# 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 destruct ion 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 Regulator y Impact Analyses.

II. Photochemical Model Versio n, Inputs and Configuration

Photochemical grid models use state of the sc ience 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 in jected 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 stru ctured system of connected mode ling-related tools and data that provide a consistent and transp arent basis for assessing the air quality response to projected changes in emissions. The base year of data us ed to construct this platform includes emissions and meteorology for 2005. The modeling system treat s 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 refere nce scenario compared to a 2005 baseline. This is done by calculating the simulated air qual ity 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 m odel that simulates the formation and fate of photochemical oxidants, ozone, primary and seconda ry 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 th at simulate the emission, production, decay, deposition and transport of organi c and inorganic gas-phase and pa rticle-phase pollutants in the atmosphere (Baker and Scheff, 2007; Nobel et al., 2001; Russell, 2008). CAMx is applied with ISORROPIA inorganic chemistry (N enes et al., 1999), a semi-volatile equilibrium scheme to

### 2 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 apport ionment tools, including the original ozone source apportionment tool (OSAT) and the an thropogenic 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 hori zontal diffusion. Horizontal advective fluxes for each of the regul ar model species that make up nitrogen oxides (NO X 

### ) 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 NO X 

### and VOC limited conditions.

Particulate matter source appor tionment 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 sp ecies. Emissions of nitr ogen oxides are tracked through all intermediate nitrogen species to particulate nitrate ion. Ammonia emissions are tracked to particulate ammonium ion. This mo deling 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 domai n 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 wester n U.S. The model extends vertically from the surface to 100 millibars (approximately 15 km) usi ng 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 change s. Table II-1 provides geographic information about the photochemical model domains.

### 3 Table II-1. Geographic elements of domain s 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 millib ar 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 west ern 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 l ooking 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

### 4 base- and future-year emissions estimates, meteorological fields, as well as initial and boundary
condition data which are all i nputs to the air quality model.

1. Emissions Input Data

The emissions data used in the base year and fu ture reference and future emissions adjustment case are based on the 2005 v4.3 platform. Emissi ons are processed to photochemical model inputs with the SMOKE emissions modeling sy stem (Houyoux et al., 2000). The 2016 reference case is intended to represent the emissions associ ated with growth and c ontrols in that year projected from the 2005 simulation year. The Un ited States EGU point source emissions estimates for the future year reference and c ontrol case are based on an Integrated Planning Model (IPM) run for criteria polluta nts. 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 star ting point for most of the 2016 SMOKE-based projections.

Table II.2 Domain total estimated sector emi ssions (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 likel y be subject to a NESHAP relate d to that sector: cement kilns, pulp & paper, refineries, coke ovens, iron/steel foundries, integr ated 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

‐ EGU point 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

### 5 furnaces, taconite mining, and ferroalloy production. The model domain total estimated emissions by source sector used in this mode ling assessment are shown in Table II.2 for 2016
and Table II.3 for 2005.

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

Other North American emissions of criteria and toxic pollutants (includi ng mercury) are based on a 2006 Canadian inventory and 1999 Mexican inventory. Both i nventories are not grown or controlled when used as part of future year base line inventories. Global em issions of criteria are included in the modeling system through boundary condition inflow.

2. Meteorological Input Data

The gridded meteorological input data for the en tire year of 2005 were derived from simulations of the Pennsylvania State University / Nationa l 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 projec tion as shown in Figure II-1.

All three meteorological model r uns 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

‐ EGU point 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

### 6 • 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 temper ature 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 10 4 

### for wind fields and temperatures and 1.0 x 10 5 

### for moisture fields. The 12 km domain nudging weighting factors
were 1.0 x 10 4 

### for wind fields and te mperatures and 1.0 x 10 5 

### 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 we re 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 millib ar top. The MM5 and CAMx vert ical structures are shown in Table II-4 and do not vary by horizontal grid re solution. The meteorological outputs from all

### 7 three MM5 sets were processed to create m odel-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 modeli ng inputs. The 2005 MM5 model performance evaluations used an approach which include d a combination of qualitative and quantitative analyses to assess the adequacy of the MM5 si mulated fields. The qualitative aspects involved comparisons of the model-estimated synoptic patterns against obser ved patterns from historical weather chart archives. Additionally, the evaluati ons compared spatial patterns of estimated to observed monthly average rainfall and check ed 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 ope rational evaluation in cluded 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 di rection. The three individual
MM5 evaluations are described el sewhere (Baker, 2009a, b, c). It wa s ultimately determined that
the bias and error values associated with a ll three sets of 2005 mete orological data were generally within the rang e of past meteorological modeling resu lts 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 verti cal layers up to 100 mb. The pred ictions 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 phot ochemical model simulation is used to supply initial and hourly boundary concen trations 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 simula tions 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 subs equent future year 12 km finer grid modeling scenarios.

### 8 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 ca rbon, etc.) was conducte d using 2005 monitoring data in order to estimate the ability of the mode ling system to replicate base year concentrations. The evaluation of PM2.5 component species incl udes comparisons of predicted and observed concentrations of sulfate (SO4), nitrate (NO3 ), ammonium (NH4), el emental carbon (EC), and organic carbon (OC). PM2.5 ambient measurements for 2005 were obtained from the Chemical Speciation Network (CSN) and the Interagency M onitoring 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 measur ements 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 me trics 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 re gional PM2.5 model applications for other, nonEPA studies. Overall, the mean bias (bias) and m ean error (error) statistic s are within the range or close to that found by other groups in recent applications (Doraiswam y, 2010; Tesche et al., 2006). The model performance results give us c onfidence that our applic ation of CAMx using this modeling platform provides a scientific ally credible approach for estimating PM2.5 concentrations for the purposes of this assessment.

### 9 TABLE III-1. Model performance metrics for sp eciated PM2.5 averaged by quarter.

B. Ozone

An operational model performance evaluation fo r 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 In formation Retrieval System (AIRS). The ozone
metrics covered in this evaluation include one-hour and eight-hour average daily maximum
ozone bias and error. The evalua tion principally consists of st atistical assessments of model versus observed pairs that are paired in time and space. This ozone model performance was limited to the prediction-observation pairs wh ere 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 qua rter 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

PM2.5 Nitrate 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

PM2.5 Organic 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

### 10 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 m odel performance metrics, goal s, 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 Concen trations with Measurements. Jour nal of Air & Waste Management Association 60, 1293-1308.

ENVIRON, 2008. User's Guide Comprehensive Air Qua lity 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.

Quarte r N Observed Predicted Bias Erro r Fractional

Bias Fractional

Erro r 2 19,821 76 65

‐ 11 12

‐ 16 18
3 22,699 80 71

‐ 9 13

‐ 13 17

Quarte r N Observed Predicted Bias Erro r Fractional

Bias Fractional

Erro r 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 de velopment and testing of a new thermodynamic aerosol module for urban and regional air quality m odels. Atmospheric Envi ronment 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 StatesEnvironmental ProtectionAgencyOffice of Air Quality Planning and StandardsAir Quality Assessment DivisionResearch Triangle Park, NCPublication No. EPA-454/R-11-006August 2011