Appendix L 

Best Available Retrofit Technology 

(BART) 

Supporting Materials

1) BART Modeling Protocol 

2) TRC Modeling Documentation 

3) TRC Florida BART Modeling 

4) BART Contribution Threshold Analysis and VOC-NH3 Exclusion 

Analysis 

5) New IMPROVE Equation 

6) Summary of BART Modeling Exemptions 

Page 1 of 266
Protocol for the Application of the 

CALPUFF Model for Analyses of Best 

Available Retrofit Technology (BART) 

December 22, 2005

(Revision 3.2 – 8/31/06)

Visibility Improvement State and Tribal Association 

of the Southeast (VISTAS)

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Table of Contents iv

TABLE OF CONTENTS

Page

SUMMARY 1

1. INTRODUCTION AND PROTOCOL OBJECTIVES 1

1.1 Background 1

1.2 Objective of this Protocol 2

2. REVIEW OF EPA’S GUIDANCE FOR BART MODELING 5

2.1 Overview of the Regional Haze BART Process 5

2.2 Model Recommendations for the BART Analysis 7

2.3 Performance of a Cap and Trade Program 8

3. OVERVIEW OF THE CALPUFF MODELING SYSTEM 10

3.1 Capabilities and features of CALPUFF 10

3.1.1 Major Features of CALMET 12

3.1.2 Major Features of CALPUFF 14

3.1.3 Major Features of Postprocessors (CALPOST and POSTUTIL) 18

3.2 Discussion of CALPUFF Applicability and Limitations 19

3.2.1 Transport and Diffusion 19

3.2.2 Aerosol Constituents 22

3.2.3 Regional Haze 30

4. VISTAS’ COMMON MODELING PROTOCOL 34

4.1 Overview of Common Modeling Approach 34

4.1.1 BART Exemption Analysis 34

4.1.2 BART Control Evaluation 36

4.1.3 VISTAS’ Treatment of VOC, NH3, and PM 36

4.2 Optional Source-Specific Modeling 38

4.3 Initial Procedure for BART Exemption 38

4.3.1 Overview of Initial Approach 38

4.3.2 Discussion of 12-km Initial Exemption Modeling 39

4.3.3 Model Configuration and Settings for Initial Analysis 41

4.4 Finer Grid Modeling Procedures 46

4.4.1 Rationale for and Overview of Finer Grid Modeling Approach 46

4.4.2 Model Configuration and Settings for Finer Grid Modeling 47

4.5 Presentation of Modeling Results 49

4.6 VISTAS Contribution to CALPUFF Modeling of BART Eligible Sources 54

5. SOURCE-SPECIFIC MODELING PROTOCOL 55

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Table of Contents iv

6. QUALITY ASSURANCE 58

6.1 Scope and Purpose of the QA program 58

6.2 QA Procedures for Common Protocol Modeling 59

6.2.1 Quality Control of Input Data 59

6.2.2 Quality Control of Application of CALMET 60

6.2.3 Quality Control of Application of CALPUFF 61

6.2.4 Quality Control of Application of CALPOST and POSTUTIL 62

6.3 Additional QA Issues for Alternative Source-Specific Modeling 63

6.4 Assessment of Uncertainty in Modeling Results 64

7. REFERENCES 65

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Summary S-1

SUMMARY

This Protocol for the Application of the CALPUFF Model for Analyses of Best Available 

Retrofit Technology (BART) for the VISTAS Regional Planning Organization (RPO) describes 

common procedures for carrying out air quality modeling to support BART determinations that 

are consistent with guidelines of the U.S. Environmental Protection Agency in 40 CFR Part 51 

Appendix W and Appendix Y. The Protocol is intended to serve as the basis for a common 

understanding among the organizations that will be performing BART analyses or reviewing the 

BART modeling results in the VISTAS region.

Background

Best Available Retrofit Technology is required for any BART-eligible source that ‘‘emits any air 

pollutant which may reasonably be anticipated to cause or contribute to any impairment of 

visibility” in any mandatory Class I federal area. According to 40 CFR Part 51 Appendix Y, “You 

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.” In the “individual source attribution approach,” a BART-eligible source that is 

responsible for a 1.0 deciview (dv) change or more is considered to “cause” visibility impairment. 

A BART-eligible source that is responsible for a 0.5 dv change or more is considered to 

“contribute” to visibility impairment in a Class I area. Any source determined to cause or 

contribute to visibility impairment in any Class I area is subject to BART.

The member states of the VISTAS RPO agreed to develop a common BART Modeling Protocol 

to guide them, their sources, and reviewers in the BART determination and review effort. The 

Protocol has been in preparation within VISTAS since January 2005. The original authors are Pat 

Brewer, VISTAS Technical Coordinator, and Ivar Tombach, VISTAS Technical Advisor. The 

VISTAS state BART contacts, particularly Tom Rogers, FL, Chris Arrington, WV, Leigh Bacon, 

AL, and Michael Kiss, VA, have directed and extensively reviewed the Protocol. The Protocol 

was enhanced and completed with the assistance of Joseph Scire, Christelle Escoffier-Czaja and 

Jelena Popovic of Earth Tech, Inc. and it has received extensive contributions and review from 

the VISTAS federal partners: Federal Land Managers and US EPA. The VISTAS RPO held a 

meeting on September 21, 2005 in Research Triangle Park, NC to discuss the Protocol with 

participants before starting a public comment period. The Protocol underwent formal external 

review during the period between September 26, 2005 and October 31, 2005. Numerous 

comments were received. All comments were carefully considered and discussed with VISTAS 

participants and federal partners. VISTAS gratefully acknowledges the very useful contributions 

of those that provided comments. On November 1st, 2005 VISTAS held another meeting with its 

participants in Nashville, TN to present and discuss the comments being considered for inclusion 

in the Protocol. No formal document will be prepared to address all the comments received on 

the Protocol. 

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Summary S-2

Objectives

The objectives of the Protocol (discussed in Chapter 1) are to provide: 

 A consistent approach to determine if a source is subject to BART

 A consistent model (CALPUFF) and modeling guidelines for BART determinations

 Clearly delineated modeling steps

 A common CALPUFF configuration

 Guidance for site-specific modeling

 Common expectations for reporting model results

The Protocol is not intended to define the engineering analyses required by the US EPA’s BART 

Guidance, nor address model alternatives to the CALPUFF model, nor address emissions trading. 

Chapter 2 is intended to provide summary background on EPA’s guidance for BART modeling. 

The CALPUFF model system is reviewed in Chapter 3, while specific recommendations for 

applying the CALPUFF model for BART purposes appear in Chapter 4. Chapter 5 describes the 

specific information that should be included in site-specific protocols. Chapter 6 describes the 

quality assurance requirements for BART analyses in the VISTAS RPO.

Recommendations

The major recommendations for VISTAS BART modeling included in this Protocol are:

I. Process

 Follow the BART process steps discussed in Chapter 2:

1. Identify BART eligible sources

2. Identify which pollutants have greater than de minimis emission levels

3. Identify sources that are subject to BART

4. Identify baseline visibility impact of each BART source

5. Identify feasible controls and emission changes

6. Identify the change in visibility impact for each candidate BART control option

7. Compare the visibility improvement of BART control options to other statutory factors in 

the engineering analysis

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Summary S-3

II. CALPUFF Model Configuration

Use the CALPUFF dispersion modeling system, as described in Chapter 4, to determine if a 

single source is subject to BART. VISTAS will use CALPUFF Version 5.754 and CALMET 

Version 5.7. These versions contain enhancements funded by the Minerals Management Service 

(MMS) and VISTAS. They were developed by Earth Tech, Inc. and are maintained on the 

CALPUFF website (www.src.com) for public access. 

VISTAS is making publicly available 12-km CALMET output files for the entire VISTAS 

modeling domain (eastern United States) and intends to also provide CALMET output files for 

five 4-km grid subdomains covering the VISTAS states and VISTAS Class I areas. To create the 

CALMET input files, Earth Tech used the MM5 databases developed by EPA for 2001, VISTAS 

for 2002, and Midwest RPO for 2003. For the 12 km grid large domain covering the entire 

VISTAS region, Earth Tech used the No-Obs setting (i.e., did not include additional surface and 

upper air observations beyond those incorporated in the MM5 calculations). For finer resolution 

subdomains (4 km grid or less), available surface and upper air observations will be used in 

addition to MM5 meteorological model outputs. The specific model settings will be provided 

with the CALMET files and via the CALPUFF website so that users can review or replicate the 

work. 

For CALPUFF modeling, source emissions should be defined using the maximum 24-hour actual 

emission rate during normal operation for the most recent 3 or 5 years. If maximum 24-hr actual 

emissions are not available, continuous emissions data, permit allowable emissions, potential 

emissions, and emissions factors from AP-42 source profiles may be used as available. 

Key points from comments received on the specific CALPUFF, CALPOST, and POSTUTIL 

configurations are highlighted below. 

• After running CALPUFF for an individual facility, repartition NO3 in POSTUTIL.

1

• Use ozone data from non-urban monitors as the background ozone input.

• Use the Pasquill-Gifford dispersion method.2

 

1 The original intent, as expressed in the Final VISTAS BART Modeling Protocol (22 December 2005) was to use 

CMAQ-derived background data for SO2, NO3 and NH3 in POSTUTIL. After extensive discussion with the EPA 

and FLMs in early 2006, EPA did not approve the recommended approach so background gaseous 

concentrations from CMAQ 2002 modeling will not be provided by VISTAS for use in POSTUTIL. Rather the 

standard default NH3 concentrations specified on page 14 of the IWAQM Phase 2 report (IWAQM, 1998) will 

be used.

2 The Final VISTAS BART Modeling Protocol (Dec. 22, 2005) recommended using turbulence-based AERMOD 

dispersion methods, citing EPA’s Revision to the Guideline on Air Quality Models: Adoption of a Preferred 

General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions; Final Rule. 70 FR 68218-

68261. 9 November 2005. Subsequently, EPA Region IV notified the VISTAS states that using turbulenceAppendix L1 

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Summary S-4

• In CALPOST, use Method 6 with monthly average RH for calculating extinction, as 

recommended by the EPA. 

• Use EPA default calculations of light extinction under current and natural background 

conditions. In addition to the default assumptions, a source may choose to also calculate 

visibility using the recently revised IMPROVE algorithm described by Pitchford, et al., 

(2005). 

Provide results in tables as illustrated in Chapter 4 that describe, for each source:

• Number of receptors within a single Class I area with impact > 0.5 dv

• Number of days at all receptors in the Class I area with impact > 0.5 dv

• Number of Class I areas with impacts > 0.5 dv

III. CALPUFF Application for BART 

For determining if a BART-eligible source is subject to BART CALPUFF modeling, use a twotier approach. For the initial exemption modeling use CALPUFF with 12-km grid CALMET. For 

finer resolution of meteorological fields, use CALPUFF with CALMET of 4-km or smaller grid 

size. 

VISTAS States are accepting EPA guidance that the threshold value to establish that a source 

contributes to visibility impairment is 0.5 deciview. 

VISTAS States are using emissions (tons per year) divided by distance (km) from a Class I area 

boundary (Q/d) as a presumptive indicator that a BART-eligible source is subject to BART. If 

Q/d for SO2 is greater than 10 for 2002 actual annual emissions, then the State presumes that the 

source is subject to BART and no exemption modeling will be performed using VISTAS funds. If 

the source agrees with this presumption, then the source can proceed to the BART determination 

using CALPUFF to evaluate impacts of control options and perform the engineering analyses. If a 

source disagrees, the source may perform fine grid modeling to determine if its impact is <0.5 dv. 

For sources with Q/d less than or equal to 10, VISTAS intends to fund TRC Environmental 

Corp.3 to assist States with the initial CALPUFF exemption modeling. Each State will prioritize 

which sources will be offered modeling by VISTAS. Modeling of these sources will be 

conducted in priority order to first accommodate States with nearer term timing constraints in 

their SIP development process. To conserve VISTAS resources, modeling will begin with 

sources at lower Q/d values and continue with sources with higher Q/d values until a Q/d value 

 

based dispersion methods would be considered a non-guideline application of CALPUFF. Thus this Protocol 

has been revised to indicate Pasquill-Gifford dispersion coefficients should be used. 

3 In April 2006, Earth Tech’s CALPUFF modeling staff became part of TRC Environmental Corporation. 

References to Earth Tech and to TRC in this protocol refer to the same technical staff, just at different times.

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Summary S-5

that consistently results in a greater than 0.5 dv impact is identified. Chapter 4 addresses the 

number of VISTAS sources eligible for BART based on Q/d analysis. 

Note that VISTAS does not propose to use Q/d to exempt BART-eligible sources, but only to 

prioritize sources for modeling purposes. Thus this application is consistent with EPA guidance 

not to use Q/d for exemption purposes. 

For the 12-km initial modeling exemption test, compare the highest single 24-hour average value 

across all receptors in the Class I area to the threshold value of 0.5 dv. If the highest 24-hr 

average value is below 0.5 dv at all Class I areas, then the source is not subject to BART. If the 

highest 24-hr average value is greater than 0.5 dv, then the source may choose to perform finer 

grid modeling for exemption purposes or may accept determination that the source is subject to 

BART and proceed to establish visibility impacts prior to and after BART controls. If using the 

single highest 24-hr average value proves, after initial 12-km grid CALPUFF modeling, to be too 

conservative a screening level, VISTAS may allow some exceedances of the threshold value for 

exemption purposes, up to no more than the 98th percentile value. 

The 12-km modeling results can be used to focus finer grid modeling for exemption purposes on 

only those Class I areas where impacts greater than 0.5 dv were projected in the 12-km modeling.

For finer grid (4 km or less) analyses, use the 98th percentile impact value for the 24-hr average. 

Use either the 8th highest day in each year or the 22nd highest day in the 3-year period, whichever 

is more conservative, for comparison to the exemption threshold.

Use the same model assumptions for pre-BART visibility impact and for BART control options 

modeling: establish baseline visibility from the pre-BART run; change one control at a time; and 

evaluate the change in visibility impact, i.e. the delta-deciview. Note that “no control” may 

constitute BART.

Visibility impact is one of the five factors considered in the engineering analysis required under 

the USEPA BART guideline. If a source accepts to institute the most stringent control, the 

engineering analyses are not required.

This common VISTAS Protocol consistently recommends conservative assumptions. Individual 

States ultimately have responsibility to determine which, if any, BART controls are 

recommended in their State Implementation Plans (SIPs).

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Introduction and Protocol Objectives 1

1. INTRODUCTION AND PROTOCOL OBJECTIVES 

1.1 Background

Under regional haze regulations, the Environmental Protection Agency (EPA) has issued final 

guidelines dated July 6, 2005 for Best Available Retrofit Technology (BART) determinations (70 

FR 39104-39172). The regional haze rule includes a requirement for BART for certain large 

stationary sources. Sources are BART-eligible if they meet three criteria including potential 

emissions of at least 250 tons per year of a visibility-impairing pollutant, were put in place 

between August 7, 1962 and August 7, 1977, and fall within one of the 26 listed source categories 

in the guidance. A BART engineering evaluation using five statutory factors -- 1) existing 

controls; 2) cost; 3) energy and non-air environmental impacts; 4) remaining useful life of the 

source; 5) degree of visibility improvement expected from the application of controls -- is 

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. (Note that, depending on the five factors, the evaluation may result 

in no control.) Air quality modeling is an important tool available to the States to determine 

whether a source can be reasonably expected to contribute to visibility impairment in a Class I 

area.

Throughout this document the term “BART-eligible emission unit” is defined as any single 

emission unit that meets the criteria described above. A “BART-eligible source” is defined as the 

total of all BART-eligible emission units at a single facility. If a source has several emission 

units, only those that meet the BART-eligible criteria are included in the definition “BARTeligible source”. 

One of the listed categories is steam electric plants of more than 250 million BTU/hr heat input. 

To determine if such a plant has greater than 250 million BTU/hr heat input and is potentially 

subject to BART, the boiler capacities of all electric generating units (EGUs) should be added 

together regardless of construction date. In this category, electric generating sources greater than 

750 MW have presumptive SO2 and NOx emission limits. States may presume the same limits for 

EGU sources between 250-750 MW. However, units at those sources constructed after the 

BART-eligibility dates are not subject to a BART engineering evaluation. EPA, in the Clean Air 

Interstate Rule (CAIR), determined that an EGU participating in the CAIR trading program 

satisfies the BART requirements for SO2 and NOx. VISTAS states are tentatively accepting this 

guidance. CAIR does not cover PM so EGUs would still need to evaluate impacts of PM if PM 

emissions are above de minimis values.

As illustrated in Table 1-1, as of December 5, 2005, VISTAS States had identified a total of 274 

BART-eligible sources that fall into 20 of the 26 BART source categories. Of the 274 sources 

with BART-eligible units, 84 sources are utility EGUs and 190 are non-EGU industrial sources. 

(Note that these numbers are not final and are subject to slight adjustments and refinements.) No 

BART sources are located on Tribal lands.

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Introduction and Protocol Objectives 2

Table 1-1. VISTAS BART Eligible Sources (not updated since December 2005)

State Total Number 

of Sources

EGU Sources Non-EGU 

Sources

AL 48 8 40

FL 50 23 27

GA 24 10 14

KY 29 12 17

MS 18 8 10

NC 16 5 11

SC 31 6 25

TN 13 2 11

VA 18 3 15

WV 26 7 19

Total 273 84 189

1.2 Objective of this Protocol

The objective of this VISTAS’ BART Modeling Protocol is to describe common procedures for 

air quality modeling to support BART determinations that are consistent with the EPA guidelines. 

The protocol will serve as the basis for establishing a common understanding among the 

organizations who will be performing the BART analyses or reviewing the BART modeling 

results, including VISTAS State and Local air regulatory agencies, EPA, Federal Land Managers 

(FLMs), source operators, and contractors for the sources. This final protocol incorporates EPA 

final guidance and comments that were received on VISTAS’ draft protocol4 and provides 

additional description of modeling procedures. The original final protocol of 22 December 2005 

has been revised since then to clarify items, resolve technical issues, and reflect decisions by the 

EPA and FLMs. This document is the third revision. 

The VISTAS States have accepted EPA’s guidance to use the CALPUFF modeling system to 

comply with the BART modeling requirements of the regional haze rule. A BART-eligible 

source will be required to submit a site-specific modeling protocol to the State for review and 

approval prior to performing CALPUFF modeling. States will consult with FLMs and the EPA 

when evaluating the site-specific BART protocols. The site-specific protocol will include the 

 

4 Draft Protocol for the Application of the CALPUFF Model for Analyses of Best Available Retrofit Technology 

(BART). VISTAS, March 22, 2005 and September 20, 2005.

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Introduction and Protocol Objectives 3

source-specific data on source location, stack parameters, and emissions. The methods of the 

VISTAS common modeling protocol will be followed in the site-specific protocol unless the 

source proposes to the State, and the State approves, alternative methods or assumptions. 

Each VISTAS State or Local agency retains responsibility for the specific procedures and 

processes it will follow in working with the BART sources under its jurisdiction, the FLMs, EPA, 

and public to determine BART controls for sources in the State. Nothing in the VISTAS process 

replaces States’ responsibility to determine BART controls. 

The remainder of this document describes the CALPUFF modeling system and the application of 

CALPUFF to two situations:

• Air quality modeling to determine whether a BART-eligible source is “subject to BART” 

and therefore the BART analysis process must be applied to its operations.

• Air quality modeling of emissions from sources that have been found to be subject to 

BART, to evaluate regional haze benefits of alternative control options and to document 

the benefits of the preferred option.

Chapters 2 and 3 of this document are intended to provide background information on EPA’s 

guidance for BART analysis modeling and on the CALPUFF modeling system. Subsequent 

chapters include more specific recommendations. Chapter 2 of this document reviews EPA’s 

guidance for regional haze BART analysis modeling, as outlined in the 6 July 2005 Federal 

Register notice. The CALPUFF model is the preferred model recommended by the EPA for 

BART modeling analyses and its characteristics and limitations are discussed in Chapter 3. The 

specific steps to determine whether a BART-eligible source is subject to BART and to evaluate 

BART controls are described in Chapter 4. The procedures include initial modeling of BARTeligible sources using CALPUFF run in a conservative mode with regional meteorological 

datasets. For sources determined to be subject to BART based on these first modeling analyses, 

further finer grid CALPUFF analyses would be performed. The model configuration for the 

common modeling protocol is described in Chapter 4. Details of the source-specific protocol are 

described in Chapter 5. A quality assurance plan is outlined in Chapter 6. 

EPA’s guidance allows for the use of appropriate alternative models, however VISTAS will not 

develop a protocol for alternative models. This protocol focuses on guidance for the application 

of the preferred CALPUFF modeling approach. If a source wants to use an alternative model in 

its BART demonstration, the source will need to submit a detailed written justification to the 

State for review and approval. The State will provide the documentation to the EPA and Federal 

Land Managers for their review. 

Also, this protocol does not address a preferred modeling approach to demonstrate the 

effectiveness of an optional emissions cap and trade program. Such a cap and trade program is 

not required, but can be implemented in lieu of BART if desired by the VISTAS States. VISTAS 

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Introduction and Protocol Objectives 4

States are not pursuing a regional trading alternative under the proposed EPA trading guidance 

(70 FR 44154-44175) that is to be promulgated in 2006.

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Review of EPA’s Guidance 5

2. REVIEW OF EPA’S GUIDANCE FOR BART MODELING

The final guidance for regional haze BART determinations was published in the Federal Register 

on 6 July 2005 (70 FR 39104 to 39172). It prescribes the modeling approaches that are to be used 

for various stages of the BART analysis process. 

This chapter provides a summary of EPA’s guidance for BART modeling. It is not intended to 

provide a comprehensive review of the guidance. Nor does this chapter address specific 

recommendations for VISTAS’ approach to CALPUFF BART modeling. Those recommendations 

appear in Chapter 4. 

2.1 Overview of the Regional Haze BART Process

The process of establishing BART emission limitations consists of four steps:

1) 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 BARTeligible. Finally, a source is eligible for BART if potential emissions of visibility-impairing air 

pollutants are greater than 250 tons per year. Qualifying pollutants include primary particulate 

matter (PM10) and gaseous precursors to secondary fine particulate matter, such as SO2 and NOx. 

Whether ammonia or volatile organic compounds (VOCs) should be included as visibilityimpairing 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 be considered as primary PM2.5 emissions and not VOCs for BART purposes.

(Note: If the source is subject to BART because one visibility impairing pollutant has potential 

emissions > 250 TPY, the State may determine that other visibility impairing pollutants are not 

subject to BART if their potential emissions are less than the de minimis levels (40 TPY for SO2

and NOx and 15 TPY of PM10 or PM2.5. This assumes that the other BART-eligibility criteria are 

met.)

2) Determine 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. The preferred approach is an assessment with an air quality model 

such as CALPUFF 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.5 The threshold to determine whether a single source “causes” visibility impairment is set at 

 

5 A recent draft settlement agreement with the EPA (to be published in the Federal Register for public comment) 

provides that a State has the discretion to decide whether annual average or 20% best natural conditions are to be 

used as the reference. This ruling resolves an ambiguity in EPA’s BART guidance, where the BART guideline 

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Review of EPA’s Guidance 6

1.0 deciview change from natural conditions over a 24-hour averaging period in the final BART 

rule (70 FR 39118). 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. The test against 

the threshold is “driven” by the contribution level, since if a source “causes”, by definition it 

“contributes”. 

EPA recommends that the 98th percentile value from the modeling be compared to the contribution 

threshold of 0.5 deciviews (or a lower level set by a State) to determine if a source does not 

contribute to visibility impairment and therefore is not subject to BART. Whether or not the 98th

percentile value exceeds the threshold must be determined at each Class I area. Over an annual 

period, this implies the 8th highest 24-hr value at a particular Class I area is compared to the 

contribution threshold. Over a 3-year modeling period, the 98th percentile value may be interpreted 

as the highest of the three annual 98th percentile values at a particular Class I area or the 22nd highest 

value in the combined three year record, whichever is more conservative.

Alternatively, States have the option of considering that all BART-eligible sources within the 

State are subject to BART and skipping the initial impact analysis. In rare cases, a State might be 

able to do exactly the opposite, and use regional modeling to conclude that all BART-eligible 

sources in the State do not cumulatively contribute to “measurable” visibility impairment in any 

Class I areas. Also, the States have an option to exempt individual sources based on model plant 

analysis conducted by EPA in finalizing the BART rule. Under this option, sources with 

potential emissions of SO2 plus NOx of less than 500 tons and a distance from any Class I area 

greater than 50 kilometers or sources with SO2 plus NOx potential emissions of less than 1000 

tons and a distance from any Class I area greater than 100 kilometers can be exempted. PM 

emissions are not specifically addressed in the model plant analysis, but subsequent discussions 

with EPA staff indicate that PM may be considered along with SO2 and NOx, so that a plant could 

be exempted if the combined potential emissions of SO2, NOx, plus PM meet the criteria above.

3) Determine BART controls 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

 

text says “natural conditions” at 70 FR 39162, col. 3, while the preamble to the BART rule says “natural visibility 

baseline for the 20% best visibility days” at 70 FR 39125, col. 1. 

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Review of EPA’s Guidance 7

e) The degree of improvement in visibility that may reasonably be anticipated to result 

from the use of such technology.

Note that if a source agrees to apply the most stringent controls available to BART-eligible units, 

the BART analysis is essentially complete and no further analysis is necessary (70 FR 39165).

4) Incorporate the BART determination into the State Implementation Plan for Regional Haze, 

which is due by December 2007.

Instead of applying BART on a source-by-source basis, a State (or a group of States) has the option 

of implementing an emissions trading program that is designed to achieve regional haze 

improvements that are greater than the visibility improvements that could be expected from BART. 

If the geographic distributions of emissions under the two approaches are similar, determining 

whether trading is “better than BART” may be possible by simply comparing emissions expected 

under the trading program against the emissions that could be expected if BART was applied to 

eligible sources. If the geographic distributions of emissions are likely to be different, however, air 

quality modeling comparing the expected improvements in visibility from the trading program and 

from BART would be required. (See the proposed BART Alternative rule, at 70 FR 44160.) EPA 

suggests that regional modeling using a photochemical grid model may be more appropriate than 

CALPUFF for this purpose.

Note that EPA has indicated in the BART rule (70 FR 39138-39139) that emissions reductions 

under the Clean Air Interstate Rule (CAIR) meet the BART requirement for SO2 and NOx control 

for those EGUs subject to BART. However, PM emissions from EGUs are not addressed by CAIR 

and therefore a BART analysis may still be required for PM. 

2.2 Model Recommendations for the BART Analysis 

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 as “the best regulatory 

modeling application currently available for predicting a single source’s contribution to visibility 

impairment” (70 FR 39162). The use of another “appropriate model” is allowed although the EPA 

prefers the use of CALPUFF. If a source wants to use an alternative model, the source needs to 

submit a written justification and source-specific modeling protocol to its State for review and 

approval. As part of the consultation process, the State will provide documentation to EPA and 

FLM. 

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 PLUVUE-II plume visibility model is mentioned as a possible model to 

consider instead of CALPUFF for a source within less than 50 km of a Class I area. 

The EPA guidance notes that “regional scale photochemical grid models may have merit, but such 

models have been designed to assess cumulative impacts, not impacts from individual sources” and 

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they are “very resource intensive and time consuming relative to CALPUFF”, but States may 

consider their use for SIP development in the future as they may be adapted and “demonstrated to 

be appropriate for single source applications” (70 FR 39123). Photochemical grid models may be 

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 (70 FR 39163).

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 BARTeligible source configurations” and factors affecting their particular Class I areas (70 FR 39126). 

In the BART modeling analyses the EPA recommends that the State use the highest 24-hour 

average actual emission rate for the most recent three to five-year period of record. Emissions on 

days influenced by periods of start-up, shutdown and malfunction are not to be considered in 

determining the appropriate emission rates. (70 FR 39129).

If a source is found 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. 

Visibility improvements may be evaluated on a pollutant-specific basis in the BART determination 

(70 FR 39129).

For evaluating the improvement in visibility resulting from the application of BART, the EPA 

guidelines state that States are “encouraged to account for the magnitude, frequency, and duration 

of the contributions to visibility impairment caused by the source based on the natural variability of 

meteorology” (70 FR 39129). 

2.3 Performance of a Cap and Trade Program

If a State or States elect to pursue an optional cap and trade program, they are required to 

demonstrate greater “reasonable progress” in reducing haze than would result if BART were 

applied to the same sources. In some cases, a State may simply be able to demonstrate that a trading 

program that achieves greater progress at reducing emissions will also achieve greater progress at 

reducing haze. Such would be the case if the likely geographic distribution of emissions under the 

trading program would not be greatly different from the distribution if BART was in place. 

If the expected distribution of emissions is different under the two approaches, then “dispersion 

modeling” of all sources must be used to determine the difference in visibility at each impacted 

Class I area, in order to establish that the optional trading program will result in visibility 

improvements aggregated over all Class I areas that are “better than BART” (70 FR 39137-39138). 

The BART guidance does not specify the method to be used for this modeling. From a technical 

perspective, either applying CALPUFF to every source or using a regional photochemical model 

would satisfy the need.

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A rulemaking procedure is currently underway to establish final guidance for such alternatives to 

BART (70 FR 44154-44175). The rule is expected to be finalized in 2006.

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3. OVERVIEW OF THE CALPUFF MODELING SYSTEM

This chapter contains a general description of the CALPUFF modeling system and its capabilities 

and limitations. It does not include specific recommendations regarding the use of the model for 

BART analysis in the VISTAS region. These specific recommendations can be found in Chapter 

4. 

3.1 Capabilities and features of CALPUFF

The CALPUFF modeling system (Scire et al., 2000a, b) is recommended as the preferred 

modeling approach for use in the BART analyses. CALPUFF and its meteorological model, 

CALMET, are designed to handle the complexities posed by the complex terrain, the large 

source-receptor distances, chemical transformation and deposition, and other issues related to 

Class I visibility impacts. The CALPUFF modeling system has been adopted by the EPA as a 

Guideline Model for source-receptor distances greater than 50 km, and for use on a case-by-case 

basis in complex flow situations for shorter distances (68 FR 18440-18482). CALPUFF is 

recommended for Class I impact assessments by the Federal Land Managers Workgroup (FLAG 

2000) and the Interagency Workgroup on Air Quality Modeling (IWAQM) (EPA 1998). The 

final BART guidance recommends CALPUFF as “the best modeling application available for 

predicting a singe source’s contribution to visibility impairment” (70 FR 39122). As a result of 

these recommendations, the VISTAS modeling protocol is based on the use of CALPUFF for its 

BART determinations.

The main components of the CALPUFF modeling system are shown in Figure 3-1. CALMET is 

a diagnostic meteorological model that is used to drive the CALPUFF dispersion model. It 

produces three-dimensional wind and temperature fields and two-dimensional fields of mixing 

heights and other meteorological fields. It contains slope flow effects, terrain channeling, and 

kinematic effects of terrain. CALMET includes special algorithms for treating the overwater 

boundary layer and coastal interaction effects. CALMET can use meteorological observational 

data and/or three-dimensional output from prognostic numerical meteorological models such as 

MM5 (Grell et al., 1995) or RUC (Benjamin et al., 2004) in the developments of its fine-scale 

meteorological fields.

CALPUFF is a non-steady-state Lagrangian puff transport and dispersion model that advects 

Gaussian puffs of multiple pollutants from modeled sources. CALPUFF’s algorithms have been 

designed to be applicable on spatial scales from a few tens of meters to hundreds of kilometers 

from a source. It includes algorithms for near-field effects such as building downwash, stack tip 

downwash and transitional plume rise as well as processes important in the far-field such as 

chemical transformation, wet deposition, and dry deposition. CALPUFF contains an option to 

allow puff splitting in the horizontal and vertical directions, which extends the distance range of 

the model. The primary outputs from CALPUFF are hourly concentrations and hourly deposition 

fluxes evaluated at user-specified receptor locations.

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Figure 3-1. CALPUFF modeling system components.

A set of postprocessing programs associated with CALPUFF computes visibility effects and 

allows cumulative source impacts to be assessed, including potential non-linear effects of 

ammonia limitation on nitrate formation. The CALPOST postprocessor contains several options 

for computing change in extinction and deciviews for visibility assessments. The POSTUTIL 

postprocessor includes options for summing contributions of individual sources or groups of 

sources to assess cumulative impacts. POSTUTIL also contains CALPUFF’s nitric acid-nitrate 

chemical equilibrium module, which allows the cumulative effects of ammonia consumption by 

background sources to be assessed in the postprocessor. In addition, the combination of 

CALPUFF and POSTUTIL allows the effects of source emissions of ammonia to be 

incrementally added to background ammonia levels when determining nitrate formation.

The rest of this chapter summarizes the capabilities and features of the CALPUFF modeling 

components in more detail.

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3.1.1 Major Features of CALMET

The CALMET meteorological model consists of a diagnostic wind field module and 

micrometeorological modules for overwater and overland boundary layers. When modeling a 

large geographical area, as would be necessary for the regional VISTAS domain, the user has the 

option to use a Lambert Conformal Projection coordinate system to account for Earth’s curvature. 

The major features and options of the meteorological model are summarized in Table 3-1. The 

techniques used in the CALMET model are briefly described below. 

Table 3-1. Major Features of the CALMET Meteorological Model

• Boundary Layer Modules of CALMET

- Overland Boundary Layer - Energy Balance Method

- Overwater Boundary Layer - Profile Method

-- COARE algorithm

-- OCD-based method

- Produces Gridded Fields of:

-- Surface Friction Velocity

-- Convective Velocity Scale

-- Monin-Obukhov Length

-- Mixing Height

-- PGT Stability Class

-- Air Temperature (3-D)

-- Precipitation Rate

• Diagnostic Wind Field Module of CALMET

- Slope Flows

- Kinematic Terrain Effects

- Terrain Blocking Effects

- Divergence Minimization

- Produces Gridded Fields of U, V, W Wind Components

- Inputs Include Domain-Scale Winds, Observations, and

 (optionally) Coarse-Grid Prognostic Model Winds

- Lambert Conformal Projection Capability

CALMET Boundary Layer Models

The CALMET model contains two boundary layer models for application to overland and 

overwater grid cells.

Overland Boundary Layer Model: Over land surfaces, the energy balance method of Holtslag and 

van Ulden (1983) is used to compute hourly gridded fields of the sensible heat flux, surface 

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friction velocity, Monin-Obukhov length, and convective velocity scale. Mixing heights are 

determined from the computed hourly surface heat fluxes and observed temperature soundings 

using a modified Carson (1973) method based on Maul (1980). The model also determines 

gridded fields of Pasquill-Gifford-Turner (PGT) stability class and hourly precipitation rates.

Overwater Boundary Layer Model: The aerodynamic and thermal properties of water surfaces 

suggest that a different method is best suited for calculating the boundary layer parameters in the 

marine environment. A profile technique, using air-sea temperature differences, is used in 

CALMET to compute the micro-meteorological parameters in the marine boundary layer. The 

version of CALMET being used by VISTAS contains improvements in the overwater boundary 

layer parameterizations (Fairall et al., 2003) based on the Coupled Ocean Atmosphere Response 

Experiment (COARE) and enhancements in the calculation of overwater mixed layer heights 

(Batchvarova and Gryning, 1991, 1994). Further details and the results of an evaluation of the 

model containing these enhancements are described in Scire et al. (2005). An upwind-looking 

spatial averaging scheme is optionally applied to the mixing heights and three-dimensional 

temperature fields in order to account for important advective effects.

Diagnostic Wind Field Module

The diagnostic wind field module uses a two-step approach to the computation of the wind fields 

(Douglas and Kessler, 1988). In the first step, an initial-guess wind field is adjusted for kinematic 

effects of terrain, slope flows, and terrain blocking effects to produce a Step 1 wind field. Gridded 

MM5 can be used to define the initial guess field. The second step consists of an objective 

analysis procedure to introduce observational data into the Step 1 wind field to produce a final 

wind field.

Step 1 Wind Field. Development of the Step 1 wind field begins with the initial guess field 

defined by the MM5 prognostic meteorological model. Normally, the CALMET computational 

domain is specified to be at finer grid resolution than the MM5 dataset used to initialize the initial 

guess field. For example, 36-km MM5 data available for VISTAS modeling may be used to 

develop the initial guess field on a 12-km or even a 1-km CALMET grid. The Step 1 algorithms 

in CALMET described below apply terrain adjustments to the initial guess field on the fine-scale 

CALMET grid. Thus, the CALMET winds are adjusted to respond to fine-scale terrain features 

not necessarily seen by the coarser scale MM5 model.

Kinematic Effects of Terrain: The approach of Liu and Yocke (1980) is used to evaluate the 

effects of the terrain on the wind field. The initial guess field winds are used to compute a terrainforced vertical velocity, subject to an exponential, stability-dependent decay function. The effects 

of terrain on the horizontal wind components are evaluated by applying a divergenceminimization scheme to the initial guess wind field. The divergence minimization scheme is 

applied iteratively until the three-dimensional divergence is less than a threshold value. 

Slope Flows: The original slope flow algorithm in CALMET has been upgraded (Scire and Robe, 

1997) based on the shooting flow algorithm of Mahrt (1982). This scheme includes both 

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advective-gravity and equilibrium flow regimes. At night, the slope flow model parameterizes the 

flow down the sides of the valley walls into the floor of the valley, and during the day, upslope 

flows are parameterized. The magnitude of the slope flow depends on the local surface sensible 

heat flux and local terrain gradients. The slope flow wind components are added to the wind field 

adjusted for kinematic effects.

Blocking Effects: The thermodynamic blocking effects of terrain on the wind flow are 

parameterized in terms of the local Froude number (Allwine and Whiteman, 1985). If the Froude 

number at a particular grid point is less than a critical value and the wind has an uphill 

component, the wind direction is adjusted to be tangent to the terrain. 

Step 2 Wind Field. The wind field resulting from the above adjustments of the initial-guess wind 

is the Step 1 wind field. The second step of the procedure may involve introduction of 

observational data into the Step 1 wind field through an objective analysis procedure. An inversedistance squared interpolation scheme is used which weights observational data heavily in the 

vicinity of the observational station, while the Step 1 wind field dominates the interpolated wind 

field in regions with no observational data. The resulting wind field is subject to smoothing, an 

optional adjustment of vertical velocities based on the O’Brien (1970) method, and divergence 

minimization to produce a final Step 2 wind field. 

The introduction of observational data in the Step 2 calculation is an option. It is also possible to 

run the model in “no observations” (No-Obs) mode, which involves the use only of MM5 gridded 

data for the initial guess field followed by fine-scale terrain adjustments by CALMET. In NoObs mode, observational data are not used in the Step 2 calculations. The No-Obs mode is 

appropriate when the MM5 simulations adequately characterize the regional wind patterns and 

when local observations, especially surface observations, reflect local conditions on a scale 

smaller than that of the CALMET domain and hence their spatial representativeness may be 

limited. Such situations are most likely to occur when the CALMET grid scale is relatively large 

i.e., coarser than the scale of variation of the true wind field, which is particularly likely to occur 

in complex terrain or along the seashore,

3.1.2 Major Features of CALPUFF

By its puff-based formulation and through the use of three-dimensional meteorological data 

developed by the CALMET meteorological model, CALPUFF can simulate the effects of timeand space-varying meteorological conditions on pollutant transport from sources in complex 

terrain. The major features and options of the CALPUFF model are summarized in Table 3-2 at 

the end of this subsection. Some of the technical algorithms are briefly described below. 

Complex Terrain: The effects of complex terrain on puff transport are derived from the 

CALMET winds. In addition, puff-terrain interactions at gridded and discrete receptor locations 

are simulated using one of two algorithms that modify the puff-height (either that of ISCST3 or a 

general “plume path coefficient” adjustment), or an algorithm that simulates enhanced vertical 

dispersion derived from the weakly-stratified flow and dispersion module of the Complex Terrain

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Dispersion Model (CTDMPLUS) (Perry et al., 1989). The puff-height adjustment algorithms rely 

on the receptor elevation (relative to the elevation at the source) and the height of the puff above 

the surface. The enhanced dispersion adjustment relies on the slope of the gridded terrain in the 

direction of transport during the time step.

Subgrid Scale Complex Terrain (CTSG): An optional module in CALPUFF, CTSG treats terrain 

features that are not resolved by the gridded terrain field, and is based on the CTDMPLUS (Perry 

et al., 1989). Plume impingement on subgrid-scale hills is evaluated at the CTSG subgroup of 

receptors using a dividing streamline height (Hd) to determine which pollutant material is 

deflected around the sides of a hill (below Hd) and which material is advected over the hill (above 

Hd). The local flow (near the feature) used to define Hd is taken from the gridded CALMET 

fields. As in CTDMPLUS, each feature is modeled in isolation with its own set of receptors.

Puff Sampling Functions: A set of accurate and computationally efficient puff sampling routines 

is included in CALPUFF, which solve many of the computational difficulties encountered when 

applying a puff model to near-field releases. For near-field applications during rapidly-varying 

meteorological conditions, an elongated puff (slug) sampling function may be used. An 

integrated puff approach may be used during less demanding conditions. Both techniques 

reproduce continuous plume results under the appropriate steady state conditions.

Building Downwash: The Huber-Snyder and Schulman-Scire downwash models are both 

incorporated into CALPUFF. An option is provided to use either model for all stacks, or make the 

choice on a stack-by-stack and wind sector-by-wind sector basis. Both algorithms have been 

implemented in such a way as to allow the use of wind direction specific building dimensions. 

The PRIME building downwash model (Schulman et al., 2000) is also included in CALPUFF as 

an option.

Dispersion Coefficients: Several options are provided in CALPUFF for the computation of 

dispersion coefficients, including the use of turbulence measurements (σv and σw), the use of 

similarity theory to estimate σv and σw from modeled surface heat and momentum fluxes, or the 

use of Pasquill-Gifford (PG) or McElroy-Pooler (MP) dispersion coefficients, or dispersion 

equations based on the CTDM. Options are provided to apply an averaging time correction or 

surface roughness length adjustments to the PG coefficients. In version 5.754 of CALPUFF 

being used by VISTAS, an option is provided to use the AERMOD turbulence profiles for 

determining dispersion rates, which is the most recent approach to dispersion in EPA-approved 

regulatory modeling. In addition, turbulence advection is included. For additional details on 

these features, see Scire et al. (2005).

Overwater and Coastal Interaction Effects: Because the CALMET meteorological model 

contains both overwater and overland boundary layer algorithms, the effects of water bodies on 

plume transport, dispersion, and deposition can be simulated with CALPUFF. The puff 

formulation of CALPUFF is designed to handle spatial changes in meteorological and dispersion 

conditions, including the abrupt changes that occur at the coastline of a major body of water.

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Dry Deposition: A resistance model is provided in CALPUFF for the computation of dry 

deposition rates of gases and particulate matter as a function of geophysical parameters, 

meteorological conditions, and pollutant species. For particles, source-specific mass distributions 

may be provided for use in the resistance model. Of particular interest for BART analyses is the 

ability to separately model the deposition of fine particulate matter (< 2.5 μm diameter) from 

coarse particulate matter (2.5-10 μm diameter).

Wind Shear Effects: CALPUFF contains an optional puff splitting algorithm that allows vertical 

wind shear effects across individual puffs to be simulated. Differential rates of dispersion and 

transport among the “new” puffs generated from the original, well-mixed puff can substantially 

increase the effective rate of horizontal spread of the material. Puffs may also be split in the 

horizontal when the puff size becomes large relative to the grid size, to account for wind shear 

across the puffs. 

Wet Deposition: An empirical scavenging coefficient approach is used in CALPUFF to compute 

the depletion and wet deposition fluxes due to precipitation scavenging. The scavenging 

coefficients are specified as a function of the pollutant and precipitation type (i.e., frozen vs. 

liquid precipitation).

Chemical Transformation: CALPUFF includes options for parameterizing chemical 

transformation effects using the five species scheme (SO2, SO4

=

, NOx, HNO3, and NO3

-

) 

employed in the MESOPUFF II model or a set of user-specified, diurnally-varying transformation 

rates. The MESOPUFF II scheme is recommended by IWAQM. It produces secondary fine 

particulate matter (sulfate and nitrate) from emissions of SO2 and NOx and thus allows analyses 

of visibility impacts. Ambient ozone concentrations are used in the parameterized chemical 

transformation module as a surrogate for OH radicals during daylight hours. Ambient ammonia 

concentrations are used together with a temperature and relative humidity-dependent equilibrium 

relationship to partition nitric acid and nitrate on an hour-by-hour and receptor-by-receptor basis. 

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Table 3-2. Major Features of the CALPUFF Dispersion Model 

• Source types

- Point sources (constant or variable emissions)

- Line sources (constant or variable emissions)

- Volume sources (constant or variable emissions)

- Area sources (constant or variable emissions)

• Non-steady-state emissions and meteorological conditions

- Gridded 3-D fields of meteorological variables (winds, temperature)

- Spatially-variable fields of mixing height, friction velocity, convective velocity scale,

 Monin-Obukhov length, precipitation rate

- Vertically and horizontally-varying turbulence and dispersion rates

- Time-dependent source and emissions data for point, area, and volume sources

- Temporal or wind-dependent scaling factors for emission rates, for all source types

• Interface to the Emissions Production Model (EPM)

- Time-varying heat flux and emissions from controlled burns and wildfires

• Efficient sampling functions

- Integrated puff formulation

- Elongated puff (slug) formulation

• Dispersion coefficient (σy, σz) options

- Direct measurements of σv and σw

- Estimated values of σv and σw based on similarity theory 

-- AERMOD turbulence profiles

-- Original turbulence profiles

- Pasquill-Gifford (PG) dispersion coefficients (rural areas)

- McElroy-Pooler (MP) dispersion coefficients (urban areas)

- CTDM dispersion coefficients (neutral/stable)

• Vertical wind shear

- Puff splitting

- Differential advection and dispersion

• Plume rise

- Buoyant and momentum rise

- Stack tip effects

- Building downwash effects

- Partial penetration

- Vertical wind shear

• Building downwash

- Huber-Snyder method

- Schulman-Scire method

- PRIME method

• Complex terrain

- Steering effects in CALMET wind field

- Optional puff height adjustment: ISC3 or "plume path coefficient"

- Optional enhanced vertical dispersion (neutral/weakly stable flow in CTDMPLUS)

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Table 3-2. Major Features of the CALPUFF Dispersion Model (Cont’d)

• Subgrid scale complex terrain (CTSG option)

- Dividing streamline, Hd, as in CTDMPLUS:

- Above Hd, material flows over the hill and experiences altered diffusion rates

- Below Hd, material deflects around the hill, splits, and wraps around the hill

• Dry Deposition

- Gases and particulate matter

- Three options:

- Full treatment of space and time variations of deposition with a resistance model

- User-specified diurnal cycles for each pollutant

- No dry deposition

• Overwater and coastal interaction effects

- Overwater boundary layer parameters (COARE algorithm or OCD-based method)

- Abrupt change in meteorological conditions, plume dispersion at coastal boundary

- Plume fumigation

• Chemical transformation options

- Pseudo-first-order chemical mechanism for SO2, SO=

4, NOx, HNO3, and NO3

(MESOPUFF II method)

- Pseudo-first-order chemical mechanism for SO2, SO=

4, NO, NO2, HNO3, and NO3 

(RIVAD/ARM3 method)

- User-specified diurnal cycles of transformation rates

- No chemical conversion

• Wet Removal

- Scavenging coefficient approach

- Removal rate a function of precipitation intensity and precipitation type

3.1.3 Major Features of Postprocessors (CALPOST and POSTUTIL)

The two main postprocessors of interest for BART applications are the CALPOST and 

POSTUTIL programs. CALPOST is used to process the CALPUFF outputs, producing 

tabulations that summarize the results of the simulations, identifying, for example, the highest and 

second-highest hourly-average concentrations at each receptor. When performing visibilityrelated modeling, CALPOST uses concentrations from CALPUFF to compute light extinction 

and related measures of visibility (haze index in deciviews), reporting these for a 24-hour 

averaging time. 

The CALPOST processor contains several options for evaluating visibility impacts, including the 

method described in the BART guidance, which uses monthly average relative humidity values. 

CALPOST contains implementations of the IWAQM-recommended and FLAG-recommended 

visibility techniques and additional options to evaluate the impact of natural weather events (fog, 

rain and snow) on background visibility and visibility impacts from modeled sources.

The POSTUTIL processor is a program that allows the cumulative impacts of multiple sources 

from different simulations to be summed, can compute the difference between two sets of 

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predicted impacts (useful for evaluating the benefits of BART controls), and contains a chemistry 

module to evaluate the equilibrium relationship between nitric acid and nitrate aerosols. This 

capability allows the potential non-linear effects of ammonia scavenging by sulfate and nitrate 

sources to be evaluated in the formation of nitrate from an individual source. CALPUFF makes 

the full ambient ammonia concentration available to each puff without regard for any scavenging 

by other puffs. POSTUTIL corrects for such scavenging when the puffs generated by the 

CALPUFF model overlap, as could be the case for a single source when the wind speed is low, or 

when nitrate formation is to be attributed to each of several sources that are in a cluster and whose 

plumes overlap, 

POSTUTIL will also compute the impacts of individual sources or groups of sources on sulfur 

and nitrogen deposition into aquatic, forest and coastal ecosystems. The postprocessor allows the 

changes in deposition fluxes resulting from changes in emissions to be quantified. For example 

the output of POSTUTIL and CALPOST can be used as input into an Acid Neutralizing Capacity 

(ANC) analysis, or for comparison to Deposition Analysis Thresholds (DATs).

3.2 Discussion of CALPUFF Applicability and Limitations

3.2.1 Transport and Diffusion

According to the IWAQM Phase 2 report (page 18), “CALPUFF is recommended for transport 

distances of 200 km or less. Use of CALPUFF for characterizing transport beyond 200 to 300 km 

should be done cautiously with an awareness of the likely problems involved.”6

IWAQM’s 200-km limitation derives from the observation that, when compared to the data of the 

Cross Appalachian Tracer Experiment (CAPTEX), the basic configuration of CALPUFF 

overestimated inert tracer concentrations by factors of 3 to 4 at receptors that were 300 to 1000 

km from the source. The apparent reason was insufficient horizontal dispersion of the simulated 

plume, presumably because an actual large plume does not remain coherent in the presence of 

vertical wind shears that typically occur, especially during the night, and of horizontal wind 

shears over the large puffs that arise over long transport distances. 

To better represent such situations, an optional puff splitting algorithm has since been added to 

CALPUFF to simulate wind shear effects across a well-mixed individual puff by dividing the 

puff horizontally and vertically into two or more pieces. Differential rates of transport among the 

new puffs thus generated can increase the horizontal spread of the material in the plume due to 

vertical wind speed shear and wind direction shear. The horizontal puff splitting algorithm is 

 

6 The IWAQM presentation at EPA’s 6th Modeling Conference provides the background for this recommendation: 

“The IWAQM concludes that CALPUFF be recommended as providing unbiased estimates of concentration 

impacts for transport distances of order 200 km and less, and for transport times of order 12 hours or less. For 

larger transport times and distances, our experience thus far is that CALPUFF tends to underestimate the 

horizontal extent of the dispersion and hence tends to overestimate the surface-level concentration maxima. This 

does not preclude the use of CALPUFF for transport beyond 300 km, but it does suggest that results in such 

instances be used cautiously and with some understanding.” (From page D-12 of the IWAQM Phase 2 report.)

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designed to allow large puffs that may grow to be several grid cells or more in size to split into 

smaller puffs that can then more accurately respond to variations in the local wind field across the 

original large puff. This will also tend to increase horizontal dispersion of the plume. Since the 

creation of additional puffs via puff splitting will increase the computational requirements of the 

model, possibly substantially, puff splitting is not enabled by default, but can be turned on at the 

option of the user. Puff splitting may be appropriate for transport distances over 200 to 300 km, 

or possibly over shorter distances in complex terrain.

Turning to the shorter distance end of the transport range, the CALPUFF section of Appendix A 

of the Guideline on Air Quality Models (40 CFR 51, Appendix W) states, “CALPUFF is intended 

for use on scales from tens of meters from a source to hundreds of kilometers.” This is supported 

by the IWAQM Phase 2 report, which indicates that the diffusion algorithms in CALPUFF were 

designed to be suitable for both short and long distances. In this regard, CALPUFF does contain 

algorithms for such near-field effects as plume rise, building downwash, and terrain impingement 

and includes routines that deal with the computational difficulties encountered when applying a 

puff model in the field near to a source.

The recommendations for regulatory use in Appendix A of the Guideline on Air Quality Models

state, “CALPUFF is appropriate for long range transport (source-receptor distance of 50 to 

several hundred kilometers)”, but provisions for using CALPUFF in the near-field in “complex 

flow” situations are also included in the regulatory guidance. Complex flow situations may 

include complex terrain, coastal areas, situations where plume fumigation is likely, and areas 

where stagnation, flow reversals, recirculation or spatial variability in wind fields (e.g., as due to 

changes in valley orientation) are important. 

The tracer studies with which CALPUFF transport and diffusion capabilities were evaluated in 

the IWAQM Phase 2 report were generally over distances greater than 50 km. More recently, 

additional studies of model performance have been performed at shorter distances, including at a 

power plant in New York state in complex terrain (at source-receptor distances of 2 to 8.5 km) 

and a second power plant in Illinois in simple terrain (at source-receptor distances in arcs ranging 

from 0.5 km to 50 km from the stack) (Strimaitis et al., 1998). Other CALPUFF evaluation 

studies over short-distances include ones by Chang et al. (2001) and Morrison et al. (2003). 

These studies demonstrate good model performance over source-receptor distances from a few 

hundred meters to 50 km.

An important factor in the performance of CALPUFF is the choice of dispersion coefficients. The 

EPA has defined the "regulatory default" option in CALPUFF to allow either Pasquill-Gifford 

(PG) or turbulence-based dispersion coefficients. CALPUFF has been evaluated and shown to 

perform better using turbulence-based dispersion for tall stacks (Strimaitis et al, 1998). 

CALPUFF with turbulence-based dispersion has also been evaluated for overwater transport and 

coastal situations (Scire et al., 2005). In many other studies, including AERMOD evaluation 

studies conducted by EPA, the use of PG-dispersion, or more specifically the lack of a convective 

probability density function (pdf) module, has been demonstrated to result in underprediction of 

peak concentrations.

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In November 2005, EPA approved the AERMOD model, which relies on turbulence-based 

dispersion, as a regulatory Guideline Model7. The ISCST3 model and its PG dispersion 

coefficients are being phased out as an acceptable regulatory approach. However, EPA Region 

IV has indicated that the application of turbulence-based dispersion coefficients in CALPUFF 

needs to be further demonstrated before they are approved for BART application. They will 

consider accepting the use of turbulence dispersion coefficients on a case-by-case basis for 

sources that are close to Class I areas. 

For regional haze light extinction calculations, use of a plume-simulating model such as 

CALPUFF is appropriate only when the plume is sufficiently diffuse that it is not visually 

discernible as a plume per se, but nevertheless its presence could alter the visibility through the 

background haze. The IWAQM Phase 2 report states that such conditions occur starting 30 to 50 

km from a source. In this light, the BART guidance strongly recommends using CALPUFF for 

source-receptor distances greater than 50 km but also presents CALPUFF as an option that can be 

considered for shorter transport distances.

As discussed above, there do not appear to be any scientific reasons why CALPUFF cannot be 

used for even shorter transport distances than 30 km, though, as long as the scale of the plume is 

larger than the scale of the output grid so that the maximum concentrations and the width of the 

plume are adequately represented and so that the sub-grid details of plume structure can be 

ignored when estimating effects on light extinction. The standard 1-km output grid that has been 

established for Class I area analyses should serve down to source-receptor distances somewhat 

under 30 km; how much closer than 30 km will depend on the topography and meteorology of the 

area and should be evaluated on a case-by-case basis. For extremely short transport distances, 

depiction of the concentration distribution will require a grid that is finer than 1 km. (For 

reference, the width of a Gaussian plume, 2σy, is roughly 1 km after 10 km of travel distance, 

assuming Pasquill-Gifford dispersion rates under neutral conditions.) 

As an additional consideration, if the plume width is small compared to the visual range, the 

atmospheric extinction along a typical sight path of tens of kilometers through the plume will be 

inhomogeneous and the simple CALPOST point estimate of regional light extinction at a receptor 

point will not be correct. However, the effect of averaging light extinction estimates for 24 hours, 

during which the plume location shifts over various receptor points, is likely to mitigate this 

problem to some degree and suggests that using CALPUFF at distances under 30 km will often be 

appropriate. For the narrow plumes that result from short transport distances, though, the modeled 

peak 24-hr average extinction at a receptor will tend to overstate the effect of the source on 

regional haze. 

 

7 Revision to the Guideline on Air Quality Models: Adoption of a Preferred General Purpose (Flat and Complex 

Terrain) Dispersion Model and Other Revisions; Final Rule. 70 FR 68218-68261. 9 November 2005.

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The U.S. EPA has suggested that the plume visibility model, PLUVUE-II, could be used in lieu 

of CALPUFF for simulating visibility effects at such short distances.8 PLUVUE-II is a Gaussian 

model that simulates the dispersion, chemical conversion, and optical effects of emissions of 

particles, SO2, and NOx from a single source. Its outputs include the discoloration of the sky by 

the plume (so called “plume blight”) and the effect of the plume on visibility along user-selected 

sight paths that pass through the plume. The impacts of the plume on visibility depend not only 

on the plume composition, but also on the sight path chosen and its direction relative to the axis 

of the plume and the location of the sun. It isn’t clear how such sight-path dependent results could 

be compared to the 0.5 and 1.0 deciview thresholds in the BART guidance. Since CALPUFF is 

designed to be useful for short transport distances (with features such as the simulation of plume 

downwash caused by structures at the source), CALPUFF seems more appropriate than 

PLUVUE-II for evaluating source impact at short distances for BART assessment purposes.

3.2.2 Aerosol Constituents

Primary PM2.5

Appendix A of the Guideline on Air Quality Models (40 CFR 51, Appendix W) states that 

CALPUFF can treat primary pollutants such as PM10. In actuality, CALPUFF can simulate PM10

or PM2.5 or some other size range, because the assumed size distribution of the particles is a user 

input. The smaller the particles, the more they disperse like an inert gas. In most cases, the 

dispersion of inert PM2.5 particles will be only minutely different from that of an inert gas, but the 

behavior of larger particles will differ.

A particularly important contributor to PM concentrations is the rate of deposition to the surface. 

PM2.5 particles, which have a mass median diameter around 0.5 μm, have an average net 

deposition velocity of about 1 cm/min (or about 14 m/day) and thus the deposition of fine 

particles is usually not significant except for ground-level emissions. On the other hand, coarse 

particles (those PM10 particles larger than PM2.5) have an average deposition velocity of more 

than 1 m/min (or 1440 m/day), which is significant, even for emissions from elevated stacks. 

CALPUFF includes parametric representations of particle and gas deposition in terms of 

atmospheric, deposition layer, and vegetation layer “resistances” and, for particles, the 

gravitational settling speed. Gravitational settling, which is of particular importance for the coarse 

fraction of PM10, is accounted for in the calculation of the deposition velocity. Effects of inertial 

impaction (important for the upper part of the PM10 distribution) and Brownian motion (important 

for small, sub-micron particles) and wet scavenging are also addressed. The BART guidance 

recommends that fine particulate matter (less than 2.5 μm diameter), which has higher light 

extinction efficiency than coarse particulate matter (2.5-10 μm diameters), should be treated 

separately in the model. CALPUFF allows for user-specified size categories to be treated as 

 

8 However, for the reasons given in this paragraph, VISTAS does not recommend PLUVUE-II for BART 

application

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separate species, which includes calculating size-specific dry deposition velocities for each size 

category.

A primary PM2.5 emission from coal-fired electric generating units (EGUs) that is of relevance to 

visibility calculations is that of primary sulfate. Although primary sulfate emissions account for 

only a small fraction of the total sulfur emissions from such sources, it may be important to 

simulate their effect with CALPUFF, especially at shorter distances before significant formation 

of secondary sulfate conversion from SO2 has taken place.

Sulfur Dioxide and Secondary Particulate Sulfate 

The MESOPUFF-II chemistry algorithm used in CALPUFF9 simulates the gas phase oxidation 

of sulfur dioxide to sulfate by a linear transformation rate that was developed using regression 

relationships derived from the analysis of chemical conversion rates produced by a complex 

photochemical box model (see Scire et al., 1984, for a description of the development of the 

chemical module). As in all empirically-derived models, the relationships are based on easilycomputed or observed parameters that are used as surrogates for the factors that control SO2

oxidation. 

The surrogate factors included in the parameterized chemistry during the daytime hours include 

solar radiation intensity, ambient ozone concentration, and atmospheric stability class. For 

example, gas phase SO2 oxidation is a function of OH radical concentrations. Ozone 

concentrations are correlated with OH radical concentrations during daytime hours, and their use 

in the daytime SO2 conversion rate in CALPUFF is based on this correlation relationship. The 

philosophy is that OH radical measurements are not available and cannot easily be computed 

within a model like CALPUFF, but ozone is commonly measured throughout the country, so the 

use of the well-known surrogate variable (ozone) is more useful in the empirical relationship than 

factors that are unknown or have a high degree of uncertainty. The same logic applies to the 

other variables in the relationship. They are surrogates for factors that the regression analysis has 

shown to be important in SO2 oxidation rates. At night, the SO2 conversion is set to a constant 

low value (default is 0.2%/hr). Aqueous phase oxidation of SO2 is represented by an additive 

term that varies with relative humidity and peaks at 3%/hr at 100% relative humidity. CALPUFF 

represents the chemical conversion as a linear process because it requires linear independence 

between puffs, although as explained below, non-linear behavior in nitrate formation can be 

modeled.

 

9 CALPUFF offers two options for parameterizing chemical transformations: the 5 species (SO2, SO4

=

, NOx, HNO3, 

and NO3

-

) MESOPUFF-II system and the 6 species RIVAD system (which treats NO and NO2 separately). 

IWAQM recommends using the MESOPUFF-II system with CALPUFF. The RIVAD system is believed to be 

more appropriate for clean environments, however, and therefore was used in the Southwest Wyoming Regional 

CALPUFF Air Quality Modeling Study in 2001. For the VISTAS region, the IWAQM- and FLM-recommended 

MESOPUFF-II chemistry is most appropriate. 

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The IWAQM Phase 2 report concludes that this chemistry algorithm is adequate for representing 

the gas phase sulfate formation but that it does not adequately account for the aqueous phase 

oxidation of SO2. Actual aqueous phase oxidation in clouds or fog can proceed at rates much 

greater than 3% per hour, leading IWAQM to suggest that sulfate might be underestimated in 

such situations. However, aqueous phase oxidation depends on liquid water content, not relative 

humidity. In reality, liquid water does not exist in the atmosphere at relative humidity much 

below 100%, while the CALPUFF aqueous reaction term produces sulfate at lower relative 

humidity. This can lead CALPUFF to overestimate sulfate concentrations when the humidity is 

high but the cloud water that enables aqueous conversion is not present. Therefore, the direction 

of the bias in the aqueous chemistry simulation of sulfate formation can vary.

Other potential sources of error in the sulfate formation mechanism of CALPUFF include (1) 

overestimation of sulfate formation when NOx concentrations in the plume are high and in 

actuality they deplete the local availability of ozone and hydrogen peroxide for oxidizing the SO2; 

and (2) lack of direct consideration of the effect of temperature on the conversion rates, which 

may cause the model to overstate sulfate formation on cold days (below 10C or 50°F) (Morris et 

al., 2003). However, in CALPUFF, the effects of temperature are, to some degree, compensated 

for indirectly by the use of the solar radiation surrogate variable in the empirical conversion 

equations. 

Whether these potential errors are important will depend on the setting. For example, Figure 3-2 

shows a comparison of predicted and observed 24-hour sulfate concentrations, due to a large 

number of SO2 sources, at the Pinedale IMPROVE site in Wyoming for the 1995 period (Scire et 

al., 2001). Overall, in this case there was very little bias in the sulfate predictions. Whether 

CALPUFF predictions would compare as well with measurements in the Southeast remains to be 

seen. 

CALPUFF does not identify the chemical form of the sulfate compound that results from its 

reactions, which will generally be some form of ammoniated sulfate whose degree of 

neutralization will depend on the availability of ammonia in the atmosphere. This consideration, 

which has been found to be relevant for calculating light extinction in the VISTAS region, is not 

addressed by CALPUFF or CALPOST.

In most applications, the ozone concentrations required for the sulfate formation calculations are 

derived from ambient measurements, although concentrations simulated by regional models can 

be used. 

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Figure 3-2. Observed vs. CALPUFF-predicted 24-hour sulfate concentrations at the IMPROVE 

monitoring site in Pinedale, Wyoming for 1995.

NOx and Secondary Ammonium Nitrate

The MESOPUFF-II chemistry algorithm used in CALPUFF simulates the oxidation of NOx to 

nitric acid and organic nitrates (both gases) by transformation rates that depend on NOx

concentration, ambient ozone concentration, and atmospheric stability class during the day. The 

conversion rate at night is set at to a constant value (default is 2.0 %/hr). The temperature- and 

humidity-dependent equilibrium between nitric acid gas and ammonium nitrate particles is taken 

into account when estimating the ammonium nitrate particle concentration, an equilibrium that 

depends on the ambient concentration of ammonia. The user supplies the value of the ambient 

concentration of ammonia. CALPUFF assumes that the sulfate reacts preferentially with that 

ammonia to form ammonium sulfate and the left over ammonia is available to form ammonium 

nitrate. 

The IWAQM Phase 2 report considers that this mechanism is adequate for representing nitrate 

chemistry. Potential situations where this assumption may not be correct, however, include (1) 

plumes with high concentrations of NOx that deplete the ambient ozone and thus limit the 

[SO4] Q-Q Plot for CALPUFF Predictions and Bridger

IMPROVE Aerosol Observations

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

Bridger IM PROVE[SO4].Units:ug/m**3 of SO4

CALPUFF Predicted [SO4]. Units: ug/m**3 of SO4

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transformation of NOx to nitric acid in the plume; and (2) when ambient temperature is below 10 

C, and thus the transformation rate is much slower and the nitrate concentration may be lower 

than that simulated by CALPUFF (Morris et al., 2003). In both cases, CALPUFF may 

overestimate the amount of nitrate that is produced. In particular, the impact of ammonium nitrate 

concentrations on visibility at Class I areas in the VISTAS region is greatest in the winter, when 

temperatures are lowest, the nitrate concentrations are the greatest, and the sulfate concentrations 

tend to be the least. CALPUFF may overstate the impacts of NOx emissions at those times, 

especially in the colder northern states. This potential overestimate of nitrate was not evident, 

however, in an evaluation of CALPUFF-modeled nitrate against actual observational data in the 

Wyoming study, as shown in Figure 3-3a (Scire et al., 2001), 

Another factor in the calculation of nitrate is that CALPUFF makes the full amount of the 

background concentration of ammonia available to each puff, and that amount is scavenged by 

the sulfate in the puff. If puffs overlap, then that approach could overstate the amount of 

ammonium nitrate that is formed in total if, in reality, the combined scavenging by the 

overlapping puffs at a location would deplete the available ammonia enough that the combined 

nitrate formation was limited by the availability of ammonia. This effect of such ammonia 

limiting can be large in summer; for a source 75 km west of Mammoth Cave National Park, one 

modeling analysis found the maximum light extinction impact of the source to be 7.4% (roughly 

0.74 deciviews) at the park when CALPUFF was used without consideration of ammonia limiting 

and about 30% less, between 5.5 and 5.8% (roughly 0.55 to 0.58 dv), when the effect of ammonia 

limiting was considered (Escoffier-Czaja and Scire, 2002).

To address the issue, since 1999 (i.e., after the IWAQM Phase 2 report) the CALPUFF system 

has included the optional POSTUTIL postprocessing program, which repartitions the ammonia 

and nitric acid concentrations estimated by CALPUFF to reflect potential ammonia-limiting 

effects on the development of nitrate. This allows non-linearity associated with ammonia limiting 

effects to be included in the CALPUFF model estimates. POSTUTIL computes the total sulfate 

concentrations from all sources (modeled sources plus inflow boundary conditions) and estimates 

the amount of ammonia available for total nitrate formation after the preferential scavenging of 

ammonia by sulfate. That is, as new sulfate, nitrate or ammonia from the source of interest is 

added to an existing mix of pollutants, POSTUTIL will estimate both the nitrate formed from the 

new source and the change in background nitrate as a result of the incremental depletion of 

ammonia (due to the new sulfate and nitrate) or addition of ammonia (from a new source of 

ammonia).

Reliable estimates of the ambient concentrations of ammonia, especially with the temporal and 

spatial resolution that would be optimal for use with CALPUFF, are needed to take full advantage 

of the increased accuracy provided by POSTUTIL. The processor requires estimated 

concentrations of ammonia throughout the modeling domain and period. Such estimates can be 

inferred from CASTNet measurements, which are integrated over a week, from 24-hr SEARCH 

measurements, or from the output of a regional photochemical model such as CMAQ or CAMx. 

The CASTNet network is fairly sparse and the uncertainty in the ammonia measurements is large, 

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so defining the ammonia concentration throughout the Southeast would require extensive 

interpolation or extrapolation from the measured values. The quality of the SEARCH 

measurements is much better, but there are only 8 sites and they do not cover the entire VISTAS 

domain. Modeled concentrations have the advantage of being resolved in space and time, but 

their accuracy should be evaluated by comparison with measurements wherever possible. 

Benefit is obtained by considering seasonal trends of ammonia and using POSTUTIL to 

determine the diurnal variability in available ammonia due to the daily cycle of nitrate formation 

associated with temperature and relative humidity effects. For example, results of the Wyoming 

study (see Figure 3-3a) show that POSTUTIL adjustments produced daily average nitrate 

concentrations well within the factor of two lines and with very little mean bias. On the other 

hand, analysis of the same results with use of constant ammonia of 0.5 ppb or 1.0 ppb produced 

consistent overpredictions of nitrate by factors of 2-3 and 3-4, respectively, as shown in Figure 3-

3b (Scire et al., 2003). 

Figure 3-3a. Observed vs. CALPUFF-predicted 24-hour nitrate concentrations at the IMPROVE 

monitoring site in Pinedale, Wyoming for 1995 using the ammonia limiting method. (Scire et al., 

2001)

[NO3] Q-Q Plot for CALPUFF Predictions and Bridger

IMPROVE Aerosol Observations

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.1 0.2 0.3 0.4 0.5 0.6

Bridger IM PROVE[NO3].Units:ug/m**3 of NO3

CALPUFF Predicted [NO3]. Units: ug/m**3 of NO3

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Figure 3-3b. Observed vs. CALPUFF-predicted 24-hour nitrate concentrations at the IMPROVE 

monitoring site in Pinedale, Wyoming for 1995 using the ammonia limiting method (blue), constant 

ammonia at 0.5 ppb (pink) and constant ammonia at 1.0 ppb (green). (Scire et al., 2003)

Secondary Organic Aerosol

Ongoing research studies at several Class I areas throughout the country (Fallon and Bench, 

2004) and at SEARCH sites in the Southeast (Edgerton et al., 2004) are finding that, typically, 90 

to 95% of the rural organic carbon fine particle concentration consists of modern carbon (e.g., 

that from the burning of vegetation and deriving from VOC emissions from vegetation) and only 

5 to 10% is attributable to man’s burning of fossil fuels. In addition, a field study at Great Smoky 

Mountains National Park in August 2002 (Tanner, et al., 2005) found that an average of 83% of 

the fine carbon was modern carbon

According to IMPROVE measurements, organics account for roughly 10% of the particle-caused 

light extinction in Class I areas in the Southeast. We can thus conclude that, in general, secondary 

organic carbon particles derived from anthropogenic fossil fuel burning emissions are unlikely to 

have a large impact (around 1%) on current visibility. (Man-caused burning of vegetation can 

have significant localized, short-term impacts, however.)

Current organic fine particle concentrations in the Southeast are typically within a factor of 2 of 

the 1.4 μg/m3 concentration assumed for natural conditions by the EPA, which means that current 

fossil fuel burning would contribute less than 2% to visibility in an atmosphere that represents 

natural conditions. Thus, it is unlikely that VOC and organic particle contributions from BART 

[NO3] Q-Q Plot for CALPUFF Predictions w ith Different NO3 and HNO3 Repartition and Bridger IM PROVE 

Observations 

0

0.2

0.4

0.6

0.8

1

1.2

0 0.2 0.4 0.6 0.8 1 1.2

Bridger IMPROVE [NO3]. Units: ug/m**3 of NO3

CALPUFF Summed Repartition (

CALPUFF Separated Repartition 

CALPUFF Separated Repartition 

Factor 1

Factor 2 Factor 3

Factor 4

Factor 2

Factor 3

Factor 4

0.4 0.6 0.8 1 1.2

Bridger IMPROVE [NO3]. Units: ug/m**3 of NO3

CALPUFF Summed Repartition (Seasonal NH3)

CALPUFF Separated Repartition (NH3=0.5ppb)

CALPUFF Separated Repartition (NH3=1.0ppb)

Factor 1

Factor 2 

or 3

Factor 2

Factor 3

Factor 4

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sources will cause a large impact to visibility at Class I areas, but a 5% (0.5 dv) localized impact 

from a particularly large VOC source cannot be dismissed out of hand. 

CALPUFF has only rudimentary capabilities for addressing formation of visibility-impairing 

organic particles from some forms of volatile organic carbon (VOC). The capabilities that do 

exist include the following. 

First, PM10 emissions (such as from power plants) are often divided into filterable and 

condensable components, with the condensable mass being 100-200% of the filterable mass. For 

purposes of visibility analyses with CALPUFF, a fraction of the condensable part is typically 

treated as organic particles, i.e., it is assumed that a fraction of the condensable components in the 

PM10 emissions condense into organic PM2.5 particles. The size of this organic fraction varies 

with process and process equipment, and can range from 20 to 100% of the condensable mass. 

These fine organic particles can be readily modeled by CALPUFF. (The remaining condensable 

material may be sulfuric, hydrochloric, or hydrofluoric acid.)

Second, a module that treats the formation of secondary organic particles from organic emissions 

was recently developed and is now part of the CALPUFF system. (Scire et al., 2001). This 

simplified secondary organic aerosol (SOA) module is a linear, parameterized representation that 

is currently considered best suited for biogenic organics. It relies on the conventional wisdom that 

only hydrocarbons with more than six carbon atoms can form significant SOA (Grosjean and 

Seinfeld, 1989). For example, according to this rule, isoprene (C5H8) does not make SOA but 

terpenes do, making pine trees more important biogenic contributors to SOA than oak trees.10

Limited evaluation of the performance of CALPUFF at simulating SOA with its biogenic SOA 

module at one IMPROVE site in a regional modeling study in Wyoming found that 95% of 101 

estimated 24-hr SOA concentrations were within 2% of the measured values (Scire et al., 2001). 

This performance seems promising, although the developers view the SOA module as needing 

more testing and evaluation.

Thus, CALPUFF includes approaches for dealing with condensable VOC emissions that are 

characterized as condensable PM10 and with biogenic VOCs, although the soundness of 

concentration estimates by these approaches when modeling a plume from a single source is 

largely untested.11 The CALPUFF simulation of VOC emissions from sources whose VOC 

emissions are predominantly anthropogenic is problematic, however. Perhaps the approach used 

for the simplified biogenic SOA module may be extended to anthropogenic VOCs when 

speciated VOC emissions information is available. If only those VOCs with more than six carbon 

atoms are presumed to be of importance, this eliminates many anthropogenic sources of VOC 

emissions. For example, the fugitive emissions of butane and ethane during petroleum processing 

 

10 Recent research suggests that isoprene may be a SOA precursor, however.

11 Note that neither of these VOC-related simulation approaches is described in the current (Version 5) CALPUFF

User’s Guide dated January 2001. See the Wyoming report referenced above for a description of this module.

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are not important, while aromatic emissions (such as of toluene and xylene) are considered by the 

SOA module’s mechanism. Development, testing, and evaluation would be needed before one 

could rely on such a module for estimating SOA from anthropogenic SOA emissions, though.

Therefore, to demonstrate the visibility impacts of VOC emissions from BART-eligible sources, 

means other than CALPUFF will be needed. A technical approach using a regional 

photochemical model to evaluate visibility impacts of VOC emissions is presented in Section 

4.1.3. CALPUFF can be used to estimate the contribution from the primary condensable fraction 

of PM10 emissions, though.

3.2.3 Regional Haze

Calculation of the impact of the simulated plume particulate matter component concentrations on 

light extinction is carried out in the CALPOST postprocessor. The formula used is the usual 

IMPROVE/EPA formula, which is applied to determine a change in light extinction due to 

changes in component concentrations. Using the notation of CALPOST, the formula is the 

following:

bext = 3 f(RH) [(NH4)2SO4] + 3 f(RH) [NH4NO3] + 4[OC] + 1[Soil] +

 + 0.6[Coarse Mass] + 10[EC] + bRay (3-1)

The concentrations, in square brackets, are in μg/m3 and bext is in units of Mm-1. The Rayleigh 

scattering term (bRay) has a default value of 10 Mm-1, as recommended in EPA guidance for 

tracking reasonable progress (EPA, 2003a).

There are a few important differences in detail and in notation between the CALPOST formula 

for estimating light extinction (i.e., Equation 3-1) and that of IMPROVE and EPA. First, the OC

in the formula above represents organic carbonaceous matter (OMC in IMPROVE’s notation), 

which is 1.4 times the OC (i.e., organic carbon alone) in the IMPROVE formula. The EC above is 

synonymous with LAC in the IMPROVE formula. CALPOST now offers the option of using the 

old IMPROVE f(RH) curve, whose values are documented in the December 2000 FLAG report, 

or the f(RH) now used by IMPROVE and EPA (as documented in EPA’s regional haze guidance 

documents). Also, CALPOST sets the maximum RH at 98% by default (although the user can 

change it), while the EPA’s guidance now caps it at 95%. 

The haze index (HI) is calculated from the extinction coefficient via the following formula:

HI = 10 ln (bext/10) (3-2)

where HI is in units of deciviews (dv) and bext is in Mm-1. The impact of a source is determined 

by comparing HI for estimated natural background conditions with the impact of the source and 

without the impact of the source. 

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CALPOST Methods

CALPOST uses Equation 3-1 to calculate the extinction increment due to the source of interest 

and provides various methods for estimating the background extinction against which the 

increment is compared in terms of percent or deciviews.

For background extinction, the CALPOST processor contains seven techniques for computing the 

change in light extinction due to a source or group of sources (called Methods 1-7). These are 

usually reported as 24-hour average values, consistent with EPA and FLM guidance. In addition, 

there are two techniques for computing the 24-hour average change in extinction (i.e., as the ratio 

of 24-hour average extinctions, or as the average of 24-hour ratios). A brief summary of the 

techniques is provided below. Method 2 is the current default, recommended by both IWAQM 

(EPA, 1998) and FLAG (2000) for refined analyses. Method 6 is recommended by EPA’s BART 

guidance (70 FR 39162).

Methods 4 and 5 use optically measured hourly background extinctions, which represent current 

actual levels of extinction and thus are not consistent with the “natural conditions” the BART 

proposal says should be used as a baseline. Methods 1 through 3 and 6 and 7 allow for user inputs 

of estimated (e.g., natural conditions) background extinction or component concentrations, and 

thus are consistent with the BART proposal.

Method 1 allows the user to specify a single value of a “dry” background extinction coefficient 

for each receptor, specify that a certain fraction of that coefficient is due to hygroscopic species, 

and use relative humidity measurements to vary the extinction hourly via a 1993 IWAQM f(RH)

curve or, optionally, the EPA regional haze f(RH) curve (EPA, 2003b). The RH is capped at 98% 

or a user-selected value (95% for the EPA curve). The same f(RH) is applied to both the modeled 

sulfate and nitrate. 

For an example of the use of Method 1, one could use the dry particle extinction coefficient of 

9.09 Mm-1 that results from EPA’s default natural conditions concentrations, together with an 

assumption that for natural conditions, say, 0.9 Mm–1 (or 10%) of this amount results from 

hygroscopic ammonium sulfate and ammonium nitrate, and then apply f(RH) to this 10%. 

In Method 2, user-specified, speciated monthly concentration values are used to describe the 

background. When applied to natural conditions, for which EPA’s default natural conditions 

concentrations are annual averages, the same component concentrations would have to be used 

throughout the year (unless potential refinements to those default values resulted in 

concentrations that vary during the year). Hourly background extinction is then calculated using 

these concentrations and hourly, site-specific f(RH) from a 1993 IWAQM curve (a different one 

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than that in Method 1) or, optionally, the EPA regional haze f(RH) curve.12 Again the RH is 

capped at either 98% (default) or a user-selected value (most commonly at 95%). 

Method 3 is the same as Method 2, except that any hour in which the RH exceeds 98% (or the 

selected maximum) is dropped from the analysis. When 24-hr extinction is computed, no fewer 

than 6 valid hours are accepted at each receptor; otherwise the value for the day is tabulated as 

“missing”.

Method 6 is similar to Method 2, except monthly f(RH) values (e.g., EPA’s monthly 

climatologically representative values in EPA (2003a, b)) are used in place of hourly values for 

calculating both the extinction impact of the source emissions and the background conditions 

extinction. Hourly source impacts, with the effect on extinction due to sulfates and nitrates 

calculated using the monthly-average relative humidity in f(RH), are compared against the 

monthly default natural background concentrations. Thus the monthly-averaged relative humidity 

is applied to the hygroscopic components (i.e., sulfate and nitrate) of both the source impact and 

the background extinction with Method 6. 

Method 7 is a new variant of Method 2 that was developed as a result of a ruling by the Assistant 

Secretary of the Interior for Fish and Wildlife and Parks, in response to a New Source Review 

case in Montana, that “natural conditions” should reflect the visibility impairment caused by 

significant meteorological events such as fog, precipitation, or naturally occurring haze (DOI, 

2003).13 Under Method 7, during hours when visibility is obscured by meteorological conditions, 

the actual measured visibility is used to represent natural conditions instead of the value that is 

calculated from EPA’s default natural conditions concentrations under Method 2. A recent 

modification developed in response to FLM comments on Method 7, in which the daily average 

natural extinction is calculated somewhat differently, is called Method 7’, i.e., “7 prime”.

Refined Estimates of Extinction and Natural Background Visibility

Separate from the BART discussions, IMPROVE, EPA, and the Regional Planning Organizations 

are evaluating whether refinements are warranted to the methods recommended in EPA’s 

guidance to calculate default estimates of natural background visibility. In particular, IMPROVE 

has recently approved an alternative to the formula (Eq. 3-1) it uses to estimate extinction from 

particle concentration measurements (Pitchford et al., 2005).

Refinements in the revised IMPROVE formula include the following:

- Adding a sea salt term, including a growth factor due to relative humidity 

 

12 Note that the hourly-varying natural background extinction in this method is not consistent with that prescribed 

by the EPA’s natural conditions guidance (EPA, 2003b), for which a “climatologically-representative” f(RH) 

that only varies monthly is to be used. Method 6 uses these monthly average humidity values.

13 The Secretary’s guidance applies only to Federal Land Managers. EPA’s position on this interpretation of natural 

conditions is unknown.

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- Increasing the factor used to calculate the mass of particulate organic matter (OC in Eq. 

3-1) from organic carbon measurements

- Modifying the relative humidity growth formula, f(RH), for sulfates and nitrates

- Revising the extinction efficiencies (the numerical constants in Equation 3-1) for 

sulfates, nitrates, and organic carbon so that they vary with concentration

- Adding a site-specific Rayleigh scattering term to the formula. Values will be calculated 

by IMPROVE for all Class I areas. 

For the purposes of calculating current, future, and natural background visibility at VISTAS Class 

I areas as part of the reasonable progress analyses, VISTAS intends to present regional air quality 

modeling results using both the current EPA recommended assumptions and the newly revised 

aerosol extinction formula. If a BART-eligible source chooses to consider its projected impacts 

using the newly revised formula as well as the current formula, then modifications would need to 

be made to CALPOST to carry out calculations with the new algorithm. 

 

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4. VISTAS’ COMMON MODELING PROTOCOL

4.1 Overview of Common Modeling Approach

In this section, guidance is provided on the use of the CALPUFF modeling system for two

purposes:

1) Evaluating whether a BART-eligible source is exempt from BART controls because it 

is not reasonably expected to cause or contribute to impairment of visibility in Class I 

areas, and 

2) Quantifying the visibility benefits of BART control options. 

For purpose 1), States must determine whether a source emits any air pollutant (SO2, NOx, PM, 

and in certain cases VOC and NH3) that “may reasonably be anticipated to cause or contribute to 

any impairment of visibility” in a Class I area. The States have 3 options to accomplish this:

A) Conclude that all BART-eligible sources in State are subject to BART. 

B) Demonstrate that all BART-eligible sources in the State together do not cause or 

contribute to any visibility impairment 

C) Determine if the impact from each individual BART-eligible source is greater than a 

threshold value. 

VISTAS States intend to follow Option C (determine if the visibility impact from individual 

sources exceeds a contribution threshold) for SO2 and NOx emissions. The methods for Option C 

are described in Section 4.1.1. In early 2006, VISTAS pursued Option B (demonstrate that all 

BART eligible sources in a State do not impact visibility) for VOC, NH3 and PM emissions. The 

approach and results for Option B are described in Section 4.1.3. As a result of this exercise, the 

VISTAS States have determined that the Option C exemption analyses should also include PM 

emissions and, for sources with large NH3 emissions, NH3. The States determined that 

anthropogenic VOC emissions do not cause or contribute to visibility impairment at VISTAS 

Class I areas and that VOC emissions do not need to be considered in BART analyses. 

4.1.1 BART Exemption Analysis

As illustrated in Figure 4-1, three steps will evaluate whether a BART-eligible source of SO2, 

NOx, or PM is subject to BART: 

1) VISTAS plans to use Q/d as a presumptive indicator that a source is subject to BART. If Q/d 

for SO2 > 10 for 2002 actual emissions, then the State presumes that the source is subject to 

BART. If the source agrees with this presumption, then no exemption modeling is required 

and the source can proceed to the BART determination using CALPUFF to evaluate impacts 

of control options and can perform the engineering analyses. If a source disagrees, the source 

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may perform fine grid modeling as described in Section 4.4 to determine if its impact is < 0.5 

dv.

Figure 4-1. Flow chart showing the components of the VISTAS common modeling protocol. 

Assessment should be made for each Class I Area. (If a source agrees to install the most stringent 

controls then the modeling steps indicated above and engineering analyses and visibility impact 

modeling would not be required.) 

2) An optional initial modeling assessment using the CALPUFF model with the coarse scale 12-

km regional VISTAS domain can be used to answer questions whether (a) a particular source 

may be exempted from further BART analyses and (b) if finer grid CALPUFF analysis were 

to be undertaken, which Class I areas should be included. Assumptions for the initial 

modeling assessment are conservative so that a source that contributes to visibility impairment 

is not exempted in error. If a source is shown not to contribute to visibility impairment using 

the initial modeling assessment, the source would not be subject to BART and would be 

exempted from further BART analyses. If a source is shown to contribute to visibility 

impairment using the initial modeling assessment, the source has the option to undertake finer 

grid CALPUFF modeling to evaluate further whether it is subject to BART. 

3) A finer grid CALPUFF modeling analysis using a subregional CALMET domain will be the 

definitive test as to whether a source is subject to BART.

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For large sources that will clearly exceed the initial screening thresholds, this step can be skipped 

and the analysis may proceed directly to the finer grid modeling analysis, which is described in 

Section 4.4. 

4.1.2 BART Control Evaluation

For sources that are determined to be subject to BART controls, part of the BART review process 

involves evaluating the visibility benefits of different BART control measures. These benefits 

will be determined by making additional CALPUFF simulations using the same CALMET and 

CALPUFF configuration as those used in the finer grid analysis of Step 2. The only exception is 

that the source and emissions data used in the CALPUFF control evaluation simulations will 

reflect the BART control measures being evaluated. Using the same model configuration will 

produce an “apples-to-apples” comparison, where differences in impacts are due to the 

effectiveness of the controls rather than model configuration differences. For example, a control 

scenario evaluation that uses more conservative assumptions than the base case simulation may 

produce results showing no or little improvement in visibility impacts. That control scenario run 

with the same model configuration as the base case may show significant visibility improvement. 

Therefore, in order to not obscure the response to predicted visibility improvements by 

differences in the modeling approach, the same model configuration should be used in the BART 

control evaluation simulation as in the base case simulation.

The base case to which the effectiveness of BART controls is to be compared is the “current 

emissions” scenario for which the finer grid Step 2 modeling was performed. The postprocessing 

steps and procedures are the same as in the BART eligibility simulation. Side-by-side 

comparison of the visibility impacts will be tabulated to quantify the effectiveness of each control 

scenario relative to the base case.

The modeling evaluation is a unit-by-unit evaluation and can be conducted on a pollutant specific 

basis. Modeling results are used with the other four statutory factors mentioned in Section 2.1 to 

decide which control technology, if any, is appropriate. Finally, if a source decides to use the 

most stringent control technology available, the BART control analysis, including modeling, is 

not necessary.

4.1.3 VISTAS’ Treatment of VOC, NH3, and PM

Volatile Organic Compounds 

CALPUFF is currently not recommended for addressing visibility impacts from VOC because its 

capability to simulate secondary organic aerosol formation from VOC emissions is not adequately 

tested, especially for anthropogenic emissions. (Separately, condensable organic carbon can be 

calculated from PM10.) 

VISTAS has performed a weight of evidence analysis to demonstrate, using the CMAQ regional 

air quality model, that the combined VOC emissions from all point sources (BART-eligible and 

non-BART) in each State do not contribute to visibility impairment. Emissions sensitivity 

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simulations run for VISTAS by Georgia Institute of Technology using VISTAS’ 12 x 12 km grid 

and CMAQ v 4.3 for episodes in July 2001 and January 2002 demonstrated very low to no 

response of organic carbon levels and light extinction at Class I areas to changing VOC emissions 

from all anthropogenic sources in the VISTAS 12-km modeling domain (eastern US). Georgia 

Tech repeated the sensitivity analyses using the VISTAS 12-km domain and CMAQ v 4.4 with a 

refined SOA module for summer (Jun 1-Jul 10) and winter (Nov 19-Dec 19) periods in 2002. 

VOC emissions from all anthropogenic point sources in every VISTAS State were reduced by 

100% (i.e., eliminated). The maximum 24-hr impact of all VOC emissions from all point sources 

throughout the VISTAS domain was thus determined to be less than 0.5 dv (compared to annual 

average natural background) at every Class I area in the VISTAS domain and in adjacent States. 

It follows that the impact of any one BART-eligible source would be much less than 0.5 dv. 

Based on these analyses, the VISTAS States have concluded that VOC emissions from BART 

sources do not cause or contribute to visibility impairment and do not need to be included in 

BART analyses. 

Ammonia 

EPA has given states the option to address ammonia (NH3) emissions from BART-eligible 

sources. VISTAS also contracted with Georgia Tech to calculate NH3 emissions sensitivities 

using CMAQ v 4.4 with a refined SOA module and the same Jun-Jul and Nov-Dec periods in 

2002 that were used for the VOC sensitivity evaluation. The NH3 emissions from all point 

sources (BART-eligible and not-BART) in every State were reduced by 100% for these analyses. 

This sensitivity evaluation showed that the collective impact of all VISTAS region point NH3

emissions is greater than 0.5 dv (compared to annual average natural background) at several Class 

I areas. When the NH3 emissions were scaled to represent 100% reduction from only the BARTeligible sources in each State, then the maximum impact of those sources was under 0.5 dv at 

most, but not all Class I areas. The high values appear to result primarily from emissions from 13 

large NH3 sources. In the absence of those 13 facilities, the scaled NH3 emissions peak impacts at 

Class I areas were 0.3 dv or less. Based on these analyses, the VISTAS States recommended that, 

except for these 13 facilities, NH3 emissions not be included in BART modeling. States will 

provide instructions to those 13 sources as to how to evaluate contributions of their NH3

emissions to visibility impairment. For documentation purposes, in summer 2006 VISTAS is 

repeating the NH3 emissions sensitivity calculations, using CMAQ v4.5 with Base F emissions 

and reducing 100% of NH3 emissions from only the BART-eligible sources in the VISTAS states. 

Primary Particulate Matter 

Primary particulate matter is considered a visibility impairing pollutant. However, the extent to 

which primary PM from BART-eligible sources contributes to impairment at Class I areas in the 

southeastern US is not clear. For EGUs, the EPA has determined that emissions reductions of 

SO2 and NOx under the CAIR rule meet the BART requirements, but these EGUs may still be 

subject to BART for primary PM. To determine the potential impacts of PM from EGU and nonEGU sources in the VISTAS states, two CMAQ sensitivity runs for the first and third quarters of 

2002 were carried out by VISTAS’ CMAQ modeling team of ENVIRON, UCR, and Alpine 

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Geophysics In one run, all primary PM from EGUs was removed while in the other run all 

primary PM from non-EGU sources was removed. All other CMAQ modeling components were 

held constant. At almost all Class I areas in the VISTAS region, primary PM emissions 

contribute to regional haze, with the collective impact of all EGU and non-EGU point primary 

PM emissions being greater than 0.5 dv compared to annual average natural background. In fact, 

the impacts of EGU PM emissions alone or of non-EGU PM emissions alone were each mostly 

greater than 0.5 dv. Although the impacts of BART sources alone would be smaller, the VISTAS 

States have concluded that all BART-eligible sources need to consider the impacts of their PM 

emissions.

4.2 Optional Source-Specific Modeling

In some circumstances, a source may want to apply techniques designed to evaluate the impacts 

in a more detailed way than the standard VISTAS common protocol. A source may propose 

source-specific modeling procedures to address special issues to the State for State review. For 

example, sources very close to Class I areas may be better treated by a finer grid resolution that 

the generic Step 2 “fine” grid resolution meteorological fields provided by VISTAS. In some 

situations, higher resolution MM5 or other prognostic meteorological datasets may be available 

than the standard 12-km or 36-km MM5 datasets provided by VISTAS. Because it is not possible 

to anticipate all of the situations where there would be a benefit to conducting more detailed 

source-specific analyses, the option to pursue this option is left as an open issue, to be resolved 

and justified based on specific factors relevant for the source in question.

A source-specific modeling protocol is required for each source. This document should describe 

the data sources and model configuration, and provide rationale for any changes in the model 

approach from the common protocol. This source-specific protocol must be provided for review 

and approval by the State. The State will share the protocol with EPA and the Federal Land 

Managers for their review. Discussion of approaches to source-specific modeling and an outline 

of the typical contents of the source-specific protocol are presented in Chapter 5. Discussions 

with the regulatory authorities should be conducted prior to development of a source-specific 

protocol to ensure all of the relevant issues are included in the protocol.

4.3 Initial Procedure for BART Exemption 

4.3.1 Overview of Initial Approach

The first step in the common protocol, the initial assessment in Figure 4-1, is a simple procedure 

to evaluate whether a source can be exempted from BART controls using a consistent set of 

meteorological and dispersion options. A pre-computed set of meteorological files and a predefined CALPUFF input option configuration, based on guidance in the final BART rule (70 FR 

39104-39172) and other EPA and FLAG model guidance, will allow relatively simple initial 

simulations. The regional initial domain is designed to allow any Class I areas within the 

VISTAS area to be evaluated with a single meteorological database and consistent CALPUFF 

modeling options. The second important question that this first screening step will answer is, if 

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initial modeling indicates a source may impact visibility significantly, what Class I areas should 

be included in a finer grid analysis? Due to the multitude of factors affecting the contribution of a 

source to visibility in a Class I area, simple screens or rules of thumb alone (such as that the 

closest Class I area will produce the controlling visibility impacts) are not likely to be universally 

reliable. 

4.3.2 Discussion of 12-km Initial Exemption Modeling

Meteorological Fields

A regional initial domain and a set of pre-computed regional CALMET meteorological files will 

be prepared for VISTAS, to allow any Class I areas within the VISTAS area to be evaluated with 

a consistent meteorological database and consistent CALPUFF modeling options. 

The following three years of MM5 meteorological data have been assembled by VISTAS for use 

in the regional CALPUFF modeling effort:

- 2001 MM5 dataset at 12 km and 36 km grid (developed for EPA)

- 2002 MM5 dataset at 12 km and 36 km grid (developed by VISTAS)

- 2003 MM5 dataset at 36 km grid (developed by the Midwest Regional Planning

Organization).

These data sets have been provided to Earth Tech by VISTAS, and from them Earth Tech has 

produced annual CALMET meteorological files at 12-km grid resolution for the domain shown in 

Figure 4-2. The CALMET modeling output files in the form of CALPUFF-ready threedimensional meteorological files will be available on external hard drives to the States and other 

parties.

The initial procedure to determine if a BART-eligible source is subject to BART uses the precomputed CALMET meteorological fields for the years 2001-2003 on the 12-km CALMET 

domain in Figure 4-2 and simulates with CALPUFF any BART-eligible source to be screened. 

The CALMET simulations will be developed using the highest resolution MM5 data available for 

each year (i.e., 36-km MM5 data for 2003, 12-km MM5 data for 2001 and 2002). 

The development of the regional CALMET meteorological fields from MM5 data will be 

conducted in No-Observations (“No-Obs”) mode. The MM5 data already reflect assimilation of 

observational data and are likely to adequately characterize regional wind patterns that are 

consistent with the 12-km grid scale. Blending of MM5 data with local observations (which are 

mainly at the surface) could lead to wind structures that may not be realistic under some 

conditions and may result in poorer characterization of the regional winds. Thus, the effort 

required to prepare observational data sets for CALMET for the large regional domain involves 

considerable effort that may not provide corresponding improvement of the wind field. 

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Figure 4-2. VISTAS Regional 12-km Resolution CALMET Modeling Domain (color area with 

terrain contours). The locations of the 36-km resolution MM5 grid points are shown on the plot. 

For 2003, the 36-km MM5 data will be used as CALMET’s initial guess field and then the 

CALMET diagnostic terrain adjustments (see Section 3.1.1) will be applied to reflect terrain on 

the scale of the CALMET grid (i.e., 12-km). When the 12-km MM5 (2001 and 2002) data are 

used, the diagnostic CALMET terrain adjustments will be turned off since the grid resolution of 

the MM5 data is the same as the CALMET grid and the terrain adjustments on the 12-km grid 

scale will already be reflected in the MM5 dataset. In this case, the MM5 winds will be 

interpolated by CALMET to the CALMET layers and CALMET’s boundary layer modules will 

compute mixing heights, turbulence parameters and other meteorological parameters that are 

required by CALPUFF. 

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Impact Threshold

The final BART guidance recommends that the threshold value to define whether a source 

“contributes” to visibility impairment is 0.5 dv change from natural conditions14 (although States 

may set a lower threshold). The 98th percentile (8th highest annual) 24-hr average predicted 

impact at the Class I area, as calculated using CALPOST Method 6 (monthly average relative 

humidity values), is to be compared to this contribution threshold value. For this comparison, the 

predicted impact at the Class I area on any day is taken to be the highest 24-hr average impact at 

any receptor in the Class I area on that day. (Note that the receptor where the highest impact 

occurs can change from day to day.) According to clarification of the BART guidance received 

from EPA, for a three-year simulation the modeling values to be compared with the threshold are 

the greatest of the three annual 8th highest values or the 22nd highest value over all three years 

combined, whichever is greater. 

For the purposes of the initial analysis, however, the highest value over the three-year period (not 

the 98th percentile value) is to be compared to the contribution threshold. This ensures a 

significant measure of conservatism in the initial approach. VISTAS will evaluate the initial 

CALPUFF results to determine if using the single highest value provides too conservative a 

screen for exemption purposes. If so, VISTAS may increase the number of exceedances of the 

contribution threshold that would be allowed and still qualify to exempt a source. 

4.3.3 Model Configuration and Settings for Initial Analysis

VISTAS will use CALPUFF Version 5.754 and CALMET Version 5.7. These versions contain 

enhancements funded by the Minerals Management Service (MMS) and VISTAS. They were 

developed by Earth Tech, Inc. and they are maintained on the CALPUFF website (www.src.com) 

for public access. This version includes CALMET, CALPUFF, CALPOST, CALSUM, and 

POSTUTIL as well as CALVIEW. 

The initial analysis uses a CALPUFF computational domain that includes all Class I areas within 

300 km of a source. These Class I areas are specified in the CALPUFF control file for analysis. 

States could decide to require a different value for the maximum distance threshold for the 

CALPUFF domain, depending on the locations of the Class I areas in their states and other 

factors such as meteorological conditions and the magnitudes of the emissions from BARTeligible sources. The regional CALMET domain will be unchanged by these adjustments. 

Also, the initial approach is designed to significantly reduce the CALPUFF simulation time by 

restricting the CALPUFF computational domain size to include only areas where significant 

impacts are feasible rather than the entire regional domain. CALPUFF allows its computational 

domain to be specified as a subset of the CALMET meteorological domain by settings within the 

 

14 As described in Footnote 5 on page 6, States have the option of defining natural conditions as either the annual 

average default conditions or the average of the 20% best natural condition days.

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CALPUFF input file. The advantage of selecting a smaller CALPUFF computational domain in 

the regional CALPUFF simulations is that CALPUFF run time is proportional to the number and 

residence time of the puffs on the domain (and other factors such as the number of receptors and 

the internal time step computed by the model). A CALPUFF domain covering an area 300 km 

from a source in all directions would involve only 50 x 50 12-km grid cells, which will require 

modest computational resources.

CALMET output files for the VISTAS regional domain shown in Figure 4-2 will be provided to 

VISTAS by Earth Tech. These files will be in CALPUFF-ready format, and as such, no 

CALMET user inputs will be required. An option in CALMET allows finer grid CALMET input 

files to be calculated from the 12-km CALMET files. 

The basic characteristics of the CALMET, CALPUFF and CALPOST configurations for the 

initial analyses are listed below.

CALMET Modeling Configuration (12-km initial exemption modeling)

The CALMET model configuration for the regional CALMET simulations will be defined by 

Earth Tech in collaboration with the VISTAS States. The basic model configuration will follow 

the recommended IWAQM guidance (EPA, 1998; Pages A-1 through A-6), except as noted 

below. 

The basic features of the modeling simulation are the following:

- Modeling period: 3 years (2001-2003)

- Meteorological inputs: MM5 data provide initial guess fields in CALMET 

- CALMET grid resolution: 12-km (same Lambert Conformal coordinate system and grid 

cells as the 12-km 2001/2002 MM5 simulations)

- CALMET vertical layers: 10 layers. Cell face heights (meters): 0, 20, 40, 80, 160, 320, 

640, 1200, 2000, 3000, 4000.

- CALMET mode: No-Observations mode including option to read overwater data directly 

from MM5.

- Diagnostic options: IWAQM default values, except as follows: diagnostic terrain 

blocking and slope flow algorithms used for 2003 simulations (using 36-km MM5 data), but 

no diagnostic terrain adjustments in 2001 and 2002 simulation (using 12-km MM5 data)

- CALMET options dealing with radius of influence parameters (R1, R2, RMAX1, RMAX2, 

RMAX3), BIAS, ICALM parameters are not used in No-Observations mode. 

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- TERRAD (terrain scale) is required for runs with diagnostic terrain adjustments (i.e., the 

2003 simulations). Values of ~10-20 km will be tested, and an appropriate value 

determined.

- Land use defining water: JWAT1 = 55, JWAT2 = 55 (large bodies of water). This feature 

allows the temperature field over large bodies of water such as the Atlantic Ocean and the 

Great lakes to be properly characterized by buoy observations.

- Mixing height averaging parameter (MNMDAV) will be determined by Earth Tech for the 

regional simulations based on sensitivity tests. The purpose of the testing is to optimize the 

variable to allow spatial variability in the mixing height field, but without excessive noise.

- Geophysical data for regional runs: SRTM-GTOPO30 30-arcsec terrain data, Composite 

Theme Grid (CTG) USGS 200m land use dataset. References for these and other CALMET 

datasets can be found on the CALPUFF data page of the official CALPUFF site 

(www.src.com). 

CALPUFF Modeling Configuration (Initial exemption modeling)

The CALPUFF model configuration for the regional CALPUFF initial simulations will follow the 

recommended IWAQM guidance (EPA, 1998; Pages B-1 through B-8), except as noted below: 

- CALPUFF domain configured to include the source and all Class I areas within 300km of 

the source plus 50km buffer zone in each direction. CALPUFF is recommended for all 

source-receptor distances to be considered in the BART analyses.

- Chemical mechanism: MESOPUFF II module

- Species modeled: SO2, SO4, NOx, HNO3, NO3 and particulate matter in size categories of 

<0.625 μm, 0.625-1.0 μm, 1.0-1.25 μm, 1.25-2.5 μm, 2.5-6.0 μm and 6-10 μm aerodynamic 

diameters. As noted below, the particulate matter emissions by size category will be 

combined into the appropriate species for the visibility analysis (i.e., elemental carbon (EC), 

fine PM or “soil” (< 2.5 μm in diameter), coarse PM (between 2.5-10 μm in diameter) and 

organics (called secondary organic aerosols (SOA) in the CALPOST postprocessor).

- Emission rates for modeling based on EPA BART guidance, i.e., maximum 24-hour actual 

emission rate with normal operations from the highest emitting day of the meteorological 

period modeled (excluding days where start-up, shutdown or malfunctions occurred 

sometime during the day.) Note that potential emissions are used to determine if a source is 

BART-eligible, but 24-hour average maximum emissions are used for modeling purposes 

(70 FR 39162). Pollutants considered include SO2, H2SO4, NOx and PM10. 

Condensable emissions are considered as primary fine particulate matter and allocated 

equally to the two submicrometer-particle size classes. If actual source emissions data are 

not available, the modeling should be based on permit limits. If source-specific size 

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categories are not available, then AP-42 factors may be used for sources where AP-42 

factors are available. For sources where AP-42 factors are not available, alternative 

approaches to speciation are given below. 

Excluded from the modeling are pollutants with plant-wide emissions less than de minimis

levels (40 tons per year for SO2 and NOx and 15 tons per year for PM10). De minimis levels 

are plant wide for each visibility-impairing pollutant, so individual units may be modeled 

even if they have emissions below de minimis if the plant total is greater than de minimis.

- Particulate emissions speciation: Break down, as appropriate, filterable and condensable 

particulate matter into the following species categories: elemental carbon (soot), “soil” (fine 

PM < 2.5 μm diameter), coarse particulate matter (2.5-10 μm diameter) and organics. The 

process is illustrated in Figure 4-3. If source-specific speciated emissions factors are not 

available, AP-42 factors or speciation information developed by the National Park Service 

(http://www2.nature.nps.gov/air/permits/ect/index.cfm) can be used to estimate the PM 

speciation for many source sectors. 

Figure 4-3. Speciation of PM-10 Emissions. (PMC is coarse particulate matter -- 2.5 to 10 μm 

diameter.)

PM-10

Filterable Condensable

EC PM-2.5 PMC PM

(including 

H2SO4)

As Prim. SO4)

Organic PM

(SOA)

EC Soil PMC SO4

f(RH)

Non-SO4

Inorganic

OC

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Otherwise, assumptions will need to be proposed by the source, and reviewed and approved 

by the State. Possible acceptable alternative approaches to estimating speciation include the 

following:

 Speciation profiles developed by the SMOKE emissions model for use in 

VISTAS’ CMAQ regional air quality modeling (available at http://www.vistassesarm.org/BART/calpuff.asp).

 The approach described in a memo available at http://www.vistassesarm.org/BART/calpuff.asp, which provides reasonably conservative estimates 

in situations where data are incomplete.

- Class I receptors: Use FLM Class I receptor list with receptor elevations provided 

(available from the NPS).

- CALPUFF model options: Use IWAQM (EPA, 1998) default guidance, including 

Pasquill-Gifford dispersion coefficients. 

- Ozone dataset – use observed ozone data for 2001-2003 from CASTNet and AIRS stations. 

Only non-urban ozone stations should be used in the OZONE.DAT file. Monthly average 

ozone (backup) background values are to be computed based on daytime average ozone 

concentrations from the OZONE.DAT file (6am-6pm average ozone concentrations 

computed by month). 

- Background ammonia concentration: In CALPUFF, use constant (0.5 ppb) value for 

ammonia. 

- Puff representation: integrated puff sampling methodology.

- Building downwash: Ignore building downwash unless source is within 50-km of a Class I 

area and the State instructs the source to specifically consider building downwash. 

CALPOST and POSTUTIL Configuration (Initial exemption modeling)

- Use Visibility Method 6 in CALPOST

- Species considered in visibility analysis: SO4, NO3, EC, SOA (i.e., condensable organic 

emissions), soil, coarse PM

- Natural background light extinction: Several options are acceptable at the discretion of the 

State: (1) A single annual average natural background extinction for each Class I area, as 

presented in Appendix B of EPA’s natural conditions guidance (EPA, 2003b); (2) A single 

value that represents the average haze index on the 20% best natural conditions days, again 

as presented in the same Appendix B; or (3) A monthly average natural background as 

calculated by CALPOST under Method 6, based on annual average default natural 

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conditions component concentrations and monthly average f(RH) values for the centroid of 

the Class I area, from Table A-3 in the natural conditions guidance document,. 

A special procedure is needed for options 1 and 2, since CALPOST requires input of natural 

background concentrations of PM components while the backgrounds for options 1 and 2 

are expressed in EPA’s guidance document as extinction coefficients or haze indices (in 

deciviews). In order to produce the appropriate natural background in CALPOST for these 

options, use Equation 3-2 to calculate the extinction coefficient that corresponds to EPA’s 

haze index value for the Class I area (if necessary), subtract the Rayleigh scattering value of 

10 Mm-1, and enter a soil concentration (in μg/m3) into CALPOST that is numerically equal 

to this result. (Since the extinction efficiency of soil is 1 m2/g, Equation 3-1 shows that this 

process produces a background extinction that equals the EPA’s value.) Leave the 

concentrations of all other species blank, since the number that is entered represents 

extinction by all components.

- Light extinction efficiencies: Use EPA (2003a) values. If a source chooses, the new 

IMPROVE algorithm for calculating light extinction (see Section 3.2.3) may be used in 

addition to the default IMPROVE algorithm. (Calculations would need to be performed 

outside CALPOST or CALPOST would need to be modified to accommodate the new 

algorithm.)

- Nitrate repartitioning in POSTUTIL: Do not use for the initial modeling. 

The initial run results will be based on the highest change in light extinction (deciviews) from 

natural conditions over the three-year modeling period for each Class I area considered. 

Predicted changes exceeding the “contribution” threshold (0.5 deciviews) will trigger a finer grid 

CALPUFF modeling analysis.

4.4 Finer Grid Modeling Procedures

4.4.1 Rationale for and Overview of Finer Grid Modeling Approach

There are two potential applications for finer grid CALPUFF modeling: 

BART Exclusion Modeling. First, finer grid CALPUFF modeling can be used to demonstrate 

that a source does not cause or contribute to visibility impairment in any Class I areas, and thus 

can be excluded from BART controls. As shown in Figure 4-1, if the initial regional modeling 

results are not below the threshold for visibility impacts, the next step is to conduct modeling 

using a finer grid resolution for the meteorological fields and the treatment of terrain effects and 

land use variability. In the finer grid modeling the predicted visibility impairment that is 

compared to the threshold is based on the BART guidance of the 98th percentile change in 

deciviews value rather than the more conservative highest value used in the initial analysis.

The BART guidance indicates that the emissions rate to be used for such modeling is the highest 

24-hr rate during the modeling period. Depending on the availability of source data, the following 

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emissions information (listed in order of priority) should be used with CALPUFF for BART 

exclusion modeling:

- 24 hr maximum value emissions for the period 2001-2003 (Continuous Emission Monitor, 

CEM data)

- 24 hr maximum value from continuous emissions monitoring data 

- facility stack test emissions

- potential to emit

- permit allowable emissions, if available

- emissions factors from AP-42 source profiles 

Quantify Benefits of BART. The second application of refined modeling is to quantify the 

visibility benefits from the BART control options. This is accomplished by running CALPUFF 

with the baseline emissions rates and again with emissions after BART controls. It is important 

that emission reductions be evaluated in the postprocessing step rather than by using “negative” 

emission rates in the CALPUFF model. The chemical scheme requires that emission rates always 

be positive. 

For any of these applications, a source-specific modeling protocol that defines source properties 

and the specific model configuration is required. As discussed in Section 5, the source specific 

protocol should include source-specific emissions data and can refer to this document for all 

methods and assumptions that follow this common protocol. 

4.4.2 Model Configuration and Settings for Finer Grid Modeling

Grid resolution substantially better than 12-km is needed for a finer grid CALPUFF assessment of 

visibility impacts in most cases involving Class I areas in complex terrain or coastal areas. Thus, 

the CALMET fine grid resolution in the subregional modeling domains used for finer grid 

modeling will depend on the terrain, land use (especially coastal boundaries), location of the 

source, distance of the source from Class I areas, and total size of the subregional modeling 

domain. 

VISTAS States have 2001-2003 CALMET files for five 4-km sub-regional domains as illustrated 

in Figure 4-4. The subdomains are designed to address all BART eligible sources within each 

VISTAS states and all Class I areas within 300 km of the BART-eligible sources. For application 

for a single source, a smaller domain of roughly 200-300 km by 200-300 km is recommended. 

Requests to obtain the 4-km CALMET files should be made to the State BART representatives. 

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Figure 4-4. The five subregional domains for 4-km CALMET modeling.

In some instances, as part of the source-specific protocol, a source may propose to the State to use 

an even finer grid simulation to properly characterize the flow fields and land use changes that 

affect dispersion. An application for source-receptor distances within about 50 km may require a 

grid resolution less than 1 km if complex terrain effects are likely to be important. This 

determination should be made on a case-by-case basis. There is not a single distance at which a 

particular grid size is appropriate. It depends on factors such as the complexity of the terrain, the 

source-receptor distances involved, the location of the source relative to the terrain features, the 

physical stack parameters (e.g., a tall stack in complex terrain may be unaffected by the terrainforced flow), proximity of the source and Class I area to a coastline, and other factors including 

availability of representative observational data.

The finer grid CALMET simulations were run in hybrid mode, using both MM5 data to define 

the initial guess fields and meteorological observational data in the Step 2 calculations. 

Overwater (buoy) data will be provided in addition to the hourly surface meteorological 

observations, precipitation observations and twice-daily upper air sounding data. 

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A domain-specific set of modeling parameters will be defined for each subregional domain. The 

proper selection of the CALMET diagnostic wind field parameters that are used to blend 

observations with the Step 1 wind field depends on factors such as the locations of the 

meteorological stations relative to terrain and coastal features (which affects the 

representativeness of the observational data), the terrain length scale, and the quality (resolution) 

of the MM5 data used to define the initial guess field and its ability to properly resolve wind 

flows on the fine-scale CALMET domain. The definition of the proper CALMET parameters is 

done as part of sensitivity testing where model performance is evaluated against available 

observations and expected terrain effects, such as channeling of flows within a valley. 

In addition to the better grid resolution and the introduction of observational data in the finer grid 

simulations, several other modeling refinements can enhance the accuracy of the finer grid 

modeling. These include use of the higher resolution terrain DEM data (~3 arc sec USGS data) 

in defining the gridded terrain fields and application of the ammonia limiting method in the 

POSTUTIL post-processor. Otherwise, the source configuration, emissions, pollutant speciation, 

Class I receptors, ozone datasets and CALPUFF model options will be the same as in the initial 

runs. Similarly, CALPOST will be used in the same manner as for the initial analyses. However, 

POSTUTIL can be used to repartition nitrate in the finer grid modeling, using background 

ammonia concentrations according to the IWAQM Phase 2 report (IWAQM, 1998).

For the finer grid BART exclusion analysis, the test for evaluating whether a source is 

contributing to visibility impairment is based on the 98th percentile modeled value (rather than the 

highest predicted value used for the initial evaluation), which is consistent with EPA’s BART 

guidance.

4.5 Presentation of Modeling Results

The CALPOST processing computes the daily maximum change in deciviews. A sample of the 

summary table produced by CALPOST is shown in Table 4-1. For evaluating compliance with 

the VISTAS screening threshold, the highest change in extinction value, located at the bottom of 

the CALPOST list file is compared to the threshold value (e.g., 0.5 dv). For example, in the 

sample shown in Table 4-1, the summary at the bottom shows that the highest visibility impact is 

1.219 dv, with 9 days over the year showing values greater than 0.5 dv. Therefore this source 

would not pass the initial analysis, and finer grid modeling would be required. 

In addition to the highest change in deciview value on each day over all the receptors in a 

particular Class I area, the CALPOST summary table in Table 4-1 contains the coordinates of the 

receptor, receptor type (D indicates discrete receptors), the total haze level (background + source, 

in dv), the background haze in deciviews, the change in haziness (delta dv), the humidity term 

applied to hygroscopic aerosols (f(RH)), and the contribution of each species to light extinction 

(in percent of the total source contribution) for SO4, NO3, organics, elemental carbon, coarse and 

fine particulate matter.

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Table 4-1. Example of CALPOST Output, Showing Maximum Daily Impacts of Source and Locations of Those Impacts.

YEAR DAY HR RECEPTOR COORDINATES (km) TYPE DV(Total) DV(BKG) DELTA DV F(RH) %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF 

2001 2 0 3 20.540 79.782 D 5.397 5.358 0.039 4.314 44.33 47.22 3.07 1.07 0.00 4.30 

2001 3 0 9 31.680 79.822 D 4.566 4.421 0.145 1.767 40.75 33.89 9.19 3.24 0.00 12.94 

2001 4 0 1 24.723 77.951 D 4.540 4.540 0.000 2.076 0.00 0.00 0.00 0.00 0.00 0.00 

2001 5 0 77 30.228 94.571 D 4.950 4.939 0.011 3.144 43.13 44.74 4.64 1.45 0.00 6.05 

2001 6 0 1 24.723 77.951 D 5.181 5.166 0.015 3.772 38.58 56.05 1.90 0.70 0.00 2.76 

2001 7 0 3 20.540 79.782 D 6.366 5.745 0.620 5.439 44.98 44.99 3.69 1.26 0.00 5.08 

 .

 .

 .

2001 363 0 113 27.414 103.782 D 5.725 5.652 0.073 5.164 53.49 35.51 4.03 1.39 0.00 5.58 

2001 364 0 113 27.414 103.782 D 6.554 6.521 0.033 7.826 48.12 47.09 1.67 0.64 0.00 2.48 

2001 365 0 1 24.723 77.951 D 6.499 6.499 0.000 7.757 0.00 0.00 0.00 0.00 0.00 0.00 

--- Number of days with Delta-Deciview => 0.50: 9

--- Number of days with Delta-Deciview => 1.00: 2

--- Largest Delta-Deciview = 1.219

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For the finer grid analysis, the data in the table can be imported into a spreadsheet and sorted on 

the delta dv column. Table 4-2 shows an example of the ranked visibility impacts (change in dv) 

for each of three years at six different Class I areas. The 98th percentile (8th highest value) in the 

sorted table would be compared to the contribution threshold (e.g., 0.5 dv). In the example 

shown in this table, the source passes the finer grid analysis because the highest 98th percentile 

visibility impact is below the contribution threshold of 0.5 dv. 

The Results section of the CALPUFF modeling report should contain the following information:

1. Map of source location and Class I areas within 300 km of the source 

2. For the VISTAS 12-km CALPUFF initial exemption modeling domain, a table listing all 

Class I areas in the VISTAS domain and those in neighboring states and impacts at those 

Class I areas within 300 km of the source, as illustrated in Table 4-3. 

3. A discussion of the number of Class I areas with visibility impairment from the source on 

98th percentile days in each year greater than 0.5 dv (total visibility impairment minus 

impairment on 20% best days for natural background visibility equals delta-dv, the 

visibility impact attributed to the source). 

4. For the Class I area with the maximum impact, discussion of the number of days below 

the 98th percentile that the impact of the source exceeds 0.5 dv, the number of receptors in 

the Class I area where the impact exceeds 0.5 dv, and the maximum impact. 

5. For finer grid CALPUFF exemption modeling, results for those Class I areas for which 

impacts of the source exceeded 0.5 dv in the 12-km initial exemption modeling. Report 

same results as provided for 12-km initial exemption modeling.

6. For control option modeling, each control option tested should be listed in tabular format. 

For each control option and for each Class I area where the impact of the source exceeded 

0.5 dv, report the change in pollutant emissions and the change in visibility impact from 

the source as a result of the control option. The effectiveness of candidate control options 

are to be compared to each other, not to a specific target improvement. 

States will provide further guidance on graphic presentation of results to simplify 

evaluation of effectiveness of control measures. For example, a temporal plot of the 

change in deciviews between the controlled and uncontrolled cases could be developed for 

the receptor with the maximum modeled impact in each Class I area. 

7. Copies of all input files and input data in electronic format for the CALMET, CALPUFF, 

CALPOST and POSTUTIL runs should be archived and provided to the State.

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Table 4-2. Example of Visibility Impact Rankings at Six Class I Areas

Class I Area 2001 2002 2003

DeltaDeciview

Ranks 1-8

DeltaDeciview

Ranks 1-8

DeltaDeciview

Ranks 1-8

Great Smoky NP

0.99

0.88

0.62

0.59

0.55

0.52

0.48

0.47

0.95

0.63

0.51

0.50

0.46

0.42

0.37

0.36

1.20

0.90

0.73

0.72

0.59

0.47

0.45

0.42

Linville Gorge

0.67

0.45

0.43

0.33

0.29

0.27

0.25

0.23

0.81

0.69

0.65

0.50

0.45

0.33

0.31

0.29

0.76

0.47

0.37

0.35

0.31

0.30

0.28

0.28

Shining Rock

0.66

0.43

0.41

0.35

0.26

0.24

0.23

0.22

0.73

0.69

0.63

0.52

0.46

0.34

0.29

0.26

0.75

0.45

0.36

0.34

0.28

0.27

0.26

0.25

Cohutta

0.26

0.23

0.22

0.21

0.20

0.19

0.18

0.16

0.54

0.47

0.43

0.37

0.37

0.31

0.31

0.30

0.61

0.42

0.30

0.29

0.28

0.28

0.25

0.25

Joyce Kilmer-Slickrock

0.34

0.33

0.31

0.26

0.24

0.20

0.18

0.17

0.52

0.43

0.32

0.31

0.30

0.28

0.24

0.24

0.27

0.24

0.23

0.20

0.14

0.13

0.11

0.10

Mammoth Cave NP

0.56

0.44

0.38

0.29

0.25

0.24

0.22

0.21

0.57

0.56

0.53

0.35

0.33

0.33

0.30

0.29

0.50

0.37

0.36

0.35

0.31

0.24

0.21

0.19

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Table 4-3. Format of Summary of Results for CALPUFF Modeling in VISTAS’ 12-km Modeling 

Domain to Determine if a BART Eligible Source is Subject to BART. 

Class I area Distance 

(km) 

from 

source to 

Class I 

area 

boundary

# of days1

and # of 

receptors 

with impact 

> 0.5 dv in 

Class I area: 

2001

# of days1

and # of 

receptors 

with impact 

> 0.5 dv in 

Class I area: 

2002

# of days1

and # of 

receptors 

with impact 

> 0.5 dv in 

Class I area: 

2003

# of days1 and 

# of receptors 

with impact 

> 1.0 dv in 

Class I area 

for 3-yr 

period

Max. 24-hr 

impact over 

3-yr period

Dolly Sods, WV

Shenandoah, VA

James River 

Face, VA

Mammoth Cave, 

KY

Sipsey, AL

Great Smoky 

Mtns, TN

Cohutta, GA

Shining Rock, 

NC

Linville Gorge, 

NC

Swanquarter, NC

Cape Romain, 

SC

Okefenokee, GA

Saint Marks, FL

Chassahowitzka, 

FL

Everglades, FL

Brigantine, NJ

Breton Island, 

LA

Caney Creek, 

AR

Upper Buffalo, 

AR

Mingo, MO

Hercules Glade, 

MO

1

Days below the 98th percentile of days in each year or the three-year modeling period, as appropriate

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4.6 VISTAS Contribution to CALPUFF Modeling of BART Eligible Sources

VISTAS will provide updates and supporting information concerning the Common Modeling 

Protocol (this document) on the VISTAS website. In addition, VISTAS will make publicly 

available the following data bases developed by Earth Tech:

• VISTAS version of the CALPUFF modeling system, maintained on the CALPUFF website. 

Version 5.754 includes CALMET, CALPUFF, CALPOST, and POSTUTIL files, updated in 

December 2005. The last update in this VISTAS version is a CALMET update that addresses 

over water dispersion, which was developed for the Minerals Management Service (MMS) in 

fall 2005. This VISTAS version of CALPUFF will not be updated further unless errors are 

found in the code, except that a new one-step POSTUTIL procedure will be incorporated. 

BART-eligible sources in the VISTAS states will be able to use this VISTAS version 

throughout the BART modeling exercise. 

• 12-km CALMET output files for 2001, 2002, and 2003 produced as described in previous 

sections. Further detail on model configuration and settings will be provided with the output 

files and will be made available on the CALPUFF website.

• CALMET will include a software modification to allow the meteorological data inputs into 

CALMET to be used to generate finer grid CALMET files without having to go back to the 

original MM5 output files

• Five 4-km CALMET subdomains for 2001, 2002, and 2003, produced as described in 

previous sections. Further detail on model configuration and settings will be provided with 

the output files and will be made available on the website.

• File with CALPUFF model configuration and settings sufficient to replicate CALPUFF 

modeling done for VISTAS using 12 km CALMET, including

o Ozone data used to run CALPUFF

o Ammonia concentrations used to run CALPUFF.

o All other set up files used in VISTAS 12-km CALPUFF run

Samples of these data files and examples of their application with CALPUFF for BART 

screening analyses can be found on the CALPUFF web site at 

(http://www.src.com/verio/download/sample_files.htm).

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Source-Specific Modeling Protocol 55

5. SOURCE-SPECIFIC MODELING PROTOCOL

Sources are required to submit a source-specific protocol to the State for review and approval 

prior to source-specific modeling. States will provide the documentation to EPA and FLM for 

their review. An outline of the typical contents of the site-specific protocol is provided in Table 

5-1.

If a source-specific modeling approach is proposed that differs from the common approach in 

Chapter 4, a more-detailed modeling protocol than that required under the common procedures is 

required. This protocol must explain the data sources, model configuration, and rationale for 

changes in the model approach from the common protocol and must be approved by the State.

Unit-specific source data include the following parameters:

- Location (e.g., UTM coordinates, UTM zone and datum)

- Stack height above the ground

- Stack diameter

- Exit velocity

- Exit temperature

- Emission rates (SO2, H2SO4, NOx and PM10).

Additional building dimension information (building width, length, height and corner locations) 

is needed for short stacks that are less than Good Engineering Practice (GEP) height. This 

information is used in providing effective structure dimensions for building downwash 

calculations. (The requirement to conduct building downwash modeling may be waived by 

individual States or if the transport distance is greater than 50 km.)

The source coordinates must be expressed in the coordinate system used to define the CALMET 

and CALPUFF modeling domains. For the regional screening simulations, a Lambert Conformal 

Conic (LCC) coordinate system will be used. The required parameters to define an LCC 

coordinate include two matching parallels, latitude/longitude of the projection origin, coordinate 

datum, and false Easting and Northing (if used) of the projection origin. Subregional and sourcespecific domains may be using either an LCC or UTM projection. 

The CALPUFF Graphical User Interface (GUI) system provides software (called COORDS) to 

compute to/from latitude/longitude, LCC and UTM coordinates for a large number of datums. In 

addition, the CALVIEW graphics feature allows the use of georeferenced satellite or aerial 

photographs to be used as base maps to confirm source locations. Links to sources of suitable 

base maps can be found on the CALPUFF data site (www.src.com) in the section on “Aerial 

Photos”.

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Table 5-1. Sample Table of Contents of a Source-Specific Fine-Scale Modeling Protocol.

1. INTRODUCTION

1.1 Objectives

1.2 Location of Source vs. Relevant Class I Areas

1.3 Source Impact Evaluation Criteria

2. SOURCE DESCRIPTION

2.1 Unit-specific Source Data

2.2 Boundary Conditions

3. GEOPHYSICAL AND METEOROLOGICAL DATA

3.1 Modeling Domain and Terrain

3.2 Land Use

3.3 Meteorological Data Base

3.3.1 MM5 Simulations

3.3.2 Measurements and Observations

3.4 Air Quality Data Base

3.4.1 Ozone Concentrations – Measured or Modeled

3.4.2 Ammonia Concentrations – Measured or Modeled

3.4.3 Concentrations of Other Pollutants – Measured or Modeled

3.5 Natural Conditions at Class I Areas

4. AIR QUALITY MODELING METHODOLOGY

4.1 Plume Model Selection

4.1.1 Major Relevant Features of CALMET

4.2.2 Major Relevant Features of CALPUFF

4.2 Modeling Domain Configuration

4.3 CALMET Meteorological Modeling

4.4 CALPUFF Computational Domain and Receptors

4.5 CALPUFF Modeling Option Selections

4.6 Light Extinction and Haze Impact Calculations

4.7 Modeling Products

5. REVIEW PROCESS

6.1 CALMET Fields 

6.2 CALPUFF, CALPOST, and POSTUTIL Results 

6. REFERENCES

APPENDICES

A.1 VISTAS BART MODELING PROTOCOL 

A.2 ... other appendices as needed

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An example of the data that need to be reported is provided in Table 5-2. More detail on the 

stack data, emissions species, and particulate size fractions to be reported will be made available 

on the CALPUFF website, www.src.com, Check with your State for the more detailed format of 

Table 5-2 that is to be used. 

Discussions with the regulatory authorities should be conducted prior to development of a 

protocol to ensure all of the relevant issues are included in the protocol. 

Table 5-2. Example of Source Documentation for BART Eligible Source. 

Unit name 

and/or 

description

Start-up dates SO2 potential 

emissions (tpy)

NOx potential 

emissions (tpy)

Total PM 

potential 

emissions (tpy) 

Emissions source

name

...

Total emissions

Potential BARTeligible 

emissions

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Quality Assurance 58

6. QUALITY ASSURANCE

6.1 Scope and Purpose of the QA program

Air quality modeling covered under this protocol is an important tool for use in determining 

whether a BART-eligible source can be reasonable expected to cause or contribute to visibility 

impairment in a Class I area, and therefore whether this source should be subject to BART 

controls, and if so, to determine the relative benefits of various BART controls. The purpose of 

the quality assurance (QA) program is to establish procedures for ensuring that products produced 

by the application of the modeling techniques for BART studies satisfy the regulatory objectives 

of the BART program.

The scope of the QA program affects different users differently. Common features of most 

applications will be the setup and execution of the CALPUFF air quality model and processing of 

modeling results to determine if a source contributes to visibility impairment at a Class I area. In 

many cases, users will be provided meteorological datasets that have been developed with 

VISTAS funding under a suitable QA program for use in the BART modeling. Other users will 

be involved in site-specific or source-specific analyses that will use additional datasets and 

potentially different modeling options and/or tools. More extensive quality assurance will be 

required in these latter types of applications. It is the responsibility of the modeler to ensure that 

an adequate QA protocol is in place for a particular application.

The CALPUFF modeling system contains built-in features to facilitate quality assurance of the 

modeling results. These include the automatic production of “QA” files for various datasets, 

including geophysical fields, sources and receptors, and imbedded tracking of model options and 

switches within the output files from the major modeling units of the modeling system. The 

Graphical User Interface system (GUI) provided as part of the latest CALPUFF modeling system 

allows these QA files to be displayed graphically.

In addition, a detailed software management system is in place to track version and level numbers 

associated each program and utility within the CALPUFF modeling system. This information is 

carried forward in all of the output files to create an audit trail of software versions and major 

model options used that can be retrieved and displayed from the model output files.

Because the required QA procedures will depend heavily on the exact application, there will be 

differences among different users and different applications.

In addition, the BART modeling process involves multiple organizations. The States have overall 

responsibility for the process and may also execute some or all of the modeling. VISTAS is 

contributing general guidance via this protocol and is preparing meteorological fields and 

performing modeling under the guidance of the States. The sources that are BART-eligible need 

to provide process information and emissions data for use in the analyses. In addition, those 

sources that are involved in BART assessments will need to be actively involved in control 

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technology decisions and assessments. Finally, some of the modeling steps may be carried out by 

contractors on behalf of VISTAS, a State, or a source.

Each of these organizations has a responsibility to ensure that it is providing correct information 

to others and to evaluate the quality of any analyses it is performing, whether with data of its own 

or from others. This chapter provides general guidance and information on those aspects of 

quality assurance that are specific to the CALPUFF modeling effort, irrespective of which 

organization is carrying out the effort. The focus is on the common protocol efforts described in 

Chapter 4. As described in Section 6.3, more comprehensive QA may be needed for the unique 

aspects of the source-specific modeling described in Chapter 5.

6.2 QA Procedures for Common Protocol Modeling

The VISTAS common protocol (Section 4) describes the methods and procedures for use in 

conducting regional scale screening modeling to determine the whether a particular source or 

group of sources is subject to BART controls. In the initial application, the regional CALPUFFready meteorological data files will be provided by VISTAS. The amount of effort for end-users 

performing QA of these pre-defined meteorological fields will be reduced from what is required 

in developing source-specific meteorological fields, as described below. Also, VISTAS is 

planning to provide five subregional CALMET meteorological datasets in a CALPUFF-ready 

format. The development of these CALMET datasets will be subject to a QA program as part of 

their development, so the necessary quality assurance activity of end-users is again reduced from 

what would be required in the development of the dataset. It is not expected that the quality 

assurance steps in the development will be repeated in each application. The VISTAS-provided 

regional and subregional meteorological fields will include a test case simulation for 

demonstrating that expected modeling results are obtained on the user’s computer platform. This 

test should be repeated by every user.

Although the CALPUFF modeling system is recommended by the U.S. Environmental Protection 

Agency for application to BART analyses, a considerable amount of expertise and modeling 

judgment is needed at certain stages of the analysis. The modeling is not a “cookbook” exercise, 

a fact that was recognized by the U.S. EPA in describing the expertise needed for CALMET 

modeling (EPA, 1998; pp. 9-10,). Current methods for performing refined chemistry calculation 

also require an understanding of the chemical and meteorological processing affecting 

ammonium nitrate formation. VISTAS has committed to provide appropriate CALPUFF training 

to assist States in obtaining the necessary expertise with the latest CALPUFF modeling tools and 

techniques. An appropriate level of knowledge of the model formulation, technical approach and 

assumptions is essential for successful BART modeling. 

6.2.1 Quality Control of Input Data

The input data required by the model depends on the application. At a minimum, source data is 

required by CALPUFF (see Section 6.2.3) along with a list of choices made about model options 

and switches. Most of the modeling option choices are specified or recommended by regulatory

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guidance and default values (see references in Section 4.3.3). However, remodeling of the 

boundary conditions is not required for VISTAS-provided finer grid domains so the expertise 

level is not as high as it would be for development of the boundary conditions files from scratch.

To the extent that modeling applications are using pre-defined CALMET files and CALPUFF 

templates, the quality assurance will be straightforward. More detailed steps are needed for the 

setup of modeling files for source-specific applications of subregional domains finer than 4 km. 

The basic procedures that will apply to all CALPUFF model applications will include a 

confirmation of the source data, including units, verification of the correct source and receptor 

locations, including datum and projection, confirmation of the switch selections relative to 

modeling guidance, checks of the program switches and file names for the various processing 

steps, and confirmation of the use of the proper version and level of each model program. It is a 

common and recommended procedure for an independent modeler not involved in the setup of 

the modeling files to independently confirm the model switches and data entry in the actual 

model input files and to conduct an independent run of the worst case event as a confirmation 

check.

In addition, common practice requires that a model project CD (or DVD or set of DVDs) be 

created that contains all of the data and program files needed to reproduce the model results 

presented in a report. The model list files from each step are included on the project CD. This 

information allows independent checking and confirmation of the modeling process.

6.2.2 Quality Control of Application of CALMET

For users of the VISTAS CALPUFF-ready CALMET meteorological files, a number of large 

datafiles will be provided by VISTAS on external USB2 or Firewire hard drives in a format ready 

for use with the CALPUFF model. The QA steps associated with the development of the 

VISTAS common datasets will be provided separately as part of the modeling documentation. It 

is not expected that the QA steps conducted in the development of the meteorological datasets 

will be repeated in each application, although tests to confirm that the dataset is suitable for the 

application for which it is being used should be performed as part of the QA. This is discussed in 

more detail below.

The regional screening CALMET grid is defined in Chapter 4 on a 12-km Lambert Conformal 

Conic (LCC) grid system. The subregional and source-specific domains may be defined in either 

LCC or Universal Transverse Mercator (UTM) coordinates. In the case of the LCC projection, 

two matching parallels, latitude/longitude of the projection origin, coordinate datum, and false 

Easting and Northing (if used) of the projection origin must also be defined. For any domains in 

UTM coordinates, the UTM zone (see Appendix D of the CALMET User’s Guide) and datum 

must be defined. The appropriate projection and map factors are provided as part of the 

definition of the VISTAS regional grid system. For a source-specific domain, the grid parameters 

will be provided as part of the source-specific protocol.

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Appendix A of the IWAQM report (EPA, 1998) contains a list of recommended CALMET switch 

settings. Except as modified in Chapter 4 of this protocol or in a source-specific protocol, the 

IWAQM guidance should be used in setting up the CALMET simulations. The CALMET model 

obtains the switch settings from an ASCII “control file” with a default name of CALMET.INP. 

Whether the model is run using a GUI or from the control line in a DOS, Linux, or Unix window, 

it is essential that the control file be reviewed as part of the CALMET QA analysis. The 

CALMET GUI retains all of the input descriptive information that is part of the standard 

CALPUFF.INP file structure. This includes the default value for each variable, a text description 

of the variable, the meaning of each variable option, the units of the variable and interrelationships among variables indicating if/when the variable is used. Some third-party 

commercial GUIs strip out this descriptive information, which makes the QA step more difficult, 

although it is essential for perform nonetheless using the variable names as references for the 

variables in the file.

Part of the CALPUFF modeling system’s built-in QA capabilities is a variable tracking system 

that retains the control file inputs for CALMET and CALPUFF in the output files create by the 

models. This information includes the Version and Level numbers of the processor codes and

main model codes used in the simulations as well as the control files from the main models 

(CALMET and CALPUFF). The information from the preprocessing steps and the CALMET 

and CALPUFF model simulations is all carried forward and saved in the 

CALPUFF/postprocessor output files so that the final concentration/flux files contain a history of 

the model options and switch settings. This allows a user or reviewing agency to confirm the 

switch settings provided in a control file with that actually used in the model simulations. An 

optional switch in the CALPOST processor creates a complete listing of the QA data. This step 

requires access to the output CALPUFF concentration and/or flux files, which are normally 

practical to store on CDs or DVDs and to provide a part of the Project CD/DVD set.

6.2.3 Quality Control of Application of CALPUFF

The quality assurance of the source and emissions data is a major component of the CALPUFF 

modeling. Also, many errors are found in source coordinates and related projection/datum 

parameters, so confirmation of the source location is an important part of the modeling QA.

The locations of the Class I area receptors are another important CALPUFF input. The use of 

pre-defined receptors as provided by the National Park Service (NPS) receptor dataset is 

recommended in the VISTAS common protocol. However, although the latitude and longitude of 

each receptor point is provided, it is necessary to ensure that the proper UTM or LCC coordinates 

have been computed for computational domain selected. In particular, the datum of the NPS 

conversion software is not specified, so it is recommended that coordinates be checked using the 

CALPUFF GUI’s COORDS software or another comparable coordinate translation software 

package that recognizes various datums.

Most of the CALPUFF input variables contain default values. Appendix B of the IWAQM report 

contains a list of recommended CALPUFF switch settings. Except as modified in Chapter 4 of 

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this protocol or in a source-specific protocol, the IWAQM guidance should be used in setting up 

the CALPUFF simulations. The CALPUFF model obtains the switch settings from an ASCII 

“control file” with a default name called the CALPUFF.INP file. As is the case with the 

comparable CALMET file, it is essential that the control file be reviewed manually as part of the 

CALPUFF QA analysis. To facilitate this process, as was the case with the CALMET GUI, the 

CALPUFF GUI retains all of the input descriptive information that is part of the standard 

CALPUFF.INP file structure. Some third-party commercial GUIs strip out this descriptive 

information, which makes the QA step more difficult, although it is essential for perform 

nonetheless using the variable names as references for the variables in the file.

6.2.4 Quality Control of Application of CALPOST and POSTUTIL

CALPOST is run separately for each Class I area in order to obtain the necessary visibility 

statistics for evaluating compliance with the BART screening and finer grid modeling thresholds. 

The inputs to CALPOST involve selection of the visibility method (Method 6 in the standard 

EPA BART guidance), entry of Class I area-specific data for computing background extinction 

(either average or best 20% natural conditions, as prescribed by the State) and monthly relative 

humidity factors for hygroscopic aerosols. CALPOST contains a receptor screening that allow 

subsets of a receptor network modeling in CALPUFF to be selected for processing in a given 

CALPOST run. This is how receptors within a single Class I area are selected for processing 

from a CALPUFF output file that may contain receptors from several Class I areas. CALPOST 

contains options for creating plot files that will help in the confirmation that the proper receptor 

subset is extracted.

The CALPOST output file contains a listing of the highest visibility impact each day of the model 

simulation over all receptors included in CALPOST analysis. Receptors will normally be 

selected in each CALPOST run so that each CALPOST run represents the impacts at a single 

Class I area. The table includes the data shown in the example in Table 4-1. For a screening 

assessment, the peak value of the change in extinction is shown at the bottom of the visibility 

table (see Table 4-1). For a finer grid simulation, the 98th percentile value (8th highest day) is 

used for comparison against the BART threshold of 0.5 deciviews. It is necessary to import the 

results of the CALPOST table into a sorting program such as a spreadsheet to rank the daily 

change in extinction values such as is presented in Table 4-2.

The CALPOST inputs that need to be carefully checked as part of the CALPOST quality 

assurance are:

- Visibility technique (Method 6 in the common VISTAS protocol)

- Monthly Class I-specific relative humidity factors for Method 6

- Background light extinction values

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- Inclusion of all appropriate species from modeled sources (e.g., sulfate, nitrate, 

organics, (as SOA), coarse and fine particulate matter and elemental carbon.

- Appropriate species names for coarse PM used

- Extinction efficiencies for each species

- Appropriate Rayleigh scattering term (10 Mm-1 for screening modeling but Class I area 

specific value for finer grid modeling)

- Screen to select appropriate Class I receptors for each CALPOST simulation.

The CALPOST program produces plot files compatible with CALVIEW that allow confirmation 

of receptor locations that is useful in evaluating the receptor screening step.

POSTUTIL allows the user to sum the contributions of sources from different CALPUFF 

simulations into a total concentration file. In addition, it contains options to scale the 

concentrations from different modeled species (e.g., different particle sizes) into speciesdependent size distributions for the particulate matter. For example, PM is often simulated with 

unit emission rates for each particle size category and, in the POSTUTIL stage, the contributions 

of each size category based on the species being considered (e.g., elemental carbon, coarse 

particulate matter, etc.) are combined to form the species concentrations for input into 

CALPOST. This process, although simple, requires a careful review of the weighting factors for 

each source. POSTUTIL also allows a repartitioning of nitric acid and nitrate to account for the 

effects of ammonia limiting conditions. 

If source-specific modeling is performed using different sources of data or different techniques, 

the source-specific modeling protocol should provide justification for deviations from the 

VISTAS common protocol, and a QA plan specific for the application provided to address the 

quality assurance of the data used.

6.3 Additional QA Issues for Alternative Source-Specific Modeling

The level of QA required for application of source-specific protocols will be substantially higher 

than for the use of datasets that have already been subject to a QA procedure. For example, 

source-specific protocols may include the use of on-site meteorological datasets, the use of higher 

resolution prognostic meteorological (e.g., MM5) datasets, alternative visibility calculations, 

different extinction coefficients, or other changes to the common protocol. In addition to 

providing a source-specific modeling protocol describing and justifying the changes to the 

modeling approach from the VISTAS common protocol, the site-specific applications should 

include the development of a QA plan to properly evaluate the data used in the site-specific 

modeling.

The critical CALMET input parameters depend on the mode in which the model is run 

(observations mode, hybrid mode or no-observations mode), and the location and spatial 

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representativeness of any observational data. In a site specific protocol involving the 

development of a meteorological dataset, the elements of the QA process include preparation of 

wind rose (using observed, MM5 and CALMET-derived data), including examination of the data 

as a function of season and time of day (e.g., 4am, 10am, 4pm wind roses), time series analyses, 

and presentation of 2-D vector plots illustrating terrain effects/sea breeze circulation or other 

features of the flow expected to occur within the domain. For example, 2-D vector plots 

produced during light wind speed stable conditions (e.g., early morning such as 4 am) are good 

for assessing the performance of the CALMET model configuration and switches in reproducing 

terrain effects because these conditions are likely to maximize the terrain impacts in the model. 

Season wind roses at 4 am, 10 am and 4 pm would be expected to show the development of sea 

breeze circulations that may be important for certain applications. Customization of the QA 

process for the individual site-specific domain based on the availability of data and the physical 

processes expected to be important at that location should be conducted as part of the site-specific 

QA plan development.

If site-specific CALPUFF simulations involving the Ammonia Limiting Method are conducted, 

performance of the model in reproducing observed CASTNet or IMPROVE sulfate and nitrate 

concentrations at measurement sites within the site-specific modeling domain should be 

evaluated. The use of alternative ammonia concentration data (e.g., CMAQ output rather than 

derived ammonia based on aerosol measurements) will require an evaluation of the model 

performance relative to the techniques in the VISTAS common protocol. 

In any site-specific protocol a site-specific QA plan should be prepared.

6.4 Assessment of Uncertainty in Modeling Results

Chapter 3 discussed the uncertainties and known limitations in CALPUFF. The source specific 

modeling report does not need to repeat the uncertainties listed in Chapter 3, but the reviewer 

should interpret results in light of these limitations. It is expected that the performance of the 

model will be better in predicting changes in visibility impacts due to BART controls than in 

predicting absolute visibility values. This is because uncertainties in meteorological conditions 

transport and dispersion are expected to be less important in evaluating a change in impact, since 

a comparable effect will be included in both the base and sensitivity simulations. 

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References 65

7. REFERENCES

Allwine, K.J. and C.D. Whiteman, 1985: MELSAR: A Mesoscale Air Quality Model for Complex 

Terrain: Volume 1--Overview, Technical Description and User's Guide. Pacific Northwest Laboratory, 

Richland, Washington.

Batchvarova, E. and S.-E. Gryning, 1991: Applied model for the growth of the daytime mixed layer. 

Boundary-Layer Meteorol., 56, 261-274.

Batchvarova, E. and S.-E. Gryning, 1994: An applied model for the height of the daytime mixed layer 

and the entrainment zone. Boundary-Layer Meteorol., 71, 311-323.

Benjamin, S.G, D. Devenyi, S.S. Weygandt, K.J. Brundage, J.M. Brown, G.A. Grell, D. Kim, B.E. 

Schwartz, T.G. Smirnova, T.L. Smith, and G.S. Manikin, 2004: An Hourly Assimilation Forecast Cycle: 

the RUC, Mon. Wea. Rev., 132, 495-518.

Carson, D.J., 1973: The development of a dry, inversion-capped, convectively unstable boundary layer. 

Quart. J. Roy. Meteor. Soc., 99, 450-467.

Chang, J.C. P. Franzese, K. Chayantrakom and S.R. Hanna, 2001: Evaluations of CALPUFF, HPAC and 

VLSTRACK with Two Mesoscale Field Datasets. J. Applied Meteorology, 42(4): 453-466.

Department of the Interior, 2003: Letter from the Assistant Secretary of the Interior to the Director of the 

Montana Department of Environmental Quality.

Douglas, S. and R. Kessler, 1988: User's Guide to the Diagnostic Wind Model. California Air Resources 

Board, Sacramento, California.

Edgerton, E., 2004: Natural Sources of PM2.5 and PMcoarse Observed in the SEARCH Network. 

Presented at the A&WMA Specialty Conference on Regional and Global Perspectives in Haze: Causes, 

Consequences and Controversies, Asheville, NC, October 2004.

Environmental Protection Agency, 2003a: Guidance for Tracking Progress under the Regional Haze Rule; 

EPA-54/B-03-004, U.S. Environmental Protection Agency, Research Triangle Park, NC.

Environmental Protection Agency, 2003b: Guidance for Estimating Natural Visibility Conditions under 

the Regional Haze Rule. EPA-454/B-03-005. U.S. Environmental Protection Agency, Research Triangle 

Park, NC.

Environmental Protection Agency, 1998: Phase 2 Summary Report and Recommendations for Modeling 

Long Range Transport and Impacts. Interagency Workgroup on Air Quality Modeling (IWAQM). EPA454/R-98-019, U.S. Environmental Protection Agency, RTP, NC.

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Escoffier-Czaja, C., and J. Scire. 2002: The Effects of Ammonia Limitation on Nitrate Aerosol Formation 

and Visibility Impacts in Class I Areas. Paper J5.13, 12th AMS/A&WMA Conference on the Applications 

of Air Pollution Meteorology, Norfolk, VA. May 2002.

Fairall, C.W., E.F. Bradley, J.E. Hare, A.A. Grachev, and J.B. Edson, 2003: 

Bulk parameterization of air-sea fluxes: Updates and verification for

the COARE algorithm. Journal of Climate, 16, 571-591.

Fallon, S. and G. Bench, 2004: IMPROVE Special Study: Hi-Vol Sampling for Carbon-14 Analysis of 

PM 2.5 Aerosols. Draft Report, Lawrence Livermore National Laboratory. 

FLAG, 2000. Federal Land Managers’ Air Quality Related Values Workgroup (FLAG). Phase I Report. 

U.S. Forest Service, National Park Service, U.S. Fish and Wildlife Service.

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.

Grosjean, D., and J. Seinfeld, 1989: Parameterization of the Formation Potential of Secondary Organic 

Aerosols. Atmos. Environ., 23, 1733-1747.

Holtslag, A.A.M. and A.P. van Ulden, 1983: A simple scheme for daytime estimates of the surface fluxes 

from routine weather data. J. Clim. and Appl. Meteor., 22, 517-529.

Irwin, J.S., J.S. Scire and D.G. Strimaitis, 1996: A Comparison of CALPUFF Modeling Results with 

CAPTEX Field Data Results. In Air Pollution Modeling and Its Applications, XII. Edited by S.E. 

Gryning and F.A. Schiermeier. Plenum Press, New York, NY.

IWAQM, 1998: Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary Report 

and Recommendations for Modeling Long Range Transport Impacts. EPA-454/R-98-019.

Liu, M.K. and M. A. Yocke, 1980: Siting of wind turbine generators in complex terrain. J. Energy, 4, 

10:16.

Mahrt, L., 1982: Momentum Balance of Gravity Flows. J. Atmos. Sci., 39, 2701-2711.

Maul, P.R., 1980: Atmospheric transport of sulfur compound pollutants. Central Electricity Generating 

Bureau MID/SSD/80/0026/R. Nottingham, England.

Morris, R., C. Tang and G. Yarwood, 2003: Evaluation of the Sulfate and Nitrate Formation Mechanism 

in the CALPUFF Modeling System. Proceedings of the A&WMA Specialty Conference – Guideline on 

Air Quality Models: The Path Forward. Mystic, CT, 22-24 October 2003.

Morrison, K., Z-X Wu, J.S. Scire, J. Chenier and T. Jeffs-Schonewille, 2003: CALPUFF-Based 

Predictive and Reactive Emissions Control System. 96th A&WMA Annual Conference & Exhibition, 22-

26 June 2003, San Diego, CA.

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O'Brien, J.J., 1970: A note on the vertical structure of the eddy exchange coefficient in the planetary 

boundary layer. J. Atmos. Sci., 27, 1213-1215.

Pitchford, M., W. Malm, B. Schichtel, N. Kumar, D. Lowenthal, and J. Hand, 2005: Revised IMPROVE 

Algorithm for Estimating Light Extinction from Particle Speciation Data. Report to IMPROVE Steering 

Committee, November 2005

Perry, S.G., D.J. Burns, L.H. Adams, R.J. Paine, M.G. Dennis, M.T. Mills, D.G. Strimaitis, R.J. 

Yamartino, E.M. Insley, 1989: User’s Guide to the Complex Terrain Dispersion Model Plus Algorithms 

for Unstable Situations (CTDMPLUS) Volume 1: Model Description and User Instructions. EPA/600/8-

89/041, U.S. EPA, Research Triangle Park, North Carolina.

Schulman, L.L., D.G. Strimatitis, and J.S. Scire, 2000: Development and Evaluation of the PRIME Plume 

Rise and Building Downwash Model, J. Air & Waste Manage. Assoc., 50, 378-390. 

Scire, J.S., F.W. Lurmann, A. Bass and S.R. Hanna, 1984: Development of the MESOPUFF II 

Dispersion Model. EPA-600/3-84-057, U.S. Environmental Protection Agency, Research Triangle Park, 

NC. 

Scire, J.S. and F.R. Robe, 1997: Fine-Scale Application of the CALMET Meteorological Model to a 

Complex Terrain Site. Paper 97-A1313. Air & Waste Management Association 90th Annual Meeting & 

Exhibition, Toronto, Ontario, Canada. 8-13 June 1997.

Scire, J.S., D.G. Strimaitis, and R.J. Yamartino, 2000a: A User’s Guide for the CALPUFF Dispersion 

Model (Version 5). Earth Tech, Inc., Concord, Massachusetts

Scire, J.S., F.R. Robe, M.E. Fernau, and R.J. Yamartino, 2000b: A User’s Guide for the CALMET 

Meteorological Model (Version 5). Earth Tech, Inc., Concord, Massachusetts.

Scire, J.S., Z-X Wu, D.G. Strimaitis and G.E. Moore, 2001: The Southwest Wyoming Regional 

CALPUFF Air Quality Modeling Study – Volume I. Prepared for the Wyoming Dept of Environmental 

Quality. Available from Earth Tech, Inc., 196 Baker Avenue, Concord, MA.

Scire, J.S., Z.-X. Wu, 2003: Evaluation of the CALPUFF Model in Predicting Concentration, Visibility 

and deposition at Class I Areas in Wyoming. Presented at the A&WMA Specialty Conference, Guideline 

on Air Quality Models: The Path Forward. Mystic, CT, 22-24 October 2003.

Scire, J.S., D.G. Strimaitis and F.R. Robe, 2005: Evaluation of enhancements to the CALPUFF model for 

offshore and coastal applications. Proceedings of the Tenth International Conference on Harmonisation 

within Atmospheric Dispersion Modelling for Regulatory Purposes. Sissi (Malia), Crete, Greece. 17-20 

October 2005.

Strimaitis, D.G., J.S. Scire and J.C. Chang, 1998: Evaluation of the CALPUFF Dispersion Model with 

Two Power Plant Data Sets. Tenth Joint Conference on the Applications of Air Pollution Meteorology, 

Phoenix, Arizona. American Meteorological Society. 11-16 January 1998.

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References 68

Tanner, R., M. Zheng, K. Lim, J. Schauer, and A. P. McNichol, 2005. Contributions to the Organic 

Aerosol Fraction in the Tennessee Valley: Seasonal and Urban-Rural Differences. Paper presented at the 

AAAR International Specialty Conference, PM: Supersite Program and Related Studies, Atlanta, 7-11 

February 2005.

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Modeling Support to VISTAS State 

Agencies in Analyses of Best 

Available Retrofit Technology 

(BART)

August 2007 

Prepared For: 

VISTAS 

526 Forest Parkway, Suite F 

Forest Park, GA 30297 

Submitted By: 

TRC Environmental Corporation 

Wannalancit Mills 

650 Suffolk Street 

Lowell, MA 01854

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i

TABLE OF CONTENTS 

Page

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

2.0 GEOPHYSICAL AND METEOROLOGICAL DATA ..........................................................2-1

2.1 Modeling Domains, Terrain .....................................................................................................2-1

2.2 Land Use.................................................................................................................................2-12

2.3 Meteorological Data Base ......................................................................................................2-12

2.4 Air Quality Monitoring Data ..................................................................................................2-23

2.5 Class I Areas...........................................................................................................................2-23

2.6 Model Configuration and Settings for Initial Analysis...........................................................2-26

2.7 CALMET Modeling Configuration (12-km initial exemption modeling)..............................2-26

2.8 CALMET Modeling Configuration (4-km refined exemption modeling)..............................2-27

2.9 CALPUFF Modeling Configuration (Initial exemption modeling)........................................2-28

2.10 CALPOST and POSTUTIL Configuration (Initial exemption modeling) .............................2-30

3.0 MODELED FACILITIES FOR BART ....................................................................................3-1

3.1 Visibility Results ......................................................................................................................3-1

3.2 NOx Considerations in Respect to Visibility ............................................................................3-5

3.3 “Q over D” analysis..................................................................................................................3-9

3.4 Use of CMAQ for ALM analysis ...........................................................................................3-11

4.0 UPDATE OF CALPUFF MODEL CODES .............................................................................4-1 

4.1 Introduction ..............................................................................................................................4-1 

4.2 Results using the EPA Evaluation Tools..................................................................................4-3

4.3 Analysis of Typical Class I Impacts.........................................................................................4-9

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

APPENDIX A – LAND USE AND STATION DATA FOR REGIONAL SUBDOMAINS.............A-1 

APPENDIX B – MEMO ON CALMET OBS/NO-OBS MODES ...................................................... B-1 

APPENDIX C – SUMMARY OF ALM ANALYSES AND RECOMMENDATIONS ....................C-1 

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ii

LIST OF FIGURES 

Page

Figure 2-1 States and Class I areas in VISTAS region......................................................................2-2

Figure 2-2 Terrain elevations for the CALMET 12-km regional domain. .......................................2-4

Figure 2-3 Locations of the five subregional VISTAS 4-km domains..............................................2-6

Figure 2-4 Terrain elevations for the CALMET/CALPUFF Domain 1. ..........................................2-7

Figure 2-5 Terrain elevations for the CALMET/CALPUFF Domain 2. ..........................................2-8

Figure 2-6 Terrain elevations for the CALMET/CALPUFF Domain 3. ..........................................2-9

Figure 2-7 Terrain elevations for the CALMET/CALPUFF Domain 4. ........................................2-10

Figure 2-8 Terrain elevations for the CALMET/CALPUFF Domain 5. ........................................2-11

Figure 2-9 Land Use for the CALMET/ CALPUFF VISTAS regional domain. ............................2-13

Figure 2-10 Plot of the 475 surface stations for the CALMET subregional domains. ......................2-17

Figure 2-11 Plot of the 728 precipitation stations for the CALMET subregional domains. .............2-18

Figure 2-12 Plot of 23 upper air stations for the CALMET subregional domains. ...........................2-19

Figure 2-13 Plot of 12 buoy stations for the CALMET subregional domains...................................2-20

Figure 2-14 Plot of MM5 12-km grid used in CALMET simulations for 2001 and 2002. ...............2-21

Figure 2-15 Plot of MM5 36 km grid used in CALMET simulations for 2003. ...............................2-22

Figure 2-16 Plot of the 497 ozone stations used for the CALPUFF simulations. .............................2-24

Figure 2-17. Speciation of PM10 emissions. The numbers in the bottom row are the extinction 

efficiencies applied in the standard IMPROVE extinction equations............................2-31

Figure 3-1. Locations of the modeled facilities in the VISTAS Region.............................................3-3

Figure 3-2. Locations of the two hypothetical NOx-dominated facilities and Class I areas in 

the NO2 absorption sensitivity tests .................................................................................3-6 

Figure 3-3. Q/D plot where Q comprises of SO2 and NOx emissions. .............................................3-10

Figure 3-4. Q/D plot where Q comprises of SO2, NOx and PM10 emissions. ...................................3-10

Figure 3-5. Quantile-quantile plot of NO3 predictions (CMAQ in pink and from CALPUFF 

chemistry in blue) as a function of NO3 observations (CASTNet dataset at the 

top, weekly averages and IMPROVE dataset at the bottom, daily averages)................3-12

Figure A-1. Land Use for the CALMET/ CALPUFF Domain 1........................................................A-2

Figure A-2. Land Use for the CALMET/ CALPUFF Domain 2........................................................A-3

Figure A-3. Land Use for the CALMET/ CALPUFF Domain 3........................................................A-4

Figure A-4. Land Use for the CALMET/ CALPUFF Domain 4........................................................A-5

Figure A-5. Land Use for the CALMET/ CALPUFF Domain 5........................................................A-6

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iii

LIST OF TABLES 

Page

Table 2-1 Coverage of the 4-km Subregional VISTAS Domains....................................................2-5

Table 2-2 Meteorological Data Sources and Parameters Available...............................................2-16

Table 2-3 Number of Surface, Precipitation, Upper Air and Buoy Stations used in the 

Subregional Modeling....................................................................................................2-16

Table 2-4 Number of Ozone Stations used in the Modeling..........................................................2-25

Table 2-5 Example of Speciation of PM10 emissions.....................................................................2-32

Table 3-1 List of BART Eligible Facilities (Sources) Modeled.......................................................3-2

Table 3-2 Sample Source 1 – Visibility Results, Maximum 24-hr Contribution.............................3-7

Table 3-3 Sample Source 2 – Visibility Results, Maximum 24-hr Contribution.............................3-7

Table 3-4 Sample Source 1 – Stack Parameters and Emissions.......................................................3-8

Table 3-5 Sample Source 2 – Stack Parameters and Emissions.......................................................3-8

Table 4-1 Results of EPA Evaluation Tool......................................................................................4-4

Table 4-2 Comparison of Predicted SO2 Concentrations (μg/m3) at Six Class I Areas for 

Different Model Versions. ...............................................................................................4-7

Table 4-3 Comparison of Predicted PM10 Concentrations (μg/m3) at Six Class I Areas for 

Different Model Versions. ...............................................................................................4-7

Table 4-4 Comparison of Predicted NOx Concentrations (μg/m3) for Different Model 

Versions. ..........................................................................................................................4-8

Table 4-5 Comparison of Predicted Sulfur Deposition Fluxes (kg/ha/yr) at Six Class I Areas 

for Different Model Versions...........................................................................................4-8

Table 4-6 Comparison of Predicted Nitrogen Deposition Fluxes (kg/ha/yr) at Six Class I 

Areas for Different Model Versions. ...............................................................................4-8

Table 4-7 Comparison of Predicted Visibility Impacts at Six Class I Areas for Different 

Model Versions. (Version C results are not available). The values shown are the 

highest percent change in extinction (Bext). .....................................................................4-9

Table A-1 Upper Air Stations used in the Subregional Modeling ...................................................A-7

Table A-2 Buoys used in the Subregional Modeling .......................................................................A-8

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1-1

1.0 INTRODUCTION 

The Visibility Improvement State and Tribal Association of the Southeast (VISTAS) and the 

Southeastern States Air Resources Mangers, Inc. (SESARM) issued a contract with TRC 

Environmental Corporation in May 2006 to continue work providing support to the VISTAS 

State Agencies with analyses of Best Available Retrofit Technology (BART). This contract 

continued work conducted by the project staff while previously employed by Earth Tech, Inc. 

This summary report outlines the major tasks completed under the project. This includes: 

• Assistance in the development of a standard modeling protocol document and the 

modeling procedures and techniques for both screening and refined BART analyses. 

• Analysis and evaluation of various technical methods and procedures including 

meteorological modeling (observations vs. no-observations mode simulations, grid 

spacing, domain definitions), importance of absorption by NO2 on light extinction, 

dispersion methods and evaluation of the impact of ammonia limitation on nitrate aerosol 

formation. 

• Development of a standard 3-year regional meteorological dataset and five subregional, 

higher resolution datasets for BART simulations. 

• Development and distribution through a web page of BART input datasets suitable for 

use in refined, site-specific modeling, including meteorological observations at 475 

stations, 728 precipitation stations, 23 upper air stations, 12 buoys and at 497 ozone 

monitoring stations. In addition, geophysical datasets and modeling input files were 

produced and distributed along with a version of the CALPUFF modeling codes 

recommended by the VISTAS states for BART analyses. 

• Default pollutant speciation and size distribution assumptions for use in BART visibility 

analyses in the absence of source-specific detailed data. The default methods were 

designed to produce reasonable but conservative estimates of visibility impact. 

• BART exemption modeling for 530 individual BART-eligible sources at 71 facilities in 

ten states at 21 different Class I areas in the VISTAS region. 

• Production of site specific and state summary reports for the BART exemption modeling 

analysis. 

• Model code maintenance and interaction with the U.S. Environmental Protection Agency 

(EPA) to update and gain acceptance of a new version of the official EPA-approved 

CALPUFF system model codes to reflect enhancements and corrections included in the 

VISTAS-recommended codes. TRC and VISTAS worked successfully with the EPA to 

provide additional documentation and test results and to introduce a “regulatory switch” 

in the CALMET meteorological model and changes to CALPUFF to achieve EPA 

acceptance of the updated model as the official regulatory version. 

Appendix L2 

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1-2

Background 

Best Available Retrofit Technology (BART) is required for any BART-eligible source that “emits 

any air pollutant which may reasonably be anticipated to cause or contribute to any impairment of 

visibility in any of the 156 mandatory federal Class I areas”. As outlined in 40 CFR Part 51 

Appendix Y, air quality modeling can be used to determine whether an individual source would 

be reasonably expected to cause or contribute to visibility impairment. EPA guidance 

recommends the use of the CALPUFF models as “the best regulatory modeling application 

currently available for predicting a single source’s contribution to visibility impairment” (70 FR 

39162). The VISTAS modeling protocol (VISTAS, 2005) focuses on the use of CALPUFF as the 

preferred modeling approach for BART demonstrations. 

Under the regional haze regulations, EPA issued final guidelines dated July 6, 2005 for BART 

determinations. The guidelines define BART-eligible sources as certain large stationary sources 

that meet three criteria: (1) potential emissions of at least 250 tons per year of a visibilityimpairing pollutant; (2) sources put in place between August 7, 1962 and August 7, 1977; and 

(3) sources falling within one of the 26 listed source categories specified in the guidance. If a 

source cannot demonstrate that it does not cause of contribute to visibility impairments, a BART 

engineering evaluation is required that uses five statutory factors including (a) existing controls in 

place at the facility; (b) cost of additional controls; (c) energy and non-air environmental impacts; 

(d) remaining useful life of the facility; and (e) the degree of visibility improvement expected 

from the application of controls. 

The CALPUFF modeling system (Scire et al., 2000a, b) is recommended in the VISTAS protocol 

as the preferred modeling approach for use in the BART analyses. CALPUFF and its 

meteorological model, CALMET, are designed to handle the complexities posed by the complex 

terrain, the large source-receptor distances, chemical transformation and deposition, and other 

issues related to Class I visibility impacts. The CALPUFF modeling system has been adopted by 

the EPA as a Guideline Model for source-receptor distances greater than 50 km, and for use on a 

case-by-case basis in complex flow situations for shorter distances (68 FR 18440-18482). 

CALPUFF is recommended for Class I impact assessments by the Federal Land Managers 

Workgroup (FLAG, 2000) and the Interagency Workgroup on Air Quality Modeling (IWAQM) 

(EPA, 1998). The final BART guidance recommends CALPUFF for “predicting a single source’s 

contribution to visibility impairment” (70 FR 39122). As a result of these recommendations, the 

VISTAS modeling effort is based on the use of CALPUFF for its BART determinations. 

Description of the CALPUFF modeling system can be found in Section 3 of the VISTAS protocol 

(VISTAS, 2005). 

Appendix L2 

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2-1

2.0 GEOPHYSICAL AND METEOROLOGICAL DATA 

The jurisdiction represented by VISTAS members include the Eastern Band of Cherokee Indians; 

the states of Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, 

Tennessee, Virginia, and West Virginia; and the local air pollution control agencies in the states 

of Alabama, Florida, Kentucky, North Carolina, and Tennessee. Figure 2-1 shows VISTAS area 

as well as the Class I areas in the region. 

2.1 Modeling Domains and Terrain 

Two types of VISTAS CALMET domains have been created for the BART modeling effort in the 

VISTAS region. The first type is the VISTAS 12-km CALMET regional domain which covers 

the entire VISTAS region (southeastern United States). The second type includes five subregional 

4-km VISTAS CALMET domains covering one to three VISTAS states. 

Gridded terrain elevations for the 12-km regional VISTAS modeling domain are derived from the 

SRTM30 data set. SRTM30 is a near-global digital elevation model (DEM) comprising a 

combination of data from the 11-day Shuttle Radar Topography Mission (SRTM), flown in 

February, 2000 and the U.S. Geological Survey's GTOPO30 data set. It is formatted and 

organized in a fashion that mimics the GTOPO30 convention. SRTM30 data set spans the globe 

from 60°N to 56°S, approximately from the southern tip of Greenland to below the southern tip of 

South America. It has a horizontal grid spacing of 30 arc-seconds (approximately 1 kilometer). 

While SRTM30 has the same resolution as GTOPO30, it can be considered a more accurate 

global digital data set compared to GTOPO30 because of its seamless and uniform representation, 

due to the fact that it was created over a short period of time from a single source rather than from 

the numerous sources spanning many decades that went into creating the GTOPO30 data set. 

The horizontal coordinate system is decimal degrees of latitude and longitude referenced to the 

WGS84 datum. The vertical units represent elevation in meters above mean sea level. SRTM30 

been divided into 27 smaller pieces, or tiles. Two of these tiles cover regional VISTAS domain: 

W100N40 and W100N90. 

Gridded terrain elevations for the 4-km subregional VISTAS modeling Domains 1 through 5 are 

derived from 3 arc-second DEMs produced by the USGS. Data are provided in files covering 

1-degree by 1-degree blocks of latitude and longitude. The 1-degree DEMs are produced by the 

Defense Mapping Agency using cartographic and photographic sources. USGS 1:250,000 scale 

topographic maps are the primary source of 1-degree DEMs. One-degree DEM data consists of 

an array of 1201 by 1201 elevations referenced on the geographic (latitude/ longitude) coordinate 

system of the World Geodetic System 1972 Datum. Elevations are in meters relative to mean sea 

level, and the spacing of the elevations along each profile is 3 arc-seconds, which corresponds to 

a spacing of approximately 90 meters. SRTM30 data were also used as a backup in case of 

missing data. 

The VISTAS 12-km CALMET regional domain was used in CALPUFF modeling and BART 

visibility analysis for the facilities that are more than 50 km away from any of the Class I areas. 

Appendix L2 

Regional Haze SIP for Florida

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2-2

Figure 2-1. States and Class I areas in the VISTAS region. 

Appendix L2 

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2-3

Figure 2-2 shows contours of the terrain averaged to 12-km grid cells on the VISTAS 12-km 

regional domain. The CALPUFF computational subdomains were chosen to cover all Class I 

areas that fall into the 300 km circle around the particulate state sources, as well as a 50 km buffer 

zone from these Class I areas and that state’s facilities. The subdomains extend at least 50 km 

from the Class I areas and all of the state’s facilities in order to provide an adequate buffer zone at 

the boundaries, and to allow the effects of flow curvature and possible small-scale recirculation to 

be evaluated. 

Five subregional VISTAS domains were designed for the refined modeling of the BART eligible 

sources. Each subregional VISTAS domain covers one to three individual states. Table 1-1 lists 

the states and Class I areas inside each of the VISTAS subregional domains. Figure 2-3 shows 

positions and extends of the five subregional domains. These domains were designed so that they 

cover entire state, all Class I areas that are within 300 km from any of the sources in that 

particular state, as well as a 50 km buffer zone from all those Class I areas and all of the 

particular state’s facilities. For example, VISTAS Domain 3 was designed for the refined 

modeling of the Kentucky and Tennessee sources. It covers entire states of Kentucky and 

Tennessee, all Class I areas that are within 300 km from any of the sources in KY and TN, as well 

as a 50 km buffer zone from these Class I areas and KY and TN sources. 

All of the VISTAS domains were defined in the Lambert Conformal Conic (LCC) projection. The 

parameters of the LCC projection used in the VISTAS modeling was chosen to match the LCC 

parameters of the MM5 data sets. The LCC projection is based on standard parallels of 33.0 N 

and 45.0 N, an origin latitude and longitude of 40.0 N and 97.0 W, and datum NWS-84. 

As listed in Table 1-1, VISTAS Domain 1 was designed for the refined modeling of the 

Mississippi sources, Domain 2 for Florida sources, Domain 3 for Kentucky and Tennessee 

sources, Domain 4 for Georgia, South Carolina and Alabama sources and Domain 5 for sources 

in Virginia, West Virginia and North Carolina. Table 1-1 also lists the Class I areas inside each of 

the subregional domains as well as the size and the number of grid cells for each of the domains. 

Terrain contours averaged to 4-km grid cells for each of the five domains and Class I areas within 

them are shown in Figure 2-4 through Figure 2-8. In the refined 4-km runs, CALPUFF 

computational domain is the same as the CALMET Domain. 

Appendix L2 

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2-4

Figure 2-2. Terrain elevations for the CALMET 12-km regional domain. The Class I areas are 

also shown. 

200 400 600 800 1000 1200 1400 1600 1800 2000

LCC East (km)

-1600

-1400

-1200

-1000

-800

-600

-400

-200

0

200

400

LCC North (km)

LCC Origin: 40.0 N, 97.0 W

Standard Parallels: 33.0 N, 45.0 N

Datum: NWS-84

Elevation

(meters)

AL GA

TN

SC

NC

VA

FL

KY

MS

WV

Everglades

Breton

Sipsey

Cohutta

Mammoth Cave

Mingo

Cape Romain

Chassahowitzka

Great Smoky Mountains

Joyce Kilmer-Slickrock

Linville Gorge

Okefenokee

Shining Rock

St Marks

Wolf Island

James River Face

Swanquarter

Shenandoah

Dolly Sods

Otter Creek

Brigantine

-25

0.1

50

100

150

200

300

400

500

600

700

800

900

1000

1100

1200

1300

1400

Appendix L2 

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2-5

Table 1-1. Coverage of the 4-km Subregional VISTAS Domains 

States Class I Areas Area of the Domain/ 

Number of grid cells 

Domain 1 Mississippi Sipsey 

Breton Island 

464 km by 728 km/ 

116 by 182 grid cells 

Domain 2 Florida 

Breton Island 

Chassahowitzka 

Everglades 

Okefenokee 

St Marks 

Wolf Island 

1052 km by 824 km/ 

263 by 206 grid cells 

Domain 3 Kentucky 

Tennessee 

Cohutta 

Dolly Sods 

Great Smoky Mountains 

James River Face 

Joyce Kilmer-Slickrock 

Linville Gorge 

Mammoth Cave 

Mingo 

Otter Creek 

Shining Rock 

Sipsey 

1052 km by 740 km/ 

263 by 185 grid cells 

Domain 4 

Alabama 

Georgia 

South Carolina 

Breton Island 

Cape Romain 

Chassahowitzka 

Cohutta 

Great Smoky Mountains 

Joyce Kilmer-Slickrock 

Linville Gorge 

Mammoth Cave 

Okefenokee 

St Marks 

Shining Rock 

Sipsey 

Wolf Island 

992 km by 1028 km/ 

248 by 257 grid cells 

Domain 5 

North Carolina 

Virginia 

West Virginia 

Brigantine 

Cape Romain 

Cohutta 

Dolly Sods 

Great Smoky Mountains 

James River Face 

Joyce Kilmer-Slickrock 

Linville Gorge 

Otter Creek 

Shenandoah 

Shining Rock 

Swanquarter 

912 km by 928 km/ 

228 by 232 grid cells 

Appendix L2 

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2-6

Figure 2-3. Locations of the five subregional VISTAS 4-km domains. 

200 400 600 800 1000 1200 1400 1600 1800 2000

LCC East (km)

-1600

-1400

-1200

-1000

-800

-600

-400

-200

0

200

400

LCC North (km)

LCC Origin: 40.0 N, 97.0 W

Standard Parallels: 33.0 N, 45.0 N

Datum: NWS-84

Elevation

(meters)

Domain 1

Domain 2

Domain 3

Domain 4

Domain 5

AL GA

TN

SC

NC

VA

FL

KY

MS

WV

Everglades

Breton

Sipsey

Cohutta

Mammoth Cave

Mingo

Cape Romain

Chassahowitzka

Great Smoky Mountains

Joyce Kilmer-Slickrock

Linville Gorge

Okefenokee

Shining Rock

St Marks

Wolf Island

James River Face

Shenandoah

Dolly Sods

Otter Creek

Swanquarter

Brigantine

-25

0.1

50

100

150

200

300

400

500

600

700

800

900

1000

1100

1200

1300

1400

Appendix L2 

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2-7

Figure 2-4. Terrain elevations for the CALMET/CALPUFF Domain 1. The Class I areas are also 

shown. 

500 600 700 800 900

LCC East (km)

-1100

-1000

-900

-800

-700

-600

-500

LCC North (

km)

LCC Origin: 40.0 N, 97.0 W

Standard Parallels: 33.0 N, 45.0 N

Datum: NWS-84

Elevation

(meters)

AL

TN

MS

Breton

Sipsey

-25

0.1

50

100

150

200

300

400

500

600

700

800

900

1000

1100

1200

1300

1400

Appendix L2 

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2-8

800 900 1000 1100 1200 1300 1400 1500 1600 1700

LCC East (km)

-1500

-1400

-1300

-1200

-1100

-1000

-900

-800

LCC North (km)

LCC Origin: 40.0 N, 97.0 W

Standard Parallels: 33.0 N, 45.0 N

Datum: NWS-84

Elevation

(meters)

FL

MS AL GA

-25

0.1

50

100

150

200

300

400

500

600

700

800

900

1000

1100

1200

1300

1400

Everglades

Chassahowitzka

St Marks

Okefenokee

Wolf Island

Breton

Figure 2-5. Terrain elevations for the CALMET/CALPUFF Domain 2. The Class I areas are also 

shown. 

Appendix L2 

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600 700 800 900 1000 1100 1200 1300 1400 1500

LCC East (km)

-600

-500

-400

-300

-200

-100

0

100

LCC North (km)

LCC Origin: 40.0 N, 97.0 W

Standard Parallels: 33.0 N, 45.0 N

Datum: NWS-84

Elevation

(meters)

TN

KY

WV

VA

NC

MS AL GA SC

Mammoth Cave

Mingo

Great Smoky Mountains

Joyce Kilmer-Slickrock

Linville Gorge

Shining Rock

James River Face

Dolly Sods

Otter Creek

-25

0.1

50

100

150

200

300

400

500

600

700

800

900

1000

1100

1200

1300

1400

Figure 2-6. Terrain elevations for the CALMET/CALPUFF Domain 3. The Class I areas are also 

shown. 

Appendix L2 

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2-10

800 900 1000 1100 1200 1300 1400 1500 1600 1700

LCC East (km)

-1200

-1100

-1000

-900

-800

-700

-600

-500

-400

-300

-200

LCC North (km)

LCC Origin: 40.0 N, 97.0 W

Standard Parallels: 33.0 N, 45.0 N

Datum: NWS-84

Elevation

(meters)

FL

GA

TN

SC

NC

VA

WV

KY

AL MS

Chassahowitzka

St Marks

Okefenokee

Wolf Island

Cape Romain

Sipsey

Cohutta

Joyce Kilmer-Slickrok

Great Smoky Mountains

Shining Rock

Linville Gorge

Breton

-25

0.1

50

100

150

200

300

400

500

600

700

800

900

1000

1100

1200

1300

1400

Mammoth Cave

Figure 2-7. Terrain elevations for the CALMET/CALPUFF Domain 4. The Class I areas are also 

shown. 

Appendix L2 

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Page 93 of 266
2-11

Figure 2-8. Terrain elevations for the CALMET/CALPUFF Domain 5. The Class I areas are also 

shown. 

1100 1200 1300 1400 1500 1600 1700 1800 1900

LCC East (km)

-600

-500

-400

-300

-200

-100

0

100

200

LCC North (km)

LCC Origin: 40.0 N, 97.0 W

Standard Parallels: 33.0 N, 45.0 N

Datum: NWS-84

Elevation

(meters)

GA

SC

NC

TN

VA

WV

KY

Cape Romain

Cohutta

Joyce 

Kilmer-Slickrok

Great Smoky 

Mountains

Shining Rock

Linville Gorge

Swanquarter

James 

River Face

Shenandoah

Otter Creek Dolly Sods

Brigantine

-25

0.1

50

100

150

200

300

400

500

600

700

800

900

1000

1100

1200

1300

1400

Appendix L2 

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2-12

2.2 Land Use 

The USGS Land Use data within the CALMET regional and subregional domains have been used 

to produce a gridded field of dominant land use categories. The land use data were obtained in 

Composite Theme Grid format (CTG) from the USGS, with a resolution of 200 m. For the 

portions of the domains where CTG data were missing, North America USGS land use/land cover 

data have been used. The North America land cover data base is one portion of a global land 

cover characteristics data base that was developed on a continent-by-continent basis with 1-km 

nominal spatial resolution. 

Land use data were processed to produce a 12-km and a 4-km resolution gridded field of 

fractional land use categories for the regional domain and the subregional domains. The 37 USGS 

land use categories were then mapped into 14 CALMET land use categories. Surface properties 

such as albedo, Bowen ratio, roughness length, and leaf area index were computed proportionally 

to the fractional land use. The USGS land use categories and their associated geophysical 

parameters are described in Scire et al. (2000a). 

Figure 2-9 shows the dominant land use categories for each 12-km CALMET grid cell on the 

VISTAS 12-km regional domain. Figures A-1 to A-5 in Appendix A show the dominant land use 

categories for each 4-km CALMET grid cell on the subregional VISTAS 4-km Domains 1 to 5. 

2.3 Meteorological Data Base 

The meteorological data used include the three dimensional gridded model output from the Fifth 

Generation Penn State/NCAR MM5 (Grell 1995) provided by VISTAS. TRC has assembled three 

years of MM5 meteorological data for the regional CALPUFF modeling. These MM5 datasets 

have been developed by EPA for 2001, VISTAS for 2002 and Midwest Regional Planning 

Organization (RPO) for 2003. The MM5 gridded data set consists of hourly values of wind speed, 

wind direction, temperature and pressure on a grid covering the VISTAS regional 12-km 

CALMET domain and VISTAS subregional 4-km CALMET Domains 1 to 5. The MM5 data was 

run at a horizontal spatial resolution of 12-km for 2001 and 2002 and 36-km for 2003. 

The considerations related to a) time and space resolution of observational data; b) time and space 

resolution of 3-D prognostic data; c) representativeness of observational data; d) discrepancies 

between prognostic winds and observations; e) model biases; and f) level of effort vs. benefit 

were presented in the Appendix B. Given those consideration, it has been recommended for the 

VISTAS 12-km CALMET regional simulations to be conducted in No-Observations mode using 

the highest resolution MM5 data available for each year modeled. It has also been recommended 

for the 4-km subregional CALMET domains to be modeled using the hybrid mode where the 

MM5 datasets are used to define the initial guess fields, but surface and upper air observational 

data are used to refine the meteorological parameters locally in the vicinity of the observational 

sites. 

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Figure 2-9. Land use for the CALMET/ CALPUFF VISTAS regional domain. The Class I areas 

are also shown. 

200 400 600 800 1000 1200 1400 1600 1800 2000

LCC East (km)

-1600

-1400

-1200

-1000

-800

-600

-400

-200

0

200

400

LCC North (km)

LCC Origin: 40.0 N, 97.0 W

Standard Parallels: 33.0 N, 45.0 N

Datum: NWS-84

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95

100

Urban/

built-up land

Agricultural

land

Rangeland

Forest land

Water

Wetland

Barren land

Tundra

Snow / Ice

Everglades

Chassahowitzka

St Marks

Okefenokee

Wolf Island

Cape Romain

Sipsey

Cohutta

Joyce Kilmer-Slickrok

Great Smoky Mountains

Shining Rock

Linville Gorge Swanquarter

James River Face

Shenandoah

Dolly Sods Otter Creek

Mammoth Cave

Breton

Brigantine

Mingo

FL

MS AL GA

TN

SC

NC

VA

WV

KY

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The regional wind patterns are adequately characterized by the MM5 simulations. Many 

observations, especially surface observations, may reflect local conditions on a scale smaller than 

the grid resolution of the regional domain (12 km). The introduction of the local observations into 

the regional modeling domain may extend the influence of the observational data beyond its true 

representativeness and result in internally inconsistent flow features. In particular the time 

interpolation of the 12-hourly upper air sounding data may wash out structure in the MM5 fields 

that are appropriate to retain. Given that the regional modeling domain includes areas of complex 

terrain, coastal regions and relatively flat terrain, a single set of representative weights that allows 

significant influence of the observations where appropriate, will involve a considerable effort and 

substantial testing. The internally consistent MM5 fields are considered to be appropriate for the 

regional simulations, and the incremental benefit of adding the observational data into the 

regional CALMET simulations is not considered worthwhile. 

However, on the smaller subregional domains, with the higher CALMET grid resolution and the 

smaller domain size, more control over the region of influence of the meteorological observations 

can be achieved. It is easier for the diagnostic model to allow the local flow observations to have 

appropriate influence in the vicinity of the observation, but allow terrain-adjusted flow to 

dominate away from the observations. Given that the subregional domains have been used 

especially in complex flow situations (complex terrain and coastal regions), the relatively coarsescale MM5 simulations are less likely to be adequate, and the introduction observational data is 

expected to achieve improvements in the resulting meteorological fields. 

Therefore, for the regional 12-km domain, CALMET runs have been made in the 

No-Observations mode. The MM5 data already reflect assimilation of observational data and 

adequately characterize regional wind patterns that are consistent with the 12-km grid scale. For 

2003, the 36-km MM5 data were used as CALMET’s initial guess field and then the CALMET 

diagnostic terrain adjustments (see Section 3.1.1 of the Protocol) were applied to reflect terrain on 

the scale of the CALMET grid (i.e., 12-km). When the 12-km MM5 (2001 and 2002) data were 

used, the diagnostic CALMET terrain adjustments were turned off since the grid resolution of the 

MM5 data is the same as the CALMET grid and the terrain adjustments on the 12-km grid scale 

have been already reflected in the MM5 dataset. In this case, the MM5 winds have been 

interpolated by CALMET to the CALMET layers and CALMET’s boundary layer modules has 

computed mixing heights, turbulence parameters and other meteorological parameters that are 

required by CALPUFF. 

For the five subregional 4-km domains, CALMET runs have been made in the refined mode, with 

all available surface, upper air, buoy and precipitation stations included in the simulations along 

with the gridded MM5 data. In the refined mode, the CALMET diagnostic model requires surface 

observations of wind speed, wind direction, temperature, cloud cover, ceiling height, surface 

pressure, relative humidity, and precipitation type (e.g., snow, rain, etc.). These variables are 

routinely measured at the National Weather Service (NWS) surface stations. The upper air data 

include twice-daily observations of vertical profiles of wind speed, wind direction, temperature, 

and pressure. In addition, hourly precipitation measurements are required for wet deposition 

calculations in CALPUFF. Table 2-2 lists the types of observational and modeled data used in the 

modeling 

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All available meteorological data within the VISTAS regional domain were assembled and 

processed. All surface and precipitation data were combined into SURF.DAT and PRECIP.DAT 

files for each of the modeled years. These standard VISTAS data files that included all available 

surface and precipitation stations within the regional domain are available at http://www.src.com

web page. For each of the five CALMET subregional modeling domains, smaller SURF.DAT and 

PRECIP.DAT files were extracted to include only those stations that are located within the 

particular domain and its vicinity. The extraction was performed using the program SUBDOMN, 

a utility program run through the CALPUFF Graphical User Interface (GUI). Figure 2-10 and 

Figure 2-11 show locations of all the surface and precipitation stations used in the CALMET 

modeling of the five subregional domains. Total of 475 surface stations and 728 precipitation 

stations are shown on these plots. The number of surface and precipitation stations used in each 

of the subregional domains is listed in Table 2-3. Locations of upper air stations used in the 

subregional domains are shown in Figure 2-12. For each sounding, individual UP.DAT files is 

created for CALMET simulations. Similarly, individual SEA.DAT files were created from data 

observed at each of the buoys. VISTAS standard data set consists of the 23 upper air stations 

shown in Figure 2-12 and 12 buoys shown in Figure 2-13. Only those upper air stations and 

buoys within or in the vicinity of the subregional domains were included in the simulations. The 

number of upper air stations and buoys used in each of the subregional domains is listed in Table 

2-3. Table A-1 and Table A-2 list the upper air stations and buoys used for each of the five 

domains. 

Figure 2-14 and Figure 2-15 show locations of the MM5 grid points used in CALMET 

simulations. For 2001 and 2002, a horizontal space resolution of MM5 grid points was 12-km 

(Figure 2-14), while for year 2003, MM5 horizontal resolutions was 36 km (Figure 2-15). For 

practical purposes, for each of the subregional domains, smaller MM5 files were extracted from 

the full MM5 data set to cover the entire subregional domain and to include additional two rows 

of the MM5 grid points beyond each of the four sides of the domain. 

Appendix L2 

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Table 2-2. Meteorological Data Sources and Parameters Available. 

Type of Dataset Frequency Source Parameters 

Surface observations Hourly NWS/NCDC Wind speed, wind direction, temperature, 

ceiling height, cloud cover, relative humidity, 

surface pressure, precipitation type 

Upper Air Twice-daily NWS/NCDC Soundings of wind speed, wind direction, 

temperature, and pressure 

Precipitation Hourly NWS/NCDC Hourly precipitation amounts 

Buoy Hourly NWS/NCDC Air-sea surface temperature difference, air 

temperature, relative humidity, overwater 

mixing height, temperature lapse rate below 

the mixing height overwater, temperature 

lapse rate above the mixing height overwater, 

wind speed, wind direction, dominant wave 

period, significant wave height 

MM5 data Hourly EPA for 2001, 

VISTAS for 2002 

Midwest RPO for 2003 

Hourly gridded fields of winds, temperature, 

pressure, and humidity and liquid water 

content on a 12-km grid for 2001 and 2002 

and 36 km grid for 2003. 

Table 2-3. Number of Surface, Precipitation, Upper Air and Buoy Stations used in the Subregional 

Modeling. 

Number of 

Surface 

Stations 

Number of 

Precipitation 

Stations 

Number of 

Upper Air 

Stations 

Number of 

Buoys 

2001 2002 2003 2001 2002 2003 2001- 2003 2001- 2003 

Domain 1 66 62 65 126 125 135 6 3 

Domain 2 109 116 100 96 103 92 10 9 

Domain 3 175 166 190 418 406 423 13 - 

Domain 4 206 194 217 306 298 318 13 6 

Domain 5 232 228 248 347 331 342 9 3 

Appendix L2 

Regional Haze SIP for Florida

Page 99 of 266
2-17

Figure 2-10. Plot of the 475 surface stations for the CALMET subregional domains. 

200 400 600 800 1000 1200 1400 1600 1800 2000

LCC East (km)

-1600

-1400

-1200

-1000

-800

-600

-400

-200

0

200

400

LCC North (km)

LCC Origin: 40.0 N, 97.0 W

Standard Parallels: 33.0 N, 45.0 N

Datum: NWS-84

Elevation

(meters)

Domain 1

Domain 2

Domain 3

Domain 4

Domain 5

AL GA

TN

SC

NC

VA

FL

KY

MS

WV

-25

0.1

50

100

150

200

300

400

500

600

700

800

900

1000

1100

1200

1300

1400

Appendix L2 

Regional Haze SIP for Florida

Page 100 of 266