Appendix H.6 

BART Modeling Protocols 
Vistas BART Modeling Protocol...........................................................................................3 

Bowater Final BART Modeling Protocol..............................................................................80 

FLM comments on Bowater Modeling Protocol ...................................................................185 

Bowater Final BART Modeling Analysis..............................................................................191 

DAK Final BART Modeling Protocol...................................................................................259 

EPA comments on DAK Modeling Protocol.........................................................................381 

DAK Final BART Modeling Analysis ..................................................................................383 

VISTAS Final BART Modeling Report (to include INVISTA Final Modeling Protocol) ...459 

U.S. EPA Region IV comments on INVISTA Modeling Protocol........................................493 

INVISTA Final BART Modeling Analysis ...........................................................................494 

IP Georgetown Final BART Modeling Protocol ...................................................................841 

IP Georgetown Addendum to Final BART Modeling Analysis............................................935 

U.S. EPA Region IV comments on IP Georgetown Modeling Protocol ...............................946 

IP Georgetown response to SC DHEC comments on Addendum .........................................951 

ISG Georgetown Final BART Modeling Protocol ................................................................957 

VISTAS Final BART Modeling Report ................................................................................1057 

MeadWestvaco Final BART Modeling Protocol...................................................................1063 

U.S. EPA Region IV comments on MeadWestvaco Modeling Protocol...............................1190 

MeadWestvaco Final BART Modeling Analysis ..................................................................1191 

SC DHEC comments on Santee Cooper Modeling Protocol.................................................1332 

Santee Cooper Final BART Modeling Analysis....................................................................1334 

SCE&G Canadys Final BART Modeling Protocol ...............................................................1540 

SCE&G Canadys Final BART Modeling Analysis...............................................................1559 

SCE&G Wateree Final BART Modeling Protocol................................................................1609 

SCE&G Wateree Final BART Modeling Analysis ...............................................................1628 

SCE&G Williams Final BART Modeling Protocol...............................................................1665 

SCE&G Williams Final BART Modeling Analysis ..............................................................1684 

Stone Container Final BART Modeling Protocol..................................................................1715 

FLM Comment on Stone Container Final BART Modeling Protocol...................................1730 

VISTAS Final BART Modeling Report (to include Stone Container Final BART 

Modeling Analysis) 

Wellman Final BART Modeling Protocol.............................................................................1730 

VISTAS Final BART Modeling Report (to include Wellman Final BART Modeling 

Analysis) 

Appendix H.6 - 1
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)

Appendix H.6 - 2
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

Appendix H.6 - 3
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

Appendix H.6 - 4
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. 

Appendix H.6 - 5
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

Appendix H.6 - 6
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 H.6 - 7
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.

Appendix H.6 - 8
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).

Appendix H.6 - 9
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.

Appendix H.6 - 10
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.

Appendix H.6 - 11
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 

Appendix H.6 - 12
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.

Appendix H.6 - 13
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 

Appendix H.6 - 14
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. 

Appendix H.6 - 15
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 

Appendix H.6 - 16
Review of EPA’s Guidance 8

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.

Appendix H.6 - 17
Review of EPA’s Guidance 9

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.

Appendix H.6 - 18
Overview of the CALPUFF Modeling System 10

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.

Appendix H.6 - 19
Overview of the CALPUFF Modeling System 11

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.

Appendix H.6 - 20
Overview of the CALPUFF Modeling System 12

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 

Appendix H.6 - 21
Overview of the CALPUFF Modeling System 13

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 

Appendix H.6 - 22
Overview of the CALPUFF Modeling System 14

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

Appendix H.6 - 23
Overview of the CALPUFF Modeling System 15

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.

Appendix H.6 - 24
Overview of the CALPUFF Modeling System 16

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. 

Appendix H.6 - 25
Overview of the CALPUFF Modeling System 17

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)

Appendix H.6 - 26
Overview of the CALPUFF Modeling System 18

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 

Appendix H.6 - 27
Overview of the CALPUFF Modeling System 19

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.)

Appendix H.6 - 28
Overview of the CALPUFF Modeling System 20

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.

Appendix H.6 - 29
Overview of the CALPUFF Modeling System 21

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.

Appendix H.6 - 30
Overview of the CALPUFF Modeling System 22

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

Appendix H.6 - 31
Overview of the CALPUFF Modeling System 23

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. 

Appendix H.6 - 32
Overview of the CALPUFF Modeling System 24

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. 

Appendix H.6 - 33
Overview of the CALPUFF Modeling System 25

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

Appendix H.6 - 34
Overview of the CALPUFF Modeling System 26

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, 

Appendix H.6 - 35
Overview of the CALPUFF Modeling System 27

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

Appendix H.6 - 36
Overview of the CALPUFF Modeling System 28

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

Appendix H.6 - 37
Overview of the CALPUFF Modeling System 29

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.

Appendix H.6 - 38
Overview of the CALPUFF Modeling System 30

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. 

Appendix H.6 - 39
Overview of the CALPUFF Modeling System 31

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 

Appendix H.6 - 40
Overview of the CALPUFF Modeling System 32

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.

Appendix H.6 - 41
Overview of the CALPUFF Modeling System 33

- 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. 

 

Appendix H.6 - 42
VISTAS’ Common Modeling Protocol 34

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 

Appendix H.6 - 43
VISTAS’ Common Modeling Protocol 35

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.

Appendix H.6 - 44
VISTAS’ Common Modeling Protocol 36

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 

Appendix H.6 - 45
VISTAS’ Common Modeling Protocol 37

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 

Appendix H.6 - 46
VISTAS’ Common Modeling Protocol 38

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 

Appendix H.6 - 47
VISTAS’ Common Modeling Protocol 39

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. 

Appendix H.6 - 48
VISTAS’ Common Modeling Protocol 40

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. 

Appendix H.6 - 49
VISTAS’ Common Modeling Protocol 41

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.

Appendix H.6 - 50
VISTAS’ Common Modeling Protocol 42

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. 

Appendix H.6 - 51
VISTAS’ Common Modeling Protocol 43

- 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 

Appendix H.6 - 52
VISTAS’ Common Modeling Protocol 44

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

Appendix H.6 - 53
VISTAS’ Common Modeling Protocol 45

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 

Appendix H.6 - 54
VISTAS’ Common Modeling Protocol 46

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 

Appendix H.6 - 55
VISTAS’ Common Modeling Protocol 47

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. 

Appendix H.6 - 56
VISTAS’ Common Modeling Protocol 48

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. 

Appendix H.6 - 57
VISTAS’ Common Modeling Protocol 49

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.

Appendix H.6 - 58
VISTAS’ Common Modeling Protocol 50

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

Appendix H.6 - 59
VISTAS’ Common Modeling Protocol 51

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.

Appendix H.6 - 60
VISTAS’ Common Modeling Protocol 52

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

Appendix H.6 - 61
VISTAS’ Common Modeling Protocol 53

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

Appendix H.6 - 62
VISTAS’ Common Modeling Protocol 54

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).

Appendix H.6 - 63
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”.

Appendix H.6 - 64
Source-Specific Modeling Protocol 56

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

Appendix H.6 - 65
Source-Specific Modeling Protocol 57

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

Appendix H.6 - 66
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 

Appendix H.6 - 67
Quality Assurance 59

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

Appendix H.6 - 68
Quality Assurance 60

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.

Appendix H.6 - 69
Quality Assurance 61

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 

Appendix H.6 - 70
Quality Assurance 62

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

Appendix H.6 - 71
Quality Assurance 63

- 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 

Appendix H.6 - 72
Quality Assurance 64

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. 

Appendix H.6 - 73
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.

Appendix H.6 - 74
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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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References 67

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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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.

Appendix H.6 - 77
 

 

Protocol for the Application of the 

CALPUFF Model in Support of the 

Best Available Retrofit Technology 

(BART) Regulations – 40 CFR 51.300 

and Appendix Y 

Bowater Coated and Specialty Papers Division 

Catawba, South Carolina 

Prepared by: 

URS Corporation 

Morrisville, North Carolina 

Greenville, South Carolina 

March 17, 2006 

Appendix H.6 - 78
Protocol for the Application of the 

CALPUFF Model for Analyses of Best 

Available Retrofit Technology (BART) 

 Prepared for: 

 

Bowater Coated and Specialty Papers Division 

 Catawba, South Carolina 

March 17, 2006 

Prepared by: 

URS Corporation 

Morrisville, North Carolina 

Greenville, South Carolina 

Appendix H.6 - 79
CALPUFF Modeling Protocol 

BART Exemption Modeling Analysis 

Bowater Coated and Specialty Papers Division 

March 17, 2006 Page ii 

Table of Contents 

1.0 Introduction and Background ......................................................................................... 1

1.1 Objective of Protocol .............................................................................................. 3 

1.2 Facility Location and Relevant Class I Areas......................................................... 4 

1.3 Source Impact Evaluation Criteria.......................................................................... 6 

1.4 General Overview of the CALPUFF Modeling System......................................... 6 

2.0 BART Source Descriptions .............................................................................................. 7

2.1 Unit Specific Source Data....................................................................................... 7 

2.2 Tabulated Source Data.......................................................................................... 10 

3.0 Geophysical and Meteorological Data .......................................................................... 16

4.0 CALPUFF Modeling Methodology ............................................................................... 16

4.1 Methodology......................................................................................................... 17 

4.2 CALMET Model Configuration and Application................................................. 19 

4.3 CALPUFF Model Configuration and Application ............................................... 19 

4.3.1 Model Codes............................................................................................ 19 

4.3.2 Domain Definition................................................................................... 19 

4.3.3 Model Set-Up .......................................................................................... 21 

4.3.4 Emissions Input Development................................................................. 21 

4.3.5 Additional CALPUFF Input Information and Settings ........................... 22 

5.0 POSTUTIL PROCESSING ........................................................................................... 24

6.0 CALPOST PROCESSING............................................................................................. 24

6.1 Visibility Assessment............................................................................................ 24 

7.0 ALTERNATIVE MODELING METHODOLOGY ................................................... 27

8.0 REPORTING .................................................................................................................. 33

8.1 Presentation of Modeling Results ......................................................................... 33 

8.2 Reporting of CALPUFF Modeling Results .......................................................... 33 

9.0 REFERENCES................................................................................................................ 34

Tables 

2-1 BART Eligible Emission Units – Stack Parameters (English Units) ................................10 

2-2 BART Eligible Emission Units – Stack Parameters (Metric Units)..................................11 

2-3 BART Eligible Emission Units – Emission Rates (English Units) ...................................12 

2-4 BART Eligible Emission Units – Emission Rates (Metric Units).....................................13 

2-5 BART Eligible Emission Units – Speciated Emissions (English Units) ...........................14 

2-6 BART Eligible Emission Units – Speciated Emissions (Metric Units).............................15 

Appendix H.6 - 80
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7-1. Example of LOS Modeling Results ...................................................................................26 

Figures 

1-1 Facility Location Relative to Class I Areas .........................................................................4 

4-1. BART Modeling Flow Chart .............................................................................................13 

4-2. VISTAS CALMET Domains with Proposed CALPUFF Sub-domains............................15 

7-1. Example of LOS Modeling Approach ...............................................................................25 

Appendices 

Appendix A CALPUFF Configuration 

Appendix B POSTUTIL Screening Configuration 

Appendix C CALPOST Screening Configuration 

Appendix D Class I Receptors in Lambert Conformal Coordinates 

Appendix E North Dakota Background Procedure 

Appendix F EPA Natural Background Values 

Appendix G EPA Monthly F(RH) Values 

Appendix H Paper Describing the Deciview Metric 

Appendix I CALPUFF Plots Showing Plume Distribution 

Appendix H.6 - 81
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1.0 Introduction and Background 

Bowater Coated and Specialty Papers Division (Bowater) Pulp and Paper mill has retained URS 

Corporation (URS) to prepare this Protocol for the Application of the CALPUFF Model in 

Support of the Best Available Retrofit Technology (BART) Regulation – 40 CFR 51.300 and 

Appendix Y. This protocol was prepared at the request of the South Carolina Department of 

Health and Environmental Control (SCDHEC) in order to document the modeling procedures 

that will be used to perform CALPUFF dispersion modeling in support of the EPA BART 

regulation. Bowater is proposing to perform modeling analyses to determine whether air 

emissions from potentially BART eligible emission units at the Bowater Catawba, South 

Carolina Pulp and Paper mill cause or contribute to regional haze in any Class I area within 300 

kilometers of the mill as defined by 40 CFR 51, Appendix Y. The following provides a 

regulatory background of the BART regulation and a summary of the modeling procedures 

detailed in this protocol. 

The Clean Air Act established goals for visibility in many national parks and wilderness areas. 

Through the 1977 amendments to the Clean Air Act, Congress set a national goal for visibility as 

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

mandatory Class I Federal areas which impairment results from manmade air pollution.” The 

Amendments required EPA to issue regulations to assure “reasonable progress” toward meeting 

the national goal. 

In 1980, EPA promulgated regulations to address the visibility issues that is “reasonably 

attributable” to a single source or small group of sources. In 1988, the States, Federal Land 

Managers (e.g., National Park Service, U.S. Forest Service, U.S. Fish and Wildlife Service, 

Bureau of Land Management), and EPA began monitoring of fine particle concentrations and 

visibility in 30 national parks and wilderness areas across the country. 

The Clean Air Act Amendments of 1990 required EPA to take regulatory action on regional haze 

and they proposed the Regional Haze Regulations in July 1997 in conjunction with issuing new 

national ambient air quality standards for fine particulate matter. 

On July 1, 1999, EPA promulgated the final Regional Haze Regulation. The final Regional Haze 

Regulation calls for state and federal agencies to work together to improve visibility in 156 

national parks and wilderness areas in the United States by developing and implementing longterm air quality protection plans to reduce the pollution that causes visibility impairment in these 

protected areas. 

 

The Regional Haze regulation provides States flexibility in determining reasonable progress 

goals for protected areas by conducting certain analyses to ensure that they consider the 

possibility of setting an ambitious reasonable progress goal, one that is aimed at reaching natural 

background conditions by the year 2064. The regulation requires States to establish goals for 

each affected area to (1) improve visibility on the haziest days, and (2) ensure no degradation 

occurs on the clearest days over the period of each implementation plan. 

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The Regional Haze regulation also requires States to develop long-term strategies including 

enforceable measures designed to meet reasonable progress goals. The first long-term strategy 

will cover 10 to 15 years, with reassessment and revision of those goals and strategies in 2018 

and every 10 years thereafter. State’s strategies will address their contribution to visibility 

problems in Class I areas both within and outside the State. 

One of the principal elements of the visibility protection provisions of the Clean Air Act 

addresses installation of best available retrofit technology (BART) for certain existing sources. 

“BART-eligible” sources are those sources built between 1962 and 1977 that have the potential 

to emit more than 250 tons per year of one or more visibility-impairing compounds including 

sulfur dioxide (SO2), nitrogen oxides (NOx), particulate matter (PM), and volatile organic 

compounds (VOCs), and that fall within 26 industrial source categories (including Kraft pulp and 

paper manufacturing). 

Soon after the Regional Haze Regulation was finalized, several parties filed petitions to 

challenge the rule with the U.S. Court of Appeals for the D.C. Circuit. In April 2004, EPA 

Administrator Mike Leavitt signed a proposed amendment to the 1999 Regional Haze 

Regulation. The proposed rule satisfied the terms of a May 2002 ruling by the U.S. Court of 

Appeals for the D.C. Circuit, which vacated parts of the BART provisions of the 1999 Regional 

Haze Regulation (American Corn Growers et. al. v. EPA, 291 F. 3d 1 (D.C. cir. 2002). 

The rule requires states to consider the visibility impacts of an individual facility when 

determining whether they have to install controls, and what those controls would be. The final 

BART implementation and guidance rule (40 CFR Part 51, Appendix Y) was published on July 

6, 2005 and it allows for a BART evaluation 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. 

Pursuant to the rule, States have the option of exempting a BART-eligible source from the 

BART requirements based on dispersion modeling demonstrating that the source cannot 

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

Regional Planning Organizations (RPOs), such as the Visibility Improvement State and Tribal 

Association of the Southeast (VISTAS) have prepared guidance for performing the dispersion 

modeling analyses. According to 40 CFR Part 51, Appendix Y, a BART-eligible source is 

considered to “contribute” to visibility impairment in a Class I area if the modeled 98th percentile 

change in deciviews (dv) is equal to or greater than the “contribution threshold.” Any BARTeligible source determined to cause or contribute to visibility impairment in any Class I area is 

subject to a BART evaluation. 

The Application of the CALPUFF Model for Analyses of Best Available Retrofit Technology 

(BART), December 22, 2005, was prepared by the VISTAS RPO to provide some common 

guidance for performing conservative BART exemption and determination modeling evaluations 

as allowed by the regulation. 

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It should be noted that the final BART rule defines a “contribution threshold” of 0.5 dv as the 

value where a modeled BART eligible source may “contribute” to visibility impairment and 

the threshold to determine whether a single source “causes” visibility impairment is set at a 1.0 dv 

change from natural conditions (background visual range) over a 24-hour averaging period in the 

final BART rule (70 FR 39118). An approximate 1.0 deciview change was defined by Pitchford 

and Malm (1992) as a “just noticeable change” to the observer when the background visual range 

equals the line of sight of the observer. According to L. Willard Richards in “Use of the Deciview 

Haze Index as an Indicator for Regional Haze” if a shorter line of sight distance than the 

background visual range (natural conditions) is used in performing the calculations then a higher 

extinction value, or deciview, is needed to cause a “just noticeable change.” In other words, when 

the line of sight is less than the background visual range, then it would require a higher deciview 

value in order to be a “just noticeable change.” However, this protocol uses a conservative 

modeling approach as defined by the VISTAS protocol by comparing the extinction changes from 

a single source at Class I area receptors only (not along the line of sight) to the background visual 

range (natural conditions) in order to compare to the 0.5 deciview change threshold. We are also 

proposing in this protocol to use the refined line-of-sight modeling approach if needed in order to 

better determine the actual impact that a single source would have on natural visibility conditions 

in a Class I area (Section 7.0). 

1.1 Objective of Protocol 

The objective of this BART modeling protocol is to obtain approval, from the SCDHEC, for the 

modeling procedures that will be used to support the BART exemption modeling for the 

Bowater Pulp and Paper mill located in Catawba, South Carolina. This protocol incorporates 

guidance developed by VISTAS for conducting a BART modeling analysis and, where needed, 

expands on that final modeling guidance. 

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. This protocol is 

intended to satisfy the BART requirement that a BART-eligible source must submit a sitespecific modeling protocol to the State for review and approval prior to performing CALPUFF 

modeling. Many of the methods and procedures recommended in the VISTAS common 

modeling protocol will be followed for modeling and the results will be presented in the standard 

recommended format. 

Bowater is also proposing to conduct more refined CALPUFF modeling to better quantify the 

estimated impact of the mill’s potentially BART-eligible units on regional haze as necessary. 

The refined modeling procedures, including examples, and the justification for these procedures 

are presented in this protocol. Bowater requests that the SCDHEC carefully evaluate and 

approve these modeling procedures or provide detailed comments to Bowater of why these 

procedures are not acceptable. Bowater and our environmental consultant, URS Corporation is 

willing to discuss these procedures in greater detail with the SCDHEC and supply additional 

material demonstrating why these modeling procedures are appropriate for calculating changes 

in regional haze from individual point sources. 

Appendix H.6 - 84
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The remainder of this document describes the CALPUFF modeling system and the application of 

CALPUFF to two situations: 

1) Air Quality modeling to determine whether a potentially BART-eligible source is 

exempt from a BART control technology evaluation. 

2) Air Quality modeling of emissions from all BART-eligible sources that are required 

to perform a BART control technology evaluation for the purpose of determining the 

relative benefits of potential alternative control options as it pertains to reducing 

regional haze in Class I areas. 

1.2 Facility Location and Relevant Class I Areas 

The Bowater Catawba, South Carolina pulp and paper mill is located at 5300 Cureton Ferry 

Road in Catawba, South Carolina. The Universal Transverse Mercator (UTM) coordinates, in 

kilometers (km), for the mill are Zone 17, 510.014 East and 3855.589 North. The approximate 

Lambert Conformal Conic (LCC) coordinates are 1458.14516 km East and -441.09012 km

North. 

There are several Class I areas near the Bowater mill: Linville Gorge, Shining Rock, and Joyce 

Kilmer-Slickrock Wilderness Areas; Great Smoky Mountains National Park; and Cape Romain 

National Wildlife Refuge. Figure 1-1 displays the location of the mill and the Class I areas. 

Linville Gorge, Shining Rock, and Joyce Kilmer-Slickrock Wilderness Areas are located 

approximately 140 km, 180 km, and 280 km west-northwest of the mill, respectively. Great 

Smoky Mountains National Park is located approximately 216 km west-northwest of the mill at 

its’ nearest boundary. Cape Romain National Wildlife Refuge is located approximately 230 km 

southeast of the mill.

Appendix H.6 - 85
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-86 -85 -84 -83 -82 -81 -8

Longitude

32333435363738

Latitude

Great Smoky Mts. Linville Gor

Joyce Kilmer Slickrock

Shining Rock

Bowater

SCALE: DRAWN BY: SCL DATE: 2/10/06 AS SHOWN CHECKED BY: DATE:

Bowater – C

Facility L

Relative t

Are
CALPUFF Modeling Protocol 

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March 17, 2006 Page 6 

1.3 Source Impact Evaluation Criteria 

To assess whether the Bowater Mill is exempt from performing a BART control technology 

evaluation, a two-tiered modeling approach is being proposed. For the initial exemption 

modeling, the CALPUFF model will be used with 12-km grid CALMET data in a screening 

mode. If the initial modeling results show haze impacts less than the recommended visibility 

threshold value of 0.5 dv, no further modeling would be necessary. If the initial 12-km grid 

modeling results show haze impacts greater than the recommended visibility threshold of 0.5 dv, 

then a finer resolution 4-km CALMET meteorological grid will be used in the CALPUFF 

modeling. If the finer resolution modeling results show haze impacts less than the visibility 

threshold on 0.5 dv, then no further modeling would be necessary. All modeling results will be 

presented in detail in a report that will be submitted to the SCDHEC for review and approval. 

If the initial 12-km grid model greatly exceeds the recommended threshold or finer grid 

modeling results exceed the recommended threshold then BART determination CALPUFF 

modeling will be conducted using finer resolution CALMET data. It is expected that the 

SCDHEC will accept EPA guidance that the threshold value to establish that a source 

contributes to visibility impairment is 0.5 deciview. 

Since the EPA BART guidance rule specifically states that modeling results should be based on 

the 98th percentile value, we are proposing that the 98th percentile be applied to all CALPUFF 

modeling results. Thus, for the 12-km initial modeling exemption analysis, the 24-hour 98th

percentile value across all receptors in the Class I area will be compared to the threshold 

value of 0.5 dv. If the 24-hour 98th percentile value is below 0.5 dv, then Bowater will be 

exempt from performing a BART evaluation. If the 24-hour 98th percentile value is greater 

than 0.5 dv, then Bowater may choose to perform finer grid modeling for exemption 

purposes or use the alternative modeling approaches described in Section 7 of this protocol. 

Bowater will use the 12-km modeling results to focus finer grid modeling for exemption 

purposes on only those Class I areas where impacts greater than 0.5 dv are projected in the 12-

km modeling. 

For finer grid (4 km or less) analyses, the 98th percentile value for the 24-hour average will also 

be used. 

1.4 General Overview of the CALPUFF Modeling System 

The CALPUFF modeling system consists of four main processors: CALMET, CALPUFF, 

POSTUTIL and CALPOST. CALMET is the meteorological model that generates hourly threedimensional meteorological fields of variables such as wind and temperature. CALPUFF 

simulates the transport, dispersion, and transformation of compounds emitted from a source and 

calculates hourly concentration values for visibility impairing compounds at each receptor 

located in the modeling domain. POSTUTIL can perform many post processing tasks on the 

CALPUFF output data file. CALPOST calculates time-averaged concentration values from the 

Appendix H.6 - 87
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CALPUFF predictions and performs regional haze calculations like those described in the 

Section 6.1 of this protocol. 

2.0 BART Source Descriptions 

The Bowater Catawba Mill is located in Catawba, South Carolina, along the Catawba River. The 

primary activities at Catawba Mill are pulp and paper production (SIC code 2611, NAICS Codes 

32211 and 322121). The mill began operations in 1959. Primary operations at the mill include: 

kraft pulping, bleaching, and associated chemical recovery operations; thermo-mechanical 

pulping and associated bleaching; coated paper production and market pulp drying; wood and 

wood waste receiving, storing, and handling; wastewater treatment operations; process steam 

generation; and additional operations and equipment necessary to support these operations. 

2.1 Unit Specific Source Data 

According to VISTAS, the emission estimates used in the CALPUFF model are intended to reflect 

steady-state operating conditions during periods of high capacity utilization. As suggested in the 

VISTAS common protocol, modeled emissions should not reflect periods of start-up, shutdown, 

and malfunction since this would produce higher than normal impacts on visibility impairment. 

VISTAS recommends that modeling be based on the 24-hour average actual emission rate from the 

highest emitting day of the meteorological period modeled. VISTAS and the SCDHEC will 

accept emission estimates based on maximum actual emission rates during the most recent 3 to 5 

year period. VISTAS has supplied the following prioritization for developing the emission 

estimates: 

• Continuous Emissions Monitoring (CEM) data; 

• Facility emissions tests; 

• Emissions factors; 

• Permit limits; or lastly, 

• Potential to emit. 

The Catawba Mill has developed emission estimates based on source testing and accepted 

emission factors used for routine annual emissions reporting. Should Bowater elect to develop 

emission rates for future subject-to-BART modeling, case-by-case procedures will be developed 

in consultation with the SCDHEC. In general, the following emission rates are considered 

acceptable: 

• Short-term (24-hours) allowable emission rates (e.g., emission rates calculated using the 

maximum rated capacity of the source). 

• Federally enforceable short-term limits (24-hours). 

• Peak 24-hour actual emission rates (or calculated emission rates) from the most recent 3 

to 5 years of operation that account for “high capacity utilization” during normal 

operating conditions and fuel/material flexibility allowed under a source’s permit. In 

Appendix H.6 - 88
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situations where a unit is allowed to use more than one fuel, the fuel resulting in the 

highest emission rates will be used for the modeling. 

Short-term emission rates (24-hours) for SO2, NOx, H2SO4 mist and PM10 (including condensible 

and filterable direct PM10) will be modeled since visibility changes are calculated for a 24-hour 

averaging period. All BART-eligible emission units at the mill will be modeled together in the 

CALPUFF model. 

Listed below is a brief description of all the BART-eligible emission units at the mill: 

• No. 2 Recovery Furnace (RF2): This recovery furnace primarily fires spent kraft 

cooking liquor (black liquor) at approximately 68% solids. No. 6 fuel oil is also used 

during startup and as auxiliary fuel. The No. 2 recovery furnace production capacity 

is 1,129 tons of black liquor solids per day, and 360 MM Btu per hour when firing 

2.1% sulfur No. 6 fuel oil. The unit is controlled by an electrostatic precipitator. 

• No. 2 Smelt Dissolving Tank (ST2): This smelt dissolving tank processes the molten 

smelt from the bottom of the No. 2 recovery furnace. The No. 2 smelt dissolving 

tank production capacity is 1,129 tons of black liquor solids per day. The No. 2 smelt 

dissolving tank does not burn any fuels, but emissions are evolved from the smelt 

when being processed. This unit is controlled by a wet scrubber. 

• No. 2 Evaporator Set (vents to NCG1 or NCG2): This evaporator set concentrates 

spent kraft cooking liquor prior to firing in the No. 2 recovery furnace. This unit is 

controlled by the No. 1 or No. 2 combination boilers, which generates SO2 emissions 

as a result of burning non condensable gases (NCG’s) from the evaporator bodies 

that contain total reduced sulfur (TRS) compounds. 

• No. 2 Combination Boiler (CB2): This multi-fuel boiler primarily fires biomass 

(bark, wood, fines, etc.), along with No. 6 fuel oil, natural gas, and tire derived fuel 

(TDF). The No. 2 Combination Boiler heat input capacity 498 MM Btu per hour 

firing biomass, 700 MM Btu per hour firing 2.1% sulfur No. 6 fuel oil, and 720 MM 

Btu per hour firing natural gas. The boiler is also capable of burning up to 1.5 tons 

per hour of TDF. 

• No. 3 coated paper machine (PM3). This paper machine was originally built as a 

newsprint machine, and was converted to coated paper production in August 2003. 

The coated paper production capacity is 1,049 air dried short tons per day. The 

coating drying section installed in 2003 burns natural gas as the primary fuel, with 

propane and kerosene as back-up fuels. The design heat input capacity to the coating 

section is 48 MM Btu per hour, however no fuels were burned prior to August 2003. 

• Chlorine Dioxide Plant (CLO2): This unit generates chlorine dioxide for use in the 

kraft bleaching system by reacting sodium chlorate, methanol, and sulfuring acid. 

There are no fuels burned in this unit, and only nominal emissions of VOC. The 

production capacity is 33 tons per day of ClO2. The unit is controlled using a series 

of two wet scrubbers. 

Appendix H.6 - 89
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The following BART-eligible units do not emit any significant levels of SO2, NOx, H2SO4

mist, or PM10 and will not be modeled. 

• Chlorine Dioxide Plant 

Table 2-1 and 2-2 provide stack parameters for all BART-eligible sources at the facility in 

English and metric units, respectively. Tables 2-3 and 2-4 provide emission rates for all BARTeligible sources in English and metric units, respectively. Tables 2-5 and 2-6 provide particulate 

speciation information for all BART eligible sources in English and metric units, respectively. 

Appendix H.6 - 90
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2.2 Tabulated Source Data 

TABLE 2-1 

BART ELIGIBLE EMISSION UNITS – STACK PARAMETERS (English Units) 

STACK SPECIFIC DETAILS 

Lambert Conformal 

Conic U.S.G.S 

Equipment Description 

Stack ID 

UTM East 

(km) 

UTM North 

(km) 

UTM East 

(km) 

UTM North 

(km) 

UTM Zone 

DATUM 

Stack Height 

 (feet) 

Base Elevation 

(feet above MSL) 

Stack Diameter or 

Equivalent 

Chlorine Dioxide Plant CLO2 1459.0480 -440.6843 510976 3855638 17 NAD 27 81 532 1 

No. 3 Paper Machine PM3 1457.7943 -441.0181 509677 3855529 17 NAD 27 i 532 i 

No. 2 Smelt Tank ST2 1458.1175 -440.9312 510012 3855558 17 NAD 27 218 532 6 

No.2 Evaporator Set NCG1c 1458.1131 -441.0314 509990 3855460 17 NAD 27 228 532 10 

No. 2 Recovery Furnace RF2 1458.1776 -440.9621 510066 3855517 17 NAD 27 195 532 7 

No. 2 Combination Boiler CB2 1458.0964 -441.0456 509971 3855449 17 NAD 27 228 532 10 

NOTES: c No. 2 evaporator set is controlled using No.1 and No. 2 combination boilers. CB1 stack parameters shown. 

d Highest average 24-hour emissions shown, except NOX from CB2 is maximum emissions burning natural gas at capacity (720 MM Btue ST2, RF2, and CB2 AP-42 PM6 size distribution multiplied by ratio of NCASI TB 884 PM10 to AP-42 PM10. f ST2, RF2, and CB2 AP-42 PM1.25, PM1.0, and PM0.625 size distributions multiplied by ratio of NCASI TB 884 PM2.5 to AP-42 PM2.5. g Based on emission factors for PM10 emissions. hBased on representative CMAQ speciation data. IEmissions through multiple stacks, historically modelled as area source 80 feet high, 50 feet wide, and 100 feet long. 
BA

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March 17, 2006 

TABLE 2-2 

BART ELIGIBLE EMISSION UNITS – STACK PARAMETERS (Metric Units) 

STACK SPECIFIC DETAILS 

Lambert Conformal 

Conic U.S.G.S 

Equipment Description 

Stack ID 

UTM East 

(km) 

UTM North 

(km) 

UTM East 

(km) 

UTM North 

(km) 

UTM Zone 

DATUM 

Stack Height 

(meters) 

Base Elevation 

(meters above MSL) 

Stack Diameter or 

Equivalent (meters) 

Chlorine Dioxide Plant CLO2 1459.0480 -440.6843 510976 3855638 17 NAD 27 24.7 162.2 0.3 

No. 3 Paper Machine PM3 1457.7943 -441.0181 509677 3855529 17 NAD 27 i 162.2 i 

No. 2 Smelt Tank ST2 1458.1175 -440.9312 510012 3855558 17 NAD 27 66.4 162.2 1.8 

No.2 Evaporator Set NCG1c 1458.1131 -441.0314 509990 3855460 17 NAD 27 69.5 162.2 3.0 

No. 2 Recovery Furnace RF2 1458.1776 -440.9621 510066 3855517 17 NAD 27 59.4 162.2 2.1 

No. 2 Combination Boiler CB2 1458.0964 -441.0456 509971 3855449 17 NAD 27 69.5 162.2 3.0 

NOTES: c No. 2 evaporator set is controlled using No.1 and No. 2 combination boilers. CB1 stack parameters shown. 

d Highest average 24-hour emissions shown, except NOX from CB2 is maximum emissions burning natural gas at capacity (720 MM Btue ST2, RF2, and CB2 AP-42 PM6 size distribution multiplied by ratio of NCASI TB 884 PM10 to AP-42 PM10. f ST2, RF2, and CB2 AP-42 PM1.25, PM1.0, and PM0.625 size distributions multiplied by ratio of NCASI TB 884 PM2.5 to AP-42 PM2.5. g Based on emission factors for PM10 emissions. hBased on representative CMAQ speciation data. IEmissions through multiple stacks, historically modelled as area source. 
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March 17, 2006 

TABLE 2-3 

BART ELIGIBLE EMISSION UNITS – EMISSION RATES (English Units) 

SHORT TERM EMISSIONS, g/sec

(highest 24hr average actual emission rate 

 for the years 2001-2003)d

Equipment Description 

SO2 H2SO4 NOX PM10 PM2.5 NH3 

Chlorine Dioxide Plant none none none none none none 

No. 3 Paper Machine 0.0 0.0 3.4 0.5 0.5 none 

No. 2 Smelt Tank 0.2 0.0 0.9 6.3 5.2 5.5 

No.2 Evaporator Set 49.0 1.5 none none none none 

No. 2 Recovery Furnace 627.7 27.9 89.5 15.0 11.3 none 

No. 2 Combination Boiler 969.3 43.1 201.6 46.1 23.6 none 

NOTES: c No. 2 evaporator set is controlled using No.1 and No. 2 combination boilers. CB1 stack parameters shown. 

d Highest average 24-hour emissions shown, except NOx from CB2 is maximum emissions burning natural gas at capae ST2, RF2, and CB2 AP-42 PM6 size distribution multiplied by ratio of NCASI TB 884 PM10 to AP-42 PM10. f ST2, RF2, and CB2 AP-42 PM1.25, PM1.0, and PM0.625 size distributions multiplied by ratio of NCASI TB 884 PM2.5 to APg Based on emission factors for PM10 emissions. hBased on representative CMAQ speciation data. IEmissions through multiple stacks, historically modelled as area source 80 feet high, 50 feet wide, and 100 feet long. 
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March 17, 2006 

TABLE 2-4 

BART ELIGIBLE EMISSION UNITS – EMISSION RATES (Metric Units) 

SHORT TERM EMISSIONS, g/sec

(highest 24hr average actual emission rate 

 for the years 2001-2003)d

Equipment Description 

SO2 H2SO4 NOX PM10 PM2.5 NH3 

Chlorine Dioxide Plant none none none none none none 

No. 3 Paper Machine 0.0 0.0 0.4 0.1 0.1 none 

No. 2 Smelt Tank 0.0 0.0 0.1 0.8 0.7 0.7 

No.2 Evaporator Set 6.2 0.2 none none none none 

No. 2 Recovery Furnace 79.1 3.5 11.3 1.9 1.4 none 

No. 2 Combination Boiler 122.1 5.4 25.4 5.8 3.0 none 

NOTES: c No. 2 evaporator set is controlled using No.1 and No. 2 combination boilers. CB1 stack parameters shown. 

d Highest average 24-hour emissions shown, except NOx from CB2 is maximum emissions burning natural gas at capacie ST2, RF2, and CB2 AP-42 PM6 size distribution multiplied by ratio of NCASI TB 884 PM10 to AP-42 PM10. f ST2, RF2, and CB2 AP-42 PM1.25, PM1.0, and PM0.625 size distributions multiplied by ratio of NCASI TB 884 PM2.5 to AP-4g Based on emission factors for PM10 emissions. hBased on representative CMAQ speciation data. IEmissions through multiple stacks, historically modelled as area source 80 feet high, 50 feet wide, and 100 feet long. 
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March 17, 2006 

TABLE 2-5 

BART ELIGIBLE EMISSION UNITS – SPECIATED EMISSIONS (English Units

Speciated Emissions for BART CALPUFF Modeling Analyses 

Particle Size Distribution Emission Rate Summary 

Equipment Description 

Stack ID 

Geometric Mass Mean 

Diameter 

6-10 um 

(lbs/hr) 

2.5-6 um

e

(lbs/hr) 

1.25-2.5 um 

(lbs/hr) 

1.0-1.25 um

f

(lbs/hr) 

0.625-1.0 um

f

(lbs/hr) 

0.5-0.625 um

f

(lbs/hr) 

Filterable PM10

g

Chlorine Dioxide Plant CLO2 no data n/a n/a n/a n/a n/a n/a n/a 

No. 3 Paper Machine PM3 no data 0.5 0.5 0.5 0.5 0.5 0.5 86.8 

No. 2 Smelt Tank ST2 no data 6.3 6.3 5.2 4.4 3.9 3.2 81.0 

No.2 Evaporator Set NCG1c no data n/a n/a n/a n/a n/a n/a n/a 

No. 2 Recovery Furnace RF2 no data 15.0 14.5 11.3 9.3 8.2 6.6 80.7 

No. 2 Combination Boiler CB2 no data 46.1 43.6 23.6 22.6 21.9 20.3 81.9 

NOTES: c No. 2 evaporator set is controlled using No.1 and No. 2 combination boilers. CB1 stack parameters shown. 

d Highest average 24-hour emissions shown, except NOx from CB2 is maximum emissions burning natural gas at capacity (e ST2, RF2, and CB2 AP-42 PM6 size distribution multiplied by ratio of NCASI TB 884 PM10 to AP-42 PM10. f ST2, RF2, and CB2 AP-42 PM1.25, PM1.0, and PM0.625 size distributions multiplied by ratio of NCASI TB 884 PM2.5 to AP-42 g Based on emission factors for PM10 emissions. hBased on representative CMAQ speciation data. IEmissions through multiple stacks, historically modelled as area source 80 feet high, 50 feet wide, and 100 feet long. 
BA

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March 17, 2006 

TABLE 2-6 

BART ELIGIBLE EMISSION UNITS – SPECIATED EMISSIONS (Metric Units) 

Speciated Emissions for BART CALPUFF Modeling Analyses 

Particle Size Distribution Emission Rate Summary 

Equipment Description 

Stack ID 

Geometric Mass Mean 

Diameter 

6-10 um 

(g/sec) 

2.5-6 um

e

(g/sec) 

1.25-2.5 um 

(g/sec) 

1.0-1.25 um

f

(g/sec) 

0.625-1.0 um

f

(g/sec) 

0.5-0.625 um

f

(g/sec) 

Filterable PM10

g

 

Chlorine Dioxide Plant CLO2 no data n/a n/a n/a n/a n/a n/a n/a 

No. 3 Paper Machine PM3 no data 0.1 0.1 0.1 0.1 0.1 0.1 86.8 

No. 2 Smelt Tank ST2 no data 0.8 0.8 0.7 0.6 0.5 0.4 81.0 

No.2 Evaporator Set NCG1c no data n/a n/a n/a n/a n/a n/a n/a 

No. 2 Recovery Furnace RF2 no data 1.9 1.8 1.4 1.2 1.0 0.8 80.7 

No. 2 Combination Boiler CB2 no data 5.8 5.5 3.0 2.9 2.8 2.6 81.9 

NOTES: c No. 2 evaporator set is controlled using No.1 and No. 2 combination boilers. CB1 stack parameters shown. 

d Highest average 24-hour emissions shown, except NOx from CB2 is maximum emissions burning natural gas at capacity (e ST2, RF2, and CB2 AP-42 PM6 size distribution multiplied by ratio of NCASI TB 884 PM10 to AP-42 PM10. f ST2, RF2, and CB2 AP-42 PM1.25, PM1.0, and PM0.625 size distributions multiplied by ratio of NCASI TB 884 PM2.5 to AP-42 g Based on emission factors for PM10 emissions. hBased on representative CMAQ speciation data. IEmissions through multiple stacks, historically modelled as area source 80 feet high, 50 feet wide, and 100 feet long. 
CALPUFF Modeling Protocol 

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March 17, 2006 Page 16 

3.0 Geophysical and Meteorological Data 

URS will use the geophysical and meteorological data developed by VISTAS for initial 12-km 

and 4-km BART exemption modeling. The development of this information is discussed in 

detail in the VISTAS common protocol. 

In the event that source-specific fine-scale (<4-km grid) modeling is needed, a revised protocol 

would be submitted to the SCDHEC for review and comment. Since the scope of this modeling 

cannot be determined, the exact configuration of any refined modeling domain also cannot be 

determined. Since domain placement and grid size selection cannot be determined until either 

12-km or 4-km grid modeling has been completed a detailed discussion cannot be presented at 

this time describing a more refined modeling domain and the interactions of terrain, land use, 

MM5 data, observational data, monitoring data and other parameters. In the event a more 

refined modeling domain is developed a revised protocol will be prepared and submitted to the 

SCDHEC for review and comment using the following outline to discuss the use of 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 

 3.4.2 Ammonia Concentrations – Measured 

 3.4.3 Concentration of Other Pollutants – Measured 

3.5 Natural Conditions at Class I Areas 

4.0 CALPUFF Modeling Methodology 

Initial CALPUFF modeling will be performed using a screening level approach in order to 

efficiently and conservatively determine whether the Bowater Mill can be exempted from a 

BART evaluation. The screening method described in this section will help identify specific 

Class I areas that might be most affected by emissions from BART eligible emission units 

located at the Bowater Mill. It will also supply useful information on whether performing a 

more time consuming and refined “source-specific” analysis would be beneficial. Should 

source-specific modeling become necessary, this information will assist URS in tailoring the 

modeling domain to focus on the areas of greatest concerns. 

CALPUFF modeling will be performed using a standard set of default meteorological, air quality 

and dispersion conditions that have been developed by VISTAS for a 12- and 4-km gridded 

CALMET domain. These data were developed to be consistent with recommendations developed 

by the Interagency Workgroup on Air Quality Modeling (IWAQM, 1998) and FLAG (2000). 

 

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As mentioned earlier, the results from the CALPUFF screening analysis has a high degree of 

conservatism (i.e., systematic tendency to over-predict visibility impacts) as compared to a 

source-specific methodology. Therefore, predicted impacts on visibility impairment will be 

overstated by using these screening modeling methodologies. 

4.1 Methodology 

The screening level methodology will use the CALPUFF model with three years of 

meteorological data with the standard compliment of model algorithms invoked and will use the 

most conservative of all the conditions likely to be examined for the source in question. For 

example, there are many factors that influence the contribution of a source beyond just the 

distance to the Class I area. The frequency of winds transporting the compounds toward the 

Class I area may often be important to include for a reliable screening analysis. Also, a more 

distant Class I area downwind in the predominant wind direction from a source may receive a 

higher visibility impact than a closer Class I area that is infrequently downwind of the source. 

Further, there may be correlations between winds from certain directions and meteorological 

conditions conducive for higher visibility impacts. Such effects and relationships will be 

addressed in the screening approach. 

If initial screening level 12-km CALPUFF modeling is too conservative, a less conservative 

modeling approach (finer grid) can and will be performed by URS using a standard set of 4-km 

gridded CALMET data developed by VISTAS. URS will use of the 98th percentile impacts 

when using the 12 and 4-km data as recommended under 40 CFR 51, Appendix Y. We may also 

develop even more refined gridded data (< 4 km) depending on the results from standard 4-km 

grid modeling. All modeling will be based on using a 98th percentile value when comparing to 

thresholds. As discussed in Section 3.0 a detailed site-specific protocol would need to be 

developed and approved before proceeding with this step. 

The regional haze impacts at each Class I area will be calculated from the daily visibility values 

for each receptor by determining the change in deciviews compared against natural visibility 

conditions. EPA’s “Guidance for Estimating Natural Visibility Conditions Under the Regional 

Haze Rule,” EPA-454/B03-005 (September 2003) lists recommended natural visibility 

conditions. To determine whether Bowater may reasonably be anticipated to cause or contribute 

to visibility impairment at a nearby Class I area, the impacts predicted by CALPUFF will be 

compared against the pertinent natural visibility background and the threshold that has been 

selected. URS is also proposing refinements to the natural visibility background values as 

discussed in Section 7. 

Figure 4-1 presents the BART modeling process flow chart that will be followed.

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FILE NO.

15266083 

FIG. NO.

SCALE: DRAWN BY: SCL DATE: SCL

AS SHOWN CHECKED BY: DATE:

BART Modeling 

Flow Chart 

4-1 

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4.2 CALMET Model Configuration and Application 

 

Sections 4.3.2 and 4.4.2 within the VISTAS common protocol discuss in detail the model 

configurations used to generate the common CALMET meteorological files for modeling BART 

eligible sources. The configuration is reported to follow the IWAQM recommendations (EPA, 

1998, Appendix A), except as noted in the protocol. For CALPUFF screening and initial fine 

grid assessments, there is no need to compile CALMET inputs, run the CALMET model or 

evaluate the outputs. 

The model-ready meteorological data sets have been developed by VISTAS for one large 36 

(12)-km regional domain and five smaller sub-regional domains. The Bowater Catawba Mill is 

located in sub-regional domain number 5 depicted in the VISTAS common protocol. Figure 4-2 

displays the configuration of the regional domain and the potential location of a smaller 

CALPUFF modeling domain. 

4.3 CALPUFF Model Configuration and Application 

 

4.3.1 Model Codes 

Bowater will use CALPUFF Version 5.8 and CALMET Version 5.7. These versions contain 

enhancements funded by the Minerals Management Service (MMS) and VISTAS. This version 

includes CALMET, CALPUFF, CALPOST, CALSUM, and POSTUTIL as well as CALVIEW 

and will be obtained from the CALPUFF website. 

Should these recommended versions not be available in time for final modeling then the latest 

‘beta’ versions of the CALPUFF modeling system (CALPUFF ver 5.711a, Level 040716, 

POSTUTIL ver 1.4, Level 040818, CALPOST ver 5.51, Level 030709) will be used. It should 

be noted that this model is not the EPA guideline codes but rather updated versions containing 

science improvements and bug fixes. (The guideline CALPUFF code is ver 5.7, level 030402). 

This substitution results from EPA phasing out the legacy Pasquill-Gifford (P-G) dispersion 

parameters with the introduction of AERMOD as a new guideline model. 

 

CALPUFF employs the AERMOD turbulence-based dispersion coefficients and probability 

density function (pdf) dispersion methods scheme instead of P-G. The appropriate model codes 

will be obtained and used. The sequence of model processors for all modeling is CALPUFF, 

POSTUTIL and finally CALPOST. CALMET and associated preprocessors are not discussed 

since VISTAS performed these model runs. 

4.3.2 Domain Definition 

The meteorological modeling data sets cover three contiguous years (2001, 2002, and 2003) and 

were resolved to a 12 and 4 km horizontal resolution grid using MM5 data. Details of the 

modeling domains and the meteorological databases for 2001, 2002, and 2003 are discussed in 

detail in the VISTAS common protocol. 

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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)

Mill

Cape Romain

Linville Gorge

Shining Rock

Great Smoky Mts.

Joyce Kilmer/

Slickrock

FILE NO.

15266083 

FIG. NO.

SCALE: DRAWN BY: SCL DATE: SCL

AS SHOWN CHECKED BY: DATE:

Bowater – Catawba, SC

VISTAS 

CALMET DOMAINS 

with Proposed 

CALPUFF 

Sub-domain 4-2 

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Receptor Network and Class I Receptors. Discrete receptor coordinate data for the Federal Class 

I areas within 300 km of the source were developed using the National Park Service (NPS) 

Convert Class I Areas (NCC) computer program. The receptor elevations provided by the NPS 

will be used for modeling. All receptors for each Class I area will be included in single 

CALPUFF simulations. Appendix D contains a listing of the Lambert Conformal coordinates for 

each Class I area with the associated receptors heights. 

4.3.3 Model Set-Up 

The modeling will use a CALPUFF computational domain that includes all applicable Class I 

areas within 300 km of the source and will include a large buffer around applicable Class I areas 

and the mill. The size and location of the potential 12- and 4-km CALPUFF computational 

domains are shown in Figure 4-2. 

As depicted in Figure 4-2, the CALPUFF modeling domains are a subset of the larger regional 

CALMET meteorological domain. A smaller CALPUFF domain is being used to reduce the 

CALPUFF run times. It is expected that the CALPUFF 12-km sub domain will be 

approximately 900 kilometers in the east/west direction and 756 kilometers in the north/south 

direction. Using a 12-km grid spacing from the CALMET files, this would relate to 75 x 63 grid 

squares. It is expected that any potential CALPUFF 4-km sub domain will be the same size with 

more grid cells unless one dominant Class I area is identified. Should this occur then a smaller 

sub-domain maybe developed which only includes the critical Class I area. Appendix A contains 

a summary of the input options, which will be selected when performing CALPUFF modeling. 

4.3.4 Emissions Input Development 

Stack Parameters. Point source stack parameters required for modeling BART-eligible units 

include: height of the stack opening from ground, inside diameter, exit velocity, exit gas 

temperature, elevation of ground, location coordinates of the stack and emission rates. 

Stack Emission Rates. Emission rates for the CALPUFF modeling analyses will be developed 

following EPA’s BART guidance and recent staff recommendations. Source terms in the initial 

modeling will be based on periods of high capacity utilization associated with normal operating 

conditions. If more accurate short-term data become available that reflects the 24-hour average 

actual emission rate with normal operations from the highest emitting day of the meteorological 

period it will be used for the modeling. Periods of start-up, shutdown or malfunctions will not be 

included in the modeling. 

The compounds that will be included in the model include SO2, NOx, H2SO4 mist, and PM10

(including condensable and filterable direct PM10). Compounds with emissions that are less than 

the de minimis levels (40 tons per year for SO2 and NOx and 15 tons per year for PM10) will be 

excluded from the modeling. 

 

In cases where a unit may burn more than one fuel, the fuel resulting in the highest short-term 

emission rates will be used for the modeling. 

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Emissions Speciation. Defining an applicable PM speciation profile for the highest 24-hour 

average actual emissions will prove particularly challenging to Bowater. However, Bowater 

understands that reliable estimates are necessary given the widely varying effects of different 

types of particulate matter on visibility. For example, the extinction coefficient ranges in value 

from 0.3 to 0.6 m2

/g for coarse particles, to 1.0 to 1.25 m2/g for fine inorganic particulate matter, 

to 1.5 to 4.0 m2

/g for sulfate and nitrate precursors, to 1.8 to 4.7 m2/g for organic aerosols, and 

up to 8-12 m2/g for elemental carbon (Tombach and McDonald). Thus, generalized, 

conservative, or arbitrary assignments of particulate emissions to different pollutant categories 

can have a considerable influence on modeled visibility impacts attributable to a single facility. 

For this reason, all PM10 emissions have been initially categorized using representative industry 

and EPA CMAQ speciation profiles to quantify fine particulate matter (PMF), coarse particulate 

matter (PMC), soot or elemental carbon (EC), organic aerosols (SOA), and sulfate (SO4). 

Bowater will evaluate the relative contribution of all visibility impairing compounds including 

SO4 and NO3. Should these components by themselves prevent the facility from being exempted 

then no additional exemption modeling will be conducted at the screening level. 

Condensable Emissions. Condensable emissions will be considered primary fine particulate 

matter. For the screening assessment all condensable mass will be assigned to the < 0.625 μm 

category. Using this single category maintains conservatism in the analysis where there may be 

uncertainty regarding the exact size of condensable PM mass. 

 

Size Classification of Primary PM Emissions. When modeling PM size classes, an appropriate 

“mass mean diameter” will be used that is within the specified particle size range. Bowater 

understands that the use of a mass mean diameter equal to the top of the range is inappropriate 

since it will overestimate PM deposition and possibly underestimate PM concentrations and 

visibility impacts. 

This section discusses the procedures and input assumptions that Bowater will follow in applying 

the CALPUFF model for BART exemption and determination modeling. 

4.3.5 Additional CALPUFF Input Information and Settings 

CALPUFF Model Options. The model options, parameter settings, and ‘switches’ for exercising 

CALPUFF for BART modeling are discussed below. Appendix A contains tables that list the 

recommended configurations for VISTAS BART modeling. The tables also identify the default 

recommendations from the IWAQM Phase 2 Report (EPA, 1998). 

 

Visibility Modeling Domain. The CALPUFF domain will be configured to include the source 

and all Class I areas within 300 km. An additional large buffer zone will be established in each 

cardinal direction from the source and class I area. Bowater acknowledges the fact that VISTAS 

recommends CALPUFF for all source-receptor distances. 

 

Building Downwash. Bowater will include building/structure information for point sources 

subject to plume downwash. 

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Puff Dispersion. The EPA (1998) guidance for plume dispersion modeling will be followed with 

the important exception that the turbulence-based dispersion coefficients and probability density 

function (pdf) dispersion methods (i.e., the AERMOD dispersion method in the CALPUFF 

‘beta’ version) will be used instead of the legacy Pasquill-Gifford curves. 

Puff Representation. Bowater will use the default integrated puff sampling methodology in 

CALPUFF. 

Puff Splitting. There is no quantitative evidence that the horizontal and vertical puff-splitting 

algorithms in CALPUFF yield improved accuracy and precision in model estimates of inert or 

linearly reactive pollutants although conceptually the methods are appealing because they mimic 

lateral and vertical wind speed and direction shears. Therefore puff splitting will not be invoked. 

Chemical Mechanism. The MESOPUFF II module will be used for BART modeling. For the 

aqueous phase conversion of SO2 to sulfate (important when the plume interacts with clouds and 

fog), the IWAQM defaults will be used, i.e., nighttime SO2 loss rate (RNITE1) is assumed to be 

0.2 percent per hour. The nighttime NOx loss rate (RNITE2) and HNO3 formation rate 

(RNITE3) are both set to 2.0 percent per hour. 

Species Modeled. Species to be modeled in CALPUFF include SO2, SO4, NOx, NO3 and 

particulate matter. Initially all PM will be modeled in the < 0.625 μm size category. Bowater 

will evaluate the relative contribution of all visibility impairing compounds including SO4 and 

NO3. Should these components, by themselves, prevent the facility from being exempted then no 

additional exemption modeling will be needed or conducted. Once BART determination modeling 

begins a more detailed investigation into PM speciation and size distribution may begin. 

Should initial dv predictions exceed the contribution threshold by a small amount then absent 

more detailed speciation and size distribution data, PM will be modeled in five (5) size 

categories: 

> < 0.625 μm, 

> 0.625-1.0 μm, 

> 1.0-1.25 μm, 

> 2.5-6.0 μm, and 

> 6-10 μm aerodynamic diameters. 

Particulate matter emissions by size category will be combined wherever possible into the 

appropriate species for the visibility analysis. These species include (a) elemental carbon (EC), 

(b) fine PM or “soil” (< 2.5 μm in diameter), (c) coarse PM (between 2.5-10 μm in diameter) and 

(d) organics, referred to as secondary organic aerosols in the CALPOST postprocessor. If 

source-specific emissions factors are not available, AP-42 factors will be used to estimate the 

PM speciation for those source sectors for which AP-42 emissions factors have been developed. 

Otherwise assumptions will need to be proposed by Bowater and approved by SCDHEC. 

Background Ozone Concentrations. Ozone concentration data for 2001-2003 from ambient 

AIRS/CASNET/SCDHEC monitors located within the particular domain being modeled will be 

used to develop background estimates. Only non-urban ozone stations will be used in the 

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OZONE.DAT file. Monthly average ozone background values will be computed from daytime 

average ozone concentrations (6 am to 6 pm average). 

Background Ammonia Concentrations. Bowater will use a constant (0.5 ppb) value for ammonia. 

For each applicable Class I area, CMAQ NH3 data will be used in POSTUTIL to repartition 

HNO3 and NO3. 

Other Background Concentrations. Concentrations of SO4 and TNO3 (HNO3 + NO3) from 

CMAQ 2001-2003 will be used for modeling. 

5.0 POSTUTIL PROCESSING 

POSTUTIL Parameters. User-selected options, parameter settings, and ‘switches’ for exercising 

POSTUTIL are presented in Appendix B. This appendix contains tables that list the proposed 

screening and default configurations for the BART modeling. The ammonia-limiting method 

(ALM) in CALPUFF (Escoffier-Czaja and Scire, 2002, 2005) repartitions nitric acid and nitrate 

on a receptor-by-receptor and hour-by-hour basis to account for the models systematic overprediction due to overlapping puffs. Bowater will set the parameter MNIRATE=1 in POSTUTIL 

to implement this approximate correction in its simplest form. Bowater will use ammonia from 

CMAQ to define NH3 for each Class I area. Bowater will choose ammonia from either the 

CMAQ grid cell where the IMPROVE monitor is located or the grid cell of the centroid of the 

Class I area (the later in the case that the IMPROVE monitor is located outside the Class I area 

or there is no IMPROVE monitor.) 

6.0 CALPOST PROCESSING 

CALPOST Parameters. Appendix C summarizes the CALPOST post-processor options, 

parameters, and switches. Tables are presented containing the proposed and default 

configurations for the BART modeling. While all receptors will be included in a single 

CALPUFF simulation, Bowater will calculate the visibility impacts in CALPOST for each Class 

I area separately using the NDRECP parameter. It specifies the receptor range to be processed in 

CALPOST. Given the importance of the CALPOST processor to the entire BART visibility 

estimation a brief overview of how CALPOST calculates visibility impacts is presented in the 

following section. 

6.1 Visibility Assessment 

The recommended procedure for quantifying visibility impacts is described in detail in the 

VISTAS common protocol. The key point is that the light extinction coefficient (bext) can be 

calculated from the IMPROVE Equation as: 

bext = 3 f(RH) [(NH4)2SO4] + 3 f(RH) [NH4NO3] + 4[OC] + 1[Soil] 

+ 0.6[Coarse Mass] + 10[EC] + bray 

 

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The monthly site-specific f(RH) values will be obtained for each mandatory Federal Class I Area 

from Table A-3 in the EPA (2003) guidance document. Then, the haze index (HI), in deciviews, 

will be calculated in terms of the extinction coefficient via: 

HI = 10 ln (bext/10) 

The change in visibility (measured in terms of ‘delta-deciviews’) will then be compared against 

background conditions. The delta-deciview, .dv, value will be calculated from the Catawba 

Mill’s contribution to extinction, bsource, and background extinction, bbackground, as follows: 

dv = 10 ln ({bbackground+ bsource}/ bbackground) 

If the dv value is greater than the 0.5 dv threshold, then Bowater could contribute to visibility 

impairment and maybe ‘subject to BART’ controls. If not, Bowater will be BART-exempt. 

Visibility Impacts from BART-Eligible Sources

 

Visibility Impact Method. CALPOST will be run initially using Method 6 (MVISBK=6) for 

calculating extinction. That is, monthly f(RH) adjustment factors will be applied directly to the 

background and modeled sulfate and nitrate concentrations, as recommended in the BART 

guidelines. Note that the RHMAX parameter (the maximum relative humidity factor used in the 

particle growth equation) is not used when Method 6 is selected. Similarly, the relative humidity 

adjustment factor (f(RH)) curves in CALPOST (e.g., IWAQM growth curve and the 1996 

IMPROVE curve) are not used when MVISBK is equal to 6. 

Monthly average Class I area-specific relative humidity values will be employed in the 

extinction analysis (EPA, 2003, Table A-3). Species to be considered include SO4, NO3, 

EC, SOA (i.e., condensable organic emissions), soil, and coarse PM. With Method 6, 

background extinction coefficients are computed from EPA (2003) monthly estimates of 

concentrations of ammonium sulfate (BKSO4), ammonium nitrate (BKNO3), coarse particulates 

(BKPMC), organic carbon (BKOC), soil (BKSOIL), and elemental carbon (BKEC). Values for 

these coefficients are listed in CALPOST input group 2 contained in Appendix C. In screening 

analyses, the extinction due to Rayleigh scattering (i.e., the scattering of light by natural particles 

much smaller than the wavelength of the light) will be set to 10 Mm-1 (BEXTRAY = 10.0) for all 

modeled Class I areas. 

Natural Background Light Extinction. The Appendix Y BART guidance recommends that 

visibility impacts should be evaluated against ‘natural’ background conditions. EPA (2003) 

describes the calculation of the annual average background extinction (in 1/Mm) for a Class I 

area using the area's annual f(RH) and average natural concentrations based on the area's 

geographic location. Annual average background extinction values (in 1/Mm) are converted to 

annual average Haze Index (HI) values (in deciview or dv). The average HI value is for the 20% 

best visibility days (Best Days (dv)) is estimated from 10th percentile of the annual average HI 

value for a Class I area assuming a normal distribution. Thus, no average natural concentrations 

are provided for determining extinction for the 20% best visibility days. EPA maintains that the 

above definition of natural visibility baseline as the 20% best visibility days is likely to be 

reasonably conservative and consistent with the Regional Haze Rule goal of natural conditions. 

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There are major technical issues with this approach: (a) the same concentrations assumed at all 

Class I areas in the East or West, (b) the same concentrations assumed to occur every month of 

the year, and (c) fine sea salt and associated water is not included. Also, in the calculation of 

20% best visibility days, the same frequency distribution is assumed for every Class I area in the 

East or in the West. In other words, ‘one size fits all’ (Tombach, 2004). But this really is not the 

case. 

The background extinction computation with Method 6 in CALPOST involves user-supplied 

monthly concentrations of SO4, NO3, PM coarse, organic carbon, soil, and

elemental carbon species. In practice, concentrations for only 2 species, SO4 ([BKSO4]) and 

soil ([BKSOIL]), are supplied in the CALPOST input file to represent hygroscopic and nonhygroscopic portions of background extinction, respectively. Furthermore, the species 

concentrations are held constant over the annual cycle (i.e., no daily, monthly, or seasonal 

variation). Finally, the EPA natural background default values are defined separately for the 

eastern and western U.S. result in natural background extinction values that vary spatially and 

temporally only in response to the spatial distribution and monthly variation of climatologicallyrepresentative relative humidity values (EPA, 2003, Table A-3). Thus, the default definition of 

natural conditions does not take into account meteorologically caused visibility impairment. 

For CALPUFF analyses, these EPA (2003) default procedures for calculation of light extinction 

will be used for current and natural background conditions. 

To determine background extinction for the BART analysis with CALPOST, average natural 

concentrations that represent average natural background visibility for the best 20% days need to 

be determined. Bowater will use the approach developed by the North Dakota Department of 

Health. This approach is discussed in Appendix F. 

Impact Threshold. The EPA BART guidance recommends that the threshold value for defining 

whether a source “contributes” to visibility impairment is 0.5 dv change from natural conditions. 

BART determinations are based upon the 98th percentile of the predicted 24-hour average 

deciview change obtained from the CALPOST postprocessor. When 98th percentile modeling is 

conducted the highest modeled delta deciview value for each modeling day for each modeled 

receptor will be determined. The value is then compared to the 0.5 dv contribution threshold 

value. If the value exceeds the “contribution” threshold of 0.5 dv the source will be subject to a 

BART evaluation. If the value is less than the “contribution” threshold 0.5 dv, the source is 

exempted from the BART requirements. 

To conserve computational and analysis resources, the CALPUFF modeling will be performed 

sequentially for calendar years 2001, 2002, and 2003. URS understands that evaluation of all 

three years will be required to exclude a BART eligible source from the BART requirements. 

Since the current regulatory version of CALPOST does not generate 98 percentile results, 

Bowater will use a modified version of CALPOST that generates a file with a full distribution of 

daily delta-deciview values for each receptor should source-specific modeling be performed. The 

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Colorado Department of Public Health and Environment (CDPHE, 2005) has developed a 

FORTRAN processor to generate 98th percentile results and it is available upon request. 

7.0 ALTERNATIVE MODELING METHODOLOGY 

The Bowater Mill is located 140 kilometers from the nearest Class I Area. Over the years the 

accepted practice for modeling a single point source for regional haze was to use the CALPUFF 

model and follow guidance developed by the Federal Land Manager. These FLM modeling 

procedures have always included many highly conservative modeling assumptions. One of these 

assumptions is that the delta dv value at a single “worst-case” receptor location within the Class I 

area is the value used to compare to the just-noticeable-change haze threshold of 1.0 dv or a 10 

percent change in extinction. 

URS researched the formulation of the deciview metric and discovered that it is based on a line 

of sight (LOS) concept. Appendix H contains a technical paper on the development and 

application of the dv. The LOS concept is not new and was originally discussed by the VISTAS 

technical consultant in the initial draft of the VISTAS Modeling Protocol dated January 31, 2005 

under section 4.1.4 (Additional Technical Considerations). The draft protocol actually gave an 

example of how an analysis could be conducted using CALPUFF by averaging predicted change 

in dv along a LOS. 

The following paragraphs have been extracted from the Initial Draft VISTA Modeling Protocol 

regarding the LOS modeling approach when using CALPUFF to estimate regional haze impacts 

from a single source. 

Statement 1 from Initial VISTAS Draft Protocol: 

“A more difficult issue to address is that CALPOST calculates the extinction coefficient at each 

receptor point. That value represents extinction in the vicinity of that point, but does not 

necessarily represent the impact of the source on extinction over any sight path, particularly 

over the longer sight paths that are likely under natural conditions. The human perception of 

visibility takes place over sight paths, not at points. 

For example, consider a 40-km sight path that represents the visual range and includes 10 

CALPUFF receptor points on a 4 km grid. If there is a 10% change in extinction (a 1 dv change) 

at two receptors and no change at the others, the actual change in haziness over the sight path is 

2% (or 0.2 dv). If, as the EPA asserts in its BART proposal, a 0.5 dv change is barely 

perceptible, then this change in haziness would be wholly imperceptible even though two 

receptor points exceed the 0.5 dv threshold! 

Thus, if the scale of the plume is small compared to the visual range under natural conditions, 

which is likely to be the case at all but the longest transport distances (and will be especially true 

for the small plumes very close to the source), the change in extinction at any receptor point is 

not representative of the effect of the source on the ability to see through the haze. Rather, in 

such cases, a sight path must be selected and the average change in extinction over that sight 

path calculated.” 

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Statement 2 from Initial VISTAS Draft Protocol: 

“... Also, when the plume is narrow, the aerosol concentrations across it may vary considerably 

from one CALPUFF receptor to the next, and the concentration field is unlikely to be uniform 

over the distance one can see (the visual range), especially if the background is assumed to be at 

default natural conditions. Therefore the light extinction impact of the source will vary 

depending on the sight path. In such situations, this variability of the aerosol from one 

CALPUFF receptor to the next should be taken into account by averaging the CALPOSTcalculated light extinction over all receptors along each of the sight paths of interest within a 

Class I area.” 

After our further review of the formulation of the deciview metric, URS agrees that this approach 

should be applicable for BART exclusion and determination analyses and URS proposes to use 

the LOS approach for BART modeling if initial predicted values suggest this approach would 

provide a more accurate representation and comparison to the 0.5 contribution threshold value. 

 

After reviewing many ground level plume footprint plots from CALPUFF for a point source near 

a Class I area it was verified that a plume is not widely dispersed under “worst-case” 

meteorological conditions (see Appendix I). Therefore using one “worst-case” receptor location 

to determine if a “just noticeable change” in regional haze is occurring does not properly 

represent a facility’s impact on the change in deciview based on the formulation of the deciview 

metric. 

It should be noted that the deciview is properly applied for ambient monitoring studies or one 

atmosphere modeling that include a homogeneous mix of visibility impairing compounds 

resulting from a wide variety of emission sources over a large geographical region thereby 

causing relatively small changes in deciviews over the length of the visual range. 

Therefore in order to properly apply the 0.5 deciview threshold for BART modeling, URS is 

proposing to conduct (if needed) additional regional haze modeling based on averaging the 

predicted change in deciviews along a line of receptors extending from a worst-case receptor 

location within the Class I area extending to a distance equal to the visual range for that day. 

It should be noted, as a preliminary modeling step for either screening level or refined level 

modeling, URS will still conduct the standard “worst-case” receptor modeling and present results 

in a standard format. However if initial modeling indicates that a 0.5 deciview threshold is 

exceeded by a factor of 2 or less then the more refined LOS modeling approach will be used for 

screening and refined level CALPUFF modeling. These modeling steps have been discussed in 

this protocol An example of how the modeling approach will work is presented below assuming 

that a visual range approach is acceptable. 

Maximum hourly emissions of SO2, NOx, filterable and condensable PM10 will be input to the 

CALPUFF model. Results from the initial VISTAS modeling will be used to determine the 

“worst-case” LOS receptor locations within the Class I area. CALPUFF will be used to 

determine the highest delta dv receptor location along the front and back edges of the Class I 

area. The worst-case receptor location along the front and back edges will be considered the 

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starting points for the LOS analysis. It will be assumed that observers look into the Class I area 

and the LOS ends at the visual range for the 24-hour time period being investigated. 

Once the worst-case receptors have been identified several other analyses will be conducted. 

The initial maximum modeling results for each time period will be evaluated to determine if any 

of the predicted “worst-case” extinctions occurred during periods of fog, rain or other naturally 

occurring haze. Should any of these conditions actually occur then a new 24-hour natural 

background value will be determined using the Method 7 modeling approach as discussed in a 

previous draft of the VISTAS protocol. The CALPOST model currently has the ability to 

address this issue using Method 7 for a 24 hour time period. 

 

For the 24-hour averaging period the top 50 delta dv values will be studied with respect to 

the default monthly F(rh) values to determine whether using actual relative humidity data and 

the EPA F(rh) curve for the time period results in a higher F(rh) value. This type of analysis 

was discussed in the December 2005 draft VISTAS protocol. If a higher F(rh) value is 

determined then the CALPOST program will be run using the actual relative humidity and 

associated EPA F(rh) value to determine a new initial maximum delta dv value. To limit the 

number of times CALPOST is executed, only periods with higher EPA F(rh) values will be 

recalculated to determine the final worst-case receptor locations. 

Two additional CALPUFF analyses will be conducted in order to produce a final 24-hour LOS 

visibility impairment estimate. A string of LOS receptors will be developed that extend 

backwards towards the Bowater Mill and another set of receptors that extends downwind alone 

the same line-of-sight (forward). The backward facing analysis will begin at a receptor location 

directly downstream but on the opposite boundary from the “worst-case” receptor located nearest 

the Bowater Mill. The LOS will extend through the Class I area and over the worst-case receptor 

on the front side of the Class I area and end at the actual visual range for the time period under 

investigation. The forward facing LOS will begin at the nearest worst-case receptor on the front 

side of the Class I area (nearest the Bowater Mill) and look through the Class I area and extend 

downwind ending at the applicable visual range. The length of each string of receptors will vary 

depending on the actual visual range for the 24-hour time period under study and each string will 

end at the most sensitive receptor location required by the deciview metric. A total of 50 evenly 

spaced receptors will be placed along each string. This limit is dictated by CALPOST printed 

output information. The time specific natural background visual range can be provided from 

CALPOST and will be based on using actual EPA adjusted F(rh) data. 

The following formula will be used to convert background extinction from CALPUFF to a visual 

range: 

VR = 3912/bext(NC) 

VR = visual range (km) 

bext(NC) = background extinction for natural conditions (Mm-1) 

Figure 7-1 is an illustration of how the modeling would be conducted using the LOS modeling 

approach for two receptor strings. A 75 km LOS was assumed for illustration. 

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Tabulated results from the analysis would be prepared and presented in the simple format 

presented in Table 7-1. This example assumes a 170-kilometer visual range is applicable for 

both time periods. The actual table would include 50 receptors. 

Appendix H.6 - 111
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Swanquarter NWR

visual range north 75 km

visual range south

Demo Source

860 880 900 920 940 960 980

UTM East (km)

3860

3880

3900

3920

3940

3960

3980

UTM North (km)

Observer location looking north >

<Observer location looking south

75 km 

Observer locations obtained 

from 5-year CALPUFF modeling

with receptors placed on 

northern & sourthern boundary

<50 evenly spaced receptors 

 located on arrow

50 evenly spaced receptors >

placed along arrow

FILE NO.

15266083 

FIG. NO.

SCALE: DRAWN BY: SCL DATE: SCL

AS SHOWN CHECKED BY: DATE:

Bowater – Catawba, South Carolina 

Example of LOS 

Modeling Approach

7-1 

Appendix H.6 - 112
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Table 7-1: Example of LOS Modeling Results 

FORWARD LOOKING LOS 

24-Hour Period Delta 

Distance (km) Deciview 

0 1.647 

10 1.524 

20 1.246 

30 0.911 

40 0.631 

50 0.475 

60 0.39 

70 0.328 

80 0.279 

90 0.244 

100 0.217 

110 0.194 

120 0.174 

130 0.156 

140 0.137 

150 0.12 

160 0.102 

170 0.085 

 Average 0.492222 

BACKWARD LOOKING LOS 

24-Hour Period Delta 

Distance (km) Deciview 

0 1.246 

-10 1.524 

-20 1.647 

-30 2.003 

-40 1.466 

-50 0.872 

-60 0.036 

-70 0.007 

-80 0.001 

-90 0 

-100 0 

-110 0 

-120 0 

-130 0 

-140 0 

-150 0 

-160 0 

-170 0 

 Average 0.489 

Appendix H.6 - 113
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The default for Rayleigh scattering associated with higher elevations will be incorporated for all 

modeling. Initial modeling will be conducted using the standard IMPROVE coefficients. 

Again, should initial modeling results only slightly exceed ambient thresholds then the SCDHEC 

will be contacted to gain approval for using the EPRI coefficients. 

8.0 REPORTING 

8.1 Presentation of Modeling Results 

 

The CALPOST processing computes the daily maximum change in deciviews. For evaluating 

compliance with the VISTAS screening threshold, the 8th highest change in extinction value for 

each year will be compared to the threshold value (e.g., 0.5 dv). At a minimum, tabular 

presentation of the following results will be provided: 

> Number of receptors within each Class I area with impacts > 0.5 dv; 

> Number of days at all receptors within each Class I area with impacts > 0.5 dv; and 

> Number of Class I areas with impacts > 0.5 dv. 

For evaluating compliance using all grid modeling results from exemption and determination 

modeling the 98th percentile of the predicted 24-hour averaged deciview impact deduced from 

the CALPOST postprocessor will be compared to the threshold value (e.g., 0.5 dv). At a 

minimum, tabular presentation of the following results will be provided: 

> Number of receptors within each Class I area with impacts > 0.5 dv; 

> Number of days at all receptors within each Class I area with impacts > 0.5 dv; and 

> Number of Class I areas with impacts > 0.5 dv. 

A variety of other tabular and graphical summaries may also be developed. 

8.2 Reporting of CALPUFF Modeling Results 

The report accompanying the CALPUFF modeling will provide a clear description of the 

modeling procedures followed and the results of the analysis. Any departures from the approved 

modeling protocol will be discussed and justified. The report will also include a discussion of the 

uncertainty in the modeling results and the likelihood that the modeling process was effective in 

its determination. Any needs for source-specific or alternative modeling will be identified. 

Accompanying the modeling report will be an electronic archive (CDs, DVDs, or removable 

USB2/IEEE 1394 hard drives as appropriate) that includes the full set of CALPUFF inputs 

and model output fields as well as any pre- or post-processor codes used to generate the 

results. The VISTAS 12/4 km regional CALPUFF-ready meteorological fields will not be 

included in the archive. The modeling data archive will be sufficiently complete as to allow 

an independent modeler to fully corroborate the CALPUFF screening results. 

Appendix H.6 - 114
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9.0 REFERENCES

CDPHE, 2005. CALMET/CALPUFF BART Protocol for Class I Federal Area Individual Source 

Attribution Visibility Impairment Modeling Analysis”, prepared by the Air Pollution Control 

Division, Colorado Department of Public Health and Environment, enver, CO. 

Earth Tech, Inc., 2002. “Application of CALMET/CALPUFF and MESOPUFF II to Compare 

Regulatory Design Concentrations for a Typical Long-Range Transport Analysis”, prepared for 

U.S. EPA, prepared by Earth Tech, Inc, Concord, MA. 

EPA, 1995. “Interagency Workgroup on Air Quality Modeling (IWAQM): Assessment of Phase 

1 Recommendations Regarding the Use of MESOPUFF II”, EPA- 54/R-95-006. Office of Air 

Quality Planning and Standards, U.S Environmental Protection Agency, Research Triangle Park, 

NC 27711. 

EPA, 1998b. “An Analysis of the CALMET/CALPUFF Modeling System in a Screening Mode.” 

EPA-454/R-98-010. Office of Air Quality Planning and Standards, U.S. Environmental 

Protection Agency, Research Triangle Park, NC 27711. 

EPA, 1998c. “A Comparison of CALPUFF with ISC3.” EPA-454/R-98-020. Office of Air 

Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle 

Park, NC 27711. 

EPA, 1998d. Phase 2 Summary Report and Recommendations for Modeling Long Range 

Transport and Impacts. Interagency Workgroup on Air Quality Modeling (IWAQM). EPA454/R98-019, U.S. Environmental Protection Agency, RTP, NC. 

EPA. 1998e. “Response to Peer Review Comments of CALMET/CALPUFF Modeling System”. 

Research Triangle Park, NC. November. 

EPA. 1999. “Response to Peer Review Comments of the Interagency Workgroup on Air Quality 

Modeling Phase 2 Summary Report and Recommendations for Modeling Long Range Transport 

Impacts”. Research Triangle Park, NC. February. 

EPA. 1999. Regional Haze Regulations; Final Rule. Federal Register, 64, 357 13-35774. 

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

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

EPA, 2005. Regional Haze Regulations and Guidelines for Best Available Retrofit Technology 

(BART) Determinations. Federal Register, 70 (128), Wednesday, July 6, 2005. 

 

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. 

Appendix H.6 - 115
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BART Exemption Modeling Analysis 

Bowater Coated and Specialty Papers Division 

March 17, 2006 Page 35 

Escoffier-Czaja, C., and J. Scire. 2005: Comments on the Computation of Nitrate Using the 

Ammonia Limiting Method in CALPUFF”, Earth Tech, Inc., Concord, MA 

Federal Register. 2003. 40 CFR Part 51. “Revisions to the Guidelines on Air Quality Models: 

Adoption of a Preferred Long Range Transport Model and Other Revisions; Final Rule. Federal 

Register/Vol. 68, No 72/Tuesday April 15, 2003 

FLAG 2000. “Federal Land Managers’ Air Quality Related Values Workgroup (FLAG)”: Phase 

I Report. USDI – National Park Service, Air Resources Division, Denver, CO. 

Garrison, M., A. Gray, S.T. Rao, M. Scruggs, 1999. “Peer Review of the Interagency Workgroup 

On Air Quality Modeling Phase 2 Summary Report and Recommendations For Modeling Long 

Range Transport Impacts”, report compiled by: John S. Irwin Air Policy Support Branch, 

Atmospheric Sciences Modeling Division U.S. Environmental Protection Agency Research 

Triangle Park, NC 27711. 

Irwin, J.S., 1998b. “Interagency Workgroup on Air Quality Modeling (IWAQM): Phase 2 

Summary Report and Recommendations for Modeling Long Range Transport Impacts”. EPA454/R-98-019, Office of Air Quality Planning and Standards, Research Triangle Park, NC, 151 

pp. (NTIS Accession Number PB 99-121089). 

Irwin, J.S. and J.P. Notar. 2001. Long-range-transport screening technique using CALPUFF. 

Proceedings of Guideline on Air Quality Modeling: A New Beginning AW&MA Specialty 

Conference. April 4-6, 2001, Newport, RI. 

Grell, G.A., J. Dudhia, and D.R. Stauffer, 1995: A Description of the Fifth Generation Penn 

State/NCAR MM5, NCAR TN-398-STR, NCAR Technical Note. 

IWAQM. 1998. Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary 

Report and Recommendations for Modeling Long-Range Transport and Impacts on Regional 

Visibility. EPA-454/R-98-019. U.S. Environmental Protection Agency, Office of Air Quality 

Planning and Standards, Research Triangle Part, NC. 

Tonnesen G.S., R.E. Morris, M. Uhl, K. Briggs, J. Vimont and T. Moore. 2003. “The WRAP 

Regional Modeling Center – Application and Evaluation of Regional Visibility Models” 

presented at 96th Annual Conference and Exhibition of the Air and Waste Management 

Association, San Diego, California

Tombach, I., 2004. “Options for Estimating Natural Background Visibility in the VISTAS 

Region”, prepared for the VISTAS Technical Analysis Work Group (TAWG), 15 January. 

Tombach, I., and P. Brewer, 2005. Natural background visibility and regional haze goals in the 

Southeastern United States. Journal of the Air & Waste Manage. Association. (in press). 

Tombach, I., P. Brewer, T. Rogers, and C. Arrington, 2005a. “BART modeling protocol for 

VISTAS: Frst Draft”, 22 March. 

Appendix H.6 - 116
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BART Exemption Modeling Analysis 

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March 17, 2006 Page 36 

Tombach, I., P. Brewer, T. Rogers, C. Arrington, J. Scire 2005b. “Protocol for the application of 

the CALPUFF model for analyses of Best Available Retrofit Technology (BART): VISTAS 

second draft”, 22 August. 

Scire, J., I. Tombach, et al. 2005. Protocol for the application of the CALPUFF model for 

analyses of Best Available Retrofit Technology (BART): (VISTAS third draft), Prepared for the 

Visibility Improvement State and Tribal Association of the Southeast (VISTAS), Prepared by 

Earth Tech, Inc., VISTAS Technical Analysis Work Group (TAWG), the Florida Department of 

Environmental Protection, and the West Virginia Department of Environmental Protection. 

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, MA. 

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, M.A 

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, M.A. 

McNally, D. E., 2003. “Annual Application of MM5 for Calendar Year 2001”. Prepared for the 

U.S. EPA, Office of Air Quality Planning and Standards, Prepared by Alpine Geophysics, LLC, 

Arvada, CO. 178 pp. 

McNally, D. E., and T. W. Tesche, 2002. “Annual Meteorological Modeling Protocol: Annual 

Application of the MM5 to the Continental United States”, prepared for the EPA Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. 

Ryan, P., D. Lowenthal and N. Kumar, 2004: Light Extinction Reconstruction in IMPROVE. 

Presented at the A&WMA Specialty Conference on Regional and Global Perspectives in Haze: 

Causes, Consequences and Controversies, Asheville, NC, 26-29 October 2004. 

Johnson, M. T., 2003b. Iowa DNR 2002 Annual MM5 Modeling Project. Presented at the August 

11th, 2003 CENRAP Workgroup Meeting in Bloomington, Minnesota. 

Johnson, M. T., 2005. CALPUFF Modeling Protocol in Support of Best Available Retrofit 

Technology Determinations”, Prepared by the Iowa Department of Natural Resources, Air 

Quality Bureau, Des Moines, IA 

LADCo, 2005. “Single Source Modeling to Support Regional Haze BART: Version 3”, Prepared 

by the Lake Michigan Air Directors Consortium (LADCO), Des Plaines, IL. 

Levy, J., J. Spengler, D, Hlinka, D. Sullivan, and D. Moon, 2002. Using CALPUFF to evaluate 

the impacts of power plant emissions in Illinois: model sensitivity and implications. Atmospheric 

Environment. 36 (6). 1063-1075. 

Appendix H.6 - 117
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March 17, 2006 Page 37 

Morris, R., C. Tana, and G. Yarwood, 2003. “Evaluation of the Sulfate and Nitrate Formation 

Mechanism in the CALPUFF Modeling System”, Proceedings of the A&WMA Specialty 

Conference --Guideline on Air Quality Models: The Path Forward. Mystic, CT. 22-24 October. 

Morris, R.E., B. Koo, T.W. Tesche, C. Loomis, G. Stella, G. Tonnesen, and Z. Wang. 2004a. 

Modeling protocol for the VISTAS Phase II regional haze modeling. 

(http://pah.cert.ucr.edu/vistas/vistas2/)

MPCA, 2005. “Best Available Retrofit Technology (BART) Modeling Protocol to Determine 

Sources Subject to BART in the State of Minnesota”, prepared by the Minnesota Pollution 

Control Agency, St. Paul, MN. 

NDDH, 2005, “Protocol for BART-Related Visibility Impairment Modeling Analyses in North 

Dakota”, prepared by the North Dakota Department of Health, Division of Air Quality, Bismark, 

ND. 

NPS, 1993. “Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 1 Report: 

Interim Recommendation for Modeling Long Range Transport And Impacts On Regional 

Visibility”, EPA-454/R-93-015, U.S. Environmental Protection Agency, Technical Support 

Division, 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., F.W. Lurmann, A. Bass and S.R. Hanna, 1983: Development of the MESOPUFF II 

Dispersion Model. EPA-600/3-84-057, U.S. Environmental Protection Agency, Research 

Triangle Park, NC. 

Appendix H.6 - 118
APPENDIX A - CALPUFF Configuration 

The tables below identify the recommended CALPUFF configurations for VISTAS BART 

modeling. Also identified are the default recommendations from the IWAQM Phase 2 Report 

(EPA, 1998). 

Input Groups in the CALPUFF Control File. 

Input 

Group Description 

Applicable to 

VISTAS BART 

0 Input and output file names Yes 

1 General run control parameters Yes 

2 Technical options Yes 

3 Species list Yes 

4 Grid control parameters Yes 

5 Output options Yes 

6 Sub grid scale complex terrain inputs No 

7 Dry deposition parameters for gases Yes 

8 Dry deposition parameters for particles Yes 

9 Miscellaneous dry deposition for parameters Yes 

10 Wet deposition parameters Yes 

11 Chemistry parameters Yes 

12 Diffusion and computational parameters Yes 

13 Point source parameters Yes 

14 Area source parameters Yes 

15 Line source parameters No 

16 Volume source parameters Yes 

17 Discrete receptor information Yes 

Appendix H.6 - 119
CALPUFF Model Input Group 1: General Run Control Parameters 

Parameter Default VISTAS Comments 

METRUN 0 0 All model periods in met file(s) will be run 

IBYR - 2001 Starting year 

IBMO - 1 Starting month 

IBDY - 1 Starting day 

IBHR - 1 Starting hour 

XBTZ - 5 Base time zone (6 = CST) 

IRLG - 8760 Length of run 

NSPEC 5 6 Number of MESOPUFF I I chemical species 

NSE 3 4 Number of chemical species to be emitted 

ITEST 2 2 Program is executed after SETUP phase 

MRESTART 0 0 Do not read or write a restart file during run 

NRESPD 0 0 File written only at last period 

METFM 1 1 CALMET binary file (CALMET.MET) 

AVET 60 60 Averaging time in minutes 

PGTIME 60 60 PG Averaging time in minutes 

Appendix H.6 - 120
CALPUFF Model Input Group 2: Technical Options 

Parameter Default VISTAS Comments 

MGAUSS 1 1 Gaussian distribution used in near field 

MCTADJ 3 3 Partial plume path terrain adjustment 

MCTSG 0 0 Sub-grid-scale complex terrain not modeled 

MSLUG 0 0 Near-field puffs not modeled as elongated 

MTRANS 1 1 Transitional plume rise modeled 

MTBowater 1 1 Stack tip downwash used 

MSHEAR 0 0 (0, 1) Vertical wind shear (not modeled, 

modeled) 

MSPLIT 0 0 Puffs are not split 

MCHEM 1 1 MESOPUFF I I chemical parameterization 

Scheme 

MAQCHEM 0 0 Aqueous phase transformation not modeled 

MWET 1 1 Wet removal modeled 

MDRY 1 1 Dry deposition modeled 

MDISP 3 2 dispersion coefficients from internally calculated 

sigma v, sigma w using micrometeorological 

calculated variables 

Appendix H.6 - 121
CALPUFF Model Input Group 2: Technical Options 

MTURBVW 3 3 Use both σv and σw from PROFILE.DAT to 

compute σy and σz (n/a) 

MDISP2 3 2 dispersion coefficients from internally calculated 

sigma v, sigma w using micrometeorological 

calculated variables 

MROUGH 0 0 PG σy and σz not adjusted for roughness 

MPARTL 1 1 No partial plume penetration of elevated 

inversion 

MTINV 0 0 Strength of temperature inversion computed 

from default gradients 

MPDF 0 1 PDF not used for dispersion under convective 

conditions 

MSGTIBL 0 0 Sub-grid TIBL module not used for shoreline 

MBCON 0 0 Boundary concentration conditions not modeled 

MFOG 0 0 Do not configure for FOG model output 

MREG 1 0 NO checks are made 

Appendix H.6 - 122
CALPUFF Model Input Group 3: Species List-Chemistry Options. 

CSPEC Modeled1 Emitted2

Dry 

Deposition3

Output 

Group 

Number 

SO2 1 1 1 0 

SO4-2 1 1 2 0 

NOx 1 1 1 0 

HNO3 1 0 1 0 

NO3 - 1 0 2 0 

NH3 1 0 1 0 

PMC 1 1 2 0 

PMF 1 1 2 0 

EC 1 1 2 0 

SOA 1 1 2 0 

Notes: 1 0=no, 1=yes 

2 0=no, 1=yes 

3 0=none; 1=computed-gas; 2=computed-particle; 3=user-specified 

Appendix H.6 - 123
CALPUFF Model Input Group 4: Map Projection and Grid Control Parameters. 

Parameter Default VISTAS Comments 

PMAP UTM UTM Map Projection 

NX - 160 Number of X grid cells in meteorological grid 

NY - 172 Number of Y grid cells in meteorological grid 

NZ - 10 Number of vertical layers in meteorological 

Grid 

DGRIDKM - 12 Grid spacing (km) 

ZFACE - 0, 20 40, 80, 

160, 320, 640,

1200, 2000, 

3000, 4000 

Cell face heights in meteorological grid (m) 

XORIGKM - 137.973 Reference X coordinate for SW corner of 

grid cell (1,1) of meteorological grid (km) 

YORIGKM - -1625.974 Reference Y coordinate for SW corner of 

grid cell (1,1) of meteorological grid (km) 

IUTMZN - 17 UTM zone of coordinates 

IBCOMP - 70 X index of lower left corner of the 

computational grid 

JBCOMP - 67 Y index of lower left corner of the 

computational grids 

IECOMP - 145 X index of the upper right corner of the 

computational grid 

JECOMP - 130 Y index of the upper right corner of the 

computational grid 

LSAMP T F Sampling grid is not used 

IBSAMP - 0 X index of lower left corner of the sampling 

Grid 

JBSAMP - 0 Y index of lower left corner of the sampling 

Grid 

IESAMP - 0 X index of upper right corner of the 

sampling grid 

JESAMP - 0 Y index of upper right corner of the 

sampling grid 

MESHDN 1 1 Nesting factor of the sampling grid 

Appendix H.6 - 124
CALPUFF Model Input Group 5: Output Options. 

Parameter Default VISTAS Comments 

ICON 

1 

1 Output file CONC.DAT containing concentrations 

is created 

IDRY 

1 

1 Output file DFLX.DAT containing dry fluxes is 

Created 

IWET 

1 

1 Output file WFLX.DAT containing wet fluxes is 

created 

IVIS 

1 

1 Output file containing relative humidity data is 

created 

LCOMPRS T T Perform data compression in output file 

IMFLX 0 0 Do not calculate mass fluxes across specific 

boundaries 

IMBAL 0 0 Mass balances for each species not reported 

hourly 

ICPRT 0 1 Print concentration fields to the output list file 

IDPRT 0 0 Do not print dry flux fields to the output list file 

IWPRT 0 0 Do not print wet flux fields to the output list file 

ICFRQ 1 1 Concentration fields are printed to output list file 

every hr 

IDFRQ 1 1 Dry flux fields are printed to output list file every 

1 hour 

IWFRQ 1 1 Wet flux fields are printed to output list file 

every 1 hour 

BowaterR

TU 

1 3 Units for line printer output are in g/m3 for 

concentration and g/m2/s for deposition 

IMESG 2 2 Messages tracking the progress of run written to 

screen 

LDEBUG F F Logical value for debug output 

BowaterF 1 1 First puff to track 

NPFDEB 1 1 Number of puffs to track 

NN1 1 1 Meteorological period to start output 

NN2 10 10 Meteorological period to end output 

Appendix H.6 - 125
CALPUFF Model Input Group 6: Sub-Grid Scale Complex Terrain Inputs. 

Parameter Default VISTAS Comments 

NHILL 0 0 Number of terrain features 

NCTREC 0 0 Number of special complex terrain receptors 

MHILL - 2 Input terrain and receptor data for CTSG hills input 

in CTDM format 

XHILL2M 1 1 Conversion factor for changing horizontal dimensions 

to meters 

ZHILL2M 1 

1 Conversion factor for changing vertical dimensions to 

meters 

XCTDMKM - 0.0 

E+00 

X origin of CTDM system relative to CALPUFF 

coordinate system (km) 

YCTDMKM - 0.0 

E+00 

Y origin of CTDM system relative to CALPUFF 

coordinate system (km) 

CALPUFF Model Input Group 7: Dry Deposition Parameters for Gases. 

Species Default VISTAS Comments 

SO2 0.1509 0.1509 Diffusivity 

 1000. 1000. Alpha star 

 8.0 8.0 Reactivity 

 0.0 0.0 Mesophyll resistance 

0.04 0.04 Henry’s Law coefficient 

NOx 0.1656 0.1656 Diffusivity 

 1.0 1.0 Alpha star 

 8.0 8.0 Reactivity 

 5.0 5.0 Mesophyll resistance 

3.5 3.5 Henry’s Law coefficient 

HNO3 0.1628 0.1628 Diffusivity 

 1.0 1.0 Alpha star 

 18.0 18.0 Reactivity 

 0.0 0.0 Mesophyll resistance 

8.0E-8 8.0E-8 Henry’s Law coefficient 

0.000359 0.000359 Henry’s Law coefficient 

Appendix H.6 - 126
CALPUFF Model Input Group 8: Dry Deposition Parameters for Particles. 

Species Default VISTAS Comments 

SO4 0.48 0.48 Geometric mass mean diameter of SO4-2 [μm]

NO3 2.0 0.48 Geometric mass mean diameter of NO3-

 [μm] 

PMC 2.0 0.48 Geometric mass mean diameter of PMC [μm] 

PMF 2.0 0.48 Geometric mass mean diameter of PMF [μm] 

EC 2.0 0.48 Geometric mass mean diameter of EC [μm] 

SOA 0.48 0.48 Geometric mass mean diameter of SOA [μm] 

(Geometric Standard Deviation for all species assumed to be 2.0 μm). 

CALPUFF Model Input Group 9: Miscellaneous Dry Deposition Parameters. 

Parameter Default VISTAS Comments 

RCUTR 30 30 Reference cuticle resistance (s/cm) 

RGR 10 10 Reference ground resistance (s/cm) 

REACTR 8 8 Reference pollutant reactivity 

NINT 9 9 Number of particle size intervals for 

effective particle deposition velocity 

IVEG 1 1 Vegetation in non-irrigated areas is active 

and unstressed 

Appendix H.6 - 127
CALPUFF Model Input Group 10: Wet Deposition Parameters. 

Default VISTAS Comments 

SO2 3.21E-05 3.21E-05 Scavenging coefficient for liquid precipitation [s-1] 

 0.0 0.0 Scavenging coefficient for frozen precipitation [s-1] 

-2 1.0E-04 1.0E-04 Scavenging coefficient for liquid precipitation [s-1

]

 SO4 3.0E-05 3.0E-05 Scavenging coefficient for frozen precipitation [s-1] 

HNO3 6.0E-05 6.0E-05 Scavenging coefficient for liquid precipitation [s-1] 

 0.0 0.0 Scavenging coefficient for frozen precipitation [s-1] 

- 1.0E-04 1.0E-04 Scavenging coefficient for liquid precipitation [s-1]

NO2 3.0E-05 3.0E-05 Scavenging coefficient for frozen precipitation [s-1] 

NH3 8.0E-05 8.0E-05 Scavenging coefficient for liquid precipitation [s-1] 

 0.0 0.0 Scavenging coefficient for frozen precipitation [s-1] 

PMC 1.0E-04 1.0E-04 Scavenging coefficient for liquid precipitation [s-1]

 3.0E-05 3.0E-05 Scavenging coefficient for frozen precipitation [s-1] 

PMF 1.0E-04 1.0E-04 Scavenging coefficient for liquid precipitation [s-1]

 3.0E-05 3.0E-05 Scavenging coefficient for frozen precipitation [s-1] 

EC 1.0E-04 1.0E-04 Scavenging coefficient for liquid precipitation [s-1]

 3.0E-05 3.0E-05 Scavenging coefficient for frozen precipitation [s-1] 

OC 1.0E-04 1.0E-04 Scavenging coefficient for liquid precipitation [s-1]

 3.0E-05 3.0E-05 Scavenging coefficient for frozen precipitation [s-1] 

Appendix H.6 - 128
CALPUFF Model Input Group 11: Chemistry Parameters. 

Parameter Default VISTAS Comments 

MOZ 1 1 Read ozone background concentrations 

from ozone.dat file (measured values). 

BCKO3 12*80 12*40 Background ozone concentration (ppb) 

BCKNH3 12*10 12*0.5 Background ammonia concentration (ppb) 

RNITE1 0.2 0.2 Nighttime NO2 loss rate in percent/hour 

RNITE2 2 2 Nighttime NOX loss rate in percent/hour 

RNITE3 2 2 Nighttime HNO3 loss rate in percent/hour 

MH202 1 1 Background H2O2 concentrations (Aqueous 

phase transformations not modeled) 

BCKH202 1 1 Background monthly H2O2 concentrations 

(Aqueous phase transformations not 

modeled) 

BCKPMF 1. 1. Fine particulate concentration for SOA 

Option (micrograms per cubic meter) 

OFRAC 

.2 .2 Organic fraction of fine particulate for 

SOA Option 

VCNX 50. 50. VOC/NOx ratio for SOA Option 

CALPUFF Model Input Group 12: Dispersion/Computational Parameters. 

Parameter Default 

VISTAS Comments 

SYDEP 550 550 Horizontal size of a puff in meters 

beyond which the time dependant 

dispersion equation of Heffter (1965) is 

used 

MHFTSZ 0 0 Do not use Heffter formulas for sigma z 

JSUP 5 5 Stability class used to determine 

dispersion rates for puffs above 

boundary layer 

CONK1 0.01 0.01 Vertical dispersion constant for stable 

Conditions 

CONK2 0.1 0.1 Vertical dispersion constant for 

neutral/stable conditions 

Appendix H.6 - 129
Parameter Default VISTAS Comments 

TBD 0.5 0.5 Use ISC transition point for determining 

the transition point between the 

Schulman-Scire to Huber-Snyder Building

Downwash scheme 

IURB1 10 10 Lower range of land use categories for 

which urban dispersion is assumed 

IURB2 19 19 Upper range of land use categories for 

which urban dispersion is assumed 

ILANDUIN 20 * Land use category for modeling domain 

XLAIIN 3.0 * Leaf area index for modeling domain 

ZOIN -0.25 * Roughness length in meters for modeling 

domain 

ELEVIN 0.0 * Elevation above sea level 

XLATIN -999 - North latitude of station in degrees 

XLONIN -999 - South latitude of station in degrees 

ANEMHT 10 10 Anemometer height in meters 

ISIGMAV 1 1 Sigma-v is read for lateral turbulence 

data 

IMIXCTDM 0 0 Predicted mixing heights are used 

XMXLEN 1 1 Maximum length of emitted slug in 

meteorological grid units 

XSAMLEN 1 10 Maximum travel distance of slug or puff 

in meteorological grid units during one 

 sampling unit 

MXNEW 99 60 Maximum number of puffs or slugs 

released from one source during one time 

step 

MXSAM 99 60 Maximum number of sampling steps 

during one time step for a puff or slug 

NCOUNT 2 2 Number of iterations used when 

computing the transport wind for a 

sampling step that includes transitional 

plume rise 

Appendix H.6 - 130
Parameter Default VISTAS Comments 

SYMIN 1 1 Minimum sigma y in meters for a new puff 

or slug 

SZMIN 1 1 Minimum sigma z in meters for a new puff 

or slug 

SVMIN .50 .50 Minimum lateral turbulence velocities 

(m/s) 

SWMIN 0.20, 0.12, 

0.08, 0.06, 

0.03, 

0.016 

0.20, 0.12,

0.08, 0.06,

0.03, 

0.016 

Minimum vertical turbulence velocities 

(m/s) 

WSCALM 0.5 0.5 Minimum non-calm wind speeds (m/s) 

XMAXZI 3000. 3000. Maximum mixing height (m) 

XMINZI 50. 20. Minimum mixing height (m) 

SL2PF 10. 10. Maximum Sy/puff length 

PLXO 0.07, 0.07, 

0.10, 0.15, 

0.35, 0.55 

0.07, 0.07,

0.10, 0.15,

0.35, 0.55 

Wind speed power-law exponents 

WSCAT 1.54, 3.09, 

5.14, 8.23, 

10.80 

1.54, 3.09,

5.14, 8.23,

10.80 

Upper bounds 1st 5 wind speed classes 

PGGO 0.020, 

0.035 

0.020, 

0.035 

Potential temp gradients PG E & F 

(deg/km) 

CDIV 0.01 0.01 Divergence criterion for dw/dz (1/s) 

PPC 0.5, 0.5, 

0.5, 0.5, 

0.35, 0.35 

0.5, 0.5, 

0.5, 0.5, 

0.35, 0.35 

Plume path coefficients (only if 

MCTADJ=3) 

NSPLIT 3 3 Number of puffs when puffs split 

IRESPLIT - 1900 Hour(s) when puff is eligible to split 

ZISPLIT 100 100 Previous hour’s minimum mixing height, m

ROLDMAX 0.25 0.25 Previous Max mixing height/current 

mixing height ratio, must be less than 

this value to allow puff to split 

NSPLITH 5 5 Number of puffs resulting from a split 

SYSPLITH 1.0 1.0 Minimum sigma-y of puff before it may 

split 

Appendix H.6 - 131
Parameter Default VISTAS Comments 

SHSPLITH 2.0 2.0 Minimum puff elongation rate from wind 

shear before puff may split 

CNSPLITH 1.0E-07 1.0E-07 Minimum species concentration before a 

puff may split 

EPSSLUG 1.0E-04 1.0E-04 Criterion for SLUG sampling 

EPSAREA 1.0E-06 1.0E-06 Criterion for area source integration 

DSRISE 1.0 1.0 Trajectory step length for numerical rise 

algorithm 

Note: Values indicated by an asterisk (*) were allowed to vary spatially across the 

domain and were obtained from CALMET

CALPUFF Model Input Group 13: Point Source Parameters. 

Parameter Default VISTAS Comments 

NPT1 - 7 Number of point sources with constant stack 

parameters or variable emission rate scale 

factors 

BowaterTU 1 1 Units for point source emission rates are g/s 

NSPT1 0 0 Number of source-species combinations with 

variable emissions scaling factors 

NPT2 - 0 Number of point sources with variable 

emission parameters provided in external file 

Appendix H.6 - 132
CALPUFF Model Input Group 14: Area Source Parameters. 

Parameter Default VISTAS Comments 

NAR1 Varies 

by 

scenario

Number of polygon area sources 

IARU 1 1 Units for area source emission rates are 

g/m2/s 

NSAR1 0 - Number of source species combinations with 

variable emissions scaling factors 

NAR2 - - Number of buoyant polygon area sources with 

variable location and emission parameters 

CALPUFF Model Input Group 15: Line Source Parameters. 

Parameter Default VISTAS Comments 

NLN2 - - Number of buoyant line sources with variable 

location and emission parameters 

NLINES - - Number of buoyant line sources 

ILNU 1 - Units for line source emission rates is g/s 

NSLN1 0 - Number of source-species combinations with 

variable emissions scaling factors 

MXNSEG 7 - Maximum number of segments used to model 

each line 

NLRISE 6 - Number of distance at which transitional rise is 

computed 

XL - - Average line source length (m) 

HBL - - Average height of line source height (m) 

WBL - - Average building width (m) 

WML - - Average line source width (m) 

DXL - - Average separation between buildings (m) 

FPRIMEL - - Average buoyancy parameter (m4/s3) 

Appendix H.6 - 133
CALPUFF Model Input Group 16: Volume Source Parameters. 

Parameter Default VISTAS Comments 

 NVL1 

- 4 Number of volume sources 

 IVLU 1 - Units for volume source emission rates is grams 

per second 

 NSVL1 0 - Number of source-species combinations with 

variable emissions scaling factors 

IGRDVL - - Gridded volume source data is not used 

VEFFHT - - Effective height of emissions (m) 

VSIGYI - - Initial sigma y value (m) 

VSIGZI - - Initial sigma z value (m) 

Table B-18. CALPUFF Model Input Group 17: Discrete Receptor Information. 

Parameter Default 

VISTAS 

Comments 

NREC - 194 Number of non-gridded receptors 

Appendix H.6 - 134
APPENDIX B - POSTUTIL Screening Configuration 

The tables below identify the recommended POSTUTIL processor screening 

configurations for CENRAP BART modeling. 

Input Groups in the POSTUTIL Processor Control File. 

Sub 

Group Description 

Applicable to 

VISTAS BART 

0a Input and output file names Yes 

1 NMET – Number of CALMET data files (365) Yes 

2 NFILES – Number of CALMET data files (1) Yes 

POSTUTIL Processor Input Group 1: General Run Control Parameters 

Parameter DEFAULT VISTAS Comments 

ISYR -- 2001 Starting year 

ISMO -- 1 Starting month 

ISDY -- 1 Starting day 

ISHR -- 0 Starting hour 

NPER -- 8760 Number of periods to process 

NSPECINP -- 6 Number of CALPUFF species to process 

NSPECOUT -- 6 Number of species to output 

NSPECCMP -- 0 Number of species to derive 

MDUPLCT -- 1 Stop run if duplicate name 

NSCALED -- 0 Number of CALPUFF files to ‘scale’ 

MNITRATE -- 1 Recompute the HNO3/NO3 partition for 

CALPUFF modeled concentrations? 1 = yes

for all sources combined 

BCKNH3 10. CMAQ. Default NH3 concentration (ppb) for 

HNO3/NO3 partitioning 

Appendix H.6 - 135
POSTUTIL Processor Input Group 2: Species Processing Information 

Parameter DEFAULT VISTAS Comments 

ASPECI -- SO2, SO4, NOx, HNO3, 

NO3, PM10

Species to post-process 

ASPECO -- SO2, SO4, NOx, HNO3, 

NO3, PM10

Species to output 

CSPECCMP -- CSPECCMP = N Nitrogen species to be 

 SO2 = 0.0 computed by scaling and 

 SO4 = 0.291667 summing one or more of the 

NO = 0.466667 processed input species using

 NO2 = 0.304348 the scaling factors for each 

 HNO3 = 0.222222 of the NSPECINP input 

 NO3 = 0.451613 species 

 PM10 = 0.0 

CSPECCMP -- CSPECCMP = S Sulfur species to be 

 SO2 = 0.50 computed by scaling and 

 SO4 = 0.333333 summing one or more of the 

NO = 0.0 processed input species using

 NO2 = 0.0 the scaling factors for each 

 HNO3 = 0.0 of the NSPECINP input 

 NO3 = 0.0 species 

 PM10 = 0.0 

MODDAT -- A (Default=1.0) Each species in NSCALED 

 SO2 = 1.1 CALPUFF data files may be 

 SO4 = 1.5 scaled before processing 

HNO3 = 0.8 (e.g., to change the emission 

 

NO3 = 0.1 

B (Default=0.0) 

rate for all sources modeled 

in the run that produced a 

data file). For each scaled 

 SO2 = 0.0 species the scaling factors 

 SO4 = 0.0 are A and B where x' = Ax + 

 HNO3 = 0.0 B. 

 NO3 = 0.0 

Appendix H.6 - 136
APPENDIX C – CALPOST Screening Configuration 

The tables below identify the recommended CALPOST processor screening 

configurations for CENRAP BART modeling. 

Input Groups in the CALPOST Processor Control File. 

Group Description 

Applicable to 

VISTAS BART 

0 Input and output file names Yes 

1 General Run Control Parameters Yes 

2 Visibility Parameters Yes 

3 Output Options Yes 

Appendix H.6 - 137
CALPOST Processor Input Group 1: General Run Control Parameters 

Parameter DEFAULT VISTAS Comments 

ISYR -- 2001 Starting year 

ISMO -- 1 Starting month 

ISDY -- 1 Starting day 

ISHR -- 0 Starting hour 

NPER -- 8760 Number of periods to process 

NREP 1 1 Process every hour of data? Yes = 1 

ASPEC -- VISIB Process species for visibility 

ILAYER 1 1 Layer/deposition code; 1 for CALPUFF 

concentrations 

A 0.0 0.0 Scaling factor, slope 

B 0.0 0.0 Scaling factor, intercept 

LBACK F F Add hourly background concentrations or 

fluxes? 

LG F F Process gridded receptors? 

LD F T Process discrete receptors? 

LCT F F Process complex terrain receptors? 

LDRING F F Report receptor ring results? 

NDRECP -1 -1 Select all discrete receptors 

IBGRID -1 -1 X index of LL corner of receptor grid 

JBGRID -1 -1 Y index of LL corner of receptor grid 

IEGRID -1 -1 X index of UR corner of receptor grid 

JEGRID -1 -1 X index of UR corner of receptor grid 

NGONOFF 0 0 Number of gridded receptor rows 

NGXRECP 1 0 Exclude specific gridded receptors, Yes = 0 

Appendix H.6 - 138
CALPOST Processor Input Group 2: Species Processing Information 

Parameter DEFAULT VISTAS Comments 

RHMAX 98 95 Maximum RH (%) used in particle growth curve 

LVSO4 T T Compute light extinction for sulfate? 

LVNO3 T T Compute light extinction for nitrate? 

LVOC T F Compute light extinction for organic carbon? 

LVMPC T F Compute light extinction for coarse particles? 

LVMPF T T Compute light extinction for fine particles? 

LVEC T F Compute light extinction for elemental carbon? 

LVBK T T Include background in extinction calculation? 

SPECPMC PMC PMC Coarse particulate species 

SPECPMF PMF PM10 Fine particulate species 

EEPMC 0.6 0.6 Extinction efficiency for coarse particulates 

EEPMF 1.0 1.0 Extinction efficiency for fine particulates 

EEPMCBK 0.6 0.6 Extinction efficiency for coarse part. background 

EESO4 3.0 3.0 Extinction efficiency for ammonium sulfate 

EENO3 3.0 3.0 Extinction efficiency for ammonium nitrate 

EEOC 4.0 4.0 Extinction efficiency for organic carbon 

EESOIL 1.0 1.0 Extinction efficiency for soil 

EEEC 10.0 10.0 Extinction efficiency for elemental carbon 

MVISBK 2 6 Method 6 for background light extinction: 

Compute extinction from speciated PM 

measurements. FLAG RH adjustment factor 

applied to observed & modeled sulfate and nitrate 

BEXTBTBK -- 10 Background extinction for MVISBK=1 (1/Mm) 

RHFRAC -- 10 Percentage of particles affected by RH 

RHFAC 12*value Depends

on Class

I Area 

Extinction coefficients for modeled and 

background hygroscopic species computed using 

EPA (2003) monthly RH adjustment factors 

Parameter DEFAULT VISTAS Comments

BKSEC 0.02 0.02 Background elem. carbon extinct. coeff – east 

BKSO4 0.23 0.23 Background sulfate extinction coeff – east 

BKNO3 0.10 0.10 Background nitrate extinction coeff – east 

BKPMC 3.00 3.00 Background coarse part. extinction coeff – east 

BKSOC 1.40 1.40 Background organic carbon extinct. coeff – east 

Appendix H.6 - 139
BKSSOIL 0.50 0.50 Background soil extinction coeff – east 

BKSEC 0.02 0.02 Background elem. carbon extinct. coeff – east 

BEXTRAY 10.0 10.0 Extinction due to Rayleigh scattering (1/Mm) 

CALPOST Processor Input Group 3: Output Options 

Parameter DEFAULT VISTAS Comments 

LDOC F F Print documentation image? 

BowaterRT

U 

1 3 Print output units (μg/m3

) for concentrations and 

(μg/m2/sec) for deposition 

L1HR T F Report 1 hr averaging times 

L3HR T F Report 3 hr averaging times 

L24HR T T Report 24 hr averaging times 

LRUNL T F Report run-length (annual) averaging times 

LT50 T F Top 50 table 

LTOPN F F Top ‘N’ table 

NTOP 4 4 Number of ‘Top-N’ values at each receptor 

ITOP 1,2,3,4 1,2,3,4 Ranks of ‘Top-N’ values at each receptor 

LEXCD F F Threshold exceedances counts 

THRESH1 -1.0 -1.0 Averaging time threshold for 1 hr averages 

THRESH3 -1.0 -1.0 Averaging time threshold for 3 hr averages 

THRESH24 -1.0 -1.0 Averaging time threshold for 24 hr averages 

THRESHN -1.0 -1.0 Averaging time threshold for NAVG-hr averages 

NDAY 0 0 Accumulation period, days 

NCOUNT 1 1 Number of exceedances allowed 

LECHO F F Echo option 

LTIME F F Time series option 

LPLT F F Plot file option 

LGRD F F Use grid format instead of DATA format 

LDEBUG F F Output information for debugging? 

Appendix H.6 - 140
APPENDIX D – CLASS I RECEPTORS IN LAMBERT CONFORMAL COORDINATES 

Great Smoky Mountains 

Lambert 

Conforma

l X (km) 

Lambert 

Conforma

l Y (km) 

Height 

(m) 

Lambert 

Conformal 

X (km) 

Lambert 

Conformal 

Y (km) 

Height 

(m) 

Lambert 

Conformal 

X (km) 

Lambert 

Conformal 

Y (km) 

Height 

(m) 

1201.214 -417.246 521 1197.428 -412.206 944 1183.507 -410.517 1072 

1204.191 -416.805 521 1198.916 -411.986 1281 1184.994 -410.3 1199 

1189.038 -417.173 521 1200.403 -411.766 1427 1186.481 -410.083 1097 

1190.527 -416.954 522 1201.891 -411.546 1221 1187.968 -409.865 720 

1192.015 -416.736 523 1203.378 -411.325 1030 1189.455 -409.647 640 

1193.503 -416.517 521 1204.865 -411.105 983 1190.942 -409.429 873 

1194.992 -416.298 538 1206.352 -410.883 894 1192.429 -409.21 852 

1196.48 -416.079 550 1207.84 -410.662 675 1193.915 -408.991 978 

1197.968 -415.859 640 1209.327 -410.44 950 1195.402 -408.772 809 

1199.456 -415.639 607 1210.814 -410.219 915 1196.889 -408.553 761 

1200.944 -415.419 523 1212.301 -409.996 732 1198.376 -408.333 987 

1202.432 -415.199 548 1213.788 -409.774 980 1199.863 -408.113 874 

1203.92 -414.978 524 1216.762 -409.328 1020 1201.349 -407.893 1147 

1205.408 -414.757 608 1218.249 -409.105 886 1202.836 -407.673 1323 

1208.384 -414.315 546 1219.736 -408.881 615 1204.322 -407.452 1221 

1209.871 -414.093 566 1221.222 -408.657 680 1205.809 -407.231 1097 

1211.359 -413.871 541 1176.335 -413.427 767 1207.296 -407.01 800 

1212.847 -413.649 530 1177.823 -413.211 919 1208.782 -406.788 942 

1181.33 -416.432 683 1179.311 -412.995 905 1210.268 -406.566 1167 

1182.818 -416.216 711 1180.798 -412.778 911 1211.755 -406.344 1052 

1184.307 -415.999 583 1182.286 -412.561 965 1213.241 -406.122 1012 

1185.795 -415.781 599 1183.773 -412.344 837 1214.728 -405.899 1002 

1187.283 -415.564 561 1185.261 -412.127 1055 1216.214 -405.676 1143 

1188.771 -415.346 582 1186.748 -411.91 716 1217.7 -405.453 998 

1190.259 -415.128 703 1188.235 -411.692 825 1219.186 -405.23 710 

1191.747 -414.909 707 1189.723 -411.474 609 1220.672 -405.006 998 

1193.235 -414.69 522 1191.21 -411.256 814 1222.158 -404.782 907 

1194.723 -414.471 760 1192.697 -411.037 1006 1223.644 -404.558 1200 

1196.21 -414.252 846 1194.184 -410.818 1036 1225.131 -404.333 1173 

1197.698 -414.033 952 1195.672 -410.599 732 1226.616 -404.108 1067 

1199.186 -413.813 765 1197.159 -410.379 746 1228.102 -403.883 896 

1200.674 -413.593 1067 1198.646 -410.16 929 1229.588 -403.658 799 

1202.161 -413.372 915 1200.133 -409.94 1008 1231.074 -403.432 683 

1203.649 -413.152 730 1201.62 -409.72 1288 1169.857 -410.632 330 

1205.137 -412.931 822 1203.107 -409.499 1266 1171.344 -410.418 523 

1206.624 -412.71 748 1204.594 -409.278 1218 1172.831 -410.203 599 

1208.112 -412.488 544 1206.081 -409.057 959 1174.318 -409.988 769 

1209.599 -412.267 706 1207.568 -408.836 681 1175.805 -409.772 831 

1211.087 -412.045 627 1209.054 -408.614 920 1177.292 -409.556 941 

1212.574 -411.822 845 1210.541 -408.392 1170 1178.779 -409.34 1150 

1214.061 -411.6 772 1212.028 -408.17 956 1180.266 -409.124 1194 

1215.549 -411.377 836 1213.515 -407.948 818 1181.753 -408.907 1084 

1220.01 -410.707 665 1215.001 -407.725 1223 1183.24 -408.69 1206 

1175.112 -415.469 554 1216.488 -407.502 1270 1184.727 -408.473 1225 

1176.6 -415.254 760 1217.974 -407.279 872 1186.213 -408.256 987 

1178.088 -415.038 805 1219.461 -407.055 694 1187.7 -408.038 1063 

1179.576 -414.822 703 1220.947 -406.832 963 1189.187 -407.82 873 

1181.064 -414.605 729 1222.434 -406.608 859 1190.673 -407.602 921 

1182.552 -414.388 699 1223.92 -406.383 1000 1192.16 -407.384 1233 

1184.04 -414.171 1109 1170.121 -412.46 365 1193.646 -407.165 1006 

1185.528 -413.954 1001 1171.608 -412.245 406 1195.133 -406.946 1024 

1187.015 -413.737 811 1173.096 -412.03 600 1196.619 -406.726 792 

1188.503 -413.519 522 1174.583 -411.815 591 1198.106 -406.507 1015 

1189.991 -413.301 581 1176.07 -411.599 644 1199.592 -406.287 1094 

1191.478 -413.082 629 1177.558 -411.383 1036 1201.079 -406.067 996 

1192.966 -412.864 634 1179.045 -411.167 1309 1202.565 -405.846 1148 

1194.453 -412.645 582 1180.532 -410.951 1262 1204.051 -405.626 1368 

1195.941 -412.426 612 1182.019 -410.734 1261 1205.537 -405.405 1052 

Appendix H.6 - 141
Great Smoky Mountains 

Lambert 

Conforma

l X (km) 

Lambert 

Conforma

l Y (km) 

Height 

(m) 

Lambert 

Conformal 

X (km) 

Lambert 

Conformal 

Y (km) 

Height 

(m) 

Lambert 

Conformal 

X (km) 

Lambert 

Conformal 

Y (km) 

Height 

(m) 

1207.024 -405.184 958 1229.034 -400.007 802 1177.982 -403.859 634 

1208.51 -404.962 988 1230.519 -399.781 756 1179.468 -403.643 662 

1209.996 -404.74 1019 1239.43 -398.422 1213 1180.954 -403.427 567 

1211.482 -404.518 1344 1240.915 -398.195 1312 1182.44 -403.21 579 

1212.968 -404.296 1246 1169.33 -406.978 365 1183.926 -402.993 660 

1214.454 -404.073 1377 1170.816 -406.763 393 1185.411 -402.776 878 

1215.94 -403.85 1135 1172.303 -406.548 455 1186.897 -402.558 907 

1217.426 -403.627 1196 1173.789 -406.333 590 1188.383 -402.34 955 

1218.911 -403.404 851 1175.275 -406.118 775 1189.868 -402.122 1048 

1220.397 -403.18 883 1176.762 -405.902 690 1191.354 -401.904 1056 

1221.883 -402.956 1060 1178.248 -405.686 697 1192.84 -401.685 1333 

1223.369 -402.732 1207 1179.734 -405.47 678 1194.325 -401.466 1223 

1224.854 -402.508 1245 1181.22 -405.253 607 1195.81 -401.247 1411 

1226.34 -402.283 1045 1182.707 -405.037 824 1197.296 -401.028 1106 

1227.826 -402.058 1068 1184.193 -404.82 802 1198.781 -400.808 1347 

1229.311 -401.832 870 1185.679 -404.602 1033 1200.267 -400.588 1222 

1230.797 -401.607 782 1187.165 -404.385 1241 1201.752 -400.368 1524 

1241.194 -400.02 1326 1188.651 -404.167 1297 1203.237 -400.147 1386 

1169.593 -408.805 453 1190.137 -403.949 1503 1204.722 -399.926 1198 

1171.08 -408.59 439 1191.623 -403.73 1477 1206.208 -399.705 1242 

1172.567 -408.376 582 1193.109 -403.512 1533 1207.693 -399.484 1415 

1174.054 -408.16 664 1194.594 -403.293 1242 1209.178 -399.262 1358 

1175.54 -407.945 770 1196.08 -403.073 1270 1210.663 -399.04 1355 

1177.027 -407.729 853 1197.566 -402.854 1198 1212.148 -398.818 1403 

1178.514 -407.513 810 1199.052 -402.634 1372 1213.633 -398.596 1712 

1180 -407.297 979 1200.537 -402.414 1394 1215.118 -398.373 1431 

1181.487 -407.08 809 1202.023 -402.194 1187 1216.603 -398.15 1422 

1182.973 -406.864 818 1203.508 -401.973 1517 1218.087 -397.927 1236 

1184.46 -406.646 1027 1204.994 -401.752 1490 1219.572 -397.703 1228 

1185.946 -406.429 1372 1206.48 -401.531 1518 1221.057 -397.479 1492 

1187.432 -406.211 1203 1207.965 -401.31 1632 1222.542 -397.255 1372 

1188.919 -405.994 975 1209.45 -401.088 1692 1224.026 -397.031 1181 

1190.405 -405.775 1077 1210.936 -400.866 1888 1225.511 -396.806 792 

1191.891 -405.557 1343 1212.421 -400.644 1951 1226.996 -396.581 939 

1193.378 -405.338 1463 1213.906 -400.422 1603 1228.48 -396.356 737 

1194.864 -405.119 1193 1215.392 -400.199 1543 1229.965 -396.131 1213 

1196.35 -404.9 932 1216.877 -399.976 1292 1231.449 -395.905 1173 

1197.836 -404.68 1059 1218.362 -399.752 1067 1235.902 -395.226 1111 

1199.322 -404.461 1144 1219.847 -399.529 928 1237.387 -395 1138 

1200.808 -404.24 1231 1221.332 -399.305 1190 1238.871 -394.773 1362 

1202.294 -404.02 1197 1222.817 -399.081 1415 1240.355 -394.545 1709 

1203.78 -403.799 1220 1224.302 -398.856 1066 1241.839 -394.318 1585 

1205.266 -403.579 1342 1225.787 -398.632 899 1243.324 -394.09 1298 

1206.752 -403.357 1350 1227.272 -398.407 714 1244.808 -393.862 1224 

1208.237 -403.136 1251 1228.757 -398.182 703 1246.292 -393.633 1150 

1209.723 -402.914 1467 1237.666 -396.825 1097 1247.776 -393.404 1348 

1211.209 -402.692 1407 1239.15 -396.598 1224 1171.774 -402.894 383 

1212.694 -402.47 1671 1240.635 -396.37 1526 1173.26 -402.679 482 

1214.18 -402.247 1369 1242.119 -396.142 1566 1174.746 -402.464 533 

1215.666 -402.025 1412 1243.604 -395.915 1311 1176.231 -402.248 559 

1217.151 -401.802 1279 1245.088 -395.686 1283 1177.717 -402.032 598 

1218.637 -401.578 863 1246.573 -395.458 1482 1179.202 -401.816 532 

1220.122 -401.355 823 1169.066 -405.151 290 1180.688 -401.6 525 

1221.608 -401.131 1103 1170.552 -404.936 389 1182.173 -401.383 533 

1223.093 -400.906 1432 1172.038 -404.721 506 1183.659 -401.166 543 

1224.578 -400.682 1448 1173.524 -404.506 644 1185.144 -400.949 562 

1226.064 -400.457 1352 1175.01 -404.291 578 1186.63 -400.731 602 

1227.549 -400.232 1094 1176.496 -404.075 539 1188.115 -400.514 736 

Appendix H.6 - 142
Great Smoky Mountains 

Lambert 

Conformal 

X (km) 

Lambert 

Conformal 

Y (km) 

Height 

(m) 

Lambert 

Conformal 

X (km) 

Lambert 

Conformal 

Y (km) 

Height 

(m) 

Lambert 

Conformal 

X (km) 

Lambert 

Conformal 

Y (km) 

Height 

(m) 

1189.6 -400.296 817 1198.241 -397.155 768 1209.844 -393.563 1203 

1191.085 -400.077 809 1199.725 -396.935 983 1211.328 -393.341 1212 

1192.571 -399.859 920 1201.21 -396.715 1281 1212.812 -393.118 1380 

1194.056 -399.64 1017 1202.695 -396.495 1378 1214.296 -392.896 1300 

1195.541 -399.421 1006 1204.179 -396.274 1371 1215.78 -392.673 1206 

1197.026 -399.201 956 1205.664 -396.053 1135 1217.263 -392.45 1581 

1198.511 -398.982 1137 1207.148 -395.832 884 1218.747 -392.227 1457 

1199.996 -398.762 982 1208.632 -395.61 907 1220.231 -392.003 1699 

1201.481 -398.541 1187 1210.117 -395.389 1249 1221.715 -391.779 1543 

1202.966 -398.321 1365 1211.601 -395.167 1528 1223.198 -391.555 1640 

1204.451 -398.1 1245 1213.085 -394.944 1552 1224.682 -391.33 1476 

1205.936 -397.879 1074 1214.57 -394.722 1473 1226.166 -391.106 1036 

1207.42 -397.658 1365 1216.054 -394.499 1677 1227.649 -390.881 1315 

1208.905 -397.436 1401 1217.538 -394.276 1553 1229.133 -390.655 1504 

1210.39 -397.214 1080 1219.022 -394.052 1572 1230.616 -390.43 1469 

1211.874 -396.992 1260 1220.506 -393.828 1526 1232.1 -390.204 1242 

1213.359 -396.77 1660 1221.99 -393.604 1261 1233.583 -389.978 1453 

1214.844 -396.547 1703 1223.474 -393.38 1217 1235.066 -389.751 1414 

1216.328 -396.324 1525 1224.958 -393.156 1247 1236.55 -389.525 1012 

1217.813 -396.101 1299 1226.442 -392.931 947 1238.033 -389.298 1127 

1219.297 -395.878 1389 1227.926 -392.706 1163 1239.516 -389.071 1352 

1220.782 -395.654 1084 1229.41 -392.48 1434 1240.999 -388.843 1189 

1222.266 -395.43 1048 1230.894 -392.255 1373 1242.482 -388.615 1257 

1223.75 -395.206 1002 1232.378 -392.029 1170 1243.966 -388.387 1048 

1225.235 -394.981 1374 1233.861 -391.803 1293 1245.449 -388.159 856 

1226.719 -394.756 910 1235.345 -391.576 1077 1246.932 -387.931 825 

1228.203 -394.531 1109 1236.829 -391.35 953 1248.415 -387.702 1060 

1229.687 -394.306 1002 1238.312 -391.123 1202 1249.898 -387.473 1125 

1231.172 -394.08 1469 1239.796 -390.895 1602 1175.435 -396.767 429 

1232.656 -393.854 1136 1241.279 -390.668 1433 1176.92 -396.551 537 

1234.14 -393.628 1324 1242.763 -390.44 1106 1178.404 -396.335 459 

1235.624 -393.401 912 1244.246 -390.212 966 1179.889 -396.119 575 

1237.108 -393.175 1427 1245.73 -389.984 1077 1181.373 -395.903 564 

1238.592 -392.948 1487 1247.213 -389.755 884 1182.858 -395.686 646 

1240.076 -392.72 1681 1248.696 -389.526 1096 1188.795 -394.816 609 

1241.559 -392.493 1310 1174.216 -398.81 459 1190.279 -394.598 535 

1243.043 -392.265 1080 1175.701 -398.594 516 1191.764 -394.379 489 

1244.527 -392.037 1029 1177.186 -398.378 579 1193.248 -394.161 408 

1246.011 -391.808 989 1178.67 -398.162 576 1194.732 -393.942 669 

1247.494 -391.58 1038 1180.155 -397.946 656 1196.216 -393.722 675 

1248.978 -391.351 1337 1181.64 -397.729 718 1197.7 -393.503 789 

1172.995 -400.852 368 1183.125 -397.513 760 1199.184 -393.283 834 

1174.481 -400.637 411 1184.61 -397.295 994 1200.668 -393.063 852 

1175.966 -400.421 485 1187.579 -396.86 782 1202.152 -392.843 817 

1177.451 -400.205 480 1189.064 -396.642 618 1203.636 -392.622 823 

1178.936 -399.989 569 1190.548 -396.424 489 1205.12 -392.401 750 

1180.422 -399.773 610 1192.033 -396.206 549 1206.603 -392.18 930 

1181.907 -399.556 585 1193.517 -395.987 598 1208.087 -391.959 1127 

1183.392 -399.339 596 1195.002 -395.768 577 1209.571 -391.737 726 

1184.877 -399.122 591 1196.486 -395.549 809 1211.055 -391.515 1065 

1186.362 -398.905 681 1197.97 -395.329 899 1212.538 -391.293 1323 

1187.847 -398.687 787 1199.455 -395.109 1006 1214.022 -391.07 1448 

1189.332 -398.469 528 1200.939 -394.889 1262 1215.505 -390.847 1548 

1190.817 -398.251 700 1202.423 -394.669 988 1216.989 -390.624 1772 

1192.302 -398.032 615 1203.907 -394.448 1120 1218.472 -390.401 1416 

1193.786 -397.813 905 1205.392 -394.227 1054 1219.956 -390.177 1198 

1195.271 -397.594 836 1206.876 -394.006 860 1221.439 -389.954 1225 

1196.756 -397.375 827 1208.36 -393.785 1013 1222.922 -389.729 1580 

Appendix H.6 - 143
Great Smoky Mountains 

Lambert 

Conformal 

X (km) 

Lambert 

Conformal 

Y (km) 

Height 

(m) 

Lambert 

Conformal 

X (km) 

Lambert 

Conformal 

Y (km) 

Height 

(m) 

Lambert 

Conformal 

X (km) 

Lambert 

Conformal 

Y (km) 

Height 

(m) 

1224.406 -389.505 1529 1246.369 -384.282 971 1222.094 -384.254 877 

1225.889 -389.28 1165 1247.851 -384.053 945 1223.577 -384.029 892 

1227.372 -389.055 1418 1249.333 -383.824 1058 1225.059 -383.805 910 

1228.855 -388.83 1635 1192.709 -390.508 492 1226.541 -383.58 1134 

1230.339 -388.605 1459 1194.193 -390.289 640 1228.023 -383.355 1358 

1231.822 -388.379 1310 1195.676 -390.07 587 1229.506 -383.13 1512 

1233.305 -388.153 1441 1197.159 -389.851 517 1230.988 -382.904 1674 

1234.788 -387.926 1391 1198.643 -389.631 876 1232.47 -382.678 1806 

1236.271 -387.7 1337 1200.126 -389.411 983 1233.952 -382.452 1678 

1237.754 -387.473 1455 1201.609 -389.191 850 1235.434 -382.225 1725 

1239.236 -387.246 1336 1203.092 -388.97 776 1236.916 -381.999 1256 

1240.719 -387.018 1223 1204.576 -388.749 518 1238.397 -381.772 1160 

1242.202 -386.791 1226 1206.059 -388.528 555 1239.879 -381.544 1382 

1243.685 -386.563 979 1207.542 -388.307 487 1241.361 -381.317 1289 

1245.168 -386.335 1079 1209.025 -388.085 626 1242.843 -381.089 1584 

1246.65 -386.106 1104 1210.508 -387.864 794 1244.325 -380.861 1317 

1248.133 -385.877 858 1211.991 -387.641 824 1245.806 -380.633 1170 

1249.616 -385.648 1030 1213.474 -387.419 1004 1211.444 -383.99 548 

1178.139 -394.509 475 1214.957 -387.196 1101 1212.926 -383.768 730 

1179.623 -394.292 440 1216.439 -386.973 1435 1214.408 -383.545 699 

1181.107 -394.076 640 1217.922 -386.75 1039 1215.89 -383.323 749 

1191.495 -392.553 369 1219.405 -386.527 765 1217.372 -383.099 624 

1192.979 -392.334 478 1220.888 -386.303 961 1218.854 -382.876 608 

1194.462 -392.115 570 1222.37 -386.079 963 1220.336 -382.652 530 

1195.946 -391.896 552 1223.853 -385.855 1391 1221.818 -382.428 797 

1197.43 -391.677 843 1225.336 -385.63 1485 1223.3 -382.204 1103 

1198.913 -391.457 604 1226.818 -385.405 1332 1224.782 -381.98 1110 

1200.397 -391.237 605 1228.301 -385.18 1520 1226.264 -381.755 1130 

1201.881 -391.017 652 1229.783 -384.955 1705 1227.746 -381.53 1499 

1203.364 -390.796 770 1231.266 -384.729 1572 1229.228 -381.305 1742 

1204.848 -390.575 834 1232.748 -384.503 1802 1230.71 -381.079 1769 

1206.331 -390.354 725 1234.23 -384.277 1371 1232.191 -380.853 1639 

1207.814 -390.133 608 1235.713 -384.05 1674 1233.673 -380.627 1342 

1209.298 -389.911 656 1237.195 -383.823 1676 1235.155 -380.401 1247 

1210.781 -389.689 1126 1238.677 -383.596 1586 1236.636 -380.174 1168 

1212.265 -389.467 1115 1240.159 -383.369 1727 1238.118 -379.947 1012 

1213.748 -389.245 1387 1241.641 -383.142 1516 1239.599 -379.72 959 

1215.231 -389.022 1569 1243.124 -382.914 1359 1241.081 -379.492 1366 

1216.714 -388.799 1595 1244.606 -382.686 1097 1242.562 -379.265 1348 

1218.197 -388.576 1195 1246.088 -382.457 1022 1244.044 -379.037 1526 

1219.68 -388.352 961 1247.57 -382.229 1016 1245.525 -378.808 1042 

1221.163 -388.128 1084 1249.051 -382 732 1214.134 -381.72 488 

1222.646 -387.904 1324 1198.372 -387.805 652 1215.616 -381.497 515 

1224.129 -387.68 1732 1199.855 -387.585 1164 1217.097 -381.274 491 

1225.612 -387.455 1720 1201.338 -387.365 1129 1218.579 -381.051 550 

1227.095 -387.23 1512 1202.821 -387.144 1070 1220.061 -380.827 579 

1228.578 -387.005 1680 1204.304 -386.924 942 1221.543 -380.603 781 

1230.061 -386.78 1528 1205.786 -386.703 668 1223.024 -380.379 900 

1231.544 -386.554 1452 1207.269 -386.481 425 1224.506 -380.155 1059 

1233.026 -386.328 1535 1208.752 -386.26 532 1225.987 -379.93 1123 

1234.509 -386.102 1401 1210.235 -386.038 696 1227.469 -379.705 1307 

1235.992 -385.875 1483 1211.717 -385.816 673 1228.95 -379.48 1166 

1237.474 -385.648 1605 1213.2 -385.594 775 1230.432 -379.254 1302 

1238.957 -385.421 1574 1214.682 -385.371 1061 1231.913 -379.028 1672 

1240.439 -385.194 1577 1216.165 -385.148 939 1233.394 -378.802 1533 

1241.922 -384.966 1326 1217.647 -384.925 817 1234.876 -378.576 1429 

1243.404 -384.738 1393 1219.13 -384.701 763 1236.357 -378.349 1457 

1244.887 -384.51 1134 1220.612 -384.478 616 1237.838 -378.122 1391 

Appendix H.6 - 144
Great Smoky Mountains 

Lambert 

Conformal 

X (km) 

Lambert 

Conformal 

Y (km) 

Height 

(m) 

Lambert 

Conformal 

X (km) 

Lambert 

Conformal 

Y (km) 

Height 

(m) 

Lambert 

Conformal 

X (km) 

Lambert 

Conformal 

Y (km) 

Height 

(m) 

1239.319 -377.895 937 

1240.8 -377.668 1015 

1242.282 -377.44 1056 

1243.763 -377.212 1071 

1245.244 -376.984 1060 

1218.304 -379.225 427 

1219.785 -379.002 480 

1221.267 -378.778 488 

1222.748 -378.554 624 

1224.229 -378.33 761 

1225.71 -378.105 732 

1227.191 -377.88 881 

1228.673 -377.655 821 

1230.154 -377.429 1000 

1231.635 -377.204 1094 

1233.116 -376.978 1208 

1234.597 -376.751 860 

1236.078 -376.525 857 

1237.558 -376.298 1391 

1239.039 -376.071 1265 

1240.52 -375.843 1067 

1242.001 -375.616 921 

1243.482 -375.388 611 

1226.914 -376.055 608 

1229.876 -375.604 699 

1231.356 -375.379 785 

1232.837 -375.153 892 

1234.318 -374.927 622 

1235.798 -374.7 762 

1237.279 -374.473 1130 

1238.759 -374.246 1458 

1240.24 -374.019 1097 

1241.72 -373.791 823 

1243.201 -373.563 603 

1232.558 -373.328 534 

1234.039 -373.102 545 

1235.519 -372.875 582 

1236.999 -372.649 735 

1238.479 -372.422 885 

1239.96 -372.195 873 

1239.319 -377.895 937 

Appendix H.6 - 145
Shining Rock 

Lambert 

Conformal 

X (km) 

Lambert 

Conformal 

Y (km) 

Height 

(m) 

Lambert 

Conformal 

X (km) 

Lambert 

Conformal 

Y (km) 

Height 

(m) 

Lambert 

Conformal 

X (km) 

Lambert 

Conformal 

Y (km) 

Hei

ght 

(m) 

1273.44 -416.507 1629 1275.842 -412.391 1161 

1274.184 -416.39 1586 1265.287 -413.11 968 

1274.928 -416.274 1370 1266.03 -412.994 1198 

1275.672 -416.157 1274 1266.774 -412.878 1362 

1276.416 -416.04 1181 1267.518 -412.762 1470 

1268.832 -416.294 1183 1268.262 -412.646 1248 

1269.576 -416.178 1416 1269.006 -412.529 1058 

1270.321 -416.062 1541 1269.749 -412.413 1170 

1271.065 -415.945 1677 1270.493 -412.296 1415 

1271.809 -415.828 1640 1271.237 -412.18 1689 

1272.553 -415.712 1770 1271.981 -412.063 1547 

1273.297 -415.595 1679 1272.724 -411.946 1550 

1274.041 -415.478 1585 1273.468 -411.83 1437 

1274.785 -415.361 1529 1274.212 -411.713 1508 

1275.529 -415.245 1309 1274.955 -411.596 1300 

1276.273 -415.128 1128 1275.699 -411.479 1176 

1267.946 -415.498 1097 1268.863 -411.617 1012 

1268.69 -415.382 1217 1269.607 -411.501 1285 

1269.434 -415.266 1536 1270.35 -411.384 1366 

1270.178 -415.149 1463 1271.094 -411.268 1566 

1270.922 -415.033 1436 1271.837 -411.151 1451 

1271.666 -414.916 1629 1272.581 -411.034 1359 

1272.41 -414.8 1771 1273.325 -410.918 1273 

1273.154 -414.683 1622 1274.068 -410.801 1274 

1273.898 -414.566 1425 1274.812 -410.684 1280 

1274.642 -414.449 1312 1275.555 -410.567 1155 

1275.385 -414.332 1362 1270.207 -410.472 1179 

1276.129 -414.215 1067 1270.951 -410.355 1348 

1276.873 -414.098 1162 1271.694 -410.239 1488 

1277.617 -413.981 1399 1270.808 -409.443 1442 

1267.059 -414.702 1029 1271.551 -409.327 1565 

1267.803 -414.586 1227 1272.295 -409.21 1505 

1268.547 -414.47 1505 1273.038 -409.093 1409 

1269.291 -414.354 1347 1273.782 -408.977 1380 

1270.035 -414.237 1317 1274.525 -408.86 1303 

1270.779 -414.121 1536 1275.269 -408.743 1104 

1271.523 -414.004 1675 1269.922 -408.648 1500 

1272.267 -413.887 1729 1271.408 -408.415 1678 

1273.011 -413.771 1523 1272.152 -408.298 1707 

1273.754 -413.654 1544 1272.895 -408.181 1515 

1274.498 -413.537 1429 1273.638 -408.065 1321 

1275.242 -413.42 1315 1274.382 -407.948 1219 

1275.986 -413.303 1068 1271.265 -407.503 1394 

1276.73 -413.186 1066 1272.009 -407.386 1522 

1277.474 -413.069 1352 1272.752 -407.269 1411 

1266.173 -413.906 1024 1273.495 -407.152 1234 

1266.917 -413.79 1296 1271.122 -406.59 1189 

1267.661 -413.674 1404 1271.865 -406.474 1343 

1268.404 -413.558 1373 1272.609 -406.357 1265 

1269.148 -413.441 1198 1270.979 -405.678 1045 

1269.892 -413.325 1198 1271.722 -405.562 1235 

1270.636 -413.208 1419 1272.465 -405.445 1066 

1271.38 -413.092 1571 

1272.124 -412.975 1741 

1272.867 -412.859 1717 

1273.611 -412.742 1616 

1274.355 -412.625 1569 

Appendix H.6 - 146
Joyce Kilmer Slickrock 

Lambert 

Conformal 

X (km) 

Lambert 

Conformal 

Y (km) 

Height 

(m) 

Lambert 

Conformal 

X (km) 

Lambert 

Conformal 

Y (km) 

Height 

(m) 

Lambert 

Conformal 

X (km) 

Lambert 

Conformal 

Y (km) 

Height 

(m) 

1174.116 -429.607 1154 1168.724 -423.854 1065 

1174.861 -429.499 1056 1169.468 -423.747 836 

1175.607 -429.391 903 1170.213 -423.64 700 

1176.352 -429.283 820 1170.958 -423.532 782 

1177.097 -429.175 818 1171.703 -423.425 961 

1173.239 -428.8 1278 1173.937 -423.102 809 

1173.984 -428.693 1069 1174.682 -422.995 941 

1174.729 -428.585 913 1167.847 -423.047 991 

1175.475 -428.477 776 1168.592 -422.94 817 

1176.22 -428.369 788 1169.337 -422.833 751 

1176.965 -428.262 750 1170.081 -422.726 635 

1172.362 -427.994 1327 1170.826 -422.619 772 

1173.107 -427.887 1085 1171.571 -422.511 882 

1173.852 -427.779 985 1168.461 -422.026 954 

1174.597 -427.671 934 1169.205 -421.919 739 

1175.342 -427.563 978 1169.95 -421.812 620 

1176.088 -427.456 982 1170.694 -421.705 635 

1176.833 -427.348 824 1171.439 -421.597 700 

1169.995 -427.402 1317 1172.184 -421.49 865 

1170.74 -427.295 1394 1168.329 -421.113 943 

1171.485 -427.188 1487 1169.074 -421.005 742 

1172.23 -427.08 1308 1169.818 -420.898 505 

1172.975 -426.973 1237 1170.563 -420.791 558 

1173.72 -426.865 1336 1171.307 -420.684 538 

1174.465 -426.757 1290 1172.052 -420.576 686 

1175.21 -426.65 1304 1168.198 -420.199 946 

1175.955 -426.542 1111 1168.942 -420.092 760 

1176.7 -426.434 1066 1169.686 -419.984 644 

1169.863 -426.489 1189 1170.431 -419.877 491 

1170.608 -426.381 1127 1171.175 -419.77 442 

1171.353 -426.274 1299 1171.92 -419.662 491 

1172.098 -426.166 1380 1168.066 -419.285 959 

1172.843 -426.059 1553 1168.81 -419.178 752 

1173.588 -425.951 1336 1169.555 -419.071 528 

1174.333 -425.844 1219 1170.299 -418.963 598 

1175.078 -425.736 1109 1171.043 -418.856 515 

1175.823 -425.628 1133 1171.788 -418.749 400 

1176.568 -425.52 1226 1172.532 -418.641 523 

1168.987 -425.682 1280 1167.935 -418.371 712 

1169.731 -425.575 1136 1170.167 -418.05 743 

1170.476 -425.467 906 1170.912 -417.942 684 

1171.221 -425.36 999 1171.656 -417.835 538 

1171.966 -425.253 1170 1172.4 -417.727 361 

1172.711 -425.145 1376 1275.099 -412.508 1422 

1173.456 -425.037 1318 

1174.201 -424.93 1369 

1174.946 -424.822 1152 

1175.691 -424.714 924 

1176.436 -424.607 882 

1168.11 -424.875 1151 

1168.855 -424.768 1116 

1169.6 -424.661 1044 

1170.345 -424.554 772 

1171.09 -424.446 866 

1171.834 -424.339 1090 

1173.324 -424.124 1020 

1174.069 -424.016 1067 

1167.979 -423.961 1224 

Appendix H.6 - 147
Linville Gorge 

Lambert 

Conformal 

X (km) 

Lambert 

Conformal 

Y (km) 

Height 

(m) 

Lambert 

Conformal 

X (km) 

Lambert 

Conformal 

Y (km) 

Height 

(m) 

Lambert 

Conformal 

X (km) 

Lambert 

Conformal 

Y (km) 

Height 

(m) 

1349.498 -350.859 468 1346.472 -337.331 924 

1350.236 -350.735 457 1347.209 -337.207 901 

1349.345 -349.949 478 1347.946 -337.084 1114 

1350.083 -349.825 576 1348.683 -336.96 1219 

1347.717 -349.286 550 1345.583 -336.544 946 

1348.455 -349.162 472 1346.32 -336.421 983 

1349.193 -349.039 721 1347.056 -336.297 1142 

1349.931 -348.915 759 1346.167 -335.511 1063 

1350.669 -348.791 600 

1351.406 -348.667 517 

1347.564 -348.376 641 

1348.302 -348.252 439 

1349.04 -348.129 607 

1349.778 -348.005 881 

1350.516 -347.881 758 

1347.412 -347.466 920 

1348.15 -347.342 665 

1348.887 -347.219 458 

1349.625 -347.095 585 

1350.363 -346.971 870 

1347.997 -346.432 826 

1348.735 -346.308 518 

1349.473 -346.185 525 

1350.21 -346.061 695 

1350.948 -345.937 892 

1347.845 -345.522 874 

1348.582 -345.398 581 

1349.32 -345.275 573 

1350.057 -345.151 664 

1348.43 -344.488 728 

1349.167 -344.364 694 

1349.905 -344.241 914 

1348.277 -343.578 756 

1349.015 -343.454 631 

1349.752 -343.331 944 

1348.125 -342.668 826 

1348.862 -342.544 624 

1349.599 -342.42 965 

1347.972 -341.758 732 

1348.709 -341.634 685 

1349.446 -341.51 889 

1347.819 -340.848 890 

1348.557 -340.724 697 

1349.294 -340.6 827 

1346.93 -340.061 1096 

1347.667 -339.938 749 

1348.404 -339.814 816 

1349.141 -339.69 1106 

1346.777 -339.151 1055 

1347.514 -339.027 762 

1348.251 -338.904 909 

1348.988 -338.78 1092 

1345.888 -338.364 941 

1346.625 -338.241 813 

1347.362 -338.117 953 

1348.099 -337.994 1097 

1348.835 -337.87 1083 

1345.735 -337.454 923 

Appendix H.6 - 148
Cape Romain Class I Area 

Lambert Conformal X (km) Lambert Conformal Y (km) Height (m) 

1609.918 -633.619 0 

1610.682 -633.471 1 

1610.506 -632.561 1 

1611.27 -632.414 1 

1611.094 -631.504 1 

1611.858 -631.356 1 

1610.154 -630.742 1 

1610.918 -630.594 1 

1611.682 -630.446 1 

1612.446 -630.298 1 

1610.742 -629.684 1 

1611.506 -629.537 1 

1612.27 -629.389 1 

1610.566 -628.775 0 

1611.33 -628.627 1 

1612.093 -628.479 1 

1612.857 -628.331 1 

1610.39 -627.865 1 

1611.154 -627.717 1 

1610.214 -626.955 1 

1610.978 -626.807 1 

1611.741 -626.66 1 

1610.802 -625.898 1 

1611.565 -625.75 1 

1610.626 -624.988 1 

1611.389 -624.84 2 

1612.152 -624.693 1 

1615.968 -623.952 0 

1611.213 -623.931 1 

1611.976 -623.783 1 

1616.554 -622.895 0 

1611.037 -623.021 1 

1611.8 -622.873 1 

1612.387 -621.816 1 

1613.149 -621.668 1 

1617.727 -620.779 0 

1612.973 -620.758 1 

1613.736 -620.61 1 

1621.949 -618.069 0 

1622.712 -617.92 1 

1623.474 -617.772 1 

Appendix H.6 - 149