Appendix H.7 

BART Modeling Protocols 
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)
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
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
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.
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
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 turbulence-
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.
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 98thpercentile 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 98thpercentile 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).
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.
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.
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
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.
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
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 98thpercentile 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 98thpercentile value may be interpreted

as the highest of the three annual 98thpercentile 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.
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
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.
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.
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.
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.
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
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
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
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.
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.
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)
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
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.)
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.
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.
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
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.
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.
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
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,
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
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
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.
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.
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
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.
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.
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
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.
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
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
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
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.
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.
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 98thpercentile (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 98thpercentile 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.
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.
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
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
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
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 m

2

/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 98thpercentile 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
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.
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.
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 98thpercentile 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.
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
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 98thpercentile (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 98thpercentile 

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

98thpercentile 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 98thpercentile 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.
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
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 days

1

and # of

receptors

with impact

> 0.5 dv in

Class I area:

2001

# of days

1

and # of

receptors

with impact

> 0.5 dv in

Class I area:

2002

# of days

1

and # of

receptors

with impact

> 0.5 dv in

Class I area:

2003

# of days

1 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
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).
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”.
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
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
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
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
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.
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
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 98thpercentile 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
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
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.
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.
References 66

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.
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.
References 68

Tanner, R., M. Zheng, K. Lim, J. Schauer, and A. P. McNichol, 2005. Contributions to the Organic

Aerosol Fraction in the Tennessee Valley: Seasonal and Urban-Rural Differences. Paper presented at the

AAAR International Specialty Conference, PM: Supersite Program and Related Studies, Atlanta, 7-11

February 2005.