Appendix 9-9: CAMx Modeling Guidelines 
 International Corporation Air Sciences

Modeling Guidance 

Guidance for the Application of the CAMx Hybrid 

Photochemical Grid Model to Assess Visibility Impacts 

of Texas BART Sources at 

Class I Areas 

Work Order No. 582-04-65563-06-10 

Amendment No. 02 

Prepared for: 

Texas Commission on Environmental Quality 

12100 Park 35 Circle 

Austin, Texas 78753 

Prepared by: 

ENVIRON International Corporation 

101 Rowland Way, Suite 220 

Novato, California 94945 

Revised by: 

Texas Commission on Environmental Quality 

December 13, 2007

 101 Rowland Way, Suite 220, Novato, CA 94945 415.899.0700 
FOREWORD

This document provides guidance to assist BART-eligible sources prepare for the requirements 

of the BART rule in Chapter 116 adopted by TCEQ. This document does not substitute for any 

Clean Air Act provision, EPA regulation, or TCEQ regulation, nor is it a regulation itself. Thus, 

it does not impose binding, enforceable requirements on any party, nor does it assure that TCEQ 

will approve all instances of its application. The guidance may not apply to a particular situation, 

depending upon the circumstances. The TCEQ retains the discretion to adopt approaches on a 

case-by-case basis that differ from this guidance where appropriate. Any decisions by TCEQ 

regarding a particular modeling analysis will only be made based on the applicable statute and 

regulations. 

This guidance is a living document and may be revised periodically. Updates, revisions, 

and additional documentation will be provided at http://www.tceq.state.tx.us. As noted above, 

TCEQ cannot assure that actions based upon this guidance will be fully approvable in all 

instances. The TCEQ welcomes public comments on this document and will consider those 

comments in any future revisions of this guidance document, providing such approaches comply 

with all applicable statutory and regulatory requirements.

Please note several important changes to this guidance: 

• In accordance with EPA’s BART modeling guidance, Appendix Y of the BART rule 

(July 6, 2005), CALPUFF shall be used for single-source modeling where the distance 

between the BART source (or potential BART source) and the Class I area is less than 

300 km. CALPUFF may also be used at distances greater than 300 km, at the source's 

discretion. 

• Since CALPUFF may significantly over predict impacts at greater distances, CAMx is 

approved for situations where the distance is greater than 300 km. 

• In all cases, modeling protocols must be approved in advance by the TCEQ. 

• Either CAMx or CALPUFF models may be used for the engineering analysis at any 

distance between a source and a Class I area. 
December 2007 

TABLE OF CONTENTS 

Page 

GLOSSARY OF ACRONYMS .................................................................................................. iii 

1. INTRODUCTION................................................................................................................ 1-1 

 Overview .............................................................................................................................. 1-1 

 Background............................................................................................................................ 1-2 

2. CAMx BART MODELING APPROACH......................................................................... 2-1 

Version of CAMx .................................................................................................................. 2-1 

Enhancements to the PM Source Apportionment Technology.............................................. 2-5 

Updates to the PSAT Formulation and New Full-Science Chemistry PiG ........................... 2-8 

3. PROCEDURES FOR VOC AND PM EMISSIONS BART SCREENING 

 ANALYSIS USING ZERO-OUT AND INERT MODELING ............................ 3-1 

 Overview of Approach........................................................................................................... 3-1 

4. PROCEDURES FOR SCREENING BART SOURCES USING PSAT AND PiG........ 4-1 

 

 Summary of Approach........................................................................................................... 4-1 

5. DETAILS ON MODELING PROCEDURES................................................................... 5-1 

 VOC and PM Zero-Out Modeling ......................................................................................... 5-1 

 PSAT Modeling ..................................................................................................................... 5-2 

 Computational Requirements................................................................................................. 5-4

6. REFERENCES..................................................................................................................... 6-1 

APPENDICES 

Appendix A: Example CAMx Run Script for a Base Case or Zero-Out Run 

Appendix B: Example CAMx Run Script for a PSAT BART Analysis using the 

 SO4 and NO3 PSAT Tracers 
December 2007 

TABLES 

Table 5-1. Example of computer requirements on a dual-processor 

 Athlon 2800+ (2.1 Ghz) PC................................................................................. 5-4 

FIGURES 

Figure 2-1. Texas BART modeling 36/12 km modeling domain 

 and locations of the IMPROVE monitoring sites ................................................ 2-3 

Figure 2-2. Texas BART modeling 12 km modeling domain and 

 locations of the IMPROVE monitoring sites....................................................... 2-4 

Figure 2-3. Schematic representation of CAMx PiG puff shape 

 in the horizontal ................................................................................................... 2-9 

Figure 2-4. Schematic representation of a chain of PiG puffs emitted 

 from a point source into an evolving gridded wind field................................... 2-13 
December 2007 

GLOSSARY OF ACRONYMS 

AERMOD American Meteorological Society/Environmental Protection Agency 

Regulatory Model Improvement Committee Model 

AQRVs Air Quality Related Values 

BART Best Available Retrofit Technology 

bnatural Clean Natural Conditions 

bsource Total light extinction due to a source 

CAA Clean Air Act 

CAIR Clean Air Interstate Rule 

CALPUFF California Puff Model 

CAMx Comprehensive Air Quality Model with extensions 

CCRS Coarse Crustal particulate matter 

CENRAP Central Regional Air Planning Association 

CMAQ Community Multiscale Air Quality Modeling System 

CPRM Coarse other Primary particulate matter 

del-dv change in deciview (delta-deciview) 

dv deciviews 

EC Elemental Carbon 

EGU Electrical Generating Units 

EPA United States Environmental Protection Agency 

f(RH) Relative Humidity adjustment factor 

FCRS Fine Crustal particulate matter 

FIPS Federal Information Processing Standard 

FLAG Federal Land Managers Air Quality Related Values Workgroup 

FLM Federal Land Manager 

FPRM Fine other Primary particulate matter 

FR Federal Register 

GREASD Greatly Reduced and Simplified Dynamics 

Hg Mercury 

HI Haze Index 

IMPROVE Interagency Monitoring of Protected Visual Environments 

IRON Incremental Reactions for Organics and Nitrogen Oxides 

ISC Industrial Source Complex Model 

km kilometers 

LSODE Livermore Solver for Ordinary differential Equations 

Mm-1 Inverse Megameters 

MRPO Midwest Regional Planning Organization 

MW Megawatt 

NH4 Ammonium 

NO3 Particulate Nitrate 

non-EGU Non-Electrical Generating Units 

NOx Nitrogen Oxides 

NSR New Source Review 

OC Organic Carbon 

OMC Organic Matter Carbon 

OSAT Ozone Source Apportionment Technology 

PEC Primary Elemental Carbon 

PGM Photochemical Grid Model 

PiG Plume-in-Grid 
December 2007 

PLUVUE Plume Visibility Model 

PM Particulate Matter 

PM10

Particulate Matter with aerodynamic diameters less than 10 

microns 

PM2.5

Particulate Matter with aerodynamic diameters less than 2.5 

microns 

POA Primary Organic Aerosol 

PPM Piecewise-Parabolic Method 

PSAT Particulate Matter Source Apportionment Technology 

PSD Prevention of Significant Deterioration 

QAPP Quality Assurance Program Plan 

RH Relative Humidity 

RHR Regional Haze Rule 

RPO Regional Planning Organization 

SCICHEM Second-order Closure Integrated Puff model with CHEMistry 

SCIPUFF Second-order Closure Integrated Puff model 

SIP State Implementation Plan 

SMOKE Sparse Matrix Operator Kernel Emissions 

SO2 Sulfur Dioxide 

SO4 Sulfate 

SOA Secondary Organic Aerosol 

TCEQ Texas Commission on Environmental Quality 

TIP Tribal Implementation Plan 

tpy ton per year 

VISTAS 

Visibility Improvement State and Tribal Association of the 

Southeast 

VOC Volatile Organic Compounds 

WRAP Western Regional Air Partnership 
December 2007 

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INTRODUCTION 

OVERVIEW 

The final version of United States Environmental Protection Agency’s (EPA) Regional Haze 

Regulations that include the Best Available Retrofit Technology (BART) Guidelines was 

published in the Federal Register on 6 July 2005 (70 FR 39104; EPA, 2005). One of the 

provisions of the program is the requirement that certain existing stationary sources emitting 

visibility-impairing air pollutants install and operate BART. The regulations require case-by-case 

BART determinations to define specific emissions limits representing BART. The regulations also 

establish schedules for compliance for each source subject to BART. These requirements will be 

part of the State Implementation Plan (SIP) revisions that Texas must submit to EPA by 17 

December 2007. Sources are subject to BART if they can be reasonably anticipated to cause or 

contribute to visibility impairment at a Class I area. Once a source is determined to be subject to 

BART, an engineering analysis and determination of the degree of visibility improvement due to 

BART controls must be performed. The EPA BART Guidelines suggest several potential methods 

for determining whether a source is subject to BART and the methods for determining what the 

expected visibility benefits of the BART controls will be, including dispersion modeling. 

For the subject to BART assessment, EPA BART Guidelines state that “You can use CALPUFF, 

or another EPA approved model, to predict the visibility impacts from a single source at a Class I 

area” (EPA, 2005). For the visibility improvement determination, EPA BART Guidelines state 

“Use CALPUFF, or other appropriate dispersion model to determine the visibility improvement 

expected at a Class I area from the potential BART control technology applied to the source” 

(EPA, 2005). For the subject to BART modeling, cumulative modeling may also be conducted to 

show that no source in a State is subject to BART. This cumulative BART exemption modeling 

may be done “on a pollutant by pollutant basis or for all visibility-impairing pollutants to 

determine if emissions from these sources contribute to visibility impairment” (EPA, 2005). 

BART Guidelines suggest using California Puff Model (CALPUFF) or a photochemical grid 

model to perform the cumulative group exemption modeling, but notes “if you wish to use a grid 

model, you should consult with the appropriate EPA Regional Office to develop an appropriate 

modeling protocol” (EPA, 2005). 

The purpose of this document is to provide sources in Texas with guidelines for preparing a 

modeling protocol and modeling analysis using the Comprehensive Air Quality Model with 

extensions (CAMx) grid model. Sources also have the option to use CALPUFF. See TCEQ’s 

BART webpage for information on the Texas CALPUFF Modeling Guidelines 

(http://www.tceq.state.tx.us/implementation/air/sip/bart/haze.html).

Sources performing modeling will be required to submit a modeling protocol to the TCEQ for 

approval. Protocols must also be made available concurrently to EPA and Federal Land Managers

(FLM) for their review. 

The report accompanying the source-specific subject-to-BART screening analysis should 

provide a clear description of the modeling procedures and the results of the analysis. An 

electronic archive that includes the full set of inputs and model output fields should also be 

included with the report. 
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TCEQ BART Screening Exemption Modeling 

TCEQ has performed cumulative group BART screening exemption modeling (Morris and 

Nopmongcol, 2006) using the Comprehensive Air Quality Model with extensions (CAMx). 

CAMx is a photochemical grid model (PGM) that incorporates the latest scientific advances 

including current state of science treatment of transport, dispersion deposition and chemical 

transformation (gas-phase and aqueous-phase chemistry and aerosol chemistry and dynamics). As 

required in the EPA BART Guidelines, a modeling protocol was prepared (ENVIRON, 2005) and 

distributed to EPA and the FLMs prior to the BART cumulative exemptions screening analysis for 

their review. Three types of BART screening exemption modeling were conducted (Morris and 

Nopmongcol, 2006): 

• BART sources Volatile Organic Compounds (VOC) zero-out modeling to ascertain 

whether or not Texas BART VOC emissions cause or contribute to visibility 

impairment on any Class I areas; 

• BART sources primary particulate matter (PM) zero-out and chemically inert modeling 

to ascertain whether or not BART primary PM emissions cause or contribute to 

visibility impairment on any Class I areas; and 

• BART sources SO2 and NOx modeling using the PM Source Apportionment 

Technology (PSAT) and the Plume-in-Grid (PiG) subgrid-scale point source model. 

Concerns have been raised regarding the use of PGMs for single source assessments, such as a 

BART visibility assessment. These concerns revolve around: 

(1) PGMs can only resolve the dynamics and chemistry of a point source plume to the grid 

resolution specified and use of a high resolution grid (e.g., 100 m to 1 km) to resolve a 

plume would require extensive model inputs and model run times; and 

(2) to assess the impacts of a source two runs have to be performed, a base case with the 

source and a zero-out case where the emissions of the source are eliminated. As PGM runs 

are already more costly than a source-oriented plume model, like CALPUFF, the need to 

do multiple zero-out runs to assess the individual impacts of multiple sources is quite 

costly. Recent advances in the CAMx model development have alleviated these concerns 

regarding the use of PGMs for single source assessments as follows (Morris, Emery and 

Yarwood, 2006): 

• Implementation of the PM Source Apportionment Technology (PSAT) that allows the 

separate tracking of individual source PM impacts so that the individual impacts from 

many different sources can be obtained cost-effectively in one run; 

• The threading of the PSAT PM source apportionment through the full-chemistry PiG 

module so that the early plume chemistry and plume dynamics can be tracked by the 

subgrid-scale PiG module until the plume size is commensurate with the grid resolution 

when the plume can be adequately simulated by the grid model; and 
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• Implementation of flexi-nesting whereby finer nests can be specified inside of a coarser 

grid and the model can interpolate some or all of the model inputs from the coarse grid, 

which allows better resolution of point source plume chemistry and dispersion. 

Purpose 

Although CAMx now has the capability to cost-effectively estimate the air quality and visibility 

impacts of individual sources, being that it is a relatively new technology, the procedures for 

performing such a single source assessment using CAMx/PSAT/PiG are not fully documented. 

Thus, this document will provide guidance in the application of CAMx for single source 

assessments as needed for BART, New Source Review (NSR) and Prevention of Significant 

Determination (PSD) assessments. The TCEQ BART screening exemption modeling is used as an 

example approach for these types of assessments. 

BACKGROUND 

Regional Haze Rule and BART Guidelines 

Section 169A of the Federal Clean Air Act (CAA) sets forth a national goal for visibility which 

is the “prevention of any future, and the remedying of any existing, impairment of visibility in 

Class I areas which impairment results from manmade air pollution.” In 1999, EPA published a 

final rule to address a type of visibility impairment known as regional haze (64 FR 35714). The 

Regional Haze Rule (RHR) requires states to submit SIPs to address regional haze visibility 

impairment in 156 federally protected parks and wilderness areas (i.e., the Class I scenic areas 

identified in the CAA). The 1999 rule was issued to fulfill a long-standing EPA commitment to 

address regional haze under the authority and requirements of sections 169A and 169B of the 

CAA. As required by the CAA, the final RHR included a requirement for BART for certain 

large stationary sources that were put in place between 1962 and 1977. The regional haze rule 

addresses visibility impairment resulting from emissions from a multitude of sources located 

across a wide geographic area. Because the problem of regional haze is believed to be caused in 

large measure by long-range transport of emissions from multiple sources, initially EPA adopted 

an approach that required states to look at the contribution of all BART sources to the problem of 

regional haze in determining both applicability and the appropriate level of control. If a source 

potentially subject to BART is located in an area from which pollutants may be transported to a 

Class I area, that source ‘‘may reasonably be anticipated to cause or contribute’’ to visibility 

impairment in the Class I area. EPA’s BART guidelines include procedures for single source 

assessments of their visibility contributions. 

The BART guidelines were written primarily for the benefit of state, local and Tribal agencies, 

and describe a process for making the BART determinations and establishing the emission 

limitations that must be included in their SIPs or Tribal implementation plans (TIPs). The 

guidelines provide a process for making BART determinations that states can use in 

implementing the regional haze BART requirements on a source-by-source basis. States must 

follow the guidelines in making BART determinations on a source-by-source basis for 750 

megawatt (MW) power plants but are not required to use the process in the guidelines when 

making BART determinations for other types of sources, i.e., states retain the discretion to adopt 

approaches that differ from the guidelines. 
December 2007 

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Sources are BART-eligible if they meet three criteria: (a) had potential emissions of at least 250 

tons per year of a visibility-impairing pollutant, (b) were in operation between 7 August 1962 

and 7 August 1977, and (c) were one of 26 listed source categories. BART controls are required 

for any BART-eligible source which can be reasonably expected to cause or contribute to 

impairment of visibility in any of the 156 federal parks and wilderness (Class I) areas protected 

under the regional haze rule. Air quality modeling is an important tool available to the states in 

determining whether a source can be reasonably expected to contribute to visibility impairment 

in a Class I area. 

In EPA’s 1 August 2005 proposed rulemaking (70 FR 44154) entitled “Revisions to Provisions 

Governing Alternative to Source-Specific Best Available Retrofit Technology Determinations,” 

the agency provided states with a process to show that an emissions trading program may be 

used as an alternative to applying BART. Texas, however, will not be participating in this 

program. 

Role of Air Quality Models 

The EPA guidelines present several options for assessing whether or not a BART-eligible source 

is subject to BART control requirements. The options, relying on different modeling and/or 

emissions analysis approaches, are provided as guidance and the states are entitled to use other 

reasonable approaches for analyzing the visibility impacts of an individual source or group of 

sources. The options are: 

Option 1: Individual Source Attribution. States can use dispersion modeling to 

determine that an individual source cannot reasonably be anticipated to cause or 

contribute to visibility impairment in a Class I area and thus is not subject to BART. 

Under this option, states can analyze an individual source’s impact on visibility as a 

result of its emissions of SO2, NOx, and direct PM emissions. Because some dispersion 

models cannot currently be used to estimate the predicted impacts on visibility from an 

individual source’s emissions of VOC or ammonia, states may elect to use a more 

qualitative assessment to determine on a case-by-case basis, which sources of VOC or 

ammonia emissions may be likely to impair visibility and should therefore be subject to 

BART review. EPA-approved models should be used to predict the visibility impacts 

from a single source at a Class I area. 

Option 2: Use of ‘Model’ Plants. Under this option, analysis of model (or 

prototypical) plants could be used to exempt certain BART-eligible sources that share 

specific characteristics. This type of analysis may be most useful in identifying the types 

of small sources that do not cause or contribute to visibility impairment for purposes of 

BART and thus should not be subject to a BART review. Different Class I areas may 

have different characteristics, however, so care should be used to ensure that the criteria 

developed are appropriate for the applicable cases. The guidance (EPA, 2005) suggests 

that states could use modeling analyses of representative plants to reflect groupings of 

specific sources with important common characteristics and based on these analyses, 

states may determine that certain types of sources may clearly be anticipated to cause or 

contribute to visibility impairment whereas others do not. 
December 2007 

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EPA’s ‘Model’ plant example in their BART guidance pertains to exempting sources 

whose SO2, NOx or SO2 plus NOx emissions are less than 500 tons per year (tpy) and are 

greater than 50 km from any Class I area, or for sources whose emissions are less than 

1,000 tpy and are greater than 100 kilometers (km) from any Class I area. EPA directed 

that states may (if they so choose) use the example cited in the Guidelines to exempt 

sources with no further analysis. In other words, states that establish 0.5 deciviews as a 

contribution threshold may exempt sources that emit less than 500 tons per year of NOx

or SO2 (or combined NOx and SO2) with no further analysis, as long as these sources are 

located more than 50 km from any Class I area. Also, states that establish 0.5 deciviews 

as a contribution threshold may exempt sources that emit less than 1000 tons per year of 

NOx or SO2 (or combined NOx and SO2) with no further analysis, as long as these 

sources are located more than 100 km from any Class I area. Texas used the model plant 

option as an initial screening method of its BART sources. 

Option 3: Cumulative Modeling. States may also submit to EPA a demonstration, 

based on an analysis of overall visibility impacts, that emissions from BART-eligible 

sources in your state, considered together, are not reasonably anticipated to cause or 

contribute to any visibility impairment in a Class I area and thus no source should be 

subject to BART. You may do this on a pollutant by pollutant basis or for all visibilityimpairing pollutants to determine if emissions from these sources contribute to visibility 

impairment. 

While EPA identifies several options for determining whether or not a source is “subject to 

BART,” the most credible method is the use of dispersion modeling. Air quality modeling 

allows a state or source operator to analyze an individual source’s impact on visibility as a result 

of its emissions of SO2, NOx, and direct PM emissions. 

EPA assumes in the BART guidance that dispersion modeling cannot currently be used to 

estimate the predicted impacts on visibility from an individual source’s emissions of VOC or 

ammonia. Instead, EPA suggests a more qualitative assessment to determine on a case-by-case 

basis, which sources of VOC or ammonia emissions may be likely to impair visibility and should 

therefore be subject to BART review. The primary difficulty in using CALPUFF for VOCs is 

that the CALPUFF modeling system, recommended as the BART modeling platform, does not 

adequately treat PM formation from VOCs. Use of photochemical grid models to assess the 

visibility impacts of VOC emissions provides a quantitative and more scientifically defensible 

approach than a qualitative assessment. 

A primary difficulty in estimating visibility impairment resulting from ammonia is uncertainties 

in the ammonia emissions inventories. These uncertainties result in modeled impacts on PM 

levels that are not as reliable as the results of modeling other PM components. While ammonia 

emissions from industrial facilities are relatively well-characterized, livestock operations and soil 

are major sources of ammonia emissions, and their quantification is highly uncertain. In 70 FR 

39104, EPA concluded that the decision whether to consider ammonia as a visibility-impairing 

pollutant in a specific case where a potential BART source actually emits more than 250 tpy of 

ammonia is best left to the state. 
December 2007 

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EPA Guidance on Air Quality Models 

The BART determination under the Regional Haze Rule seeks to quantify the impact of source 

emissions of SO2, NOx, and direct PM (PM2.5 and/or PM10) on hourly and daily visibility 

impairment at receptors located within downwind Class I areas. Since visibility is defined in the 

context of light extinction, which itself is determined by atmospheric concentrations of specific 

fine particulate species – sulfate, nitrate, organic carbonaceous matter, elemental carbon, other 

fine particles and coarse mass – the modeling method(s) used must be capable of simulating 

these components reliably. 

EPA’s position on recommended models for fine particulate and visibility estimation from single 

point sources is clearly set out in the Final BART Rule and in the BART Modeling guidance 

document. The Final BART Rule states, “Because the air quality model CALPUFF is currently 

the best application available to predict the impact of a single source on visibility in a Class I 

area, we proposed that a CALPUFF assessment be used as the preferred approach first, for 

determining whether an individual source is subject to BART, and second, in the BART 

determination process. CALPUFF can be used to estimate not only the effects of directly 

emitted PM2.5 emissions from a source, but also to predict the visibility impacts from the 

transport and chemical transformation of fine particle precursors.” The Rule goes on to state that 

“regional scale modeling typically involves use of a photochemical grid model that is capable of 

simulating aerosol chemistry, transport, and deposition of airborne pollutants, including 

particulate matter and ozone. Regional scale air quality models are generally applied for 

geographic scales ranging from a multi-state to the continental scale. Because of the design and 

intended applications of grid models, they may not be appropriate for BART assessments, so 

states should consult with the appropriate EPA Regional Office prior to carrying out any such 

modeling.” 

In contrast, EPA’s “Guidance for Demonstrating Attainment of the Air Quality Goals for PM2.5

and Regional Haze” (EPA, 2001) sets forth the types of models that should be used for 

simulating secondary fine particulate and visibility for SIPs. EPA states, “States should use a 

regional scale photochemical grid model to estimate the effects of a control strategy on 

secondary components of PM. Changes in primary components may be estimated using a 

numerical grid model (with no chemistry), a Lagrangian model, or in some cases a receptor 

model.” Thus, in its Regional Haze and PM2.5 SIP modeling guidance, EPA indicates that 

CALPUFF (a Lagrangian non-steady-state Gaussian puff model) should not be used for 

secondary PM and visibility impacts at Class I areas, but rather is relegated to the category of 

estimating primary species. 

So, on the one hand, EPA maintains that CALPUFF is the “best regulatory modeling application 

currently available for predicting a single source’s contribution to visibility impairment” and 

notes, “it is the only EPA-approved model for use in estimating single source pollutant 

concentrations resulting from the long range transport of primary pollutants.” On the other hand, 

only regional grid models with appropriate chemistry are to be used in developing PM2.5 and 

Regional Haze SIPs. EPA has attempted to reconcile these two positions in the Final BART 

Rule by asserting that (a) regional models were not developed to treat individual point sources 

and (b) CALPUFF’s secondary aerosol chemistry is adequate for estimating relative benefits of 

controls on BART sources. 
December 2007 

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More recent developments in photochemical grid modeling should alleviate some of the concerns 

related to using them for single-source visibility assessment. In particular, the use of finer grid 

spacing and new PM PiG modeling techniques can extend the grid model’s applicability to 

assess the visibility impacts of a single source and groups of sources. As photochemical grid 

models include state-of-science representation of chemistry, this allows for the quantitative 

assessment of the visibility impacts of VOC emissions, as well as, more accurate and 

scientifically correct treatment of secondary PM formation (e.g., sulfates and nitrates) and 

regional transport and dispersion. 

Steps in the BART Modeling Process 

The BART guidelines identify three steps required to determine emission limitations for affected 

sources. 

Identify BART-Eligible Sources. The first step is to identify whether a source is 

BART-eligible based on its source category, when it was put in service, and the 

magnitude of its emissions of one or more “visibility-impairing” air pollutants. The 

BART guidelines list 26 source categories of stationary sources that are BART-eligible. 

Sources must have been put in service between 7 August 1962 and 7 August 1977 in 

order to be BART-eligible. Potential emissions of 250 tpy or more of a visibilityimpairing air pollutant are required to make a source eligible for BART. Qualifying 

pollutants include primary PM10 and gaseous precursors to secondary fine particulate 

matter such as SO2 and NOx. Whether ammonia and VOCs should be included as 

visibility-impairing pollutants for BART eligibility is left for the states to determine on a 

case-by-case basis. The guidance states that high molecular weight VOCs with 25 or 

more carbon atoms and low vapor pressure should to be considered as primary PM2.5

emissions and not VOCs for BART purposes. 

Determine Sources Subject to BART. Next, one determines whether a BART-eligible 

source can be excluded from BART controls by demonstrating that the source cannot be 

reasonably expected to cause or contribute to visibility impairment in a Class I area. 

EPA’s preferred approach is an assessment with the CALPUFF modeling system (or 

other appropriate model) followed by comparison of the estimated 24-hr visibility 

impacts against a threshold above estimated natural conditions to be determined by the 

States. The threshold to determine whether a single source “causes” visibility 

impairment is set at 1.0 deciview change from natural conditions over a 24-hour 

averaging period in the final BART rule (70 FR 39118). Any exceedance of this 

threshold would trigger a BART determination. The guidance also states that the 

proposed threshold at which a source may “contribute” to visibility impairment should 

not be higher than 0.5 deciviews, although depending on factors affecting a specific Class 

I area, it may be set lower than 0.5 deciviews. 

EPA’s guidance builds upon the 1990 National Acid Precipitation Assessment Program 

(NAPAP) that found that a 5% change in light extinction will evoke a just noticeable 

change in most landscapes. Converting the 5% change in light extinction to a change in 

deciviews yields a change of approximately 0.5 deciviews. EPA believes that this is a 

natural breakpoint at which to set the BART exemption levels. Since visibility 
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degradation may begin to be recognized by a human observer at this extinction level, the 

guidance uses a 0.5 deciview change on a 24-hour average basis for determining that an 

individual source is contributing to visibility impairment at a Class I area. This level 

would be calculated by comparing the air quality model’s results for an individual source 

against “natural visibility” conditions. To assess a source’s impact one compares the 98th

percentile modeled value (8th highest day annually at a receptor or 22nd highest over 

3-years) with the 0.5 deciview threshold to determine if the source contributes to 

visibility impairment and is therefore subject to BART. 

Determine Appropriate Types and Levels of Control. The third step is to determine 

BART for the source by considering various control options and selecting the “best” 

alternative, taking into consideration: (a) any pollution control equipment in use at the 

source (which affects the availability of options and their impacts), (b) the costs of 

compliance with control options, (c) the remaining useful life of the facility, (d) the 

energy and non air-quality environmental impacts of compliance, and (e) the degree of 

improvement in visibility that may reasonably be anticipated to result from the use of 

such technology. 

For states under the PM2.5 provisions of the Clean Air Interstate Rule (CAIR), EPA has noted 

that CAIR satisfies the BART requirements for Electrical Generating Units’ (EGU) SO2 and NOx

emissions. However, since CAIR does not address VOC and PM emissions, a state still needs to 

determine whether an EGU’s VOC and/or PM emissions contribute significantly to visibility 

impairment at any Class I area and, if so, address the rest of the BART requirements. 

According to the BART guidance, a modeling protocol should be submitted for all modeling 

demonstrations regardless of the distance from the BART-eligible source to the Class I area. 

EPA’s role in the development of the protocol is only advisory as the “states better understand 

the BART-eligible source configurations” and factors affecting their particular Class I areas 

(70 FR 39126). 

CALPUFF Modeling Recommendations 

To evaluate the visibility impacts of a BART-eligible source at Class I areas beyond 50 km from 

the source, the EPA guidance recommends the use of the CALPUFF model (EPA, 2003c). For 

modeling the impact of a source closer than 50 km to a Class I area, EPA’s BART guidance 

recommends that expert modeling judgment be used “giving consideration to both CALPUFF 

and other methods.” The Plume Visibility Model (PLUVUE)-II model is mentioned as a 

possible model to consider in addition to CALPUFF within 50 km of a source. The EPA 

guidance notes that regional scale photochemical grid models may have merit, but such models are 

resource intensive relative to CALPUFF. Photochemical grid models are clearly more appropriate 

for cumulative modeling options such as in the determination of the aggregate contribution of allBART-eligible sources to visibility impairment, but such use should involve consultation with the 

appropriate EPA Regional Office. 

CALPUFF is recommended for ascertaining whether a source may be exempted from BART. If a 

source is determined to be subject to BART, CALPUFF or another appropriate model should be 

used to evaluate the improvement in visibility resulting from the application of BART controls. 

Emissions reflecting periods of start-up, shutdown, and malfunction are not to be considered in 
December 2007 

1−9 

determining the appropriate emission rates. The EPA recommends that the state use the highest 

24-hour average actual emission rate for the most recent five-year period (excluding periods with 

start-up, shutdown, and malfunctions). Visibility improvements may be evaluated on a pollutantspecific basis. States are encouraged to account for the magnitude, frequency, and duration of the 

contributions to visibility impairment caused by the source when assessing whether the source is 

reasonably anticipated to cause or contribute to visibility impairment at a Class I area. 

Alternative Models for BART Analyses 

All air quality models potentially suited to BART analysis share a common foundation: the 

species-conservation (or atmospheric diffusion) equation. Source-oriented air quality models, 

including CALPUFF, derive from this equation. The atmospheric diffusion equation applies 

equally to many sources. The distinction lies in how the various terms are treated (CAMx, 

CMAQ) or neglected (CALPUFF) in the governing equations and the choice of coordinate 

system (Lagrangian or Eulerian). Much of the simplicity of the CALPUFF model derives from 

the fact that many chemical and physical processes known to influence visibility are simply 

ignored. In contrast, comprehensive regional models treat these processes in detail, albeit at the 

expense of greater computer resources and data needs. EPA’s BART guidance allows for the use 

of alternative models on a case-by-case basis. 

EPA’s dismissal of regional scale modeling ignores a substantial body of research and model 

development carried out at the agency and elsewhere in the U.S. over the past 20 years. 

Although grid models have generally been applied at geographic scales ranging from a multistate to the continental scale and were not initially designed to simulate individual point sources, 

modern one-atmospheric regional photochemical grid models, employing nested grid (Kumar 

and Russell, 1996) and Plume-in-Grid techniques (Karamanchandani et al., 2002; ENVIRON, 

2005), are fully applicable to the analysis of point source plumes, most especially when reactive 

atmospheric chemistry occurs. If they were not, then they would not be reliable in simulating the 

combined effects of the wide array of anthropogenic and biogenic emissions that cause gas 

phase, particulate, secondary aerosol, and visibility air pollution problems. Furthermore, the 

convergence of fast commodity-based LINUX computer clusters and recently-developed 

regional modeling emissions, meteorological, and air quality databases make application of these 

modeling platforms no longer a research or academic exercise. While regional scale modeling 

clearly requires expertise to perform properly, the actual program costs to conduct a CMAQ or 

CAMx regional modeling study are, today, quite comparable with and often less than a 

traditional PSD modeling study using plume models, such as Industrial Source Complex Model 

(ISC), CALPUFF, or The American Meteorological Society/Environmental Protection Agency 

Regulatory Model Improvement Committee Model (AERMOD). Given grid nesting and Plumein-Grid technology, modern regional models are applicable to a broad range of scales from 10-20 

km to continental scale. In fact, regional photochemical grid models have been applied with grid 

spacing down to hundreds of meters on occasion (Kemball-Cook, Emery and Yarwood, 2005). 

Regional photochemical grid models are clearly more appropriate than CALPUFF for 

cumulative modeling requirements such as in the determination of the aggregate contribution of 

all-BART-eligible sources to visibility impairment. As confirmed in EPA’s fine particulate and 

regional haze modeling guidance discussed previously (EPA, 2001) and clearly shown by Morris 

and co-workers (2003; 2005; 2006), regional photochemical grid models, such as CMAQ and 

CAMx, provide a much more accurate and technically correct representation of the formation of 
December 2007 

1−10 

secondary PM species, such as sulfate (SO4), nitrate (NO3), and secondary organic aerosols 

(SOA), than CALPUFF. 

Still, for the vast majority of potential BART-eligible sources the application of the CALPUFF 

modeling system will in all likelihood be sufficient to address the needs of the source owner, the 

state, and the approving agency (EPA and the FLM.) However, for the BART modeling in 

Texas, a photochemical grid model (PGM) is more appropriate than the single-source CALPUFF 

model for the following reasons: 

• There are a large number of potential BART-eligible sources in Texas, and the CAMx 

screening BART exemption runs allow the efficient screening of many sources in a 

scientifically defensible manner; 

• For most potential BART-eligible sources in Texas, the Class I areas where visibility 

impacts are estimated are a great distance from the source. The sources that have not yet 

screened out of BART, based on the CAMx runs already performed on behalf of TCEQ, 

are between 97 and 654 km from the nearest Class I area. Of the 67 sources remaining, 

50 are more than 300 km from the nearest Class I area; 

• TCEQ has conducted analyses to identify which of the many potential BART-eligible 

sources satisfy the three criteria for being BART-eligible. Some of these sources have 

been determined to make an insignificant contribution to visibility impairment at Class I 

areas as a group, resources can now be focused on those sources determined most likely 

to impact visibility in Class I areas (Morris and Nopmongcol, 2006); 

• Use of a PGM allows for the quantitative assessment of the visibility impacts due to 

potential BART-eligible sources’ VOC and PM emissions; 

• Use of a PGM provides an evaluation of the cumulative impact of BART-eligible sources 

on visibility in Class I areas; and 

• Use of a hybrid PGM that uses a full-chemistry PiG module for near-source plume 

chemistry and dynamics and a three-dimensional grid model for plume chemistry, 

transport and dispersion at farther downwind distances contains all of the scientific 

advantages of both CALPUFF and a PGM while treating secondary PM formation using 

full-science algorithms at all scales. 
December 2007 

2-1 

2.0 CAMx BART MODELING APPROACH 

In this section we describe the general modeling approach used to perform the BART screening 

analysis of potential BART-eligible sources in the state of Texas using the CAMx PGM. This 

modeling approach is used as an example for how the CAMx, with its PSAT and PiG features, 

can be used to perform single source or groups of sources visibility assessments as part of the 

BART exemption and visibility improvement steps. The approach could also be used for air 

quality and Air Quality Related Values (AQRVs) assessments from proposed new or modified 

sources as part of the NSR and PSD process (Morris, Emery and Yarwood, 2006). There are two 

elements that were performed in the Texas BART screening exemption modeling using CAMx: 

• Screening analysis of the potential visibility impacts at Class I areas due to all potential 

BART-eligible sources VOC and PM emissions; and 

 

• Screening analysis for groups of potential BART-eligible non-EGU sources’ SO2 and 

NOx emissions to determine whether the group’s visibility impacts at Class I areas are 

significant or not. 

Both elements of the screening analysis used the same 36/12 km 2002 annual database. The 

VOC and PM emission screening analysis was performed using emissions zero-out modeling and 

inert primary PM modeling, whereas the group of sources’ SO2 and NOx emissions screening 

analysis was performed using the PSAT that has been implemented in the full-chemistry PiG. 

The SO2 and NOx emissions analysis could also include the PM PSAT family of source 

apportionment tracers (and SOA/VOC family as well) and could also be applied for EGU 

sources. 

VERSION OF CAMx 

CAMx Version 4.4β (V4.4β) was used in the Texas BART screening exemption modeling 

analysis. CAMx V4.4β is an updated version to CAMx V4.31 (ENVIRON, 2005) that is 

currently (August 2006) publicly available on the CAMx website for no cost (www.camx.com). 

CAMx V4.4β includes several updates to CAMx V4.31, including the implementation of the 

PSAT within the full-science PiG. CAMx V4.4β is available on request, but is undergoing final 

testing and will be posted to the CAMx website with an updated user’s guide and test case 

shortly (late summer or early fall 2006). 

2002 ANNUAL 36/12 KM MODELING DATABASE 

The Texas BART screening analysis exemption modeling was performed using a 2002 annual 

regional photochemical modeling database that was developed as part of the Central Regional 

Air Planning Association (CENRAP) regional haze work. CENRAP has developed a 2002 

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

Organization (RPO) grid that covers the continental US. This database was developed following 

the procedures outlined in the CENRAP Modeling Protocol (Morris et al., 2004) and CENRAP 

modeling Quality Assurance Program Plan (QAPP) (Morris and Tonnesen, 2004). The 

CENRAP preliminary base case model performance evaluation results for the CAMx model on 
December 2007 

2-2 

the national 36 km grid using the CENRAP Base A emissions is given in Morris et al., (2005d). 

The CENRAP Modeling Protocol, QAPP and preliminary Base A evaluation reports provide 

details on the development of the CENRAP 2002 36 km annual modeling database. Below we 

provide additional information related to enhancements to the CENRAP database for use in the 

Texas BART exemption screening analysis. 

Enhancements to the CENRAP 2002 Modeling Database 

The CENRAP 2002 36 km annual CAMx evaluation using the Base A emissions and CAMx 

Version 4.20 is reported in Morris and co-workers (2005d) with additional model performance 

evaluation displays available on the CENRAP modeling Website 

(http://pah.cert.ucr.edu/aqm/cenrap/cmaq.shtml#camx). CENRAP has updated the CAMx 2002 

36 km Base A emissions to Base B and CAMx version 4.30. The Base B base case database was 

the starting point for the Texas BART exemption modeling screening analysis. 

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

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

including: 

• Big Bend, Guadalupe Mountains, Carlsbad Caverns National Parks; 

• Salt Creek Wichita Mountains Wildlife Refuges and White Mountain; and 

• Caney Creek, Upper Buffalo and Hercules Glade Wilderness Areas. 

 Figure 2-1 displays the 36/12 km nested grid structure that was used in the Texas CAMx BART 

exemption modeling analysis. The locations of the potential BART-eligible sources and Class I 

areas within the 12 km modeling domain are shown in Figure 2-2. The CAMx flexi-nesting 

feature was used to incorporate the 12 km Texas grid within the CENRAP 36 km modeling 

domain. Full flexi-nesting was invoked in which CAMx internally interpolates all of the 

meteorological, emissions and other inputs from the 36 km grid to the 12 km grid. This option 

has the desired effect of allowing the BART point source plumes’ chemistry, transport and 

dispersion to be represented and resolved by the higher resolution 12 km grid after treatment of 

their near-source plume chemistry and dynamics using the subgrid-scale PiG module when 

plume size is below 12 km. 

If BART or similar assessments using CAMx are desired, a new 12 km grid flexi-nest could be 

defined focused on the selected states and nearby Class I areas. 
December 2007 

2-3 

-2736 -1872 -1008 -144 720 1584 2448

-2088

-1656

-1224

-792

-360

72

504

936

1368

1800

km

km

36km Domain: South West Corner = (-2736 km, -2088 km) NX, NY = (148x112) 

LCP Definition: (-97.0, 40.0, 45.0, 33.0)

Figure 2-1. Texas BART modeling 36/12 km modeling domain and locations of the IMPROVE 

monitoring sites that include Class I areas (indicated by circles). 
December 2007 

2-4 

-936 -816 -696 -576 -456 -336 -216 -96 24 144 264 384

-1620

-1500

-1380

-1260

-1140

-1020

-900

-780

-660

-540

-420

km

km

12km Domain: South West Corner = (-936 km, -1620 km) NX, NY = (111x108) 

LCP Definition: (-97.0, 40.0, 45.0, 33.0)

Figure 2-2. Texas BART modeling 12 km modeling domain and locations of the IMPROVE monitoring 

sites (indicated by circles) that include Class I areas and locations of potential BART-eligible sources in 

Texas (indicated by triangles). 
December 2007 

2-5 

ENHANCEMENTS TO THE PM SOURCE APPORTIONMENT TECHNOLOGY 

To estimate the air quality and/or visibility impacts from a single source or group of sources, the 

CAMx model PSAT is used. PSAT was updated to be compatible with the CAMx PiG module 

(CAMx V4.4β). Below we describe the technical formulation of the PSAT source 

apportionment technique and the enhancements to the CAMx PiG and PSAT to make them 

compatible with each other. 

PSAT Formulation 

PSAT is designed to source apportion the following PM species modeled in CAMx: 

• Sulfate (SO4) 

• Particulate nitrate (NO3) 

• Ammonium (NH4) 

• Particulate mercury (Hg(p)) 

• Secondary organic aerosol (SOA) 

• Six categories of primary PM 

o Elemental carbon (EC) 

o Primary organic aerosol (POA) 

o Fine crustal PM (FCRS) 

o Fine other primary PM (FPRM) 

o Coarse crustal PM (CCRS) 

o Coarse other primary PM (CPRM) 

PSAT performs PM source apportionment for each user defined source group. A source group 

consists of a combination of a geographic region and emissions source category. Examples of 

source regions include states, nonattainment areas and counties, whereas examples of source 

categories include mobile sources, biogenic sources, elevated point sources or even an individual 

source. The user defines a geographic source region map to specify the source regions. The user 

then inputs each separate source category as separate gridded low-level emission and/or elevated 

point source emission inputs. The model then determines each source group by overlaying the 

source categories on top of the source region map. 

The PSAT “reactive tracers” that are added for each source category/region (i) are described 

below. In general, a single tracer can track primary PM species whereas secondary PM species 

require several tracers to track the relationship between gaseous precursors and the resulting PM. 

Particulate nitrate and secondary organics are the most complex species to apportion because the 

emitted precursor gases (NOx, VOCs) are several steps removed from the resulting PM species 

(NO3, SOA). The PSAT tracers for each type of PM are listed below. PSAT convention is that 

tracer names for particulate species begin with the letter “P.” 

Sulfur

SO2i Primary SO2 emissions 

PS4i Particulate sulfate ion from primary emissions plus secondarily formed sulfate 
December 2007 

2-6 

Nitrogen

RGNi Reactive gaseous nitrogen including primary NOx (NO + NO2) emissions plus 

nitrate radical (NO3), nitrous acid (HONO) and dinitrogen pentoxide (N2O5). 

TPNi Gaseous peroxyl acetyl nitrate (PAN) plus peroxy nitric acid (PNA) 

NTRi Organic nitrates (RNO3) 

HN3i Gaseous nitric acid (HNO3) 

PN3i Particulate nitrate ion from primary emissions plus secondarily formed nitrate 

NH3i Gaseous ammonia (NH3) 

PN4i Particulate ammonium (NH4) 

Secondary Organic Aerosol

ALKi Alkane/Paraffin secondary organic aerosol precursors 

AROi Aromatic (toluene and xylene) secondary organic aerosol precursors 

CREi Cresol secondary organic aerosol precursors 

TRPi Biogenic olefin (terpene) secondary organic aerosol precursors 

CG1i Condensable gases from toluene and xylene reactions (low volatility) 

CG2i Condensable gases from toluene and xylene reactions (high volatility) 

CG3i Condensable gases from alkane reactions 

CG4i Condensable gases from terpene reactions 

CG5i Condensable gases from cresol reactions 

PO1i Particulate organic aerosol associated with CG1 

PO2i Particulate organic aerosol associated with CG2 

PO3i Particulate organic aerosol associated with CG3 

PO4i Particulate organic aerosol associated with CG4 

PO5i Particulate organic aerosol associated with CG5 

Mercury

HG0i Elemental Mercury vapor 

HG2i Reactive gaseous Mercury vapor 

PHGi Particulate Mercury 

Primary Particulate Matter

PECi Primary Elemental Carbon 

POAi Primary Organic Aerosol 

PFCi Fine Crustal PM 

PFNi Other Fine Particulate 

PCCi Coarse Crustal PM 

PCSi Other Coarse Particulate 

PSAT includes a total of 32 tracers for each source group (i) if source apportionment is applied 

to all types of PM. Since source apportionment may not always be needed for all species, the 

PSAT implementation is flexible and allows source apportionment for any or all of the chemical 

classes in each CAMx simulation (i.e. the SO4, NO3, NH4, SOA, Hg and primary PM classes 

listed above). For example, source apportionment for sulfate/nitrate/ammonium requires 9 

tracers per source group. 

One fundamental assumption in PSAT is that PM should be apportioned to the primary precursor 

for each type of PM. For example, SO4 is apportioned to SOx emissions, NO3 is apportioned to 

NOx emissions, NH4 is apportioned to NH3 emissions, etc. As a source apportionment method, 
December 2007 

2-7 

PSAT must account for all modeled sources of a PM species. Consider two model species A 

and B that are apportioned by reactive tracers ai and bi, respectively. Reactive tracers must be 

included for all sources of A and B including emissions, initial conditions and boundary 

conditions so that complete source apportionment is obtained, i.e., A = Σai and B = Σbi. 

In PSAT, the general approach to modeling change over a model time step Δt is illustrated for a 

chemical reaction A→B. The general equation for species destruction is: 

( ) ()

i

i

i i

a

a Atatta

∑

Δ+=Δ+ 

Here, the relative apportionment of A is preserved as the total amount changes. This equation 

applies to chemical removal of A and also to physical removal of A by processes such as 

deposition or transport out of a specific grid cell. 

The general equation for species production (e.g, chemical production by the chemical reaction 

A→B) is: 

 ( ) ()

i

i

i i

a

a Btbttb

∑

Δ+=Δ+

Here, production of B inherits the apportionment of the precursor A. The same equation applies 

for “production” of B in a specific grid cell due to emissions or transport. For the case where B

increases due to emissions, ai is the apportionment of the emissions inventory. For the case 

where B increases due to transport, ai is the apportionment of the upwind grid cell. 

In some cases, source category specific weighting factors (wi) must be added to the equation for 

species destruction: 

( ) ()

ii

ii

i i

aw

aw Atatta

∑

Δ+=Δ+

An example is chemical decay of the aromatic VOC tracers (ARO,) which must be weighted by 

the average hydroxyl radical (OH) rate constant of each AROi. ARO tracers for different source 

groups have different average VOC reactivities because the relative amounts of toluenes and 

xylenes differ between source categories. 

In some cases, source category specific weighting factors (wi) must be added to the equation for 

species production: 

( ) ()

ii

ii

i i

aw

aw Btbttb

∑

Δ+=Δ+

An example is chemical production of condensable gases (CG1 or CG2) from ARO tracers, 

which must be weighted by aerosol yield weighting factors. The aerosol yield weighting factors 

depend upon the relative amounts of toluenes and xylenes in each source group. 

Several aerosol reactions are treated as equilibria, A↔B. If A and B reach equilibrium at each 

time step, it follows that their source apportionments also reach equilibrium: 
December 2007 

2-8 

( ) () () [ ]

( ) () () [ ] ⎟

⎠

⎞ ⎜

⎝

⎛

+

+=Δ+

⎟

⎠

⎞ ⎜

⎝

⎛

+

+=Δ+

BA

B tbtattb

BA

A tbtatta

i ii

i ii

 

Examples are the equilibrium between gas phase nitric acid and aerosol nitrate, gas phase 

ammonium and aerosol ammonium, and condensable organic gases (CG) and secondary organic 

aerosols (SOA). 

The PSAT source apportionment technique has been extensively tested and evaluated against 

other source apportionment techniques (e.g., ENVIRON, 2005; Morris et al., 2005; Yarwood et 

al., 2004). 

UPDATES TO THE PSAT FORMULATION AND NEW FULL-SCIENCE CHEMISTRY 

PiG 

The CAMx PSAT and PiG algorithms were updated to treat the near-source chemistry of 

secondary PM formation and to be compatible with each other. The PiG module now treats fullscience aerosol chemistry and dynamics in addition to gas-phase chemistry and has been 

extended to PSAT and Ozone Source Apportionment Technology (OSAT.) The formulation of 

the full-science PiG is described below. 

Modeling photochemistry is a highly non-linear problem because chemical rates for most 

compounds depend upon their ambient concentrations. Ambient concentrations in turn depend 

on how well the modeling grid resolves emissions, transport, and chemical history. Thus, grid 

resolution plays a vital role in the ability of the model to properly characterize photochemical 

conditions. Increasing resolution should, in theory, lead to a better model as the time/space 

discretization tends toward a continuum. However, practical and theoretical considerations 

suggest that the lower limit on horizontal grid spacing is about 1000 meters for Eulerian air 

quality models such as CAMx. Nevertheless, even higher resolution is often necessary to 

adequately simulate chemistry within concentrated point source plumes. 

As a result, many modern Eulerian models contain a Plume-in-Grid sub-model that tracks 

individual plume segments (or puffs) in a Lagrangian sense, accounting for plume-scale 

dispersion and chemical evolution, until such time as puff mass can be adequately represented 

within the larger grid model framework. It is important to understand that the inclusion of a 

Lagrangian puff model within an Eulerian grid model is a forced construct. The formulations of 

the two modeling approaches are fundamentally different and there is no theoretically “correct” 

methodology. This explains the variety of Plume-in-Grid methodologies that are in use today. 

The CAMx PiG module was recently updated in Version 4.4 of the model. 

The new PiG approach in CAMx treats the full suite of gas-phase photochemistry, aqueousphase chemistry and aerosol phase chemistry and dynamics. Chemical processes are simulated 

within each plume segment using an “incremental chemistry” approach, whereby puffs carry the 

incremental contributions of the puff relative to the grid concentrations. Incremental puff 

concentrations can be positive or negative, depending upon the species and stage of plume 

evolution. A similar chemistry approach is used in the Second-order Closure Integrated puff 
December 2007 

2-9 

model (SCIPUFF) with CHEMistry (SCICHEM) Lagrangian model (EPRI, 2000). The 

approach lends itself to incorporating chemistry for particulates as well. 

Basic Puff Structure and Diffusive Growth 

The CAMx PiG releases a stream of plume segments (puffs) from a point source specified in the 

CAMx input file by setting the point source stack diameter to a negative value. Each puff 

possesses a longitudinal length and directional orientation defined by the separation of a leading 

and a trailing point. Initial separation of these points is determined by the wind vector at final 

plume rise. Each point is then subsequently and independently transported through the gridded 

wind fields, which directly accounts for puff stretching and changes to centerline orientation due 

to deforming shears. The official "position" of each puff is defined by the center point of each 

puff between the endpoints. This position defines the grid cell in which the puff resides for the 

calculation of diffusion and chemistry. 

Like other puff models, the shape of each puff is characterized by a spread tensor, which is 

defined from a set of Gaussian standard deviations (so-called “sigmas”) along the three spatial 

axes (σx, σy, σz). Diffusive growth is defined by the growth in these sigma values. The total 

cross-sectional width extends ±1.5σ from puff centerline. The limits of ±1.5σ result in an 

average concentration across the Gaussian distribution that nearly equals a uniformly mixed 

concentration across that distance. The total longitudinal length is the distance between the puff 

endpoints ±1.5σy. Horizontal area is calculated using the formula for an ellipse. Different 

vertical constructs are employed for Greatly Reduced and Simplified Dynamics (GREASD) and

Incremental Reactions for Organics and NOx (IRON) PiG, as described later in this section. 

Figure 2-3 presents a schematic representation of each puff in horizontal cross-section. 

P (lead)

P (trail)

y 1.5

x

y 1.5

Figure 2-3. Schematic representation of CAMx PiG puff shape in the horizontal. Directional orientation 

of the puff is arbitrary, and evolves during the aging of the puff according to wind direction, shears and 

diffusive growth along its trajectory.
December 2007 

2-10 

We have developed an explicit solution approach for puff growth that shares SCICHEM theory 

and concepts (EPRI, 2000), but includes some simplifications. SCICHEM solves predictive 

spatial moment equations with second-order closure that relate the evolution of the puff spread 

tensor (σij=σi×σj) to resolved mean shears and turbulent velocity statistics. The Reynoldsaveraged second-moment transport equation is given as: 

Q

cux

Q

cux

x

u

x

u

dt

d ji ij

k

i

jk

k

j

ik

ij ′′′ +

′′′ +

∂

∂

+

∂

∂ = σσ

σ

where u is the mean wind vector component, the primed values represent turbulent fluctuations 

from the mean, and the angle brackets denote integrals over space. The Reynolds averaging 

process always introduces higher-order fluctuation correlations, and this is given by the turbulent 

flux moments cux ′′′ , where cu ′′ represents the turbulent flux of concentration. It is these last 

two diffusion terms that SCICHEM solves in its second-order closer scheme. 

For sub-puff scale turbulence, SCICHEM employs the restriction that the only off-diagonal 

component of cux ′′′ to be considered is the symmetric horizontal term (i=x, j=y), and then 

only for the large-scale (meso to synoptic) contribution to puff deformation when puff scale 

reaches such dimensions. In CAMx, we ignore this off-diagonal flux moment term altogether 

since puff mass is ultimately introduced to the grid when puff size is at the grid scale (1-50 km in 

practically all applications), and thus puffs never reach spatial scales at which this term becomes 

important. SCICHEM also makes the assumption that the horizontal turbulence is isotropic, 

′′′ = cvycux ′′′ . This results in a single diffusivity equation for both x and y directions, and a 

single diffusivity for the z direction: 

Q

cvy

KK yx

′′′

==

Q

cwz

Kz

′′′

=

In our approach for CAMx, we have adopted the SCICHEM second-order tendency equations to 

model the time-evolution of puff turbulent flux moments (represented by diffusivities Kx=Ky and 

Kz) and their contribution to the evolution of puff spread (represented by the diagonal 

components of the puff spread tensor,σx

2

 =σy

2

 and σz

2

). Note that we ignore the off-diagonal 

contributions to puff spread, since they are unnecessary in the context of the CAMx PiG. Puff 

spread is defined for puff depth (σz) and puff width (σy); the latter is also added to the 

longitudinal length to allow for diffusive growth along the puff centerline. We account for the 

effects of wind shears in the evolution of lateral spread, but assume that the evolution of vertical 

spread is solely the result of turbulent fluxes. 
December 2007 

2-11 

The resulting two Reynolds-averaged second-moment transport equations for CAMx PiG are: 

z

z K

dt

d 2

2

= σ

y zy y

y K

dz

dv

dz

du D

dt

d

2 2 2

2/1 22

2

2

+ ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = σ + σσ +

σ

where D is deformation of horizontal wind (see Section 2). 

The SCICHEM tendency equation for the horizontal turbulent flux moment is: 

cvy

q AQqcvy dt

d ′′′ Λ ′′′ −= 2

where A = 0.75, q

2

 = vv ′′ , and Λ is the horizontal turbulent length scale. Separate equations are 

given for two different boundary layer turbulence scales (shear- and buoyancy-produced), such 

that: 

shear buoyancy

′′′ = cvycvy ′′′ + cvy ′′′

Within the surface-based boundary layer, the horizontal velocity variance is given by: 

[ ( )] buoyancy i q w /exp5.1113.0 zz

2

*

2 = +

( ) shear i q /15.2 zzu

2

*

2 = −

where u* is the friction velocity, w* is the convective velocity scale, z is height above the surface, 

and zi is the height of the surface-based boundary layer. The horizontal turbulent length scale is 

given by: 

( ) ( ) 2 2 2 65.0

1

3.0

1 1

z z shear i

= +

Λ

buoyancy i Λ = 3.0 z

In the stable boundary layer, only the shear components of q

2

 and Λ are applied. Above the 

boundary layer, SCICHEM applies rough approximations for the velocity variance and turbulent 

length scale: q

2

 = 0.25 m2/s2, and Λ = 1000 m. 
December 2007 

2-12 

The SCICHEM tendency equation for the vertical turbulent flux moment is 

( cwzQK ) q Acwz

dt

d eq

z

v

v − ′′′ Λ ′′′ =

where qv

2

 = ww ′′ , Λv is the vertical turbulent length scale, and Kz

eq is the equilibrium diffusivity. 

Whereas a specific equation for Kz

eq is formulated for SCICHEM, we have chosen to specify the 

value of this parameter from the gridded fields of vertical diffusivity in CAMx. Again 

SCICHEM gives separate equations for shear- and buoyancy-produced turbulence scales. 

Within the surface-based boundary layer, the vertical velocity variance is given by 

( )i shear v q /15.1 zzu

2

*

2 = −

( ) ( ) i i buoyancy v q zzw /05.1/1.1 zz

2 3/2

*

2 = −

The vertical turbulent length scale for both shear and buoyancy is equal to Λshear given above for 

horizontal length scale. Above the boundary layer, SCICHEM again applies rough 

approximations for the velocity variance and turbulent length scale: 

qv

2

 = 0.01 m2/s2, and Λv = 10 m. 

The external variables needed by IRON PiG to complete the dispersion calculations include zi, u*

and w*. All of these are available from an internal module in CAMx that calculates these 

boundary layer similarity theory parameters. Thus, no additional parameters are needed to be 

input to the model. 

Puff Transport 

A fresh set of new puffs are released from all PiG point sources within the modeling domain for 

the duration of the smallest time step among the master and all nested grids. The length of each 

puff is determined by the combination of the mean total wind speed at the height of final plume 

rise and time step. Limits are placed on maximum extruded length based on half the finest 

resolution in the given simulation. If winds and time-steps are such that the maximum allowed 

length is violated, then several puffs are extruded from a given stack per time step. The 

orientation of the puff length is along the total wind vector. Total puff volume is determined by 

stack volumetric flow rate in conjunction with growth due to turbulent entrainment following the 

SCICHEM approach. Initial σy and σz are explicitly calculated from this entrainment 

calculation. 

Effects of wind divergence on plume deformation are treated in an explicit manner within the 

CAMx PiG using a “chained puff” approach (Figure 2-4). Points at the leading and trailing 

edges of the puff centerline are individually transported through the gridded wind fields, which 

directly accounts for puff stretching and changes to centerline orientation due to deforming 

shears. Since PiG puffs can extend over multiple layers, layer density-weighted average wind 

components are determined for each endpoint based on the vertical coverage of the puff, and 

these are used for advection of those points. GREASD PiG puffs are not allowed to expand 
December 2007 

2-13 

beyond the depth of the layer in which the centerpoint resides, so only the single layer wind 

components are used to advect the endpoints. 

The "chain" aspect means that at least initially (as puffs are extruded from the stack) the trailing 

point of a puff emitted at time t will be the leading point of a puff emitted at time t+dt. 

However, as the puffs are advected downstream, the leading point of one puff will deviate from 

the trailing point the puff behind it due to evolving puff depth and wind fields. Puff volume is 

conserved in convergent/divergent wind fields. Puff endpoints may move closer together or 

further apart, in wind fields that are slowing or accelerating downstream. We compute puff endpoint separation changes and then adjust puff widths and depths to maintain constant puff 

volume. The change in computed puff endpoint spacing defines puff length tendencies, then puff 

depth tendencies are computed from grid-resolved vertical wind shear (dw/dz), and finally we 

determine the puff width tendencies required to conserve puff volume. 

Stack

Figure 2-4. Schematic representation of a chain of PiG puffs emitted from a point source into an 

evolving gridded wind field. The red line connected by dots represents puff centerlines, with dots 

representing leading and trailing points of each puff. The CAMx computational grid is denoted by the 

blue lines.
December 2007 

2-14 

The official "position" of each puff is defined by the center point of each puff between the 

endpoints. This position defines which grid domain and grid cell the puff resides for the 

calculation of diffusion and chemistry. This definition holds even if the puff is sufficiently long 

that the endpoints are in different grid cells (or even different grid domains if near a domain 

boundary). With our definition for termination when horizontal area approaches grid cell area, 

the extents of the puff length should not extend across more than two grid cells. 

PiG Chemistry 

The primary goal of the new PiG formulation in CAMx Version 4.4 was to include a more 

complete treatment of chemistry in point source pollutant plumes, while secondarily improving 

puff-grid mass exchange and adding additional features central for treating toxic pollutants not 

normally carried by the standard CAMx chemical mechanisms. Several approaches have been 

developed to treat photochemistry within point source plume models. One of the more elegant 

methodologies is the incremental chemistry idea embodied in the SCICHEM model (EPRI, 

2000). However, we found that the implementation of incremental chemistry in SCICHEM is 

very complex, especially in its handling of the chemistry of overlapping puffs. In adopting this 

innovative approach for the new PiG, it was necessary to reformulate the physical and chemical 

configuration of the PiG puffs and to utilize an accurate numerical solution approach based on 

the Livermore Solver for ordinary differential equations (LSODE) chemical solver. 

The Concept of Incremental Chemistry

For a second-order reaction between puff species A and B, the total reaction rate is the following: 

( )( ) T = PPkR BA (1) 

where PA and PB are the total puff concentrations of each species. The total puff concentrations 

can be expressed as the sum of the background and puff perturbation concentrations: 

( ) = + CcP AAA

( ) = + CcP BBB

where C is the ambient concentration and c is the puff increment concentration. Thus the 

reaction rate is found to be: 

( )( ) T = + + CcCckR BBAA

or 

( ) T = + + + CCCccCcckR BABABABA

If we subtract the rate of change of the background, 

(2) Ambient = CkCR BA

by assuming that it is explicitly and separately treated by the grid model, we obtain the reaction 

rate for the puff increments: 

 ( ) (3) P = + + CccCcckR BABABA
December 2007 

2-15 

Equation 3 is the basis of SCICHEM incremental chemical kinetic solver. One problem with this 

approach is the mixed terms, CAcB and B cACBB. Most chemical solver packages are designed to 

solve rate equations for total concentration, as in Equation 1. Thus, for the new PiG we 

developed an alternative numerical solution scheme for puff perturbation chemistry. We note 

that the CAMx chemical solver can be independently applied to the rate equation for total puff 

concentrations, Equation 1, and to the rate equation for ambient concentrations, Equation 2. By 

subtraction of the two solutions, we obtain the solution to rate Equation 3. This requires no 

modification to, and is obviously completely self-consistent with, the CAMx chemical solvers. 

Once the incremental puff reaction rates are obtained they are applied to the incremental puff 

mass to calculate the new (adjusted for chemistry) incremental concentrations. These new puff 

increments are subsequently advected and dispersed by the transport portions of the PiG code. 

Puff Constructs for Incremental Chemistry

The new PiG sub-model includes three new constructs designed specifically to facilitate the 

incremental chemistry approach: 

• Treatments to handle puff-grid information exchange for puffs spanning multiple model 

layers; 

• The concept of “virtual dumping” to handle the chemical impacts of large puffs that can 

overlap other puffs within a given grid column; and 

• The concept of multiple puff “reactor” cells to account for the chemical effects of 

concentration distributions within each puff. 

Each of these are described below. 

Puff Layer Spanning 

The new PiG is designed to chemically process point source plume mass within streams of puffs 

until such time that each puff can be adequately resolved on the horizontal grid. Unlike the 

previous versions of the PiG approach, where the vertical layer structure dictates puff leaking 

and ultimately termination, the approach in new PiG leads to the necessity that puffs be allowed 

to vertically span multiple grid model layers before they reach horizontal grid scales. This 

introduces technical implications for defining “background” concentrations and ambient 

conditions for puff chemistry, as well as for transferring plume incremental mass to the grid. 

The solution employed in the new PiG is to: 

1) Assume that the vertical distribution of puff concentration is always uniform; 

2) Distribute puff mass transfer (via “leaking” and “dumping”) to the grid according to the 

puff fractional coverage across each model layer and by density-weighting; and 

3) Determine mean background concentrations and other ambient conditions (e.g., 

temperature, humidity, etc.) over the puff vertical span via similar fractional layer-density 

weighting. 
December 2007 

2-16 

PiG puffs are considered to be elliptical in the horizontal, with the minor axis spanning the crosswind puff width (defined as ±1.5σy), and the major axis spanning the along-wind puff length 

(defined as length ±1.5σy on each end). This is similar to GREASD PiG. However, given the 

complications associated with multiple layer spanning and mass-weighting of ambient inputs and 

dumped mass, puffs are rectangular and uniform in the vertical, with total puff depth defined as 

±1.5σz. 

Horizontally, the mean background concentration and ambient conditions are taken from the 

single host grid column containing each puff center point, even if the puff is large and/or spans a 

horizontal cell interface. 

Chemistry Solution 

In summary, chemistry is solved for each PiG puff “reactor” in three steps: 

1) The layer-mean background (grid + overlapping puff) concentrations and environmental 

conditions over the volume occupied by the puff are stored and then chemically updated 

via the LSODE gas-phase chemistry mechanism; 

2) The pre-updated mean background concentrations are added to the puff increments and 

the total concentrations are chemically updated; and 

3) The updated results from step 1 are subtracted from the updated results of step 2 to 

provide the updated incremental concentrations. 

An important consequence of this approach is that the incremental puff mass may be positive or 

negative. For example, a high- NOx puff that is destroying ambient ozone will have negative 

ozone increments. The puff increments are subsequently advected and dispersed by the transport 

portions of the IRON PiG code. The updated background concentrations, which include “virtual 

dumps” of mass from large puffs, are used for reference only in the puff incremental chemistry 

algorithm; the actual grid concentrations are updated in the grid chemistry routine. 

Puff Dumping and PiG Rendering 

Mass transfer from puff to grid can happen in two ways: slowly, termed “leaking,” or suddenly, 

termed “dumping.” As described earlier, all mass is transferred from the PiG to the vertical grid 

structure in a density-weighted fashion according to each puff’s fractional layer coverage. The 

process of leaking ensures that puff mass is transferred to the grid continuously, rather than in 

discrete lumps of pollutants with very different concentrations than those in the grid. The idea 

behind puff leakage is to account for turbulent shearing of mass from the main plume and its 

subsequent dispersion to the grid scale. This rate of transfer should be directly proportional to 

the puff size relative to the grid scale. The user can control whether a puff is leaked or not, and 

for Texas BART screening modeling, we have assumed the default mode in which puffs are not 

leaked to the grid. This will allow the full-science PiG plume model to treat the chemistry of the 

BART point source plumes as plume chemistry until the plume size is commensurate to the grid 

cell size, rather than the early dilution of the plume emissions across the grid when the puff is 

leaked. 
December 2007 

2-17 

Puff leakage is controlled by comparing the horizontal area of a puff to a specified leakage 

parameter, defined as a fraction of horizontal grid cell area. When a puff is first emitted, there is 

no leakage. As the puff grows in volume, the concentrations within the reactors are reduced 

accordingly by dilution. When the puff area exceeds the leakage onset parameter, a fraction of 

the mass in each puff reactor is transferred to the grid. This fraction is determined by the relative 

exceedance of the leakage parameter; initial leakage is slow as the exceedance is relatively small, 

but leakage rates grow as the puff continues to grow beyond the leakage parameter. 

The reduced mass from leakage is compensated by a reduced effective volume, so that 

concentrations are not artificially diluted by leakage (an essential chemical imperative). Thus, 

two distinct volumes are tracked: the actual volume (defined by the puff spread sigmas) and the 

effective volume. While these are identical before leakage, they obviously deviate after leakage 

is initiated, and thereafter the relative deformation of the actual puff volume (via diffusion, 

shearing, etc.) is used to scale the deformation of effective puff volume. 

Eventually the horizontal span of the puff will exceed the grid cell area, and the remaining mass 

is then dumped all at once to the grid. However, because of the combination of photochemical 

processing and leakage, by the time a puff dumps the potential for producing numerical shocks is 

much reduced. Furthermore, if the puff exceeds a user-defined maximum age, puff mass is 

transferred to the grid. Also, puff mass is dumped to the grid model when the chemical maturity 

of the puff is such that plume-scale chemistry is no longer appropriate. 

While the mass confined to the puffs at any given time has not yet affected the grid 

concentrations, it will eventually, so it can be somewhat misleading to sequester this mass from 

visualizations of a model simulation. The puff mass can be optionally incorporated into the 

model average output files for visualization purposes (referred to as “PiG rendering”). 

Rendering employs a “virtual dump” of the puff masses into the average concentration array 

each time step. As described for chemistry, virtual puff mass is added as an increment over the 

entire grid column according to fractional layer-density weighting over puff depth, thus diluting 

its concentrations relative to that within the puff. The actual puff mass remains within the puffs 

over the course of their lifetimes. This visualization is available for 3-D average output files, and 

can produce some rather startling effects in output displays, including very narrow virtual 

plumes, or streaks, representing mass moving through the grid in sub-grid puffs, but not subject 

to grid-scale eddy diffusion. 

High Resolution Puff Sampling 

PiG optionally employs surface-layer puff sampling of concentration species on a user-defined 

grid of arbitrary horizontal resolution, similarly to the way nested grids are defined. Sampling 

grids are entirely passive, and intended to provide a display of the plume concentrations at scales 

much smaller than typically used for the finest computational grids (i.e., <1 km), primarily 

around and downwind of a source complex. Sampled PiG concentrations are time-averaged like 

the output concentrations provided on the computational grids, and are written to files with 

similar format so that they may be readily viewed and manipulated with CAMx post-processing 

software. Additional information on configuring and using PiG sampling grids is provided in 

Section 5. 

Given that the puffs constantly evolve via diffusive growth and reshaping due to deforming 

shears, the sampling procedure includes trigonometric calculations to define which sampling 
December 2007 

2-18 

points are influenced by each puff. This influence is determined according to the puffs’ twodimensional horizontal Gaussian shape shown in Figure 2-3. To include a sufficiently large 

percentage of mass across each puff for sampling, limits of ±3σy in both horizontal dimensions 

are used to define the puffs’ total elliptical area coverage. Puffs are only sampled if they extend 

vertically into the model’s surface layer. 
December 2007 

3-1 

3.0 PROCEDURES FOR VOC AND PM EMISSIONS BART SCREENING 

ANALYSIS USING ZERO-OUT AND INERT MODELING 

This section presents the procedures that can be used to perform cumulative BART screening 

exemption modeling for combined VOC and PM emissions and for PM emissions alone. Details 

on the steps needed to perform this analysis, selection of the recommended CAMx options and 

scripts and computer operation notes are provided in Chapter 5. 

OVERVIEW OF APPROACH 

One type of BART screening analysis using CAMx would be to estimate the cumulative 

visibility impacts at Class I areas due to VOC and PM emissions from all potential BARTeligible sources. To address impacts due to both VOC and NOx emissions two CAMx 36/12 km 

simulations will be conducted for the 2002 annual period: 

• 2002 BART Base Case Emissions Scenario; and 

 

• 2002 BART VOC and PM Emissions Zero-Out Scenario. 

For the Texas screening analysis, the 2002 BART Base Case Emissions Scenario was based on 

the CENRAP 2002 Typical Base Case emissions scenario, although other base case scenarios 

could also be used (e.g., 2002 Western Regional Air Partnership/WRAP, Visibility Improvement 

State and Tribal Association of the Southeast/VISTAS, Midwest RPO base cases, CAIR 2001 

base case, etc.). The EPA BART guidelines require that BART modeling use the maximum 

actual 24-hour emissions for each BART-eligible source (excluding start up and shut down). 

The 2002 base case scenarios typically contain average actual emissions for all sources. The 

maximum 24-hour actual emission rates are not readily available for most sources. Thus, to 

account for the differences between maximum 24-hour actual and average typical actual, the 

average typical actual emissions for potential BART-eligible sources in the 2002 Base Case 

emissions scenario were doubled as recommended by EPA Region 6. Doubling the average 

emissions provides a conservative estimate of maximum 24-hour emissions for most sources. 

Visibility Calculations 

Visibility impacts are calculated at each Class I area using the differences in 24-hour PM 

concentrations between the 2002 Base Case and 2002 BART Sources VOC and PM Emissions 

Zero-Out Case scenarios following the procedures given in EPA’s BART modeling guidance 

(EPA, 2005) that are based on the Federal Land Managers Air Quality Related Values 

Workgroup report (FLAG, 2000). The FLAG (2000) procedures were developed to estimate 

visibility impacts at Class I areas from proposed new sources as part of the Prevention of 

Significant Deterioration (PSD) and New Source Review (NSR) process and were adapted to 

BART. These procedures use the Interagency Monitoring of Protected Visual Environments 

(IMPROVE) reconstructed mass extinction equation (Malm et al., 2000), only instead of using 

measured PM concentrations from an IMPROVE monitor, incremental PM concentrations from 

the differences in the CAMx 2002 Base Case and 2002 BART VOC/PM Zero-Out runs will be 

utilized in the equation. 
December 2007 

3-2 

The IMPROVE reconstructed mass extinction equation is used to estimate visibility at Class I 

areas using IMPROVE monitoring data and has also been used for evaluating visibility impacts 

at Class I areas due to new sources using modeling output of a single source or group of sources. 

The total light extinction due to a source (bsource), in units of inverse Megameters (Mm-1), is 

assumed to be the sum of the light extinction due to the source’s individual PM species 

concentration impacts times an extinction efficiency coefficient:

bsource = bSO4 + bNO3 + bOC + bEC+ bsoil + bcoarse

bSO4 = 3 [(NH4)2SO4]f(RH) 

bNO3 = 3 [NH4NO3]f(RH) 

bOC = 4 [OMC] 

bEC = 10 [EC] 

bSoil = 1 [Soil] 

bcoarse = 0.6 [Coarse Mass] 

Here f(RH) are relative humidity adjustment factors. EPA BART modeling guidance 

recommends that Class I area specific monthly average f(RH) values be used (EPA, 2005; 

2003a). The concentrations in the square brackets are in μg/m3

 and are based on the differences 

in concentrations between the 2002 BART Base Case and 2002 BART VOC/PM Zero-Out Case. 

Although CAMx explicitly models ammonia and ammonium, the IMPROVE extinction equation 

assumes that SO4 and NO3 are completely neutralized by ammonium. The OMC in the above 

equation is Organic Matter Carbon (OMC). When using IMPROVE measurements, the current 

IMPROVE extinction equation assumed an OMC/OC ratio of 1.4 (i.e., the IMPROVE Organic 

Carbon or OC measurement is multiplied by 1.4 to obtain OMC). Since CAMx directly models 

OMC, the 1.4 factor is not needed. The following species mappings are used to map the CAMx 

species to those used in the IMPROVE reconstructed mass extinction equation given above: 

[(NH4)2SO4] = 1.375 x PSO4 

[NH4NO3] = 1.290 x PNO3 

[OMC] = POA + SOA1 + SOA2 + SOA3 + SOA4 + SOA5 

[EC] = PEC 

[Soil] = FPRM + FCRS 

[Coarse Mass] = CPRM + CCRS 

Here PSO4 and PNO3 are the CAMx particulate sulfate and nitrate species. POA is the CAMx 

primary Particulate Organic Aerosol species, whereas SOA1-5 are the five Secondary Organic 

Aerosol species carried in CAMx. Primary Elemental Carbon is represented by PEC in CAMx. 

CAMx carries two species that represent the other PM2.5 components (i.e., fine particles that are 

not SO4, NO3, EC or OC), one for the crustal (FCRS) and the other for the remainder of the 

primary emitted PM2.5 species (FPRM). Similarly, CAMx carries two species to represent 

Coarse Mass (PM2.5-10), one for crustal (CCRS) and one for other coarse PM (CPRM). 

The Haze Index (HI) for the source is calculated in deciviews (dv) from the source’s extinction 

plus natural background using the following formula: 

HIsource = 10 ln[(bsource + bnatural)/10] 
December 2007 

3-3 

Here, bnatural is the Class I area specific clean natural visibility background where EPA’s default 

values will be used in this analysis (EPA, 2003b). 

The source’s HI is compared against natural conditions to assess the significance of the source’s 

visibility impact. EPA guidance lists natural conditions (bnatural) by Class I area in terms of 

Mm-1 (EPA, 2003b) and assumes clean conditions with no man-made or weather interference. 

The visibility significance metric for evaluating BART sources is the change in deciview 

(del-dv) from the source’s and natural conditions haze indices: 

del-dv = HIsource – HInatural = 10 ln[(bsource + bnatural)/10] - 10 ln[bnatural/10] 

= 10ln[(bsource + bnatural)/bnatural] 

The visibility impacts from the CAMx BART VOC/PM zero-out run can be calculated using all 

PM species to assess the visibility impacts due to the elimination of both VOC and PM 

emissions. Separate visibility calculations may be made using those PM species that may be 

associated with the elimination of the BART VOC emissions (i.e., SOA1, SOA2, SOA3, SOA4 

and SOA5) and then those species associated with the elimination of the primary PM emissions 

(i.e., PSO4, PNO3, POA, PEC, FCRS, FPRM, FCRS and CCRS). 

The del-dv impacts will be calculated at all Class I areas of interest. For each day, the maximum 

del-dv impact in a Class I area will be used to represent the visibility impact for that day at that 

Class I area. 

Significance Threshold 

The EPA BART guidance suggests that a significance threshold to determine whether a source 

significantly contributes to visibility impairment at a Class I area should be no greater than 

0.5 dv. Thus, if the del-dv due to all potential BART-eligible sources VOC and PM emissions at 

every Class I area and for all days from 2002 are < 0.5 dv, then VOC and PM emissions from all 

potential BART-eligible sources would not contribute significantly to visibility impairment, and 

therefore, the VOC and PM emissions from each individual potential BART source would not be 

significant. 

If there are any days in 2002 for which the del-dv is greater than 0.5 dv in the BART VOC/PM 

zero-out screening analysis, the results should be examined in more detail, including the analysis 

of the frequency, magnitude and duration of the visibility impacts. The BART guidance suggests 

comparing the 98th percentile del-dv at any Class I area with the 0.5 dv significant threshold to 

determine whether a significant visibility impact would reasonably be expected to occur. Using 

one year of modeling results (2002), the 98th percentile would correspond to the eighth highest 

24-hour average visibility impact at each Class I area. 

The separate assessment of the contribution of VOC (SOA1-5) versus primary PM related PM 

species should be examined to determine whether one precursor pollutant or the other is the 

cause of the del-dv exceeding the 0.5 dv significance threshold. 
December 2007 

3-4 

BART Screening Analysis for PM Emissions Only 

If BART screening analysis is desired for primary PM emissions by themselves, then it is much 

simpler and more computationally efficient to run an inert CAMx simulation with the potential 

BART-eligible primary PM emissions as input. Gas-phase species and gas-, aqueous- and 

aerosol-phase chemistry do not affect the primary PM species so these species and chemistry do 

not need to be simulated when looking at the primary PM impacts. In this case, the BART PM 

emissions are processed and simulated by the model and only one inert simulation needs to be 

conducted. Chemistry is turned off in CAMx by setting the “Chemistry” option in the CAMx 

input file to “.false.” (see Appendix A or B for example CAMx run control input files). 
December 2007 

4-1 

4.0 PROCEDURES FOR SCREENING BART SOURCES USING PSAT AND PiG 

This section discusses the procedures for using CAMx with the PSAT and full-chemistry PiG 

subgrid-scale plume module for single source or multiple source BART or similar (e.g., 

NSR/PSD) modeling. Although the Texas PSAT/PiG BART screening analysis addressed 

impacts of SO2 and NOx emissions from BART non-EGU sources, the general approach can be 

used for all visibility impairing pollutants (i.e., NOx, SO2, PM, VOC and NH3) as well as for both 

EGU and non-EGU sources. However, the PSAT computational requirements for simulating 

Secondary Organic Aerosol (SOA) formation from VOCs are more extensive than for the other 

PSAT families (see Chapter 2). In addition, the contributions of SOA due to BART type source 

VOC emissions to visibility impairment at Class I areas is extremely small. Thus, if VOC 

emissions are an issue, it is recommended that they first be addressed through a zero-out run as 

described in Chapter 3. 

Specifics on the steps and options to be specified for running CAMx/PSAT/PiG for BART and 

similar assessments are provided in Chapter 5. 

SUMMARY OF APPROACH 

The updated PSAT that includes the new PiG module in CAMx Version 4.4 (and later versions) 

is used in the single source or multiple source BART analysis. For the Texas application, the 

2002 36/12 km modeling database described in Section 2 was used. The Texas 

CAMx/PSAT/PiG source apportionment divided the non-EGU potential BART-eligible sources 

into several source groups for the screening analysis. CAMx/PSAT was run for the 2002 annual 

year on the 36/12 km grid with each potential BART-eligible source flagged to use the new 

PSAT PiG feature. 

As described in Chapter 3, if the del-dv due to all sources in a source group at every Class I area 

and for all days from 2002 is < 0.5 dv, then each individual potential BART-eligible source in 

the source group would not contribute significantly to visibility impairment. 

Visibility Impacts from PSAT 

In the Texas CAMx/PSAT/PiG BART modeling, the sulfate (SO4) and nitrate (NO3) families of 

PSAT source apportionment tracers were invoked for the PSAT BART screening analysis. 

However, in the discussion below we try to be more general and include a discussion of how you 

would use the PSAT tracers for the primary PM and SOA PSAT families of PM source 

apportionment. However, as noted above, it is highly desirable to screen out the VOC emissions 

(and primary PM if possible) as a significant visibility contributor as the computational 

requirements of the PSAT SOA family of tracers is greater than the other PSAT families. 

The visibility impacts at each Class I area will be calculated in a similar manner as described in 

Section 3 that uses the IMPROVE reconstructed mass extinction equation. Only concentrations 

from each PSAT BART source group will be used to calculate the Haze Index (HI) and change 

in deciview (del-dv) from natural conditions. 
December 2007 

4-2 

bsource = bSO4 + bNO3 + bOC + bEC+ bsoil + bcoarse

bSO4 = 3 [(NH4)2SO4]f(RH) 

bNO3 = 3 [NH4NO3]f(RH) 

bOC = 4 [OMC] 

bEC = 10 [EC] 

bSoil = 1 [Soil] 

bcoarse = 0.6 [Coarse Mass] 

Here f(RH) are the monthly average relative humidity adjustment factors from EPA’s guidance 

(EPA, 2003a). The concentrations in the square brackets are in μg/m3

 and are the concentrations 

from the PSAT output for each potential BART-eligible PSAT source group (i) with sulfate and 

nitrate assumed to be fully neutralized by ammonium. Using the PSAT species naming 

convention (see Chapter 2) these concentrations are as follows: 

[(NH4)2SO4] = 1.375 x PS4i 

[NH4NO3] = 1.290 x PN3i

[OMC] = POAi + PO1i + PO2i + PO3i + PO4i + PO5i

 [EC] = PECi

 [Soil] = PFCi + PFN i

The Haze Index (HI) for the source group is calculated in deciview from the source group’s 

extinction plus natural background using the following formula: 

HIgroup = 10 ln[(bgroup + bnatural)/10] 

The source’s HI is compared against natural conditions to assess the significance of the source’s 

visibility impact. EPA guidance lists natural conditions (bnatural) by Class I areas in terms of 

Mm-1 (EPA, 2003b) and assumes clean conditions with no man-made or weather interference. 

The visibility significance metric for evaluating BART sources is the change in deciview 

(del-dv) from the source’s and natural conditions haze indices: 

del-dv = HIgroup – HInatural = 10 ln[(bgroup + bnatural)/10] - 10 ln(bnatural/10) 

The 0.5 dv significance threshold is used to assess whether a potential BART-eligible source or 

source group contributes significantly to visibility impairment. If the del-dv for each day of 

2002 and every 12 km grid cell that intersects with any Class I area is less than 0.5 dv, then the 

source groups SO2 and NOx emissions do not contribute significantly to visibility impairment at 

any Class I area. EPA’s BART guidance suggests using the 98th percentile visibility impact at a 

Class I area in the visibility significance determination using this approach, then a source group 

would not have a significant contribution to visibility if the 8th highest 24-hour average del-dv at 

each Class I area is less than 0.5 dv. 
December 2007 

5-1 

5.0 DETAILS ON MODELING PROCEDURES 

This section provides the details on the modeling procedures discussed in Chapters 4 and 5 for a 

single BART source or a group of BART sources, or a similar type of modeling analysis using 

CAMx. Example CAMx run scripts for a base case run as used in the Texas VOC and PM zeroout run is provided in Appendix A, and an example CAMx run script for a Texas BART 

PSAT/PiG modeling analysis is provided in Appendix B. 

VOC AND PM ZERO-OUT MODELING 

The CAMx VOC and PM zero-out modeling of BART emissions is conceptually straight 

forward and involves two runs: a base case simulation and a simulation with all VOC and PM 

emissions from the BART sources eliminated. For the Texas BART screening analysis, the 

CENRAP 2002 36 km Base B base case was the starting point for the analysis. The EPA BART 

Guidelines require the use of maximum actual 24-hour emission rates. Sources will need to 

determine their actual 24-hour maximum emission rate for use in their modeling analyses. The 

first step in the analysis is the Sparse Matrix Operator Kernel Emissions (SMOKE) emissions 

processing of the BART sources 24-hour maximum emission rates. 

Compiling Emissions

1. Process the base case emissions with all emissions using SMOKE. 

2. Separate BART sources 24-hour maximum actual VOC and PM emissions from 2002 

base case emissions. 

3. Separately process VOC and PM emissions from BART sources alone using SMOKE. 

4. Zero-Out BART VOC and PM emissions inputs are obtained by subtracting the BART 

VOC and PM emissions (Step 3) from the base case emissions (Step 1). 

Once the base case with the 24-hour maximum actual BART VOC and PM emissions and zeroout BART VOC and PM emissions model-ready emission inputs have been prepared, CAMx 

modeling and post-processing is performed in the following steps: 

Running and Post-Processing CAMx 

1. Perform CAMx runs for both zero-out and 24-hour maximum actual BART VOC and 

PM emissions scenarios. 

2. Post-process the CAMx BART modeling results. This step involves extracting 

concentration data from the hourly average concentrations output files (*avrg and *favrg) 

which have a binary format (refer to the CAMx user’s guide) and calculating 24-hour 

average concentrations from the hourly concentrations. 
December 2007 

5-2 

3. For each Class I area, calculate the change in 24-hour average concentrations of SOA 

(SOA1-4) from 24-hour maximum actual scenario and the zero-out scenario. Repeat the 

same steps to obtain two 24-hour average PM (non-SOA species) concentrations. 

4. Calculate the mass extinction (bsource) and the difference in deciview (del-dv) from the 

source’s and natural conditions haze indices. The formulation is described in Chapter 4. 

5. Compare the 98th percentile del-dv at any Class I area with the 0.5 dv significance 

threshold. The delta deciview at the 98th percentile would correspond to the eighth 

highest 24-hour average visibility impact at each Class I area during the modeled year. 

Appendix A lists an example CAMx script for the 2002 base case with 24-hour maximum actual 

Texas BART VOC and PM emissions. The script for the 2002 zero-out BART VOC and PM 

emissions would be similar, with the file names changed for the output files and the point source 

emission inputs. Changes from the standard CENRAP 2002 Base Case CAMx modeling script 

are: 

• File names and locations to conform with current modeling; 

• Addition of a second grid nest centered over Texas using a 12 km resolution grid 

(e.g., Number_of_Grids = 2,); and 

• Specifying no input files for the second grid so that full flexi-nesting of the 12 km grid 

inputs from the 36 km grid will be used. 

The differences in concentrations at each Class I receptor area will be extracted from the binary 

output files from the two CAMx runs. The visibility calculations described in Chapter 4 can then 

performed using an Excel spreadsheet. 

PSAT MODELING 

PSAT apportions PM components among several source groups as specified by the user. Source 

groups consist of a source region, defined by a source region map provided as input, and source 

categories, that are defined by the low-level and point source emission inputs that can be input 

for each of the PSAT source category. For example, the source region map could divide the 

domain into 10 different geographic areas (e.g., states) and separate emissions files could be 

provided for 4 source categories (e.g., biogenic, mobile, area or point) resulting in 42 source 

groups to be tracked (initial and boundary conditions are always tracked as two source groups). 

For the PSAT BART modeling, each BART source or group of BART sources for which 

separate PM source apportionment is desired needs to be separately identified as a unique source 

group in the PSAT run. One way of doing this is to have multiple sets of the point source 

emission inputs with the first one being all point sources with their full emissions, the second one 

would be all point sources but with all emissions set to zero except those for the first BART 

source group, the third one would have all point source emissions zeroed-out except the second 

BART source group, etc. The CAMx PSAT could then be run using these different point source 

emissions source groups as inputs for each PSAT source group and with a source region map that 

included one region for the whole modeling domain. 
December 2007 

5-3 

However, for the Texas BART PSAT screening modeling we used an alternative approach using 

the PSAT/OSAT “point source override” feature. This was done by having a source region map 

with one source region for the entire domain and assigning a separate source region value in the 

point source input file that will override the source region that the point source resides in. In 

addition, a negative flag has to be set for source stack diameters in order for the BART point 

source to receive the PiG treatment. PSAT outputs require significant disk space. An annual run 

can take up to 450 gigabytes (GB) (excluding the deposition and the restart (*.depn and *.inst.1,) 

files. The steps for setting up the BART PSAT emissions are as follows: 

Compiling Emissions

1. Process emissions from BART sources separately from non-BART sources. If there is 

more than one source group, the BART emission file must carry facility information 

including Federal Information Processing Standard (FIPS) codes, plant ID and stack ID 

to cross reference each point source to a point index list. In SMOKE, users have an 

option to create an elevated-point source input file in an ASCII format that contains all of 

this necessary information. 

2. Assign the point index for each source group in the (unused) kcell1

 value on the point 

source file to a unique value for that source group. 

3. Use maximum 24-hour actual NOx and SO2 emissions for the BART sources. 

4. Set the stack diameters negative for the BART sources for PiG treatment. 

5. Steps 2-4 can be accomplished by using “PIGSET_BART” program which outputs a 

binary CAMx-ready point source file. 

6. Append BART emissions to non-BART emissions. This step can be achieved by using 

“PTSMRG” program. 

7. Make a duplicate of the emission file and rename the copy. This step is necessary because 

the PSAT source apportionment tool needs to read the same emission file as the CAMx 

host model and once the CAMx main module is accessing it you can not open the file 

again for reading by PSAT. 

Once the PSAT emission files have been generated, then CAMx can be run to obtain separate 

PM source apportionment modeling results for each individual BART source or group of BART 

sources as desired. Appendix B lists an example CAMx script for one of the Texas PSAT source 

apportionment model simulations. The screening analysis demonstrated that Texas BART 

sources’ VOC emissions are insignificant to the visibility impairments at Class I areas (Morris 

and Nopmongcol, 2006). In addition, this screening analysis indicated that only some facilities 

contribute significantly to the visibility impairments. Consequently, for the Texas BART 

screening modeling, PSAT was run only for the SO4 and NO3 families of PSAT tracers. This 

approach, however, could be extended to the PM and SOA families as well. 

Running CAMx with PSAT

8. Create a fixed-width format ASCII receptor definition input file. This file contains the 

location of Class I areas in the coordinate system of the CAMx grid. For example: 

 

1

 The kcell value for each stack is contained in the time-variant portion of the elevated point source file. The value 

is typically ignored, except as flag for OSAT/PSAT point source override. 
December 2007 

5-4 

POINT BAND1 -831.124 -424.425 

9. Generate source area region maps for a master grid and nested grids. The sources area 

mapping file assigns each grid cell to a specific geographic source region. The format of 

this file is an array of integer numbers (3i) corresponding to the CAMx domain (refer to 

the CAMx’s user guide). For PSAT run, assign the same unique number, which is 

different from BART source groups, to every grid cell. For example, if BART source 

groups range from 1-10, assign number 11 to every grid cell in the source region map 

file. This unique number represents a group number of area and non-BART sources. 

Point source override in CAMx will discard this number and replace it with a userspecified BART source groups. 

10. PSAT is invoked within the CAMx control file. In the &CAMx_Control namelist 

module, the variable Probing_Tool must be set to “PSAT” and Pig_Submodel must be set 

to “GREASD”. 

11. In the &SA_Control namelist, provide the name of SA_Receptor_Definitions and 

SA_Source_Area_Map files. For Texas application set PSAT_Treat_SULFATE_Class 

and PSAT_Treat_NITRATE_Class to “true” for SO4 and NO3 PSAT families. 

12. Emission and meteorological inputs are optionally provided for the nested grid. If these 

files are not supplied, the Flexi-Nest algorithm within CAMx will interpolate the missing 

fields from the parent grid. In the Texas screening analysis case, 12 km Flexi-Nest was 

turned on. 

13. Perform CAMx 36/12km run. 15 spin-up days is recommended. 

14. PSAT output is in the receptor concentration file (*.sa.receptor) which contains 

information for all receptors and all 24 hours for each simulation day. This is a text 

format that users can use any scripting language to extract the data. 

15. Post processing of BART results to estimate visibility for each Class I area is conducted 

as follows. First, use 24-hour average concentrations of sulfate and nitrate to calculate 

the mass extinction (bsource). Second, calculate the Haze Index (HI) and the change in 

deciview (del-dv) from the source’s and natural conditions HI. The formulations of these 

two steps are described in Chapter 4. Finally, compare the 98th percentile del-dv at any 

Class I area with the 0.5 dv significant threshold. The deciview that the 98th percentile 

would correspond to is the eighth highest 24-hour average visibility impact at each 

Class I area. 

The example CAMx script in Appendix B for a Texas PSAT SO4 and NO3 run is similar to the 

standard CENRAP CAMx 2002 base case script with the following changes: 

• File names and locations were changed to be consistent with current applications; 

• Number of grids changes to 2 with a 12 km grid added over Texas and surrounding 

regions; 

• No input files specified for 12 km grid so that full flexi-nesting can be used; 

• The “Probing Tool” option is set to PSAT; and 

• The BOTT advection solver (BOTT, A. 1989) is specified rather than PiecewiseParabolic Method (PPM). 

The PPM solver was used in the zero-out runs. However, the BOTT advection solver was used 

in the PSAT run because it is more computationally efficient. 
December 2007 

5-5 

COMPUTATIONAL REQUIREMENTS 

This section provides some example CAMx configurations for 36/12 km domain with flexinesting and associated system requirements when run on a dual-processor LINUX Power 

Management (PM) (with Operations and Management Platform-OMP). 

Table 5-1. Example of computer requirements on a dual-processor Athlon 2800+ (2.1 Ghz) PC. 

Run Configuration Memory 

Disk Usage 

(excluding *inst.1 

and *depn) CPUs 

Execution 

Time 

VOC and 

PM zero-out 

Chemistry turned on 

(Mechanism 4 CF) 

356mB 255 MB/episode day 2 4 

hrs/episode 

day 

PM zero-out Chemistry turned off 245mB 46.5 MB/ 

episode day 

2 20 mins/ 

episode 

day 

PSAT 10 Point Source 

Groups 

1278mB 1.25 GB/ 

episode day 

2 6.5 hrs/ 

episode 

day 
December 2007 

6-1 

6.0 REFERENCES 

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Pollution Control Division, Colorado Department of Public Health and Environment, 

Denver, CO. 

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Hill, NC. 

Emery, C. and G. Yarwood. 2005. Implementing PM chemistry in the CAMx "IRON PiG" 

plume-in-grid module, Prepared for the Lake Michigan Air Directors Consortium, 

Prepared by ENVIRON International Corporation, Novato, CA. 
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EPA. 1999. Regional Haze Regulations; Final Rule. Federal Register, 64, 35713-35774. 

EPA. 2001. “Draft Guidance for Demonstrating Attainment of Air Quality Goals for PM2.5 and 

Regional Haze”, U. S. EPA, Office of Air Quality Planning and Standards, Research 

Triangle Park, NC. 

EPA. 2003a. Guidance for Tracking Progress Under the Regional Haze Rule. U.S. 

Environmental Protection Agency, Research Triangle Park, NC. 

EPA. 2003b. 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. 

EPA. 2005. Regional Haze Regulations and Guidelines for Best Available Retrofit Technology 

(BART) Determinations. Federal Register, 70 (128), Wednesday, July 6, 2005. 

ENVIRON. 2005a. “User’s Guide – Comprehensive Air-quality Model with extensions, 

Version 4.20.” ENVIRON International Corporation, Novato, California. (Available at 

http://www.camx.com). 

ENVIRON. 2005b. Screening Analysis of Potential BART-eligible Sources in Texas. Prepared 

for Texas Commission of Environmental Quality, Austin Texas. Prepared by ENVIRON 

International Corporation, Novato, CA. December 15. 

EPRI. 2000. SCICHEM Version 1.2: Technical Documentation. Final Report prepared by 

ARAP/Titan Corporation, Princeton, NJ, for EPRI, Palo Alto, CA. December 2000 

(1000713). 

Federal Register. 2003. 40 CFR Part 51. “Revisions to the Guidelines on Air Quality Models: 

Adoption of a Preferred Long Range Transport Model and Other Revisions; Final Rule. 

Federal Register/Vol. 68, No 72/Tuesday April 15, 2003. 

FLAG. 2000. “Federal Land Managers’ Air Quality Related Values Workgroup (FLAG)”: 

Phase I Report. USDI – National Park Service, Air Resources Division, Denver, CO. 

Gillani, N.V. and J.M. Godowitch. 1999. Plume-in-grid treatment of major point source 

emissions. In Science algorithms of the EPA Models-3 Community Multiscale Air 

Quality modeling system. Chapter 9. D.W. Byun, and J.K.S. Ching (Eds.). EPA-600/R99/030, National Exposure Research Laboratory, U.S. Environmental Protection Agency, 

Research Triangle Park, NC, 40 pp. 

Grell, G.A., J. Dudhia and D.R. Stauffer. 1995. A Description of the Fifth Generation Penn 

State/NCAR MM5, NCAR TN-398-STR, NCAR Technical Note. 

IWAQM. 1998. Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary 

Report and Recommendations for Modeling Long-Range Transport and Impacts on 

Regional Visibility. EPA-454/R-98-019. U.S. Environmental Protection Agency, Office 

of Air Quality Planning and Standards, Research Triangle Part, NC. 
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Johnson, M.T. 2003a. Meteorological modeling protocol: IDNR 2002 annual MM5 

application, Prepared by the Iowa Department of Natural Resources, Air Quality Bureau, 

Des Moines, IA 

Johnson, M.T. 2003b. Iowa DNR 2002 Annual MM5 Modeling Project. Presented at the August 

11th, 2003 CenRAP Workgroup Meeting in Bloomington, Minnesota. 

Karamchandani, P., L. Santos, I. Sykes, Y. Zhang, C. Tonne and C. Seigneur. 2000. 

Development and evaluation of a state-of-the-science reactive plume model, Environ. Sci. 

Technol., 2000, 34, 870–880. 

Karamchandani, P., C. Seigneur, K. Vijayaraghavan and S-Y Wu. 2002. Development and 

Application of a state-of-science plume-in-grid model. J. Geophysical Research, 107, 

(D19), 4403, DOI: 10.1029/2002JD002123. 

Karamchandani, P., K. Vijayaraghaavan, S.-Y. Chen and C. Seigneur. 2005. Development and 

application of advanced plume-in-grid PM models. 4th Annual CMAS Models-3 User's 

Conference, Chapel Hill, NC, 26-28 September. 

Kemball-Cook, S., Y. Jia, C. Emery, R. Morris, Z. Wang and G. Tonnesen. 2005. Comparison 

of CENRAP, VISTAS and WRAP 36 km MM5 Model Runs for 2002, Task 3: 

Meteorological Gatekeeper Report. 

http://pah.cert.ucr.edu/aqm/cenrap/ppt_files/CENRAP_VISTAS_WRAP_2002_36km_M

M5_eval.ppt) 

Kemball-Cook, S., C. Emery and G. Yarwood. 2005. Impact and Role of Air Quality Modeling 

Assumptions in the Development of Revisions to the Houston Stat Implementation Plan 

for Attainment of the Ozone Air Quality Standard. Prepared for Houston Advanced 

Research Center (HARC) under Project H12.8HRB. ENVIRON International 

Corporation, Novato, CA. March. 

Kumar, N. and A.G. Russell. 1996. Multiscale air quality modeling of the northeastern United 

States. Atmospheric Environment, 30, 1099-1116. 

Malm, W.C., et al. 2000. Spatial and Seasonal Patterns and Temporal Variability of Haze and 

its Constituents in the Unites States – Report III. NPS, NOAA and CIRA. Fort Collins, 

CO. May. 

Morris, R. E., et al. 2002. “Recent Advances in Photochemical Air Quality Modeling Using the 

CAMx Model: Current Update and Ozone Modeling of Point Source Impacts”, 

Presented at the Air and Waste Management Association Meeting, Paper # 43180, 

prepared by ENVIRON International Corporation, Novato, CA. 

Morris, R., C. Tana and G. Yarwood. 2003. “Evaluation of the Sulfate and Nitrate Formation 

Mechanism in the CALPUFF Modeling System”, Proceedings of the A&WMA Specialty 

Conference -- Guideline on Air Quality Models: The Path Forward. Mystic, CT. 22-24 

October. 
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Morris, R.E., B. Koo, T.W. Tesche, C. Loomis, G. Stella, G. Tonnesen and Z. Wang. 2004. 

Modeling protocol for the VISTAS Phase II regional haze modeling. 

(http://pah.cert.ucr.edu/vistas/vistas2/)

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

Regional Air Planning Association (CENRAP) Emissions and Air Quality Modeling, 

Revision 0. Prepared for Central Regional Air Planning Association (CENRAP). 

November 19. 

Morris, R.E., G. 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). 

Prepared for Central Regional Air Planning Association (CENRAP). December 8. 

Morris, R.E., B. Koo, A. Guenther, G. Yarwood, D. McNally, T. W. Tesche, G. Tonnesen, J. 

Boylan and P. Brewer. 2005a. Model sensitivity evaluation for organic carbon using two 

multi-pollutant air quality models that simulate regional haze in the southeastern United 

States. Atmospheric Environment. (in press). 

Morris, R.E., D.E. McNally, T.W. Tesche, G. Tonnesen, J.W. Boylan and P. Brewer. 2005b. 

Preliminary Evaluation of the CMAQ Model for 2002 and the Southeastern U.S. Journal 

of the Air & Waste Manage. Association. (in press). 

Morris, R.E., G. Wilson and G. Yarwood. 2005c. Use of a full-science 3-D photochemical grid 

model to address the BART visibility modeling requirements. 8th Annual Electric 

Utilities Environmental Conference, Tucson, AZ, 24-25 January. 

Morris, R.E., et al. 2005d. Air Quality Modeling Analysis for CENRAP: Preliminary 2002 

Base Case CMAQ and CAMx Modeling of the Continental U.S. 36 km Domain and 

Model Performance Evaluation. Prepared for the Central Regional Air Planning 

Association, prepared by ENVIRON International Corporation, and the University of 

California, Riverside. 

Morris, R.E., S. Lau and B. Koo. 2005e. “Evaluation of the CALPUFF Chemistry Algorithms.” 

Presented at the 98th Annual Air and Waste Management Conference, Minneapolis, MN. 

June. 

Morris, R.E., G. Yarwood, C. Emery, G. Wilson and B. Koo. 2005f. “Recent Advances in OneAtmospheric Modeling Using the Comprehensive Air-quality Model with Extensions.” 

Presented at the 98th Annual Air and Waste Management Conference, Minneapolis, MN. 

June. 

Morris, R.E., C. Emery and G. Yarwood. 2006. Use of an Advanced Hybrid Plume/Grid 

Photochemical Model to Perform Single Source Assessments for PSD and BART 

analysis. Presented at the AWMA Guidelines on Air Quality Models Conference, 

Denver, Colorado. April 26-28 2006. 

Morris, R.E., and U. Nopmongcol. 2006. Screening Analysis of Potential BART-Eligible 

Sources in Texas (Draft). Prepared for Texas Commission on Environmental Quality. 

August 19 2006. 
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6-5 

MPCA. 2005. “Best Available Retrofit Technology (BART) Modeling Protocol to Determine 

Sources Subject to BART in the State of Minnesota”, prepared by the Minnesota 

Pollution Control Agency, St. Paul, MN. 

Seigneur, C., et al. 2000. “Guidance for the Performance Evaluation of Three-Dimensional Air 

Quality Modeling systems for Particulate Matter and Visibility”, Journal of the Air and 

Waste Management Association, 50. 588-599. 

Tesche, T.W., R.E. Morris, G.S. Tonnesen, D.E. McNally, P. Brewer and J. Boylan. 2005. 

CMAQ/CAMx Annual 2002 Performance Evaluation over the Eastern United States. 

Atmospheric Environment (in press). 

Tombach, I., P. Brewer, T. Rogers and C. Arrington. 2005a. “BART modeling protocol for 

VISTAS (VISTAS first draft)”, 22 March, 2005. 

Tombach, I., P. Brewer, T. Rogers, C. Arrington and J. Scire. 2005b. “Protocol for the 

application of the CALPUFF model for analyses of Best Available Retrofit Technology 

(BART). (VISTAS second draft)”, 22 August 2005. 

Tombach, I., P. Brewer, T. Rogers, C. Arrington and J. Scire. 2005c. “Protocol for the 

application of the CALPUFF model for analyses of Best Available Retrofit Technology 

(BART). (VISTAS revised draft)”, 20 September 2005. 

Tonnesen, G., et al. 2001. “Modeling Protocol and Quality Assurance Plan for the WRAP 

Visibility Modeling Project”, prepared for the Western Regional Air Partnership 

Modeling Forum, prepared by University of California, Riverside and ENVIRON 

International Corporation. 

Tonnesen, G.S., R.E. Morris, M. Uhl, K. Briggs, J. Vimont and T. Moore. 2003. “The WRAP 

Regional Modeling Center – Application and Evaluation of Regional Visibility Models” 

presented at 96th Annual Conference and Exhibition of the Air and Waste Management 

Association, San Diego, California. 

Turner, D.B. 1970. “Workbook of Atmospheric Dispersion Estimates.” Prepared for U.S. EPA, 

Office of Air Programs, Research Triangle Park, NC. 

Yarwood, G., R.E. Morris, and G. Wilson. 2004. “Particulate Matter Source Apportionment 

Technology (PSAT) in the CAMx Photochemical Grid Model." Presented at the ITM 

27th NATO Conference- Banff Centre, Canada, October. 

Yarwood, G., C. Emery, et al. 2005. Recent CAMx updates and current MRPO projects. MRPO 

Fall Modeling Meeting, Des Plaines, IL 12-13 September. 
December 2007 

A-1 

Appendix A 

Example CAMx Run Script for a 

Base Case or Zero-Out Run 
December 2007 

A-2 

#!/bin/csh 

# 

# CAMx 4.4beta OMP 

# 

setenv NCPUS 2 

setenv MPSTKZ 128M 

limit stacksize unlimited 

# 

set EXEC = 

"/disk52/tceq.bart/camx/src.v4.30.bart2/CAMx.tceq.bart.pg_linux" 

# 

set RUN = "v4.30.tceq.bart.12FE" 

set CHEM = "/disk52/cenrap/camx/inputs/others" 

set LUSE = "/disk52/cenrap/camx/inputs/landuse" 

set AHOMAP = "/disk52/cenrap/camx/inputs/ahomap" 

set PHOT = "/disk52/cenrap/camx/inputs/tuv" 

set ICBC = "/disk52/cenrap/camx/inputs/icbctc" 

set MET = "/disk52/cenrap/camx/inputs/met" 

set EMIS = "/disk52/tceq.bart/camx/input/emis" 

set OUTPUT = "/swamp4/tceq.bart/camx/outputs/cen02b/Q1_double" 

# 

mkdir -p $OUTPUT 

# 

# --- set the dates and times ---- 

# 

set STARTDATE = 2002001 

set ENDDATE = 2002090 

set JDATE = $STARTDATE 

while ( $JDATE <= $ENDDATE ) 

set RESTART = "true" 

if ( $JDATE == $STARTDATE ) set RESTART = "true" 

@ YESTERDAY = $JDATE - 1 

if ( $YESTERDAY == 2002000 ) set YESTERDAY = 2001365 

set YYYY = `j2g $JDATE | awk '{print $1}'` 

set MM = `j2g $JDATE | awk '{print $2}'` 

set DD = `j2g $JDATE | awk '{print $3}'` 

# 

# --- Create the input file (always called CAMx.in) 

# 

cat << ieof > CAMx.in 

 &CAMx_Control 

 Run_Message = 'CAMx 4.30 Mech 4 CF - TCEQ.BART 36-12kmFE' 

!--- Model clock control --- 

 Time_Zone = 0, ! (0=UTC,5=EST,6=CST,7=MST,8=PST) 

 Restart = .${RESTART}., 

 Start_Date_Hour = ${YYYY},${MM},${DD},0000, ! (YYYY,MM,DD,HHHH) 

 End_Date_Hour = ${YYYY},${MM},${DD},2400, ! (YYYY,MM,DD,HHHH) 

 Maximum_Timestep = 20., ! minutes 

 Met_Input_Frequency = 60., ! minutes 

 Ems_Input_Frequency = 60., ! minutes 
December 2007 

A-3 

 Output_Frequency = 60., ! minutes 

!--- Map projection parameters --- 

 Map_Projection = 'LAMBERT' ! (LAMBERT,POLAR,UTM,LATLON) 

 UTM_Zone = 0, 

 POLAR_Longitude_Pole = 0., ! deg (west<0,south<0) 

 POLAR_Latitude_Pole = 0., ! deg (west<0,south<0) 

 LAMBERT_Central_Meridian = -97., ! deg (west<0,south<0) 

 LAMBERT_Center_Longitude = -97., ! deg (west<0,south<0) 

 LAMBERT_Center_Latitude = 40., ! deg (west<0,south<0) 

 LAMBERT_True_Latitude1 = 45., ! deg (west<0,south<0) 

 LAMBERT_True_Latitude2 = 33., ! deg (west<0,south<0) 

!--- Parameters for the master (first) grid --- 

 Number_of_Grids = 2, 

 Master_Origin_XCoord = -2736., ! km or deg, SW corner of cell(1,1) 

 Master_Origin_YCoord = -2088., ! km or deg, SW corner of cell (1,1) 

 Master_Cell_XSize = 36., ! km or deg 

 Master_Cell_YSize = 36., ! km or deg 

 Master_Grid_Columns = 148, 

 Master_Grid_Rows = 112, 

 Number_of_Layers(1) = 19, 

!--- Parameters for the second grid --- 

 Nest_Meshing_Factor(2) = 3, ! Relative to master grid 

 Nest_Beg_I_Index(2) = 51, ! Relative to master grid 

 Nest_End_I_Index(2) = 87, ! Relative to master grid 

 Nest_Beg_J_Index(2) = 14, ! Relative to master grid 

 Nest_End_J_Index(2) = 49, ! Relative to master grid 

 Number_of_Layers(2) = 19, 

!--- Model options --- 

 Diagnostic_Error_Check = .false., ! True = will stop after 1st timestep 

 Advection_Solver = 'PPM', ! (PPM,BOTT) 

 Chemistry_Solver = 'CMC', ! (CMC,IEH) 

 PiG_Submodel = 'GREASD', ! (None,GREASD,IRON) 

 Probing_Tool = 'None', ! (None,OSAT,GOAT,APCA,DDM,PA,RTRAC) 

 Chemistry = .true., 

 Dry_Deposition = .true., 

 Wet_Deposition = .true., 

 Staggered_Winds = .true., 

 Gridded_Emissions = .true., 

 Point_Emissions = .true., 

 Ignore_Emission_Dates = .true., 

!--- Output specifications --- 

 Root_Output_Name = '$OUTPUT/camx.$RUN.$JDATE', 

 Average_Output_3D = .false., 

 HDF_Format_Output = .false., 

 Number_of_Output_Species = 35, 

 Output_Species_Names(1) = 'NO', 

 Output_Species_Names(2) = 'NO2', 

 Output_Species_Names(3) = 'O3', 

 Output_Species_Names(4) = 'PAN', 

 Output_Species_Names(5) = 'NXOY', 
December 2007 

A-4 

 Output_Species_Names(6) = 'CO', 

 Output_Species_Names(7) = 'HONO', 

 Output_Species_Names(8) = 'HNO3', 

 Output_Species_Names(9) = 'NTR', 

 Output_Species_Names(10) = 'SO2', 

 Output_Species_Names(11) = 'SULF', 

 Output_Species_Names(12) = 'NH3', 

 Output_Species_Names(13) = 'HCL', 

 Output_Species_Names(14) = 'CG1', 

 Output_Species_Names(15) = 'CG2', 

 Output_Species_Names(16) = 'CG3', 

 Output_Species_Names(17) = 'CG4', 

 Output_Species_Names(18) = 'CG5', 

 Output_Species_Names(19) = 'PNO3', 

 Output_Species_Names(20) = 'PSO4', 

 Output_Species_Names(21) = 'PNH4', 

 Output_Species_Names(22) = 'POA', 

 Output_Species_Names(23) = 'SOA1', 

 Output_Species_Names(24) = 'SOA2', 

 Output_Species_Names(25) = 'SOA3', 

 Output_Species_Names(26) = 'SOA4', 

 Output_Species_Names(27) = 'SOA5', 

 Output_Species_Names(28) = 'PEC', 

 Output_Species_Names(29) = 'FPRM', 

 Output_Species_Names(30) = 'FCRS', 

 Output_Species_Names(31) = 'CPRM', 

 Output_Species_Names(32) = 'CCRS', 

 Output_Species_Names(33) = 'NA', 

 Output_Species_Names(34) = 'PCL', 

 Output_Species_Names(35) = 'PH2O', 

!--- Input files --- 

 Chemistry_Parameters = '$CHEM/CAMx4.3.chemparam.4_CF', 

 Photolyis_Rates = '$PHOT/tuv.cenrap36km.${YYYY}${MM}.20051013.txt', 

 Initial_Conditions = '$ICBC/ic.cenrap36km.CAMx', 

 Boundary_Conditions = '$ICBC/bc.cenrap36km.CAMx.$JDATE', 

 Top_Concentrations = '$ICBC/topc.cenrap36km.CAMx', 

 Albedo_Haze_Ozone = '$AHOMAP/ahomap.${YYYY}${MM}.20051013.txt', 

 Point_Sources = 

'$EMIS/double/CAMx.pt.tceq_double.RPO_US36.cen02b.$JDATE', 

 Master_Grid_Restart = '$OUTPUT/camx.$RUN.$YESTERDAY.inst.2', 

 Nested_Grid_Restart = '$OUTPUT/camx.$RUN.$YESTERDAY.finst.2', 

 PiG_Restart = '$OUTPUT/camx.$RUN.$YESTERDAY.pig', 

 Emiss_Grid(1) = '$EMIS/CAMx.ar.tceq_zero.RPO_US36.cen02b.$JDATE', 

 Landuse_Grid(1) = '$LUSE/CAMx.cenrap36km.luse.bin', 

 ZP_Grid(1) = '$MET/camx.zp.${YYYY}${MM}${DD}.OB70.bin', 

 Wind_Grid(1) = '$MET/camx.uv.${YYYY}${MM}${DD}.OB70.bin', 

 Temp_Grid(1) = '$MET/camx.tp.${YYYY}${MM}${DD}.OB70.bin', 

 Vapor_Grid(1) = '$MET/camx.qa.${YYYY}${MM}${DD}.OB70.bin', 

 Cloud_Grid(1) = '$MET/camx.cr.${YYYY}${MM}${DD}.OB70.bin', 

 Kv_Grid(1) = '$MET/camx.kv.${YYYY}${MM}${DD}.CMAQ.kvmin1.0.bin', 

 Emiss_Grid(2) = ' ', 

 Landuse_Grid(2) = ' ', 

 ZP_Grid(2) = ' ', 

 Wind_Grid(2) = ' ', 

 Temp_Grid(2) = ' ', 

 Vapor_Grid(2) = ' ', 

 Cloud_Grid(2) = ' ', 
December 2007 

A-5 

 Kv_Grid(2) = ' ', 

 & 

!----------------------------------------------------------------------------

--- 

ieof 

# 

# --- Execute the model --- 

# 

/usr/bin/time $EXEC >& $OUTPUT/camx.$RUN.$JDATE.stdout 

@ JDATE++ 

if ( $JDATE == 2001366 ) set JDATE = 2002001 

end 
December 2007 

B-1 

Appendix B 

Example CAMx Run Script for a 

PSAT BART Analysis using the 

SO4 and NO3 PSAT Tracers 
December 2007 

B-2 

#!/bin/csh 

# 

# CAMx 4.40beta OMP 

# 

setenv NCPUS 2 

setenv MPSTKZ 128M 

limit stacksize unlimited 

# 

set EXEC = 

"/disk52/tceq.bart/camx/src.v4.40.Mar29/CAMx.tceq.bart.pg_linuxomp" 

# 

set run = Q1.tceq.psat.oldmet 

set RUN = "v4.40.tceq.bart.12FE" 

set CHEM = "/disk52/cenrap/camx/inputs/others" 

set LUSE = "/disk52/cenrap/camx/inputs/landuse" 

set AHOMAP = "/disk52/cenrap/camx/inputs/ahomap" 

set PHOT = "/disk52/cenrap/camx/inputs/tuv" 

set ICBC = "/disk52/cenrap/camx/inputs/icbctc" 

set MET = "/disk50/tceq_bart/met" 

set MAP = "/disk52/tceq.bart/camx/input/sa" 

set EMIS = "/disk52/tceq.bart/camx/input/emis/psat" 

set OUTPUT = "/swamp6/tceq.bart2/camx/outputs/Q1.psat.tceqbart.12FE.oldmet" 

# 

mkdir -p $OUTPUT $run 

# 

# --- set the dates and times ---- 

# 

set STARTDATE = 2001353 

set ENDDATE = 2002364 

set JDATE = 2002021 

while ( $JDATE <= $ENDDATE ) 

set RESTART = "true" 

if ( $JDATE == $STARTDATE ) set RESTART = "false" 

@ YESTERDAY = $JDATE - 1 

if ( $YESTERDAY == 2002000 ) set YESTERDAY = 2001365 

set YYYY = `j2g $JDATE | awk '{print $1}'` 

set Y2 = `echo $YYYY | awk '{printf("%2.2d",$1-2000)}'` 

set MM = `j2g $JDATE | awk '{print $2}'` 

set DD = `j2g $JDATE | awk '{print $3}'` 

# --- Create the input file (always called CAMx.in) 

# 

cat << ieof > CAMx.in 

 &CAMx_Control 

 Run_Message = 'CAMx 4.40 Mech 4 CF - TCEQ.BART 36-12kmFE' 

!--- Model clock control --- 

 Time_Zone = 0, ! (0=UTC,5=EST,6=CST,7=MST,8=PST) 

 Restart = .${RESTART}., 

 Start_Date_Hour = ${YYYY},${MM},${DD},0000, ! (YYYY,MM,DD,HHHH) 

 End_Date_Hour = ${YYYY},${MM},${DD},2400, ! (YYYY,MM,DD,HHHH) 
December 2007 

B-3 

 Maximum_Timestep = 20., ! minutes 

 Met_Input_Frequency = 60., ! minutes 

 Ems_Input_Frequency = 60., ! minutes 

 Output_Frequency = 60., ! minutes 

!--- Map projection parameters --- 

 Map_Projection = 'LAMBERT' ! (LAMBERT,POLAR,UTM,LATLON) 

 UTM_Zone = 0, 

 POLAR_Longitude_Pole = 0., ! deg (west<0,south<0) 

 POLAR_Latitude_Pole = 0., ! deg (west<0,south<0) 

 LAMBERT_Central_Meridian = -97., ! deg (west<0,south<0) 

 LAMBERT_Center_Longitude = -97., ! deg (west<0,south<0) 

 LAMBERT_Center_Latitude = 40., ! deg (west<0,south<0) 

 LAMBERT_True_Latitude1 = 45., ! deg (west<0,south<0) 

 LAMBERT_True_Latitude2 = 33., ! deg (west<0,south<0) 

!--- Parameters for the master (first) grid --- 

 Number_of_Grids = 2, 

 Master_Origin_XCoord = -2736., ! km or deg, SW corner of cell(1,1) 

 Master_Origin_YCoord = -2088., ! km or deg, SW corner of cell (1,1) 

 Master_Cell_XSize = 36., ! km or deg 

 Master_Cell_YSize = 36., ! km or deg 

 Master_Grid_Columns = 148, 

 Master_Grid_Rows = 112, 

 Number_of_Layers(1) = 19, 

!--- Parameters for the second grid --- 

 Nest_Meshing_Factor(2) = 3, ! Relative to master grid 

 Nest_Beg_I_Index(2) = 51, ! Relative to master grid 

 Nest_End_I_Index(2) = 87, ! Relative to master grid 

 Nest_Beg_J_Index(2) = 14, ! Relative to master grid 

 Nest_End_J_Index(2) = 49, ! Relative to master grid 

 Number_of_Layers(2) = 19, 

!--- Model options --- 

 Diagnostic_Error_Check = .false., ! True = will stop after 1st timestep 

 Advection_Solver = 'BOTT', ! (PPM,BOTT) 

 Chemistry_Solver = 'CMC', ! (CMC,IEH) 

 Aerosol_Solver = 'ISOROPIA', ! (ISOROPIA,EQSAM) 

 PiG_Submodel = 'GREASD', ! (None,GREASD,IRON) 

 Probing_Tool = 'PSAT', ! (None,OSAT,GOAT,APCA,DDM,PA,RTRAC) 

 Chemistry = .true., 

 Dry_Deposition = .true., 

 Wet_Deposition = .true., 

 Staggered_Winds = .true., 

 Gridded_Emissions = .true., 

 Point_Emissions = .true., 

 Ignore_Emission_Dates = .true., 

!--- Output specifications --- 

 Root_Output_Name = '$OUTPUT/camx.$RUN.$JDATE', 

 Average_Output_3D = .false., 

 HDF_Format_Output = .false., 

 Number_of_Output_Species = 35, 

 Output_Species_Names(1) = 'NO', 
December 2007 

B-4 

 Output_Species_Names(2) = 'NO2', 

 Output_Species_Names(3) = 'O3', 

 Output_Species_Names(4) = 'PAN', 

 Output_Species_Names(5) = 'NXOY', 

 Output_Species_Names(6) = 'CO', 

 Output_Species_Names(7) = 'HONO', 

 Output_Species_Names(8) = 'HNO3', 

 Output_Species_Names(9) = 'NTR', 

 Output_Species_Names(10) = 'SO2', 

 Output_Species_Names(11) = 'SULF', 

 Output_Species_Names(12) = 'NH3', 

 Output_Species_Names(13) = 'HCL', 

 Output_Species_Names(14) = 'CG1', 

 Output_Species_Names(15) = 'CG2', 

 Output_Species_Names(16) = 'CG3', 

 Output_Species_Names(17) = 'CG4', 

 Output_Species_Names(18) = 'CG5', 

 Output_Species_Names(19) = 'PNO3', 

 Output_Species_Names(20) = 'PSO4', 

 Output_Species_Names(21) = 'PNH4', 

 Output_Species_Names(22) = 'POA', 

 Output_Species_Names(23) = 'SOA1', 

 Output_Species_Names(24) = 'SOA2', 

 Output_Species_Names(25) = 'SOA3', 

 Output_Species_Names(26) = 'SOA4', 

 Output_Species_Names(27) = 'SOA5', 

 Output_Species_Names(28) = 'PEC', 

 Output_Species_Names(29) = 'FPRM', 

 Output_Species_Names(30) = 'FCRS', 

 Output_Species_Names(31) = 'CPRM', 

 Output_Species_Names(32) = 'CCRS', 

 Output_Species_Names(33) = 'NA', 

 Output_Species_Names(34) = 'PCL', 

 Output_Species_Names(35) = 'PH2O', 

!--- Input files --- 

 Chemistry_Parameters = '$CHEM/CAMx4.3.chemparam.4_CF', 

 Photolyis_Rates = '$PHOT/tuv.cenrap36km.${YYYY}${MM}.20051013.txt', 

 Initial_Conditions = '$ICBC/ic.cenrap36km.CAMx', 

 Boundary_Conditions = '$ICBC/bc.cenrap36km.CAMx.$JDATE', 

 Top_Concentrations = '$ICBC/topc.cenrap36km.CAMx', 

 Albedo_Haze_Ozone = '$AHOMAP/ahomap.${YYYY}${MM}.20051013.txt', 

 Point_Sources = '$EMIS/CAMx.FE.pt.tceq_psat.RPO_US36.cen02b.$JDATE', 

 Master_Grid_Restart = '$OUTPUT/camx.$RUN.$YESTERDAY.inst.2', 

 Nested_Grid_Restart = '$OUTPUT/camx.$RUN.$YESTERDAY.finst.2', 

 PiG_Restart = '$OUTPUT/camx.$RUN.$YESTERDAY.pig', 

 Emiss_Grid(1) = '$EMIS/CAMx.ar.cenrapzero.RPO_US36.cen02b.$JDATE', 

 Landuse_Grid(1) = '$LUSE/CAMx.cenrap36km.luse.bin', 

 ZP_Grid(1) = '$MET/camx.zp.${YYYY}${MM}${DD}.OB70.bin', 

 Wind_Grid(1) = '$MET/camx.uv.${YYYY}${MM}${DD}.OB70.bin', 

 Temp_Grid(1) = '$MET/camx.tp.${YYYY}${MM}${DD}.OB70.bin', 

 Vapor_Grid(1) = '$MET/camx.qa.${YYYY}${MM}${DD}.OB70.bin', 

 Cloud_Grid(1) = '$MET/camx.cr.${YYYY}${MM}${DD}.OB70.bin', 

 Kv_Grid(1) = '$MET/camx.kv.${YYYY}${MM}${DD}.CMAQ.kvmin1.0.bin', 

 Emiss_Grid(2) = '$EMIS/CAMx.ar.cenrapzero.RPO_US12.cen02b.$JDATE', 

 Landuse_Grid(2) = ' ', 

 ZP_Grid(2) = ' ', 

 Wind_Grid(2) = ' ', 
December 2007 

B-5 

 Temp_Grid(2) = ' ', 

 Vapor_Grid(2) = ' ', 

 Cloud_Grid(2) = ' ', 

 Kv_Grid(2) = ' ', 

 & 

!----------------------------------------------------------------------------

--- 

&SA_Control 

 SA_File_Root = '$OUTPUT/camx.$RUN.$JDATE', 

 SA_Summary_Output = .false. 

 SA_Master_Sfc_Output = .true., 

 SA_Nested_Sfc_Output = .true., 

 SA_Stratify_Boundary = .false., 

 SA_Number_of_Source_Regions = 11, 

 SA_Number_of_Source_Groups = 2, 

 Use_Leftover_Group = .false., 

 Number_of_Timing_Releases = 0, 

 SA_Receptor_Definitions = '$MAP/receptor.tceqbart.txt' 

 SA_Source_Area_Map(1) = '$MAP/BART_psat_regn_36km.txt' 

 SA_Source_Area_Map(2) = '$MAP/BART_psat_regn_12km.txt' 

 SA_Points_Group(1) = 

'$EMIS/CAMx.FE.pt.tceq_psat.RPO_US36.cen02b.$JDATE.copy', 

 SA_Emiss_Group_Grid(1,1) = ' ', 

 SA_Emiss_Group_Grid(1,2) = ' ', 

 SA_Points_Group(2) = ' ', 

 SA_Emiss_Group_Grid(2,1) = 

'$EMIS/CAMx.ar.cenrapzero.RPO_US36.cen02b.$JDATE.copy', 

 SA_Emiss_Group_Grid(2,2) = 

'$EMIS/CAMx.ar.cenrapzero.RPO_US12.cen02b.$JDATE.copy', 

 SA_Master_Restart = '$OUTPUT/camx.$RUN.$YESTERDAY.sa.inst.2', 

 SA_Nested_Restart = '$OUTPUT/camx.$RUN.$YESTERDAY.sa.finst.2', 

 PSAT_Treat_SULFATE_Class = .true. 

 PSAT_Treat_NITRATE_Class = .true. 

 PSAT_Treat_SOA_Class = .false. 

 PSAT_Treat_PRIMARY_Class = .false. 

 PSAT_Treat_MERCURY_Class = .false. 

 PSAT_Treat_OZONE_Class = .false. 

& 

ieof 

# 

# --- Execute the model --- 

# 

cp CAMx.in $run/camx.$RUN.$JDATE.in 

/usr/bin/time $EXEC |& tee $run/camx.$RUN.$JDATE.stdout 

@ JDATE++ 

if ( $JDATE == 2001366 ) set JDATE = 2002001 

end