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

The EPA Administrator has promulgated revised National Ambient Air Quality Standards (NAAQS) for particulate matter (PM) that include 24-hour and annual average standards for both PM-10 and PM-2.5. The implication of the new PM-2.5 standards is that more emphasis will be placed on controlling the fine fraction of PM, i.e., controlling emissions of fine particles directly emitted into the atmosphere (primary PM) as well as emissions of gases that are precursors of condensable gases that lead to PM formation (secondary PM). Such precursor gases include sulfur dioxide (SO2), nitrogen oxides (NOx) and volatile organic compounds (VOC) that lead to formation of sulfate, nitrate and organic PM, respectively.

We present in this report a critical review of existing modeling tools that are currently available for the development of source-receptor relationships for PM concentrations. This review addresses both source models and receptor models. 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 addresses these two categories of models separately. However, the two approaches are treated together in our recommendations for PM modeling for the preparation of State Implementation Plans (SIP).

Source Models

Our review of source models focuses on three-dimensional Eulerian air quality models that include a PM module. Lagrangian models such as plume trajectory models were not considered as suitable for SIP preparation as Eulerian models. Eulerian models can be grouped into two broad categories: (1) air quality models that include a detailed treatment of atmospheric chemistry but are generally limited in their applications to simulations of a few days because of their computational costs (hereafter referred to as episodic models) and (2) air quality models that use a simplified treatment of atmospheric chemistry and, therefore, can be applied to longer time periods (e.g., one year or more) without prohibitive computational costs (hereafter referred to as long-term models). Seven episodic models and three long-term models were reviewed.

The seven episodic models are:

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)

The three long-term models are:

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)

It appears from this review that none of these models is currently suitable for simulating PM ambient concentrations under a wide range of conditions.

The following limitations were identified in episodic models:

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

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

c)	The urban-scale models would require modifications if they were to be applied to regional scales.

d)	All models but one lack subgrid-scale treatment of point source plumes.

Long-term models present the same limitations as those mentioned above for episodic models. In addition, the simplified treatments of chemistry used in long-term models have not been sufficiently evaluated to be considered reliable when estimating the effect of emission changes on PM concentrations.

An alternative approach for predicting annual average PM concentrations is to apply an episodic model for several typical meteorological scenarios and to reconstruct a full year by recombining these scenarios with appropriate weighting factors. Therefore, this alternative approach involves making approximations with the representativeness of the meteorology of the selected scenarios. To date, this approach has not been the subject of a comprehensive evaluation and its reliability is, therefore, unknown.

In addition to the limitations pointed out above regarding the formulations of PM source models, it must be noted that the application of PM source models requires input data for emissions, meteorology, and ambient concentrations of PM and gases. Although several data bases exist, only a few of those were found to be appropriate for comprehensive evaluations of PM source models and significant uncertainties are still associated with emission inventories of all data bases. For example, ammonia and VOC emission inventories involve large uncertainties that will affect the predictions of secondary PM, whereas uncertainties in natural and anthropogenic emissions of mineral dust will affect the predictions of primary PM. Moreover, all the best data bases correspond to California regions and there is a serious lack of data in the rest of the United States (U.S.). Therefore, there is an urgent need, not only for improvements in the formulations of PM source models but also for the development and implementation of field programs that can provide the data bases needed to operate and evaluate the PM models.

Receptor Models

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.

In each of these categories, there exist some well established techniques as well as some recent emerging techniques. Seventeen receptor modeling techniques that range from well-established techniques such as Chemical Mass Balance (CMB) to research-grade techniques were reviewed.

For the primary component of PM, there are several receptor modeling techniques that are available to provide quantitative support for SIP development. CMB has been applied to PM-10 problems throughout the western U.S. with generally good success. New methods of factor analysis can also be employed in areas where source profiles are not available. For example, SAFER and Positive Matrix Factorizations have been successfully applied. However, one major concern is that state and local regulatory agencies may not take advantage of these latter techniques because EPA's Office of Air Quality Planning and Standards (OAQPS) currently only recognizes CMB and Principal Components Analysis (PCA) in their PM-10 SIP development guidance documents.

The reliability of receptor models for PM-2.5 is quite different since the majority of the fine particle mass is due to secondary PM. Initial studies strongly suggest some ability of the secondary PM 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 PM mass to those sources is more uncertain.

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).

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.

Recommendations for PM SIP Modeling

Table E-1 presents a summary of our recommendations for modeling PM for SIP preparation. The recommendations are similar for PM-10 and PM-2.5; however, because secondary PM constitutes a larger fraction of PM-2.5 than of PM-10, the limitations associated with secondary PM modeling are more severe for PM-2.5 than for PM-10.

For PM annual average modeling, receptor modeling techniques are recommended. Chemical Mass Balance (CMB) and Principal Component Analysis (PCA) have been used for PM-10 SIP development while Empirical Orthogonal Function Analysis (EOF) and Partial Least Squares (PLS) have proven useful in research applications. However, better receptor modeling techniques exist that can provide a more refined quantitative source apportionment. SAFER and Positive Matrix Factorization (PMF) are two such models that we recommend. Since 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. We recommend that at least two of the following approaches be used in complement of each other, since none of them has been thoroughly evaluated over a wide range of conditions.

a)	Speciated rollback (as a screening analysis)

b)	A hybrid model such as Potential Source Contribution Function (PSCF) with apportionment

c)	Source modeling using either a long-term source model or an episodic model with aggregation of episodes based on their meteorological frequency.

Table E-1. 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.

The emphasis on the more resource-intensive techniques (e.g., episodic modeling with aggregation of episodes) is only justified for cases where the secondary fraction of PM is significant (i.e., for PM-2.5 rather than for PM-10).

For PM 24-hour average modeling, we recommend to use an episodic three-
dimensional Eulerian grid model for sources with known emissions. However, one must be aware that there are some severe limitations associated with most of these models and a proper model performance evaluation must be conducted prior to their application. For cases where the source emission rate is poorly characterized, (e.g., fugitive dust, biomass burning), receptor modeling should be used to complement source modeling.

In summary, it appears that although a solid modeling framework exists to address PM NAAQS, (1) significant developments are needed in the current formulations of source models, (2) further applications of the most advanced receptor modeling techniques are warranted and (3) there is a critical need for the collection of additional data (both ambient data and emission data) that are required for the application and evaluation of source and receptor models.