Appendix 9-9: CAMx Modeling GuidelinesInternational 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 documen t does not substitute for any Clean Air Act provision, EPA regulation, or TCEQ re gulation, nor is it a regulation itself. Thus, it does not impose binding, enforceabl e 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 discreti on to adopt approaches on a case-by-case basis that differ from this guidan ce where appropriate. Any decisions by TCEQ regarding a particular modeling an alysis will only be made base d on the applicable statute and regulations.

This guidance is a living document and may be revised periodically. Updates, revisions,
and additional documentati on will be provided at http://www.tceq.state.tx.us. As noted above, TCEQ cannot assure that actions based upon th is guidance will be fu lly approvable in all instances. The TCEQ welcomes public comments on this document and will consider those comments in any future revisions of this guida nce document, providing such approaches comply with all applicable statutor y and regulatory requirements.

Please note several important ch anges to this guidance:

• In accordance with EPA’s BART modeling gui dance, 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 distan ces greater than 300 km, at the source's
discretion.

• Since CALPUFF may significantly over pr edict 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 us ed 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
SO 4 and NO 3 PSAT TracersDecember 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 mode ling 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 representa tion 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................................... 2-13December 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
b natural Clean Natural Conditions
b source 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 Qu ality 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
NH 4 Ammonium
NO 3 Particulate Nitrate
non-EGU Non-Electrical Generating Units
NO

x 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-GridDecember 2007

PLUVUE Plume Visibility Model

PM Particulate Matter

PM 10 Particulate Matter with aerodynamic diameters less than 10
microns

PM 2.5 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 Integr ated Puff model with CHEMistry
SCIPUFF Second-order Closure Integrated Puff model
SIP State Implementation Plan
SMOKE Sparse Matrix Operator Kernel Emissions
SO 2 Sulfur Dioxide
SO 4 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 PartnershipDecember 2007

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− 1 INTRODUCTION

OVERVIEW

The final version of United States Environmen tal Protection Agency’s (EPA) Regional Haze Regulations that include the Best Availabl e Retrofit Technology (B ART) 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 th at certain existing stationary sources emitting visibility-impairing air pollutants in stall and operate BART. The re gulations require case-by-case BART determinations to define specific emissions limits representing BART. The regulations also establish schedules for complian ce for each source subj ect to BART. These requirements will be part of the State Implementati on Plan (SIP) revisions that Te xas 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 se veral potential methods for determining whether a source is subject to BART and the me thods for determining what the expected visibility benefits of the BART c ontrols will be, includi ng dispersi on modeling.

For the subject to BART assess ment, EPA BART Guidelines stat e that “You can use CALPUFF, or another EPA approve d model, to predict the vi sibility impacts from a si ngle source at a Class I area” (EPA, 2005). For the visibility improvemen t determination, EPA BART Guidelines state “Use CALPUFF, or othe r appropriate dispersion model to determine the visi bility 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, cumula tive 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 contri bute to visibility impairment” (EPA, 2005). BART Guidelines suggest using California Pu ff Model (CALPUFF) or a photochemical grid model to perform the cumulative group exemption modelin g, but notes “if you wish to use a grid model, you should consult with the appropriate EP A Regional Office to develop an appropriate modeling protocol” (EPA, 2005).

The purpose of this document is to provide s ources in Texas with guidelines for preparing a modeling protocol and mode ling analysis using the Comprehensive Air Quality Model with

extensions (CAMx) grid model. Sources also have the option to use CA LPUFF. See TCEQ’s BART webpage for information on th e Texas CALPUFF Mo deling Guidelines ( http://www.tceq.state.tx.us/implem entation/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 ma de available concurrently to EPA and Federal Land Managers (FLM) for their review.

The report accompanying the source-specific su bject-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.December 2007

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− 2 TCEQ BART Screening Exemption Modeling

TCEQ has performed cumulativ e group BART screening exemption modeling (Morris and Nopmongcol, 2006) usin g the Comprehensive Air Quality Mo del with extensions (CAMx). CAMx is a photochemical grid mo del (PGM) that incorporates th e latest scientific advances including current state of science treatment of transport, dispersion deposition and chemical transformation (gas-phase and aqueous-phase chemis try and aerosol chemistr y and dynamics). As required in the EPA BART Guide lines, 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 screeni ng 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 emissi ons 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 primar y PM emissions cause or contribute to visibility impairment on any Class I areas; and

• BART sources SO 2 and NO

x 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 assess ment. These concer ns revolve around:

(1) PGMs can only resolve the dynamics and che mistry of a point source plume to the grid resolution specified and use of a high resolu tion 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 ha ve 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-orien ted plume model, like CA LPUFF, the need to do multiple zero-out runs to assess the indivi dual impacts of multip le 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 Apportionm ent Technology (PSAT) that allows the separate tracking of individual source PM impacts so that the individual impacts from many different sources can be obtaine d cost-effectively in one run;

• The threading of the PSAT PM source a pportionment 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; andDecember 2007

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− 3 • Implementation of flexi-nesting whereby finer nests can be specified inside of a coarser grid and the model can interpol ate 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 es timate 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 usin g CAMx/PSAT/PiG are no t fully documented. Thus, this document w ill provide guidance in the applica tion of CAMx for single source
assessments as needed for BA RT, New Source Review (NSR) and Prevention of Significant Determination (PSD) assessments. The TCEQ BART screening exem ption modeling is used as an example approach for thes e types of assessments.

BACKGROUND

Regional Haze Rule and BART Guidelines

Section 169A of the Federal Clean Air Act (CAA) se ts forth a national goal for visibility which is the “prevention of any future, and the remedyi ng of any existing, impairment of visibility in Class I areas which impairment results from ma nmade 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 su bmit 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 requ irements 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 a ll 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 ar ea from which pollutants ma y be transported to a Class I area, that source ‘‘may reasonably be anti cipated to cause or cont ribute’’ to visibility impairment in the Class I area. EPA’s BART gu idelines include procedures for single source assessments of their visibility contributions.

The BART guidelines were written primarily for th e 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 implementa tion 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 require d to use the process in the guidelines when making BART determinations for other types of sour ces, i.e., states retain the discretion to adopt approaches that differ from the guidelines.December 2007

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− 4 Sources are BART-eligible if they meet three criteria: (a) had pot ential emissions of at least 250 tons per year of a visibility-impairing pollu tant, (b) were in operation between 7 August 1962 and 7 August 1977, and (c) were one of 26 listed s ource categories. BART controls are required for any BART-eligible source which can be reas onably expected to cause or contribute to impairment of visibility in any of the 156 fede ral parks and wilderness (Class I) areas protected under the regional haze rule. Air qu ality modeling is an important t ool available to the states in determining whether a source can be reasonably expected to contribu te to visibility impairment
in a Class I area.

In EPA’s 1 August 2005 proposed rulemaking (70 FR 44154) entitled “Rev isions to Provisions Governing Alternative to Sour ce-Specific Best Available Retr ofit Technology Determinations,” the agency provided states with a process to sh ow that an emissions trading program may be used as an alternative to applying BART. Texa s, 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. Th e options, relying on different modeling and/or emissions analysis approaches, are provided as guidance and the st ates are entitled to use other reasonable approaches for analyzing the visibility impacts of an individu al 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 antic ipated to cause or contribute to visibility impair ment in a Class I area and t hus is not subject to BART. Under this option, states can analyze an i ndividual source’s impact on visibility as a
result of its emissions of SO 2 , NO
x , 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 cas e-by-case basis, which sources of VOC or ammonia emissions may be likely to impair vi sibility and should ther efore 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 cau se 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 re presentative plants to reflect groupings of specific sources with important common char acteristics and based on these analyses, states may determine that certain types of s ources may clearly be anticipated to cause or contribute to visibility impair ment whereas others do not.December 2007

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− 5 EPA’s ‘Model’ plant example in their BART guidance pertains to exempting sources
whose SO 2 , NO
x or SO 2 plus NO
x 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 (k m) 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 wo rds, states that esta blish 0.5 deciviews as a contribution threshold may exempt sources that emit less than 500 tons per year of NO
x or SO 2 (or combined NO
x and SO 2 ) 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 ma y exempt sources that emit le ss than 1000 tons per year of
NO
x or SO 2 (or combined NO
x and SO 2 ) with no further analysis, as long as these
sources are located more than 100 km from a ny Class I area. Texas used the model plant option as an initial screeni ng 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 polluta nt 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 SO 2 , NO
x , and direct PM emissions.

EPA assumes in the BART guidance that disp ersion 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 qualita tive 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 pr imary difficulty in using CALPUFF for VOCs is that the CALPUFF modeling system, recommende d as the BART modeling platform, does not adequately treat PM formation from VOCs. Us e of photochemical grid models to assess the visibility impacts of VOC emissi ons provides a quantitative and mo re scientifically defensible approach than a qual itative assessment.

A primary difficulty in estimating visibility impa irment 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 faciliti es are relatively well- characterized, livestock operations and soil are major sources of ammonia emissions, and their quantification is highl y 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 BA RT source actually emits more than 250 tpy of
ammonia is best left to the state.December 2007

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− 6 EPA Guidance on Air Quality Models

The BART determination under the Regional Haze Ru le seeks to quantify the impact of source
emissions of SO 2 , NO
x , and direct PM (PM 2.5 and/or PM 10 ) on hourly and daily visibility impairment at receptors located w ithin downwind Class I areas. Sin ce visibility is defined in the context of light extinction, which itself is dete rmined by atmospheric concentrations of specific fine particulate species – sulfate, nitrate, organic carbonaceous matter, elemental carbon, other fine particles and coarse mass – the modeling me thod(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, “Becau se the air quality model CALPUFF is currently the best application available to predict the impact of a single source on vi sibility in a Class I area, we proposed that a CALPUFF assessment be used as the preferre d 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 PM 2.5 emissions from a source, but also to predict the visibility impacts from the transport and chemical transformation of fine partic le precursors.” The Rule goes on to state that “regional scale modeling typically involves use of a photochemical gr id 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 ma y 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 Demonstratin g Attainment of the Air Quality Goals for PM 2.5 and Regional Haze” (EPA, 2001) sets forth the ty pes of models that should be used for simulating secondary fine particul ate and visibility for SIPs. EP A states, “States should use a regional scale photochemical grid model to es timate the effects of a control strategy on secondary components of PM. Changes in pr imary components may be estimated using a numerical grid model (with no chemistry), a Lagr angian model, or in some cases a receptor model.” Thus, in its Regional Haze and PM 2.5 SIP modeling guidance, EPA indicates that CALPUFF (a Lagrangian non-stead y-state Gaussian puff model) should not be used for secondary PM and visibility impacts at Class I ar eas, 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 fo r use in estimating single source pollutant concentrations resulting from the long range tran sport of primary pollutants.” On the other hand, only regional grid models with appropriate chemistry are to be used in developing PM 2.5 and Regional Haze SIPs. EPA has attempted to reco ncile 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 aeroso l chemistry is adequate for es timating relative benefits of
controls on BART sources.December 2007

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− 7 More recent developments in phot ochemical grid modeling should alleviate some of the concerns related to using them for single-s ource visibility assessment. In pa rticular, 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 VO C 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 categor y, 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 servic e between 7 August 1962 and 7 August 1977 in order to be BART-eligible. Potential emi ssions of 250 tpy or mo re of a visibilityimpairing air pollutant are required to ma ke a source eligible for BART. Qualifying pollutants include primary PM 10 and gaseous precursors to secondary fine particulate
matter such as SO 2 and NO
x . Whether ammonia and VOCs should be included as visibility-impairing pollutants for BART eligibil ity is left for the states to determine on a case-by-case basis. The guidance states th at high molecular weight VOCs with 25 or more carbon atoms and low vapor pressure should to be considered as primary PM 2.5 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 assessme nt with the CALPUFF modeling system (or other appropriate model) followed by compar ison of the estimated 24-hr visibility impacts against a threshold above estimated natural conditions to be determined by the
States. The threshold to determine whet her a single source “causes” visibility impairment is set at 1.0 deciview cha nge 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 determina tion. The guidance also states that the proposed threshold at which a source may “c ontribute” to visibility impairment should not be higher than 0.5 deciviews, although de pending 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 li ght 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 BA RT exemption levels. Since visibilityDecember 2007

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− 8 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-hou r average basis for determining that an
individual source is contributi ng to visibility impairment at a Class I area. This level would be calculated by comparing the air qualit y model’s results for an individual source against “natural visibility” conditions. To assess a source’s impact one compares the 98 th percentile modeled value (8 th highest day annually at a receptor or 22 nd 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 se lecting the “best” alternative, taking into consideration: (a) any pollution control equipment in use at the source (which affects the availability of op tions and their impact s), (b) the costs of compliance with control options, (c) the rema ining useful life of the facility, (d) the energy and non air-quality environmental impa cts 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 PM 2.5 provisions of the Clean Air Inters tate Rule (CAIR), EPA has noted that CAIR satisfies the BART requirement s for Electrical Generating Units’ (EGU) SO 2 and NO
x emissions. However, since CAIR does not addre ss VOC and PM emissions, a state still needs to
determine whether an EGU’s VOC and/or PM em issions contribute signifi cantly 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 pr otocol 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 fact ors affecting their par ticular Class I areas (70 FR 39126).

CALPUFF Modeling Recommendations

To evaluate the visibility impacts of a BART-e ligible 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 modeli ng 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 gr id models may have mer it, but such models are
resource intensive relative to CALP UFF. 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 involv e 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 inDecember 2007

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− 9 determining the appropriate emission rates. Th e EPA recommends that the state use the highest 24-hour average actual emission rate for the most recent five-year period (e xcluding periods with start-up, shutdown, and malfunctions). Visibility improvements may be evaluated on a pollutantspecific basis. States are enco uraged to account for the magnitude , frequency, and duration of the contributions to visibility impair ment caused by the source when assessing whether the source is reasonably anticipated to cause or contribute to visibility impa irment at a Cl ass I area.

Alternative Models for BART Analyses

All air quality models potentially suited to BA RT analysis share a common foundation: the species-conservation (or atmosphe ric diffusion) equation. Source -oriented air qua lity 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 th e simplicity of the CALPUFF model derives from the fact that many chemical and physical proce sses known to influence visibility are simply ignored. In contrast, comprehens ive regional models treat these pr ocesses 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 i gnores a substantial body of research and model development carried out at the agency and else where in the U.S. over the past 20 years. Although grid models have generally been appl ied at geographic scales ranging from a multistate to the continental scale and were not initia lly 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 poi nt 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 anthr opogenic and biogenic emissions that cause gas phase, particulate, secondary aerosol, and visi bility air pollution problems. Furthermore, the convergence of fast commodity-based LINUX co mputer clusters and recently-developed regional modeling emissions, meteorological, and ai r quality databases make application of these modeling platforms no longer a research or academ ic 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 Ameri can Meteorological Society/Envi ronmental Protection Agency Regulatory Model Improvement Committee Model (AERMOD). Given grid nesting and Plumein-Grid technology, modern regional models are applicable to a br oad range of scales from 10-20
km to continental scale. In f act, 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 cl early more appropriate than CALPUFF for cumulative modeling requirements such as in the determination of the a ggregate contribution of all-BART-eligible sources to visibility impairment . As confirmed in EPA’s fine particulate and regional haze modeling guidance discussed prev iously (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 technica lly correct representati on of the formation ofDecember 2007

1

− 10 secondary PM species, such as sulfate ( SO 4 ), nitrate ( NO 3 ), and secondary organic aerosols (SOA), than CALPUFF.

Still, for the vast majority of potential BART-e ligible 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 mo re appropriate than the single-source CALPUFF model for the following reasons:

• There are a large number of potentia l BART-eligible sources in Texas, and the CAMx screening BART exemption r uns allow the efficient scr eening 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 r uns already performed on behalf of TCEQ,
are between 97 and 654 km from the nearest Cl ass 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 wh ich 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 cont ribution 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 asse ssment of the visibi lity impacts due to potential BART-eligible sources’ VOC and PM emissions;

• Use of a PGM provides an eval uation of the cumulative impact of BART-eligible sources on visibility in Class I areas; and

• Use of a hybrid PGM that uses a full-che mistry PiG module for near-source plume chemistry and dynamics and a three-dimensional grid model for plume chemistry, transport and dispersion at fa rther downwind distances contai ns 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 modeli ng approach used to perform the BART screening analysis of potential BART-eligib le sources in the state of Texa s 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 exempti on modeling using CAMx:

• Screening analysis of the potenti al visibility impacts at Class I areas due to all potential BART-eligible sources VOC and PM emissions; and

• Screening analysis for groups of poten tial BART-eligible non-EGU sources’ SO 2 and
NO
x 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 pe rformed using emissions zero-out modeling and inert primary PM modeling, whereas the group of sources’ SO 2 and NO
x emissions screening analysis was performed using the PSAT that has been implemented in the full-chemistry PiG.
The SO 2 and NO
x emissions analysis could also in clude 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 4.4 β (V4.4 β ) was used in the Texas BART screening exemption modeling analysis. CAMx V4.4 β is an updated version to CAMx V4.31 (ENVIRON, 2005) that is currently (August 2006) publicly availabl e on the CAMx website for no cost ( www.camx.com ).
CAMx V4.4 β includes several updates to CAMx V 4.31, including the impl ementation of the
PSAT within the full-science PiG. CAMx V4.4 β 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 MO DELING DATABASE

The Texas BART screening analysis exempti on modeling was performed using a 2002 annual regional photochemical modeling da tabase that was developed as part of the Central Regional Air Planning Association (CENRAP) region al 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 continen tal US. This database was developed following the procedures outlined in the CENRAP Modelin g Protocol (Morris et al., 2004) and CENRAP modeling Quality Assurance Program Plan (QAPP) (Morris and Tonnesen, 2004). The CENRAP preliminary base case model performa nce evaluation results for the CAMx model onDecember 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 prel iminary 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 enhancem ents 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 evaluati on using the Base A emissions and CAMx Version 4.20 is reported in Morris and co-wor kers (2005d) with additional model performance evaluation displays available on the CENRAP modeling Website ( http://pah.cert.ucr.edu/aq m/cenrap/cmaq.shtml#camx ). CENRAP has updated the CAMx 2002
36 km Base A emissions to Base B and CAMx ve rsion 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 ne sted-grid that covers Texas and Class I areas in and near Texas including:

• Big Bend, Guadalupe Mountains, Carl sbad 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 wa s 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 s hown 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 whic h CAMx internally interpolates all of the meteorological, emissions and other inputs from th e 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 gr id 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

kmkm

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

kmkm

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 domai n and locations of the IMPROVE monitoring sites (indicated by circles) that include Class I are as and locations of potential BART-eligible sources in Texas (indicated by triangles).December 2007

2-5 ENHANCEMENTS TO THE PM SO URCE APPORTIONMENT TECHNOLOGY

To estimate the air quality and/or visibility imp acts 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 V4.4 β ). Below we describe the techni cal 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 (SO 4 )

• Particulate nitrate (NO 3 )

• Ammonium (NH 4 )

• 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 regi on 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 geogr aphic source region map to specify the source regions. The user then inputs each separate sour ce category as separate gridded lo w-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 adde d 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 th e most complex species to apportion because the emitted precursor gases (NO
x , VOCs) are several steps removed from the resulting PM species (NO 3 , SOA). The PSAT tracers for each type of PM are listed below. PSAT convention is that tracer names for particulate spec ies begin with the letter “P.”

Sulfur
SO2 i Primary SO 2 emissions
PS4 i Particulate sulfate ion fro m primary emissions plus secondarily formed sulfateDecember 2007

2-6 Nitrogen
RGN i Reactive gaseous nitrogen including primary NO
x (NO + NO 2 ) emissions plus nitrate radical (NO 3 ), nitrous acid (HONO) a nd dinitrogen pentoxide (N 2 O 5 ).
TPN i Gaseous peroxyl acetyl nitrate (PAN) plus peroxy nitric acid (PNA)
NTR i Organic nitrates (RNO 3 )
HN3 i Gaseous nitric acid (HNO 3 )
PN3 i Particulate nitrate ion from primary em issions plus secondarily formed nitrate
NH3 i Gaseous ammonia (NH 3 )
PN4 i Particulate ammonium (NH 4 )

Secondary Organic Aerosol
ALK i Alkane/Paraffin secondary organic aerosol precursors
ARO i Aromatic (toluene and xylene) s econdary organic aerosol precursors
CRE i Cresol secondary organic aerosol precursors

TRP i Biogenic olefin (terpene) sec ondary organic aerosol precursors
CG1 i Condensable gases from toluene an d xylene reactions (low volatility)
CG2 i Condensable gases from toluene an d xylene reactions (high volatility)
CG3 i Condensable gases from alkane reactions
CG4 i Condensable gases from terpene reactions
CG5 i Condensable gases from cresol reactions
PO1 i Particulate organic aeros ol associated with CG1
PO2 i Particulate organic aeros ol associated with CG2
PO3 i Particulate organic aeros ol associated with CG3
PO4 i Particulate organic aeros ol associated with CG4
PO5 i Particulate organic aeros ol associated with CG5

Mercury
HG0 i Elemental Mercury vapor
HG2 i Reactive gaseous Mercury vapor
PHG i Particulate Mercury

Primary Particulate Matter
PEC i Primary Elemental Carbon
POA i Primary Organic Aerosol
PFC i Fine Crustal PM
PFN i Other Fine Particulate
PCC i Coarse Crustal PM
PCS i 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 sour ce apportionment for any or all of the chemical classes in each CAMx simulation (i.e. the SO 4 , NO 3 , NH 4 , 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, SO 4 is apportioned to SO
x emissions, NO 3 is apportioned to
NO
x emissions, NH 4 is apportioned to NH 3 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 a i and b i , respectively. Reactive tracers must be
included for all sources of A and B including emissions, init ial conditions and boundary conditions so that complete source apportionment is obtained, i.e., A = Σ a i and B = Σ b i

.
In PSAT, the general approach to m odeling change over a model time step Δ t 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 am ount 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 apportionm ent 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, a i is the apportionment of the emissions inventory. For the case
where B increases due to transport, a i is the apportionment of the upwind grid cell.

In some cases, source category specific weighting factors ( w i ) 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,) whic h must be weighted by the average hydroxyl radical (OH) rate constant of each ARO i . 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 ( w i ) must be added to the equation for species production:


## ( ) ()

ii ii
i i 

### aw aw Btbttb ∑ Δ+=Δ+

An example is chemical production of condensab le gases (CG1 or CG2) from ARO tracers, which must be weighted by aerosol yield weighting factors. The aerosol yield we ighting factors depend upon the relative amounts of tolu enes and xylenes in each source group.

Several aerosol reactions ar e 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

ii i ii i

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., E NVIRON, 2005; Morris et al., 2005; Yarwood et al., 2004).

UPDATES TO THE PSAT FORMULATIO N AND NEW FULL-SCIENCE CHEMISTRY
PiG

The CAMx PSAT and PiG algorith ms were updated to treat th e near-source chemistry ofsecondary 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 Apportionmen t 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 concentra tions 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 m odel to properly characterize photochemical conditions. Increasing resolution should, in theo ry, 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 c ontain a Plume-in-Grid sub-model that tracks individual plume segments (or puffs) in a La grangian sense, acc ounting 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 th at the inclusion of a Lagrangian puff model within an Eu lerian grid model is a forced c onstruct. The formulations of the two modeling approaches are fundamentally di fferent 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 upda ted in Version 4.4 of the model.

The new PiG approach in CAMx treats the fu ll 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 us ed in the Second-order Closure Integrated puffDecember 2007

2-9 model (SCIPUFF) with CHEMistry (SCICHEM ) Lagrangian model (EPRI, 2000). The approach lends itself to in corporating chemistry for pa rticulates 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 se paration of these points is determined by the wind vector at final plume rise. Each point is then subsequently and independently trans ported through the gridded wind fields, which directly accounts for puff stre tching 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 deviat ions (so-called “sigmas” ) along the three spatial axes ( σ x , σ y , σ z ). Diffusive growth is defined by the growth in these sigma values. The total
cross-sectional width extends ±1.5 σ from puff centerline. The limits of ±1.5 σ result in an average concentration across the Gaussian distri bution that nearly equa ls a uniformly mixed
concentration across that distance. The total l ongitudinal length is the distance between the puff endpoints ±1.5 σ y . Horizontal area is calcu lated using the formula for an ellipse. Different vertical constructs are employe d for Greatly Reduced and Simplified Dynamics (GREASD) and Incremental Reactions for Organics and NO
x (IRON) PiG, as described later in this section. Figure 2-3 presents a schematic representation of each puff in horizontal cross-section.

P ( l e

a

d

)

P ( t

r a

i l ) 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 si mplifications. SCICHEM solves predictive spatial moment equations with second-order closur e that relate the evolution of the puff spread tensor ( σ ij = σ i ×σ j ) to resolved mean shears and turbulen t velocity statistics. The Reynoldsaveraged second-moment tran sport equation is given as:

Q cux
Q cux

x u

x u

dt d ij ji

k i jk

k j

ik ij ′′′

+ ′′′

+ ∂ ∂
+ ∂ ∂

= σσ σ

where 

### u is the mean wind vector component, the prim ed values represent tu rbulent fluctuations from the mean, and the angle brackets denote integrals over space. The Reynolds averaging process always introduces higher-or der 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) c ontribution 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 th at 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 K

z ′′′

=

In our approach for CAMx, we have adopted th e SCICHEM second-order tendency equations to model the time-evolution of puff turbulent flux moments (represented by diffusivities K
x =K y and

K

z ) and their contribution to the evolution of puff spread (represented by the diagonal components of the puff spread tensor, σ x 2

= σ y 2 and σ z 2 ). Note that we ignore the off-diagonal contributions to puff spread, since they are unnece ssary in the context of the CAMx PiG. Puff spread is defined for puff depth ( σ z ) and puff width ( σ y ); the latter is also added to the longitudinal length to allow for diffusive growth along the puff cen terline. We account for the
effects of wind shears in the evolu tion of lateral spread, but assume that the evolution of vertical spread is solely the resu lt of turbulent fluxes.December 2007

2-11 The resulting two Reynolds-averaged second-mome nt 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

+ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

+ + = σσ σ σ

where D is deformation of horiz ontal 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 lengt h scale. Separate equations are given for two different boundary layer turbulence scales (shear- and buoyancy-produced), such
that:

buoyancy shear cvy cvycvy ′′′ + ′′′ = ′′′

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



## ( ) 

## [ ]

i buoyancy zz w q /exp5.1113.0 2

* 2 + = 

## ( )

i shear zzu q /15.2 2

* 2

− =

where u * is the friction velocity, w * is the convective velocity scale, z is height above the surface,
and z i is the height of the surface-based boundary laye r. The horizontal turbulent length scale is given by:



## ( ) ( ) 2 2 2 65.0 1

3.0 1 1

z z i shear + = Λ

i buoyancy z 3.0 = Λ

In the stable boundary layer, only the shear components of q 2 and Λ are applied. Above the boundary layer, SCICHEM applies rough approximati ons for the velocity variance and turbulent length scale: q 2 = 0.25 m 2 /s 2 , and Λ = 1000 m.December 2007

2-12 The SCICHEM tendency equation for the vertical turbulen t flux moment is 

# ( ) cwzQK q Acwz dt d
eq

z

v v ′′′

− Λ = ′′′

where q v 2 = ww ′′

, Λ
v is the vertical turbulent length scale, and K z eq is the equilibrium diffusivity. Whereas a specific equation for K z eq is formulated for SCICHEM, we have chosen to specify the value of this parameter from the gridded fiel ds of vertical diffusivity in CAMx. Again SCICHEM gives separate equa tions for shear- and buoyancy- produced turbulence scales.

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



## ( )

i shear v zzu q /15.1 2

* 2

− =


## ( ) ( )

i i buoyancy v zz zzw q /05.1/1.1 3/2 2

* 2

− =

The vertical turbulent length scale for both shear and buoyancy is equal to Λ shear given above for horizontal length scale. Above the bounda ry layer, SCICHEM again applies rough approximations for the velocity vari ance and turbulent length scale:

q v 2 = 0.01 m 2 /s 2 , and Λ
v = 10 m.

The external variables needed by IRON PiG to complete the dispersion calculations include z i , u * and w * . All of these are availabl e from an internal module in CAMx that calculates these boundary layer similarity theory parameters. T hus, 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 ma ster 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 ma ximum 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 extr uded 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 σ y and σ z are explicitly calculated from this entrainment
calculation.

Effects of wind divergence on plum e deformation are treated in an explicit manner within the CAMx PiG using a “chained puff” approach (Figur e 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 change s to centerline orientation due to deforming shears. Since PiG puffs can extend over multip le layers, layer density-weighted average wind components are determined for each endpoint base d on the vertical coverage of the puff, and these are used for advection of those points. GREASD PiG puffs are not allowed to expandDecember 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 ex truded 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/divergen t wind fields. Puff endpoints ma y move closer together or further apart, in wind fields th at are slowing or accelerating do wnstream. We compute puff endpoint separation changes and then adjust puff wi dths 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 re quired 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. Th e red line connected by dots re presents 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 defi ned 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 chemistr y. 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 wh en horizontal area appro aches 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 sour ce pollutant plumes, while secondarily improving puff-grid mass exchange and adding additional feat ures 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 implementati on 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 nece ssary to reformulate the physical and chemical configuration of the PiG puffs and to utilize an accurate numerical so lution approach based on the Livermore Solver for ordinary differe ntial 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:


## ( ) ( )

BA T PPkR = (1)

where P A and P B are the total puff concentrations of each species. The total puff concentrations can be expressed as the sum of the bac kground and puff perturbation concentrations: 

## ( )

AAA CcP + = 

## ( )

BBB CcP + =

where C is the ambient concentration and c is the puff increment c oncentration. Thus the
reaction rate is found to be: 

## ( ) ( )

BBAA T CcCckR + + =

or 

## ( )

BABABABA T CCCccCcckR + + + =

If we subtract the rate of change of the background,

(2)
BA Ambient CkCR =

by assuming that it is explicitly and separately treated by the grid model, we obtain the reaction rate for the puff increments:

(3) 

## ( )

BABABA P 

### CccCcckR + + =December 2007

2-15 Equation 3 is the basis of SCICHEM incremental ch emical kinetic solver. One problem with this approach is the mixed terms, C A c B and c

B A C B

B . 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 numeri cal solution scheme for puff pert urbation chemistry. We note that the CAMx chemical solver can be independ ently 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 obtain ed 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 disper sed by the transport portions of the PiG code.

Puff Constructs for Incremental Chemistry

The new PiG sub-model includes three new construc ts 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 th e 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 th e vertical layer structur e 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 be fore they reach horizontal grid scales. This introduces technical implica tions 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 concentr ation is always uniform;

2) Distribute puff mass transfer (via “leaking” and “dumping”) to the grid according to the puff fractional coverage across each mode l layer and by density-weighting; and

3) Determine mean background concentrations and other ambient conditions (e.g., temperature, humidity, etc.) ove r the puff vertical span via similar fractional layer-density weighting.December 2007

2-16 PiG puffs are considered to be elliptical in the ho rizontal, with the minor axis spanning the crosswind puff width (defined as ± 1.5 σ y ), and the major axis spanning the along-wind puff length (defined as length ± 1.5 σ y on each end). This is similar to GREASD PiG. However, given the complications associated with multiple layer sp anning and mass-weighting of ambient inputs and dumped mass, puffs are rectangular and uniform in the vertical, with total puff depth defined as

± 1.5 σ z .

Horizontally, the mean backgr ound 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 e ach PiG puff “reactor” in three steps:

1) The layer-mean background (grid + overlappi ng 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 ar e chemically updated; and 3) The updated results from step 1 are subtract ed 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- NO
x 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 bac kground 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 ar e 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 accordin g to each puff’s fractional layer coverage. The process of leaking ensures that puff mass is transf erred 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 whet her a puff is leaked or not, and for Texas BART screening modeling, we have as sumed the default mode in which puffs are not leaked to the grid. This will allow the full-scie nce PiG plume model to treat the chemistry of the BART point source plumes as plume chemistry un til 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 hori zontal 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 gr id. This fraction is determined by the relative exceedance of the leakage parameter; initial leakag e is slow as the exceedance is relatively small, but leakage rates grow as the puff continue s to grow beyond the leakage parameter. The reduced mass from leakage is compensate d by a reduced effective volume, so that concentrations are not artificially diluted by leak age (an essential chemical imperative). Thus,
two distinct volumes are tracked: the actual vol ume (defined by the puff spread sigmas) and the
effective volume. While these ar e identical before l eakage, they obviously deviate after leakage is initiated, and thereafter the relative deform ation 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. Howeve r, because of the combination of photochemical processing and leakage, by the ti me a puff dumps the potential fo r producing numerical shocks is much reduced. Furthermore, if the puff exceed s 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-scal e 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 pu ff mass can be optionally incorporated into the model average output files for visualization pur poses (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, virt ual 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 visualiza tion is available for 3-D av erage output files, and can produce some rather startling effects in output displays, includi ng very narrow virtual plumes, or streaks, representing mass moving thr ough the grid in sub-grid puffs, but not subject to grid-scale eddy diffusion.

High Resolution Puff Sampling

PiG optionally employs surface-la yer puff sampling of concentrat ion species on a user-defined grid of arbitrary horizontal reso lution, similarly to the way nested grids are defined. Sampling grids are entirely passive, and intended to provid e 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. Sample d PiG concentrations are time-averaged like the output concentrations provided on the comput ational grids, and are written to files with similar format so that they may be readily vi ewed 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 di ffusive growth and reshaping due to deforming shears, the sampling procedure includes trigono metric calculations to define which samplingDecember 2007

2-18 points are influenced by each puff. This influe nce 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 ±3 σ y 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 th e 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 NO
x 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 analys is, 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, Vi sibility Improvement
State and Tribal Association of the South east/VISTAS, Midwest RP O 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 star t up and shut down). The 2002 base case scenarios typi cally contain average actual emissions for all sources. The
maximum 24-hour actual emission rates are not r eadily 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 recomme nded 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 calculat ed at each Class I area using the differences in 24-hour PM
concentrations between the 2002 Base Case a nd 2002 BART Sources VOC and PM Emissions Zero-Out Case scenarios following the proced ures 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) pr ocess 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 sour ces using modeling output of a singl e source or group of sources. The total light extinction due to a source (b
source ), in units of inverse Megameters (Mm -1 ), is assumed to be the sum of the light extincti on due to the source’s individual PM species concentration impacts times an extinction efficiency coefficient:

b source = b SO4 + b NO3 + b OC + b EC + b soil + b coarse

b SO4 = 3 [(NH 4 ) 2 SO 4 ]f(RH)
b NO3 = 3 [NH 4 NO 3 ]f(RH)
b OC = 4 [OMC]
b EC = 10 [EC]
b Soil = 1 [Soil]
b
coarse = 0.6 [Coarse Mass]

Here f(RH) are relative humidity adjustme nt factors. EPA BART modeling guidance recommends that Class I area specific monthl y average f(RH) values be used (EPA, 2005; 2003a). The concentrations in the square brackets are in μ g/m 3 and are based on the differences
in concentrations betw een the 2002 BART Base Case and 2002 BART VOC/PM Zero-Out Case. Although CAMx explicitly models ammonia a nd ammonium, the IMPROVE extinction equation
assumes that SO 4 and NO 3 are completely neutralized by a mmonium. The OMC in the above equation is Organic Matter Ca rbon (OMC). When using IMPR OVE measurements, the current IMPROVE extinction equation assu med 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 follo wing species mappings are used to map the CAMx species to those used in the IMPROVE rec onstructed mass extinction equation given above:

[(NH 4 ) 2 SO 4 ] = 1.375 x PSO4 [NH 4 NO 3 ] = 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 su lfate and nitrate species. POA is the CAMx primary Particulate Organic Aerosol species, wh ereas 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 PM 2.5 components (i.e., fine particles that are
not SO 4 , NO 3 , EC or OC), one for the crustal (FCRS) and the other for the remainder of the primary emitted PM 2.5 species (FPRM). Similarly, CAMx carries two species to represent Coarse Mass (PM 2.5-10 ), 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 usi ng the following formula:

HI source = 10 ln[(b
source + b natural )/10]December 2007

3-3 Here, b natural is the Class I area specific clean natu ral visibility background where EPA’s default values will be used in this analysis (EPA, 2003b).

The source’s HI is compared against natural c onditions to assess the signi ficance of the source’s visibility impact. EPA guida nce lists natural conditions (b natural ) 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 = HI
source – HI natural = 10 ln[(b
source + b natural )/10] - 10 ln[b natural /10] = 10ln[(b
source + b natural )/b natural ]

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 repr esent the visibility impact for that day at that
Class I area.

Significance Threshold

The EPA BART guidance suggests that a signifi cance threshold to dete rmine whether a source significantly contributes to visibility impairment at a Cla ss 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 contribu te significantly to visi bility impairment, and therefore, the VOC and PM emissions from each individual potential BA RT 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 re sults 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 98 th percentile del-dv at any Class I area with the 0.5 dv significant threshold to determine whether a significant visibility impact w ould reasonably be expected to occur. Using one year of modeling results (2002), the 98 th percentile would correspon d to the eighth highest 24-hour average visibility imp act 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 whethe r 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 fo r PM Emissions Only

If BART screening analysis is desired for prim ary PM emissions by themselves, then it is much simpler and more computationally efficient to ru n 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 primar y 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 mo del 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 pro cedures 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 SO 2 and NO
x emissions from BART non-EGU sour ces, the general approach can be used for all visibility im pairing pollutants (i.e., NO
x , SO 2 , PM, VOC and NH 3 ) 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 Cla ss 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 opti ons to be specified for runni ng CAMx/PSAT/PiG for BART and similar assessments are provided in Chapter 5.

SUMMARY OF APPROACH

The updated PSAT that includes th e new PiG module in CAMx Vers ion 4.4 (and later versions) is used in the single source or multiple source BA RT analysis. For the Texas application, the 2002 36/12 km modeling database described in Section 2 was used. The Texas CAMx/PSAT/PiG source apportion ment 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 sign ificantly to visibility impairment.

Visibility Impacts from PSAT

In the Texas CAMx/PSAT/PiG BA RT modeling, the sulfate (SO 4 ) and nitrate (NO 3 ) 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 hi ghly 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 Cl ass 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 b source = b SO4 + b NO3 + b OC + b EC + b soil + b coarse

b SO4 = 3 [(NH 4 ) 2 SO 4 ]f(RH)
b NO3 = 3 [NH 4 NO 3 ]f(RH)
b OC = 4 [OMC]
b EC = 10 [EC]
b Soil = 1 [Soil]
b
coarse = 0.6 [Coarse Mass]

Here f(RH) are the monthly average relative hum idity adjustment factors from EPA’s guidance (EPA, 2003a). The concentrations in the square brackets are in μ g/m 3 and are the concentrations from the PSAT output for each potential BART-e ligible PSAT source group (i) with sulfate and nitrate assumed to be fully neutralized by ammonium. Using th e PSAT species naming convention (see Chapter 2) these concentrations are as follows:

[(NH 4 ) 2 SO 4 ] = 1.375 x PS4 i [NH 4 NO 3 ] = 1.290 x PN3 i [OMC] = POA i + PO1 i + PO2 i + PO3 i + PO4 i + PO5 i [EC] = PEC i [Soil] = PFC i + PFN i

The Haze Index (HI) for the source group is calc ulated in deciview from the source group’s extinction plus natural backgr ound using the following formula:

HI group = 10 ln[(b group + b natural )/10]

The source’s HI is compared against natural c onditions to assess the signi ficance of the source’s visibility impact. EPA guida nce lists natural conditions (b natural ) 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 = HI group – HI natural = 10 ln[(b group + b natural )/10] - 10 ln(b natural /10)

The 0.5 dv significance threshold is used to asse ss 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 SO 2 and NO
x emissions do not contribute signifi cantly to visibility impairment at any Class I area. EPA’s BART guidance suggests using the 98 th percentile visibility impact at a Class I area in the visibility si gnificance determination using this approach, then a source group would not have a significant cont ribution to visibility if the 8 th 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 discu ssed 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 cas e 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 si mulation 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 st arting point for the anal ysis. 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 Matr ix 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 inpu ts 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 ac tual 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 pe rformed in the following steps:

Running and Post-Processing CAMx

1. Perform CAMx runs for both zero-out a nd 24-hour maximum actual BART VOC and
PM emissions scenarios.

2. Post-process the CAMx BART modeling re sults. This step involves extracting concentration data from the hourly average conc entrations output file s (*avrg and *favrg) which have a binary format (refer to the CAMx user’s guide) and calculating 24-hour average concentrations from th e 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 averag e PM (non-SOA species) concentrations.

4. Calculate the mass extinction (b
source ) and the difference in deciview (del-dv) from the
source’s and natural conditions haze indices. Th e formulation is described in Chapter 4.

5. Compare the 98 th percentile del-dv at any Class I area with the 0.5 dv significance
threshold. The delta deciview at the 98 th 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 scri pt for the 2002 zero-out BART VOC and PM emissions would be similar, with the file name s 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 c onform 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 visibili ty calculations described in Chapter 4 can then performed using an Excel spreadsheet.

PSAT MODELING

PSAT apportions PM components among several sour ce 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 th e low-level and point source em ission inputs that can be input for each of the PSAT source category. For exam ple, the source region map could divide the domain into 10 different geogra phic areas (e.g., states) and sepa rate 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 conditi ons are always tracked as two source groups).

For the PSAT BART modeling, each BART s ource 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 emissi ons set to zero except those for the first BART source group, the third one would have all poin t 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 s ource 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 PSA T 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 s ource region that the point source resides in. In addition, a negative flag has to be set for sour ce 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 separa tely from non-BART sources. If there is more than one source group, the BART emi ssion 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 f ile in an ASCII format that contains all of this necessary information.
2. Assign the point index for each source group in the (unused) kcell 1 value on the point source file to a unique value for that source group.
3. Use maximum 24-hour actual NO
x and SO 2 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 emissi ons. 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 eac h individual BART source or group of BART sources as desired. Appendix B lists an exampl e CAMx script for one of the Texas PSAT source apportionment model simulations. The screeni ng analysis demonstrated that Texas BART sources’ VOC emissions are insignificant to the vi sibility impairments at Class I areas (Morris and Nopmongcol, 2006). In addition, this screeni ng analysis indicated that only some facilities contribute significantly to the visibility impa irments. Consequently, for the Texas BART screening modeling, PSAT was run only for the SO 4 and NO 3 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 defi nition 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 contai ned in the time-variant portion of the elevated point sour ce 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 speci fic geographic source region. The format of this file is an array of integer numbers (3 i) 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 gr oups, to every grid cell. For example, if BART source groups range from 1-10, assign number 11 to every grid cell in th e source region map file. This unique number represents a group number of area and non-BART sources.
Point source override in CAMx will discard th is number and replace it with a userspecified BART source groups.
10. PSAT is invoked within the CAMx contro l file. In the &CAMx_Control namelist module, the variable Probing_T ool must be set to “PSAT” and Pig_Submodel must be set
to “GREASD”.
11. In the &SA_Control namelist, provide th e name of SA_Receptor_Definitions and SA_Source_Area_Map files. For Texas a pplication set PSAT_Treat_SULFATE_Class and PSAT_Treat_NITRATE_Class to “true” for SO 4 and NO 3 PSAT families.
12. Emission and meteorological i nputs are optionally provided for the nested grid. If these files are not supplied, the Flexi-Nest algor ithm within CAMx will interpolate the missing fields from the parent grid. In the Texas sc reening 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 concentra tion 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 scrip ting 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 concen trations of sulfate a nd nitrate to calculate the mass extinction (b
source ). Second, calculate the Haze Index (HI) and the change in deciview (del-dv) from the s ource’s and natural conditions HI . The formulations of these two steps are described in Chapter 4. Finally, compare the 98 th percentile del-dv at any Class I area with the 0.5 dv significant threshold. The deciview that the 98 th percentile would correspond to is the eighth highest 24-ho ur average visibility impact at each
Class I area.

The example CAMx script in Appendix B for a Texas PSAT SO 4 and NO 3 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 requirement s 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 dayDecember 2007

6-1 6.0 REFERENCES

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Emery, C. and G. Yarwood. 2005. Implemen ting PM chemistry in the CAMx "IRON PiG" plume-in-grid module, Prepared for the La ke Michigan Air Directors Consortium, Prepared by ENVIRON Internati onal Corporation, Novato, CA.December 2007

6-2 EPA. 1999. Regional Haze Regulations; Final Rule. Federal Register , 64, 35713-35774.

EPA. 2001. “Draft Guidance for Demonstrating Attainment of Air Quality Goals for PM 2.5 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 Natura l 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 – Comprehe nsive Air-quality Model with extensions, Version 4.20.” ENVIRON International Corporation, Novato, California. (Available at http:// www.camx.com).

ENVIRON. 2005b. Screening Analysis of Potentia l BART-eligible Sources in Texas. Prepared for Texas Commission of Environmental Qualit y, 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. “Revisi ons to the Guidelines on Air Quality Models: Adoption of a Preferred Long Range Transpor t 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 Serv ice, Air Resources Di vision, Denver, CO.

Gillani, N.V. and J.M. Godowitch. 1999. Pl ume-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 Resear ch Laboratory, U.S. Enviro nmental 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 Standa rds, Research Triangle Part, NC.December 2007

6-3 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
11 th , 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 reac tive plume model, Environ. Sci.
Technol. , 2000, 34, 870–880.

Karamchandani, P., C. Seigneur, K. Vijaya raghavan and S-Y Wu. 2002. Development and Application of a state-of-sci ence 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/cenra p/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 St andard. Prepared for Houston Advanced Research Center (HARC) under Project H12.8HRB. ENVIRON International Corporation, Novato, CA. March.

Kumar, N. and A.G. Russell. 1996. Multiscal e air quality modeling of the northeastern United
States. A tmospheric 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 – Repor t 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 Manageme nt Association Meeting, Paper # 43180, prepared by ENVIRON International Corporation, Novato, CA.

Morris, R., C. Tana and G. Yarwood. 2003. “E valuation of the Sulfate and Nitrate Formation Mechanism in the CALPUFF Modeling System”

, Proceedings of the A&WMA Specialty Conference -- Guideline on Air Qual ity Models: The Path Forward

. Mystic, CT. 22-24
October.December 2007

6-4 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 Planni ng Association (CENRAP).
November 19.

Morris, R.E., G. Mansell, B. Koo, G. Tonne sen, 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 Planni ng 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 sensit ivity evaluation for or ganic 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 Mode l for 2002 and the Southeastern U.S. Journal of the Air & Waste M anage. 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 visi bility modeling requirements. 8th Annual Electric Utilities Environmental Conference , Tucson, AZ, 24-25 January.

Morris, R.E., et al. 2005d. Air Quality M odeling 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 Internat ional Corporation, and the University of California, Riverside.

Morris, R.E., S. Lau and B. Koo. 2005e. “Eva luation of the CALPUFF Chemistry Algorithms.”
Presented at the 98 th Annual Air and Waste Management Conference, Minneapolis, MN.
June.

Morris, R.E., G. Yarwood, C. Emery, G. Wils on and B. Koo. 2005f. “Recent Advances in OneAtmospheric Modeling Using the Comprehensiv e Air-quality Model with Extensions.”
Presented at the 98 th 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 Guide lines on Air Quality Models Conference, Denver, Colorado. April 26-28 2006.

Morris, R.E., and U. Nopmongcol. 2006. Screen ing Analysis of Potential BART-Eligible Sources in Texas (Draft). Prepared for Texas Commission on Environmental Quality. August 19 2006.December 2007

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.

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Tesche, T.W., R.E. Morris, G.S. Tonnesen, D.E. McNally, P. Brewer and J. Boylan. 2005. CMAQ/CAMx Annual 2002 Performance Evalua tion over the Eastern United States. Atmospheric Environment (in press).

Tombach, I., P. Brewer, T. Rogers and C. A rrington. 2005a. “BART modeling protocol for VISTAS (VISTAS first draft)”, 22 March, 2005.

Tombach, I., P. Brewer, T. Rogers, C. Arringt on and J. Scire. 2005b. “Protocol for the application of the CALPUFF m odel for analyses of Best Available Retrofit Technology (BART). (VISTAS second draft)”, 22 August 2005.

Tombach, I., P. Brewer, T. Rogers, C. Arringt on and J. Scire. 2005c. “Protocol for the application of the CALPUFF m odel for analyses of Best Available Retrofit Technology (BART). (VISTAS revised draft)”, 20 September 2005.

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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 96 th Annual Conference and Exhibition of the Air and Waste Management Association, San Diego, California.

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Yarwood, G., R.E. Morris, and G. Wilson. 2004. “Particulate Matt er Source Apportionment Technology (PSAT) in the CAMx Photochemical Grid Model." Presented at the ITM
27th NATO Conference- Banff Centre, Canada, October.

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A-1 Appendix A

Example CAMx Run Script for a
Base Case or Zero-Out RunDecember 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., ! minutesDecember 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

endDecember 2007

B-1 Appendix B

Example CAMx Run Script for a PSAT BART Analysis using the
SO 4 and NO 3 PSAT TracersDecember 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