REVIEW OF AIR QUALITY MODELS FOR

PARTICULATE MATTER

Prepared for the

American Petroleum Institute

1220 L Street, Northwest

Washington, DC 20005

Prepared by

Christian Seigneur

Prasad Pai

Jean-Francois Louis

Atmospheric & Environmental Research, Inc.

2682 Bishop Drive, Suite 120

San Ramon, California 94583

Philip Hopke

Department of Chemistry

Clarkson University

Potsdam, New York 13699

Daniel Grosjean

Daniel Grosjean & Associates, Inc.

4526 Telephone Road, Suite 205

Ventura, California 93003

Document Number CP015-97-1b

5 December 1997

TECHNICAL SUMMARY

T.1 Background

The U.S. Environmental Protection Agency (EPA) has promulgated the following revised particulate matter (PM) National Ambient Air Quality Standards (NAAQS):

a)	A PM-10 annual average standard of 50 g/m3.

b)	A PM-10 24-hour average standard of 150 g/m3.

c)	A new PM-2.5 annual average standard of 15 g/m3.

d)	A new PM-2.5 24-hour average standard of 65 g/m3.

EPA based these revised NAAQS on the recommendations made in the EPA Staff Paper (EPA, 1996b) and the conclusions presented in the Air Quality Criteria for Particulate Matter (EPA, 1996a). In that latter document, EPA presented a wide range of health effects that could be attributed to PM, such as premature mortality, increased hospital admissions and emergency room visits, increased respiratory symptoms and diseases, decreased lung function, and alterations in lung tissue and structure and in respiratory defense mechanisms. Many of these effects tend to affect primarily the elderly, children and/or individuals with cardiopulmonary diseases.

The objective of this report is to critically review existing modeling tools available for the simulation of PM concentrations, make recommendations for models suitable for analyzing PM concentrations for State Implementation Plan (SIP) preparation, and discuss areas where these models require improvements. Available data bases and additional data needs for PM modeling are also discussed. The review addresses all pertinent aspects of PM standards: i.e., PM-10 and PM-2.5, annual average and 24-hour average concentrations, urban scale and regional scale airsheds.

Both source models and receptor models are addressed in this review. Source models are based on fundamental or empirical representations of the relevant physico-chemical atmospheric processes. They predict ambient concentrations of PM knowing the characteristics of the sources and their emission rates of PM and precursor gases. Receptor models are based on statistical analyses of ambient PM data, and, in some cases, data on PM emission characterization. They provide empirical relationships between ambient data at the receptors and PM emissions (and, in some cases, PM precursor emissions) by source category.

Because source models and receptor models are based on different conceptual approaches, our review presents these two categories of models separately. However, the two approaches are treated together in our recommendations for SIP modeling.

T.2 Particulate Matter Source Models

T.2.1 Overview

The simulation of PM atmospheric concentrations through source modeling for SIP applications requires the use of a three-dimensional Eulerian air quality model that includes a PM module. 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 information on meteorological variables (wind flow vector, atmospheric turbulence, temperature, pressure and relative humidity, and, in some cases, information on fog, clouds and/or precipitation), 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 species as a function of time (typically with a one-hour average resolution) and location (i.e., within each grid cell of the modeling domain). Additionally, some models also calculate the dry and wet deposition rates of chemical species to the ground. Eulerian air quality models can be categorized according to their spatial and temporal attributes, as follows.

Some air quality models have been developed primarily for application to urban areas (i.e., for airsheds of the order of 100 km x 100 km, hereafter referred to as urban scale). Such models typically do not provide sufficient treatment of upper air dynamics and, therefore, are generally not applicable to regions of the order of 1,000 km x 1,000 km (hereafter referred to as regional scale), where vertical transport in the free troposphere, up to several kilometers above ground, may be important. Regional scale models have been developed that allow the simulation of the transport and dispersion of chemicals over long distances; such models can generally also be applied to the urban scale by using finer grid resolutions. Therefore, we will refer to these models as mesoscale models. Clearly, urban scale models will be appropriate to address isolated urban airsheds but will not be appropriate for areas where PM formation and transport processes occur over scales of the order of 1,000 km. For such areas, therefore, a mesoscale model, possibly with nested grids, will be more appropriate.

Air quality models that include a detailed treatment of chemistry tend to be limited in their applications to a few days of simulation because of the computational costs associated with the numerical integration of the chemical kinetic equations (we will refer to these models as episodic models). On the other hand, air quality models that use a simplified treatment of atmospheric chemistry can be applied to longer time periods (e.g., one year or more) without prohibitive computational costs (we will refer to these models as long-term models). In other terms, the ability to simulate long time periods is generally obtained at the expense of some accuracy (since the treatment of chemistry is less accurate in long-term models). Another 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 factors. This approach involves making approximations with the representativeness of the meteorology whereas the use of a long-term model involves making approximations with the chemistry. To date, no comprehensive evaluation of these two methods has been conducted and it is debatable which approach is the most desirable.

Seven Eulerian episodic PM air quality models were identified for this review.

a)	The California Institute of Technology model (CIT)

b)	The Denver Air Quality Model (DAQM)

c)	The Gas, Aerosol, Transport and Radiation model (GATOR)

d)	The Regional Particulate Model (RPM)

e)	The SARMAP Air Quality Model with aerosols (SAQM-AERO)

f)	The Urban Airshed Model Version IV with aerosols (UAM-AERO)

g)	The Urban Airshed Model Version IV with an aerosol module based on the Aerosol Inorganic Model (UAM-AIM)

These models include three urban-scale models (CIT, UAM-AERO and UAM-AIM) and four mesoscale models (DAQM, GATOR, RPM, and SAQM-AERO) that can be applied to urban or regional scales. Among the mesoscale models, two have been applied with nested-grids to allow simulation at a regional scale with a coarse grid resolution (say, of the order of 100 km) and, imbedded within the regional grid system, a finer grid resolution (say, of the order of a few km) for urban areas of interest. We first identify the major attributes of these episodic PM models. Then, we discuss in greater detail the characteristics, strengths and weaknesses of each one of these models.

Table T-1 presents a list of these PM episodic models along with their air quality modeling framework, the meteorological model used to drive the air quality model, and the areas where the PM model has been applied.

Two PM models use RADM as the air quality model; two PM models use UAM-IV as the air quality model, two other models use an established air quality model (CIT and SAQM); and the seventh PM model (GATOR) was developed specifically for the purpose of PM simulations. Among the five air quality models, three have been used in previous applications with a specific meteorological model: RADM has been used with the Meteorological Mesoscale model version 4 (MM4) or version 5 (MM5), and SAQM

Table T-1. Air Quality and Meteorological Model Components of Episodic PM Models and Applications to Date

PM Model

Air Quality Model

Meteorological Model

(used in previous applications of the air quality model)

Applications to Date

CIT

CIT

Diagnostic model

Los Angeles Basin, CA

DAQM

RADM

MM4(1)

Denver, CO

GATOR

GATOR

MMTD

Los Angeles Basin, CA

RPM

RADM

MM4(1)

Eastern North America

SAQM-AERO

SAQM

MM5

San Joaquin Valley, CA

UAM-AERO

UAM-IV

Various models

Los Angeles Basin and San Joaquin Valley, CA

UAM-AIM

UAM-IV

Various Models

Los Angeles Basin
(1)	Future applications are planned with MM5.

with the Mesoscale Meteorological model version 5 (MM5). GATOR has been applied to date in combination with the Mesoscale Meteorological and Tracer Dispersion model (MMTD). The other two models have been applied with a variety of meteorological models.

Table T-2 presents a summary of the major attributes of the aerosol modules of the seven episodic PM models. Only four gas-phase mechanisms are used among the six episodic PM models: The Carbon-Bond Mechanism version IV (CBM-IV) of Gery et al. (1989), the RADM mechanism version 2 (RADM2) of Stockwell et al. (1990), the Statewide Air Pollution Research Center mechanism (SAPRC) of Carter (1990, 1995), and a mechanism developed explicitly for GATOR (Jacobson, 1994) that is based primarily on SAPRC for the inorganic chemistry and CBM-IV for the organic chemistry (the extended LCC mechanism (Lurmann et al, 1987, Harley et al., 1993) that is used in CIT is derived from an early version of the SAPRC mechanism). The CBM-IV, RADM2 and SAPRC mechanisms have been subjected to comprehensive performance evaluations against smog chamber data. The GATOR gas-phase chemical kinetic mechanism has not been evaluated against smog chanber data but has been used as part of the overall GATOR operational performance evaluation.

Five thermodynamic modules are used to simulate the equilibrium between the gas and particulate phases: The Model of an Aerosol Reacting System (MARS) (Saxena et al., 1986), the SEQUILIB model (Pilinis and Seinfeld, 1987), the SCAPE2 model (Kim et al., 1993a, 1993b; Kim and Seinfeld, 1995; Meng et al., 1995), EQUISOLV, a model developed specifically for GATOR (Jacobson, 1994) and AIM2, an updated version of AIM (Wexler and Seinfeld, 1991). These models differ in the number of chemical species treated as well as in the methods used to calculate activity coefficients of the chemical species dissolved in the PM aqueous phase. All PM models include treatment of sulfate, nitrate, ammonium and water; some models include the explicit treatment of other species with GATOR and CIT providing the most detailed treatments of the inorganic PM composition. For example, MARS treats only sulfate, nitrate, ammonium and water using some approximations (e.g., the presence of mixed salts of ammonium sulfate and nitrate is neglected) whereas SEQUILIB treats these same species with, in

Table T-2. Major attributes of Aerosol Modules in Episodic PM Models

PM Model	Gas-phase Chemistry	Aerosol Thermo- dynamics	Inorganic PM	Organic PM	Size Distribution
CIT

Extended LCC	SCAPE2	Sulfate, nitrate, ammonium, chloride, sodium, potassium, calcium, magnesium, carbonate, water, elemental carbon, other PM material	Variable fractional yields of aromatics oxidation by OH	Sectional distribution (8 sections from 0.039 to 10 m in current application)
DAQM

RADM2	MARS	Sulfate, nitrate, ammonium, elemental carbon, geologic material, water	Fixed fractional yields of VOC oxidation by OH	Fine and coarse modes
GATOR

GATOR	GATOR	Sulfate, nitrate, ammonium, chloride, sodium, potassium, calcium, magnesium, elemental carbon, 7 metal oxides, other PM material	10 soluble and condensable organic compounds	Sectional distribution (16 sections up to 74 m in latest application)
RPM

RADM2	MARS	Sulfate, nitrate, ammonium, other PM material	Fixed fractional yields of VOC oxidation by OH	Lognormal distributions with two modes up to 2.5 m
SAQM-
AERO

CBM-IV & SAPRC	SEQUILIB	Sulfate, nitrate, ammonium, chloride, sodium, other PM material	Fixed fractional yields of VOC oxidation by OH	One PM-10 mode
UAM-
AERO

CBM-IV & SAPRC	SEQUILIB	Sulfate, nitrate, ammonium, chloride, sodium, other PM material	Fixed fractional yields of VOC oxidation by OH	Sectional distribution
UAM-
AIM	CBM-IV & SAPRC	Neural network represen- tation of AIM2	Sulfate, nitrate, ammonium, chloride, sodium, other PM material	Fixed fractional yields of VOC oxidation by OH	Sectional distribution
addition, chloride and sodium to account for the effect of sea salt on PM thermodynamics. To our knowledge, there has been limited comparison between these aerosol modules, and there is a need for a more comprehensive evaluation of these various thermodynamic PM modules (i.e., MARS, SEQUILIB, SCAPE2, EQUISOLV and AIM2). All models treat sulfate PM formation through a mass transfer process. All models but CIT, GATOR, and UAM-AIM treat nitrate PM formation assuming equilibrium between the bulk gas phase and the particulate phase; CIT, GATOR and UAM-AIM use a mass transfer process for nitrate PM formation (UAM-AERO uses a hybrid approach where equilibrium is assumed over the entire PM but gas/particle conversion is distributed among particle sizes via a mass transfer formulation). The formation of organic aerosols is included in all models. All models, but GATOR and CIT, treat organic PM formation with a mass transfer process using fixed fractional yields (see Section T.2.6 for a discussion of fractional yields). CIT uses a variable fractional yield for the oxidation of aromatics, where the yield depends on the organic PM mass. GATOR uses an approach based on the solubility of organics in water.

Table T-3 presents a summary of the improvements recommended for episodic models. The following general statements can be made about the status of existing episodic models:

a)	Most models need improvements, albeit to various extents, in their treatment of sulfate and nitrate formation in the presence of fog and/or clouds.

b)	All models need improvements, albeit to various extents, in their treatment of secondary organic aerosol formation.

c)	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.

d)	All models but one (SAQM-AERO) lack subgrid scale treatment of plumes.

Table T-3. Improvements Recommended for Episodic PM Models

Model	Air Quality Model	Inorganic PM	Organic PM

CIT	•	For regional scale applications, treatment of free troposphere

•	Subgrid scale treatment of plumes	•	Treatment of fog and cloud chemistry

•	Wet deposition	•	SOA formation extended to other VOC beside aromatics, also aromatics oxidation by O3 and NO3
DAQM	•	Subgrid scale treatment of plumes	•	Treatment of fog microphysics	•	Improved SOA formation
GATOR	•	Subgrid scale treatment of plumes	•	Subgrid scale treatment of convective clouds	•	Improved SOA formation
RPM	•	Subgrid scale treatment of plumes	•	Treatment of fog microphysics	•	Improved SOA formation
SAQM-
AERO	•	Subgrid scale treatment of plumes extended to PM	•	Improved treatment of fog/cloud chemistry	•	Improved SOA formation
UAM-AERO	•	For regional scale applications, treatment of free troposphere

•	Subgrid scale treatment of plumes	•	Improved treatment of fog/cloud chemistry	•	Improved SOA formation
UAM-AIM	•	For regional scale applications, treatment of free troposphere

•	Subgrid scale treatment of plumes	•	Improved treatment of fog/cloud chemistry	•	Improved SOA formation

T.2.3 Review of Long-term PM Models

Table T-4 presents a list of the three PM long-term models identified for this review, along with their air quality modeling framework, the meteorological model required to drive the air quality model, and the areas where the PM model has been applied. These three models are the following:

a)	The Regulatory Modeling System for Aerosols and Deposition (REMSAD)

b)	The Urban Airshed Model Version IV with linearized chemistry (UAM-LC)

c)	The Visibility and Haze in the Western Atmosphere model (VISHWA)

All these long-term models correspond to episodic air quality models that have been modified by use of a simplified chemistry and the addition of an aerosol module. All three models are flexible in terms of the meteorological model being used to generate the meteorological fields, as long as the proper interface between the meteorological model output and the air quality model input is developed.

Table T-5 presents a summary of the major attributes of the aerosol modules of the three long-term PM models. All three models include sulfate, nitrate, ammonium and water, and one model (VISHWA) also treats secondary organic PM formation. Two models (REMSAD and VISHWA) provide the PM-2.5 mass and one model (UAM-LC) provides the PM-10 mass. None of the models has a detailed particle size distribution treatment.

Long-term models represent one approach for calculating annual-average concentrations (the other approach consists in constituting a synthetic long-term average by aggregation of several episodic simulations). In this approach, the atmospheric chemistry of secondary PM formation is considerably simplified to allow for the simulation of a full year without excessive computational costs. Therefore, the ability to correctly represent the major chemical processes relevant to sulfate, nitrate and organic PM is a critical attribute of a long-term model.

Table T-4. Air Quality and Meteorological Model Components of

Long-term PM Models and Applications to Date

PM Model

Air Quality Model

Meteorological Model

(used in previous applications)

Applications to Date

REMSAD

UAM-V

Diagnostic model or MM4

Eastern U.S.

UAM-LC

UAM-LC

Diagnostic model

Los Angeles Basin

VISHWA

ADOM

NGM and RAMS

Southwestern U.S.

Table T-5 Major attributes of Aerosol Modules in Long-term PM Models

PM Model	Gas-phase Chemistry	Aerosol Thermo- dynamics	Inorganic PM	Organic PM	Size Distribution
REMSAD	Parameterized chemistry	Empirical algorithm	Sulfate, nitrate, ammonium, water, other PM material

None	One PM-2.5 mode
UAM-LC

Parameterized chemistry	Parameterized gas/particle conversion	Sulfate, nitrate, ammonium, sodium, chloride, other PM material

None	One PM-10 mode
VISHWA

GRS	Empirical algorithm

Sulfate, nitrate, ammonium, water, other PM material

VOC oxidation by OH	One PM-2.5 mode

From our review of long-term models, it appears that there are two approaches for simulating the atmospheric chemistry:

a)	The use of response-surfaces for parameterizing the major reactions leading to secondary PM formation (hereafter referred to as "parameterized chemistry").

b)	The use of a chemical mechanism that includes only a limited number of reactions compared to the standard mechanisms used in episodic model (hereafter referred to as "reduced chemistry").

The first approach, as it is used in UAM-LC and in the first version of REMSAD, relies on O3 concentrations specified externally to the model. This approach may lead to significant inconsistencies when analyzing emission control strategies for VOC and NOx, since these compounds affect the O3 concentrations. Therefore, we recommend either using a reduced chemistry approach or upgrading the parameterized chemistry approach by adding response surfaces for the O3 concentrations.

Table T-6 presents a summary of our recommended improvements for the three long-
term models reviewed here.

T.2.4 Review of the Speciated Rollback Model

The speciated rollback model is an empirical model used to predict the effect of changes in emission 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. Since PM consists of several chemical species (e.g., sulfate, nitrate, organic compounds, elemental carbon, dust), the speciated rollback model is more appropriate to use than a standard linear rollback approach.

In the speciated rollback models, the source receptor relationship over an airshed is assumed to be linear:

Table T-6. Improvements Recommended for Long-Term PM Models

Model	Air Quality Model	Gas-phase Chemistry	Inorganic PM	Organic PM

REMSAD	•	Evaluation of mechanism for a variety of atmospheric conditions	•	Treatment of fog microphysi cs	•	Improved SOA formation
UAM-LC	•	For regional scale applications, treatment of free troposphere	•	Upgrade of gas-phase mechanism	•	Improved treatment of fog/cloud chemistry

•	Wet deposition	•	Addition of SOA formation
VISHWA	•	Evaluation of mechanism for a variety of atmospheric conditions	•	Treatment of fog microphysi cs	•	Improved SOA formation

Ci = ai Si + bi i = 1, . . ., N	(T-1)

where Ci is the ambient concentration of chemical i at the receptor of interest, Si is the airshed wide emission rate of chemical i, ai is an empirical proportionality constant and bi represents the background concentration of chemical i due to sources outside of the airshed. Clearly, this empirical model is location and time specific, i.e., the parameters ai and bi cannot be used in a setting different than the one for which they were developed.

The speciated rollback model is correct only if the spatio-temporal 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 SO2/sulfate, NOx/nitrate, and VOC/organic PM relationships will be significantly different for the local sources and the upwind sources.

T.2.5 Review of Inverse Modeling Techniques

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, 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 have to be in order to best fit model predictions to the measured concentrations.

In PM studies, inverse modeling can be used to bound the likely errors and identify any missing data gaps in the emission inventories that will be used for source-based modeling. For example, the biases in existing ammonia emission inventories can be estimated and corrected using an inverse method based on a Kalman filter. The role of ammonia and ammonium is essential 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, there are still large uncertainties in the ammonia emission estimates. Using a model of the atmospheric transport and transformation processes and the available surface concentration data of NH3 and NH4+ (or NH4+ wet deposition data), the ammonia emission inventory could be adjusted by inverse modeling. This approach can be applied similarly to other emission inventories which have large uncertainties (e.g., VOC emissions).

T.2.6 Simulation of Secondary PM

The simulation of secondary PM is an essential component of a PM air quality model, particularly to address PM-2.5 concentrations, since secondary PM constitutes a significant fraction of PM-2.5. We discuss in this section the current status and deficiencies of the treatment of secondary inorganic aerosol (SIA) formation and secondary organic aerosol (SOA) formation in existing PM source models.

Treatment of Secondary Inorganic PM

Secondary inorganic aerosols consist primarily of sulfate and nitrate compounds. Sulfate is formed from the oxidation of sulfur dioxide (SO2) whereas nitrate is formed from the oxidation of nitrogen oxides (NOx). These chemical transformations take place in the gas-phase as well as in the aqueous phase.

The oxidation of SO2 to sulfate in the gas phase occurs by reaction with hydroxyl (OH) radicals. This reaction leads to a linear relationship between SO2 and sulfate concentrations, i.e., a reduction in SO2 concentrations leads to a proportional reduction in sulfate concentrations. The oxidation of SO2 to sulfate in the aqueous phase occurs by reaction with hydrogen peroxide (H2O2), ozone (O3), and oxygen (O2) when catalyzed by iron or manganese. These reactions generally lead to non-linear relationships between SO2 and sulfate because the H2O2 reaction can be oxidant limited and the O3 and O2 reactions have self-limiting rates. Therefore, a reduction in SO2 concentrations may not lead to a proportional reduction in sulfate concentrations. This result is important when assessing the effect of emission reduction strategies and it is essential to assure that such atmospheric chemical relationships are correctly represented in PM models.

The oxidation of NO2 to nitric acid (HNO3) occurs in the gas-phase by reaction with OH radicals, primarily during daytime, and by reaction with nitrate (NO3) radicals, primarily during nighttime. The NO3 radical is formed by reaction of NO2 with O3; NO2 and NO3 then react to form N2O5, which is subsequently hydrolyzed to HNO3. The rate of hydrolysis increases with ambient humidity. In the presence of fog or cloud droplets, nitrate formation can take place either on the surface of the droplet (hydrolysis of N2O5) or inside the droplet (reactions involving the NO3 radical). HNO3 is a gas at ambient temperature. It can, however, under certain atmospheric conditions depending on ammonia (NH3) concentrations, temperature and relative humidity, be converted to PM nitrate. The NO2/HNO3 relationship is typically non-linear since the concentrations of oxidants such as OH radicals and O3 are a function of both NOx and VOC emissions. Therefore, it is essential to have a correct representation of oxidant chemistry in order to develop a reliable assessment of NOx emission reduction strategies.

Table T-7 presents a summary of the treatment of secondary inorganic aerosol formation in existing PM source models. It appears that several of these models include only gas-phase chemistry (CIT) or gas-phase chemistry with a highly parameterized treatment of droplet chemistry (REMSAD, SAQM-AERO, UAM-AERO, UAM-AIM, UAM-LC). The other models (DAQM, GATOR, RPM and VISHWA) include some treatment of droplet chemistry.

Treatment of Secondary Organic PM

Secondary organic aerosols are formed in-situ in the atmosphere by gas-phase photochemical oxidation of volatile organic compounds (VOC). These VOC include alkanes, alkenes, aromatics, aldehydes, other oxygenates and biogenic compounds such as terpenes. The reactions relevant to SOA formation include reactions of VOC with the hydroxyl radical (OH), ozone (O3) and the nitrate radical (NO3). While most of the

Table T-7. Treatment of SIA and SOA formation in PM models

PM Model	Secondary PM Treatment
Gas Phase Chemical Mechanism (a)	SIA Formation	SOA Formation
CIT	extended LCC	gas-phase chemistry	absorption mechanism with mass transfer
DAQM	RADM2	gas-phase, droplet chemistry	lumped aerosol yields of Pandis et al., 1992
GATOR	GATOR	gas-phase, droplet chemistry	solubility
REMSAD	condensed CBM-IV chemistry	gas-phase, parameterized droplet chemistry	VOC emission fraction
RPM	RADM2	gas-phase, droplet chemistry	lumped aerosol yields of Pandis et al.,1992
SAQM-AERO	CBM-IV and SAPRC	gas-phase, parameterized droplet chemistry	lumped aerosol yields of Pandis et al., 1992
UAM-AERO	CBM-IV and SAPRC	gas-phase, parameterized droplet chemistry	lumped aerosol yields of Pandis et al., 1992
UAM-AIM	CBM-IV and SAPRC	gas-phase, paramaterized droplet chemistry	lumped aerosol yields of Pandis et al., 1992
UAM-LC	parameterized chemistry	gas-phase, parameterized droplet chemistry	None
VISHWA	condensed chemistry (7 reactions and 7 species)	gas-phase, droplet chemistry	2-component approach with aromatic and terpene lumped aerosol yields of Pandis et al., 1992 adjusted downward to match ambient observations
(a)	The Carbon Bond Mechanism (CBM-IV), RADM2, GATOR, SAPRC and extended LCC gas phase chemical mechanisms are described by Gery et al., 1989, Stockwell et al., 1990, Jacobson, 1994, Carter, 1990, 1995, and Harley et al., 1993b, respectively.

products of these reactions remain in the gas phase, some of the less volatile products may accumulate in the condensed phase.

The urban atmosphere contains hundreds of VOC, and each VOC may lead to many reaction products. As a result, it is difficult to obtain quantitative estimates of SOA formation. Nevertheless, literature data clearly indicate that SOA formation cannot be ignored in air quality models that are used to design control strategies for particulate matter (PM). For example, results obtained during the 1987 Southern California Air Quality Study (SCAQS) showed that SOA accounted for up to 70% of the total particulate organic carbon during photochemical smog episodes (Turpin and Huntzicker, 1991, 1995). Since virtually all SOA are associated with fine particles, the importance of SOA would increase substantially if control strategies were based on PM-2.5, rather than PM-10, NAAQS.

The formation of SOA involves complex chemical and physical processes which are not fully characterized (McMurry and Grosjean, 1985; Grosjean and Seinfeld, 1989; Grosjean et al., 1994; Odum et al., 1996, 1997a, 1997b). As a result, and considering further the significant uncertainties associated with model input data – for example emissions of VOC that are SOA precursors – the treatment of SOA in current PM models is very limited.

As shown in Table T-7, eight PM models include a description of SOA formation. UAM-LC does not currently treat SOA formation and REMSAD uses fixed yields for VOC emissions, i.e., it does not have a kinetic treatment of SOA formation. Of the eight models that treat SOA formation, six (DAQM, RPM, SAQM-AERO, UAM-AERO, UAM-AIM and VISHWA) use the lumped SOA yields approach of Pandis et al. (1992). (In turn, the lumped SOA yield approach of Pandis et al. (1992) makes use of the VOC- specific fractional aerosol coefficients (FAC) of Grosjean and Seinfeld (1989)). In the lumped SOA yields approach, 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. For example, the reaction of aromatic compounds with OH radicals will be assumed to lead to x% of SOA, where x is deduced from the review of Pandis et al. (1992).

The six PM models that employ the lumped SOA yield approach of Pandis et al. (1992) use different gas-phase mechanisms. Three of these models can use either the Carbon Bond mechanism or the SAPRC mechanism (UAM-AERO, UAM-AIM and SAQM-AERO), two models use the RADM mechanism (DAQM and RPM), and one model uses a highly condensed chemistry with only two lumped SOA precursors, aromatics and terpenes (VISHWA).

In five of the six PM models listed above, the lumped SOA yields of Pandis et al. have been used without modification (if changes or updates were made, they are not mentioned in the original publications). In the VISHWA model, the lumped SOA yields of Pandis et al. for the two lumped SOA precursors used, i.e. aromatics and terpenes, have been adjusted downward by a factor of about eight, reportedly to match experimental observations.

GATOR does not use the lumped SOA yield approach but instead describes gas-
particle partitioning of about ten condensable and soluble organic species. However, the organic species now included in GATOR are not representative of condensable reaction products that contribute to SOA under atmospheric conditions.

In CIT, secondary organic aerosol (SOA) is modeled by the mass transport between the gas and particulate phases, which is governed by the same formulation as that for inorganic volatile compounds. The surface vapor concentrations of semi-volatile organic compounds are calculated according to an absorption mechanism,

(T-2)

where Fi ( g m-3) is the concentration of organic species i in the aerosol phase, KiOM (m3 g-3) is the absorption partitioning coefficient, OM ( g m-3) is the absorbing organic mass concentration. This approach is in better agreement with recent experimental data on SOA formation than the fixed yield approach of Pandis et al. (1992). In the present published version of CIT, however, only aromatics are considered for the production of secondary organic PM.

T.2.7 Data Bases for PM Source Modeling

The application of PM source models requires input data for the initial and boundary conditions of the model as well as ambient data for model performance evaluation. Several data bases exist that range from nationwide monitoring networks to urban airshed episodic intensive field programs and site-specific research-grade investigations.

Table T-8 presents a summary of the major relevant PM field programs that have been conducted in the past decade or are planned to occur sometime in 1997. Only the major characteristics of these programs are presented in this table, and the listed references should be consulted if more detailed information is desired.

Among these data bases, the SCAQS data base for the Los Angeles Basin and the SARMAP and IMS 95 data bases for the California San Joaquin Valley are the most comprehensive in terms of spatial coverage and PM information. Each of these programs also included many special studies.

Both SCAQS and IMS 95 will be followed by new studies. SCOS took place in the Los Angeles basin in summer 1997, however, it focused primarily on ozone and other photochemical oxidants and limited measurements were made for PM. Field programs are being planned to study PM transport and formation for 1998-2000 in the California San Joaquin Valley.

The NFRAQS data bases are useful because they provide PM data for a non-
Californian airshed, i.e., the Denver area, which is the subject of ongoing modeling and monitoring studies on PM and visibility impairment. Similarly, the TSPMP data base will provide one year of PM data for several locations in Texas.

As is apparent from Table T-8, although several oxidant field studies have been conducted over the past several years in the eastern U.S. (e.g., Southern Oxidant Study, NARSTO-Northeast), no significant airshed-based PM air quality studies have been conducted there.

Table T-8. Summary of Selected PM Field Measurement Programs

AIRS	IMPROVE/

NESCAUM

SCAQS

PTEP

SCOS

SARMAP

IMS 95

NFRAQS

TSPMP
Location	Nationwide (primarily urban sites)	Nationwide (primarily national parks and wilderness areas)	Los Angeles Basin, CA	Los Angeles Basin, CA	Los Angeles Basin, CA	San Joaquin Valley, CA	San Joaquin Valley, CA	Denver, CO	TX
Period	Ongoing since 1985	Ongoing since 1988	June - December 1987	Ongoing since November 1994	June - October 1997	July - August 1990	November 1995 - January 1996	January - February 1996

July - August 1996

December 1996 - January 1997	March 1997 -

March 1998
Monitoring Sites	Over 1,300	Over 50 sites	47 sites	6 sites	Several ground sites and upper air data	10 sites and upper air data	4 core sites and 56 other sites	1 to 3 core sites and up to 6 satellite sites	12 sites
Major Measurements(1)	PM-10 mass, some sites with PM-2.5 mass	PM 10 & PM 2.5 mass, some PM 2.5 chemical composition	PM 10 & PM 2.5 (mass, ions, OC, EC, trace elements, size distributions), HNO3, NH3, VOC, criteria gases, H2O2 at 11 sites; criteria gases and intermittent PM 10 (mass, sulfate, nitrate) at other sites	PM 10 & PM 2.5 (mass, 5 ions, OC, EC, 36 trace elements), HNO3, NH3, criteria gases	PM 10 & PM 2.5 with chemical speciation and criteria gases at several sites; single particle analyses at 3 sites; PM mass, size distribution and chemical composition aloft	PM 10 & PM 2.5 (mass, ions, EC, OC, elements), HNO3, NH3 and SO2 aloft	PM 10 & PM 2.5 (mass, 7 ions, OC, EC, trace elements), HNO3, and NH3 at 30 sites; sulfate at 2 additional sites; NH3 at 15 additional sites; criteria gases at 7 sites; fog chemistry at 3 sites; VOC and OC speciation at 3 sites	PM 10 & PM 2.5 (mass, 3 ions, speciated OC, EC), HNO3, NH3; SO2	PM 10 mass &

PM 2.5 (mass, 4 ions, OC, EC)
Reference	AIRS, 1995	Sisler et al., 1993; Poirot et al., 1990	Lawson, 1990	SCAQMD, 1996	B. Croes, 1997	Chow et al., 1994	Solomon et al., 1996	CSU, 1996	Loos, 1997
(1)	Ions: sulfate, nitrate, ammonium, and in some cases, chloride, sodium, fluoride and potassium

OC: organic carbon

EC: elemental carbon	Criteria gases: NO/NOx, SO2, O3 and CO

As discussed above, there is a need to further evaluate existing PM source models before their application for SIP preparation. Such an evaluation should test, not only the ability of the model to reproduce observed PM concentrations (i.e., operational evaluation), but also the reliability of individual model components (i.e., diagnostic evaluation). To accomplish such model performance evaluations, it is essential to possess a comprehensive data base in terms of emissions, meteorology, tracer experiments, ambient concentrations of gases and speciated PM at ground level and aloft, and, if relevant, fog/cloud droplet chemistry.

Emission inventories are a critical component of any PM source modeling study. Inventories of precursor gases such as SO2 and NOx are generally reliable, however, large uncertainties remain in the inventories of precursor gases such as VOC (with speciation) and NH3. Inventories of PM also have significant uncertainties, particularly for sources such as fugitive dust (both natural and anthropogenic).

A dense network of meteorological measurements is essential, particularly in areas affected by complex terrain and land/sea (or land/lake) breeze effects. In addition, many air pollution episodes occur under calm conditions, which can be difficult to properly model; therefore, actual measurements, particularly aloft, are very useful. Tracer experiments are useful to characterize atmospheric transport and dispersion, particularly, from large point sources. Ambient concentrations of PM precursors and PM components should be collected with sufficient resolution (both temporal and spatial) at the ground as well as aloft. Speciation of VOC is essential to evaluate the emission model as well as the air quality model. Chemical speciation of PM-10 and PM-2.5 is very important for diagnostic evaluations because it can provide a test for the treatment of secondary PM formation. The speciation should address both the inorganic and organic fractions of PM.

Among the data bases presented in Table T-8, the SCAQS, SARMAP and IMS 95 approach the requirements listed above. However, significant uncertainties remain, particularly, in the emission inventories. Moreover, all these data bases are for California regions (Los Angeles Basin or San Joaquin Valley). Planned or ongoing field programs in Colorado and in Texas will provide valuable information for the operational evaluation of PM source models but will not allow diagnostic evaluations. Moreover, no comprehensive PM data base currently exists for the eastern U.S.

T.2.8 General Recommendations

In addition to carrying out the model-specific improvements listed in the previous sections, there appears to be a real need to conduct a systematic assessment of the various modules that are essential to a PM air quality source model.

a)	The chemical kinetic mechanisms for secondary inorganic aerosol formation (i.e., sulfate and nitrate PM) should be critically compared between models and against data. This assessment should address both gas-phase and aqueous-phase chemistry.

b)	The formulation of secondary organic aerosol formation should be modified and/or extended in all models.

c)	The five thermodynamic equilibrium aerosol modules currently in use in episodic models (i.e., MARS, SEQUIL, SCAPE2, EQUISOLV and AIM2) need to be compared with each other so that we can quantitatively assess the differences in predicted PM concentrations and chemical composition due to model assumptions and numerical solutions. The neural network approach used in conjunction with AIM2 should be included in this comparison since it offers an interesting approach to reduce computational requirements.

d)	The assumption of gas/aerosol equilibrium versus the mechanistic treatment of mass transfer of condensable species should be critically assessed over a broad range of conditions to evaluate the uncertainties associated with the simpler, but computationally more efficient, equilibrium approach.

Once these systematic comparisons have been performed, we should be able to decide on a quantitative basis how to compromise between model accuracy and computational costs.

The other area that requires further investigation is the performance evaluation of PM air quality models. To date, existing models have been evaluated by their developers in different regions and for different episodes. There is a need, therefore, for an objective evaluation of the most promising models against one or more comprehensive data bases (e.g., the SCAQS, SARMAP and IMS 95 data bases). Such an evaluation could follow, for example, the protocol used in the Consortium for Regional Model Evaluation (CReME) study where four ozone air quality models are compared against the same data bases.

For the calculation of annual average PM concentrations, the use of episodic models with aggregation of several meteorological episodes to construct a synthetic year should be evaluated against the alternative approach of using a long-term model with simplified chemistry.

The fundamental principle of receptor modeling is 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:

a)	Models that apportion primary PM using source information

b)	Models that apportion primary PM without using source information

c)	Models that apportion primary and secondary PM.

The application of these models has typically been done using information on the average chemical composition of particles. 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 exist some well established techniques as well as some recent emerging techniques. Table T-9 summarizes the major characteristics of each one of these receptor modeling techniques. The information provided in Table T-9 includes the following:

a)	Whether the receptor model is available from EPA

b)	Whether it requires source information (first vs second category)

c)	Whether it provides quantitative apportionment of source contribution

d)	Whether it apportions secondary aerosol (first/second categories vs third category)

e)	Other advantages

f)	Other disadvantages.

The methods, their data requirements and the software available to implement the receptor modeling techniques described in Table T-9 are presented in Table T-10. One of the major concerns is that state and local regulators may not take advantage of these methods because EPA's Office of Air Quality Planning and Standards (OAQPS) has only recognized chemical mass balance (CMB) and principal components analysis (PCA) as part of their SIP development guidance documents.

It is clear that CMB can be used effectively to identify and apportion particle sources whose elemental characteristics are clearly defined. The method has been applied to PM-10 problems throughout the western United States with generally good success. 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. New methods of factor analysis can also be employed in areas where source profiles are not available and cannot be easily obtained. SAFER has been effectively applied to airborne organic compounds in Atlanta. 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. Thus,

Table T-9. Summary of the Receptor Modeling Methods that are Available for PM Source Identification and Apportionment.

Model	EPA Model Available	Requires Source Information	Quantitative Apportionment	Source Location Information	Apportions Secondary Aerosol	Other Advantages	Other Disadvantages
Chemical Mass Balance (least- squares) (CMB)	yes

yes	yes

propagation	no	no	Easy to use, includes errors in source compositions, accepted by OAQPS	Can misspecify sources; does not include variability in source compositions; Sensitive to collinearity
Partial Least Squares	no	yes	yes	no	no	Identifies completeness of source profile set	Requires specialized software
Artificial Neural Networks (Backpropagation)	no	yes	yes	no	no	Least sensitive to collinearity	Requires specialized software; can be time consuming
Genetic Algorithm	no	no	no	no	yes	uncertain	Requires specialized software; can be time consuming
Principal Components Analysis (PCA)	no	no	no	no	no	Easy to use, identification of source types; identification of unusual samples, recognized by OAQPS	Depends on correlation that can be driven by meteorology or colocation
Absolute Principal Components Analysis	no	no	yes	no	no	Estimates source profiles and contributions;	Propogates error in centering and uncentering of data; assumes that all sources have been identified
Target Transformation Factor Analysis	no	no	yes	no	no	Does not center; estimates uncertainties	Assumes that all sources have been identified

Table T-9. Summary of the Receptor Modeling Methods that are Available for PM Source Identification and Apportionment (continued).

Model	EPA Model Available	Requires Source Information	Quantitative Apportionment	Source Location Information	Apportions Secondary Aerosol	Other Advantages	Other Disadvantages
SAFER	no	no	yes	no	no	Includes external constraints and other information	Assumes that all sources have been identified

Positive Matrix Factorization	no	no	yes	no	sometimes	Treats missing and detection limit data well; estimates uncertainties	Assumes that all sources have been identified; for secondary apportionment, requires correlation with other species (black carbon, MSA, etc.)
Artificial Neural Networks (Kohonen)	no	no	Can lead to one	yes	uncertain	Provides a map that can be related to geographical distribution of sources	Better if multiple source locations; need at least 35 samples per site; time consuming
Empirical Orthogonal Function Analysis	no	no	sometimes	yes	yes	Easy to use, can estimate source contributions.	Requires sufficiently large spatial extent in sampling sites.
Residence Time Analysis	no	no	no	yes	yes	Provides likely source areas	Needs sufficient nos. of samples
Area of Influence Analysis	no	no	no	yes	yes	Provides likely source areas	Needs sufficient nos. of samples

Table T-9. Summary of the Receptor Modeling Methods that are Available for PM Source Identification and Apportionment (continued).

Model	EPA Model Available	Requires Source Information	Quantitative Apportionment	Source Location Information	Apportions Secondary Aerosol	Other Advantages	Other Disadvantages
Quantitative Trajectory Bias Analysis	no	no	yes	yes	yes	Incorporates wet and dry deposition; combines measurements from multiple sites	Requires detailed information of precipitation along trajectory
Potential Source Contribution Function	no	no	no	yes	yes	Provides likely source areas	Needs sufficient nos. of samples
PSCF with Apportionment	no	yes	yes	yes	yes	Provides likely source areas	Needs sufficient nos. of samples; depends on quality of emissions inventory
Residence Time Weighted Concentration	no	no	Possibly	yes	yes	Provides likely source areas and estimates of their relative contributions	Needs sufficient nos. of samples

Table T-10. Summary of the Receptor Modeling Methods, Data Requirements, and Software to Implement Them

Model	Information Required	Other Data	Software	Ease of Use
Source

Profiles	# of Ambient Samples	Requirements	Availability
Chemical Mass Balance	Yes	≥ 1	None	EPA TTN2000

WWW Site	Easy
Partial Least Squares	Yes	≥ 1	None	SCAN1,

UnScrambler2,

SIMCA3	Moderate
Artificial Neural Networks (Backpropogation)	Yes	≥ 1	None	MATLAB4	Difficult
Genetic Algorithm	Yes	≥ 1	None	MATLAB	Difficult
Simulated Annealing	Yes	≥ 1	None	MATLAB	Difficult
Artificial Neural Networks (Kohonen)	No	≥ 30 to 40;

more is better	None	Anonymous FTP at sun.mes.clarkson.edu;/pub

/hopkepk	Moderate

to Difficult
Principal Components Analysis	No	≥ 30 to 40;

more is better	None	Most standard statistical packages	Moderate
Absolute Principal Components Analysis	No	≥ 30 to 40;

more is better	Total sample mass	Most standard statistical packages	Moderate

Table T-10. Summary of the Receptor Modeling Methods, Data Requirements, and Software to Implement Them

(continued)

Model	Information Required	Other Data	Software	Ease of Use
Source

Profiles	# of Ambient Samples	Requirements	Availability
Target Transformation Factor Analysis	No	≥ 30 to 40;

more is better	Total sample mass	Available from P.K. Hopke	Difficult
SAFER	No	≥ 30 to 40;

more is better	Total sample mass;

any other elemental relationships	Available from R.C. Henry	Difficult
Positive Matrix Factorization	No	≥ 30 to 40;

more is better	Total sample mass;

estimates of uncertainty for each datum point	Available from Pentti Paatero	Difficult
Empirical Orthogonal Function Analysis	No	Multiple sites;

≥ 30 samples per site	None	Most standard statistical packages	Easy
RMAPS (SAFER)	No	Multiple sites;

≥ 30 samples per site	Any other spatial relationships	Available from R.C. Henry	Difficult
Residence Time Analysis	No	≥ 40	Back trajectories	No directly available software	Easy if trajectories are available

Table T-10. Summary of the Receptor Modeling Methods, Data Requirements, and Software to Implement Them

(continued)

Model	Information Required	Other Data	Software	Ease of Use
Source

Profiles	# of Ambient Samples	Requirements	Availability
Quantitative Trajectory Bias Analysis	No	≥ 20; better with multiple site data	Back trajectories, precipitation data along path of back trajectory	No directly available software	Difficult
Area of Influence Analysis	No	≥ 35	Back trajectories	No directly available software	Easy if trajectories are available
Potential Source Contribution Function	No	≥ 35	Back trajectories	No directly available software	Easy if trajectories are available
PSCF with Apportionment	Yes; gridded emission inventory	≥ 35	Back trajectories	No directly available software	Easy if trajectories are available
Residence Time Weighted Concentration	No	≥ 35	Back trajectories	No directly available software	Easy if trajectories are available
1 Commercial program available from Minitab, Inc.
2 Commercial program available from Camo USA
3 Commercial program available from Umetrics, Inc.
4 Commercial program available from The Math Works, Inc.

receptor modeling methods are available to provide quantitative support for PM-10 SIP development.

The situation for PM-2.5 is quite different. The majority of fine particle mass is due to secondary compounds. Initial studies strongly suggest some ability of the secondary particle receptor models to identify the likely locations of major sources of secondary particle precursors. However, the ability of these models to provide quantitative apportionment of the measured aerosol mass to those sources is more uncertain.

Several methods have been developed and applied to the problem of particle source identification and quantitative mass apportionment. For many problems, receptor methods can provide useful information that can be used to plan air quality management strategies. However, there are limits to the ability of such methods to resolve sources, particularly for those precursors that lead to the formation of secondary particles. In such cases additional information needs to be introduced. Spatial variability can be used as the basis of the analysis (Henry, 1997a, b, c) and in theory temporal variation could be used in a similar manner. Meteorological information can be included in the form of back trajectories which model the movement of the air carrying the particles to the site backward in time from the time during which the sample has been collected (Gao et al., 1993; 1994). These approaches can give an estimation of source apportionment for secondary PM (Gao et al., 1993; Cheng et al., 1996), although they depend on the availability of a source inventory. Other approaches that do not require inventory data are under development, but are clearly not sufficiently developed at this moment to be used in routine air quality planning.

There are also several methods that have been used in research studies. Thus, receptor modeling is a field in which developments are on-going and the ability to perform substantial receptor-oriented analyses opens many additional possibilities for further improvements in apportioning PM-10 and PM-2.5. However, although these methods are clearly accepted by the scientific community (as evidenced by the acceptability of this work in well respected scientific publications), it is not clear if OAQPS will accept them as part of the SIP process. An effort should be made to facilitate the issuance of new guidance to the state and local air quality managers indicating a willingness to consider more than CMB 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. Training of agency staff to use the more advanced methods will also be needed.

Receptor models require ambient PM data and, for models that need source information, emission profiles by source category. Measurement programs are needed to augment and update existing data bases (e.g., source profiles) as well as develop new data bases (e.g., speciated PM-2.5 ambient data).

T.4 Recommendations for PM SIP Modeling

As indicated above, there are significant limitations in the capabilities of available PM models and data bases. Nevertheless, Table T-11 may be viewed as a starting point for recommendations for modeling PM for SIP preparation. The following subsections present discussions of these recommendations for each one of the PM NAAQS.

T.4.1 PM-10 Annual Average NAAQS

PM source models have not at this point received sufficient scrutiny in terms of their ability to (1) reproduce annual average concentrations of PM and (2) correctly represent the effect of emission control strategies on PM annual average levels. Moreover, PM-10 concentrations are strongly affected by natural and anthropogenic emissions of mineral dust, which cannot presently be modeled with sufficient accuracy for source modeling.

Consequently, we recommend that receptor models be used for analyzing PM-10 annual average levels. Among receptor models, Chemical Mass Balance (CMB), Partial Least Squares (PLS), Principal Component Analysis (PCA) and Empirical Orthogonal Function Analysis (EOF) are the easiest to use at this moment; two of these techniques,

Table T-11. Recommended Modeling Approaches for PM SIP Preparation

NAAQS	Modeling Technique	Comments

Annual average	For the primary fraction of PM-
10 and PM-2.5:

•	Receptor model(1)

For the secondary fraction of PM-10 and PM-2.5, at least two different approaches should be used in complement:

•	Long-term source model(2)

•	Episodic model with episode aggregation(2)

•	Speciated rollback

•	Hybrid model	•	Currently, there is no suitable, evaluated modeling technique for quantitatively apportioning the secondary fraction of PM-10 and PM-2.5. Until suitable techniques are developed and evaluated, the use of two or more different approaches in complement of each other will help minimize major errors. Speciated rollback should be considered a screening method.

24-hour average	For the primary fraction of PM-10 and PM-2.5:

•	Receptor model(1)

For the primary fraction of PM-10 and PM-2.5 with known emissions and the secondary fraction of PM-10 and PM- 2.5:

•	Episodic source model(2)	•	Natural and anthropogenic sources such as fugitive dust and biomass burning have large emission uncertainties and are better characterized through receptor modeling than source modeling.

•	All episodic source models have some deficiencies in their formulation, albeit to various extents, and need to be subjected to a comprehensive performance evaluation before application and possibly additional development.
(1)	Candidate receptor modeling techniques include Chemical Mass Balance (CMB), Partial Least Squares (PLS), Principal Component Analysis (PCA), SAFER, and Positive Matrix Factorization (PMF).

(2)	Three-dimensional Eulerian grid models are recommended.

CMB and PCA, are approved as guideline modeling techniques by EPA and have been used in the preparation of SIPs (e.g., SCAQMD, 1996). PCA and EOF can be used to identify the major source categories that correspond to the PM ambient sample chemical composition. If source profiles for all these source categories are available, then a quantitative source apportionment can be carried out using CMB or PLS. CMB is directly available from EPA. Software is also available for PLS; PLS offers the advantage over CMB that it provides a test regarding whether additional source profiles are needed to explain the variations seen in the ambient samples.

Better receptor modeling techniques exist that can provide a more refined quantitative source apportionment. SAFER and Positive Matrix Factorization (PMF) are two models that we recommend. Software is available for these receptor modeling techniques, however, they are more difficult to apply than the other receptor modeling techniques mentioned above. Therefore, we recommend that technical staff with the proper training be selected to implement techniques such as SAFER and PMF.

A pure receptor modeling approach will not provide a spatially distributed allocation of precursor sources of secondary PM mass (i.e., sulfate, nitrate and organic compounds). Various approaches are available and, since none of these approaches is currently reliable, we recommend that a combination of those be used. A screening analysis can be conducted using speciated rollback, however, there are some severe limitations associated with this method and one should assure that (1) none of the method's limitations is a critical flaw for the specific area of study and (2) the results obtained with speciated rollback are checked against those obtained with other methods. A hybrid model such as Potential Source Contribution Function (PSCF) with apportionment can be used if wind trajectories can be constructed and validated. However, no standard software is available for hybrid modeling and some model development (or, at the minimum, adaptation from a past study) will be required. Finally, source models can be used to assess the contributions of precursors to PM secondary mass. If a sufficient data base exists, episodic modeling with aggregation of episodes based on their meteorological frequency is the best approach. However, since secondary mass is typically not the dominant component of PM-10 mass, this approach may be too resource intensive for its application to PM-10. A long-term model with reduced chemistry (for sulfate, nitrate and organic PM) or parameterized chemistry (for sulfate only) may be used to supplement the speciated rollback and/or hybrid modeling approaches. However, the limitations discussed above must be taken into consideration before applying such models.

T.4.2 PM-10 24-hour Average NAAQS

Seven episodic source models, CIT, DAQM, GATOR, RPM, SAQM-AERO, UAM-
AERO, and UAM-AIM, were reviewed. However, two of these models, CIT and GATOR, are very comprehensive models that have extensive computational requirements. Therefore, CIT and GATOR should be considered to be "research-grade" models and are not recommended for SIP applications. They should, however, be used as benchmarks for the other models. Of the five other models, three are mesoscale models (DAQM, RPM and SAQM-AERO) that can be applied to any scale (urban or regional) and two (UAM-AERO and UAM-AIM) are urban-scale models that should only be used for urban areas and not for regional areas.

These episodic source models have some limitations and whether any of these limitations are critical for the specific area of study should be assessed. If there are critical limitations (e.g., importance of fog prior to PM events), then, some model improvements will need to be carried out. In any case, a model performance evaluation must be conducted prior to the application of a model for SIP preparation.

Receptor models are applied to 24-hour average PM values in the same manner as they are applied to annual average PM values (as a matter of fact, the application of receptor models to annual averages uses a set of 24-hour average PM concentrations, typically collected every sixth day). Therefore, the recommendations made above for receptor modeling of annual average PM-10 concentrations apply to 24-hour average concentrations as well.

The use of receptor models is particularly important for the cases where source models are unlikely to perform well such as, for example, for the fugitive dust component of PM-10 because of the uncertainties associated with natural and anthropogenic fugitive dust emissions. Consequently, episodic source modeling should then be complemented with a receptor modeling approach for the primary coarse component of PM-10.

T.4.3 PM-2.5 Annual Average NAAQS

The same general recommendations as those made for PM-10 annual average modeling apply for PM-2.5 annual average modeling. However, the fact that secondary PM constitutes a much larger fraction of PM-2.5 mass than of PM-10 mass places more limitations on current modeling techniques, and therefore suggests the use of receptor models that use spatial, temporal or meteorological information (e.g., Potential Source Contribution Functions, Residence Time Analysis, Area of Influence Analysis).

Long-term source modeling should, in theory, be the method of choice for simulating the secondary fraction of PM-2.5. However, there are still some severe limitations to long- term models because of their simplified treatment of atmospheric chemistry (using either reduced chemistry or parameterized secondary PM formation rates). For example, the aqueous-phase formation of sulfate is treated in a manner that is too simplistic in REMSAD and UAM-LC. Clearly, existing long-term modeling techniques need further development if one wants to correctly assess source contributions for annual-average PM-2.5 concentrations.

A scientifically more sound approach may be the use of an episodic model to simulate a large number of meteorological scenarios and to weigh these scenarios according to the frequency of meteorological conditions to construct a synthetic annual average. This approach has been used with RPM in the eastern U.S. and is planned for the southern Appalachian mountains region. Clearly, this approach is resource intensive, but it offers the advantage over long-term modeling that episodic models have a better performance evaluation record and, potentially, a better representation of precursor/PM relationships.

At this moment, we recommend the use of an approach based on episodic modeling with the construction of an annual average using the frequency of meteorological conditions of the simulated episodes. When applying a source model to simulate annual average PM concentration, one must be aware, however, that the background PM-2.5 values need to be accurately determined through comprehensive monitoring programs, because they are likely to contribute a significant fraction of PM-2.5 mass.

The application of receptor modeling techniques to PM-2.5 will require using research-grade techniques because a significant fraction of PM-2.5 is due to secondary material. Therefore, the simpler methods, such as CMB, will not be very useful and a significant effort will need to be put into training technical staff into using more sophisticated receptor modeling techniques.

T.4.4 PM-2.5 24-hour Average NAAQS

The four source models mentioned above for PM-10 SIP applications (DAQM, RPM, SAQM-AERO and UAM-AERO) are also suitable to the simulation of PM-2.5 episodic concentrations, taking into account, however, their current limitations. These limitations pertain primarily to (1) the formation of inorganic PM through aqueous reactions in fog and/or clouds, (2) the treatment of secondary organic aerosol formation and (3) the lack of subgrid-scale treatment of plumes from large point sources.

For PM-2.5 mass, the fugitive dust component is unlikely to be significant, and, therefore, there may not be any need for receptor modeling support. However, uncertainties in primary emissions of certain source categories may need to be reconciled through inverse modeling or receptor modeling. Such needs will have to be assessed after performance evaluation of the source model. The limitations mentioned above regarding the application of receptor modeling to PM-2.5 will apply here as well. Therefore, the proper modeling of PM-2.5 24-hour average concentrations will require significant efforts for model improvements and applications.