ENVIRONMENTAL POLICY ANALYSIS
PARTICULATES
Modeling Atmospheric Particulate Matter C H R I S T I A N SEIGNEUR AND PRASAD PAI Atmospheric and Environmental Research, Inc. 2682 Bishop Drive, Suite 120 San Ramon, CA 94583 P H I L I P K. HOPKE Clarkson University Potsdam, NY 13699 DANIEL GROSJEAN DGA 4526 Telephone Road, Suite 205 Ventura,CA93003
The new National Ambient Air Quality Standards (NAAQS) for particulate matter (PM), promulgated by the U.S. Environmental Protection Agency, include 24-hour and annual average standards for fine particles (PM25), in addition to the previous PM10 standards. Numerical models are needed to develop the emission control strategies that will bring polluted areas into attainment of the standards. Because the fraction of PM material that is formed in the atmosphere (secondary PM) is more significant in PM 25 than in PM10, the numerical models required to develop reliable source receptor relationships must take this secondary PM into account. We review numerical modeling techniques in terms of their ability to address the PM standards. We recommend that various techniques be used (sometimes in combination) to address the different PM standards. Further model development and evaluation, additional field data collection, and training of agency staff in the use of more advanced modeling techniques are recommended.
8 0 A • FEB. 1, 1999/ENVIRONMENTAL SCIENCE & TECHNOLOGY / NEWS
In July 1997, EPA promulgated NAAQS for atmospheric PM that include annual and 24-hour average standards of 50 ug/m 3 and 150 ug/m3, respectively, for PM10, as well as new annual and 24-hour average standards of 15 ug/m 3 and 65 pg/m 3 , respectively, for PM2 5 (fine particles). For areas that are designated by EPA to be in nonattainment of these standards, state and local air quality agencies will need to prepare State Implementation Plans (SIPs) that present the emission controls proposed to bring those areas into attainment of the standards. The new fine particle standards will prompt emission controlstrategies that may significantly differ from those used to meet the existing PM standards because PM contains a large fraction of particulate material that was formed in the atmosphere (secondary PM) whereas PM tends to be dominated by particulate material that was directly emitted into the atmosphere (primary PM) Models that were develODed for PM will not aDDly to PM if they cannot properly address secondary PM Our objective is to critically review existing models available for the simulation of PM concentrations, recommend models suitable for developing emission control strategies, and discuss areas where these models require improvements. Available databases and additional data needs for PM modeling are also discussed. We address both source- and receptororiented models and the major features of these two distinct approaches to modeling (see Figure 1). Source models predict ambient concentrations of PM with the characteristics of the sources and their emission rates of PM and precursor gases explicitly incorporated within them. As discussed later, PM ambient data can be used in source models to improve their PM predictions (inverse modeling). Receptor models provide empirical relationships between ambient data at the receptors and PM emissions fcind in some esses P]Vt precursor emissions) by source category (so-called source apportionment) Because source models and receptor models are based on different conceptual aDDroaches our review presents these two categories of models separately However the two approaches can be complementary as discussed later in our recommendations We present here an overview of the current capabilities of source and receptor models pertaining to their applications in addressing the PM standards. Technical details regarding specific models (major attributes of source models, a list of inverse modeling techniques, and major attributes of receptor models) and discussions of their relative advantages and shortcomings are provided in the supporting information to this article.
© 1999 American Chemical Society
PM source models The simulation of PM atmospheric concentrations through source modeling for SIP applications requires the use of a three-dimensional Eulerian air quality model that numerically solves the mass conservation equation for PM. Input data required by Eulerian air quality models include initial and boundary conditions of the chemical concentrations of the species modeled, emission rates of these chemical species in the modeling domain, three-dimensional meteorological variables (wind flow vector atmospheric turbulence, temperature, pressure and relative humidity and
FIGURE 1
Major features of source and receptor models Source models are based on fundamental or empirical representations of the relevant physicochemical atmospheric processes. Receptor models are based on statistical analyses of ambient PM data, and, in some cases, PM emission data. Solid arrows denote the fundamental informatton flows; dashed arrows indicate additional data uses required by some modeling techniques or for model evaluation.
1T1 SflTTlf1 PriSf1^ inTflt*—
mation on fog clouds and/or cipitation) physiographic data (such as land use and elevation) and time and location of the scenario being simulated (for the calculation of the photolysis rates of photochemical reactions) The model then calculates the concentrations of the chemical snpries as a function of time (tvnirallv with a one hour averaee resolution) and location (within each grid cell of the modeling domain)
Some models also calculate the dry and wet deposition rates of chemii
I
4i_
J
cal species to the ground. We differentiate between episodic models that include a detailed treatment of chemistry, which are generally limited in their applications to a few days of simulation, due to computational costs associated with the numerical integration of the chemical kinetic equations and long-term models that typically use a simplified treatment of atmospheric chemistry and can be applied to longer time periods (e.g., one year or more, without prohibitive computational costs). Another approach that has been considered for the estimation of annual-average concentrations is based on simulating various meteorological scenarios (see Figure 2) To date no comprehensive evaluation of episodic and longterm models has been conducted and it is debatable which approach is the most desirable
FIGURE 2 Simulation of annual average concentrations An approach that has been considered for the estimation of annual-average concentrations is to apply an episodic model for several typical meteorological scenarios and to reconstruct a full year by combining these scenarios with appropriate weighting (right branch of illustration). This approach involves making approximations with the representativeness of the meteorology, whereas the use of a long-term model (left branch of illustration) involves making approximations with the chemistry.
Episodic PM models We selected seven episodic PM models for tiiis review. These models include three urban-scale models (CIT, UAM-AERO, and UAM-AIM) and four mesoscale models (DAQM, GATOR, RPM, and SAQMAERO). (RPM is equivalent in its scientific formulation
to the Models-3 aerosol module.) Mesoscale models can be applied to urban or regional scales. The vertical extent of the urban-scale models is limited to the atmospheric boundary layer (typically up to 2 km), whereas the mesoscale models extend into the free troposphere. FEB. 1, 1999 /ENVIRONMENTAL SCIENCE S TECHNOLOGY / NEWS " 8 1 A
TABLE 1
Episodic PM source model applications The indicated data (and references) summarize the performance of episodic models in recent applications to PM 25 and PM, 0 and suggest the current reliability of such models.
Secondary inorganic PM consists primarily of sulfate and nitrate compounds. Sulfate is formed from the oxidation of sulfur dioxide (S02), whereas nitrate is formed from the oxidation of nitrogen oxides (NOJ. These chemical transformations take place in the gas phase as well as in the aqueous phase. All episodic PM models use comprehensive gas-phase chemical kinetic mechanisms that include of the order of 100 reactions among NO*, volatile organic compounds (VOCs), S0 2 , and oxidants. However, only two models (GATOR and RPM) include a comprehensive treatment of droplet chemistry. All PM models include treatment of sulfate, nitrate ammonium and water; some models in clude the explicit treatment of other species; GATOR and CIT provide the most detailed treatments of the inorganic PM composition. Secondary organic aerosols (SOAs) are formed in situ in the atmosphere by gas-phase photochemical oxidation of volatile organic compounds (VOCs). SOAs can account for a significant fraction (up to 70%) of total particulate organic carbon (i). The formation of SOAs involves complex chemical and physical processes, which are not fully characterized and, as a result, the treatment of SOAs in current PM models is approximate. Most PM models that include a description of SOA formation use the lumped SOA fixed-yields approach of Pandis and co-workers (2). 8 2 A • FEB. 1, 1999 / ENVIRONMENTAL SCIENCE & TECHNOLOGY / NEWS
In the lumped SOAfixed-yieldsapproach, each class of VOC in the gas-phase chemical kinetic mechanism is assumed to lead to a fixed fraction of secondary organic aerosol product through its oxidation reactions (3). GATOR uses an approach based on the water solubility of VOCs to describe gas-particle partitioning; this approach is suitable for polar organic compounds (4). In CIT, SOA formation is modeled with an absorption mechanism, where condensable organics dissolve into an organic particulate phase. This approach is in better agreement with recent experimental data on SOA formation (5) than the fixedyields approach of Pandis and co-workers (2). Most episodic models offer a resolution of the particle size distribution that is finer than the two regulatory PM size fractions of particle aerodynamic diameters less than 2.5 um and 10 pm. Whether those size distributions, using either sectional or modal representations (6), are necessary to address the PM NAAQS is debatable. Processes such as dry and wet deposition, cloud processes, light scattering and, in some cases, gas particle partitioning, strongly depend on particle size. However, uncertainties in tiie parameterization of these processes are likely to exceed the uncertainty that is introduced by using a default particle distribution (for example, deduced from relevant ambient measurements) instead of calculating the evolution of the modeled size distribution.
Episodic model performance (see Table 1) (719) for PM mass, specific particulate chemical species (e.g., sulfate, nitrate, ammonium, and carbon) and precursor gases is available for several applications to PM2 s and PM10 and has been summarized by Seigneur and co-workers (20). Because the model performance evaluations differ for the area, episode, and/or performance statistics, intercomparison of the models is not feasible. Five models have been applied to various episodes in the Los Angeles Basin, Calif. The normalized absolute error between PM2 5 model predictions and observations ranges from 17 to 46%. Applications of two models to the Los Angeles Basin and the San Joaquin Valley, Calif., for PM showed poorer performance (error in the 17-147% range) than for Plvl This can be attributed to large uncertainties in the emissions of Diimary coarse PM Although RPM has been applied to eastern North America no performance evaluation is available The current performance of episodic models for ozone is tvoically better than 30 to 35% error and 5 to 15% bias (21 22) Although performance for ozone cannot be directly related to PM (because of different averaging times and more comDlex chemistry for secondary PM) the performance of enisodic PM mndpls should be improved before thev are annlied to dpsien emission control scenarios timlarlv sinrp nprfnrmanrp for individual particulatp chemical species can be significantly worse than for total L iv22 5 or Pis/iiQ concentrations. The following general statements can be made about the current status of existing episodic models: • Most models need improvements, albeit to various extents, in their treatment of sulfate and nitrate formation in the presence of fog and/or clouds. • All models need improvements, albeit to various extents, in their treatment of secondary organic aerosol formation. • The urban-scale models would require modifications to treat the dynamics of pollutants in the free troposphere if they were to be applied to regional scales. • All models lack subgrid-scale treatment of plumes emitted from large point sources and may, therefore, misrepresent the impact of such plumes on air quality. Long-term PM models Three long-term PM models are currently available. All three models treat sulfate, nitrate, ammonium, and water; one model (VISHWA) also treats secondary organic PM formation. Two models (REMSAD and VISHWA) provide the PM2 5 mass, and one model (UAM-LC) provides the PM10 mass. Not one of these models has a detailed particle size distribution treatment. REMSAD, UAM-LC, and VISHWA have been applied to the eastern United States (23), the Los Angeles Basin (24) and Phoenix, Ariz. (25), and the southwestern United States (26), respectively. Performance of long-term models is typically poorer than that of episodic models very probably because of their simplified chemistry treatment. There are two approaches for simulating the atmospheric chemistry in long-term models: • The use of a parameterized chemistry, in which
secondary PM formation is approximated by mathematical functions, for example, as in UAM-LC. • The use of a reduced chemistry that includes only a limited number of reactions compared with the standard mechanisms used in episodic models, for example, as in REMSAD and VISHWA. Because of the approximations involved, both approaches may lead to significant uncertainties when analyzing emission control strategies. Therefore, we recommend that long-term models be carefully evaluated, not only for their ability to reproduce observed PM ambient concentrations, but also for their ability to reproduce the response of PM ambient concentrations to changes in emission levels. The speciated rollback model The speciated rollback model is an empirical model used to predict the effect of changes in emissions of PM and precursor gases on PM ambient concentrations. It is an extension of the linear rollback model from a single air pollutant to a multicomponent pollutant mixture (e.g., sulfate, nitrate, organic compounds, elemental carbon, and dust). The speciated rollback model is correct only if the spatiotemporal distributions of the emissions before and after reduction are identical and if the source ambient concentration relationships are linear. For cases where such assumptions are not satisfied, these limitations of the speciated rollback model can lead to either over- or underestimates in the effects of emission reduction strategies on PM levels. For example the speciated rollback model will not be appropriate for an area that is affected by local sources as well as long-range transport because the actual S0 sulfate NO -nitrate and VOC-organic PM relationships will be significantly different for the local sources and the upwind sources Inverse modeling Inverse modeling is a technique that relates emissions of pollutants (or precursors) to measured ambient concentrations by using ambient data and a variety of approaches (e.g., variational method and Kalman filter) to calculate the emissions that provide the best agreement with the observed ambient concentrations. This technique uses existing air quality data and models of the transport and transformation processes to infer what the inputs must be to best fit model predictions to the measured concentrations. In PM studies, inverse modeling can be used to bound the likely errors and identify the major data gaps in the emission inventories that are used in source modeling. For example, ammonia plays an essential role in governing atmospheric concentrations of secondary PM. Nevertheless, even in areas like the Los Angeles Basin, where large resources have been spent to develop better emission inventories, large uncertainties still exist in the ammonia emission estimates. Using a model of the atmospheric transport and transformation processes and the available surface concentration data of ammonia, the emission inventory could be adjusted by inverse modeling. FEB. 1, 1999/ENVIRONMENTAL SCIENCE S TECHNOLOGY / NEWS " 8 3 A
PM receptor models The fundamental principles of receptor modeling are that mass conversion can be assumed and a mass balance analysis can be used to identify and apportion sources of airborne particulate matter in the atmosphere. Receptor models can be grouped into three major categories (27): • models that apportion primary PM using source information, • models that apportion primary PM without using source information, and • models that apportion both primary and secondary PM. The application of these models typically uses information on the chemical composition of PM. An additional subcategory can be defined for applications that use information of individual particles using electron microscopy. So far, this approach has been used to apportion primary PM using source information. In each of these categories, there are some wellestablished techniques, as well as some recent emerging techniques. It is clear that chemical mass balance (CMB) can be used effectively to identify and apportion particle sources whose elemental characteristics are clearly defined. The method has been applied to PM10 problems throughout the western United States with generally good success (28). It has recently been applied to the apportionment of particulate organic compounds to primary sources using detailed information on source profiles and ambient concentrations of speciated particulate organic compounds (29). Even higher source specificity could be obtained using automated electron microscopy data and any problems of collinearity can generally be resolved with the increased number of variables defining the source profiles (30). Other methods that use source information are the multivariate techniques such as partial least squares. When no source information is available, a general category of methods known as factor analysis can be used, in which the source information is extracted from the data by means of specific mathematical analyses. Among the available factor analysis methods, principal components analysis (PCA) is the easiest to use, but it does not provide a quantitative source apportionment. New methods of factor analysis can be used for quantitative apportionment in areas where source profiles are not available and cannot be easily obtained. SAFER has been effectively applied to airborne organic compounds in Atlanta, Ga. (31). Positive matrix factorization has proven to be very effective in separating sources, even in remote areas like Alaska, where the variability in the sample compositions are strongly affected by long-range transport rather than the variability of source emissions (32) Thus receptor modeling methods are available to provide quantitative support for PM SIP development The situation for PM2 5 is quite different because the majority of fine-particle mass is due to secondary compounds. Initial studies have shown the 8 4 A • FEB. 1, 1999 / ENVIRONMENTAL SCIENCE & TECHNOLOGY / NEWS
ability of some receptor models to identify the likely locations of major sources of secondary PM precursors. However, the ability of these models to provide quantitative apportionment of the measured aerosol mass to those sources has not yet been demonstrated. Because secondary PM has been formed in the atmosphere and not directly emitted from a source, additional information needs to be introduced (compared with the information available for characterizing primary PM). Two major approaches have been used. Instead of examining the variation of chemical speciation in the ambient PM data (as is done for primary PM apportionment) spatial variation in the data has been used to apportion precursors of secondary PM (and, in theory, temporal variation could be used in a similar manner) with techniques such as empirical orthogonal function analysis and SAFER (33)) However White eas suggested that there ITI&V be difficulties using such spatial methods and further development and testing are needed (34). The second approach uses meteorological information that is included in the form of back trajectories Back trajectories model the movement of the air parcel carrying PM and its precursors to the site backward in time from the time during which the sample has been collected (35 36) This approach can give an estimation of source appor tionmentfor secondarvPM (35 37) although it de pends on trie availabilitv of a source inventorv A major concern is that state and local regulators may not take advantage of these methods because EPA has only formally recognized CMB and PCA as part of the SIP development guidance documents. For techniques that have been successfully tested, issuance of new guidance to state and local air quality managers is needed that would show a willingness to consider more than CMB and PCA as part of SIP development and, to the extent possible, to develop standardized software that can then be distributed to all of the state and local air quality management agencies. Databases for PM modeling Ambient PM data are required by both source and receptor models. Source models require ambient data to specify the model's initial and boundary conditions and to evaluate the model's performance. Such an evaluation should test, not only the ability of the model to reproduce observed PM concentrations (operational evaluation), but also the reliability of individual model components (diagnostic and mechanistic evaluations). To accomplish such model performance evaluations, it is essential to possess a comprehensive database of emissions, meteorology, tracer experiments, ambient concentrations of gases and speciated PM at ground level and aloft, and, if relevant, fog cloud droplet chemistry. Receptor models require ambient PM data with chemical speciation and for models thcit need source information emission profiles by source categorv Several databases exist that range from nationwide monitoring networks to urban airshed episodic intensive field programs, public health monitoring studies, and site-specific research-grade
TABLE 2 Approaches for PM modeling Techniques (and limitations) are recommended for modeling PM in preparation of State Implementation Plans. The recommendations are similar for PM ]0 and PM 25 ; however, because secondary PM constitutes a larger fraction of PM 25 than of PM10, the limitations associated with secondary PM modeling are more severe for PM 25 than for PM10.
investigations. (See the supporting information to this article for a summary of the major relevant PM field programs that have been conducted in the past decade.) Among these databases, the SCAQS database for the Los Angeles Basin and the SARMAP and IMS 95 databases for the California San Joaquin Valley are the most comprehensive in terms of spatial coverage and PM information. However, these databases still include significant uncertainties in the emissions inventories (e.g., VOC, PM, and ammonia) and PM speciation (e.g., secondary organic and inorganic fractions). Although several oxidant field studies have been conducted over the past several years in the eastern United States such as Southern Oxidant Study and NARSTO-Northeast no significant airshed-based PM air quality studies have been conducted there. For receptor modeling surement programs are urgently needed to ment and update existing databases (e g source profiles) and to develop new databases (e g speciated PM ambient data) Recommendations for PM modeling As indicated above, significant limitations exist in the capabilities of available PM models and databases. Nevertheless, a starting point for recommendations for modeling PM for SIP preparation can be suggested (see Table 2). For PM annual average modeling, receptor modeling techniques are recommended. Because a pure
receptor modeling approach will not provide a spatially distributed allocation of precursor sources of secondary PM, other approaches must be used to complement receptor modeling. Because none of these approaches has been thoroughly evaluated over a wide range of conditions, we recommend that at least two of the following approaches be used in complement of each other: • speciated rollback (as a screening analysis only); • a hybrid model such as potential source contribution function, or PSCF, with apportionment; and • source modeling using either a long-term source model or an episodic model witii aggregation of episodes based on their meteorological frequency. The emphasis on the more resource-intensive techniques (e.g., episodic modeling with aggregation of episodes) is only justified for cases in which the secondary fraction of PM is significant (for PM2 5 rather than for PM10). For PM 24-hour average modeling, we recommend using an episodic three-dimensional Eulerian grid model for sources with known emissions. However, one must be aware that severe limitations are associated with most of these models, and a proper model performance evaluation must be conducted before their application. For cases in which the source emission rate is poorly characterized (e.g., fugitive dust and biomass burning), receptor modeling should be used to complement source modeling. FEB. 1, 1999 / ENVIRONMENTAL SCIENCE & TECHNOLOGY / NEWS • 8 5 A
In summary, it a p p e a r s t h a t although a solid m o d eling framework exists to address PM NAAQS, significant developments are n e e d e d in t h e current formulations of source models; further applications a n d testing of t h e m o s t a d v a n c e d receptor modeling t e c h n i q u e s are warranted; training of agency staff to u s e m o r e adv a n c e d source a n d receptor m o d e l s is needed; a n d a critical n e e d exists for t h e collection of additional a m bient a n d emission d a t a that are required for t h e application a n d evaluation of source a n d receptor models.
Acknowledgments This work was supported by the American Petroleum Institute (API). The authors wish to thank Howard Feldman, API Project Manager, and the API reactive modeling task force, chaired by Charles Schleyer, of the Mobil Technology Company.
Supporting information Supporting information for this paper is available as photocopy or microfiche. It is also available free of charge on the Internet. See current masthead page for ordering and access information.
References (1) Turpin, B. J.; Huntzicker, J. /. Atmos. Environ. 1995, 29, 3527-3544. (2) Pandis, S. N.; Harley, R. A.; Cass, G. R.; Seinfeld, J. H. Atmos. Environ. 1992, 26A, 2269-2282. (3) Grosjean, D.; Seinfeld, J. H. Atmos. Environ. 1989,23,17331747. (4) Saxena, P.; Hildemann, L. M. J. Atmos. Chem. 1996, 24, 57-109. (5) Odum, J. R.; Jungkamp, T. P. W.; Griffin, R. J.; Forstner, H. J. L.; Flagan, R. C; Seinfeld, J. H. Environ. Sci. Technol. 1997, 31, 1890-1897. (6) Seigneur, C; Hudischewsky, A. B.; Seinfeld, J. H.; Whitby, K. T.; Whitby, E. R.; Brock, J. R.; Barnes, H. M. Aerosol. Sci. Technol. 1986, 5, 205-222. (7) Meng, Z.; Dabdub, D.; Seinfeld, J. H. /. Geophys. Res. 1998, 103, 3419-3435. (8) Middleton, P. /. Air Waste Manage. Assoc. 1997, 47, 302316. (9) Middleton, R; Lansford, H.; Zhang, K.; Beauhamols, M.; Hopkins, D.; Cheng, J.; Hash, L.; Blackmail, S.; Flaming, D. Brown Cloud II: The Denver Air Quality Modeling Study, Final Technical Report, University Corporation for Atmospheric Research: Boulder, CO, 1993. (10) Jacobson, M. Z. Atmos. Environ. 1997, 31, 131-144. (11) Jacobson, M. Z. Atmos. Environ. 1997, 31, 587-608. (12) Binkowski, F. S.; Shankar, U. /. Geophys. Res. 1995, 100, 26,191-26,209. (13) Dabdub D.; DeHaan, L.; Kumar, N.; Lurmann, F.; Seinfeld, J. H. Computationally Efficient Acid Deposition Model for California; Draft Report 92-304; California Air Resources Board: Sacramento, CA, 1998. (14) Pai R; Vijayaraghavan, K.; Seigneur, C. Particulate Modeling in the Los Angeles Basin Using MM5 and SAQMAERO. Presented at the Air & Waste Management Association International Specialty Conference: PM2 5—A Fine Particle Standard, Long Beach, CA, Jan. 1998.
8 6 A • FEB. 1, 1999 / ENVIRONMENTAL SCIENCE & TECHNOLOGY / NEWS
(15) Kumar, N.; Lurmann, F.; Wexler, A. S.; Pandis, S.; Seinfeld, J. H. Development and Application of a ThreeDimensional Aerosol Model. Presented at tlie Conference on Computing in Environmental Resource Management, Research Triangle Park, NC, Dec. 1996. (16) Lurmann, F. W.; Wexler, A. S.; Pandis, S. N.; Musarra, S.; Kumar, N.; Seinfeld, J. H. Atmos. Environ. 1997,31,26952715. (17) Lurmann, F. W.; Wexler, A. S.; Pandis, S. N.; Musarra, S.; Kumar, N.; Hering S. V.; Seinfeld, J. H. Development of an Acid Deposition Model for the South Coast Air Basin, California Institute of Technology, Final Report 92-311; California Air Resources Board: Sacramento, CA, 1997. (18) Lurmann, F. W; Kumar, N.; Loomis, C; Cass, G. R.; Seinfeld, J. H.; Lowenthal, D.; Reynolds, S. D. PM10Air Quality Models for Application in theSanJoaquinValleyPM10 SIP; Report STI-94250-1595-FR; California Air Resources Board: Sacramento, CA, 1996. (19) Sun, Q.; Wexler, A. S. Atmos. Environ., 1998, 32, 35333542. (20) Seigneur, C; Pai, P.; Louis, J. E; Hopke, P.; Grosjean, D. Review of Air Quality Models for Particulate Matter, Report No. 4669; American Petroleum Institute: Washington, DC, 1998. (21) Rethinking the Ozone Problem in Urban and Regional Air Pollution; National Research Council, National Academy Press, Washington, DC, 1991. (22) Guideline for the Regulatory Application of the Urban Airshed Model; U.S. Environmental Protection Agency, U.S. Government Printing Office: Washington, DC, 1991. (23) Guthrie, P. D.; Gao, D. Evaluation ofREMSADfine particulate modeling; Memorandum to Richard Scheffe, U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards: Durham, NC, 1996. (24) Modeling and Attainment Demonstrations; South Coast Air Quality Management District: Diamond Bar, CA, 1996. (25) Technical Support Document for Regional PMI0 Modeling in Support of the 1997 Serious Area PM10 Plan forMaricopa County Nonattainment Area; Maricopa Association of Governments: Phoenix, AZ, 1997. (26) Venkatram, A.; Karamchandani, P.; Pai, P.. Sloane, C; Saxena, R; Goldstein, R. /. Air Waste Manage. Assoc. 1997, 47, 286-301. (27) Hopke, P. K. Receptor Modeling in Environmental Chemistry, Wiley & Sons: NY, 1985. (28) Air Quality Criteria for Particulate Matter, EPA/600/P95/001aF; U.S. Environmental Protection Agency, U. S. Government Printing Office: Washington, DC, 1996, pp. 5-59-5-66. (29) Schauer J. J.; Rogge W. F.; Hildemann L. M.; Mazurek M. A.; Cass G.; Simoneith, R. Atmos. Environ. 1996,30,38373855. (30) Song, X. H.; Hadjiski, L.; Hopke, P. K.; Ashbaugh, L. L.; Casuccio, G. S.; Schlaegle, S. /. Air Waste Manage. Assoc, in press, 1998. (31) Henry, R. C; Lewis, C. W; Collins, J. F. Environ. Sci. Technol. 1994, 28, 823-837. (32) Polissar, A. V; Hopke, P. K.; Malm, W C; Sisler, J. F. /. Geophys. Res., in press, 1998. (33) Henry, R. C. J. Air Waste Manage. Assoc. 1997, 47, 216219. (34) White, W. H. J. Air Waste Manage. Assoc, in press, 1998. (35) Gao, N.; Cheng, M-D.; Hopke, E K. Anal. Chim.Acta 1993, 277, 369-380. (36) Gao, N.; Cheng M-D.; Hopke, R K. Atmos. Environ. 1994, 28, 1447-1470. (37) Cheng, M-D.; Gao, N.; Hopke, P. K.J. Environ. Eng. (N.Y.) 1996, 122, 183-190.
