Air Quality Modeling Technical Support Document: Final 

EGU NESHAP 
EPA-454/R-11-009 

October 2011 

Air Quality Modeling Technical Support Document: Final 

EGU NESHAP 

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 of the final 

National Emissions Standard for Hazardous Air Pollutants (NESHAP) related to electrical 

generating utilities. 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, 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 other air pollutants. 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 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. 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, and visibility impairment. Model 

predictions are used to estimate future-year design values of PM2.5 and ozone. Specifically, we 

compare a 2017 reference scenario to a 2017 control scenario. 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). 
2 

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 other pollutants 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). 

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 v4.3 platform. Emissions are processed to photochemical model 

inputs with the SMOKE emissions modeling system (Houyoux et al., 2000). The 2017 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 2017 reference case. The 2005 

v4 platform projection factors were the starting point for most of the 2017 SMOKE-based 
4 

projections. The estimated total anthropogenic emissions and emissions for the utility sector used 

in this modeling assessment over the entire model domain are shown in Table II.2. 

Table II.2 Model domain total estimated total inventory and EGU sector emissions for each 

modeling scenario.

Other North American emissions 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 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: 

• 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. 

Scenario Sector CO NOX NH3 SO2 SULF

Primary

PM2.5

2005 baseline EGU(PTIPM) 603,788 3,729,157 21,995 10,380,870 224,859 496,874

All Other 102,946,238 22,523,479 4,805,389 6,675,740 88,714 4,567,808

2017 baseline EGU(PTIPM) 873,345 1,930,767 40,259 3,281,361 73,994 276,428

All Other 71,652,291 16,171,166 4,998,214 6,063,388 50,506 4,040,380

2017 control case EGU(PTIPM) 707,641 1,789,788 35,493 1,866,245 41,592 223,319

All Other 71,652,291 16,171,166 4,998,214 6,063,388 50,506 4,040,380
5 

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. 
6 

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 CMAQ 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. 

Initial and boundary conditions for the projected future year 36 km simulations are the same as 

the 2005 base year. 

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

Specie 1 2 3 4

N Sulfate Ion 8,493 8,916 8,229 8,155

Nitrate Ion 8,143 8,518 7,942 8,016

Ammonium Ion 4,723 4,746 4,546 4,461

Organic Carbon 8,038 8,485 7,939 7,874

Elemental Carbon 8,543 8,857 8,145 8,218

Mean Observed (ug/m3

) Sulfate Ion 2.1 3.0 4.3 2.0

Nitrate Ion 2.1 0.8 0.5 1.4

Ammonium Ion 1.7 1.4 1.7 1.3

Organic Carbon 1.7 1.8 2.3 2.2

Elemental Carbon 0.5 0.4 0.5 0.6

Mean Predicted (ug/m3

) Sulfate Ion 1.9 2.7 3.4 1.9

Nitrate Ion 1.9 0.9 0.4 1.4

Ammonium Ion 1.2 1.2 1.1 1.1

Organic Carbon 1.7 1.2 1.6 1.5

Elemental Carbon 0.8 0.6 0.7 0.8

Bias (ug/m3

) Sulfate Ion ‐0.2 ‐0.2 ‐0.8 ‐0.1

Nitrate Ion ‐0.1 0.2 ‐0.1 0.1

Ammonium Ion 0.0 0.2 ‐0.2 0.2

Organic Carbon 0.0 ‐0.7 ‐0.7 ‐0.6

Elemental Carbon 0.3 0.2 0.2 0.2

Error (ug/m3

) Sulfate Ion 0.9 1.0 1.4 0.6

Nitrate Ion 1.3 0.7 0.5 1.1

Ammonium Ion 0.8 0.7 0.7 0.7

Organic Carbon 1.0 1.0 1.2 1.2

Elemental Carbon 0.5 0.3 0.4 0.4

Fractional Bias (%) Sulfate Ion 0.2 ‐3.8 ‐11.9 6.2

Nitrate Ion ‐33.0 ‐47.2 ‐85.5 ‐25.5

Ammonium Ion 4.5 27.3 7.6 27.5

Organic Carbon 0.8 ‐35.8 ‐33.1 ‐23.2

Elemental Carbon 29.8 16.9 22.3 13.4

Fractional Error (%) Sulfate Ion 43.3 35.3 39.6 39.1

Nitrate Ion 86.3 103.5 119.8 97.8

Ammonium Ion 50.9 56.4 56.6 59.4

Organic Carbon 55.2 62.2 59.9 60.1

Elemental Carbon 63.3 60.9 60.8 58.1

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10 

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

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

national regulations (USEPA, 2010).

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. 

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. 

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. 

456789

N Daily 1‐hr maximum 5,704 7,173 9,553 9,522 8,433 7,118

Daily 8‐hr maximum 5,705 7,180 9,557 9,529 8,437 7,120

Mean observed (ppb) Daily 1‐hr maximum 73 75 79 81 81 78

Daily 8‐hr maximum 68 68 71 72 71 70

Mean predicted (ppb) Daily 1‐hr maximum 64 67 72 77 75 70

Daily 8‐hr maximum 59 62 66 70 68 64

Bias (ppb) Daily 1‐hr maximum ‐9 ‐8 ‐7 ‐4 ‐5 ‐8

Daily 8‐hr maximum ‐9 ‐7 ‐5 ‐2 ‐3 ‐6

Error (ppb) Daily 1‐hr maximum 10 10 10 11 11 10

Daily 8‐hr maximum 9 8 8 9 9 9

Fractional bias (%) Daily 1‐hr maximum ‐13 ‐12 ‐10 ‐6 ‐7 ‐11

Daily 8‐hr maximum ‐14 ‐11 ‐8 ‐3 ‐5 ‐10

Fractional error (%) Daily 1‐hr maximum 14 14 14 14 15 14

Daily 8‐hr maximum 15 13 12 12 13 13

Month
11 

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. 

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. 

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

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

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. 
United States 

Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 

Publication N