calibration to athin metal sheet. Hourly PM-10 measurements are possible

Andersen (GRA) FH621-N PM-10 Beta Attenuation Monitor, EQPM-0990-076, 55 FR 38387,09/18/90

Similar in principle to Met One BAM, but with different radioactive electron source, tape advance unit, and data-processing system

Wedding (TEI) 650 PM-10 Beta Attenuation Monitor, EQPM-0391-081,56 FR 9216, 03/05/91

Similar in principle to the Met One unit, but using a Teflon membrane tape. No longer available but included for completeness

Rupprecht & Patashnick (R&P) TEOM Series 1400/140Oa PM-10 Monitors, EQPM1090-079, 55 FR 43406,10/29/90

Flat-top 2.78 x lO^nrVs [16.7L/min] PM-10 inlet. Aluminum inlet, copper transfer tube, and a heater (323 K [500C]) for PM-10 equivalence, but adjustable to other temperatures. 5 x 10"5 m3/s [3 L/min] of air is drawn through a 12.7 mm diameter quartz filter on a hollow glass tube that vibrates. The vibration frequency changes as the filter mass increases with aerosol deposit, and this is related to mass concentration. Five minute averages are attainable in ambient air. A bypass is available to collect the remaining 2.28 x lO^nrVs [13.7 L/min] of makeup air on a filter for other analyses

a

Reference and equivalent method requirements are specified in 40 CFR Part 50 and by the US. EPA (1987,1997b). FEMs are designated after collocated sampling with FRMs in several different environments.

3.3 x 10"4 to 1.9 x 10~3m3/s [20 to 113L/min] have been drawn through the filter packs, with a makeup flow supplying the difference to retain the inlet cut point. A Bendix 240 PM-2.5 replaces the SA254I inlet for PM-2.5 particle measurements. Chow et al. (1993) demonstrated how a variation of this sampler could be coated with PFA Teflon for sampling of reactive species. The /nteragency Monitoring of Protected Visual Environments (IMPROVE) network has operated at U.S. National Parks and Monuments since 1987 to provide a long-term database of chemical concentrations in support of regional visibility assessment. The IMPROVE sampler (Eldred et al., 1988) consists of different modules controlled by a common sample switching system. Each module can be tailored to a specific type of measurement. The standard configuration acquires one PM-IO sample for gravimetric and proton-induced X-ray emission (PIXE) elemental analyses and three PM-2.5 samples, one on a Teflon membrane for gravimetric and PIXE analyses, one on a nylon membrane for ion analysis, and one on a quartz fiber filter for carbon analysis. Up to four sequential samples can be taken without operator intervention by using a timer to switch solenoid valves. Flow rates are controlled by critical orifices. The initiation of a PM-2.5 speciation monitoring network by the U.S. EPA has resulted in several commercially available monitoring systems. Figure 27-2 shows a typical configuration

Teflon-coated louvered inlet 24 Umin

24 Umin

P M 2 5 cyclone

P M 2 5 cyclone

Manifold Channel 116.7 Umin

Teflon membrane filter Mass Gravimetry Elements XRF Nitrate, Sulfate IC Absorption Transmission Ammonium AC SolubleV.Cr, Mn, Fe, NI, Cu, Zn, As, Se ICP/MS

Channel 27.3 Umin

Quartz fiber filter Chloride, Nitrate, Sulfate IC Ammonium AC SolubleV.Cr, Mn, Fe. NI, Cu, Zn, As, Se ICP/MS Soluble Na', K* AAS Carbon Fractions TOR

Quartz fiber filter

Quartz fiber filter

Carbon Fractions TOR

Carbon Fractions TOR

Manifold Channel 3OUmin

Field blank

Channel 47.3 Umin

Channel 58.7 Umin

Channel 68 Umin

X A D organic carbon annular denuder No Analysis

Magnesium oxide H N O 3 annular denuder Nitric Acid IC

Citric a d d N H 3 annular denuder Ammonia AC

Quartz fiber filter

Quartz fiber filter

Quartz fiber filter

Carbon Fractions TOR

Chloride, Nitrate, Sulfate IC

Quartz fiber filter Carbon Fractions TOR

Sodium ch londe impregnated cellulose fiber filter Volatilized Nitrate /C

Ammonium

AC

Citric acid impregnated cellulose fiber filter Volatlzed Ammonium AC

Fig. 27-2. Example sample configuration and analyses for the AND PM-2.5 RAAS. AAS, atomic absorption spectrometry; AC, automated colorimetry; IC, ion chromatography; ICP/MS, inductively coupled plasma/mass spectrometry; TOR, thermal/optical reflectance; Transmission, light transmission; and XRF, X-ray fluorescence.

based on the Reference Ambient Air Sampler (RAAS) speciation monitor (GRA). The sampler pulls 8 x lO^nrVs [48L/min] through a louvered PM-IO inlet without impaction jets that removes very coarse particles to minimize maintenance of the PM-2.5 inlets. The flow is split and directed through two AIHL cyclone inlets into manifolds to which custom filter holders are attached. The filter holders are configured to contain up to three filters in series. Flow is controlled by critical orifices behind each filter with a vacuum created by a large rotary vane pump. The six available channels can be configured in different ways with flow rates varied by changing the orifices. All of the aluminum and stainless steel parts are coated with PTFE Teflon. Similar speciation monitors include the MASS and VAPS units from URG, Inc. (URG), the SASS from MetOne (PAC) and the Partisol 2300 and 2025 from Rupprecht & Patashnick, Inc. (R&P). Good examples of aerosol samplers assembled from existing parts for specific purposes are represented by the "BOSS" (Brigham Young University Organic Sampling System) series of samplers (Eatough et al., 1993, 1995, 1996; Tang et al., 1994; Cui et al., 1997, 1998). These samplers allow filters to be located before and after denuders to evaluate the difference between artifact vapor adsorption and adsorption of volatilized particles. The PCBOSS (Cui et al., 1998; Pang et al., 1998) uses the Harvard virtual impactor to concentrate particles for organic compound speciation. Annular denuders are used for inorganic species, while parallel plate denuders lined with carbon-impregnated filters are used for organic measurements. Several cascade impactors have been developed to partition PM-IO into smaller size ranges on substrates suitable for chemical analysis. Each of these impaction methods uses low pressure stages to attain the lower particle cut points. The Low Pressure Impactor (LPI) (Hering et al., 1979a,b), the Davis Rotating-drum Unit for Monitoring (DRUM) (Raabe

et al., 1988), the Berner impactor (Berner et al., 1979), and the Micro Orifice Uniform Deposit Impactor (MOUDI) (Marple et al., 1981) have been applied in ambient aerosol chemistry and visibility studies. The low flow rate (1.7 x 10~5m3/s [lL/min]) and nonuniform deposits, as well as substantial handling of the substrates, make the LPI impractical for many studies, though it was a precursor to the later impactors and provided valuable information for its time. The DRUM is a cascade impactor sampling at 5 x 10~4m3/s [30L/min] with low-pressure stages that acquires particle deposits in nine size ranges from 0.07 to 8.5 urn. Mylar impaction substrates are mounted on cylinders that rotate below the impaction jet so that particle deposits can be monitored as a function of time and analyzed by PIXE. The MOUDI and Berner cascade impactors are different in design but similar in function. These impactors operate at 5 x IQr4Tn3Is [30L/min] and have been used to acquire carbon and ion deposits on substrates that are amenable to chemical analysis by thermal evolution carbon analysis on aluminum foils and sulfate and nitrate analyses on Teflon substrates in at least eight size ranges from 0.03 to 15jnm. SELECTING A SAMPLING SYSTEM This chapter is not intended to identify every existing or every conceivable ambient aerosol sampling system. It concentrated on sampler components and sampler configurations in common use for which a body of knowledge and confidence have been established. Nevertheless, the practitioner is presented with many choices when confronted with the challenge of designing, installing, operating, and using the data from a sampling network to accomplish a specific purpose. The following steps can be used to select and implement a particle sampling system. The first step is to clearly define monitoring objectives (see Chapter 7). Five types of monitoring goals were given as examples in the first part of this chapter. The specific objectives should be as comprehensive as possible. For example, networks of PM-IO and PM-2.5 FRMs have been established for the immediate objective of determining compliance with NAAQS. If one or more of these samples exceeds a standard, then the objectives broaden to include source apportionment. Sampling that meets the first objective is typically inadequate for the source apportionment objective that requires a speciation monitor and chemical analysis. The second step is to determine the particle size fractions, chemical analyses, sampling frequencies, and sample durations needed to address the objectives. More frequent samples, or samples taken at remote locations, may require a sequential sampling feature. Shorter sample durations may require a larger flow rate to obtain an adequate sample deposit for analysis. The third step is to calculate the expected amount of deposit on the filter for each chemical species and compare it with typical detection limits for the analyses being considered. The types of analyses and size fractions desired affect the number of sampling ports and different filter media needed. Chow (1995a) specifies lower quantifiable limits for many analysis methods that can be translated into the needed aerosol deposit. Urban samples acquire adequate deposits for analysis with flow rates as low as -3.3 x 10"4HxVs [20L/min] for as low as 4h durations, while samples at nonurban sites may require larger flows and sample durations to obtain an adequate deposit. The analytical laboratory should be involved at the sampler design stage to ensure compatibility between sampling methods, analysis methods, filter media, and detection levels for the analytical methods. The fourth step is to create, adapt, or purchase a sampling system that provides the most cost-effective and reliable means of meeting the monitoring needs. It is also worthwhile to contact the authors of cited references for the noncommercial sampling systems because these investigators may be in a position to loan, rent, sell, or build one of these units to meet

a special need. If no existing unit can fulfill all of the requirements, then it will be necessary to assemble different sampling components into a new configuration that will meet those needs. The final step is to create or adapt an operating procedure that specifies methods and schedules for inlet cleaning, filter transport and handling, calibration and performance tests, and record keeping. Sampling systems described here have such written procedures that can serve as guides for specific procedures.

CONCLUSIONS Ambient aerosol sampling systems have evolved in order to meet different monitoring needs. Sampler inlets, monitoring surfaces, denuders, filter media, filter holders, flow monitors, flow movers, and operating procedures have been developed and tested. These components have been integrated into various sampler configurations for application in dozens of studies. Using these components and the lessons learned from previous studies, it is possible to develop sampling systems customized to meet specific objectives without extensive original development and testing.

ACKNOWLEDGMENTS The authors are grateful to Barbara Hinsvark and Steve Kohl of DRI for compiling data on different inlet and aerosol samplers. Fruitful discussions with Mr. Robert Gussman of BGI, Inc., on inlet testing and FRM development history and with Ms. Julie Morris of URG, Inc., on sampling surfaces and filter holders also contributed to this chapter. Norman Mankim of DRI assisted with the manuscript and references.

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TABLE 28-1. Generic Categories of Open Dust Sources

Unpaved travel surfaces Roads Parking lots and staging areas Storage piles Paved travel surfaces Streets and highways Parking lots and staging areas Exposed areas (wind erosion) Storage piles Bare ground areas Materials handling Batch drop (dumping) Continuous drop (conveyor transfer, stacking) Pushing (dozing, grading, scraping) Tilling

site characteristics that cause these variations may be grouped into (1) properties of the exposed surface material from which the dust originates and (2) measures of energy expended by machinery interacting with the surface. These site characteristics are further discussed below. Surface Material Texture and Moisture. The dry-particle size distribution of the exposed soil or surface material determines its susceptibility to mechanical entrainment. The upper size limit for particles that can become suspended has been estimated at about 75 urn in aerodynamic diameter (Cowherd et al., 1974). Conveniently, 75 (xm in physical diameter is also the smallest particle for which size analysis by dry sieving is practical (ASTM, 1984). Particles passing a 200-mesh screen (74 urn opening) on dry sieving are termed silt by highway officials. Note that for fugitive dust particles, the physical diameter and aerodynamic diameter are roughly equivalent because of the offsetting effects of higher density and irregular shape. Dust emissions are known to be strongly dependent on the moisture level of the mechanically disturbed material (Cowherd et al., 1974). Water acts as a dust suppressant by forming cohesive moisture films among the discrete grains of surface material. In turn, the moisture level depends on the moisture added by natural precipitation, the moisture removed by evaporation, and moisture movement beneath the surface. The evaporation rate depends on the degree of air movement over the surface, material texture and mineralogy, and the degree of compaction or crusting. The moisture-holding capacity of the air is also important, and it correlates strongly with the surface temperature. Vehicle traffic intensifies the drying process primarily by increasing air movement over the surface. Mechanical Equipment Characteristics. In addition to the material properties discussed above, it is clear that the physical and mechanical characteristics of materials handling and transport equipment also affect dust emission levels. For example, visual observation suggests (and field studies have confirmed) that vehicle emissions per unit of unpaved road length increase with increasing vehicle speed (Cowherd et al., 1974). For traffic on unpaved roads, studies have also shown positive correlations between emissions and (1) vehicle weight and (2) number of wheels per vehicle (Cowherd et al., 1979). Similarly, dust emissions from materials-handling operations have been found to increase with increasing wind speed and drop distance.

Windblown Dust Generation

Wind-generated emissions from open dust sources also exhibit a high degree of variability from one site to another, and emissions at any one site tend to fluctuate widely. The site characteristics that cause these variations may be grouped into (1) properties of the exposed surface material from which the dust originates and (2) measures of energy expended by wind interacting with the erodible surface. These site characteristics are further discussed below. Surface Material Texture and Moisture. As in the case of mechanical entrainment, the dry-particle size distribution of the exposed soil or surface material determines its susceptibility to wind erosion. Wind forces move soil particles by three transport modes: saltation, surface creep, and suspension. Saltation describes particles, ranging in diameter from about 75 to 500 urn, that are readily lifted from the surface and jump or bounce within a layer close to the air-surface interface. Particles transported by surface creep range in diameter from about 500 to 1000 um. These large particles move very close to the ground, propelled by wind stress and by the impact of small particles transported by saltation. Particles smaller than about 75 um in diameter move by suspension and tend to follow air motions. As stated above, the upper size limit of silt particles (75 urn in physical diameter) is roughly the smallest particle for which size analysis by dry sieving is practical. The threshold wind speed for the onset of saltation, which drives the wind erosion process, is also dependent on soil texture, with 100 to 150 urn particles having the lowest threshold speed. Saltation provides energy for the release of particles in the PM-10 size range that typically are bound by surface forces to larger clusters. Dust emissions from wind erosion are known to be strongly dependent on the moisture level of the erodible material (Woodruff and Siddoway, 1965). The mechanism of moisture mitigation is the same as that described above for mechanical entrainment. Nonerodible Elements. Nonerodible elements, such as clumps of grass or stones (larger than about 1 cm in diameter) on the surface, consume part of the shear stress of the wind that otherwise would be transferred to erodible soil. Surfaces impregnated with a large density of nonerodible elements behave as having a "limited reservoir" of erodible particles, even if the material protected by nonerodible elements is itself highly erodible. Windgenerated emissions from such surfaces decay sharply with time, as the particle reservoir is depleted. Surfaces covered by unbroken grass are virtually nonerodible. Crust Formation. Following the wetting of a soil or other surface material, fine particles will move to form a surface crust. The surface crust acts to hold in soil moisture and resist erosion. The degree of protection that is afforded by a soil crust to the underlying soil may be measured by the modulus of rupture and thickness of the crust (Cowherd et al., 1985). This modulus of rupture is roughly a measure of the hardness of the crust. Exposed soil that lacks a surface crust (e.g., a disturbed soil or a very sandy soil) is much more susceptible to wind erosion. Frequency of Mechanical Disturbance. Emissions generated by wind erosion are also dependent on the frequency of disturbance of the erodible surface. A disturbance is defined as an action that results in the exposure of fresh surface material. This would occur whenever a layer of aggregate material is either added to or removed from the surface. The disturbance of an exposed area may also result from the turning of surface material to a depth exceeding the size of the largest material present. Each time that a surface is disturbed, its erosion potential is increased by destroying the mitigative effects of crusts, vegetation, and friable nonerodible elements and by exposing new surface fines.

Wind Speed. Under high wind conditions that trigger wind erosion by exceeding the threshold velocity, the wind speed profile near the erodible surface is found to follow a logarithmic distribution (Gillette, 1978b): (28-1) where u is wind speed (cm/s), w* is friction velocity (cm/s), z is height above test surface (cm), Zo is roughness height (cm), and 0.4 = von Karman's constant (dimensionless). The friction velocity (w*) is a measure of wind shear stress on the erodible surface, as determined from the slope of the logarithmic velocity profile. The roughness height (z0) is a measure of the roughness of the exposed surface as determined from the y-intercept of the velocity profile (i.e., the height at which the wind speed is zero) on a logarithmic-linear graph. Agricultural scientists have established that total soil loss by continuous wind erosion of highly erodible fields is dependent roughly on the cube of wind speed above the threshold velocity (Woodruff and Siddoway, 1965). More recent work has shown that the loss of particles in suspension mode follows a similar dependence. Soils protected by nonerodible elements or crusts exhibit a weaker dependence of suspended particulate emissions on wind speed (Cowherd, 1988). Wind Gusts. Although mean atmospheric wind speeds may not be sufficient to initiate wind erosion from a particular "limited-reservoir" surface, wind gusts may quickly deplete a substantial portion of its erosion potential. In addition, because the erosion potential (mass of particles constituting the "limited reservoir") increases with increasing wind speed above the threshold velocity, estimated emissions should be related to the gusts of highest magnitude. The current meteorological variable that appropriately reflects the magnitude of wind gusts is the fastest 2min wind speed from the "First Order Summary of the Day," published by the U.S. Weather Service for first order meteorological stations (NEDS, 1992). The quantity represents the wind speed corresponding to the largest linear passage of wind movement during a 2min period. The duration of the fastest 2min wind speed matches well with the half-life (i.e., the time required to remove one-half the erodible particles on the surface) of the erosion process. It should be noted that instantaneous peak wind speeds can significantly exceed the fastest 2min wind speed. Because the threshold wind speed must be exceeded to trigger the possibility of substantial wind erosion, the dependence of erosion potential on wind speed cannot be represented by any simple linear function. For this reason, the use of an average wind speed to calculate an average emission rate is inappropriate. Wind Accessibility. If the erodible material lies on an exposed area with little penetration into the surface wind layer, then the material is uniformly accessible to the wind. If this is not the case, it is necessary to divide the erodible area into subareas representing different degrees of exposure to wind. For example, the results of physical modeling show that the frontal face of an elevated pile is exposed to surface wind speeds of the same order as the approach wind speed upwind of the pile at a height matching the top of the pile (BillingsStunder and Arya, 1988); on the other hand, the leeward face of the pile is exposed to much lower wind speeds. EMISSION CALCULATION PROCEDURE A calculation of the estimated emission rate for a given source requires data on source extent, uncontrolled emission factor, and control efficiency. The mathematical expression for this calculation is as follows:

(28-2) where R is estimated mass emission rate in the specified particle size range; M is source extent; e is uncontrolled emission factor in the specified particle size range, that is, mass of uncontrolled emissions per unit of source extent; and c is fractional efficiency of control. The source extent (or activity level) is the appropriate measure of source size or the level of activity that is used to scale the uncontrolled emission factor to the particular source in question. For process sources of fugitive particulate emissions, the source extent is usually the production rate (i.e., the mass of product per unit time). Similarly, the source extent of an open dust source entailing a batch or continuous drop operation is the rate of mass throughput. For other categories of open dust sources, the source extent is related to the area of the exposed surface that is disturbed by either wind or mechanical forces. In the case of wind erosion, the source extent is simply the area of erodible surface. For emissions generated by mechanical disturbance, the source extent is also the surface area (or volume) of the material from which the emissions emanate. For vehicle travel, the disturbed surface area is the travel length times the average daily traffic (ADT) count, with each vehicle having a disturbance width equal to the width of a travel lane. If an anthropogenic control measure (e.g., treating the surface with a chemical binder that forms an artificial crust) is applied to the source, the uncontrolled emission factor in Eq. 28-2 must be multiplied by an additional term to reflect the resulting fractional control. In broad terms, anthropogenic control measures can be considered as either continuous or periodic, as the following examples illustrate: Continuous Controls

Periodic Controls

Wet suppression at conveyor transfer points

Watering or chemical treatment of unpaved roads Enclosures/wind fences around storage piles Sweeping of paved travel surfaces Continuous vegetation of exposed areas Chemical stabilization of exposed areas The major difference between the two types of controls is related to the time dependency of performance. For continuous controls, efficiency is essentially constant with respect to time. On the other hand, the efficiency associated with periodic controls tends to decrease (decay) with time after application until the next application, at which time the cycle repeats but often with some residual effects from the previous application. To quantify the performance of a specific periodic control, two measures of control efficiency are required. The first is "instantaneous" control efficiency and is defined by (28-3) where c(t) is instantaneous control efficiency (percent), ec(t) is instantaneous emission factor for the controlled source, eu is uncontrolled emission factor, and t is time after control application. The other important measure of periodic control performance is average efficiency, (28-4)

where c{t) is instantaneous control efficiency at time t after application (percent) and T is time period over which the average control efficiency is referenced. The average control efficiency is needed to estimate the emission reductions due to periodic applications.

To estimate the emissions of a specific contaminant that is present within the disturbed surface material, the emission rate should be adjusted by the fraction of contaminant in the specified airborne-particle size range (Cowherd et al., 1985). However, as an approximation, the fraction of contaminant in the silt portion of the disturbed surface material may be used to adjust the uncontrolled emission factor and, in turn, the calculated emission rate, so that it applies to the contaminant of interest. EMISSION QUANTIFICATION TECHNIQUES Fugitive dust emission rates and particle size distributions are difficult to quantify because of the diffuse and variable nature of such sources and the wide range of particle sizes, including particles which deposit immediately adjacent to the source. Standard source testing methods, which are designed for application to confined flows under steady-state, forced-flow conditions, are not suitable for the measurement of fugitive emissions unless the plume can be drawn into a forced-flow system. The available source testing methods for fugitive dust sources are described in the following paragraphs. Mechanical Entrainment Processes Because it is usually impractical to enclose open dust sources or to capture the entire emissions plume, only two methods are suitable for the measurement of particulate emissions from most open dust sources: 1. The upwind-downwind method involves the measurement of upwind and downwind particulate concentrations, utilizing ground-based samplers under known meteorological conditions, followed by a calculation of the source strength (mass emission rate) with atmospheric dispersion equations (Kolnsberg, 1976). 2. The exposure-profiling method involves simultaneous, multipoint measurements of particulate concentration and wind speed over the effective cross section of the plume, followed by a calculation of the net particulate mass flux through integration of the plume profiles (Cowherd et al., 1974). In both cases it is customary to use high-volume air samplers so that quantifiable sample mass can be accumulated in sampling periods no longer than about 6 hours. Upwind-Downwind Method The upwind-downwind method involves the measurement of airborne particulate concentrations both upwind and downwind of the pollutant source. The number of upwind sampling instruments depends on the degree of isolation of the source operation of concern (i.e., the absence of interference from other sources upwind). Increasing the number of downwind instruments improves the reliability in determining the emission rate by providing better plume definition. To reasonably define the plume emanating from a point source, instruments need to be located at a minimum of two downwind distances and three crosswind distances. The same sampling requirements pertain to line sources except that measurements need not be made at multiple crosswind distances. Net downwind (i.e., downwind minus upwind) concentrations are used as input to atmospheric dispersion equations (normally of the Gaussian type) to back-calculate the particulate emission rate (i.e., source strength) required to generate the pollutant concentrations measured. Emission factors are obtained by dividing the calculated emission rate by the source extent. A number of meteorological parameters must be concurrently recorded for input to this dispersion equation. As a minimum, the wind direction and speed must be recorded on site.

While the upwind-downwind method is applicable to virtually all types of sources, it has significant limitations with regard to the development of source-specific emission factors. Because of the impracticality of adjusting the locations of the sampling array for shifts in wind direction during sampling, it may be questionable to assume that the plume position is fixed in the application of the dispersion model. In addition, the usual assumption that a line or area source is uniformly emitting may not allow for a realistic representation of spatial variation in source activity. Exposure-Profiling Method. As an alternative to conventional upwind-downwind sampling, the exposure-profiling technique utilizes the emission profiling concept, which is the basis for conventional ducted source testing (Method 5; U.S. EPA, 1977), except that, in the case of exposure-profiling, the ambient wind directs the plume to the sampling array. The passage of airborne particulate matter immediately downwind of the source is measured directly by means of a simultaneous, multipoint sampling of particulate concentration and wind velocity over the effective cross section of the fugitive emissions plume. For the measurement of nonbuoyant fugitive emissions using exposure profiling, sampling heads are distributed over a vertical network positioned just downwind (usually about 5 m) from the source. Particulate sampling heads should be symmetrically distributed over the concentrated portion of the plume containing at least 80% of the total mass flux. A vertical line grid of at least three samplers is sufficient for the measurement of emissions from line or moving point sources (Fig. 28-1), while a two-dimensional array of at least five samplers is required for quantification of the fixed virtual point source of emissions. For quantifying emissions of particles larger than about 10 um, the particulate samplers should have directional intakes, as discussed below. At least one upwind sampler must be operated to measure the background concentration, and wind speed and direction must be measured concurrently on site. The particulate emission rate is obtained by a spatial integration of the distributed measurements of exposure (accumulated mass flux), which is the product of mass concentration and wind speed: (28-5) where R is emission rate (g/s), C is net particulate concentration (g/m3), u is wind speed (m/s), h is vertical distance coordinate (m), w is lateral distance coordinate (m), and A is effective cross-sectional area of plume (m2).

Profiler Head Cyclone/lmpactor Anemometer Wind Vane

Fig. 28-1. Exposure profiling method—roadway.

Usually, a numerical integration scheme is used to calculate the emission rate. This massbalance calculation scheme requires no assumptions about plume dispersion phenomena. Isokinetic Sampling. Regardless of which method is used, isokinetic sampling is required for a representative collection of particles larger than about 10 um in aerodynamic diameter. The directional sampling intakes are pointed into the mean wind direction, and the intake velocity of each sampler is periodically adjusted (usually with intake nozzles) to closely match the mean wind velocity approaching the sampling intake. Because of natural fluctuations in wind speed and direction, some anisokinetic sampling effects will always be encountered. If the angle a between the mean wind direction and the direction of the sampling axis equals 30°, the sampling error is about 10% (Watson, 1954). For an isokinetic flow ratio of sampling intake speed to approach wind speed between 0.8 and 1.2 (Watson, 1954), the sampling error is about 5%. Wind Erosion

The two wind erosion source testing methods of interest are the upwind-downwind method as described above and the portable wind tunnel method. The wind tunnel method involves the use of a portable open-floored wind tunnel for in situ measurement of emissions from representative surfaces under predetermined wind conditions (Cuscino et al., 1983). Upwind-Downwind Method The upwind-downwind method is burdened with practical difficulties for the study of wind erosion in that the onset of erosion and its intensity are beyond the control of the investigator. In addition, background (upwind) particulate concentrations tend to be high during erosion events, making source isolation very difficult. Wind Tunnel Method The most common version of the wind tunnel method utilizes a pullthrough wind tunnel with an open-floored test section placed directly over the surface to be tested. A device of this type is shown in Figure 28-2. Air is drawn through the tunnel at controlled velocities. The exit air stream from the test section passes through a circular duct fitted with a directional sampling probe at the downstream end. Air is drawn isokinetically through the probe by a high-volume sampling train. The wind tunnel method incorporates the essential features of Method 5 stack sampling (U.S. EPA, 1977). The one prime difference, the use of single-point sampling, is justified by the high turbulence levels in the sampling module. The measurement uncertainty inherent in this method is of the same order as that in Method 5, which has been subjected to extensive collaborative testing by the EPA. The wind tunnel method relies on a straightforward mass-balance technique for the calculation of emission rate. By sampling under light ambient wind conditions, background interferences from upwind erosion sources can be avoided. Although a portable wind tunnel does not generate the larger scales of turbulent motion found in the atmosphere, the turbulent boundary layer formed within the tunnel simulates the smaller scales of atmospheric turbulence. It is the smaller scale turbulence that penetrates the wind flow in direct contact with the erodible surface and contributes to the particle entrainment mechanisms (Gillette, 1978a,b). Particle Sizing

Concurrent with the measurement of mass emissions, the aerodynamic particle size distribution should be characterized. Chemical, biological, and morphological analyses may also be performed to characterize the nature and origin of the particles. For particle sizing, a high-volume cyclone/cascade impactor featuring isokinetic sample collection has been used

Blower

Clutch Gasoline ' Engine

Flexible Hose

lsokinetic probe Cyco l ne precollector, Cascade m i pactor

High-volume sampler

Pressure gauges Screens Pitot tube port Contraction

Tail section 92-30 woot port wind tun 080692

Honeycomb FIg. 28-2. Portable open-floored wind tunnel equipment.

(Cowherd et al., 1986). A cyclone preseparator (or other device) is needed to remove the coarse particles, which otherwise would bounce off the greased substrate stages within the impactor, causing fine-particle bias. Once again, the sampling intake is pointed into the wind and the sampling velocity adjusted to the mean local wind speed by fitting the intake with a nozzle of appropriate size. This system offers the advantage of a direct determination of aerodynamic particle size. Another particle-sizing option includes an analysis of the particulate deposit by optical or electron microscopy. Disadvantages include (1) potential artificial disaggregation of particle clusters during sample preparation and (2) uncertainties in converting physical size data to equivalent aerodynamic diameters. In a collaborative field test of the exposure-profiling method, the cyclone/impactor method was judged to be more suitable than microscopy for the particle sizing of fugitive dust emissions (McCain et al., 1985). Control Efficiency Estimation

Field evaluation of the control efficiency requires that the study design include not only adequate emission measurement techniques but also a proven "control application plan." In the past, two major types of plans have been used. Under the Type 1 plan, controlled and uncontrolled emission measurements are obtained simultaneously. Under Type 2, uncontrolled tests are performed initially, followed by controlled tests. To ensure comparability between the operating characteristics of the controlled and uncontrolled sources, many evaluations are forced to employ Type 2 plans. An example would be a wet suppression system used on a primary crusher. One important exception to this, however, is unpaved-road dust control. In this instance, testing under a Type 1 plan may be conducted on two or more contiguous road segments. One segment is left untreated, and the others are treated with the dust suppressant. Under a Type 2 plan, a normalization of

emissions may be required to allow for potential differences in source characteristics during the uncontrolled and controlled tests because they do not occur simultaneously.

EMISSION MODELS Early in the U.S. EPA field testing program to develop emission factors for fugitive dust sources, it became evident that uncontrolled emissions within a single generic source category may vary over two (or more) orders of magnitude as a result of variations in source conditions (equipment characteristics, material properties, and climatic parameters). Therefore, it would not be feasible to represent an entire generic source category in terms of a single-valued emission factor, as traditionally used by the U.S. EPA to describe average emissions from a narrowly defined ducted source operation. In other words, it would take a large matrix of single-valued factors to adequately represent an entire generic fugitive dust source category. To account for emissions variability, therefore, the approach was taken that fugitive dust emission factors be constructed as mathematical equations for sources grouped by the dust generation mechanisms. The emission factor equation for each source category would contain multiplicative correction parameter terms that explain much of the variance in observed emission factor values on the basis of variances in specific source parameters. Such factors would be applicable to a wide range of source conditions, limited only by the extent of experimental verification. For example, the use of the silt content as a measure of the dust generation potential of a material acted on by the forces of wind or machinery proved to be an important step in extending the applicability of the emission factor equations to a wide variety of aggregate materials of industrial importance. A compendium of emission factors (referred to as AP-42) is maintained on a CD-ROM (Air CHIEF, 1998) by the U.S. EPA. Chapter 13 of AP-42 contains the predictive emission factor equations for fugitive dust sources. Also with each equation is provided a set of particle size multipliers for adjusting the calculated emission factors to specific particle size fractions. The ratios of PM-2.5 to PM-IO typically range from 0.15 to 0.25. Example: Vehicle Traffic on Unpaved Roads For the purpose of estimating uncontrolled emissions, the AP-42 emission factor equation applicable to vehicle traffic on unpaved roads takes source characteristics into consideration (MRI, 1998): (28-6) where s is surface material silt content (%), W is mean vehicle weight, Mg (ton), and M is surface material moisture content (%). The denominators in each of the multiplicative terms of the equation constitute normalizing default values, in case no site-specific correction parameter data are available. The default moisture content represents dry (worst-case) road conditions. Extrapolation to annual average uncontrolled emission estimates (including natural mitigation) is accomplished by assuming that emissions are occurring at the estimated rate on days without measurable precipitation and, conversely, are absent on days with measurable precipitation.

EMISSION CONTROL OPTIONS

Typically, there are several options for the control of fugitive particulate emissions from any given source. This is clear from Eq. 28-2, used to calculate the emission rate. Because the uncontrolled-emission rate is the product of the source extent and the uncontrolled-emission factor, a reduction in either of these two variables produces a proportional reduction in the uncontrolled-emission rate. In the case of open sources, the reduction in the uncontrolled-emission factor may be achieved by adjusted "work practices." The degree of the reduction of the uncontrolledemission factor can be estimated from the known dependence of the factor on source conditions that are subject to alteration. For open dust sources, this information is embodied in the predictive emission factor equations for fugitive dust sources as presented in Section 13 of AP-42. The reduction of source extent and the incorporation of adjusted work practices that reduce the amount of exposed dust-producing material are preventive measures for the control of fugitive dust emissions. Add-on controls can also be applied to reduce emissions by reducing the amount (areal extent) of dust-producing material, other than by clean-up operations. For example, the elimination of mud/dirt carryout onto paved roads at construction and demolition sites is a costeffective preventive measure. On the other hand, mitigative measures involve the periodic removal of dust-producing material. Examples of mitigative measures include clean up of spillage on travel surfaces (paved and unpaved) and clean up of material spillage at conveyor transfer points. Mitigative measures tend to be less favorable from a cost-effectiveness standpoint. Periodically applied control techniques for open dust sources begin to decay in efficiency almost immediately after implementation. The most extreme example of this is the watering of unpaved roads, where the efficiency decays from nearly 100% to 0% in a matter of hours (or minutes). On the other hand, the effects of chemical dust suppressants applied to unpaved roads may last for several months.

TABLE 28-2. Controls for Fugitive Dust Sources Source Category Paved roads

Unpaved roads

Storage piles (transfer operations) Construction/demolition

Open area wind erosion

Control Action Water flushing/sweeping Improvements in sanding/salting applications and materials Truck covering Prevention of track-on/wash-on Construction site measures Curb installation Shoulder stabilization Storm water drainage Paving Chemical stabilization Surface improvement (graveling) Vehicle speed reduction Wet suppression Paving permanent roads early in project Truck covering Access apron construction and cleaning Watering of graveled travel surfaces Revegetation Limitation of off-road vehicle traffic

Consequently, to describe the performance of most intermittent-control techniques for open dust sources, the "time-weighted average" control efficiency must be reported along with the time period over which the value applies. For continuous-control systems (e.g., wet suppression for continuous-drop materials transfer), a single control efficiency is usually appropriate. Table 28-2 lists fugitive dust control measures that have been judged to be generally cost-effective for application to metropolitan areas unable to meet PM-IO standards. The most highly developed performance models available apply to application of chemical suppressants on unpaved roads. These models relate the expected instantaneous control efficiency to the application parameters (application intensity and dilution ratio) and to the number of vehicle passes (rather than time) following the application. More details on available dust control measure performance and cost are presented by Cowherd et al. (1988) and Cowherd (1991). REFERENCES Air Chief. 1998. CD-ROM, Version 6.0, U.S. EPA. Research Triangle Park, NC, September 1998. ASTM. 1984. American Society of Testing and Materials, Subcommittee C09.03.05. Standard Method for Sieve Analysis of Fine and Coarse Aggregates. Method C-136, 84a. Philadelphia: ASTM. Billings-Stunder, B. J. and S. P. S. Arya. 1988. Windbreak effectiveness for storage pile fugitive dust control: A wind tunnel study. JAPCA 38:135-143. Cowherd, C. Jr. 1988. A refined scheme for calculation of wind generated PM-10 emissions from storage piles. In Proceedings: APCAJEPA Conference on PM-10: Implementation of Standards. San Francisco: U.S. EPA. Cowherd, C. Jr. 1991. Best Available Control Measures (BACM) for Fugitive Dust Sources. Revised Draft Guidance Document submitted to Air Quality Management Division. Research Triangle Park, NC: U.S. EPA. Cowherd, C, Jr., K. Axetell Jr., C. M. (Guenther) Maxwell, and G. A. Jutze. 1974. Development of Emission. Factors for Fugitive Dust Sources, EPA Publication No. EPA-450/3-74/037, NTIS Publication No. PB-238 262. Cowherd, C. Jr., R. Bohn, and T. Cuscino Jr. 1979. Iron and Steel Plant Open Source Fugitive Emission Evaluation, EPA-600/2-79/103, NTIS Publication No. PB-299 385. Cowherd, C. Jr., J. S. Kinsey, D. D. Wallace, M. A. Grelinger, T A. Cuscino, and R. M. Neulicht. 1986. Identification, Assessment, and Control of Fugitive Paniculate Emissions, EPA-600/8-86-023, NTIS Publication No. PB86-230083. Cowherd, C, Jr., G. E. Muleski, P. J. Englehart, and D. A. Gillette. 1985. Rapid Assessment of Exposure to Paniculate Emissions from Surface Contamination Sites, EPA/600/8-85/002. Washington, DC: U.S. EPA. Cowherd, C, Jr., G. E. Muleski, and J. S. Kinsey. 1988. Control of Open Fugitive Dust Sources, EPA 450/388-008. Research Triangle Park, NC: U.S. EPA. Cuscino, T, Jr., G. E. Muleski, and C. Cowherd Jr. 1983. Iron and Steel Plant Open Source Fugitive Emission Evaluation. EPA-600/2-83-110, NTIS Publication No. PB84-110568. Gillette, D. A. 1978a. A wind tunnel simulation of the erosion of the soil. Atmos. Environ. (Great Britain) 12:1735-1743. Gillete, D. A. 1978b. Tests with a portable wind tunnel for determining wind erosion threshold velocities. Atmos. Environ. 12:2309. Kolnsberg, H. J. 1976. Technical Manual for the Measurement of Fugitive Emissions: Upwind/Downwind Sampling Method for Industrial Fugitive Emissions. EPA-600/2-76-089a, NTIS Publication No. PB253092. McCain, J. D., B. E. PyIe, and R. C. McCrillis. 1985. Comparative study of open source particulate emission measurement techniques. In Proceedings of the Air Pollution Control Association Annual Meeting. Pittsburgh, PA: APCA.

MRI. 1998. Midwest Research Institute. Emission Factor Documentation for AP-42, Section 13.2.2, Unpaved Roads, Final Report. Kansas City, MO. NAAQS. 1990. CAA 109(b), 42 US.C. 7409(b) (1990), Public Law 101-549,15 November 1990. NEDS. 1992. National Environmental Data Service. Local Climatological Data Annual and Monthly Summaries. Asheville, NC: National Climatic Center. U.S. EPA. 1977. Standards of performance for new stationary sources, revision to reference methods 1-8. Federal Register. 18 August 1977, Part II. Watson, H. H. 1954. Errors due to anisokinetic sampling of aerosols. Am. Ind. Hyg. Assoc. Q. 15:21. Woodruff, N. P. and F. H. Siddoway. 1965. A wind erosion equation. Soil ScL Soc. Am. Proc. 29:602-608.

The focus here is primarily limited to assessments in nonoccupational settings. Occupational indoor microenvironments impose special requirements on aerosol characterization, which are discussed separately in Chapter 25. Outdoor aerosol sampling is important on its own as a regulated macroenvironment and is described in Chapter 27. The reader is cautioned to be aware of inadvertent biasing of nonoccupational air samples by occupational exposures (e.g., metal dusts resuspended into the breathing zone from work clothing worn while at home, as reported by Cohen et al., 1984). The ubiquitous contributions of ambient aerosol to indoor spaces and to integrated exposure assessments, however, also merits limited discussion in this chapter. The ability of outdoor aerosols to penetrate into indoor spaces is well documented (e.g., Alzona et al., 1979; Dockery and Spengler, 1989; Lioy et al., 1990; Wallace, 1996; Pellizzari et al., 1999a). A recent paper by Thornburg et al. (2001) demonstrated that the penetration process is complex and dependent on a number of factors, and it is a function of particle size. These latter considerations are especially important when attempting to understand the relationships between concentrations and exposures or between exposures and doses. The methodologies and equipment needed to measure both fixed-location indoor concentrations and personal exposures to aerosols are described in modest detail. A representative review of the relevant literature is provided, and specific references should be reviewed for additional details. The limited scale and empirical nature of current indoor air and research studies has not prompted a wide range of commercially available measurement options for nonoccupational exposure assessments. Although occupational personal exposure monitors (PEMs) are readily available and have occasionally been pressed into service, their weight and obtrusiveness are not always suitable for use in nonoccupational studies. This is especially true for exposure studies where the selected participants exhibit a wide range of ages and physical sizes and may be reluctant to carry burdensome samplers for extended periods.

CONCENTRATIONS VERSUS EXPOSURES The study of aerosols in nonoccupational indoor air, and especially the assessment of personal exposures, are relatively recent activities. Concentrations in outdoor and occupational microenvironments were studied extensively as important exposure predictors until the recognition of the relative importance of the time spent indoors (Ott, 1995) and the contributions of indoor aerosol sources to personal exposures (Wallace, 1996). A comprehensive summary of the key findings of recent nonoccupational indoor air and personal exposure studies, plus discussions of the factors most likely to contribute to elevated exposures, is provided by Wallace (1996). The observation that close human proximity to localized indoor sources (e.g., Sherwood, 1966; Rodes et al., 1991) could significantly bias exposure estimates made from fixed-location, residential concentration measurements by median factors as high as 3.3 also supported the move toward personal exposure assessments. The control of outdoor and occupational aerosol source emissions, and their resulting concentrations, have historically (e.g. Buckley et al., 1991; Ogden et al., 1993) been used to minimize adverse health effects. A risk paradigm can be used to illustrate the intervening steps in the relationship between toxic emissions from sources and adverse health effects (NRC, 1999): sources —> emissions —> concentrations —> exposures —> doses —» effects

(29-1)

While concentrations can be linked statistically to effects through epidemiology, the sequential, direct linkage across the paradigm in Eq. 29-1 requires an understanding of the intervening relationships, including those between concentrations and human exposures. The

transition from concentration measurements to the assessment of human exposure requires a shift in the strategies used to characterize aerosol, along with implicit requirements in the hardware technology. Relocating the sampler from a fixed location to a movable reference frame that follows the breathing zone location has proved to be a difficult transition for aerosol assessments. Significant improvements in the technology to provide acceptable data quality with relatively unobtrusive samplers have greatly facilitated occupational and indoor aerosol studies. Personal exposure assessments for the general population, however, require technologies with an even more stringent level of unobtrusiveness (quieter, smaller, lighter) while still maintaining the robust sampling volumes needed for subsequent sample analysis. Although many occupational settings are "indoors," the aerosol concentration levels and chemical compositions can be substantially different from those in residential settings. They can also pose conflicting study design constraints. The simplest approach is to avoid overlapping the occupational and nonoccupational assessments, employing the respective measurement strategies that are appropriate. When overlapping activity patterns cannot be avoided, separate integrated aerosol collections for each microenvironment or time interval can be considered if the individual sampled aerosol loadings are sufficient to provide the necessary analytical data quality. The constraints placed on aerosol exposure samplers, when considering children and the elderly, are more restrictive than those required for the general nonoccupational population. Children and the elderly have increasingly become the focus of aerosol exposure studies because of their identification as sensitive subpopulations with greater health risks (e.g., Liao et al., 1999). Compromises may be needed to study these populations, such as accepting the representativeness of area concentration measurements, in lieu of using unacceptably obtrusive personal monitors. The risks inherent in attempting to use obtrusive PEMs are the potential for substantial modifications of normal personal activity patterns and excessive drop-out rates during the study by the participants. The latter concern can affect the validity of population exposure studies in which participants are identified using a probability-based sample selection. The time-weighted contribution of indoor air to total personal exposure is substantial, given the typical preponderance of time spent in all indoor microenvironments. While the substantially larger collection volumes of outdoor samplers readily lend themselves to complex sampling and analysis strategies, unobtrusive personal samplers with very small sampled volumes are still technologically limited to either relatively simple approaches or modest minimum detection limits. The information base concerning aerosol character and concentrations in indoor air has grown steadily in the past two decades. Until 10 to 15 years ago, it was commonly believed that the general quality of indoor air was superior to that of the outdoor (ambient) air. However, even though ambient air quality may have improved for many pollutants, the indoor air quality for some compounds generated indoors (especially volatile organics) has not. The general decline in smoking during the past decade has significantly reduced the presence and mean concentrations of aerosols found indoors. Studies relating indoor aerosol concentrations and personal exposures to ambient concentrations have consistently shown that while indoor residential concentrations are generally lower than outdoors, personal aerosol exposures are significantly higher than fixed location concentrations of either (e.g., Alzona et al., 1979; Hayes, 1989; Rodes et al., 1991; Pellizzari et al., 1999b). This is especially true for particles <2.5 urn (PM-2.5), but less so for PM-IO, due to the self-generation of particles during personal activities, including the resuspension of particles greater than about lum while walking on carpeting (Thatcher and Lay ton, 1998). Changes in people's life styles have introduced new contaminants into the indoor environment, including synthetic fibers, residues from spray propellants, deodorants, pesticides, and combustion particles from kerosene space heaters. The average adult now spends 61 %

to 78% of the day indoors in their private residence (U.S. EPA, 1989; Jenkins et al., 1990) and at least 87% to 89% indoors including occupational microenvironments (Ott, 1995). It could be surmised that less active individuals, and especially the elderly and those with preexisting health problems, spend an even greater fraction of time indoors. Rodes et al. (2000) showed that the elderly in retirement centers spent as much as 95% of their time indoors. MEASUREMENT STRATEGIES Several questions are provided in Table 29-1 that can be reviewed to assist in developing the indoor or exposure aerosol study design and selecting the appropriate methodologies. Defining a clear understanding of the objectives of the study, the required data quality objectives, and the use of the collected data are essential in the selection of the sampling and analysis approaches and the development of the measurement strategy. The simplest indoor aerosol measurements are made using a sampling device in a fixed location. These devices are often referred to as microenvironmental monitors (MEMs) and can provide acceptable data for well-mixed spaces. After a location is chosen that is expected to represent a microenvironment, sampling is conducted for a defined integration period. If a sufficient number of indoor (and outdoor) microenvironments are also characterized, integrated exposures can be estimated using a simple time-weighted-average model. Wallace (1996) noted, however, that it was often difficult to reconcile the differences between such simple model estimates and the consistently higher aerosol concentrations found using personal samplers carried by subjects in the same microenvironments. It was concluded that personal proximity to localized sources was the most likely contributor to elevated personal exposure mass concentration. PEMs are small, self-contained, battery-powered sampling systems that can be carried by an individual to mimic the proximity of the breathing zone to local sources or spatial concentration gradients (Fig. 29-1). These relatively unobtrusive samplers are used to assess the

Inlet system

Size

Inlet near breathing zone

classifying inlet Filter holder

Pumping system

Flow controller Low-flow pump Pumping system

Cover with 'inlet holes Oiled frit impactor plate Filter holder base Exploded view

Battery pack 10 umCutpoint version

Optional AC adapter

(a)

(b)

(C)

Fig. 29-1. Schematic diagram of a personal aerosol exposure monitor, showing (A) individual wearing the monitoring system, (B) block diagram of personal exposure system components, and (C) Diagram of MSP Model 200 Personal Environmental Monitoring Impactor. (Adapted from U.S. Environmental Protection Agency, 1990.)

TABLE 29-1. Design Considerations for Indoor Air and Exposure Aerosol Sampling Studies

No.

Design Consideration

1.

How arefixed-location,indoor and outdoor aerosol concentration measurements related to personal exposures? What specific aerosol characteristics are expected (e.g., size distributions, presence of volatile components, etc.), and how will they affect the collection and analysis methodologies? Are specific cut point size ranges or sampling efficiency curve shapes required by regulations or conventions? What are the expected aerosol concentration levels, and are high and low concentrations equally important? What are the expected influences of spatial and temporal gradients in aerosol concentration and size distribution across the important microenvironments? What are the aerosol sources, their strengths, and their locations relative to the proposed sampling locations? Is identification of the aerosol source categories contributing to collected samples important? What are the ventilation characteristics of the indoor microenvironments or meteorology in the outdoors that may adversely affect aerosol sampling (e.g., turbulence levels, frequency of high winds, etc.)? What supplemental measurements may be useful to collect indoors to support IAQ aerosol modeling and to assist in linking concentrations to sources? Will speciation beyond mass be needed, will the sampler cut point influence the collected samples, and will multiple substrate collections be required to match the analysis methodology? Is the majority of the aerosol collection expected to occur in occupational microenvironments? Are viable biological aerosols a concern? Are there suitable locations within the microenvironments (fixed locations) to accommodate the monitors to minimize safety concerns and alteration of normal activities? Are suitably precise and unobtrusive personal monitors available for the desired measurement, especially for special populations such as the elderly or children? What are the biases that may affect the MEM and PEM measurements? Is there a sensitive focus population, and is it important to define the most exposed in the population?

2.

3. 4. 5. 6. 7. 8.

9. 10.

11. 12. 13.

14. 15. 16.

Section Heading Concentrations versus Exposures; Relating Indoor, Outdoor, and Personal Concentrations Measurement Strategies; Sampling and Analysis Methods Measurement Strategies Sampling and Analysis Methods Introduction Measurement Strategies Measurement Strategies Indoor Air Studies

Indoor Air Studies Sampling and Analysis Methods

See Chapter 25 See Chapter 24 Exposure Studies

Exposure Studies Sampling and Analysis Methods; Personal Aerosol Sampling Biases Exposure Studies; Focused Panel Studies

time-integrated exposure of an individual to aerosols, with hopefully minimal influence on normal activity patterns. Although sampling near the breathing zone is usually desirable, alternative locations (e.g., at the waist) may be acceptable for a chosen particle size range. An intercomparison study should be considered before the exposure study to demonstrate the absence of a bias (relative to the breathing zone) from using an alternative location. Although breathing zone sampling is considered ideal, certain situations may require resorting to alternative locations (e.g., sampling adults with a waistpack or children using a backpack sampler; Figs. 29-2 and 29-3).

Fig. 29-2. Personal exposure monitoring system with aerosol inlet mounted on the front of a waistpack (Reproduced by permission; Research Triangle Institute, Research Triangle Park, NC 27709).

Fig. 29-3. Personal Exposure Monitoring System with the Aerosol Inlet Mounted on a Backpack for a Study of Children (Reproduced by permission; Research Triangle Institute, Research Triangle Park, NC 27709).

Fig. 29-4. Microenvironmental exposure monitoring system used indoors with the aerosol inlet mounted on the front of a "bluff" body (Reproduced by permission; Research Triangle Institute, Research Triangle Park, NC 27709).

Because air exposure is defined to occur when an aerosol particle reaches the entrance to a person's nose or mouth (Lioy, 1990), the best measurements for the assessment of exposure to aerosols typically require a sampler inlet be placed within the breathing zone. When personal exposure assessments are not possible or cost effective, a microenvironment (e.g., a room, work area) can be selected and monitored using a MEM or fixed-location sampler as shown in Figure 29-4. These stationary samplers operate at varying distances from the subject's breathing zone and provide correlated but biased measurements compared with personal exposures. The aerosol dose that an individual receives in both industrial and residential settings is a combination of the exposure and specific elements of the breathing process (e.g., inhalation rate, tidal volume, respiratory physiology). The key exposure elements, include such factors as the aerosol size distribution, morphology, bulk or surface chemistry, and so forth. The aerodynamic size of aerosol particles provides a direct relationship to the deposition of such particles in the human respiratory system. This forms the most logical basis for selecting inlet sampling size cut points for health-based studies. Another important factor to consider is the distinct chemical compositions that may exist between aerosol size distribution

modes (Wilson and Suh, 1997). Selecting an inlet cut point that clearly separates distributional modes may be more important to the study objectives than mimicking deposition in portions of the human respiratory system. The respiratory system's particle deposition processes of impaction, settling, interception, and diffusion are best defined in terms of aerodynamic diameter (see Chapter 25). One or more of these removal processes may operate simultaneously. Depending on the location of particles in the individual's respiratory system, the action of the removal processes may produce different aerosol deposition patterns. Particles greater than about 50 um in diameter are considered inhalable (see Chapter 25) and capable of entering the respiratory system by crossing the oral/nasal entry plane. Particles greater than 10 urn in diameter are deposited on the ventilation pathway surfaces above the trachea, while fine particles less than ~2um in aerodynamic diameter are removed primarily in the gas-exchange (alveolar) region of the lungs. Integrated sampling with either PEM or MEM samplers can use inlets with selected cut points, such as those just described, that approximate exposures in specific size fractions. Individuals are exposed to ambient aerosols when they spend time outdoors; however, they may be exposed indoors due to the infiltration of ambient aerosols inside (Colome et al., 1982; Hayes, 1989). Vu Due and Favez (1981) found lead (Pb) particles indoors, for which 60% of the total mass was submicrometer-sized particles, and attributed it to automobile exhaust. Polycyclic aromatic hydrocarbons (PAHs) were also found on submicrometer-sized particles by these researchers and were attributed to the infiltration of combustion aerosols. In fact, PAH concentrations are often higher indoors than outdoors (Pellizzari et al., 1983; Wallace et al., 1986; Wallace, 1987; Chuang et al., 1987). The identification of an aerosol of ambient source origin for indoor and exposure samples has been studied for years (e.g., Alzona et al., 1979), but improvements in the methodologies are still considered an important research priority (NRC, 1999), especially for personal exposures. The commonality of significant components of both indoor- and outdoor-generated aerosol sometimes makes accurate source identification difficult, if not impossible. A potential assessment methodology is the application of ambient source apportionment to estimate the relative contributions of indoor and outdoor sources (Sexton and Hayward, 1987; Koutrakis et al., 1992). This powerful tool requires the establishment of singular measurement "fingerprints" for the potential indoor and outdoor source categories (a task requiring creative sampling and analysis schemes). The characteristic receptor sample chemistries are then related to these fingerprints through an apportionment model to estimate the relative contributions by source category. While ambient air samplers are sufficiently robust—a common flow rate is 2.78 x 10"4InVs [16.7L/min]—to collect the sample volumes and parallel collection substrates to define these fingerprints and receptor samples, indoor air and especially personal exposure samplers often operate at flow rates that are two to eight times lower. Space limitations (even using indoor MEM samplers) can also preclude the luxury of parallel sampling channels to collect supplemental aerosol size ranges and substrates (e.g., Teflon filters for mass, ions, metals, and pre-fired quartz filters for elemental and volatile carbon). SAMPLING AND ANALYSIS METHODS Aerosol exposure sampling methods can be generally separated into three groups: physical, chemical, and biological. Physical sampling methods provide information regarding particle size, number, morphology, and mass concentration. Chemical sampling methods provide information on elemental and chemical constituents, as well as phase distributions. These two groups can be further subdivided into integrated sample assessments and those that are made as near-real-time measurements on a continuous basis. Biological exposure sampling is

discussed in Chapter 24. For indoor and personal exposure measurements, the significant sampling and analysis factors must be overlaid on the physical size and flow rate limitation imposed by miniature personal aerosol exposure devices. Personal exposure devices also require consideration of the energy required (batteries) to operate the subcomponents, as well as the burden associated with their weight, bulk, and noise. Particle Sizing and Characterization

The types of sampling methods selected for indoor exposure measurements depend initially on the physical characteristics of the aerosol being studied, as described in Chapter 8. Compromises are often required for exposure measurements, however, due to inherent limitations, including allowable size, noise, and weight. Integrated sample collection typically utilizes a simple fractionating inlet to provide an upper cut point for the collected sample. Inlets used in indoor aerosol sampling are often similar to those for ambient air sampling (Chapter 27). Personal exposure sampling inlets, however, are severely limited in size and weight, which strongly influences their design. Similarly, continuous measurements of aerosol optical properties indoors use the same devices for monitoring ambient air (Chapter 14). Continuous personal exposure measures are typically limited to miniature single-channel nephelometers. Personal and microenvironmental inertial impactors, virtual impactors, and cyclones have been developed that are suitable for use in both indoor air and exposure studies. Some specific samplers were described by Turner et al. (1979), Rubow et al. (1985), Spengler et al. (1989), Dockery and Spengler (1981), McKenzie et al. (1982), Buckley et al. (1991), and Marple et al. (1988). A representative list of commercially available devices as of August 2000 are listed in Table 29-2. Inlets with specific cut points, such as 2.5 urn, have been developed for indoor sampling (Marple and McCormack, 1983; Marple et al., 1987, 1989; Buckley et al., 1991) that use impaction on oil-soaked or greased, sintered metal surfaces. Cascade impactors can also be used to develop the mass size distribution from which the contribution of specific size fractions can be estimated (Martonen et al., 1984). The measurement of respirable particles that would be deposited in the alveolar region of the lungs can be approximated by using an aerosol inlet with an impactor with a 2.5 um cut point. This size range is the PM-2.5 fraction described by the U.S. EPA (1997) and is often measured in outdoor settings. Different types of inlets may be required to sample various micro- and macroenvironments, especially the outdoor macroenvironment with its wide range of wind speeds and precipitation types. While outdoor ambient samplers may be required to meet regulated performance standards, they are often unacceptably large and noisy for indoor usage. The sampling performance of the MSP* Model 200 PM-2.5 personal aerosol inlet operating at 3.33 x 10"5m3/s [2L/min] outdoors is compared with the EPA ambient cut point requirements in Figure 29-5. This intercomparison suggests that the MSP inlet can be used for indoor and outdoor sampling, as well as PM-2.5 personal exposure assessment. Limited data are available on the size distribution of residential indoor aerosols or on the distribution of particles to which people are commonly exposed (Kamens et al., 1988; Owen et al., 1990). Generally, indoors one would expect a bimodal (fine, coarse) size distribution, similar to that found outdoors. Indoor fine particles (<2.5 urn) typically originate from condensation and nucleation processes, such as smoking, heating, cooking, and infiltration of ambient particles. Coarse particles (>2.5 um) can be inhalable and are generated from a wide variety of sources in the indoor environment. Mechanical (as opposed to chemical)

* See Appendix I for full manufacturer addressess referenced to the italicized three-letter codes.

TABLE 29-2. Representative Personal Exposure Monitor (PEM) and MicroEnvironmental Monitor (MEM) Components that are Commercially Available Type MEM indoor sampling systems

Ambient samplers Biological samplers PEM personal sampling inlets

PEM cascade impactor Occupational PEMs Personal sampling pumps

Denuders Ultrafine aerosols Aerosol nephelometers

Supplemental indoor environmental measurements

Name

Vendor Code6

AD&E Indoor Area Monitor (Harvard Impactor) MEM system MEM system MEM system

ADE

Up to 20L/min with 1.0,2.5, or 10 urn

SKC MSP RTI

Series 200 Impactors

MSP

Cyclones Respicon

BGI TSI

Spiral Inlet IOM Personal Samplers Model 290

SKC SKC URG MSP

AFC123 Air Check Escort Gilian-Sensidyne PEM system

BGI SKC MSA GIL RTF

Annular Denuder Indoor Sampling System P-Trak RAM

URG

2.5 or 10 um cutpoints lOL/min dual flow 2.5 and 10 um Up to 3 parallel channels; 2.5 um, 10 um, and inhalable; 2 or 4L/min See Chapter 27 See Chapter 24 2,4, or lOL/min for 1.0,2.5, or 10 um 1.7 to 2.21pm 3.1 L/min with simultaneous 2.5 um, 10 urn, and inhalable 2 L/min for 2.5 jam Total inhalable 4 L/min with 1.0,2.5, and 10 um 2 L/min, 8 stages, from 0.6 to 20 um See Chapter 25 0.5 to 4.5 L/min Various ranges Various ranges Various ranges 1 to 4 L/min; incorporates activity sensor 2,4 L/min flow, 2.5 um cut size

TSI MIE

Hand-held; 0.01 to 0.10 um range Active sampling

MIE TSI GRI TSI SOL INN

Passive sampling (no pump) 1.4 to 2.4 L/min Inlets for 2.5 and 10 um CO, CO2, Temp., RH Temp., RH, air velocity Temp., RH, air velocity

INN ALR

SF6; air exchange rate Temp., RH, air velocity

MINI-RAM Dust-Trak Aerosol spectrometer Q-Trak IAQ monitor MPM 50Oe Thermal Comfort Logger Air Tech Compuflow

Remarks

"Table entries represent only selected models; check with vendors for more complete information. RH, relative humidity. Appendix I contains vendor information indexed to codes. c The RTI system was not yet commercially available as of August 2000.

h

generation and resuspension of dust are constantly occurring because of all human activities. Penetration of outdoor coarse particles to the indoor environment occurs infrequently because these particles are easily lost to surfaces. Inlet testing may be necessary to measure the sampling efficiencies for the inlets in still air, at typical indoor air or walking velocities, and at various wind speeds, if used outdoors.

Collection Efficiency, %

MSP PEM data: University of Minnesota data 8/9/97 - V. A. Marple; private communication to C. E. Rodes PM25 FRM Requirements: EPA Fed. Rea. Vol. 62, No. 138, 7/18/97]

MSP 2.5 |j.m impactor MSP scalping stage EPA PM2 5 FRM

Aerodynamic Diameter, jam Fig. 29-5. Comparison of the sampling efficiences for the MSP Model 200 21pm, 2.5 urn inlet, compared with the EPA Federal Reference Method requirements for PM25 (Sources: Rodes, 1997; Rodes, 1999).

In evaluating the performance of personal sampler inlets, it is important to consider bluff body effects (Vincent and Gibson, 1981; Wiener and VanOsdell, 1990; Ingham and Yan, 1996). This effect occurs as a result of the changes in aerosol particle trajectories induced by the alterations in the flow stream trajectories around the human body immediately adjacent to the PEM inlet (also see Chapter 8). A potential bias in collection performance for a PEM can occur if the PEM is used as a fixed-location sampler without a form (simulating the body shape) immediately behind the inlet. The interaction of human activity and the flow field particle trajectories may further bias the measurement. The microenvironmental indoor sampler shown in Figure 29-4 illustrates the use of an aluminum, elliptical backing body behind the inlet to minimize this effect. For integrated methods, the characteristics of the filter media or impaction substrate become important. Chapter 9 provides a detailed discussion of the characteristics of filters and their particle capture mechanisms. Improper selection of the filter substrate can cause substantial biases in low-flow-rate indoor and personal exposure sampling systems. Teflon filters of 2 or 3 urn porosity are perhaps the most widely used substrates for indoor and personal exposure sampling and can be used for subsequent ion and XRF analyses. Lower porosity filters used for personal exposures are prone to clogging if the sampler collects heavy loadings of environmental tobacco smoke. While Teflon filters are generically susceptible to nitrate losses (Hering and Cass, 1999), the typically lower face velocities of personal exposure samplers may provide reduced losses compared with ambient samplers.

Continuous Monitoring

Only a limited number of miniature, portable aerosol spectrometers using optical nephelometry are currently commercially available (e.g., MiniRam [MIE] or the Dust Trak [TST]; see Table 29-2) to provide estimates of the particle concentrations on a nongravimetric basis. The conversion of photometer response to estimated mass concentration is provided by the manufacturer's internal calibrations against test aerosols, such as Arizona test dust (PTf). The ability of these calibrations to represent the aerosols across a variety of microenvironments, however, is not clear. A portable ultrafine aerosol monitor (P-Trak TSI; based on total condensation nuclei counting, see Chapter 19) is also available for making real-time indoor surveys. To characterize indoor air aerosols in even greater detail (e.g., count, surface area, and volume distributions), the measurement devices described in Chapter 13 can be used indoors, most of which are limited to fixed-location sampling because of their physical size, vibration sensitivity, or dependence on AC power. Optical particle counters (e.g., LAS-X, PMS), aerodynamic particle sizers (e.g., APS TSI; see Chapter 17), and ultrafine monitors (e.g., SMPS system, TSI; see Chapter 18) have been used in tandem in indoor studies to define the number distribution over a broad size. Merging and presentation of composite size distributions produced by different measurement techniques are complex tasks, however, and the reader is referred to Chapter 22 for additional information. Gravimetric, Elemental, and Chemical Analyses

An extensive discussion of aerosol sample analysis methods are provided in Chapter 11 and need not be repeated here. Most of the same sampling considerations are used by indoor and personal exposure samplers (e.g., filter types, archive concerns) as their larger ambient sampler relatives. Sampling residential indoor and personal exposure aerosols poses special analysis problems because of the relatively low concentrations of the pollutants sometimes present (often less than 10 to 20jig/m3) and the resulting small collections for low-flow-rate samplers. This is especially true of gravimetric analysis, which is limited to the minimum detection levels of currently available electronic balances. For example, applying gravimetric analyses to samples of less than -50 ug can result in poor precision because of the 2 to 5ug composite error imposed by the weighing process (Lawless and Rodes, 1999).* Rather than attempt collection of larger sample volumes, Lawless and Rodes (1999) addressed improvements in the gravimetric analysis process for personal exposure samples in order to improve the minimum detection levels. Serial inlet sampling systems have been developed in reduced scale versions for lower flow rates that sequentially combine inertial impactors with annular denuders and filter holders (sometimes stacked) to separate particles and gases (Koutrakis et al., 1989; Vossler et al., 1988). The phase distribution of species that are present as both particles and gases may be assessed with these devices. This can be especially important to accurately quantify nitrates (Hering and Cass, 1999) or the lighter semivolatile organic compounds (McDow and Huntzicker, 1990). Inlet denuder systems can also protect collected aerosols by removing potentially interactive vapors (e.g., removing ammonia to protect acid aerosol already deposited). The denuders can be used with either a MEM or, with some burden penalty, a PEM. A pre-separator, such as a cyclone or inertial impactor, is used to remove particles larger than 2.5 urn. The denuder removes gases of interest, and then an after-filter removes fine particles. The large particles, adsorbed gases, and fine particles can then be analyzed separately. *It is encouraging to note that the estimated gravimetric error inherent in gravimetric analyses reported in the 1993 edition of this text were twice as large as are now possible—the result of significant improvements in the methodologies.

Personal Aerosol Sampling Biases

A number of important factors have been identified that can significantly influence the precision and accuracy of personal exposure measurements. Lawless and Rodes (1999) discuss in detail the gravimetric analysis errors that can result in significant precision and accuracy problems for lightly loaded, personal exposure filters. Key suggestions were the application of computer control with an electronic balance to provide paired average weighings for each filter (pre- and post-weight) and minimal operator intervention that may inadvertently alter the balance zero. Baron (1998) discusses a number of issues biasing personal exposure sampling, including sealing problems and the advantages of conductive construction materials to minimize static charge problems. Willeke and Baron (1990) also discussed various aspects that might bias sampling results and included subsequent treatment options and pitfalls to minimize misinterpretation of collected data. Substrate handling and storage may cause loss of large particles from filter surfaces. The sampling media should remain in an airtight enclosure during shipping and handling to prevent nonsampled exposures. If the presence of semi-volatile organic carbon (SVOCs) is suspected, it is necessary to enhance their stability on the substrate through cold (<273K [<0°C]), dark storage because of chemical and photochemical reactivity and artifact production. Flow measurement and control are key factors in integrated sampling accuracy and precision. Changes in flow rate will alter the effective cut diameter, as well as change the amount of mass collected and the computed concentration. A personal sampler pumping system with no means of recording the flow continuously during the sampling period may experience periods of significant flow reduction, resulting from inlet obstruction (e.g., from overcoats). A robust flow control system may, however, consume as much power as the pumping system, significantly shortening battery life. Compromises may be required if longer sampling durations are necessary. Other Sampling Considerations

In addition to meeting the study objectives, the sampling integration period must be selected based on other specific factors, including the battery life (for personal samplers), the expected temporal patterns of the aerosol source emissions and human activity patterns, and the time required to acquire enough material for analysis without overloading the filters. Levels of particulate matter and SVOCs in indoor environments can vary substantially because of local, strong sources such as environmental tobacco smoke (ETS). If analytical sensitivity is sufficient, it is sometimes desirable to make separate measurements for sampling by daytime and nighttime periods to highlight the influence of personal activity sources and outdoor diurnal chemistry. The accuracy and precision of the measurements determine the level of researchers' confidence in the data collected. Collocated placement of instruments (for replicate samples) are used to estimate precision. Coefficients of variation (CVs) for ambient aerosol monitors meeting outdoor equivalency requirements (Code of Federal Regulations, 1990) range from 3% to 10% and those for indoor MEMs range from 3% to 15%. PEMs have poorer precision, primarily resulting from smaller sample collection and poorer flow controls, with CVs ranging from 5% to 25%. The accuracy of aerosol measurements can only be estimated by calibrating the instruments under controlled test atmosphere conditions, such as in wind tunnels. Accuracy is inferred from comparison with isokinetic nozzle measurements. There are no mandatory federal indoor air quality or exposure standards for nonoccupational aerosols in the United States. The current outdoor standards apply to PM-2.5 and PM10 aerosols in ambient air. The US EPAs national ambient air quality standard (NAAQS) for PM-IO is set at 50jig/m3 for a 24h annual average and at 150|ig/m3 for an individual 24 h daily limit.

When present, ETS is one of the most significant indoor aerosols to be addressed. Personal samplers must be capable of handling the elevated pressure drops that may occur on the filters, caused by elevated ETS aerosol deposits. Additionally, the complex mixture of vapor and aerosol phase compounds in ETS may interfere with some analytical schemes and result in volatilization losses after collection if not properly stored. ETS exposure studies have been performed by a number of investigators (e.g., Vaughan and Hammond, 1990). Repace and Lowrey (1983) estimated a mean exposure of 1.43mg/day, with a range of 0 to 14mg/day for a series of public buildings not randomly selected. The Harvard Six-Cities Study (Spengler et al., 1981; Samfield, 1985) found a mean increase in indoor PM-2.5 concentration of 13 |ig/m3 when one smoker was present. Several methods are available to apportion ETS from the bulk respirable suspended particles in indoor aerosol samples. The tests rely on marker species and metabolites to determine the degree of exposure to ETS. Nicotine is the most commonly used marker for ETS and is found almost exclusively in the vapor phase in air samples of ETS (Eatough et al., 1986; Hammond et al., 1987). Nicotine has a short halflife (30min), but is stable once frozen. A number of specific tests for nicotine in indoor air are available (Caka et al., 1990). Cotinine, a metabolite of nicotine with a long half-life (2Oh), can be measured in urine or saliva as a surrogate for aerosol exposure. Cotinine analyses are expensive, but specific, because cotinine is endogenously produced.

INDOOR AIR ASSESSMENTS Focused Studies Focused indoor aerosol studies are designed to better understand and evaluate selected physical parameters governing specific aspects of the generation of, distribution of, sampling of, and exposure to aerosols. The studies can be performed under controlled conditions in the laboratory or in field environments. Laboratory studies may include special studies to address aerosol source characteristics, sampler performance factors, aerosol distribution, and transport. Quality assurance studies may be required for sampler inlet validation and method standardization. Precision can be obtained from sampler intercomparison and collocational testing of inlets and sampling systems (Lioy et al., 1988). Estimation of aerosol sampling accuracy can only be made in controlled laboratory wind tunnel conditions. The physical differences between the measurements from PEM and MEM sampling can be studied using aerosols in a test chamber. While PEMs provide the best estimates of aerosol exposure, they often have a number of drawbacks, including poorer precision and sensitivity, anisokinetic sampling biases, and higher study costs. MEMs may provide a cost-effective substitute for personal exposures, especially in situations where the microenvironments are few and well mixed. Field studies are needed to assess aerosol concentrations and exposures in complex indoor, microenvironmental, and macroenvironmental conditions and to determine source contributions (source/receptor analyses). Although respiratory deposition of specific aerosols can be measured in the laboratory, actual human exposure to aerosols cannot be studied directly. PEMs and MEMs are used as surrogate samplers to approximate the level of particulate matter to which subjects are exposed. Each microenvironment in which the subject might be significantly exposed must be considered. Most early residential indoor air quality (IAQ) studies used only fixed-location microenvironmental measurements. Exposure studies also require the use of questionnaires, often referred to as survey instruments. Survey instruments are used to obtain information from the study participants, including information on activity patterns, source activities, ventilation system settings, and health responses. These questionnaires and activity logs should be pre-tested using focus

groups in laboratory situations and using pilot field tests, with participants from the population intended for study. In contrast with most laboratory or field studies, the success of human exposure assessment studies depends on the degree of cooperation and the performance of the subjects in the study. In standard industrial hygiene sampling, the hygienist closely monitors the participant. Typically, the participant in an occupational setting is engaged in repetitive, well-defined tasks. When studying exposure in residential settings, the investigator must be as unobtrusive as possible to minimize biases in participant activity patterns. Building Studies

The sick building syndrome (SBS) has been a key factor in promoting studies of IAQ and human exposure. SBS is specifically defined by the World Health Organization (1983) as symptoms from which building occupants suffer during the time they spend in the building that diminish when they leave the building. Some symptoms may have delayed onset and may occur after the occupant leaves the building; however, the symptoms diminish after he or she spends time away from the building. These conditions cannot be readily traced to specific pollutants within the building. Typical health responses include the following: irritation of eye, nose, and throat; dry mucous membranes and skin; erythema; mental fatigue; headache; airway infections; cough; hoarseness; wheezing; unspecific hypersensitivity reactions; nausea; and dizziness (Molhave, 1987). Tight building syndrome (TBS) has symptoms similar to SBS and is due to poor ventilation practices, causing thermal discomfort or extremes in humidity, often resulting from energy conservation measures. The National Institute for Occupational Safety and Health (NIOSH) Hazard Evaluation Program concluded that about one-third to one-half of sick buildings have this problem. At least 5% of the SBS and illnesses are due to biological aerosol contamination problems (Wallingford and Carpenter, 1986). Building-related illness (BRI) is defined as a discrete, identifiable disease or illness that can be traced to a specific pollutant or source within a building. Aerosols (especially bioaerosols) are undoubtedly a leading cause of SBS, in combination with ventilation deficiencies (Brief and Bernath, 1988; LaForce, 1984). Initial preparation for a sick building study is important. Discussions before sampling must be conducted with the occupants regarding the complaints and symptoms reported. An assessment must also be made of (1) the mitigation steps already taken to improve the IAQ and (2) the heating, ventilation, and air conditioning (HVAC) system operation and maintenance schedules. A thorough inspection of the building must be conducted inside and out, including the HVAC system. The following determinations must be made: the type of system; the configuration, location, and effectiveness of air handlers, chillers, cooling tower, filtration or particle-removal devices, air supply and air return ducts, and air circulation path; location of outside air intake vents (check for cross-contaminants) and thermostats; and the presence of moisture, standing water, or microbial contaminants in the HVAC system. The building must be surveyed for sources and strengths of suspected contaminants (e.g., tobacco smoke, combustion products), build up of pollutants from poor air ventilation/high occupant density (carbon dioxide monitoring can be used as a surrogate), insulation materials (fiberglass, urea-formaldehyde foam, asbestos), age and cleanliness of carpets and furnishings, radon decay products from soil or concrete blocks, construction materials or activities, aerosol cleaning products, pesticides, moisture (condensation on windows, walls), biological species (fungi or mold on walls or in closets or enclosed spaces), and the presence of unvented combustion heaters (kerosene heaters, unvented gas ranges). It is also desirable to determine if sociological or psychological factors have been introduced that would make personnel more receptive or responsive to sick building problems.

Relating Indoor, Outdoor, and Personal Concentrations

Rodes et al. (1991) suggested that strong spatial gradients, either indoors or outdoors, can cause substantial biases between MEM and PEM measurements. They also reported that there are lognormal relationships between microenvironmental and personal exposures when results are plotted as cumulative distributions (Fig. 29-6). The slopes of these relationships are hypothesized to be affected by a combination of source/receptor proximity and the spatial distribution of the sources. MEMs tend to underrepresent exposure when the aerosol being measured is being generated by personal activities. If the outside aerosol that infiltrates indoors is homogeneous, the indoor gradients are most likely due to the generation of particles indoors. A number of researchers have recently reported methodologies for relating integrated personal exposures with indoor and outdoor aerosol concentrations (e.g., Rojas-Bracho et al., 2000; Janssen et al., 2000; Williams et al., 1999). Both PM-2.5 and PM-10, personal samples were collected by Rojas-Bracho et al. (2000) using reduced flow rate, impactor-based inlets and a single pump (with a flow splitter) operating at 5.33 x 105m3/s (3.2L/min). A 0.1m elutriator channel was added to the inlets to reduce the unwanted collection of clothing fibers. Janssen et al. (2000) utilized miniature personal cyclones to provide PM-2.5 cut points operating at 6.66 x 105m3/s (4L/min), weighing 1.5 kg. They reported a sample failure rate of 38%, attributed to battery failures using the portable pumps. This highlights a current problem in personal aerosol exposure measurements because the available miniature pump technologies require compromises among the factors of battery life, noise level, and system weight. Williams et al. (1999) described using a single-channel, impactor-based PM-2.5 inlet system mounted on a vest with a total weight just under 1 kg. Battery problems were eliminated by

PEM / MEM Ratio

Stevens (1969) Fletcher and Johnson (1988) Parker etal. (1990) Lioyetal. (1990) EPA PTEAM data

Data median og 13.40 1.98 1.78 1.17 5.70 3.40 1.58 1.53 1.98 162

Cumulative % less than Fig. 29-6. Distributions of Personal exposure monitor (PEM) to microenvironmental monitor (MEM) ratio from literature data (Source: Rodes et al., 1991).

RTI indoor-outdoor data 2/3/99; range bars are std. devs.; corrected for sampling line losses Personal activities: particle re-suspension and opening doors

Inside/Outside Ratio

SMPS morning LAS-X morning LAS-X late evening

file m_ouo t2a

SMPS or LASX Mean Bin Size, jum

Fig. 29-7. Indoor-to-outdoor particle count ratios by size, using two different measurement devices (TSI SMPS and a PMS LAS-X); illustrating the influence of personal activities on particle resuspension from carpeting (Source: C. E. Rodes, 1999 unpulished data; Research Triangle Park, NC 27709).

using a low power drain pump/motor/flow controller combination and alkaline, rather than rechargeable, batteries. A more robust (but nongravimetric) approach to studying indoor-outdoor relationships involves simultaneous collections of particle count distributions in both microenvironments. This permits assessing the time history of ratios as a function of particle size, allowing direct comparison of periods with and without indoor sources (or participants) present. An example of this approach is shown in Figure 29-7 in which a TSI SMPS and a PMS LAS-X aerosol spectrometer were used in tandem to cover the particle size range from 0.01 to 2.5 urn (data from Rodes, 1999). The data illustrate the influence of personal activities on the larger particle sizes. To eliminate the need to have duplicate pairs of monitors collecting indoor and outdoor data simultaneously, a low-loss manifold system was used, operating on a 5min cycle. Note that the losses in this sampling manifold were of sufficient magnitude (and size dependent) to require correction. Indoor Source Characterization

With the exception of ETS, only limited data are available characterizing aerosol emissions from indoor sources. There are few standardized test protocols for determining indoor source emission factors. Studies have characterized unvented combustion sources and particles generated by home humidifiers, leakage rates of particles from vented combustion sources, and the deposition rates of particles on indoor surfaces (Highsmith et al., 1988; Girman et

al., 1982; Leaderer et al., 1990; Traynor et al., 1987; Traynor and Nitschke, 1984; Tu and Hinchliffe, 1983). Thatcher and Layton (1995) reported that resuspension of particles above 1 urn occurred readily by walking on carpeting, with the predominant larger sizes have a significant affect on the PM-IO fraction. Lioy et al. (1999) characterized the aerosol emissions as a function of particle size for several commercially available vacuum cleaners and found emissions in both the ultrafine and fine particle ranges. Associated Indoor Air Quality Measures

Indoor ventilation parameters are important for understanding (1) mixing and dilution of aerosol sources, (2) aerosol transport from sources to receptors, (3) the loss mechanisms that occur in both the physical structure and internally in the HVAC system, and (4) the penetration of ambient aerosol indoors. Whole-house ventilation rate is measured as air exchange rate (AER) in units of air changes per unit of time. It is governed by factors such as the tightness of the building, the HVAC system's design, and the use of windows and doors. Aerosols can be transported throughout the microenvironment via the HVAC system or by natural ventilation pathways. Outdoor aerosols are lost during penetration through the building "shell" to the indoors as a function of particle size. Once indoors, aerosols are lost by a variety of mechanisms to indoor surfaces. The collective loss process is often referred to as deposition losses. Particles are also removed by the HVAC system as a function of particle size, filtration efficiency, and operational duty cycle. Supplemental makeup air introduces outside aerosols to the indoor environment in many commercial HVAC systems and is an obvious source of ambient aerosols. Thornburg et al. (2001) suggest that variables associated with the HVAC system must be added to IAQ models to appropriately account for all sources and sinks indoors. Note that filtered air emitted from an HVAC vent will gradually reduce the indoor aerosol concentrations if no other contributing sources are present. Even with no personal activity sources, this removal is balanced against the continuous penetration of outdoor aerosols. Portable high-efficiency particulate air (HEPA) filtration devices are commonly used indoors to provide supplemental air cleaning. Offermann et al. (1985) tested the removal efficiencies as a function of particle size for several commercially available units and showed a substantial performance range, especially for ETS. The presence of supplemental filtration devices can significantly alter the expected indoor concentration levels, and their presence and operational schedules should be documented by questionnaires. Methods and instrumentation used to measure building tightness include tracer gases such as sulfur hexafluoride (SF6) and perfluorocarbons, blower doors (whole-house pressurization or component pressurization), and thermography (U.S. EPA, 1990). Measurement of the effectiveness of mechanical ventilation equipment and characterization of fluid flow can be made with hot wire/film anemometry, tracers, neutral-density bubbles, and smoke pencil testing (flow visualization). EXPOSURE STUDIES The critical elements in designing a personal aerosol exposure study are a thorough understanding of the purpose and objectives and the establishment of experimental hypotheses. The overall expense of most exposure studies may be minimally impacted by collecting supplemental measures that might greatly enhance the subsequent interpretation of the data. What population is being studied, and are there special burden considerations? Are especially susceptible populations, such as asthmatic children, of interest? What is the specific aerosol feature to be measured, and for what period of time? How many samples will be needed to define the upper 95th percentile?

If the objective is to examine exposure to combustion aerosols, using PM-2.5 inlets would gather samples that would be more representative of that exposure than using PM-IO or total suspended particulate. If the purpose of the exposure study is for subsequent risk analyses, will exposure—dose relationships be needed? Will an IAQ model be used to interrelate the study data? If so, what additional measurements (e.g., AER) are required to apply the model? When designing aerosol exposure studies, questions regarding whether to include or exclude smokers are especially important. Including smoking participants confounds the relationship between personal and microenvironmental data. The most significant personal exposure to aerosol particles of the smoker is undoubtedly the mainstream tobacco smoke. On the other hand, excluding smokers jeopardizes the representativeness of the study population because of possible cluster effects. Smokers are not uniformly distributed in the population, and other demographic biases are incorporated into the study by the exclusion of smokers. An exposure survey study may be used to assess the distribution of exposure of a population. Inferences to a larger population (or frame) can be made by using statistical probability sampling methods to select the study population. The survey and sampling instruments, methodologies, and protocols used in defined-focus/objective studies can be adapted for this type of study. The ingredients of exposure assessment include taking a representative probability sample, measuring pollutant concentrations, measuring body burden (not possible for generic aerosols without a regular and identifiable chemical constituency), and recording daily personal activities. The objectives of population exposure studies are to produce representative frequency distributions of the exposure of human beings to aerosols, to determine how exposures compare with existing regulatory standards or mitigation guidelines, to establish indoor/outdoor/personal relationships for aerosol exposure, and to determine the significant sources of aerosol exposure (Ott, 1985,1990; Wallace et al., 1986; Wallace, 1987). Each microenvironment encountered in daily exposures must be defined. Microenvironments typically consist of the home (with one or more cells), the workplace, the transportation vehicle, and the ambient environment near the home. Relationships between personal and microenvironmental sampling, spatial and temporal variance in aerosol concentrations, exposure and activity, and the frequency distribution of exposure must be determined for the test population (Ju and Spengler, 1981). Once exposure information has been collected, it can be used for risk assessment. Risk assessment requires determining the number of persons exposed, the sources and transport factors relating to the pollutant from its source to receptor (person or sampler), the exposure-related significance of sampling data, and the health effects of exposure. Risk assessment also requires an estimate of the population at risk. The risk assessment can then be used to help define the relationship of exposure to outdoor standards. Focused Panel Studies

Exposure studies are designed to meet many objectives. Some are collecting data simply to expand the exposure knowledge base, while others are conducted concurrently with health studies attempting to associate various health indicators with aerosol features. A number of studies are reported in the literature that collect data to better define nonoccupational aerosol exposures. Only recently have "panel" studies been reported, where an exposure study is conducted in parallel with collection of health data on a small selected population of subjects. Williams et al. (1999) described the design of an aerosol exposure panel study in a retirement center. The aerosol measurements included a combination of integrated 24 h sampling (personal, indoors, and outdoors) for PM-2.5, as well as several continuous monitors and size distribution analyzers to supplement the collected data. Bahadori et al. (1999) described the design of a multifaceted aerosol study focusing on data collection for

inhalation epidemiology, including a concurrent exposure assessment component. Lippmann et al. (1999) discussed a planned exposure study to define the contributions of aerosols originating both outdoors and indoors for asthmatic and chronic obstructive pulmonary disease study subjects. Rodes et al. (2001) summarized the personal aerosol exposure assessments across three panel studies of retirement center residents and provided estimates of the "personal cloud"* contributions that increased the personal exposures, and the suggested aerosol sources. Population Exposure Studies

Exposure studies that provide the ability to extrapolate the results to the general population are highly desirable but costly as the result of the participant selection process and typically large number of participants. The probability-based selection of participants is complex, hypothesis driven, and dependent on a robust participation rate that is partly a function of the obtrusiveness of the samplers and the survey instruments. Only a limited number of aerosol population studies have been reported in the literature, including those of Ozkaynak et al. (1990), Pellizzari et al. (1999b), Clayton et al. (1999), and Rotko et al. (2000). Participants in this type of study are selected from a known population, or frame, through a random draw. The frame must have a complete demographic evaluation. Random selection from the frame provides a probability sample. By this method, the participant data can later be used to make statistically valid inferences to the frame. Particular factors of interest in the population (e.g., smoking habits, vocation) can be used to stratify the sample. Weighting factors can then be applied so that specific subpopulations of interest can be represented in sufficient number to provide statistically significant approximations of exposure. The response rate during selection and the subsequent participation rate during sampling must be kept high so that the test population is randomly represented. Low respondent rates suggest that possible biases may have been introduced to the study and are most often caused by excessive study burden on the participants. The potential burdens of both the personal exposure samplers and the survey instruments must be carefully considered as part of the total burden on the study population. Personal aerosol sampling systems should have minimal weight and noise levels, while indoor samplers should also be unobtrusive (see next section). Survey materials—such as diaries, logs, and questionnaires—must be easy to understand and simple to complete to reduce their burden level. Collection of survey data is critical because the information provides the ability to interpret the exposure measurement data relative to sources and/or activity patterns. Thus, the combined burden of the sampling systems and the survey instruments on the study population must be considered carefully. Sampler Obtrusiveness and Burden

Indoor and personal exposure samplers can be obtrusive to individuals whose environments are being studied. The degree of allowable obtrusiveness and the participant burden are important factors in avoiding biasing normal activity patterns and in obtaining sustained participation by the study subjects. Obtrusiveness depends on a number of factors. For an indoor fixed-location sampler, this includes primarily the relative size of the sampler, relative to that of the microenvironment, and its noise level; sampler configuration (packaging, size, and weight); how the sampler is to be employed (fixed location or mobile); the environment in which it will be used (i.e., children or pets present); noise produced by the system relative to background noise level (for homes or offices, total instrument noise should be less than * The aerosol personal cloud is generally denned as the portion of the measured concentration that cannot be reconciled with a time-weighted concentration calculation that uses "% of time spent" data for each microenvironment taken from activity diaries and microenvironmental, fixed-location concentration measurements.

40dbA at 1 m to avoid nuisance; in bedrooms, instruments may need to be even quieter); conspicuousness (prefer dull color so as not to be interest-provoking); safety of operation (no sharp edges and tamperproof to prevent injury, sample bias, or destruction and to reduce liability); power requirements (battery operation is the least obtrusive); and operational independence (does not require technical maintenance during sampling period). For personal aerosol exposure samplers, it is important that the sampling package be comfortable to carry for extended periods and not obstruct routine activities. The personal exposure sampling system shown in Figure 29-2 is contained in a waistpack to minimize obtrusiveness and simplify its application for extended exposure periods (e.g., up to 7 days). The proper use of equipment depends on the participant's behavior. Minimizing requirements for the participant's time and minimizing interruptions in his or her daily activities (by judicious scheduling of field visits) further reduces participant burden and its resulting bias. It is imperative that the researcher compromise his or her desire to constantly monitor the participant's activities and the need to not significantly alter the participant's behavior. Outdoor, ambient aerosol samplers have often been used to characterize indoor microenvironments when less obtrusive samplers are not available with the desired capabilities. Although smaller MEM samplers used indoors can often be suitably designed to operate outdoors, using ambient monitors indoors imposes some additional considerations. In addition to the obvious obtrusiveness concerns, the following should be considered when using high-flow-rate ambient samplers indoors: (1) Provide an outside exhaust for pumps to reduce either the contributions of filtered air or pump emissions (e.g., carbon, copper, oil vapor) into the space; (2) account for the possible influence of the sampler on the AER if simultaneous sampling is conducted with several monitors; and (3) place pumps outdoors if possible to reduce the unnatural heat loads they may pose on the operation (and filtration) of the HVAC system.

MODELING Exposure Modeling

Personal exposure monitoring studies to estimate population exposure levels can be expensive and, in some cases, impractical. An alternative approach combines limited-scale measurement studies and existing data with an appropriate exposure model. The most widely used exposure model (e.g., Letz et al., 1984; Spengler et al., 1985) is the simple summation model of the measurements of interest (e.g., mass concentration for a specific aerosol size fraction) in each microenvironment, weighted by the time spent in each microenvironment. These relationships can be expressed as the simple summation of concentrations: (29-2) where E is mean exposure over N microenvironments,// is fraction of time spent in microenvironment /, and C1 is mean concentration in microenvironment /. A study participant would be required to keep a time-activity diary that would provide information on the fraction of time spent in each compartment. The mean concentration measurement would be obtained either from a PEM carried by the participant while he or she was in the microenvironment or, less desirably, from a fixed-location monitor positioned to gather readings considered representative of the microenvironment. As noted by Letz et al. (1984), the variance about the mean exposure can be estimated from the variances of the mean concentrations and exposure times. If these individual parameter variances are small relative to their means and the parameters are uncorrelated, Gauss' law of error propagation (see Bevington, 1969; Chapter 22) can be applied. The

mean exposures and variance estimates representing specific microenvironments can be catalogued and used subsequently to predict distributions of exposures for other combinations of activity patterns. Wilson et al. (2000) describe a more complex version of Eq. 29-1 that accounts the contributions of outdoor aerosol to personal exposures for each microenvironment. Compartmental Indoor Air Quality Modeling

The mean value of the aerosol mass concentration for a microenvironment can be estimated using compartmental models. These models are based on differential mass balances and assume that the contaminant accumulation in a room or compartment is equal to the sum of material entering or being formed, less that which is being lost. Analytical solutions are possible for the least complicated differential equations, although more complex situations typically use numerical methods to estimate the time-dependent or equilibrium concentration. Compartmental models compute an integrated average value that may not exist at any single point in the real microenvironment. Because aerosol properties typically vary by particle size, the accuracy of the compartmental averages is improved by application to specific size fractions. Several general compartmental models have been proposed for aerosols in indoor environments, including those of Offermann et al. (1989), Raunemaa et al. (1989), and Nazaroff and Cass (1989). These models account for a variety of situations, including polydisperse size distributions, transport between multiple microenvironments, coagulation, gas-phase reaction kinetics, and wall losses. The model of Offermann et al. (1989) was used to describe the relationship of the aerosol removal performance of air cleaners, as a function of particle diameter, and the mean room concentration. Raunemaa et al. (1989) noted that a remission term for indoor aerosols released after loss to surfaces should be included in compartmental models. They also noted that the size distributions of these resuspended particles differ from those during deposition. Nazaroff et al. (1989) applied the aerosol transport and fate model of Nazaroff and Cass (1990) to an estimate of aerosol soiling in museums from surface deposition. More recent compartmental models (Thatcher and Layton, 1995; Thornburg et al., 2001) have been applied to estimate the penetration of ambient particles into indoor microenvironments. The Thornburg et al. (2001) model is the most comprehensive and includes the loss mechanisms added by the HVAC system. The model was applied to calculate particle penetrations and the resultant indoor/outdoor (I/O) ratios for a range of sizes for structures with and without HVAC systems. It was determined that I/O ratios are a maximum (-40% to 60%) for ambient particles in the 0.3 to 0.5 um size range for all structure types. The I/O ratios for particles above about 5um were significantly lower, ranging from 0 to 40%, with the lowest values resulting from the removal mechanisms in the HVAC systems. REFERENCES Alzona, I, B. L. Cohen, H. Rudolph, H. N. Jow, and J. O. Frohliger. 1979. Indoor-outdoor relationships for airborne particulate matter of outdoor origin. Atmos. Environ. 11:55-60. Bahadori, T., M. Van Loy, and P. Saxena. 1999. Paper 295P. In Proceedings of the Joint ISEE/ISEA Annual Conference, Athens, Greece. Boston: International Society for Exposure Analysis, p. S107. Baron, P. A. 1998. Personal Aerosol Sampler design: A review. Appl Occup. Environ. Hyg. 13:313-320. Bevington, P. R. 1969. Data Reduction and Error Analysis for the Physical Sciences. New York:

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Owen, M. K., D. S. Ensor, L. S. Hovis, W. G. Tlicker, and L. E. Sparks. 1990. Particle size distributions for an office aerosol. Aerosol ScL Technol 13:486-492. Ott, W. R. 1985. Total human exposure. Environ. ScL Technol 19:880-886. Ott, W. R. 1995. Human exposure assessment: The birth of a new science. JEAEE 5:449-472. Ozkaynak, H., J. D. Spengler, C. A. Clayton, E. Pellizzari, and R. W. Wiener. 1990. Personal exposure to particulate matter: Findings from the particle total exposure assessment methodology (PTEAM) prepilot study. In Proceedings of the 5th International Conference on Indoor Air Quality and Climate, Vol. 2, ed. D. S. Walkinshaw. Ottawa: Canada Mortgage and Housing Corporation, pp. 571-576. Pellizzari, E. D., C. A. Clayton, C. E. Rodes, R. E. Mason, L. L. Piper, B. Fort, G. Pfeifer, and D. Lynam. 1999a. Particulate matter and manganese exposures in Toronto, Canada. Atmos. Environ. 33:721-734. Pellizzari, E. D., T. D. Hartwell, C. Leininger, H. Zelon, S. Williams, J. Breen, and L. Wallace. 1983. Human exposure to vapor-phase halogenated hydrocarbons: Fixed-site vs. personal exposure. In Proceedings from the National Symposium on Recent Advances in Pollutant Monitoring of Ambient Air and Stationary Sources. Pub. No. EPA/600/9-83/007. Research Triangle Park, NC: U.S. Environmental Protection Agency, pp. 264-288. Pellizzari, E. D., R. L. Perritt, and C. A. Clayton. 1999b. National Human Exposure Assessment survey: Exploratory survey of exposure among population subgroups in EPA region V. JEAEE 9(1): 49-55. Raunemaa, T, M. Kulmala, H. Saari, M. Olin, and M. H. Kulmala. 1989. Indoor air aerosol model: Transport indoors and deposition of fine and coarse particles. Aerosol ScL Technol. 22:11-25. Repace, J. L. and A. H. Lowrey. 1980. Indoor air pollution, tobacco smoke and public health. Science 208:464-472. Rodes, C. E. 1997. Unpublished data, Research Triangle Institute, Research Triangle Park, NC 27709; from private communication with V. A. Marple, University of Minnesota, Minneapolis, MN. Rodes, C. E. 1999. Unpublished data, Research Triangle Institute, Research Triangle Park, NC 27709; partially supported by U. S. Environmental Agency Contract 68-D5-0040, WA041, Research Triangle Park, NC 27711. Rodes, C. E., R. M. Kamens, and R. W. Wiener. 1991. The significance and characteristics of the personal activity cloud on exposure assessment measurements for indoor contaminants. Indoor Air 2: 123-145. Rodes, C. E., P. A. Lawless, G. F. Evans, L. S. Sheldon, R. W. Williams, A. F. Vette, J. P. Creason, and D. Walsh. 2001. The relationships between personal PM exposures for elderly populations and indoor and outdoor concentrations for three retirement center scenarios. JEAEE 11:103-105. Rojas-Bracho, L., H. Suh, and P. Koutrakis. 2000. Relationships among personal, indoor, and outdoor fine and coarse particle concentrations for individuals with COPD. JEAEE 10:294-306. Rotko, T, L. Oglesby, N. Kunzli, and M. Jantunen. 2000. Population sampling in European air pollution exposure study, EXPOLIS: Comparisons between the cities and representativeness of the samples. JEAEE 10:355-364. Rubow, K. L., V. A. Marple, J. Olin, and M. A. McCawley. 1985. A Personal Impactor: Design, Evaluation, and Calibration. Pub. No. 469. Minneapolis: University of Minnesota, Particle Technology Laboratory. Samfield, M. 1985. Importance of cigarette sidestream smoke—Further aspects. Tabak J. Int. 6:448. Sexton, K. and S. B. Hayward. 1987. Source apportionment of indoor air pollution. Atmos. Environ. 21(2):407-418. Sherwood, R. J. 1966. On the interpretation of air sampling for radioactive particles. Am. Ind. Hyg. Assoc. J. 27:98-109. Spengler, J. D., D. W Dockery, W. A. Turner, J. M. Wolfson, and B. G. Ferris, Jr. 1981. Long-term measurements of respirable sulfates and particulates inside and outside homes. Atmos. Environ. 15:23-30. Spengler, X, H. Ozkaynak, J. Ludwig, G. Allen, E. Pellizzari, and R. Wiener. 1989. Personal exposures to particulate matter: Instrumentation and methodologies (P-TEAM), In Proceedings of the 1989 EPAJAWMA [American Waste Management Association] Symposium on Measurement of Toxic and Related Air Pollutants, eds. R. K. M. Jayanty and S. Hochheiser. Pub. No. VIP-13. Pittsburgh: AWMA, pp. 449-463.

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mental Panel on Climate Change, 1994). Satellite validation studies have helped determined the accuracy of satellite measurements and data retrieval processes. OBJECTIVES TO BE ACHIEVED IN AIRBORNE AEROSOL SAMPLING AND MEASUREMENT Airborne instruments are used to characterize all the properties of aerosol that are studied in ground-based measurements. The investigators' objectives for the airborne measurements are often the same as those motivating ground-based measurements. Most instruments or samplers accept air streams that are moving at much smaller velocities than the aircraft platform. The pressure at which the samplers or instruments operate may nearly equal the stagnation pressure rather than the ambient static pressure. Therefore, it is convenient to characterize the aerosol in terms of mixing ratios rather than concentrations. Mixing ratios are defined as the ratio of a given aerosol property to the mass of air sampled. The mixing ratios of aerosol properties will remain unchanged as air density changes as long as the particles follow the streamlines and the size of the sampled particles remains unchanged. A sample is representative of the ambient aerosol if its aerosol properties expressed in terms of mixing ratios are the same as those found in the free air stream. The systems that extract a sample from the air stream and deliver it to instruments are judged by different criteria depending on the objectives of the measurements. In some investigations, inlets are expected to provide a representative sample of the ambient aerosol for characterization by procedures that determine integral properties of the size distribution without performing size-discriminated selection on the sampled aerosol. Examples include determination of aerosol mass by weighing of filter samples and measurement of lightscattering coefficients by use of a nephelometer. In such cases, the integral property is determined without determining the details of the size distributions, and size-dependent corrections are impossible. It is not always possible to achieve representative sampling. The sampling and transmission efficiencies of the inlet and transport systems are a function particle of size, and the sampled particles may suffer significant size-dependent losses or enhancements. If the efficiencies are known and the size distribution is measured, or if size-segregated samples are acquired, then the ambient properties describing each size range can be calculated by dividing the measured results by the corresponding sampling-system efficiencies. Not all sampling systems are designed to achieve representative sampling. For example, virtual impactors can be incorporated into the sampling system to improve the counting statistics for large particles or to provide sufficient mass to permit chemical analysis of sizesegregated samples (Porter et al., 1992), and some inlets intentionally enhance particles. The ambient mixing ratios must again be calculated from the measured ones using the quantitative description of the impactor or inlet performance. Inlets have also been used to separate particles from the flow in order to deliver nearly particle-free air to gas analyzers. The objective is to measure mixing ratios of gases such as water or nitric acid in the gas phase. If the particles were not removed from the air in the inlet, they would vaporize and confound the determination of gas phase mixing ratios. The separation of the phases depends on particle inertia, and small particles may follow the sampled streamlines and not be removed. To study the composition of particles, particle inertia has been used to remove particles from the ambient gas stream and to deposit them in another gas of known composition. Inlets that accomplish this separation perform like virtual impactors. To avoid partial volatilization and loss of particle mass, the inertial separation must occur before the particles are heated significantly by ram heating or by heat transfer from the instrument. Following that separa-

SIZING PHOTODIODE/ PRE-AMP MASKED APERTURE (ACCEPTANCE) PHOTODIODE/ PRE-AMP

633nm

COLLECTiNQ OBJECTIVE F1.8 55mm

SPIKE FILTER (Optional)

RIGHT ANQLE PRISM

50% BEAM SPLITTER DUMP SPOT

SCATTERING PHOTODETECTOR MODULE

PARTICLE PLANE

CONDENSING LENS REFERENCE PHOTODIODE

„ 45* ANQLE HE-NE HYBRID LASER

FIg. 30-1. Optical diagram of the forward-scattering spectrometer probe (FSSP). The particle plane is in the free stream. (Courtesy of PMS,)

tion, the particles may be analyzed using aerosol techniques or vaporized to permit analysis with gas samplers. In either case, the composition of the gas phase in the free stream is not permitted to contaminate the sampled particles. Small particles may escape the separation and not be sampled. AIRBORNE AEROSOL MEASUREMENT TECHNIQUES Aerosol Measurements Made Outside the Aircraft Bringing particles into the aircraft for measurement frequently causes significant heating and often alters their size and composition. Therefore, it is often desirable to measure the particles in the air stream to the extent possible. Three manufacturers market instruments capable of measuring particles without bringing a sample into the aircraft (DMT, PMI, and SFE).* PMI markets some probes that were formerly sold by PMI. These measurements are easier to make on large particles than small ones, and most of these instruments are most effective on supermicrometer aerosol and even larger cloud particles. The PMI forward-scattering spectrometer probe (FSSP; Fig. 30-1) measures the optical diameter of single aerosol particles having diameters larger than about 0.4 um in the air stream in real time. The instrument case is constructed so that the measuring volume is outside the aircraft and is surrounded with a cylindrical housing aligned with the air flow. The objective of this design is to perturb the air stream and particles as little as possible. Particles pass through an HeNe laser beam and the forward-scattered light is measured to determine particle size. The concentration of particles is determined from the air speed and the size of the viewing volume. Aerosol spectrometers of this type collect light scattered in the near-forward direction. They often have a multivalued response over a portion of the size range that they measure. Discussions of aerosol and cloud measurements with the FSSP may * See Appendix I for full manufacturer addresses referenced to the italicized three-letter codes.

be found in Knollenberg (1981), Pinnick et al. (1981), Baumgardner (1984), Cerni (1983), Dye and Baumgardner (1984), Baumgardner et al. (1985), Brenguier and Amodei (1989), and Brenguier (1989). An instrument capable of measuring scattering into multiple angles by particles in the free stream has been developed. Information about the angular scattering can be used to determine the refractive index of the particles (Baumgardner et al., 1996). It is also possible to acquire images of larger particles in the free stream. These images provide information on shape and size and are of particular interest in cloud and precipitation studies. Imaging probes are available from the three companies listed above.

Instruments Requiring Sampling and Transport of Aerosols

Many instruments used on the ground have been used or adapted for use aboard aircraft and require the user to provide an inlet. These instruments include condensation nuclei counters (CNCs), optical particle counters (OPCs), differential mobility analyzers (DMAs), diffusion batteries (DBs), aerodynamic particle sizers (APSs), electrostatic precipitators, nephelometers, optical extinction cells, aethelometers, impactors, quartz crystal microbalances, and filtration devices (Woods and Chuan, 1983; Hering, 1987; Dreiling and Jaenicke, 1988; Leaitch et al., 1989; Brock et al., 1990). The PMI PCASP is an OPC with its own inlet and capable of sensing submicrometer particles. It is available in an aircraft-mountable package (Strapp et al., 1992; Liu et al., 1992). When aerosol instruments designed for use at 1 atm pressure are operated at reduced pressures, the assumptions and approximations used in the design of the instruments may no longer be valid. For example, Heintzenberg and Ogren (1985) recommend that the TSI Model 3020 CNC not be used at pressures less than -250 mb. Noone and Hansson (1990) have calibrated the TSI Model 3760 CNC at various pressures and find that it also functions at pressures down to 250 mb. At higher altitudes and lower pressures, other designs are required (Wilson et al., 1983). Impact or performance depends on Reynolds number as well as Stokes number, and impactor performance can degrade with altitude. Aerosol jets formed by inlets in optical counters may change shape as pressure falls, causing particles to miss the beam. The precision of counting depends on the number counted, and small aerosol concentrations are usually encountered at higher altitudes. Thus, normal, terrestrial sample flow rates may provide inadequate counting statistics in the free troposphere or stratosphere. Investigators are strongly cautioned to calibrate their instruments at the pressures to be encountered and to evaluate the response of instruments to the concentrations to be sampled. The U.S. National Aeronautics and Space Administration (NASA), the U.S. Office of Naval Research (ONR), and the National Center for Atmospheric Research (NCAR) have programs that encourage development or use of instruments capable of operating on conventional or uninhabited aircraft (UAVs). The Center for Interdisciplinary Remotely Piloted Aircraft Studies (CIRPAS) is operated by the ONR and located at the Naval Post Graduate School. The Environmental Research Aircraft and Sensor Technology (ERAST) program is operated by NASA. The High Performance Instrumented Airborne Platform for Environmental Research (HIAPER) initiative is being conducted by NCAR. Because of the increasing complexity of research payloads, programs like these encourage development of autonomous aerosol instruments able to operate on aircraft and to meet space, weight, and strict airworthiness requirements. A sampling of such instruments includes scanning, radial differential mobility analyzers (Russell et al., 1996), aerosol mass spectrometers able to provide composition information on individual particles (Murphy et al., 1998), and aerosol spectrometers able to determine aerosol size distributions at stratospheric concentrations in the diameter ranges of 4 to 50 nm (Brock et al., 2000) and 90 to 2000nm (Jonsson et al, 1995).

EFFECTS COMPLICATING AEROSOL SAMPLING FROM AIRCRAFT Alteration of the Size Distribution by Disturbance of Air due to the Aircraft

Several experimental and theoretical studies have examined the effects of the air flow distortion on airborne measurements (Beard, 1983; Baumgardner, 1984; King, 1984, 1986a,b; Drummond, 1984; Drummond and MacPherson, 1984,1985; MacPherson and Baumgardner, 1988; Norment, 1988; Twohy and Rogers, 1993). Convergence and divergence of streamlines around the fuselage, wings, and instrument pods can lead to areas of enhanced particle concentrations as well as zones where concentrations are greatly reduced. These effects result from the fact that particles have significant inertia and may not be able to follow the flow streamlines. The effects are more significant for particles with large Stokes numbers. Thus, although samples are drawn from beyond the aircraft's boundary layer, they may not contain a sample that is representative of the particles in the free stream. Rather, if the stream lines are bent upstream of the sample point due to the aircraft or probe, the sampled concentration may differ from that in the free stream. The effects vary substantially from aircraft to aircraft depending on fuselage shape and diameter, wing thickness and aspect ratio, placement of the engines and propellers, and the location of appendages and of the sample point. Sampling locations thought to minimize effects of air flow distortion include below the center of each wing, on the lower aircraft fuselage, on the upper fuselage ahead of the trailing edge of the wings but well behind the cockpit, and extending into the air stream ahead of the aircraft nose. These locations minimize the influences of wing tip vortices and the distorted flow created by the nose and windshield areas on the fuselage. Less preferable locations include wing tip, near-wing tip, forward, dorsal fuselage mounts, and regions of propwash and engine exhaust. The area aft of the wing along the side of and over the top of the fuselage is a region that may be swept by horseshoe vortices produced at the juncture of the wing root and fuselage. This area may also present unexpected flows and sampling difficulties. Aircraft are often electrified with respect to the air. Laboratory studies suggest that sharpedged inlets should not be located at points on the airframe where electrical discharges are likely (Romay et al., 1996). Following studies of sampling on propeller aircraft, it has been argued that the effects of airflow distortion are limited to particles larger than approximately 10 um. However, aircraft such as the NASA ER-2, the NASA DC-8, the proposed NCAR HIAPER, and passenger jets reach speeds in excess of 200 m/s and often operate at higher altitudes. Under these conditions, aerodynamic distortion of streamlines upstream of sample points can affect sampling efficiencies of particles having diameters smaller than 10 um. Anecdotal information suggests that upwind interactions with the fuselage or protuberances on the surface of the aircraft can remove submicrometer particles from the sample. Wings bend upstream streamlines and induce an upwash velocity and angle of attack. These effects can be estimated and taken into account in designing orientation of probes located on the wings or wing pods (Soderman et al., 1991). Some published studies involve detailed flow modeling for specific aircraft. Some models use supercomputers, and such models may not be available for every installation. Studies of the impact of the flow around the fuselage or wing tanks can be done with simplified geometries and commercial programs such as FLUENT (FLT) that run on personal computers. Such models can provide an indication of what size particles are likely to be affected by the passage of the fuselage or of a wing tank through the sampled air. Effects of Sensor Housings on Airflow

The sensor housing may also distort the airflow. The airflow effects of the PMI FSSP and OAP cloud and precipitation probes have been studied in some detail (MacPherson, 1985;

King, 1986b; MacPherson and Baumgardner, 1988; Norment, 1988). As an example, the FSSP sensing volume is housed within a cylindrical tube. Based on detailed numerical simulation of the air flow around this tube, its support arms, and the FSSP nacelle itself (Norment, 1988), the instrument was found to undercount particles with dp > 20urn by -10%. Norment (1988) also found that approximately half of the flux and speed distortions at the point of measurement were caused by the presence of the sensor housing. Additionally, Norment pointed out that distortions induced by the aircraft and those caused by the sensor interact synergistically and thus should not be evaluated independently. Anisokinetic Sampling

When the velocity of the air entering the inlet is different from the velocity upstream of the inlet, streamlines bend upwind of the sampling inlet, and particle mixing ratios entering the inlet can differ from ambient mixing ratios. This condition is referred to as anisokinetic sampling (see Chapter 8). Anisokinetic sampling results in size-dependent biases in the sample. Investigators routinely correct for the effects of anisokinetic sampling. Such corrections require knowledge of the free stream velocity, the flows entering the inlet, and the size and density of particles whose mixing ratio is being corrected. Claims of isokinetic sampling can be verified if the sample flow and free stream velocity are known. A number of the inlets described in this chapter are intentionally anisokinetic. Slowing the flow outside of the inlet with shrouds or restricted ducts causes streamlines to bend upstream of the inlet and leads to enhancements for large particles. These inlets may not provide a representative sample for all particle sizes. Inertial Effects and Turbulent Deposition in the Inlet

Some inlets use diffusers whose role is to slow the flow and the particles. The relaxation time for a unit density 10 um particle is approximately 3 x 10"4S at the surface of the earth. If an aircraft is traveling at 100 m/s, that particle will have a stopping distance of 3 cm (see Chapter 4). This dimension is significant compared with the distances over which streamlines diverge in practical diffusing inlets. Thus, concentrations of particles in this size range are likely to be enhanced near the center of flows in diffusing inlets. The stopping distance for particles with diameters near 1 urn is smaller by a factor of -100 at 1 atm pressure, and these particles will not suffer as much enhancement near the center of the flow. This inertial redistribution of the aerosol may cause the samples to be nonrepresentative in cases where the diffuser flow is not ingested by a single instrument. If only part of the diffuser flow is delivered to the instrument or if the flow is split among several instruments, larger particles may not be accurately sampled by any or all of the instruments. Diffusers generate turbulence as well as slow the flow. The turbulence will act to redistribute the particles in the flow and to deposit particles on the wall of the diffuser (Huebert et al., 1990; Sheridan and Norton, 1998). These studies have showen that as much as 50% of the mass can end up on the wall and that the effect is particle size dependent, with supermicrometer particles suffering larger losses than submicrometer particles. Standard turbulence models and particle motion models may fail to accurately predict both the inertial effects on the spatial distribution of particles at the outlet of the diffuser and turbulent losses in the diffuser. The equations that are usually solved in turbulence calculations represent the mean flow, and all the scales of the turbulence are modeled. The user can vary five parameters in standard models, and the calculation of particle motion is likewise modeled (Fluent manual, FLT). Parameters may be chosen that produce desired results in a given situation, but, because the fundamental physics of fluid and particle motion are not captured in the models, use of the these parameters in other situations is not guaranteed to produce accurate calculations. Therefore, even if a model produces accurate particle deposi-

tion rates for a given flow and geometry, the same choice of parameters may fail in another geometry or flow regime. The situation is different for calculations of laminar flow and particle trajectories in laminar flow. The accuracy of the laminar calculations may be limited by availability of computing resources, but the theory that is applied does not suffer any fundamental limitations. Calculations of particle motion in laminar flow have been successful in explaining the performance of inertial impactors (see Chapter 10), the losses in transport around bends and effects of anisokinetic sampling (see Chapter 8), and the performance of the TSI Aerodynamic Particle Sizer (see Chapter 17). Misalignment of the Inlet with the Free-Stream Flow

Misalignment of the inlet with the air stream of a few degrees can lead to separation and turbulence generation near the tip of sharp-edged inlets (Soderman et al., 1991). Turbulence can lead to particle deposition and alteration of particle mixing ratios. Misaligned inlets also cause the streamlines to bend before entering the inlet. If particles fail to follow the streamlines, then the sample will be altered (see Chapter 8). It is often difficult to know the precise direction of the local wind vector at the sampling location. Numerical modeling, wind tunnel testing, and in-flight measurements can be used to examine the air flow field around specific inlet locations on research aircraft. The angle of attack of the aircraft may vary depending on altitude, rate of descent or climb, aircraft weight, true air speed, and the presence of turbulence, while aircraft yaw may vary with wind speed, turn coordination, and so forth. It is possible to measure the wind vector at a particular location on the aircraft with an instrumented probe such as the 858 Flow Angle Sensor (ROS). The wind vector should be characterized for all anticipated sampling conditions. A number of investigators have devised and tested shrouded inlets to align the flow with the inlets (see below). The shrouds have been shown to straighten the flow before it impinges on the inlet. Proper shroud design takes the inertia of particles into account. There is an upper limit on the diameter of particles whose trajectories can be straightened by a given shroud (Twohy, 1998). Inlets with an elliptical leading edge profile have been shown to be more resistant to turbulence generation in the inlet as the angle between the inlet and the flow changed (Soderman et al., 1991). A blunt leading edge increases the surface presented to the flow, and some area inside of the forwardmost contour is occupied by solid material. The definition of isokinetic sampling is less clear in this instance. If the flow velocity in the throat of the inlet is set equal to the free stream velocity, then streamlines are forced to curve around the blunt leading edge in order to flow around the inlet. Then particles will certainly strike the inlet and may shatter (Webber et al., 1998). If the flow is set so that the stagnation point is poised on the forwardmost contour, then air entering the inlet will have been forced to flow around the curve and particles may be lost by impaction and may also shatter. Rader and Marple (1988) calculate the enhancements for a tube with a wall of finite thickness presented to the flow and show that representative sampling is possible for some flow ratios. Solid-walled, diffusing inlets generate turbulence. There is no evidence suggesting that this turbulence can be eliminated by careful alignment of the inlet axis with the free stream or by use of a shroud. Losses in Transport to the Instruments

Flows carrying aerosol samples from external inlets to the instruments inside aircraft often experience bends and can be turbulent. Estimates of particle losses in transport can be made using the techniques in Chapter 8. Care should be taken in this analysis because the assumptions underlying the techniques in Chapter 8 may not correspond with the actual sampling

situation. For example, the spatial distribution of particles entering the bend may not be uniform, or the flow may not be fully developed in the sampling situation. However, in the studies used to produce the relationships in Chapter 8, the flows were usually well developed and the particles were usually distributed uniformly. Despite this, the analysis may still provide important guidance concerning the size range of particles that experience significant sampling errors (see Chapter 2: Aerosol Calculator). The most prudent application of this type of analysis is in the design of transport systems and not in the correction of measurements. Inertial effects often result in a rather steep curve of transmission efficiency versus aerodynamic diameter. It is preferable to design the transport system so that the transition from high transmission efficiency to low efficiency occurs for particle diameters somewhat larger than those of interest. Gravitational sedimentation must also be considered in studies of supermicrometer particles. As in the case of losses in diffusers, computational fluid mechanics (CFD) codes can be used to calculate particle deposition in transport to the instruments. However, the cautions stated above concerning calculation of turbulent deposition apply here as well. Alteration of Particles by Thermodynamic Changes in Sampling or Transport to the Instruments Particles are often heated on the way from the inlet to the instrument. The heating due to slowing of the flow is unavoidable and can be estimated from the Mach number of the freestream flow relative to the aircraft. Heating of the transported aerosol by conduction from the warm cabin may be avoided in some instances. However, the instrument often heats the aerosol sample before the measurement. The heating can volatilize part or all of sampled particles, and changes can occur within fractions of a second. In some cases, it is possible to reconstruct the ambient size distribution from the measured one if the composition and evaporation rates of the particles are understood (Wilson et al., 1992; Hermann et al., 2000). In other cases, it may be impossible to determine the volatile components from the ones that survived to the measurement.

REVIEW OF INLETS Table 30-1 lists several inlets and selected characteristics including references. A number of investigators operate inlets that are not described in the literature. Inlets 1,2, and 3 employ diffusers to slow the flow. Inlets 2 and 3 have blunt leading edges. The inlets are designed to achieve isokinetic sampling. Inlet 1 provides near isokinetic sample flows for Mach numbers between 0.4 and 0.8 without use of a pump or flow control system. A tube near the center line of the main diffuser extracts the sample and transports it to the instruments. A second diffuser on the sample extraction tube reduces the sample velocity to ~10m/s. All flows are measured, and corrections are made for anisokinetic sampling at both the main and sample extraction diffuser. Although the flow in the diffuser of inlet 1 is turbulent, comparisons with LIDAR and SAGE II satellite extinctions show that the inlet provides accurate samples for submicrometer particles in cases where the number distribution is dominated by particles smaller than 0.3 urn and larger than 0.7 urn in diameter. Transmission of aerosol particles was studied in inlet 3 in the laboratory using Reynolds, Stokes, and Schmidt similarity. Inlet 3 provides representative samples of submicrometer aerosol. Approximately 50% of the 1.3 um particles are delivered to the sampling device. Inlets 4 and 5 use shrouded diffusers. Figure 30-2 shows inlet 4, which is similar to inlet 5. The flow out the rear of the shroud is restricted, and the velocity of the air impinging on the entrance of the diffuser is much less than the free-stream velocity. Thus the streamlines upstream of the intake diverge, and these inlets are designed to be anisokinetic. Wind tunnel

TABLE 30-1. Inlet Characteristics

Inlet No. 1 2 3 4 5 6

7 8 9 10 11 12 13

Characteristics

True Air Speed, Altitude

Sharp-edged diffuser Blunt-edged diffuser Blunt-edged diffuser Shrouded diffuser Shrouded diffuser

A passive, instrumented, near-isokinetic inlet A near-isokinetic inlet with a velocity reduced by a factor of 16 Intended to be isokinetic

200 m/s 20 km 60 m/s 3.5 km 235 m/s 12 km 15 m/s

Wilson et al. (1992), Jonsson et al. (1995) Pena et al. (1977)

100 m/s 6.1km

Counterflow virtual impactor (CVI) Shrouded CVI

Separates large particles and droplets from the atmosphere and deposits them in a gas of known composition for analysis Shroud straightens flow up stream of the CVI. Shroud does not slow flow Shroud straightens flow and provides high-speed sample to the capillary Anisokinetic inlet. Turbulence is prevented from disturbing the slowed sample flow Low turbulence in the diffuser. Elliptical leading edge and isokinetic operation

Ram et al. (1995), Cain and Ram (1998), Cain et al. (1998) Noone et al. (1988), Laucks and Twohy (1998)

Classification

Shrouded capillary Nonseparating, double duct Boundary-layer suction diffuser Inertial aerosol separator

Tolerates variations in angle of attack. Anisokinetic Tolerates variations in angle of attack. Anisokinetic

Large particles are unable to follow curved streamlines upstream of a sampling inlet Variable geometry permits the inlet to Gas/particle operate either as a CVI or an inertial separating aerosol separator inlet Trans-sonic and Solid wall diffusers supersonic inlets

100 m/s

References

Hermann et al. (2000) McFarland et al. (1989)

250 m/s

Twohy (1998)

200 m/s 20 km 200 m/s 20 km

Murphy and Schein (1998) Soderman et al. (1991)

100 m/s 6 km

Lafleur et al. (2000)

200 m/s 20 km

Fahey et al. (1989)

200 m/s 20 km

Dhaniyala et al. (2000) Martone et al. (1980), Me et al. (1990)

Free stream U0 Shroud

Probe

Sample flow EIg. 30-2. Inlet number 4 is a shrouded, diffusing inlet. The sample flow is brought into the aircraft with a pump. The flow through the shroud is slowed by the restriction caused by the probe, and the velocity entering the probe is smaller than the free stream velocity. (After McFarland et al., 1989.)

studies of inlet 4 demonstrated transmission ratios between 0.93 and 1.11 for wind speeds of less than 15m/s and for particles as large as lOjum. Wind tunnel tests demonstrated the ability of the shroud in inlet 5 to straighten the flow. The investigators initially proposed this inlet for study of insoluble aerosol because the suggested collection method involves washing the inlet after sampling to recover particles deposited by turbulence on the diffuser wall. Application of corrections for enhancements due to anisokinetic sampling requires knowledge of particle size. Calculations of transmission efficiency of particles through the turbulent diffuser are presented for users who do not wish to wash down the inlet.These results are subject to the limitations of such calculations discussed above. The counterflow virtual impactor (CVI), inlets 6 and 7, separates large particles from the air stream and deposits them in a flow of controlled composition. Gas is forced through the porous material near the front of the probe and is pulled back to the sampling instruments (Fig. 30-3). The counter flow gas and the free stream flow create a stagnation zone, and particles having sufficient inertia penetrate this zone and are deposited in the counterflow gas and carried to the sampling instruments. Wind tunnel tests showed that the cut point for separation could be varied from 9 to 30 um by controlling the flow to and from the probe tip. Wind tunnel tests and numerical predictions were used to design a shroud for the CVI that reduced the impact of variations in the angle of attack. Inlet 9 employs a double duct. The sample flow is slowed to approximately 20m/s from 200 m/s and is laminar upon delivery to the inner duct. The inlet is mounted on a wing pod. The main duct has a rounded lip and is oriented into the incoming flow to prevent flow separation. Analysis of the impact of anisokinetic flow on particle sampling is not presented. Turbulence normally generated in a diffuser is reduced to negligible levels by applying boundary layer suction through a porous wall in inlet 10 (Fig. 30-4). Velocity reductions by factors exceeding 20 are achieved, and an elliptical leading edge reduces the impact of variations in angle of attack. Large particles are enhanced in the laminar flow delivered to the rear of the inlet. The magnitude of the enhancement is determined by calculations of laminar flow and particle trajectories (Fig. 30-5).The resulting sample is not representative for supermicrometer particles, and measured concentrations of these particles must be corrected for inertial enhancements that depend on particle size. Flight tests have demonstrated the ability of this inlet to maintain laminar, isokinetic flow. An obstacle in the form of a double ellipse of revolution resembling an American football is mounted below the fuselage in inlet 11. Streamlines reaching the sampling inlet near the rear of the football are forced to curve around the obstacle, and large particles are unable to follow the flow. At 10 kPa [100 mb] pressure and 200 m/s, particles larger than 5 urn in diam-

Q1

Q2

L

Q3

X

Ux Fig. 30-3. Counterflow virtual impactor (CVI). The probe moves through the air at a velocity Ux. Flow Ql is filtered air and passes through the porous section of length x. Flow Q3 goes out the end. Particles with sufficient inertia to traverse distance Lt + L are entrained in flow Q2 and carried to instruments. (Courtesy of C. Twohy, National Center for Atmospheric Research, Boulder, CO.)

Porous diffuser

Free stream U0

Sample flow

Suction flow

Enhancement Factor

Fig. 30-4. Inlet 10 reduces the turbulence generated in the diffuser by boundary layer suction. The sample flow and suction flow are brought into the aircraft by pumps. The flows are controlled to maintain isokinetic sampling and laminar flow.

P, TATT =1007 m b STATIC

P. T . _=722 mb STATIC

Aerodynamic Diameter (|um) Fig. 30-5. Calculations of laminar flow and particle trajectories in inlet 10 show enhancements for large particles. In this case, 27% of the flow is delivered as sample flow, and the rest is suction flow. Thus, the maximum enhancement possible is 3.7 for large particles. The ambient mixing ratio equals the mixing ratio in the sample flow divided by the enhancement. Results are shown for two static pressures.

eter are excluded from the sample. This excludes most precipitation and type two polar stratospheric cloud particles. Inlet 12 is designed for sampling vapors and semivolatile aerosol. Figure 30-6 shows the aerosol separating/enhancing geometry. Flow channels that are not shown in the figure deliver the sample stream to the rectangular slit intake. In the particle mode, particles larger than approximately 0.2 urn are separated from the 200 m/s free stream and deposited in the

D B

A

C

D

A

B

C E

B D

D

(a)

B

(b)

Fig. 30-6. a, Inlet 12 in the aerosol mode. Sampled air is delivered to point A by a flow system not shown here. Gas of known composition is injected at point B. A smaller flow goes to the instrument at C so some of the supplied gas flows out of the nozzle and away at D. The sampled air does not enter the sample nozzle and exits at D. Particles larger than about 0.2 (xm in diameter have sufficient inertia to cross into flow C and are transported to the instrument in the injected gas. (Courtesy of S. Dhaniyala, California Institute of Technology, Pasadena, CA.) b, Inlet 12 in the gas mode. Sampled air is delivered to point A by a flow system not shown here. Gas of known composition is injected at point B, but this flow is less than the flow to the instrument at C. Some of the sampled air enters at E, and particles larger than about 0.2 um in diameter fail to follow the streamlines at E and are removed from the air that enters the sample nozzle. Air that has contacted the inlet is removed through D. Sampled air is transported to the instrument with the gas injected at B.

gas stream forming the counterflow in the inlet. The inlet is upstream of a chemical ionization mass spectrometer, and the particles are volatilized and are analyzed in a flow of known composition. In the gas mode, particles larger than approximately 0.2 um are separated from the sample flow that is analyzed. The inlet shifts back and forth from particle to gas mode during a flight. CONCLUSIONS Inlet designers should consider the impact of the aircraft on the flow upstream of the inlet, the impact of the inlet itself, and the impact of the transport system from the inlet to the instruments. Investigators should also validate the performance of the instruments under the conditions to be encountered. Improvements in our ability to manipulate flows and predict the motion of particles in laminar flows are leading to inlets with improved characteristics. Several inlets appear to provide representative samples of submicrometer aerosol. Supermicrometer particles and high speeds present difficult problems that appear to be yielding to engineering advances and may be ameliorated by choosing slower platforms. REFERENCES Baumgardner, D. 1984. The effects of airflow distortion on aircraft measurement: A workshop summary. Bull. Am. Meteorol. Soc. 65:1212-1213.

Baumgardner, D., J. E. Dye, B. Gandrud, K. Barr, K. Kelly, K. R. Chan. 1996. Refractive indices of aerosols in the upper troposphere and lower stratosphere. Geophys. Res. Lett. 23:749-752. Baumgardner, D., W. Strapp, and J. E. Dye. 1985. Evaluation of the forward scattering spectrometer probe. Part II: Corrections of coincidence and dead-time losses. /. Atmos. Ocean. Technol. 2:626-632.

Beard, K. V. 1983. Reorientation of hydrometeors in aircraft accelerated flow. /. CUm. Appl. Meteorol 22:1961-1963. Brenguier, J. L. 1989. Coincidence and dead-time corrections for particle counters. Part II: High concentration measurements with and FSSP. /. Atmos. Ocean. Technol. 6:585-598. Brenguier, J. L. and L. Amodei. 1989. Coincidence and dead-time corrections for particle counters. Part I: A general mathematical formalism. /. Atmos. Ocean Technol. 6:575-584. Brock, C. A., L. F. Radke, and P. V. Hobbs. 1990. Sulfur in particles in arctic hazes derived from airborne in situ and lidar measurements. /. Geophys. Res. 95:22369-22387. Brock, C. A., F. Schroder, B. Karcher, A. Petzold, R. Busen, and M. Fiebig. 2000. Ultrafine particle size distributions measured in aircraft exhaust plumes. /. Geophys. Res. 105:26555-26567. Cain, S. A. and M. Ram. 1998. Numerical simulation studies the turbulent airflow through a shrouded airborne aerosol sampling probe and estimation of the minimum sampler transmission efficiency. 1998. /. Aerosol Set 29:1145-1156. Cain, S. A., M. Ram, and S. Woodward. 1998. Qualitative and quantitative wind tunnel measurements of the airflow through a shrouded airborne aerosol sampling probe. 1998. /. Aerosol Sci. 29:1157-1169. Cerni, T. A. 1983. Determination of the size and concentration of cloud drops with an FSSP. /. Appl. Meteorol 22:1346-1355. Dhaniyala, S., R. Flagan, and P. O. Wennberg. 2000. A gas/particle separating inlet for sampling vapors and semi-volatile aerosols. American Association for Aerosol Research 2000 Conference, St. Louis, MO, November 6-10. Dreiling, V. and R. Jaenicke. 1988. Aircraft measurement with condensation nuclei counter and optical particle counter. /. Aerosol Sci. 19:1045-1050. Drummond, A. M. 1984. Aircraft flow effects on cloud droplet images and concentrations. Aeronautical Note NAE-AN-21 (NRC No. 23508). National Research Council Canada, 30 pp. Drummond, A. M. and J. E. MacPherson. 1984. Theoretical and measured airflow about the Twin Otter wing. Aeronautical Note NAE-AN-19 (NRC No. 33184), National Research Council, Canada, 33 pp. Drummond, A. M. and J. E. MacPherson. 1985. Aircraft flow effects on cloud drop images and concentrations measured by the NAE Twin Otter. /. Atmos. Ocean. Technol. 2:633-643. Dye, J. E. and D. Baumgardner. 1984. Evaluation of the forward scattering spectrometer probe. Part I: Electronic and optical studies. /. Atmos. Ocean. Technol. 1:329-344. Fahey, D. W., K. K. Kelly, G. V. Ferry, L. R. Poole, J. C. Wilson, D. M. Murphy, M. Loewenstein, and K. R. Chan. 1989. In situ measurements of total reactive nitrogen, total water and aerosol in a polar stratospheric cloud in the Antarctic. /. Geophys. Res. 94:11299-11315. Heintzenberg, J. and J. A. Ogren. 1985. On the operation of the TSI-3020 condensation nuclei counter at altitudes up to 10 km. Atmos. Environ. 19:1385-1387. Hering, S. V. 1987. Calibration of the QCM impactor for stratospheric sampling. Aerosol Sci. Technol. 7:257-274. Hermann, M., F. Stratmann, M. Wilck, and A. Wiedensohler. 2001. Sampling characteristics of an aircraftborne aerosol inlet system. /. Atmos. Ocean. Technol. 18:7-19. Huebert, B. X, G. L. Lee, and W. L. Warren. 1990. Airborne aerosol inlet passing efficiency measurement. / Geophys. Res. 95:16369-16381. Intergovernmental Panel on Climate Change. 1994. Radiative Forcing of Climate Change. Cambridge University Press, Cambridge, Great Britain. Ivie, J. X, L. X Forney, and R. L. Roach. 1990. Supersonic particle probes: Measurement of internal wall losses. Aerosol Sci. Technol. 13:368-385. Jonsson, H. H., X C. Wilson, C. A. Brock, R. G. Knollenberg, R. Newton, J. E. Dye, D. Baumgardner, S. Borrmann, G. V. Ferry, R. Pueschel, D. C. Woods, and M. C. Pitts. 1995. Performance of a focused cavity aerosol spectrometer for measurements in the stratosphere of particle size in the 0.06-2.0 um diameter range. /. Ocean. Atmos. Technol 12:115-129. King, W. D. 1984. Airflow and particle trajectories around aircraft fuselages. I: Theory. J. Atmos. Ocean. Technol 1:5-13. King, W. D. 1986a. Airflow and particle trajectories around aircraft fuselages. IV: Orientation of ice crystals. / Atmos. Ocean. Technol 3:439.

King, W. D. 1986b. Airflow around PMS canisters. /. Atmos. Ocean. Technol 3:197-198. Knollenberg, R. G. 1981. Techniques for probing cloud microstructure. In Clouds, Their Formation, Optical Properties, and Effects, eds. P. V. Hobbs and A. Deepak. New York: Academic Press, pp 15-91. Lafleur, B. G., J. C. Wilson, and W. R. Seebaugh. 2000. Flight testing of low turbulence aerosol inlets. American Association For Aerosol Research 2000 Conference, St. Louis, MO, November 1-6. Laucks, M. L. and C. H. Twohy. 1998. Size dependent collection efficiency of an airborne counterflow virtual impactor. Aerosol ScI Technol. 28:40-61. Leaitch, W. R., R. M. Hoff, and J. I. MacPherson. 1989. Airborne and lidar measurements of aerosol and cloud particles in the troposphere over Alert, Canada in April 1986. /. Atmos. Chem. 9:187-211. Liu, P. S. K., W. R. Leaitch, J. W. Strapp, and M. A. Wasey. 1992. Response of Particle Measuring Systems Airborne ASASP and PCASP to NaCl and Latex Particles. Aerosol Sci. Technol. 16:83-96. MacPherson, J. 1.1985. Wind tunnel calibration of a PMS canister instrumented for airflow measurement. Aeronautical Note NAE-AN-32 (NRC No. 24922). National Research Council, Canada. MacPherson, J. E. and D. Baumgardner. 1988. Airflow about King Air wingtip-mounted cloud particle measurement probes. /. Atmos. Ocean. Technol. 5:259-273. Martone, J. A., P. S. Daley, and R. W. Boubel. 1980. Sampling submicrometer particles suspended in near sonic and supersonic free jets. JAPCA 30:898-903. McFarland, A. R., C. A. Ortiz, M. E. Moore, R. E. DeOtte, Jr., and S. Somasundaram. 1989. A shrouded aerosol sampling probe. Environ. Sci. Technol. 23:1487-1492. Murphy, D. M. and M. E. Schein. 1998. Wind tunnel tests of a shrouded aircraft inlet. Aerosol Sci. Technol. 28:33-39. Murphy, D. M., D. S. Thomson, and M. J. Mahoney. 1998. In situ measurements of organics, meteoritic material, mercury and other elements in aerosols at 5 to 19 kilometers. Science 282:1664-1669. Noone, K. J., R. X Charlson, D. S. Covert, J. A. Ogren, and J. Heintzenberg. 1988. Design and calibration of a counterflow, virtual impactor for sampling of atmospheric fog and cloud droplets. Aerosol. Sci. Technol. 8:235-244. Noone, K. X and H.-C. Hansson. 1990. Calibration of the TSI3760 condensation nucleus counter for nonstandard operating conditions. Aerosol Sci. Technol. 13:478-485. Norment, H. G. 1988. Three-dimensional trajectory analysis of two drop sizing instruments: PMS OAP and PMS FSSR /. Atmos. Ocean. Technol. 5:743-756. Pena, X A., X M. Norman, and D. W. Thomson. 1977. Isokinetic sampler for continuous airborne aerosol measurements. JAPCA 27:337-341. Pinnick, R. G, D. M. Garvey, and L. D. Duncan. 1981. Calibration of Knollenberg FSSP light-scattering counters for measurements of cloud droplets. /. Appl. Meteorol 20:1049-1057. Porter, X N, A. D. Clarke, G. Ferry, and R. F. Pueschel. 1992. Studies of size dependent aerosol sampling through inlets. /. Geophys. Res. 97:3815-3824. Rader, D. X and V. A. Marple. 1988. A study of the effects of anisokinetic sampling. Aerosol Sci. Technol. 8:293-299. Ram, M., S. A. Cain, and D. B. Taulbee. 1995. Design of a shrouded probe for airborne aerosol sampling in a high velocity air stream. /. Aerosol Sci. 26:945-962. Reagan, X A., X D. Spinhirne, D. M. Byrne, D. W. Thomson, R. G de Pena, and Y. Mamane. 1977. Atmospheric particulate properties inferred from lidar and solar radiometer observations compared with simultaneous in situ aircraft measurements: A case study. / Appl. Meteorol. 16:911-928. Romay, F. X, D. Y. H. Pui, T. X Smith, N. D. Ngo, and X H. Vincent. 1996. Corona discharge effects on aerosol sampling efficiency. Atmos. Environ. 30:2607-2613. Russell, L. M., M. R. Stolzenburg, S. H. Zhand, R. Caldow, R. C. Flagari, and X H. Seinfeld. 1996. Radially classified aerosol detector for aircraft-based submicron aerosol measurements. /. Atmos. Ocean. Technol. 13:568.

Sheridan, P. X and R. B. Norton. 1998. Determination of the passing efficiency for aerosol chemical species through a typical aircraft-mounted, diffuser-type aerosol inlet system./. Geophys. Res. 103:8215-8225. Soderman, P T, N. L. Hazan, and W. H. Brune. 1991. Aerodynamic design of gas and aerosol samplers for aircraft. NASA Technical Memorandum 103854. National Aeronautics and Space Administration, Ames Research Center, Moffatt Field, CA.

Strapp, W. I, W. R. Leaitch, and P. S. K. Liu. 1992. Hydrated and dried aerosol-size-distribution measurements from the Particle Measuring Systems FSSP-300 Probe and Deiced PCASP-IOOx probe. /. Atmos. Ocean. Technol 9:548-555. Torgeson, W. L. and S. C. Stern. 1966. An aircraft impactor for determining the size distributions of tropospheric aerosols. /. Appl Meteorol 5:205-210. Twohy, C. H. 1998. Model calculations and wind tunnel testing of an isokinetic shroud for high-speed sampling. Aerosol ScI Technol 29:261-280. Twohy, C. H. and D. Rogers. 1993. Airflow and water-drop trajectories at instrument sampling points around the Beechcraft King Air and Lockheed Electra. /. Atmos. Ocean. Technol. 10:566-578. Webber, R. X, A. D. Clarke, M. Litchy, J. Li, G. Kok, R. D. Schillawski, and P. H. McMurry. 1998. Spurious aerosol measurements when sampling from aircraft in the vicinity of clouds. /. Geophys. Res. 103:28337-28346. Wilson, J. C, J. H. Hyun, and E. D. Blackshear. 1983. The function and response of an improved stratospheric condensation nucleus counter. /. Geophys. Res. 88:6781-6785. Wilson, J. C, M. R. Stolzenberg, W. E. Clark, M. Loewenstein, G. V. Ferry, K. R. Chan, S. R. Kawa, and K. K. Kelly. 1992. Stratospheric sulfate aerosol in and near The northern hemisphere polar vortex: The morphology of the sulfate layer, multimodal size distributions and the effect of denitrification. /. Geophys. Res. 97:8:7997-8013. Woods, D. C. and R. L. Chuan. 1983. Size-specific composition of aerosols in the El Chichon volcanic cloud. Geophys. Res. Lett. 10:1041-1044. World Meteorological Organization. 1988. Scientific Assessment of Ozone Depletion: 1988. Geneva: World Meteorological Organization, pp. 3.1-3.40,7.6-7.16.

measurement. If the real-time measuring instruments described earlier in this book are to be used, the number concentration and temperature must be reduced to within the operational limits of the instrument. To alleviate some of these problems, in situ techniques can be used. The measurement of high-temperature and high-concentration aerosols using these two methods is described. Impactors used in high-temperature applications are also discussed. This is followed by a discussion of the in situ techniques for measurement of hightemperature aerosols in combustion environments. DILUTION SYSTEMS The hot gases with the particles must be diluted and cooled in the sampling system. The aerosol dynamics must be rapidly quenched, and so must changes in chemistry. High dilution ratios are often required to reduce the number concentration to the operational range of the aerosol instruments. Such high dilution ratios often require that multistage dilution systems be used. Dilution systems used by various researchers are summarized in Table 31-1. Primary Dilution Systems A simple design of a primary dilution probe used by Linak and Peterson (1984) is shown in Figure 31-1. Dry, filtered compressed air is used to entrain a constant flow rate sample by TABLE 31-1. Summary of Dilution Systems Researchers

Dilution Ratio Primary

Newton et al. (1980) Pedersen et al. (1980) Ulrich and Riehl (1982) Houck et al. (1982) Sousa et al. (1987), Linak and Peterson (1984) Du and Kittleson (1984) Bonfanti and Cioni (1986) Wu and Flagan (1987) Biswas et al. (1989) Hildemann et al. (1989) Sethi and Biswas (1990) Pratsinis et al. (1990) Zimmer (2000)

Secondary

Overall

Dilution Gas Used

N.A.

5

Air

N.A.

20

20

Air

N.R.

N.A.

N.R.

N.A.

30

30

Air

21.2

39.5

837.4

Air

10

33

330

Nitrogen

6

N.A.

6

Nitrogen, air

N.R.

2000

2000

Nitrogen, air

101

3131

Argon, air

5

31 N.A.

25-100

19.5

25

5

200

1000

Argon, air

10

100

1000

Air

N.A., not applicable; N.R., not recorded.

25-100

Nitrogen

487.5

Air Argon, air

Sampled Source Fluidized bed combustor Organic aerosols, automobiles Silica flame reactor Organic aerosols, stack Pulverized coal combustion Organic aerosols, diesel engines Organic aerosols, stack Silicon, tubular reactor Silica, tubular reactor Organic aerosols, stack Lead, flame incinerator Titania, tubular reactor Arc welding process

WATER JACKET SAMPLE INLET

TO AEROSOL INSTRUMENTS

DILUTION EXHAUST DILUTION EXHAUST COOLING AIR AIR WATER Fig. 31-1. A two-stage dilution system consisting of a particle probe as the primary dilution system. (Adapted from Linak and Peterson, 1984.)

Sample and dilution gas

Sample Dilution gas Fig. 31-2. Schematic diagram of a primary dilution probe. The dilution gas enters through the perforations in the central tube and mixes with the sample.

aspiration. Constant-pressure dilution air is supplied through an outer annulus, and, as it enters the inner tube, a sample stream is drawn through the orifice due to the lower pressure region that is created. This sample then mixes with the filtered and compressed dilution air. The dilution ratio is determined based on the capillary dimension, clearance between outer and inner annuli, and diluent gas pressure. The dilution ratio is not controlled directly (by flow control), and there may be associated fluctuations in the sample flow. Isokinetic conditions may also be difficult to maintain in such systems. An aspirated isokinetic probe consisting of a water-jacketed air delivery system has been used by Peterson and co-workers (see Scotto et al., 1988; Gallagher et al., 1990) for sampling aerosols for studying alkali metal partitioning in ash from pulverized coal combustors. Sampling conditions were isokinetic, thus allowing sampling of larger particles. The aerosol sample was rapidly quenched (cooled and diluted) by a free turbulent jet that provided a high mixing rate. The problem of not having direct control of the dilution ratio is alleviated by using a direct flow-controlled primary sampling probe. Newton et al. (1980) describe a radially injected dilution probe for sampling in fluidized bed coal combustion systems. The inlet surfaces of the probe are chamfered to reduce edge effects. Dilution air enters through a porous stainless steel cylinder perpendicular to the direction of the sample flow. The dilution air flow is independently controlled and is particle free. The system tends to compress the aerosol sample, allowing cooling and mixing to occur away from the walls, thus reducing losses. A sample flow of 4.17 x lO^nrVs [25L/min] was diluted with a total air flow of 1.67 x 10"3m3/s [lOOL/min], leading to a dilution ratio of 5. While thermophoretic losses are shown to be minimized, alteration of the aerosol by condensation and coagulation is not necessarily eliminated. A design of a flow-controlled primary dilution probe is shown in Figure 31-2 (Biswas et al., 1989). The probe is made of quartz and is constructed using two concentric tubes. Small 1/6 inch (4 mm) diameter holes are drilled on the walls of the inside tube. The dilution

Clean air for secondary dilution Mass flow controller

secondary

o aerosol n i strument T measuring instruments

Clean air for primary Q dilution "prm i ary Mass flo^w controller

^exhaust

Filter

Sampling probe

Qsampel

Mass flowVacuum controller pump

Dilution bottle

Fig. 31-3. Schematic of a dilution probe for flame measurements. Also shown is the secondary dilution system.

gas can thus enter the inside tube and mix and dilute the sample aerosol. The probe is easily movable to sample at different locations in the system. In systems where chemical reactions are rapid, an inert gas such as argon is used for dilution as it participates in no chemical reactions. A more rugged version of a probe designed by Sethi and Biswas (1990) for measurement in flame environments is shown in Figure 31-3. The following criteria are adopted in this modified design: The cross section of the probe tip should be small so as not to disturb the flow in the system at the sampling location; cleaning and fabrication should be easy; and material should withstand flame environments. A stainless steel probe with a small outer diameter (1/8 inch, 3.2mm) is used for the sampling end of the probe and is housed in a slightly larger diameter tube. The outer tube also serves as the outer jacket for the dilution gases. The sample is drawn out of the probe from a tube housed inside the outer tube.Thermophoretic and diffusional particle losses in the probe are negligible. The residence time in the sampling tube is short. Sampling losses due to anisokinetic sampling are estimated using the criteria for probe alignment and sampling to mainstream velocity ratio. The probe is aligned along the flame axis; hence there are no losses due to misalignment. The Stokes number of particles sampled in the flame is very much smaller than 0.01; thus, the losses due to velocity differences are unimportant (Hinds, 1999). A similar design probe has been used by Zimmer and Biswas (2001) for effectively monitoring the evolution of the aerosol size distribution during an arc welding process. Very high dilution ratios had to be used due to the high number concentrations and high temperatures in the zone of the plasma arc. A nano-differential mobility analyzer was successfully used in conjunction with the sampling system to map out the evolution of the ultrafine particle size distribution, both spatially and temporally. Ulrich and Riehl (1982) used a nitrogen-quenched, sonic expansion probe for collection of flame-generated silica particles for surface area analysis. The probe is constructed using two concentric tubes with nitrogen gas flowing in the region between the inner and outer tubes. A vacuum is applied at the inner tube that pulls some sample from the mainstream. The nitrogen gas not only cools and dilutes the sample but also prevents clogging of the probe tip.

In the above designs, a probe-type configuration is used to draw a small sample from the mainstream flow and dilute it by mixing with some particle-free gas. This is done so that higher dilution ratios can be attained. Designs where the entire reactor flow is diluted have been used by Alam and Flagan (1986) and then modified and used by Wu and Flagan (1987). A schematic diagram is shown in Figure 31-4. As the aerosol comes out of the reactor, a coaxial flow is introduced from the sides of a sintered tube. This prevents the aerosol from coming into contact with the cooler walls and depositing due to thermophoresis. Downstream, a large volume of diluent gas is mixed with the aerosol. The flow becomes turbulent, and good mixing is obtained with the gases, causing both dilution and cooling simultaneously. A similar set up used by Pratsinis et al. (1990) is shown in Figure 31-5. The reactor effluent is rapidly mixed and cooled with argon in a perforated quartz dilutor. The perforated quartz dilutor is used in place of the sintered tube used by Alam and Flagan (1986) for ease of fabrication and cleaning. AEROSOL REACTOR

Primary dilution gas Secondary dilution gas

Fig. 31-4. Dilution system (primary and secondary) wherein the entire reactor flow is diluted. (Adapted from Alam, 1984.)

DILUTION ARGON

REACTOR FLOW REACTOR FLOW AN DILUTION GAS DILUTION ARGON Fig. 31-5. Details of a primary dilution system wherein the entire reactor flow is diluted with a volume of inert diluent gas to quench chemical reactions. (Adapted from Pratsinis et al., 1990.)

The following conditions must be met in the design of primary dilution probes: 1. Sampling under as close to isokinetic conditions as possible (see Chapter 8), matching the sample velocity to the face stream velocity 2. Choosing the dilution ratio to ensure minimal deposition losses due to thermophoresis and diffusion and minimal alteration of the size distribution by various dynamic aerosol phenomena such as nucleation, coagulation, and condensation Secondary Dilution

The primary dilution probes should rapidly cool and dilute a sample in order to quench aerosol growth and chemical reaction. As described earlier, in the case of high mainstream aerosol concentrations (1016 to 1021 particles/m3 [1010 to 1015 particles/cm3]), a secondary dilution is often necessary as real-time aerosol instruments are limited to measuring a maximum of about 1013 particles/m3 [107 particles/cm3]. Secondary dilution can be done in configurations similar to the primary dilution system. The flow with the primary dilution system mixes with a large volume of particle-free air, and a part of this flow is pulled into the aerosol sampling instrument (Linak and Peterson, 1984; Fig. 31-1). As the residence times are relatively short, proper mixing has to be ensured to obtain a representative sample. In the dilution system developed by Alam and Flagan (1986), a coaxial flow is used to obtain secondary dilution (see Fig. 31^). In both these systems, as the entire flow from the primary dilution system is being diluted, a relatively high flow rate of diluent gas must be chosen, depending on the number concentration of the aerosol in the mainstream (see Example 31-1). An alternative secondary dilution system developed by Biswas (1985) and used for particle sampling in high-temperature systems (Biswas et al., 1989; Sethi and Biswas, 1990) is shown in Figure 31-6. The dilution takes place in a 5OL bottle, with a small flow from the dilution system being drawn in and mixed with a large volume of particle-free air. Very high dilution ratios can be obtained as precise flow control is possible by using mass flow controllers. To minimize uncertainties in the dilution ratio, the dilution flow is often cooled, filtered, and recirculated (Biswas, 1985:125; Biswas and Flagan, 1988; Wu and Flagan, 1987). A schematic diagram of the set up indicating the various flows is shown in Figure 31-6. The residence time is sufficiently long to allow good mixing. The expression developed by Crump et al. (1983) can be used to calculate the losses in the dilution vessel (31-1) where Cn is the number concentration of the aerosol after being resident in the dilution vessel for a time r, cn0 is the number concentration at time t = 0, and p is the wall loss coefficient (Table 31-2).

TABLE 31-2. Fraction of Particle Loss in the Dilution Bottle Particle Diameter (jam) 0.024 0.042 0.34 0.51 0.79

P (s"1) Loss Coefficient (Crump et al., 1983) 1.8 8.4 1.5 4.8 1.26

4

x 10x 10-5 x 10~5 xlO~ 5 x IQ-4

Fraction of Particle Loss 0.05 0.02 0.01 0.02 0.04

EXAMPLE 31-1 (1) For the dilution system shown in Figure 31-6, if the sample flow rate is <2SamPie,in a n d the primary dilution gas flow rate is 2primary,in, compute the dilution ratio in the probe. If the flow rate of clean air (particle free) mixed with a flow of QSamPie,out coming from the probe is <2Secondary,in> compute the overall dilution ratio. The following are a set of flow rates used in the experiments described by Biswas et al. (1989):

Answer: Primary dilution: Assuming that the number concentration of the aerosol in the sample is Cn and that in the diluted sample is c nl , a mass (or number, particle properties unchanged) balance yields

where it is assumed that the diluent gas is particle free (filtered). The dilution ratio is defined as the ratio by which the number concentration must be multiplied to obtain the number concentration of the aerosol in the system. Therefore, the dilution ratio in the sampling probe, DRU is

Secondary dilution: In a similar manner, the dilution ratio for the secondary system, DR2, is

The overall dilution ratio, DR is thus given by

The number concentration of the aerosol in the system is then determined by multiplying the measured size distribution of the aerosol by DR. For the given flow rates, using the formulae above, one computes DR = 3131. (2) If the number concentration in a system is 1016 particles/m3 [1010 particles/cm3], what flow rates are to be used in the dilution system illustrated in Figure 31-6 for measurement with commonly used real time aerosol instruments? Answer: As most instruments have a measurement upper limit of around 1013 particles/m 3 [107 particles/cm3], and using a margin of safety, we choose an overall dilution ratio, DR = 104. Hence, for a number concentration in the system of 1016 particles/m3 [1010 particles/cm3], the instrument would see a concentration of 1016/104 = 1012 particles/m3. Let Gprimaryjn = GPrimary,out = 5 x KT5nr7s [3L/min], <2sampie,in = 1.67 x lO^nrVs [0.1 L/min] as in Example 31-1 a. Therefore, DR1 = 5.17 x KT5/1.67 x 10"6 [3.1/0.1] = 31. Hence, the required DR2 = DRIDR, = 104/31 = 322.6. If Gsampie,out = 1-67 x KT6In3Zs [0.1 L/min], then we obtain Gsecondary,in = DR2 ftample,out - Gsample,out = 538 X 10"4 - 0.017 X 10~4 [32.26 - 0.1] = 5.36 X 10-4m3/s [32.16L/min]. The required degree of accuracy of flow control should be appreciated.

TUBULAR REACTOR

^primary, in

^sample, in FLOW CONTROLLER TEMPERATURE SENSOR ^secondary, in

EXHAUST FILTER

^•primary, out FLOW CONTROLLER

VACUUM PUMP EXHAUST

^instrument ^secondary, out FILTER

VACUUM PUMP

AEROSOL INSTRUMENT

DILUTION BOTTLE Fig. 31-6. Entire dilution system used by Biswas et al. (1989) showing primary dilution probe and secondary dilution system. Also indicated are the various flow rates through the different sections.

EXAMPLE 31-2 Consider the secondary dilution bottle system to be spherical with a volume of 0.05 m3 [50L]. Assuming a total flow rate of 1.67 x lO^mVs [lOL/min], estimate the fraction of particle loss in the bottle for 0.024, 0.042,0.34,0.51, and 0.79 urn particles. Answer: A simplistic calculation will be carried out, using Eq. 31-1 and the measured values of p reported by Crump et al. (1983). The residence time in the dilution bottle is 50/10 = 5 min = 300 s. The results, computed assuming that p remains constant for this time period for particles in a certain size range, are listed in Table 31-2.

Dilution Sampling of Organic Aerosols

Due to the toxic nature of organic compounds, it is of interest to determine the contribution of emission sources to ambient organic aerosol concentrations (Daisey et al., 1986).The ratio of the vapor phase to particulate organics is a strong function of the temperature. Direct filtration of hot gases may not lead to the capture of the organic aerosols that exist primarily as vapors at these temperatures, leading to underestimation of source organic aerosol contributions. Alternatively, the use of cooled impingers or cryogenic traps results in an overestimation of organic aerosol concentrations due to the condensation of organics that normally would not condense. Thus, the sampling of organic aerosols from combustion sources requires that dilution systems be used. Hildemann et al. (1989) have reviewed the various systems for organic aerosol sampling. Large, nonportable dilution tunnels have been used for automotive emissions measurements. These systems were designed to simulate the dilution and cooling that take place in the atmosphere when exhaust gases leave the exhaust pipe of a vehicle (Pedersen et al., 1980). The exhaust was partially cooled (to 433 K [1600C] to prevent the condensation of moisture)

and mixed with dilution air at a dilution ratio of 20 to further cool the sample to 308 K [350C]. Du and Kittleson (1984) used a sampling system capable of removing, quenching, and diluting the entire contents of a cylinder during actual engine combustion. The extracted gas was made to go through an adiabatic expansion into the sampling apparatus whereby it was diluted with nitrogen at a ratio of about 10. These diluted combustion chamber contents were further diluted in a polyethylene sampling bag with nitrogen to give an overall dilution ratio of about 330. Portable dilution systems for stack sampling of organic aerosols have been described by Houck et al. (1982), Hyunh et al. (1984), Bonfanti and Cioni (1986), Sousa et al. (1987), and Hildemann et al. (1989) with dilution ratios in the range of 6 to 100. The important design issues for such systems have been summarized by Hildemann et al. (1989): 1. Simulation of atmospheric dilution as closely as possible to collect a sample that includes all organics that would condense into the aerosol phase in the atmosphere. This is achieved by a high degree of dilution and cooling to ambient temperatures. The system should allow a long residence time for mixing. 2. Sampler surfaces should be inert and withstand rigorous cleaning and heat treatment. 3. The sampler should be configured and operated to minimize particle and vapor losses. EPA STACK SAMPLING METHODS There are two U.S. EPA proposed methodologies for obtaining particle mass concentrations in stacks: an external filtration method (Method 5) and an in-stack filtration method (Method 17). The U.S. EPA Method 5 Sampling Train is used to determine total particulate emissions on a mass basis from stationary sources such as utility plants, coal-fired boilers, incinerators, and other stacks (U.S. EPA, 1990). It is widely used for stationary-source sampling for air pollution control equipment performance evaluation for determining the effect of operating variables on mass emissions and for regulatory compliance tests. The sampling train (Fig. 31-7) consists of a nozzle, probe, filter housing, impingers, pitot tube, and metering system. The nozzle is designed to operate under isokinetic conditions, and its size is chosen accordingly. It has a button hook design to create a smooth change in the flow direction by 90°. Typical nozzle sizes range from 1/6 inch (1.6 mm) to 1.2 inches (12.7 mm) diameter and are constructed of 316 stainless steel. The function of the probe is to support the nozzle and connect it to the filter housing. The probe length varies from 2 feet (0.61m) to 12 feet (3.67m) and is typically made of borosilicate glass. When long lengths are required (>15 feet, 4.6m), they are made of stainless steel. The probe and the filter assembly is maintained at 393 K [1200C] to prevent moisture condensation. Glass fiber filters are used as the sampling media. The recommended point of sample extraction should be 10 duct diameters away from the nearest upstream disturbance (bends, dampers, duct outlets) and two duct diameters away from the nearest downstream disturbance. The number of sampling or traverse points ranges from 8 to 24, depending on the stack diameter and the downstream distance from the nearest flow disturbance. The sampling time should be long enough to collect 0.6 m3 at a rate no greater than 0.021 nvVmin. Leak tests also need to be performed to ensure that the correct volume of gas is sampled. The particulate sample is recovered from all the components, including the nozzle, probe, cyclone, filter holder, filter, rubber gasket, and impingers, by washing with water or acetone. This extract is then dried and the amount of particulate matter weighed to determine the mass concentration. The EPA Method 5 uses a flexible connecting hose (in contrast to a rigid connection) linking the probe to the impingers, thus making it simpler to operate. The other difference is that the filter is placed in the stack. Thus, the filter material has to be able to withstand the

Temperature measurement system

Heated filter compartment Filter assembly

Nozzle Heated probe sheath

lmpingers Pitot tube Orifice meter

Vacuum pump Dry gas meter Fig. 31-7. The EPA Method 5 Sampling Train showing the various components. (Adapted from U.S. EPA, 1990.)

local stack temperatures. The measurements taken by EPA Method 5 and 17 may be vastly different, especially if there are condensibles present in the stack gases. HIGH-TEMPERATURE IMPACTORS Inertial impactors have been discussed in Chapter 10. To prevent bounce and reentrainmnet of particles, the collection surfaces are often coated with a thin layer of grease or oil. These coatings are not practical for use in sampling of high-temperature gas streams. Other substrates such as ceramic fibers have been used at temperatures as high as 870K, with no apparent shift in the 50% cut-off size; however, the size cuts were considerably less sharp when compared to greased plate impaction at room temperature (Parker et al., 1981). Newton et al. (1982) used ungreased stainless steel substrates for high-temperature and high-pressure measurements; however, the impaction velocities were limited to prevent particle bounce. Pilat et al. (1970) designed a source stack cascade impactor for measuring particle size distributions in stacks and ducts of emission sources. By operating the impactor inside the duct, true isokinetic sampling could be achieved with minimum wall losses and condensation problems. However, the problem of particle bounce would remain in these impactors at temperatures greater than 423 K [1500C] as the collection surfaces cannot be coated with a sticky medium. Pilat and Steig (1983) used a version of this impactor with filter media as the collection surface for sampling in a pressurized fluidized bed coal combustor. Although filter media reduce particle bounce in comparison with uncoated collection surfaces, impactor col-

lection efficiency characteristics were found to be significantly different when compared to greased collection surfaces (Rao and Whitby, 1977). Virtual impactors are a good alternative as the particles are collected off line and greased plates do not need to be used (Schott and Ranz, 1976; Marple and Chien, 1980). A disadvantage of the virtual impactor is that secondary flows need to be controlled precisely at every stage, and a multistage device becomes cumbersome and difficult to operate. A particle trap impactor was developed by Biswas and Flagan (1988) for use in hightemperature systems. The particles were collected in a cavity instead of having a secondary flow stream as in a virtual impactor. Extensive calibration at room temperatures was carried out, and the device yielded sharp efficiency characteristics. The impactor could also be used under high jet velocity conditions for classification of submicrometer-sized aerosols. The impactor was also demonstrated to be functional up to 770K [5000C] with sharp cut-off characteristics (Biswas and Flagan, 1988). Distortions may also occur in the size measurements due to condensation of vapors and other species. The distortion of the particle size distributions in both conventional and lowpressure cascade impactors was determined for aerosols at different humidities by Biswas et al. (1987). Differential impaction of aerosols within the impactor may also cause shifts in the measured distributions as high number concentrations are encountered in these systems. The allowable upper limit in the number concentration for sampling of such aerosols has been determined by Biswas (1988). IN SITU MEASUREMENTS In addition to the probe techniques described earlier, a variety of diagnostic techniques have been used to measure particle sizes inflames.Thermophoretic sampling techniques have been used by Megaridis and Dobbins (1989) to deposit particles on a cold probe and then examine them by electron microscopy. Many optical techniques have been used as they offer advantages of in situ, nonintrusive measurements and greater optical resolution. The principles of light scattering for particulate measurements have been outlined in Chapters 15 and 16. Instruments that are commercially available for in situ measurements using optical techniques have been discussed in Chapter 17. Two techniques, elastic light scattering (ELS) and dynamic light scattering (DLS), used recently by various researchers for measurement in high-concentration aerosol systems are discussed here (see Table 31-3).

Elastic Light Scattering

An elastic light scattering process is one in which there is no energy exchange between the incident photons of light and target particles, resulting in no shift in the incident light frequency. These techniques have been used by many researchers in various ways such as combination of scattering and extinction, angular dissymetry, polarization ratio measurements, and two-color techniques. Lights of different wavelengths (two colors) provide a small range of measurements and are costly to implement (Zachariah et al., 1989); they are not discussed in this chapter. Combination of Scattering and Extinction. Santoro et al. (1983, 1987) and Presser et al. (1990) summarized the measurement procedure for light-absorbing aerosols using a combination of scattering and extinction measurements. The technique was demonstrated by measurements of soot particles formed by combustion of a gaseous fuel in a diffusion flame. The scattered light detection system allowed an absolute determination of the differential scattering cross section per unit volume. Light scattering and extinction measurements were

TABLE 31-3. Light Scattering Techniques for Particle Measurement

Elastic light scattering Scattering/extinction Angular dissymetry Polarization ratio Nonelastic light scattering Dynamic light scattering (also called photon correlation spectroscopy)

Mean particle size and number concentration for absorbing aerosols. No size limitation Mean size, number concentration and spread in distribution Measurable particle size greater than 0.1A, Good for nonabsorbing aerosols with known refractive index No size limitation; sensitivity decreases with particle size, Determines diffusion coefficient; hence requires system temperature to compute particle size. Refractive index of particles not needed

obtained simultaneously at different radial and axial positions. The scattering cross section applicable for the vertically polarized light, Q11 and the extinction coefficient, fcext, are determined from the measurements. Using the Rayleigh theory (Chapter 15), expressions for Q11 and kext are obtained, and the ratio of the two is proportional to an average particle diameter, dp63 defined by (31-2) where f(dp) is the size distribution function, m is the complex refractive index of the particles, E(m) = -Im [(m2 - V)I{m2 + 2)], and F(m) = |(m2 - I)I {m2 + 2)|. If a certain shape of the size distribution is assumed, dp6,3 can be related to the volume mean diameter, dp3$. Knowing dp3)o, the particle number concentration, cn, can be determined (Santoro et al., 1983): (31-3) Santoro et al. (1983) suggested that, due to high number concentrations encountered in flames, the size distribution can be assumed to be self-preserving. They assumed (dp6^/dp3yo)3 = 2 for the self-preserving size distribution and, using the experimentally measured kext, determined the number concentration using Eqs. 31-2 and 31-3. Angular Dissymetry Measurements. The scattering to extinction ratio technique described above is not applicable to the study of nonabsorbing aerosols such as silica. The dissymmetry technique utilizes the angular dependence of the scattered-light intensity for particles of known refractive indices in the Mie regime. The intensity of the scattered light from the particles is a function of the measured angle, the refractive index of the particle, and particle size in the Mie regime. The well-established relation between particle size and the scattering intensity is described by van de Hulst (1981). A description of the angular dissymmetry technique to determine the mean particle size and number concentration is provided by Zachariah et al. (1989). The vertically polarized scattering intensity, / u can be measured and is given by (31-4) where a= ndpIX, X is the wavelength of the incident light beam, C u is the Mie scattering cross section for spherical particles and the subscript (1,1) denotes state of vertical polarization, Cn

is the number concentration, and S is the optical system parameter. Zachariah et al. (1989) assumed the aerosol size to be described by a mean size (monodisperse) and determined the dissymmetry ratio from their measurements: (31-5) With the Mie scattering cross section (Dave 1968), the above equation can be solved to determine a and, therefore, the mean particle size. Using the value of the mean particle size thus determined, Eq. 31-4 can be used to determine the number concentration, cn, with S(O) determined from calibration experiments. Thus, intensity measurements at two angles could provide information on the mean particle size and number concentration. The angular dissymmetry technique has been extended by Chang and Biswas (1992) to determine the spread of the particle size distributions. The dissymmetry ratio for a size distribution of particles is given by (31-6) As the above equation has an unknown f(dp), the size distribution function, it cannot be solved directly as the monodisperse case described earlier. Chang and Biswas (1992) represented a flame-generated silica aerosol with a lognormal distribution, and Eq. 31-6 can then be written with two unknowns, the geometric mean size and the geometric standard deviation. Thus, two dissymmetry ratios were determined by scattering-intensity measurements at three angles and then used to determine the geometric mean diameter and the geometric standard deviation. Chang and Biswas (1995) further developed a statistical regression procedure for determining the best-fit parameters of the size distribution function using measurements at a number of angles. They also applied this technique to the measurement of multicomponent aerosols where the refractive index is unknown and is used as a fitting parameter. Polarization Ratio Measurements. The ratio of the intensity of the parallel to perpendicular component of the scattered light for unpolarized incident light is termed the polarization ratio. Kerker and La Mer (1950) suggested that the polarization ratio at any angle could be used to determine the particle size. This technique has been used by several researchers and has been reviewed by Bonczyk (1979). The polarization ratio (/11//12) is a function of the refractive index, the size distribution of the scatterer, and the angle of the measurement (Bonczyk and Sangiovanni, 1984): (31-7) where Iu is the vertically polarized scattered-light intensity, / u is the horizontally polarized scattered-light intensity (see Chapter 15), and C is the Mie scattering cross section with subscripts (1,1) and (1,2) denoting states of vertical and horizontal polarization, respectively. If the aerosol is assumed to be monodisperse, the mean particle size can be determined from Eq. 31-7 using the expressions for the scattering cross section (Dave, 1968) and the measured polarization ratio. The advantage of the technique is that the ratio is independent of geometrical and system parameters. The disadvantage is that the polarization ratio measurements may not be very reliable for high-concentration aerosols due to the depolarization effects resulting from agglomerated nonspherical scatterers (Maron and Elder, 1963).

Beam Splitter

Polarizer

View Volume Photodiode

LASER Chopper

Photodiode

Laser Line Filter

COMPUTER

A/D CONVERTER

LOCK IN AMPLIFIER

Fig. 31-8. An optical system for light scattering and extinction measurements. The detection system is rotated for measurement at different angles.

Elastic Light-Scattering Measurement System. This section is concluded with a description of the components of an optical system used to carry out light-scattering and transmission measurements. A schematic diagram of such a system is shown in Figure 31-8. It consists typically of a laser light source with a mechanically chopped beam divided into two parts by a beam splitter. One beam is directed through the region of the flame for the light-scattering and extinction measurements. The other beam is directed to a photodiode that provides a reference signal to a lock-in amplifier, thus distinguishing the scattered light intensity signal from the unchopped background noise. If the polarization ratio is to be measured, an unpolarized light source is used. The scattered signal from the probe volume is measured using a photomultiplier tube (PMT) connected to a lock-in amplifier for signal conditioning. Two slits on the PMT allow a very small scattering volume to be defined, thus enhancing spatial resolution. The laser line filter centered at the light source wavelength with a AX of around 1 nm is also mounted on the PMT to reject the extraneous signals from flame emissions. A polarizer is used before the PMT to ensure that the scattered light corresponds to the incident beam if the source is also polarized. The PMT is typically installed on a rotator so that it can measure the scattered-light intensities at various angles. The optical system or the burner can be installed on a moveable system that can be traversed for spatial measurements. The output signal from the lock-in amplifier is digitized with an AfD converter and sent to a microcomputer for data storage and further processing. Dynamic Light Scattering

Dynamic Light Scattering (DLS), also known as photon correlation spectroscopy, has been used in the measurement of soot formation (Flower 1983a). Monochromatic light incident on particles in a defined probe volume results in a fluctuating scattering intensity due to the Brownian motion of the particles. These fluctuations are correlated to the mean speed or the diffusion coefficient of the particles, both of which are related to the particle diameter. DLS is discussed in Chapter 15. The procedure to obtain the particle size is discussed herewith (Flower and Hurd, 1987; Zachariah et al., 1989). Flower (1983a,b) used DLS techniques for the measurement of soot particles in flames. The advantage of the DLS technique is that the measurement is independent of the particle refractive index. However, the technique provides a value for the particle diffusion coeffi-

cient, which is used to compute the particle size. If the mean motion of the particles is small, the power spectra P(^) of the scattered light exhibits a Lorentzian function (Cummins and Swinney, 1970) (31-8) where / is the intensity of the scattered light, co is the angular frequency, q is the magnitude of the scattering wave vector [= 4;rsin(0/2)/Ao], Pi0 is the wavelength of the incident light, D is the diffusion coefficient, and 6 is the scattering angle measured in the forward direction. When the scattered light is focused on the PMT, self beating of the scattered light results in photocurrent fluctuations. From the above equation, the frequency at half-width is given by (31-9) The frequency at half-width, A/, can be determined from the spectral plot described above, and the diffusion coefficient can be computed using Eq. 31-9. Knowing the relationship of D to particle size (Z) = kTCJln r\ dp), the particle size, dp, can be determined. However, an accurate estimate of the temperature and the properties of the medium are necessary. Nonspherical Scatterers

Due to high number concentrations encountered in combustion environments, particle agglomerates may be formed. Light scattering has been used for examining such aerosol agglomerates. However, the relation of the scattered intensity to particle size from even a small agglomerate is rather complicated. The fractal dimension can characterize statistically similar but geometrically irregular objects. Light scattering has been used to determine the fractal dimension of clusters in liquid media (Schaefer and Martin, 1984) and in gas media (Martin et al., 1986; Gangopadhyay et al., 1991; Yang and Biswas, 1997). Details are provided in Chapter 23, and only a brief description is provided here. Mountain and Mulholland (1988) established light-scattering relationships from simulated smoke agglomerates. The measured scattering intensity and its angular dependence were related to the fractal dimension, the radius of gyration of the agglomerate, and the total number of primary particles in the agglomerate. They also reported the resulting error in the volume mean diameter between an effective sphere model and their proposed agglomerate model. The error increased with the number of primary particles in the agglomerate, with a 26% difference being obtained for a cluster with 119 primary particles and an 8% difference for a cluster with 32 primary particles. Hurd and Flower (1988) studied flame-generated silica particle aggregates by in situ dynamic and static light scattering and reported fractal dimensions. A morphological description of flame-generated fractal particles is provided by Megaridis and Dobbins (1990), and the absorption and scattering of light by polydisperse aggregates is discussed by Dobbins and Megaridis (1991). Assuming Rayleigh-Debye scattering (Kerker, 1969), the differential scattering coefficient, Qx b for an aggregate of particles can be expressed as (31-10) where cN is the number concentration of agglomerates, Np is the number of primary particles in an agglomerate, and Ci,i,Rayieigh is the Rayleigh scattering cross section of a primary parti-

cle in an agglomerate. For a single agglomerate, the structure factor, S(q) is defined as (Berne and Pecora, 1976) (31-11) where 8 is the phase lag of the scattered beam caused by the interference between two distinct primary particles in an agglomerate. Expanding the structure factor for small values of q leads to the relation (31-12) where q is [= 4/rsin(0/2)//lo]; RG is the radius of gyration and is defined as

(31-13) where r} is the distance from the center of mass of the agglomerate to the /th primary particle. For small values of q (so that RGq «: 1), Eq. 31-11 can be rewritten as (31-14) For larger values of q (RG q > 1), the behavior of S(q) becomes (Mountain and Mullholland, 1988) (31-15) Df is the fractal dimension and defines the mass distribution in an agglomerate by the relation (31-16) where a is the radius of a primary particle and k0 is a constant, different values of which have been established both experimentally and computationally (details are provided in Chapter 23). For a fixed position, the intensity <2u(#) c a n be measured at different angles (or q). As CN Q,i,Rayieigh Np2 is a constant at a fixed location, the intensity measurement is proportional to S(q). With Eq. 31-15, a plot of S(q) with respect to q on a logarithmic scale yields a slope that is equal to -Dh the fractal dimension. At small q, S(q) has a parabolic dependence on q (Eq. 31-14), and fitting the data yields a value of the radius of gyration. If the primary particle size is known (determined from microscopy and assumed to be monodisperse), Eq. 31-16 can be used to determine Np, the number of primary particles in an aggregate. These parameters can then be substituted in Eq. 31-10 to determine cN, the number concentration of the agglomerate particles. Ex situ small-angle X-ray scattering and small-angle neutron-scattering measurements have been used to elucidate the morphological characteristics of fumed aggregates (Schaefer et al., 1991,1994). In situ light-scattering measurements have also been extensively used to establish the fractal dimensions of flame-generated particles (Hurd and Flower, 1988; Gangopadhyay et al., 1991; Yang and Biswas, 1997). A schematic diagram of a system to perform multiangle measurements is shown in Figure 31-9. Yang and Biswas (1997) have used in situ light-scattering measurements in conjunction with TEM observations to study the growth, aggregation, and evolution of the aggregate structure of titania particles in flame

Laser beam

Premixed burner Clean dry air Mass flow controller

Methane Mass flow controller

Heatinq tape Titanium isopropoxide liquid in bubbler (a)

6 WAr + Laser Chopper

Burner

Light trap

Spatial filter polarizer

Polarizer line filter PMT (b) Fig. 31-9. a, Schematic diagram of the premixed flame aerosol reactor system, b, Schematic illustrating multiangle light-scattering measurement system.

reactors. Using their experimental light-scattering measurements, they determined (using Eq. 31-15) the fractal dimension of the aggregates as a function of position along the burner axis. The fractal dimension of these small aggregates was found to vary (unlike the scale invariance observed in large agglomerates) and was attributed to sintering-type processes. They also used a numerical simulation (Yang and Biswas 1999) to relate the change in fractal dimension to the change in the normalized surface area. Using this relationship, they were able to establish the characteristic sintering time of the agglomerates. Results of the experiments are illustrated in Figure 31-10. Laser-Induced Fluorescence

To gain a detailed understanding of the particle formation mechanisms, it is essential to map out the vapor phase precursor concentrations. Moreover, there are several engineered

A

B

C

D

E Sintering & Collision Characteristic Times (s)

a

crossover of xc and t f Xiongetal., 1993 Haul and Dumbgen, 1965 Kobata et al., 1991; Seto et al. 1995 Astier and Vergnon, 1976

this work collision charact. time

b Distance From Burner Outlet (mm) Fig. 31-10. a, Transmission electron microscopy scans of particles in the premixed flame at different axial distances (X) above the burner outlet, b, Characteristic sintering time as a function of the center line axial distance.

sorbent processes that rely on gas to particle conversion for control of fine particle emissions (Biswas and Wu, 1998), and the vapor phase concentration profiles are important at establishing the gas to particle conversion rates. Laser-induced fluorescence is an in situ measurement technique that allows the mapping of the gas phase precursors to particle formation. The theory and application of laser-induced fluorescence spectroscopy has been described in detail elsewhere (Eckbreth, 1988). Planar laser-induced fluorescence (PLIF) is a wellestablished spectroscopic flow diagnostic that is based on the spontaneous radiative emission (fluorescence) following absorption of laser photons by a specific molecular species. In a typical PLIF measurement, a thin sheet of frequency-tuned pulsed-laser light is used as the excitation source and directed through the flow region of interest (Fig. 31-11). The resulting broadband fluorescence from the illuminated region is imaged with a lens onto an intensified charge-coupled device (CCD) camera. Through various excitation and detection strategies, the pulse integrated fluorescence signal can be related to the absorbing species concentration (McMillin et al., 1996; Biswas and Zachariah, 1997). The absorbing species concentration generally requires knowledge of the local collisional quenching rate coefficient and temperature. One strategy that is used is to assume a constant quenching rate (Zachariah and Burgess, 1994; McMillin et al., 1996) and an absorption transition that is insensitive to temperature (discussed later). With these approximations, the fluorescence signal is a direct representation of the relative vapor phase precursor concentration in the flame. It is important to know the partial fluorescence excitation-detection scheme and associated energy level diagram, and an example for PbO is shown in Figure 31-12a (Biswas et al., 1998b). Represented schematically is the A3TT(O+) <- X^(0 + )(0,3) excitation and the ground level vibration states, v" = 0,1,2,3,4,5, and 6. The upward-pointing arrow indicates the laser excitation frequency that was tuned to excite transitions from a particular rotational level within the v" = 3 ground state to the v' = 0 level in the excited A state. The downwardpointing arrows depict the radiative (fluorescent) or nonradiative (quenched) decay from the excited state (A) to various lower states. The partial excitation frequency spectrum obtained by scanning the laser from 567.5 to 571.5 nm in steps of 0.001 nm is shown in Figure 31-12b. With the spectroscopic constants reported by Oldenberg et al. (1973), the synthetic spectra were calculated for the above-mentioned transition, and rotational line assignments were made (Fig. 31-12b).

Tunable dye laser

Cylindrical lens

Pulsed excimer laser

Flow exiting reactor

Optical sink

Flow reactor Computer Metal aerosol/vapor and sorbent precursor Gating device Fig. 31-11. Schematic diagram of the system for performing laser-induced fluorescence measurements.

A

LIF Signal (a.u.)

X

b

Wavelength (nm) Fig. 31-12. a, Partial energy level diagram for PbO showing the excitation and detection scheme, b, Laser excitation spectrum of lead oxide between 567.5 and 571.5 nm established with a resolution of 0.001 nm. Also shown are rotational states for a few lines.

While transition probabilities are relatively independent of rotational state, the population in each rotational state is a function of temperature. Hence the absorption and fluorescence are also a function of temperature, this being related to the fractional population (FB) of the y'th rotational state (B anwell, 1983) being excited

(31-17) where h is the Planck's constant, c is the velocity of light, k is the Boltzmann constant, T is the temperature, Te is the electronic energy level, G(v) is the vibrational energy level, F(J) is the rotational energy level, Qe (= 1) is the electronic partition function, Qv (= 1/[1 - exp(hccoJkT)\) is the vibrational partition function, <x>& is the vibrational frequency, Qx (= kTlhcBv) is the rotational partition function, and Bv is a rotational constant. It is important to choose the appropriate frequencies that have a minimal dependence on the temperature (Biswas et al., 1998b).

CHARACTERIZATION OF COMBUSTION AEROSOLS Several characterization techniques can be used in the analysis of combustion-generated particulate matter. These include electron microscopy and X-ray diffraction, which are routinely used and are discussed in Chapter 12. Two additional spectroscopic techniques—Mossbauer and Raman—are briefly discussed here. Raman Spectroscopy

Due to quantum absorption and re-emission, light can be scattered at different wavelengths from the incident beam; this is known as inelastic scattering. Raman scattering is an inelastic scattering process that provides information on the different vibrational states of the molecule. The intensities of the Raman scattering signal are orders of magnitude smaller than elastic scattering signals, and limited number of in situ measurements have been made, especially in combustion environments. However, ex situ Raman measurements have been extensively used for the chemical analysis of particles. Biswas et al. (1998a) have used Raman scattering to characterize the optical phonons and crystallinity of flame-processed titania powders and films. The Eg mode in nanophase titania (20 nm) was broadened and blue shifted, similar to trends observed by Gonzalez and Zallen (1997). The mode frequency shift is due to a relaxation of the k = 0 selection rule, leading to blue or red shifts depending on the w(k) dispersion of phonon modes in the Brilloin zone near k = 0. These shifts can be quantified and related to particle size. Wang et al. (2000) have used this to study iron-doped titania samples and showed elegantly the decrease in grain size with increasing iron dopant concentration. Thus, in addition to the chemical analysis of the combustion-generated particles, Raman spectroscopy can be used to establish the grain size of particles and other characteristics of particles such as oxygen deficiency (Parker and Siegel, 1990). Mossbauer Spectroscopy

Mossbauer spectroscopy provides microscopic information on the chemical environment of the emitting or absorbing nuclei of particles. It involves the measurement of resonant absorption occurring in a solid as a result of the resonant emission taking place from another solid. y-Rays are emitted (or absorbed) by energy level transitions in the

nucleus without recoil. The y-ray energies associated with resonant emission and absorption are sensitive to changes in the solid-state environment via hyperfine interactions. When the resonant absorption is analyzed by monitoring the number of y-rays passing through the absorber, the measurement is said to be carried out in the transmission mode. One obtains a spectrum of transmitted rays versus velocity (emitted by the source and passed through the absorber) as a function of the velocity of the source relative to the absorber. This has been extensively used to study the characteristics of aerosol reactor synthesized superconducting powders and to establish the role of oxygen deficiency (Boolchand et al., 1990). The Mossbauer effect has been successfully used to obtain the kinetics of formation of the y-phase iron oxide during aerosol synthesis. Not only was the Mossbauer effect used as an atomic scale probe of the iron rich phases, but could also be readily used to discriminate small (nano) sized grains of y-Fe2O3 grains from the "large-sized" ones (Lin et al., 1996). REFERENCES Alam, M. K. 1984. Nucleation and Condensational Growth of Aerosols: Application to Silicon Production. Ph.D. thesis, California Institute of Technology, Pasadena, CA. Alam, M. K. and R. C. Flagan. 1986. Controlled nucleation aerosol reactors. Production of bulk silicon. Aerosol ScL Technol 5:237-248. Banwell, C. N. 1983. Introduction to Molecular Spectroscopy. New York: McGraw Hill. Berne, B. R. and R. Pecora. 1976. Dynamics of Light Scattering. New York: John Wiley & Sons. Biswas, P. 1985. Impactors for Aerosol Measurements: Developments and Sampling Biases. Ph.D. thesis, California Institute of Technology, Pasadena, CA. Biswas, P. 1988. Differential impaction of aerosols. /. Aerosol ScL 19:603-610. Biswas, P. and R. C. Flagan. 1988. The particle trap impactor. /. Aerosol ScL 19:113-121. Biswas, P., C. L. Jones, and R. C. Flagan. 1987. Distortion of size distributions by condensation and evaporation in aerosol instruments. Aerosol ScL Technol. 7:231-246. Biswas, P., X. Li, and S. E. Pratsinis. 1989. Optical waveguide preform fabrication: Silica formation and growth in a high temperature aerosol reactor. /. Appl. Phys. 65:2445-2450. Biswas, P. and C. Y. Wu. 1998. Control of toxic metal emission from combustors using sorbents: A review. /. Air Waste Manage. 48:113-127. Biswas, P., G. Yang, W. Bresser, and P. Boolchand. 1998a. An aerosol process for depositing multifunctional titania films. Adv. Technol. Part. Process. New York: AICHE Publications, vol. I, pp. 239-245. Biswas, P., G. Yang, and M. R. Zachariah. 1998b. In situ processing of ferroelectric materials from lead waste streams by injection of gas phase titanium precursors: Laser induced fluorescence and X-ray diffraction measurements. Combust. ScL Technol. 134:183-200. Biswas, P. and M. R. Zachariah. 1997. In situ immobilization of lead species in combustion environments by injection of gas phase silica sorbent precursors. Environ. ScL Technol. 31:2455-2463. Bonczyk, P. A. 1979. Measurement of particulate size by in situ laser-optical methods: A critical evaluation applied to fuel-pyrolyzed carbon. Combust. Flame 35:191-206. Bonczyk, P. A. and J. J. SangiovannL 1984. Optical and probe measurements of soot in a burning fuel droplet stream. Combust. ScL Technol. 36:135-147. Bonfanti, L. and M. Cioni. 1986. Sampling of polynuclear aromatic hydrocarbons at stack with a dilution sampler. In Aerosols: Formation and Reactivity. 2nd International Aerosol Conference, Berlin, pp. 952-955. Boolchand, P., C. Blue, K. Eglaid, W. Huff, A. Kilinic, D. McDaniel, P. Biswas, D. Zhou, and J. Oostens. 1990. Signature of Twin Planes in Mossbauer Spectroscopy of Cuprate Superconductors", Hyperfine Interactions, 62: 73-88. Chang H. and P. Biswas. 1992. In situ light scattering dissymmetry measurements of the evolution of the aerosol size distribution in flames. /. Colloid Interface ScL 153:157-166.

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Martin, I. E., D. W. Schaefer, and A. I. Hurd. 1986. Fractal geometry of vapor-phase aggregates. Phys. Rev. A 33:3540-4353. McMillin, B., P. Biswas, and M. R. Zachariah. 1996. In situ characterization of vapor phase growth of iron oxide-silica nanocomposites, Part 1.2-D planar laser induced fluorescence and imaging / Mater. Res. 11:1552-1561. Megaridis, C. M. and R. A. Dobbins. 1989. Comparison of soot growth and oxidation in smoking and nonsmoking ethylene diffusion flames. Combust. ScL Technol. 66:1-16. Megaridis, C. M. and R. A. Dobbins. 1990. Morphological description of flame generated materials. Combust. ScL Technol. 71:95-109. Mountain, R. D. and G. W. Mullholland. 1988. Light scattering from simulated smoke agglomerates. Langmuir 4:1321-1326. Newton, G. L, R. L. Carpenter, Y. S. Cheng, E. B. Barr, and H. C. Yeh. 1982. High temperature-high pressure: cascade impactor design, performance and data analysis methods. /. Colloid Interface ScL 87: 279-290. Newton, G. I., R. L. Carpenter, H. C. Yeh, and E. R. Peele. 1980. Respirable aerosols from fluidized bed coal combustion. 1. Sampling methodology for an 18-inch experimental fluidized bed coal combustor. Environ. ScL Technol 14:849-853. Oldenborg, R. C, C. R. Dickson, and R. N. Zare. 1973. Oldenborg et al. Title: Spectroscopic Bands of PbO in the 450-850nm range. /. MoI. Spectrosc. 58:283. Parker, R., S. Calvert, D. Drehmel, and I. Abbott. 1981. Inertial impaction of fine particles at high temperature and high pressure. /. Aerosol Sci. 12:297-306. Parker, J. C. and R. W. Siegel. 1990. Calibration of the Raman spectrum to oxygen stoichiometry. Appl. Phys. Lett. 57:943-945. Pedersen, P. S., I. Ingwersen, T. Nielsen, and E. Larsen. 1980. Effects of fuel, lubricant, and engine operating parameters on the emission of polycyclic aromatic hydrocarbons. Environ. ScL Technol. 14: 71-79. Pilat, M. I., D. S. Ensor, and I. C. Bosch. 1970. Source test cascade impactor. Atmos. Environ. 4:671-679. Pilat, M. I. and T. W. Steig. 1983. Size distribution of particulate emissions from a pressurized fluidized bed coal combustion facility. Atmos. Environ. 17:2429-2433. Pratsinis, S. E., H. Bai, P. Biswas, M. Frenklach, and S. V. R. Mastrangelo. 1990. Kinetics of titanium (IV) chloride oxidation. /. Am. Ceram. Soc. 73:2158-2162. Presser, C, A. K. Gupta, H. G. Semerjian, and R. I. Santoro. 1990. Application of laser diagnostic techniques for the examination of liquid fuel spray structure. Chem. Eng. Commun. 90:75-102. Rao, A. K. and K. T. Whitby. 1977. Nonideal collection characteristics of single stage and cascade impactors. Am. Ind. Hyg. Assoc. J. 38:174-179. Santoro, R. L, H. G. Semerjian, and R. A. Dobbins. 1983. Soot particle measurements in diffusion flames. Combust. Flame 51:203-218. Santoro, R. I., T. T. Yeh, 1.1. Horvath, and H. G. Semerjian. 1987. The transport and growth of soot particles in laminar diffusion flames. Combust. ScL Technol. 53:89-115. Schaefer, D. W. and I. E. Martin. 1984. Fractal geometry of colloidal aggregates. Phys. Rev. Lett. 52(26): 2371-2374. Schaefer, D. W., B. X Oliver, A. J. Hurd, G. Beaucage, J. J. Ivie, and C. R. Herd. 1991. Structure of combustion aerosols. /. Aerosol ScL 22:S447-S450. Schaefer, D. W., B. J. Oliver, C. Ashley, G Beaucage, D. Richter, B. Fargo, B. Frick, and A. Fischer. 1994. Structure and Topology of Silica Aerogels During Densification./ Non-Cryst. Solids 172-174:647-655. Schott, I. H. and W. E. Ranz. 1976. Jet-cone impactors as aerosol separators. Environ. ScL Technol. 10: 1250-1256. Scotto, M. V., E. A. Bassham, J. O. L. Wendt, and T. W. Peterson. 1988. Quench induced nucleation of ash constituents during combustion of pulverized coal in a laboratory furnace. In Proceedings of 22nd International Symposium on Combustion, pp. 239-247. The Combustion Institute. Sethi, V. and P. Biswas. 1990. Fundamental studies on particulate emissions form hazardous waste incinerators. In Proceedings of the 16th Annual RREL Hazardous Waste Research Symposium. EPA/600/990073, pp. 59-67. Cincinnati: USEPA.

Sousa, I. A., J. E. Houck, I. A. Cooper, and I. M. Daisey. 1987.The mutagenic activity of particulate organic matter collected with a dilution sampler at coal fired power plants. JAPCA 37:1439-1444. Stamatakis, P., C. A. Natalie, B. R. Palmer, and W. A. Yuill. 1991. Research needs in aerosol processing. Aerosol ScL Technol. 14:316-321. Ulrich, G. D. and J. W. Riehl. 1982. Aggregation and growth of submicron oxide particles in flames. /. Colloid Interface ScL 87:257-265. U.S. EPA. 1990. Code of Federal Regulations, CFR 42.9. Van de Hulst, H. C. 1981. Light Scattering by Small Particles. New York: Dover. Wang, Z. M., G. Yang, P. Biswas, W. Bresser, and P. Boolchand. 2001. Processing of iron-doped titania powders in flame aerosol reactors. Powder Technol. 114:197-204. Wu, J. J. and R. C. Flagan. 1987. Onset of run away nucleation in aerosol reactors. /. Appl. Phys. 61: 1365-1371. Yang, G. and P. Biswas. 1997. Study of the sintering of nanosized titania agglomerates in flames using in situ light scattering measurements. Aerosol ScL Technol. 27:507-521. Yang, G. and P. Biswas. 1999. Computer simulation of the aggregation and sintering restructuring of fractal-like clusters containing limited number of primary particles. / Colloid Interface Sci. 211: 142-150. Zachariah, M. R. and D. R. F. Burgess. 1994. Strategies for laser-excited fluorescence spectroscopy: Measurements of gas-phase species during particle formation. /. Aerosol ScL 25:487-497. Zachariah, M. R., D. Chin, H. G. Semerjian, and J. L. Katz. 1989. Dynamic light scattering and angular dissymmetry for the in situ measurement of silicon dioxide particle synthesis in flames. Appl. Opt. 28:530-536. Zimmer, A. 2000. Aerosol Formation Mechanisms, Metallurgical Aspects and Engineering Control of Fumes Generated During Arc Welding Operations. Ph.D. thesis, Aerosol and Air Quality Research Laboratory, University of Cincinnati, Cincinnati, OH. Zimmer, A. and P. Biswas. 2001. Characterization of arc welding aerosols as a function of distance from welding zone. /. Aerosol ScL Submitted.

(Mezey, 1966). The aggregation of particles is usually controlled by addition of traces of potassium salts in the flames (Dannenberg, 1971). Particle formation proceeds by hydrocarbon oxidation in the diffusion flame which is followed by soot particle formation by cyclization reactions at the fuel rich side of the flame (Santoro and Miller, 1987). The soot particles then grow by coagulation and surface reactions (material addition from the gas phase). Measurement problems that arise in this case include determination of particle size and morphology. The other major application of aerosol reactors is the generation of pigmentary titania and fumed silica powders in flame reactors. These reactors are similar to those used in manufacture of carbon blacks. Precursor vapor (TiCl4 or SiCl4) with small amounts of hydrocarbon fuel are injected with nearly stoichiometric amounts of oxygen into the reactor. Rapid, exothermic oxidation of the vapor takes place forming oxide powders and chlorine gas (Clark, 1977). These powders are loose aggregates of submicrometer particles. The crystalline structure of the particles can be controlled by adding dopant vapors in the feed stream and by tailoring the temperature history of the reactor (Mezey, 1966). A number of other examples of ceramic powder generation by aerosol routes exist. Spray pyrolysis is used on an industrial scale to produce simple and complex metal oxides (Ruthner, 1983). These ceramic powders are used in a wide variety of applications for manufacture of substrates for catalysts and integrated circuits, structural ceramics and ceramics with specific optical, magnetic and electrical properties. In most of these cases, manufacture of submicrometer powders with precisely controlled physical and chemical properties is of utmost importance in making ceramic parts with the minimal number of imperfections (microcracks and flaws). Currently, ceramic powders are made primarily in spray pyrolysis and flame reactors. Other types of reactors such as furnace, plasma, and laser reactors are used on a much smaller scale. In all of these cases, measurement problems include determination of particle size distributions and morphology, chemical and phase content, and microstructural information. Examples of different types of materials and the determination of their physical and chemical properties are presented in later sections. Generation of powders using flame processes relies on introducing species into the flame that react to form gaseous species with low vapor pressures (Table 32-1). The reactants that have been used most often are metal chlorides because of their low cost and high volatility. Some processes have relied on using precursors in the aqueous phase. The solution is formed into droplets that are sent into the flame where evaporation takes place to provide gaseous reactants (Zachariah and Huzarewicz, 1991). Aerosol routes also allow direct production of thick and thin films of a variety of materials via aerosol deposition onto surfaces. A variety of aerosol processes for film formation exist. Films can be formed by either deposition of droplets or by deposition of solid particles onto surfaces. Examples of deposition of droplets onto surfaces include spray pyrolysis (see reviews by Tomar and Garcia, 1981; Mooney and Redding, 1982). This route begins with a solution of molecular precursors which is converted into droplets. These droplets are directed onto a heated surface where the solvent evaporates and the precursors decompose resulting

TABLE 32-1. Powders Made by Flame Reactors Product Al 2 O 3 SiO2 TiO 2 Carbon black Al 2 O 3 , Cr 2 O 3 , Fe 2 O 3 , GeO 2 , SiO2, SnO 2 , TiO 2 , V 2 O 5 , ZrO 2

Reactant

Author

Al acetylacetonate SiCl4 TiCl4 Alkanes Chlorides

Sokolowski et al. (1977) Ulrich (1984) George et al. (1973) Dannenberg (1971) Formenti et al. (1972)

in film formation. A related process involves delivery of volatile species into a reactor using an aerosol (Koukitu et al., 1989; Viguie and Spitz, 1975; Siefert, 1984; Salazar et al., 1992). An example of solid particle deposition for film formation is the cluster deposition process (Takagi et al., 1980; Andres et al., 1989). Many other examples also exist including formation of thick films of ceramics (Kodas et al., 1988; Baker et al., 1989; Komiyama et al., 1987; Adachi et al., 1988; Koguchi et al., 1990). However, the most well known and successful example of a solid particle deposition process is optical fiber generation in which the unique capacity of aerosol processes for manufacture of high-purity particles has been exploited (MacChesney et al., 1974). Optical fibers are made by fabrication of a preform glass rod, rod sintering and fiber drawing and coating (Nagel et al., 1982). The key process with respect to the light transmission and mechanical properties of the fiber is the fabrication of the preform rod. The manufacturing goal of the latter process is to make a preform with a prescribed radial refractive index profile at the maximum yield. Preforms are made by silica and dopant particle deposition onto thin substrate rods (external processes) or into hollow, substrate glass tubes (internal processes). In the external processes that were invented by Siecor, Inc. (SIC), the particles are generated by a flame reactor and deposit primarily by thermophoresis onto the substrate. The flame-generated particles are too large for rapid Brownian diffusion and too small for inertial impaction to be important for particle deposition. In the internal processes that were invented at Lucent Technologies (LUC), halide vapors and oxygen flow through a rotating, hollow, quartz tube that is externally heated by a slowly, axially traversing torch (or plasma). Inside the tube, the halide gases are oxidized forming particles that either deposit to the tube wall by thermophoresis or exit the tube with the process gases. The preform is completed when the quartz tube is almost filled with glassy layers of particles that were fused by the torch. Currently, preforms are made on an industrial scale at rather low yields [50% for silica and much lower for costly dopants such as germania (Bohrer et al., 1985)]. A major challenge in manufacture of optical fibers is to understand the relationship between deposit properties, glass particle size and composition and process conditions. This is important since it has been observed that during sintering of multicomponent particulate deposits, small particles tend to be depleted of dopants much faster than large particles and bubbles can be present in deposits. This relationship is also important for improvement of the current low process yields. As the fiber optics market becomes more competitive, the low yields become a critical issue from both economic and environmental viewpoints (toxic halide vapors can be released when they are not converted to particles). Thus, there is a need for in situ measurements of particle size and composition.

AEROSOL PROCESSES In general, aerosol processes are attractive for their simplicity since they do not involve large volumes of liquid byproducts and can be used for production of high-purity materials (Pratsinis and Mastrangelo, 1989). Most aerosol measurement techniques have been developed for aerosols in environmental systems. Industrial aerosols, however, are quite different from their environmental counterparts. Typically, high concentrations (1014 to 1020 particles/m3) of irregular, fine particles suspended in a variety of gases are encountered in industrial processes. These aerosols are made at high temperatures (1300K) and massive production rates (10 tons/h) in closed systems and experience a spectrum of physicochemical phenomena within a few seconds. Product yields vary widely. The yield, for example, for pigmentary titania is usually over 90% (Xiong and Pratsinis, 1991), while that of optical fibers is under 50% (Bohrer et al., 1985).

Despite the economic significance of industrial aerosol processes and the importance of aerosol characterization in operation and control of these processes, very little has been done for the development of real-time instruments for aerosol characterization in manufacturing processes. There are three reasons for this oddity. First, industrial aerosols have notoriously high concentrations, and furthermore, particle formation, growth and transport take place simultaneously over very short time scales. Removing an industrial aerosol from its environment for sampling and analysis, may drastically change its characteristics. For example, an aerosol with 1016 particles/m3 of average diameter 0.1 um reduces its concentration by coagulation by 90% within Is (Friedlander, 1977). Second, powders made by aerosol processes tend to be nonspherical aggregate structures making difficult their rapid characterization by conventional aerosol instruments. Third, the development of industrial aerosol processes has historically been Edisonian rather than guided by the principles of aerosol science. In fact, the development of some of these processes was well ahead of aerosol science! Aerosol routes in material synthesis can be classified as gas-to-particle and droplet-toparticle conversion depending on the starting material. In gas-to-particle conversion, precursor gases or vapors react forming product particles that grow further by coagulation and/or surface reactions. Powders made by gas-to-particle conversion have relatively narrow size distributions and consist of aggregates of nonporous primary particles. Production of chemical commodities such as carbon black, silica and titania involves gas-to-particle conversion at high temperatures in the so-called flame reactors (Ulrich, 1984). In the droplet-to-particle route, solution or slurry droplets are suspended in gases by liquid atomization. These droplets are converted to powders by direct pyrolysis or by in-situ reactions with another gas. The product powder distribution is determined primarily by the droplet distribution and in some cases by particle break-up during pyrolysis or drying. Powders made by this route are rarely agglomerated but can be porous depending on precursor solute concentration and drying rate (Zhang et al., 1990; Charlesworth and Marshall, 1969; Leong, 1981,1987). Spray drying (Lukasiewicz, 1989) and spray pyrolysis (Kodas, 1989) are typical industrial processes employing droplet to powder conversion. Solids can also be used as starting materials in aerosol processes involving either complete or partial vaporization or melting of the solid and subsequent formation of the product (Bolsaitis et al., 1987; Weimer et al., 1991). In this chapter, processes and materials employed in industrial scale manufacture of aerosols are reviewed and the most common techniques for particle characterization are outlined. Gas-to-Powder Conversion

The gas-to-powder conversion route refers to "building" particles from individual molecules in the gas phase (Fig. 32-1). The particle formation process is driven by the generation of molecules by chemical reaction from precursor gases or by the rapid cooling of a superheated vapor. Cooling of the vapor can be accomplished by contact with a cooler inert gas or by expansion through a nozzle even of a supercritical fluid (Petersen et al., 1986; Matson et al., 1987). The process of evaporation-condensation is carried out either under atmospheric pressure in inert gases or in a vacuum chamber that can be evacuated to pressures of 10~6 to 10"10 Torr in order to reduce contamination levels. High temperatures are usually required to accomplish the reaction or to bring the vapor to the superheated state. Evaporation followed by condensation of a species is a convenient method for the formation of nanometersize particles. Depending on the thermodynamics of the process (Ulrich, 1971; Rao and McMurry, 1989; Xiong and Pratsinis, 1991), the product molecules can form particles either by uninhibited collisions (collision-controlled nucleation) or by balanced condensation and evaporation to and from molecular clusters (condensation-evaporation-controlled nucleation). The newly formed particles grow further by collision with product molecules

Gas-to-Particle R o u t e Vapor or gas

(Solids

Heating or

Condensation or Reaction

Liquids Chemical Reaction & controlled nucleation (coagulation)

Molecules & Clusters Condensation Evaporation controlled nucleation

Particles k ,Surface Reactions Coagulation & Aggregation Coagulation & Aggregation

Powder Fig. 32-1. Physicochemical processes occurring during powder production from gases (gas-to-particle conversion). (Adapted from Pratsinis, 1990.)

(condensation) and/or particles (coagulation). When the rate of particle collision is faster than that of particle coalescence (fusion), aggregates of spherical (primary) particles are formed. The latter ones are termed hard or soft aggregates (or agglomerates) depending on how easy it is to break the bonds connecting the primary particles. Rapid cooling favors the formation of aggregates (Schaefer and Hurd, 1990; Dobbins and Megaridis, 1987; Hurd and Flower, 1988; Ulrich and Riehl, 1982). Various energy sources are used to create the high temperatures required during gas-toparticle conversion. The name of these sources is frequently used to signify a specific aerosol reactor in which gas-to-particle conversion takes place. Thus, flame reactors use combustion of hydrocarbons in premixed or diffusion flames. Plasma reactors utilize the high energy of an ionized gas (plasma). Laser reactors employ the high energy and precision of a laser beam. Furnace reactors electrically heat ceramic tubes through which precursor gases flow. The gas-to-particle conversion route is used industrially for production of pigmentary titania, fumed silica and carbon blacks and on a laboratory scale for production of metals, semiconductors, and oxide and nonoxide ceramics in the nanometer range. As a result, this route has been studied the most and mathematical models describing particle formation in various gas-to-particle conversion processes have been developed. The effects of various process variables on average powder properties (polydispersity and average diameter) are easily described by moment models (Dobbins and Mulholland, 1984; Pratsinis, 1988). The

evolution of the detailed powder size distribution in gas-to-particle processes has been also modeled (Wu and Flagan, 1988; Landgrebe and Pratsinis, 1990). More recently these models have been used to create design diagrams facilitating the understanding of the effect of process variables on powder size characteristics (Pratsinis et al., 1989; Landgrebe et al., 1990). The gas-to-particle coversion route is most useful for generation of single-component powders. It is not as convenient, however, for multicomponent materials where differences in the vapor pressures and subsequently in the nucleation and growth rates of the various species may lead to nonuniform product composition from particle to particle and even within particles. The primary advantages of gas-to-particle routes are: small particle size, narrow particle size distribution, solid particles, and high purity. Disadvantages include difficulties in producing multicomponent materials, and problems in handling hazardous gases. Flame Reactors. These reactors involve formation of spherical or aggregate particles from gaseous precursors in diffusion or premixed flames (Ulrich, 1984; Pratsinis, 1998). Aromatic hydrocarbons are used as precursors of carbon blacks in fuel rich flames (Medalia and Rivin, 1976; Ivie and Forney, 1988). A variety of metal oxides, most notably silica and titania, has been produced by oxidation of the respective metal halide vapors in hydrocarbon supported flames (Ulrich and Riehl, 1982; Kriechbaum and Kleinschmidt, 1989; Formenti et al., 1972; Hurd and Flower, 1988; Sokolowski et al., 1977; George et al., 1973). Additives are introduced in the process to control the phase, shape and size distribution of the product particles (Mezey, 1966; Dannenberg, 1971; Haynes et al., 1979). Electrical charges offer another tool for control of the characteristics of flame-made powders. Hardesty and Weinberg (1973) found that the primary particle size of product silica was reduced up to a factor of 3 with increasing applied electric potential. This was attributed to the rapid particle deposition on the electrodes as a result of reduced residence time for particle sintering. Katz and Hung (1990), found that the number density of the aggregates decreased and the size of titania and germania particles made in an H2/O2 counterflow diffusion flame reactor increased by 3 to 10 times upon the application of an electrical field across the flame. In premixed and diffusion flames (Vemury and Pratsinis, 1995,1996; Vemury et al., 1997) making titania and silica, the aggregate and primary particle size decrease proportionally to the applied electric field as they were measured by transmission electron microscopy (TEM) and specific surface area analysis. Flame reactors provide an attractive method for particle generation since they are simple in construction and operation relative to other systems. Particle formation in flame reactors closely follows the standard description of the gas-toparticle route. Flame-generated aggregates contain from a few up to many thousand primary particles. The typical size of the primary particles ranges from 1 to 500 nm. Maximum flame temperatures are usually on the order of 2500K. In contrast to other gas to particle conversion systems, temperatures are lower in flame reactors than in plasmas but much higher than in furnace, evaporation-condensation and laser reactors. The residence time and temperature determine whether the product powder is amorphous or crystalline and determines the degree of agglomeration and coalescence. Advantages of flame reactors include: Scale up has been demonstrated for carbon blacks, silica, titania and other oxides; simplicity; ability to use volatile and nonvolatile precursors; high product purity; and large range of diameters for particles in aggregates. The disadvantages of flame reactors are that hard agglomerates are formed under most conditions and that broad particle size distributions (coagulation controlled) are usually obtained. A detailed review of this technology can be found in Pratsinis (1998) Furnace, Laser and Plasma Reactors. Furnace reactors involve driving chemical reactions of powder precursor vapors in a tubular, furnace-heated reactor (Masdiyasni et al., 1965;

Kanapilly et al., 1970; Suyama and Kato, 1976; Prochaska and Greskovich, 1978; Alam and Flagan, 1986; Kruis et al., 1998a; Okuyama et al., 1986; Kim, 1997). The reactants are introduced into the reactor either as a gas or as an aerosol of a solution. In the case of solution droplets, the solution and precursors in the droplet evaporate in the reactor before gas phase reaction occurs. Thus, the aerosol facilitates introduction of low volatility precursors into the reactor (Kagawa et al., 1983). Particle formation in furnace reactors is reasonably well understood through detailed models for specific chemical systems such as silicon particle formation by silane decomposition (Wu et al., 1988), silica by SiCl4 oxidation (Kim and Pratsinis, 1988,1989), titania by oxidation OfTiCl4 or titanium isopropoxide (Landgrebe et al., 1990; Okuyama et al., 1986,1989). Furnace reactors provide excellent control of temperature and flow velocity so they are frequently used for measurement of global chemical reaction rates of particle forming systems (SiO2: Powers, 1978; French et al., 1978; TiO2: Pratsinis et al., 1990). Lasers can be used to drive chemical reactions that result in particle formation by photothermal and photochemical processes (Cannon et al., 1982; Casey and Haggerty, 1987). In the photothermal process, the laser energy is absorbed by the carrier gas and raises its temperature, thus, driving the chemical reactions. In the photochemical process, the reactant molecules absorb the laser light and then dissociate to form condensable species. Thus, temperatures can be near ambient in contrast to the photothermal case where temperatures are near 1300K or more (Kruis et al., 1998b). The photochemical process presently requires expensive reactants and lasers. In a typical photothermal process, a CO2 laser is used to heat gas molecules by light absorption (Alexandrescu et al., 1998). The laser acts as a heat source since molecules are in thermal equilibrium with the gas. High energy lasers can provide sufficient energy to force coalescence of agglomerated particles. This can be used to produce spherical, nonagglomerated particles in the nanometer range (Lee and Choi, 2000). The absence of walls leads to high-purity products. The advantage of using a laser is that its narrow spectral width allows coupling between the light source and the molecular precursor. The coupling requires near coincidence between laser light wavelength and wavelength of absorption. Efficiencies for use of the laser power are about 15%. Models for particle formation in laser reactors have been developed indicating that particle coagulation determines the product powder size distribution (Flint and Haggerty, 1990). Plasma reactors use an ionized gas (plasma) to provide the energy required to drive reactions that result in particle formation (Phillips and Vogt, 1987; Pickles and McLean, 1983; Gani and McPherson, 1980; Girshick and Chiu, 1989,1990). A plasma is a system with a high energy content in which a significant fraction of the species are ionized. Two types of plasma systems are of interest: high temperature equilibrium thermal plasmas and low temperature nonequilibrium plasmas. In the thermal plasmas, the temperatures of the electrons and ions are equal. In low temperature plasmas such as glow discharges, the temperature of the electrons is much greater than that of the ions. Thermal plasmas are most common for powder generation. Applications of thermal plasma for processing of materials include plasma synthesis, spraying, and consolidation (Peng et al., 1998). Glow discharges are not used extensively for powder generation or processing (Singh and Doherty, 1990) though they are used in film coating. Numerous variations of plasma reactors exist depending on how the reactants are introduced into the system. Temperatures (600 to 25,000K) are higher than in all other aerosol reactors and complete destruction of reactants is common. This allows use of molecular and solid feed streams so, in principle, any material can be processed. Common processes for all of these methods are formation of product species that nucleate to form particles during cooling while exiting the plasma, following the standard formation pathways of gas-toparticle conversion. Two broad classes of thermal plasma reactors are used: DC arc jet and high frequency (microwave or radio frequency) induction systems. In the case of the DC arc jet, current is supplied to the ionized gas (plasma) by physical contact with a metallic

electrode surface. This system is relatively simple and inexpensive. However, the electrodes are consumed and end up in the product, resulting in contamination. In the case of a high frequency induction plasma (microwave or radio frequency plasma reactor), there is no contact between the plasma and its power source. The induction coil lies outside the reactor walls like the electrical thermal elements in furnace reactors. Energy transfer takes place through the electromagnetic field of the induction coil, so there is no contamination of the product. An overview of microwave plasmas can be found in Vollath et al. (1997). Variables that can be controlled are plasma composition and frequency. The most common case is an argon plasma operated at 20OkHz to 20MHz with typical temperatures about 15,00OK. As a result, plasma reactors can handle high melting point materials and solid powder feeds. Evaporation-Condensation. Evaporation followed by condensation of a species is a convenient method for the formation of nanometer-size particles that is used for the synthesis of metal nanoparticles. The technique is called inert gas condensation in that field. Metals and nanocrystalline ceramics with improved mechanical properties can be produced by these methods (Siegel, 1990; Ramsey and Avery, 1974; Granqvist and Buhrman, 1976). A metal is vaporized into a host gas where the vapor is cooled resulting in particle formation by nucleation. Cooling of the vapor can be accomplished by contact with a cooler inert gas or by expansion through a nozzle. Expansion of a supercritical fluid through a nozzle can also be used for particle formation (Petersen et al., 1986; Matson et al., 1987). The process of evaporation-condensation is often carried out below atmospheric pressure in inert gases. A typical system involves a vacuum chamber that can be evacuated to pressures of 10~6 to 10"10 Torr in order to reduce contamination levels. Often a boat is used to hold a metal heated to vaporize it into the gas phase. A natural plume is established by free or forced convection from the metal containing boat. This boat can be heated continously or in pulses. Temperatures are usually lower than those in plasma and flame reactors. In evaporation-condensation/reaction processes, reactions take place at the newly formed particle surface altering the chemical and/or phase composition of the particles, A precursor compound is vaporized into a carrier gas, which is then cooled resulting in droplet formation by nucleation (Fig. 32-1) (Matijevic, 1986, 1987; Ingebrethsen et al., 1983; Visca and Matijevic, 1979; Ingebrethsen and Matijevic, 1980; Kodas et al., 1987). After the droplet formation a reaction step particle formation takes place resulting in solid. Evaporation-condensation processes are reasonably well understood especially when precursors with known physicochemical properties are employed and well-defined flow patterns exist such as in tube flows (Tsantilis et al., 1999). Aerosol processes are used industrially to produce a variety of products. Each of the gas to particle conversion processes discussed in this chapter has its own advantages and disadvantages. Table 32-2 presents a comparison of these different processes. Droplet-to-Particle Conversion

The most common processes for generation of powders with liquid phase precursors are spray pyrolysis, spray drying and freeze drying (Fig. 32-2). The common step in all of the processes is formation of droplets containing molecular or particulate precursors using some type of droplet generator. In spray drying processes a variety of physical and chemical processes can occur while the particles are in the gas phase. These processes include: droplet generation, evaporation of solvent from droplets, initial crystallization of the solute in the droplet and further evaporation from the droplet consisting of solution/solid along with further solute crystallization to form a dried particle. Spray and freeze drying is similar to spray pyrolysis since it only involves heating the droplets to lower temperatures so solid phase reactions do not occur (Masters, 1972; Johnson,

TABLE 32-2. Comparison of Aerosol Processes for Powder Production Flame

Maximum size Spread Morphology

Maximum temperature (K) Material

Complexity

Evap.Cond. Reaction

Laser

Plasma

Hot Wall

1 urn

0.1-10 urn 1 urn

1 urn

Broad Agglomerates solid

Narrow Solid

Narrow Solid

Narrow Broad Agglomerates Spherical solid solid

<2,000

1,000

25,000

Metals and oxides Low

Nonoxides Nonoxides oxides oxides

2,500

Oxides

Low

Medium

High

1.0 um

1,000

Spray Pyrolysis 0.10-100 jim Broad Spherical solid, porous hollow 1,600

Nonoxides Nonoxides oxides oxides semiconductors Low Low

Droplet-to-Particle Route Solution or Slurry Atomization

Droplets

Evaporation

Reaction (Pyrolysis)

Surface reaction

Drying

Powder Fig. 32-2. Physicochemical processes occurring during powder production from liquid droplets (dropletto-particle conversion). (Adapted from Pratsinis, 1990.)

1981). An additional difference is that much larger droplets are used in spray drying. This is a consequence of the goal of spray drying, which is usually to form powder consisting of relatively large free-flowing granules (100um). Charlesworth and Marshall (1969) have discussed some of the possible particle morphologies and how they are produced in spray drying.

In the case of spray pyrolysis, these steps are followed by: reaction of the precursors in the dried particle to form the product powder and gaseous products, intraparticle transport processes resulting in changes of the particle morphology, evaporation of volatile metals, metal oxides, and other species followed by condensation on reactor walls and on particles and by new particle formation and coagulation. During freeze drying, once the droplets are formed and collected in the cold liquid (hexane or nitrogen), the characteristics of the product powder are determined by the nature of the cold liquid, solvent, precursors, drying rate, and subsequent processing conditions. Freeze drying processes are similar to spray drying as far as droplet generation, but the droplets are frozen and then dried once in the solid state (Johnson et al., 1987). The solution containing dissolved compounds is sprayed into liquid nitrogen or some other cold liquid where the droplets freeze. The liquid is removed by sublimation and the dry product is converted to the desired product by further heating. In addition to the processes discussed above, coagulation, diffusion, sedimentation, impaction, and thermophoresis in the process vessels and manifold can modify the particle size distribution and influence yields. Further operations may be required to form the final powder product when droplet-to-solid processes are involved. These processes are typically operated at atmospheric pressure and involve particles in the 0.1 to 100 urn range. In general, advantages of all of these processes are the ability to process organic and inorganic materials; the ability to form a variety of multicomponent materials; simplicity; cost effectiveness (only a few unit operations); many choices for inexpensive liquid phase precursors; doping is possible; scale-up has been already demonstrated to ton quantities; uniform chemical composition; relatively safe process since volatile precursors not required. The disadvantages for these processes are: porous or hollow particles can be formed at certain conditions and the spread of particle sizes is limited by the spread of the generated droplets. Droplet Generation, The droplet generation process is critical to all droplet-to-particle processes since it largely controls the particle size distribution of the product. A variety of generators are available (Lefebvre, 1989; Kerker, 1975): pressure, rotary, air-assist, air-blast, ultrasonic, electrostatic, and vibrating capillary atomizers. Other atomizers such as sonic, windmill, flashing liquid jets and effervescent atomization are also used. Each of these types of droplet generators produces different particle size distributions and has different advantages and disadvantages. Typical average droplet diameters are 1-100 urn. Pressure atomizers work by discharging a liquid through a small aperture under high pressure into a slow-moving gas stream. Rotary atomizers operate by centrifugal atomization. A liquid is directed onto a rotating element that converts the liquid stream to droplets. Airassist atomizers involve exposing the liquid to a stream of air or steam flowing at high velocity. Air-blast atomizers are similar to air-assist nozzles with the exception that the former use much higher quantities of air or steam and at lower velocities. Ultrasonic atomizers use a transducer or horn which vibrates at ultrasonic frequencies to produce the short wavelength required for atomization. Flow rates are lower than can be obtained for air blast, air assist, pressure and rotary atomizers. Electrostatic atomizers expose a liquid jet or film to a strong electrical pressure. The expansion in the droplet is opposed by surface tension forces. Although this method is capable of producing submicrometer droplets, it does not allow high liquid flow rates and is commonly used on a laboratory scale. Vibrating capillary atomizers produce droplets down to 30 um. Again, only low liquid flow rates are possible and the latter atomizers are mostly used on a laboratory scale. Measurements of droplet size distributions are routinely made by optical techniques. The prediction of droplet size distributions is accomplished through empirical correlations accounting for liquid properties, atomizer design and fluid flows (Lefebvre, 1989). The most relevant properties of a liquid are surface tension, viscosity, and density.

Spray Pyrolysis. Spray pyrolysis involves passing suspended precursor droplets through a high temperature region (furnace or a flame). There, rapid evaporation of the volatile components of the droplet takes place first. Further residence in this region allows chemical reactions to occur in the solid phase that form the product powder. Depending on the process conditions and the material properties solid or porous powders are formed and collected on a filter. This process has been called evaporative decomposition, spray roasting, spray calcining, Aman process, atomizing burner technique, decomposition of misted solutions, aerosol or mist decomposition method and others (Sokolowski et al., 1977; Pebler and Charles, 1989; Chadda et al., 1991; Wenckus and Leavitt, 1957; Vollath, 1990; Epstein, 1976; Imai and Takami, 1985; Kodas et al., 1988,1989a,b; Biswas et al., 1989b, 1990; Zhang et al., 1990; Gardner et al., 1984; Zachariah and Huzarewicz, 1991; Messing et al., 1993). Spray pyrolysis allows generation of particles with a uniform chemical composition from particle to particle since the precursors are present in the correct stoichiometry in each droplet. Volatile precursors are not required; thus, a wide variety of reactants can be used while avoiding the need for carbon-containing precursors. This allows use of inexpensive reactants such as metal nitrates, chlorides, and fluorides. The generation of multicomponent ceramic powders can be carried out on an industrial scale using only a few unit operations; the ability to scale up these systems for production of ton quantities of conventional ceramic oxide powders has already been demonstrated (Ruthner, 1979). Unagglomerated particles are produced since particle growth rarely involves collisions between particles. High purities are also obtained since milling is not required. Key challenges include determining the morphology of the particles, their chemical composition, their phase composition, and their behavior in liquids during ceramic powder processing operations. Recently, Brewster and Kodas (1997) produced nonaggregated, dense BaTiO3 particles by flame-spray pyrolysis of aqueous solutions of barium acetate and titanium lactate. Low flame temperatures resulted in hollow particles while high temperatures or long residence times resulted in dense and homogeneous particles. Xiong and Kodas (1993) and Messing et al. (1993) have nicely summarised some of the current quantitative understanding on particle formation by spray drying, along with applications for synthesis of solid or hollow particles and even nanoparticles. Superconducting powders can be made by flame processes also. Merkle et al. (1990) produced YBa2Cu3Ox superconducting oxide by reacting an atomized nitrate solution containing yttrium: barium: copper in the atomic ratio 1:2:3, respectively, in an oxy-hydrogen diffusion flame. The primary particle size was about 100 to 300 nm. X-ray diffraction measurements of oxygen-annealed particles revealed a crystal structure identical with that of the conventionally produced superconducting oxide. Magnetic susceptibility measurements demonstrated that the material is superconducting with a Tc of about 93 K. Zachariah and Huzarewicz (1991) also made submicrometer YBa2Cu3O78 particles with Tc = 92 K in an aerosol flame reactor by a flame spray pyrolysis method in which an aerosol composed of an aqueous solution of Y, Ba, and Cu nitrate salts was introduced into a H2/O2/Ar flame (1000 to 1400K) as was pointed out earlier in this review. Lewis (1991) has produced mullite and other mixed oxides (SiO2, Al2O3) by spray combustion of triethylaluminum with various organosilicon compounds and solvents. Recently, Bickmore et al. (1996), have further developed a similar flame spray pyrolysis process for synthesis of spinel powders from Mg-Al alkoxides with diameters 10 to lOOnm and specific surface areas of 40 to 60m2/g at a production rate of 50 to 100 g/h. MEASUREMENT TECHNIQUES Physical and chemical characterization of materials produced by aerosol processes involves many techniques that are summarized in Tables 32-3 and 32-4. Niessner (1990) has reviewed in

TABLE 32-3. Summary of Characterization Techniques for Collected Particles Physical

Chemical

Size Sedimentation velocity Electrical mobility measurements Image analysis and microscopy Light scattering Sieving Chromatography and field flow fractionation Electronic methods

Phase composition XRD Analytical TEM

Shape SEM TEM Optical microscopy STM/AFM Image analysis and microscopy

Elemental composition Bulk X-ray fluorescence Emission and absorption spectroscopies FTIR PIXE NRA GC/MS NMR Surface sensitive AES EDS XPS Electron photoemission spectroscopy Mass spectrometry

Charge Electrometer Microstructure/Porosity Mercury porosimetry Surface area He density Pellet green density

Thermochemical and thermophysical DTA/DSC TGA TGA/MS

Microstructure/grain size TEM SEM STM/AFM

Other SAXS SANS ESR EPR Raman spectroscopy Magnetic and electrical properties Optical properties (pigments)

situ aerosol measurement techniques, most of which are included in Chapters 12 and 13 in this book. Physical Properties Size. A wide variety of methods exist for measurement of particle size distributions in the gas and liquid phases. Measurements of particle size distributions in the gas phase are discussed in Chapters 15,16, and 18. Recent advances in particle size measurements for aerosol particles that have been collected and placed in liquids have been summarized by Miller and Lines (1988). Table 32-4 lists techniques for measurement of particle size. A critical problem

TABLE 32-4. Particle Size A nalysis Techniques Method Sedimentation velocity Gravity Centrifuge Electrical SEM TEM Optical microscope Sieving Electronic methods Chromatography and field flow fractionation

Particle Size Oun)

Sample Size

0.2-100 0.02-100 0.003-1 0.01-50 0.001-1 0.2-400 30-5000 0.3-400 0.001 to 200 (depending on technique)


that arises when using several techniques for measurement of size is that each technique relies on different physical characteristics of the particles such as optical properties, electrical properties and sedimentation velocity. As a result, particle sizes determined from different techniques can vary significantly. Examples of this will be given in later sections. Light extinction by aerosols is a powerful method for determination of particle size distributions. The method can be applied in-situ in the gas phase and can also be used for particles that have been collected and suspended in liquids. Details of light absorption and scattering processes (static and dynamic) are discussed in Chapters 15 and 16. The most basic particle size analysis involves a microscope. In recent years scanning and transmission electron microscopes (SEM and TEM) are used for direct size characterization of fine particles. These methods involve the examination of particle images followed by collection of the images by a computer and presentation to an image analyzer for automatic characterization. In addition to particle size distributions, particle shape is also best determined by SEM and TEM examination of particles. The most common image analysis systems consist of a microscope, a video camera and an image analyzing computer. The images of the particles can be collected with optical, scanning electron and transmission electron microscopes, depending on the size range of interest. For example, TEM allows sizing of particles from the nanometer up to the micrometer range. Only small amounts of materials are needed. For example, a filter can be inserted into a flow path to collect particles. A good dispersion of particles is required so only single particles are present on the filter surface. The filter surface can then be examined by a microscope. Figure 32-3 shows a TEM photograph OfTiO2 particles produced by a furnace aerosol reactor. A comparison of results from SEM/image analysis and a differential mobility particle size analyzer are shown in Figure 32-4. Sedimentation velocity measurements of particle size rely on Stokes law (Reed, 1988). Particles are collected and then placed in a fluid. The settling velocity of the particles is detected by measuring light or X-ray transmission. Calculation of particle size distributions assumes that the particles are solid and spherical since Stokes law is used to relate velocity to diameter. In practice, particles greater than 0.1 urn can be sized by this method while particles below 0.1 urn are influenced too strongly by Brownian motion to allow measurements unless centrifuges are used. Particles larger than 50 urn do not follow Stokes law, defining an upper limit for this technique. However, extending or converting the Sedigraph (MCM) distributions for example to sieve distributions and vice versa is reasonably well successful, allowing easy and robust comparisons (Austin and Shah, 1983). Figure 32-5 shows a particle size distribution obtained by this technique for Ba0 SeCa014TiO3

Fig. 32-3. TEM of titania particles produced at 1300K by TiCl4 oxidation in a furnace aerosol reactor. (Courtesy of M. K. Akhtar.)

particles produced by spray pyrolysis using a mixture of metal acetates, lactates and nitrates (Ortega et al., 1991). Sieving is a simple and easy way to fractionate particles according to size (Reed, 1988). This method is limited to particles that are much larger than 1 urn, usually greater than about 50 urn. Agglomeration becomes a problem below this size. Smaller particle sizes can be sized using liquids, but these methods are often difficult to carry out reproducibly. Chromatography and Field Flow Fractionation. These are separation methods whereby a liquid suspension of particles is classified into size fractions that must later be quantified in order to produce a particle size distribution (Miller and Lines, 1988). The chromatographic techniques involve separation of particles according to size within a flow channel as in conventional chromatography. In field flow fractionation, the particle retention is induced by an electrical, centrifugal or thermal gradient. Hydrodynamic chromatography (HDC) involves passing particles through a packed bed of non-porous column material. The rate of transport of particles depends on the size of the particles, the size of the spheres in the bed, and the flow rate and ionic strength of the eluting solution. This technique is useful for particles from 20 nm to 2 urn. Size exclusion chromatography utilizes a porous bed. The flow through the porous material imposes a separation effect by steric exclusion. The upper limit of this technique is roughly 500 nm with a lower limit of essentially molecular size. Capillary particle chromatography is an extension of HDC. This method used a long capillary tube instead of a packed bed to provide the separation. Particles from 20 nm to 200 urn can be separated. Field flow fractionation methods can be used for particles from 10 nm to lum. Fractionation in the centrifugal sedimentation version of this process occurs within closely spaced parallel plates. The particles are caused to roll or tumble along the outer wall to produce the segregation. These methods have not been used extensively for characterization of particles produced by aerosol processes, primarily because they require resuspension of collected particles in a liquid. Resuspension of submicrometer particles into a liquid is not simple and can lead to significant errors in measurements of size distributions.

(a)

A N/N

SEM DMPS

Particle Diameter, \xm (b) Fig. 32-4. a, Scanning electron micrograph (SEM) of titania particles produced at 1723 K by TiCl4 oxidation in a furnace flow reactor, b, A comparison of the size distributions obtained by image analysis of the SEM micrograph and those obtained by the differential mobility particle sizer (DMPS). (Courtesy ofM.K.Akhtar.)

Electrical Sensing Methods (Coulter Counter). Particles to be sized are suspended at low concentration in an electrolyte solution, which is drawn through a small aperture in an insulating wall, across which a current also flows (Miller and Lines, 1988). Each particle that passes through the sensing zone displaces a certain amount of electrolyte solution that results in a momentary change in the electrical impedance across the aperture. The volume of solution displaced by the particle is determined by the amplitude of the electrical pulse produced as the particle passes the sensing zone. Extreme dilution is required to avoid coincidence errors. Particles 100 nm to roughly 400 um can be sized using different size apertures.

Mass finer (%)

Conventional •P-1600

Particle diameter (jam) Fig. 32-5. Particle size distribution obtained by sedimentation velocity for Ba0^CaCuTiO3 particle produced by spray pyrolysis (P-1600) and by conventional milling of ceramic oxide mixtures. (Adapted from Ortega et al, 1991.)

Microstructure

The description of the microstructure of particles involves characterization of porosity, surface area, density, and grain size. The microstructure of particles plays a critical role in determining rates of densification of ceramic particles. Similarly, particles used for catalytic applications require knowledge of total surface areas. Mercury Porosimetry. This technique utilizes mercury intrusion to obtain pore size distributions in powders and solids (Reed, 1988). A sample is placed into the instrument where mercury under a controlled pressure is forced into the pores of the material. As the pressure is increased, more mercury enters the pores. The accessible pore size corresponding to a given pressure is obtained from the Washburn equation which relates the applied pressure to the intruded radius of a cylindrical pore. Thus, mercury porosimetry is useful for determining the pore size distribution in particles. Surface Area. Surface areas of powders are usually measured using nitrogen or helium adsorption followed by analysis of the data using the Brunauer-Emmet-Teller isotherm (Reed, 1988). Estimates of surface areas provide information about the porosity of particles and the primary particle size in particles that consist of agglomerates of smaller particles. The primary particle size can be related to surface area assuming that the particles are smooth, monodisperse spheres. Specific surface areas can range from a few m2/g to hundreds of m2/g in the case of highly porous materials or very fine particles. Density. In many cases, it is useful to know the density of a material in particles. Helium density measurements provide the density of a material assuming that no closed porosity is present (Reed, 1988). A sample is weighed and placed in a vessel into which helium is introduced. The volume of the sample is obtained from the volume of gas displaced by the powder. The density is obtained from the volume and weight of the sample. For this reason, hollow particles with no porosity can lead to erroneously low densities. In contrast, if the density is

known, He density measurements become a method for the determination of the amount of closed porosity in a sample. Pellet Green Density. An indirect but practical measure of the quality of a powder is the green density that can be obtained by dry pressing or other powder processing techniques. Typically about 100 mg of powder is needed. Hollow particles usually lead to low green densities, 20% to 40%. In contrast, solid particles can provide green densities of 50% to 60%. It must be noted that this is a crude measure since the type of processing can result in large variations in green density (Fig. 32-6). A related measurement is the tap density. This technique, which involves measuring the apparent density of powders, also provides a way of quantifying the packing characteristics of powders from an applications point of view. Tranmission Electron Microscopy. Processing of ceramic or metallic powders involves particle sintering. Sintering rates and the grain size distribution of densified ceramics depend on the initial grain size distribution within each particle and the size distribution of the particles. For example, Edelson and Glaser (1988) have shown that sintering of spherical particles consisting of agglomerates of smaller particles can result in broad grain size distributions. Broad grain size distributions are usually undesirable (Barringer et al., 1984).Thus, it is useful to know the size of grains in particles. In addition, particles with smaller grain sizes have larger grain surface areas which provides a large driving force for increase of grain size (Kingery et al., 1976). Thus, particles consisting of nanocrystalline grains are usually more active than single-crystal particles. TEM provides a powerful method for determination of grain sizes. Crystallites with dimensions ranging from nanometer to micrometer can be observed. Figure 32-7 shows how TEM can be used to distinguish if particles are nanocrystalline or single crystals (Feldman and Mayer, 1986). The separate crystallites in the particles are visible within each particle. Special 3D TEM techniques can enhance our understanding of the morphological properties of particles being examined (Hoppe et al., 1968; Frank, 1995). To extract volume information the probe is rotated and different projections of the sample are imaged. At present these methods are time intensive and therefore not widespread. However, advances in computing power, electro-optical imaging and microscope design promise much faster and versatile acquisition of 3D TEM pictures.

% Theoretical Density

Specific Surface Area (m2/g)

Aerosol Reactor Temperature (0C) Fig. 32-6. Green density (left-hand scale) and surface area (right-hand scale) of Ba0SeCa0I4TiO3 particles produced by spray pyrolysis as a function of production temperature. Particles produced at lower temperatures are hollow and lead to low green densities. (Adapted from Ortega et al., 1991.)

a

b Fig. 32-7. a, Superconducting (so-called 123 superconductors) (YBa2Cu3O7.,, particles with nanometer grains. (Adopted from Carim et al., 1989.) b, Single-crystal YBa2Cu3O7x particles (bar = 10nm). Particles were produced by spray pyrolysis.

Charge. Multiply charged particles with unipolar charges tend to agglomerate less (Friedlander, 1977). Therefore it can be of interest to know the charge per aerosol particle. To measure the particle charge electrometers coupled with particle counters are often used. The electrometer is a well insulated total particle filter. The charge accumulating on the filter is responsible for a small potential difference between ground and the particle filter. By amplifying this signal, typically by a factor of 1010 to 1012 the amount of charge being deposited

on the filter can be measured. If the particle count is known the average particle charge can be easily obtained. Phase Composition

X-Ray Diffraction, X-ray diffraction (XRD) is a standard tool for identification of crystalline phases in powder samples (Feldman and Mayer, 1986). The crystalline phases in the sample diffract X-rays according to Bragg's equation which relates lattice spacing to the wavelength of the X-rays used as a probe. The amount of sample needed in practice is roughly 100 mg or more. Crystalline phases present at levels of about 1 percent or greater can be detected. The unit cell dimensions (a, b, c) of the crystal can also be determined and provide more information for complex structure materials (Biswas et al., 1990). Amorphous materials are not observed. In routine work, XRD relies on availability of standards that allow identification of peaks in the diffraction pattern. Figure 32-8 shows phases of aerosol made titania and aluminum nitride identified by XRD (Akhtar et al., 1991). TEM/Electron Diffraction. Microdiffraction with transmission electron microscopy (TEM) allows determination of the crystalline structure of phases present in particles (Feldman and Mayer, 1986). Since single particles can be examined, only very small amounts of a sample are needed. The electron diffraction pattern allows determination of whether the particles is poly crystalline (ring patterns), amorphous, or a single crystal (spot patterns). Figure 32-9

Intensity x 10 (counts/s)

ANATASE

RUTlLE

a

26 (Degrees)

Fig. 32-8. X-ray diffraction (XRD) patterns for a, titania powder produced in a furnace aerosol reactor. The peaks resulting from the two different crystal phases (anatase and rutile) in which titania can be found are clearly visible; and for b, aluminum nitride powder produced in a furnace aerosol (Courtesy of M. K. Akhtar.)

IntensityxiO (counts/s) b

26 (Degrees) Fig. 32-8. Continued

Fig. 32-9. Microdiffraction pattern for crystallite in a YBa2Cu3O7x particle. Spot diffraction pattern demonstrates that particle is a single crystal. (Courtesy of Larry Allard and Abhaya Datye.)

shows how different phases in a submicrometer sized particle can be identified by electron diffraction by TEM (Carim et al., 1989). Chemical Composition

A variety of techniques exist for determining the chemical composition of aerosol particles. A more extensive discussion of X-ray fluorescence, proton-induced X-ray emission, nuclear reaction analysis, and emission and absorption spectroscopies is presented in Chapter 13. This chapter outlines these methods and provides extensive reference materials in the context of material synthesis. Methods for Bulk Materials X-Ray Fluorescence. X-ray fluorescence (XRF) is a method for determination of the chemical composition of liquid and solid samples (Feldman and Mayer, 1986). The sample is irradiated with X-rays, which results in fluorescence. The wavelength of the emitted light is characteristic of the elements in the sample. This technique can be used for examination of multicomponent solid samples where it is desirable to determine the ratio of the elements present in the sample. Elements with Z > 11 are detectable down to lOppm. Only 10 to 100 ng levels of particles are required on a filter in order to determine chemical composition. Fourier Transform Infrared Spectroscopy. Fourier transform infrared spectroscopy (FTIR) measures the amount of infrared radiation absorbed by a sample as a function of frequency (Reed, 1988). This technique provides information about the different types of bonds present in a sample. The amount of sample required for obtaining a spectrum depends on the nature of the sample, but 100 mg amounts are often adequate. If the FTIR spectra are obtained in-situ, the technique permits measurement of the aerosol temperature. A black body temperature can be obtained both for the gas and the particles as demonstrated by Morrison et al. (1997). Also by analyzing the IR spectra the gas as well as particle chemical composition can be obtained. Proton-Induced X-Ray Emission Spectroscopy. Proton-induced X-ray emission (PIXE) spectroscopy involves the use of ions to induce emission of x-rays from a sample (Feldman and Mayer, 1986). Elements from Na to U in the periodic table can be detected. PIXE allows the use of 10 urn beams for high sensitivity detection of trace elements. PIXE also allows identification of a number of elements simultaneously in complex samples. Nuclear Reaction Analysis. Nuclear reaction analysis (NRA) (also known as instrumental neutron activation analysis) involves probing a sample using an ion beam and examining the light ion reaction products (Feldman and Mayer, 1986). The probe beam induces radioactivity in the sample, which is examined to determine the nature of the elements in the sample. Light elements such as H, Be, B, C, O, F can be detected. The detection limit for thermal neutron activation analysis is 10"8 to 10"10g. Because this method has poor spatial resolution, it is commonly used for samples of particles collected on filters of impactors. A drawback of this technique is that it requires a nuclear reactor and specialized expertise. However, several laboratories provide this service on a routine basis. Emission and Absorption Spectroscopies. Emission and absorption spectroscopies are extremely sensitive methods for detection of trace amounts of elements in samples (Reed, 1988). Emission spectroscopy provides analyses to the ppm level with 5mg samples. The powder is usually excited in an electric arc or by a laser flash. Chemical information is

provided by the wavelength and intensities of the spectra of the emitted light. This technique is often used for quick qualitative surveys of samples. Flame Emission Spectroscopy provides quantitative analyses of alkali elements and boron to the ppm level, with ppb detectability for some elements. This technique is commonly used for liquid samples that can be sprayed into flames. Inductively coupled plasma emission spectroscopy (ICP-ES) also utilizes liquid samples and is a reliable, fast and multielement method. Atomic absorption spectroscopy (AAS) can analyze 30 to 40 elements present at concentrations of <0.1%.This technique is the industry standard for quantitative analysis to ppm levels for solution samples though it analyzes one element at a time whereas ICP can analyze about 40. The sample in solution form is sprayed into a flame where the radiation from a lamp is passed through the flame. Dissociated atoms in the flame absorb the radiation and reduce the transmitted intensity. The identity of the species present is determined from the absorption wavelengths. Surface Sensitive Methods Auger Electron Spectroscopy. Auger electron spectroscopy (AES) is a surface sensitive method for determination of the chemical composition of materials (Feldman and Mayer, 1986). This method requires an ultrahigh vacuum chamber and involves directing an electron beam at a sample which results in emission of electrons (the so-called Auger electrons) with energies that are characteristic of the elements in the sample. Depth profiling can be obtained in combination with sputtering for solid samples. This method is surface sensitive with a penetration depth of 0.5 to 2nm and can detect elements from Li through U with up to 0.3% sensitivity. Instruments with 0.1 (Xm spatial resolution are available. Since even single particles can be examined, the amount of material required is very small. Due to the special properties of nanoparticles, it is possible to extract electrons from aerosol particles under ultraviolet light irradiation (Schleicher et al., 1993). The photons are absorbed by surface atoms and the deposited energy results in the subsequent emission of a photoelectron (Greber et al., 1995). The probability of the emitted photoelectron returning to the surface is small, and therefore the particles can be efficiently charged. After removing the highly mobile negative ions from the gas stream the accumulated charge can be counted by an electrometer. The method is very sensitive allowing for the detection of submonolayer coating of particles. The photoemission depends on the photon energy being equal or bigger than the energy an electron needs to escape from the atom. Therefore this technique is sensitive to the particle surface composition. For example, most metal and most carbonaceous particles give a strong signal (Hiiglin et al., 1997; Skillas et al., 1999) for wavelengths around 200 nm. Due to space charge effects there is an upper limit on the particle concentration of about 1012 particles/m3 (Mohr et al., 1993). A detailed description can be found in Chapter 14. For the PAS to be used effectively in an industrial environment it has to be combined with an effective dilution system (Hueglin et al., 1997). Energy Dispersive Spectroscopy. Energy dispersive spectroscopy (EDS) involves the probing of a sample with electrons resulting in emission of X-rays with energies characteristic of the elements present in the sample (Feldman and Mayer, 1986). Because an electron beam is used to probe the sample, the composition of particles in the nm range can be examined. Single particles can be examined by this technique. The probing depth is on the order of 1 jam. EDS has 0.1% detectability for Z > 11. Thus, elements with atomic weights greater than sodium can be detected in most instruments (Fig. 32-10). Resolution down to 1 um is available with SEM and down to 50 nm is available with TEM instruments. X-ray maps of a particle can be used to examine the chemical homogeneity of multicomponent particles. These maps show regions of high or low concentrations of a given element. The technique can also be used to examine a large number of particles and to look for variations in stoichiometry

COUNTS

C U B A

A L C U

B A

Y

B A

A G.

C U

ENERGY, keV Fig. 32-10. Energy dispersive spectrometer results for an Ag-YBa2Cu3O7x particle. The location of the peaks and their intensities determine the element and the relative amount of the element present in the probed portion of the sample. (Adapted from Carim et al., 1989.)

from particle to particle. Quantitative measurements usually require flat surfaces, but semiquantitative information can be obtained for particles. X-Ray Photoelectron Spectroscopy. X-ray photoelectron spectroscopy (XPS) (also known as electron spectroscopy for chemical analysis) can detect elements from Li—U (Feldman and Mayer, 1986). This method irradiates the particles with x-rays resulting in the emission of photoelectrons with energies characteristic of the elements in the sample and their chemical bonding. The effective probing depth is 3nm and the method has 0.1 monolayer sensitivity. Spatial resolution of 10 um can be obtained in state-of-the-art instruments. This limits this technique to relatively large particles. Because monolayer amounts can be detected, the amount of sample needed is small. The primary advantage of XPS over AES and EDS is that the chemical bonding of the elements can be determined. Mass Spectrometric Methods. Mass spectrometers (MS) are extremely sensitive instruments for detection of elements from H to U and can detect ppm levels (Feldman and Mayer, 1986). The various mass spectrometric methods differ by the method used to introduce the sample into the MS. Particles on surfaces can be vaporized using pulsed lasers or can be introduced directly from the gas phase into ionization regions. These methods are usually not used on a routine basis because of their complexity. Thermophysical and Thermochemical Analysis

Thermogravimetric Analysis. Thermogravimetric analysis (TGA) measures the change in the weight of a sample as it is heated at a known rate (Hench and Gould, 1971). The primary use of TGA for materials produced by aerosol processes is for determination of the extent of reaction and for detection of species such as water vapor. For determination of the identity of the evolved species, TGA/mass spectrometry can be used. This variation determines the identity of the species released at a certain temperature. Using rate of weight loss, kinetic parameters can also be determined (Biswas et al., 1989a,b). TGA and differential thermal

Temperature / C

Weight / %

Time / s Fig. 32-11. Weight loss as a function of temperature for a silica-carbon nanocomposite. The first and second weight loss stem from the removal of physically adsorbed and chemically bound water. The third step stems from the oxidation of the carbon (Courtesy of R. Miiller).

analysis (DTA) (below) typically require 50 mg samples. Figure 32-11 shows TGA for a silicacarbon nanocomposite produced in a flame reactor. Differential Thermal Analysis. This technique (DTA) measures the amount of heat released or absorbed by a sample as it is heated at a known rate (Hench and Gould, 1971). When the enthalpy change is determined, the method is called differential scanning calorimetry (DSC). The presence of exothermic or endothermic processes at certain temperatures provides information about the nature of phase changes and chemical reactions occurring in the material as it is heated. DTA can often be used as a sensitive method for establishing the presence or absence of secondary phases in samples if these phases undergo phase transformations at known temperatures. Surface Properties

The surface properties of powders suspended in liquids can play a critical role in powder processing. For this reason, it is often important to understand the behavior of the particles in solution. A variety of measurements are available to characterize surface properties in solution: electrophoresis, sedimentation potential, electro-osmosis, streaming potential, Zeta potential. These measurements are related and can provide information about the nature of ionized functional groups on the surfaces of the particles. Other Methods

A variety of other methods exist for characterization of particles produced by aerosol processes. These methods include Raman spectroscopy, electron spin resonance (ESR), small angle X-ray scattering (SAXS), small-angle neutron scattering (SANS), and other processes. These are relatively specialized techniques that are expensive and are not usually used on a routine basis.

A class of techniques that is being used on an increasing basis is scanning tunneling microscopy (STM) and atomic force microscopy (AFM). These techniques provide information about structure down to the atomic level (Neddermeyer, 1990) for both conductors and insulators. This resolution is important in the case of nanometer-size clusters on surfaces that may be difficult to image by techniques such as TEM.

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conductor chips, magnetic head assemblies, and other precision products, vertical laminar flow (VLF) is the rule. The entire ceiling of the cleanroom is covered with HEPA or ultra low penetrating air (ULPA) filters (see Chapter 9), which feed air into the work space beneath at flow velocities typically in the 0.25 to 0.50 m/s range. In like fashion, the entire floor of the work space is the return duct so that in an empty cleanroom the air flow is essentially laminar and directed vertically downward. This arrangement means that the air reaching the work space level is freshly filtered and that any contaminant emissions generated at the work level by the ongoing activity are convected away from the work space and into the floor return ducts. The exhaust air recirculates through conditioning filters and re-enters the cleanroom through the ceiling ULPA filters along with any makeup air needed to replace air losses (hoods venting to the outside, leaks, and so forth). Figure 33-1 is a photograph of the interior of a cleanroom used for manufacturing state-of-the-art semiconductor chips. The ceiling surface facing the work space is composed of 100% filters except for the light fixtures. The perforated squares making up the floor are the entry ports to the return ducts for recirculating the cleanroom air. The primary impetus for cleanroom development has come from those industries most vulnerable to particulate contamination. The concentration of aerosol particles in the ambient air can be greater than 109m"3.The concentration of aerosol particles in a cleanroom is often less than 10 m"3. This dramatic reduction in concentration is the primary value contributed by cleanroom suppliers of which there are many with many differing designs. As a result, standards for cleanroom performance were initiated as part of the pioneering cleanroom work at Sandia and since then have been periodically revised and updated by broadbased industrial/professional groups having vital interests in the use and performance of cleanrooms. The most recent versions of these standards have been developed by international committees and are now internationally accepted. The International Cleanroom Standards

According to ISO 14644-1 (ISO, 1999), a cleanroom is a "room in which the concentration of airborne particles is controlled and which is constructed and used in a manner to minimize the introduction, generation and retention of particles inside the room and in which other relevant parameters, e. g. temperature, humidity and pressure, are controlled as necessary." No mention of contaminants other than aerosol particles appears in this definition. The concentration of nonparticulate contaminants ("molecular contaminants") in a cleanroom is not part of the definition and has no bearing or influence in the system used by ISO 14644-1 to classify cleanrooms. While ignoring the concentration of molecular contaminants in a cleanroom may be a serious oversight in many applications, this chapter, following the lead of the standards and in accordance with the subject of this handbook, discusses only the measurement of the concentration and size distribution of aerosol particles in a cleanroom. The aerosol particles found in a state-of-the-art cleanroom represent one extreme of the aerosol particle size/concentration spectrum. The particles of interest in the highest quality cleanrooms are those at the smallest end of the aerosol particle size range, and the concentrations of interest are among the lowest detectable. A state-of-the-art cleanroom is a space in which particles have not only been controlled but in many cases eliminated insofar as possible. Measuring aerosol particle concentration in such a cleanroom thus means measuring small-sized aerosol particles at low concentrations. This chapter consists of three major topics: 1. A detailed review of the newly published ISO 14644-1 standard and its companion standard, ISO 14644-2 (ISO, 2000). ISO 14644-1 defines an international system for classifying the quality of cleanroom air, defined in terms of aerosol particle

Fig. 33-1. Contemporary VLF cleanroom used in semiconductor manufacturing. Particle-free air continuously enters through the ceiling filters and exits through the perforated floor panels. (Courtesy of Semiconductor International Magazine.)

concentration, and spells out procedures for verifying that the air quality in a given cleanroom meets the requirements of its claimed classification. This verification procedure requires measuring the concentration and size distribution of aerosol particles in that cleanroom. ISO 14644-2 offers monitoring guidelines that provide some assurance that the performance of a clean room, previously certified by the methods of ISO 14644-1, has been maintained. 2. A brief description of the commercially available instrumentation suitable for making measurements of aerosol particle concentration and size distribution in a cleanroom. This list of instrumentation properties is based on literature from U.S. instrument suppliers only. However, the performance specifications given here for just U.S. manufacturers can be assumed to be representative of instrumentation that is commercially available from manufacturers worldwide, as reported in Chapter 15. 3. A test method proposed for measuring the emission rate of aerosol particles from specific particle sources inside a cleanroom, such as apparatus, equipment, or even personnel. This test method provides a basis for ranking candidate cleanroom apparatus according to their propensity to introduce particulate contamination into a cleanroom. It is taken from an Institute of Environmental Sciences and Technology (IEST) recommended practice (IEST, 1992). Thus the primary goals of the chapter are to provide a contemporary summary of (1) the measurements of aerosol particle concentrations that are required in today's cleanrooms in order to satisfy business contracts and to maintain control of the environment for manufacturing precision products; and (2) the performance and properties of the instrumentation that is commercially available for making those required measurements of aerosol particle concentration in a cleanroom. A secondary goal is to emphasize that anything brought into a cleanroom is a potential source of airborne particulate contamination and to present a test method for measuring the emission rate of aerosol particles from the tools and other objects so introduced.

INTERNATIONAL STANDARDS FOR CLASSIFYING, VERIFYING, AND MONITORING CLEANROOMS: ISO 14644-1 AND -2 Cleanroom standards classify cleanroom air quality by code names that specify a maximum allowable concentration of aerosol particles equal to or greater than a specified reference particle size. ISO 14644-1 defines particle size as the "the diameter of a sphere that produces a response by a given particle-sizing instrument that is equivalent to the response produced by the particle being measured." The composition of the reference sphere is not specified by ISO 14644-1, but optical particle counters are most often calibrated using polystyrene latex spheres. Typically, cleanroom standards use a code name composed of numbers from which the maximum allowable aerosol particle concentration permitted under that class code name can be directly read for the reference particle size. For example, Class 10 air quality, according to Federal Standard 209E (1992), means that the concentration of aerosol particles, 0.5 urn and larger, is no greater than 10 particles/ft3 (350 particles/m3). Class 1 implies a concentration of aerosol particles, 0.5 urn and larger, that is no greater than 1 particle/ft3 (35 particles/m3), and so on. In this specific U.S. federal standard, the reference particle size is always 0.5 urn and larger. Other cleanroom standards specify other reference particle sizes and use other units of aerosol particle concentration. The discussion in this section is of one classification system only—the classification system defined by the newly published ISO 14644-1 (ISO, 1999) standard and its companion standard, ISO 14644-2 (ISO, 2000).

ISO Standards 14644-1 and -2 On May 1,1999, the International Organization for Standardization (ISO), Geneva, Switzerland, issued ISO 14644-1: Cleanrooms and Associated Controlled Environments—Part 1: Classification of Air Cleanliness (ISO, 1999). Its companion standard was issued on September 15, 2000: ISO 14644-2: Cleanrooms and Associated Controlled Environments—Part 2: Specifications for Testing and Monitoring to Prove Continued Compliance with ISO 14644-1 (ISO, 2000). These documents were prepared by an international committee with the goal of replacing the many differing national standards previously used for cleanroom classification. While the general scope and purpose of all the preceding national standards have always been basically the same, each country developed its own definitions so that a given class code name in one country's standard invariably had a different name than the same cleanroom air quality would have in another country's standard. When, by coincidence perhaps, the same class code name was used in two different national standards, it invariably referred to a different concentration of aerosol particles in one standard than in the other. The particle size to which the class code name referred varied among the various national standards. Except for the most recent version (FED-STD-209E, which used both English and its own unique, metric class designations), the U.S. 209 standards expressed particle concentrations exclusively in ft"3; all other national standards used m~3, and so it went. Table 34-2 of Fissan et al. (1993) presents a short summary comparing the designations used by five of the predecessor national standards. No easy conversion from the usage of one national standard to that of another existed. This confusing jumble of conflicting names and unique designations has long made obvious the need for a single, universal standard for cleanroom classification. The ISO has now provided just such a consensus international standard, which is the topic of this section. Class Codes as Defined by ISO 14644-1 Table 33-1 (a reproduction of Table 1 from ISO 14644-1), and Figure 33-2 (a modified reproduction of Fig. A.I from ISO 14644-1) summarize the classifications of this new international standard. The class designations are expressed as "ISO Class W where N can be any number between 1.0 and 9.0 in increments of 0.1. As in predecessor standards, N describes the maximum allowable concentration for its class. The maximum allowed aerosol particle concentration for ISO Class N is 10N particles/m3 that are 0.1 urn in size or larger. While the reference particle size in the international standard is 0.1 um, a maximum allowable concentration can be calculated for any particle size between 0.1 and 5.0 um by the following equation (Eq. 1 from ISO, 1999): (33-1) where Cmax (particles/m3) is the maximum allowed concentration of aerosol particles of size d and larger; N is the class code (9.0 > TV > 0.1); and d (um) is the lower bound on the particle size at which the aerosol particle concentration is measured. Only for d = 0.1 um is Cmax = 10^. At all other values of d, a factor less than 1, as given in Eq. 33-1, reduces the maximum allowed aerosol particle concentration. The standard specifies that d must always be in the range 5.0 um > d > 0.1 um. For most values of N, allowable values of d are further restricted as indicated in both Table 33-1 and Figure 33-2. The standard does not allow Cmax to be measured at particle sizes outside the range of the filled blocks in the row corresponding to each integer class code in Table 33-1 or beyond the solid circles on the end of the curves plotted in Figure 33-2 for each integer class. For example, ISO classes 7,8, and 9 can be verified by measuring the concentration of aerosol particles only in the size

TABLE 33-1. Cleanroom Classification Codes as Defined in ISO 14644-1 ISO Classification Number (AO ISO Class 1 ISO Class 2 ISO Class 3 ISO Class 4 ISO Class 5 ISO Class 6 ISO Class 7 ISO Class 8 ISO Class 9

Maximum Concentration Limits (Particles/m3 of Air) for Particles Equal to and Larger Than the Considered Sizes Shown Below (Concentration Limits are Calculated in Accordance with Equation [1] in 3.2) 0.1 um

0.2 um

0.3 um

0.5 um

lum

5um

10 100 1,000 10,000 100,000 1,000,000

2 24 237 2,370 23,700 237,000

10 102 1,020 10,200 102,000

4 35 352 3,520 35,200 352,000 3,520,000 35,200,000

8 83 832 8,320 83,200 832,000 8,320,000

29 293 2,930 29,300 293,000

Airborne particle concentrator*, C^ax particles/m3

Note: Uncertainties related to the measurement process require that concentration data with no more than three significant figures be used in determining the classification level. Source: From ISO (1999), Table 1.

Particle size, D, \n pm Fig. 33-2. Maximum allowable aerosol particle concentrations for the Class codes of ISO 14644-1. Cmax represents the maximum permitted concentration (in particles per cubic metre of air) of airborne particles equal to and larger than the considered particle size. N represents the specified ISO class number. (ISO 1991.)

range 5.0 um > d > 0.5 um. Similarly, showing compliance with the requirements for ISO Class 1 air quality requires measurements in the size range 0.2 urn > d > 0.1 um. The range of d allowed for non-integer values of Af is not explicitly defined in the standard. Because one intent of limiting the ranges of d is to not allow verification of cleanrooms of high quality (low values of N) at large particle sizes where aerosol particle concentrations are very low, the safe procedure is to assume that the allowable particle size range for non-integer values of JV less than 5 is that of the next lowest integer range. For N = 4.5, for example, the allowed range of d should be assumed to be the same as that for N = 4 or 1.0 urn > N > 0.1 urn. Similarly, at large values of N, the d ranges specified in Table 33-1 and Figure 33-2 are those that avoid measuring excessively large concentrations of small particles, vulnerable to errors associated with coincidence counting (see Chapter 15), so the reasonable approach for noninteger values of N > 6 is to assume the d range of the next larger integral value of N. For N = 6.2, for example, the allowed value of d should be those of the N=I curve or 5.0 um> J > 0.5 jim. Equation 33-1 assumes an inverse power-law relationship between concentration and particle size with an exponent of 2.08. This size distribution is unlikely to be an accurate description of most (or even any) size distributions encountered in actual operating cleanrooms. Conceivably, the air quality in a given cleanroom could meet the requirements for its claimed classification at one of the allowed particle sizes but not at another or any other. Thus the particle size at which the measurement of aerosol particle concentration is made must be specified in advance and be included in the test report. When measurements at multiple particle sizes are part of the test protocol, the different particle diameters at which the measurement of aerosol particle concentration are made must differ by a factor of at least 1.5: . . . d3 > 1.5 d2 > 1.5 ^ 1 . . . Because all d{ must be within the allowed ranges of d, this additional restriction means that ISO Class 1 can be measured at no more than two different particle sizes in the 0.1 to 0.2 um range. "U" and "M" Descriptors

While any measurement of aerosol particle concentration that will be used to verify air quality class must be made in the particle size range 5.0 um > d > 0.1 |im, the standard provides guidance for reporting the concentrations of particles outside this size range. A "U descriptor" is the concentration (m"3) of all measurable aerosol particles including the ultrafine particles, where ultrafine particles are defined as aerosol particles smaller than 0.1 um. It is specified and reported as U (x; y), where x is the maximum permitted or, in a test report, the actually measured concentration (m"3) of aerosol particles, including the ultrafine particles, of size greater than y (um) y is the lower cut-off limit (um at 50% counting efficiency) of the particle counter used to measure the U descriptor. A maximum permitted U descriptor of 140,000 m"3 in the particle size range >0.01um, for example, would be written "U (140,000; 0.01)." Similarly, an "M descriptor" is the measured concentration of macroaerosol particles, defined as aerosol particles larger than 5.0 um. It is specified and reported as M (a; b); c, where a is the maximum permitted or, in a test report, the actually measured concentration of macroparticles (m~3)

b is the lower bound on the particle size of the measuring instrument (|im) c identifies the measurement method A maximum permitted M descriptor of 10,000 aerosol particles >5um measured by a time-of-flight aerodynamic particle counter would be written "M (10,000; 5); time of flight aerodynamic particle counter." Both the U descriptor and the M descriptor simply provide supplementary information regarding the air quality. In principle, these descriptors can have any value whatsoever without affecting the ISO classification codes of the cleanroom air quality. Procedures for Verifying a Cleanroom Classification per ISO 14644-1 As in previous cleanroom standards, ISO 14644-1 spells out, in its Annex B, explicit procedures for verifying that a given cleanroom does in fact meet the requirements of its claimed classification. Simply making a measurement of aerosol concentration at a random location or two within the cleanroom does not constitute adequate verification. This section summarizes the procedures given in ISO 14644-1 as a reference test method for demonstrating compliance. However, alternative test methods "having comparable accuracy" may be used if specified and agreed on by both the certifier and his customer. Number of Sampling Locations; Number of Separate Measurements/Location. The minimum number of sampling locations required to certify a given cleanroom is the square root of the cleanroom area in m2: (33-2) where NL is the minimum number of required sampling locations (rounded up to the next whole number) and A is the area of the cleanroom or clean zone in m2. These sampling locations "are evenly distributed throughout the area of the cleanroom or clean zone and positioned at the height of the work activity." For small area clean zones the minimum number of locations can be one. However, the minimum number of individual sample measurements required for the verification of any cleanroom or clean zone is three. If only one location is required and chosen, at least three separate, individual sample measurements must be made at that one location. If more than one location is required, only one sample measurement per location is permissible provided that the total number of separate sample measurements made at all locations in the cleanroom is still at least three. Sample Volume. The minimum air volume required for any one individual measurement is the larger of (1) 2 L or (2) that air volume that would be required to contain 20 particles, assuming that the particles are uniformly distributed in the air at the maximum allowed concentration of the designated ISO class: Vs = 20/C^,^

x 1,000

(33-3)

where Vs (liters) is the minimum air volume required to be sampled in each individual measurement 20 is an arbitrarily selected minimum number of particles assumed to represent an acceptable balance between sampling practicality and statistical significance

Cmax,dmax (m 3) is the maximum allowed particle concentration at the largest particle size, dmax, to be measured 1,000 converts m3 to liters Regardless of Eq. 33-3 and particle counter flow rates, the sample collection time at each location must be at least 1 min. Sample Probe Size and Orientation. While sizing a sampling probe to sample air flow isokinetically would seem to be good measurement practice (see Chapter 8), ISO 14644-1 does not require it, recognizing that the errors introduced by anisokinetic sampling of cleanroom air are negligible for particles smaller than 0.5 urn and likely to be minor for aerosol particles smaller than 5jim (Appendix C, FED-STD-209E). In cleanrooms with unidirectional air flow, the center line of the probe should be aligned so as to face into the direction of the air flow streamlines. In those cleanrooms characterized by nonunidirectional air flow, the probe should be oriented vertically, 180° to the direction of the gravitational force. Acceptance Criteria for Meeting the Class Requirement. Two conditions must be satisfied in order to verify a claimed cleanroom classification. Condition 1. The average aerosol particle concentration calculated from all the individual measurements made at one location cannot exceed the maximum concentration allowed for the claimed cleanroom classification, Cmax, as calculated by Eq. 33-1. While an individual measurement at a given location can exceed the target Cmax concentration, the average concentration, calculated by combining all individual measurements made at that same location, cannot. (33-4)

where Ax is the average particle concentration at location /, ci>n are the particle concentrations of the individual measurements at location /, and n is the number of measurements made at location L Individual values of c u can exceed Cmax without causing the cleanroom to fail the verification as long as no value of Ax does. Condition 2. The mean of all the location averages must also meet the target Cmax concentration with a 95% upper confidence limit as calculated by the Student t statistic. Demonstration of compliance with this condition is required only when fewer than 10 locations are sampled (/ < 10), because the probability of failing this requirement is vanishingly small when all 10 or more locations satisfy condition 1. (33-5) where M is the mean of the location averages, Ax are the individual location averages (Eq. 33-4), and L is the total number of locations. The 95% upper confidence limit (UCL) of M is calculated from (33-6) where J0-95 is the Student t distribution factor for 95% UCL (see Table 33-2) and s is the standard deviation of M: (33-7)

TABLE 33-2. Student t Distribution Factor for the 95% UCL Number of locations, L 495

2

3

4

5

6

7-9

>9

6.3

2.9

2.4

2.1

2.0

1.9

Not applicable

Observed count, C

Stop counting, FAIL

C= 3.96+ 1.03E Continue counting C =-3.96+ 1.03E Stop counting, PASS

Expected count, E Fig. 33-3. Graph for making pass/fail decisions per the sequential sampling procedure of ISO 14644-1. (Adapted from ISO 1991.)

Sequential Sampling. ISO 14644-1 recognizes that Eq. 33-3 can call for impracticably long sampling times in state-of-the-art cleanrooms. For example, when certifying ISO Class 1 air quality at dmax = 0.2 urn, Table 33-1 shows that Cmax,dmax = 2 m"3 and hence, from Eq. 33-3, Vs = 10,000L. With a particle counter sampling air at a rate of 4.67 x lO^nrVs [28L/min or lcfm, a typical flow rate for a particle counter], collecting a single sample of the required volume would take nearly 6h! The standard provides potential relief in such instances by allowing the use of a sequential sampling procedure. This procedure can lead to early pass/fail decisions based on observed cumulative particle count versus cumulative sampling volume rather than the concentration determined only after measuring the entire volume prescribed by Eq. 33-3. When the aerosol particle concentration in the air being sampled is either significantly greater or significantly less than the maximum allowed particle concentration of the claimed class code, sequential sampling spots these trends early in the sampling period, dramatically reducing the time required to verify class compliance. At the same time, the pass/fail probability of the sequential sampling procedure is essentially the same as for the single sampling procedure (Cooper and Milholland, 1990). Unlike the single, fixed-volume sampling procedure, sequential sampling requires continuous data monitoring and reduction in order to make the pass/fail decision as soon as the data indicate sufficiently large departures from the observed concentration versus sampling time (or volume) curve expected for air at the maximum particle concentration allowed by the claimed class code. Software for automating the required data logging and real-time analyses is readily available. Figure 33-3 illustrates the procedure by plotting the boundaries of the observed particle count, C, against the expected particle count, E. The expected particle count in a valid sample of air at the maximum allowed aerosol particle concentration of any class code is 20. Thus the abscissa in Figure 33-3 starts at 0 and ends at 20. The upper and lower boundaries of C plotted in Figure 33-3 are derived from published formulas for sequential sampling, assuming a 0 to 20 range for the expected particle counts (Cooper and Milholland, 1990). As soon as the cumulative particle count exceeds the upper bound (C = 3.96 + 1.03 E), counting stops

TABLE 33-3. Pass/Fail Criteria per the Sequential Sampling Plan in ISO 14644-1 Passes if Count, C, Comes Later Than Expected

Fails if Count, C, Comes Earlier Than Expected Fractional Time (t) 0.0019 0.0505 0.0992 0.1476 0.1961 0.2447 0.2932 0.3417 0.3902 0.4388 0.4873 0.5359 0.5844 0.6330 0.6815 0.7300 0.7786 1.0000

Observed Count

Fractional Time (t)

Observed Count

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

0.1922 0.2407 0.2893 0.3378 0.3864 0.4349 0.4834 0.5320 0.5805 0.6291 0.6676 0.7262 0.7747 0.8233 0.8718 0.9203 0.9689 1.0000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Note: Fractional times are given as the fraction of total times (t = 1.0000 at the class limit). Source: ISO (1999).

and the sample is declared to have failed to meet the class code requirement. Conversely, as soon as the cumulative particle count falls below the lower bound (C = -3.96 + 1.03), counting again stops with the verdict that the sample has passed the class code requirement. Counting continues as long as the cumulative counts remain between the two bounds. If the cumulative counts remain between the two bounds throughout the sampling period, the sequential sample provides a concentration measurement that meets the class code requirement and that can serve as a single measurement of the concentration average for that location. Table 33-3 is an alternative presentation of the sequential sampling process. Here the upper and lower bounds are expressed in terms of the fractional time at which these bounds equal the observed counts. An expected count of 20 equals a normalized time of unity; the real time is that time required by the selected particle counter to sample the required volume, V8 (Eq. 33-3).The left-hand side of Table 33-3 describes the upper bound. When the observed count reaches the listed value before the corresponding fractional time, the sample fails the class requirement. Conversely, the right-hand side of Table 33-3 represents the lower bound. When the fractional sampling time reaches the listed value before the corresponding number of counts is observed, the sample passes the class requirement. When neither of these conditions is met, the sampling continues. The software merely has to note the time at which each integral count is reached and determine if that time is earlier than the upper bound (a failure), longer than the lower bound (a pass), or in between the two (no decision, so counting continues). The number of required sampling locations and the acceptance criteria for sequential sampling are similar to those of single-volume sampling. What changes is the procedure for making the pass/fail decision. Sequential sampling allows an early decision to be made on the basis of one trial per location with potentially a smaller sample volume. A fail decision rendered by that trial can be overturned only by reverting to the single-volume sampling procedure with the data from the failed sequential sample included in the calculation of the

average particle concentration for that location. A second sequential sampling at a location is not part of the procedure. When the number of locations sequentially sampled is less than 10, the 95% UCL calculation is carried out by assuming each location average to be the cumulative particle count at the time the pass decision was reached divided by the cumulative sample volume at the same time. For the ISO Class 1 verification example cited previously (certifying ISO Class 1 air quality at dmax = 0.2 jim), sequential sampling would allow the observation of five counts in less than 18min to render a fail decision and stop the counting of that sample at that time. Conversely, sequential sampling would allow the absence of any observed counts in 68min to render a pass decision and stop the counting of that sample, a significant reduction in sampling time from the nearly 6h needed by the standard's single sampling procedure. While 68min is still a long sampling period, this example represents a worse case in that the CmaX)dmax value of 2 m"3 is the smallest listed in Table 33-1 and represents the largest required sampling volume (hence the longest sampling time) to be calculated from Eq. 33-3. Test Report

ISO 14644-1 requires a "comprehensive" report, including an explicit statement of compliance or noncompliance with a specific classification code of cleanroom air cleanliness. The report must include all details of the test method used and present the concentration data measured at all sampling locations, identified by coordinates within the cleanroom. Any special conditions or departures from the test method need to be noted. The occupancy state of the clean room—as-built, at-rest, or operational*—is part of the report. The report must name the particle counter used and provide a copy of its current calibration certificate. Any measurements of U descriptors or M descriptors also become part of the report even though they do not impact the verdict of compliance with a class code. Maintaining Continued Compliance

ISO 14644-2 specifies requirements for demonstrating, by monitoring procedures, that a cleanroom continues to comply with its class code as designated by ISO 14644-1. The document considers two cases: (1) a cleanroom having no monitoring in place following its initial verification and (2) a cleanroom operating with "continuous or frequent monitoring" of aerosol concentration following its initial verification. Monitoring is defined as "observations made in accordance with a defined method and plan to provide evidence of the performance of an installation." The monitoring plan must be written, and the standard suggests that it be based on risk assessment. At a minimum it must specify the 1. 2. 3. 4. 5.

Predetermined sample locations Minimum air volume and time per sample Required replicates Time interval between measurements Measured particle size(s)

* Cleanroom occupancy states: • As-built: an empty cleanroom without any equipment or personnel inside but with filter fans operating • At-rest: a cleanroom with filter fans operating, equipment in place, and idling power at each work station but no processing underway or personnel present (the state of a cleanroom between shifts; the overnight state of a cleanroom that runs just two shifts) • Operational: a cleanroom performing its usual functions; equipment is processing product, and operating personnel are present.

TABLE 33-4. Schedule of Testing to Demonstrate Continued Particle Count Compliance Class Code

Maximum Time Interval

Test Method

6 mo 12 mo

ISO 14644-1, Annex B ISO 14644-1, Annex B

ISO Class 5 Source: ISO (2000), Table 1.

6. Acceptance, excursion, and alert limits 7. Types of response actions required and when Placing a particle counter at a critical location or two within a cleanroom for continuous logging of particle concentration, while not a satisfactory verification procedure, could be part of an adequate monitoring plan. Specific details, however, of how to monitor a given cleanroom are left to the cleanroom owner. Without such a monitoring plan, Table 3 3 ^ (Table 1 of ISO 14644-2) lists the maximum time interval allowed between repeating the test method of ISO 14644-1. With a monitoring plan in place, the maximum time interval between requalification can "be extended, provided the results of continuous or frequent monitoring remain within the specified limit(s)." No explicit time interval is specified for a monitored cleanroom. Monitoring simply exempts a cleanroom from the maximum requalification intervals given in Table 33-4. The following conditions also trigger a requalification (repeating the test procedures of ISO 14644-1): 1. Remedial action to correct an out-of-compliance condition 2. Significant change in the operational use of the cleanroom or its performance specification 3. Interruption of air movement that significantly affects the cleanroom operation 4. Major maintenance such as a filter changeout. ISO 14644-2 also discusses the monitoring of other variables not directly related to the measurement of aerosol particle concentration, such as air pressure, air flow velocity and volume, temperature, and humidity. Annex B of ISO 14644-2 lists a number of useful optional tests, including installed filter leakage and air flow visualization, that are based on test methods to be specified in ISO 14644-3 (ISO, 2002), yet to be issued. COMMERCIALLY AVAILABLE AEROSOL PARTICLE COUNTERS FOR CLEANROOM CLASSIFICATION AND MONITORING While a large number of particle-counting techniques and commercial versions exist, as discussed in Chapter 15, not all of them match up well with the cleanroom application. To be useful in a cleanroom, a particle counter must be able to count individual particles. Instruments that measure the properties of particle ensembles, while important in some aerosol measurement roles, are of limited value in cleanroom measurements. The primary properties of interest for a cleanroom particle counter are size sensitivity (the smallest particle size at which the counter detects just 50% of the particles of that size actually passing through the counter); the background counting rate (generally expressed as the particle concentration that corresponds to the number of counted particles/unit time when measuring "zero aerosol" air [the absence of any "real" particles]); and the sampling volume

flow rate. (See Chapter 15 for a fuller discussion of the properties of optical particle counters and their calibration). Size resolution is typically of less interest in cleanroom measurements because the primary measurements required by the classification standards have always been expressed in terms of the concentrations of cumulative size distributions. However, being able to measure cumulative concentration at any of the allowed particle size cuts affords flexibility in sampling strategy that can be advantageous. Selecting the optimum particle counter for a cleanroom depends on all three of the primary properties. First, the size sensitivity of the particle counter must cover the particle size range of interest. For certifying cleanroom air quality according to ISO 14644-1, particle counters that can count particles as small as 0.1 um are those best suited for state-of-the-art cleanrooms. Table 33-1 and Figure 33-3 show that in the size distribution assumed by ISO 14644-1, the allowed particle concentrations are about an order of magnitude larger at O.ljLim than at 0.3 urn and about four times larger than at 0.2 um. This greater particle concentration at 0.1 Jim means that sampling volumes required for certification can be smaller (Eq. 33-3) and hence be less time consuming than at the larger particle sizes. Regardless of the sample flow rate through the counter, counting 20 particles when the particle concentration is high requires less sample volume and takes less sampling time than when the particle concentrations are lower. Similarly, higher volume air flow rates through a particle counter also mean less time to sample a required volume of air, regardless of the value of the required sampling volume. The most common sampling air flow rates of today's cleanroom particle counters are either 4.67 x 10"5m3/s or 4.67 x lO^nrVs [2.8L/min or 28L/min]. The product of particle concentration x air flow rate yields the 100% particle-counting rate—the maximum number of particles passing through the counter per unit time. This value increases with both increasing particle concentration and sampling flow rate. Counting 0.3 um particles at the maximum allowed concentration of ISO Class 3 air with a particle counter whose sampling flow rate is 0.028 m3/min yields the same 100% particle counting rate as counting 0.1 um particles at 0.0028 m3/min: 102 particles/m3 x 0.028 m3/min = 2.8 particles/min = 1000 particles/m3 x 0.0028 m3/min = 2.8 particles/min Reaching 20 particles with either of these configurations requires about 7min. However, measuring particles >0.1um at 0.028 mVmin reduces the required sampling time to about 0.7 min (remember, however, in all cases ISO 14644-1 requires a minimum sampling time of lmin). For verifying ISO Class 3 and cleaner air, counting particles >0.1u at 0.028 mVmin makes even the ISO fixed-volume sampling procedure practical. A 4.67 x 10"4In3Zs [28L/min] counter also accelerates the sequential sampling procedure, which can be used to verify these very clean air qualities. The third property of importance in selecting a particle counter for verifying air quality in a cleanroom is the background count rate. This background count rate is that which is characteristic of the counter response when measuring air having no particles. It can be determined by placing a ULPA filter on the inlet of the counter or by measuring the particle counts with no air flowing through the counter (pump Off). Light scattering from air molecules contributes to this count as well as cosmic ray strikes on the detector and electronic noise. Chapter 15 presents data showing that air flow anomalies often dominate the background count, which is significantly lower when measured with the pump Off than with the pump running and a filter on the inlet. Ideally, the background count rate should be less than 10% of the particle count rate expected at the maximum allowed concentration of the class code (the 100% counting rate for that particle size). Table 33-5 lists the particle counter properties suitable for measuring particle concentration at the classifications and particle sizes explicitly called out in Table 33-1. This table

TABLE 33-5. Preferred Particle Counter Properties for Verifying the ISO 14644-1 Cleanroom Classifications ISO Class Code 1 2

3

4

5

6

7 8,9

Detection Limit of Particle Counter Used (Smallest Detectable Particle Size [um])

Recommended Volume Air Flow Rate (L/min)

Preferred Background Concentration at Measured Particle Size (m"3)

0.1 0.2 0.1 0.2 0.3 0.5 0.1 0.2 0.3 0.5 1.0 0.1 0.2 0.3 0.5 1.0 0.1 0.2 0.3 0.5 1.0 5.0 0.1 0.2 0.3 0.5 1.0 5.0 0.5 1.0 5.0 0.5 1.0 5.0

28 28 28 28 28 28 2.8; 28 28 28 28 28 2.8; 28 2.8; 28 2.8; 28 28 28 2.8; 28 2.8; 28 2.8; 28 2.8; 28 2.8; 28 28 2.8; 28 2.8; 28 2.8; 28 2.8; 28 2.8; 28 28 2.8 2.8; 28 2.8; 28 2.8 2.8 2.8

<1 «1 <10 <2 <1 «1 <100 <30 <10 <4 <1 <1000 <300 <100 <400 <10 <1000 <1000 <1000 <400 <100 <3 <1000 <1000 <1000 <1000 <1000 <300 <1000 <1000 <1000 <1000 <1000 <1000

assumes that the only available particle counters suitable for cleanroom measurements are those having flow rates of either 4.67 x 10~5m3/s or 4.67 x KTW/s [2.8 or 28L/min]. Lower flow rates are not suitable, except for measuring particle concentration in the lowest quality air (ISO Class 7 and above). A 9.33 x 10~4m3/s [56L/min or 2cfm] particle counter has been advertised but seems to be not readily available or in widespread use. Its higher flow rate would be highly suitable for the highest quality air (ISO Classes 1 to 3). The criteria for choosing the particular set of particle counter properties given in Table 33-5 are 1. Background concentration <10% of the maximum allowed concentration, Cmax, for the classification and in the size bin(s) at which the measurement(s) is (are) made 2. Maximum particle concentration <35 x 105/m3 [105/ft3] to keep coincident counting errors less than 10% for all counters

3. Sampling time per individual measurement <10min as an arbitrarily selected, practical sampling time 4. Minimum sampling time of lmin as required by ISO 14644-1. A 4.67 x lO^nrVs [28L/min] counting rate offers no time advantage when certifying cleanrooms of air quality having Cmax values exceeding 7000 m"3. However, higher total counts imply higher quality measurements. The first three of these targets are arbitrary, not always achievable, and, for some particle counters, unduly restrictive. Particle counters having background concentrations less than 10% of Cmax will not always be available; some particle counters can operate with less than 10% coincidence error at aerosol particle concentrations exceeding 35 x 105m"3; and, obviously, limiting sampling time to 10 minutes per sample point is important primarily for certifying cleanrooms of large areas. A typical background concentration claimed by manufacturers of optical particle counters is on the order of 35/m3 [I/ft3] at the sensitivity limits of the counter. This background concentration exceeds that of some of the values listed in column 4 of Table 3-5. In such instances, counter selection becomes a trade-off to achieve the best compromise. The reduced background concentration typically exhibited by a particle counter in a larger particle size bin may be worth the longer sampling time required by certifying at that larger particle size. Condensation particle counters (see Chapter 19) usually have background concentrations on the order of 0.35/m3 [0.01/ft3]—one to two orders of magnitude lower than that of an OPC. They are highly suitable for measuring U descriptors, but their lack of sizing discrimination without additional upstream conditioning stages and their typical flow rate limitation of 0.0028 m3/min make them less useful for verifying classifications by measuring particle concentrations in the size ranges >0.1 urn. Observations

ISO Class 1 can be very time consuming to certify (the Cmax of ISO Class 1 at 0.5 um, if allowed for certification, which it is not, would be that corresponding to FED-STD-209E Class 0.01). Even at a flow rate of 4.67 x IQT4WL3IS [28 L/min] using a particle counter sampling 0.1 um particles, the sampling time per individual measurement is 71 min. As the area of the cleanroom in which air of this quality must be certified increases, the required number of sampling locations also increases so that the sequential sampling plan becomes increasingly attractive. Without a particle counter having a flow rate of 4.67 x IQT4WL3IS [28 L/min] and 0.1 urn sensitivity, the certification of ISO Class 1 air quality could become onerous indeed. On the other hand, lower quality air can be reasonably certified with less sensitive particle counters having lower flow rates, as indicated in Table 33-5.

MEASURING PARTICULATE EMISSIONS FROM CLEANROOM EQUIPMENT In most contemporary cleanrooms the major sources of particulate contamination are within the cleanroom itself rather than the outside. The air filters used in today's cleanrooms can reduce particle penetration in both the make-up air, introduced from the outside ambient, and the exhaust air being recirculated from the cleanroom to very low concentrations. Aerosol particle concentration in many cleanrooms, when measured in the as-built state (earlier definition), is often at the background concentration of the particle counter making the measurement. This conclusion holds especially true inside small clean zones that have their own filters, air flow controls, and enclosures to isolate a single processing unit within a larger cleanroom from the environment of that larger "ballroom" cleanroom (a small, "super

clean" cleanroom within a standard cleanroom—the so-called minienvironment). In its operational state, cleanroom ambient air is not as contaminant-free as that in either the as-built or the at-rest state because of the particulate emissions from the equipment itself. Thus the measurement of particulate emissions from operating equipment is important and can provide an additional property for specifying and selecting process equipment. Neither ISO 14644-1 or -2 specifies how to measure this potentially important property (although aggressive manufacturers sometimes claim "Class 1" performance or use similar phrasing in touting the merits of their equipment or product). Methods for making such measurements are given in the literature, however (Donovan et al., 1987; Donovan, 1990) and have been incorporated into a Recommended Practice prepared by the IEST (1992). The discussion presented here follows the methods described in these publications. Relating measurements of aerosol particle concentration, made by a particle counter in the vicinity of a source, to the particle emission rates characterizing that source requires that the volume into which the particles are emitted be known and also that the air flow through that volume, if any, be controlled and known. In the closed box method, the source is completely isolated inside an enclosure that confines the particle emissions. A particle counter monitors the particulate concentration within the enclosure as a function of time following source activation. There is no air flow except for what is drawn through a sampling line leading to the particle counter located outside the enclosure. Make-up air for this sampling loss is through an orifice containing an air filter that removes all aerosol particles from the make-up air. In the flowing duct method, the source is located inside a duct open at one end and having filters at the opposite end through which air continuously enters. Particle concentration is measured downstream of the source placed inside the duct and after a steady state exists following source activation. In both methods, complete mixing of the aerosol particles throughout the closed box or across the flowing duct is assumed and must be promoted either by the insertion of mixing fans or, in the flowing duct, by flow conditions that create turbulent mixing. The following section presents the analyses appropriate for calculating a particle emission rate with either of these configurations. Closed Box Method

The closed box configuration allows two independent determinations of particle emission rate, both based on the following rate equation: (33-8) where S is the number of particles emitted/unit time [V1]*, V is the volume of the enclosure [I3], C is the measured particle number concentration [I"3], t is time measured starting with the source activation [t], Kx is the wall loss rate [IV1], and Qx is the sampling flow rate of the particle counter [Pt"1]. Equation 33-8 relates the rate of change of the particle concentration within the enclosure to the particle generation rate of the source less the particle loss rate to the enclosure walls and less the particle loss rate attributable to the air sample drawn by the particle counter. Determination 1, Assuming that the starting particulate concentration within the enclosure is zero (this condition can be approximated by allowing the particle counter, or other external pump, to replace the initial air within the enclosure with filtered make-up air), the particle emission rate is related to the initial time rate of increase in particle concentration by * Symbols in brackets give the dimensions of the variables being defined: t = time; 1 = length.

(33-9) Equation 33-9 is just Eq. 33-8 with C = 0, as established by the above starting conditions. The volume of the enclosure is presumed to be known, and the initial slope of the concentration versus time plot provides a value of dC/dt. Thus, Eq. 33-9 provides a measure of the particle emission rate, S, of the source within the enclosure. Equations 33-8 and 33-9 assume that the particles emitted by the source are uniformly distributed throughout the volume. A mixing fan within the enclosure helps create this condition, although such a fan is also a source of particulate emissions. The fan's particle emission rate must be measured first and subsequently subtracted from any value of S determined when both it and the source under evaluation are operating. Determination 2. Under continued source operation and consequent build up of particle concentration within the enclosure, C eventually reaches a value at which the sum of particle loss terms in Eq. 33-8, both of which depend linearly on C, equals the particle emission rate of the source (S). At this particle concentration, C remains constant and the dCldt term of Eq. 33-8 goes to 0. Under these conditions, Eq. 33-8 becomes (33-10) Cs is the steady-state concentration measured by the particle counter sampling at a known volume flow rate, Qx. Both of these factors are thus known. Kx, however, is not known and must be measured. Kx can be measured by deactivating the source once Cs has been reached and recorded. Deactivating the source is equivalent to setting S = 0 in Eq. 33-8, which then becomes (33-11) or (33-12) The slope of a plot of InC versus t, following source deactivation, then provides a value of d(lnC)/dr to use in Eq. 33-12 to solve for Kx. This Kx value inserted in Eq. 33-10 then yields a second independent measure of S, which can be compared with that obtained from the conditions of Eq. 33-9. Flowing Duct Method

The flowing duct method consists of inserting the source to be evaluated inside a duct through which filtered ("particle-free") air continuously flows. Once the source is activated, the emitted particles increase the particle concentration measured by a particle counter sampling the air downstream of the source. The particle emission rate of the source is given by: S = CQ

(33-13)

where S is the particle emission rate [t"1], C is the downstream concentration measured after activating the source [I 3 ], and Q is the volume air flow rate through the duct [l3/t]. Particle concentration upstream of the source is assumed to be 0 so that all particles measured downstream of the source are assumed to originate from the source under test. The air flow dilutes the particle concentration in the downstream air, making this method less appro-

priate than the closed box method for measuring sources of low emission rates. Wall losses can be ignored—any KxC term, analogous to that in Eq. 3-8, will usually be small compared with the CQ term of Eq. 33-13. The dominant particle removal mechanism in the flowing duct method is the convective flow of particles out the end of the duct. Turbulent mixing of the air flow can be achieved by making the QIA of the duct (A is the cross-sectional area of the duct) that needed to produce a Reynold's number in excess of, say, 2000 to 4000. Sampling under these conditions lessens the cross-sectional position dependency of the sampling probe. Ideally the probe should be 10 or so duct widths downstream of the source. Isokinetic sampling is preferred but not critical for the particle sizes likely to be of most importance in a cleanroom. Limitations These methods for measuring particle emissions rates of sources work well with sources that can be easily moved in and out of enclosures or ducts and that can be activated or not by the simple flip of a switch. Large, permanently anchored sources that are always powered and activated present additional but not insurmountable problems. Enclosures or ducts can be built around such sources. The vertical laminar flow in a cleanroom represents a source of particle-free air that can feed air into a temporary duct, perhaps assembled out of flexible plastic sheets, that surrounds a stationary source. The floor vents already in place then serve as the open exhaust end of the duct. If powering a source On and Off is not practical, particle concentration upstream and downstream of the source can be used to determine a AC that can be used in Eq. 33-13 in place of C. The closed box method, however, depends on being able to transition from source On to source Off. It is thus more appropriate for measuring the emission rates of equipment undergoing acceptance checks at the manufacturer's site where startup and shutdown do not yet interfere with production schedules. The closed box configuration can be simulated by simply dropping a tent constructed with flexible walls over the equipment to be tested. Sealed entry ports for the sampling line and the power cords are easily installed. The analyses presented in the preceding paragraphs then apply to these large, immobile sources.

CONCLUSIONS The aerosol technology developed over the past half century proves very useful in both the design of today's cleanrooms and in assessing their performance. Contract specifications between suppliers and buyers of cleanrooms rely heavily on highly developed, sophisticated particle counters as the means of verifying performance and winning customer acceptance. Conversely, the needs of the cleanroom user community with its significant market impact have motivated manufacturers of particle counters to focus particular attention on this segment of the particle counter spectrum. About 75% of all aerosol particle-counting instruments sold today are for cleanroom applications. Without this driving force, the impressive performance of today's particle counters, as presented here and in Chapter 15, most likely would not be available. Indeed, instrumentation for measuring the concentration and size of aerosol particles in a cleanroom and the general aerosol technology base developed over the years have become key elements in preventing the specter of contamination limitations creating a road block to the manufacture of new products. Thus far, continually improving aerosol particle measuring instrumentation, a prerequisite for improving cleanroom design and performance, has maintained the pace of development needed to create the more demanding cleanroom work spaces required by each new generation of precision products.

Aerosol particle counters can be used for more than just verifying and monitoring the particulate concentration in the ambient cleanroom air. They can also be used to measure particulate emissions from sources within a cleanroom and to discriminate between more cleanroom-compatible and less cleanroom-compatible hardware and processing equipment. The methods for measuring particle emissions presented here are for determining the contribution of particulate sources within a cleanroom to the cleanroom ambient. Other researchers have adapted sampling probes to measure particulate concentrations inside process equipment, including equipment operating at subatmospheric pressures. This use of an aerosol particle counter is an industry-specialized application not reviewed here but is noted as simply another example of the versatility and importance of aerosol measurements in cleanrooms. Future industrial needs will likely lead to more capable and versatile instrumentation for measuring particles of interest within a cleanroom, wherever they are deemed to be important and whatever their size and concentration.

REFERENCES Cooper, D. W. and D. C. Milholland. 1990. Sequential sampling for Federal Standard 209 for cleanrooms. / Instrum Environ. ScL 33:28-32. Donovan, R. P. 1990. Measurement of particle emission rates from equipment. In Particle Control for Semiconductor Manufacturing, R. P. Donovan, ed. New York: Marcel Dekker, pp. 243-253. Donovan, R. P., B. R. Locke, and D. S. Ensor. 1987. Measuring particle emissions from cleanroom equipment. Microcontamination 5(10):36-39, 60-63. Federal Standard 209E. 1992. Airborne Particulate Cleanliness Classes in Cleanrooms and Clean Zones. September 11,1992. Mount Prospect, IL: IEST. Fissan, H., W. Schmitz, and A. Trampe. 1993. Clean-room measurements. In Aerosol Measurement: Principles, Techniques and Applications, K. Willeke and P. Baron, eds. New York: Van Nostrand Reinhold. pp. 747-767. IEST. 1992. IEST Recommended Practice CC018.1, Cleanroom Housekeeping—Operating and Monitoring Procedures. Mount Prospect, IL: IEST ISO. 1999. ISO 14644-1. Cleanrooms and Associated Controlled Environments—Part 1: Classification of Air Cleanliness. May 1,1999. Mount Prospect, IL: IEST. ISO. 2000. ISO 14644-2. Cleanrooms and Associated Controlled Environments—Part 2: Specifications for Testing and Monitoring to Prove Continued Compliance with ISO 14644-1, September 15,2000. Mount Prospect, IL: IEST ISO. 2002. ISO 14644-3. Cleanrooms and Associated Controlled Environments—Part 3: Metrology and Test Methods. In press.

Reactor Safety [U.S. NRC, 1980]). Detailed review and application of this information, as well as the development of new techniques and applications, continue to occupy the careers of many aerosol scientists and health protection professionals. This chapter provides an overview of the principles, techniques, and applications of measuring radioactive aerosols. Information needed for a basic understanding is presented, and several newer measuring techniques are described.

RADIATION AND RADIOACTIVE DECAY Aerosols of radioactive materials have all the physical and chemical forms of nonradioactive aerosols. They can range from ultrafine metal fumes to large liquid droplets. Their physical form has the usual influence on their aerodynamic behavior and on the choice of measurement technique. Their chemical form has a similarly important influence on their biological or environmental behavior and on the technique that will be used to confirm their chemical form after collection. It is their radioactive properties that can make them easier to detect, yet, in many cases, more hazardous to handle.

Types of Radiation

Three types of radiation are of concern in studies of airborne radioactive materials: alpha, beta, and gamma radiation (Fig. 34-1). Neutrons and positrons are also of concern in some special circumstances. Details of the origin and characteristics of all types of radiation as well as the radioactive decay schemes for radionuclides are presented in references such as the Table of Isotopes (Lederer, 1978) and the Handbook of Health Physics and Radiological Health (Schleien et al., 1998). Alpha Radiation. Alpha radiation is the least penetrating, but most highly ionizing type of radiation. Alpha particles consist of two neutrons and two protons, carry two positive charges, are identical to a helium nucleus, and are created spontaneously during the radioactive decay of high atomic number elements such as radium, uranium, and plutonium. Alpha-emitting radionuclides can be identified by the characteristic energy of their emissions. They are of special concern when they are inhaled and deposited in the respiratory tract. If inhaled in large quantities, damage to cells in the lung or in other organs to which the material is translo-

TYPEOF RADIATION

COMPOSITION

ABIUTY TO PENETRATE THROUGH MATERIALS

ALPHA PARTICLES

BETA PARTICLES

e*

GAMMA OR XRAYS

electromagnetic energy

Fig. 34-1. Characteristics of the three major forms of radiation of concern for measurements of radioactive aerosols: Alpha particles, beta particles, and gamma radiation.

cated can cause acute health effects such as fibrosis and long-term health effects such as cancer (e.g., Hobbs and McClellan, 1986). Beta Radiation. Beta radiation is more penetrating than alpha radiation and consists of negatively charged particles that are identical to electrons. Beta radiation can penetrate the skin and, like alpha radiation, is of concern when large amounts of beta-emitting radionuclides are deposited in the body. The energy spectrum of beta emissions covers a broad range up to a characteristic maximum. Iodine-131, cesium-137, and strontium-90 are beta-emitting radionuclides that are of special concern for accidents involving nuclear reactors. Gamma Radiation. Gamma radiation and X-rays are penetrating quanta of electromagnetic energy. They are not charged particles, but they can cause ionization in materials and biological damage in tissues. Gamma radiation originates in the nucleus of atoms during radioactive decay. Gamma radiation is emitted at discrete, characteristic energies for each radionuclide. Many alpha- and beta-emitting radionuclides also emit gamma radiation. X-rays originate in disruptions of electrons from their orbits around the nucleus. X-rays can also arise as a secondary form of radiation called bremsstrahlung (braking radiation). Bremsstrahlung is created when the path of a negatively charged beta particle is altered by the coulombic attraction to positively charged nuclei of the absorbing material. This alteration of direction causes a radial acceleration of the beta particle, which is accompanied, in accordance with classical theory, by a loss of electromagnetic energy at a rate proportional to the square of the acceleration (e.g., Cember, 1996).

Half-Life for Radioactive Decay

The rate at which a radioactive material decays is described by its half-life: the time for half of the atoms present to undergo a spontaneous nuclear transformation. Half-life is an important consideration in determining how to collect, handle, and quantify samples of a radioactive material. For example, samples of short-lived materials may need to be analyzed immediately after collection, or even in real time. Conversely, samples of longlived radionuclides may remain radioactive for years, centuries, or even millennia. Experimental determination of half-life is also one way of helping to identify a given radionuclide. The equation describing radioactive decay is (34-1) where tm is the decay half-life for the radionuclide (in time units), t is the elapsed time (in the same time units as tl/2), e is the base of the naperian logarithm system (2.718), 0.693 is the natural logarithm of 2, A0 is the activity of the sample at time t = 0 (in disintegrations per second), and A{i) is the activity of the sample after an elapsed time of t. Another way of writing the relationship involves the decay constant, X, where (34-2) In terms of the number of elapsed half-lives, n, the amount of activity remaining can be determined as (34-3)

Thus, after two half-lives, only one-fourth (25%) of the original activity is present, and after seven half-lives only 1/128 (0.8%) of the original activity remains. Specific Activity

The specific activity of a radioactive material is the rate of decay per unit mass. Materials with a short half-life have a high specific activity. The historical unit for specific activity, the Curie (2.22 x 1012 disintegrations per minute), was defined in terms of the specific activity of radium (1 Ci/g). The system international (SI) unit for radioactive decay is the Becquerel, which is 1 disintegration per second. Thus, InC is 37Bq. Specific activity is of biological concern for radioactive materials in the body because it determines the rate at which energy will be deposited and damage will occur in tissues. Specific activity is of practical concern for aerosol measurement because it determines the amount of radioactivity that will be present in a sample of a given mass and the mass or number of particles that will be associated with a sample of a given activity. The dependence of airborne mass concentration, cm (g/m3), on activity concentration, ca (Bq/m3), involves the specific activity of the material, A (Bq/g), in the following way: Cm=Ca/A

(34-4)

The number of particles per cubic meter, Cn (particles/m3), required to provide a given activity air concentration, ca, involves a similar relationship of specific activity, A, particle density, p(g/m3), and the volume of the airborne particles. For a simple example in which particles are assumed to be monodisperse in size and spherical in shape (volume = TK/P3/6, with dp in m), this relationship is (34-5) Inhalation Exposure Limits

Statutory limitations on exposures of humans to radioactive materials are provided in regulations such as the U.S. Nuclear Regulatory Commission Standards for Protection Against Radiation (U.S. NRC, 1991) and the U.S. Department of Energy Standards for Protection Against Radiation (U.S. DOE, 1993). Specific activity and biological behavior of materials are considered in determining acceptable annual limits on intake (ALIs) for workers exposed to radioactive aerosols (see ICRP [1979] and its addendums). A related concept is the derived air concentration (DAC). A DAC is the calculated air concentration to which a worker could be exposed 8h per day, 5 days per week, for 50 weeks (an entire work year of 200Oh), without exceeding the ALI for the radionuclide. The worker is assumed to breathe at the rate of 20L/min as given for the ICRP Reference Man (ICRP, 1975). Thus, the DAC is the ALI divided by the volume of air inhaled during the working year (2400m3). Particle number concentrations for several materials at their DAC are illustrated in Table 34-1 as a function of particle size. These materials include three that are radioactive (238PuO2, 239 PuO2, and enriched uranium) and one that is nonradioactive (beryllium, a toxic metal of concern in many nuclear facilities). Insoluble 238Pu has a specific activity of 6.44 • 1011 Bq/g and a DAC of 0.3 Bq/m3. Insoluble 239Pu has a specific activity of 2.26 109 Bq/g and a DAC of 0.2 Bq/m3. For enriched uranium, the specific activity is 2.35 • 106Bq/g (dominated by the contribution from 234U, which is present at 1% by mass), and the DAC for the insoluble material is 0.6 Bq/m3. The occupational exposure limit for the nonradioactive beryllium is 2ug/m3. This example demonstrates that the number of particles of concern for different radionuclides covers a broad range.

TABLE 34-1. Number of Particles Per Cubic Meter as a Function of Monodisperse Particle Size for Selected Toxic Materials at Their Derived Air Concentration (DAC) Particle Diameter (Mm)

238

10 5 3 1 0.5

0.0001 0.0008 0.004 0.1 0.8

PuO 2

239

PuO 2

0.02 0.15 0.7 19 150

Enriched Uranium

Beryllium Metal

54 433 2,007 54,180 433,443

2,065 16,518 76,471 2,064,715 16,517,716

Note the following: Insoluble 238Pu has a specific activity of 6.44 • 1011 Bq/g and a DAC of 0.3Bq/m3. Insoluble 239Pu has a specific activity of 2.26 109 Bq/g and a DAC of 0.2Bq/m3. For enriched uranium, the specific activity is 2.35 • 106 Bq/g (dominated by the contribution from 234U, which is present at 1% by mass), and the DAC is0.6Bq/m3. The effective density of beryllium metal aerosol particles with a slight oxide coating is 2g/cm3 (Hoover et al., 1989) and the occupational exposure limit for beryllium is 2ug/m3.

RADIATION DETECTION Radioactive materials can be detected in a number of ways. Most of them depend on the ionization process that occurs when gamma rays or charged particles pass through a gas, liquid, or solid and form ion pairs by disrupting atoms and electrons in the material. Radiation detection methods can be used as direct tools for quantifying radioactive aerosols and as support tools for the safe handling of radioactive materials. Measurement of alpha-emitting radionuclides generally requires direct, or nearly direct, contact between the alpha-emitting material and the detection device. Proximity requirements are less severe for the more penetrating beta and gamma forms of radiation. Investigators must have a thorough understanding of the decay schemes of the radionuclides of interest. In many cases the radioactive emissions of progeny radionuclides are of greater biological concern than the emissions of the parent radionuclide. For example, although strontium-90 has a relatively long half-life (28.8 years) and emits beta radiation at a relatively low energy (0.54MeV), its progeny radionuclide yttrium-90 has a short half-life (64.2 h) and is a high-energy beta emitter (2.28 MeV). An overview of the measuring processes for all three types of radiation is given below. Additional details can be found in resources such as Evans (1955), the chapter on health physics instrumentation in Cember (1996), or the section on radiation detection principles in the Operational Health Physics Training manual (U.S. DOE, 1988). The Cloud Chamber

Condensation tracks are visible in a cloud chamber as charged particles pass through a supercooled vapor (e.g., carbon dioxide from dry ice). Photographs of the particle tracks observed in such devices readily reveal the manner in which charged particles travel through a medium and deposit their energy (e.g., Rutherford et al., 1930; Rasetti, 1947). Cloud chambers provide a useful insight into the physical processes that make radiation detection devices work. Improved versions of the cloud chamber are still used in high-energy physics experiments.

A popular, simple, historical experiment involved placing a camping lantern mantle (WeIsbach mantle) in a cloud chamber and observing the tracks from the naturally occurring uranium and thorium progeny in the phosphors of the mantle. In the mid-1990s, Coleman Outdoor Products (Wichita, KS) stopped using uranium and thorium in their lantern mantles. Before that, concerns for inhalation hazards from aerosols of the older mantles led to the warnings that lantern mantles should be installed and lighted in well-ventilated areas and that users should avoid breathing the vapors. Scintillation Counting Phosphors are materials that absorb energy during the ionization process and re-emit a fraction of it as light flashes (scintillations). Light emission occurs when electrons are elevated to a higher energy state and then make the transition back to the ground state. Scintillation bursts can be detected with a photodiode or photomultiplier device and counted to determine the number of charged particles or gamma rays that passed through the material. The intensity and duration of the scintillation can also be analyzed to determine the energy of the radiation being detected. A number of different phosphors are suitable for radiation detection. For gamma or lowlevel beta detection, the most notable are solid phosphors, such as thallium-doped sodium iodide (NaI[Tl]), cesium iodide (CsI[Tl]), or potassium iodide (KI[Tl]). The thallium activator serves as an impurity in the crystal structure and converts the energy absorbed in the crystal to light. Zinc sulfide (usually doped with silver), ZnS(Ag), is an excellent detector for alpha radiation. Scintillations from the interactions of alpha particles with ZnS are bright enough to be viewed with the human eye in a darkened room. ZnS must be used as a thin layer (usually a fine crystalline coating on a clear plastic film) because it has a low transmission factor for visible light. Plastic or liquid scintillators such as trans-stilbene are also available. Samples containing radioactive materials can be dissolved in a scintillation "cocktail" involving a solvent such as toluene to improve the contact between the radionuclides being detected and the phosphors. There is a wide range of commercially available cocktail media, many with optimized features for dissolving the test material or functioning at special conditions of pH or salt content. Ionization Chamber Devices Gas proportional counters and other ionization chamber devices can detect the number and, in some cases, the energy of radioactive emissions. When gamma rays or charged particles pass through the chamber, ion pairs are formed in the gas. These bursts of ion pairs are drawn by an electric field to a charged sensor, and the number and energy of the emissions can be recorded. Counting efficiency, fidelity of energy determination, and counting rate limits are available for standard ionization devices. Solid-State Detection More recently, solid-state detectors have been developed that use special layers of semiconducting materials to provide a site for ion-pair production. These high-efficiency devices provide excellent energy resolution and can be used with a multichannel analyzer to identify the energy spectrum of the detected radiation. In detectors such as germanium-lithium (GeLi) or high-purity germanium (HPGe), the sensitive region extends far enough into the surface that they are highly effective for gamma rays. Diffused-junction or surface-barrier detectors have a thin, sensitive layer that is especially useful for alpha and beta radiation detection. When these detectors are used for alpha spectroscopy, the energy resolution is highest when the alpha-emitting material is placed directly on the detector surface or when the air in the gap between the alpha source and the detector is evacuated. New developments

in surface coatings for solid-state detectors are making them highly resistant to damage from liquids, acids, bases, and abrasion. Autoradiography, Track-Etch, and Other Detection Systems

Radiation-induced ionization can cause chemical changes in materials like photographic film. The film can be developed to reveal the location of the damage tracks. This process is known as autoradiography; the radioactive material provides its own exposure of the film. Solid-state track recorders can also be made from materials such as polycarbonate or cellulose nitrate. Following exposure to radiation, the surface of the material can be chemically etched to reveal the location, length, and diameter of the damage tracks. Other detection systems use materials that respond to ionizing radiation by undergoing optical density changes, radiophotoluminescence, thermoluminescence, or conductivity changes (e.g., U.S. DOE, 1988, Chapter 13). Calibration Considerations

Calibration is a critical part of the detection process. Background samples are analyzed, followed by counting of calibration standards of known activity. The normal calibration process involves the use of sealed sources or electroplated sources with radioactivity levels that are traceable to the National Institute of Standards and Technology. Quantities of radioactivity are generally chosen to correspond to those expected in the samples to be analyzed. This allows appropriate corrections to be made for nonlinear phenomena such as instrument dead time (the inability of a system to detect a new event until it has finished reporting or recovering from a previous event). At high count rates (several million counts per minute or more, depending on the instrument), effects such as dead time can be substantial, but at very low count rates such errors are negligible. When instruments are to be used at very low count rates, higher activity standards are used to ensure that statistically valid counting efficiencies are determined for the instruments. Note that detection uncertainty is generally calculated from Poisson statistics as being proportional to the square root of the number of events detected. Thus, the relative uncertainty is high for low-activity sources or for short counting times. The influence of background interference must also be taken into account. For errors of less than 1 %, net counts of 10,000 or more are generally required (Price, 1965).

SAFE HANDLING OF RADIOACTIVE AEROSOLS The handling techniques for radioactive aerosols are usually very stringent. Personal protection and contamination control are major concerns. Contamination of people, equipment, other samples, the workplace, and the environment must be avoided. Facility Design and Licensing

Radioactive materials are generally handled in special facilities with access control, HEPA filtration, and ventilation systems that provide successively more negative air pressures from the outside environment to general office areas, to laboratory or work areas, and finally to any special enclosures in which actual handling occurs. Figure 34-2 illustrates a laboratory design with graded pressure zones and access control. Operation of facilities that handle radioactive materials may require federal, state, and local approvals. Laboratories involved in measuring radioactive aerosols must meet the same standards for worker protection, posting of warning signs, effluent monitoring, and waste disposal as the production or reactor facilities to which they provide measuring services.

GRADEDPRESSURE DESIGN FOR A RADIOACTIVITY HANDLING AREA ATMOSPHERIC PRESSURE (Pa) HOOD (Ph)

ACCESS CORRIDOR (Pc)

GLOVE BOX (Pg) Air Lock

RADIOACTIVE MATERIALS HANDLING ROOMS (Pr)

MAIN LABORATORY BUILDING (Pb)

Pg < Ph < Pr < Pc < Pb < Pa Fig. 34-2. Illustration of the successively decreasing pressure conditions in a facility designed for handling radioactive aerosols.

Use of Special Enclosures Special laminar flow hoods, glovebox enclosures, and handling rooms are generally needed for work involving radioactive materials. Flow hoods for handling radioactive materials draw room air into the handling area and exhaust it through HEPA filtration. Temporary, specialuse enclosures may be required for nonroutine activities such as maintenance or decommissioning that are being performed (e.g., Newton et al., 1987; Fig. 34-3). Such systems may have to be built when existing radioactivity handling areas are being fitted with special features for aerosol sampling or when special sampling efforts are underway. Portable aerosol sampling systems may include local ventilation and filtration around the sampling instruments (e.g., Hoover et al., 1983). Ultraviolet and chemically resistant glove materials such as Hypalon® and specially designed modular glovebox links may used to minimize the spread of contamination and exposures of workers (Hoover et al., 1999). Contamination Control for Radioactive Aerosol Samples Samples of radioactive aerosols collected for radiation detection can be stabilized for sample preservation and contamination control by several techniques. For example, filters containing gamma-emitting samples can be stored in sealed containers or sandwiched between tape without degrading the radioactivity counting results. For low-energy, beta-emitting materials or for alpha-emitting materials, samples may be sandwiched between tape and a thin (e.g., 1.5 um thick) piece of Mylar®film.A slight energy correction must be made to account for energy loss in the Mylar®. In a further variation of this technique, alpha-emitting particles collected on substrates such as a filter can be prepared for counting by sandwiching the substrate between a piece of tape and a piece of ZnS(Ag)-coated clear plastic with the scintillator side facing the particles. This stabilizes the sample and prevents contamination of the counting equipment or laboratory. Requirements for Radiation Shielding Workers and radioactivity-counting equipment must also be appropriately shielded from radiation. As shown in the radiation penetration illustration in Figure 34-1, shielding requirements for alpha-emitting radionuclides are minimal; normally the packaging used for contamination control is adequate. Conversely, shielding requirements for gamma-emitting radionuclides may involve several centimeters of lead, steel, or other high atomic number

EXHAUST DUCT

CUTTING TABLE AND PIPE VISE AIR FILTERS OBSERVATION WINDOW BELOW AIR FILTER LUClTE OBSERVATION WINDOWS

2"x4" FRAME WITH RRE RETARDANT FILM (BOTHSIDES)

(a)

(b) Fig. 34-3. Sketches of the temporary containment structure ("tent") built to enable controlled cutting of radioactively contaminated pipe. Sampling probes were placed at breathing zone levels for a worker at the cutting table.

(high-Z) material. Shielding for beta radiation involves two considerations: attenuation of the beta particles themselves and attenuation and minimization of secondary X-rays from bremsstrahlung. Because bremsstrahlung production increases with the atomic number of the absorber, low-Z materials (less than atomic number 13, aluminum) are normally used to shield beta-emitting radionuclides. Thus, a typical storage unit for aerosol samples of betaemitting radionuclides involves an inner layer of several millimeters or centimeters of methylmethacrylate acrylic plastic or graphite for beta attenuation, supplemented with an outer layer of high-Z material for absorption of any bremsstrahlung photons. Failure to provide adequate shielding for radioactive samples can cause unnecessary radiation exposures of people and may interfere with proper radioactivity counting.

Proper Handling and Disposal of Radioactive Materials

Federal, state, and local laws must be met in the packaging, labeling, and transportation of radioactive materials (U.S. DOT, 1990). Federal regulations for handling and disposal of radioactive wastes are contained in Title 10 of the Code of Federal Regulations Part 20 and related documents (U.S. NRC, 1991). State and local laws must also be consulted. Even when radioactive particles from air streams that have concentrations below legal limits for environmental release are sampled, the process of concentrating the particles may create samples that eventually must be treated as radioactive waste. Minimization of Mixed Wastes

"Mixed waste" is a concept defined under the U.S. Resource Conservation and Recovery Act (RCRA) (U.S. EPA, 1990). Wastes that are mixtures of chemically toxic and radioactive materials cannot, by law, be disposed of in either chemical disposal sites or radioactive disposal sites. The lead used for shielding samples of radioactive aerosols and many of the chemicals such as toluene used for scintillation cocktails to count radioactive samples are listed as hazardous wastes under RCRA. Production of mixed wastes should be minimized because they must be handled at special processing facilities, usually at great cost. Mismanagement of mixed wastes can result in fines and/or imprisonment. OBJECTIVES FOR MEASURING RADIOACTIVE AEROSOLS As is true for all air sampling and air monitoring, the selection of methods for measuring radioactive aerosols should be determined by the underlying objectives of the sampling effort. "What information is really needed?" This concept seems trivial, but it must be emphasized so that needed data will not be missed and time and fiscal resources will not be wasted. As shown in Figure 34-4, sampling objectives can be grouped into six areas: basic characterization and toxicological testing, process control, health protection, environmental monitoring, emergency response, and demonstration of compliance. These objectives are not mutually exclusive, and other special objectives may also exist.

BASIC CHARACTERIZATION AND TOXICOLOGICAL TESTING Understanding the materials being used.

PROCESS CONTROL

HEALTH PROTECTION

ENVIRONMENTAL MONITORING

EMERGENCY RESPONSE

Making sure the process is running properly.

Keeping worker exposures within limits and ALARA.

Keeping environmental releases within limits and AL^RA.

Having a basis for action when things go wrong.

Fig. 34-4. Illustration of six major objectives for sampling radioactive aerosols. The objectives are not mutually exclusive, and other special objectives may also exist.

Basic Aerosol Characterization and Toxicological Testing

Basic aerosol characterization and toxicological testing are ideally done before initiation of any process involving radioactive materials. They involve both collecting and characterizing relevant samples of selected materials and creating laboratory surrogate aerosols that have well-controlled and well-characterized properties. The full spectrum of aerosol sampling tools is usually applied to measure parameters such as particle size, concentration, morphology, chemical composition, and solubility. This approach provides a technical basis for selecting appropriate requirements for designing and managing the entire process in order to keep exposures of people within limits and "as low as reasonably achievable" (ALARA). It also provides a technical basis for balancing the relative risks of internal radiation exposures from inhalation or ingestion and external radiation exposures from working or living in a radiation area. Because both internal and external radiation doses pose risks to people, choices must be made. Historically, the choice has been to accept larger than necessary external exposures in an effort to avoid inhalation exposures. This is not reasonable in light of the underlying risks, and an equal treatment of both types of exposures is recommended by bodies such as the International Commission on Radiological Protection (ICRP, 1973). Dorrian and Bailey (1995) have summarized the particle size distribution of radioactive aerosols that have been measured in a wide range of industrial operations. The typical particle size distribution had an activity median aerodynamic diameter of 5 |xm, with a geometric standard deviation of 2, although smaller size distributions were observed in operations involving high temperatures or fumes, and larger particle size distributions were observed in operations such as coarse powder handling. The typical size distribution values reported by Dorrian and Bailey have now been accepted as the default values for use in ICRP Report 66 on the new Human Respiratory Tract Model for Radiological Protection (ICRP, 1994b). The previous default assumption for aerosol particle size in the workplace had been an activity median aerodynamic diameter of lum (ICRP, 1979). The lum aerodynamic diameter is still the default value for the particle size of radioactive aerosols in the environment (ICRP, 1994b). The recommendations of the ICRP are internationally accepted as a coherent and consistent approach to radiation protection. An alternate model developed by the National Council on Radiation Protection and Measurements (NCRP, 1997) focuses on fundamental considerations of human respiratory tract structure and function in deriving an alternate mathematical model to describe the deposition, clearance, and dosimetry of inhaled radioactive substances. The characteristics of aerosols from a wide range of activities such as powder handling, spills, and fires have also been summarized in a handbook on airborne release fractions/rates and respirable fractions for nonreactor nuclear facilities (U.S. DOE, 1994). Process Control

Routine sampling or monitoring for aerosols in processes such as nuclear fuel fabrication, reactor operations, or radioactive waste disposal ensures that the processes are working properly or provides an early warning that conditions are changing. Sampling refers to methods that involve off-line analysis, and monitoring refers to methods that provide a real-time response. Such measurements are usually made within containment areas and generally focus on concentration or size measurements. Other parameters may be measured periodically. Health Protection

Measurements for health protection purposes can be taken in the breathing zone of workers using fixed or personal samplers or can be taken in the general work area. Results of breath-

ing zone and area sampling can indicate the need for bioassay, the need for improved containment or work practices, and the need for air monitoring and personal protective equipment. Neither breathing zone nor area sampling data should be used to assign radiation dose to workers except in cases where no bioassay method is available and where the sampling method can reasonably be expected to provide a representative sample of the airborne radioactivity (U.S. DOE, 1998). Problems with correlations between air sampling results and bioassay can be especially severe for high specific activity materials such as plutonium-238 because a small number of particles can be significant from a dose standpoint (Scott et al., 1997; Scott and Fencl, 1999). Stochastic concerns for whether or not an individual particle was inhaled do not exist for isotopes with lower specific activity (e.g., tritiated particulate materials) where the aerosol cloud can be expected to be more homogenous at concentrations of concern. Basic characterization and process control measurements provide the guidance for determining the appropriate level of health protection measurements; processes involving potentially small source terms of low-toxicity materials do not require as extensive a measurement program as would be appropriate for potentially large source terms of high-toxicity materials. In all cases, the objective is to keep worker exposures within limits and ALARA. Concerns for particle solubility are generally high because of the influence it has on the biological behavior on inhaled material and because knowledge of solubility is needed to correctly apply biokinetic models and interpret bioassay information from urine, fecal, or blood samples obtained from workers. Other measurement techniques, such as radiation monitoring for hand and foot contamination or for contamination of work place surfaces, are also part of a total health protection program. They often signal increased airborne concentrations before worker exposures become excessive. Environmental Monitoring

Because the Clean Air Act sets limits on allowable releases of radioactive materials to the environment (U.S. EPA, 1991), effluent monitoring or stack sampling is required. This must be done routinely to ensure that environmental releases are within limits and ALARA. Measurements are often made both on site and off site. Because concentration limits for environmental releases are generally lower than for the workplace, greater sensitivity is usually required. This is achieved by using higher sampling flow rates, sampling for longer periods of time, or more sensitive analytical techniques. Emergency Response

Good emergency response involves graded levels of reaction, depending on the severity of any releases. This implies a greater reliance on real-time or near-real-time information than is necessary during normal operations. In addition, instruments may need to remain operational and provide useable results at aerosol concentrations much higher than are seen in routine process, health protection, or environmental monitoring. It is sometimes necessary to deploy portable or mobile instrumentation. Instruments may also need more frequent cleaning or replacement. Demonstration of Compliance

Throughout all the measuring regimes, it is necessary to demonstrate that the instruments are working properly and to document actual releases of airborne radionuclides in comparison to statutory release limits. In a sense, this effort is a subset of all other measuring objectives. Once the operator is convinced of what has occurred, the regulators and other interested parties must be convinced.

APPLICATION OF STANDARD MEASURING TECHNIQUES Nearly all standard aerosol sampling techniques can be applied to the measurement of radioactive aerosols. Some limitations are related to the amount of material that may be significant or available. For example, the mass amounts of concern for radionuclides may be below the limits of detection of piezoelectric mass monitoring systems or optical monitoring devices. On the other hand, radioactivity levels associated with particles collected for electron microscopy may require use of designated and restricted equipment. Considerations such as these are discussed below for a number of standard measuring techniques. Optical Particle Counting

Optical particle counters have not been widely used for radioactive aerosols, but there are circumstances under which they might provide useful information to help protect workers from exposures to radioactive aerosols, especially to warn of unusual particle releases. Optical particle counters have two main advantages: They sample air streams continuously, and they provide real-time information. Their main disadvantages are that radioactive particles cannot be differentiated from inert particles on the basis of light-scattering behavior and that light scattering generally provides an estimate of the physical size distribution of the observed particles rather than an estimate of the aerodynamic size distribution, which is more relevant for assessing inhalation risks. A number of commercially available instruments are being used to detect nonradioactive aerosols in clean rooms, to monitor work area dust levels in industries such as mining and textiles, and to provide quality control monitoring for processes such as paint pigment preparation that fabricate or use fine particles. Optical monitoring has also been used in systems for generating and characterizing inhalation exposure atmospheres of radioactive aerosols (Hoover et al., 1988a). Issues related to their application for radioactive aerosols include (1) level of detection compared with allowed air concentrations for radioactive aerosols, (2) level of detection compared with background levels of nonradioactive aerosols in the workplace, (3) aerosol characterization requirements to determine relationships between radioactive and nonradioactive aerosols in the workplace, (4) calibration requirements to quantify instrument response to specific aerosols, and (5) health protection management strategies for using optical monitoring information in a total program for workplace control and worker protection. Figure 34-5 illustrates a graphical scheme that we recently developed (Hoover and Newton, 1991) to evaluate whether a given optical particle counter can meet useful requirements for a level of detection. The abscissa of the two-dimensional field describes the level of an airborne radionuclide concentration in relevant units such as Bq/m3.This permits placement of a vertical line for any concentration level of interest, such 1 DAC for a specific radionuclide. For convenience, we refer to this level-of-interest line as the "alarm limit." This line divides the field in half. The ordinate of the two-dimensional field describes the particle number concentration (particles/m3) or mass concentration (mg/m3) that corresponds to a given radionuclide concentration. The ordinate uses the units in which the optical counter measures or reports information, and it permits placement of two horizontal lines corresponding to the lower limit of detection and the upper limit of detection for the instrument. The lower limit of detection depends intrinsically on the sensitivity of the instrument (a function of flow rate, sensing volume dimensions, and internal signal-to-noise ratio), and it also depends in practice on the concentration of background dust (external signal to noise ratio). Except in very clean environments, the lower limit of detection is due to the background aerosol concentration. The upper limit of detection is caused by saturation of the counter (more than one particle in the sensing volume at once). In very dusty environments, the concentration of background aerosols may actually be above the upper limit of detection due to coincidence (the

ALARM LIMIT

NUMBER OR MASS CONC.

(p/m3) OR (mg/m3)

V

Vl

Aerosol releases will be under reported

Aerosol releases will be under reported

III

IV

The monitor can detect releases before they become significant

The monitor can detect significant releases

I The monitor cannot detect releases before they become significant

Il The monitor cannot detect significant releases

UPPER LIMIT OF - DETECTION

LOWER LIMIT OF DETECTION

RADIONUCLIDE CONCENTRATION (Bq /m3) OR UNITS OF DERIVED AIR CONCENTRATION (DAC) Fig. 34-5. Format of graphical scheme for evaluating if an optical monitor could be useful for detecting radioactive or other toxic aerosols.

monitor is saturated by background aerosols). The combination of the horizontal limit of detection lines and the vertical concentration of interest line divides the field into six sectors. When a curve of mass versus activity or particle number versus activity (assuming a particle size) is added to this plot, it reveals the usefulness of the instrument for detecting a given radionuclide. The ideal situation would involve a monitor whose effectiveness spans sectors III and IV. This would involve a robust instrument that tracks routine airborne levels at concentrations well below the alarm limit, allows detection of increased airborne particle levels (thus providing opportunity for preventive action or progressive responses), provides a reliable alarm when airborne concentrations exceed an action level, and continues to track airborne concentrations well above the alarm limit (thus providing reliable information for estimating human exposures or environmental releases if accidents have occurred). Figure 34-6 illustrates a practical application of the evaluation scheme for applying an optical particle monitor to the three radioactive materials (238PuO2,239PuO2, and enriched uranium) and one nonradioactive material (beryllium) presented in Table 34-1. For convenience, the abscissa units of Figure 34-6 are DAC. That equalizes the scale for all radioactive materials and also allows treatment of nonradioactive materials. The particle size in the Figure 34-6 example is assumed to be monodisperse 3um diameter spheres. Optical particle counters typically give information as "number greater than a given size." The saturation limit is assumed to be 108/m3. The lower limit of detection depends on the flow rate and sampling interval. For a flow rate of 0.03m3/min (nominal 0.1 cfm, typical of small, portable units) and a sampling interval of 1 min, detection of a single particle during the sample interval would correspond to a concentration of 347 particles/m3. Figure 34-6 indicates that optical particle

AIRBORNE CONCENTRATION (partlcles/m3)

UPPER LIMIT OFDETECTION BERYLLIUM ENRICHED URANIUM

LOWER LIMIT OF DETECTION

DERIVED AIR CONCENTRATION (DAC) Fig. 34-6. Illustration of the evaluation scheme for usefulness of optical particle monitors. Particles are assumed to be monodisperse with diameters of 3um. Sampling flow rate is 5 x 10"5m3/s [3L/min].

counting will not be effective for 238Pu and 239Pu because number concentrations of concern are below normal limits of detection. However, optical particle counting may be useful for enriched uranium and beryllium metal (assuming that background levels of other dusts are not excessive). Parameters that still need to be evaluated include the unique response of light-scattering devices to each material. This is of special concern because the particles are unlikely to be spherical. Correlations of optical diameter or real diameter with aerodynamic diameter are also important because movement of the aerosol through the workplace and inhalation of the aerosol by workers will depend on aerodynamic, not optical, diameter. Strategies also need to be developed on precisely how optical monitors would be integrated into a total monitoring program (placement, worker response to alarm, record keeping, and other considerations). The use of optical monitors as early warning devices to detect radioactive releases appears promising, especially for special applications, such as during maintenance operations to detect unusual leaks and to guide in the placement of local ventilation. Early warning could speed reactions to protect workers and regain control of the workplace. Particle Collection for Microscopy

Collection of radioactive particles for morphological examination by transmission or scanning electron microscopy can easily be done using standard instruments such as the pointto-plane electrostatic precipitator (Morrow and Mercer, 1964). A small hood is normally sufficient for sample handling. The air flow rate through the hood opening should be maintained at the lowest effective level (approximately 75 linear feet per minute) to avoid

problems in handling the small, fragile electron microscope grids. Standard Formvar-coated copper grids can be used for transmission electron microscopy. Degradation of the Formvar by radiation damage is usually only a problem for high specific activity radionuclides such as plutonium-238 (half-life = 87.7 years). Care should always be taken to use contamination control features, such as liquid nitrogen cold fingers, to minimize the spread of contamination within the microscope. Highspecific-activity alpha-emitting radionuclides such as plutonium-238 are prone to migration from the collection grid by radioactive decay-induced recoil and spallation. The need for respiratory protection and radiation monitoring should be considered before servicing any microscope used for radioactive materials. Filtration

Filtration is the most widely used method for collecting samples of radioactive aerosols. The methods and equipment range from high-volume samplers (sampling rates up to about 60m3/h) for environmental or short-term workplace sampling to low-volume, miniature lapel samplers (1 L/min or less) for collecting aerosols in the breathing zone of individual workers (U.S. DOE, 1988). Low-pressure-drop, cellulose filters are commonly used, and samples can be easily reduced to ash or dissolved for analysis by analytical chemistry or radiochemistry. Concerns for penetration of particles into the filter matrix are a function of the type of filter, the type of radiation, and the radiation counting method being used. Membrane filters with their superior front-surface collecting characteristics are preferred over fibertype filters when alpha-particle spectroscopy is applied. Shielding by the filter media is seldom a concern for detection of gamma radiation. Although energy degradation concerns are greatest for alpha-emitting radionuclides, we have found that even glass microfiber filters such as the Gelman A/E glass (GEL)* collect particles near enough to the filter surface that radioactivity counting results from the ZnS method are as accurate as radiochemical results. Long-term storage of filter samples for archival purposes is not always feasible for highspecific-activity alpha-emitting radionuclides such as plutonium-238. Radiation damage to the filter, packaging tape, or plastic container may allow release of radioactivity. Care should be taken when handling samples that have aged. Inertial Sampling

Inertial sampling using cascade impactors, spiral duct centrifuges, and cyclones has been the major approach for characterizing the aerodynamic particle size distribution of radioactive aerosols. A number of specialized versions of these instruments have been developed specifically for use with radioactive aerosols (Mercer et al., 1970; Kotrappa and Light, 1972). Special requirements for instruments used in handling radionuclides generally include being compact and easy to assemble and disassemble while wearing protective gloves in a confined space such as a glove box enclosure. They also need to be easy to clean. Low sample collection rates are usually adequate for collection of small sample masses. Analytical methods for collected samples are straightforward by radioactive counting. The spiral duct centrifuge has been widely used to estimate the density or shape factor of individual particles. This works equally well for radioactive and nonradioactive particles. Because the aerodynamic diameter associated with each particle deposition location is known, electron microscopy can be used to determine the physical size and shape of particles found at those locations, and density or shape factor can be calculated (Stober and Flachsbart, 1969). Real-time inertial techniques, such as time-of-flight measurements of particles accelerated through a nozzle, are also useful for radioactive aerosols. This assumes a willingness to * See Appendix I for full manufacturer addresses referenced to the italicized three-letter codes.

purchase dedicated instruments for use with radioactive aerosols because decontamination of equipment for return to unrestricted use is not always easy. Measurement of Electrical Properties

Measurement of aerosol electrostatic charge distribution can be done using a standard aerosol charge spectrometer (Yeh et al., 1976). The theory of Yeh et al. (1976,1978) predicts that self-charging due to alpha or beta emission in radioactive aerosols will occur in addition to friction charging due to comminution. Even when aerosols are created by highly charging processes like grinding, radioactive decay processes such as alpha or beta decay may quickly result in a charge distribution that is near Boltzmann equilibrium. Measurements of particle charge distribution on a plutonium-uranium aerosol obtained by Yeh et al. (1978) with and without use of a krypton-85 discharge unit were identical. At high alpha radioactivity concentrations (>25nCi/L), it is likely that sufficient ion pairs are present to reduce the charge on the aerosols to near Boltzmann equilibrium. At lower radioactivity concentrations, this equilibrium condition may not be reached. Raabe et al. (1978) reported the anomalous results of two cascade impactor samples taken without the use of a 85Kr discharge unit in the blending step of a mixed plutonium-uranium oxide fuel preparation process. The alpha radioactivity concentration at the time those samples were taken was only 1 to 2nCi/L. Because the activity median aerodynamic diameter of these samples was larger than observed in samples taken with a discharger, it is likely that anomalous deposition on the upper stages of the impactors occurred as a result of electrostatic charge effects. It is therefore recommended, without advance information on the radioactivity concentration being sampled, to include an in-line 85Kr discharge unit as a standard procedure, even though it may not be needed to reduce the charge distribution to Boltzmann equilibrium. Fabrication and use of 85Kr discharge units are described by Teague et al. (1978). Volumetric Grab Samples, Impingers, Cold Traps, and Adsorbers

Sampling techniques such as evacuated volumes, liquid bath impingers, cold traps, and activated charcoal adsorbers work equally well for capturing radioactive and nonradioactive vapors and particles. Standard radioactivity counting techniques can be applied, depending on sample geometry. The Lucas cell is an interesting variation of a grab sampling approach (see Fig. 35-3). Radioactive particles or gases are drawn into a chamber whose interior walls are coated with a layer of crystalline ZnS(Ag) or other scintillator. Nearly 100% of the alpha rays reaching the scintillator will result in a flash of light. Any flashes of light occurring within the chamber are observed by a detector. This method can be applied to radon gas, radon progeny, or other alpha-emitting radionuclides that can be drawn into the chamber. The halflife of the radionuclides being sampled influences the delay time or cleaning requirements before the cells can be reused. Analytical Chemical Techniques

Traditional analytical chemistry methods such as infrared spectrometry, flame or furnace atomic absorption spectrometry, energy dispersive X-ray analysis, electron or neutron diffraction, and inductively coupled plasma (with either emission spectroscopy or mass spectroscopy) have sensitivities that are compatible with the small sample sizes usually associated with radioactive aerosols. Dedicated equipment is usually required for handling radionuclides, and appropriate controls must be applied. Techniques requiring tens or hundreds of milligrams, such as X-ray diffraction, have much more limited application for radioactive aerosols.

SPECIAL TECHNIQUES FOR RADIOACTIVE AEROSOLS Detection of Individual Particles by Autoradiography The interaction of ionizing radiation with photographic film or nuclear track detector foils such as CR-39 creates tracks or material defects than can be photographically developed or chemically etched for observation by scanning electron microscopy or light microscopy. The location, number, and length of the tracks can be used to determine the position and estimate the radioactivity of individual radioactive particles. In one example, Voigts et al. (1986) identified single aerosol particles as the alpha sources from industrial plume samples with particle number concentrations of 2000 particles/mm2. Cohen et al. (1980) used cellulose nitrate track etch film to measure the alpha radioactivity on human autopsy specimens of the bronchial epithelium. In combination with microscopy, autoradiography can be used to determine how cells and organelles are irradiated by particles that are inhaled and deposited in the body. Figure 34-7 shows an example of autoradiography used with histological slides of lung tissue to determine the microdosimetry of an inhaled uraniumplutonium oxide aerosol. Measurement of Particle Solubility and Biological Behavior Particle solubility and biokinetic studies can be done on all classes of aerosol particles, but detection of dissolved material is especially straightforward for radioactive aerosols (Kanapilly et al., 1973). Samples can be sandwiched between filters and subjected to continuous solvent flow (dynamic systems) or placed sequentially in fresh containers of solvent (static systems). Particles can also be placed in a tube with the solvent and periodically centrifuged to concentrate the particles in the bottom of the tube and to allow sampling of dissolved material from the supernatant. Radioactivity counting provides the very low limits of detection needed for accurate determination of particle dissolution, especially for highly insoluble materials, and radioactive aerosols have played a unique role in many unusual

Fig. 34-7. Autoradiograph of alpha-emitting particles of varied specific activity in the lungs of a rat following inhalation exposure to mixed uranium plutonium oxide aerosols. (Adapted from Mewhinney, 1978.)

DENSITY GRADIENT

TECHNIQUE

THALLIUM FORMATE SOLUTION TEST PARTICLES CENTRIFUGATION ALIQUOT NUMBER (a)

(b)

(C) ALIQUOTS WEIGHED AND ANALYZED Fig. 34-8. Illustration of the density gradient ultracentrifugation technique for determining the density of individual particles. (Adapted from Finch et al., 1989.)

discoveries about aerosol behavior. The high degree of measurement sensitivity was a major factor in the studies of Mewhinney et al. (1987a) that showed a rapid initial release of material whenever particles were reintroduced into a solvent. That work provided insight into the possible environmental effects of wet and dry weathering on particles released to the biosphere. Density Measurement by Isopycnic Gradient Ultracentrifugation

Isopycnic density gradient ultracentrifugation has been shown to be a useful technique for measuring the density of small quantities (0.1 to 5mg) of a variety of particles (Allen and Raabe, 1985; Finch et al., 1989). Normal density measurement techniques, such as air or gas pycnometry or liquid displacement, are not suitable for the small sample volumes normally associated with radioactive aerosols. Thallium formate has been the usual heavy-metal solution for this technique, but sodium metatungstate has been shown to be an economical, nontoxic alternative (Hoover et al., 1991). Figure 34-8 illustrates the technique. Particles are added to a centrifuge tube containing the heavy liquid. The tube is subjected to centrifugation to form a gradient of density from top to bottom. Density near the top of the tube normally approaches that of water, and higher density near the bottom of the tube can be higher than 3.0g/cm3. Particles move to the location within the tube where their density equals that of the surrounding liquid. Successive samples of known volume are then removed, weighed to confirm the density of the liquid in the sample, and then analyzed by radioactivity counting or other suitable methods to determine the fraction of the particle material in the sample. Refractive index measurements of the liquid samples can also be used to determine the density of the liquid samples, but that requires a dedicated refractometer and is usually more time-consuming than simple weighing. The density gradient ultracentrifugation technique provides information about the density distribution of particles within an aerosol sample.

Surface Area Measurement by Krypton-85 Adsorption

Particle-specific surface area (m2/g) influences the rate of surface phenomena such as dissolution. When adequate sample masses are available (10 to 50mg or more), measurement of specific surface area is reliable and straightforward. The most widely used approach involves the Brunauer, Emmett, and Teller method of calculating nitrogen adsorption onto the surface of a sample of known mass. A number of commercial instruments are available. Rothenberg et al. (1982,1987) have focused attention on the special problems of surface area measurement when sample size is less than 10 mg, which is a typical restriction when characterizing radioactive particles. They have described and evaluated a method for adsorbing 85Kr gas onto the sample surface (Rothenberg et al., 1987). Radioactive decay of 85Kr emits a 0.514MeV gamma ray that can be readily detected by a standard scintillation method such as NaI(Tl). They note that a lcm 2 monolayer of 85Kr gas having a specific activity of lOCi/g of gas will give approximately 10,000 disintegrations per minute that can easily be measured with an uncertainty of less than l%.The major statistical uncertainty is associated with blank correction for the sample holder. The 85Kr-adsorption technique has been successfully applied to the characterization of small samples of mixed uranium and plutonium dioxide particles (Mewhinney et al., 1987b), and the technique can be used for samples as small as lmg, with specific surface areas greater than 1 m2/g. However, the disadvantage of the method rests in the lack of a commercially available instrument and the significant effort required for a user to set up the method. Real-Time Monitoring for Airborne Radionuclides

The International Electrotechnical Commission has developed standards for radiation protection instrumentation for continuously monitoring radioactivity in gaseous effluents, including (1) general requirements and (2) specific requirements for (a) radioactive aerosol monitors including transuranic aerosols, (b) radioactive noble gas monitors, (c) iodine monitors, and (d) tritium monitors (IEC, 2000a-e). Many instruments for real-time monitoring of airborne radioactivity are commercially available. The typical instrument configuration involves a radiation detector in close proximity to a filter that is collecting aerosols. Radioactivity concentration information can be accessed at the instrument itself, or instruments can be networked to central monitoring stations. Radiation shielding and background correction for external radiation sources are required for beta and gamma radiation detection systems, and background correction, calibration, and geometry considerations come into play for alpha radiation detection systems. Some of the important considerations for design, calibration, and operation of continuous air monitors (CAMs) for alpha-emitting radionuclides are given below. Mitigation of Interference from Radon Progeny. The alpha emissions of naturally occurring radon progeny such as polonium-218 and bismuth-212 (with alpha energies of 6.0 and 6.08MeV, respectively) are similar enough in energy to the alpha emissions of plutonium-239 (alpha energy 5.2MeV) and plutonium-238 (alpha energy 5.5MeV) to cause interference or false-positive reports of plutonium air concentrations. Figure 34-9 shows a typical radon progeny spectrum as it is detected by an alpha spectrometer. Early alpha CAM designs did not include spectrometry, but used a single-channel analyzer to detect radioactivity in a plutonium energy region of interest (ROI) and a second analyzer to both detect radon progeny activity in a second energy region and allow a simple background correction for the counts seen in the plutonium ROI. The correction method was crude and the limit of detection high, but it provided a useful, real-time means for detecting relatively large airborne releases of plutonium. An alternate approach to handling radon progeny background interference was developed at the Department of Energy Savannah River Site (Tait, 1956; Alexander, 1966). It used

PERCENT OF COUIfTS

* « PO 7,68 MeV

212 P0 212 Bi 6.08 MeV

2 A and

8.78 MeV ISp0

6.00 MeV

ALPHA ENERGY (MeV) Fig. 34-9. Typical radon progeny spectrum collected in an alpha continuous air monitor. The energy region for 234Pu (5.5MeV) is in the tail of the 218Po peak.

an impactor jet to deposit aerosol particles directly onto a photomultiplier tube that was coated with a thin layer of ZnS(Ag). This eliminates most of the radon progeny, which are usually attached to the small particle fraction of ambient aerosol (diameter less than 0.3 um) and are smaller than the effective cut-off diameter of the impactor, and provides a real-time capability for detecting plutonium particles deposited on the collection substrate (Chen et al., 1999). Smaller plutonium particles are also undetected, but most aerosol releases in the workplace have an activity median aerodynamic diameter of about 5um with a geometric standard deviation of about 2, and therefore most of the radioactivity is associated with larger particles (Dorrian and Bailey, 1995). Radioactivity counting efficiency for the collected plutonium was approximately 50% (nearly 100% of the emissions occurred in the direction of the ZnS layer) because the light emissions from the ZnS(Ag) are emitted isotropically, and all emissions can be seen with equal likelihood by a photomultiplier tube or photodiode detector. A recent variation of the Savannah River approach uses an impactor jet to deposit the aerosol directly onto the surface of a solid-state detector (Model 8300, KUR). Detection efficiency is excellent, and direct detection of the alpha particles causes good separation of the plutonium and radon progeny peaks. Particle bounce off the detector surface degrades the collection efficiency of large particles (we found that more than 90% of the 10 um aerodynamic diameter particles are lost to bounce), but this might be solved by use of a virtual impactor or a particle trap approach (for a description of the particle trap, see Biswas and Flagan, 1988). Recent advances in microcomputers have enabled a major development effort to include alpha spectroscopy in solid-state detector CAMs for real-time monitoring of alpha-emitting radionuclides. Again, a solid-state detector is placed just above the surface of a filter onto which alpha-emitting aerosols are drawn, but the detector output goes to an embedded spectrometer in the CAM rather than to single-channel analyzers. In a batch operation, the air gap between the filter and the detector can be evacuated. This prevents attenuation of the alpha-particle energies as they pass through the air gap and minimizes any spectral overlap of the plutonium and radon progeny alpha energy peaks. For real-time sampling of the aerosol, however, the region between the filter and the detector cannot be evacuated, and attenuation of the alpha particle energies occurs in the air gap.

The first successful algorithm for subtraction of radon progeny interference in the plutonium energy region was developed by Unruh (1986) at Los Alamos National Laboratory. Four ROIs are established: ROI-I on the lower energy tail of the 218Po peak, where plutonium is expected; ROI-2 on the upper energy tail of the 218Po peak; ROI-3 on the lower energy tail of the 214Po peak; and ROI-4 on the upper energy tail of the 214Po peak. Basically, the 218 Po and 214Po peaks are assumed to be congruent in shape, and the radon progeny activity in the plutonium ROI (ROI-I) can be estimated by taking a ratio of the radon progeny counts in the other three ROIs: (35-6) This approach is used in the Eberline Alpha 6 (EBE) and in the Victoreen Model 758 (Victoreen Inc., Cleveland, OH; note that the Model 758 is still in use in some nuclear facilities, but it is no longer sold, and no replacement is available). The Radeco Model 452 (SAIC/Radeco, San Diego, CA) uses a similar detector and filter arrangement with a triple window background subtraction algorithm (counts in regions above and below the plutonium region are used to make the background subtraction) or a peak-shape subtraction algorithm that uses peak stripping to remove interfering counts from the plutonium ROI. The Canberra Alpha Sentry (Canberra Industries, Inc., Meriden, CT; McFarland et al., 1992) and the Eberline Alpha 7 (EBE) use peak-shape algorithms for correction of radon progeny background. The Canberra Alpha Sentry also includes a fine screen on the aerosol inlet to prevent unattached radon progeny from penetrating to the aerosol collection region of the instrument (McFarland et al., 1992). The Merlin-Gerin alpha-beta CAM (MGP) uses a very large detector-to-filter separation distance (more than 1 cm) and a stainless steel baffle assembly (installed between the filter and the detector) to collimate the "beam" of alpha emissions reaching the detector. Although the overall detection efficiency is severely reduced because of the small solid angle of view from the detector to the filter, the collimation results in a very narrow full-width-at-halfmaximum for the alpha energy peaks. That obviates the need for a correction for radon progeny interference in the plutonium ROI. Other instruments have used coincidence-counting measurements of beta or gamma emissions from radon progeny to correct for alpha emission interference. However, these indirect measurements are susceptible to errors when the composition or concentration of the radon progeny background is altered. Performance testing of alpha continuous air monitors is addressed in an international standard promulgated by the International Electrotechnical Commission (IEC, 1997). Test facilities meeting the requirements of that standard have been described by Hoover and Newton (1998) and Grivaud et al. (1998). Concerns relate to particle collection efficiency and stability of the radon correction algorithms over a range of radon progeny concentrations. Filter Requirements for Continuous Alpha Air Monitoring. An active area of recent work has related to the selection of appropriate filter media for alpha CAMs. The quality of the alpha energy spectrum reported by a solid-state detector system is strongly dependent on the type of filter substrate used for particle collection. Membrane filters have long been accepted as having both the excellent particle collection efficiency and the excellent surface collection characteristics needed for alpha spectroscopy. Lindeken et al. (1964) demonstrated that, compared with submicrometer filters, large-pore membrane filters show no serious sacrifice in collection efficiency until the pore diameters exceed 5 um. Thus, larger pore filters, with their lower pressure drop, are preferred. The ANSI N13.1 (1999) standard states that if a filter with a collection efficiency lower than 95% is required to meet the overall sampling objectives, then a correction for efficiency shall be made.

The filter that has been widely recommended by CAM manufacturers is the 5 urn pore size, membrane filter (mixed cellulose ester, type SMWP; MIL). However, we found the fragile nature of that filter to be unacceptable. Particle collection efficiency and gravimetric confirmation of particle mass collection were unreliable because of filter breakage under field conditions. Results of evaluations to find a more rugged membrane filter that would have a reasonably low pressure drop and excellent surface collection efficiency are summarized in Annex D of ANSI N13.1 (1999). The Versapor-3000 (an acrylic co-polymer on a nonwoven nylon fiber support, from Gelman Sciences, now Pall-Gelman [/^4X]) performs similarly to that of the SMWP {MIL). The Durapore 5um pore size, polyvinylidene fluoride membrane filter {MIL) is acceptable but provides a poorer alpha energy spectrum than the SMWP. The AW19 filter {MIL) is a reinforced mixed cellulose ester filter that is rugged and provides a better alpha energy spectrum than the SMWP. It is also easy to dissolve for radiochemical analysis. We found the performance of the Fluoropore FSLW filter {MIL) to be superior to all the others because it has a very low pressure drop and it is an excellent front-surface-collecting filter. It is a polypropylene-backed, polytetrafluoroethylene (PTFE) filter that is extremely rugged and provides excellent alpha energy spectral separation of the collected radionuclides. The qualities of radon progeny spectra collected with the Fluoropore FSLW {MIL) and SMWP {MIL) membrane filters are compared in Figure 34-1Oa. Note the much lower tail of the 218Po peak in the plutonium ROI (around 5.2MeV). As a warning, Figure 34-1Ob shows the extremely poor spectral quality obtained when a conventional fiber filter such as the Whatman 41 (WHA) is used in an alpha CAM. Thus, the use of fiber-type filters must be avoided. These and other factors that affect alpha particle detection in continuous air monitor applications are presented by Moore et al. (1993). A useful innovation in filtration technique involves mounting each filter in a thin cardboard carrier. The card protects the filter during handling and can be labeled to readily identify the sample. Bar code labeling and computer data logging can be used for both the filter cards and the sampling instruments. This improves chain-of-custody control and facilitates long-term archiving of samples. If samples are to be destructively analyzed instead of

Percent of Counts

Fluoropore

Millipore

Whatman 41

Alpha Energy (Mev) Fig. 34-10. Illustration of the influence of filter type on the quality of the radon progeny energy spectrum in an alpha continuous air monitor. The Fluoropore filter provides superior resolution.

archived, they can be easily removed from the cards. The choice of filter for use in the cards depends on the quality of the energy spectrum that is required and on compatibility requirements of the filter for the analytical technique to be used. Mitigation of Interference from Airborne Dust. The accumulation of ambient dust on the collection filter of an alpha CAM leads to attenuation of alpha energy, just as the air gap above the filter degrades the alpha energy. Such burial of plutonium leads to underreporting of air concentrations, ranging from 10% to 100% when airborne dust concentrations are greater than lmg/m3 (Hoover et al., 1988b, 1990). Alpha particles from plutonium that is buried by 20jig/mm2 of salt on a filter are prevented from reaching the detector. This does not prevent the CAM from responding to large puff releases of radioactivity, but it does raise the limit of detection for slow, continuous releases. Dust concerns are primarily associated with decommissioning activities where metal piping and structures are being cut (see Newton et al., 1987), with environmental restoration activities where soil is being disturbed, and with special activities such as in the underground salt mine environment of the Department of Energy Waste Isolation Pilot Plant near Carlsbad, New Mexico. Efficiency Considerations for Filter/Detector Geometry. Concerns for dust loading make it necessary to consider some trade-offs in filter and detector size. Many CAMs use a 25 mm diameter detector and a 25 mm diameter filter. This arrangement has an overall detection efficiency of approximately 20%. Retaining the same detector size but increasing the diameter of the filter collection surface to 43mm cuts the detection efficiency in half (down to 10%), but increases the collection surface area by nearly a factor of 4. At first, this appears to be a reasonable trade-off. However, a closer examination of detection efficiency as a function of filter diameter reveals that material collected at the filter edges contributes very little to overall efficiency. This is because of solid angle considerations that reduce both the efficiency at which alphas are intercepted by the detector and the energy at which they are detected. With a larger filter, alphas from the filter edge lose a significant amount of energy as they travel to the detector. Alphas traveling from one edge of the filter to the opposite edge of the detector have the longest path through air and thus suffer the greatest energy loss, often enough to remove them from the plutonium energy region. Plutonium collected at the center of the filter is detected in the plutonium ROI at an efficiency of 30%, with little energy degradation. At a diameter of 25 mm, detection efficiency has dropped to 15%, but only marginal energy degradation has occurred. At a diameter of 43 mm, only 0.04% of the emitted alpha particles reach the detector and have energies in the plutonium ROI. Thus, the larger filter has marginal utility, especially in the presence of dust, where additional energy degradation will degrade any alphas from the filter edge to energies below the plutonium region. Remote Detection of Radioactive Particles

A new technique has been developed at Los Alamos National Laboratory for remotely detecting alpha radioactivity contamination on internal surfaces of equipment or in areas that cannot be directly surveyed by radiation instrumentation (MacArthur and Allander, 1991). It uses the fact that ion pairs persist in the air after they are formed by the movement of positively charged alpha particles through air. This persistence was a surprise to many who believed that the recombination of ion pairs was nearly instantaneous. By drawing clean air across a contaminated surface, ion pairs can be carried to an electrometer and detected. The new technique should receive wide use in facilities having airborne alpha-emitting aerosols and makes it possible to identify contamination with a greater degree of certainty than was previously possible.

Sampling from Stacks and Ducts

The original version of the American National Standard Guide to Sampling Airborne Radioactive Materials in Nuclear Facilities (ANSI, 1969) has been revised and reissued as the American National Standard for Sampling and Monitoring for Releases of Airborne Radioactive Substances from the Stacks and Ducts of Nuclear Facilities (ANSI, 1999). Both versions of the standard emphasize the importance of obtaining a representative sample. However, the original standard included faulty assumptions about aerosol mixing in exhaust ducts and faulty technical guidance on the use of multipoint sampling to obtain a representative sample. The new standard minimizes reliance on simplistic concepts such as isokinetic sampling (considered by many to be broadly misapplied in turbulent sampling conditions) and emphasizes "qualification" of a well-mixed sampling location, which enables the more efficient collection of samples from a single point. Compact and cost-effective mixer designs have been developed to create a well-mixed condition in an exhaust stack (McFarland et al., 1999a,b). The revised standard also contains information about using the new and more efficient shrouded probes for sample extraction (McFarland et al., 1989; Rodgers et al., 1996). Significant improvements have also been made in modeling the deposition of radioactive aerosols in sampling transport lines. Modern codes such as DEPOSITION 4.0 (Anand et al., 1996; Riehl et al., 1996) can now be used to predict particle deposition as a function of transport line geometry. An overview of the methodology for sampling effluent air from stacks and ducts of the nuclear industry is available (McFarland, 1998).

PRACTICAL OPTIONS FOR DATA TRANSMISSION AND NETWORKING For many years, radiation monitoring instruments were considered stand-alone devices that provided information locally. A new emphasis on demonstrating "control of the workplace" has lead many health protection professionals to consider networking and central monitoring capabilities as essential. Applications in nuclear power plants have long demonstrated the advantages of networking, but those applications have largely involved fixed physical situations with hardwired cables installed during original construction. Development of new networking capabilities is especially difficult when cables must be installed in radioactive areas or when work tasks change and new monitoring locations are needed. The cost of installing the cables for data transmission can be prohibitive. Some of the most notable breakthroughs in recent years are in new radiation monitoring devices that provide continuous dose rate information by radio signal to a remotely located individual. To accomplish this, the most beneficial devices use spread spectrum radio transmission. This is basically the same high-technology approach used for cellular telephones and remote microphones. For the first time, it is possible to cut radiation doses in half by removing the health physics technician from direct proximity to the work site. Someone totally outside a contaminated area can now monitor air concentrations and worker whole-body exposures and provide instructions and guidance for minimizing dose. This is an area of rapid progress.

ADEQUACY OF THE EXISTING AEROSOL SCIENCE DATA BASE An ever-increasing emphasis is being placed on having a "technically defensible basis" for measuring radioactive aerosols in the workplace and environment. New terms such as conduct of operations, total quality management, and integrated safety management have been coined to provide new paradigms for management and worker attitudes and responsibilities, record keeping and quality assurance requirements, demonstration of compliance, and proper integration of concerns for industrial hygiene, health physics, and traditional safety hazards.

Some of the notable deficiencies in the technical basis for radioactive aerosol measurement are quality and handling of radioactive calibration sources, selection of appropriate instrumentation for specific tasks, placement of instrumentation in the workplace or environment, and criteria for collection of an adequate sample. Improvements in the limit of detection for workplace and environmental releases are also needed. This might come through wider application and improvements in virtual impaction techniques to concentrate airborne particles before collection. Pressure for improved quality and capabilities can be expected to be the greatest in four classes of nuclear applications: environmental restoration of radioactively contaminated facilities such as those located throughout the Department of Energy weapons production complex; design, construction, and operation of facilities for safe disposal of low-level and high-level radioactive waste; monitoring and handling of radioactive materials in medical settings; and continued operation or design of new nuclear facilities for generation of electricity, including those that would use mixed uranium-plutonium oxide fuels from disposition of nuclear weapons. Opportunities are available for improving the quality of regulations and standards and for preparing guidance documents for state-of-the-art sampling and monitoring. The American National Standard for Radiation Protection Instrumentation Test and Calibration (ANSI, 1983) is being revised to include a specific new standard to be issued as ANSI-N323C for air monitoring instruments. International standards for air monitoring and radiation protection instrumentation are under continuous development and improvement. However, continued work is needed to fully understand the basic behavior of radioactive aerosols and to synthesize total measurement approaches that are technically defensible and cost effective. CONCLUSIONS The measurement of radioactive aerosols is a challenging and specialized subset of aerosol science. The basic physics of radiation and radioactivity provides some unique technical advantages and disadvantages for accomplishing the task. Overall, the advantages outweigh the disadvantages. At the same time, a range of political, regulatory, and emotional obstacles arise from both the justifiable and the unreasonable health and environmental concerns that surround radioactivity and the use of radioactive materials. Because the health, economic, psychological, and political costs to society can be high, the tools of aerosol science must be used in wise and technically defensible ways when dealing with radioactivity. ACKNOWLEDGMENTS Preparation of this manuscript was supported by the U.S. Department of Energy under Cooperative agreement No. DE-FC04-96AL76406.

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TABLE 35-1. Parent Nuclide and Radon Isotope of the Natural Radioactive Series Long-Lived Parent

Series Uranium Thorium Actinium

Isotope 238

U Th 235 U 232

Half-life (xlO9 years) 4.5 14.1 0.7

Radon Isotope

Crustal Abundance ppm 2.7 8.5 0.02

Bq/kg 33 34 1.5

pCi/g 0.89 0.92 0.04

Isotope 222

Rn Rn 219 Rn 220

Common Name

Half-life

a-Particle Energy (MeV)

Radon Thoron Actinon

3.82 days 55.6 s 3.96 s

5.49 6.29 6.82

Source: Adapted from U.S. NCRP (1988).

life of the radon, that is, the time needed for half of a given quantity to decay. The half-life is different for each radon isotope (Table 35-1). The concentration of a radioactive element may be designated by mass, that is, mg/kg or ppm, or by activity (i.e., Bq/kg or pCi/g). The unit of activity is the becquerel (Bq): (35-1) Thus, 1 Bq represents one transformation or disintegration per second. The conventional unit of activity is the curie (Ci): (35-2) Further conversions are shown in Table 35-2. Both 238U and 232Th are present in all rocks and soils at concentrations of roughly 1 to 5 and 2 to 12 ppm, respectively, or about 7 to 60 Bq/kg of soil for both nuclides. The 232Th concentration of some rocks is as high as 20ppm (U.S. NCRP, 1988). The crustal abundance (by mass) of 232Th averages about three times that of 238U, but the activity concentrations are about equal because of the much longer half-life of 232Th (Table 35-1). The content of 235U in crustal matter is less than 1% that of 238U. These nuclides and their progeny will be in radioactive equilibrium unless separated by a physical process, such as diffusion of the gas radon away from its site of origin. The most significant isotope with respect to exposure of the population to background radiation is 222Rn, of the 238U decay series. Its half-life of 3.82 days allows time for substantial diffusion to soil gas and to the atmosphere. 220Rn (thoron), with a half-life of 55 s, contributes less significantly to the airborne inventory of radon. 219Rn (actinon) is the least abundant isotope both because of its short half-life (3.96 s) and because of the low content of 235U in crustal matter. The following discussion centers on sampling and evaluation of airborne 222Rn because (1) the atmospheric concentration far exceeds that of 220Rn and (2) the radiation dose that results from exposure to 220Rn is only about one-third as great as that from an equal activity concentration of 222Rn. For special circumstances (e.g., geological formations with highly elevated thorium concentrations or manufacturing processes utilizing thorium), thoron exposure may be significant and must be recognized if radon sampling is required. A few methods are available for sampling thoron progeny (U.S. NCRP, 1988), but the need is rare and is not considered further in this chapter. Detailed information on concentrations of the radon isotopes, sources, emanation rates, atmospheric mixing and transport, the radiation dose resulting from exposure and the measurement of radon and radon daughters in air can be found in a series of reports issued by the National Council on Radiation Protection and Measurements (U.S. NCRP, 1984a,b, 1987,1988,1989). These authoritative reports have been freely consulted in the preparation of this chapter. Primary sources for material referenced to U.S. NCRP documents can be found in those reports.

TABLE 35-2. Units and Conversion Factors for Radon and Radon

Activity Becquerel (SI unit) Curie (conventional unit) Useful conversions I C i = 3.7 x 1010Bq IpCi = 3.7 x 10"2Bq IpCiL" 1 = 37 Bq nT3

IBq = I s 1 ICi = SJxIO 10 S- 1 lBq = 27pCi 100Bq = 2JpCiL" 1

Special Units SI Units 222 Rn

220

WL = 2.8 x 10"5 (A) + 1.4 x 10"4 (B) + 1 x 10"4 (C) EEC in Bq nT3 = 0.105 (A) + 0.516 (B) + 0.379 (C) where A, B, C = concentrations of 218Po, 214Pb, and EEC in Bq m~3 = (3700) (WL)

214

Bi in Bq m"3

WL = 3.3 x 10"5 (B) + 3.1 x 10"4 (C) EEC in BqnT 3 = 0.91 (B) + 0.09 (C) where B, C = concentrations of 212Pb and 212Bi in Bq m"3. The contribution of 216Po to WL and E E C is negligible EEC in Bq nT 3 = 275 (WL)

Rn

Jm" 3 = 5.6 x 10-9 (EEC in Bq m"3) JhnT 3 = (Jm" 3 ) (hours exposed)

Either Conventional Units 222 Rn

WL = 1.05 x 10"3 (A) + 5.16 x 10"3 (B) + 3.79 x 10"3 (C) E E C in p Q L 4 = 0.105 (A) + 0.516 (B) + 0.379 (C) where A, B, C = concentrations of 218Po, 214Pb, and 214Bi in pCiL" 1 EEC in pCiL" 1 = (100) (WL)

EEC, equilibrium equivalent concentration; WL, working level.

TABLE 35-3. Major Sources of Worldwide Atmospheric Radon-222 Source

Ci/year

Emanation from soil Ground water (potential) Emanation from oceans Phosphate residues Uranium tailings piles

2x 5x 3x 3x 2x

109 108 107 106 106

Source: Adapted from U.S. NCRP (1984b).

Sources

Direct emanation to the atmosphere from soils and rocks is the major source of atmospheric radon (Table 35-3). Ground water is the second most significant source. Radium, the immediate predecessor of radon, is dissolved by ground water and carried into streams and rivers; the decay of the radium provides a reservoir of potential atmospheric radon. Lesser amounts are contributed from the ocean and other radium-containing crustal materials. The major source of indoor radon is the infiltration of soil gas into a structure drawn by pressure differentials between the indoor air and the soil gas. Small pressure differentials caused by temperature differences between indoor and outdoor air, or surface winds, draw soil gas from large areas into a dwelling (U.S. NCRP, 1989).

Airborne Concentrations

Typical atmospheric concentrations of radon in the United States range from about 4 to 15BqnT3 [0.1 to (UpCiL"1] (U.S. NCRP, 1988), with an average of about 7Bqm"3 (U.S. NCRP, 1989). Concentrations are much lower over the oceans. Indoor concentrations are reported to be lognormally distributed with a geometric mean of about 48Bqm"3 [IJpCiL"1] (U.S. EPA, 1992a) and a geometric standard deviation (GSD) of about 2 to 2.5 (Harley, 2000). This implies that about 5% of homes exceed 1500 Bqm~3 [4pCiL"!], the level at which the U.S. EPA (1992) suggests remedial action be considered. These concentrations represent a few thousand atoms of radon and only a few atoms of the short-lived progeny per liter so that identification by chemical methods is not possible. The radiometric properties of the nuclides are used for detection; these radiometric properties also confer the potential for induction of biological effects.

RADIOMETRIC PROPERTIES OF RADON AND DAUGHTERS Radon, an alpha-particle emitter, transforms to a polonium isotope that further decays through a series of short-lived isotopes of bismuth, lead, and polonium (also thallium for 220 Rn and 219Rn) (Figs. 35-1 to 35-3). The short-lived progeny of 222Rn include the series from 218 Po to 214Po (RaA to RaC). This portion of the decay chain has an overall half-life of about 30min. Various alpha, beta, and gamma rays emitted from these nuclides are detectable (Table 35-4), and specific radiations may be used to quantitate the air concentration of individual progeny. The next nuclide in the series, 210Pb, has a half-life of 21 years, is largely

238 y 4.5x109y 4.2 MeV

234 U 2.5x105y 4.7 - 4.8 MeV 234m pa 1.2 m 2.3 MeV

234

Th 24 d 0.2, 0.1 MeV

230 Th 8.0x104y 4.6 - 4.7 MeV

Beta Decay Alpha Decay

226 Ra 160Oy 4.8 MeV 222 Rn 3.82 d 5.5 MeV

214

210

Bi (RaC) 19.7 min 0.4 - 3.3 MeV Pb(RaB) 26.8 min 0.7,1.0MeV

Po(RaF) 138 d 5.3 MeV

Po(RaC) 1.6x10-4s 7.7 MeV

Po(RaA) 3.05 min 6.0 MeV

214

210

214

218

Bi (RaE) 5.Od 1.2MeV

210

Pb (RaD) 22y <0.1 MeV

206

Pb(RaG) Stable

Fig. 35-1. Decay scheme of the uranium-238 series. The hatched area highlights radon and its short-lived progeny.

232 Th 1.4x10l0y 4.0 MeV

228

228 Ac 6.13 h '0.4 - 2.2 MeV

228 Th 1.91 y 5.3, 5.4 MeV

Beta Decay

224

Ra 3.64 d 5.7 MeV

Ra 5.8 y <0.1 MeV

Alpha Decay

220

Rn (Tn) 55 s 6.3 MeV

216

212

Po(ThA) 0.15 s 6.8 MeV

Po (ThC) 3 x 10'7S 8.8 MeV

212

Bi (ThC) 60.6 min 2.2 MeV 6.1 MeV

212

208

Pb(ThB) 10.6 h 0.3, 0.6 MeV

Pb(ThD) Stable

208

TI (ThC") 3.1 min 1.0-1.8MeV Fig. 35-2. Decay scheme of the thorium-232 series. The hatched area highlights thoron and its short lived progeny. 235 U 7.1 x108y 4.4 MeV 231 Th 25.5 h 0.09 - 0.30 MeV

231 p a 3.2x104y 5.0 MeV 227 Ac 21.6 y 0.05 MeV

227 Th 18.2d 5.8 - 6.0 MeV

223

Ra 11.4d 5.5 - 5.7 MeV

Beta Decay' Alpha Decay

2

^Rn 4.0 s 6.4 - 6.8 MeV 21

SPo (AcA) 1.8 x 10"3S 7.4 MeV

211

Pb(AcB) 36.1 min 1.4, 0.5 MeV

211

Bi (AcC) 2.15 min 6.3, 6.6 MeV 207 Pb Stable 207

TI (AcC") 4.79 min 1.44 MeV Fig. 35-3. Decay scheme of the uranium-235 series.

TABLE 35-4. Uranium Series from Rn-222 to Pb-210 Nuclide

99.98%

99.98%

Historical Name

Half-life

Major Radiation Energies (Mev) and Intensities

Emanation Radon (Rn)

3.823 days 5.49 (99.92%)

Radium A

3.05 min

6.00 (99.98%)

0.02% Radium B

26.8 min

Astatine

~2 s

Radium C

19.9 min

0.178 (2.4%) 0.665 (46.1%) 0.295 (18.4%) 0.722 (40.8%) 0.352 (35.4%) 1.02 (9.5%) 0.768 (1.04%) 6.65 (6.4%) 6.99 (90%) 6.76 (3.6%)

1.06 (5.56%) 1.15 (4.25%) 1.41 (8.15%) 1.50 (16.9%) 1.54 (17.5%) 1.89 (7.56%) 3.27 (19.8%)

0.02% Radium C

164 us

Radium C"

1.30min

Radium D

22.3 years

0.609 (44.8%) 0.768 (4.76%) 1.12 (14.8%) 1.24 (5.83%) 1.76 (15.3%) 2.20 (4.98%)

7.69 (100%) 1.86(24%) 0.298(79.1%) 2.02 (10%) 0.800 (99.0%) 2.41 (10%) 1.07 (12%) 4.20 (30%) 1.21 (17%) 4.38(20%) 1.36(21%) 0.0165 (87%) 0.046 (4.18%) 0.063 (18%)

Source: From Cohen, B. Sampling airborne radioactivity. In Air Sampling Instruments for Evaluation of Atmospheric

Contaminants, 7th Ed. S. Hering, ed. Cincinnati, OH: American Conference of Governmental Industrial Hygienists, Inc., pp. 73-109.

removed from the atmosphere before disintegrating, and ultimately decays to stable lead (Evans, 1969). AEROSOL PROPERTIES OF RADON AND DAUGHTERS Unattached Fraction When 222Rn decays, the atom of freshly formed 218Po may quickly attach to a particle of the ambient aerosol. Those atoms that remain unattached will rapidly grow by reacting with trace gases, or if charged, they will attract ambient water molecules to form clusters. These small, highly diffusive particles, or the resulting clusters, are known as the "unattached fraction." A

TABLE 35-5. Characteristics of Radon Progeny Aerosols

Atmosphere Outdoor Indoor Mines

Number Concentration (No. cm"3)

AMD (nm)

Equilibrium Factor

103-105 103-105 104-105

30-500 5-150 90-300

0.7a, 0.8* Q4a,b

0.3*

AMD, activity median diameter. US. NCRP (1987). 6 UNSCEAR (1988). Source: U.S. NCRP (1988). 0

review of the diffusion coefficients measured for this size fraction can be found in U.S. NCRP (1988) and Hopke (1989). They range from 0.0025 to 0.08Cm2S"1. This corresponds to a size range of about 0.5 to 5.0 nm. These highly mobile particles diffuse rapidly and deposit on larger atmospheric particles or plate out to the walls and other surfaces, but because new RaA is constantly formed, there is always a small percentage of unattached 218Po. Similarly, a few 214Pb or 214Bi (RaB or RaQC) remain unattached, but the fraction is even lower than that of the RaA. It is estimated that, after formation, about 90% of the RaA atoms have a single positive charge at the end of the recoil path, the rest being neutral (Hopke, 1989; U.S. NCRP, 1988). Although the 218Po is rapidly neutralized, there remains some charged fraction. U.S. NCRP (1988) reports that a positive charge is regained during growth or attachment to ambient particles. Attached Fraction

Most of the radon progeny attach to ambient aerosol particles, the size of the fraction depending on the number of airborne particles available. Their subsequent physical behavior tracks that of the stable airborne particles except for radioactive decay. The latter is the primary mode of removal from the atmosphere because the overall radiological half-life, 30min, is faster than removal by deposition or plateout. The important measurable quantities for the attached fraction are the concentration and particle size. The size distribution of the aerosol determines its physical behavior in the environment, including particle transport and deposition. Particle size is the most important factor in determining whether the particles are inhaled and where they will deposit in the respiratory tract. For these reasons, particle size is a critical determinant of the radiation dose to the respiratory airways. The size distribution and, thus, the radiation dose differ for mine, outdoor, and indoor air (Table 35-5). For radioactive aerosols, the diameter of interest is the activity median diameter (AMD), that is, the geometric mean diameter determined by measuring the radioactivity of the size fractionated sample, assuming a lognormal distribution. The AMD reported for radon progeny varies with ambient conditions, as seen from the representative ranges shown in Table 35-5, but it is always less than 1 jam. AMD is one of the most significant variables when comparing the dose per unit exposure for underground miners with that of the general population. HUMAN EXPOSURE PARAMETERS When atmospheric radon decays, both attached and unattached short-lived progeny are inhaled and deposit on the bronchial epithelium, where they decay before they can be removed by natural clearance processes. The dose delivered by the alpha particles emitted

after deposition is the significant quantity in determining the carcinogenic potential of a particular radon atmosphere. For 222Rn the alpha emitters are 218Po (RaA) and 214Po (RaC) (Table 35-4). The 214Pb (RaB) and 214Bi (RaC), both beta-particle emitters, are not significant dosimetrically, but their behavior determines the ultimate concentration of 214Po. Units of Exposure The historical unit of exposure is the working level (WL). This is defined as equivalent to an exposure atmosphere containing 10OpCiL"1 of 222Rn in equilibrium with all of the short-lived progeny. The alpha-particle energy that would eventually be emitted into this air volume is 1.3 x 105MeVL"1 (Table 35-6). In practice, one WL is any combination of short-lived daughters in I L of air that will result in the emission of 1.3 x 105MeV of potential alpha energy. An exposure atmosphere with this quantity of potential alpha-particle energy is called a WL regardless of whether equilibrium exists. Note that WL is a measure of the air concentration of short-lived decay products, not of the radon gas. The unit of cumulative exposure is the working level month (WLM) and is equal to exposure in WL multiplied by exposure duration in multiples of the occupational month (17Oh). To calculate the cumulative exposure of a member of the population in terms of WLM, the hours must be adjusted because exposure times exceed 170h/month. Then WLM = WL[(hours exposed)/170]

(35-3)

Atmospheric clearance processes, such as deposition to surfaces, usually result in disequilibrium between the concentration Of222Rn and its offspring. The airborne activity concentration of 218Po is usually less than that of radon and so on for each successive decay product, except that the extremely short half-life of 214Po (RaC) ensures that it remains in equilibrium with 214Bi (RaC). The resulting airborne radioactivity concentration may be described as an equilibrium equivalent concentration (EEC). The EEC is the concentration of radon that in equilibrium with each of the daughters would have the same potential alpha energy per unit volume as the actual mixture. By calculating the fractional alpha energy shown in the last column of Table 35-6, it can be seen that (35-4) where, [A], [B], and [C] are the concentrations of 218Po,214Pb, and 214Bi, respectively, in either Bqnr 3 or pCiL 1 . EEC and WL are related as follows: (35-5)

TABLE 35-6. Isotope 222

Rn Po 214 Pb 214 Bi 214 Po 238

222

Rn Short-Lived Decay Products

Alpha Ray Energy (MeV)fl

Half-Life

Number of Atoms/100 pCi

5.49 6.00 0 0 7.68

3.82 days 3.05 min 26.8 min 19.7 min 2.7 x 10"6min

1.76 x 106 977 8580 6310 8.5 x 10"4

Ultimate Alpha Ray Energy (MeV)

Total Alpha Ray Energy (MeV)

6.00 + 7.68 7.68 7.68 7.68 12.7 x 104

1.34 x 104 6.50 x 104 4.85 x 104 0.007

"The total amount of alpha ray energy that will ultimately be emitted by the short-lived decay products of the 100pCi of 222Rn = 1.3 x 105MeV.

(35-6) The state of equilibrium is also described by the equilibrium factor, F F is the ratio of the potential alpha-energy concentration that exists in the mixture to that which would exist if all the decay products were in equilibrium with the radon present. Then F is the ratio of EEC to the radon concentration: (35-7) where [Rn] is the concentration of radon gas. If F is about 0.4, a level commonly found in homes in the United States (Table 35-5; U.S. NCRP, 1987), in an atmosphere with lOOpCiL"1 Rn, the EEC is 4OpCiL"1 and the exposure is 0.4WL; in an atmosphere with 4pCiL -1 of Rn, the EEC is 1.6 for an exposure of about 0.02 WL. Dose Conversion Factor

The dose conversion factor (DCF) gives the relationship between the radon progeny exposure and the radiation dose to the critical target cells of the lung. It is derived from lung model calculations taking into account ambient aerosol conditions. When calculating the dose to the lungs from inhaled short-lived radon progeny, particle size is very important. Particle number concentration is important because of the relationship between particle number and the unattached fraction (Porstendorfer and Reineking, 1999). Porstendorfer and Reineking (1999) calculated average dose conversion factors (DCF) for a variety of well-characterized atmospheres. They took into account the AMD,
AIR SAMPLING FOR RADON AND ITS SHORT-LIVED DECAY PRODUCTS Sampling Strategy When air sampling is planned, the reason for making a measurement will determine the type of measurement that is needed. Additional considerations are the estimated amount of the activity present and whether suitable equipment is available. For any measurement, it is necessary to have adequate sensitivity and adequately calibrated instruments. The purpose of the measurement, in order of complexity, may range from simply determining the indoor or outdoor air concentration, through quantitation of sources, to a health effects evaluation. If a determination is to be made as to whether there is a potential for people to be significantly exposed, then decision-making criteria will be required. Various national and international regulatory bodies have recommended limits for air concentrations of radon and radon daughters in buildings. These range from 74 to 400 Bqm"3 [0.02 to 0.11 WL]. The Indoor Radon Abatement Act of 1988, which is an amendment to the Toxic Substances Control Act, established a long-term national goal of reducing radon in U.S. buildings to ambient outdoor levels. There is some controversy about the appropriate limit for initiating remedial action, but there is general agreement that remedial action be taken if radon concentrations are greater than about 800Bqm"3, or 0.1WL (2OpCiL"1 Rn, EEC = IOpCiL-1). Activity concentrations can be determined by measuring radon, the daughters, or both. Measurement of Rn is easier and will give an upper limit to the amount of potential alpha

energy (WL) in the atmosphere. In addition, if an average equilibrium factor for homes of about 0.4 is assumed, the radiation dose that will result from inhalation exposure can be estimated. Determining the sources of radon is more complex. This may require a variety of measures to assess the emanation rate from surfaces, soil, building materials, water, and so forth. To perform a complete health effects evaluation requires the measurement of both the concentration and size distribution of the radon progeny. Sampling Methods

Sampling for either radon or progeny may be accomplished by grab sampling, continuous sampling, or collecting an integrated sample. Grab samples are either instant or very short period samples. Either radon or daughter samplers may be used. Grab samples are useful for screening on a small scale. Samples can be collected periodically over several seasons, and seasonal average concentrations can be assessed from a few measurements taken in each season. Continuous sampling with an instrument that produces sequential on-site readout is more descriptive because there are spatial and temporal variations in radon and progeny concentrations. It is necessary for any in-depth assessment of exposure, but requires skilled personnel, a high level of quality control, and relatively complex instrumentation. Continuous sampling is essential, however, for remedial work so that sources and ventilation effects may be correlated with the observed concentrations. Some of the modern real-time air monitors for alpha-emitting radionuclides are described in Chapter 34. Integrated sampling will result in a single value for the average concentration over the duration of the sampling. Average sampling times may be days, weeks, or months. Three important advantages for radon sampling are that (1) integrated samples enable the measurement of concentrations that are too low for grab sampling, (2) many long-duration samples can be collected at the same time, and (3) the samples can be analyzed at a convenient time and place. Both passive and active integrated samplers for radon are available. These samplers are generally less expensive than continuous samplers. Most measurements are undertaken to measure the air concentration of radon. Separation of radon from its daughters is feasible because radon is a gas and the progeny are particles. To sample radon only, the ambient daughters are removed by filtration. The progeny that are subsequently formed by the decay of the isolated radon are either counted or quantitatively removed from the sample volume. Counting the progeny increases the sensitivity of the sampling system because it provides three alpha particles for each 222Rn decay (see Fig. 35-1 or Table 35^-). It is only necessary to wait for radioactive equilibrium to be established. This will occur in about six half-lives, or approximately 3 h for the 222Rn short-lived decay products. Grab samplers and integrating samplers have been developed for both radon and the short-lived decay products. Continuous monitors are available for radon, but only semicontinuous monitors are available for the short-lived progeny. The operation of these samplers is based on a few collection and detection principles, but there are multiple variations of many of the basic types of monitors. This chapter presents at least one example of each type of monitoring system (grab, continuous, and integrating) and some of the more common variations. Details are given where the information is instructive of the sampling principles. Standard methods for indoor air measurements are presented; however, because new monitors are developed frequently, it is advisable to review the recent literature before selecting a sampling method for any extensive radon detection program. Commercially available samplers and monitoring equipment are described and discussed in the current edition of the air sampling instruments text published by the American Conference of Governmental (Cohen and Heikkinen, 2001).

Detection Principles. Most detection systems rely on counting of the alpha particles emitted from radon, 218Po (RaA), and 214Po (RaC). The methods are limited by the range of the alpha particles, which can penetrate only a few centimeters in air and less than 0.1mm in unitdensity material. However, this limited range permits the development of very low background detection systems for alpha-particle counting. When counting the gamma rays emitted from 214Pb (RaB) and 214Bi (RaC), it is more difficult to reduce the always-present background radiation and, thus, more difficult to detect low levels of activity. To calculate WL, it is necessary to know the concentrations of 218Po,214Pb,214Bi, and 214Po (RaA, RaB, RaC, and RaC). Either alpha, beta, or gamma spectrometers may be used to identify the radiation emitted at a specific energy for the identification of individual decay products. The different half-lives of the radon progeny provide another means of identification, and several sampling systems utilize the temporal pattern of the decay of total alpha activity to separate out 218Po,214Pb, 214 Bi, and 214Po. Lower Limits of Detection. An important measure of system performance is the smallest quantity of radon or progeny that can be detected. This will generally depend on both the volume sampled and the duration of the sampling. For example, if radon gas is concentrated from an air volume, increasing the air volume will allow adequate detection of a lower initial concentration. Similarly, increasing the exposure time of an integrating sampler will generally permit quantitation of lower concentrations. The determinants of the lowest detectable amount are the sensitivity with which the instrument responds to radiation (i.e., counts/min/unit activity) and the background count rate. A good measure is the lower limit of detection (LLD) as defined by Pasternack and Harley (1971). For a 5% risk of concluding falsely that activity is present and for a 95% predetermined degree of confidence for detecting its presence, a simplified expression is LLD = 3.29 yan

(35-8)

where /is a calibration constant to convert counts into activity and (Jn is the standard deviation of the net count rate. Grab Sampling for Radon

A common and popular device for grab sampling of radon is the scintillation cell or flask (Fig. 35^). It consists of a 100 to 100OmL glass, plastic, or metal chamber with a flat transparent bottom and either one or two valves. Either the flask is evacuated at the laboratory and filled by opening the valve in the field or an air sample may be drawn through the cell. The inside surfaces, except for the base, are coated with ZnS phosphor. The phosphor emits a flash of light when struck by an alpha particle. The air sample is held for six half-lives to allow the decay of any progeny originally present and for build up of equilibrium with the trapped radon. Counting is done by viewing the scintillations with a photomultiplier tube through the optically transparent base. Detection limits are about 1OpCiL"1, but can be as low as 0.IpCiL"1 with longer counting periods. The system is simple and reliable as long as the containers are thoroughly leak tested. The drawbacks are that the flasks are fragile and that time must be allowed for decay and to establish radioactive equilibrium. A second grab-sampling method for radon is the "two-filter method" (Fig. 35-5). Air is drawn through a tube with filters at both inlet and outlet. The first filter removes all airborne decay products. As the radon traverses the tube, some of it decays and the resulting progeny are collected on the second filter. Knowing the time for flow through the tube, the tube volume, and the deposition loss to the walls as a function of the time of formation of the 218Po

12/5 5 Semi Ball Micro Stopcock Brass

Kovar Seal Scintillator

Quartz Window (conductive coating)

R-3T3 Adhesive

Photomultiplier Tube

Fig. 35-4. Radon grab sampler. Alpha particles emitted by 222Rn, 218Po, and 214Po in the sampled air volume interact with phosphor coated on the inner walls to produce scintillations. The light pulses are transmitted through the optically clear base to a photomultiplier tube and converted to electrical pulses. The signal is then amplified and counted. (Adapted from Lucas, 1977.)

TWO FILTER TUBE AIfFlQW Filter 1 Collects Radon Progeny from tr*& fr*©o«nn i &§ Ak

Filter a Collects Radon Progeny F&med during Transit through th& Tube FIg. 35-5. Schematic of a two-filter tube for measurement of radon in air. A holding tank may be placed in front of the first filter to allow time for decay of any thoron that may be present.

(RaA), assay of the second filter permits a calculation of the radon concentration. The sensitivity depends on the tube dimensions, but can be as low as 0.IpCiL"1. Grab Sampling for Radon Daughters Determination of EEC or WL is made by collecting the short-lived progeny from a known air volume onto a filter and measuring the amount of each decay product collected on the filter. The collected progeny continue to decay during sampling; this complicates the analysis. In addition, the amount collected is often close to the LLD of the detection system, so counting errors are relatively large. A method still in use for mine atmospheres was devel-

oped by Kusnetz (1956). He used the simple approach of alpha counting the filter sample at 60min after sampling. The basis of the method is that if an atmosphere of 10OpCiL"1 of radon is in equilibrium with the short-lived decay products (IWL), an air filter will collect 10OpCiL"1 of each daughter. At 60min after collection there will be 112.6 disintegrations per minute (dpm) on the filter, all due to 214Po (RaC). The number of multiples of 112^dPmL"1 is a measure of the number of WL. The complications are that the equilibrium ratios in the air vary, the samples may not be counted at precisely 60min, and build up and decay occur on the filter during sampling. Kusnetz showed that for a sample collected for 5 or lOmin, the error due to build up and decay on the filter will be <12% under normal mine conditions, and he provided correction factors for counting times from 40 to 90min. The method has been modified and improved by several investigators (Harley and Pasternack, 1969; Rolle, 1972) and can measure down to 0.0005 WL with a reproducibility of 35%. At 0.04 WL the reproducibility is about 4%. If a substantial amount of thoron is present the filter may be counted again after about 4h have elapsed (to permit decay of the short-lived 222Rn daughters) to obtain a correction factor. The later count will result from remaining 212Bi (ThC) and 212 Po (ThC) that are formed after decay of 212Pb (ThB) (10.6h half-life). Another grab-sampling method, the Thomas method (Thomas, 1972), is a modification of an older method (Tsivoglou et al., 1953). A filter sample is collected for exactly 5min, and the total number of alpha counts is determined for the periods 2 to 5, 6 to 20, and 21 to 30min. These counts are entered into three simultaneous equations, together with the air sampling rate in 1 min"1 and the counting efficiency of the system, from which the concentration in air of 218Po,214Pb, and 214Bi, (RaA, RaB, and RaC) may be individually determined. This method does not make any assumptions about equilibrium, but it is slow. Scott (1981) has adapted the method for a reduced counting time. Alpha spectrometry can directly measure the concentration of each of the progeny on a filter sample. It requires the use of solid-state detectors and a spectrometry system that is more expensive than a simple alpha-counting system. Counting at two separate times is required in order to calculate the initial concentration of each nuclide. Continuous Sampling for Radon

Continuous radon samplers count continuously, but totals are recorded for specified time periods (e.g., 30 or 40min counts). The continuous scintillation cell is based on the same principle as the Lucas flask. Air passes through the cell and the counts are recorded for 30 min counting periods, and then another count cycle begins. Calculation of the radon concentration in these samplers is complicated by the "memory effect," that is, the presence of radon progeny formed and deposited on cell walls during an earlier sampling period. Corrections needed for this effect are determined by sampling from a test radon atmosphere with a clean sampler (Thomas and Countess, 1979). Another continuous sampler for radon is an automated version of the two-filter method. Air flows continuously through a two-filter tube, and an alpha particle detector is placed facing the second filter. Alternatively, the second filter may be replaced by a moving tape so that the sample is collected for a specified time period, after which the tape moves the collected sample under an alpha-particle detector and is replaced by a clean filter. A useful class of radon samplers operate by allowing radon to diffuse into a detection volume from which the progeny are excluded. The atmospheric particles are filtered by a diffusion barrier of foam or other material. The progeny that subsequently form in the volume are collected by a charged surface. The shapes and sizes of these monitors vary, as do the means for maintaining charge on the particle collector. Alpha particles emitted from the progeny collected on the charged surface are counted with a surface barrier detector, or the surface may be coated with ZnS and coupled to a photomultiplier tube for scintillation counting. The response of these detectors must be determined in a calibration atmosphere.

These monitors also suffer from the "memory effect," which can be troublesome in a changing radon atmosphere. The same principle is applied in several integrating radon monitors that use film or thermoluminescent dosimeters as alpha detectors. This popular method is sensitive to relative humidity, which alters the collection efficiency of the progeny by the charged surface, thus changing the calibration of the system. The details of individual monitors based on this method can be found in U.S. NCRP (1988). An alternate approach used by Chittaporn et al. (1981) is to collect the progeny on a surface that keeps the alpha particles out of the range of the detector. The radon progeny are collected by an electret that can maintain a field of 100 V cm"1 in the sensitive volume. The sensitive volume, coated with scintillation phosphor, detects only the alpha particles from the decay of the parent radon. Removing the daughters from the counting volume avoids any changes in efficiency due to humidity. The monitor can detect 0.03 pCi L"1 for a 1 h counting time. To avoid counting thoron in continuous radon monitors, a decay chamber may be placed in front of the detection volume. The volume needed will depend on the sampling flow rate and must be large enough to allow the decay of the 55 s half-life thoron. Passive radon samplers, that is, those that depend on diffusion of the gas into the sensitive volume, can reduce thoron detection by use of a diffusion barrier thick enough to delay the thoron beyond its average life. Continuous Samplers for Radon Daughters

Radon daughters are sampled by collecting them onto a filter and then counting the alpha particles emitted as they decay. The samplers can only be semicontinuous, but sampling can be done for 2 to 5 min and counting for 2 to 3 min so that a measurement can be made every lOmin. Membrane filters with pore sizes <1 urn are usually used because they are highly efficient for all particle sizes and because the counting efficiency of the samples thus collected is not too variable. Some other filters exhibit a variability in counting efficiency caused by self-absorption of alpha particles in the filter material. As described for the grab samplers, the filter may be counted in place, or it may move to a counting position and be replaced by the next filter collector. Counting the filter in place avoids mechanical problems that are troublesome in harsh environments. Such instruments frequently incorporate computer capability to directly calculate the concentrations of each of the progeny and the WL from the measured 218Po (RaA) and RaC alpha particles or decay of the beta count rate. Several commercial continuous "working level monitors" are available (Cohen and Heikkinen, 2001). Integrated Sampling for Radon

Integrating samplers for radon may be either passive or active. Passive samplers that require no power supply are extremely useful field instruments. A significant advantage is that they are quiet and, thus, acceptable to occupants when measurements are needed in homes. There are currently four types of passive radon monitors. As with other radon samplers, the progeny particles are excluded by diffusion barriers. The two most common are activated-charcoalcontaining collectors and monitors containing etched-track detectors. Activated-charcoalcontaining monitors are perforated cannisters 2.5 to 5 cm [1 to 2 in] high and 5 to 10 cm in diameter. The container is sealed at the laboratory and then opened to the test atmosphere for a fixed time period, usually 4 to 7 days for a cannister with >50g of charcoal, less for smaller quantities. Atmospheric radon diffuses onto the charcoal where it is adsorbed; the container is resealed and returned to the laboratory for counting. Counting is usually done by gamma-ray spectrometry of the entire cannister. Charcoal adsorbs water vapor as well as radon so that for open-faced carbon containers an experimentally derived correction factor

is needed when the humidity is high. Each sampler must be weighed before and after exposure; any difference is attributed to adsorbed water vapor, and an experimentally derived correction factor is applied. These detectors can be reused after regeneration by heating. The heating drives off the water vapor before the sampler is resealed. George and Weber (1990) report that if a sintered metal filter is used as a diffusion barrier over the inlet, no correction is needed for adsorbed water vapor under most conditions. Activated charcoal adsorbant is satisfactory for most sampling environments, but if the concentration of radon varies dramatically, sample loss may occur by desorption when air concentration declines. The sintered filter cannister is less sensitive to changing Rn concentrations than other open-faced or diffusion barrier cannisters (George and Weber, 1990). Etched-track detectors are plastic films, usually cellulose nitrate (Kodak LRl 15, Eastman Kodak, Rochester, NY) or allyl diglycol carbonate (CR-39, Tech/OPs Landauer, Inc., Glenwood, IL) that develop tracks when traversed by an alpha particle. Chemical etching enlarges the tracks so they can be optically counted. These detectors do not respond to beta or gamma radiation. The detectors are placed in a container into which the radon diffuses. The alpha particles from the subsequent progeny expose the detector. The results are in terms of tracks cm"2 day 1 . Calibration must be done in a test atmosphere because the geometry of the detector and container will determine the detection efficiency. They are usually left in place for several months or a year, thus giving a seasonal or annual average exposure. Another passive monitor is the passive environmental radon monitor (PERM). This unit, developed by Breslin and George (1978), is similar to the continuous passive diffusion radon monitors described above. Radon diffuses through a silica gel and filter barrier, the progeny are collected by electrostatic attraction to a rod on which a voltage (900 V) is maintained by batteries, and the radiation dose is monitored by a thermoluminescent dosimeter (TLD). The PERM sampler is usually deployed for a few weeks, and then the TLD is collected and read out on a TLD analyzer. A small passive monitor, the environmental gamma and radon detector (EGARD) (Maiello and Harley, 1987), also utilizes TLDs to record exposure. The detector consists of a small aluminum container providing a 0.001 m3 [1 L] volume into which the radon diffuses. There are three sets of TLDs. One set is covered with aluminized mylar to prevent alpha particles from reaching the TLDs and records background gamma radiation. The second set is placed on an electret to which the progeny are attracted and records both background gamma and the alpha radiation. The third set is for quality control. This small detector is easy to mail and has an LLD of 100pCiL-1days. Active sampling into gas sampling bags or sampling tanks may be used for radon as for other gases. The sample is transported to the laboratory, where the gas is transferred to a detection system. Integrated Sampling for Radon Daughters

There are no passive samplers for radon daughters. Because radon progeny aerosols must be sampled onto filters, power is required. Once collected, the resulting radiation may be measured by an integrating detector such as a film, a TLD, or an etched-track detector. One such monitor, the RPISU (for radon progeny integrating sampling unit), collects daughters on a filter near a TLD (Schiager, 1974). Radon and radon progeny measurement devices and instruments are rapidly becoming more portable and more often incorporate microprocessors and data-recording devices. However, all operate within the basic framework for measurement discussed above. A recent review of available instruments and sources will be found in Cohen and Heikkinen (2001). Additional information on instrument suppliers can be found at www.epa.gov/radonpro/ manufact.htm.

Other Monitors Several active and passive samplers have been developed that are small enough, sensitive enough, and reliable enough to be useful as personal monitors, while others are under development. The operating principle of these monitors is basically the same as for those described above. A personal radon monitor that can also be used for thoron has recently been developed (Chittaporn and Harley, 1999). The monitor is made of lightweight conducting plastic and has three chambers. Radon diffuses into each chamber through a different diffusion barrier for signal differentiation. CR-39 plastic is the radon detector. The new monitor is based on miniaturization of an earlier version (Harley et al., 1991b). These monitors were worn by subjects to make "personal" exposure measurements, and more of the monitors were simultaneously placed in the individual's home to measure 222Rn on different levels (Harley et al., 1991a). The personal exposure correlated well with exposure on the first floor of the homes tested. The reported ratio of personal to first floor exposure was 0.71. A system for measuring the fraction of airborne radon decay products that will deposit in the nasal cavity or on the bronchial tree has been suggested (Hopke et al., 1990). Wire screen collectors are used preceding a filter. The wire screen sizes and flow rates are selected based on lung deposition models to mimic respiratory deposition. A parallel filter sampler measures the total airborne activity. The activity deposited is then obtained from the difference between the amount of alpha activity measured on the two filters. Special techniques have been developed for measuring the flux of radon from soils and building materials. It is difficult to measure radon flux without perturbing the system under study. Various methods are described in U.S. NCRP (1988). Detection of the radon and progeny for this special class of samplers is also based on the principles described above.

CALIBRATION All sampling and counting systems must be calibrated to ensure accurate measurement. Flow calibration is required for active sampling systems (see Lippmann, 2001). Calibration of counting systems may be accomplished with standard reference sources traceable to the National Institute of Standards and Technology (NIST). Determination of the counting efficiency of any detector, that is, the counts per minute per unit activity of radon or daughters, is an essential part of any quality control program. Two reports present detailed information on calibration procedures (U.S. NCRP, 1988; Beckman, 1972). The counting efficiency of radon samplers may be determined by emanating a known amount of radon into the sampler from a standard solution of 226Ra obtainable from NIST. A few radon chambers exist in which carefully controlled atmospheres are maintained. These atmospheres are standardized by using samplers that have been standardized with reference sources. Among these are a chamber at the Environmental Measurements Laboratory of the U.S. Department of Energy (New York, NY) and a few that are commercially operated. Arrangements can usually be made to calibrate sampling equipment at one of these chambers. For radon progeny there is no standard atmosphere available. Thus, alphaemitting standard sources are used for comparison with laboratory counters of known efficiency; alternatively, beta or gamma standards of appropriate energy are used. If counters and detectors are properly calibrated, it is then assumed that the progeny count rates are correct. In addition to counting efficiency it may be necessary to determine the collection efficiency of a sampling system (e.g., by passive monitors containing etched-track detectors). This can only be done in a chamber with a known radon atmosphere. If any changes are made in the detection system, the calibration may no longer be valid and should be reevaluated.

PROTOCOLS FOR INDOOR MEASUREMENT The U.S. Environmental Protection Agency (U.S. EPA, 1992b, 1993) has prepared a set of protocols to be used for obtaining standardized indoor measurements. The protocols require sampling to be done in a closed house with minimum ventilation in order to maintain a stable environment. The locations at which measurements are to be made are also specified. The purpose is to permit an intercomparison of the measurements in different houses. The values of airborne radon and decay product concentrations obtained using these protocols do not necessarily reflect the concentrations to which occupants would be exposed. The EPA gives specifications for measurements of radon with continuous radon monitors, charcoal cannisters, alpha track detectors, and grab samplers. For radon daughters, procedures have been specified for the continuous working-level monitors, the RPISU, and for grab samplers. For each of these, requirements are listed for the location of the measurement device and the house conditioning; a set of minimum requirements for quality control are established for each procedure.

CONCLUSIONS The study of radon and its progeny is challenging. There are few atoms of either radon or its daughters in air, even at concentrations that are considered high in terms of the potential for causing harm to lung tissue. The size distribution of the progeny aerosol particles is of special significance because it is an important determinant of the efficiency with which they will deposit in the lung. This, in turn, determines the radiation dose that results from a particular exposure, and it is the radiation dose that confers the health risk. A variety of sensitive methods for radon (and progeny) sampling have been developed, many of which have been noted in this chapter. These methods, and those for sample analyses, must take into account the formation and decay of the short-lived progeny. The time frame (minutes to a few hours) for the build up and decay of these nuclides complicates the measurements. It is not possible to simply allow decay until only one nuclide is present or to assay samples quickly before the build up of interferences. New sampling instruments are rapidly being developed and marketed because of the current intense interest in determining the exposure of the general population. Thus, the reader who plans to sample for these airborne materials is advised to investigate the current literature as well as sources that compile and update a list of available instruments (e.g., www.epa.gov/iaq/radon).

REFERENCES Beckman, R. T. 1972. Calibration Procedures for Radon and Radon-Daughter Measurement Equipment. Informational Report 1005. Denver, CO: United State Department of the Interior, Denver Technical Support Center. Breslin, A. J. and A. C. George. 1978. An Improved Time Integrating Radon Monitor. Presented at the NEA Specialist Meeting on Personal Dosimetry and Area Monitoring Suitable for Radon and Daughter Products, November 20-22,1978, Paris. Chittaporn, P., M. Eisenbud, and N. H. Harley. 1981. A continuous monitor for the measurement of environmental radon. Health Phys. 41:405-410. Chittaporn, P. and N. H. Harley. 1999. A new personal 222Rn and 220Rn (RnTn) monitor. Health Phys. 76: S163-S164. Cohen, B. S. and M. L. Heikkinen. 2001. Sampling airborne radioactivity. In Air Sampling Instruments for Evaluation of Atmospheric Contaminants, eds. B. S. Cohen and C. S. McCammon, 9th Ed. Cincinnati, OH: American Conference of Governmental Industrial Hygienists, pp. 221-240.

Evans, R. D. 1969. Engineers' guide to the elementary behavior of radon daughters. Health Phys. 17: 229-252. George, A. C. and T. Weber. 1990. An improved passive activated C collector for measuring environmental 222Rn in indoor air. Health Phys. 58:583-589. Harley, N. H. 2000. Radon and daughters. In Environmental Toxicants, 2nd Ed., ed. M. Lippmann. New York: Wiley Interscience. Harley, N. H., P. Chittaporn, M. H. Roman, and J. Sylvester. 1991a. Personal and home 222Rn and gammaray exposure measured in 52 dwellings. Health Phys. 61:737-744. Harley, N. H., P. Chittaporn, and S. C. Scarpitta. 1991b. The Influence of Time-Activity Patterns and Lifestyle on Human Exposure to Radon in Air. Part 1: Development of a Personal Radon Monitor. Report to New Jersey Department of Environmental Protection, Trenton, NJ. Harley, N. H. and B. S. Pasternack. 1969. The rapid estimation of radon daughter working levels when daughter equilibrium is unknown. Health Phys. 17:109-114. Hopke, P. K. 1989. Initial behavior of 218Po in indoor air. Environ. Int. 15:299-308. Hopke, P. K., M. Ramamurthi, and E. V. Knutson. 1990. A measurement system for Rn decay product lung deposition based on respiratory models. Health Phys. 58:291-295. Kusnetz, H. L. 1956. Radon daughters in mine atmospheres—A field method for determining concentrations. Ind. Hyg. Q. 3:85-88. Lippmann, M. 2001. Calibration of air sampling instruments. In Air Sampling Instruments for Evaluation of Atmospheric Contaminants, 9th Ed. eds. C. S. McCammon and B. S. Cohen. Cincinnati, OH: American Conference of Governmental Industrial Hygienists, pp. 73-109. Lucas, H. F. 1977. Alpha scintillation counting. In Atomic Industrial Forum Workshop on Methods for Measuring Radiation In and Around Uranium Mills, Vol. 3, No. 9, ed. E. D. Harwood. Washington, DC: Atomic Industrial Forum. Maiello, M. L. and N. H. Harley. 1987. EGARD: An environmental gamma-ray and radon detector. Health Phys. 53:301-305. Pasternack, B. S. and N. H. Harley. 1971. Detection limits for radionuclides in the analyses of multicomponent gamma-ray spectrometer data. Nucl Instrum. Methods 91:533-540. Porstendorfer, J. and A. Reineking. 1999. Radon: Characteristics in air and dose conversion factors. Health Phys. 76:300-305. Rolle, R. 1972. Rapid working level monitoring. Health Phys. 22:233-238. Schiager, K. J. 1974. Integrating radon progeny air sampler. Am. Ind. Hyg. Assoc. J. 35:165-174. Scott, A. G. 1981. A field method for measurement of radon daughters in air. Health Phys. 41:403405. Thomas, J. W. 1972. Measurement of radon daughters in air. Health Phys. 23:783-789. Thomas, J. W. and R. J. Countess. 1979. Continuous radon monitor. Health Phys. 36:734-738. Tsivoglou, E. E., H. E. Ayers, and D. A. Holaday. 1953. Occurrence of non-equilibrium atmospheric mixtures of radon and its daughters. Nucleonics 11:40-45. UNSCEAR. 1988. Sources, Effects and Risks Of Ionizing Radiation. Report to the General Assembly, United Nations Scientific Committee on the Effects of Atomic Radiation. U.S. EPA. 1992a. A Citizens Guide to Radon. EPA 402-K-92-001. Washington, DC: Environmental Protection Agency, Office of Air and Radiation. U.S. EPA. 1992b. Indoor Radon and Radon Decay Product Measurement Device Protocols. EPA 402R-92-004. Washington, DC: Environmental Protection Agency, Office of Air and Radiation. U.S. EPA. 1993. Protocols for Radon and Radon Decay Product Measurements in Homes. EPA 402R-92-003. Washington, DC: Environmental Protection Agency, Office of Air and Radiation. U.S. NCRP. 1984a. Exposures From the Uranium Series with Emphasis on Radon and its Daughters. NCRP Report No. 77. Bethesda, MD: National Council on Radiation Protection and Measurements. U.S. NCRP. 1984b. Evaluation of Occupational and Environmental Exposures to Radon and Radon Daughters in the United States. NCRP Report No. 78. Bethesda, MD: National Council on Radiation Protection and Measurements.

U.S. NCRP. 1987. Exposure of the Population in the United States and Canada from Natural Background

Radiation. NCRP Report No. 94. Bethesda, MD: National Council on Radiation Protection and Measurements. U.S. NCRP. 1988. Measurement of Radon and Radon Daughters in air. NCRP Report No. 97. Bethesda,

MD: National Council on Radiation Protection and Measurements. U.S. NCRP. 1989. Control of Radon in Houses. NCRP Report No. 103. Bethesda, MD: National Council on Radiation Protection and Measurements.

derived from natural products administered as smokes as far back as 2000 to 4000 BCE. in China and India (Sciarra, 1970). In recent times, pharmaceutical aerosol products were developed for inhalation in the mid-1950s (Sciarra and Cutie, 1990). Routes of Administration

While inhalation aerosol products represent the most sophisticated delivery system for therapeutic agents, another major application of aerosol technology involves topical administration for local activity. The physicochemical characteristics of droplets delivered from topical aerosol delivery systems are different from those required for delivery by inhalation and are considered later in the text. Types of Pharmaceutical and Diagnostic Aerosols

Pharmaceutical aerosol delivery systems may be divided in five general categories of drug formulation/device combinations. These categories are topical aerosol products, intranasal pumps, propellant-driven metered dose inhalers, dry powder inhalers, and nebulizer systems. Each of these categories of delivery technology allows for presentation of the aerosol as an aqueous solution or suspension, a nonaqueous solution or suspension, or a dry powder. Each of these approaches has merit, and the purpose for which the drug formulation/device is being selected must be considered to allow optimal use of the available techniques (Dalby et al., 1996). Diagnostic aerosols may be delivered from more traditional aerosol generators. Because these devices do not have to be portable, as the subject will be studied in a clinical setting, more sophisticated and fundamental approaches can be taken to aerosol generation (Gonda, 1990). Physicochemical Properties of Pharmaceutical and Diagnostic Aerosols

The physicochemical properties of pharmaceutical and diagnostic aerosols play a major role in the ease of dispersion, site of deposition, and rate and route of clearance from the lungs. Among aerosol measurement applications, aerosols in the health care field have certain unique features that bear on the appropriate measurement methods. One important feature is the presence in many (but not all) such aerosols of substances, which are extremely biologically potent, including macromolecules of various configurations and weights. Some of these substances have the capacity to initiate marked biological changes at very small concentration and are able to be detected and quantified with extremely sensitive and specific tests (Byron and Patton, 1994). Such macromolecules and other biologically active substances are often physicochemically fragile, and their existence in the aerosol state may alter their biological activity. Other aerosol substances in the health care field carry living organisms such as fungi, bacteria, or viruses. Such aerosols can be detected and quantified with sensitive and selective methods of bacteriology and virology. They are similarly often physiologically fragile in certain environments, and these features are the concern of a significant historical interest in airborne infection. The introduction of vaccines and other preventive agents has markedly reduced the concern about many classic diseases spread by aerosols. However, the concern with newly discovered pathogens has renewed the interest in airborne infection, and the continuing inability to prevent infection from the common cold or influenza provides impetus to understand the airborne route. Despite a lack of any positive evidence to date, there is continuing interest in the possibility that human immunodeficiency virus infection may have an airborne component, and many diseases, such as tuberculosis and measles, are known to be transmitted in air.

PHARMACEUTICAL AEROSOLS BY ROUTE OF ADMINISTRATION Topically Applied Aerosols Types of Topically Applied Aerosols. A number of therapeutic aerosols are applied topically. This may be for antiseptic applications or for antimicrobial activity, for example, in burn treatment. These products operate on the principle of top pressurization with gases or by the use of liquefied propellants. Where the head space in the canister is filled with gas above atmospheric pressure, the ideal gas law may be used to predict the loss of pressure over the lifespan of the product. For liquefied propellant systems Raoult's and Dalton's laws may be used to predict the vapor pressure over the lifetime of the product. Principles of Delivery and Delivery Systems. Figure 36-1 depicts a typical topical aerosol product. The product is displaced through the valve by depressing the valve stem using the actuator as can be seen in the schematic Figure 36-lb. The product consists of a number of components. Typically the container is composed of stainless steel or tin-plate stainless steel. The valve contains a number of components including, potentially, delrin and/or buna or neoprene rubber gaskets, stainless steel springs, a variety of possible metal mounting cups, and a polypropylene or polyethylene dip tube. Each of these components must be compatible with the product. These aerosols, while having a broad consumer application, are not a major focus for evaluation in this chapter as the droplet size of interest is quite large, generally >50jim. Actuator button Valve . stem

Aerosol

Canister

Dip tube Product

(a)

(b)

Fig. 36-1. a, An aerosol canister for topical products operated by a button actuator in a vertical orientation, b, A vertical section showing the dip tube, continuous valve, gas headspace and product. Dotted lines indicate direction of product into dip tube and aerosol from actuator.

Nasal applicator and cover

Pump

Pump seating

Container

(a)

(b)

Fig. 36-2. a, A nasal spray product, b, A schematic vertical cross section through the product indicating the metering chamber, dip tube, and swirl nozzle. Dotted lines indicate direction of flow.

Large droplets are required as the intention is to use this product to meter the therapeutic substance to a local site and deposit it by inertial impaction. Intranasal Aerosols

Types of Intranasal Aerosols. Intranasal aerosols are delivered for a number of purposes. Saline sprays are applied to rehydrate the nasal mucosa. Steroidal and nonsteroidal antiinflammatory agents are delivered to alleviate allergic rhinitis. More recently, calcitonin, a systemically active agent that is absorbed through the nasal mucosa, has been delivered for the treatment of osteoporosis in the elderly. Principles of Delivery and Delivery Systems. Figure 36-2 illustrates a typical pharmaceutical nasal spray product. Figure 36-2b shows that the principle of delivery is similar to that of a topical product except the force for emission of the product is now supplied by the patient. As the base of the product is pushed the container moves upward with respect to the actuator finger grip, and the contents of the metering valve are propelled throughout the nozzle, which, based on its geometry, can swirl the product or deliver it directly thereby dictating the droplet size and plume geometry of the emitted spray. The droplet size emitted from a nasal product is generally >20jnm and is intended for direct application to the nasal mucosa by inserting the nozzle into the nostril. Inhalation Aerosols

Types of Inhalation Aerosols. A therapeutic aerosol for inhalation is intended to deliver a substance to the respiratory tract that will bring about some desired physiological change

(Newman, 1984). The respiratory tract may be the locus of activity, or it may be the portal of entry for systemic distribution of a therapeutic drug. Several different types of therapeutic agents may be delivered to the respiratory tract. These include bioactive molecules, which act locally or systemically at the cellular level, substances that physically alter the properties of respiratory secretions, and substances that treat infection. They range in chemical complexity from macromolecular solutions to water or saline aerosols. For systemic drugs, it is acceptable to deliver the substance to any site of the respiratory tract that will permit the desired absorption characteristics (usually rapid absorption). Therapeutic agent aerosols that act locally usually require a more specific site of delivery, depending on the desired site of action. For example, aerosol bronchodilator drugs that are acting locally must be delivered to the bronchial airways in sufficient quantity to achieve the desired result. If the aerosol is entirely deposited in the airways above the trachea or predominantly delivered to the alveolar spaces, the local bronchodilation may not occur. The considerable body of knowledge that now exists concerning the deposition of "ideal" aerosols in the respiratory tract (Heyder and Rudolph, 1984) has been drawn on to predict and design aerosol delivery systems for therapeutic substances. However, as is detailed below, there are a number of "nonidealities" concerning many therapeutic aerosol systems that hinder reliable prediction, even though some general statements about deposition can be made. Measurement methods for therapeutic aerosols must take these nonidealities into account in linking aerosol properties to deposition sites. Principles of Delivery and Delivery Systems. A number of different techniques for aerosol generation are employed for therapeutic aerosols, each of which bears on the appropriate measurement methods for these aerosols. The initial physical state of the substance to be aerosolized is a bulk dry powder, a solution, an emulsion, or a solid-particle suspension. Depending on the method of generation, the liquid vehicle may be either water or a liquid propellant. Properties of aerosols differ according to the device utilized to generate the aerosol. Some of the properties are listed in Table 36-1. Several methods exist for aerosolizing bulk powders of therapeutic agents (Dunbar et al., 1998). The Spinhaler was originally described by Bell et al. (1971). Bulk powder in a gelatin capsule is mounted in a rotor holder inside the inhalation tube. Two holes for powder dissemination are punched just before use. The rapid inspiratory flow of the patient causes the rotor to spin and vibrate on the undersized shaft, causing the powder to be discharged out of the capsule into the air stream. In its original form, the bulk powder was a mixture of the micronized drug and powdered lactose, the latter intended to enhance aerosolization by preventing excessive cohesion of the drug particles. Currently, the micronized drug is loaded into the capsule without lactose or other diluent. The Rotahaler (Fig. 36-3) is a similar device for delivery of a dry powdered drug in which the powder is discharged into a small cylindrical chamber before inspiration and is aerosolized during rapid inspiration (causing high air velocity and turbulence in the chamber) and transported through a mouth tube to the respiratory tract (Kjellman and Wirenstrand, 1981).

TABLE 36-1. Characteristics of Aerosols (Velocity, Particle Size, and Concentration) Actively or Passively Delivered from a Variety of Devices Device

Velocity

Size (|im)

Concentration

Emission/Sampling

pMDI DPI Nebulizer

High Variable Low

1-10 1-10 1-5

Variable High Low

Active Passive or active Passive or active

DPI, dry powder inhaler; pMDI, propellant-driven metered dose inhaler.

Turbulence Grid Split Capsule

Aerosol

Airflow

Split Capsule Fig. 36-3. Schematic of a Rotahaler indicating the two sections and groove by means of which rotation is controlled and showing the position of the capsule and the grid through which particles pass on the patient's inspiratory flow. The broken capsule moves on the inspiratory flow and disperses aerosol particles.

One-way valve housing Baffle Solution reservoir

Air supply

(a)

(b)

Fig. 36-4. a, Schematic depiction of a Pari LC-Star nebulizer, b, A section through the device indicating the coaxial liquid feed and air supply orifices and the blade baffle upon which the atomized spray impinges. Solid directional lines indicate aerosol flow; dotted lines indicate large impact and return to reservoir.

Both of these devices require a loading step before inspiration. In the Turbohaler, a disk containing many powder charges (up to several hundred) can be rotated for each application to locate a fresh powder charge in the path of inspired air. Entry of the inspired air through the device suspends the powder charge. The turbulent flow through the flow channel to the mouth tube is intended to disperse and mix the powder so that primarily individual powder particles enter the respiratory tract (Jaegfeldt et al., 1987). Many therapeutic aerosols are generated by nebulizers (Niven, 1996; Mercer, 1981). Jet nebulizers employ high-velocity gas (usually compressed air) to shear bulk liquid into fine particles (for example, Fig. 36-4). This principle was originally applied to generate liquid aerosols at least 150 years ago, and all such devices are linearly descended from the original. It is well known that for a given liquid the median particle size produced decreases with increasing air velocity, reaching a practical limit at sonic velocity. For a given air velocity, the median-size particle decreases with decreasing liquid viscosity and surface tension. Modern nebulizers achieve smaller median particle sizes by inertial removal of larger particles, thus "recycling" a large fraction (usually 99%) of the liquid mass atomized and, therefore, reducing the airborne droplet mass concentration. AU aerosols produced by jet nebulizers are poly-

disperse such that the majority of the aerosol mass (even after inertial removal) is usually found in a relatively small number of large particles whose size is much greater than the count median diameter. The other major type of nebulizer employed for therapeutic aerosols is the ultrasonic nebulizer, in which ultrasonic energy originating from an electric transducer is focused within a bulk liquid, producing airborne particles whose median size is related to the liquid properties and the ultrasonic frequency. The amount of aerosol produced is determined by the ultrasonic energy level, and the aerosol mass concentration is determined by the generation rate and air flow dilution rate, which can be independently set. This is the primary practical difference between jet and ultrasonic nebulizers, in that the aerosol mass concentration of the jet is limited by the jet flow for atomization. The propellant-driven metered dose inhaler (pMDI) is a widely used method of generating therapeutic aerosols. In the pMDI, drug is aerosolized by the release of a mixture of drug, propellant, and (in some instances) surfactant to maintain a stable dispersion of the drug in the liquid propellant within the canister before release (Purewal and Grant, 1998). This is the well-known "aerosol can" concept (used to dispense numerous commercial products) miniaturized and fitted with a dosing valve that dispenses a given liquid volume on each depression of the valve cap (Fig. 36-5). In most instances, the drug is maintained as a suspension of micronized powder within the propellant. Some drugs are liquid emulsions or solutes. The propellant normally makes up most of the material within the canister; a surfactant to prevent particle agglomeration is sometimes a minor constituent. The MDI enjoys wide popularity as a therapeutic drug generator because it is selfcontained (requires no air pressure, battery, or other energy source), pocket sized, generates a relatively constant mass of drug, and is relatively easy to use. The phase-out of widely used chlorofluoromethane propellants for environmental reasons has clouded the future use of MDIs. Alternative propellants are now in development that should have similar generative properties provided they do not demonstrate undesirable physicochemical interactions with therapeutic agents. Some aerosols for localized delivery in the nasal or oral airways are generated by liquid spray devices. These generation devices are known technically as hydraulic atomizers as opposed to the jet nebulizer; in the former a liquid is mechanically forced through a small orifice and breaks up to form large droplets (50 to 200 urn) while, as described above, jet nebulizers use air streams to break up liquids, producing much smaller particles. Liquid spray devices differ from MDIs in that no explosive evaporation of the droplets takes place to disperse the particles and reduce particle size. Because therapeutic aerosols are meant to be breathed, a system is needed to couple the aerosol generator to the breathing pattern of the user (Swift, 1989). Account must be taken of both the generative process of the aerosol and the breathing pattern of the user. A mismatch between these may result in nonoptimal conditions under which either aerosol is generated but not breathed or breathing takes place without sufficient aerosol being provided. Because the object of aerosol therapy is to deliver an optimum amount of agent to a specified respiratory tract location, nonoptimal conditions can result in the potentially undesirable conditions of underdose or overdose. In the case of dry-powder aerosol generators, the delivery system is simply a suitably sized tube to be inserted into the oral or nasal passage. Ideal coupling between breathing and aerosol generation is ensured by making generation of the aerosolized powder breath actuated. The only condition of failure occurs when the user cannot achieve a high enough inspiratory flow rate to aerosolize the dry powder. Exhaling through the system before inhalation could humidify the powder and cause particle adhesion, reducing the ability to aerosolize individual particles; therefore, such devices usually include some means to reduce or obviate such conditions. Jet and ultrasonic nebulizers normally run at a constant flow rate of 1.3 to 1.7 x 10"4m3/s [8 to lOL/min], thus the necessity to provide a path for exhaled air and aerosol generated

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(b) Particles suspended in propellent Metering valve

Metering chamber

Heterogeneous droplet size and vapor concentration plume Valve stem Actuator Actuator orifice

(C) Fig. 36-5. a, A pressure-packaged metered dose inhaler shown in its operational orientation with canister inverted and valve down, b, Metering Valve, c, Vertical cross section through device showing aerosol generation.

during the exhalation phase. Because inspiratory flow rate varies, it is also necessary to provide for extra "make-up" air when the inspiratory rate exceeds the generation flow rate (and vice versa). In some jet nebulizer systems, an air pressure bypass is provided to permit generation only during inspiration; the user simply covers the bypass by finger pressure during the inspiratory phase to produce aerosol. This does eliminate aerosol generation during exhalation, but does not provide for flow matching during inspiration. Because ultrasonic generation does not require a constant flow for aerosol generation, the flow through the generator can be inspiratory-flow-driven, resulting in some temporal variation in aerosol mass concentration. The temporal generation pattern of MDIs is quite different from the above types; when the actuator is pressed, an aerosol is formed during a very short period of time (-0.1 s). This

aerosol generation phase is accompanied by a very high air flow rate resulting from the rapid evaporation of the propellant and the induction of air. It is impossible to match the temporal flow pattern of the MDI to the inspiratory flow of the user. Thus, if the canister is fired directly into the oral cavity through a short mouth tube, the particles, which consist of the evaporating propellant and agent, have a very high velocity and a strong tendency to preferentially impact on the posterior pharyngeal wall. In studies of deposition of MDI aerosols in the human respiratory tract, it has been shown (Newman et al., 1982) that only 6% to 14% of the aerosol reaches the thoracic region of the respiratory tract distal to the trachea. Efforts to reduce high pharyngeal deposition and improve the matching between aerosol generation and inspiratory breathing have centered on "spacers." These are conduits of various geometries and volumes that are placed between the MDI and the mouth into which the MDI is discharged.The aerosol is then inspired from the spacer (Moren, 1985).The spacer is intended to allow the aerosol particles to both slow down and evaporate before they are inhaled, resulting in greater deep-lung delivery. It is observed that spacers do not result in greater thoracic deposition but do reduce pharyngeal deposition; larger particles impact or settle in the spacer, and the remaining aerosol is inhaled with less resulting oral-cavity deposition. This delivery system allows generation and breathing to be temporally separated. A special issue associated with delivery systems for therapeutic aerosols is product utilization. Many currently used drug substances are relatively expensive, and there is considerable motivation to reduce the cost by utilizing as much of the drug as possible. The ultimate measurement of utilization is how much of the provided therapeutic substance ends up at the desired site in the respiratory tract during the breathing periods. Often, the utilization efficiency is a very small percent, even in the case of MDIs, in which practically all of the agent in the container is available for aerosolization. Other therapeutic aerosol systems have lower utilization for a variety of reasons. Optimal design of generation-delivery systems should strive for high utilization as well as proper particle size (Swift, 1989).

DIAGNOSTIC AEROSOLS Types of Diagnostic Aerosols Diagnostic aerosols differ from therapeutic aerosols in that the intention for their use is to learn something about the state of the respiratory tract rather than the delivery of an agent to produce a specific local or systemic effect (Wagner, 1976). It is usually important to characterize properly the properties of such a diagnostic aerosol because the information to be determined may be critically dependent on the quantity and site of deposition of the aerosol. There are two widely used diagnostic procedures involving aerosols: the measurements of pulmonary ventilation and those of airway reactivity. The remainder of the diagnostic methods employing aerosols are rather more specialized and, thus, limited to research laboratory settings where special equipment and expertise is available. Ventilation measurements using aerosols may have several purposes, including the diagnosis of pulmonary embolism (PE) and the identification of regions of either acute or chronic airway constriction. Ventilation is defined as the actual quantity of air in various compartments of the lungs compared to what would be expected if the lung were operating normally with respect to the inspired air. The measurement of ventilation for diagnosis of PE is carried out in tandem with a lung perfusion measurement. Lung perfusion is likewise defined as the quantity of blood delivered to various vascular compartments of the lung compared to the expected quantity. A specific region of the lung that has both a ventilation and a perfusion defect, as evidenced by a significantly reduced amount of radioactivity of both aerosol and vascular injected labeled microspheres, is considered a positive indication for PE. The use of

aerosols for ventilation measurements is not universal; many clinicians avoid using aerosols (despite their superiority in several respects) and prefer to measure ventilation with 133Xe or 82 Kr gas. While these gas methods do not require aerosol generation equipment, the radiation dose required to obtain several views of the thoracic region for thorough analysis is greater than that for aerosols. In the case of aerosol use, one administration of the radiolabeled aerosol is adequate for all views that can be taken sequentially. It is common to use a jet nebulizer (see below) for such aerosols. The other common diagnostic aerosol procedure is the measurement of airway hyperreactivity, often referred to as bronchoprovocation. In this method aerosol containing a specific activating substance, usually methacholine or histamine, is administered to the subject in progressively concentrated solutions. Measurement of pulmonary mechanical behavior is made after each administration. When the mechanical behavior has changed by a significant amount, the test is stopped, noting the total dose required to achieve the change. It is found that allergic and asthmatic individuals have a markedly greater sensitivity to these agents than do individuals who have no such clinical history, and the test is a convenient means to quantify the degree of sensitivity. Other environmental agents may modulate the response of sensitive individuals. This provides a convenient means for investigating the interaction between common environmental airborne agents (indoor or outdoor air pollutants) and specific reactivity producing agents or the conditions to which individuals may be exposed. Aerosols are also employed in diagnostic procedures for measuring the permeability of the alveolar membrane, the degree of constriction of medium and small bronchi, the mucociliary system, and the mixing of tidal and residual air in the lungs (Agnew, 1984). All these methods are rather more specialized, requiring a greater degree of understanding of the aerosol characteristics and the means for delivery. Aerosols used for alveolar permeability must not only have the proper biological properties to differentiate high from low permeability, but must be of such size and nature to achieve predominantly alveolar deposition. High degrees of bronchoconstriction measured by aerosol penetrance, the ratio of the quantity of aerosol in the lung periphery to the quantity in the central hilar zone, are difficult to make without well-defined aerosols and techniques such as gamma-camera scintiography. Measurements of lung mucociliary clearance are usually made with radioisotope-labeled particles, which are not readily dissolved in the lung fluid or otherwise transported away from the airway epithelium by means other than mucus transport. The particles must deposit predominantly on ciliated airway surfaces. Similarly, measurements of mucociliary clearance in the nasal airway require particles to deposit or be transported to ciliated epithelium. Aerosol mixing experiments require that the aerosols suffer minimal respiratory deposition, a condition that can be realized with a highly monodisperse aerosol of a specific size range. Principles of Delivery and Delivery Systems

For the commonly used aerosol diagnostic methods, it is usual for the aerosol to be generated by a jet or ultrasonic nebulizer, with the liquid containing the radioisotope in solution or colloidal suspension (Newman, 1984). Although this may not be the ideal method, the equipment and expertise to generate stable monodisperse aerosols for specific procedures are not usually available. It is likely that the aerosol generated for bronchoprovocation tests has a large mass fraction, which deposits in the alveolar region; even so, the test is reasonably reproducible for a single subject. Bronchoconstriction aerosol measurements are also made with poly disperse radiolabeled aerosols generated from jet or ultrasonic nebulizers. If the mucociliary clearance of large bronchi is to be measured, large-diameter particles (da > 5 urn) must predominate in the aerosol, which must be breathed at high inspiratory rate to achieve significant deposition at bifurcations of large airways. Under such conditions, a significant fraction of the aerosol is deposited in the posterior oral cavity, reducing effective delivery. Nebulization of suspended large particles (3 to lOjim diameter) can be employed

for such procedures. For mixing studies, it is necessary to measure aerosol concentration profiles during inspiration and expiration. This is normally accomplished by photometric light-scattering measurement of a monodisperse aerosol generated by an evaporation condensation (Sinclair-LaMer) generator. This method of generation requires expertise in determining the degree of aerosol monodispersity. Most delivery systems for diagnostic aerosols are similar to certain delivery systems for therapeutic aerosols. The primary difference in most cases is that the material limitations on substances used for diagnosis are usually less stringent. The exception is that the utilization of radioisotopic aerosols used in diagnosis is very low. Thus, most of the isotope remains behind after the procedure. This is a situation, which may be costly or involve high activity, that must be shielded during handling and use. In some diagnostic procedures it is important to measure the breathing characteristics during delivery to estimate aerosol deposition mass. Because diagnosis is usually done in a clinical laboratory, the space and portability features of delivery systems, which are important for some therapeutic aerosols, are not a factor (Heyder, 1988). The MDI is rarely, if ever, used in diagnosis employing aerosols. In bronchoprovocation aerosol tests the usual method of aerosol delivery is by nebulized solution.

CHARACTERIZATION OF PHARMACEUTICAL AND DIAGNOSTIC AEROSOLS Measurement of Physicochemical Properties Topically Applied Noninhaled Aerosols. Some therapeutic agents appear in aerosol form, although they are not intended for inhalation. Usually, these aerosols are to be transported to some surface where their therapeutic action occurs. This was the case with Lister's aerosol generation of phenol, which presumably had its antiseptic action by depositing on biological and other surfaces to render harmful pathogens impotent. It is not proposed that inhalation of the phenol had any therapeutic benefit; in fact, it was ultimately abandoned because of the irritant effect of the aerosol on the eyes and mucous membranes. Such agents, which may be used to deliver therapeutic agents by aerosol to the skin and other body surfaces (such as during surgery), must have desired characteristics. One key feature is a high probability of being directly deposited on the intended surface, with a minimum of "overspray" that might lead to other undesirable effects or (at minimum) less than optimal utilization of the agent. Agents could be in the form of dry dusts, solutions, or suspensions, but in order to achieve even coverage of a surface the use of propellants as "drivers" and dispersers of the agent from pressure-packaged canisters ("aerosol" cans) seems the most ideal approach. As has been detailed above, the aerosol emerging from such a device is composed primarily of large propellant drops (-25 urn) containing the active substance and travelling at high velocity (-4OmS"1). According to the theory of inertial deposition, these are the ideal conditions for a high percentage of the droplets to deposit on a surface perpendicular to the spray axis and near enough to prevent total evaporation and deceleration by viscous drag. The major disadvantage of this method of administration of aerosols to biological surfaces is the cooling effect on the surface as a result of the propellant evaporation. However, this is not ordinarily a significant problem if a distance is maintained to allow some evaporation to take place in the gas phase before deposition. In fact, some applications of propellantdriven droplets are actually intended to retard inflammation and pain by "freezing" the tissues, similar to the effect of cold water or other cooling upon burns. Little information exists in the scientific literature on the optimal conditions for therapeutic aerosol delivery to surfaces, but manufacturers of these and other consumer products applied to surfaces by propellant atomization (e.g., waxes, cleansers, polishes, and hair sprays)

have determined by experiment the conditions maximizing even-spray application and minimizing overspray. Some of these products are irritants to some individuals when inhaled. Notable among these products are nonstick spray containing lecithin and certain hair sprays. Therapeutic skin sprays contain agents such as bactericides, steroids for control of inflammation and itch, antibiotics, and antifungal agents. In their normal use, the lifetime of the aerosol between generation and surface deposition is short enough to render standard methods of aerosol sampling and measurement ineffective. Aerosol overspray of such applications can be characterized in a standard form using area cascade impactors, filter cassettes, or personal aerosol samplers with cyclones to determine the respirable fraction. However, the irritant effect of such aerosols is likely to be observed for any region of the respiratory tract. In summary, no aerosol measurement techniques unique to the noninhalation aerosol area have been developed despite the importance of proper minimization of overspray for many of the agents used. Inhaled Aerosols. The properties of therapeutic aerosols necessary to describe their behavior are similar to the properties of other aerosols, that is, size distribution (number, surface, and volume), number and mass concentration, electric charge, hygroscopicity, and the distribution of the active ingredient among the particles. Because inhaled particle transport and deposition depend on particle aerodynamic behavior in a flowing gas, particles in the inertial regime are properly characterized by their aerodynamic equivalent diameter. The conditions of generation, inhalation, and aerodynamic behavior in the respiratory tract are such that for isometric particles with da < 5.0 um, this is the appropriate size descriptor. There are three major problems with therapeutic aerosols that complicate the measurement process. The first is that, in many instances, the distance between the generator and inhalation site of the aerosol is very short, and there is not an opportunity to place aerosolmeasuring devices in this location. It is conceivable to sample (remove) the aerosol being generated before it is inhaled, but the influence of sampling (for subsequent measurement) on inhalation of the aerosol is not known in general. A second related issue is the matching of flows between generation, inhalation, and measurement involving a sampling flow. As discussed above, generation of therapeutic aerosols may be either at a constant flow rate (e.g., nebulizers) or under pulse flow conditions (e.g., MDIs). In the first case, the matching between the generation and sampling can be achieved, but the cyclical breathing pattern is not matched to either generation or sampling. In the case of pulse generation, neither sampling nor breathing can be properly flow matched to the pattern of generation, and this leads to potential errors in aerosol characterization. In the case of dry-powder aerosol generation, the generation is forced to match the inspiratory flow, but if sampling is envisioned for aerosol characterization, the sampling flow cannot readily be matched to the inspiratory flow. Thus, under any circumstance, it is not feasible to match generation, sampling, and breathing flows simultaneously. The third problem of therapeutic aerosols that make aerosol characterization difficult is the unstable nature of the liquid aerosols produced by nebulizers and MDIs. Aqueous aerosols produced by jet or ultrasonic nebulizers are subject to rapid changes in particle diameter and other properties due to evaporation or growth of aqueous particles as they seek equilibrium at the changing conditions of temperature and humidity within the delivery system. This is particularly true when the delivery systems encounter both inspiratory and expiratory flows; the expiratory flow consists primarily of air at near to the body core temperature and fully saturated, while aerosol generated by nebulizers is often at 5 to 7 K below the ambient temperature (Mercer, 1981). In the case of MDIs the aerosol emerging from the canister consists of propellant particles containing the active ingredient and other substances (e.g., surfactant). These particles are travelling at high velocity and are rapidly evaporating, with attendant cooling. The

particle size distribution at the front end of the distribution tube is likely not the same as the distribution at the distal end of the oral passage. Because of these experimental difficulties in the measurement of aerosols entering the respiratory tract during actual breathing, the usual practice has been to connect the delivery system output directly to an appropriate instrument for aerosol measurement. The most common instrument used for nebulizer-generated aerosols is the cascade impactor (see Chapter 10). This size separation device is operated at a constant flow rate, which can be set according to one of the several mutually exclusive criteria. The first is that of exactly matching the output flow rate of the delivery system so that all the aerosol generated enters the impactor, but this requires that the impactor be capable of such matching, a situation not possible with all commercially available impactors. Next, the flow rate into the impactor can be less than the output flow of the nebulizer, with the excess allowed to escape. Third, if the impactor flow rate exceeds the output from the delivery system, additional air must be drawn in, simulating the possible real situation during the breathing cycle when inspiratory flow exceeds nebulizer output flow. As alluded to above, no one of these criteria is totally satisfactory when the inspiratory breathing is cyclical and the nebulizer output is constant, especially if the size and concentration of the aerosol depend strongly on the make-up air temperature and humidity. No impactor presently exists that can be operated predictably under cyclical flow conditions to match the breathing profiles. A further difficulty with using cascade impactors to measure aerosols from nebulizers is the apparent evaporation of the particles taking place as they traverse the stages of the impactor. It is observed that when aqueous aerosols containing a solute such as 0.9% NaCl (isotonic with respect to extracellular fluids) are passed through a cascade impactor, the upper-stage deposition is that of liquid particles while the final stage(s) collect solid salt particles. The evaporation may be primarily due to the pressure drop through the impactor in conjunction with the increase in the vapor pressure of submicrometer-sized particles (Kelvin effect) (Hinds, 1999). The situation when dry particle aerosols are generated by breath actuation presents many of the same difficulties when the cascade impactor is employed to measure the particle size distribution during actual or simulated inspiration. A sample can be drawn from the delivery system to an impactor during this period of generation and delivery, but it would be normally done at constant flow, whereas the flow, particle concentration, size distribution, and static pressure are likely to vary markedly during the inspiratory period. The equal weighting of all time periods during inspiratory flow by the cascade impactor will not likely reflect the highly time-dependent character of the aerosol. Aerosols produced from MDIs have similar problems when sampled by instruments such as cascade impactors during their passage from the canister to the respiratory tract. In this case, the very rapid evaporation of the propellant and the pulsatile velocity of the generated particles, as described above, are not consistent with conditions ideal for cascade impactor sampling. To avoid the practical and theoretical difficulties and sampling complications associated with therapeutic aerosols outside the respiratory tract, many investigators have elected to deliver the aerosol into a large-volume chamber. The residual aerosol from this chamber is sampled into a cascade impactor for size and mass analysis (Mercer et al., 1968), after a short period of evaporation or mixing. If the mixed aerosol is sampled, integration over the time of generation has already been performed, and the aerosol sampled represents the mixed average throughout the entire period of generation. While this method offers a means of comparison for various methods and conditions of aerosol generation and delivery, the aerosol that is eventually sampled is the "fossil remains" of the aerosol, which is delivered to the respiratory tract in the actual therapeutic application. It remains, therefore, for the investigator to calculate or estimate backward in time and space to the aerosol condition at the point of

delivery in order to determine what aerosol actually entered the respiratory tract at a particular time during the inspiratory maneuver. The use of size-selective instruments such as cascade impactors faces similar interpretive difficulties when other occupational or environmental aerosols are sampled due to their labile nature and spatial and temporal variability. However, these difficulties seem to be more severe in the case of therapeutic aerosols because less time is available between their generation and inhalation compared with other situations. A number of particle sizing techniques and their application to pharmaceutical aerosols have been reviewed in a series of articles in the pharmaceutical literature. These techniques include not only microscopic (Evans, 1993) and inertial sampling methods (Atkins, 1992; Milosovich, 1992), but also laser diffraction (Ranucci, 1992), laser time-of-flight (Niven, 1993), laser holography (Gorman and Carroll, 1993), right-angled light scattering (Jager et al., 1993), and phase Doppler anemometry (Ranucci and Chen, 1993). Respiratory Tract Deposition

The difficulties of measuring therapeutic aerosols outside the respiratory tract and the objective of delivering a specified quantity of an agent to some respiratory tract region make it desirable to conduct practical measurements of aerosol deposition within the respiratory tract. This has led to the development of an expanding technology of aerosol deposition measurement. It should be noted that mathematical models have been used successfully to describe lung deposition and clearance (Swift, 1996). These models are currently being validated using imaging techniques to allow prediction of lung disposition. In almost all cases, this is accomplished by means of gamma-emitting radioisotopes, which are either physically or chemically attached to the aerosol particles inhaled. The isotopes emit gamma rays, which are detected outside the body by a radiation detector (Taplin and Chopra, 1978). The type of technology used depends on the level of spatial resolution required to describe the location of the aerosol particle. In the simplest example, one might wish simply to estimate the quantity of aerosol deposited in the entire respiratory tract. Even then, it is usually necessary to separate the respiratory tract into the thoracic and extrathoracic compartments, the division line usually taken to be at the mid-trachea. In this division, the extrathoracic airways include the upper trachea, larynx, pharynx, oral, and nasal passages. These are alternatively known as the head airways or (taking the division slightly more proximal) the nasaloralpharyngeal-laryngeal (NOPL) region. The simplest measurement of deposition in this extrathoracic region is obtained with a single scintillation detector, with some collimation to exclude thoracic deposition oriented either to view from lateral, anterior, or posterior positions. Similarly, an estimate of the thoracic deposition can be made with a single scintillation detector aimed at the lungs and situated far enough from the chest to accept gamma rays from the lung periphery. Such measurements provide an estimate of the total amount of deposition in one or the other major region, without any further spatial information. With this degree of spatial information, the amount of activity required to exceed background radiation is not great; subjects can inhale aerosol containing a radioisotope and receive a dose within the annual limit considered safe for the general population. When the isotope used is 99mTc, several breaths can be taken by the same subject to achieve a total dose well below the annual exposure limit of 0.5 rem. A gamma scintillation camera can detect the deposited aerosol if a greater degree of spatial resolution is desired. The amount of radioactivity required for a detailed image of the thoracic or extrathoracic region is much greater than with an uncollimated detector because of the collimation required to attain good spatial resolution (~5mm). The dose received for a single study limits the number of studies that can be performed with the same subject to

keep within the annual dose requirement. Even so, the information from such a study can only be grouped into about three concentric regions for each lung or three regions in the head airways. A planar (two-dimensional) image of activity in the lung does not allow a good degree of separation between the peripheral (alveolar) region and the medium and small bronchial airways because these regions overlap in such a planar view. An even more detailed spatial look at the respiratory tract deposition of aerosol can be obtained using the technique of SPECT (single-photon emission computed tomography) (Phipps et al., 1989). This technique allows the activity in the lung or extrathoracic region to be assigned to three-dimensional volume elements, "voxels," the equivalent of twodimensional "pixels," so that activity distributions in various planes can be visualized and quantified. This added spatial information is obtained at the cost of additional radioactivity, a measurement time for a single image of ~20min (compared with ~5min for a gammacamera scintigram), and additional computer capacity to process all of the information from the multiposition scan about the region. The scan time of 20min may be unacceptably long because rapid upper bronchial or extrathoracic airway clearance is taking place. For example, aerosol deposition onto the mucociliary surface of the nasal passage is normally cleared to the pharynx and swallowed within about 15 min.The SPECT procedure is suitable for aerosol deposition and clearance measurement only when the temporal scales of clearance are much greater than the scanning time. In some cases of therapeutic aerosol delivery to the respiratory tract the spatial distribution is not necessary, and the uptake of the agent can be monitored by serial measurements of blood or urine concentration of the agent or its metabolites (Walter et al., 1972). Likewise, in the extrathoracic region, the aerosol deposition can sometimes be measured by washing out the deposited material for quantitative analysis of an appropriate tracer. For example, it is well known that MDIs deliver large fractions of drug to the posterior pharyngeal wall, which can readily be removed by oral rinse and gargle for analysis. Nonideal Behavior of Therapeutic Aerosols

The quantitative measurement of aerosol deposition of therapeutic agents is important because in many instances it is difficult to predict how therapeutic aerosol will behave based on well-established models of respiratory deposition. This is because such models are based on a number of simplifying assumptions defining an "ideal" aerosol. This means that the particles are of spherical shape, are solid, nonhygroscopic, nonevaporating, noninteracting, are present in the air at moderate concentration, and are travelling through the airways essentially at the velocity of the inspired or expired air. In most therapeutic aerosols, one or more of these conditions does not hold (Newman, 1984). For example, the aerosol from an MDI begins as a very high velocity jet of large, rapidly evaporating propellant particles at high concentrations. After losing most of the propellant, the particles are often hygroscopic and, thus, subject to growth by accretion of water at the temperature and humidity of the central airways. The jet begins in the center of the inhaled air and may not be spatially uniform during part of its path. Because of its short duration, the inspired dose is an aerosol bolus, not a continuous volume of aerosol, subject to dispersion within the respiratory tract. Other therapeutic aerosols consisting of liquid droplets or dispersed solid particles are likewise "nonideal" to a degree as a result of their physicochemical properties and/or their mode of generation and exposure. An additional factor that makes the prediction of therapeutic aerosol deposition difficult is the nonideal nature of the respiratory tract in many people using such aerosols to treat respiratory diseases. Even in "normal" individuals, aerosol deposition exhibits significant biological variability, but, in many individuals using therapeutic aerosols, lung morphology and/or breathing pattern is markedly abnormal. For example, individuals who have cystic

fibrosis inhale bland saline aerosols to "liquify" the secretions. However, observations suggest that the preferential sites of deposition of these aerosols are in the "healthy" regions of the lung while the diseased regions receive little aerosol because they are poorly ventilated (Alderson et al., 1974). Much work remains to be done to clarify this situation. Respiratory Tract Disposition (Pharmacokinetics)

Disposition of airborne particles from the lungs occurs by absorption, mucociliary transport, and cell-mediated (macrophage) disposition. The pharmacokinetics of disposition depend on the site of deposition and the residence time of the particle; the latter is related to the dissolution rate of the particle. Particles deposited in the upper airways are subject to rapid mucocilary transport through the trachea to the throat where they are swallowed. Particles deposited in the periphery of the lungs may be absorbed as a function of their dissolution rate, solubility, and partition coefficient. Sparingly soluble particles will be phagocytosed and subjected to intracellular mechanisms of dissolution and potentially degradation including continued hydrolysis and enzyme activity. Particles exhibiting very long dissolution times will be translocated by the macrophages from the lungs to the lymphatics and ultimately to the systemic blood circulation. Various models have been proposed for the general disposition of drugs from the lungs (Byron, 1986; Gonda, 1988) and for the disposition of specific drugs from the lungs (Hochhaus et al, 1998; Edsbacker et al., 1998). The pharmacokinetics of disposition can be conducted utilizing radiolabeled materials, which can be detected by imaging techniques or by scintillation counting in blood samples. Chromatographic separation methods such as high-performance liquid chromatography with drug-specific detection methods such as ultraviolet, fluorescence, or mass spectroscopy may be employed to detect drug in plasma, serum, or other biological fluids (e.g., urine, bronchoalveolar lavage fluid) to monitor the appearance/disappearance of drug. Model independent methods may be employed to analyze the data, or mathematical models can be employed to predict the disposition of drug.

Efficacy and Toxicity

Pharmaceutical Aerosols. Pharmaceutical aerosols exhibit a dose-dependent efficacy. For most aerosols delivered to the lungs the therapeutic ratio is large, which is to say that the toxic dose is much larger than the therapeutic dose. Targeted delivery in itself allows small doses to be delivered, thereby circumventing both local and systemic toxicity. The use of specific adrenergic agonists and anticholinergics has long been regarded as both safe and efficacious. The local delivery of glucocorticosteroids is considered much less hazardous than systemic administration of these drugs. However, the potential for local immunosuppression, particularly in the oropharynx and upper respiratory tract, where significant deposition of aerosols occurs, does present the increased risk of local infection most frequently in the form of candidiasis. The safety concerns regarding drug delivery will become increasingly important as the lungs are considered as a route of administration for systemically acting agents. The chronic delivery of materials, some of which are of biological origin, to the periphery of the lungs may require unique immunological and toxicological considerations. Some studies endeavor to relate the lung dose and distribution as measured by gamma scintigraphy with clinical response to therapeutic aerosols (Dolovich, 1993). This integration of aerosol measurements, lung imaging, pharmacokinetics/pharmacodynamics, and efficacy are a desirable goal to strive for in product development. Diagnostic Aerosols. The major risk associated with the inhalation of diagnostic aerosols relates to the use of radioactive isotopes for imaging. These risks are minimized by the use

of short half-life radiation sources such as technetium-99m and by advances in the sensitivity of detection systems. Nevertheless, all studies involving radioactivity are subject to oversight by radiation safety committees and Institutional Review Boards on the protection of the rights and safety of human subjects. Inadvertent Exposure to Aerosols in Health Care. There are a number of potential and actual aerosol exposure situations in the health care field that are unintended and can result in a risk of ill effect. In such cases, aerosol measurement provides the estimates of potential exposure and expected benefit of exposure control methodology. Some of these instances involve chemical therapeutic agents that have potent biological effects, while other instances involve living organisms or their bioactive remains that may result in infections, transmission of disease, or other ill effects. The anecdotal observations of airborne (aerosol) transmission of disease have an illustrious history dating back at least to the Greeks. Despite their lack of knowledge of the nature of the agents (usually attributing such transmission to "bad airs"), modern biochemistry, physiology, bacteriology, virology, and parasitology have provided the tools to understand the nature of the airborne transmission of many diseases, ranging from the common cold to tuberculosis. The understanding of immunologic defense mechanisms has led to the development of vaccines and other strategies to prevent or attenuate the harmful spread of these diseases. Despite these advances, the appearance of "new" diseases such as AIDS, the proliferation of potent therapeutic agents such as antitumorals, and the increased processing of human and other biological tissues and fluids in diagnostic and research laboratories has made an understanding of airborne exposure and transmission as important now as in the days when tuberculosis, influenza, and pneumonia were responsible for pandemics. Human-Human Aerosol Transmission of Disease. It is a well-established observation that aerosols are produced from the respiratory tract by a number of means, including sneezing, coughing, conversation, and singing (Wells, 1955). Particles thus emitted have a wide size range, the larger of which (d > 10 um) settle rapidly to horizontal surfaces. Smaller particles emitted as such or resulting from evaporation remain airborne for longer time periods. These "droplet nuclei," which may contain any of the numerous biological organisms, are the means by which disease is transmitted over distance by air between humans. Depending on their environmental "hardiness," such droplets may even be transmitted through ventilation systems and infect individuals quite remote from the source individual (Riley, 1974). Two approaches toward aerosol characterization have been used for such agents: (1) the number concentration of organisms collected as a suitable medium and determined by colony (or equivalent) counts and (2) the empirical number of "infectivity units" (each of which contains the minimum number of organisms needed to produce an active disease case). In some cases this may be only a single viable organism, while in other cases many organisms are needed. Control of transmission by air dilution, killing of organisms, or physical removal (filtration) can be measured by the reduction of organisms or disease units by standard measurement methods. Various schemes for killing organisms have been proposed. For example, reduction of viable aerosol particles by ultraviolet light has been quantified with liquid impingers or agar-plate cascade impactors (see Chapter 24). With extensive use of vaccines and effective chemotherapy for tuberculosis, it was thought, not long ago, that airborne transmission of disease in developed countries was limited to the common cold, influenza, and other treatable illnesses. However, the recurrence of tuberculosis as a side effect of AIDS and other immune deficiency conditions has revived the concern, particularly among health care personnel, that more serious effects of airborne transmission may still occur. Although there is no evidence that AIDS itself is transmitted by the airborne route, there are other opportunistic pathogens that deserve attention with respect to airborne spread. Thus, viability techniques to assess the magnitude of the problem

and the effect of control technology employing aerosol measurement need to be developed (Riley, 1972). Inadvertent Exposure to Therapeutic-Agent Aerosols. There is a growing concern that the production, handling, and administration of therapeutic agents may pose health hazards for health care workers and others who routinely or infrequently are exposed by several routes, including the formation of aerosols. Measurement techniques for bioaerosols, which result during bioprocessing, are discussed in Chapter 24. However, it is important to emphasize that the rapid development of new therapeutic agents (many of which result from developments in genetic engineering and molecular biology) should be accompanied by an examination of the consequences resulting from exposure of pharmaceutical workers and health care providers to these agents. These include not only the agents for treating human disease, but an increasingly expanding array of agents used in animal husbandry and veterinary medical practice. Means must be developed to ensure that exposure, including that by airborne particles, is controlled to minimize or eliminate ill effects. The detection and quantification of airborne concentrations of such substances can be carried out with standard aerosol collection techniques as long as the collection substrates do not interfere with the sensitive and selective bioassay methods available for many of these substances. One of the advantages of environmental sampling for such agents is that their biological potency permits highly sensitive and selective detection tests to be used; these detection capabilities are already in place in many laboratories where the agents are employed. Inadvertent Exposure to Bioaerosols from Tissue, Cell, and Fluid Tests. The increasing number of routine tests being carried out with tissues, cells, and body fluids (including blood) of living organisms (mainly human) and the possibility that many routes of infection may contribute to disease transmission to health care workers (Zimmerman et al., 1981) is a concern similar to that referred to above. Although high on the list of transmission routes is the introduction of substances directly into the blood via inadvertent needle sticks, and so forth, the possibility of aerosol formation and transmission in such settings has received considerable attention. Numerous processes such as centrifugation, stirring, and pipetting can lead to aerosol formation as well as poor housekeeping procedures such as spilling liquids that may dry and become aerosolized. Even procedures that are intended to minimize other transmission routes, such as hypodermic needle clipping, can produce aerosols directly or result in fluid spills on surfaces (Binley et al., 1984). These possibilities have achieved heightened public interest with the advent of AIDS. Even without the fear of AIDS transmission, the possible transmission of much more robust organisms such as hepatitis virus ought to be reason enough to understand and develop control measures to eliminate such pathways. The health literature contains several occupational epidemiology studies demonstrating that transmission takes place in such settings, but little environmental sampling including the airborne route has been done. As with therapeutic agents, good microbiological techniques for the detection of most organisms are available and can be combined with appropriate aerosol sampling technology when indicated. Inadvertent Exposure to Bioaerosols in Dental Procedure. It has been known for some time that many dental procedures such as tooth drilling, abrasive cleaning, and certain oral surgical procedures can produce airborne particles containing irritant dust and pathogenic flora from the mouth (Macdonald, 1987). The newer technologies being introduced into dentistry, such as laser cutting, water abrasive cleaning, and dry powder abrasive cutting, are all sources of aerosols that could pose hazards for the dental patient, the dentist and staff, and other

patients being treated in the same area (Pagniano et al., 1986). Greater drilling efficiency with very high-speed drills produces finer sized dust, and the escape of this dust from the mouth due to its high velocity leads to concern for aerosol exposure. Aerosol samples taken in dental suites have demonstrated the existence of viable and pathogenic airborne organisms. This has led to the introduction of exhaust ventilation and other methods to eliminate or minimize exposure. A practical problem in developing such exhaust systems is the difficulty of getting good capture efficiency without interfering with access to the work area. With new techniques and materials, it is important to evaluate the degree of aerosol exposure (as well as other routes) in dental practice and minimize the risk of infection. The amount of inhaled aerosol producing an effect may be very small for some highly infectious and pathogenic organisms. Inadvertent Exposure to Aerosols in Surgery. Advances in surgical techniques, as in dentistry, are sometimes accompanied by new modes of transmission, affecting not only the patient but also health care workers. This has been demonstrated in the case of the increasing use of lasers and electrocautery in many surgical procedures. Both these techniques, as well as bone and tissue cutting procedures, are capable of producing airborne particles that can transmit infections and pathogenic organisms both from one region of the patient to another and from the patient to other individuals (Hoye et al., 1968). This is a reversal of the traditional concept of infection transmission, where concern was (and still is) centered on preventing infection agents arising from surgeons or other operating-room personnel from gaining access to the patient via an open surgical wound. This was attempted (not always successfully) by using clean clothing, skin cleansing and disinfection, surgical masks, and (more recently) filtered laminar flow of air over the site of surgery as well as the application of disinfecting materials directly on the wound. This becomes a difficult task, especially in large-area surgical procedures such as hip replacement or open-heart surgery. Aerosol characterization of smokes and other aerosols produced during surgery is very limited and mostly anecdotal at present, but it has been demonstrated that viable organisms can be aerosolized by these procedures (Merritt and Myers, 1991). Because both lasers and electrocautery produce high temperatures at the site of cutting, it might be questioned how aerosol generated by such processes could indeed remain viable. It appears that boiling of tissues adjacent to the cutting site results in the emission of cells and tissue fragments into the air. No studies linking particle size to the nature of the viable organisms have been performed, although such investigations do not appear to be beyond the present capability of aerosol sampling and analysis by one of the several biological tests for viable species. Inadvertent Exposure to Aerosol in Aerosol Therapy. There are several therapeutic aerosol procedures currently used in which aerosol production, either from the therapeutic agent source or from the patient, poses a concern for health workers (Arnold and Buchan, 1991). One of these that has achieved some attention is the increasing use of aerosol pentamidine in the treatment of AIDS-related Pneumocystis carinii pneumonia (PCP). This is presently carried out in hospitals where several patients are normally placed in a room, each with an aerosol delivery system containing an aqueous solution of pentamidine. Although it is the intent of the systems to deliver all the pentamidine to the patient's alveolar compartment, it is often the case that pentamidine aerosol escapes the delivery system and is transported throughout the room. Health care workers in the room and nearby are exposed to this aerosol, which is designed to be accessible to the deep lung, and there are anecdotal reports of cough and irritation among health care workers (Boulard, 1991). The possible long-term effects of this exposure are unknown.

Aerosol measurements of the pentamidine have been performed in a few instances employing fairly crude means for area aerosol sampling, and these confirm that pentamidine aerosol of inhalable size is present. Several methods to reduce exposure have been proposed, but none has been evaluated; these include increased room ventilation, isolation of the patients in booths containing a separate air supply and filtered exhaust, and respiratory protection devices for the health care personnel. The disadvantages of all these proposed control methods have been recognized, but ideal solutions, which do not entail high cost or questionable benefits, have not been proposed. Another widely used aerosol treatment method is the administration of saline aerosol to induce sputum production for the collection of bronchial cells and fluid and cytological examination. It is well known that the coughing that accompanies the aerosol administration and aids in bringing the bronchial secretions upward also produces aerosols, which may contain pathogenic organisms. As above, little quantitative aerosol characterization has been performed to test the control methods to reduce exposure. It is likely that in the future many more drug substances will be administered to patients in similar settings. It is important that aerosol measurement technology be employed to evaluate both the effectiveness of the treatment and the possibility that health care workers may be inadvertantly exposed at harmful levels. CURRENT ISSUES IN PHARMACEUTICAL AND DIAGNOSTIC AEROSOL MEASUREMENT It is clear that advances in many of the health care areas alluded to above carry with them both the possibility of increased effectiveness of treatment and diagnosis employing aerosolized substances and the possibility of undesirable exposure of health care workers to biologically potent aerosol agents. Aerosol measurement technology has an important part to play in these developments both to guide in the most effective and efficient use of therapeutic and diagnostic aerosols and in the evaluation of the degree of exposure and the effectiveness of exposure control strategies. It is not surprising therefore that there are ongoing regulatory and pharmacopeial considerations regarding the control and characterization of pharmaceutical aerosols. The regulatory (FDA) and pharmacopeial (USP) agencies of the United States are currently collaborating with similar agencies worldwide to harmonize (ICH) the requirements for evaluation and release of products thereby standardizing approaches to health and safety internationally and facilitating commerce. CONCLUSIONS There is a long history of combining effective aerosol measurement and biological assay techniques to characterize aerosols of importance to health. Some of this effort has been directed toward health care worker protection, but much more has been directed toward workers in the "dusty trades." The design of methods to deliver therapeutic and diagnostic aerosols has borrowed heavily from the knowledge base developed by industrial hygienists, health physicists, and air pollution researchers. These interactions ought to be fostered by continued professional contacts through conferences, journals, and collaborative research projects and ought to be supported by both public and private funding. The results of these efforts will be beneficial to health care consumers and health care providers, both in cost and life quality. These developments give further strength to the widening role of aerosol science and technology, including aerosol measurement, as a scientific discipline and practice that touches many important fields of endeavor.

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BASIC ATMOSPHERE GENERATION AND CONTROL SYSTEMS The types of exposure systems used in inhalation toxicology are whole-body exposure chambers, nose-only and head-only exposure systems, or single or multiple animal, closedloop, metabolism chambers. The construction and operation of these devices have been described elsewhere (Phalen, 1984; Hinner et al., 1968; Moss, 1995; McClellan and Henderson, 1995; Leavens et al., 1996). In this section the basic properties of air flow in each of these systems is discussed and related to the concepts of air changes per hour and system half-time. Whole-Body Exposure Chamber

Whole-body exposure chambers are either well-mixed systems or laminar or plug flow systems (Fig. 37-1). In a well-mixed system, it is assumed that any incremental volume of air entering the chamber will be instantaneously and completely mixed with all other volume elements present. With this assumption, the concentration build up and decay curves as described by Silver (1946) can be calculated. The equations are based on the exponential relations used to predict reaction rates in continuously stirred tank reactors and in predicting heat transfer and mixing rates in heating and air-conditioning systems. Airflow through a well-mixed or completely mixed whole-body exposure chamber is produced in either a "push," a "pull," or a "push-pull" mode. Energy (to create compression) is used to push exposure air into the inlet of the chamber. Energy (to create suction) is used to pull exposure air from the outlet of the chamber. In the "push-pull" mode of operation two driving systems, one on the inlet side and the other on the exhaust side, are adjusted to maintain a proper atmospheric pressure inside the chamber. In a "pull" system, energy is used to pull exposure atmosphere from the chamber, while the total input into the chamber is partially supplied from a pressure-driven exposure atmosphere generation system and partially from passive suction of air from the room or supply duct into the chamber. Although seldom used, a "push-only" system is operated by pushing exposure atmosphere into the chamber and letting the increased pressure drive air out of the chamber, through filtration systems and into an exhaust area. The latter approach is not commonly used because it would result in the chamber being operated above atmosphere pressures, increasing the possibility for contamination of the surrounding room and personnel. The "pull" system naturally operates at lower than ambient pressure, and the "push-pull" system is generally adjusted to ensure that the chamber is at a pressure lower than ambient. Thus, if there are leaks in this system, room air will be sucked into the chamber, preventing the exposure atmosphere from

Push system

Common zone Well mixed nose-only, whole-body head-only exposure chamber exposure unit

Directed flow nose only exposure unit

Laminar flow exposure chamber

Fig. 37-1. Configurations of four basic exposure systems.

Pull system

escaping. Such an inward leak will, however, change the exposure atmosphere disposition within the chamber. Laminar (or Plug) Flow Exposure Chamber

In a laminar flow or plug flow exposure chamber, an incremental element of exposure air volume, upon entering the chamber, maintains its uniqueness throughout its passage through the chamber to the exhaust side. It is assumed that there is no mixing, horizontally or vertically, for any element of volume and that the flow is simple plug flow from entrance to exhaust. A laminar flow exposure chamber is normally operated as a push-pull system (exposure air is pushed into the horizontal or vertical laminar flow portion of the exposure chamber and then pulled from the chamber by equipment on the exhaust side). The pressure of the chamber and total flow through the chamber are controlled. A laminar flow whole-body exposure chamber can also be operated using a pull system (whereby exposure atmosphere is pulled from the chamber by equipment in the exhaust side and exposure air is introduced into the laminar flow system, in part by a pressure-driven aerosol generation system and in part by passive suction of material from the surrounding room or open supplied air duct [Fig. 37-1]). Nose-Only and Head-Only Exposure Systems

Nose-only and head-only exposure systems have important applications in inhalation toxicology, especially when only the head or nose of the animal is to be exposed to eliminate surface contamination or when very little research chemical is available for study. These systems have been described previously by Phalen (1984), McClellan and Henderson (1995), Cannon et al. (1983), and Hemenway and Jakab (1990). Nose-only exposure systems, by their very design, are push-pull systems for they all include some point where compressed air flow is balanced or exceeded by suction. There are two basic types of nose-only exposure systems: the ones with a common distribution zone and the ones that incorporate a directed flow system (Fig. 37-1). Nose-only exposure systems with common distribution zones are designed and operated essentially around a small cavity that is several times larger than the head of the animal. The nose, or the head of the animal, is placed through the wall of this cavity, and air is directed through the cavity. The exhaled breath from the animal is captured in this region and may be carried past other animals before passing into the exhaust side of the noseonly exposure system. Usually re-breathing of air from other animals is avoided by staggering the position of the animals in the common distribution zone. The assumption is made that there is very little mixing of the exposure atmosphere with the cleaner air exhaled by the animal and thus very little dilution of atmosphere delivered to the subsequent levels of animals. Directed flow, nose-only exposure systems were designed to eliminate the potential dilution of exposure air by the exhaled air of other animals and to reduce the consumption of material used in the exposure. Such systems have a separate, small-volume exposure air distribution line that feeds short delivery tubes positioned and sized so that the flow through them is directed at the breathing zone of the animal. The nose of the animal is positioned in a side port of an exhaust tube or duct. The exhaled breath of the animal is directed into the exhaust tube or duct and carried away. Each animal is exposed to a unique, well-controlled exposure atmosphere, undiluted by its own breath or the breath of other animals present on the system. Such systems as described by Cannon et al. (1983), Baumgartner and Coggins (1980), and Hemenway and Jakab (1990) operate on the premise that the directed exposure air has, at least, the minimum velocity and mass flow to dominate the air space in front of the nose of the animal at the beginning of each breath. In general, such systems must still be operated at least with air flows above 1.5 times and preferably above 2 times the minute ventilation of the rodent (Moss and Asgharian, 1994).

Closed-Loop and Partial Closed-Loop Exposure Systems

In single or multiple animal exposure systems used in metabolism studies, the animal is placed in a closed vessel that is attached to a closed-loop air circulation system. All air withdrawn from the system must be replaced in order to preserve the closed loop during sampling and analysis of the atmosphere. Such systems are predominantly used for gases and aerosols that have very low diffusion and sedimentation loss to the fur of the animal, to the sides of the tubing, or to the walls of the animal-holding chamber. Partial recirculation has successfully been used in whole-body systems to reduce the impact adsorption on exposure concentration (National Toxicology Program, 1999).

PROPERTIES OF EXPOSURE SYSTEMS An operating exposure system has two basic measured properties: volume (L) and flow (L/min). In discussing the operation of an exposure system, the flow through the system is normally described in terms of air changes per hour. The flow, Q (L/min), is equal to n, the number of air changes per minute, times the volume, v, of the system (Fig. 37-2). The number of air changes per hour is equal to (60min/h) x n, where n = QIv. The definition of volume, v, is the key in this normalization. For example, the volume of a single-animal closed-loop exposure system is the entire volume of the animal containment vessel and all tubing and lines associated with the closed recirculating air system minus the volume of the animals. The volume of a directed flow, nose-only exposure system is taken to be the volume of the system contained between the end of the delivery tube and the nose of the rodent. The key factors affecting exposure in such nose-only systems are the animal's minute ventilation, the peak flow rate that occurs during each breath, the velocity of the exposure air as it exits the end of the delivery tube, and the air-exchange rate in the volume. Directed flow nose-only exposure systems must be set so that the flow rate through the delivery tube is greater than the maximum inspiratory flow rate of the animal. This is necessary in order to minimize dilution of the delivered exposure air by exhaled air. In practice a flow of two times the animals' minute ventilation is sufficient to keep the inhaled concentration greater than 90% of the concentration of the exposure atmosphere at the end of the delivery tube.

Qnlet

Volume v

Air change per hour

Chamber concentration buildup ^chamber - Qnlet (1~e "*)

^chamber

Chamber concentration decrease QnIeI= ° a n d ^chamber, f=0 =C0

for

^chamber -CQO

n

Fig. 37-2. Air flow and concentration in a well-mixed exposure chamber. Q is the flow through the system (L/min); v is the relevant chamber volume (L); and n is the number of air changes per minute.

In common distribution zone nose-only exposure systems the volume of the system is the volume of the common distribution zone in which the nose of the animal or the head of the animal is placed. In a laminar flow whole-body exposure system, the volume of the system is taken to be the volume in which each level of animals are housed. Finally, in a well-mixed whole-body exposure chamber, the chamber volume is defined as that volume above and below the location of the animals where there is contribution (mixing) to the inhaled exposure atmosphere. This latter definition is essential in order to meet the basic assumption of complete and thorough mixing made in the operation of these chambers. This assumption also leads to subtle differences in the definitions of volumes for whole-body exposure chambers. For example, in exposures where air is drawn from and mixed in both the top and bottom of the chamber, the entire volume of the chamber and its transition pieces must be taken to be the volume of the chamber. In exposure systems where there is no back flux of exposure air, once the housing level of the animal is passed, the exposure system volume is the volume of the top transition piece plus the volume of the region housing the animals. The term half-time comes from the equations used to describe concentration change in a well-mixed exposure system and is a function of the air-exchange rate (such as "air changes per hour or minute"). In a well-mixed system, the rate of change in concentration is directly proportional to its current value. Under such an ideal system, concentration build up in the chamber is equal to the concentration of the air entering the chamber times the quantity 1 - e~ni (where n is the air change rate). When the incoming concentration is set to zero, the concentration in such a chamber at any subsequent time is equal to Coe~n\ where C0 is the concentration in the chamber at the start of the time period. In both build up and decay of concentration, the time it takes to move from any starting concentration to 50% closer to the equilibrium concentration (i.e., the target concentration for build up and zero for decay) is given as the half-time, t1/2, and is equal to (In2)/n (i.e., 0.693/n). For example, if it takes 3min for one chamber volume of air to be pulled into the chamber inlet (i.e., n = Q/v = (1/3) min 1 ), then t\a = 0.693 x 3 = 2.1 min. Thus, after 2.1 min from any starting concentration, the chamber concentration becomes 50% closer to equilibrium concentration. Two half-times, or 4.2 min from the start of the concentration, would be 75% closer to the equilibrium concentration. The relation can be generalized. The time it takes to change in concentration from the start to some percentage, P, of the difference between the starting concentration and the equilibrium concentration can be calculated as t(mo0) = -(1M)In(I - /7100). For example, r(1/2) = 0.693/«; and similarly t(0J5) = 1.386/«; r(o.9o) = 23/n; t^95) = 3/n; and %).99) = 4.61/«. The term half-time does not accurately apply to a true laminar flow or plug flow system. In a perfect laminar flow or plug flow system the concentration changes instantaneously from its current value to the new equilibrium value as the "plug" moves past the levels of the animals and the sampling probe. In such an ideal system the characteristic time of the system is taken to be the time for an increment of air to reach the animals after it leaves the point where the generator air is first diluted. In such cases, the reciprocal of this time is n, the air-change rate for the system. However, there is usually some level of mixing in such systems, and there is a build up or decay of concentration near the breathing zone of the animal that can be discussed on the basis of a half-time, as described above for the well-mixed system where the volume in this case is taken to be the volume of the housing around the animals.

BASIC SAMPLING TECHNIQUES AND STRATEGIES The sampling and analyses of aerosol and gas concentrations in support of inhalation toxicology must be carried out so that the measurement process does not significantly alter the stability of the sampling device or change concentration or total air flow through the system.

Direct Sampling System Sample inlet Qs

Qsd Sampling Device Qd On/off valve (A)

On /off valve (B) Stable Vacuum Source Clean Up (exhaust) Devices rotameter and flow control valve

Clean Compressed Air for (optional) dilution

Fig. 37-3. Direct sample system for measuring concentrations from inhalation exposure systems. <2sd is the total flow through the sampling device; Qd is the flow of dilution air (if required); and Qs is the sample flow.

The latter is important in dealing with well-mixed systems because changing the total air flow can significantly change the half-time of the system and concentration level. Nearly all exposure systems are constant-flow systems except for the case of exposure of a single animal by head-only or intratracheal intubation through a non-rebreathing valve. In such cases the flow in the system fluctuates with the breathing cycle of the animal. Sampling System for Constant Flow Exposure Systems

In the commonly used direct sample system (Fig. 37-3), a stable vacuum source or vacuum pump internal to the sampling device is used to pull a sample at flow rate Qs from the exposure chamber. Exhaust from the sampling device is passed through a clean-up system and then through a flow control valve to a stable vacuum source and then into the facility exhaust system. An optional source of clean compressed air at flow rate Qd may be used to continuously dilute the sample before entry of the combined flow (Qsd = QS + Qd) into the sampling device. Such direct sampling systems are turned on or off by closing the air and vacuum valves, shutting off the sampling device, or unplugging the sampling line from the exposure system. The operation of a direct sampling system may change the concentration and air flow rate in the exposure system. However, in most cases Qs is less than 2% of the total air flow through the chamber, and the effect on concentrations can be ignored. The goal of stabilizing the sampling device by leaving it on at all times can be met by incorporating an additional clean air line of flow Qs in the direct sample system (Figs. 37-3 and 37-4). Sampling of exposure air in such a system is turned off by diverting a flow of clean sample-replacement air into the sampling device. The total clean air flow rate matches the air flow rate through the sampling device and shuts off flow from the sampling inlet, eliminating the need for a valve in the sampling line. Clean air is sampled at rate Qs directly into the sampling device after passing a Y connector. Clean air is provided to one or two rotameters at the front of the system as shown. Sampling is initiated when the flow, Qs, of clean replacement air through the first rotameter (just below three-way valve C in Fig. 37-4a) is diverted to the chamber exhaust. The total flow, Qsd, being pumped by the sampling device is maintained by removing an equal flow rate, Qs, from the exposure chamber (Fig. 37-4). The flows in Figure 37-4a are "balanced" when the flow through the first rotameter is diverted to the sampling device and set equal to the sampling flow, Q% and Qs + Qd = Qsd. One way to set this rotameter is to place a soap bubble volume meter at the sample inlet. Flow is balanced when the soap bubble does not move.

(a) Generic sampling system: balanced flows

Sample inlet OLVmin-

Qsd

Sampling device

On/off valve (B)

Clean up devices

(C), 0 L/min. Replacement air dump

Stable Vacuum Source (exhaust)

rotameter and flow control

valve Qs 1

Qd

I Three way On/off I valve valve (C) (A)

replacement air

Clean compressed air for replacement and (optional) dilution

(b) Generic sampling system: sample collection

On / off valve I

(B) I Stable •Vacuum Source (exhaust) Sample inlet Qs

Qsd

Sampling device

Clean up devices

(O,

Qs Replacement air dump

Q5

rotameter and flow control valve

Qd

three way valve (C)

On 7 off valve (A)

Clean compressed air for replacement and (optional) dilution

replacement air

Fig. 37-4. Generic sampling system, a, Diversion of Qs to the sample device with valve (C) allows continuous operation of the sampling device without pulling a sample from the exposure system, b, Diversion of Qs to the sample exhaust with valve (C) allows chamber air to enter the sampling device with the same flow rate Qs. Valves (A) and (B) together can remain on or off and will not affect chamber concentration. Qsd is the total flow through the sampling device; Qd is the flow of dilution air (if required); and Qs is the sample flow. Sampling System for Constant Flow: Push-Pull Exposure Systems

A direct sampling system (Fig. 37-3) should not be used when the sample flow rate, Qs, is greater than 2% to 5% of the total flow through the chamber. The generic sampling system, shown in Figures 37-4 and 37-5, can be hooked to an exposure chamber with the sample being pulled from the breathing zone of the animals and system flow balance maintained by attaching the "clean replacement air dump" to the chamber exhaust (Fig. 37-5). Sampling commences by turning the three-way valve so that "clean replacement air," Qs, flows from the rotameter to the chamber exhaust. With such a generic system, both the total flow and the concentration within the exposure chamber remain constant. The concentration in the exhaust line decreases downstream of the entry of clean air at flow rate Qs. On the other hand, if the direct sample system shown in Figure 37-3 is attached directly to a pull-only

Exhaust Generic sampling Replacement system air

Exhaust Direct sampling system

Pull system

Pull system]

Fig. 37-5. Direct (a) and generic (b) sampling systems attached to a well-mixed exposure chamber operated in the "pull-only" mode. Qs is the sample flow; Qx is the total flow before (Qtl) and during (Q12) sampling; and QE is the exhaust flow from the exposure chamber.

Exhaust Generic sampling system Replacement air Fig. 37-6. Generic sampling system attached to a directed flow nose-only exposure system. Qs is both the sample flow and the replacement air exhaust flow.

exposure system (Figs. 37-1 and 37-5), then during the sampling cycle the flow through the system is increased by the amount Q8. The new total flow, Qt2, through the chamber is equal to the original flow, QtU plus the sampling flow: Q12= Qa + Q8-The concentration will decrease by the factor [l-Gs/(Gti+Gs)]. Sampling System for Directed Flow Nose-Only Exposure Systems

In sampling directed flow nose-only exposure systems, special care must be taken to maintain the flow balance. The generic sampling system must be attached as shown in Figure 37-6. The clean replacement air exhaust is so attached that it vents into the exhaust duct near the sampling site. The inlet sampling point must be placed so that it is in the same relative position as would be the nose of the animal. Sampling System for Pulsed Flow Exposure Systems

Pulsed flows are produced when an exposure system is attached to a single-direction nonrebreathing valve, being used by a subject or an animal. Such flows can also be produced when a constant flow sampling system (such as a cascade impactor) is being used to collect the entire output of a pulsed generator (such as a metered dose inhaler). If sampling can be accomplished upstream from the "T" used to divert flow to the non-rebreathing valve, then the sampling techniques for whole-body or nose-only chambers apply as discussed above. If, on the other hand, sampling of concentration in the delivery air must take place down stream from the "T" or "Y" used to divert flow to the non-rebreathing valve, then the flow through the system will fluctuate while the concentration remains constant. In such a case, for a sampling system that operates

Exhaust

t(sec) Generic sampling system

Replacement air t(sec)

Exhaust Two-way

non-rebreathing respiratory valve (inhalation)

Filter

Room air

Fig. 37-7. Sampling for concentration of afluctuatingair stream where the total flow in the air stream is always greater than the samplingflow.Qg is the total flow entering the system from the generator; Q1 is the pulsating suction from the non-rebreathing valve; Qs is the constant sampleflow;Qm is theflowof make-up air from the room; and QE is the exhaust flow. under constant sampling flow, Qs, the generic sampling system shown in Figure 37-7 should be used. In this system, the fluctuating total flow, Qx, is the result of the difference in the constant flow from the generator, Qg, minus the fluctuating inhalationflow,Qx: Qx=Qg- Qi- The total flow, Qx, is divided between the sampling device and the exhaust system. The sample flow, Qs, is kept less than the lowest value of Q1. The loss of flow caused by the inspiration of the animal, Qx, is passively replaced by suction of clean air from the room through a low-pressure clean-up device, Qm=-Qi. In this way the flows in the system can be fully controlled for accurate sampling. The amount of exposure atmosphere sampled at flow Q5 is a true measure of the concentration inhaled through the non-rebreathing valve. To measure the total amount of material exhaled from the exhaust side of a nonrebreathing valve, or from the output of a pulsed generator, a different sampling train must be used (Fig. 37-8). Constant total flow, Qx, in this sampling system is maintained by using a flow of clean compressed air, Qa, that is either vented into the room (or hood) or sucked into the sampling system. In the schematic shown in Figure 37-8, the flow of exposure air, Q1, is from the exhaust side of the non-rebreathing valve. The flow of Qa is set to be equal to or greater than the peak flow rate coming from Qx, the non-rebreathing valve (Qa > Qx). As the animal exhales, an additional portion of the clean air flow Qa, is diverted through the filter, flow Qh and into the room or into the exhaust system (Qx < Qf). As the animal stops exhaling and Qx decreases to zero, the total flow through the system is made up by ga.This dynamic air flow system allows a constant flow, Qx, to be established at the middle of the system dividing the sampling side from the non-breathing valve. The flow through the generic sampling system, Qs, is less than or equal to the maximum value of Qx. CONCLUSIONS Even though the sampling device itself may be very accurate, the measurement may be inaccurate because the process of sampling changes the equilibrium of the exposure system. The

Exhaust

t(sec)

Generic sampling system Replacement air

Two-way non-rebreathing respiratory valve (exhalation)

Exhaust

Filter Room exhaust

Rotameter and flow On/Off control valve valve

Clean compressed air

Fig. 37-8. Sampling for total output from a fluctuating air stream where the total flow periodically drops to zero. Qi is the flow entering the system from the non-rebreathing valve; Qa is the flow of clean air into the system; Q5 is the constant sample flow; Q1 is the flow of clean air out to the room; and QE is the exhaust flow. basic sampling systems shown meet the general requirement of obtaining a concentration measurement from a variety of exposure systems without any significant change in concentration or total air flow. For large flow systems, where the sampling flow, g s , is small compared with the total flow through the system, the use of the direct sample system will not change the exposure concentration. However, for lower flow systems, or for systems requiring special air flow balancing, such as directed flow nose-only and closed-loop exposure systems, care must be taken to allow sampling to proceed in an accurate and efficient manner.

REFERENCES Baumgartner, H. and C. R. E. Coggins. 1980. Description of a continuous-smoking inhalation machine for exposing small animals to tobacco smoke. Beitrage Tabakforschung Int. 10:169-174. Cannon, W. C, E. F. Blanton, and K. E. McDonald. 1983. The flow-past chamber: An improved nose-only exposure system for rodents. Am. Ind. Hyg. Assoc. J. 44:923-928. Cohen, B. S. and S. V. Hering, eds. 1995. Air Sampling Instruments for Evaluation of Atmospheric Contaminants, 8th Ed. Cincinnati, OH: American Conference of Governmental Industrial Hygienists, Inc. Hemenway, D. R. R. and G. J. Jakab. 1990. Nose-only inhalation system using the fluidized-bed generation system for coexposures to carbon black and formaldehyde. Inhal Toxicol. 2:69-89. Hinner, R. G., J. K. Burkart, and E. L. Punte. 1968. Animal inhalation exposure chambers. Arch. Environ. Health 16:194-206. Leavens, T. L., O. R. Moss, and J. A. Bond. 1996. A dynamic inhalation system for individual whole-body exposure of mice to volatile organic chemicals. Inhal Toxicol. 8:655-677. McClellan, R. O. and R. F. Henderson, eds. 1995. Concepts in Inhalation Toxicology, 2nd Ed. Washington, DC: Taylor and Francis.

Moss, O. R. 1995. Calibration of gas and vapor samplers. In Air Sampling Instruments for Evaluation of Atmospheric Contaminants, 8th Ed., eds. B. S. Cohen and S. V. Hering. Cincinnati, OH: American Conference of Governmental Industrial Hygienists, Inc., pp. 151-164. Moss, O. R. and B. Asgharian. 1994. Precise inhalation dosimetry with minimum consumption of product: The challenge of operating inhalation exposure systems at their design limits. In Respiratory Drug Delivery TV, eds. P. R. Byron, R. N. Dalby, and S. J. Farr. Richmond, VA: Virginia Commonwealth University, pp. 197-201. National Toxicology Program. 1999. Toxicology and Carcinogenesis Studies of Glutaraldehyde (CAS No. 111-30-8) in F344/N Rats and B6C3F1 Mice (Inhalation Studies). Technical Report No. 490, Public Health Service, National Institutes of Health, NIH Publication No. 99-3980, Research Triangle Park, NC Phalen, R. F. 1984. Inhalation Studies: Foundations and Techniques. Boca Raton, FL: CRC Press. Silver, S. D. 1946. Constant flow gassing chambers: Principles influencing design and operation. /. Lab. CHn. Med. 31:1153-1161.

Term

Explanation

agglomerate

group of particles held together by van der Waals forces or surface tension heterogeneous particle in which the various components are not easily broken apart sampling and analysis of air to determine the quantity of pollutants present condition of materials present in the air at levels detrimental to the health and/or welfare of human beings level beyond which air pollutants can cause damage to humans, animals, plants, or materials disk-like image of a small point produced by an optical system with diffraction-limited resolution atmospheric particles in the approximate size range of 0.01 to 0.1 urn microscopic plant part of the respiratory system in which gas exchange occurs; alveoli are small sacs at the ends of the bronchioles surrounding air sampling condition in which the air flowing into an inlet has a different direction from the ambient air flow sample taken in a fixed location assumed to be representative of the area being investigated fraction of particles entering an inlet from the ambient environment device used to produce droplets by mechanical disruption of a bulk liquid relationship of measured values to previously measured ones single-celled microorganisms; some genera produce endospores method of mass measurement that relies on the attenuation of a beam of beta particles energetic electron emitted in certain nuclear decay processes Brunauer-Emmett-Teller method; procedure using the adsorption isotherm of a material to measure its surface area consistent difference between a measurement and a true or accepted value particle size distribution with two distinct maxima

aggregate air monitoring air pollution air quality standards Airy disk Aitken nuclei algae alveolar ambient air anisoaxial sampling area sample aspiration efficiency atomizer autocorrelation bacteria beta gauge beta particle BET method bias bimodal size distribution bioaerosol

bipolar ion field Boltzmann charge distribution boundary layer

suspension of particles of biological origin; viable or dead cells; spores or pollen grains; fragments, products, or residues of organisms region in which ions of both polarities exist residual or minimum charge distribution on particles after exposure to a bipolar ion field region of flow near bounding surface where the flow is dominated by friction forces resulting in reduced flow velocity relative to the free stream

Term breathing zone sample Brownian motion bubble meter bulk analysis capillary pore filter carcinogen cascade impactor

centrifuge

closed-face sampler colony-forming units cloud coagulation coarse particle mode

coincidence comminution condensation condensation nuclei counter confidence limits continuum flow corona Coulter counter

Explanation sample taken as close as possible to the point at which the subject inhales air, usually within 0.3 m of the nose and mouth; represents a subject's inhaled air random motion of particles due to collisions with gas molecules tube with a defined volume into which bubbles are injected to measure flow rate analysis of a sample in its entirety versus analysis of individual particles filter consisting of solid membrane with an array of cylindrical holes of uniform size penetrating the membrane agent that causes cancer device that uses a series of impaction stages with decreasing particle cut size so that particles can be separated into relatively narrow intervals of aerodynamic diameter; used for measuring aerodynamic size distribution of an aerosol device in which particles are removed by centrifugal forces from an aerosol flowing in a helical path; device is usually characterized by high-resolution particle size separation filter cassette sampler with the inlet smaller than the filter, as used in in-line liquid filtration number of biologically active entities (e.g., bacteria, fungi) that can form a colony present in a unit volume of air assembly of particles with an aerosol density that is more than about 1 % higher than the density of the gas alone aerosol growth process resulting from the collision of aerosol particles with each other largest particle mode (>2|im) in atmospheric particle size distributions, consisting primarily of particles generated by mechanical processes simultaneous presence of two or more particles in the sensing volume of a particle counter breakup of particles by mechanical action process with more vapor molecules arriving at a particle's surface than leaving the surface, resulting in a net growth of the particle device in which submicrometer-sized particles are grown by vapor supersaturation to a larger size and are detected by light scattering values defining a range around a sample statistic flow governed by the macroscopic properties of the gas or fluid such as viscosity and density region of intense ionization, often surrounding an electrode at high voltage instrument that measures individual particle volume in a liquid by measuring the change in resistivity of the liquid as it passes through an orifice

Term

Explanation

cowl

cylindrical tube used in front of filter cassette to prevent direct impaction or contamination of samples; used primarily for asbestos fiber sampling an orifice through which there is a constant air flow when a sufficient pressure drop across the orifice causes sonic flow same as slip correction factor

critical orifice Cunningham slip correction factor cut-off particle diameter

cyclone Dean number dichotomous impactor differential mobility classifier diffraction diffusion diffusion battery

diffusion charging diffusion denuder diffusion (equivalent) diameter diffusiophoresis dilution ratio dilution system disinfection dispersion drag coefficient drag force dust

diameter of a particle that has 50% probability of being removed by the device or stage and 50% probability of passing through; also called 50% cut point, d50, or the effective cut-off diameter device in which particles are removed by centrifugal forces in a cyclonic path ratio of square root of the product of inertial and centrifugal forces to viscous forces virtual impactor with two emerging aerosol flows device that passes only particles within a narrow range of electrical mobilities change in direction and amplitude of radiation after passing near an object or through an orifice net movement of particles or gas from a higher to a lower concentration aerosol spectrometer used for submicrometer-sized aerosols in which size is measured by the diffusive loss of particles in an arrangement of ducts (e.g., tubes, filters, screens) process by which airborne particles acquire charge from ions undergoing Brownian motion device that passes particles (low diffusivity) and removes gases (high diffusivity) diameter of a unit-density sphere with the same rate of diffusion as the particle in question particle motion under the influence of a gas concentration gradient factor by which measured concentration is multiplied to obtain mainstream concentration system wherein aerosol is mixed with particle-free dilution gas in known volumetric ratio to reduce concentration destruction of the majority of microorganisms, not necessarily of all the spores system consisting of particles suspended in a fluid coefficient that relates the particle's drag force to the velocity pressure resistance experienced by a particle when moving in a fluid solid particles formed by erosion or other mechanical breakage of a parent material; generally consists of particles of irregular shape and larger than about 0.5 urn

Term dust generator

Explanation

device used to disperse dry particles in the air in a controlled fashion dynamic shape factor ratio of the drag force on a particle to that on a sphere of equivalent diameter eddy diffusion aerosol transport due to gas turbulence; theoretical description is similar to molecular diffusion effective density density of a particle with voids, in contrast to a compact particle's bulk material density elastic light scattering process in which there is no energy exchange between incident photons of light and target particles electrical aerosol aerosol size spectrometer in which the particles are separated by analyzer removing those with an electrical mobility greater than a selected value electrical aerosol aerosol size spectrometer in which the particles are separated by classifier selecting those within a narrow range of electrical mobilities electrical mobility parameter that indicates a particle's ability to move in an externally applied flow field electrical mobility diameter of a unit-density spherical particle moving at the same (equivalent) diameter velocity in an electric field as the particle in question electrodynamic device that uses superimposed ac and dc fields to levitate balance particles electrophoresis charged particle motion induced by an electric field electrostatic balance device that uses a dc field to levitate particles (e.g., the Millikan condenser) electrostatic device in which airborne particles are charged in a unipolar ion precipitator field and deposited with a high-voltage electric field elutriator device used to separate particles by aerodynamic diameter by allowing them to settle in a moving air stream emission material being discharged into the outdoor atmosphere endotoxin toxic cell wall component of gram-negative bacteria envelope (equivalent) diameter of a sphere composed of the particle bulk material and diameter included voids that has the same mass as the particle in question epiphaniometer instrument that measures the surface area of aerosol particles equivalent diameter diameter of a sphere having the same value of a specific physical property (activity) as the particle in question evaporation process with more vapor molecules leaving a particle's surface than arriving at the surface, resulting in shrinkage of the particle extinction coefficient measured parameter given by the amount of light scattered and absorbed by a particle divided by the amount incident upon it extrathoracic region of the respiratory system above the larynx containing the nose and mouth fabric filter filter consisting of a woven or felted fabric fastest 2 minute wind wind speed corresponding to the largest linear passage of speed wind movement during a 2 minute period

Term Feret's diameter fibrous filter field charging filter fine particle

flocculate fluidized bed generator fly ash fog fractal dimension free molecular flow Froude number fume fungi Gaussian curve Geiger-Miiller tube geometric geometric standard deviation graticule

gravitational deposition parameter gravitational settling velocity half-life Hatch-Choate equations heterogeneous

Explanation particle dimension determined by the projection of the particle's silhouette onto a selected axis filter consisting of a mat of individual fibers process by which particles are charged by ions moving in a strong electric field porous membrane or mat of fibers used to collect particles from the air particle less than about 2 urn in size, consisting of particles in the nuclei and accumulation modes; term used in describing atmospheric aerosols group of particles very loosely held together, often by electrostatic forces; flocculates can easily be broken apart by shear forces within the air device using an agitation force, e.g. air pressure, to fluidize a powder to release dust particles particles of ash entrained in flue gas produced by fossil fuel combustion liquid particle aerosol, typically formed by condensation of supersaturated vapors measure of complexity of a particle's shape flow governed by discrete impacts of gas molecules proportional to inertial force/gravitational force small particles that are usually the result of condensed vapor (often from combustion) with subsequent agglomeration multicellular organisms that produce spores profile of distribution or curve similar to that observed for the normal distribution radiation-sensing instrument that relies on an avalanche process initiated by the production of electron-ion pairs refers to a size parameter on a logarithmic size scale where a given ratio of two sizes appears as the same linear distance measure of dispersion in a lognormal distribution (always >1) transparent disk with calibrated scale placed in the focal plane of an optical system (e.g., a microscope) used for measurement of particles or other objects ratio of particle settling distance during transport in the sampling inlet region to the diameter of the inlet particle velocity in a gravitational field after equilibrium between gravity and aerodynamic drag forces has been reached time interval required to reduce the rate of emission of a radioisotope by a factor of two expressions that, given a characteristic diameter and geometric standard deviation of a distribution, allow the calculation of any other characteristic diameter of the distribution consisting of individual components that may differ from each other in size, shape, and chemical composition

Term

Explanation

heterogeneous nucleation homogeneous nucleation horizontal elutriator

formation of droplets on condensation nuclei (existing submicrometer particles) formation of droplets in the absence of condensation nuclei; also called self-nucleation horizontal channel through which aerosol flows and particles above a given size or size range are removed by gravitational settling device used to measure air velocity by measuring the change in resistance of a heated wire common insects living in mattresses and carpets; excreta are common allergens hypothetical diameter of an object equal to four times the object's cross-sectional area divided by the perimeter of that area suspension of particles in a liquid property of a chemical that indicates its tendency to absorb water from the air chain of fungal cells hypothetical fluid having no viscosity device in which aerosol particles with sufficiently high inertia in a deflected air stream are impacted onto a surface device in which particles are removed by impacting the aerosol particles into a liquid fraction of an aerosol that can enter the human respiratory system (defined for sampling purposes) fraction of ambient particles that is delivered to the aerosol transport section of a sampling system by the inlet; it is the product of the aspiration and transmission efficiencies same as inhalable; inhalable is the currently preferred term collision with and deposition of a particle on an object when the particle passes within one particle radius of the object radiation-sensing instrument that relies on the detection of free electron-ion pairs sampling condition in which the air flowing into an inlet has the same direction as the ambient air flow sampling condition in which the air flowing into an inlet has the same velocity and direction as the ambient air flow a nebulizer employing air pressure to aerosolize a bulk liquid increase in partial vapor pressure for a particle's curved surface required to maintain mass equilibrium relative to the vapor pressure above a flat liquid surface ratio of gas molecular mean free path to the physical dimension of the particle; indicator of free molecular flow versus continuum gas flow solution of the two-dimensional viscous flow field for a system of cylinders perpendicular to the flow, taking into account the interference effects of neighboring fibers (has also been applied to spheres); used to model flow in fibrous filters

hot wire anemometer house-dust mites hydraulic diameter hydrosol hygroscopicity hyphae ideal impactor

fluid

impinger inhalable inlet efficiency

inspirable interception ionization chamber isoaxial sampling isokinetic sampling jet nebulizer Kelvin effect

Knudsen number

Kuwabara

flow

Term laminar

Explanation flow

gas flow with a smooth, nonturbulent pattern of streamlines, with no streamline looping back on itself; usually occurs at very low Reynolds numbers light scattering change in direction of light radiation due to reflection, diffraction, and refraction from a particle lognormal size particle size distribution characterized by a bell-shaped or distribution Gaussian distribution shape when plotted on a logarithmic size scale lung model representation of the respiratory system used to make quantitative estimates of particle deposition Mach number ratio of gas to acoustic velocity; indicator of compressibility manometer device used to measure pressure differences Martin's diameter length of horizontal line bisecting particle cross section into equal areas mass (equivalent) diameter of a sphere composed of the particle bulk material diameter with no voids that has the same mass as the particle in question mass median size size with an equal mass of particles above and below this value (see also median size) mean free path mean distance a molecule in a gas travels before colliding with another molecule mean size average of all sizes, that is, the sum of all sizes divided by the number of particles mechanical mobility aerosol parameter that indicates a particle's ability to move in a suspending medium; see mobility median size size with an equal number of particles above and below this value (see also mass median size) membrane filter filter that is formed as a gel from a colloidal suspension; characterized by tortuous air passages micronize process by which coarse powders are mechanically reduced to a particle size suitable for redispersion as an aerosol from a solvent or propellant microparticles particles with sizes of the order of micrometers Mie scattering theory general theory describing light scattered by spherical particles mildew visible fungal growth on surfaces minute volume volume of air passing in and out of the respiratory system in 1 min mist liquid particle aerosol, typically formed by physical shearing of liquids, such as in nebulization, spraying, or bubbling mobility ratio of particle velocity to the force producing that velocity mobility (equivalent) diameter of a spherical particle with the same dynamic mobility diameter as the particle in question mode value occurring most often in a distribution of values; peak value of a distribution mold visible fungal growth on surfaces monodisperse composed of particles with a single size or a small range of sizes mycelium mass of fungal hyphae mycotoxin toxic chemical produced by fungi

Term

Explanation

nanoparticles

general term indicating particles with sizes on the order of nanometers, usually limited to less than 100 nm portion of the respiratory tract between the epiglottis and the anterior nares device in which droplet aerosols are produced by dispersion of a bulk liquid instrument that measures the amount of light scattered from an aerosol; also called a photometer reduction in electronic charge on particles by exposure of the aerosol to ion clouds (often produced by radioactive sources) particle size distribution characterized by a bell-shaped or Gaussian distribution shape when plotted on a linear size scale process of initial formation of particles from a vapor smallest mode in atmospheric particle size distributions, formed by condensation of atmospheric gases or emissions from hot processes, typically containing particles <0.1 urn in size degree to which an aerosol obscures an observer's view filter cassette sampler with the inlet approximately the same size as the filter diameter of a calibration particle that scatters as much light in a specific instrument as the particle being measured aerosol size spectrometer that differentiates particles by the amount of light scattered by each particle device used to measure flow rate in a duct by measuring the pressure drop across a calibrated constriction device used to measure diameter of monodisperse aerosol particles by illumination with white light and detection of higher order Tyndall spectra ratio of fiber or membrane volume of a filter to its total volume; also solidity pressure that a vapor would exert if it were the only component present in a volume of gas small discrete object, often having a density approaching the intrinsic density of the bulk material; it may be chemically homogeneous or contain a variety of chemical species; it may consist of solid or liquid materials or both rebound of particles that fail to adhere after impacting on a collecting surface relationship expressing the quantity of a particle property (activity) associated with particles in a given size range an adjective indicating that the material in question has particle-like characteristics; this term is also used colloquially as a noun to describe particles or particle-containing material

nasopharyngeal compartment nebulizer nephelometer neutralizing normal size distribution nucleation nuclei mode

opacity open face sampler optical (equivalent) diameter* optical (single) particle counter orifice meter owl

packing density partial pressure particle

particle bounce particle size distribution particulate

* For glossary of terms related to optics, see the MIE internet site.

Term

Explanation

passive sampling

aerosol measurement using natural convection or diffusion to draw the air into the measurement device, as opposed to active sampling microorganism that causes disease ratio of a particle's convective to diffusive transport device attached to a person in order to sample air in the person's immediate vicinity particles that appear in a measured distribution that are due to coincidence or other nonideal aspects of the measurement process and not due to real particles instrument that measures the amount of light scattered from a particle cloud; also called a nephelometer particle motion under the influence of asymmetrical light absorption within a particle device used to measure velocity pressure in a flow stream gas flow through a tube characterized by a uniform velocity across the entire cross section of visible effluent from an outlet (e.g., a stack or vent) particulate mass; usually used in relation to regulatory standards having a specific particle aerodynamic diameter cut-off (e.g. PM-IO) electrostatic precipitator using a corona from a single point to deposit particles onto a flat plane laminar flow with a parabolic velocity profile occurring in a circular duct; the gas velocity in the center of the tube equals twice the average velocity in the tube mathematical function relating the number of particles in a given volume element to the average concentration of randomly distributed particles in the entire volume composed of particles with a range of sizes (1 - packing density) an indication of the degree of variation in the results of repeated measurements of a variable device that removes particles ahead of an aerosol sensor, often in a manner similar to the particle removal occurring ahead of the respiratory region of interest; also called a pre-

pathogen Peclet number personal sampler phantom particles

photometer photophoresis Pitot tube plug plume PM

flow flow

point-to-plane precipitator Poiseuille flow

Poisson distribution

polydisperse porosity precision pre-classifier

separator or a pre-cutter

primary particle projected area (equivalent) diameter pulmonary compartment pycnometer radiometric force Rayleigh scattering

particle introduced into the air in solid or liquid form diameter of a circle that has the same area as the projected area of a particle seen under a microscope portion of the respiratory tract in which gas exchange occurs (includes alveoli and respiratory bronchioles) device for measuring density of particles force produced by light pressure scattering of radiation occurring when the size of the scattering object is much smaller than the radiation wavelength

Term

Explanation

re-entrainment

return of particles to an air stream after deposition on a surface change in speed and direction of radiation passing from one medium into another ratio of the speed of light in a vacuum to that in a material in question ratio of the terminal gravitational settling velocity to sampling air velocity in an inlet ratio of standard deviation to the mean; also described as coefficient of variation (CV) time for a particle to reach 1/e of its final velocity from an initial velocity or from rest when subjected to an external force; an indicator of a particle's ability to adjust to changes in flow velocity fraction of an aerosol that can reach the gas exchange region of the human respiratory system (defined for sampling purposes) transparent disk with lines or other marks placed in the focal plane of optical systems for calibration or alignment flow similitude parameter, expressed as the ratio of the inertial force of the gas to the friction force of the gas moving over the surface of an object; flow Reynolds number is the gas flow in a tube, and particle Reynolds number describes the gas flow around a particle device used to measure flow rate as indicated by the height of a float centered in a vertical tapered tube device to withdraw aerosol from a system ratio of the ambient air velocity to the air velocity in an inlet ratio of the partial pressure of a vapor to its saturation vapor pressure partial pressure of a liquid's vapor required to maintain the vapor in equilibrium with the condensed liquid or solid; also referred to as vapor pressure diameter of a droplet whose surface to volume ratio is equal to the mean of all the surface-to-volume ratios of the droplets in a spray distribution; also referred to as the surface area mean diameter or the mean volume-surface diameter ratio of Peclet number to Reynolds number, or the ratio of kinematic viscosity to diffusion coefficient radiation-sensing instrument that relies on the excitation of optical emission followed by detection with a photomultiplier tube particle formed in the air, usually by gas to particle conversion; also sometimes used to describe agglomerated or redispersed particles movement of particles by the influence of gravity radiation-sensing instrument that relies on the generation of free carriers in a semiconductor material

refraction refractive index relative settling velocity relative standard deviation relaxation time

respirable fraction reticle Reynolds number

rotameter sampling probe sampling ratio saturation ratio saturation vapor pressure Sauter mean diameter

Schmidt number scintillation spectrometer secondary particle

sedimentation semiconductor detector

Term shape factor

Explanation

factor that relates the drag force on a particle to that on an equivalent sphere Sherwood number dimensionless mass transfer coefficient that relates the particle's diffusive deposition velocity to the particle's diffusion coefficient Sinclair-LaMer device that produces monodisperse aerosols by condensation of generator vapor onto nuclei slip correction factor factor that allows slip flow behavior to be calculated using continuum gas flow equations slip flow regime transition between free molecular flow and continuum gas flow smog an aerosol consisting of solid and liquid particles, created, at least in part, by the action of sunlight on vapors; the term is a combination of the words smoke and fog and often refers to the entire range of such pollutants, including the gaseous constituents smoke solid or liquid aerosol, the result of incomplete combustion or condensation of supersaturated vapor; most smoke particles are submicrometer in size SnelFs law fundamental principle in optics that the sines of the angles of incidence and refraction are in a constant ratio to one another solidity see packing density soot conglomeration of particles formed by incomplete combustion of carbonaceous material source apportionment analysis of an aerosol sample so that fractions of the aerosol can be assigned to specific sources source sampling collection of materials emitted from an air pollutant-generating source specific surface particle surface area per unit mass or volume of particles spinning disk device that produces monodisperse droplets from the break up of atomizer a thin film of liquid ejected from the surface of a spinning disk spirometer device used to measure gas volume (or flow rate with a timer) using an expandable can sealed with a liquid spores dormant cells of microorganisms standard maximum allowable level of an air contaminant established by law Stephan flow special case of diffusiophoresis with particle motion toward or away from evaporating or condensing surfaces; also written as Stefan flow sterilization complete destruction of microorganisms and their spores Stokes diameter diameter of a spherical particle with the same density and settling velocity as the particle in question Stokes law flow flow around a body under the influence of viscous, but not inertial forces (nonturbulent flow) Stokes number ratio of a particle's stopping distance to a characteristic dimension; generally used as an indicator of similitude in particle behavior in a given aerosol flow configuration Stokes regime condition for which Stokes law applies

Term stopping distance

Explanation

product of relaxation time and the initial particle velocity; an indicator of a particle's ability to adjust to directional changes in aerosol flow subisokinetic sampling condition in which the air flowing into an inlet has a sampling lower velocity than the ambient air flow superisokinetic sampling condition in which the air flowing into an inlet has a sampling higher velocity than the ambient air flow surface barrier a type of semiconductor detector used primarily for charged detector particle emissions terminal settling equilibrium velocity of a particle approached when falling under velocity the opposing influences of gravity and fluid drag thermal precipitator device that deposits particles using a temperature gradient thermophoresis particle's motion in a temperature gradient (i.e., from a hotter to a colder region) thoracic region of the respiratory tract from the larynx down thoracic fraction fraction of an aerosol reaching the respiratory region below the larynx system (defined for sampling purposes) tidal volume volume of gases inhaled or exhaled during each breath total lung capacity volume of air contained in the lung at maximum inspiration tracheobronchial region of the respiratory tract from the larynx to the terminal compartment bronchioles transfer function the change of one function into another; used to describe the change in size distribution or spatial distribution as an aerosol passes through a classifier transmission fraction of aspirated particles that is transmitted through an efficiency inlet to the rest of the sampling system turbulent flow chaotic flow with streamlines looping back on themselves; less "well behaved" than laminar flow ultrasonic nebulizer a nebulizer employing focused sound waves to aerosolize a liquid into droplets ultra-Stokesian condition in which flow velocity relative to an object is high enough to be outside the Stokes regime unipolar ion field region containing ions of only one polarity vapor pressure partial pressure of a liquid's vapor required to maintain the vapor in equilibrium with the condensed liquid or solid; also referred to as saturation vapor pressure variability measure of spread of repeated measurements of a parameter variance square of the standard deviation; a measure of variability vena contracta flow contraction with flow separation from the wall, usually occurring after constriction of a flow channel or just downstream of the entry point of an inlet Venturi meter device used to measure flow rate in a duct by measuring the pressure drop across a calibrated streamlined constriction vertical elutriator vertical channel that gravitationally retains or removes particles above a given size or size range and emits the remaining airborne particles

Term

Explanation

virtual impactor

device in which particles are removed by impacting them through a virtual surface into a stagnant volume, or a volume with a slowly moving air flow, so that large particles remain in this volume, while smaller particles are deflected with the bulk of the original air flow; the dichotomous impactor is a frequently used virtual impactor microorganism that needs a complete cell to reproduce maximum volume of gas that can be exhaled from the lung after maximum inhalation deposition of particles in a sampler on surfaces other than those designed for particle collection indicates the ratio of the air pressure force to the surface tension force for a liquid droplet undergoing acceleration in a gas application of a factor to one particle activity to obtain another activity (e.g., count, surface, volume, or mass) unicellular fungus

virus vital capacity wall loss Weber number

weighting yeast

APPENDIX B Conversion Factors Length 1 micrometer (um) = 1(T6m = 10"4Cm = 10"3mm = 10 3 nm = 10 4 A = 3.937 x 1(T5 in = 3.281 x 10-6ft 1 nanometer (nm) = 10~3um = 10~9m 1 Angstrom (A) = 10"Vm = 10"10In 1 inch (in) = 2.540 cm 1 foot (ft) = 12 in = 0.3048 m

Volume Im 3 = 10 1 Vm 3 = 10 3 L = 6.102 x 104in3 = 35.31ft3 1 um 3 = 10"15L = 10-18m3 = 6.102 x 10"14in3 = 3.531 x 10"17ft3 1 liter (L) = 1015um3 = 10"3m3 = 61.02in3 = 3.531 x 10"3ft3 lin 3 = 5.787 x 104ft3 = 1.639 x 10 3 um 3 = 1.639 x 10"2L = 1.639 x 10"5m3 lft 3 = 1.728 x 103in3 = 2.832 x 1016um3 = 28.32L = 2.832 x 10"2m3

Force 1 1 1 1 1 1

Newton (N) = 105 dyne = 0.22481b = 102.1 g dyne = 10"5N = 2.248 x 10"6Ib = 1.021 x 10"3g pound (Ib) = 4.448 x 105 dynes = 4.448 N = 453.6 g gram (g) force = 980.7 dyne = 9.807 x 10"3N = 2.205 x 10"3Ib grain (gr) = 63.55 dynes poundal = 1.383 x 104 dynes

Temperature degrees Kelvin (K) = T 0 C + 273.15 = 5/9 (T 0 F + 459.67) degrees Celsius ( 0 C) = T K - 273.15 = 5/9 (T 0 F -32) degrees Fahrenheit ( 0 F) = 1.8T°C + 32 = 1.8T K - 459.67 degrees Rankine (R) = T 0 F + 459.67 where T is temperature in the indicated units Pressure 1 Pascal (Pa) = lN/m 2 = 10 dyne cm2 = 9.869 x 10"6atm = 1.450 x 10~4lb/in2 = 7.501 x 10" 3 mmHg = 4.015-10"3in H 2 O 1 atmosphere (atm) = 1.013 x 105N/m2 = 1.013 x 10 5 Pa = 101.3 kPa = 1.013 x 106 dyne/cm2 = 14.701b/in2 = 760mmHg = 406.8in H 2 O

1 inch of water (in H2O) (at 4°C) = 2.458 x l(r 3 atm = 2491 dyne/cm2 = 249.1 N/m2 = 3.613 x 1(T3IMn2 = 1.868mmHg lmm of mercury (mmHg) (at 00C) = 1.316 x l(T3atm = 1.333 x 103 dyne/cm2 = 1.333 x 102N/m2 = 0.535 in H2O = 1.934 x 10"2lb/in2 ltorr = lmm Hg Viscosity lPa-s = 10 poise (P) = 10g/cms = 10 dynes/cm2 Electrical Units electronic charge = 1.6022 x 10"19C 1 ampere (amp) = 2.998 x 109 statamp 1 statampere (statamp) = 3.336 x 10~10amp 1 volt (V) = 3.336 x 10~3statV 1 statvolt (statV) = 299.8 V 1 farad (F) = 106^iF = 8.987 statF 1 statfarad (statF) = 1.113 x 10"12F lohm = 1.113 x 10"12statohm lstatohm = 8.987 x 10nohm

APPENDIX C Commonly Used Constants Boltzmann constant Avogadro's number gas constant Stephan-Boltzmann constant

k Na R s

elementary charge permittivity of free space speed of light in vacuum gravitational acceleration

e E0 c g

1.381 x l(r23N-m/K [1.381 x 10"16 dynecm/K] 6.022 x 1023 molecules/mole 8.314 J/(moleK) [8.314 x 107 dynecm/mole-K] 5.670 x 10-8N/(m2s K4) [5.670 x 10~5 dyne/(cm 2 sK 4 )] 1.602 x 10"19C [4.803 x 10-10statC] 8.854 x 10"12F/m [1 electrostatic unit] 2.998 x 108m/s [2.998 x 1010cm/s] 9.807 m/s2 [980.7 cm/s2]

APPENDIX D Some Properties of Air and Water Air at 293.13 K [200C] and 101.3 kPa [latm] (NTP) density (pg) viscosity (v) mean free path (X) average molecular weight (M) specific heat ratio (/) diffusion coefficient (D)

1.205 kg/m3 [1.205 x 10"3 g/cm3 = 1.205 g/L = 0.075 lb/ft3] 1.832 x 10"4P [1.832 x 10" 5 Pas] 0.0665 urn 28.96 g/mole 1.40 1.9 x 10"5m2/s [0.19cm2/s]

For some properties of other gases, see Table 4-1. Composition of Dry Air by Volume Gas

Content (Volume %)

Molecular Weight (g/mole)

78.08 20.95 0.934 0.033a <0.003

28.01 32.00 39.95 44.01

N2 O2 A CO 2 Other a

CO 2 concentration may vary from place to place.

Water at 293 K [200C] viscosity surface tension vapor pressure

1.002 x 10" 3 Ns/m 2 [0.01002 dynes/cm 2 ] 0.07275 N/m [72.75 dyne/cm] 2.338 kPa [17.54 mm Hg]

Water Vapor at 293 K [200C] Diffusion coefficient Density

2.4 x 10"3m2/s [0.24cm2/s] 0.75 kg/m3 [0.75 x 10"3 g/cm3]

APPENDIX E Major Dimensionless Numbers

Froude (Fr)

U2 =—

inertial force/gravitational force

Knudsen (Kn) = — dv

mean free path of molecules/particle diameter

Mach (Ma)

flow velocity/sonic velocity

= U sonic

Peclet (Pe)

=

bulk mass transfer/diffusive mass transfer

Prandtl (Pr)

=— a

momentum diffusivity/thermal diffusivity

Reynolds (Re) = —2— v

inertia force/viscous force

Schmidt (Sc) = —

momentum diffusivity/mass diffusivity

Stokes (Stk)

stopping distance/characteristic flow dimension

=— JLJ

APPENDIX F Properties of Particles Standard Density (1000kg/ni3) Spheres at NTP-293.15K, 101.3 kPa [latmj Particle Diameter (|xm)

Slip Correction Factor

Settling Velocity (m/s)

0.00037* 0.001 0.002 0.005 0.01 0.02 0.05 0.1 0.2 0.5 1 2 5 10 20 50 100

611.5 226.5 109.9 44.31 22.45 11.53 5.014 2.888 1.879 1.333 1.166 1.083 1.033 1.017 1.008 1.003 1.002

2.49OxIO"9 6.737 XlO"9 1.326XlO-8 3.34OxIO"8 6.768XlO"8 1.390xl0" 7 3.779 XlO"7 8.708 xlO~7 2.267x10^ 1.005 XlO-5 3.515 XlO-5 0.0001306 0.0007787 0.003065 0.01216 0.07395 0.2605

"Displacement in 10 s. b Effective diameter of air molecule.

Diffusion Coefficient (m2/s) 3.93OxIO"5 5.381 x 10"6 1.305XlO-6 2.105 x 10"7 5.332 x 10"8 1.369 x 10~8 2.382 x 10"9 6.86IxIO- 10 2.232 x 10"10 6.335 x IO"11 2.769 XlO"11 1.286XlO"11 4.908 x 10"12 2.415 x 10"12 1.198 XlO"12 4.766 x 10"13 2.379 x 10"13

Mobility m/(Ns)

rms Brownian Displacement (m) a

9.70OxIO15 1.33OxIO15 3.227 x 1014 5.203 x 1013 1.318 x 1013 3.385 x 1012 5.888 x 1011 1.696 x 1011 5.517 x 1010 1.566 x 1010 6.846 x 109 3.179 x 109 1.213 x 109 5.969 x 108 2.96OxIO 8 1.178 x 108 5.88IxIO 7

26.58 10.37 5.110 2.052 1.033 0.5233 0.2182 0.1171 0.06681 0.03559 0.02353 0.01604 0.009908 0.006949 0.004894 0.003087 0.002181

APPENDK G Geometric Formulas Circle circumference area Ellipse circumference (approx.) area where a and b are the major and minor semi-axes, respectively Sphere surface area volume Ellipsoids: prolate surface area

volume Ellipsoids: oblate surface area volume Right cylinder surface area where L is the length volume

APPENDIX H Bulk Densities of Some Common Aerosol Materials*

Material

Solids Aluminum Corundum (Al2O3) Ammonium sulfate Asbestos Calcite (CaCO3) Coal Coal fly ash Glass Granite Iron Iron oxide Limestone Lead Lead oxide Methylene blue Mineral wool Plant particles Paraffin Pollen Polystyrene Polyvinyl toluene Portland cement Potassium biphthalate Rock wool Quartz Sodium chloride Sulfur Starch Talc Titanium dioxide Uranine dye Wood Zinc oxide

Density (xlOOO) (kg/m3) 2.70 4.0 1.77 2.4-3.3 2.7-2.9 1.2-1.8 ca. 2.0 2.4-2.8 2.4-2.7 7.86 5.2-5.7 2.1-2.9 11.3 8.0-9.5 1.26 ca. 2.7 1.1-1.5 0.9 ca. 1.4 1.05 1.03 3.2 1.64 ca. 2.5 2.64-2.66 2.17 2.07 1.5 2.6-2.8 4.26 1.53 ca. 1.5 5.61

Continued Material

Liquids Isopropyl alcohol Dibutyl phthalate Dioctyl phthalate (DOP) Dioctyl sebecate Hydrochloric acid Mercury Oils Oleic acid Polyethylene glycol Sulfuric acid

Density (xlOOO) (kg/m3) 0.7855 1.043 0.981 0.915 1.19 13.6 0.88-0.94 0.894 1.13 1.84

* Additional references for particle densities and other properties: McCrone, W. C. and J. G. Delly. 1973. The Particle Atlas, Vol. IV. Ann Arbor: Ann Arbor Science Publishers Inc.; Weast, R. C. 1994. Handbook of Chemistry and Physics. Cleveland, OH: CRC Press.

APPENDIX I Manufacturers and Suppliers Note that addresses and internet sites may not be up to date. Companies have been included that are not mentioned elsewhere in the book, but may have useful aerosol-related products. AGI Ace Glass, Inc. P.O. Box 688 1430 Northwest Blvd. Vineland, NJ 08360-0688 Tel: 609-692-3333 or 800-223-4524 Fax: 800-543-6752 www.aceglass.com Chapters 10,24

ADT Adept Scientific, Inc. 257 Great Rd. Acton, MA 01720 Tel: 978-635-5360 Fax: 978-635-5330 www.adeptscience.com Chapter 2

ADE Air Diagnostics and Engineering, Inc. 110 Alpine Village Rd. Harrison, ME 04040 Tel: 207-583-4834 www.airdiagnostics.com Chapters 10,29

AIR Air Techniques, Inc. 11403 Cronridge Dr. Owings Mills, MD 21117 Tel: 410-363-9696 Fax: 410-363-9695 www.atitest.com

AER Aerosol Dynamics, Inc. 2329 Fourth St. Berkeley, CA 94710 Tel: 510-649-9360 Fax: 510-649-9260 Chapter 16 &LL Allergen LLC Allergenco/Blewstone Press P.O. Box 8571 Wainwright Station San Antonio, TX 78208-0571 Tel: 210-822-4116 Fax: 210-829-1883 www.txdirect.net/corp/allergen Chapter 24 ALR Alnor Instrument Company (part of TSI) 7555 N Linder Ave. Skokie, IL 60077-3223 Tel: 847-677-3500 Fax: 847-677-3539 www.alnor.com Chapter 29 AND (Andersen Instruments) Thermo Andersen 500 Technology Court Smyrna, GA 30082-5211 Tel: 770-319-9999 or 800-241-6898 Fax: 770-319-0336 www.anderseninstruments.com Chapters 10,14, 24,25,26, 27

API Apiezon Products M&I Materials, Ltd. RO. Box 136 Manchester M60 IAN, UK Tel: 44-161-875-4444 www.apiezon.com Chapter 10

BAS BASF Aktiengesellschaft Carl-Bosch-Strasse 38 D-67056 Ludwigshafen Germany Tel: 49-621-600 Fax: 49-621-604-25-25 www.basf.de Chapters 24,32

ARE ARELCO 2 Avenue Ernest Renan F-94120 Fontenay-sous-Bois France Tel: 33-1-48-75-82-82 Fax: 33-1-43-94-07-21 www.arelco-arc.com Chapter 25

BAY Bayer AG Werk Leverkusen D-51368 Leverkusen Germany Tel: 49-214-301 www.bayer.de Chapter 32

BEI Baldwin Environmental, Inc. 895 E. Patriot Blvd., Suite 107 Reno, NV 89511 Tel: 775-828-1300 or 888-234-7366 Fax: 775-828-1305 www.bei-reno.com

EEC Beckman Coulter, Inc. 4300 N. Harbor Blvd. PO. Box 3100 Fullerton, CA 92834-3100 Tel: 714-871-4848 Fax: 714-773-8283 www.beckmancoulter.com Chapter 16

BAN Bangs Laboratories, Inc. 9025 Technology Dr. Fishers, IN 46038-2886 Tel: 317-570-7020 or 800-387-0672 Fax: 317-570-7034 www.bangslabs.com Chapters 16,21

BEL Belfort Instrument Co. 727 S. Wolfe St. Baltimore, MD 21231 Tel: 410-342-2626 Fax: 410-342-7028 www.belfortinstrument.com

BAR Barramundi Corp. P.O. Drawer 4259 Homosassa Springs, FL 34447 Tel: 352-628-0200 Fax: 352-628-0203 www.mattson-garvin.com Chapter 24

BGI BGI Incorporated 58 Guinan St. Waltham, MA 02154 Tel: 781-891-9380 Fax: 781-891-8151 www.bgiusa.com Chapters 2,10,25

BSI Bioscience International 11607 Magruder Lane Rockville, MD 20852-4635 Tel: 301-230-1418 www.biosci-intl.com Chapter 24

BII BIOS International 10 Park Place Butler, NJ 07405 Tel: 973-492-8400 or 800-663-4977 Fax: 973-492-8270 www.biosint.com Chapters 21,24

BIO Biotest Diagnostics Corporation 66 Ford Rd., Suite 131 Denville, NJ 07834 Tel: 973-625-1300 or 800-522-0090 F • 973 625 9454 www.biotest.com Chanter 24 Chapter24

BKR Booker Systems, Ltd. P.O. Box 5894 Towcester, Northants NN12 8ZX U.K. Tel:44-(0)870-241-1557 Fax: 44-(0)870-241-1558 e-mail: [email protected] Chapter 14

BIR Bristol Industrial and Research Associates, Ltd. P.O. Box 2,1 Beach Road West Portishead, Bristol BS20 7JB UK. Tel: 44-(O) 1275 847787 Fax: 44-(O) 1275 847303 www.biral.com Chapters 15,23

BRK Brookhaven Instruments Corp. 750 Blue Point Rd. Holtsville, NY 11742-1896 Tel: 631-758-3200 Fax: 631-758-3255 www.bic.com Chapter 16

BUC A.P. Buck, Inc. 7101 Presidents Dr., Suite 110 Orlando, FL 32809 Tel: 407-851-8602 Fax: 407-851-8910 www.apbuck.com Chapter 21

^U^

^x/r . ^ T A r Manufacturing Co., Ltd. Woodcock Hill Industrial Estate Richmansworth, Hertfordshire WD3 IPJ England Tel:44-(0)923-773134 Fax: 44-(0)923-774790 www.burkard.co.uk Chapter 24

Burkard

CAB

Cabot Corp. 700 E u & H i g h w a y 36

T\iscola, IL 61953-9643 217-253-3370 www.cabot-corp.com/cabot Chapter 32

Tel:

CAN Canberra Industries 800 Research Parkway Meriden, CT 06450 Tel: 203-238-2351 or 800-243-3955 Fax: 203-235-1347 www.canberra.com

CMI California Measurements, Inc. 150 E. Montecito Ave. Sierra Madre, CA 91024 Tel: 626-355-3361 Fax: 626-355-5320 www.californiameasurements.com Chapters 10,14 ^Ag Casella Group, Ltd. Regent House, Wolseley Rd. KeLpston, Bedford MK42 7JY U.K. Tel: 44-(0)1234-841441 Fax: 44-(0)1234-841490 www.casella.co.uk Chapters 23,24,25 CIi Climet Instruments Co. 1320 W. Colton Ave. Redlands, CA 92374 Tel: 909-793-2788 Fax: 909-793-1738 www.climet.com Chapters 7,15 ClL (CILAS) Compagnie Industrielle des Lasers Route de Nozay BP 27 F-91460 Marcoussis France Phone: 33-1-64-54-48-00 Fax:33-1-69-01-37-39 www.cilas.com cha ter16 P COL Columbian Chemicals Company 1800 West Oak Commons Ct. Marietta, GA 30062-2253 Tel: 770-792-9400 Fax: 770-792-9625 www.columbianchemicals.com Chapter 32

COP Copley Scientific, Ltd. Private Road #7, Cowlick Industrial Estate Cowlick, Nottingham NG 2ER U.K. Tel: 44-115-96-16229 Chapter 10 CRL Corel Corp. 1600 Carlin

S Ave" ' °°tan° ™R7 Canada l e L 3 3 1 213 3912 ^ " " www.corel.com Chapter 2

Ottawa

CCO Corning Costar One Alewife Center Cambridge, MA 02140 Tel: 617-868-6200 or 800-492-1110 Fax: 617-868-2076 www.corningcostar.com Chapters 9,27 COR

Corning, Inc. 45 Nagog Park Acton, MA 01720-3413 Tel: 978-635-2200 or 800-492-1110 Fax: 978-635-2476 www.scienceproducts.corning.com Chapter 24 DM D A N Ind

c

Ltd

j Tel: 03-488-1111 Fax:03-488-1118 DAN Dantec Dynamics, Inc. 777 Corporate Dr. Mahway, NJ 07430 Tel: 201-512-0037 Fax: 201-512-0120 www.dantecmt.dk Chapters 16,21

DAT Datatest 6850 Hibbs Lane Levittown, PA 19057 Tel: 215-943-0668 Fax: 215-547-7973 www.datatest-inc.com DEG Degussa-Huls AG Hauptverwaltung Weissfrauenstrasse 9 D-60311 Frankfurt am Main

DUK Duke Scientific Co. 2463 Faber PL Palo Alto, CA 94303 Tel: 650-424-1177 or 800-334-3883 Fax: 650-424-1158 www.dukescientific.com Chapters 16,21

nirp

Tel- 49 69 218 01 Fax: 49-69-218-3218 www.degussa.de Chapter 32

T^TJ i o 1007 Market St. Wilmington, DE 19898 Tel: 302-774-1000 or 800-441-3515 www.dupont.com Chapter 32

DEK Dekati, Ltd. Osuusmyllynkatu 13 FIN-33700 Tampere Finland Tel: 358-3-3578-100 Fax: 358-3-3578-140 www.dekati.fi Chapters 10,14

DWY Dwyer Instruments, Inc. PO. Box 373 Michigan City, MI 46361 Tel: 219-879-8000 Fax: 219-872-9057 www.dwyer-inst.com Chapter 21

f

DEV The DeVilbiss Co. P.O. Box 635 Somerset, PA 15501 Tel: 814-443-4881 www.sunrisemedical.com Chapter 21 DOW

^ ^ ^ . t^ The Dow Chemical Company Midland, MI 48667 www.dow.com Chapter 32 DMT Droplet Measurement Technologies, Inc. P.O. Box 20293 Boulder, CO 80308 Tel: 303-440-5576 Fax: 303-440-1965 www.dropletmeasurement.com Chapter 30

DYN D

ynal Partlcles A S Svellevn.29 ^* B o x 1 6 N-2001 Lillestr0m, Norway Tel: 47-63-89-71-00 Fax:47-63-89-74-72 www.dynalbiotech.com nu n. O1 Chapter 21 EBE Eberline Instrument Corp. P.O. Box 2108 504 Airport Rd. Santa Fe, NM 87504 Tel: 505-471-3232 Fax: 505-473-9221 www.eberlineinst.com Chapter 34

ECO EcoChem Technologies, Inc. 22605 Valerio St. West Hills, CA 91307 Tel: 818-347-4369 Fax: 818-347-5639 www.ecochem-analytics.com Chapter 14 EDC Environmental Devices Corporation 88 Essex St. Haverhill, MA 01832-5675 Tel: 978-521-1514 Fax: 978-521-1628 www.hazdust.com ESM ESM-Andersen Instruments GmbH Frauenauracher Strasse 96 D-91056 Erlangen Germany Tel: 49-9131-909-0 Fax: 49-9131-909-156 http://www.esm-andersen.de unapter 14 FSI

Fisher Scientific, Inc. 585 Alpha Dr. Pittsburgh, PA 15283 Tel: 800-766-7000 Fax:800-926-1166 www.flsherscientific.com Chapter 9

GEL Gelman Sciences (Pall-Gelman) 600 South Wagner Rd. Ann Arbor, MI 48103-9019 Tel: 734-665-0651 or 800-521-1520 Fax: 734-913-6114 www.pall.com/gelman Chapters 9,19,26,27,34 GEN Genitron Instruments GMBH Heerstrasse 149 D-60488 Frankfurt am Main-90 Germany Tel: 49-69-976-514-0 Fax: 49-69-765-327 www.genitron.de GIL

Gilian Instrument Corp. (part of SEN) Chapters 21,29 ^ 1 mr Graseby-GMW (part of AND) Chapters 10,25 GRA

Graseby-Andersen (part of AND) Chanter 27 Chapter 27

FLU Fluid Energy Aljet P.O. Box 428-T Plumsteadville, PA 18949 Tel: 215-766-0300 Fax: 215-766-0555

GRE Greenfield Instruments RO. Box 971 Amherst, MA 01004 Tel: 413-253-4216 Fax: 413-253-1397 www.greenfieldinst.com Chapter 27

FLT FLUENT, Inc. 10 Cavendish Court, Centerra Park Lebanon, NH 03766 Tel: 603-643-2600 Fax: 603-643-3967 www.fluent.de Chapters 2,30

GRT GreenTek, Inc. 295 NW Hillcrest Drive Grants Pass, OR 97526 Tel: 541-955-5386 Fax: 541-479-4285 www.greentekusa.com Chapter 27

GRI Grimm Technologies, Inc. 9110 Charlton Place Douglasville, GA 30135 Tel: 877-474-6872 Fax: 770-577-0955 www.dustmonitor.com Chapters 25,29 HAA Haan and Wittmer GmbH Birkenstrasse 31 D-71292 Friolzheim Germany Tel: 49(0)-7044-4064 Fax: 49(0)-7044-4040 HAM Hampshire Glassware 77-79 Dukes Rd. Portswood, Hampshire, Southampton S014 OST U K Tel: 44-02380-553755 Fax: 44-02380-553020 www.hgl-uk.com Chapter 24 HAU

Hauke GmbH KG P.O. Box 63 A-4810 Gmunden Austria Tel: 43-(0)76-12-63758 Tel: 43-(0)76-12-64133 members.aon.at/meissl/hauke.html n, f i n 1Q Chapters 10,18 HIA Hiac-Royco (part of PAC) 141 Jefferson Dr. Menlo Park, CA 94025 Chapter 15 HIQ Hi-Q Environmental Products Co. 7386 Trade St. San Diego, CA 92121 Tel: 858-549-2820 Fax: 858-549-9657 www.hi-q.net

HOR Horiba 1080A East Duane Ave. Sunnyvale, CA 94086 Tel: 408-730-4772 Fax: 408-730-8975 www.horibastec.com Chapter 16

HOS Hosokawa Micron Powder Systems 10 Chatham Rd. Summit, NJ 07901 Tel: 908-273-6360 Fax: 908-273-7432 www.hosokawamicron.com Chapter17

HUN Helmut Hund GmbH Wilhelm-Will-Str. 7

35580 Wetzlar

Germany Tel: 49-6441-20040 Fax: 49-6441-200444 www.hund.de Chapters 7,15,25

INC

^

M

l

„, c S Street West' ^iT^T™, M5H4B7 Canada Tel: 416-361-7511 Fax: 416-361-7781 www.inco.com Chapter 32 145 Km

e . iCnn Suite 1500

INN

Innova AirTech Instruments A/S Energivej 30 2750 Ballerup Denmark Tel: 45-44-20-01-00 Fax: 45-44-20-01-01 www.innova.dk Chapter 29

INO Inovision Radiation Measurements (includes Victoreen) 6045 Cochran Rd. Cleveland, OH 44139 Tel: 440-248-9300 Fax: 440-349-2307 or 800-850-4608 www.victoreen.com Chapter 34

TV% - ^ l n Interfacial Dynamics Corp. S m t e 12 ° 17300 SW Upper Boones Ferry Rd. Portland, OR 97224 Tel: 503-684-8008 or 800-323-4810 Fax:503-684-9559 .JlA www.idcla ex.com Chapter21

INS Insitec Measurement Syst. (see MAL and

ISH Ishihara Sangyo Kaisha, Ltd. 3-15, Edobori 1-chome Nishi-ku, Osaka 550 Japan Tel: 06-444-1451 Fax: 06-445-7798 Chapter 32 JSR Japan Synthetic Rubber 11-24, Tsukiji 2-chome, Chuo-ku Tokyo 104, Japan Tel: 03-5565-6521 Fax- 03 5565 6645 ^ ! 7* „ 0 0 4 D Cha ter 21 P , VT TTCA T Kanomax USA, Inc. ^ 25Q ^ Suite m New York, NY 10107 Tel: 212-489-3755 Fax: 212-489-4104 www.kanomax-usa.com Chapter 14

PRM) 2110 Omega Rd., Suite D San Ramon, CA 94583 Tel: 510-837-1330 Fax: 510-837-3864 www.insitec.com

rnM

Institute of Occupational Medicine Ltd. 8 Roxburgh Place Edinburgh EH8 9SU b

^ V AA 1 *1 ^l

<111

Tel: 441 31 667 5131 Fax- 44 31 667 0136 ^Chapter 7 25 L ^ INT In-Tox Products P.O. Box 2070 Moriarty, NM 87035 Tel: 505-832-5107 Fax: 505-832-5092 www.intoxproducts.com Chapter 10

KEM Kemira Pigments B.V. P.O. Box 1013 NL-3180 AA Rozenburg The Netherlands Tel: 31-10-295-2540 Fax: 31-295-2536 www.kemira.com ^ KFR

$ \ * n Kerr-McGee R Q

B

°X

25861

^1. ,

^. ^x^^i-ic City, OK 73125 405-270-1313 , www.kerr-mcgee.com Chapter 32 KIM Kimoto Electric Co., Ltd. 3-1 Funahashi-cho, Tennoji-ku, Osaka 543-0024, Japan Tel: 81-6-6-768-3401 Fax: 81-6-6-764-7040 www.kimoto-electric.co.jp Chapter 14

TOklahoma el:

KOE Koenders Instruments Postbus 1189 1300 BD Almere The Netherlands Tel: 31-36-5480101 Fax: 31-36-5480102 www.koendersgroup.com Chapter 25

^HA Kratel SA CH-1222 Geneve-Vesenaz 64 Ch. De St. Maurice Switzerland

AUK

Kurz Instruments, Inc. 2411 Garden Rd. Monterey, CA 93940 Tel: 831-646-5911 or 800-424-7356 Fax:831-646-8901 www.kurz-instruments.com Chapter 34 T A*i x .c D T Lanzom, S. R. L. Via Michelino 93/B 40127 Bologna, Italy Tel: 39-(0)51-504810 Fax: 39-(0)51-6331892 www.lanzoni.it Chapter 24 LAS Laser Holography Mammoth Lakes, CA 93546 Chapter 17 LEA Monitor Labs, Inc. (supports Lear Siegler Corp. equipment) 74 Inverness Dr. East Englewood, CO 80112-5189 Tel: 303-792-3300 or 800-422-1499 Fax: 303-799-4853

LOT LOT—ORIEL GmbH Im Tiefen See 58 6100 Darmstadt, Germany Tel: 49(0)6151-88060 Fax: 49(0)6151-896667 www.lot-oriel.com LUC

Lucent Technologies 600 Mountain Ave. Murray Hill, NJ 07974 www.lucent.com Chapter 32 ^ Ludlum Measurements, Inc. P.O. Box 810 501 Oak Street Sweetwater, TX 79556 T e l . 9i5.235.5494 o r 800-622-0828 Fax:915-235-4672 www.ludlums.com Chapter 34 MMM 3M 3M Center Building St Paul, MN 55144 '_ Tel: 612-737-6501 www.3m.com Chapter 21 MAG Magee Scientific 1829 Francisco Street Berkeley, CA 94703 Tel: 510-845-2801 Fax: 510-845-7137 www.mageesci.com Chapter 11 MAL Malvern Instruments, Inc. 10 Southville Rd. Southborough, MA 01772 Tel: 508-480-0200 Fax: 508-460-9692 www.malvern.co.uk Chapter 16

MAS MathSoft Engineering & Education, Inc. 101 Main Street Cambridge, MA 02142-1521 Tel: 617-577-1017 Fax: 617-577-8829 www.mathsoft.com Chapter 2

MAT Matter Engineering Bremgarterstrasse 62 CH-5610 Wohlen Switzerland Tel: 41-56-618-66-30 Fax: 41-56-618-66-39 www.matter-engineering.com Chapter 14

MET (see PAC) Met One, Inc. 481 California Ave. Grants Pass, OR 97526 Tel: 503-479-1248 or 800-866-7889 Fax: 503-479-3057 www.pacsciinst.com

MGP MGP Instruments, 5000 Highlands Parkway, Suite 150

MFS Advantec MFS, Inc. (Formerly Micro Filtration Systems, Inc.) 6723 Sierra Ct., Suite A Dublin, CA 94568 Tel: 925-479-0625 or 800-334-7132 Fax: 925-479-0630 www.advantecmfs.com Chapter 9 MCM

Micromentics Instrument Corp. Micromeritics Dr. Norcross G A ' 30093-1877 Tel: 770-662-3620 www.micromeritics.com Chapter 32 One

MSI Micron Separations, Inc. (see OSM) 5951 Clearwater Dr. Minnetonka, MN 55343 www.osmolabstore.com Chapter 9 MIC Microsoft, Inc. Redmond, WA 98052 Tel: 425-882-8080 www.microsoft.com Chapter 2 MCR Microtrac, Inc.

I™?™' ? ^ 3 T 2

12501-A 62nd Street North

Tel: 770-432-2744 Fax:770-432-9176 ww.mgpixom Chapter 34

p L ^3773 Tel: 727-507-9770 Fax: 727.507.9774 www.microtrac.com Chapter 16

MEL MetroLaser, Inc. 18010 Skypark Circle, Suite 100 Irvine, CA 92614 Tel: 949-553-0688 Fax: 949-553-0495 www.metrolaserinc.com Chapters 15,16

MIE MIE, Inc. 7 Oak Park Bedford, MA 01730 Tel: 781-275-1919 Fax: 781-275-2121 www.mieinc.com Chapters 15,17,23,25,26,29

L

MLN Millennium Inorganic Chemicals 3901 Fort Armistead Rd. Baltimore, MD 21226 Tel: 410-355-3600 Fax: 410-229-5003 www.millenniumchem.com Chapter 32 MIL Millipore Corp. 80 Ashby Rd. Bedford, MA 01730 Tel: 781-533-6000 Fax: 781-533-3110 www.millipore.com Chapters 9,24,26,34

NAL Nalge Nunc International 75 Panorama Creek Dr. Rochester, NY 14602-0365 Tel: 716-586-8800 www.nalgenunc.com Chapter 9 NAN Nanophase Technologies Corp. 453 Commerce Street Hinsdale, IL 60521-0750 www.nanophase.com Chapter 32 NAT

TLfVA

National Instruments Corp.

Mine Safety Appliances Co.

1

RIDC Industrial Park Pittsburgh, PA 15238-2919 Or P.O. Box 426 Pittsburgh, PA 15230 Tel: 412-967-3000 or 800-672-2222 Fax: 412-967-3451 www.msanet.com Chapters 10,26,29 wcD/!^S"?; o o • .,n* 1313 Fifth Street S.E., Suite 206 RO. Box 549 Minneapolis MN 55414 www.mspcorp.com Chapters 10,25,26,29 MUL Multidata LLC, Operations 4838 Park Glen Rd. St. Louis Park, MN 55416 Tel: 952-285-9890 or 800-264-1338 Fax: 952-285-9902 www.multidata.com Chapter 10 NAC Nanochem, Inc. 2901 Maximillian Albuquerque, NM 87104-1817 Chapter 32

^500

K

J^eF^-

JTS^S* 50 * Tel: 512-794-0100 Fax:512-683-8411 www.ni.com Chapter 2

NBS N e w Brunswick Scientific Co., Inc. P.O. Box 4005 44TalmadgeRd. Edison, NJ 08818-4005 T d . 732.2g7.x200 o r 800-631-5417 Fax:732-287-4222 www.nbsc.com NOV Novelec, North America, Inc. 113 w. Outer Dr. PO. Box 6621 Oak Ridge, TN 37831 i y . 423-482-9287 F a x . 423-483-0305 OSI Omega Specialty Instrument Co. 4 Kidder Rd., Unit 5 Chelmsford, MA 01842 Tel: 978-256-5450 or 800-343-8253 Fax: 978-256-8015 www.omegaspec.com

OPS Opsis AB Box 244 SE-244 02 Furulund, Sweden Tel: 46-46-722500 Fax: 46-46-722501 www.opsis.se Chapter 14 OSM Osmonics 5951 Clearwater Dr. Minnetonka, MN 55343-8995 Tel: 612-933-7979 or 800-848-1750 www.osmonics.com Chapter 9 OXF Oxford Lasers, Inc. 29 King Street Littleton, MA 01460-1528 Tel* 978-742-9000 Fax: 978-742-9100 www.oxfordlasers.com Chapter 21 PAC Pacific Scientific Instruments

PAR PARI GmbH Moosstrasse 9 D-82319 Starnberg Germany Tel: 49(0)8150-2790 Fax: 49(0)8151-279101 www.pari.de Chapter 15 PMS Particle Measuring Systems, Inc. 5475 Airport Blvd. Boulder, CO 80301-2339 Tel: 303-443-7100 or 800-238-1801 Fax: 303-449-6870 www.pmeasuring.com Chapters 7,15,16,29 PMI , Part cle M e t n c s I n c

!

' ' Monarch Park Place, Suite B Longmont, CO 80503 JeI: 303-247.0411 Fax:303-247-1318 Chapter 30 p s s

P a r t i d e sizi

g

S^SoRW526

75 A C r C a m i n

° ' SuitC B

Tel: 541-479-1248 or 800-866-7889 Fax:541-479-3057 www.pacsciinst.com Cha ter15 P

Te? 805 968^149^ ^ ^ Fax:805-968-0361 www.pssnicomp.com Chapter 16

PAS Palas, GmbH Greschbachstrasse 3B D-76229 Karlsruhe, Germany Tel: 49-721-96213-0 Fax: 49-721-96213-33 www.palas.de Chapters 15,16,21

m Particle Technologies, LLC 14554 Lee Rd. Chantilly, VA 20151-1632 Tel: 703-378-6200 or 301-896-0257 F a x : 703-378-7274 Chapters 29, 32

PAL Pall Corp. 25 Harbor Park Dr. East Hills, NY 11548 Tel: 516-484-5400 or 800-645-6532 Fax: 516-484-5228 www.pall.com Chapters 9,11,26,27,34

°

PBI International PBI Via Novara, 89 20153 Milan Italy Tel: 39(0)2-487791 Fax: 39(0)2-40090010 www.wheatonsci.com Chapter 24

s

PIX PIXE International, Inc. 2306 Domingo Dr. Tallahassee, FL 32303 Tel: 850-574-6469 p££ Pollution Control Systems Corp. P.O. Box 15770 Seattle, WA 98115 Tel: 206-523-7220 Fax: 206-523-7221 www.cascadeimpactor.com Chapter 10 POL Polysciences, Inc. 400 Valley Rd. Warrington, PA 18976 Tel: 215-343-6484 or 800-523-2575 Fax: 215-343-0214 www.polysciences.com Chapter 21

PlX Polytec Pl, GmbH Polytec Platz 5-7 P.O. Box 1140 W-7517 Waldbronn, Germany Tel:49(0)-7243-60-0 Fax: 49(0)-72436994 www.polytecpi.com Chapters 15,16

POR Poretics, see OSM

PTI Powder Technology, Inc. P.O. Box 1464 Burnsville, MN 55337 Tel: 952-894-8737 or 800-718-8737 Fax: 952-894-0734 www.powdertechnologyinc.com Chapter 29

PPM ppm Inc. 9737 Cogdill Rd. Suite 215 Knoxville TN 37932 Tel: 865-966-8796 Fax: 865-966-2750 www.ppmcorp.com

PRM Process Metrix (Formerly Insitec; see also MAL) 2110 Omega Rd., Suite D San Ramon, CA 94583-1295 Tel: 925-837-1330 Fax: 925-837-3864 www.processmetrix.com Chapter 16

PYL Pylon

Electronics, Inc. 147 Colonnade Rd. Nepean, Ontario Canada K2E 7L9 Tel: 613-226-7920 or 800-896-4439 Fax: 613-226-8195 www.pylonelectronics.com

OCM QCM Research 2825 Laguna Canyon R d L a g u n a Beach> C A 92651

Or P.O. Box 277 Laguna Beach, CA 92652 Tel: 949-497-5748 Fax: 949-497-9828 www.qcmresearch.com Chapters 10,14

RAD Rad Elec, Inc. 5714-C Industry Lane Frederick, MD 21704 Tel: 301-694-0011 or 800-526-5482 Fax: 301-694-0013 www.radelec.com

RIO RION Co., Ltd. Ikeda Bldg. 7-7 Yoyogi 2-chome 151 Tokyo Japan Tel: 33-79-23-52 Fax: 33-70-48-28 www.rion.co.jp

SAR Sartorius North America, Inc. 131 Heartland Blvd. Edgewood, NY 11717 Tel: 631-254-4249 or 800-635-2906 Fax: 631-254-4253 www.sartoriuscorp.com Chapter 9

jlTj Research Triangle Institute P.O. Box 12194 Research Triangle Park, NC 27709 Tel: 919-541-6000 www.rti.org Chapter 29

SAS SAS Institute, Inc. SAS Campus Dr. Cary, NC 27513-2414 Tel: 919-677-8000 F *x: 919-677-4444 www.sas.com Chapter 22

ROS ~ A RosemountA Aerospace

Schleicher and Schuell, Inc. .~ _ . .A

/u

J * i-

J - u A•

S&S

r* o

\

(changed to Goodrich Aircraft Sensors) ,, nA 6 T . . . 1 D . ' 14300 Judicial Rd. Burnsville, MN 55306-4898 Tel: 952-892-4000 Fax:952-892-4800 ,www.bfg-sensors.com Chapter 30

10 Optical Ave.

,, r T T T M , . . Keene, XNH 03431 ' el: J ^ ^ ° 7 Fax:603-357-3627 wwws-and-s.com Chapter 9 r

^*"" Rupprecht & Patashnick Co. 25 Corporate Circle Albany, NY 12203 Tel: 518-452-0065 Fax: 518-452-0067 www.rpco.com Chapters 7,11,14,26,27

5C/ Scientific Computing & Instrumentation Magazine 3 0 1 Gibraltar Dr. B 0 x 559 Morris Plains, NJ 07950-0650 T e l : 973-292-5100 F a x : 973-539-3476 www.scimag.com Chapter 2

SAM (Sampling Technologies, Inc.) Multidata LLC 4838 Park Glen Rd. St. Louis Park, MN 55416 Tel: 952-285-9890 or 800-264-1338 Fax: 952-285-9902 www.multidata.com Chapter 24

STS (SciTech Science) Cole-Parmer 625 E. Bunker Ct. Vernon Hills IL 60061-1844 Tel: 847-549-7600 or 800-323-4340 Fax: 847-247-2929 www.coleparmer.com Chapter 2

SEN Sensidyne, Inc. 16333 Bay Vista Dr. Clearwater, FL 33760 Tel: 727-530-3602 or 800-451-9444 Fax: 727-539-0550 www.sensidyne.com Chapter 10

SRD Seradyn Particle Technology 7998 Georgetown Rd., Suite 1000 Indianapolis, IN 46268 Tel: 317-610-3800 or 800-428-4072 Fax: 317-610-3888 www.seradyn.com Chapter 16

SHI Shimadzu Scientific Instruments, Inc. 7102 Riverwood Dr. Columbia, MD 21046 Tel: 410-381-1227 or 800-477-1227 Fax: 410-381-1222 www.ssi.shimadzu.com Chapter 16

SIB Sibata Scientific Technology, Ltd. 1-25, Ikenohata 3-Chome, Taito-Ku, Tokyo 110-8701 Japan Tel: 81-3-3822-2112 Fax: 81-3-5685-1394 www.sibata.co.jp

SIC

(Siecor, Inc.) Corning Cable Systems 800 17th St. NW Hickory, NC 28601 Tel: 828-327-5000 or 800-743-2671 Fax: 828-327-5973 www.corningcablesystems.com Chapter 32

SIE Siemens AG Energie- und Automatisierungstechnik Balanstrasse 73 8000 Muunchen 80 Germany Tel: 49(0)89-4144-0 Fax: 49(0)89-4144-8002 www.siemens.com SKC SKC, Inc. 863 Valley View Rd. Eighty Four, PA 15330-9614 Tel: 412-941-9701 or 800-752-8472 Fax: 412-941-1369 www.skcinc.com Chapters 10,24,25,26,29 SOL Zellweger Analytics, Inc. Neotronics/Solomat Division, 4331 Thurmond Tanner Rd. PO. Box 2100, Flowery Branch, GA 30542 Tel: 770-967-2196 or 800-535-0606 www.zelana.com Chapter 29 SON Sono-Tek Corp. 2012 Route 9W, Building 3 Milton, NY 12547 Tel: 845-795-2020 Fax: 845-795-2720 www.sono-tek.com Chapter 21 SPI Spiral Biotech, Inc. 7830 Old Georgetown Rd. Bethesda, MD 20814 Tel: 877-657-2030 www.spiralbiotech.com Chapter 10,24 STA Staplex Co., Air Sampler Div. 777 Fifth Ave. Brooklyn NY 11232-1695 Tel: 718-768-3333 or 800-221-0822 Fax: 718-965-0750 www.staplex.com

SPE Stratton Park Engineering Company (SPEC), Inc. 5401 Western Boulder, CO 80301 Tel: 303-449-1105 Fax: 303-449-0132 www.specinc.com Chapter 30

STR

Strohlein GmbH & Co. Girmeskreuzstrasse 55 P.O. Box 14 63 D-41564 Kaarst 1 Germany Tel: 49-21-31-606-0 Fax: 49-21-31-606-167 Chapter 25

SUM Sumitomo Electric USA, Inc. Park Avenue Tower, 65 East 55th St., 16th Floor

New York, NY 10022 Tel: 212-308-6444 Fax: 212-308-6575 www.sumitomo.com Chapter 32

TEC Technical Associates 7051 Eton Ave. Canoga Park, CA 91303-2197 Tel: 818-883-7043 Fax: 818-883-6103 www.tech-associates.com TOP

Topas GmbH Wilischstrasse 1 D-01279 Dresden Tel: 49-351-2-54-10-08 F a x . 49.351.2.54.IO-I3 www.topas-gmbh.de F

5

TSI TSI Inc. 500 Cardigan Rd. P.O. Box 64394 St. Paul, MN 55164 Tel: 651-483-0900 Fax: 651-490-2748 www.tsi.com C h a t e r s 2 6 7 8 13 14 15 16 1 7 1 8 19 P >>>> > > > > > ' > 21 24 2 5 2 9 3 0 > > > > UNI

(United Sdences Inc}

Monitor Labs 5310 N pioneer R d

Gibsonia, PA 15044 T e l : 724-443-8610

Fax: 724-443-4025 www.monitorlabs.com SUN Sun Nuclear Corp. 425-A Pineda Court Melbourne, FL 32940-7508 Tel- 321-259-6862 Fax:321-259-7979 www.sunnuclear.com Chapter 34

SYM Sympatec Inc., System-Partikel-Technik 3490 U.S. Route 1 Princeton, NJ 08540-5706 Tel: 609-734-0404 Fax: 609-734-0777 www.sympatec.com Chapter 16

URG U R G Cor

P'

1 1 6 S o u t h M e r r i t M i U Rd

* Chapel Hill, NC 27516 Tel: 919-942-2753 Fax: ^19-942-3522 www.urgcorp.com Chapters 10,27,29 VER Verewa Umwelt- und ProzeBmeBtechnik GmbH Kollaustrasse 105 D-22453 Hamburg, Germany Tel: 49-40-55-42-18-0 Fax: 49-40-58-41-54 www.durag.de Chapter 14

VIR The VirTis Co. 815 Route 208 Gardiner, NY 12525-9989 Tel: 914-255-5000 or 800-765-6198 Fax: 914-255-5338 www.virtis.com

WOL Wolfram Research, Inc. 100 Trade Center Dr. Champaign, IL 61820-7237 Tel: 217-398-0700 or 800-965-3726 Fax: 217-398-0747 www.wolfram.com Chapter 2

WED Wedding & Associates, Inc. (Part of AND) www.anderseninstruments.com Chapter 10

WYA Wyatt Technologies 30 South La Patera Lane, B-7 Santa Barbara, CA 93227-3253 Tel: 805-681-9009 Fax: 805-681-0123 www.wyatt.com Chapter 16

WHA Whatman International Ltd. Whatman House St. Leonard's Rd. 20/20 Maidstone Kent ME16 OLS, U.K. Tel: 44(0)-1622-676670 or (USA) 800-441-6555 Fax: 44(0)4622-677011 www.whatman.com Chapters 9,27,34

ZAA Zefon Analytical Instruments 2860 23rd Avenue North St. Petersburg, FL 33713-4211 Tel: 727-327-5449 or 800-282-0073 Fax: 727-323-6965 www.zefon.com Chapter 24

Index

Note that many terms are listed primarily in terms of their acronyms. This has been done to include definitions and references for acronyms in the index rather than in a separate list. See also Appendix A for the glossary of terms. *Indicates commercial designations of products

Index terms

Links

A AAS, See atomic absorption spectroscopy Absorption of light

80 921

of particles

274

424

462

830

917

431

448

540

125 806

137 809

240

246

782

806

1046

of vapor accumulation mode

85

96 1065

102 552

106 113 804 1065

See also size distribution accuracy

118 1065

See also analytical techniques; bias; error analysis ACGIH (American Conference of Governmental Industrial Hygienists)

106 629

114 782

inhalable sampling criterion

114

782

respirable sampling criterion

125

136

240

246

thoracic sampling criterion

782 262

269

347

See also TLV®

acid aerosol

109

See also nitric acid; pH ACM (asbestos containing material)

738

acoustic coagulation

80

fluidized bed generator

736

oscillation field, particle motion in

496

95 504

pressure This page has been reformatted by Knovel to provide easier navigation.

1111

1112

Index terms

Links

acoustic (Continued) velocity

64

actinomycetes

753 1065

active surface (or Fuchs surface)

404

activity

411

113 406 591 671 676 1012 1017 1021 1026 1065

981

See also radioactivity activity median diameter. See equivalent diameter ADAM* (Atmospheric Dust Automatic Monitor) adhesion force

395 57

adsorption

58 85 404 781 998 1065

96 788

112 830

225 837

282 944

ADT (average daily traffic)

849

AEM [analytical electron microscopy (or microscope)]

310

339

353

73 80 484 499 782 1065

99 508

129 628

254 706

69 499

75

78

81

128

495

520

7

31

45

externally mixed

106

284

365

focusing

366

368

314

See also EDS; EELS; EFTEM; TEM; SEM AER (air exchange rate)

876

aerodynamic diameter

50 401 729

measurement. See calibration; centrifugal classification; equivalent diameter; impactor; inertial classification; gravitational settling aerodynamic drag force on particle aerodynamic lens

58 99

66 183

236

368

Aerodynamic Particle Sizer*, See APS calibration

657

Aerosizer*

121

calibration

657

Aerosol Calculator, see spreadsheet program aerosol dilution. See dilution; sampling, diluter

This page has been reformatted by Knovel to provide easier navigation.

61 1065

1113

Index terms

Links

aerosol (Continued) health effects

780

high concentration

903

high temperature

903

internally mixed

106

personal cloud

878

turbidity

428

860 1034 1040 1046

284

365

280 433 1071 1073

644

See also aerosol generation; particle; size distribution aerosol generation air blast nebulizer or atomizer

938 1032 1036

Berglund-Liu. See vibrating orifice bioaerosols

643

calibration

632

condensation

641

751 936

See also Sinclair-LaMer generator droplet

938

droplet-to-particle conversion

936

dry dispersion

645

845 1035

electrospray

548

642

electrostatic

938

flame reactor

934

937

fluidized bed

531

645

furnace reactor

934

937

freeze drying

936

from carpets

756

gas-to-powder conversion

932

high temperature furnace or reactor

632

impactor classification

644

laser reactor

935

moisture

846

multijet vibrating orifice

472

nebulizer

433

of fibers

643

of fugitive dust

845

of non-spherical particles

643

861

736 1070

873

934 937

644 1035

This page has been reformatted by Knovel to provide easier navigation.

876

1114

Index terms

Links

aerosol generation (Continued) plasma reactor Sinclair-LaMer generator

935 1041

sonic fluidized bed

649

spinning disk

637 1076

spray pyrolysis

939

tagged aerosol

650

test aerosol

635

test chamber

632

therapeutic aerosols

1033

ultrasonic nebulizer

638

644

938 1037 1077

vibrating capillary or orifice

433

473

514

water effects

846

wind tunnel

633

Wright dust feed

645

637

938

184 917

393 933

647

See also test aerosols; instruments, tables of Aerosol measurement

54

ideal instrument

24

365

AES (Auger electron spectroscopy)

950

aethalometer

265

274 1065

AFM (atomic force microscopy)

297

349

AFRICA [(U. K. International) Asbestos Fibre Regular Interchange Counting Arrangement]

302

agglomerate or aggregates

48 51 565 705 944 1066

AHERA [(U S.) Asbestos Hazard Emergency Relief Act]

330

AIDS (acquired immunodeficiency syndrome)

55 915

739

1048

AIHA (American Industrial Hygiene Association)

302

aircraft-based aerosol measurement

887

629

air flow rate. See flow rate measurement air velocity. See velocity measurement, air Aitken nuclei

803 1066

See condensation nuclei

This page has been reformatted by Knovel to provide easier navigation.

449 939

1115

Index terms

Links

ALARA (as low as reasonably achievable) radiation exposure

988

algae

752

ALI (annual limit on intake)

982

allergens

752

alpha radiation

756 754

769

845

222 980 1014 1018

983

986

994

998

223 275 394 828

225 282 398 831

261 351 583 833

279

282

788

See also radiation, high energy alveolar region

125 1066

alveoli

68

AMAD (activity median aerodynamic diameter), See equivalent diameter AMD (activity median diameter) American Association for Aerosol Research ammonium ion and ammonia

113 1017 1019 32 106 263 373 585 871

108 266 375 614

185 269 379 822

261

939

995

222 829

272 999

277

See ionic species ammonium fluorescein amosite

640 572

See also asbestos analytical techniques for particles sample blanks

See also AAS; AES; BET; DSC; DTA; EDXA EELS; EPMA; FT-IR; GC; HDC; HPLC; IC; ICP; INAA; IR; LC; LMMS; MS; NRA; PCM; PIXE; PLM; Raman Spectroscopy; SEM; STM; TEM; TGA; XRD; XRF ANC (Aitken nuclei counter). See CNC anemometer

655

See also LDV; PIV ANOVA (analysis of variance)

685

See also error analysis

This page has been reformatted by Knovel to provide easier navigation.

1116

Index terms

Links

anion. See ionic species anisoaxial sampling

151

159

165 1066

ANSI (American National Standards Institute)

148

629

980 1001

anthophyllite

739 120 526 994

124 628

991 1066

See also sampling

See also asbestos APS* (aerodynamic particle sizer)

99 508 890

calibration

657

phantom particles

129

area (or fixed site) sampling

785

810

ASAS* (active scattering aerosol spectrometer)

129

427

asbestos

529

649

806

874

AHERA

330

739

conductivity

527

dielectrophoresis

566

direct reading instrument

527

electrical alignment

527

electron diffraction

311

electrostatic effects in sampling

220

generation

735

health effects

22

image analysis

740

in buildings

738

725

726

737

735

741

128 644

732

434 761

495 870

735

795

See also AHERA light scattering

22

See fiber, light scattering magnetic alignment

732

PCM counting

301

739

polarized light microscope analysis

301

740

properties

725

regulations

737

sampler cowl

170

192

222

This page has been reformatted by Knovel to provide easier navigation.

739 1068

1117

Index terms

Links

asbestos (Continued) sampling

22

shear flow alignment

170

192

222

739

729

SEM analysis

22

305

307

330

TEM analysis

22

304

330

738

679

726

737

279

950

113

terminology

738

See also fiber; PCM; PLM; SEM; TEM; fibrous aerosol monitor asbestosis

22

aspiration efficiency

1066

See inlet; Chapter 8 atmospheric aerosols. See Chapters 6, 17, 30; PM-2.5; PM-10 atomic absorption spectroscopy

201

See also GFAAS atomizer

1066

See aerosol generator, nebulizer Auger electron emissiont (spectroscopy)

314

950

autoradiography

985

996

6 379

12 519

14 49 752 1066

B bacteria

222

BAM (beta attenuation monitor), See beta attenuation monitor BC (black or elemental carbon). See carbon black; EC; TC BCR (Community Bureau of Reference)

483

Beer’s law. See Lambert-Beer law Bernoulli equation

513

BET (Brunauer-Emmett-Teller) method

404

944

998 1066

beta (β) attenuation monitor (BAM)

121

264

282

beta radiation (particle)

389

658 1066

222 389 541 631 640 658 980 995 998 1000 1014 1018 1021 1024 1066

This page has been reformatted by Knovel to provide easier navigation.

1118

Index terms bias

Links 124

map

134

301

388

393

397

402 407 785 1066

411

413

515

525

867 1066

137

bimodal size distribution

109

114

135

14

16

18

751

779

220 565

541 544 632 1066

550

163

198

893

125

137

240

67

92

145

See also size distribution, multimodal bioaerosol bipolar ions persistence

49

519

643

873 1048 1066 560

563

254

782

807

209

528

540

1002

See also charge neutralization blunt sampler

149

See also sampling BMRC (British Medical Research Council) respirable dust definition Boltzmann constant

125

595 1081 Boltzmann charge distribution

507

542

545 1066

BOM [(U. S.) Bureau of Mines]

395

527

809

814

58

240

247

627

631

640

761 1073

771

788

853

912

999

63

156

172

176

180

209

408 1066

585

852

887

891

896

989

994 1055

bounce, particle

See also impactor; saltation boundary layer

Bq (becquerel)

982

Bragg cell breathing zone sample

991 1012

49

473

502

789

859

865

1057 1059 1067

See also PEM; personal sampling BRI (building related illness)

873

This page has been reformatted by Knovel to provide easier navigation.

1119

Index terms Brownian motion or diffusion

Links 50

52

67

69

503

529

540

544

564

916 1067

bubble meter

629

651

653

789 1058 1067

button sampler

785

See diffusion Brunauer-Emmett-Teller surface area measurement.

See BET

C calibration. See Chapter 22 and other chapters on instrumental techniques aerodynamic size

484

by primary standards

483

test aerosol

635

test chamber

632

657

CAM (continuous air monitor)

998

CAMM (Continuous Ambient Mass Monitor)

282

Capillary, for particle focusing

368

381

18 277 824

24 278 829

203 302 985

215 307

220 407

222 770

45 273 348 794 838

106 277 376 809 879

108 279 380 812 903

201 284 413 829

225 340 631 833

262 344 782 836

black

45

428

631

929

932

fibers

732

735

586

812

capillary pore (also straight-through pore, polycarbonate, or Nuclepore*) filter

carbon

See also BC; CC; EC; nanotubes; OC; PAH; SVOC; TC

cascade impactor

1067

See impactor, cascade; inertial classification cation. See ionic species CBED (convergent beam electron diffraction)

311

CC (carbonate carbon)

270

272

This page has been reformatted by Knovel to provide easier navigation.

1120

Index terms

Links

CDMA (cylindrical differential mobility analyzer),

See electrical mobility classifier CEN [Comité Européen de Normes (European Standards Committee)]

629

782

centrifugal classification

229

761

770

780

795

941

18

74

76

229

255

644

657

941

cascade cyclone centrifuge conifuge

220 994 1067

18

cyclone

74

99

120

125

129 135 138 283 388 435 520 588 644 795 806 810 994 1042 1068

51

59

229 449 657 813

234 513 730 824

245 515 785 852

33 936

46 939

726 944

903

929

933

738

912

34

894

cgs units

46

222

752

757

767

773

chains of particles

76

726

728

143

170

185

631

640

648

See also inertial classification; instruments, tables of ceramics ceramic fibers CFD (computational fluid dynamics) cfu. See colony forming units

chamber animal exposure deposition in for testing samplers

1054 18

73

76

191

229

254

872 1026

charge, electrostatic acquisition mechanisms

541

See also particle charging equilibrium

541

measurement of a particle limit on droplet neutralization (reduction to Boltzmann equilibrium)

20

615

946

217

541

549

736

789 1017 1073

616

This page has been reformatted by Knovel to provide easier navigation.

1121

Index terms

Links

charge, electrostatic (Continued)

See also electric field; electrical mobility; electrostatic; particle charging charged particle drift velocity in field

20 182

52 186

57 199

59 496

77 537

124 551

generation

400

537

736

motion in oscillating field

496

chemical reaction with particle

84 200 620

96 225 645

102 261 908

106 404 930

119 412 932

188 603 934

262 281

268 284

271

273

275

279

674

682

684

686

695

24

201

276

982

995

See also nitrate and nitric acid Chemical Speciation Network chemisorption

97

chi-square test for comparing distributions chromatography

See GC; ion chromatograph CHS (collimated hole structure) Ci (curie)

589 632 1018

998 1012 1014

cigarette smoke. See smoke, cigarette CIRPAS (Center for Interdisciplinary Remotely Piloted Aircraft Studies)

890

classification. See centrifugal classification; inertial classification cleanroom

959

measurement

974

standards (e.g., Federal Standard 209D)

672

960

closed face sampler

127

199

785 1067

closed box method for particle emissions

975

cloud

4 474

6 479

49 673

5

983

chamber aerosol measurement

887

This page has been reformatted by Knovel to provide easier navigation.

107 352 887 1067

455

1122

Index terms

Links

cloud (Continued) personal

878

CMD (count median diameter) CNC or CPC (condensation nuclei (nucleus or particle) counter)

93

101

113

127

676

19 569 793

24 590 890

120 593

124 596

445 644

560 657

91 803 932

147 187 880 903 938 1067

455 905

444

calibration

657

conductive-cooling type

577

expansion type

574

mixing type

577

P-Trak*

793

870

coagulation

53 637 908

83 640 930

acoustic

80

95

coefficient

91

gradient

95

See also condensation; instruments, tables of

high concentration

147

in mine aerosol

804

kinematic

91

monodisperse (Smoluchowski)

91

polydisperse

93

thermal

91

turbulent

95

932 95

coal dust

114

126

134

424

426

644

803

806

809

814

102 402 833

106 448 837

109 525 853

114 245 673 804 867 1067

in mines

801

rock dust

813

workers’ pneumoconiosis

801

coarse particle mode

7 277 824

This page has been reformatted by Knovel to provide easier navigation.

1123

Index terms coincidence error, particle counters

Links 126

128

438

441

456

459

466 531 974

483 509 657 674 991 1067

512 689

516 943

525 965

106 340 779 866 913

261 398 781 873 917

83 282 653

96 374 779

APS*

516

Aerosizer*

525

See also error analysis; phantom particles collection efficiency. See losses in tubes and centrifugal classification; electrostatic precipitator; filters; gravitational settling; impactor; inertial classification; sampling lines collision diameter, molecular

67

colony forming units

752

colorimetry

270

combustion

33 272 403 802 875 923

761 1067 49 274 412 813 877 933

52 299 455 831 903 939

104 302 468 861 910

870

commercial photometers MicroDust Pro*

793

MINIRAM*

448

793

814

RAM*

448

793

814

Respicon*

792

TM Digital µP*

137

447

793

comminution

738

802

995 1067

concentration distribution

673

Commercial instrument tables. See instruments, tables of; Appendix I

See also size distribution condensation

46 49 102 104 564 569 873 1067

53 113 611

See also cloud chamber This page has been reformatted by Knovel to provide easier navigation.

81 200 618

1124

Index terms

Links

condensation (Continued) concentration increase at high humidity

755

in dilution or sampling system

145

147

908

910

in indoor air

867

in mine aerosol

803

generation of aerosol

217

632

184

455

903

905

635

641

929

932

936 1041 mode in atmosphere

104

119

nuclei (Aitken nuclei)

4

19

88

569

nucleus (particle) counter

4

19

84

123

407

445

569 627 974 1067

657

793

870

560 890

See also CNC on a droplet

86

confidence limits

1067

See error analysis conifuge

18

See also centrifugal classification continuum regime

61

64

70

183

482

538

547 1067

565

618

620

725

731

548

551

566 1067

832

836 1068

control charts

682

corona

400

409

679

685

48

79

See also particle charging correlation coefficient Coulomb’s law Coulter counter*

943 1067

count median diameter. See CMD; diameter CPC (condensation particle counter). See CNC CPSC [(U. S.) Consumer Product Safety Commission]

738

critical orifice

400

526

651

crocidolite

733

736

739

See also asbestos

This page has been reformatted by Knovel to provide easier navigation.

1125

Index terms

Links

cumulative size (or frequency) distribution. See size distribution, cumulative Cunningham slip correction factor

51 507

66 595

70 371 482 610 1068 1076

cutoff particle diameter (d50, also cut point)

102 822

484 825

627 912

759 965

595

690

See also centrifugal classification; gravitational settling; inertial classification CV (coefficient of variation, or relative standard deviation)

871

CVI (counterflow virtual impactor)

896

cyclone. See centrifugal classification

D d50 See cutoff particle diameter DAC (derived air concentration)

982

991

inversion

253

561

regression

677

source apportionment (chemical mass balance) model

813

stripping

595

data analysis

880 1075

See also error analysis; size distribution DB (diffusion battery)

890

See also diffusion battery, GDB, PFDB DBS (di-butyl sebacate)

618

DBS (diffusion broadening spectroscopy)

481

DCF (dose conversion factor)

1019

Deagglomeration

16

149

648

Dean number

64

584 1068

deconvolution. See data inversion DEHS (di-2-ethylhexyl sebacate)

442

density air

62

855

bulk materials

76

706

716

722

correlation method gas

944 1091

62 This page has been reformatted by Knovel to provide easier navigation.

498

761 770 999 1068

1126

Index terms

Links

density (Continued) microbes

752

particle

50

134

705

710

713

722

999 1017

997 water

856

denuder. See diffusion denuder deposition in tubes and sampling lines. See losses in tubes and sampling lines detection limits. See IDL; LLD; LOD diameter activity median

113

676

989

995

aerodynamic

50 725

59 73 729 1042

75

count (or number) median

100

99

706

cutoff (d50) See cutoff particle diameter effective sphere mass median (also MMAD) median

917 76

671

676 99

783

676

optical equivalent

50

52

Sauter mean

52

456

Stokes

99

See also equivalent diameter; Kelvin diameter; size distribution; Stokes diameter dichotomous impactor

277

398

583

565

732

741

272

275

403

809

903

829

833 1068

412

781

See also impactor; inertial classification dielectrophoresis

See also electrical mobility diesel fume analysis

482

sampler

809

differential mobility analyzer

1068

See electrical mobility analyzer; electrical mobility classifier differential scanning calorimetry

952

This page has been reformatted by Knovel to provide easier navigation.

803

1127

Index terms differential thermal analysis diffraction

Links 952 1068

See light, diffraction; electron diffraction; XRD diffusion

1068

battery

121

charging

124

587 1068

400

408

411

441

560

563

565

731 1068

67

81

87

92

144

208

481 569 593 603 916 1017

572 618

579 723

582 731

587 828

denuder

67 822

282 832

569 837

581 596 870 1068

eddy

186 1069 87

90

coefficient

gas

67

262 828 81

641

828 1012 1024

in annular tubes

282

581

583

in coil

584

in collimated hole structure

589

in cylindrical tubes

579

583

588

in filters

208

in parallel disks

580

588

in rectangular channels

580

588

in screens

122

569

595

696 1026

motion due to

579

581

540

731

737

916

59

81

613 1068

176

188

904 1068

dilution ratio (rate)

188

904 1068

organic aerosols

910

See also DLS; losses in tubes and sampling lines; instruments, tables of diffusion (equivalent) diameter. See equivalent diameter diffusiophoresis deposition due to dilution

81

This page has been reformatted by Knovel to provide easier navigation.

550

97

618

588

590

593

67

of fibers

541

1128

Index terms

Links

dilution (Continued) sample conditioning

126 659

147 904

184 188 950 1055

512

632

dipole scattering. See Rayleigh scattering disinfection

772 1031 1049 1068

direct reading instruments, selection of

119

distribution size. See size distribution concentration. See concentration distribution DLS (dynamic light scattering)

481

696

913

916

DMA* (differential mobility analyzer). See electrical mobility classifier DMPS* (Differential mobility particle sizer). See electrical mobility classifier DOE [(U. S.) Department of Energy]

979

DOP [di-octyl phthalate; also bis(2-ethylhexyl)phthalate]

134

137

213

217

397

434

482 830

516

577

611

631

794

806

813

526

610

See also test aerosols Dorr-Oliver* cyclone (also 10-mm nylon cyclone)

See also centrifugal classification dose conversion factor drag coefficient drag force, on particle

1019 37

70

523 1068

58 706

69 723

404 500 730 1068

752

755 1047

droplet carrying microorganisms condensational growth

87

641

657

571

936

515

523

drying time

89

641

evaporation

80

85

89

107

433

611

613

618

620

evaporation coefficient

603

620

explosion

616

deformation under acceleration

Fuchs correction

88

572

This page has been reformatted by Knovel to provide easier navigation.

603

1129

Index terms

Links

droplet (Continued) gas absorption

96

growth from vapor

86

lifetime

89

light scattering

572

603

See Chapters 15, 16 mode in atmosphere

106

reaction with a gas

96

sampling

888

temperature

87

-to-particle conversion

929

DR (dilution rate)

188

DRUM (Davis Rotating drum Universal size cut Monitoring impactor)

247

DSC (differential scanning calorimetry)

952

DTA (differential thermal analysis)

952

dust

49

935

837

779 1068

See aerosol; respirable dust; inhalable dust; thoracic dust Dust Dosimeter*

814

dust generator. See aerosol generation dust mites. See house-dust mites dynamic light scattering. See DLS; light scattering dynamic measurement. See direct reading instrument dynamic shape factor

70

75

515

705

723

730

265 394

272 631

283 810

340 812

347

1069

See also fiber shape factor

E EAA* (electrical aerosol analyzer)

1069

See electrical mobility analyzer EAC* (electrical aerosol classifier). See electrical mobility classifier EBSD (electron back scatter diffraction)

331

EC (elemental carbon)

262 375

This page has been reformatted by Knovel to provide easier navigation.

1130

Index terms

Links

ECAD (effective cut aerodynamic diameter), See equivalent diameter EDB. See electrodynamic balance EDS [energy dispersive spectroscopy (also EDXA or ED-XRFA)]

310

330

353

947

950

77

732

934

984

182

See also SEM; STEM; TEM EDTA (ethylene diamine tetraacetic acid)

273

EDXA [energy dispersive x-ray analysis (also ED-XRFA or EDS)]. See ED-XRFA ED-XRFA or EDXRF (energy dispersive x-ray fluorescence analysis)

263

277

See XRF EEC (equilibrium equivalent concentration)

1013 1018 1022

EELS (electron energy loss spectroscopy)

310

EFTEM (energy filtered TEM)

315

EGARD (environmental gamma and radon detector)

313

1025

electrical charge on particles. See charge, electrostatic; charge neutralization; particle charging electrical forces

20

59

electrical low pressure impactor. See ELPI electrical units

48 1018

electric field

11

32

oscillating, particle motion in

20

496

static, particle motion in

47

52

78

124

144

401

409

500

504

603

78 1069

401

538

540

547

554

analyzer (EAA*)

102

106

classifier (CDMA, DMA*, RDMA*, SMEC, SMPS*)

120 870

124 875

402 943

407

445

547

732

741

See Chapter 20

electrical mobility

calibration

657

tandem

564

time of flight

565

dielectrophoresis

565

This page has been reformatted by Knovel to provide easier navigation.

1131

Index terms

Links

electrical mobility (equivalent) diameter. See equivalent diameter electrodynamic balance (EDB)

449

603 1069

electrometer, aerosol

122 563

399 573

409 946

547 550 950 1002

330

739

947

electron charge

560

1081

electron energy loss spectroscopy. See EELS electron microprobe

316

See EBSD; EDS; ED-XRFA; EELS; EPMA; microanalysis; PIA; SEM; STEM; TEM electron microscopy. See SEM; TEM; STEM electron diffraction, in TEM

309

See also EBSD; SAD; SAED electrophoresis

77

electrospray

548

952 1069 642

electrostatic balance

1069

See Chapter 20 deposition in sampling lines

182

827

effects at low humidity

566

732

image force

404

411

precipitator

13 929

77 548 551 993 1069 1074

point-to-plane precipitator

551

993 1074

pulsed precipitator

551

space charge

401

736 632

890

283 887

813 949

See also charge; electrical field; electrical forces; particle charging; losses in tubes and sampling lines elemental analysis

222 830

262 833

265 836

276 870

ELPI* (electrical low pressure impactor)

121

387

399

409

ELS (elastic light scattering)

913

elemental carbon. See EC

See also light scattering

This page has been reformatted by Knovel to provide easier navigation.

1132

Index terms elutriator

Links 1069

See gravitational settling EM, electron microscopy (or microscope) scanning. See SEM transmission. See TEM also, expectation-maximization emission spectroscopy

595

696

279

See also ICP endotoxin

751

771

773 1069

419

427

456

47 527 833

106 628 854

118 262 738 821 911 1027

266 827

398 830

262

268

273

275

279

281

energy dispersive spectroscopy. See EDS; EDXA; ED-XRFA energy filtered TEM. See EFTEM ensemble detection techniques, optical

475

See also nephelometer; photometer envelope (equivalent) diameter. See equivalent diameter EPA [(U. S.) Environmental Protection Agency]

Chemical Speciation Network

284 Supersites program EPCS* (ensemble particle concentration and size)

262

281

480

EPI (epiphaniometer). See epiphaniometer epiphaniometer

121

123

404

406 1069

297

305

309

316

457

668 1069

EPM [electron probe microscopy (or microscope)] EPMA (electron probe microanalysis) equation of state

64

equivalent diameter

50

activity

113

aerodynamic

50

73

diffusion

50

99 1068

electrical mobility

50

envelope

51 1069

mass

51

mobility

50 1072

70

99

76 1072

This page has been reformatted by Knovel to provide easier navigation.

328

1133

Index terms

Links

equivalent diameter (Continued) optical

50

99

projected area

50

450

Sauter mean

52

volume

70

117 1073

449

ERDA [(U.S.) Energy Research and Development Authority]

112

erosion

847

error analysis

124

ANOVA (analysis of variance)

685

confidence limits

682

linear regression

677

mean

675

propagation of errors

684

standard error

677

t-test

685

variance

675

682

879

See also data analysis; size distribution errors, aerosol measurement

124

871

See also analytical techniques for particles; data analysis; sampling; size distribution ESP. See electrostatic precipitator ESR (electron spin resonance)

952

E-SPART* [electrical single particle aerodynamic relaxation time (analyzer)]

495

See SPART ETH (Eidgenössische Technische Hochschule)

412

etched track detector

985

ETS (environmental tobacco smoke). See smoke, cigarette evaporation of a droplet

1069

See droplet evaporation; SVOC EVE (extreme value estimation)

691

696

exposure assessment

111

127

737

738

244

See also Chapters 24, 25, 26, 27, 29

This page has been reformatted by Knovel to provide easier navigation.

527

629

726

1134

Index terms

Links

extinction. See light extinction extrathoracic region

784 1044 1069

F FAM*. See fibrous aerosol monitor Faraday cage (cup). See electrometer, aerosol fastest 2 minute wind speed

848 1069

FEG-SEM (field emission gun SEM)

324

FEM (federal equivalent method)

833

fiber charging

731

dielectrophoresis

565

732

22

726

-related disease

see also mesothelioma, asbestosis generation

735

length classification

565

length measurement

531

light scattering from

528

733

orientation in flow field

515

729

orientation in electric field

527

729

orientation in magnetic field

729

732

real-time counting

741

rotational motion

527

730

shape

76

726

shape factor

76

729

size distributions

330

727

translational motion

728

741 732

See also asbestos; PCM; SEM; TEM fibrous aerosol monitor (FAM*)

23

496

527

741

field charging

541

547

551

565

field flow fractionation

942

filter

1070

bypass leakage

794

capillary pore

18

203

199

829

cassettes or filter holders

This page has been reformatted by Knovel to provide easier navigation.

1135

Index terms

Links

filter (Continued) characteristics

829

charge neutralization

268

831

clearing for optical microscopy

300

302

collection mechanisms

208

collection systems

198

cost

206

efficiency

205

electrostatic effects

268

fabric

831

1069

fibrous

201 1070

granular bed

204

for bioaerosols

770

for chemical analysis

829

for gravimetric analysis

220

for radioactive aerosols

994 1000

holders

199

loading

212

membrane filter

17

774

829 202

minimum efficiency (most penetrating size)

213

packing density

207 1073

porous foam

204

pressure drop

215

282

815

872

7

16

202

204

soluble theory

205

types

201

218

788

16

201

206

218

268

202

206

221

788 1001

218

830

994

See also filter materials; HPA filter; instruments, tables of; membrane filters; ULPA filter filter materials cellulose

830

994 cellulose acetate cellulose ester

218 18

cellulose nitrate

218

Fluoropore*

282 1001

glass fiber

201

206

This page has been reformatted by Knovel to provide easier navigation.

1136

Index terms

Links

filter materials (Continued) Nuclepore*. See polycarbonate; filter, capillary pore Nylon*

206

225

polystyrene fiber

201

218

polytetrafluoroethylene (PTFE)

224

269

830

Polycarbonate. See capillary pore filter

See also Teflon* polyvinyl chloride (PVC)

202

206

219

547

quartz fiber

201

206

218

220

222

268

283

830

silver membrane

219

222

813

stainless steel fiber

202 218

268

280

283

218

268

830

Teflon* Teflon*-coated glass fiber fine particle

202

206

830

869

202

206

1070

See also PM-2.5; respirable dust flocculate

49 1070

flowing duct method for particle emissions

976

flow rate measurement

651

832

bubble meter

629

651

bubble tracer

876

dry gas meter

651

venturi meter

651

orifice meter

651

rotameter

198

531

wet test meter

651

655

653 1058

653

651

See also velocity measurement, air; pressure measurement, volume measurement, air fluidized bed generator. See aerosol generation fluorescence, laser induced fog

919 49 1070

Fourier transform. See infrared, microanalysis, and Raman fractal

55

707 1070

Fraunhofer diffraction. See light

This page has been reformatted by Knovel to provide easier navigation.

789 1058

1137

Index terms free molecular flow (regime) FRM (federal reference method) Froude number

Links 61

404

620 1070

262 833

266 838

281

821

827

829

352

949

766

771

1085

FSSP* (forward scattering spectrometer probe)

466

486

889

891

FT-IR (Fourier transform infrared)

269

271

297

348

FT-LMMS (Fourier transform laser microprobe mass spectrometry)

337

See LMMS Fuchs correction

87

Fuchs (active) surface

404

fugitive dust

845

fume

49

fungi

754

spores

779 1070 763

643 751 774 1070

772 1072 756

G Gaussian distribution

668 1070

See size distribution, normal GAW (Global Aerosol Watch) program

408

GC (gas chromatograph)

275

GC-MS (gas chromatograph-mass spectrometer)

265

GCV (generalized cross validation)

695

geometric standard deviation

100

340

671 1070

See also size distribution generation, aerosol. See aerosol generation GFAAS (graphite furnace atomic absorption spectroscopy)

283

granular bed filter

201

graticule

204

15 1070

May

15

Patterson-Cawood

15

Walton-Beckett

734

740

gravimetric measurement. See mass measurement, aerosol; filter for gravimetric analysis This page has been reformatted by Knovel to provide easier navigation.

763

1138

Index terms

Links

gravitational acceleration

58

gravitational deposition parameter

88 1070

gravitational settling

71

in filters

71 1081 229 1070

711

210

in horizontal elutriator

18

53

125

254

257

657

730

807

874 1071

71

729 254

630

in inlets. See inlets in still air in tubes

730

settling chamber settling plate

73

229

110

761

stirred

73

terminal velocity

37

72

in vertical elutriator

18

73

824 1077

See also losses in tubes and sampling lines grit pot

245

H half-life

981 1070

Hatch-Choate equations

676 1070

haze

49

HDC (hydrodynamic chromatography)

942

HEPA (high efficiency participate air) filter

213

876

960

986

See also filter heterogeneous nucleation

89

603 1071

HIAPER (high performance instrumented airborne platform for environmental research) Chapter 30 holography. See optical imaging homogeneous nucleation horizontal elutriator

88 1071 1071

See gravitational settling in horizontal elutriator hot wire anemometer

655 1071

house-dust mites

756 1071

HVAC (heating, ventilating and air conditioning)

753

873

876

hydrocarbon. See PAH This page has been reformatted by Knovel to provide easier navigation.

879

711

644

1139

Index terms

Links

hydrodynamic factor

209

chromatography (HDC)

942

582

hydrogen ion. See pH hydrosol

7

hygroscopicity

45 1071

1071

of aerosols

112

220

571

640 1019 1032

220

268

393

876

880

1045 of filters

200

I IAEA (International Atomic Energy Agency)

979

IAQ (indoor air quality)

872

IC (ion chromatograph)

270

ICDD (International Center for Diffraction Data)

333

ICP (inductively coupled plasma-atomic emission spectroscopy)

279

ICP-MS (inductively coupled plasma-mass spectroscopy)

279

950

ICRP (International Commission on Radiological Protection) ideal fluid

979

982

989

1071

IDL (instrumental detection limit)

270

See also LLD; LOD IEST (Institute of Environmental Sciences and Technology)

962

image analysis

35

50

55

628

740

941

impactor

24

121

229

791 1067 1071

See Chapter 10 after-filter

238

Andersen*

9

110

149

758

766

769

791

827

body impactor

230

236

239

245

calibration

627

632

9

121

760

791

760

See impactor, commercial instruments

cascade

See also DRUM, MOUDI* This page has been reformatted by Knovel to provide easier navigation.

837

762

1140

Index terms

Links

impactor (Continued) collection substrates

238

241

251

711

commercial instruments

231

825

condensation in

913

counter-flow virtual impactor (CVI)

896

dichotomous impactor

277

398

583

829

237

243

246

250

92

240

247

402

912

809

867

512

762

906

833 1068

Davis rotating-drum unit for monitoring. See DRUM efficiency curve electrical low pressure impactor. See ELPI impinger

1071

See impinger instruments

231

interstage losses

240

244

8

780

low pressure

239

837

micro-orifice

239

644

konimeter

See MOUDI* time-resolved

247

measurement strategy

247

overloading

240

particle bounce

58

particle trap

913

personal sampling

792

See also PEM PIDS (personal inhalable dust spectrometer)

792

Respicon*

792

rotary

110

respirable

240

sampling time

251

single stage

237

245

See also konimeter slotted rod

695

Stokes diameter

73

Stokes number

75

99 1076 368

401

1076 1085

This page has been reformatted by Knovel to provide easier navigation.

1141

Index terms

Links

impactor (Continued) substrate

121 761 866

241 765 869

247 788 871

251 791 903

virtual impactor

229

824

913 1077

wall or interstage losses

627

635

650

WINS (well impactor ninty six) impactor

824

827

833

weighing accuracy

268

870

263 837 912

402 853 999

496 780

667 910

912

See also inertial classification; size distribution; data analysis impinger

6 9 14 758 761 768 995 1047 1071

16 771

IMPROVE (interagency monitoring of protected visual environments)

273

278

836

INAA (instrumental neutron activation analysis)

278

index of refraction

14 200 432 479 613 914

35 52 299 301 443 449 481 513 630 657 931 1075

123 419 460 529 723

129 424 467 603 793

134 426 471 610 890

of gases

432

461

indoor air

859

aerosols

112

173

179

190

See also inertial classification imprecision. See precision; error analysis

industrial aerosol

276

113

See Chapters 25, 26 inertial classification or deposition

59

calibration

657

in a bend

39 512

in filters

210

in flow constrictions

180

in inlets and sampling lines

151

229 63

171

This page has been reformatted by Knovel to provide easier navigation.

1142

Index terms

Links

inertial classification or deposition (Continued) spectrometer

242

instruments

231

respirable impactor

240

stratification

190

512

39

152

157

241

892

896

296

348

with turbulence

162

171

189

792

795 1071

See also impactor; inlets; centrifugal classification; cutoff particle diameter; dichotomous impactor; impactor; impinger; INSPEC*; losses in tubes and sampling lines; impactor, virtual; instruments, tables of PERSPEC* infrared microscopy

See also FT-IR inhalable dust

866

sampling criteria

114

IOM (Institute of Occupational Medicine) sampler

785

inlet

266

781

159

165

151

143

ambient air

822

anisoaxial

151

anisokinetic

151

aniso-mean-velocity

151

aspiration efficiency

144

146

blunt

149

163

calibration

146

dead volume

150

diffusing

892

diffusion losses

144

efficiency

125

144 1071

gravitational settling in

154

159

39

144

isoaxial

151

164

isokinetic

151

852

iso-mean-velocity

151

inertial losses in

895

897

152

nonisoaxial. See anisoaxial This page has been reformatted by Knovel to provide easier navigation.

198

1143

Index terms

Links

inlet (Continued) null-type

150

plugging

189

shrouded sampling probe

150

894

stack-sampling

150

911

super-isokinetic

151

supe-iso-mean-velocity

151

thin-walled

150

transmission (transport) efficiency

39

146

turbulence

700

vapor deposition in

184

vena contracta

152

160

199

829

486

658

157

See also sampling; losses in tubes and sampling lines in-line sampler inspirable. See inhalable particulate sampling criteria instrument intercomparison instruments, tables of aircraft-based instruments

895

ambient aerosol samplers

629

beta attenuation monitors

395

bioaerosol samplers

759

CNCs and diffusion batteries

573

differential mobility analyzers

559

filters and filter holders

206

218

impactors and cyclones

231

868

indoor environmental samplers

868

industrial workplace samplers

629

nebulizers

638

optical devices

440

radon monitors

1027

825

834

770

221

786 443

487

See also commercial photometers; Appendix I interception, particle

209

INSPEC* (inertial spectrometer)

242

instrumental neutron activation analysis

278

731 1071

This page has been reformatted by Knovel to provide easier navigation.

198

1144

Index terms

Links

ion detection from radiation

1002

generation

548

mobility

542

552

trap

400

410

412

ion chromatograph

201 587

223

263

ionic (chemical) species

268

282

ionization

373

See ionization; particle charging; sticking coefficient 268

282

IPM (inhalable particulate matter). See inhalable dust IR (infrared) microscopy. See infrared ISE (ion selective electrode)

270

ISO (International Organization for Standardization)

216

629

clean room standards

439

960

sampling criteria

782

isoaxial, isokinetic sampling

16

151

164 1071

See also sampling

J Junge size distribution. See size distribution

K Kelvin diameter

85

effect

85

equation

85

KHP (potassium hydrogen phthalate) kinematic viscosity konimeter

570 1043 1071

273 62

69

8

780

64

538

183

186

498

See also impactor, inertial classification Knudsen number ion

572 1071 1085

543

Kuwabara flow

209 1071

Kolmogorov-Smirnov test

135

686

This page has been reformatted by Knovel to provide easier navigation.

284

1145

Index terms 85

Kr (Krypton-85)

Links 392

631

640

271

274

428

995

998

See also charge neutralization

L Lambert-Beer (also Beer’s, Beer Lambert) law laminar flow

62 1072

LAPS* (Lovelace aerosol particle separator)

255

LAS* (laser aerosol spectrometer)

435

440

445

870

875

ablation

124

334

372

374

378

active cavity sensor

435

Doppler anemometer

655

496

501

508

655

laser Doppler velocimeter. See LDV laser 381

See also LDV LASPEC* (large inertial spectrometer)

242

latex particles. See DVB; PSL; PVT; test aerosols LCL (lower confidence limit)

791

LDV (laser Doppler velocimeter)

469

lead (Pb)

22

277

280

284

319

333

337 583

339 866

343 986

347 404 988 1014

406

753

772

345 830

lens. See aerodynamic lens Legionella levitation, particle

22

See Chapter 20 light absorption

80 348 917

271 375 921

274 428 923

296 462 935

335 482 949

dipole (or Rayleigh) scattering

419

422

430

462

914 1074

419

422

449

463

470

diffraction

483 elastic light scattering. See ELS fluorescence. See fluorescence

This page has been reformatted by Knovel to provide easier navigation.

477

1146

Index terms

Links

light (Continued) Fraunhofer diffraction

423

449

464

extinction

421

428 1069

momentum

80

477

photon correlation spectroscopy. See DLS; PCS Rayleigh scattering. See light, dipole scattering reflection

422

424

refraction

35 470

298 419 484 1074

scattering

6 420

at low angles (forward)

434

by agglomerates or aggregates

449

718

by irregular particles

449

465

by fibers

733

741

by gas molecules

431 34

coefficient

449

457

463

471

610

419 1069 1072

angle

codes

464

420

917

603

315

dynamic. See DLS elastic

461

913

See ELS Frauenhofer

464

geometric optics

463

inelastic

461

Lorenz-Mie theory

420

430

735

914 1072

morphology dependent resonances

613

multiple

430

463

467

464

nephelometer. See nephelometer Photometer. See photometer turbidity

428

stray

420

transmittance

428

435

446

See also aerosol; optical microscopy; particle limit of detection. See LOD; LLD

This page has been reformatted by Knovel to provide easier navigation.

460

1147

Index terms linear regression LLD (lower limit of detection)

Links 677

685

693

1021 1025

See also LOD LM [light microscopy (or microscope)]

14

297

353

LMMS (laser microprobe mass spectrometry)

297

334

353

LOD (limit of detection)

268

62 888

63 892

See also microanalysis and optical microscopy

See also LLD lognormal size distribution. See size distribution London-van der Waals force

57

losses in chambers and bags

185

losses in tubes and sampling lines

39 758

calculation of diffusion

38

827

401 908

199

400

548

198

906

908

gravitational

171

inertial deposition in bends

179

inertial deposition in flow constrictions

180

particle reentrainment

189

plugging of lines

189

thermophoretic

183

turbulent inertial

176

See also inlets; sampling; spreadsheet program 123

See optical particle counter 239

837

510

513

See also ELPI; impactor

LV-SEM (low vacuum-scanning electron microscope)

550

199 906

182

lung deposition of particles

569

170 581

electrostatic

LPP (large particle processor)

558

894 1003

577 184

LPI (low pressure impactor)

639

144

diffusiophoretic

LPC (laser particle counter)

146 442 905 1003

46

517

781 1034

307

This page has been reformatted by Knovel to provide easier navigation.

1148

Index terms

Links

M Mach number

64

523

manometer

655 1072

MAP* [(U.K.) Manchester Asbestos Program]

740

894 1072 1085

mass (equivalent) diameter. See equivalent diameter mass measurement, aerosol

122 281

220 387

filter pressure drop

282

814

from single particle

615

237 430

250 448

263 870

266

See also beta attenuation monitor; filter for gravimetric analysis; size distribution, mass; PM-2.5; PM-10; TEOM* mass median diameter

671

676

mass spectrometry. See MS; LMMS; SIMS, TOFMS MDI (metered dose inhaler) MDR (morphology dependent resonance)

1035 1037 1041 1045 613

See also light scattering mean free path

65

mean of a population

371 1072

675

See also data analysis; error analysis; size distribution MEL (maximum exposure limit)

784

MEM (microenvironmental monitor)

862

865

870

874

879

17

201

214

216

220

238

282 347 393 657 994 1000 1024 1072

770

membrane filter

278 822

See also filter; filter materials mesothelioma

22

726

microanalysis

222

295

electron beam

303

353

ion beam (secondary ion)

340

353

laser microprobe

334

353

optical

221

298

Raman

348

353

scanning probe

350

737

353

This page has been reformatted by Knovel to provide easier navigation.

1149

Index terms

Links

microanalysis (Continued)

See also EELS; EDXA; EPMA; SEM; TEM microenvironment micron

859 46

microscope. See LM; SEM; TEM; IR microscope micro-Raman. See Raman spectroscopy Mie theory. See light scattering, Lorenz-Mie theory mildew. See fungi Millikan cell

450

mine aerosol

801

Mini-RAM* (miniature real-time aerosol monitor)

793

mist

603 814

870

49 1072

mixing ratio

888

892

MMAD (mass median aerodynamic diameter). See equivalent diameter; diameter, mass median MMD (mass median diameter)

676

See also equivalent diameter; diameter, mass median mobility

1072

electrical

78

mechanical

67

401

539

69 1072

See electrical mobility spectrometer mobility (equivalent) diameter. See equivalent diameter mode

102 1072

accumulation

106

droplet

107

condensation

106

coarse ion

108

804

804

see nuclei, accumulation, coarse particle mode molecular weight of air

65 1083

velocity in air

65

See also mean free path mold. See fungi

This page has been reformatted by Knovel to provide easier navigation.

547

554

1150

Index terms monodisperse aerosol

Links 53

92

100

216

401

428

430 448 507 577 670 915

433 450 518 588 677 944

437 468 521 593 732 982

440 444 446 482 483 496 526 553 573 628 632 641 736 752 903 992 1040 1072

MOUDI* (micro-orifice uniform deposit impactor)

239

791

809

838

Mössbauer spectroscopy

923

MRE [(U.K.) Mining Research Establishment]

254

807

MS (mass spectroscopy)

124

275

377

951

784

801

806

838

845

See also size distribution; aerosol generation

quadrupole

377

time of flight

377

ion trap

379

See also GC-MS; ICP-MS; SIMS; LMMS; TOF-MS MSHA [(U.S.) Mine Safety and Health Administration]

629

738

mycotoxin

751

773 1072

N NAA (neutron activation analysis)

24

See INAA NAAQS [(U.S.) National Ambient Air Quality Standard]

266

NAMS (National Air Monitoring Network)

262

nanoparticle

821

827

49 1073

charging

550

nanotechnology

32

nanotubes

727

NASA (National Aeronautics and Space Administration)

890

nasopharyngeal compartment

1073

National Institute for Occupational Safety and Health. See NIOSH NBS [(U.S.) National Bureau of Standards]

345

See NIST NCAR (National Center for Atmospheric Research)

890

This page has been reformatted by Knovel to provide easier navigation.

871

1151

Index terms

Links

NCRP [(U.S.) National Council on Radiation Protection]

979

NDIR (non dispersive infrared)

273

nebulizer, liquid

989 1012 1017

280 433 1071 1073

644

938 1032 1036

6 1073

19

121

123

419

448

224 738

275 784

301 809

395 812

527 873

628

273

324

483

628

985 1026

324

337

345

383

483

6 274 583

106 277 585

223 282 828

261 339 869

266 375

269 398

108 583

110 585

223 586

225 828

261 888

269

See also aerosol generation nephelometer

See photometer neutron activation analysis. See INAA; NRA NIOSH [(U.S.) National Institute for Occupational Safety and Health] NIST [(U.S.) National Institute for Standards and Technology (formerly NBS, the National Bureau of Standards)] SRM (standard reference materials) nitrate

See nitric acid; particulate nitrate; ionic species nitric acid NMD (number median diameter). See equivalent diameter nonisoaxial. See anisoaxial; sampling NOAA [(U.S.) National Oceanic and Atmospheric Administration] NOPL (nasal-oral-pharyngeal-laryngeal)

381 1044

normal distribution. See Gaussian distribution; size distribution; concentration distribution normal temperature and pressure

47

NRA (nuclear reaction analysis)

949

62 1083

See also INAA NRC [(U.S.) Nuclear Regulatory Commission] [(U.S.) National Research Council] NSOM (near field scanning optical microscopy)

979

982

859 349

This page has been reformatted by Knovel to provide easier navigation.

1152

Index terms NTP (normal temperature and pressure)

Links 47

62 1083

nuclear reaction analysis. See NRA; INAA nucleation

87

nucleation

84

88 1073

homogeneous

88 1071

heterogeneous

88 1071

nuclei insoluble

88

mode

102 1073

soluble

88

number (or count) median diameter

671

676

See also CMD; diameter NVLAP [(U.S.) National Voluntary Laboratory Accreditation Program]

331

O OAP* [optical array (imaging) probe]

474

484

OC (organic carbon)

262 809

265 812

272

OEL (occupational exposure limit)

791

OES (occupational exposure standard)

784

ONR [(U.S.) Office of Naval Research]

890

829

283

OPC [optical (single) particle counter]. See optical particle counter open face sampler

127

199

optical array probe

474

484

optical depth

428

optical (equivalent) diameter. See equivalent diameter optical ensemble measurement

See DLS; light, Fraunhofer diffraction; optical imaging optical fiber

33

46

929

optical imaging

455

465

474

holography

475

optical microscopy bright field

300 This page has been reformatted by Knovel to provide easier navigation.

931

340

375

1153

Index terms

Links

optical microscopy (Continued) depth of field

303

differential interference contrast

300

fluorescence

301

for particle counting

303

near field scanning optical microscopy (NSOM)

349

phase contrast (PCM)

300

496

polarized light (PLM)

300

739

20 657

background noise calibration

772

527

531

738

120 890

419 974

433 484 991 1073

630

339

916

972

426

428

433

444

449

460

467

472

480

483

657

461

464

See also microanalysis optical particle counter (OPC)

See Chapter 21 coincidence. See coincidence error counting efficiency

440

electronics recovery time

438

for radioactive aerosols

991

imaging

473

instrument comparisons

129

486

instruments

440

443

487

313

428

446

multiple scattering

481 particle density, refractive index effect

134

resolution

444

sensing volume

126

stray particles

435

438

See also calibration; coincidence; light scattering; optical imaging; SPC optical scattering or extinction. See light scattering; light extinction; optical particle counter optical spectroscopy

949

organic carbon. See OC organic compounds

267

See also OC This page has been reformatted by Knovel to provide easier navigation.

479

1154

Index terms orifice meter

Links 651

654

832 1073

orifice transmission

180

371

512

OSHA [(U.S.) Occupational Safety and Health Administration]

302

629

737

784

339

347

397

648 1037

See also critical orifice

P packing density, filter. See filter packing density PAH (polycyclic aromatic hydrocarbon) particle

108

111

224

412

866

934

49 1073

ablation

124

374

381

adhesion

57

165

242

366

368

373

beam bounce. See particle bounce in impactors. See inertial classification characterization. See Chapters 12, 13 charging

400

diffusion

408

electrolytic

540

field

547

spray

540

contact

540

induced

540

limit on droplet

616

nanoparticle

550

photoemission

412

thermionic

540

radioactive self-

541

concentrator

541

540

550

995 1017

244

See also impactor, virtual See also coincidence error density

50

134

706

997

411

422

449

732

deposition. See losses in tubes and sampling lines ionization. See charged particle polarizability

375

This page has been reformatted by Knovel to provide easier navigation.

1155

Index terms

Links

particle (Continued) porosity

77

707

944

relaxation time

74 19 75 255 330 433 483 615 668 780

35 99 295 350 445 509 631 673 793

45 123 299 368 449 515 635 705 814

49

99

371

997

571

787

45

52

95 299 422 550 641 727 906 944

100 343 431 565 648 732 930 950

374

397

refractive index. See index of refraction shape

48 125 309 404 457 521 643 753 846

55 129 316 420 474 529 645 757 941

70 251 320 427 481 565 657 762 994

79

79

84

86

123 347 464 570 667 737 932 998

127 349 471 571 673 781 934

130 373 527 616 676 794 936

253 404 541 618 720 814 939

795

814 1020 1024

See also shape factor size size distribution. See size distribution solubility surface (area)

vaporization

See size distribution particulate

49 1073

partial pressure

83 1073

PAS (photoelectric aerosol sensor) passive sampling PAT [(AIHA) Proficiency Analytical Testing Program]

412

950

628 1074

793

302

PCM [phase contrast microscope (microscopy)] See optical microscopy, phase contrast

This page has been reformatted by Knovel to provide easier navigation.

1156

Index terms

Links

PCP (pneumocystic carinii pneumonia)

1049

PCS (photon correlation spectroscopy)

481

See also DLS PCSV* (particle counter sizer velocimeter)

468

PDA* (particle dynamic analyzer)

472

PDPA* (phase Doppler particle analyzer; also PDA*)

471

PE (pulmonary embolism) Peclet number

484

486

484

486

1039 69 1074 1085

PEL (permissible exposure limit)

784

806

PEM (personal exposure monitor)

860

862

percentiles of a distribution

671

864

867

767 802

771 785 806 1074

789

134 136 446 627 870 1074

216 658

See also size distribution PERM (passive environmental radon monitor)

1025

PERSPEC* (personal inertial spectrometer)

243

792

personal cloud

878

personal sampling

628 792

634 795

pesticides

861

873

PFA (perfluoro alkoxy) Teflon*

828

836

PFDB (parallel flow diffusion battery)

590

pH (negative log10 of hydrogen ion concentration)

269

phantom particles

517

526 1074

22 254 792

123 419 795

See also PEM; exposure assessment

See also coincidence error photoelectric aerosol sensor. See PAS photometer

applications

446

calibration

448

linearity range

446

real-time sensing

123

response

431

stray light background

446

126 427 814

See commercial photometers; nephelometer This page has been reformatted by Knovel to provide easier navigation.

1157

Index terms photon correlation (PCS or DLS)

Links 481

696

59

80

913

916

photoelectric emission. See particle charging-photoemission photophoresis PIA (phase identification analysis)

333

PIDS (personal inhalable dust spectrometer)

792

613 1074

See impactor; inertial classification piezoelectric mass monitor particle coupling

121

387

388

pitot tube

651

655 1074

PIXE (proton induced x-ray emission)

265

278

PLIF (planar laser induced fluorescence)

921

PLM [polarizing light microscope (microscopy)]

300

739

plug flow

561

583 1054 1074

PM (particulate matter)

261 1074

health effects

836

838

949

268

PM-2.5 (particulate matter <2.5 µm aerodynamic diameter)

PM-10 (particulate matter <10 µm aerodynamic diameter; also PM-10)

PM-15 (particulate matter <15 µm aerodynamic diameter) pMDI (propellant-driven, metered-dose inhaler) PMT (photomultiplier tube)

106

112

237

262

266

281

283 861

398 867

448 871

821 874

823 877

833

112 398 845

149 628 861

237 821 868

266 823 871

268 830 874

281 833

277 1035 1037 472

521

611

PN (particulate nitrate). See nitrate; nitric acid pneumoconiosis asbestosis

113

coal workers’

801

silicosis

813

6

210

Po (Polonium-210)

632

640

See also charge neutralization Poiseuille flow

62 1074

Poisson distribution

674

682

686

This page has been reformatted by Knovel to provide easier navigation.

689 1074

1158

Index terms Pollak counter

Links 573

657

643

751

See also condensation nucleus counter pollen

755

758

761

772

845 polycyclic aromatic hydrocarbon. See PAH porosity

944

filter

1074

See filter packing density PortaCount*

794

power-law distribution. See size distribution PPAH (particle-bound polycyclic aromatic hydrocarbons)

413

Prandtl number

1085

precision

1074

See coefficient of variation; error analysis pre-classifier

125

135 1074

manometer

651

655

Magnehelic*

651

655

pressure transducer

651

655

See centrifugal classification; diffusion; gravitational settling; inertial classification pressure measurement

pressure units

1079

primary particle projected area (equivalent) diameter

49

261 1074

1074

See also equivalent diameter PSI (Paul Scherrer Institute)

406

PSL (polystyrene latex) spheres

134

402

433

444

449

483

505 635

507 644

521 649

526

609

614

PTEAM (particle total exposure assessment methodology)

111

PTFE (polytetrafluoroethylene) filter. See filter material, PTFE P-Trak*

793

870

See also CMC

This page has been reformatted by Knovel to provide easier navigation.

1159

Index terms pumps, air flow control

Links 198

200

229

240

366

407

502 794

509 815

526 832

530

558

788

117 263 627 735 1020 1025

271 741

279 862

301 331 872 1003

22

47

PVC (polyvinyl chloride) filter. See filter material, polyvinyl chloride PVT (polyvinyl toluene) spheres

635

Q QCM* [quartz crystal microbalance (aerosol sensor)].

See piezoelectric mass monitor QELS (quasi-elastic light scattering)

481

QLFT (qualitative fit test)

793

QNFT (quantitative fit test)

793

quality assurance or control

See also error analysis quartz crystal microbalance aerosol sensor. See piezoelectric mass monitor quartz (common type of crystalline silica) dust

7 483

15 806

R radiation pressure, light

613

radiation, high energy

980

detection

983 1019

exposure limits

982

half-life

981 1011

sources

548 1012

specific activity

982

See also beta attenuation monitor; beta radiation; charge, electrostatic, neutralization radioactive aerosols

979

material handling safety

985

measurement

983 1019

This page has been reformatted by Knovel to provide easier navigation.

445

450

1160

Index terms radioactive charge neutralizer

Links 631

85

See also Kr; bipolar ion; charge neutralization radius of gyration radon and radon progeny (or daughters)

712

917

1011

air concentration

113 1013

measurement methods

591

998 1019

See also EGARD; PERM; RPISU; TLD human exposure

1017

radioactive decay chain

1014

sources

113 1013

RAM* (real-time aerosol monitor)

814

Raman spectroscopy

297

345

353

Rayleigh (or dipole) scattering

419

422

431 1074

RCD (respirable combustible dust)

810

813

RCRA [(US.) Resource Conservation and Recovery Act]

988

RDMA* (radial differential mobility analyzer)

554

556

558

58

145

620

923

See electrical mobility classifier reaction, chemical

96

receptor modeling

262

re-entrainment, in sampling lines

16 189

824 1075

784

801

84

109

reference materials. See SRM; NIST refractive index. See index of refraction REL (recommended exposure limit) relative humidity relative settling velocity

1075

relative standard deviation

1075

754

See also data analysis relaxation time

74 1075

Respicon*

792

See also impactor; inertial classification respirable dust

7

criterion

1075

combustible. See RCD ACGIH definition

782

This page has been reformatted by Knovel to provide easier navigation.

147

149

164

1161

Index terms

Links

respirable dust (Continued) BMRC definition

782

concentration

811

ISO definition

782

measurement

447

real-time monitoring

446

807

806

respirator fit testing

793

reticle

483 1075

Reynolds number

61

370 1075 1085

flow

62 1074

particle

62

RH (relative humidity)

84

70 1074 109

RICE [(U.K.) Regular Inter-laboratory Counting Exchange]

302

ROI (region of interest)

998 1000

rotorod sampler

245

754

761

See impactor, body rotameter

651 1075

RPISU (radon progeny integrating sampling unit) RSF (relative sensitivity factor)

1025 1027 337

344

S SAD (selected area diffraction)

311

See microanalysis safety considerations aerosol generation

631

laser

461

radioactive materials

985 1047

Saffman force

172

saltation

110

sample transport

143

847

See also sampling; losses in tubes and sampling lines; inlets sampling active

448 1025 1026

aircraft-based

887

This page has been reformatted by Knovel to provide easier navigation.

1162

Index terms

Links

sampling (Continued) anisoaxial

151

159

165

anisokinetic

151

aniso-mean-velocity

151

area (or fixed site)

785

810

989

aspiration efficiency

143

632

761

199

785 1067

781

785

792

634

794

See also inlet bioaerosol

757

calibration. See calibration calm air

168

closed-face

127

collection time

764

dilution

176

188

904

extractive

146

455

911

errors

124

871

from fluctuating flow

150 1060

from still (calm) air

168

from turbulent air

151

grab

995 1020 1027

gravitational losses in inlet

154

high velocity

888

inertial losses in inlet

144

See also dilution

162

190

159 152

inlet efficiency. See inlet inlet transmission efficiency

156

in situ

455

isoaxial

151

164 1071

isokinetic

151

852 1071

leakage in sampler

189 829

200 440 911 1021

mixing ratio

888

892

on aircraft

887

still air criteria

168

nonisoaxial. See anisoaxial nonrepresentative

144

open-face

127

199

829

This page has been reformatted by Knovel to provide easier navigation.

560

1163

Index terms

Links

sampling (Continued) passive

115

850

628

793

personal. See personal sampling; PEM passive

795

814 1020 1024

1074 ratio

143 1075

representative

144

166

190

197

767

789

852 908

877 879 990 1003

888

891

903

shrouded sampling probe

150

stack

150

911

subisokinetic

151

512 1077

superisokinetic

151

512

super-iso-mean-velocity

151

velocity ratio

155

See also inlets; losses in tubes and sampling lines SANS (small angle neutron scattering)

952

SAS* (surface air sampler)

767

770

saturation ratio

84 1075

saturated vapor pressure

84 1075

Sauter mean diameter

52 1075

SAXS (small angle X-ray scattering)

952

SBS (sick building syndrome)

873

scanning electron microscope (or microscopy)

120

See SEM scanning mobility particle sizer (SMPS*). See electrical mobility classifier SCAQS (Southern California Air Quality Study) Schmidt number scintillation counting secondary particle sedimentation

106 69

108

110

174 1075 1085

984 49

261 1075

1075

See gravitational settling self nucleation. See homogeneous nucleation

This page has been reformatted by Knovel to provide easier navigation.

1164

Index terms SEM [scanning electron microscope (or microscopy)] sample preparation

Links 120

297

247

330

275

374

310

353

943

75

515

705

See also EPMA; LV-SEM semivolatile organic carbon settling chamber. See gravitational settling settling due to gravity. See gravitational settling settling plate

110

SFS (sequential filter sampler)

833

shape factor

55 1076

70

See dynamic shape factor; particle shape Sherwood number SI (Système International; international system of units) sick building syndrome

174 46

584 1076 351

873

Silica, see quartz silicosis

7

silt

846

SIMS (secondary ion mass spectrometry)

296

801 340

347

353

See also microanalysis Sinclair-LaMer generator

1076

See aerosol generator size distribution

99

667 1073

“bell-shaped”, See size distribution, normal calculation

250

comparison using bias map

137

comparison using chi-square test

674

682

comparison using Kolmogorov-Smirnov test

135

686

cumulative

126

135

668

differential

100

126

135

54

101

671

histogram

100

126

253

669

Junge

671

694

54

100

250

670 1072

684

686

695

Gaussian. See size distribution, normal geometric standard deviation

See power-law lognormal

This page has been reformatted by Knovel to provide easier navigation.

723

1165

Index terms

Links

size distribution (Continued) mass median diameter mean

671

676 1072

54

See Chapter 22 modified gamma

102

673

53

100

677

668

673 1073

53

100

103 1074

power-law

102

671

677

presentation of

679

probit values

680

Rosin-Rammler

102

smoothing

691

monodisperse multimodal

102

normal (Gaussian)

54

of fibers

728

percentiles

680

polydisperse

standard deviation

54

volume

100

Weibull

102

Whitby model

102

673 668

See also equivalent diameters; data analysis size fractionator. See preclassifier size, particle

46

See diameter size-selective inlet

779

794

809

See also inhalable dust; PEM; PM-2.5; PM-10; preclassifier; respirable dust; thoracic dust sampling SLAMS (State and Local Air Monitoring Network) slip correction factor

262 1076

See Cunningham slip correction factor slip flow regime SM (silver membrane)

61

65 1076

813

See also filter materials, silver membrane SMD (surface median diameter)

676

This page has been reformatted by Knovel to provide easier navigation.

822

1166

Index terms SMEC (Spectrometre de Mobilite Elitrique Circulaire)

Links 558

See electrical mobility classifier smog

49 1076

smoke

6 13 551 707 1049 1076

cigarette

111

794

48 779

112 876

271 398 917 1032

753

869

871

875

450 828

See also ETS tracer

148

SMPS* (scanning mobility particle sizer). See electrical mobility classifier, Chapters 14, 18 Snell’s law

464 1076

sodium chloride (NaCl)

89 109 465 571 833 1043

284 639

345 643

402 794

soil particles

109

261

271

845

873 1002

solidity (or packing density) of a filter

207

213

215 1076

64

367

371

263 838

278 866

280 349 872 1076

16

276

121

495

520

637

641

643

255

644

994

sonic velocity

520

906 1036

See also critical orifice source apportionment

813

821

644

670

See also data analysis Soxhlet filter SPART* [single particle aerodynamic relaxation time (analyzer)]

See also E-SPART SPC (single particle counter)

456

See also optical particle counter SPECT (single photon emission computed tomography) spinning disk atomizer

1045 635 1076

See also aerosol generation spiral centrifuge SPM (scanning probe microscopy)

19 349

This page has been reformatted by Knovel to provide easier navigation.

1167

Index terms SPP (small particle processor) Spray Pyrolysis spreadsheet program (Aerosol Calculator) SPX (sphericity index)

Links 510

513

517

49

52

936

939

34

130

894

682

684

445

SRM (standard reference material). See NIST SRM standard error

677

See also error analysis standard deviation

675

See also error analysis standard temperature and pressure

47

Stefan (or Stephan) flow

81 1076

STEL (short-term exposure limit)

784

791

STEM [scanning transmission electron microscope (or microscopy)]

307

314

Stephan (or Stefan) flow

81 1076

sterilization

772 1076

sticking coefficient

404

408

STM (scanning tunneling microscopy)

349

953

73

75

70

706

Stokes diameter

99

450 1076

See also impactor; impinger; inertial classification or deposition Stokes law flow or drag Stokes number

75 368 1076 1085

941 1076 401

512

762

906

230

234

See also impactor; impinger; inertial classification or deposition Stokes regime outside stopping distance STP (standard temperature and pressure) Student’s t distribution

70

72

497 1076

36

75

495

74 762

96 145 892 1077

47 683

See also error analysis; data analysis

This page has been reformatted by Knovel to provide easier navigation.

176

1168

Index terms sulfate

Links 67

105

111

223

225

261

266 373

269 375

282 394

339 586

347 622

351

275

871

272

809

812

297

304

315

711

945

281

395

403

814

636

639

See also ionic species surface area. See particle surface area SVOC (semivolatile organic carbon)

T tapered element oscillating microbalance. See TEOM TBS (tight building syndrome)

873

TC (total carbon)

265

See also carbon black; BC; CC; EC; OC t distribution

683

TEM [transmission electron microscope (or microscopy)]

120 994

analysis for asbestos

330

electron diffraction. See EDS filters for

222

sample preparation

330

See also AEM; EDS; EELS; EFTEM; microanalysis TEOM* (tapered element oscillating microbalance) calibration

121 658

terminal settling velocity

72 1077

See also gravitational settling test aerosols

636

ammonium fluorescein

636

640

Arizona road dust

636

870

coal dust

134

444

636

473

523

DOP, See DOP fiber

735

oleic acid

468

PSL. See PSL PVT

635

See also aerosol generation; Chapter 22 test chamber

632

This page has been reformatted by Knovel to provide easier navigation.

1169

Index terms TGA (thermogravimetric analysis)

Links 951

therapeutic aerosols, Chapter 36 thermal precipitator

11

80

229

256

59

79

613 1077

11

80

229

150

154

See thermophoresis thermophoresis deposition due to thermal precipitator thin-walled inlet or sampling nozzle

711

See also inlet; sampling thoracic

1077

dust

448

dust sampling criteria

782 1077

tidal volume

865 1040 1077

Timbrell spectrometer

254

523

See also gravitational settling in horizontal elutriator TLD (thermoluminescent detector)

1025

TLV® (threshold limit value)

784

809

TM digital µP*

136

447

tobacco smoke

112

See also ETS TOF (time-of-flight) aerodynamic sizing. See APS*; Aerosizer* TOFMS, mass spectrometry

284

TOF LMMS

337

336

341

TOR (total optical reflectance)

264

273

TOT (thermal optical transmission)

264

273

283

7

448

806

TPV (temperature programmed volatilization)

265

275

tracheobronchial compartment or region

781

784 1077

total carbon. See TC total dust TPM (thoracic particulate mass). See thoracic dust

transition regime

61

65

transmission electron microscope (or microscopy). See TEM

This page has been reformatted by Knovel to provide easier navigation.

256

780 1077

1170

Index terms transmission or transport efficiency

Links 1077

See inlet; sampling; losses in tubes and sampling lines tremolite

735

739

tropospheric aerosol

408

890

TSDF, hazardous-waste (treatment, storage and disposal facility)

845

TSP (total suspended particulate)

266

turbidity

428

741

See also asbestos

turbulent flow

821

823

62 1077

in inlets

151

TUT-ELPI (Tampere University of Technology-ELPI)

399

402

784

790

See ELPI TWA (time-weighted average) tyndallometer

20

See also light scattering

U UAV (uninhabited air craft)

890

UCL (upper confidence limit)

791

UICC (Union Internationale Centre le Cancer)

735

ULPA (ultra low particulate air) filter

960

ultramicroscope

967

970

972

19

See also light scattering ultrasonic nebulizer

1077

See also aerosol generation ultra-Stokesian

1077

unfolding. See data analysis, inversion unipolar charging ion field

399

408

541

547

563

565

731

946

1077

units

46 1013 1079

uranium

113

333

UV-APS (Ultraviolet Aerodynamic Particle Sizer)*

509

761

344

This page has been reformatted by Knovel to provide easier navigation.

550

560

1171

Index terms

Links

V vapor pressure

84 1077

saturation

84

partial

84

variability

570 1075

1077

See error analysis variance

675 1077

VDI [Verein Deutscher Ingenieure (German engineering association involved in standard setting)]

440

velocity measurement, air hot wire anemometer

651

655

pitot tube

651

655

laser anemometer

655

particle imaging velocimeter

655

vena contracta

63

venturi meter

153 1077

651 1077

vertical elutriator

1077

See gravitational settling vibrating orifice monodisperse aerosol generator. See aerosol generation virtual impactor

1077

See impactor, virtual virus

112 222 751 1032 1048 1077

viscosity air

62 1083

water

1083

vital capacity

1077

VLF (vertical laminar flow)

960

VMD (volume median diameter)

101

637

644

See also MMD VOC (volatile organic compounds)

275

volume measurement, air

651

volatility, particle. See particle vaporization

This page has been reformatted by Knovel to provide easier navigation.

755

757

761

1172

Index terms

Links

VOMAG* (vibrating orifice monodisperse aerosol generator; also VOAG*). See aerosol generation, vibrating orifice

W WAA (Whitby Aerosol Analyzer)

552

See electrical mobility analyzer wall or interstage loss

1077

See inertial deposition WDXRF (wavelength dispersive x-ray fluorescence)

277

Weber number

514

516

523 1077

welding fumes

113

119

781

55

126

130 1077

827

833

803

806

903

869

949

weighing samples. See mass determination, aerosol; filter for gravimetric measurement weighing factor (weighting) WHO (World Health Organization)

873

WINS (well impactor ninty six) impactor

824

See also impactor, virtual; inertial classification WL(M) [working level (month)] WMO (World Meteorological Organization)

1013 1018 408

X XPS (x-ray photoelectron spectroscopy)

951

XRD (x-ray diffraction)

947

XRF(A) [(x-ray fluorescence (analysis)] microanalysis

265

271

276

308

Y yeast

753 1077

Z ZAF [atomic number (Z), absorption within sample and detector (A), X-ray induced fluorescence (F)]

322

This page has been reformatted by Knovel to provide easier navigation.

308