International Corporation Air Sciences

Revised Draft Final Modeling Protocol 

Screening Analysis of 

Potentially BART-Eligible 

Sources in Texas 

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

Prepared for: 

Texas Commission on Environmental Quality 

12118 Park 35 Circle 

Austin, Texas 78753 

Prepared by: 

ENVIRON International Corporation 

101 Rowland Way, Suite 220 

Novato, California 94945 

September 27, 2006 

 101 Rowland Way, Suite 220, Novato, CA 94945 415.899.0700 
September 2006 

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TABLE OF CONTENTS 

Page 

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

 

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

 Purpose .............................................................................................................................. 1-8 

 

2. MODELING APPROACH ................................................................................................. 2-1 

2002 Annual 36/12 km Modeling Database .......................................................................... 2-1 

Enhancements to the PM Source Apportionment Technology.............................................. 2-3 

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

3. PROCEDURES FOR VOC AND PM EMISSIONS 

 BART SCREENING ANALYSIS ................................................................................ 3-1 

4. PROCEDURES FOR SCREENING BART 

 SOURCES SO2 AND NOx EMISSIONS ..................................................................... 4-1 

 

 Visibility Impacts from PSAT ............................................................................................... 4-1

5. REFERENCES..................................................................................................................... 5-1 

FIGURES 

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

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

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

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

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

 in the horizontal ................................................................................................... 2-8 

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

 from a point source into an evolving gridded wind field................................... 2-12 
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1.0 INTRODUCTION 

OVERVIEW 

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

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

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

visibility-impairing air pollutants install and operate the Best Available Retrofit Technology 

(BART). The regulations require case-by-case BART determination to define specific emissions 

limitations representing BART and schedules for compliance for each source subject to BART. 

These requirements would be part of the SIP revisions that each state must submit to EPA by 

December 17, 2007. EPA’s BART Rule requirements are summarized below, particularly with 

respect to the modeling analyses that are required to determine (a) if a source is BART-eligible, 

and (b) what levels of emissions controls might be necessary for sources shown to cause or 

contribute to visibility impairment at federally-protected Class I areas. This summary is based on 

EPA’s BART guidelines (EPA, 2005) and BART modeling protocols prepared by Visibility 

Improvement State and Tribal Association of the Southeast (VISTAS) (2005) and Central Regional 

Air Planning Association (CENRAP) (Alpine and ENVIRON, 2005). 

Regional Haze Rule and BART Guidelines 

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

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

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

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

Regional Haze Rule (RHR) requires States to submit state implementation plans (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 longstanding EPA commitment to address regional haze under the authority and requirements of 

sections 169A and 169B of the CAA. As required by the CAA, the final RHR included a 

requirement for BART for certain large stationary sources that were put in place between 1962 

and 1977. The regional haze rule addresses visibility impairment resulting from emissions from 

a multitude of sources located across a wide geographic area. Because the problem of regional 

haze is believed to be caused in large measure by long-range transport of emissions from 

multiple sources, initially EPA adopted an approach that required states to look at the 

contribution of all BART sources to the problem of regional haze in determining both 

applicability and the appropriate level of control. If a source potentially subject to BART is 

located in an area from which pollutants may be transported to a Class I area, that source ‘‘may 

reasonably be anticipated to cause or contribute’’ to visibility impairment in the Class I area. 

EPA’s BART guidelines include procedures for single source assessments of their visibility 

contributions. 

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

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

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

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

implementing the regional haze BART requirements on a source-by-source basis. States must 
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follow the guidelines in making BART determinations on a source-by-source basis for 750 

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

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

approaches that differ from the guidelines. 

A BART eligible source or existing stationary facility means any stationary source of air 

pollutants, including any reconstructed source, which: (a) “was not in operation prior to August 

7, 1962, and was in existence on August 7, 1977," (b) "has the potential to emit 250 tons per year 

or more of any air pollutant" and (c) falls within one or more of 26 specifically listed source 

categories (40 CFR Section 51.301). BART controls are required for any BART-eligible source 

that can be reasonably expected to cause or contribute to impairment of visibility in any of the 

156 federal parks and wilderness (Class I) areas protected under the regional haze rule. Air 

quality modeling is an important tool available to the states in determining whether a source can 

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

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

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

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

used as an alternative to applying BART. 

Role of Air Quality Models 

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

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

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

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

sources. The options are: 

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

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

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

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

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

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

individual source’s emissions of volatile organic compounds (VOC) or ammonia, states 

may elect to use a more qualitative assessment to determine on a case-by-case basis, 

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

should therefore be subject to BART review. EPA approved models should be used to 

predict the visibility impacts from a single source at a Class I area. 

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

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

specific characteristics. This type of analysis is used to identify the types of small 

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

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

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

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

that states could use modeling analyses of representative plants to reflect groupings of 
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specific sources with important common characteristics and based on these analyses, 

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

contribute to visibility impairment whereas others do not. 

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

whose sulfur dioxide (SO2), nitrogen oxides (NOx) or SO2 plus NOx emissions are less 

than 500 tpy and are greater than 50 kilometers (km) from any Class I area, or for 

sources whose emissions are less than 1,000 tpy and are greater than 100 km from any 

Class I area. EPA directed that states may (if they so choose) use the example cited in 

the Guidelines to exempt sources with no further analysis. In other words, states that 

establish 0.5 deciviews (dv) as a contribution threshold may exempt sources that emit 

less than 500 tpy of NOx or SO2 (or combined NOx and SO2) with no further analysis, as 

long as these sources are located more than 50 km from any Class I area. Also, states 

that establish 0.5 deciviews as a contribution threshold may exempt sources that emit less 

than 1,000 tpy of NOx or SO2 (or combined NOx and SO2) with no further analysis, as 

long as these sources are located more than 100 km from any Class I area. 

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. Therefore, no source should be 

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

impairment. 

This modeling protocol discusses the use of this third option to exempt potential BARTeligible sources’ VOC and particulate matter (PM) emissions from BART and screen 

non-electric generating units (NEGU) potential BART-eligible sources’ SO2 and NOx 

emissions to eliminate those that have insignificant visibility impacts from further BART 

analysis. 

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

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

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

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

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

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

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

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

therefore be subject to BART review. The primary difficulty in using California Puff Model

(CALPUFF) for VOCs is that the CALPUFF modeling system, recommended as the BART 

modeling platform, does not adequately treat PM formation from VOCs. Use of photochemical 

grid models to assess the visibility impacts of VOC emissions provides a quantitative and more 

scientifically defensible approach than a qualitative assessment. 

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

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

levels that are not as reliable as the results of modeling other PM components. While ammonia 
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emissions from industrial facilities are relatively well-characterized, livestock operations and soil 

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

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

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

ammonia is best left to the state. 

EPA Guidance on Air Quality Models 

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

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

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

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

fine particulate species (like sulfate, nitrate, organic carbonaceous matter, elemental carbon, 

other fine particles and coarse mass), logic dictates that the modeling method(s) used must be 

capable of simulating these components reliably. 

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

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

document. The Final BART Rule (pg 101) states, “Because the air quality model CALPUFF is 

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

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

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

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

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

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

(pg 110) that “regional scale modeling typically involves use of a photochemical grid model that 

is capable of simulating aerosol chemistry, transport, and deposition of airborne pollutants, 

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

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

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

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

modeling.” 

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

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

simulating secondary fine particulate and visibility for SIPs. EPA states (pg 169): “States should 

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

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

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

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

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

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

estimating primary species. 

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

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

notes, “it is the only EPA-approved model for use in estimating single source pollutant 
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concentrations resulting from the long range transport of primary pollutants.” On the other hand, 

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

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

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

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

controls on BART sources. 

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

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

spacing and new PM Plume-in-Grid (PiG) modeling techniques can extend the grid model’s 

applicability to assess the visibility impacts of a single source and groups of sources. As 

photochemical grid models include state-of-science representation of chemistry, this allows for 

the quantitative assessment of the visibility impacts of VOC emissions. 

Steps in the BART Modeling Process 

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

sources. 

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

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

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

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

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

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

pollutants include primary particulate matter (PM10) and gaseous precursors to secondary 

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

included as visibility-impairing pollutants for BART eligibility is left for the states to 

determine on a case-by-case basis. The guidance states that high molecular weight VOCs 

with 25 or more carbon atoms and low vapor pressure should to be considered as primary 

PM2.5 emissions and not VOCs for BART purposes. 

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

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

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

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

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

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

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

is set at 1.0 deciview change from natural conditions over a 24-hour averaging period in 

the final BART rule (70 FR 39118). Any exceedance of this threshold would trigger a 

BART determination. The guidance also states that the proposed threshold at which a 

source may “contribute” to visibility impairment should not be higher than 0.5 deciviews, 

although depending on factors affecting a specific Class I area, it may be set lower than 

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

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

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

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

natural breakpoint at which to set the BART exemption levels. Since visibility 

degradation may begin to be recognized by a human observer at this extinction level, the 

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

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

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

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

98th percentile modeled value (8th highest day annually at a receptor or 22nd highest over 

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

visibility impairment and is therefore subject to BART. 

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

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

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

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

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

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

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

such technology. 

Note that for states under the PM2.5 provisions of the Clean Air Interstate Rule (CAIR), EPA has 

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

and NOx emissions. However, since CAIR does not address VOC and PM emissions, a state 

still needs to determine whether an EGU’s VOC and/or PM emissions contributes significantly 

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

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

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

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

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

(70 FR 39126). 

CALPUFF Modeling Recommendations 

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

the source, the EPA guidance recommends the use of the CALPUFF model. For modeling the 

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

expert modeling judgment be used “giving consideration to both CALPUFF and other methods.” 

The Plume Visibility Model (PLUVUE)-II model is mentioned as a possible model to consider in 

addition to CALPUFF within 50 km of a source. The EPA guidance notes that regional scale 

photochemical grid models may have merit, but such models are resource intensive relative to 

CALPUFF. Photochemical grid models are clearly more appropriate for cumulative modeling 

options, such as in the determination of the aggregate contribution of all-BART-eligible sources to 

visibility impairment, but such use should involve consultation with the appropriate EPA Regional 

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

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

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

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

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

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

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

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

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

One approach being considered by several Regional Planning Organization (RPOs) (e.g., 

Midwest RPO, Western Regional Air Partnership-WRAP and VISTAS) is to perform a weightof-evidence analysis using a photochemical grid model (e.g., CAMx and/or CMAQ) to assess 

whether VOC emissions from all potential BART-eligible point sources in a state contribute to 

visibility impairment. If the impact of eliminating all VOC emissions from all potential BARTeligible point sources in a state is shown to be less than 0.5 dv, then, logically, the impact of any 

one potential BART-eligible source would be less than 0.5 dv. Some RPOs are considering 

weight-of-evidence modeling with Community Multiscale Air Quality Modeling System 

(CMAQ) or Comprehensive Air Quality Model with extensions (CAMx) to address ammonia in 

the same way. 

Alternative Models for BART Analyses 

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

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

including CALPUFF, derive from this equation that applies equally to one source or a million or 

more 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). As shown in the next section, much of the simplicity of the CALPUFF 

model derives from the fact that many chemical and physical processes known to influence 

visibility are simply ignored. In contrast, comprehensive regional models treat these processes in 

detail, albeit at the expense of greater computer resources and data needs. EPA’s BART 

guidance allows for the use of alternative models on a case-by-case basis. 

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

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

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

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

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

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

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

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

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

convergence of fast commodity-based Linux computer clusters and recently-developed regional 

modeling emissions, meteorological, and air quality data bases make application of these 

modeling platforms no longer a research or academic exercise. While regional scale modeling 
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clearly requires expertise to perform properly, the actual program costs to conduct a CMAQ or 

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

Prevention of Significant Deterioration (PSD) modeling study using the Industrial Source 

Complex Model (ISC), CALPUFF, or American Meteorological Society/Environmental 

Protection Agency Regulatory Model Improvement Committee Model (AERMOD). Given grid 

nesting and Plume-in-Grid technology, modern regional models are applicable to a very broad 

range of scales from 10-20 km to continental scale. In fact, regional photochemical grid models 

have been applied with grid spacing down to hundreds of meters on occasion (Kemball-Cook, 

Emery and Yarwood, 2005). Regional photochemical grid models are clearly more appropriate 

than CALPUFF for cumulative modeling requirements such as in the determination of the 

aggregate contribution of all-BART-eligible sources to visibility impairment. And as confirmed 

in EPA’s fine particulate and regional haze modeling guidance discussed previously (EPA, 2001) 

and clearly shown by Morris and co-workers (2003; 2005), regional photochemical grid models, 

such as CMAQ and CAMx, provide a much more accurate and technically correct representation 

of the formation of secondary PM species, such as SO4, NO3 and secondary organic aerosols 

(SOA), than CALPUFF. 

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

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

state, and the approving agency (EPA and the Federal Land Manager-FLM). However, for the 

specific BART exemption screening analysis being undertaken in this study, a photochemical 

grid model is more appropriate than the single-source CALPUFF model for the following 

reasons: 

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

runs planned will allow the efficient screening of many sources in a scientifically 

defensible manner; 

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

impacts will be estimated are between 97 and 654 km from the source; 

• TCEQ is trying to identify which of the many potential BART-eligible sources satisfy the 

three criteria for being BART-eligible. If many of these sources can be determined to 

make an insignificant contribution to visibility impairment at Class I areas as a group, 

resources can then be focused on those sources determined most likely to impact 

visibility in Class I areas; 

• Use of a photochemical grid model would allow the quantitative assessment of the 

visibility impacts due to potential BART-eligible sources’ VOC and PM emissions, and; 

• Use of a photochemical grid model will provide an evaluation of the cumulative impact 

of BART-eligible sources on visibility in Class I areas. 

PURPOSE 

The Texas Commission on Environmental Quality (TCEQ) is currently identifying which 

sources in Texas may be subject to the BART requirements. TCEQ has compiled a list of 

potential BART-eligible sources and desires to perform a screening analysis using an appropriate 

technique to eliminate sources or precursor pollutants that clearly do not contribute significantly 

to visibility impairment at any Class I area so is therefore exempt from the BART process. 

TCEQ is currently evaluating their list of sources against the three BART-eligible criteria. The 

screening analysis will test whether it is appropriate to exclude VOC and PM emissions and 

groups of potential BART-eligible sources from the list based on their visibility impairment 
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contributions. The resulting screened Texas potential BART-eligible sources list would be more 

manageable, allowing the TCEQ to focus their efforts on determining whether BART controls 

are needed for the remaining BART-eligible sources. 

This modeling protocol describes an initial screening analysis that is aimed at determining the 

following: 

• Whether VOC and/or PM emissions from potential BART-eligible sources in Texas can 

be shown to not contribute significantly to visibility impairment at Class I areas so may 

be exempt from the BART process; and 

 

• Whether there are groups of non-EGU potential BART-eligible sources whose total SO2

and NOx emissions can be shown to not contribute significantly to visibility impairment 

at Class I areas so each source may be exempt from BART. 

The Texas BART exemption screening analysis would build off the regional photochemical 

modeling (Morris et al., 2005d) being conducted by CENRAP). In particular, the CENRAP 

2002 36 km CAMx modeling database would be enhanced to include a 12 km grid over Texas 

and vicinity, and CAMx zero-out modeling will be conducted to determine whether potential 

BART-eligible VOC and PM emissions can be shown to not contribute significantly to visibility 

impairment at any Class I area. CAMx PM Source Apportionment Technology (PSAT) 

modeling would also be conducted for groups of potential BART-eligible sources’ SO2 and NOx 

emissions. Sources in groups that are shown not to contribute significantly to visibility 

impairment at any Class I area would be excluded. Note that a new version of CAMx will be 

used in this work that has incorporated PM chemistry and PSAT within its Plume-in-Grid (PiG) 

module that will be used to treat the early chemical evolution of potential BART-eligible sources 

SO2 and NOx emissions. This approach is fully consistent with the CENRAP draft BART 

Modeling Protocol (Alpine Geophysics, 2005).
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2.0 MODELING APPROACH 

This section describes the modeling approach and databases that will be used to perform the 

BART exemption screening analysis of potential BART-eligible sources in Texas. There are two 

elements that will be performed under this work: 

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

BART-eligible sources VOC and PM emissions; and 

 

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

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

not significant, in which case the visibility impacts from each individual source in the 

group is not significant. 

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

VOC and PM emission screening analysis will be performed using emissions zero-out modeling, 

whereas the group of sources’ SO2 and NOx emissions screening analysis will be performed 

using an updated version of the PSAT. 

2002 ANNUAL 36/12 KM MODELING DATABASE 

The Texas BART screening analysis exemption modeling was performed using CAMx 

(ENVIRON, 2005) and the 2002 annual regional photochemical modeling database developed as 

part CENRAP. CENRAP has developed a 2002 annual modeling database for CAMx on the 36 

km unified national RPO grid that covers the continental United States. This database was 

developed following the procedures outlined in the CENRAP Modeling Protocol (Morris et al., 

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

2004). The CENRAP preliminary base case model performance evaluation results for the CAMx 

model on the national 36 km grid using the CENRAP Base A emissions is given in Morris et al., 

(2005d). The CENRAP Modeling Protocol, QAPP, and preliminary Base A evaluation reports 

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

Below, additional information is provided on enhancements to the CENRAP database for use in 

the Texas BART exemption screening analysis. 

Enhancements to the CENRAP 2002 Modeling Database 

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

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

evaluation displays available on the CENRAP modeling Website 

(http://pah.cert.ucr.edu/aqm/cenrap/cmaq.shtml#camx). CENRAP is currently updating the 

CAMx 2002 36 km base case simulation using the Base B base case emissions and CAMx 

version 4.30. The Base B base case database and CAMx Version 4.30 will be the starting point 

for the Texas BART exemption modeling analysis. 
September 2006 

2-2 

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

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

including: 

• National Parks: Big Bend (BIBE), Guadalupe Mountains (GUMO), and Carlsbad 

Caverns 

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

• Wilderness Areas: White Mountain (WHIT), Caney Creek (CACR), Upper Buffalo 

(UPBU), and Hercules-Glade (HEGL). 

Figure 2-1 displays the 36/12 km nested grid structure being proposed for the CAMx BART 

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

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

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

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

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

-2736 -1872 -1008 -144 720 1584 2448

-2088

-1656

-1224

-792

-360

72

504

936

1368

1800

km

km

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

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

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

Monitoring of Protected Visual Environments (IMPROVE) monitoring sites that include Class I areas 

indicated by circles. 
September 2006 

2-3 

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

-1620

-1500

-1380

-1260

-1140

-1020

-900

-780

-660

-540

-420

km

km

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

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

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

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

Texas (triangles). 

ENHANCEMENTS TO THE PM SOURCE APPORTIONMENT TECHNOLOGY 

For the potential BART-eligible group of sources SO2 and NOx emissions screening analysis, the 

CAMx PSAT will be used. PSAT is currently being updated to be compatible with the CAMx 

PiG module. The next section describes the technical formulation of the PSAT source 

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

compatible with each other. 
September 2006 

2-4 

PSAT Formulation 

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

• Sulfate (SO4) 

• Particulate nitrate (NO3) 

• Ammonium (NH4) 

• Particulate mercury (Hg(p)) 

• Secondary organic aerosol (SOA) 

• Six categories of primary particulate matter (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 regions 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 and even an 

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

regions. The user then inputs each separate source category as separate gridded low-level 

emission and/or elevated point source emission inputs. The model then determines each source 

group by overlaying the source categories on top of the source region map. 

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

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

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

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

emitted precursor gases (NO, VOCs) are several steps removed from the resulting PM species 

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

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

Sulfur

SO2i Primary SO2 emissions 

PS4i Particulate sulfate ion from primary emissions plus secondarily formed sulfate 

Nitrogen

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

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

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

NTRi Organic nitrates (RNO3) 

HN3i Gaseous nitric acid (HNO3) 

PN3i Particulate nitrate ion from primary emissions plus secondarily formed nitrate 

NH3i Gaseous ammonia (NH3) 

PN4i Particulate ammonium (NH4) 
September 2006 

2-5 

Secondary Organic Aerosol

ALKi Alkane/Paraffin secondary organic aerosol precursors 

AROi Aromatic (toluene and xylene) secondary organic aerosol precursors 

CREi Cresol secondary organic aerosol precursors 

TRPi Biogenic olefin (terpene) secondary organic aerosol precursors 

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

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

CG3i Condensable gases from alkane reactions 

CG4i Condensable gases from terpene reactions 

CG5i Condensable gases from cresol reactions 

PO1i Particulate organic aerosol associated with CG1 

PO2i Particulate organic aerosol associated with CG2 

PO3i Particulate organic aerosol associated with CG3 

PO4i Particulate organic aerosol associated with CG4 

PO5i Particulate organic aerosol associated with CG5 

Mercury

HG0i Elemental Mercury vapor 

HG2i Reactive gaseous Mercury vapor 

PHGi Particulate Mercury 

Primary Particulate Matter

PECi Primary Elemental Carbon 

POAi Primary Organic Aerosol 

PFCi Fine Crustal PM 

PFNi Other Fine Particulate 

PCCi Coarse Crustal PM 

PCSi Other Coarse Particulate 

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

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

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

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

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

nine tracers per source group. 

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

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

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

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

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

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

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

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

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

 ( ) ()

i

i

i i

a

a

a t t a t A

∑

+ Δ = + Δ
September 2006 

2-6 

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

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

deposition or transport out of a specific grid cell. 

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

A→B is: 

 

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

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

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

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

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

species destruction: 

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

the average OH rate constant of each AROi. ARO tracers for different source groups have 

different average VOC reactivities because the relative amounts of toluenes and xylenes differ 

between source categories. 

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

species production: 

An example is chemical production of condensable gases (CG1 or CG2) from aromatic VOC 

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

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

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

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

 

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). 

( ) ()

i

i

i i

a

a b t t b t B

∑

+ Δ = + Δ

( ) ()

i i

i i

i i

w a

w a

a t t a t A

∑

+ Δ = + Δ

( ) ()

i i

i i

i i

w a

w a b t t b t B

∑

+ Δ = + Δ

( ) () () [ ]

( ) () () [ ] ⎟

⎠

⎞ ⎜

⎝

⎛

+

+ Δ = +

⎟

⎠

⎞ ⎜

⎝

⎛

+

+ Δ = +

A B

B b t t a t b t

A B

A

a t t a t b t

i i i

i i i
September 2006 

2-7 

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

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

al., 2004). 

Updates to the PSAT Formulation 

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

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

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

the full-science PiG is described below. 

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

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

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

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

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

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

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

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

adequately simulate chemistry within concentrated point source plumes. 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

approach lends itself to incorporating chemistry for particulates as well. 

Basic Puff Structure and Diffusive Growth 

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

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

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

and a trailing point. Initial separation of these points is determined by the wind vector at final 
September 2006 

2-8 

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

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

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

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

calculation of diffusion and chemistry. 

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

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

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

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

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

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

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

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

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

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

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.

P (lead)

P (trail)

y 1.5

x

y 1.5
September 2006 

2-9 

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

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

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

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

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

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

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

flux moments x ′u ′c ′ , where u ′ c ′ 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 x ′u ′c ′ to be considered is the symmetric horizontal term (i=x, j=y), and then 

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

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

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

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

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

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

single diffusivity for the z direction: 

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

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

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

components of the puff spread tensor,σx

2

 =σy

2

 and σz

2

). The off-diagonal contributions to puff 

spread were ignored, since they are unnecessary in the context of the CAMx PiG. Puff spread is 

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

to allow for diffusive growth along the puff centerline. We account for the effects of wind shears 

in the evolution of lateral spread, but assume that the evolution of vertical spread is solely the 

result of turbulent fluxes. 

Q

x u c

Q

x u c

x

u

x

u

dt

d i j j i

k

i

jk

k

j

ik

ij ′ ′ ′

+

′ ′ ′

+

∂

∂

+

∂

∂ = σ σ

σ

Q

y v c

Kx Ky

′ ′ ′

= =

Q

z w c

Kz

′ ′ ′

=
September 2006 

2-10 

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

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

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

where A = 0.75, q

2

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

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

that: 

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

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

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

given by: 

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

2

 and Λ are applied. Above the 

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

length scale: q

2

 = 0.25 m2/s2, and Λ = 1000 m. 

y y z y

y K

dz

dv

dz

du D

dt

d

2 2 2

1/ 2 2 2

2

2

+ ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = σ + σ σ +

σ

z

z K

dt

d 2

2

= σ

y v c

q

y v c Qq A

dt

d ′ ′ ′ Λ ′ ′ ′ = −2

shear buoyancy

y′v′c′ = y′v′c′ + y′v′c′

( ) shear i q 2.5u 1 z/ z

2

*

2 = −

[ ( )] buoyancy i q 0.13w 1 1.5 exp z / z

2

*

2 = +

( ) ( ) 2 2 2 0.65

1

0.3

1 1

z z shear i

= +

Λ

buoyancy i Λ = 0.3 z
September 2006 

2-11 

The SCICHEM tendency equation for the vertical turbulent flux moment is 

where qv

2

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

eq is the equilibrium diffusivity. 

Whereas a specific equation for Kz

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

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

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

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

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

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

approximations for the velocity variance and turbulent length scale: 

qv

2

 = 0.01 m2/s2, and Λv = 10 m. 

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

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

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

input to the model. 

Puff Transport 

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

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

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

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

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

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

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

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

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

calculation. 

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

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

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

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

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

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

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

(QK z w c ) q

z w c A

dt

d eq

z

v

v − ′ ′ ′ Λ ′ ′ ′ =

( )i shear v q 1.5u 1 z / z

2

*

2 = −

( ) ( ) i i buoyancy v q 1.1w z/ z 1.05 z/ z

2 2 / 3

*

2 = −
September 2006 

2-12 

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

components are used to advect the endpoints. 

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

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

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

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

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

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

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

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

determine the puff width tendencies required to conserve puff volume. 

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

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

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

blue lines.

Stack
September 2006 

2-13 

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

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

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

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

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

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

PiG Chemistry 

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

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

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

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

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

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

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

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

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

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

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

The Concept of Incremental Chemistry

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

(1) 

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

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

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

reaction rate is found to be: 

or 

If we subtract the rate of change of the background, 

(2) 

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

rate for the puff increments: 

 (3) 

( )( ) T PA PB R = k

( ) A A CA P = c +

( ) B B CB P = c +

( )( ) T A A B CB R = k c + C c +

( ) T A B A B ACB CACB R = k c c + C c + c +

Ambient ACB R = kC

( ) P A B A B ACB R = k c c + C c + c
September 2006 

2-14 

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

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

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

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

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

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

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

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

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

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

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

Puff Constructs for Incremental Chemistry

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

incremental chemistry approach: 

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

layers; 

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

overlap other puffs within a given grid column; and 

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

concentration distributions within each puff. 

Each of these is described below. 

Puff Layer Spanning 

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

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

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

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

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

introduces technical implications for defining “background” concentrations and ambient 

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

The solution employed in the new PiG is to: 

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

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

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

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

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

weighting. 
September 2006 

2-15 

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

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

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

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

±1.5σz. 

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

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

horizontal cell interface. 

Chemistry Solution 

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

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

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

via the LSODE gas-phase chemistry mechanism; 

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

the total concentrations are chemically updated; and 

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

provide the updated incremental concentrations. 

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

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

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

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

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

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

Puff Dumping and PiG Rendering 

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

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

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

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

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

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

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

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

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

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

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

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

leaked. 
September 2006 

2-16 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

to grid-scale eddy diffusion. 

High Resolution Puff Sampling 

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

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

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

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

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

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

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

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

Section 5. 

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

shears, the sampling procedure includes trigonometric calculations to define which sampling 
September 2006 

2-17 

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

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

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

vertically into the model’s surface layer. 
December 2005 

BART_TX_Screen_Protocol.doc 3-1 

3.0 PROCEDURES FOR VOC AND PM EMISSIONS BART SCREENING ANALYSIS 

The first screening analysis will estimate the cumulative visibility impacts at Class I areas due to 

VOC and PM emissions from all potential BART-eligible sources in Texas. 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. 

The 2002 BART base case emissions scenario will be based on the CENRAP 2002 typical base 

case emissions scenario. The BART guidelines require that BART modeling use the actual 

maximum 24-hour emissions for each BART-eligible source. The CENRAP 2002 typical 

scenario contains average actual emissions for all sources. The maximum 24-hour actual 

emission rates are not readily available for most sources. Thus, to account for the differences 

between maximum 24-hour actual and average typical actual, the average typical actual 

emissions for potential BART-eligible sources in the CENRAP 2002 typical base case emissions 

scenario will be doubled. This provides a conservative estimate of maximum 24-hour emissions 

for these sources. 

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

concentrations between the two scenarios above following the procedures given in EPA’s BART 

modeling guidance (EPA, 2005) and in the Federal Land Managers Air Quality Related Values 

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

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

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

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

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

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

the differences in the CAMx 2002 BART base case and 2002 BART VOC/PM zero-out runs will 

be utilized in the equation. 

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

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

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

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

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

concentration impacts times a extinction efficiency coefficient plus Rayleigh (i.e., blue sky) 

background, that is assumed to be 10 Mm-1.

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

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

bNO3 = 3 [NH4NO3]f(RH) 

bOC = 4 [OMC] 

bEC = 10 [EC] 

bSoil = 1 [Soil] 

bcoarse = 0.6 [Coarse Mass] 
December 2005 

BART_TX_Screen_Protocol.doc 3-2 

bray = 10 Mm-1

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

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

2003a), which is what we will use in this study. The concentrations in the square brackets are in 

μg/m3

 and are based on the differences in concentrations between the 2002 BART base case and 

2002 BART VOC/PM zero-out case. Although CAMx explicitly models ammonia and 

ammonium, the IMPROVE extinction equation assumes that SO4 and NO3 are completely 

neutralized by ammonium. The OMC in the above equation is Organic Matter Carbon. When 

using IMPROVE measurements, the current IMPROVE extinction equation assumed an 

OMC/OC ratio of 1.4 (i.e., the IMPROVE Organic Carbon, or OC, measurement is multiplied by 

1.4 to obtain OMC). Since CAMx directly models OMC, the 1.4 factor is not needed. The 

following species mappings will be used to map the CAMx species to those used in the 

IMPROVE reconstructed mass extinction equation given above: 

[(NH4)2SO4] = 1.375 x PSO4 

[NH4NO3] = 1.290 x PNO3 

[OMC] = POA + SOA1 + SOA2 + SOA3 + SOA4 

[EC] = PEC 

[Soil] = FPRM + FCRS 

[Coarse Mass] = CPRM + CCRS 

Here PSO4 and PNO3 are the CAMx particulate sulfate and nitrate species. POA is the CAMx 

primary Particulate Organic Aerosol species, whereas SOA1-4 are the four Secondary Organic 

Aerosol species carried in CAMx. Primary Elemental Carbon is represented by PEC in CAMx. 

CAMx carries two species that represent the other PM2.5 components (i.e., fine particles that are 

not So4, NO3, EC or OC), one for the crustal (FCRS) and the other for the remainder of the 

primary emitted PM2.5 species (FPRM). Similarly, CAMx carries two species to represent 

Coarse Mass (PM2.5-10), one for crustal (CCRS) and one for other (CPRM). 

The Haze Index (HI) for the source is calculated in deciviews (dv) from the source’s extinction 

plus natural background using the following formula: 

HIsource = 10 ln[(bsource + bnatural)/10] 

The source’s HI is compared against natural conditions to assess the significance of the source’s 

visibility impact. EPA guidance lists natural conditions (bnatural) by Class I areas in terms of 

Mm-1 (EPA, 2003b) and assumes clean conditions with no man-made or weather interference. 

The visibility significance metric for evaluating BART sources is the change in deciview 

(del-dv) from the source’s and natural conditions haze indices: 

del-dv = HIsource – HInatural = 10 ln[(bsource + bnatural)/10] - 10 ln[bnatural/10] 

= 10ln[(bsource + bnatural)/bnatural] 

The visibility impacts from the CAMx BART VOC/PM zero-out run will first be calculated 

using all PM species. Depending on these results, then separate visibility calculations may be 

made using just those PM species that may be associated with the elimination of the BART VOC 

emissions (i.e., PSO4, PNO3, SOA1, SOA2, SOA3 and SOA4) and then just those species 

associated with the elimination of the primary PM emissions (i.e., PSO4, PNO3, POA, PEC, 
December 2005 

BART_TX_Screen_Protocol.doc 3-3 

FCRS, FPRM, FCRS and CCRS). Note that SO4 and NO3 are associated with both VOC and 

PM: VOC through photochemical reactivity and formation of secondary PSO4/PNO3; and 

primary emitted PSO4/PNO3 for PM emissions. 

The del-dv impacts will be calculated at all 12 km receptor grid cells in which the 12 km grid 

intersects any portions of a Class I area. For each day, the maximum del-dv impact across all 

12 km grid cells that intersect a Class I area will be used to represent the visibility impact for that 

day at that Class I area. 

The EPA BART guidance suggests that a significance threshold to determine whether a source 

significantly contributes to visibility impairment at a Class I area should be no greater than 

0.5 dv. Thus, if the del-dv due to all potential BART-eligible sources VOC and PM emissions at 

every Class I area and for all days from 2002 are < 0.5 dv, then VOC and PM emissions from all 

potential BART-eligible sources would not contribute significantly to visibility impairment and 

therefore the VOC and PM emissions from each individual potential BART source would not be 

significant. Under these conditions, VOC and PM emissions no longer need to be considered in 

the BART analysis for Texas. 

If there are any days in 2002 for which the del-dv is greater than 0.5 dv in the BART VOC/PM 

zero-out screening analysis, the results would be examined in more detail including the analysis 

of the frequency, magnitude and duration of the visibility impacts. The BART guidance suggests 

comparing the 98th percentile del-dv at any Class I area with the 0.5 dv significant threshold to 

determine whether a significant visibility impact would reasonably be expected to occur. Using 

one year of modeling results (2002), the 98th percentile would correspond to the eighth highest 

24-hour average visibility impact at each Class I area. A separate assessment of the contribution 

of VOC versus PM related PM species may also be performed to determine whether one 

precursor pollutant or the other is the cause of the del-dv exceeding the 0.5 dv significance 

threshold. TCEQ will also attempt to identify whether a particular source or group of sources are 

responsible for the exceedance of the visibility threshold. Additional VOC/PM zero-out runs 

may be performed using subgroups of potential BART-eligible sources. For example, not 

including a source that is believed responsible for the visibility threshold exceedance or 

separating the potential BART-eligible sources into EGU and non-EGU groups. 

BART Screening Analysis for PM Emissions Only 

If BART screening analysis is desired for primary PM emissions by themselves, then it is much 

simpler and more computationally efficient to run an inert CAMx simulation with the potential 

BART-eligible primary PM emissions as input. Gas-phase species and gas-, aqueous- and 

aerosol-phase chemistry do not affect the primary PM species so these species and chemistry do 

not need to be simulated when looking at the primary PM impacts. In this case, the BART PM 

emissions are processed and simulated by the model and only one inert simulation needs to be 

conducted. Chemistry is turned off in CAMx by setting the “Chemistry” option in the CAMx 

input file to “.false.” 
September 2006 

4-1 

PROCEDURES FOR SCREENING BART SOURCES SO2 AND NOx EMISSIONS 

The screening analysis for potential BART-eligible non-EGU sources SO2 and NOx emissions 

will use the updated PM Source Apportionment Technology (PSAT) in CAMx and the 2002 

36/12 km modeling database described in Section 2. The non-EGU potential BART-eligible 

sources will be divided up into approximately 10 source groups for the PSAT screening analysis. 

CAMx/PSAT will be 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 for the zero-out run, if the del-dv due to all sources in a source group at every 

Class I area and for all days from 2002 is < 0.5 dv, then each individual potential BART-eligible 

source in the source group would not contribute significantly to visibility impairment. 

Therefore, the SO2 and NOx emissions from each individual potential BART source would not 

be subject to BART. 

At this time we are only considering performing the potential BART-eligible source group PSAT 

modeling for SO2 and NOx emissions from sources that are not Electrical Generating Units 

(EGUs). As Texas is covered under the PM2.5 provisions of the Clean Air Interstate Rule 

(CAIR) and EPA BART guidance (EPA, 2005) states that CAIR satisfies the BART SO2 and 

NOx requirements for CAIR PM2.5 States, then only SO2 and NOx emissions from non-EGU 

potential BART-eligible sources need be considered in this screening analysis. 

VISIBILITY IMPACTS FROM PSAT 

The sulfate and nitrate families of PSAT source apportionment tracers will be invoked for the 

PSAT BART screening analysis. The visibility impacts at each Class I area will be calculated in 

a similar manner as described in Section 3, only the PSO4 and PNO3 concentrations from each 

PSAT BART source group will be used in the extinction equation that is then used to calculate 

the haze index (HI) and change in deciview (del-dv) from natural conditions due to the SO2 and 

NOx emissions from all of the potential BART-eligible sources in each source group. In this 

case the IMPROVE reconstructed mass extinction equation for each potential BART-eligible 

source group will only include the sulfate and nitrate components, as well as Rayleigh 

background: 

bgroup = bSO4 + bNO3 + bray

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

bNO3 = 3 [NH4NO3]f(RH) 

bray = 10 Mm-1

Here f(RH) are the monthly average relative humidity adjustment factors from EPA’s guidance 

(EPA, 2003a). The concentrations in the square brackets are in μg/m3

 and are the sulfate and 

nitrate from the PSAT output for each potential BART-eligible PSAT source group (i) that are 

assumed to be fully neutralized by ammonium: 

[(NH4)2SO4] = 1.375 x PS4i

[NH4NO3] = 1.290 x PN3i
September 2006 

4-2 

The haze index for the source group is calculated in deciview from the source groups extinction 

plus natural background using the following formula: 

HIgroup = 10 ln[(bgroup + bnatural)/10] 

The source’s HI is compared against natural conditions to assess the significance of the source’s 

visibility impact. EPA guidance lists natural conditions (bnatural) by Class I areas in terms of 

Mm-1 (EPA, 2003b) and assumes clean conditions with no man-made or weather interference. 

The visibility significance metric for evaluating BART sources is the change in deciview (deldv) from the source’s and natural conditions haze indices: 

del-dv = HIgroup – HInatural = 10 ln[(bgroup + bnatural)/10] - 10 ln[bnatural/10] 

The 0.5 dv significance threshold will be used to assess whether a potential BART-eligible 

source group does not contribute significantly to visibility impairment. If the del-dv for each 

day of 2002 and every 12 km grid cell that intersects with any Class I area is less than 0.5 dv, 

then the source groups SO2 and NOx emissions do not contribute significantly to visibility 

impairment at any Class I area. EPA’s BART guidance suggests using the 98th percentile 

visibility impact at a Class I area in the visibility significance determination using this approach, 

then a source group would not have a significant contribution to visibility if the 8th highest 

24-hour average del-dv at each Class I area is less than 0.5 dv. Under these conditions the SO2

and NOx emissions from each individual potential BART eligible source in the source group also 

does not contribute significantly to visibility at any Class I area so can be exempt from BART. 

Selection of Potential BART-eligible Source Groups for PSAT Modeling 

TCEQ is currently discussing the optimal approach for selecting non-EGU potential BARTeligible source groups for PSAT modeling. Originally, the thought was to rank the potential 

BART-eligible sources by emissions (SO2 and NOx) over distance from the nearest Class I area 

(Q/D) and then divide the list up into 10 decile source groups. This approach would include the 

smallest emitting sources that are furthest from the Class I areas that are most likely to have an 

insignificant contribution in one source group, with the largest emitting sources closest to the 

Class I areas that are most likely to have a significant contribution in another source group. 

Upon further considerations, however, there may be other criteria that should be factored in 

when defining the potential BART-eligible source groups for PSAT modeling. For example, it 

may be advantageous to group sources by source type, which might make control strategy 

planning and defining what BART is for a particular source type easier in later stages of the 

BART process. Another consideration may be to use one source group for west Texas EGU 

potential BART-eligible sources in the event EPA grants the Petition for Reconsideration to 

remove west Texas EGUs from CAIR. The final configuration may be a hybrid of the above 

considerations and is under review at TCEQ. 
September 2006 

5-1 

5.0 REFERENCES 

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