Air Quality Modeling Technical Support Document: 

Source Sector Assessments 
EPA-454/R-11-006 

August 2011 

Air Quality Modeling Technical Support Document: 

Source Sector Assessments 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 27711 
1 

I Introduction 

This document describes the air quality modeling performed by EPA in support or air quality and 

deposition assessments related source sector groups. A national scale air quality modeling 

analysis was performed to estimate the impact of current and future year sector emissions on 

annual and 24-hour PM2.5 concentrations, 8-hr maximum ozone, as well as visibility 

impairment. Air quality benefits are estimated with the Comprehensive Air Quality Model with 

Extensions (CAMx) model. CAMx simulates the numerous physical and chemical processes 

involved in the formation, transport, and destruction of ozone, particulate matter and air toxics. 

In addition to the CAMx model, the modeling platform includes the emissions, meteorology, and 

initial and boundary condition data which are inputs to this model. It is important to note that the 

inventories used in the air quality modeling may be different than a final sector inventory 

presented in a Regulatory Impact Analyses. 

II. Photochemical Model Version, Inputs and Configuration 

Photochemical grid models use state of the science numerical algorithms to estimate pollutant 

formation, transport, and deposition over a variety of spatial scales that range from urban to 

continental. Emissions of precursor species are injected into the model where they react to form 

secondary species such as ozone and then transport around the modeling domain before 

ultimately being removed by deposition or chemical reaction. 

The 2005-based CAMx modeling platform was used as the basis for the air quality modeling. 

This platform represents a structured system of connected modeling-related tools and data that 

provide a consistent and transparent basis for assessing the air quality response to projected 

changes in emissions. The base year of data used to construct this platform includes emissions 

and meteorology for 2005. The modeling system treats the emissions, transport, and fate of 

criteria pollutants. 

The modeling system was used to calculate daily and annual PM2.5 concentrations, 8-hr 

maximum ozone, and visibility impairment. Model predictions are used to estimate future-year 

design values of PM2.5 and ozone using a 2016 reference scenario compared to a 2005 baseline. 

This is done by calculating the simulated air quality ratios between any particular future year 

simulation and the 2005 base. These predicted ratios are then applied to ambient base year design 

values. The design value projection methodology used here followed EPA guidance for such 

analyses (USEPA, 2007). 

A. Model version 

CAMx version 5.30 is a freely available computer model that simulates the formation and fate of 

photochemical oxidants, ozone, primary and secondary PM concentrations, and air toxics, over 

regional and urban spatial scales for given input sets of meteorological conditions and emissions. 

CAMx includes numerous science modules that simulate the emission, production, decay, 

deposition and transport of organic and inorganic gas-phase and particle-phase pollutants in the 

atmosphere (Baker and Scheff, 2007; Nobel et al., 2001; Russell, 2008). CAMx is applied with 

ISORROPIA inorganic chemistry (Nenes et al., 1999), a semi-volatile equilibrium scheme to 
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partition condensable organic gases between gas and particle phase (Strader et al., 1999), 

Regional Acid Deposition Model (RADM) aqueous phase chemistry (Chang et al., 1987), and 

Carbon Bond 05 (CB05) gas-phase chemistry module (ENVIRON, 2008; Gery et al., 1989). 

CAMx contains a variety of ozone source apportionment tools, including the original ozone 

source apportionment tool (OSAT) and the anthropogenic pre-cursor culpability assessment 

(APCA) tool (ENVIRON, 2008). Ozone source apportionment tracers are treated using the 

standard model algorithms for vertical advection, vertical diffusion, and horizontal diffusion. 

Horizontal advective fluxes for each of the regular model species that make up nitrogen oxides 

(NOX) and volatile organic compounds (VOC) are combined and normalized by a concentration 

based weighted mean. Separate ozone tracers are used in CAMx to track ozone formation that 

happens under NOX and VOC limited conditions. 

Particulate matter source apportionment technology (PSAT) implemented in CAMx estimates 

the contribution from specific emissions source groups to PM2.5 and all forms of mercury using 

reactive tracers (ENVIRON, 2008; Wagstrom et al., 2008). The tracer species are estimated with 

source apportionment algorithms rather than by the host model routines. PSAT tracks 

contribution to PM2.5 sulfate, nitrate, ammonium, secondary organic aerosol, and inert primarily 

emitted species. Non-linear processes like gas and aqueous phase chemistry are solved for bulk 

species and then apportioned to the tagged species. Emissions of nitrogen oxides are tracked 

through all intermediate nitrogen species to particulate nitrate ion. Ammonia emissions are 

tracked to particulate ammonium ion. This modeling assessment used the PSAT approach to 

estimate source contribution to PM2.5 species and the APCA method to estimate source 

contribution to modeled ozone. 

B. Model domain and grid resolution 

The modeling analyses were performed for a domain covering the continental United States as 

shown in Figure II-1. This domain has a parent horizontal grid of 36 km with two finer-scale 12 

km grids over portions of the eastern and western U.S. The model extends vertically from the 

surface to 100 millibars (approximately 15 km) using a sigma-pressure coordinate system. Air 

quality conditions at the outer boundary of the 36 km domain were taken from a global model 

and vary in time and space. The 36 km grid was only used to establish the incoming air quality 

concentrations along the boundaries of the 12 km grids. Only the finer grid data were used in 

determining the impacts of the emissions changes. Table II-1 provides geographic information 

about the photochemical model domains. 

 
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Table II-1. Geographic elements of domains used in photochemical modeling. 

 Photochemical Modeling Configuration 

 National Grid Western U.S. Fine Grid Eastern U.S. Fine Grid 

Map Projection Lambert Conformal Projection 

Grid Resolution 36 km 12 km 12 km 

Coordinate Center 97 deg W, 40 deg N 

True Latitudes 33 deg N and 45 deg N 

Dimensions 148 x 112 x 14 213 x 192 x 14 279 x 240 x 14 

Vertical extent 14 Layers: Surface to 100 millibar level (see Table II-3) 

Figure II-1. Map of the photochemical modeling domains. The black outer box denotes the 36 

km national modeling domain; the red inner box is the 12 km western U.S. grid; and the blue 

inner box is the 12 km eastern U.S. grid. 

C. Modeling Time-period 

The 36 km and both 12 km modeling domains were modeled for the entire year of 2005. Data 

from the entire year were utilized when looking at the estimation of PM2.5 and visibility 

impacts. Data from April through October is used to estimate ozone impacts. 

D. Model Inputs: Emissions, Meteorology and Boundary Conditions 

The 2005-based modeling platform was used for the air quality modeling of future emissions 

scenarios. In addition to the photochemical model, the modeling platform also consists of the 
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base- and future-year emissions estimates, meteorological fields, as well as initial and boundary 

condition data which are all inputs to the air quality model. 

1. Emissions Input Data 

The emissions data used in the base year and future reference and future emissions adjustment 

case are based on the 2005 v4.3 platform. Emissions are processed to photochemical model 

inputs with the SMOKE emissions modeling system (Houyoux et al., 2000). The 2016 reference 

case is intended to represent the emissions associated with growth and controls in that year 

projected from the 2005 simulation year. The United States EGU point source emissions 

estimates for the future year reference and control case are based on an Integrated Planning 

Model (IPM) run for criteria pollutants. Both control and growth factors were applied to a subset 

of the 2005 non-EGU point and non-point emissions to create the 2016 reference case. The 2005 

v4 platform projection factors were the starting point for most of the 2016 SMOKE-based 

projections. 

Table II.2 Domain total estimated sector emissions (TPY) for 2016 modeling scenario.

The future year projection includes emissions reductions related to the NOx SIP Call, Boiler 

MACT, RICE, and the proposed Transport Rule (TR1). The residential wood combustion 

category is defined based on SCC codes 2104008 and 2104009. Certain sectors are based on a 

list of facilities that would likely be subject to a NESHAP related to that sector: cement kilns, 

pulp & paper, refineries, coke ovens, iron/steel foundries, integrated ion/steel, electric arc 

Sector Year VOC NOX Primary PM2.5 SO2 NH3

Biogenics 2016 59,388,365 2,218,207 0 0 0

Cement Kilns 2016 3,059 130,536 1,106 48,737 679

Pulp & Paper 2016 121,597 240,139 10,067 170,393 10,859

Refineries 2016 111,391 118,206 7,379 132,337 3,556

Coke Ovens 2016 7,821 16,110 368 27,952 1,084

Iron/Steel Foundries 2016 14,384 5,867 1,366 3,590 166

Integrated Iron/Steel 2016 9,620 31,925 2,856 29,045 167

Electric Arc Furnaces 2016 3,560 15,707 622 6,088 119

Taconite Mining 2016 606 41,350 884 8,823 4

Ferroalloy Production 2016 150 3,412 201 4,580 510

Residential Wood 2016 538,466 33,786 192,492 4,720 6,586

Non‐point Other 2016 9,380,925 1,633,261 325,820 1,243,154 126,802

Non‐EGUpoint Other 2016 1,259,745 1,263,276 67,614 877,620 140,948

Average Fires 2016 1,874,035 189,428 459,641 49,094 36,777

Air/Locomotives/Marine 2016 43,547 1,342,849 35,604 9,087 940

Nonroad mobile 2016 1,953,067 1,259,578 106,975 2,879 2,345

Onroad mobile 2016 2,357,108 4,239,971 118,986 26,786 82,094

Canada + Mexico 2016 5,615,200 2,841,702 242,412 2,855,974 1,077,333

Point EGU 2016 63,198 1,826,582 30,078 3,793,362 36,706

Commercial Marine (SECA‐C3) 2016 66,093 1,534,234 7,407 439,987 0

Area Fugutive Primary PM2.5 2016 0 0 47,438 0 0
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furnaces, taconite mining, and ferroalloy production. The model domain total estimated 

emissions by source sector used in this modeling assessment are shown in Table II.2 for 2016 

and Table II.3 for 2005. 

Table II.3 Domain total estimated sector emissions (TPY) for 2005 modeling scenario

Other North American emissions of criteria and toxic pollutants (including mercury) are based 

on a 2006 Canadian inventory and 1999 Mexican inventory. Both inventories are not grown or 

controlled when used as part of future year baseline inventories. Global emissions of criteria are 

included in the modeling system through boundary condition inflow. 

2. Meteorological Input Data 

The gridded meteorological input data for the entire year of 2005 were derived from simulations 

of the Pennsylvania State University / National Center for Atmospheric Research Mesoscale 

Model. This model, commonly referred to as MM5, is a limited-area, nonhydrostatic, terrainfollowing system that solves for the full set of physical and thermodynamic equations which 

govern atmospheric motions. Meteorological model input fields were prepared separately for 

each of the three domains shown in Figure II-1 using MM5 version 3.7.4. The MM5 simulations 

were run on the same map projection as shown in Figure II-1. 

All three meteorological model runs were configured similarly. The selections for key MM5 

physics options are shown below: 

Sector Year VOC NOX Primary PM2.5 SO2 NH3

Biogenics 2005 59,388,365           2,218,207 000

Cement Kilns 2005 9,044                          217,948                 2,342        157,163                861

Pulp & Paper 2005 128,390                     246,714              11,979        345,917          10,990

Refineries 2005 112,622                     150,924                 7,750        246,351            3,556

Coke Ovens 2005 7,914                            16,145                    416          29,239            1,089

Iron/Steel Foundries 2005 15,522                             5,985                 1,435            3,611                166

Integrated Iron/Steel 2005 9,687                            31,961                 2,862          32,646                167

Electric Arc Furnaces 2005 3,630                            15,707                    622            6,088                119

Taconite Mining 2005 607                                41,350                 1,035          11,285                     4

Ferroalloy Production 2005 150                                   3,412                    201            4,580                510

Residential Wood 2005 647,141                       38,190            222,081            5,280            7,238

Non‐point Other 2005 12,453,787           1,659,651            326,140    1,251,930        126,802

Non‐EGUpoint Other 2005 1,747,491              1,297,668              71,899    1,215,203        141,230

Average Fires 2005 1,874,035                  189,428            459,641          49,094          36,777

Air/Locomotives/Marine 2005 58,890                    1,909,526              53,671        154,016                773

Nonroad mobile 2005 3,353,497              2,083,093            181,038        195,597            1,969

Onroad mobile 2005 4,734,097              9,006,389            258,251        176,525        156,276

Canada + Mexico 2005 5,615,200              2,841,702            242,412    2,855,974    1,077,333

Point EGU 2005 63,404                    3,726,455              39,544 10,380,773          21,684

Commercial Marine (SECA‐C3) 2005 42,438                    1,169,907              10,501        740,998 0

Area Fugutive Primary PM2.5 2005 0 0 47,315               0 0
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• Pleim-Xiu PBL and land surface schemes 

• Kain-Fritsh 2 cumulus parameterization 

• Reisner 2 mixed phase moisture scheme 

• RRTM longwave radiation scheme 

• Dudhia shortwave radiation scheme 

Three dimensional analysis nudging for temperature and moisture was applied above the 

boundary layer only. Analysis nudging for the wind field was applied above and below the 

boundary layer. The 36 km domain nudging weighting factors were 3.0 x 104 for wind fields and 

temperatures and 1.0 x 105 for moisture fields. The 12 km domain nudging weighting factors 

were 1.0 x 104

 for wind fields and temperatures and 1.0 x 105 for moisture fields. 

Table II-4. Vertical layer structure (heights are layer top). 

CAMX Layers MM5 Layers Sigma P Approximate Height 

(m) 

Approximate Pressure 

(mb) 

0 0 1.000 0 1000 

1 1 0.995 38 995 

2 2 0.990 77 991 

3 3 0.985 115 987 

4 0.980 154 982 

4 5 0.970 232 973 

6 0.960 310 964 

5 7 0.950 389 955 

8 0.940 469 946 

6 

9 0.930 550 937 

10 0.920 631 928 

11 0.910 712 919 

7 

12 0.900 794 910 

13 0.880 961 892 

14 0.860 1,130 874 

8 

15 0.840 1,303 856 

16 0.820 1,478 838 

17 0.800 1,657 820 

9 18 0.770 1,930 793 

19 0.740 2,212 766 

10 20 0.700 2,600 730 

21 0.650 3,108 685 

11 22 0.600 3,644 640 

23 0.550 4,212 595 

12 

24 0.500 4,816 550 

25 0.450 5,461 505 

26 0.400 6,153 460 

13 

27 0.350 6,903 415 

28 0.300 7,720 370 

29 0.250 8,621 325 

30 0.200 9,625 280 

14 

31 0.150 10,764 235 

32 0.100 12,085 190 

33 0.050 13,670 145 

34 0.000 15,674 100 

All three sets of model runs were conducted in 5.5 day segments with 12 hours of overlap for 

spin-up purposes. All three domains contained 34 vertical layers with an approximately 38 m 

deep surface layer and a 100 millibar top. The MM5 and CAMx vertical structures are shown in 

Table II-4 and do not vary by horizontal grid resolution. The meteorological outputs from all 
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three MM5 sets were processed to create model-ready inputs for CAMx using the MCIP 

processor. 

Before initiating the air quality simulations, it is important to identify the biases and errors 

associated with the meteorological modeling inputs. The 2005 MM5 model performance 

evaluations used an approach which included a combination of qualitative and quantitative 

analyses to assess the adequacy of the MM5 simulated fields. The qualitative aspects involved 

comparisons of the model-estimated synoptic patterns against observed patterns from historical 

weather chart archives. Additionally, the evaluations compared spatial patterns of estimated to 

observed monthly average rainfall and checked maximum planetary boundary layer (PBL) 

heights for reasonableness. 

Qualitatively, the model fields closely matched the observed synoptic patterns, which is not 

unexpected given the use of nudging. The operational evaluation included statistical 

comparisons of model/observed pairs (e.g., mean normalized bias, mean normalized error, index 

of agreement, root mean square errors, etc.) for multiple meteorological parameters. For this 

portion of the evaluation, five meteorological parameters were investigated: temperature, 

humidity, shortwave downward radiation, wind speed, and wind direction. The three individual 

MM5 evaluations are described elsewhere (Baker, 2009a, b, c). It was ultimately determined that 

the bias and error values associated with all three sets of 2005 meteorological data were 

generally within the range of past meteorological modeling results that have been used for air 

quality applications. 

3. Initial and Boundary Conditions 

The lateral boundary and initial species concentrations are provided by a three-dimensional 

global atmospheric chemistry model, the GEOS-CHEM model (standard version 7-04-11). The 

global GEOS-CHEM model simulates atmospheric chemical and physical processes driven by 

assimilated meteorological observations from the NASA’s Goddard Earth Observing System 

(GEOS). This model was run for 2005 with a grid resolution of 2.0 degree x 2.5 degree 

(latitude-longitude) and 30 vertical layers up to 100 mb. The predictions were used to provide 

one-way dynamic boundary conditions at three-hour intervals and an initial concentration field 

for the 36 km CAMx simulations. The 36 km photochemical model simulation is used to supply 

initial and hourly boundary concentrations to the 12 km domains. The 36 km domain simulation 

includes 10 days of spin-up before the start of each calendar quarter that are not used in the 

analysis. The 12 km domain simulations include 3 days of spin-up before each calendar quarter. 

The future base conditions from the 36 km coarse grid modeling were used as the 

initial/boundary state for all subsequent future year 12 km finer grid modeling scenarios. 

 
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III. Base Case Model Performance Evaluation 

A. PM2.5 

An operational model performance evaluation for the speciated components of PM2.5 (e.g., 

sulfate, nitrate, elemental carbon, organic carbon, etc.) was conducted using 2005 monitoring 

data in order to estimate the ability of the modeling system to replicate base year concentrations. 

The evaluation of PM2.5 component species includes comparisons of predicted and observed 

concentrations of sulfate (SO4), nitrate (NO3), ammonium (NH4), elemental carbon (EC), and 

organic carbon (OC). PM2.5 ambient measurements for 2005 were obtained from the Chemical 

Speciation Network (CSN) and the Interagency Monitoring of PROtected Visual Environments 

(IMPROVE). The CSN sites are generally located within urban areas and the IMPROVE sites 

are typically in rural/remote areas. The measurements at CSN and IMPROVE sites represent 24-

hour average concentrations. In calculating the model performance metrics, the modeled hourly 

species predictions were aggregated to the averaging times of the measurements. 

Model performance statistics were calculated for observed/predicted pairs of daily concentrations 

(Boylan and Russell, 2006). The aggregated metrics and number (N) of prediction-observation 

pairs are shown by chemical specie and quarter in Table III-1. Model performance was 

compared to the performance found in recent regional PM2.5 model applications for other, nonEPA studies. Overall, the mean bias (bias) and mean error (error) statistics are within the range 

or close to that found by other groups in recent applications (Doraiswamy, 2010; Tesche et al., 

2006). The model performance results give us confidence that our application of CAMx using 

this modeling platform provides a scientifically credible approach for estimating PM2.5 

concentrations for the purposes of this assessment. 

 
9 

TABLE III-1. Model performance metrics for speciated PM2.5 averaged by quarter. 

B. Ozone 

An operational model performance evaluation for hourly and eight-hour daily maximum ozone 

was conducted in order to estimate the ability of the modeling system to replicate the base year 

concentrations. Ozone measurements were taken from the 2005 State/local monitoring site data 

in the Air Quality System (AQS) Aerometric Information Retrieval System (AIRS). The ozone 

metrics covered in this evaluation include one-hour and eight-hour average daily maximum 

ozone bias and error. The evaluation principally consists of statistical assessments of model 

versus observed pairs that are paired in time and space. This ozone model performance was 

limited to the prediction-observation pairs where observed ozone exceeded or equaled 60 ppb. 

This cutoff was applied to evaluate the model on days of elevated ozone which are more policy 

relevant. Aggregated performance metrics by quarter are shown in Table III-2 for daily 

maximum 1-hr average ozone and Table III-3 for daily maximum 8-hr average ozone. 

 

Specie Quarter N Observed Predicted Bias Error

PM2.5 Sulfate Ion 1 6,218 2.7 4.2 1.6 2.0

2 6,537 3.8 4.4 0.7 1.6

3 6,108 5.4 5.2 0.0 1.9

4 5,894 2.5 3.7 1.3 1.5

PM2.5Nitrate Ion 1 5,870 2.4 1.9 ‐0.4 1.3

2 6,146 0.8 0.6 ‐0.1 0.6

3 5,828 0.5 0.2 ‐0.2 0.4

4 5,757 1.3 1.1 ‐0.1 0.8

PM2.5 Ammonium Ion 1 4,045 1.7 1.8 0.4 0.8

2 4,075 1.5 1.5 0.3 0.7

3 3,869 1.8 1.5 0.0 0.8

4 3,763 1.3 1.4 0.4 0.7

PM2.5Organic Carbon 1 5,882 1.7 2.2 0.6 1.1

2 6,180 2.1 1.7 ‐0.4 0.9

3 5,836 2.4 2.3 ‐0.1 0.9

4 5,600 2.0 2.0 0.1 1.0

PM2.5 Elemental Carbon 1 6,368 0.5 1.0 0.5 0.6

2 6,552 0.5 0.7 0.3 0.4

3 6,061 0.5 0.8 0.3 0.4

4 5,937 0.6 0.9 0.4 0.5
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TABLE III-2. Model performance metrics for daily maximum 1-hr avg ozone by quarter. 

TABLE III-3. Model performance metrics for daily maximum 8-hr avg ozone by quarter. 

This model performance is consistent with photochemical modeling used to support other 

national regulations (USEPA, 2010).

IV. References

Baker, K., Dolwick, P., 2009a. Meteorological Modeling Performance Evaluation for the Annual 2005 Continental 

U.S. 36-km Domain Simulation. US Environmental Protection Agency OAQPS. 

Baker, K., Dolwick, P., 2009b. Meteorological Modeling Performance Evaluation for the Annual 2005 Eastern U.S. 

12-km Domain Simulation. US Environmental Protection Agency OAQPS, RTP. 

Baker, K., Dolwick, P., 2009c. Meteorological Modeling Performance Evaluation for the Annual 2005 Western U.S. 

12-km Domain Simulation, in: EPA, U. (Ed.). US Environmental Protection Agency OAQPS. 

Baker, K., Scheff, P., 2007. Photochemical model performance for PM2.5 sulfate, nitrate, ammonium, and precursor 

species SO2, HNO3, and NH3 at background monitor locations in the central and eastern United States. 

Atmospheric Environment 41, 6185-6195. 

Boylan, J.W., Russell, A.G., 2006. PM and light extinction model performance metrics, goals, and criteria for threedimensional air quality models. Atmospheric Environment 40, 4946-4959. 

Chang, J.S., Brost, R.A., Isaksen, I.S.A., Madronich, S., Middleton, P., Stockwell, W.R., Walcek, C.J., 1987. A 3-

Dimensional Eulerian Acid Deposition Model - Physical Concepts and Formulation. J. Geophys. Res.-Atmos. 92, 

14681-14700. 

Doraiswamy, P., Hogrefe, C., Hao, W., Civerolo, K., Ku, J., Sistla, G., 2010. A Retrospective Comparison of 

Model-Based Forecasted PM2.5 Concentrations with Measurements. Journal of Air & Waste Management 

Association 60, 1293-1308. 

ENVIRON, 2008. User's Guide Comprehensive Air Quality Model with Extensions. ENVIRON International 

Corporation, Novato. 

Gery, M.W., Whitten, G.Z., Killus, J.P., Dodge, M.C., 1989. A Photochemical Kinetics Mechanism for Urban and 

Regional Scale Computer Modeling. J. Geophys. Res.-Atmos. 94, 12925-12956. 

Houyoux, M.R., Vukovich, J.M., Coats, C.J., Wheeler, N.J.M., Kasibhatla, P.S., 2000. Emission inventory 

development and processing for the Seasonal Model for Regional Air Quality (SMRAQ) project. Journal of 

Geophysical Research-Atmospheres 105, 9079-9090. 

Quarter N Observed Predicted Bias Error

Fractional

Bias

Fractional

Error

2 19,821 76 65 ‐11 12 ‐16 18

3 22,699 80 71 ‐9 13 ‐13 17

Quarter N Observed Predicted Bias Error

Fractional

Bias

Fractional

Error

2 19,833 69 60 ‐9 11 ‐15 17

3 22,714 70 64 ‐6 10 ‐10 16
11 

Nenes, A., Pandis, S.N., Pilinis, C., 1999. Continued development and testing of a new thermodynamic aerosol 

module for urban and regional air quality models. Atmospheric Environment 33, 1553-1560. 

Nobel, C.E., McDonald-Buller, E.C., Kimura, Y., Allen, D.T., 2001. Accounting for spatial variation of ozone 

productivity in NOx emission trading. Environmental Science & Technology 35, 4397-4407. 

Russell, A.G., 2008. EPA Supersites Program-related emissions-based particulate matter modeling: Initial 

applications and advances. J. Air Waste Manage. Assoc. 58, 289-302. 

Strader, R., Lurmann, F., Pandis, S.N., 1999. Evaluation of secondary organic aerosol formation in winter. 

Atmospheric Environment 33, 4849-4863. 

Tesche, T.W., Morris, R., Tonnesen, G., McNally, D., Boylan, J., Brewer, P., 2006. CMAQ/CAMx annual 2002 

performance evaluation over the eastern US. Atmospheric Environment 40, 4906-4919. 

USEPA, 2007. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality 

Goals for Ozone, PM2.5, and Regional Haze, RTP. 

USEPA, 2010. Air Quality Modeling Technical Support Document: Boiler Source Sector Rules (EPA-454/R-10-

006), Research Triangle Park, North Carolina. 

Wagstrom, K.M., Pandis, S.N., Yarwood, G., Wilson, G.M., Morris, R.E., 2008. Development and application of a 

computationally efficient particulate matter apportionment algorithm in a three-dimensional chemical transport 

model. Atmospheric Environment 42, 5650-5659. 
United States 

Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 

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