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21 February 2013

MEMORANDUM

To: Ellen Belk, EPA Region 6

Cc: Susan Wolf, RTI International 

From: Uarporn Nopmongcol, Greg Yarwood

Subject: 2002 Baseline CAMx Simulation, Texas Regional Haze Evaluation

[Contract EP-W-011-029]

ENVIRON is assisting EPA by performing modeling of regional haze impacts of selected sources 

in Texas The analysis builds upon modeling of 2002 conducted previously for CENRAP by 

ENVIRON. The CENRAP database was enhanced to include a 12 km grid over Texas and nearby 

Class I areas, and updated to use with the latest version of the Comprehensive Air Quality 

Model with extensions (CAMx; ENVIRON, 2012). This memorandum documents the new 2002 

baseline modeling setup and results. 

INTRODUCTION

The Clean Air Act (CAA) establishes special goals for visibility in many national parks, wilderness 

areas, and international parks. Through the 1977 amendments to the Clean Air Act, Congress 

set a national goal for visibility as “the prevention of any future, and the remedying of any 

existing, impairment of visibility in mandatory Class I Federal areas which impairment results 

from manmade air pollution” (40 CFR 51.300). The goal of the Regional Haze Rule (RHR) is to 

achieve natural visibility conditions at 156 Federally mandated Class I areas by 2064. To achieve 

this goal, the RHR has set up milestone years of 2018, 2028, 2038, 2048, 2058, and 2064 to 

monitor progress toward natural visibility conditions. The 2000-2004 period has been used by 

states as a baseline from which to demonstrate progress toward natural visibility conditions in 

2018.

EPA Region 6 is presently under a court-ordered Consent Decree deadline regarding action on

the Texas Regional Haze (RH) State Implementation Plan (SIP). Regional haze is linked to fine 

particulate matter (PM2.5), for which EPA has a new standard. Air quality modeling is an 

important tool for determining whether a source can be reasonably expected to contribute to 

visibility impairment at a Class I area. ENVIRON is assisting EPA to evaluate regional haze 

impacts of sources in Texas that may be used to assess the Texas Regional Haze 

Implementation Plan. 

The Texas Haze analysis was built upon the regional photochemical modeling (ENVIRON and 

CERT, 2007) conducted for CENRAP. In particular, the CENRAP 2002 and 2018 36 km modeling 
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database for CAMx was enhanced to include a 12 km grid over Texas and nearby Class I areas. 

The overall approach to the project includes the following steps: 

 Update CENRAP 2002 and 2018 modeling database to use with the latest release of 

CAMx (v5.41)

 Conduct 2002 modeling with Plume-in-Grid (PiG) and a 12-km flexi-nest grid to provide 

the new 2002 baseline RH modeling

 Conduct 2018 modeling with PiG and the CAMx PM Source Apportionment Technology 

(PSAT) for sources selected by EPA

 Evaluate impact of selected sources on visibility in Class I areas

 Conduct any additional future-year scenarios to access various control strategies (to be 

determined)

This memorandum documents the new 2002 baseline modeling setup and results.

CAMx MODELING APPROACH

Air quality modeling was performed with version 5.41 of the Comprehensive Air quality Model 

with extensions (CAMx; ENVIRON, 2012) with input data developed by CENRAP. Several 

features have been introduced to CAMx since CENRAP modeling, thus, many CAMx inputs had 

to be updated. CAMx Input development and updates are described separately below.

2002 Annual 36 km CENRAP modeling database 

The Texas Haze analysis was built upon the 2002 annual regional photochemical modeling 

database developed as part of the CENRAP (ENVIRON and CERT, 2007). CENRAP developed a 

2002 annual modeling database for CAMx on the 36 km unified national Regional Planning 

Organization (RPO) grid that covers the continental United States. The CENRAP modeling 

protocol (Morris et al., 2004), CENRAP modeling Quality Assurance Program Plan (QAPP; Morris 

and Tonnesen, 2004), and base model evaluation (ENVIRON and CERT, 2007) reports provide 

details on the development of the CENRAP 2002 36-km annual modeling database. Emissions 

inputs were based on 2002 Base G Typical (Typ02G) annual emissions database. Numerous 

iterations of the emissions modeling were conducted using interim databases before arriving at 

the final Base G emission inventories (e.g., Morris et al., 2005). 

Enhancement to the CENRAP 2002 Modeling Database

The CENRAP Base G 2002 36 km annual CAMx photochemical modeling database was updated 

to include a 12 km nested-grid that covers Texas and Class I areas in and near Texas including:

 National Parks: Big Bend (BIBE), Guadalupe Mountains (GUMO), and Carlsbad Caverns;

 Wildlife Refuges: Salt Creek (SACR) and Wichita Mountains (WIMO);

 Wilderness Areas: Breton (BRET), White Mountain (WHIT), Caney Creek (CACR), Upper 

Buffalo (UPBU), Bandeller (BAND), Hercules-Glade (HEGL), and others (see Table 1).
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Figure 1 displays the 36/12 km nested grid structure used for the CAMx modeling. The locations 

of the Interagency Monitoring of Protected Visual Environments (IMPROVE) sites that includes 

Class I areas within and near the 12 km modeling domain are shown in Figure 2. The CAMx flexinesting feature was used to specify a 12 km Texas fine grid within the CENRAP 36 km modeling 

domain. Full flexi-nesting was invoked in which CAMx internally interpolates meteorological 

data, gridded emissions and other inputs from the 36 km grid to the 12 km grid. Flexi-nesting 

does not interpolate point source emissions because exact source coordinates are known 

enabling each point source to be placed within the correct 36 km or 12 km grid cell. 

Figure 1. Texas RH modeling 36/12 km modeling domain and the locations of the IMPROVE

monitoring sites that include Class I areas, indicated by circles.

-2736 -2304 -1872 -1440 -1008 -576 -144 288 720 1152 1584 2016 2448

-2088

-1656

-1224

-792

-360

72

504

936

1368

1800

36km: SW = (-2736,-2088) NX,NY = (148x112) DX,DY = (36x36)

12km: SW = ( -936,-1620) NX,NY = (144,108) DX,DY = (12x12)

LCP Definition: (-97.0,40.0,45.0,33.0)
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Figure 2. Texas RH modeling 12 km modeling domain and the locations of the IMPROVE

monitoring sites (circles) that include Class I areas.

Table 1. Class I areas to be included in the Texas RH analysis.

Site State Code

State

FIPS County

County 

FIPS Latitude Longitude

LCPX 

(km)

LCPY 

(km) Grid

Breton Wilderness 

Area LA BRET1 22

St. Bernard 

Parish 87 29.1189 -89.2066 763 -1176 12km

Big Bend National 

Park TX BIBE1 48

Brewster 

County 43 29.3027 -103.178 -604 -1167 12km

Guadalupe 

Mountains TX GUMO1 48

Culberson 

County 109 31.833 -104.809 -738 -873 12km

Wichita Mountains 

Wilderness OK WIMO1 40

Comanche 

County 31 34.7323 -98.713 -156 -581 12km

Caney Creek 

Wilderness Area AR CACR1 5 Polk County 113 34.4544 -94.1429 261 -610 12km

Upper Buffalo 

Wilderness Area AR UPBU1 5

Newton 

County 101 35.8258 -93.203 341 -455 12km

Bandelier 

Wilderness Area NM BAND1 35

Los Alamos 

County 28 35.7797 -106.266 -831 -424 12km

Bosque del Apache 

Wilderness Area NM BOAP1 35

Socorro 

County 53 33.8695 -106.852 -906 -629 12km

Carlsbad Caverns 

NP. NM GUMO1 31.833 -104.809 -738 -873 12km

Gila Wilderness 

Area NM GICL1 35

Catron 

County 3 33.2204 -108.235 -1042 -686 36km

Pecos Wilderness 

Area NM WHPE1 36.5854 -105.452 -750 -343 12km

Salt Creek 

Wilderness Area NM SACR1 35

Grant 

County 17 33.4598 -104.404 -685 -696 12km

San Pedro Parks 

Wilderness Area NM SAPE1 35

Rio Arriba 

County 39 36.0139 -106.845 -880 -393 12km

BIBE1 BRET1

GUMO1

WIMO1 CACR1

UPBU1 BAND1

BOAP1

GICL1 SACR1

SAPE1

WHPE1

WHIT1

HEGL1

MING1

GRSA1

ROMO1
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Site State Code

State

FIPS County

County 

FIPS Latitude Longitude

LCPX 

(km)

LCPY 

(km) Grid

Wheeler Peak 

Wilderness Area NM WHPE1 35 Taos County 55 36.5854 -105.452 -750 -343 12km

White Mountain 

Wilderness Area NM WHIT1 35

Lincoln 

County 27 33.4687 -105.535 -790 -686 12km

Hercules-Glades 

Wilderness Area MO HEGL1 29

Taney 

County 213 36.6138 -92.9221 362 -366 12km

Mingo MO MING1 29

Stoddard 

County 207 36.9717 -90.1432 606 -312 36km

Great Sand Dunes CO GRSA1 8

Saguache 

County 109 37.7249 -105.519 -744 -217 36km

Rocky Mountain 

National Park CO ROMO1 8

Larimer 

County 69 40.2783 -105.546 -720 65 36km

Preparing Emissions Data

CENRAP processed emission inputs using the Sparse Matrix Operator Kernel Emissions (SMOKE) 

version 2.3 with chemical speciation for the CB4 mechanism. Several versions of SMOKE have 

been released since then. This study used the latest SMOKE version 3.1, although some SMOKE 

modules came from an older version to maintain compatibility with the format of data from 

CENRAP. All SMOKE configurations and SMOKE input files were the same as used in CENRAP

with selected updates. Provided below is a summary of the emission updates made to the 

CENRAP database for use in this study.

 Used CB05 chemical speciation profiles for area and point sources

 Processed point source emissions in CAMx input format to preserve stack parameters 

and location 

 Changed the earth sphere variable (IOAPI_ISPH) in SMOKE from 19 to 20. CENRAP 

SMOKE setup used IOAPI_ISPH=19 that could cause inconsistencies between point 

source locations and the meteorological points. IOAPI_ISPH=20 assumes a 6740 km 

earth radius consistent with MM5 and CAMx

 Tagged Plume-in-Grid (PiG) for large SO2 and NOx sources 

 Added organic aerosol precursor emissions from biogenic sources based on CENRAP 

BEIS CB4 emissions

 Adjusted primary organic aerosol mass from POC to POA mass

The criteria used for elevated point and PiG source selection are described below:

Non-Selected sources

 A plume rise cutoff of 50 meter (i.e., any source having estimated plume rise lower than 

50 m. was treated as a surface source). 

 NOx emissions thresholds for PiG selection varied by region: 5 tons per day (TPD) for 

Texas, 10 TPD for neighboring states (NM, CO, OK, AR, LA, and KS), and 20 TPD for the 

rest. 

Selected sources

 All selected sources were treated as elevated sources

 PiG criteria are stack height of 50 ft (~15 m.) and NOx or SO2 emissions higher than 20 

TPD
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Table 2 provides an emission summary for EPA selected sources.

Table 2. Annual emissions (TPY) of EPA selected sources by pollutant in the CENRAP 2002 

Typical G inventory.

Number Plant Name FIPS Plant ID NOX CO VOC SO2 PM2.5 PM10

1 BIG BROWN 48161 2 7,213 20,553 134 77,860 395 933

2 BIG SPRING CARBON BLACK 48227 2 629 3,927 38 9,880 5 76

3 BORGER CARBON BLACK 48233 1 483 10,351 340 3,609 69 96

4 BORGER CARBON BLACK PLT 48233 2 822 757 3 5,148 110 242

5 COLETO CREEK PLANT 48175 2 3,563 500 60 14,289 84 173

6 FAYETTE POWER PROJECT 48149 5 19,120 1,778 231 31,798 432 1,454

7 FULLERTON GAS PLANT 48003 10 1,470 759 326 2,375 23 23

8 GIBBONS CREEK 48185 2 2,218 54 58 10,816 130 295

9 GOLDSMITH GASOLINE PLANT 48135 22 888 551 204 1,597 7 7

10 GREAT LAKES CARBON LLC 48245 23 824 43 5 9,795 178 245

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GUADALUPE COMPRESSOR 

STATION 48109 5 668 102 1 2 4 4

12 HARRINGTON STATION 48375 22 13,140 1,119 149 26,967 41 2,178

13 HOLCIM (TEXAS) LP 48139 22 4,204 5,052 627 3,167 378 379

14 HW PIRKEY POWER PLT 48203 22 4,953 30,216 58 19,476 405 799

15 KEYSTONE COMPRESSOR STN. 48495 30 2,870 619 61 0 19 19

16 KEYSTONE PLANT 48495 6 2,208 359 134 526 29 29

17 LIGNITE-FIRED POWER PLANT 48013 7 6,702 1,089 37 13,167 69 225

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MARTIN LAKE ELECTRICAL 

STATION 48401 11 18,473 47,553 257 66,402 593 881

19 MIDLOTHIAN PLANT 48139 9 4,221 763 43 2,099 115 301

20 MONTICELLO STM ELE STN 48449 3 15,924 38,339 245 86,236 1,446 3,297

21 NEWMAN STATION 48141 8 1,717 552 38 5 63 63

22 NORTH TEXAS CEMENT CO. 48139 2 2,572 418 15 4,434 349 451

23 ODESSA CEMENT PLANT 48135 23 1,758 1,296 119 329 136 291

24 OKLAUNION POWER STATION 48487 10 8,711 363 14 3,751 171 392

25 PEGASUS GAS PLANT 48329 6 2,095 423 199 84 7 7

26 RELIANT ENERGY LIMESTONE 48293 10 13,461 1,021 232 29,268 496 546

27 SANDOW STEAM ELECTRIC 48331 5 7,681 4,059 67 23,327 649 735

28 SHERHAN PLANT 48195 6 2,419 727 294 537 34 34

29 SOMMERS DEELY SPRUCE PWR 48029 63 10,915 2,020 147 26,301 745 1,513

30 STREETMAN PLANT 48349 11 691 215 284 3,468 104 187

31 TEXARKANA MILL 48067 5 1,619 788 1,322 374 521 578

32 TNP ONE STEAM ELECTRIC ST 48395 13 2,400 823 2 5,088 153 318

33 TOLK STATION 48279 18 12,117 1,087 143 24,874 139 1,183

34 W A PARISH STATION 48157 5 15,903 8,027 163 60,238 992 1,026

35 WAHA PLANT 48389 2 478 271 48 3,386 42 42

36 WELSH POWER PLANT 48449 5 13,316 35,744 107 35,838 2,581 2,585

37 WORKS NO 4 48485 15 5,317 12 1 371 377 396
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Preparing Other CAMx Inputs 

Meteorological data were from the MM5 prognostic meteorological model for the calendar 

year of 2002. The CENRAP meteorological modeling utilized data assimilation to incorporate 

observations into the MM5 simulations. MM5 provided CAMx with hourly input data for winds, 

temperature, clouds, precipitation and vertical mixing. MM5CAMx version 5.2 was used to 

reduce 34 MM5 vertical layers to 19 CAMx layers.

Several GIS and Perl-based processors were used to prepare landcover and Leaf Area Index 

(LAI) input datasets for CAMx. This new landcover format is required in new Zhang deposition 

scheme in CAMx. 

Albedo-Haze-Ozone Inputs (AHO files) were updated to include snow cover and allow inline 

TUV cloud and aerosol adjustments. 

The photolysis rates were generated for CB05 chemistry using the updated AHO files. The TUV 

program reads the categorical values in each AHO file and creates a lookup table listing the 

photolysis rates for each combination of the categorical values of albedo, haze, and ozone 

column at various solar angles and heights above the ground. 

Other inputs including boundary conditions and initial conditions were from the CENRAP 

database. 

CAMx Model Configuration 

CAMx v5.41 was chosen for this study because it was the most recent version at the time 

modeling task was initiated. The CAMx configuration is shown in Table 3. CENRAP CAMx 

configurations also are provided in Table 3 for comparison. The CAMx model was run 

separately for each of four quarters of 2002 using a 15 day spin up period to limit the influence 

of the assumed initial concentrations.

Table 3. Model Configurations Options for CAMx model.

Science Options Texas RH CENRAP

Version Version 5.41 Version 4.40

Vertical Grid Mesh 19 Layers 19 Layers

Horizontal Grids 36/12 km using two-way nesting 36 km

Initial Conditions 15 days full spin-up 15 days full spin-up

Boundary Conditions

2002 GEOS-CHEM day

specific 3-hour average data

2002 GEOS-CHEM day

specific 3-hour average data

Sub-grid-scale Plumes PiG treatment No Plume-in-Grid (PiG)

Chemistry

Gas Phase Chemistry CB05 CB4 with isoprene updates

Aerosol Chemistry ISORROPIA equilibrium ISORROPIA equilibrium

Secondary Organic Aerosols SOAP SOAP

Cloud Chemistry

RADM-type aqueous

chemistry

RADM-type aqueous

chemistry
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Science Options Texas RH CENRAP

Meteorological Processor MM5CAMx v5.2 MM5CAMx v1

Horizontal Transport

Eddy Diffusivity Scheme

K-theory with Kh grid size

dependence

K-theory with Kh grid size

dependence

Vertical Transport

Eddy Diffusivity Scheme K-Theory K-Theory

Diffusivity Lower Limit

Kzmin = 0.1 to 1.0 (Land use 

dependent Kzmin)

Kzmin = 0.1 to 1.0 (Land use 

dependent Kzmin)

Planetary Boundary Layer

From MM5 with PBL below 

convective clouds raised to cloud 

top From MM5

Deposition Scheme Zhang Wesely

Numerics

Gas Phase Chemistry Solver

Euler Backward Iterative (EBI) 

solver CMC fast solver

Horizontal Advection Scheme

Piecewise Parabolic Method (PPM 

scheme)

Piecewise Parabolic Method

(PPM) scheme

Parallelization OMP-MPI OMP

CAMx MODELING RESULTS

Air quality modeling results for the 2002 baseline simulations are presented by pollutant of 

concern, including ozone and PM. The analysis of air quality results focus on high ozone and 

PM levels, i.e. maximum 8-hour ozone and maximum 24-hour PM. Annual average levels for 

PM and its constituents are also presented. Visibility analysis comparing model results to the 

observational data is provided at the end of this section. This project did not include a complete 

statistical performance evaluation.

Ozone 

The annual maximum 8-hour ozone may be susceptible to model artifacts and so we focus on 

the annual 4th highest 8-hour ozone (Figure 3). The 2002 baseline shows high values of 4th

highest daily maximum 8-hour ozone (above 75 ppb, the current level of the ozone NAAQS) in 

several western locations such as California, Colorado and Arizona. Much of this western ozone 

is associated with wildfire emissions that can be identified by their smoke plumes (i.e., high 

organic PM). These wildfire emissions also contain high levels of NOx which is an ozone 

precursor. High ozone concentrations (above 100 ppb) also occur over water bodies close to 

major urban/industrial areas near the Great Lakes, Gulf Coast and the Northeast Seaboard, 

where emissions are transported over water and confined to a shallow boundary layer. Ozone 

exceeds 75 ppb in several urban areas in Texas including Dallas, Houston, and San Antonio. 

New Orleans and Baton Rouge, Louisiana, also exceed over 75 ppb. 
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Figure 3. 4th highest daily maximum 8-Hour ozone (ppb) from the 36-km simulation (left) and 

12-km simulation (right) for the 2002 baseline. 

Particulate Matter

PM2.5 results are presented for daily design-value relevant measures (98th percentile of all daily 

concentrations) and annual average concentrations. The baseline 8th highest 24-hour average 

concentrations of fine PM2.5 (Figure 4) shows the highest peak values occurring in the Western 

United States but more uniformly high values occurring in the Eastern United States. Elevated 

PM2.5 concentrations in the 12 km domain are seen in western Louisiana and western Arkansas. 

Upon inspecting emissions it appears that intense fire activities in these areas are the major 

cause of the high modeled PM concentrations. The causes of the high modeled PM 

concentrations may also be inferred from the chemical composition of the PM (shown in 

Appendix A). Some peaks in the Western United States occur in urban areas, such as Los 

Angeles and Seattle (characterized by high nitrate and organic carbon) whereas others are 

associated with prescribed burn and wildfire emissions (indicated by high primary organic 

carbon). High particulate matter concentrations in the Eastern United States have large sulfate 

and nitrate contributions with additional contributions from primary organic PM in the south. 

Annual average concentrations of PM2.5 and PM10 (Figure 5 and Figure 6) show a similar pattern 

of widespread but the fire influence is less pronounced. 
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Figure 4. Maximum 24-Hour PM2.5 (μg m-3) from the 36-km simulation (left) and 12-km 

simulation (right) for the 2002 baseline. 

Figure 5. Annual average PM2.5 (μg m-3) from the 36-km simulation (left) and 12-km 

simulation (right) for the 2002 baseline. 

Figure 6. Annual average PM10 (μg m-3) from the 36-km simulation (left) and 12-km 

simulation (right) for the 2002 baseline. 
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Visibility 

The IMPROVE algorithm for estimating light extinction from PM data has been a useful tool for 

understanding haze in terms of the various PM components of aerosols. EPA adopted this 

algorithm as the basis for the regional haze metric for visibility impact calculations under the 

1999 RHR. This work used the new IMPROVE algorithm (Pitchford et al., 2007) which 

reconstructs the light-extinction coefficient (bext, expressed in units of inverse megameters, 

Mm-1) using the following equation:

bext ≈

2.2 x fS(RH) x [small sulfate] + 4.8 x fL(RH) x [large sulfate]

+ 2.4 x fS(RH) x [small nitrate] + 5.1 x fL(RH) x [large nitrate]

+ 2.8 x [small organic mass] + 6.1 x [large organic mass]

+ 10 x [elemental carbon]

+ 1 x [fine soil]

+ 1.7 x fSS(RH) x [sea salt]

+ 0.6 x [coarse mass]

+ Rayleigh scattering (site-specific)

+ 0.33 x [NO2 (ppb)]

The apportionment of the total concentration of sulfate compounds into the concentrations of 

small and large size fractions is accomplished using the following equations:

[large sulfate] = [total sulfate/20] x [total sulfate], for [total sulfate] < 20 μg/m3

[large sulfate] = [total sulfate], for [total sulfate] ≥ 20 μg/m3

[small sulfate] = [total sulfate] - [large sulfate]

The similar equations are used to apportion total nitrate and total organic mass into small and 

large size fractions. The new algorithm contains three distinct water growth terms, designated 

fS, fL, and fSS for the small and large sulfate and nitrate fractions, and for sea salt, respectively. 

Monthly average f(RH) values are used as following FLAG2010 procedure (FLAG, 2010). 

EPA’s visibility natural conditions guidance document lists three default natural condition 

values corresponding to best 20% (B20%) days, worst 20% (W20%) days, and annual average 

(EPA, 2003b). Our analysis calculates the 2002 visibility and compares to the measurements at 

Class I areas on the B20% and W20% days. Similar analysis based on CMAQ simulation results 

was conducted by CENRAP for the worst 20% days and documented in Appendix D of the 

CENRAP Technical Support Document (ENVIRON and CERT, 2007). CENRAP analysis on the 

W20% days was provided here for comparison. 

Figures 7-10 demonstrate visibility comparisons at the Class I areas in Texas (i.e., Big Bend and 

Guadalupe Mountains) and nearby Texas (i.e., Caney Creek and White Mountain). Additional 

visibility comparison for other sites can be found in Appendix B. Performance for the B20%

days is mostly characterized by an overestimation bias; whereas the W20% days is 

characterized by an underestimation bias. Measurement data show that on average sulfate 
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dominates light-extinction coefficient on both B20% and W20% cases, however model results 

show variation of dominant light-extinction component. On the W20% days, underestimation 

of sulfate is observed across all sites. The sulfate underestimation was also seen in the CENRAP 

analysis. Soil performance on the B20% days at CACR and WIMO Class I areas is suspect and 

care should be taken in the interpretation of the visibility projections at these Class I areas. 

(a)

Figure 7 (continued). Comparison of observed (left bar) and 2002 Base G modeled (right bar) 

daily extinction for Caney Creek (CACR), Arkansas during (a) Best 20% (B20%) and (b) Worst 

20% (W20%) days in 2002. CENRAP results on the W20% days are shown in (C).
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(b)

(c)

Figure 7 (completed). Comparison of observed (left bar) and 2002 Base G modeled (right bar) 

daily extinction for Caney Creek (CACR), Arkansas during (a) Best 20% (B20%) and (b) Worst 

20% (W20%) days in 2002. CENRAP results on the W20% days are shown in (C). 
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(a)

(b)

Figure 8 (continued). Comparison of observed (left bar) and 2002 Base G modeled (right bar) 

daily extinction for Big Bend National Park (BIBE), Texas during (a) Best 20% (B20%) and (b) 

Worst 20% (W20%) days in 2002. CENRAP results on the W20% days are shown in (C).
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(c)

Figure 8 (completed). Comparison of observed (left bar) and 2002 Base G modeled (right bar) 

daily extinction for Big Bend National Park (BIBE), Texas during (a) Best 20% (B20%) and (b) 

Worst 20% (W20%) days in 2002. CENRAP results on the W20% days are shown in (C).
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(a)

(b)

Figure 9 (continued). Comparison of observed (left bar) and 2002 Base G modeled (right bar) 

daily extinction for Big Guadalupe Mountains National Park (GUMO), Texas during a) Best 

20% (B20%) and (b) Worst 20% (W20%) days in 2002. CENRAP results on the W20% days are 

shown in (C).
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(c)

Figure 9 (completed). Comparison of observed (left bar) and 2002 Base G modeled (right bar) 

daily extinction for Big Guadalupe Mountains National Park (GUMO), Texas during a) Best 

20% (B20%) and (b) Worst 20% (W20%) days in 2002. CENRAP results on the W20% days are 

shown in (C).
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(a)

(b)

Figure 10 (continued). Comparison of observed (left bar) and 2002 Base G modeled (right bar) 

daily extinction for Wichita Mountains Wilderness Area (WIMO), Oklahoma during a) Best

20% (B20%) and (b) Worst 20% (W20%) days in 2002. CENRAP results on the W20% days are 

shown in (C).
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(c)

Figure 10 (completed). Comparison of observed (left bar) and 2002 Base G modeled (right

bar) daily extinction for Wichita Mountains Wilderness Area (WIMO), Oklahoma during a) 

Best 20% (B20%) and (b) Worst 20% (W20%) days in 2002. CENRAP results on the W20% days 

are shown in (C).

SUMMARY

Air quality modeling results for the 2002 baseline simulations are presented by pollutant of 

concern, including ozone and PM. Peak values for both ozone and PM outside of urban areas 

are influenced by fire emissions as indicated by high organic particulate concentrations. 

Overall, the air quality maps show predicted ozone and PM concentrations within reasonable 

ranges. Additional analysis would have to be undertaken to examine in details the model’s 

ability to treat ozone and fine particulate.

The model evaluation focused on the model’s ability to predict the components of light 

extinction at the Class I areas. Similar visibility analysis by CENRAP concluded that the CMAQ 

2002 36 km model appears to be good enough to make future-year projections for changes in 

SO4, NO3, EC and OMC at the rural Class I areas. The CAMx 2002 model appears to be 

comparable to the CENRAP 2002 model on the worst 20% days. Underestimation bias of 

sulfate is seen in both simulations with a larger bias seen in this study. Performance for Soil 

and especially CM is suspect and care should be taken in interpreting these modeling results. 
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REFERENCES

ENVIRON. 2012. “User’s Guide – Comprehensive Air-quality Model with extensions, Version 

5.40.” ENVIRON International Corporation, Novato, California. (Available at

http://www.camx.com).

ENVIRON and CERT. 2007. “Technical Support Document for CENRAP Emissions and Air Quality 

Modeling to Support Regional Haze State Implementation Plans”, prepared for the 

Central Regional Air Planning Association, prepared by ENVIRON International 

Corporation, and the University of California, Riverside. September 12.

EPA. 2003. Guidance for Estimating Natural Visibility Conditions under the Regional Haze Rule. 

EPA-454/B-03-005. U.S. Environmental Protection Agency, Research Triangle Park, NC.

FLAG. 2010. Federal Land Managers’ Air Quality Related Values Work Group (FLAG) Phase I 

report-Revised (2010). Natural Resource Report NPS/NRPC/NRR—2010/232

Morris, R.E., G.E. Mansell, B. Koo, G. Tonnesen, M. Omary and Z. Wang. 2004. Modeling

Protocol for the CENRAP 2002 Annual Emissions and Air Quality Modeling, Draft 2.0.

(http://pah.cert.ucr.edu/aqm/cenrap/docs/CENRAP_Draft2.0_Modeling_Protocol_1208

4.pdf). December 8.

Morris, R.E. and G. Tonnesen. 2004. Quality Assurance Project Plan (Draft) for Central Regional 

Air Planning Association (CENRAP) Emissions and Air Quality Modeling.

(http://pah.cert.ucr.edu/aqm/cenrap/docs/CENRAP_QAPP_Nov_24_2004.pdf).

December 23.

Morris, R.E., A. Hoats, S. Lau, B. Koo, G. Tonnesen, C-J. Chien and M. Omary. 2005. Air Quality 

Modeling Analysis for CENRAP – Preliminary 2002 Base Case CMAQ and CAMx Modeling 

of the Continental US 36 km Domain and Model Performance Evaluation. ENVIRON 

International Corporation, Novato, California. April 30.

Pitchford, M.L., Malm, W.C., Schichtel, B.A., Kumar, N., Lowenthal, D., and Hand, J.L. 2007. 

Revised algorithm for estimating light extinction from IMPROVE particle speciation data. 

Journal of the Air and Waste Management Association. 57, 1326-1336.
APPENDIX A

Annual Average PM Constituents from the CAMx 2002 Base Case results
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Appendix A: Annual Average PM Constituents from the CAMx 2002 Base Case 

results

(a) Sulfate

(b) Nitrate

(c) Ammonium
(d) Organic aerosols

(e) Elemental Carbon

(f) Secondary organic aerosols
(g) Other fine particulates and fine crustal materials
APPENDIX B

Visibility Analysis for 2002 Typical Base G Emission Scenario
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Appendix B: Visibility Analysis for 2002 Typical Base G Emission Scenario

(a)

(b)

Figure B-1. Comparison of observed (left bar) and 2002 Base G modeled (right bar) daily 

extinction for Upper Buffalo Wilderness (UPBU), Arkansas during (a) Best 20% (B20%) and (b) 

Worst 20% (W20%) days in 2002.
(a)

(b)

Figure B-2. Comparison of observed (left bar) and 2002 Base G modeled (right bar) daily 

extinction for Hercules-Glades Wilderness (HUGL), Missouri during (a) Best 20% (B20%) and 

(b) Worst 20% (W20%) days in 2002. 
(a)

(b)

Figure B-3. Comparison of observed (left bar) and 2002 Base G modeled (right bar) daily 

extinction for Salt Creek Wilderness (SACR), New Mexico during (a) Best 20% (B20%) and (b) 

Worst 20% (W20%) days in 2002. 
(a)

(b)

Figure B-4. Comparison of observed (left bar) and 2002 Base G modeled (right bar) daily 

extinction for White Mountains Wilderness (WHIT), New Mexico during (a) Best 20% (B20%) 

and (b) Worst 20% (W20%) days in 2002. 
(a)

(b)

Figure B-5. Comparison of observed (left bar) and 2002 Base G modeled (right bar) daily 

extinction for Bosque del Apache Wilderness (BOAP), New Mexico during (a) Best 20% (B20%) 

and (b) Worst 20% (W20%) days in 2002. 
(a)

(b)

Figure B-6. Comparison of observed (left bar) and 2002 Base G modeled (right bar) daily 

extinction for Bandelier Wilderness Area (BAND), New Mexico during (a) Best 20% (B20%) 

and (b) Worst 20% (W20%) days in 2002. 
(a)

(b)

Figure B-7. Comparison of observed (left bar) and 2002 Base G modeled (right bar) daily 

extinction for Breton Wilderness (BRET), Louisiana during (a) Best 20% (B20%) and (b) Worst 

20% (W20%) days in 2002.