Air Quality Modeling Technical Support Document: 

National Emission Standards for Hazardous Air Pollutants 

from the Portland Cement Manufacturing Industry 
2

EPA-454/R-10-004 

July 2010 

Air Quality Modeling Technical Support Document: 

National Emission Standards for Hazardous Air Pollutants 

from the Portland Cement Manufacturing Industry 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 27711 

July 2010 
3

I Introduction 

This document describes the air quality modeling performed by EPA in support of the Portland 

Cement NESHAP. A national scale air quality modeling analysis was performed to estimate the 

impact of the sector emissions changes on future year: annual and 24-hour PM2.5 

concentrations, total mercury deposition, 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. 

Emissions and air quality modeling decisions are made early in the analytical process. For this 

reason, it is important to note that the inventories used in the air quality modeling and the 

benefits modeling are slightly different than the final adjusted cement kiln sector inventories 

presented in the RIA. However, the air quality inventories and the final rule inventories are 

generally consistent, so the air quality modeling adequately reflects the effects of the rule. 

II. Photochemical Model Version, Inputs and Configuration 

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

this final rule. 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 platform is intended to support a variety of regulatory 

and research model applications and analyses. This modeling platform and analysis is described 

below. 

A. Model version 

CAMx version 5.10 is a freely available computer model that simulates the formation and fate of 

photochemical oxidants, 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 

(Nobel, McDonald-Buller et al. 2001; Baker and Scheff 2007; Russell 2008). CAMx is applied 

with ISORROPIA inorganic chemistry (Nenes, Pandis et al. 1999), a semi-volatile equilibrium 

scheme to partition condensable organic gases between gas and particle phase (Strader et al., 

1999), Regional Acid Deposition Model (RADM) aqueous phase chemistry (Chang, Brost et al. 

1987), and Carbon Bond 05 (CB05) gas-phase chemistry module (Gery, Whitten et al. 1989; 

ENVIRON 2008). 
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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 did not change over the simulations. In turn, 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 emission standard program changes. Table II-1 

provides some basic geographic information regarding the photochemical model domains. 

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 domain. 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. 
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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, total mercury 

deposition, and visibility impacts from the regulation. 

D. Model Inputs: Emissions, Meteorology and Boundary Conditions 

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

scenarios. As noted in the introduction, in addition to the CAMx model, the modeling platform 

also consists of the base- and future-year emissions estimates (both anthropogenic and biogenic), 

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 platform. The emissions cases use some different emissions data 

than the official v4 platform to use data intended only for the rule development and not for 

general use. Unlike the 2005 v4 platform, the configuration for this modeling application 

included some additional hazardous air pollutants (HAPs) and a cement kiln sector emissions 

inventory more consistent with the engineering analysis of potential control options. 

The 2013 reference case is intended to represent the emissions associated with growth and 

controls in that year. The US 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, 

hydrochloric acid, and mercury in 2013 (though hydrochloric acid was not modeled). Both 

control and growth factors were applied to a subset of the 2005 non-EGU point and nonpoint to 

create the 2013 reference case. The 2002 v3.1 platform 2020 projection factors were the starting 

point for most of the 2013 SMOKE-based projections. 

The 2013 reference scenario for the cement kiln sector assumed no growth or control for the 

industry from the 2005 sector emissions estimates with the exception that facilities that closed 

between 2005 and 2010 were removed from the 2013 inventory. The length of time required to 

conduct emissions and photochemical modeling preclude the use of the final facility specific 

emissions estimates based on controls implemented for this rule. A 2013 “control” or emissions 

adjustment case was developed by removing all Portland Cement sector emissions from the 2013 

baseline inventory. Portland Cement sector emissions in this analysis refer to facilities that burn 

hazardous waste and those that do not burn hazardous waste. This “zero-out” of the sector 

creates a policy space where potential controls would be maximized at all locations. Since this is 

unrealistic, the air quality estimates from the 2013 “zero-out” or “control” case are adjusted to 

reflect nation-wide estimates of control percentages by pollutant. 

As part of the analysis for this rulemaking, the modeling system was used to calculate daily and 

annual PM2.5 concentrations, annual total mercury deposition levels and visibility impairment. 
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Model predictions are used in a relative sense to estimate scenario-specific, future-year design 

values of PM2.5 and ozone. Specifically, we compare a 2013 reference scenario, a scenario 

without the cement kiln controls, to a 2013 control scenario which includes the adjustments to 

the cement kiln sector. 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). Additionally, the raw model outputs are also 

used in a relative sense as inputs to the health and welfare impact functions of the benefits 

analysis. Only model predictions for mercury deposition were analyzed using absolute model 

changes, although these parameters also considered percent changes between the control case 

and two future baselines. 

Table II.2 2005 and estimated future year sector emissions 

Specie 2005 Future Year Baseline

Nitrogen Oxides 216,525 199,391

Volatile Organic Compounds 8,817 8,419

Sulfur Dioxide 158,560 149,013

Primary PM2.5 16,758 15,403

PM2.5 Mercury 0.8 0.7

Reactive Gas Phase Mercury 6.2 6.0

Elemental Mercury 3.8 3.6

Cement Kiln Emissions (TPY)

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: 

• 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 
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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. 

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-3 and do not vary by horizontal grid resolution. The meteorological outputs from all 

three MM5 sets were processed to create model-ready inputs for CAMx using the MM5CAMx 

processor to derive the specific inputs. 

Table II-3. 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 

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 
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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 2009; Baker 2009; Baker 2009). 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 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. 

E. Base Case Model Performance Evaluation 

1. PM2.5 

An operational model performance evaluation for PM2.5 and its related speciated components 

(e.g., sulfate, nitrate, elemental carbon, organic carbon, etc.) was conducted using 2005 

state/local 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 
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metrics, the CAMx hourly species predictions were aggregated to the averaging times of the 

measurements. 

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

concentrations. These metrics are averaged by season. Statistics were generated for the following 

geographic groupings: domain wide Eastern and Western United States. The “acceptability” of 

model performance was judged by comparing our 2005 performance results to the range of 

performance found in recent regional PM2.5 model applications for other, non-EPA studies. 

Overall, the mean bias (bias) and gross mean error (error) statistics shown in Table II-4 and 

Table II-5 are within the range or close to that found by other groups in recent applications. The 

model performance results give us confidence that our application of CAMx using this modeling 

platform provides a scientifically credible approach for assessing PM2.5 concentrations for the 

purposes of this assessment. The number (N) of monitor locations used to estimate the 

aggregated metrics are shown by chemical specie, quarter, network, and domain in Table II-6. 

TABLE II-4. Bias (μg/m3

) metric by quarter, network, and model domain. 

Domain Network Quarter Metric SO4 NO3 NH4 OC EC CRUSTAL

EAST IMPROVE 1 bias (ug/m3) 1.02 0.37 0.75 0.33 0.09 1.93

EAST IMPROVE 2 bias (ug/m3) 0.37 -0.03 0.91 -0.14 -0.07 1.03

EAST IMPROVE 3 bias (ug/m3) -0.34 -0.06 0.88 0.08 -0.09 1.07

EAST IMPROVE 4 bias (ug/m3) 0.79 0.34 0.98 0.08 0.01 1.54

EAST CSN 1 bias (ug/m3) 1.53 0.55 0.54 0.61 0.49 3.82

EAST CSN 2 bias (ug/m3) 0.58 0.11 0.28 -0.62 0.28 2.40

EAST CSN 3 bias (ug/m3) -0.42 -0.15 -0.15 -0.54 0.28 2.62

EAST CSN 4 bias (ug/m3) 1.21 0.49 0.50 -0.13 0.29 3.41

WEST IMPROVE 1 bias (ug/m3) 0.31 -0.04 0.03 0.03 0.66

WEST IMPROVE 2 bias (ug/m3) 0.05 -0.24 -0.08 0.01 0.42

WEST IMPROVE 3 bias (ug/m3) -0.28 -0.21 0.39 0.05 0.70

WEST IMPROVE 4 bias (ug/m3) 0.14 0.04 -0.17 -0.02 0.55

WEST CSN 1 bias (ug/m3) 0.42 -0.44 0.14 -0.08 0.55 3.32

WEST CSN 2 bias (ug/m3) -0.04 -0.54 0.01 -0.26 0.35 1.61

WEST CSN 3 bias (ug/m3) -0.66 -0.82 -0.29 -0.13 0.44 1.90

WEST CSN 4 bias (ug/m3) 0.14 -0.78 -0.13 -1.57 0.18 2.63
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TABLE II-5. Gross error (μg/m3

) metric by quarter, network, and model domain. 

Domain Network Quarter Metric SO4 NO3 NH4 OC EC CRUSTAL

EAST IMPROVE 1 error (ug/m3) 1.23 0.99 0.86 0.63 0.20 1.93

EAST IMPROVE 2 error (ug/m3) 1.43 0.40 1.09 0.61 0.19 1.04

EAST IMPROVE 3 error (ug/m3) 1.77 0.21 1.14 0.55 0.17 1.08

EAST IMPROVE 4 error (ug/m3) 1.03 0.74 1.01 0.57 0.17 1.54

EAST CSN 1 error (ug/m3) 1.99 1.76 0.90 1.36 0.61 3.83

EAST CSN 2 error (ug/m3) 1.85 0.84 0.76 1.18 0.45 2.41

EAST CSN 3 error (ug/m3) 2.38 0.52 0.80 1.09 0.45 2.64

EAST CSN 4 error (ug/m3) 1.55 1.22 0.73 1.39 0.57 3.45

WEST IMPROVE 1 error (ug/m3) 0.43 0.54 0.35 0.13 0.67

WEST IMPROVE 2 error (ug/m3) 0.50 0.34 0.42 0.12 0.43

WEST IMPROVE 3 error (ug/m3) 0.56 0.25 0.80 0.16 0.71

WEST IMPROVE 4 error (ug/m3) 0.34 0.49 0.49 0.14 0.58

WEST CSN 1 error (ug/m3) 0.91 2.33 0.89 2.83 0.84 3.35

WEST CSN 2 error (ug/m3) 0.79 0.84 0.44 1.23 0.52 1.62

WEST CSN 3 error (ug/m3) 1.00 0.90 0.50 1.23 0.58 1.90

WEST CSN 4 error (ug/m3) 0.71 2.46 0.96 3.21 0.85 2.74

TABLE II-6. Number (N) of monitors used for metric estimation by quarter, network, and 

model domain. 

Domain Network Quarter Metric SO4 NO3 NH4 OC EC CRUSTAL

EAST IMPROVE 1 N 2,586 2,586 409 2,666 2,670 2,419

EAST IMPROVE 2 N 2,868 2,868 406 2,861 2,867 2,704

EAST IMPROVE 3 N 2,633 2,633 394 2,585 2,573 2,411

EAST IMPROVE 4 N 2,513 2,513 382 2,503 2,511 2,388

EAST CSN 1 N 3,640 3,292 3,640 3,224 3,706 3,580

EAST CSN 2 N 3,669 3,278 3,669 3,319 3,685 3,609

EAST CSN 3 N 3,475 3,195 3,475 3,251 3,488 3,441

EAST CSN 4 N 3,381 3,244 3,381 3,097 3,426 3,359

WEST IMPROVE 1 N 2,833 2,833 0 2,740 2,760 2,548

WEST IMPROVE 2 N 3,043 3,043 0 2,994 3,002 2,879

WEST IMPROVE 3 N 2,648 2,648 0 2,632 2,615 2,462

WEST IMPROVE 4 N 2,809 2,809 0 2,714 2,733 2,632

WEST CSN 1 N 1,089 1,010 1,087 970 1,086 1,046

WEST CSN 2 N 1,085 990 1,076 975 1,055 1,032

WEST CSN 3 N 1,080 1,011 1,076 1,011 1,037 1,027

WEST CSN 4 N 1,100 1,052 1,088 1,021 1,071 1,051

III. Model Results 

As described above, we performed a series of air quality modeling simulations for the 

continental U.S in order to assess the impacts of emissions adjustments to the Portland cement 

kiln sector. We looked at impacts on future ambient PM2.5, total mercury deposition levels and 

visibility impairment. In this section, we present information on current and projected levels of 

pollution for 2013. 
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A. Impacts of Sector on Total Mercury Deposition 

This section summarizes the results of our modeling of differences in total mercury deposition 

impacts in the future based on changes to the cement kiln emissions. Specifically, we compare a 

2013 reference scenario to a 2013 emissions change scenario. Model results for the eastern and 

central United States indicate that total mercury deposition (wet and dry forms) would be 

reduced by a total of 44,946 μg/m2

. A reduction of 19,231 μg/m2 is estimated for the western 

United States. 

Figure III-1. Changes in Total Mercury Deposition (μg/m2) Between the Reference Case 

and the Emissions Reduction Scenario for the Eastern and Central United States

Figure III-2. Changes in Total Mercury Deposition (μg/m2) Between the Reference Case 

and the Emissions Reduction Scenario for the Eastern and Central United States
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The reductions to total annual mercury deposition estimated by the photochemical model show 

that the reductions tend to be greatest nearest the sources. 

B. Impacts of Sector on Future Annual PM2.5 Levels 

This section summarizes the results of our modeling of annual average PM2.5 air quality impacts 

in the future due to reductions in emissions from this sector. Specifically, we compare a 2013 

reference scenario to a 2013 control scenario. The modeling assessment indicates a decrease up 

to 0.3 μg/m3

 in annual PM2.5 design values is possible given an area’s proximity to controlled 

sources and the amount of reduced sulfur dioxide and primary PM2.5 emissions. The median 

reduction over all monitor locations is 0.09 μg/m3

. An annual PM2.5 design value is the 

concentration that determines whether a monitoring site meets the annual NAAQS for PM2.5. 

The full details involved in calculating an annual PM2.5 design value are given in appendix N of 

40 CFR part 50. Projected air quality benefits are estimated using procedures outlined by United 

States Environmental Protection Agency modeling guidance (USEPA 2007). 

C. Impacts of Sector on Future 24-hour PM2.5 Levels 

This section summarizes the results of our modeling of 24-hr average PM2.5 air quality impacts 

in the future due to reductions in emissions from this sector. Specifically, we compare a 2013 

reference scenario to a 2013 control scenario. The modeling assessment indicates a decrease up 

to 0.5 μg/m3

 in 24-hr average PM2.5 design values at most monitor locations in the United States 

is possible given an area’s proximity to controlled sources and the amount of reduced sulfur 

dioxide and primary PM2.5 emissions. The median reduction over all monitor locations is 0.1 

μg/m3. The maximum reduction was 1.5 μg/m3 at a monitor located in Oklahoma. A 24-hour 

PM2.5 design value is the concentration that determines whether a monitoring site meets the 24-

hour NAAQS for PM2.5. The full details involved in calculating a 24-hour PM2.5 design value 

are given in appendix N of 40 CFR part 50. Projected air quality benefits are estimated using 

procedures outlined by United States Environmental Protection Agency modeling guidance 

(USEPA 2007). 

D. Impacts of Sector on Future Visibility Levels 

Air quality modeling conducted for this final rule was used to project visibility conditions in 138 

mandatory Class I federal areas across the U.S. in 2013 (USEPA 2007). The level of visibility 

impairment in an area is based on the light-extinction coefficient and a unitless visibility index, 

called a “deciview”, which is used in the valuation of visibility. The deciview metric provides a 

scale for perceived visual changes over the entire range of conditions, from clear to hazy. Under 

many scenic conditions, the average person can generally perceive a change of one deciview. 

Higher deciview values are indicative of worse visibility. Thus, an improvement in visibility is a 

decrease in deciview value. The modeling assessment indicates a decrease up to 0.31 deciviews 

in annual 20% worst visibility days is possible given an area’s proximity to controlled sources 

and the amount of reduced sulfur dioxide and primary PM2.5 emissions. Median reductions are 

0.01 deciviews to the 20% worst days and 20% best days over all monitor locations. 
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IV. References 

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

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

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

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

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

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

Baker, K. and P. Scheff (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. 

Chang, J. S., R. A. Brost, et al. (1987). "A 3-DIMENSIONAL EULERIAN ACID DEPOSITION MODEL - 

PHYSICAL CONCEPTS AND FORMULATION." Journal of Geophysical Research-Atmospheres

92(D12): 14681-14700. 

ENVIRON (2008). User's Guide Comprehensive Air Quality Model with Extensions. Novato, ENVIRON 

International Corporation. 

Gery, M. W., G. Z. Whitten, et al. (1989). "A PHOTOCHEMICAL KINETICS MECHANISM FOR URBAN AND 

REGIONAL SCALE COMPUTER MODELING." Journal of Geophysical Research-Atmospheres

94(D10): 12925-12956. 

Nenes, A., S. N. Pandis, et al. (1999). "Continued development and testing of a new thermodynamic aerosol module 

for urban and regional air quality models." Atmospheric Environment 33(10): 1553-1560. 

Nobel, C. E., E. C. McDonald-Buller, et al. (2001). "Accounting for spatial variation of ozone productivity in NOx 

emission trading." Environmental Science & Technology 35(22): 4397-4407. 

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

applications and advances." Journal of the Air & Waste Management Association 58(2): 289-302. 

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. 
United States Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 

Publication N