Air Quality Modeling Technical Support Document: Point 

Source Sector Rules 
EPA-454/R-11-003 

February 2011 

Air Quality Modeling Technical Support Document: Point 

Source Sector Rules 

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 

mercury deposition assessments related to large stationary point sources that generate electricity. 

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, 8-hr maximum 

ozone, total mercury deposition, as well as visibility impairment. Air quality benefits are 

estimated with the Community Multi-scale Air Quality (CMAQ) model. CMAQ 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 CMAQ 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 may be slightly different than the final utility 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 

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 CMAQ modeling platform was used as the basis for the air quality modeling for 

this 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 modeling system treats the emissions, transport, and 

fate of criteria pollutants and certain toxics including hydrogen chloride (HCL) and speciated 

mercury: Hg(0) (gaseous elemental), Hg(II) (oxidized gaseous), and Hg(p) (particle-bound). This 

modeling platform and analysis is described below. 

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

annual PM2.5 concentrations, 8-hr maximum ozone, annual total mercury deposition levels and 

visibility impairment. Model predictions are used to estimate future-year design values of PM2.5 

and ozone. Specifically, we compare a 2016 reference scenario, a scenario without the boiler 

sector controls, to a 2016 control scenario which includes the adjustments to the boiler 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 
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predictions for mercury deposition were analyzed using absolute model changes, although 

percent changes between the control case and two future baselines are also estimated. 

A. Model version 

The Community Multi-scale Air Quality (CMAQ) model v4.7.1 (www.cmaq-model.org) is a 

state of the science three-dimensional Eularian “one-atmosphere” photochemical transport model 

used to estimate air quality (Appel et al., 2008; Appel et al., 2007; Byun and Schere, 2006). 

CMAQ 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. CMAQ is applied with the AERO5 

aerosol module, which includes the ISORROPIA inorganic chemistry (Nenes et al., 1998) and a 

secondary organic aerosol module (Carlton et al., 2010). The CMAQ model is applied with 

sulfur and organic oxidation aqueous phase chemistry (Carlton et al., 2008) and the carbon-bond 

2005 (CB05) gas-phase chemistry module (Gery et al., 1989). Mercury oxidation pathways are 

represented for both the gas and aqueous phases in addition to aqueous phase reduction reactions 

(Bullock and Brehme, 2002). 

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. 

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) 
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, total mercury 

deposition, and visibility impacts from the regulation. 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 

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 v4.1 platform. The emissions cases use different emissions data for 

some pollutants than the official v4 platform to use data intended only for this rule. Unlike the 

2005 v4 platform, the configuration for this modeling application included mercury emissions 

from the National Air Toxics Assessment Inventory and some industrial boiler sector mercury 

emissions more consistent with the engineering analysis for the 

Industrial/Commercial/Institutional Boilers and Process Heaters NESHAP. Emissions for the 

future years for the EGU sector utilized information collected from the utility MACT 

information collection request. The information collection request informed existing HCL, NOX, 

HG, and PM controls. In addition this data was used to supply HCL removal rates from selected 
4 

control technology. 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, hydrochloric acid, and mercury in 

2016. Both control and growth factors were applied to a subset of the 2005 non-EGU point and 

non-point to create the 2016 reference case. The 2005 v4 platform 2014 projection factors were 

the starting point for most of the 2016 SMOKE-based projections. The mercury projections for 

non–EGU point sources accounted for emission reductions expected in the future due to 

NESHAP for various non-EGU source categories that were finalized or expected to be finalized 

prior to the rule proposal including the Boiler MACT, Gold Mine NESHAP and Electric Arc 

Furnace NESHAP. The estimated total anthropogenic emissions and emissions for the utility 

sector used in this modeling assessment are shown in Table II.2. 

Table II.2 Estimated total inventory and EGU sector emissions for each modeling scenario.

Scenario Sector VOC NOx CO SO2 PM10 PM2.5

2005 baseline EGU(PTIPM) 40,950 3,726,459 601,564 10,380,786 615,095 508,903

All 17,613,543 22,216,093 83,017,436 15,050,209 13,031,716 4,400,680

2016 baseline EGU(PTIPM) 40,845 1,769,764 691,310 3,577,698 523,504 384,320

All 14,390,421 15,019,836 59,148,384 7,245,595 12,772,091 4,022,846

2016 control case EGU(PTIPM) 38,217 1,618,199 656,245 1,220,379 358,165 291,044

All 14,387,792 14,868,270 59,113,319 4,888,276 12,606,752 3,929,570

Scenario Sector Hg(0) Hg(II) Hg(p) HCL CL2 NH3

2005 baseline EGU(PTIPM) 30 21 1.6 351,592 99 21,684

All 64 33 8.5 429,223 6,409 3,762,641

2016 baseline EGU(PTIPM) 21 7 0.7 74,089 36,655

All 42 16 5.9 140,638 6,050 3,897,033

2016 control case EGU(PTIPM) 5 2 0.4 8,802 36,982

All 26 11 5.6 75,351 6,050 3,897,360

Emissions (tons/year)

Emissions (tons/year)

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 and 

toxic pollutants (including mercury) are included in the modeling system through boundary 

condition inflow. 
5 

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 

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. 
6 

Table II-3. Vertical layer structure (heights are layer top). 

CMAQ 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 CMAQ 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 CMAQ 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 
7 

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 CMAQ simulations. The 36 km photochemical model simulation is used to supply 

initial and hourly boundary concentrations to the 12 km domains. 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. Mercury initial and boundary conditions were 

based on a GEOS-CHEM simulation using a 2000 based global anthropogenic emissions 

inventory that includes 1,278 Mg/yr of Hg(0), 720 Mg/yr of Hg(II), and 192 Mg/yr of particle 

bound mercury (Selin et al., 2007). 

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 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 

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. The aggregated metrics and number (N) of prediction-observation pairs are 
8 

shown by chemical specie and quarter in Table III-1. 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 mean error (error) statistics shown in Table III-1 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 CMAQ using this modeling 

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

purposes of this assessment. 

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

Metric Quarter Sul fate Ion Nitrate Ion

Ammonium

Ion

Organic

Carbon

Elemental

Carbon

Number 1 6,218 5,870 4,045 6,368 6,305

2 6,537 6,146 4,075 6,552 6,516

3 6,108 5,828 3,869 6,061 6,152

4 5,894 5,757 3,763 5,697 5,792

Mean Observed 1 2.69 2.39 1.72 0.51 0.40

(μg/m3

) 2 3.80 0.83 1.49 0.48 0.66

3 5.43 0.45 1.84 0.52 0.78

4 2.45 1.28 1.26 0.61 0.52

Mean Predicted 1 2.37 2.36 1.50 0.82 5.20

(μg/m3

) 2 3.45 1.21 1.47 0.64 4.06

3 4.39 0.48 1.39 0.66 4.69

4 2.31 1.84 1.32 0.81 5.46

Bias 1 -0.27 0.12 0.05 0.34 4.87

(μg/m3

) 2 -0.28 0.49 0.33 0.15 3.50

3 -0.88 0.07 -0.12 0.16 4.03

4 -0.08 0.63 0.36 0.24 5.01

Error 1 1.05 1.37 0.77 0.47 4.90

(μg/m3

) 2 1.25 0.89 0.81 0.35 3.65

3 1.73 0.46 0.80 0.35 4.24

4 0.74 1.17 0.66 0.44 5.05

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 were paired in time and space. This ozone model performance was 

limited to the prediction-observation pairs where observed ozone exceeded or equaled 60 ppb. 
9 

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. 

TABLE III-2. Model performance metrics for daily maximum ozone by quarter. 

Quarter N Observed Predicted Bias Error

2 24,357 76.0 69.6 ‐6.4 9.2

3 27,430 79.7 75.3 ‐4.4 10.6

Quarter N Observed Predicted Bias Error

2 24,370 69.3 63.9 ‐5.4 8.1

3 27,447 70.5 68.0 ‐2.5 8.6

*metrics estimated where observed ozone > 60 ppb

1‐hr Daily Maximum Ozone (ppb)

8‐hr Daily Maximum Ozone (ppb)

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

national regulations (USEPA, 2010).

C. Mercury Wet Deposition 

Model estimated weekly mercury wet deposition is compared to observation data to assess model 

skill simulating this component of mercury deposition. Mercury wet deposition measurements 

are weekly totals taken at sites that are part of the Mercury Deposition Network 

(http://nadp.sws.uiuc.edu/MDN/) which operates under the National Atmospheric Deposition 

Program. In addition to mercury wet deposition, the network sites also collect rainfall data which 

is also evaluated against estimates used by the photochemical model from prognostic 

meteorological model output. 

TABLE III-3. Model performance metrics for mercury wet deposition and rainfall by quarter. 

Quarter N Observed Predicted Bias Error

1 798 0.15 0.23 0.14 0.18

2 853 0.26 0.28 0.06 0.24

3 840 0.31 0.16 ‐0.12 0.24

4 748 0.13 ‐0.80 ‐1.27 1.47

Quarter N Observed Predicted Bias Error

1 1,073 17.27 18.10 0.83 8.34

2 1,082 19.41 23.50 4.09 14.45

3 1,118 20.64 26.51 5.87 19.77

4 1,037 20.18 17.22 ‐2.97 10.38

Total Mercury Wet Deposition (μg/m2

)

Rainfall (mm)

A comparison of observed and predicted mercury wet deposition is shown in Table III-3. 

Mercury wet deposition performance (annual average normalized mean bias of 34% and annual 
10 

average normalized mean error of 52%) compares well to other photochemical model 

applications published in literature (Bullock and Brehme, 2002; Vijayaraghavan et al., 2007). 

IV. Post Processing Mercury Deposition 

CMAQ outputs hourly wet and dry deposition estimates (kg/ha) in each grid cell of speciated 

mercury: Hg0, Hg2, and PM2.5 Hg. Hourly outputs are averaged by day, then month, and then to 

an annual estimate. CMAQ model estimates of annual total mercury deposition from both 12 km 

model domains (12EUS1 and 12WUS1) were joined into a single 12 km model file covering the 

entire continental United States. Total mercury deposition is defined as the sum of all wet and 

dry deposition of elemental mercury, divalent gas-phase mercury, and PM2.5 mercury. Gridded 

12 km total mercury estimates from 2005, 2016, and emissions sensitivity CMAQ simulations 

were linked to an ArcInfo shape file of the model domain. ArcGIS software was then used to 

calculate weighted areal ratios between the 12 km grid cells and 12 digit hydrologic unit code 

(HUC) boundaries to produce a shape file of emissions estimates for each HUC within the 

domain. 
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V. References

Appel, K.W., Bhave, P.V., Gilliland, A.B., Sarwar, G., Roselle, S.J., 2008. Evaluation of the community multiscale 

air quality (CMAQ) model version 4.5: Sensitivities impacting model performance; Part II - particulate matter. 

Atmospheric Environment 42, 6057-6066. 

Appel, K.W., Gilliland, A.B., Sarwar, G., Gilliam, R.C., 2007. Evaluation of the Community Multiscale Air Quality 

(CMAQ) model version 4.5: Sensitivities impacting model performance Part I - Ozone. Atmospheric Environment 

41, 9603-9615. 

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. 

Bullock, O.R., Brehme, K.A., 2002. Atmospheric mercury simulation using the CMAQ model: formulation 

description and analysis of wet deposition results. Atmospheric Environment 36, 2135-2146. 

Byun, D., Schere, K.L., 2006. Review of the governing equations, computational algorithms, and other components 

of the models-3 Community Multiscale Air Quality (CMAQ) modeling system. Applied Mechanics Reviews 59, 51-

77. 

Carlton, A.G., Bhave, P.V., Napelenok, S.L., Edney, E.D., Sarwar, G., Pinder, R.W., Pouliot, G.A., Houyoux, M., 

2010. Model Representation of Secondary Organic Aerosol in CMAQv4.7. Environmental Science & Technology 

44, 8553-8560. 

Carlton, A.G., Turpin, B.J., Altieri, K.E., Seitzinger, S.P., Mathur, R., Roselle, S.J., Weber, R.J., 2008. CMAQ 

Model Performance Enhanced When In-Cloud Secondary Organic Aerosol is Included: Comparisons of Organic 

Carbon Predictions with Measurements. Environmental Science & Technology 42, 8798-8802. 

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. 

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

Regional Scale Computer Modeling. Journal of Geophysical Research-Atmospheres 94, 12925-12956. 

Nenes, A., Pandis, S.N., Pilinis, C., 1998. ISORROPIA: A new thermodynamic equilibrium model for multiphase 

multicomponent inorganic aerosols. Aquatic Geochemistry 4, 123-152. 

Selin, N.E., Jacob, D.J., Park, R.J., Yantosca, R.M., Strode, S., Jaegle, L., Jaffe, D., 2007. Chemical cycling and 

deposition of atmospheric mercury: Global constraints from observations. Journal of Geophysical ResearchAtmospheres 112. 

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

006), Research Triangle Park, North Carolina. 
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Vijayaraghavan, K., Seigneur, C., Karamchandani, P., Chen, S.Y., 2007. Development and application of a 

multipollutant model for atmospheric mercury deposition. Journal of Applied Meteorology and Climatology 46, 

1341-1353. 
United States 

Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 

Publication N