CENRAP BART MODELING GUIDELINES 

 12 km Terrain Field (m) 6 km CALMET Wind Field 

 

 Three CALMET Modeling Domains 

Prepared by 

T. W. Tesche 

Dennis E. McNally 

George J. Schewe 

Alpine Geophysics, LLC 

Ft. Wright, KY 41017 

 Prepared for 

Ms. Annette Sharp 

Executive Director 

Central Region Air Planning Assoc. 

10005 S. Pennsylvania, Ste C 

Oklahoma City, OK 73159

15 December 2005 

www.alpinegeophysics.com
 

i 

TABLE OF CONTENTS 

List of Figures .......................................................................................................................... iv 

List of Tables ........................................................................................................................viii 

PREFACE ....................................................................................................................... P-1 

ACKNOWLEDGEMENTS ................................................................................................................ P-3 

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

 1.1 Overview........................................................................................................................1-2 

 1.2. Objectives of These Guidelines....................................................................................1-2 

 1.3 Overview of Recommended BART Modeling Procedures........................................1-4 

 1.3.1 Three BART Modeling Approaches..................................................................1-4 

 1.3.2 Exemption Modeling ..........................................................................................1-5 

 1.3.3 Modeling the Effects of BART Controls on Visibility ....................................1-5 

 1.3.4 Modeling Protocols .............................................................................................1-6 

 1.4 Organization of this Document....................................................................................1-6 

2.0 BART MODELING REQUIREMENTS ................................................................................2-1 

 2.1 Regional Haze Rule and BART Guidelines ................................................................2-1 

 2.2 Role of Air Quality Models ..........................................................................................2-2 

 2.3 EPA Guidance on Air Quality Models........................................................................2-3 

 2.4 Steps in the BART Modeling Process..........................................................................2-4 

 2.5 CALPUFF Modeling Recommendations. ...................................................................2-6 

 2.6 Alternative Models for BART Analyses .....................................................................2-7 

 

3.0 CALPUFF FORMULATION AND IMPLEMENTATION ..................................................3-1 

 3.1 Original Model Development.......................................................................................3-1 

 3.2 CALPUFF Model Formulation....................................................................................3-2 

 3.2.1 Model Concept and Governing Equations........................................................3-2 

 3.2.2 Transport and Dispersion ..................................................................................3-3 

 3.2.3 Primary Particulates...........................................................................................3-4 

 3.2.4 Gas-Phase Chemistry..........................................................................................3-5 

 3.2.5 Aerosol Chemistry...............................................................................................3-6 

 3.2.6 Surface Removal ...............................................................................................3-10 

 3.3 CALMET Meteorological Preprocessor ...................................................................3-10 

 3.3.1 Boundary Layer Modules.................................................................................3-10 

 3.3.2 CALMET Diagnostic Wind Field Module......................................................3-11 

 3.4 Estimation of Regional Haze Contributions.............................................................3-12 

 3.4.1 CALPOST Methods..........................................................................................3-14 

 3.4.2 POSTUTIL ........................................................................................................3-15 

 3.4.3 Refined Extinction and Background Visibility Estimates.............................3-15 

 3.5 Model Availability.......................................................................................................3-16 

 3.6 CALPUFF Evaluation Studies...................................................................................3-16 

 

4.0 ALTERNATIVE MODELING ................................................................................................4-1 

 4.1 Overview .......................................................................................................................4-1 

 4.2 Available Alternative Models.......................................................................................4-4 
 

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 4.3 Treatment of Sub-Grid-Scale Plumes .........................................................................4-6 

 4.3.1 CMAQ PinG ........................................................................................................4-7 

 4.3.2 CMAQ-APT-PM .................................................................................................4-7 

 4.3.3 CMAQ-MADRID-APT ......................................................................................4-8 

 4.3.4 CAMx PM IRON PiG........................................................................................4-8 

 4.3.5 CAMx Multi-Scale Flexi-Nesting......................................................................4-8 

 4.4 Steps in an Alternative Modeling Study .....................................................................4-9 

 4.5 Accuracy of Regional PM and Visibility Models .......................................................4-9 

 4.6 Data Base Requirements ............................................................................................4-12 

 4.7 Resource Requirements..............................................................................................4-12 

 4.7.1 Computers..........................................................................................................4-12 

 4.7.2 Staff Expertise ...................................................................................................4-13

 4.7.3 Schedule ............................................................................................................4-13 

 4.8 Approval Process for an Alternative Model.............................................................4-14 

 4.9 Cost of Alternative Modeling.....................................................................................4-15 

5.0 DATA BASES FOR CALPUFF MODELING........................................................................5-1 

 5.1. Development of CALMET Meteorological Files........................................................5-1 

 5.1.1 MM5 Data Sets....................................................................................................5-1 

 5.1.2 CALMET Model Configuration........................................................................5-2 

 5.1.3 MM5/CALMET Processing ...............................................................................5-5 

 5.1.4 Evaluation of the CALPUFF-Ready Meteorological Data Fields ..................5-6 

 5.1.5 Meteorological Data Archive and Distribution................................................5-8 

 5.2 Aerometric Monitoring Networks ..............................................................................5-8 

 

6.0 CALPUFF SCREENING APPLICATIONS..........................................................................6-1 

 6.1 Methodology ..................................................................................................................6-1 

 6.2 Protocol Development and Negotiation ......................................................................6-2 

 6.3 CALMET Model Configuration and Application......................................................6-2 

 6.4 CALPUFF Model Configuration and Application ....................................................6-2 

 6.4.1 Model Codes ........................................................................................................6-2

 6.4.2 Domain Definition...............................................................................................6-2 

 6.4.3 Model Set-Up.......................................................................................................6-3

 6.4.4 Emissions Input Development ...........................................................................6-4 

 6.4.5 CALPUFF Model Configuration.......................................................................6-5 

 6.4.6 Post Processing .....................................................................................................6-6

 6.5 Visibility Assessment ....................................................................................................6-7 

 6.5.1 Visibility Impacts from BART-Eligible Sources..............................................6-8 

 6.5.2 Change in Visibility Due to BART Controls ..................................................6-10 

 6.6 Presentation of Modeling Results ..............................................................................6-10 

 6.7 Reporting of CALPUFF Screening Assessment Modeling Results ........................6-10 

7.0 CALPUFF SOURCE-SPECIFIC APPLICATIONS.............................................................7-1 

 7.1 Methodology ..................................................................................................................7-1 

 7.2 Source-Specific Considerations ...................................................................................7-2 

 7.2.1 Protocol Development and Negotiation ............................................................7-2 

 7.2.2 Domain Definition...............................................................................................7-2 

 7.3 CALMET Configuration for Source-Specific Assessments ......................................7-2 
 

iii 

 7.4 CALPUFF Configuration for Source-Specific Assessments .....................................7-4 

 7.4.1 Background Conditions......................................................................................7-5 

 7.4.2 Ammonia Limiting Method (ALM) ..................................................................7-5 

 7.4.3 Computing Light Extinction ..............................................................................7-8 

 7.4.4 Estimating Rayleigh Scattering .........................................................................7-9 

 7.4.5 Natural Baseline Conditions ..............................................................................7-9 

 7.4.6 Visibility Impact Assessment ...........................................................................7-10 

 7.5 Assessment of Modeling Results ................................................................................7-10 

8.0 ALTERNATIVE MODEL APPLICATIONS.........................................................................8-1 

 8.1 Use of One-Atmosphere Models in Refined Assessments .........................................8-1 

 8.1.1 Regional Haze Modeling Data Bases.................................................................8-2 

 8.1.2 Obtaining Regional Haze Modeling Data Bases...............................................8-3 

 8.1.3 Protocol Development.........................................................................................8-3 

 8.2 Model Applications .......................................................................................................8-4 

 8.2.1 Regional Base Case Simulation..........................................................................8-4 

 8.2.2 BART Source Simulation with SGS Technology .............................................8-5 

 8.2.3 Assessing Modeled BART Source Impacts.......................................................8-5 

 8.3 Estimation of Natural Visibility Conditions...............................................................8-5 

 8.3.1 Flexibility Suggested by EPA.............................................................................8-5 

 8.3.2 Refined Procedures for One-Atmosphere Modeling .......................................8-6 

 8.4 Cumulative Impact Assessments .................................................................................8-7 

 8.5 Concluding Remarks ....................................................................................................8-7 

9.0 QUALITY ASSURANCE .........................................................................................................9-1 

 9.1 Objective of the QA Program ......................................................................................9-1 

 9.2 Quality Assurance Procedures.....................................................................................9-1 

 9.2.1 Input Data............................................................................................................9-2 

 9.2.2 MM5 and CALMET Applications.....................................................................9-2 

 9.2.3 CALPUFF Applications .....................................................................................9-3 

 9.2.4 Application of CALPOST and POSTUTIL......................................................9-4 

 9.2.5 QA Issues for Alternative Source-Specific Modeling ......................................9-5 

 9.3 Quality Assessment Summary .....................................................................................9-6 

REFERENCES ...................................................................................................................................R-1 

 
 

iv 

LIST OF FIGURES 

Figure 1-1. Federal Class I Areas in the United States .................................................................1-7 

Figure 1-2. U.S. Forest Service Class I Areas.................................................................................1-8 

Figure 1-3. National Park Service Class I Areas ...........................................................................1-9 

Figure 1-4. Fish and Wildlife Service Class I Areas....................................................................1-10 

Figure 1-5. Location of Visibility Monitoring Sites in the CENRAP Domain ..........................1-11 

Figure 1-6. CENRAP BART Modeling Flow Chart ....................................................................1-12 

Figure 3-1. CALPUFF Modeling System Components...............................................................3-18 

Figure 4-1. Plume Trajectory Paths and Growth Rates of Sub-Grid-Scale Reactive 

 Plumes within the CMAQ-PinG Model Simulation of the July 1995 SOS 

 Field Program in Nashville, TN.................................................................................4-17 

Figure 4-2. Reactive Plume Species Concentrations in the Cross Section from a High 

 NOx/Low SO2 Emissions Source at 5 Hours after Release .....................................4-18 

Figure 4-3. Aerosol Sulfate (SO4) Difference Field Determined by Subtracting the 

 CMAQ-NoPinG Concentrations from the CMAQ-PinG Concentrations 

 at 2 pm EDT on 7 July 1995.......................................................................................4-18 

Figure 4-4. Change in the Contributions of Fourteen (14) EGUs to Local 

Sulfate (SO4) Concentrations When the CMAQ-APT-PM Plume-in-Grid 

 Approach is Used .......................................................................................................4-19 

Figure 4-5. Change in the Contributions of Fourteen (14) EGUs to Local 

Nitrate (NO3) Concentrations When the CMAQ-APT-PM Plume-in-Grid 

 Approach is Used .......................................................................................................4-19 

Figure 4-6. CAMx PM PiG Simulation of Ozone Over the Upper Midwest on 

 14 June 2002: Plot on Right Reveals the Ozone Increments Associated 

 with Sub-Grid-Scale Treatment of Reactive Plumes...............................................4-20 

Figure 4-7. CAMx PM PiG Simulation of SO2 and Sulfate Over the Upper Midwest 

 on 14 June 2002: Plot on Right Reveals the SO2 and SO4 Increments 

 Associated with Sub-Grid-Scale Treatment of Reactive Plumes...........................4-20 

Figure 4-8. CAMx PM PiG Simulation of NO3 and NH4 Over the Upper Midwest 

 on 14 June 2002: Plot on Right Reveals the NO3 and NH4 Increments 

 Associated with Sub-Grid-Scale Treatment of Reactive Plumes............................4-21 
 

v 

Figure 4-9. Mean Fractional Bias and Error in Fine Particulate Species Predictions 

 from the CAMx, CMAQ, URM, and CMAQ-MADRID Models 

 over Several Regional PM and Visibility Model Evaluation Studies.....................4-22 

Figure 5-1. CENRAP North, Central, and South 6 km Meteorological Domains....................5-12 

Figure 5-2. CENRAP South Domain ............................................................................................5-13

Figure 5-3. CENRAP Central Domain .........................................................................................5-14 

Figure 5-4. CENRAP North Domain ............................................................................................5-15

Figure 5-5. Locations of IMPROVE, CASTNet, SEARCH, STN, and NADP 

 Monitoring Sites in and Near the CENRAP States .................................................5-16 

Figure 5-6. Locations of AQS Monitoring Sites in and Near the CENRAP States ..................5-17 

Figure 5-7. Spatial Mean Relative Humidity (%) over the Central Domain: 

 July 2002 States ...........................................................................................................5-18 

Figure 5-8. Spatial Mean Surface Temperature (deg C) over the Central Domain: 

 July 2002 5-21. .............................................................................................................5-18 

Figure 5-9. Wind Speed Index of Agreement over the Central Domain: July 2002.................5-19 

Figure 5-10. Standard Deviation in Wind Speed (m/s) over the Central Domain: 

 July 2002 5-21. .............................................................................................................5-19 

Figure 5-11. Root Mean Square Error in Wind Speed (m/s) over the Central Domain: 

 July 2002 5-21. .............................................................................................................5-20 

Figure 5-12. Scalar Mean Wind Speed (m/s) over the Central Domain: July 2002. .................5-20 

Figure 5-13. Vector Mean Wind Speed (m/s) over the Central Domain: July 2002...................5-21 

Figure 5-14. Normalized Bias in Relative Humidity (%) over the Central Domain: 

 July 2002 5-21. .............................................................................................................5-21 

Figure 5-15. Normalized Error in Relative Humidity (%) over the Central Domain: 

 July 2002 5-21. .............................................................................................................5-22 

Figure 5-16. Relative Humidity (%) at Kenosha, WI: July 2002 .................................................5-22 

Figure 5-17. Relative Humidity (%) at Topeka, KS: July 2002 ...................................................5-23 

Figure 5-18. Normalized Bias in Surface Temperature (%) over the Central 

 Domain: July 2002 ......................................................................................................5-23 
 

vi 

Figure 5-19. Normalized Error in Surface Temperature over the Central 

 Domain: July 2002 ......................................................................................................5-24 

Figure 5-20. Surface Temperature (deg C) at Kenosha, WI: July 2002......................................5-24 

Figure 5-21. Surface Temperature at Topeka, KS: July 2002......................................................5-25 

Figure 5-22. MM5/CALMET Relative Humidity Bias (%) by Month for Three 

 BART Modeling Years (2001, 2003, and 2003) .......................................................5-26 

Figure 5-23. MM5/CALMET Relative Humidity Error (%) by Month for Three 

 BART Modeling Years (2001, 2003, and 2003) .......................................................5-27 

Figure 5-24. MM5/CALMET Temperature Bias (deg C) by Month for Three 

 BART Modeling Years (2001, 2003, and 2003). ......................................................5-28 

Figure 5-25. MM5/CALMET Temperature Error (deg C) by Month for Three 

 BART Modeling Years (2001, 2003, and 2003) .......................................................5-29 

Figure 5-26. MM5/CALMET Wind Speed Index of Agreement by Month for 

 Three BART Modeling Years (2001, 2003, and 2003) ............................................5-30 

Figure 5-27. MM5/CALMET Relative Humidity Bias (%) over Three Years 

 in all CENRAP Domains ............................................................................................5-31 

Figure 5-28. MM5/CALMET Relative Humidity Error (%) over Three Years 

 in all CENRAP Domains ............................................................................................5-32 

Figure 5-29. MM5/CALMET Surface Temperature Bias (deg C) over Three Years 

 in all CENRAP Domains ............................................................................................5-33 

Figure 5-30. MM5/CALMET Surface Temperature Error (deg C) over Three 

 Years in all CENRAP Domains .................................................................................5-34 

Figure 5-31. MM5/CALMET Wind Speed Index of Agreement over Three 

 Years in all CENRAP Domains .................................................................................5-35 

Figure 6-1. Division of the U.S. for the Purpose of Assigning Default Natural 

 Background Concentrations (μg/m3) for Eastern and Western 

 U.S. Class I areas ...................................................................................................... 6-14 

Figure 7-1. POSTUTIL Processing of the Ammonia Limiting Method (ALM), 

 NO3/HNO3 Repartitioning for an Individual Source, and Estimating 

 Ammonia Consumption from Modeled Sources and Boundary Conditions ...... 7-15 

Figure 8-1. CENRAP 36/12 km CMAQ/CAMx Domain Used for 2002 Annual One- 

 Atmosphere Regional Haze Simulations .................................................................. 8-8 
 

vii 

Figure 8-2. MRPO 36 km CAMx Domain Used for 2001, 2002, and 2003 Annual One- 

 Atmosphere Regional Haze Simulations .................................................................. 8-9 

Figure 8-3. MRPO 36/12 km CAMx Domain Used for 2002 Annual One-Atmosphere 

 Regional Haze Simulations ...................................................................................... 8-10 

Figure 8-4. VISTAS 36/12 km CMAQ/CAMx Domain Used for 2002 Annual One- 

 Atmosphere Regional Haze Simulations ................................................................ 8-11 

Figure 8-5. WRAP 36 km CMAQ/CAMx Domain Used for 2002 Annual One- 

 Atmosphere Regional Haze Simulations ................................................................ 8-12 

Figure 8-6. Five States Study 36/12/4 km PM2.5/8-hr Ozone Study Using CAMx for 

 the 2002 Annual Period............................................................................................ 8-13 

Figure 8-7. Incremental 24-hr Average SO2, Sulfate, Nitrate, and NH4 

 Concentrations Fields on 14 June 2002 from the CAMx PM PiG 

 Simulation Over the Upper Midwest ...................................................................... 8-14 
 

viii 

LIST OF TABLES 

Table 1-1. Federally Mandated Class I Areas in the CENRAP States.....................................1-13 

Table 1-2. Federally Mandated Class I Areas Near the CENRAP States..................................1-3 

Table 3-1. Major Features of the CALMET Meteorological Model ........................................3-19 

Table 3-2. Major Features of the CALPUFF Dispersion Model ..............................................3-20 

Table 3-3. Parameter Variations in Box Model Simulations Used to Develop the 

 CALPUFF Sulfate and Nitrate Formation Algorithms...........................................3-22 

Table 5-1. CENRAP Lambert Conic Conformal Modeling Domain Specifications.................5-9 

Table 5-2. Vertical Layer Structure in CALMET Fields ............................................................5-9 

Table 5-3. Meteorological Model File Sizes for CENRAP BART Modeling .............................5-9 

Table 5-4. Statistical Evaluation of the CALMET Meteorological Fields for 2001-2003.......5-10 

Table 5-5. Overview of Ambient Data Monitoring Networks Covering the CENRAP 

 Domain ......................................................................................................................5-11 

Table 6-1. Contents of a Typical Screening BART Modeling Protocol ...................................6-12 

Table 6-2. CALMET/CALPUFF Model Codes for Screening Applications ...........................6-13 

Table 6-3. Default Natural Background Concentrations for Eastern and Western 

 U.S. Class I Areas........................................................................................................6-13 

Table 6-4. Federal Class I Areas in the CENRAP Domain.......................................................6-14 

Table 7-1. Contents of a Typical Source-Specific BART Modeling Protocol..........................7-11 

Table 7-2. Example of Hypothetical Impact Ranking at Six CENRAP Class I Areas ...........7-13 
 

P-1

PREFACE 

 On 6 July 2005, the U.S. Environmental Protection Agency (EPA) published final amendments 

to its 1999 Regional Haze Rule in the Federal Register, including Appendix Y, the final guidance for 

Best Available Retrofit Technology (BART) determinations (70 FR 39104-39172). ). The BART rule 

requires the installation of BART on emission sources that fit specific criteria and “may reasonably be 

anticipated to cause or contribute” to visibility impairment in any Class I area. Air quality modeling is 

the preferred method for determining which emission sources cause or contribute to visibility 

impairment. 

 This document establishes guidelines for performing dispersion modeling analyses in support 

of BART determinations for sources located within the CENRAP domain. Largely consistent with 

EPA guidelines, these CENRAP BART Modeling Guidelines cover the many assumptions, data base 

elements, model configurations and analytical methods that CENRAP states and/or source operators 

should consider using in their assessments of whether BART eligible sources are indeed ‘subject to 

BART’, or may be exempted. For those sources determined to be subject to BART, these guidelines 

provide for a common and consistent methodology for estimating the visibility improvements 

associated with BART emissions reductions. 

 Each state is required to develop a BART Modeling Protocol setting forth the required steps in 

assessing the levels of controls needed on sources subject to BART. At the same time, the five (5) 

Regional Planning Organizations (RPOs) are addressing the requirement of the Regional Haze Rule in 

a generally consistent manner, with some exceptions for how BART is treated. The Midwest RPO for 

example, is expected to conduct most of the BART modeling for sources within its region. In the 

VISTAS states, the BART modeling is expected to be a shared exercise between VISTAS and the 

states. In the central states, the states themselves will take the lead in the BART modeling, but 

CENRAP will provide assistance in the form of modeling guidance (this document) and readilyavailable modeling data bases (delivered separately) that can be used by states and/or source operators 

to conduct their analyses. 

 Deferring to the states and RPO’s, EPA has placed the responsibility of preparing modeling 

protocols with the groups who will actually be performing the modeling, rather than developing a 

prescriptive protocol covering all states and sources. EPA has provided so-called BART modeling 

guidelines (EPA, 2005) which set for the general framework for performing the analyses. It is up to 

the states and RPO’s to develop the modeling protocols governing the BART modeling activities 

within their jurisdictions. 

 Several BART modeling protocols have been developed within the past year prior to and in 

parallel with these CENRAP BART Modeling Guidelines. In the interest of providing consistency in 

the selection, use, and interpretation of models and analytical methods, we have attempted to 

synthesize key unifying elements of these protocols and weave them into a consistent set of modeling 

procedures for the CENRAP region, paying attention to the unique characteristics of this region and 

the sources located within. At the same time, EPA guidance on how the BART modeling analyses will 

be judged has evolved and many protocols as well as this guideline document have been refined to 

reflect EPA’s clarifications. 

 Several modeling protocols developed by other RPOs and State agencies are particularly 

relevant to CENRAP’s effort to develop consistent guidelines for the Central states. Six BART 

protocols are especially significant: 
 

P-2

 > “VISTAS Protocol for the Application of the CALPUFF Model for Analyses of Best 

Available Retrofit Technology (BART)”, (VISTAS, 2005). Prepared by the Visibility 

Improvement State and Tribal Association of the Southeast (VISTAS) Technical 

Analysis Work Group (TAWG), and Earth Tech, Inc. 

> “CALPUFF Modeling Protocol in Support of Best Available Retrofit Technology 

Determinations”, (Johnson, 2005). Prepared by the Iowa Department of Natural 

Resources, Air Quality Bureau, Des Moines, IA. 

 > “Best Available Retrofit Technology (BART) Modeling Protocol to Determine Sources 

Subject to BART in the State of Minnesota”, (MPCA, 2005). Prepared by the Minnesota 

Pollution Control Agency, St. Paul, MN. 

 

> “Modeling to Support BART”, (Baker, 2005). Prepared by the Midwest Regional 

Planning Organization and the Lake Michigan Air Directors Consortium, Des Plaines, 

IL. 

> “CALMET/CALPUFF BART Protocol for Class I Federal Area Individual Source 

Attribution Visibility Impairment Modeling Analysis”, (CDPHE, 2005). Prepared by the 

Air Pollution Control Division, Colorado Department of Public Health and 

Environment, Denver, CO. 

 > “Screening Analysis of Potentially BART-eligible Sources in Texas”, (ENVIRON, 

2005). Prepared for the Texas Commission on Air Quality, Austin, TX. 

All of these protocols address a common subject; thus, there is substantial overlap in the discussions of 

numerous topics such as Federal requirements, EPA guidance, CALMET/CALPUFF model 

descriptions, and recommended modeling procedures and so on. There are differences, of course, due 

to local meteorology, topography, location of Class I areas, the mix of BART-eligible sources, and 

established modeling procedures within the state, local and federal land manager (FLM) groups. 

 To promote consistency in BART modeling applications across the central and eastern U.S., in 

preparing these CENRAP Guidelines, we have incorporated common elements of existing protocols 

where possible. The VISTAS protocol deserves special mention. It was prepared jointly by members 

of the VISTAS Technical Analysis Work Group (TAWG) led by Ms. Pat Brewer, VISTAS’ Technical 

Advisor Dr. Ivar Tombach, and Mr. Joe Scire of Earth Tech, Inc. The VISTAS protocol represents 

the culmination of several draft versions and considerable public discussion and input. At least two 

public workshops were convened in the autumn of 2005 to solicit comments and suggestions on draft 

versions and to receive input from EPA staff on evolving agency guidance. Details of these public 

discussions may be found at: http://www.vistas-sesarm.org/BART/BARTComments.asp. Because the 

VISTAS protocol reflects the work of a large number of organizations, we have incorporated a number 

of the recommendations set for the therein..

 In summary the CENRAP BART Modeling Guidelines presented in this document represent 

the integrated writing efforts of many groups, especially the modelers and regulatory decision-makers 

instrumental in developing the VISTAS, MRPO, Iowa, Minnesota, Texas, and Colorado BART 

Modeling Protocols. The authors are indebted to the contributions of these groups, and assume 
 

P-3

responsibility for any misstatements or errors in incorporating written descriptions from the 

aforementioned documents. 

ACKNOWLEDGMENTS 

The authors wish to acknowledge specifically the assistance and, in several cases, written 

contributions by the following individuals in the preparation of this guidance document: 

Mr. Chris Arrington, West Virginia Department of Environmental Protection; Mr. Bret 

Anderson, U.S. EPA; Dr. Kirk Baker, Lake Michigan Air Directors Consortium; Ms. Pat 

Brewer, VISTAS Technical Coordinator; Mr. Chad Daniels, Iowa Department of Natural 

Resources; Dr. Bright Dornblaser, Texas Commission on Environmental Quality; Mr. 

Todd Hawes, EPA/OAQPS; Ms. Cyndi Huber, U.S. Forest Service; Mr. Matthew 

Johnson, Iowa Department of Natural Resources; Ms. Margaret McCourtney, Minnesota 

Pollution Control Agency; Mr. Bruce Polkowsky, National Park Service; Mr. Tom 

Rodgers, Florida Department of Environmental Protection; Ms. Rosalina Rodriguez, 

North Carolina Department of Natural Resources; Mr. Joe Scire, Earth Tech, Inc.; Dr. 

Ivar Tombach, VISTAS Technical Advisor; Mr. John Vimont, National Park Service; and 

Ms. Lee Warden, Oklahoma Department of Environmental Quality. 

Particular recognition is due Mr. Ralph Morris of ENVIRON International Corporation who served as 

an independent technical reviewer throughout the course of this study and provided many useful 

suggestions, insightful recommendations and key text. 
 

1-1

1.0 INTRODUCTION 

On 6 July 2005, the U.S. Environmental Protection Agency (EPA) published final amendments 

to its 1999 Regional Haze Rule (RHR) in the Federal Register, including Appendix Y, the final 

guidance for Best Available Retrofit Technology (BART) determinations (70 FR 39104-39172). The 

rule applies to 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 

(Figures 1-1 through 1-4). States retain the authority to exempt certain BART-eligible sources based 

on dispersion modeling demonstrating that the source cannot reasonably be anticipated to cause or 

contribute to visibility impairment in a Class I area. States also have the authority to define the 

modeling procedures used to establish BART emissions limits on those sources for which controls are 

required. To assist the states, the EPA has offered guidelines for how BART modeling should be 

conducted (EPA, 2005). Although the Federal regulations implementing the BART requirement afford 

States latitude in establishing criteria for BART-exemptions and for determining levels of BART 

controls required on specific sources, the dispersion modeling analyses must either conform to 

established EPA guidance for regulatory modeling or demonstrate, through an acceptable modeling 

protocol, that alternative modeling methods are equally consistent with the overall aim of the RHR. 

States have the responsibility for preparing modeling protocols that describe both the modeling 

analyses for the ‘subject to BART’ test and the visibility improvement analyses as part of the BART 

determination. In developing modeling protocols, states are expected to collaborate with all 

stakeholders including Tribes, EPA, Federal Land Managers (FLMs), Regional Planning Organizations 

(RPOs) and the various source operators. Ultimately, EPA has the authority to approve or disapprove 

a state’s State Implementation Plan (SIP). A flawed BART modeling analysis, including deviations 

from an agreed upon protocol, would be considered in the SIP approval process. 

At least three CENRAP States (Iowa, Minnesota, and Texas) have already developed BART 

modeling protocols: 

> “CALPUFF Modeling Protocol in Support of Best Available Retrofit Technology 

Determinations”, (Johnson, 2005). Prepared by the Iowa Department of Natural 

Resources, Air Quality Bureau, Des Moines, IA 

> “Best Available Retrofit Technology (BART) Modeling Protocol to Determine Sources 

Subject to BART in the State of Minnesota”, (MPCA, 2005), prepared by the Minnesota 

Pollution Control Agency, St. Paul, MN. 

 > “Screening Analysis of Potentially BART-eligible Sources in Texas”, (ENVIRON, 

2005), prepared for the Texas Commission on Air Quality, Austin, TX. 

Protocols may be under development in other States as well. To promote consistency between states in 

the development of BART modeling protocols and to harmonize the approaches between adjacent 

RPOs, CENRAP commissioned the development of the BART Modeling Guidelines set forth in this 

document. These guidelines are intended to assist CENRAP states and source operators in the 

development of state-wide and source-specific modeling protocols specifically tailored to the needs 

and requirements of a given facility and Class I area(s). 

 In addition to the Iowa, Minnesota, and Texas protocols, the states of Colorado (CDPHE, 2005) 

and North Dakota (NDDH, 2005), VISTAS (VISTAS, 2005), the Midwest RPO (Baker, 2005; 
 

1-2

LADCo, 2005) have also developed BART protocols. Since they all address a common subject, there 

is substantial overlap in the discussions of numerous topics such as Federal requirements, EPA 

guidance, CALMET/CALPUFF model descriptions, and recommended modeling procedures. 

Differences among the protocols exist, of course, due to local meteorology, topography, the location of 

Class I areas, the mix of BART-eligible sources, and established modeling procedures within the state, 

local and FLM groups. For consistency in BART modeling applications across the central U.S., in 

preparing these CENRAP Guidelines we have utilized relevant elements of each of these protocols. 

 The VISTAS protocol deserves special mention. It was prepared by members of the VISTAS 

Technical Analysis Work Group led by Ms. Pat Brewer, VISTAS’s Technical Advisor Dr. Ivar 

Tombach and Mr. Joe Scire of Earth Tech, Inc. The VISTAS BART protocol represents the 

culmination of several draft versions and considerable public discussion and input. At least two public 

workshops were convened in the autumn of 2005 to solicit comments and suggestions on draft versions 

and to receive input from EPA and FLM staff on evolving agency guidance. See full comments at: 

http://www.vistas-sesarm.org/BART/BARTComments.asp. Thus, element of the VISTAS protocol, 

reflecting the work of a very large number of organizations, were incorporated as appropriate into the 

CENRAP modeling recommendations.

 The guidelines presented in this document therefore represent the integrated writing efforts of 

many groups, especially the modelers and regulatory decision-makers instrumental in developing the 

VISTAS, MRPO, Iowa, Minnesota, Colorado and North Dakota protocols. 

 

1.1 Overview 

EPA’s BART requirements (70 FR, 39104-39172) are mandatory for electric generation units 

(EGUs) > 750 MW capacity. States are not required to follow the EPA BART guidelines for all 

sources, but are encouraged to apply the guideline concepts to all sources. States have the authority to 

make any or all aspects of the EPA BART guidelines mandatory for all BART determinations. 

Sources are BART-eligible if they meet three specific criteria including (a) emissions of at least 

250 tons per year of a visibility-impairing pollutant, (b) begin operation between 7 August 1962 and 7 

August 1977, and (c) are listed as one of the 26 listed source categories in the guidance. BART 

controls are required for any BART-eligible source that can be reasonably expected to “cause” or 

“contribute” to impairment of visibility in any of the 156 federal parks and wilderness (Class I) areas 

protected under the regional haze rule. Air quality modeling is an important tool available to the States 

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

Class I area. 

A “BART-eligible emission unit” is defined as any single emission unit that meets the BART 

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 “BART-eligible source”. 

1.2. Objectives of These Guidelines 

Overall compliance with RHR visibility improvement goals in the CENRAP region entails four 

phases of visibility modeling: 
 

1-3

> Single-source modeling to determine which BART-eligible sources are ‘subject to 

BART’; 

> Single-source modeling to determine the degree of visibility improvement attributable 

to proposed BART controls for each source subject to BART; 

> Cumulative modeling to determine the combined effect of proposed BART controls for 

sources subject to BART in each CENRAP state; and 

> Regional-scale modeling to determine if the combined effect of proposed BART 

controls for all CENRAP states ultimately satisfy the RHR visibility improvement 

goals. 

The responsibility for the first three modeling analyses belong to the individual states. CENRAP’s 

mandate is to assess the aggregate effects of controls on progress toward attaining the visibility goals 

across the domain. Tables 1-1 and 1-2 identify the Class I areas in and adjacent to the CENRAP 

domain. Figures 1-2 through 1-4 indicate the location of Class I areas by federal land manager. Figure 

1-5 identifies where visibility monitoring is currently occurring in the CENRAP region. 

This document applies only to the first two phases involving single-source modeling (i.e., 

screening to determine which BART-eligible sources are subject to BART, and single-source modeling 

to determine the degree of improvement related to the proposed BART control). The third phase will 

be addressed by the states in their Regional Haze SIPs due in 2008 and will most likely use the same or 

similar full-science modeling tools (CAMx or CMAQ) being used by the various RPOs dealing with 

the fourth phase. Individual CENRAP states will most likely conduct most, if not all, visibility 

modeling to determine which BART-eligible sources are subject to BART. Operators of sources 

determined to be ‘subject to BART’ may wish to conduct their own single-source modeling to 

determine the degree of visibility improvement, as they consider a variety of BART control options. 

The division of BART modeling between state and source operators will likely vary widely across the 

CENRAP states. Ultimately, it is the state’s responsibility to review and verify all single-source 

visibility modeling analyses. Note that all BART modeling for sources in the CENRAP states must 

follow a written and approved protocol either developed by the state for general application or by the 

source operator for a source-specific analysis. Thus, depending upon the state in which they operate, 

BART-eligible sources may or may not have to develop individual protocols. This issue must be 

worked out with the state and cognizant FLM. 

Given the focus on the first two visibility assessment phases, these guidelines focus on 

common procedures for air quality modeling in support of BART determinations, consistent with the 

EPA guidelines and harmonized with the methods employed by other RPOs. (Figure 1-6 depicts this 

process). The guidelines are intended to provide a common understanding among the organizations 

performing or reviewing BART analyses – CENRAP state and local air agencies, EPA, FLMs, source 

operators, and contractors. Each state retains responsibility for the processes and procedures it will 

follow in satisfying the requirements of the RHR. Nothing in the CENRAP process replaces States’ 

responsibility to determine BART controls. Therefore, this document describes suitable BART 

modeling systems and their application 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. 
 

1-4

 > 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. 

These guidelines incorporate EPA final guidance and results of extensive public comments on BART 

modeling procedures recently proposed by several organizations. 

1.3 Overview of Recommended BART Modeling Procedures 

States must determine whether a source in their jurisdiction emits any air pollutant (SO2, NOx, 

PM, VOC) that “may reasonably be anticipated to cause or contribute to any impairment of visibility” 

in a Class I area. EPA identifies three options for the states to consider. First, a state might conclude 

that all BART-eligible sources are subject to BART. Alternatively, a state might demonstrate that all 

BART-eligible sources together do not cause or contribute to any visibility impairment. Finally, a 

state may seek to determine if the impact from each individual BART-eligible source on any day is 

greater than a threshold value. If so, then the source is subject to BART. 

The CENRAP states intend to pursue the third option, utilizing dispersion modeling to calculate 

impacts from individual sources with respect to a prescribed visibility threshold in Class I areas. This 

section discusses how the CALPUFF modeling system can be used to: (a) evaluate whether a BARTeligible source is exempt from BART controls, or to (b) quantify the visibility benefits of control 

options for sources that are determined to be subject to BART. 

 1.3.1 Three BART Modeling Approaches 

The visibility modeling methods described in these guidelines are grouped into three categories: 

 > Screening Modeling: The screening analysis uses the CALPUFF modeling system in 

default mode with pre-prepared modeling domains and model-ready meteorological 

inputs. This simple methodology, described in detail in Chapter 6, is intended to 

quickly distinguish between sources that have no significant impacts on Class I area 

visibility and those sources for which impacts are demonstrable or for which sourcespecific analyses are warranted. 

 > Source-Specific Modeling : A source-specific CALPUFF analysis (the subject of 

Chapter 7) is intended to remove some of the conservatism of the screening approach 

and bring more realism to bear in the analysis of a specific BART-eligible source. Here, 

the state or source operator may elect to introduce greater source specificity into the 

modeling through the use of variable background ammonia, ozone, and visibility levels; 

puff-splitting; finer horizontal grid meshes in the meteorological and air quality 

modeling, and so on. 

 > Alternative Modeling: Refined full-science modeling is available to states or source 

operators on a case-by-case basis. Described in Chapter 8, this entails the application of 

a state-of-the-science regional modeling system and readily available RPO visibility 

data bases for either single source or cumulative source impact assessments. 

For most sources in the CENRAP region, the use of the screening or source-specific approaches will 

satisfy the BART regulatory requirements. Separate chapters of this protocol are devoted to each of 
 

1-5

these BART modeling approaches. Regardless of the approach, exemption modeling is the initial task 

at hand, followed if necessary by visibility impact modeling. 

 1.3.2 Exemption Modeling 

Exemption modeling is intended as a definitive test of whether a source is subject to BART. A 

state and/or source operator may use one or more of three modeling approaches just introduced in 

performing exemption modeling. For most sources, an initial screening assessment with CALPUFF 

should be considered to assess whether a BART-eligible source is subject to BART. As described in 

Chapter 6, a screening CALPUFF dispersion modeling analysis is performed to assess whether a 

particular source is exempt from BART, obviously ‘subject to BART’, or should consider additional 

source-specific modeling analyses to explore further its exemption status. Assumptions for the 

screening assessment are purposefully conservative so that a source that contributes to visibility 

impairment is not inadvertently exempted. If a source is shown not to contribute to visibility 

impairment using the CALPUFF screening assessment, the state may choose to exempt the source 

from BART or to require additional source-specific modeling to confirm it is not subject to BART. 

If a source is shown to contribute to visibility impairment in the screening assessment, the 

source has the option to undertake source-specific CALPUFF modeling to evaluate further whether it 

is subject to BART (see Chapter 7). Source-specific exemption modeling analysis can be used for 

sources that do not pass the screening analysis above or for which a more detailed modeling 

investigation is warranted. These analyses would typically include exercising CALPUFF with finer 

grid resolution (e.g., 1-2 km), more realistic estimates of background pollutant concentrations, sitespecific estimates of background natural visibility conditions, and so on. 

In some circumstances, a state or source operator may wish to apply full-science modeling 

methods to overcome CALPUFF’s inherent limitations (see Chapter 8). For example, sources close to 

Class I areas, sources located in proximity to other large industrial or urban source complexes, or 

sources located great distances from a Class I area may be better treated by a comprehensive fullscience modeling system. If alternative modeling is pursued, the requirements of the modeling protocol 

are increased. An alternative modeling protocol must justify the need for more advanced modeling 

methods, identify the availability of pertinent data sets, address model selection and configuration 

issues, and describe the methods to be used in applying the model to estimate visibility impacts. 

Discussions with state, EPA and FLM representatives should be conducted prior to development of an 

alternative modeling protocol to ensure all of the relevant issues are addressed in the document. 

1.3.3 Modeling the Effects of BART Controls on Visibility 

For sources determined to be ‘subject to BART’, the next step involves estimating the visibility 

benefits of possible BART control measures. These benefits are determined by re-running the 

dispersion model using the screening modeling procedures or more resolved source-specific inputs. A 

key difference from the exemption modeling is that the source terms and/or emissions data are 

modified to reflect the BART control measures being evaluated. The base case to which the 

effectiveness of BART controls is compared is the “current emissions” scenario for which the 

exemption modeling analysis was performed. The post processing steps and procedures are the same. 

Side-by-side comparison of the visibility impacts provides a direct estimate of the effectiveness of each 

control scenario relative to the base case. 
 

1-6

1.3.4 Modeling Protocols 

Regardless of the BART modeling approach (e.g., screening, source-specific, or alternative 

modeling), a formal modeling protocol is required. A modeling protocol should be submitted for all 

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

The protocol is typically written by the state or the source operator and then reviewed with state, EPA, 

and the FLM. EPA’s role in the development of the protocol is advisory only as the “States better 

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

(70 FR 39126). 

A protocol consists of many elements but mainly serves as a means for planning and 

communicating how a BART modeling study will be performed before it occurs. Examples of 

required elements in the modeling protocol: 

> The meteorological and terrain data that will be used; 

> The source-specific information (stack height, temperature, exit velocity, elevation, and 

emission rates, speciation and size fractionization of applicable pollutants); and 

> Receptor data from appropriate Class I areas. 

The protocol guides the technical details of a modeling study and provides a formal framework for 

reviewing technical assumptions, operational details, commitments and expectations of the 

participants. It also provides means for resolution of potential differences of technical and policy 

opinion in an open process within prescribed time and budget constraints. 

Modeling protocols all too often fall short of providing sufficient detail in the description of the 

modeling assumptions and procedures to be employed (Roth et al., 2005). To assist the states and 

source operators in development of a protocol specific to a particular facility, protocol templates are 

given in Chapters 6 and 7 containing key elements that should be include in screening and sourcespecific applications, respectively. Of course, each protocol will have some unique features depending 

on the nature of the sources(s) and the Class I area(s) of interest. Also, the requirements for technical 

detail escalate from a screening application to source-specific application and this should be reflected 

in the content of the protocol. 

1.4 Organization of this Document 

The remainder of this document describes available models and data bases and the 

recommended procedures for their application. Chapter 2 reviews EPA’s guidance for regional haze 

BART analysis modeling, as outlined in the 6 July 2005 Federal Register notice. Readers already 

familiar with the BART Rule may wish to skip to Chapter 3 where the CALPUFF model, EPA’s 

preferred tool for BART analyses, is introduced and its characteristics and limitations discussed. 

EPA’s guidance allows for the use of appropriate alternative models and Chapter 4 gives an overview 

of pertinent state-of-science regional photochemical/aerosol models and currently available data bases. 

However, these CENRAP guidelines focus primarily on the application the CALPUFF modeling 

system. Chapter 5 identifies existing data bases developed by CENRAP and others to support BART 

modeling. Then, Chapters 6, 7 and 8 present the three BART modeling approaches. 
 

1-7

Chapter 6 discusses the specific steps needed to determine whether a BART-eligible source is 

subject to BART using the CALPUFF model in the screening mode using pre-compiled regional 

meteorological modeling data sets and CALPUFF run in a conservative mode. Procedures are also 

identified for evaluating the effects of BART controls on those sources that are identified as ‘subject to 

BART. For sources determined to be subject to BART based on screening analyses, additional sourcespecific CALPUFF modeling might be performed as described in Chapter 7. In a limited number of 

cases, it may be appropriate to use a full-science modeling system to develop more accurate estimates 

of a sources’ BART eligibility and/or to assess the effects of BART controls. Chapter 8 discusses the 

applications of state-of-science alternative models for this purpose. For each modeling approach 

described in Chapters 6 through 8, information regarding protocol development is provided as a 

template. Finally, a quality assurance methodology for CALMET/CALPUFF BART modeling is 

outlined in Chapter 9. 

Figure 1-1. Federal Class I Areas in the United States. 
 

1-8

Figure 1-2. U.S. Forest Service Class I Areas. 
 

1-9

Figure 1-3. National Park Service Class I Areas. 
 

1-10

Figure 1-4. Fish and Wildlife Service Class I Areas. 
 

1-11

Figure 1-5. Location of Visibility Monitoring Sites in the CENRAP Domain. 
 

1-12

Figure 1-6. CANRAP BART Modeling Flow Chart.

Screening 

CALPUFF Application 

(2001-2003 at 6 km) 

High ∆dv 

> 0.5 

Source-Specific 

CALPUFF Application 

(2001-2003 at 1-6 km)

98% ∆dv 

> 0.5 

Source subject to BART controls

Source not subject 

to BART controls 

Source not subject 

to BART controls 

CENRAP BART 

Modeling Flow Chart 

NO

YES 

NO

YES 

YES 
 

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Table 1-1. Federally Mandated Class I Areas in the CENRAP States. 

U. S. Forest Service 

Caney Creek Wilderness (AR) 

Upper Buffalo Wilderness (AR) 

Boundary Waters Canoe Area (MN) 

Hercules - Glades Wilderness (MO) 

National Park Service 

Big Bend National Park (TX) 

Guadalupe Mountains National Park (TX) 

Voyageurs National Park (MN) 

U.S. Fish and Wildlife Service 

Wichita Mountains Wilderness (OK) 

Mingo Wilderness (MO) 

Breton Wilderness (LA) 

Table 1-2. Federally Mandated Class I Areas Near the CENRAP States. 

U.S. Forest Service 

Eagles Nest Wilderness (CO) 

Rawah Wilderness (CO) 

Pecos Wilderness (NM) 

Wheeler Peak Wilderness (NM) 

White Mountain Wilderness (NM) 

Sipsey Wilderness (AL) 

Rainbow Lakes Wilderness (WI) 

National Park Service 

Arches National Park (CO) 

Bandelier National Monument (NM) 

 Carlsbad Caverns National Park (NM) 

Chiricahua National Monument (NM) 

Great Sand Dunes National Monument (CO) 

Rocky Mountain National Park (CO) 

Badlands National Park (SD) 

Isle Royale National Park (MI) 

Theodore Roosevelt National Park (ND) 

Wind Cave National Park (SD) 

Mammoth Cave National Park (KY) 

U.S. Fish and Wildlife Service 

Bosque Del Apache Wilderness (NM) 

Salt Creek Wilderness (NM) 

Seney Wilderness (MI) 

St. Marks Wilderness (FL) 

Lostwood Wilderness (ND)
 

2-1

2.0 BART MODELING REQUIREMENTS 

The final version of EPA’s Regional Haze Regulations was published in the Federal Register on 

6 July 2005 (70 FR 39104). One of the provisions of the program is the requirement that certain existing 

stationary sources emitting visibility-impairing air pollutants install and operate the Best Available 

Retrofit Technology (BART). The regulations require case-by-case BART determination to define 

specific emissions limitations representing BART and schedules for compliance for each source ‘subject 

to BART’. These requirements would be part of the SIP revisions that each State must submit to EPA by 

17 December 2007. This chapter summarizes the requirements of EPA’s BART Rule, particularly with 

respect to the modeling analyses that are required to determine (a) if a source is ‘subject to BART’, and 

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

visibility impairment at federally-protected Class I areas. 

2.1 Regional Haze Rule and BART Guidelines 

 Section 169A of the Clean Air Act (CCA) sets forth a national goal for visibility which is the 

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

which impairment results from manmade air pollution.” In 1999, EPA published a final rule to address 

a type of visibility impairment known as regional haze (64 FR 35714). The Regional Haze Rule (RHR) 

requires States to submit implementation plans (SIPs) to address regional haze visibility impairment in 

156 federally-protected parks and wilderness areas (i.e., the Class I scenic areas identified in the Clean 

Air Act). The 1999 rule was issued to fulfill a long-standing EPA commitment to address regional haze 

under the authority and requirements of sections 169A and 169B of the CAA. As required by the CAA, 

the final RHR included a requirement for Best Available Retrofit Technology (BART) for certain large 

stationary sources that were put in place between 1962 and 1977. The regional haze rule addresses 

visibility impairment resulting from emissions from a multitude of sources located across a wide 

geographic area. Because the problem of regional haze is believed to be caused in large measure by 

long-range transport of emissions from multiple sources, EPA adopted an approach that requires states 

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

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

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

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

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

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

that must be included in their SIPs or Tribal implementation plans (TIPs). Because the individual states 

have the authority to require source operators to assume part of the analytical burden in the BART 

analysis, there may be some differences in how the analyses are carried out across the CENRAP 

region. The BART guidelines also recognize that data collection, analysis, and rule development may 

be performed by Regional Planning Organizations, for adoption within each SIP or TIP. 

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

implementing the regional haze requirements on a source-by-source basis. States must follow the 

guidelines in making BART determinations on a source-by-source basis for > 750 megawatt (MW) 

power plants but are not required to use the process in the guidelines when making BART 

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

differ from the guidelines. 
 

2-2

Sources are BART-eligible if they meet three criteria: (a) emissions of at least 250 tons per 

year of a visibility-impairing pollutant, (b) in operation between 7 August 1962 and 7 August 1977, 

and (c) one of 26 listed source categories in the guidance. BART controls are required for any BARTeligible source which can be reasonably expected to cause or contribute to impairment of visibility in 

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

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

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

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

Provisions Governing Alternative to Source-Specific Best Available Retrofit Technology 

Determinations” the agency addressed BART for electric generating units (EGUs). Theses newlyproposed guidelines establish certain control levels or emission rates as presumptive standards for 

facilities of greater than 200 MW capacity at plants with total generating capacity in excess of 750 

MW. These presumptive levels, developed through a formal rulemaking process, are to be applied on a 

source-specific basis. EPA believes that it appropriate to apply them in a trading context where the 

burden to meet BART-equivalent reductions may be shared among non-BART eligible sources as well. 

Thus, when states estimate emissions reductions achievable from source-by-source BART, they must 

assume that all EGUs which would otherwise be subject to BART will control at the presumptive 

level, unless they demonstrate such presumptions are not appropriate at particular units. 

2.2 Role of Air Quality Models 

The EPA guidelines present several options for assessing whether or not sources are subject to 

BART. The options, relying on different modeling and/or emissions analysis approaches, are provided 

as guidance and the states are entitled to use other reasonable approaches for analyzing the visibility 

impacts of an individual source or group of sources. The options are: 

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

that an individual source cannot reasonably be anticipated to cause or contribute to visibility 

impairment in a Class I area and thus is not subject to BART. Under this option, states can 

analyze an individual source’s impact on visibility as a result of its emissions of SO2, NOx and 

direct PM emissions. Because EPA’s recommended CALPUFF dispersion model cannot 

currently be used to reliably estimate the predicted impacts on visibility from an individual 

source’s emissions of VOC or ammonia, states may elect to use a more qualitative assessment 

to determine on a case-by-case basis which sources of VOC or ammonia emissions may be 

likely to impair visibility and should therefore be subject to BART review. EPA approved 

models should be used to predict the visibility impacts from a single source at a Class I area. 

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

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

characteristics. EPA (2005) provides a model plant analysis assuming potential emissions as 

an example of how states might use this approach. The plume and stack characteristics of the 

model plants were developed considering a broad range of sources, and the analysis was based 

on impacts at two hypothetical Class I areas. States may develop their own model plant 

analyses to account for the meteorology and terrain in their region. Alternatively, a state could 

determine that the assumptions in EPA’s model plant analyses accurately reflect the 

characteristics of the sources and region at issue and no further analysis would be necessary. 

Because PM was not evaluated in EPA’s model plant analysis, states must determine whether 

PM emissions are significant enough to warrant further analysis. 
 

2-3

It may be most useful to use this type of analysis to identify the types of small sources that do 

not cause or contribute to visibility impairment for purposes of BART, and thus should not be 

subject to a BART review. Different Class I areas may have different characteristics, however, 

so care must be taken to ensure that the criteria developed are appropriate for the applicable 

cases. States could use modeling analyses of representative plants to reflect groupings of 

specific sources with important common characteristics. Based on these analyses, states may 

find that certain types of sources are clearly anticipated to cause or contribute to visibility 

impairment. States can then choose to categorically require those types of sources to undergo a 

BART determination. 

Option 3: Cumulative Modeling. States may also submit to EPA a demonstration, based on 

an analysis of overall visibility impacts, that emissions from BART-eligible sources in a state, 

considered together, are not reasonably anticipated to cause or contribute to any visibility 

impairment in a Class I area and thus no source should be subject to BART. States may do this 

on a pollutant-by-pollutant basis or for all visibility-impairing pollutants to determine if 

emissions from these sources contribute to visibility impairment. 

While EPA identifies several options for determining whether a source is “subject to BART”, the most 

credible method is the use of dispersion modeling. Air quality modeling allows a state or source 

operator to analyze an individual source’s impact on visibility as a result of its gaseous SO2, NOx and 

direct PM emissions. 

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

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

owing to uncertainties in the ammonia inventories1. Presumably, EPA believes that the uncertainty in 

current biogenic and anthropogenic ammonia inventories are sufficiently large that dispersion 

modeling of such sources would not provide meaningful results. However, this view does not comport 

with EPA’s CAIR modeling or ongoing RPO regional haze visibility modeling in which existing 

ammonia inventories are indeed used to calculate daily and annual PM2.5 and visibility impact 

assessments. EPA suggests a more qualitative assessment to determine on a case-by-case basis which 

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

subject to BART review. CENRAP is considering the use of advanced one-atmosphere models 

(CAMx) to investigate whether VOC and ammonia emissions from BART eligible sources in the 

region constitute a problem. Findings from these CENRAP modeling studies are expected to provide 

information helpful to the States or source operators in deciding whether and to what extent ammonia 

and VOCs need to be considered in the BART modeling. 

2.3 EPA Guidance on Air Quality Models 

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

emissions of SO2, NOx, and direct PM (PM2.5 and/or PM10) on 24-hr visibility impairment at receptors 

located within downwind Class I areas. Since visibility is defined in the context of light extinction, 

which itself is determined by atmospheric concentrations of specific fine particulate species -- 

 

1

 We believe this viewpoint is outdated, give recent improvements in modeling data bases and one-atmosphere models by 

EPA ORD/OAQPS and others (Seigneur et al., 2000; Arnold et al., 2003; Morris et al., 2005a,b; Tesche et al, 2005). 
 

2-4

ammonium, sulfate, nitrate, organic carbonaceous matter, elemental carbon, and coarse mass – logic 

dictates that the modeling method(s) used must be capable of simulating these components reliably. 

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

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

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

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

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

whether an individual source is subject to BART, and second, in the BART determination process. 

CALPUFF can be used to estimate not only the effects of directly emitted PM2.5 emissions from a 

source, but also to predict the visibility impacts from the transport and chemical transformation of fine 

particle precursors.” The Rule goes on to state (pg 110) that “regional scale modeling typically 

involves use of a photochemical grid model that is capable of simulating aerosol chemistry, transport, 

and deposition of airborne pollutants, including particulate matter and ozone. Regional scale air quality 

models are generally applied for geographic scales ranging from a multi-state to the continental scale. 

Because of the design and intended applications of grid models, they may not be appropriate for BART 

assessments, so States should consult with the appropriate EPA Regional Office prior to carrying out 

any such modeling”. 

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

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

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

scale photochemical grid model to estimate the effects if a control strategy on secondary components 

of PM. Changes in primary components may be estimated using a numerical grid model (with no 

chemistry), a Lagrangian model, or in some cases a receptor model”. Thus, in its Regional Haze and 

PM2.5 SIP modeling guidance, EPA indicates that CALPUFF (a Lagrangian non-steady-state Gaussian 

puff model) should not be used for secondary PM and visibility impacts at Class I areas, but rather is 

relegated to the category of estimating primary species. 

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

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

and notes that “it is the only EPA-approved model for use in estimating single source pollutant 

concentrations resulting from the long range transport of primary pollutants”. On the other hand, only 

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

SIPs. EPA attempted to reconcile these two positions in the Final BART Rule by asserting that (a) 

regional models were not developed to treat individual point sources and (b) CALPUFF’s secondary 

aerosol chemistry is adequate for estimating relative benefits of controls on BART sources. The first 

point ignores the significant amount of advanced plume-in-grid model development carried out at EPA 

and elsewhere over the past 20 years, while the second point is the focus of continuing debate in the 

scientific modeling community. Regardless, the BART modeling guidance stands and CALPUFF is 

the recommended model for BART determinations. EPA does allow for the use of alternative models 

on a case-by-case basis. Thus, this guidance addresses both approaches with CALPUFF being the 

primary tool for most BART visibility assessments. 

2.4 Steps in the BART Modeling Process 

The BART guidelines identify four steps required to determine emission limitations for 

affected sources. 
 

2-5

Identify BART-Eligible Sources. The first step is to identify whether a source is “BARTeligible” 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 7 August 1962 and 7 August 177 in order to be BART-eligible. Potential 

emissions of 250 tons per year or more of a visibility-impairing air pollutant are required to 

make a source eligible for BART. Qualifying pollutants include primary particulate matter 

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

Whether ammonia and VOCs should be included as visibility-impairing pollutants for BART 

eligibility is left for the states to determine on a case-by-case basis. The guidance states that 

high molecular weight VOCs with 25 or more carbon atoms and low vapor pressure should be 

considered as primary PM2.5 emissions and not VOCs for BART purposes. 

Determine Sources Subject to BART. To determine whether a source or facility is ‘subject to 

BART’, EPA recommends modeling all units and all pollutants together. This entails using 

plant-wide emissions and considering the effect of all pollutants combined for comparison to 

the visibility threshold. EGUs participating in the CAIR program may be BART-eligible for 

PM if plant wide potential emissions of PM are greater than de minimus. If the EGU is BARTeligible for PM and if the source emissions result in impairment above the state-determined 

threshold, the source may be deemed subject to BART. 

To determine whether a BART-eligible source can be excluded from BART controls, a 

modeling demonstration is required. EPA’s preferred approach is an assessment with 

CALPUFF followed by comparison of the estimated 24-hr visibility impacts against a threshold 

above estimated natural conditions to be determined by the states. The threshold to determine 

whether a single source “causes” visibility impairment is set at 1.0 deciview change from 

natural conditions over a 24-hour averaging period. Any exceedance of this threshold would 

trigger a BART review. 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. 

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

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

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

a change of approximately 0.5 deciviews. EPA believes that this is a natural breakpoint at 

which to set the BART exemption levels. Since visibility degradation may begin to be 

recognized by a human observer at this extinction level, the guidance uses a 0.5 deciview 

change on a 24-hour average basis for determining that an individual source is causing 

visibility impairment at a Class I area. This level would be calculated by comparing the air 

quality model’s results for an individual source against’ natural visibility’ conditions. A 

source’s impact is assessed by comparing the 98th percentile modeled value (8th highest day 

annually at a receptor or 22nd highest over 3-years) with the 0.5 deciview threshold. An impact 

is declared if the source contributes to visibility impairment. If so, it is ‘subject to BART’. 

If individual BART-eligible units at a facility have impacts that are less than 0.5 dv but in 

aggregate with other BART eligible sources at the facility are greater than 0.5 dv, the source is 

‘subject to BART’. In other words, if the combined impact of all units is greater than the 

threshold for contribution determined by the state (such as 0.5 dv), the source is declared 
 

2-6

‘subject to BART’. This does not imply that controls are necessary for each unit; the control 

technology analysis, which follows the ‘subject to BART’ test, can be a unit-by-unit evaluation 

and a visibility analysis can be conducted for single units and individual pollutants. 

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

for the source by considering various control options and selecting the “best” alternative, taking 

into consideration: (a) any pollution control equipment in use at the source which affects the 

availability of options and their impacts, (b) the costs of compliance with control options, (c) 

the remaining useful life of the facility, (d) the energy and non air-quality environmental 

impacts of compliance, and (e) the degree of improvement in visibility that may reasonably be 

anticipated to result from the use of such technology. 

Incorporate BART-Determinations into the State’s Regional Haze SIP. Results of the 

BART determinations must be incorporated into the state’s regional haze SIP which is due 17 

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 designed to achieve 

regional haze improvements that are greater than the visibility improvements that could be 

expected from BART2

 alone. 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, regional air quality modeling comparing the 

expected improvements in visibility from the trading program and from BART would be 

required. 

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. 

2.5 CALPUFF Modeling Recommendations 

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

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

sources closer than 50 km to a Class I area, the guidance recommends that expert modeling judgment 

be used “giving consideration to both CALPUFF and other methods.” While the PLUVUE-II model is 

mentioned as a possible tool to consider in addition to CALPUFF within 50 km of a source, it seems 

unlikely this technique would be any more credible than CALPUFF. The EPA guidance notes that 

regional scale photochemical grid models may have merit, but such models are resource intensive 

relative to CALPUFF. Photochemical grid models are clearly more appropriate than CALPUFF for 

cumulative modeling options such as in the determination of the aggregate contribution of all-BARTeligible sources to visibility impairment, but such use should involve consultation with the appropriate 

EPA Regional Office. 

 

2

 EPA’s BART guidance suggests that CAIR would satisfy the BART requirements for affected EGUs, which 

could affect the modeling efforts a State would need to undertake. 
 

2-7

For both BART exemption and BART determination modeling with CALPUFF, emissions 

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

appropriate emission rates. The EPA recommends that the states use the highest 24-hour average actual 

emission rate for the most recent five-year period (excluding periods with start-up, shutdown and 

malfunctions). Visibility improvements may be evaluated on a pollutant-specific basis. 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. 

EPA does not recommend CALPUFF for addressing visibility impacts from VOCs because its 

capability to simulate secondary organic aerosol formation from VOC emissions is deficient, 

especially for anthropogenic emissions. Currently, VISTAS is performing a weight-of-evidence 

analysis using EPA’s CMAQ regional air quality model to assess whether VOC emissions from all 

BART-eligible and non-BART point sources in each VISTAS State contribute to visibility impairment. 

If the impact of eliminating all VOC emissions from all point sources in a VISTAS state is shown to be 

less than 0.5 dv, then, logically, the impact of any one BART-eligible source would be less than 0.5 dv. 

If the VISTAS regional modeling analysis confirms this expectation, the CENRAP States might argue 

similarly that VOC emissions should not be subject to BART. 

EPA has given states the option to address ammonia (NH3) emissions from BART-eligible 

sources. VISTAS weight-of-evidence modeling with CMAQ is also addressing ammonia in the same 

way VOCs are being examined. If the CMAQ sensitivity evaluations shows that the collective impact 

of all point NH3 emissions in a state is less than 0.5 dv, then the impact of a single BART eligible 

source would be less than 0.5 dv. Should the VISTAS modeling confirm the expected insignificant 

role of direct NH3 emissions on Class 1 area visibility, the CENRAP States might argue similarly that 

VOC emissions should not be subject to BART. CENRAP is presently considering similar modeling 

for both ammonia and VOCs. If the VISTAS modeling demonstrates the importance of VOCs and/or 

NH3 emissions on Class I area visibility, then CENRAP will need to consider the use of advanced 

regional models to assist the States in establishing BART controls for these pollutants. 

2.6 Alternative Models for BART Analyses 

All air quality models share a common foundation: the species-conservation (or atmospheric 

diffusion) equation. Source-oriented air quality models including CALPUFF derive from this equation 

(Tesche, 1983) which applies equally to one source or a million or more sources. The distinction lies 

in how the various terms are treated (CAMx, CMAQ) or neglected (CALPUFF) in the governing 

equations and the choice of coordinate system (Lagrangian or Eulerian). As shown in the next 

Chapter, much of the simplicity of the CALPUFF model derives from the fact that many chemical and 

physical processes known to influence visibility are simply ignored. More sophisticated plume models 

such as SCIPUFF and SCICHEM (Karamchandani et al., 2000) rely on a second order closure 

treatment of turbulent mixing and detailed photochemical reaction schemes and to provide more 

realistic simulations than simpler approaches such as CALPUFF. Full-science regional models such as 

CAMx and CMAQ treat the various physical and chemical processes in detail, albeit at the expense of 

greater computer resources. Furthermore, detailed plume-in-grid treatment is available in several 

regional scale models, at least in CAMx, it is now possible to perform particulate and gas-phase source 

apportionment within the plume-in-grid formulation. Because EPA allows the use of alternative 

models on a case-by-case basis, these CENRAP guidelines include descriptions of these models and 

how they can be used. 
 

2-8

EPA essentially dismisses the use of regional scale models because they are “generally applied 

for geographic scales ranging from a multi-state to the continental scale and that by design they may 

not be appropriate for simulating individual point sources”. This posture ignores a very substantial 

body of research and model development carried out at the agency and elsewhere in the U.S. over the 

past 20 years. Modern one-atmospheric regional photochemical grid models, employing multi-scale 

nested grids (Kumar and Russell, 1996) and plume-in-grid techniques (Karamanchandani et al., 2002), 

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

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

of the wide array of anthropogenic and biogenic emissions that cause gas phase, particulate, secondary 

aerosol, and visibility air pollution problems—precisely the problem currently being addressed by all 

five RPOs. Furthermore, the convergence of fast commodity-based Linux computer clusters and the 

recently-developed RPO regional modeling emissions, meteorological, and air quality data bases make 

application of these comprehensive models no longer a research or academic exercise. While regional 

scale modeling clearly requires expertise to perform properly, the actual program costs to conduct a 

CMAQ or CAMx regional modeling study are today quite comparable with and occasionally less that a 

traditional PSD modeling study using ISC, CALPUFF, or AERMOD. Given grid nesting and plumein-grid technology, modern regional models are applicable to a very broad range of scales from 10-20 

km to continental scale. Regional photochemical grid models are clearly more appropriate than 

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

contribution of all-BART-eligible sources to visibility impairment. 

States may elect to perform cumulative modeling in order to examine the feasibility of 

exempting BART-eligible sources, but this is certainly not an EPA or BART Rule requirement. The 

regional haze rule requires States to consider the degree of visibility improvement resulting from a 

source's installation and operation of retrofit technology, along with the other statutory factors when 

making a BART determination. However, there is no longer a cumulative modeling requirement in 

determining the degree of visibility improvement. 

For the vast majority of potential BART-eligible sources in the CENRAP domain, application 

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

operator, the state, EPA and the FLM. For those special cases where more accurate modeling results 

are required, appropriate full-science modeling systems and supporting RPO data bases are available. 

Their broad use by the five RPOs in the past three years amply demonstrate that these comprehensive 

models are no longer arcane academic/research institution projects but applications-ready tools 

suitable for regulatory decision-making. 
 

3-1

3.0 CALPUFF FORMULATION AND IMPLEMENTATION 

The RHR relates visibility attenuation to extinction coefficient (bext) which is a measure of light 

scattering and absorption due to atmospheric constituents. Values for bext are estimated using an 

empirically derived equation which relates the extinction coefficient to relative humidity and the 

following components of particulate matter mass: (a) sulfates (SO4); (b) nitrates (NO3); (c) organic 

carbon (OC); (d) elemental carbon (EC); (f) particulate matter (IP) (“crustal material”); and (g) coarse 

mass (CM) (i.e., PM10 – PM2.5). The BART guidance requires the use of modeled concentrations of 

these components, together with a “humidity correction factor”, to estimate values for bext on all days 

within a three year period. These estimates, when compared with naturally occurring background 

extinction, are used to determine whether a source is causing or contributing to visibility impairment 

and also to measure the effectiveness of emissions controls on the source aimed at mitigating such 

effects. EPA notes that secondary particulate matter constitutes an important fraction of PM2.5 and that 

the modeling requirements for secondary and primary particulate matter differ in their need to consider 

atmospheric chemistry and in the degree of spatial resolution needed for the modeling (EPA, 2001, pg 

22). 

This chapter introduces the formulation of the CALPUFF modeling system. We summarize the 

model capabilities as described in the user’s manuals (Scire et al., 2000a,b) and discuss the capabilities 

and limitations of the model. Equipped with this information, states and source operators can identify 

those situations for which screening and/or source-specific applications of CALPUFF are appropriate. 

In most cases, we expect that application of the CALPUFF system will be sufficient to meet the 

BART Rule requirements. For that subset of conditions requiring advanced methods, Chapter 5 

provides details on full-science alternative models and available data bases for BART modeling. Such 

conditions might include a situation where the default modeling shows that a source just barely causes 

or contributes to visibility degradation or in negotiations over the final BART determination that 

weighs technical and economic feasibility against expected air quality benefits. In both situations, a 

more accurate estimate of a source’s impacts may be very important to source operators. 

3.1 Original Model Development 

 

The CALPUFF modeling system was originally developed as a component of a three-part 

modeling system sponsored by the California Air Resources Board (ARB) in the mid-1980s. The ARB 

sought to develop a new puff-based model, a new grid-based model and an improved meteorological 

processor that would support application of the two. CALGRID was the urban-scale photochemical 

grid model resulting from the project (Yamartino et al., 1992) comparable in science and capabilities to 

the Urban Airshed Model (UAM-IV) (Scheffe and Morris, 1993). The model formulation was aimed 

at overcoming the deficiencies in EPA’s steady-state Gaussian plume models that were routinely used 

in California for inert and linearly reactive materials (principally SO2) from elevated point sources. 

Thus, the CALGRID model was designed to treat the complexities of urban-scale photochemical 

processes while CALPUFF was formulated to treat the non-steady state transport, diffusion, linear 

reaction, and deposition of primary pollutants from point sources. CALPUFF was not designed to 

address photochemical oxidants or and secondary aerosol formation production processes in a 

scientifically rigorous manner. 

 

In recent years, CALPUFF and its meteorological pre-processor (CALMET) have been used in 

a range of regulatory modeling studies to address point source issues that include complexities posed 

by complex terrain, large source-receptor distances, parameterized chemical transformation and 
 

3-2

deposition, and issues related to Class I visibility impacts. These applications are more complex than 

the California ARB’s non-steady-state, linear chemistry formulation of the mid-1980s. 

The CALPUFF modeling system has been adopted by the EPA as a guideline model for sourcereceptor distances greater than 50 km, and for use on a case-by-case basis in complex flow situations 

for shorter distances. It was recommended for Class I impact assessments by the FLM Workgroup 

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

directed in the BART guidance, CALPUFF is the primary modeling system for screening and sourcespecific BART applications in the CENRAP region. Thus, examination of the model’s formulation 

provides the context for assessing the extent to which it suitable for simulating the various physical 

processes and gas-phase, aerosol, and aqueous-phase chemical processes that influences visibility. 

3.2 CALPUFF Model Formulation 

The CALPUFF user’s guide (Scire et al., 2000a) depicts the modeling system as shown in 

Figure 3-1. CALMET is a diagnostic/interpolation model that provides meteorological inputs to 

CALPUFF. These fields include hourly-averaged three-dimensional wind and temperature fields and 

two-dimensional fields of mixing heights and other meteorological parameters. CALMET uses routine 

surface and aloft meteorological observations and/or three-dimensional output from prognostic 

numerical models such as MM5 (Grell et al., 1995) or RUC (Benjamin et al., 2004) to construct the 

meteorological inputs. Other inputs to the air quality program include emissions information, receptor 

locations, ancillary geophysical information, and estimated concentrations of ambient pollutants that 

are entrained by the modeled puffs as each is carried downwind. Tables 3-1 and 3-2 summarize the 

key features of the CALMET/CALPUFF models as described in the user’s guides. 

Two post-processor routines are included to facilitate cumulative source impacts (POSTUTIL) 

and estimates of light extinction and visibility attenuation at Class I receptors of interest (CALPOST). 

In particular, CALPOST contains several options for computing change in extinction and deciviews for 

visibility assessments while the POSTUTIL postprocessor includes options for summing contributions 

of individual sources or groups of sources to assess cumulative impacts. POSTUTIL also contains an 

empirical nitric acid-nitrate chemical equilibrium module to estimate the cumulative effects of 

ammonia consumption by background sources once the simulation is completed. 

 3.2.1 Model Concept and Governing Equations 

The starting point for the CALPUFF development was the choice of the fundamental reference 

system of which there are two: Eulerian and Lagrangian. Consistent with the original ARB design 

criteria, the Lagrangian (moving puff) reference system was chosen for CALPUFF. In the Eulerian 

approach, the behavior of pollutants is described relative to a fixed coordinate system. The Lagrangian 

reference frame, in contrast, relates the behavior of pollutants relative to a coordinate system that 

moves with the average wind. These two approaches yield different mathematical relationships for 

pollutant concentrations that are equally valid. The choice of which approach to adopt depends upon 

the specific design goals of the modeling system. 

 The advantages and drawbacks of each approach are thoroughly discussed in the literature 

(Tesche, 1983; Seinfeld and Pandis, 1998; Jacobson, 1999; Russell and Dennis, 2000). One of the 

criticisms of early Eulerian grid models was their ‘over-dilution’ of point source emissions into the 

fixed grid cells; but for the past twenty years, this limitation has been overcome through with the 

development of sub-grid-scale, plume-in-grid algorithms (Seigneur, et al., 1981; Godowitch, 2004; 
 

3-3

Karamchandani et al., 2005; Emery and Yarwood, 2005) and the use of multi-scale nested grids 

(Russell and Dennis, 2000). While the Lagrangian approach is conceptually simple, flexible, and 

computationally inexpensive, the governing equations are not directly applicable to situations 

involving non-linear chemical reactions (Seinfeld and Pandis, (1998) and it is awkward to handle a 

large number of sources realistically. 

 3.2.2 Transport and Dispersion 

Adopting the Lagrangian concept, CALPUFF simulates the transport, dispersion, linear 

chemical transformation, and deposition of individual puffs carried downwind by the threedimensional fields generated by CALMET. The model’s implementation follows puffs from the near 

source region (a few tens of meters) to hundreds of kilometers downwind. Its puff-based formulation, 

in conjunction with three-dimensional hourly meteorological data, allow CALPUFF to simulate the 

effects of time- and space-varying meteorological conditions on pollutants emitted from a variety of 

source types. The major features and options of the CALPUFF model are summarized below: 

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 stackby-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: Turbulent dispersion in CALPUFF is treated with the K-theory (fluxgradient) closure scheme, defined for a Lagrangian frame of reference. 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. Recently, the EPA 

AERMOD dispersion parameters have been included in CALPUFF and are used regularly. 

Puff Sampling Functions: Puff sampling routines are included in CALPUFF to address 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. 

Wind Shear Effects: A key underpinning of the Lagrangian concept is that the modeled puffs retain 

their identity over the time- and spatial-scale associated with the effects the model is attempting to 

predict (i.e., visibility impairment at 200 km or beyond) While discrete puffs emitted from a source 

retain their physical integrity for a period of time, at some point the action of horizontal and vertical 

variations in wind speed and direction (i.e. ‘wind shear’) shred the puff into multiple elements. These 

new puff parcels, composed of remnants of the old puff, continue to be diffused and dispersed by the 

wind. The point where significant puff shredding occurs is difficult to define since it depends 

substantially upon the complexity of the meteorological conditions and the underlying terrain. But 

when shredding occurs, the Lagrangian concept in CALPUFF breaks down. By ignoring puff 

shredding (i.e., by keeping puffs intact), the model will systematically over-predict pollutant 

concentrations. 
 

3-4

To deal with this conceptual limitation, CALPUFF contains an optional puff splitting algorithm 

to simulate vertical wind shear effects across individual puffs. Differential rates of dispersion and 

transport among the “new” puffs generated from the original, well-mixed puff act to increase the 

effective rate of horizontal spread of the material as would be expected in the real atmosphere. 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. Detailed guidance on when and how the puff-splitting algorithm 

should be used and actual verification studies demonstrating that the technique operates as intended are 

not discussed in the model documentation or presented in the science literature.

Complex Terrain: 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 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, 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. 

Overwater and Coastal Interaction Effects: The CALMET processor contains overwater and overland 

boundary layer parameterizations allowing certain of the effects of water bodies on plume transport, 

dispersion, and deposition to be estimated. In a sense, CALPUFF operates as a hybrid model, by 

utilizing gridded fields of meteorology and dispersion conditions as well as grid-based descriptions of 

underlying land use. This includes the abrupt changes that occur at the coastline of a major body of 

water. 

Dry Deposition: A resistance model is used 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). 

Wet Deposition: An empirical scavenging coefficient approach is used 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). 

 

 3.2.3 Primary Particulates 

CALPUFF is designed to simulate PM10 or PM2.5 or other user defined size distributions of 

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

dispersion of inert PM2.5 particles will differ only slightly from that of an inert gas. A key primary 
 

3-5

PM2.5 emission from coal-fired electric generating units (EGUs) of relevance to visibility calculations 

is particulate sulfate. Although primary sulfate emissions account for only a small fraction of the total 

sulfur emissions from such sources, it is appropriate to include their effect if reasonable estimates of 

primary sulfate emissions from the source are available. Treating primary sulfate emissions is likely to 

be most important at short distances from the stack before significant SO2 to secondary sulfate 

conversion has taken place. 

 3.2.4 Gas-Phase Chemistry 

Chemical reactions in the gas-phase play an important role in secondary aerosol formation by 

generating radical concentrations (e.g., the hydroxyl radical). These radical species oxidize SO2 and 

NOx, providing the precursors to aqueous–phase chemistry (i.e., chemistry in liquid water droplets) 

that convert SO2 to sulfate (e.g., H2O2 and O3), and form condensable gases from some volatile organic 

compounds (VOCs) that can then condense into particulate secondary organic aerosols (SOA). The 

levels of NOx, VOC, and O3 concentrations along with the reactivity of the VOCs, sunlight, 

temperature, and water vapor are all key variables that influence the radical cycle and consequent 

sulfate and nitrate formation rates. 

CALPUFF neglects realistic gas-phase processes entirely. The chemistry in CALPUFF 

parameterizes chemical transformation effects using five species (SO2, SO4

=

, NOx, HNO3, and NO3

-

) 

via a set of user-specified, diurnally-varying transformation rates. The model estimates secondary fine 

particulate matter (sulfate and nitrate) from emissions of gas-phase SO2 and NOx. Rather than 

simulating important non-linear gas phase oxidant chemistry, the model employs a user-supplied 

hourly ozone concentration as a surrogate for the hydroxyl radical and other oxidizing radical species. 

Ambient ammonia concentrations are also a user input along with temperature and relative humidity. 

Although simplifications of photochemistry have been attempted in the past, correct 

representation of the gas-phase photochemistry and the radical cycles are critically important in order 

to properly characterize sulfate and nitrate formation in the real atmosphere. Seigneur et al., (2000) 

demonstrated this fact in their evaluation of full-science representations of photochemistry against 

simplified representations (but more advanced than CALPUFF). They concluded that simplified 

linearized transformation schemes are inadequate for describing sulfate and nitrate formation 

processes: 

“These results indicate that the accurate prediction of source-receptor relationships for PM2.5

requires a comprehensive treatment of PM2.5 formation from gaseous precursors for the 

secondary components of PM2.5 and a spatially resolved treatment of transport processes for 

primary PM2.5. Simplified treatments of either atmospheric chemistry or transport are 

appropriate only when the secondary or primary components of PM2.5, respectively, are not 

significant. Therefore, the development of source-receptor relationships for PM2.5 should be 

based on air quality models that provide comprehensive descriptions of atmospheric chemistry 

and transport.” 

Morris et al., (1998) also compared the sulfate and nitrate particulate estimates from a comprehensive 

full-science regional model with those from a model incorporating a simplified empirical chemical 

mechanism developed in a manner similar to the mechanism in CALPUFF. Evaluating the full-science 

and empirical chemistry models against observed concentrations, Morris and co-workers concluded: 
 

3-6

“Given the importance of the radical cycle for determining secondary PM formation rates, it 

appears that empirical gas-phase algorithms are inadequate for determining secondary PM 

formation.” 

The uncertainty and potential biases introduced into the CALPUFF visibility estimates due to neglect 

of gas phase oxidant chemistry remain unknown. 

3.2.5 Aerosol Chemistry 

 

Formation of secondary fine particulate matter (e. g., nitrates, sulfates, organic aerosols) in 

point source plumes is strongly dependent on the rate of mixing with ambient (background) air and the 

chemical composition of this background. The rates of oxidation of sulfur dioxide (SO2) and nitrogen 

dioxide (NO2) to sulfate and nitric acid can be very different within a power plant or industrial plume 

compared to that in the background air (Gillani and Godowitch, 1999; Karamchandani et al., 2000). 

Similarly, the formation of secondary organic aerosols from emitted VOCs and those from other 

anthropogenic and biogenic sources, adds yet another pathway in the formation of visibility-impairing 

aerosols. The presence of atmospheric ammonia introduces further nonlinearities into the gas phase 

and aerosol reactions. Accordingly, for a model to realistically simulate the production of secondary 

particulate sulfate, nitrate, and organic aerosols from a potential BART source, the mixing processes 

and chemical reactions within and outside of the plume must be treated realistically. If the chemical 

interactions between these two fundamentally different and interactive chemical environments are 

overly-simplified or neglected altogether, the ability of the model to correctly calculate plume 

concentrations, deposition, or visibility impacts is lost. 

Sulfate and Nitrate Formation. Two SO2 and NOx chemical transformation schemes are available in 

CALPUFF: the MESOPUFF-II algorithm (Scire et al., 1983; Atkinson et al., 1982) and the RIVAD 

algorithm (Latimer et al., 1986). These algorithms calculate sulfate and nitrate formation rates based 

on the puff concentrations, background environmental parameters provided by CALMET, and 

background ozone and ammonia concentrations provided as input by the user. SOA particulates are not 

treated by either mechanism. The parameters used are as follows (note that each method does not use 

all of these parameters). 

Puff Average Concentrations (from CALPUFF)

• NOx concentration 

• SO2 concentration 

Environmental Parameters (from CALMET)

• Temperature 

• Surface Relative Humidity (RH) 

• Atmospheric Stability 

• Solar Radiation 

Background Concentrations (User Input)

• Ozone (O3) 

• Ammonia (NH3) 
 

3-7

The MESOPUFF-II chemical transformation scheme is EPA’s recommended approach for Class I area 

impact assessment (IWAQM, 1998). It entails pathways for five active pollutants (SO2, SO4, NOx, 

HNO3, and NO3) as follows: 

k1

SO2 Æ SO4

k2

NOx Æ HNO3 (+RNO3) 

k3

NOx Æ HNO3

NH3

HNO3 (g) ÅÆ NO3 (PM) 

where, 

SO2 is the puff average sulfur dioxide concentration; 

NOx is the puff average oxides of nitrogen concentrations; 

SO4 is sulfate concentrations formed from the SO2; 

HNO3 is the nitric acid formed from the NOx; 

NO3 is the particulate nitrate that is in equilibrium with the nitric acid; and 

NH3 is the background ammonia concentration. 

Daytime Rates

k1 = 36 x R0.55 x [O3]

0.71 x S-1.29 + k1(aq) 

k1(aq) = 3 x 10-8 x RH4 (added to k1 above during the day) 

k2 = 1206 x [O3]

1.5 x S-1.41 x [NOx]-0.33

k3 = 1261 x [O3]

1.45 x S-1.34 x [NOx]-0.12 

Nighttime Rates

k1 = 0.20 (%/hr) 

k2 = 0.00 (%/hr) 

k3 = 2.00 (%/hr) 

with, 

k1 is the SO2 to SO4 gas-phase transformation rate (%/hr)

k1(aq) is the SO2 to SO4 aqueous-phase transformation rate (%/hr) 

k2 is the NOx to HNO3+RNO3 transformation rate (%/hr) 

k3 is the NOx to HNO3 (only) transformation rate (%/hr) 

S is the stability index ranging from 2 to 6 

(PGT class A&B=2, C=3, D=4, E=5, F=6) 

R is the total solar radiation intensity (kw/m2) 

RH is the relative humidity (%) 

[O3] is the user provided background ozone concentrations (ppm) 

[NOx] is the plume average NOx concentration (ppm) 

NH3 is the user provided background ammonia concentrations 
 

3-8

Daytime chemical transformations are based on statistically analyzed hourly transformation 

rates (Scire et al., 1983) obtained from box model simulations using the Atkinson et al., (1982) 

photochemical mechanism. In this scheme, gas-phase oxidation of SO2 and NOx depends on the 

hydroxyl (OH) radical concentrations for which background ozone, solar intensity (R), and stability 

index are used as surrogates. At night, OH concentrations are much lower and default SO2 and NOx 

oxidation rates of 0.2 %/hr and 2.0 %/hr are assumed. The k1(aq) sulfate formation rate is added to the 

k1 rate during the day as a surrogate for aqueous-phase sulfate formation which begins to assume 

importance above approximately 50% RH (~0.2 %/hr sulfate formation rate) and peaks at 100% RH 

(3%/hr sulfate formation rate). 

The sulfate and nitrate formation rate equations used in the MESOPUFF II scheme were 

originally generated by developing regression equations for a few key variables on the results of 144 

box model simulations that used the 1982 photochemical mechanism of Atkinson et al. These box 

model simulations varied ambient temperature, ozone concentration, sunlight intensity, VOC 

concentrations, atmospheric stability, and plume NOx concentrations as shown in Table 3-1. The 

actual environmental conditions used to generate the sulfate and nitrate transformation equations were 

extremely limited. For example, the transformation rates did not cover temperatures below 10 deg C 

(50 deg F) or cleaner rural atmospheric conditions with VOC concentrations less than 50 ppbC. 

The CALPUFF MESOPUFF-II chemistry clearly neglects several environmental parameters 

and chemical processes that are important in simulating sulfate and nitrate formation in NOX/SO2

emissions source plumes. In many cases these deficiencies lead to an overestimation bias of the 

source’s sulfate and nitrate impacts. Factors that lead such a bias include: 

Lack of Temperature Effects: Photochemistry is known to be highly temperature sensitive, as 

evidenced by the fact that elevated ozone concentrations tend to occur on hot summer days. 

Lower temperatures produce lower OH and other radical concentrations and consequently 

lower sulfate and nitrate formation rates. The CALPUFF sulfate and nitrate formation rates, 

however, do not adequately incorporate temperature effects. The MESOPUFF-II chemical 

transformation algorithm was developed under conditions with a minimum temperature of only 

10° C (50° F). Thus, under conditions colder than 10° C, CALPUFF will overpredict sulfate 

and nitrate formation rates and impacts. CALPUFF typically estimates maximum sulfate and 

visibility impacts during the late fall/early spring and winter months; these are the same months 

when the CALPUFF overestimation bias from not considering temperature effects will be 

greatest. In addition, under colder temperatures, NOx will be converted to peroxyacetyl nitrate 

(PAN) so that the NOx is no longer available to be converted to nitrate. Since the CALPUFF 

chemistry ignores the PAN sink for NOx, it will systematically overpredict nitrate impacts. 

Effects of NOx Emissions on Sulfate Chemistry: Downwind of a point source with significant 

NOx/SO2 emissions, high NOx and SO2 concentrations co-exist. Under high NOx 

concentrations, radical concentrations are greatly reduced, resulting in very low ozone, sulfate, 

and nitrate formation rates. This is due to the NOx inhibition effect on photochemistry 

whereby: (1) the titration of NO with ozone eliminates ozone and its source as a radical 

generator; and (2) the high NO2 concentrations eliminate the OH radical via the NO2 + OH 

reaction thereby effectively shutting down photochemistry. Thus, in a NOx/SO2 point source 

plume near the source, there will be very low OH radical and ozone concentrations and 

consequently very low sulfate and nitrate formation. Since the simple MESOPUFF-II 

transformation equations cannot account for the NOx effect on the sulfate formation, 

CALPUFF will tend to over-predict sulfate formation rate in a NOx/SO2 point source plume 
 

3-9

near the source, which in turn leads to overstating the sulfate formation rate. Because 

NOx/SO2 point sources are typically buoyant, they are frequently be emitted aloft in a stable 

layer where the high NOx concentrations and inhibited sulfate and nitrate formation rates could 

persist 100 km or more downwind. 

Aqueous-Phase Sulfate Formation Algorithm. CALPUFF’s MESOPUFF-II chemistry treats aqueousphase sulfate formation solely as a function of relative humidity (RH), which actually has no direct 

affect on aqueous-phase sulfate formation chemistry. The CALPUFF MESOPUFF-II aqueous-phase 

sulfate formation rate ranges from values of approximately 0.2 %/hr at 50% RH to 3.0 %/hr at 100% 

RH. Relative humidity (RH) is a measure of the content of water vapor in the atmosphere. However, 

in reality aqueous-phase sulfate formation will depend on the amount of atmospheric liquid water 

content (LWC) in cloud or fog droplets, the pH of the water droplets, and the level of H2O2, ozone, and 

SO2 concentrations. Accordingly, in the atmosphere, aqueous-phase sulfate formation chemistry is not 

affected by RH. Thus, the CALPUFF aqueous-phase chemistry parameterization is incorrect. 

Although under conditions of clouds and fog there will be high RH, the occurrence of high RH with 

very little or no clouds or fog can be quite frequent. 

In a liquid water droplet, the reaction of SO2 with H2O2 to form sulfate is essentially 

instantaneous and is usually limited by the amount of H2O2 present (i.e., oxidant limited) for a 

NOx/SO2 point source. Once the H2O2 is reacted away within the water droplet, sulfate formation via 

this pathway slows to the rate of H2O2 formation, which would be extremely slow to nonexistent in a 

large point source plume due to the scavenging of radicals by the high NOx concentrations. This 

introduces an inaccurate representation of sulfate formation in CALPUFF that creates uncertainties and 

bias in modeled visibility impacts. Whether this uncertainty results in an under- or overestimate of 

sulfate formation is difficult to determine since the approach is scientifically invalid. Under conditions 

of high RH and little clouds or little plume interaction with clouds, it will clearly overstate sulfate 

formation. However, under conditions of cloudy conditions with available photochemical oxidants 

(i.e., H2O2 and O3) and a dilute NOx/Sox point source plume, it may understate sulfate formation. 

Near large NOx/SO2 point source where the elevated NOx concentrations scavenge and limit 

photochemical oxidants, the MESOPUFF-II algorithm will likely overstates sulfate formation. 

Thus, the CALPUFF aerosol chemistry fails to account for many environmental parameters that 

are necessary to simulate sulfate and nitrate formation rates, including VOCs and their reactivity, 

temperature, liquid water content, and NOx concentrations. In their evaluations against full-science 

PM models and observations, Seigneur et al., (2000) and Morris et al., (1998) both independently 

found that the empirical chemistry modules, such as employed by CALPUFF, are inadequate for 

estimating sulfate and nitrate formation. These findings are supported by EPA’s PM2.5 and Regional 

Haze SIP modeling guidance (EPA, 2001) that recommends against using Lagrangian models such as 

CALPUFF for simulating secondary PM. 

 From the foregoing, it is clear that the CALPUFF chemical transformation algorithms neglect 

important chemical processes necessary to accurately estimate the sulfate and nitrate impacts due to 

SO2 and NOX emissions. Given that EPA recommends the model for BART determinations, a key 

question is “What is the influence of the simplified chemistry on modeled estimates of visibility 

impacts from BART sources? In some cases, the inadequacies in the CALPUFF chemistry algorithms 

may simply introduce broader uncertainties into the calculation of estimated sulfate and nitrate 

impacts. In many cases, however, the simplifications made in the CALPUFF description of chemical 

processes result in a systematic bias in the estimated concentrations and visibility impacts due to SO2

and NOX emissions sources. For large point sources that emit SO2 and NOx emissions, such as EGUs, 
 

3-10

petrochemical process heaters, cement plant kilns, etc., many of the limitations in the CALPUFF 

MESOPUFF-II SO2 and NOx transformation algorithms would result in an overestimation bias. While 

models that are systematically biased high (i.e., over-predict impacts) may be appealing to regulatory 

decision-makers because they are ‘conservative’, the overprediction tendency may well lead to 

unwarranted and excessive control of emissions from some sources. Thus, the tradeoff between 

simplicity and conservativism on the one hand and technical credibility and unbiased answers on the 

other is a key element in the negotiation of modeling protocols developed by the states or source 

operators. 

 3.2.6 Surface Removal 

An especially important contributor to particulate 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 

not usually 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 separate species, which includes calculating sizespecific dry deposition velocities for each size category. 

3.3 CALMET Meteorological Preprocessor 

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

micrometeorological modules for over-water and overland boundary layers. When modeling a large 

geographical area such as the CENRAP 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. 

3.3.1 Boundary Layer Modules 

The CALMET processor contains two boundary layer modules for application to overland and 

overwater grid cells. 

Overland Boundary Layer Module: 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 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 module also determines gridded fields of PGT stability 

class and hourly precipitation rates. 
 

3-11

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

suggest that a different method is needed for estimating 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. 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. 

3.3.2 CALMET Diagnostic Wind Field Module 

The CALMET wind model was constructed from two other meteorological models used in 

California in the late 1970s. One was the California Institute of Technology (CIT) mass consistent 

interpolation model described by Goodin et al., (1980). The other was the Complex Terrain Wind 

Model (CTWM) developed at Systems Applications, Inc. (Tesche and Yocke, 1978; Yocke and Liu, 

1978). The CTWM terrain adjustments used to modify the flow fields were assembled in the 1970s as 

part of research into fire spread and avalanche forecasting in mountainous regions of California. 

Various heuristic algorithms were developed to approximate down slope drainage flows, terrain 

blocking and channeling (Geiger, 1965), thermal heat islands (Stern and Malkus, 1953), surface 

friction retardation, capping by an elevated inversion and so on. These algorithms were based on 

empirical studies in wind tunnels, numerical modeling experiments, and field studies in the Alps, some 

dating back to the 1930s (Defant, 1933). Later work by Tesche et al., (1986), Kessler et al., (1987) and 

Douglas and Kessler (1988) integrated the CIT and CTWM modeling system into a single 

meteorological model that included algorithms to blend observational data with prognostic 

meteorological model output. The combined model was used extensively for urban-scale ozone 

studies throughout the U.S. prior to the switch to MM5 as the preferred meteorological model for SIP 

studies in the mid-1990s. 

The CALMET model development incorporated the main features of the CTWM and CIT wind 

model and significantly updated the physical parameterizations and improved model input/output (I/O) 

schemes (Scire et al., 2000a). Today, CALMET uses the CTWM two-step approach to the 

computation of the wind fields. In the first step, an ‘initial-guess’ wind field is constructed and then 

adjusted to approximate the kinematic effects of terrain, slope flows, and terrain blocking. Currently, 

the gridded MM5 field is used as the initial guess prior to terrain-perturbation. The second step 

consists of an objective analysis procedure to blend the MM5 field with observational data to produce 

a final wind field. This introduction of observational data in the second step of the CALMET wind 

field development is optional. 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 finescale terrain adjustments on the scale of the CALMET domain. 

Normally, the CALMET computational domain is specified to be at smaller grid spacing than 

the MM5 dataset used to initialize the initial guess field. For example, 36/12 km MM5 data sets 

available for 2000-2003 over the CENRAP domain have been used to develop the 6 km CALMET 

grids shown in Figures 5-1 through 5-4. 

The current thermal, kinematic, and dynamic effects parameterized in CALMET, used in the 

first step of the windfield development, are as follows: 
 

3-12

Kinematic Effects of Terrain: The CTWM algorithms for kinematic effects (Liu and Yocke, 1980) is 

used to evaluate the influence of the terrain on the wind field. The initial guess field winds are used to 

compute a terrain-forced vertical velocity, subject to an exponential, stability-dependent decay 

function. The effects of terrain on the horizontal wind components are evaluated by applying a 

divergence-minimization 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 (Defant, 1933) has been upgraded (Scire and Robe, 

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

3.4 Estimation of Regional Haze Contributions 

The default procedure for quantifying visibility impacts is described in several documents 

(IWAQM, 1998; FLAG, 2000). Implementation of these procedures in CALPUFF is described in the 

user’s documentation (Scire et al., 2000b). Generally, ‘visibility’ may be quantified either by visual 

range (the greatest distance that a large object can be seen) or by the light extinction coefficient, which 

is a measure of the light attenuation per unit distance due to scattering and absorption by gases and 

particles. Visibility is impaired when light is scattered in and out of the line of sight and by light 

absorbed along the line of sight. The light extinction coefficient (bext) considers light extinction by 

scattering (bscat) and absorption (babs): 

 

b

ext

 = b

scat

 + b

abs

 

The scattering components of extinction (bscat) are represented by light scattering due to air molecules 

(i.e., Rayleigh scattering, brayleigh) and light scattering due to particles, bsp. The absorption components 

of extinction (babs) include light absorption due to gases (bag) and particles (bap). Furthermore, particle 

scattering, bsp, can be expressed by its components: 

 

b

sp

 = b

SO4

 + b

NO3

 + b

OC

 + b

SOIL

+ b

Coarse

 

where the chemical species and soot scattering coefficients are given as: 

 

b

SO4 = 3 [(NH4

)

2

SO

4

] f(RH) 

b

NO3 = 3 [NH4

NO

3

]f(RH) 
 

3-13

b

OC = 4 [OC] 

b

SOIL= [Soil] 

b

Coarse= 0.6 [Coarse Mass] 

 

b

ap

 = 10 [EC] 

 

The numeric coefficient at the beginning of each equation is the dry scattering or absorption efficiency 

in meters-squared per gram. The f(RH) term is a monthly-average relative humidity adjustment factor. 

The terms in the brackets are the estimated concentrations fro CALPUFF (or other model) in 

micrograms per cubic meter (μg/m3

). 

 

Finally, the total atmospheric extinction is estimated as: 

 

b

ext

 = b

SO4

 + b

NO3

 + b

OC

 + b

SOIL

+ b

Coarse

+ b

ap

+ b

rayleigh 

or, substituting in the above terms, 

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

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

This is the so-called IMPROVE extinction equation currently recommended by EPA (2003). Note that 

the sulfate (SO4) and nitrate (NO3) components are hygroscopic because their extinction coefficients 

depend upon relative humidity. 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). The effect of relative humidity variability on 

the extinction coefficients for SO4 and NO3 can be estimated in several ways, but following the EPA 

BART guidelines, the Class I area-specific monthly f(RH) values shown in Table 6-1 should be used. 

 

Modeled ground level concentrations of each of the above visibility impairing pollutants are 

used with the IMPROVE equation to deduce the extinction coefficient. The change in visibility 

(measured in terms of ‘deciviews’) is compared against background conditions. The delta-deciview, 

Δdv, value is calculated from the source’s contribution to extinction, bsource, and background extinction, 

bbackground, as follows:

 

Δdv = 10 ln((bbackground

+ bsource)/ bbackground) 

 

The impact of a source is determined by comparing the Δdv, or haze index (HI), for estimated natural 

background conditions with the impact of the source and without the impact of the source. If the Δdv 

value is greater than the 0.5 dv threshold the source is said to contribute to visibility impairment and is 

thus subject to BART controls. 

CALPOST uses a previous IMPROVE f(RH) curve (FLAG, 2000) which differs slightly from 

the f(RH) now used by IMPROVE and EPA (2003), mainly at high relative humidity. 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% (easily modified in the CALPUFF input file). 
 

3-14

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. This 

is consistent with the BART guidance recommendation for using CALPUFF for source-receptor 

distances greater than 50 km. But, CALPUFF is also recommended by EPA as an option that can be 

considered for shorter transport distances when the plume may in fact be discernible from the 

background haze. 

Apart from the chemistry issues discussed previously, there do not appear to be any major 

reasons why CALPUFF cannot be used for even shorter transport distances than 30 km, 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 with individual CENRAP State modelers. (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.) 

3.4.1 CALPOST Methods 

Calculation of the impact of the simulated plume particulate matter component concentrations 

on light extinction is carried out in the CALPOST postprocessor. For BART applications, this 

processor is of considerable importance. 

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 visibility-related modeling, CALPOST uses 

concentrations from CALPUFF to compute light extinction and related measures of visibility 

(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 IWAQMrecommended 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. 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 (i.e., Methods 1 through 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). Method 2 is the current default, 

recommended by both IWAQM (EPA, 1998) and FLAG (2000) for source-specific. Method 6 is 

recommended by EPA’s BART guidance (70 FR 39162). 
 

3-15

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 or, optionally, the EPA regional haze f(RH) 

curve.1

 Again the RH is capped at either 98% (default) or a user-selected value (most commonly at 

95%). 

Method 6 is similar to Method 2, except monthly f(RH) values (e.g., EPA’s monthly 

climatologically representative values) 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. 

3.4.2 POSTUTIL 

The POSTUTIL processor allows the cumulative impacts of multiple sources from different 

simulations to be summed, including computing the difference between two sets of predicted impacts 

(useful for evaluating the benefits of BART controls). It also 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 background sulfate and nitrate sources to be 

approximated in the formation of nitrate from an individual source. The processor can compute the 

impacts of individual sources or groups of sources on sulfur and nitrogen deposition into aquatic, forest 

and coastal ecosystems, thereby allowing changes in deposition fluxes resulting from changes in 

emissions to be quantified. 

The POSTUTIL processor attempts to overcome the bias introduced when CALPUFF assumes 

that the full background ammonia concentration is entrained into each discrete puff. For a single puff, 

this may be satisfactory, but the model overestimates the production of ammonium nitrate when 

multiple puffs co-exist and overlap. The POSTUTIL processor re-partitions the ammonia and nitric 

acid concentrations to conform to the ammonia-limiting processes influencing nitrate formation. 

Though based on recognized science, this approximate post-processing method is fundamentally 

dependent on reliable estimates of ambient NH3 at the Class I receptor of interest. 

3.4.3 Refined Extinction and Background Visibility Estimates 

EPA, the IMPROVE Steering Committee, and the RPOs are evaluating whether refinements 

are warranted to the methods recommended for calculating extinction and the default estimate of 

natural background visibility. Whether EPA will approve of any changes to the IMPROVE equation is 

uncertain at this time. Also, the responsibility for incorporating any changes to the algorithms in 

CALPUFF (e.g., new f(RH) curves) is unclear. If changes to these methods are recommended by 

EPA, CENRAP is encouraged to adopt them. However, details of the process for incorporation of any 

 

1

 Note that the hourly-varying natural background extinction here 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. 
 

3-16

refinements to the IMPROVE equations in the CALPUFF system should be addressed in the State’s or 

source operators modeling protocol. 

3.5 Model Availability 

The EPA-approved version of the CALPUFF modeling system is available from Earth Tech, 

Inc., (http://www.src.com/calpuff/calpuff1.htm). The main models (CALMET, CALPUFF, and 

CALPOST), their GUIs, and many of the processors are available to download. One may also register 

to receive notices of model updates. The most recent update to the system (25 May 2005) is a new 

version of CALMM5 (MM5 V3) that has been added to the Download BETA-Test page. This version 

of CALMM5 processes MM5 Version 3 output data directly. 

Earth Tech offers CALPUFF training courses that include a description of the technical 

formulation of the models, overviews of each of the processor programs, and hands-on application of 

the models to several case study data sets. Attendees of the course receive a training notebook, a 

workbook of case study problems, exercises, and data sets, updates on recent and future model 

enhancements, and the latest (proprietary) versions of the models and Graphical User Interfaces 

(GUIs). Other third-party training courses and materials are also available. 

3.6 CALPUFF Evaluation Studies 

Tesche (2002, 2003) reviewed results of various CALPUFF evaluation studies and reached the 

following conclusions: 

> There is a paucity of model evaluation information for CALPUFF at scales of 50 to 200 

km and beyond; 

> Based on the limited information available, CALPUFF may be able to give unbiased 

estimates of short-term (i.e., 3-10 hr) concentrations of non-reactive contaminants to 

within a factor of two (e.g. 200%) out to distances of about 200 km from a source. This 

level or uncertainty in a 200 km radius around a source is increased if one examines 

CALPUFF’s predictions in a particular modeling cell (e.g., one containing a population 

center) at a specific hour as opposed to considering the question of bias generally over 

the entire 200 km region irrespective of location and time of occurrence; 

> For time periods of a day or less, CALPUFF is unable to produce reliable predictions of 

non-reactive concentrations at a specific location and time; 

> What limited experimental data do exist suggest that the accuracy and reliability of the 

model’s predictions degrade as the distance scale increases; 

> While the IWAQM recommendations on the range of applicability of the CALPUFF 

model (50 to 200 km) rests on very sparse model evaluation information, EPA’s 

suggestion that the model can be used for scales beyond 200 km, even with case-bycase approval, is not based on model evaluation data; and 

> For chemically reactive pollutants such as SO2, NOx, sulfate, nitrate, nitric acid, and 

other secondary reaction products, the testing of CALPUFF model over extended spatial 

scales (50 km and beyond) has not been attempted in a rigorous manner. 
 

3-17

Scire et al., (2001) report an evaluation of CALPUFF sulfate, nitrate, light extinction, and 

sulfur and nitrogen deposition at a Class I areas over a range of source-receptor distances. In this 

study, in which a large number of sources were modeled simultaneously, sulfate and nitrate predictions 

at the CASTNet monitoring site in Pinedale, Wyoming were evaluated against observations, and light 

extinction predictions were evaluated using transmissometer measurements. Wet sulfur and nitrogen 

predictions were compared to observations at several acid deposition monitoring sites. This study is 

especially relevant because it evaluates the performance of the model to predict variables of direct 

interest in Class I visibility analyses, such as sulfate and nitrate concentrations and light extinction 

coefficients 

More recently, Chang et al., (2003) reported an intercomparison of CALPUFF with two other 

transport and dispersion models with high resolution field data. CALPUFF predictions for inert SF6

were compared using two recent mesoscale field datasets: the Dipole Pride 26 (DP26) and the 

Overland Along-wind Dispersion (OLAD). Both field experiments involved instantaneous releases of 

sulfur hexafluoride tracer gas in a mesoscale region with desert basins and mountains. Tracer 

concentrations were observed along lines of samplers at distances up to 20 km. CALPUFF predictions 

were evaluated using the maximum 3-h dosage (concentration integrated over time) along a sampling 

line. At the DP26 sampler array, CALPUFF had mean biases within 35% and random scatters of about 

a factor of 3–4. About 50%–60% of the CALPUFF predictions were within a factor of 2 of the 

observations. At the OLAD site, the model underpredicted by a factor of 2–3, on average, with 

random scatters of a factor of 3–7. Only about 25%–30% of the CALPUFF predictions of inert SF6

were within a factor of 2 of observations. 

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, model 

performance has been performed at shorter distances including a 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). Another CALPUFF evaluation study over short-distances is reported by Morrison et al. 

(2003). These studies address model performance over source-receptor distances from a few hundred 

meters to 50 km. 
 

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Figure 3-1. CALPUFF Modeling System Components. (Scire et al., 2000a) 
 

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Table 3-1. Major Features of the CALMET Meteorological Model. (Scire et al., 2000b)

 • Boundary Layer Modules of CALMET 

 - Overland Boundary Layer - Energy Balance Method 

 - Overwater Boundary Layer - Profile 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 
 

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Table 3-2. Major Features of the CALPUFF Dispersion Model (Scire et al., 2000a) 

 • 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 

 - 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 

 
 

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

 • 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) 

 • 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 

 - 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 
 

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Table 3-3. Parameter Variations in Box Model Simulations Used to Develop the 

CALPUFF Sulfate and Nitrate Formation Algorithms. (Morris et al., 2003).

Surrogate 

Parameter 

Number of 

Variations 

Model Input Parameters And Variations 

Season 3 Temperatures of 30, 20 and 10 °C were used for the, 

respectively, summer, fall and winter seasons. 

Diurnally varying clear skies solar radiation was 

assumed for each season corresponding to a latitude of 

40°. 

Background Air 

Reactivity 

4 For the summer season the following four levels of 

background ozone and VOCs were used: 

Ozone 

(ppb) 

VOC 

(ppbC) 

20 50 

50 250 

80 500 

200 2,000 

For fall and winter the ozone concentrations were 

assumed to be 75% and 50% of the summer levels. 

Dispersion 2 Two different rates of plume dispersion were used: (1) 

a stable case with a wind speed of 1.5 m/s and; (2) a 

slightly unstable case with a wind speed of 5.0 m/s. 

Release Time 2 Photochemical box model simulations were performed 

with release times of sunrise and noon. 

Plume NOx 

Concentration 

3 Initial plume NOx concentrations of 7, 350 and 1400 

ppb were used. 
 

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4.0 ALTERNATIVE MODELING 

 In some situations, the modeling of a BART-eligible source may require more rigorous 

simulation tools in order to adequately estimate the potential that the source adversely impacts a Class 

I receptor or to develop reasonable, cost effective controls on BART sources. Situations where more 

comprehensive fine particulate/visibility modeling might be required might include: 

 > The source’s plume traverses over other strong discrete sources of visibility-reducing 

emissions (e.g., SO2, NOx); 

> Co-mingling of the sources plume with emissions from one or more urban or metropolitan 

areas; 

> Complex meteorological flows entailing significant lateral and/or vertical wind shear or 

stagnation; and 

> Extensive transport distance between source location and the nearest Class I area (e.g., > 

300 km). 

Another significant motivation for some BART sources may be the need to develop more accurate, less 

biased estimates of the source’s impact on the Class I area given the propensity of CALPUFF to overpredict fine particulate aerosol concentrations and attendant visibility effects. In these or other 

situations, alternative full-science modeling systems and associated regional data bases are available 

and allowed by EPA on a case-by-case basis. Though somewhat more costly than routine CALPUFF 

modeling, the potential costs savings associated with more reasoned BART control requirements may 

prompt some states or BART source operators to pursue alternative modeling. 

 This chapter summarizes these tools and provides an assessment of their accuracy and 

reliability in estimating the impacts of BART-eligible sources on visibility in Class I areas. Of course, 

use of a more advanced modeling framework would necessitate a detailed ‘source-specific’ modeling 

protocol and discussions with the state, EPA and FLM. 

4.1 Overview 

 All source-oriented models, including CALPUFF, are derived from the fundamental atmospheric 

species conservation equation (Tesche, 1983). Much of the difference between simple and 

comprehensive models stems from the extent to which the various chemical and physical atmospheric 

processes are treated: rigorously, heuristically, in a highly parameterized fashion, or neglected 

altogether. Model developers must choose what pollutants and processes need to be treated rigorously 

and which ones may be parameterized or neglected altogether when constructing a model to fulfill its 

design objectives. Simple screening tools such as the Gaussian Plume model neglect a host of 

atmospheric processes for the sake of providing solutions on a hand-held calculator or nomograph. As 

noted in Chapter 3, CALPUFF entails a number of simplifications in atmospheric chemistry and 

transport and dispersion in order to yield a system that runs swiftly on a PC with minimal data 

requirements. 

 At the other end of the spectrum, full-science photochemical dispersion models rigorously treat 

the major processes that govern the formation, transport, gas-phase and secondary aerosol chemical 

transformation and removal of atmospheric gas phase and aerosol species and their precursors. These 
 

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state-of-science ‘one-atmosphere’ models include five basic components: a chemical kinetic 

mechanism, an emissions model(s), a meteorological model of pollutant transport, a model for 

pollutant removal at the earth's surface, and a set of numerical algorithms for integrating the governing 

species conservation equations. The scientific rigor in the development, testing, and peer-review of 

one-atmosphere models is well-described in a recent annual report by the National Oceanic and 

Atmospheric Administration (NOAA, 2005) to the EPA. Other in-depth reviews of photochemical and 

meteorological models, chemical kinetic mechanisms, emissions inventorying techniques, deposition 

modules, numerical methods, and data base preparation procedures appear extensively in the literature 

(see, for example: http://www.cmascenter.org/index.html). 

 One-atmosphere photochemical dispersion models employ nested, stationary three-dimensional 

arrays of grid cells within which pollutants are emitted, transported from cell to cell, diffused by 

turbulence, undergo chemical reactions, and are removed from the grid region by precipitation, 

adsorption on the ground, and other means. This Eulerian grid model concept has been usefully 

applied and tested across a large range of domain scales, from microscale to regional and continental 

scales. Current generation ‘one-atmosphere’ models estimate concentrations of VOCs, NO, NO2, 

NOx, O3, fine particulate aerosol including sulfate, nitrate, ammonium, elemental and organic species, 

and product species such as HNO3 and PAN. Model predictions are generated at roughly 6 to 10 

minute intervals and then hourly averaged for each grid cell in the three-dimensional computational 

domain. Current regional models are operated for episodes of several weeks duration up to a year or 

more. Model output is displayed as time series plots at all monitoring stations and as two-dimensional 

‘tile’ plots that show the hourly modeled concentrations for each species across the full domain. 

Comparisons between hourly model estimates of gas phase (e.g., ozone, NOx, VOCs, PAN) and 

particulate (sulfate, nitrate, ammonium, organic and elemental carbon, etc.) species and observations 

are readily made. 

 One-atmosphere models offer numerous advantages for certain BART analyses: 

 > They include detailed characterization of all relevant man-made and natural sources of 

NOx, VOCs, SOx, and primary particulates (e.g., sulfate, nitrate, ammonia, carbon) across 

the entire modeling domain on an hourly basis for the entire year. 

 

 > They include state-of-science treatment of the coupled non-linear atmospheric chemistry of 

gas phase photochemical and secondary aerosol species within comprehensive, tested and 

documented chemical mechanism packages. 

 

 > They include detailed characterization of three-dimensional meteorological processes in 

each grid cell of the modeling domain, account for wind speed and direction shears, 

turbulent mixing, precipitation processes, solar radiation, and pressure variations; 

 > They contain modern procedures for specifically treating the near source plume chemical 

and transport dynamics in the near-field before the plume grows to the size of the finest 

Eulerian grid nest (typically 4-12 km). Elimination of the over-dilution problem in the 

original grid models is accomplished through use of high resolution multi-scale nested 

grids and integrated plume-in-grid (PiG) sub-grid-scale (SGS) modules that simulate the 

full interactive dynamics and chemistry of point source plumes until they are assimilated 

into the grid nest; 
 

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 > As the result of detailed mulsti-scale nesting or plume-in-grid treatment and accounting for 

all manmade and natural source emissions in the entire region, one-atmosphere models are 

well suited to the analysis of single BART sources and especially the combination of 

BART sources in a region, a state, or the entire CENRAP domain; 

 > They can evaluate relative benefits of actual control measures on various man-made 

sources that might influence the fate of BART-eligible plumes, including differences in 

location, source type, and composition; 

 > The results provide complete spatial and temporal mapping of concentration fields (with 

and without the BART-eligible source included) that can be used for direct plume impact 

assessment; there is no need to compute or estimate an artificial ‘natural background 

extinction’ level since the base case model simulation (without BART sources) produces 

this automatically. Furthermore, these background estimates can be rigorously evaluated 

using existing CASTNet, IMPROVE, SEARCH, and related aerosol data sets; 

 > They have been widely applied by state and federal regulatory agencies, industrial 

organizations and academic/consulting organizations worldwide; 

 > They are accompanied by in-depth EPA regulatory guidance documents (for 8-hr ozone, 

PM2.5, and Regional Haze) specifying regarding procedures for justifying model usage in a 

protocol; developing emissions, meteorological, and air quality inputs; evaluating model 

performance; and usage in control strategy and SIP development: 

 > There now exist up-to-date, detailed regional date bases for the entire U.S. (plus northern 

Mexico and southern Canada) to support model applications, including high resolution 

36/12 km data bases over the CENRAP region; 

 While these science advantages over CALPUFF are substantial, they come at a price. Oneatmosphere models are (a) computationally intensive relative to puff models, (b) they have significant 

data requirements, and (c) they require a high level of expertise in their use. However, for some 

BART sources in the CENRAP region, these drawbacks may not dissuade source operators from 

considering their use. When compared to the likely cost of BART controls, the marginal increased 

cost of an alternative modeling study is modest indeed. In fact, recent experience in the RPO modeling 

programs suggests that application of ‘one-atmosphere’ regional models with existing annual regional 

haze data sets can be roughly equivalent to the costs of a typical PSD modeling exercise using 

CALPUFF. The regional models run on inexpensive clusters of Linux PCs. Furthermore, in the U.S. 

today there are over a hundred agency, industry, academic, government laboratory, and consulting 

groups using one-atmosphere models, providing an ample resource base for on-line help, training, 

development assistance, documentation, data bases (e.g., the CMAS clearing house) and so on. 

Several questions need to be addressed in determining whether use of a one-atmosphere model 

is preferred over EPA’s default CALPUFF modeling system: 

> What alternative models are available? 

> How do the models treat the sub-grid-scale interaction of BART plumes with the 

surrounding atmosphere containing emissions from other man-made and biogenic 

emissions sources? 
 

4-4

> What are the steps in using an alternative model? 

 > What are the performance capabilities of alternative models for gas phase and fine 

particulate concentrations and visibility impacts? 

 > What data bases are available in the CENRAP domain to support full-science modeling? 

> What are the computational requirements? 

> What are the schedule implications of using an alternative model? 

 > What is the approval process for an alternative model? 

 > What are the costs of using an alternative model instead of CALPUFF? 

 Each of these questions is addressed in the following section. 

4.2 Available Alternative Models 

Though no atmospheric model is wholly accurate and reliable in estimating visibility and fine 

particulate impacts, two models clearly constitute the state-of-science in modeling fine particulate 

aerosol and visibility: 

> CMAQ: EPA’s Models-3/Community Multiscale Air Quality (CMAQ) modeling 

system is a ‘one-atmosphere’ photochemical grid model capable of addressing ozone, 

particulate matter (PM), visibility and acid deposition at regional scale for periods up to 

one year (Byun and Ching, 1999). Developed by EPA over a 12 year period, the 

CMAQ modeling system is designed to address air quality as a whole by including 

state-of-the-science capabilities for modeling multiple air quality issues, including 

tropospheric ozone, fine particles, toxics, acid deposition, and visibility degradation. As 

with CAMx, CMAQ was designed to have multi-scale capabilities so that separate 

models were not needed for urban and regional scale air quality modeling. The CMAQ 

modeling system contains three types of modeling components: (a) a meteorological 

module for the description of atmospheric states and motions, (b) an emission models 

for man-made and natural emissions that are injected into the atmosphere, and (c) a 

chemistry-transport modeling system for simulation of the chemical transformation and 

fate. 

> CAMx: The Comprehensive Air Quality Model with Extensions (CAMx) modeling 

system is a second state-of-science ‘one-atmosphere’ photochemical grid model capable 

of addressing ozone, particulate matter (PM), visibility and acid deposition at regional 

scale for periods up to one year (ENVIRON, 2004). CAMx is a publicly available 

open-source computer modeling system for the integrated assessment of gaseous and 

particulate air pollution. CAMx is designed to (a) simulate air quality over many 

geographic scales, (b) treat a wide variety of inert and chemically active pollutants 

including ozone, inorganic and organic PM2.5 and PM10 and mercury and toxics, (c) 

provide source-receptor, sensitivity, and process analyses and (d) be computationally 

efficient and easy to use. EPA has approved the use of CAMx for numerous ozone and 
 

4-5

PM SIPs throughout the U.S, and has used both CAMx and CMAQ to evaluate regional 

ozone and fine particulate strategies (e.g., CAIR, CAMR). 

Each of these models have been shown to be significantly more accurate and free from systematic 

bias in estimating visibility reducing air pollutants when compared with CALPUFF (Tesche, 2002; 

Morris et al., 2003, 2005a,b; Tesche et al., 2005). 

A potential compromise between the CMAQ/CAMx comprehensive three-dimensional 

photochemical grid grids with full-science that simulates all sources and the single-source CALPUFF 

Lagrangian model with simplified chemistry is the SCICHEM model (Karamchandani et al., 2000), 

which is also a Lagrangian model designed to treat a single source but uses full-science chemistry: 

> SCICHEM: SCICHEM is the reactive chemistry version of SCIPUFF, which stands 

for Second-Order-Closure (SOC) Integrated Puff, and includes comprehensive 

treatment of gas-phase and aqueous-phase chemistry and aerosol processes. SCIPUFF 

simulates plume transport and dispersion using a second-order closure approach to 

solve the turbulent diffusion equations (Sykes et al., 1988, 1993; Sykes and Henn, 

1995). SCIPUFF represents a plume by a collection of three-dimensional puffs that are 

advected and dispersed according to the local meteorological and micrometeorological 

characteristics. Each puff has a Gaussian representation of the concentrations of emitted 

inert species. The overall plume, however, can have any spatial distribution of these 

concentrations, since it consists of a multitude of puffs that are independently affected 

by the transport and dispersion characteristics of the atmosphere. The turbulent 

diffusion parameterization used in SCIPUFF is based on the second-order turbulence 

closure theories of Donaldson (1973) and Lewellen (1977), providing a direct 

connection between measurable velocity statistics and predicted dispersion rates. The 

closure model has been applied on local scales up to 50 km range (Sykes et al., 1988) 

and also on continental scales up to 3000 km range (Sykes et al., 1993c). Gabruk et al. 

(1999) have shown that the second-order closure algorithm provides better model 

performance than both empirical algorithms such as the Pasquill-Gifford-Turner (PGT) 

method or first-order closure algorithms that use similarity theory to relate the 

dispersion coefficients to micrometeorological variables (e.g., as used in CALPUFF). 

SCICHEM can simulate the effect of wind shear since individual puffs will evolve 

according to their respective locations in an inhomogeneous velocity field. As puffs 

grow larger, they may encompass a volume that cannot be considered homogenous in 

terms of the meteorological variables. A puff splitting algorithm accounts for such 

conditions by dividing puffs that have become too large into a number of smaller puffs. 

Conversely, puffs may overlap significantly, thereby leading to an excessive 

computational burden. A puff merging algorithm allows individual puffs that are 

affected by the same (or very similar) micro-scale meteorology to combine into a single 

puff. Also, the effects of buoyancy on plume rise and initial dispersion are simulated by 

solving the conservation equations for mass, heat, and momentum. The gas-phase 

chemical reactions within the puffs are simulated using a general framework that allows 

any chemical kinetic mechanism (e.g., CB-IV, SAPRC) to be treated. SCICHEM 

includes an aqueous-phase chemistry module based on Walcek and Taylor (1986). The 

module includes the major pathways for aqueous-phase conversion of SO2 to sulfate. A 
 

4-6

pathway is also included for the heterogeneous production of nitrate from N2O5

hydrolysis in the presence of cloud or fog droplets. For PM2.5 calculations, SCICHEM 

includes modules to simulate the partitioning of species among the gas, aqueous, and 

solid phases. Particle size distribution is simulated using a sectional representation with 

2 size sections. 

In the 1 August 2005 Federal Register notice (70 FR, 44154), EPA invited comments on the 

use of CMAQ or CAMx in estimating the BART ``benchmarks'' for controls on sources subject to 

BART. For example, regional scale models can be used to inform BART determinations at many 

sources simultaneously through the use of source apportionment techniques which track multiple 

single source contributions of individual pollutant species that combine to reduce visibility. EPA 

notes that this “benchmark” modeling is different from the traditional use of regional scale models 

like CMAQ and/or CAMx to assess the cumulative impact on visibility after controls on all “subject 

to BART” sources have been made. Indeed, this usage is consistent with EPA’s application of 

CMAQ and CAMx in support of the Clean Air Interstate Rule (CAIR) and of the most-stringent-case 

BART for EGUs. 

 What EPA has not addressed, both in the BART Rule and the BART Modeling Guidelines, is 

the capability of full-science models such as CMAQ and CAMx to reliably model the photochemical 

and aerosol processes in individual plumes. EPA’s characterization of regional models in the BART 

Rule and the Guidelines on Air Quality Models makes no mention of the substantial body of model 

research and development performed at EPA and elsewhere over the past 10-20 years in extending grid 

model to treat sub-grid-scale reactive point source plumes. Below, we summarize highlights of the 

model development activities at EPA and other organizations that have resulted in full-science models 

that are indeed capable of simulating the fine details of BART plumes within the context of a regionalscale domain. 

4.3 Treatment of Sub-Grid-Scale Plumes 

The Plume-in-Grid (PiG) technique constitutes an attempt to account rigorously for the subgrid-scale treatment of the dynamic and chemical processes governing gas-phase and aerosol species 

concentrations in isolated, major point source plumes within the grid-based (Eulerian) modeling 

system. Since the original PiG model formulation and atmospheric testing in the 1970s sponsored by 

the Electric Power Research Institute (Seigneur et al., 1981; Tesche et al., 1981), there have been 

sustained research efforts within and outside of EPA to improve the characterization of reactive point 

source plumes within a variety of host photochemical grid models. 

Indeed, development and verification of plume-in-grid (PinG) treatment has been an integral 

component of EPA’s CMAQ modeling system. EPA’s PinG approach was specifically developed to 

simulate the relevant processes governing pollutant concentrations in subgrid scale plumes released 

from major point sources within the CMAQ grid modeling domain. Since excessive dilution occurs 

when high NOx or SOx point source emissions are instantly emitted into large volumes of Eulerian 

grid cells defined for typical regional modeling domains, chemical processes and pollutant 

concentration levels from point source emissions are greatly impacted. Consequently, the PinG 

approach models the meteorological, photochemical, and aerosol processes at the appropriate spatial 

dimension by allowing for the realistic, gradual horizontal/vertical growth of each subgrid scale plume. 

The PinG treatment has been fully integrated into the CMAQ chemical transport model (CTM) 
 

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4.3.1 CMAQ PinG 

Godowitch and Young (2005) describe the current status of EPA’s program to implement and 

verify the plume-in-grid (PinG) technique in the CMAQ chemical transport model. As described by 

Gillani and Godowitch (1999) EPA’s original goal was to provide a realistic treatment of the dynamic 

and chemical/aerosol processes impacting pollutant concentrations in major point source plumes. They 

formulated the CMAQ/PinG model with an imbedded Lagrangian plume technique to simulate the 

relevant atmospheric dispersion processes governing vertical and horizontal plume expansion. This 

more realistic treatment of plume growth allows photochemistry and aerosol formation to evolve at the 

proper spatial and temporal scales. (Godowitch and Young, 2005). The current PinG module in CMAQ 

utilizes the same chemical mechanisms (i.e., CB-IV, SAPRC-99) and aerosol modules used by the 

parent chemical transport model. The updated AE3 aerosol algorithm in CMAQv4.4 was incorporated 

and successfully tested in the PinG module. The current 2005 public release (CMAQ4.5) contains 

EPA’s most recent PinG module that has significantly improved capability to simulate aerosol species 

and PM2.5 along with gas-phase photochemistry in the subgrid plumes. 

During a simulation in which point sources are treated as PinG, CMAQ provides boundary 

conditions at the plume edges in the PinG model. For example, in Figure 4-1, the five individual 

plumes embedded in the regional grid all receive gaseous and aerosol boundary condition updates at 

each integration time step (~6 min). In the PinG model, a continuous plume is simulated by hourly 

emissions released into a new plume section. The PinG model resolves the detailed horizontal internal 

structure of each plume section by an attached set of plume cells. Figure 4-2 shows the modeled cross 

section of a high NOx/ low SO2 industrial plume in the middle Tennessee area. Once a plume section 

reaches the grid size, its subgrid PinG simulation ceases and a feedback of plume material into the 

CTM grid is performed. The net impact of a potential source is determined by subtracting a base case 

simulation (i.e., a NoPinG run) from a full CMAQ-PinG simulation (see Figure 4-3 for example). A 

full description of the capabilities of EPA’s CMAQ-PinG model and its technical formulation were 

described in EPA’s original science document (Gillani and Godowitch, 1999) and more recently by 

Godowitch (2001, 2004) and NOAA (2005). 

4.3.2 CMAQ-APT-PM 

Another PinG formulation incorporated into EPA’s CMAQ model and evaluated with 

monitoring program data is described by Vijayaraghavan, et al., (2004). CMAQ-APT-PM consists of 

the EPA CMAQ model with an embedded reactive plume model, the Second-order Closure Integrated 

puff model (SCIPUFF) with CHEMistry (SCICHEM). SCICHEM uses a second-order closure 

approach to solve the turbulent diffusion equations. The plume is represented by a myriad of threedimensional puffs that are advected and dispersed according to the local micrometeorological 

characteristics. Each puff has a Gaussian representation of the concentrations of emitted inert species. 

The overall plume, however, can have any spatial distribution of these concentrations, since it consists 

of a multitude of puffs that are independently affected by the transport and dispersion characteristics of 

the atmosphere. SCICHEM can simulate the effect of wind shear since individual puffs will evolve 

according to their respective locations in an inhomogeneous velocity field. The effects of buoyancy on 

plume rise and initial dispersion are simulated by solving the conservation equations for mass, heat, 

and momentum. The formulation of nonlinear chemical kinetics within the puff framework is 

described by Karamchandani et al. (2000). Chemical species concentrations in the puffs are treated as 

perturbations from the background concentrations. The chemical reactions within the puffs are 

simulated using a general framework that allows any chemical kinetic mechanism to be treated. The 
 

4-8

full CMAQ PM aerosol and aqueous-phase chemistry is included in the PinG formulation of CMAQAPT-PM (Karamchandani et al., 2005). 

 4.3.3 CMAQ-MADRID-APT 

 Karamchandani, et al., (2005) describe another implementation of plume-in-grid technology 

into the CMAQ framework, but this modeling system used an alternative state-of-science gas-phase 

and aerosol chemical mechanism. CMAQ-MADRID (CMAQ with the Model of Aerosol Dynamics, 

Reaction, Ionization and Dissolution, MADRID) is a version of CMAQ that is publicly available and 

currently distributed by EPA’s Community Modeling and Analysis System (CMAS) center. CMAQMADRID-APT utilizes the same emissions, meteorological and air quality files as the other CMAQ 

versions discussed above. It also has been tested with atmospheric data. 

 4.3.4 CAMx PM IRON PiG

 Implementation of plume-in-grid algorithms in CAMx has been underway since the mid 1990s. 

Emery and Yarwood (2005) and Yarwood et al., (2005) describe the science formulation, 

implementation and testing of two versions: IRON PiG and PM PiG. The CAMx IRON (Incremental 

Reactions for Organics and NOx) Plume-in-Grid model features: 

> New puff structure and dynamics to better account for deforming shears; 

> Second-order closure puff spread calculation following SCIPUFF equations; 

> Puff initialization and transport performed on the time step of finest grid; 

> Full photochemistry using CB-IV or SAPRC99; 

> Revised puff dumping approach for IRON PiG based on comparison to cell area; and 

> "Virtual dumping" of puff mass into average output concentration fields for postprocessing and analysis. 

Implementation of PM chemistry into the IRON PiG closely paralleled the approach for grid 

chemistry. The PM chemistry modules are used in each puff following the same incremental 

chemistry approach that IRON PiG uses for the gas-phase ozone chemistry; i.e., by separate 

integrations for background and puff + background (to determine the evolution of puff incremental 

concentrations.) The PM PiG updates have been incorporated into CAMxv4.20_PMPiG. Details on 

the PiG implementations in CAMx are available at www.camx.com) 

 4.3.5 CAMx Multi-Scale Flexi-nesting

A final procedure for treating sub-grid-scale plume chemistry and physics takes advantage of 

the ‘flexi-nesting’ capability of CAMx. In this method, a series of nested grids are defined with 

successively smaller horizontal grid spacing, provide a cascading multi-scale representation from local 

plume scale (say a few hundred meters) to regional scale (12 or 36 km). Through careful assessment 

of the meteorological conditions influencing the BART source and Class I receptor(s), a multi-scale 

nested grid system can be defined that directly overcomes the plume over-dilution problem of older 

Eulerian models while retaining manageable computational times over the annual period. CENRAP’s 

existing MM5/CALMET meteorological fields for 2001-2003 can be used to define the sector(s) of 

greatest concern for the BART plume, thereby allowing the modeler to ensure that the fine grid mesh is 

used to best effect. 
 

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4.4 Steps in an Alternative Modeling Study 

 The motivation to consider an alternative model to CALPUFF for the BART exemption and/or 

BART determination analysis would typically come from a State or source operator. The first step in 

the process would be to discuss the rationale for use of an alternative model with representatives from 

the state, EPA, and FLM. Since decisions on acceptability of an alternative model is a joint 

responsibility, it is prudent to engage the EPA and FLM early in this process. Section 4.8 describes the 

specific steps in the approval process. 

 Consistent with the approval process and to guide the actual modeling study, a detailed sourcespecific modeling protocol must be prepared. While this protocol would generally follow the content 

of the source-specific CALPUFF protocol (see Chapter 7), additional technical details would be 

required in the description of the model configuration, model-set up procedures, data bases to be 

employed, model evaluation and sensitivity tests, and procedures for application. 

 Once the alternative modeling protocol is approved, the state or source operator (or consultant) 

would implement the procedures set forth in the document. Typically, these would include: selection of 

appropriate emissions, meteorological, and air quality models; selection of modeling year(s) to use; 

selection and acquisition of pertinent regional modeling data bases (e.g., from CENRAP, VISTAS, 

MRPO, or WRAP); base case model simulation; model performance evaluation and sensitivity testing; 

modeling of BART source(s) with appropriate sub-grid-scale plume technology; calculation of 

background visibility; comparison of BART source plume increment relative to background visibility; 

uncertainty analysis; reporting; and modeling data archival and distribution. 

4.5 Accuracy of Regional PM and Visibility Models 

A major motivation for using full-science models for BART analysis is the expectation that 

they provide more accurate and less biased predictions of visibility reducing fine particulate aerosols 

than CALPUFF. While this likelihood is easily seen in comparative evaluations of CALPUFF and 

one-atmosphere model science formulations (e.g., Morris et al., 2003), a comparison of model 

performance against actual gas-phase, fine particulate aerosol, and visibility measurements is also 

instructive. Indeed, as noted in Section 4.8 below, one of the factors influencing EPA and FLM 

assessment of an alternative model is whether it performs better for the application than CALPUFF 

and better satisfies the BART Rule regulatory requirements. 

 Unfortunately, it is not possible to carry out a clean, head-to-head comparative model 

evaluation between CALPUFF and full-science models for their ability to simulate fine particulate 

species and visibility. First, there simply are no detailed atmospheric plume data bases that record the 

chemical evolution of point source plumes at numerous downwind locations (i.e., from point of 

emission to downwind distances of 200-400 km where the concentrations of sulfates, nitrates, EC, OC, 

secondary organic aerosols (SOA), and extinction coefficient are measured.) If such data bases 

existed, it would be a simple matter to compare alternative modeling platforms. Second, CALPUFF 

has no reliable means for accounting for the effects of all other manmade and natural emissions 

sources in the region that participate in the chemical transformation of point source emissions. Thus, 

one cannot evaluate CALPUFF predictions against downwind secondary aerosol measurements or 

visibility. Measurements reflect the interaction of a variety of upwind sources; CALPUFF outputs 

principally the fate of emissions from the point source(s) under consideration. 

 
 

4-10

Several evaluations of CALPUFF that have been carried out using inert atmospheric tracers 

(see Section 3.6). These studies are useful in elucidating the model’s ability to simulate the transport 

and dispersion of non-reactive materials. In contrast, there is a rich and evolving performance 

evaluation history of regional one-atmosphere models, stimulated in large measure by the CMAQ 

and/or CAMx modeling being performed by WRAP, CENRAP, MRPO, VISTAS and MANE-VU for 

regional haze. These regional modeling studies have examined one or both models over grid domains 

of 36 km and 12 km for the years 2001, 2002, and 2003 (depending upon the RPO). Detailed 

performance evaluations of the models have been carried out by various groups, focusing on several 

performance attributes of relevance to BART analyses. For the CENRAP modeling (Morris et al., 

2005d), these evaluations, for NO, NO2, ozone, sulfate, nitrate, ammonium, ammonia, EC, OC, 

secondary organic aerosol, PM2.5, extinction coefficient, and various other species are reported as: 

 > Daily scatter and time-series plots at each individual site.

 > Monthly average model performance metrics. Bar plots for 12 months showing bias 

and error averaged over all sites in the CENRAP region, providing a quick 

summary of the variability in seasonal performance. 

 > Bugle plots grouped both by species and by monitoring network. Monthly average 

error and bias terms are plotted in a Bugle plot as a function of the species 

concentration. This helps characterize differences in model performance at clean 

versus polluted conditions. These plots also include curves showing model 

benchmarks and performance goals. Benchmark curves indicate the skill of a wide 

array of previous PM modeling studies. Performance goals are arbitrarily defined 

based on ozone performance goals. 

 > Animated spatial plots with data overlaid on the model predictions: These animated, 

daily average plots show the IMPROVE and AQS data superimposed over the 

model simulated data. These are useful for showing spatial distributions and 

instances where poor model performance occurs because the model mis-locates the 

high concentrations by just a few grid cells from the observed high concentrations. 

 > Stacked bar time-series plots for each site: These plots show contributions of each 

PM component to visibility impairment for the daily average for each day with 

ambient data in one plot, and for each of the 365 modeled days in a second plot. 

 > Stacked bar plots for the best & worst 20% day average at each site using (1) 

matched in space & time; (2) relaxed in time; and (3) relaxed in both space and 

time: These comparisons show whether the model is capable of simulating the full 

range of clean to polluted conditions seen in the ambient data at the individual sites. 

It increases confidence in the model as a "reasonable version of reality" if it can 

simulate the range of clean to polluted conditions. 

Three recent studies summarize the current performance capabilities of the CMAQ and CAMx 

regional models for aerosol species and visibility. Morris et al., (2005d) summarize the performance of 

CAMx and CMAQ over the 36 km CENRAP domain for the full calendar year 2002. For VISTAS, 

Morris et al., (2005a,b) and Tesche et al., (2005a) summarize detailed performance evaluations of 

CAMx and CMAQ for the VISTAS region, also for 2002. 
 

4-11

Boylan and Russell (2005) proposed PM model performance goals (the level of accuracy that is 

considered to be close to the best a model can be expected to achieve) and criteria (the level of 

accuracy that is considered to be acceptable for regulatory applications) that vary as a function of 

concentration and extinction. Specifically, it has been proposed that a model performance goal has 

been met when both the mean fractional error (MFE) and the mean fractional bias (MFB) are less than 

or equal to +50% and ±30%, respectively. Additionally, the model performance criteria has been met 

when both the MFE ≤ +75% and MFB ≤ ±60%. Less abundant species would have less stringent 

performance goals and criteria. These recommendations are based upon an analysis of numerous PM 

and visibility modeling studies performed throughout the country. 

In particular, Boylan and Russell (2005) tabulated the mean fractional bias and mean fractional 

error for several recent one-atmosphere modeling studies: SAMI, WRAP, VISTAS, Midwest RPO, and 

EPRI. The one-atmosphere models evaluated included CAMx, CMAQ, CMAQ-MADRID, and URM. 

Figure 4-9 presents so-called ‘bugle plots’ of fractional bias and fractional error for each component of 

PM as well as PM10 and PM2.5. Overall model performance for PM2.5 is fairly good with about 50% of 

the points meeting the goals and a large majority meeting the criteria. Sulfate performance is better 

than most components of PM2.5 that were examined, with a majority of the points meeting the goal and 

only two points falling outside the criteria. This was attributed to good estimates of SO2 emissions, 

sulfate chemistry being less complex than other components of PM species, and the high spatial 

homogeneity of sulfate. On the contrary, nitrate performance is poorer with approximately 40% of the 

points falling outside the criteria and only values smaller 0.8 μg m-3 meeting the goal. As pointed out 

by recent EPA researchers (Yu et al., 2005) nitrate performance with all atmospheric models continues 

to be a significant challenge. Ammonium performance falls between that of sulfate and nitrate. 

Overall, the performance is fairly good because the majority of the ammonium is associated with 

sulfate which performs well. Less than 40% of the organic performance assessments meet the goal and 

a fair number fall outside the criteria. The large errors are mainly due to simulated organic levels 

being lower than observations, especially at the urban STN sites. Elemental carbon concentrations are 

typically below 1.0 μg m-3 and the performance is within the performance goals for all assessments 

except one, which is just barely outside the goal. Approximately 40% if the soil points meet the goal, 

while another 45% are outside the criteria. Most of the soil error is caused by large overestimations by 

the model. Finally, coarse mass performance is poor with only 2 points meeting the goal and a 

majority falling outside the criteria. 

Boylan and Russell (2005) performed an identical analysis for components of light extinction. 

They report similar performance patterns to those of PM for sulfate, nitrate, ammonium, organics, and 

elemental carbon. These similarities are due to the fact that each of these components receive 

multipliers that are on the same order of magnitude as the scale change when converting from mass to 

light extinction. However, since soils and coarse mass do not get large multipliers due to their minimal 

impact on visibility, their performance is much improved over that of their PM mass performance. In 

fact, only a handful of soils and coarse mass points fall outside the criteria lines. Using the identified 

metrics, sulfate and EC components of PM2.5 and light extinction are generally the most accurately 

simulated, while nitrate and organic carbon performance are poor. Poor soil performance is of concern 

for PM2.5, but not visibility. Poor coarse mass performance does not impact total PM2.5 and has 

minimal impact on total light extinction. Detailed model evaluation results for the CENRAP, MPPO, 

VISTAS, and WRAP modeling studies may be found at: 

 CENRAP: http://pah.cert.ucr.edu/aqm/cenrap/cmaq.shtml

 MRPO: http://www.ladco.org/
 

4-12

 VISTAS: http://pah.cert.ucr.edu/vistas/vistas2/results.shtml

 WRAP: http://pah.cert.ucr.edu/aqm/308/cmaq.shtml

In summary, the performance status of current one-atmosphere models such as CMAQ and 

CAMx reveals that for some species (sulfate, PM2.5) the models do quite well while for certain others 

the models exhibit bias and large uncertainty (e.g., nitrate concentrations, secondary organic aerosols). 

The models have been tested over a full range of atmospheric conditions covering the CENRAP region 

and have been evaluated for up to three continuous years (2001, 2002, and 2003). This performance 

record clearly surpasses – in rigor, scope, and statistics – any evaluation that has been published to date 

for CALPUFF. The current status of one-atmosphere models in simulating fine particulate aerosols 

and visibility impairment is thus well established and this information would be quite helpful in 

supporting discussions with EPA Regional Offices and the FLM on the merits of using full-science 

visibility models as alternatives to CALPUFF.

4.6 Data Base Requirements 

The data sets needed to support the evaluation and application of alternative regional models 

may be grouped into three general categories: emissions, meteorology, and air quality. Fully 

developed CAMx and CMAQ modeling data sets at 36/12 km grid scales are available over the 

CENRAP states and may be obtained from one or more RPOs. Details are described in Chapter 5.

 

4.7 Resource Requirements 

 

The current availability of RPO-developed regional PM modeling data bases and publiclyavailable modeling tools and technical support venues now make it feasible to perform a BART 

visibility impact study in a matter of a few months at a cost that is not too much larger than a 

traditional PSD modeling exercise. A summary of current computer, staff, and schedule requirements 

associated with a BART exemption or BART determination study is given below.

 4.7.1 Computers 

 While the computational costs to run CMAQ or CAMx for a typical BART application far 

exceed those of a guideline CALPUFF analysis, the hardware requirements are still well within the 

range of modern scientific computing set-ups. Given the availability of model-ready meteorological, 

emissions, and air quality data bases from the various RPOs, the only significant computational cost in 

alternative modeling is in running the base case simulation to establish background conditions, and the 

BART impact cases to assess determine if the source is ‘subject to BART’ and if so, what appropriate 

controls might be. As with the CALPUFF applications, there is no need to develop new meteorology. 

Unlike CALPUFF, there is no need to develop estimates of background emissions, or air quality; these 

are already compiled in the existing RPO data bases. The only data input development need, common 

to CALPUFF too, is the characterization of the point source’s emissions. 

 CMAQ and CAMx run efficiently on modern commodity PCs under the Linux operating 

system. For example, a typical Linux cluster configuration for applying CMAQ in multi-processor 

mode might include 3 dual Athlon MP 2800+ computers (6 total processors), 1 to 2.5 Gybte RAM for 

each node, a GigE switch connection, and 1-3 Tbyte external hard disk storage for model input and 

output files. With four such clusters, an annual CMAQ simulation can be performed by quarter with 15 

days spin-up. Four quarters are run in parallel, saving project calendar time. In this example, four (4) 

sub-clusters of three dual-processor Athlon MP machines would be employed with the ‘master" node 
 

4-13

of each sub-cluster maintaining one 250 Gbyte drive for meteorological data per quarter and one 250 

Gbyte drive for output for quarter. Experience shows that using more than 3 nodes (i.e., 6 processors) 

per quarter does not yield substantial run-time speed up. 

 CAMx4.2 employs a shared memory scheme rather than a parallel processing scheme, so 

model simulations are performed on one stand-alone system. Multiple jobs can be run in parallel 

however. The overall computer run times of a projects do not seem to vary significantly between 

CMAQ and CAMx platforms. 

 Finally, EPA provides a website for the community to share different experiences in 

acquiring, configuring, and applying different hardware solutions for CMAQ and other oneatmosphere tools and data bases. Go to: http://www.cmascenter.org/html/hardware.html. 

4.7.2 Staff Expertise 

Setting up and exercising the CMAQ or CAMx modeling system requires a high level of 

atmospheric modeling expertise and typically transcends the skill level normally associated with 

operating EPA guideline models. Particular areas of modeling expertise involve meteorology, 

atmospheric chemistry, emissions inventory development, regional air quality modeling, data analysis, 

and computational science. However, there are over 100 groups in the U.S. today that possess this 

level of expertise; many reside within state agencies, regional EPA offices, governmental laboratories, 

and academic institutions. There is also a fairly large consultant community with the requisite level of 

experience. Accordingly, for the limited set of states or source operators for whom alternative regional 

modeling may be attractive, there should be no shortage of skilled practitioners available to assist with 

the modeling analyses. 

 4.7.3 Schedule 

Typically a 3 to 4 month time period is required to design, negotiate, implement, and complete 

CALPUFF modeling for a nominal BART application. Of course, some studies can be performed 

more quickly and others may take longer than 4 months. 

 An alternative modeling study will require more time than a CALPUFF application for three 

reasons. First, because the use of advanced regional models may be somewhat new to some CENRAP 

state or federal agencies, the length of time to negotiate a consensus CMAQ or CAMx BART 

Modeling Protocol will take longer. Part of this stems from the requirement to justify the need and 

appropriateness of alternative models to CALPUFF. In this connection, there are some important 

decisions to be ratified (calculation of ‘natural visibility background’, number of years to be modeled, 

appropriate sub-grid-scale plume technology to be used, etc) and this will require discussion and 

negotiation with state and federal agencies. Second, the full-science models require a couple of weeks 

or more to simulate a full calendar year. So, depending on the number of years modeled and the 

number of BART control strategies examined, computer simulations alone may necessitate two months 

or more of calendar time.1

 Third, unlike CALPUFF model applications, the alternative model protocol 

development and annual visibility simulations can not realistically be carried out in parallel. 

Negotiation of the protocol must first be completed before the commitment to run annual CMAQ or 

 

1

 Most modeling centers operate several clusters of high-speed Linux machines; by configuring the clusters and model 

simulation jobs thoughtfully, multiple annual CAMx or CMAQ simulations can be carried out in parallel, thus 

condensing elapsed time for the model applications.
 

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CAMx simulations is actually implemented. Based on these factors, it is reasonable to expect that an 

alternative BART modeling application would require on the order of 4 to 6 months to complete. 

4.8 Approval Process for an Alternative Model 

 The decision to use a full-science alternative model would typically be made by the State or the 

particular source. To justify the use of an alternative model, one must follow the procedures set forth 

in the Guideline on Air Quality Models for obtaining approval for the use of a non guideline models. 

EPA encourages selection of the best techniques for each individual air quality analysis, but requires 

that the selection be done in a consistent manner. 

Determination of acceptability of an alternative model is a Regional Office responsibility. If it 

can be demonstrated that in a specific application the alternative model is more appropriate than 

CALPUFF, the model may be used subject to certain requirements. This demonstration before the 

Regional Administrator normally requires that the State or the source show that either CALPUFF is 

not appropriate for the specific application or the alternative model is available and is applicable. 

There are three separate conditions under which an alternative model will normally be approved for 

use: 

> If a demonstration can be made that the model produces concentration estimates 

equivalent to the estimates obtained using a preferred model; 

> If a statistical performance evaluation has been conducted using measured air quality 

data and the results of that evaluation indicate the alternative model performs better for 

the application than a comparable model in appendix A; and 

> If there is no preferred model for the specific application but a refined model is needed 

to satisfy regulatory requirements. 

Any one of these three separate conditions may warrant use of an alternative model. 

The procedures and techniques for determining the acceptability of a model for an individual 

case based on superior performance is contained in the document entitled “Interim Procedures for 

Evaluating Air Quality Models” should be followed. Preparation and implementation of an evaluation 

protocol which is acceptable to both control agencies and regulated industry is an important element in 

such an evaluation. If it can be shown that CALPUFF is not applicable to the BART source-specific 

modeling problem at hand, an alternative refined model may be used provided that: 

> The model can be demonstrated to be applicable to the problem on a theoretical basis; 

> The data bases which are necessary to perform the analysis are available and adequate; 

and 

> Performance evaluations of the model in similar circumstances have shown that the 

model is not biased toward underestimates. 

In short, the guidance requires that the alternative model be evaluated from both a theoretical and a 

performance perspective. 
 

4-15

EPA has prepared a document entitled “Interim Procedures for Evaluating Air Quality Models” 

to assist in developing a consistent approach when justifying the use of other than the preferred 

modeling techniques. Another EPA document “Protocol for Determining the Best Performing 

Model”provides a general framework for objective decision-making on the acceptability of an 

alternative model for a given regulatory application. These documents contain procedures for 

conducting both the technical evaluation of the model and the field test or performance evaluation. 

Ultimately, the Regional Administrator has the authority to determine whether an alternative 

model is acceptable for a source-specific BART analysis. However, recognizing the need for 

assistance and guidance in the selection process so that fairness and consistency in modeling decisions 

is fostered among the various Regional Offices and the States, EPA’s Model Clearinghouse serves as 

the final authority on modeling issues. 

4.9 Cost of Alternative Modeling 

The cost of a CALPUFF dispersion modeling study for a source can vary depending upon 

several factors including: (a) availability of meteorological and air quality data sets, (b) availability of 

emissions source characterization (especially particulate matter speciation and size fractionization), (c) 

the number of Class I areas to be examined, (d) the distance(s) from source to Class I area(s), (e) the 

effort required to prepare and successfully negotiate the modeling protocol with the state, EPA and 

FLM, and various other factors. Computer costs are minimal since the CALPUFF system runs on 

personal computers equipped with inexpensive external hard disk drive storage. (Current storage costs 

run less than $1 per Gigabyte). Experience shows that the cost of a typical CALPUFF modeling 

exercise for a BART source2

 is in the range of $20,000 to $50,000 depending upon the nature of the 

problem, the expertise and cost structure of the organization performing the modeling, the modeling 

experience of the state or source operator, and the extent of interaction required with the reviewing 

agencies. 

 An alternative modeling study using CAMx or CMAQ costs more than a CALPUFF BART 

application but the differential is much less today given the ready availability of applicable regional 

data sets. Key cost elements in an alternative modeling study include: (a) protocol development and 

negotiation with the reviewing agencies, (b) obtaining and setting up one or more RPO data bases on 

the host computer network3

, (c) running the base case regional model simulation to establish 

‘background visibility conditions, (d) running the regional model with the BART source included, and 

(e) post processing and reporting the results. Assuming that the alternative modeling was performed 

for one calendar year (e.g., 2001, 2002, or 2003), the cost range is $50,000 to $75,000. 

 The greatest cost variable in alternative modeling is whether the state, EPA, and/or FLM will 

require that more than one calendar year be modeled to establish whether a source is ‘subject to 

BART’ and/or the level of BART emissions controls needed. If a full three year CMAQ or CAMx 

modeling analysis is required, the aforementioned cost range could double. However, even if the 

reviewing agencies require a full three years of CMAQ or CAMx modeling, there are still several 

opportunities for cost reduction. Opportunities include: 

 

2 This exercise would includes analyses to determine whether the source is ‘subject to BART’, followed by several 

CALPUFF model runs to estimate the visibility impacts of different levels of BART emissions controls. 3

 The computational platform needed to efficiently run the full-science models is a Linux- or Unix-based cluster with 

multiple nodes and several TBytes of external disk storage. 
 

4-16

> If three full years of alternative modeling is performed to confirm that a source is 

subject to BART, then the BART control modeling can likely focus just on that year for 

which the maximum visibility impacts were calculated. 

> Exercise the CAMx or CMAQ models in an episodic mode only, simulating only those 

infrequent time periods when peak 24-hr average visibility impacts are greatest. 

 > Invoke the plume-in-grid or multi-scale grid nesting SGS technologies in the regional 

model only during those episodic periods where peak 24-hr average impacts were 

estimated in the initial modeling; 

> Exercise the CAMx or CMAQ models in an episodic mode only, simulating only those 

infrequent time periods when peak 24-hr average visibility impacts are greatest. 

Thoughtful application of alternative models can very likely reduce the overall program cost. The 

greatest variable in estimating the resources required to employ CAMx or CMAQ in BART modeling 

is the level of effort required to work with the state, EPA, and FLM to reach a consensus on how the 

model(s) should be applied. Since this is likely to be ‘new territory’ for some agency staff, it may 

require somewhat more effort to reach consensus. 
 

4-17

 

Figure 4-1. Plume Trajectory Paths and Growth Rates of Sub-Grid-Scale Reactive Plumes 

within the CMAQ-PinG Model Simulation of the July 1995 SOS Field Program in 

Nashville, TN. Plumes Released at 1500 UTC on 7 July 1995 on the Modeling 

Domain with the 36 km Grid. (Source: Godowitch, 2004). 
 

4-18

Figure 4-2. Reactive Plume Species Concentrations in the Cross Section from a High NOx/Low 

SO2 Emissions Source at 5 Hours After Release. Ozone (O3) is the solid red line. 

Units: SO4 (μg/m3), OH (ppt), SO2 and HNO3 (ppb). (Source: Godowitch, 2004). 

Figure 4-3. Aerosol Sulfate (SO4) Difference Field Determined by Subtracting the CMAQNoPinG Concentrations from the CMAQ-PinG Concentrations at 2 pm EDT on 7 

July 1995. (Source: Godowitch, 2004). 
 

4-19

Figure 4-4. Change in the Contributions of Fourteen (14) EGUs to Local Sulfate (SO4) 

Concentrations When the CMAQ-APT-PM Plume-in-Grid Approach is Used. 

(Source: Karamchandani et al., 2005). 

Figure 4-5. Change in the Contributions of Fourteen (14) EGUs to Local Nitrate (NO3) 

Concentrations When the CMAQ-APT-PM Plume-in-Grid Approach is Used. 

(Source: Karamchandani et al., 2005). 
 

4-20

Figure 4-6. CAMx PM PiG Simulation of Ozone Over the Upper Midwest on 14 June 2002: Plot 

on Right Reveals the Ozone Increments Associated with Sub-Grid-Scale Treatment 

of Reactive Plumes. (Source: Yarwood et al., 2005). 

Figure 4-7. CAMx PM PiG Simulation of SO2 and Sulfate Over the Upper Midwest on 14 June 

2002: Plot on Right Reveals the SO2 and SO4 Increments Associated with Sub-GridScale Treatment of Reactive Plumes. (Source: Yarwood et al., 2005). 
 

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Figure 4-8. CAMx PM PiG Simulation of NO3 and NH4 Over the Upper Midwest on 14 June 

2002: Plot on Right Reveals the NO3 and NH4 Increments Associated with SubGrid-Scale Treatment of Reactive Plumes. (Source: Yarwood et al., 2005). 
 

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(a) Mean Fractional Bias in 24-hr Average Model Predictions 

(b) Mean Fractional Error in 24-hr Average Model Predictions 

Figure 4-9. Mean Fractional Bias and Error in Fine Particulate Species Predictions from the 

CAMx, CMAQ, URM, and CMAQ-MADRID Models over Several Regional PM and 

Visibility Model Evaluation Studies. (Source: Boylan and Russell, 2005). 
 

5-1

5.0 DATA BASES FOR CALPUFF MODELING 

To support BART modeling by the states and source operators, both meteorological and 

aerometric data sets are required. Regional meteorological data sets generated by the CALMET model 

suitable for direct input to the CALPUFF modeling system have been developed and archived. These 

data sets cover calendar years 2001, 2002, and 2003 for three sub-regional grid domains shown in 

Figures 5-1 through 5-4. The procedures used in developing the CALMET data sets generally follow 

the IWAQM recommendations (EPA, 1998), except for a few notable refinements. The processed 

CALMET files, in CALPUFF-ready input format, are available from CENRAP on hard disk drives to 

interested states and stakeholders. 

This chapter describes how these meteorological modeling sets were developed and evaluated. 

The basic CALMET model configuration used to generate the three years of CALPUFF-ready 

meteorology is described in detail so that users of this information have a clear understanding of the 

data sets and their applicability. In addition, for those states or source operators who elect to conduct 

more source-specific CALMET/CALPUFF modeling, the information in this chapter may be helpful in 

guiding specification of revised CALMET model inputs and generation of revised CALMET data sets. 

Also included in Section 5 .2 is a discussion of routinely available air quality monitoring data 

sets available to the states and source operators in support of screening and source-specific BART 

modeling exercises. 

5.1 Development of CALMET Meteorological Files 

5.1.1 MM5 Data Sets 

 Alpine Geophysics developed a consistent set of CALMET regional meteorological modeling 

data sets for use by the CENRAP States, BART eligible sources within the region and others. These 

meteorological modeling data sets were constructed through the joint use of the CALMET processor 

and results from existing annual three-dimensional MM5 meteorological simulations. The specific 

annual prognostic model simulations available for CENRAP BART modeling included: 

> 2001 MM5 data set at 36/12 km resolution developed for EPA by Alpine Geophysics 

(McNally and Tesche, 2002; McNally 2003); 

> 2002 MM5 data set at 36 km resolution developed for CENRAP by Iowa DNR 

(Johnson, 2003a,b), 

> 2003 MM5 data set at 36 km resolution developed for the Midwest RPO (Baker, 

2005; Baker et al., 2004; Kembell-Cook et al., 2005) 

Each of these studies included a performance evaluation of the MM5 generated data sets against 

surface meteorological observations and the results of these evaluations are contained in the reports or 

presentations cited above. While there exists a set of annual 12 km MM5 meteorology for 2002, this 

data set was developed by four independent CENRAP modeling centers and these data sets have not 

been concatenated into one master data base. More importantly, there has been no systematic, rigorous 

model performance evaluation performed on the CENRAP 2002 12 km MM5 data yet. Accordingly, 

until such time as the 2002 12 km data set has been evaluated and shown to be of comparable 

reliability as the aforementioned MM5 data sets, it’s use is contraindicated. 
 

5-2

5.1.2 CALMET Model Configuration 

 

The CALMET modeling procedures used to construct meteorological inputs to CALPUFF for 

visibility screening of BART eligible sources generally follows the IWAQM recommendations (EPA, 

1998), except as noted below. 

CALMET Model Options. The CALMET model has a number of user-selected options, parameter 

settings, and ‘switches’ that must be defined prior to exercising the processing system. These options 

and settings are well-described in the CALMET User’s Guide (Scire et al., 2000a) and in the 

CALMET input file to the executable code. Appendix A of this protocol summarizes the CALMET 

configurations used in developing the processed 6 km meteorological fields over the three CENRAP 

BART modeling domains. Also included in the tables in Appendix A are the default CALMET 

options and parameter settings recommended in the IWAQM Phase 2 Report (EPA, 1998). 

 

CALMET Domain. Three slightly overlapping modeling domains were defined by CENRAP to 

support BART modeling. These domains are shown in Figures 5-1 through 5-4 and Table 5-1. The 

processors used to generate the domain, land use, and elevation data for the CALMET/CALPUFF 

system include TERREL, CTGPROC, and MAKEGEO, as described below. 

> TERREL is the terrain pre-processor that averages terrain features to the modeling grid 

resolution; TERREL constructs the basic properties of the gridded domain and defines 

the coordinates upon which meteorological data are stored. Key parameters include 

specification of grid type, location, resolution and terrain elevation. 

> CTGPROC computes the fractional land use for the modeling grid resolution. Land use 

characteristics for each grid cell are assigned using CTGPROC. The primary variable 

adjustment associated with CTGRPOC is selection of an appropriate land use database. 

Version 2.0 of the North American Land Cover Characteristics database is used. 

> MAKEGEO is the final pre-processor that combines the terrain and land use data for 

input to CALMET. Generating the appropriate MAKEGEO.INP control file requires 

only minimal alteration of the default assignments. Key modifications include 

specifying domain attributes and ensuring input files are correctly referenced. 

Terrain. CALMET requires both terrain height and land use/land cover for the application region. 

These are generated using the CALMET CTGPROC, TERREL and MAKEGEO processors. The 

terrain data were created using the TERREL (version 3.311, level 030709) processor and the Shuttle 

Radar Topography Mission (SRTM)-GTOPO 30 second (~1 km) resolution dataset. 

Land Use. The landuse data set was created using the Composite Theme Grid CTGROC processor 

(version 2.42, level 030709) and the United States Geological Survey (USGS) Global Land Cover 

Characterization (GLCC) version 2.0 database. The GLCC database is available at 30 second (~1km) 

resolution. References for these and other modeling datasets can be found at www.src.com. 

Vertical Layer Structure. The vertical layer structure for the CALMET/CALPUFF screening 

applications is more refined than the general suggestions of IWAQM. The CENRAP vertical structure 

was designed to reduce the need for vertical interpolation while simultaneously improving vertical 

resolution within the planetary boundary layer (PBL). Table 5-2 identifies the 11 layer interfaces 

required to define the 10 layer vertical CALMET grid structure. The top interface in the CALMET 

simulation is 4000 meters. 
 

5-3

Use of Observations. Based on considerable discussions with State and Federal managers and agency 

personnel, CENRAP has elected to use the No-Obs mode in CALMET for constructing the 6 km 

meteorological fields for CALPUFF screening exercises. The three annual MM5 simulations (2001, 

2002, and 2003) will be used as the sole source for meteorological data within CALMET. Blending 

observational data with the MM5 data within CALMET (i.e., use of the “OBS” option is essentially a 

redundant use of the same data. Substantial improvement in the MM5 initialization data and in the use 

of four dimensional data assimilation (FDDA) has been achieved in recent years using observational 

data. The ETA analysis data used in initial and boundary conditions estimates as well as within the 

FDDA fields derive from 3-hourly, 40 km objective analysis fields computed using an extensive 

supply of observational data (National Weather Service surface and upper air data, GOES satellite 

precipitable water; VAD wind profiles from NEXRAD; ACARS aircraft temperature data; SSM/I 

oceanic surface winds; daily NESDIS snow cover and sea-ice analysis data; RAOB balloon drift; 

GOES and TOVS-1B radiance data; 2D-VAR SST from NCEP Ocean Modeling Branch; radar 

estimated rainfall; and surface rainfall). The complexity, resolution, and accuracy of the ETA data that 

is used to initialize and ‘nudge’ the MM5 forecasts is extensive indeed. Particularly at the 12-36 km 

horizontal grid scales over the flat to modestly rolling topography of the CENRAP domain, there is no 

need to introduce local meteorological observations in order to retrieve local terrain effects, for 

example. Thus, mesoscale wind patterns are likely to be adequately characterized by the MM5 

simulations. 

Many observations, especially surface observations, reflect local conditions on a scale smaller 

than the 6 km CENRAP CALMET fields. The introduction of the local observations into the regional 

modeling domain may extend the influence of the observational data beyond its true representativeness 

and result in internally inconsistent flow features. In particular the time interpolation of the 12-hourly 

upper air sounding data may wash out structure in the MM5 fields that are appropriate to retain. Given 

that the CENRAP domain as a whole includes areas of moderately rolling terrain, coastal regions and 

relatively flat terrain, a single set of representative weights1

 that allows significant influence of the 

observations where appropriate, will involve a considerable effort and substantial testing. The 

internally consistent MM5 fields are considered likely to be appropriate for the regional simulations, 

and the incremental benefit of adding the observational data into the regional CALMET simulations is 

not considered worthwhile. 

However, on the smaller domains likely to be considered in source-specific modeling (e.g., 1-4 

km in scale) with the higher CALMET grid resolution and the smaller domain size, more control over 

the region of influence of the meteorological observations can be achieved. It is easier for the 

diagnostic model to allow the local flow observations to have appropriate influence in the vicinity of 

the observation, but allow terrain-adjusted flow to dominate away from the observations. Given that 

the fine scale source-specific domains will be used especially in irregular and/or meteorologically 

complex settings, the relatively coarser-scale MM5 simulations are less likely to be fully adequate, and 

the introduction observational data into CALMET is more likely to achieve improvements in the 

resulting meteorological fields. 

Diagnostic Model Settings

 A number of diagnostic model settings must be selected for CALMET to properly process 

representative diagnostic meteorological data sets. These are summarized in Appendix A, compared to 

the default CALMET settings, and discussed in the following: 

 

1 Weights are assigned in CALMET to control the ‘blending’ of observations and MM5 predictions. 
 

5-4

> CALMET options dealing with radius of influence parameters (R1, R2, RMAX1, 

RMAX2, RMAX3), BIAS, ICALM parameters are not used in No-Observations mode; 

> Gridded cloud data were inferred from the MM5 relative humidity fields (ICLOUD=3); 

> Given that all state variables are MM5-derived (IPROG=14; ITPROG=2), surface layer 

winds were not extrapolated to the upper layers (IEXTRP = -1); 

 > The IWAQM recommendation for disabling the computation of kinematic effects in the 

wind field options and parameters was selected. This was selected in light of the very 

modest elevated terrain in the CENRAP domain, relative to the mountainous regions in 

the U.S. and Alps where the kinematic parameterizations were originally developed. 

Thus, the option for computing kinematic effects was disabled (IKINE = 0). 

 > The BIAS array was set to 0. in the CALMET control file because surface and upper air 

data were not used (NOOBS = 2); 

 > Because the MM5 wind fields supply CALMET with the initial guess fields to the 

diagnostic wind model (IWFCOD =1, IPROG = 14) and observational data are not 

reintroduced, the following variables were set to nominal values: 

 The minimum distance for which extrapolation of surface winds should occur 

was set to -1 (RMIN2 = -1.). 

 RMIN was left at the IWAQM recommendation of 0.1 km. 

 RMAX1 and RMAX2 were each assigned a value of 30 km. RMAX3 was 

assigned a value of 50 km. 

 R1 and R2 were each assigned the value of 1.0. 

 ISURFT and IUPT were assigned placeholder values of 4 and 2, respectively. 

 > The radius of influence regarding terrain features is comparable to the resolution of the 

processed terrain data: 12 km. 

 > The radius of influence for temperature interpolation is set to 36 km (TRADKM), a 

value considered appropriate given the 6 km CALMET domain and 36/12 km MM5 

domain. 

 > The beginning/ending land use categories for temperature interpolation over water are 

assigned category 55: (JWAT1 = JWAT2 = 55). 

 > SIGMAP was set to 50 km, while the IWAQM recommendation is 100 km, but with no 

supporting documentation. Because precipitation rates are explicitly incorporated from 

the MM5 data, a lower radius of influence was deemed appropriate. 

> Diagnostic options: IWAQM default values were used (see Appendix A); 

> TERRAD (terrain scale) is required for runs with diagnostic terrain adjustments (i.e., 

the 2003 simulations). Values of ~10-20 km were tested, and an appropriate value 

determined.
 

5-5

> 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 Gulf of 

Mexico and the Great lakes to be properly characterized by buoy observations.

> Mixing height averaging parameter (MNMDAV) were determined 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.

Obviously, there are some instances where more advanced and/or recently developed procedures for 

constructing the CALMET fields have been used compared with the IWAQM (1998) guidance. For 

example, one agency expressed concern about the choice to employ prognostic model-derived gridded 

cloud cover data in CALMET (ICLOUD = 3). While this is admittedly a ‘non-guideline’ option, in 

our view it represents the best science option currently available. In particular, the EPA CAIR and 

CAMR rulemaking modeling and the CAMx/CMAQ modeling being performed by the RPOs for 

regional haze all utilize the gridded moisture fields in the MM5 model as a basis for estimating cloud. 

Presumably, if the method is suitable for such advanced visibility modeling, it is adequate for 

CALPUFF modeling. Of course, in the protocol negotiation, the States, source operators, and 

regulatory agencies have an opportunity to re-examine the CALMET diagnostic model settings used in 

creating the CENRAP gridded fields and modify them if warranted. 

In summary, the development of the regional CALMET meteorological fields from MM5 data 

was conducted in No-Observations (“No-Obs”) mode. CALMET’s boundary layer modules were used 

to compute mixing heights, turbulence coefficients and other meteorological parameters required as 

input to CALPUFF. 

 

 5.1.3 MM5/CALMET Processing 

Construction of the CALPUFF-ready meteorological fields entails a two-step process. First, 

the MM5 prognostic model output fields are extracted and processed for input to CALMET. This step 

entails running various extraction software routines followed by the CALMM5 code. Then, CALMET 

is exercised for the full three year period over each sub-regional CENRAP domain. 

CALMM5. Previous applications of the prognostic Mesoscale Meteorological model version 5 (MM5) 

served as the source of the gridded meteorological fields for calendar years 2001, 2002, and 2003. 

The actual CALMM5 configuration entailed modification of a few user-specified variables. However, 

two setting are of primary importance: 

> All vertical layers from MM5 were extracted, providing CALMET configuration 

flexibility, and 

> Vertical velocity, relative humidity, cloud/rain fields, and ice/snow fields were 

extracted. (Graupel was extracted for 2001, the only year where the data were available 

in the MM5 datasets.) 

CALMET. CALMET (v5.53a, lev 040716) was applied consistent with CENRAP’s recommendation 

that the 6 km be generated using the ‘No-Obs’ option. The specific options used have been discussed 

above and are summarized in Appendix A. 

 
 

5-6

 5.1.4 Evaluation of the CALPUFF-Ready Meteorological Data Sets 

In typical applications the adequacy of the CALMET fields is seldom evaluated using 

independent measurements. Often, only cursory visual examination of wind vector plots or time series 

is considered. This evaluation is important because the CALMET performance analysis gives direct 

insight into the adequacy of the model-processed fields on a subregional basis. It also serves as an 

independent quality assurance tool. Alpine’s MAPS evaluation software to perform an independent 

evaluation of the processed CALMET data bases. MAPS was used in conjunction with the NCAR 

DS472 TDL data sets to evaluate the surface winds and temperatures for 2001-2003 across all three 

domains. Since only a small portion of the meteorological content of these data were ingested in the 

MM5 data assimilation routines (see Johnson, 2003a), these data sets are essentially an independent, 

quantitative means for evaluating the adequacy of the meteorological fields input to CALPUFF. 

CALMET Evaluation Methodology 

Several statistical measures were calculated as part of the CALMET meteorological evaluation 

using established procedures (e.g., Tesche et al., 1990; Emery et al., 2001). Additional plots and 

graphs are used to present these statistics on both hourly and daily time frames over the full annual 

cycle. For this study, evaluation measures were calculated for wind, temperature, and relative humidity 

because these parameters are the principal meteorological inputs to CALPUFF. The full set of 

CALMET evaluation statistics and graphical displays generated with the AG-MAPS software 

(McNally and Tesche,1994) are contained on a DVD available from CENRAP. 

The statistics used to evaluate the meteorological fields for 2001-2003 are generated in both 

absolute terms (e.g., wind speed error in m/s), and relative terms (percent error) as is commonly done 

for air quality assessments. Obviously, a very different significance is associated with a given relative 

error for different meteorological parameters. For example, a 10% error for wind speed measured at 

10 m/s is an absolute error of 1 m/s, a minor error. Yet a 10% error for temperature at 300 K is an 

absolute error of 30 K, a ridiculously large error. On the other hand, pollutant concentration errors of 

10% at 1 ppb or 10 ppm carry practically the same significance. 

Three key meteorological metrics include the bias, error, and index of agreement (IOA) for 

wind speed, temperature and relative humidity. These measures are defined as follows: 

Bias (B): Calculated as the mean difference in prediction-observation pairings with valid data within a 

given analysis region and for a given time period (hourly or daily): 

Error (E): Calculated as the mean absolute difference in prediction-observation pairings with valid 

data within a given analysis region and for a given time period (hourly or daily). 

Note that the bias and gross error for winds are calculated from the predicted-observed residuals in 

speed and direction (not from vector components u and v). The direction error for a given predictionobservation pairing is limited to range from 0 to ±180°. 

∑∑( ) = =

= −

J

j

I

i

i

j

i Pj O

IJ

B

1 1

1

∑∑= =

= −

J

j

I

i

i

j

i Pj O

IJ

E

1 1

1
 

5-7

Index of Agreement (IOA): calculated following the approach of Willmont (1981). This metric 

condenses all the differences between model estimates and observations within a given analysis region 

and for a given time period (hourly and daily) into one statistical quantity. It is the ratio of the total 

RMSE to the sum of two differences – between each prediction and the observed mean, and each 

observation and the observed mean: 

Viewed from another perspective, the index of agreement is a measure of the match between the 

departure of each prediction from the observed mean and the departure of each observation from the 

observed mean. Thus, the correspondence between predicted and observed values across the domain at 

a given time may be quantified in a single metric and displayed as a time series. The index of 

agreement has a theoretical range of 0 to 1, the latter score suggesting perfect agreement. 

CALMET Evaluation Results 

Table 5-5 summarizes the statistical measures, averaged over the month, for temperature, wind 

speed, and relative humidity for all three years. The CALMET evaluation DVD contains a full 

compilation of the statistical and graphical results. Figures 5-7 through 5-31 present a variety of 

graphical displays of processed and observed surface temperature, relative humidity, and wind across 

the three CENRAP subdomains for the three-year period 2001-2003. Figures 5-28 through 5-31 

provide convenient summaries of the bias and error in the relative humidity, temperature, and wind 

speed fields across the continuous 36 month period by subdomain. 

Thorough discussion of the performance findings is beyond the scope of these guidelines. 

However, a few key findings of the evaluation are worth noting here. From Table 5-5, the wind speed 

index of agreement, a general measure of correlation between measured and observed winds, is 

systematically greater than a value of 0.8 for virtually every month. These values are typically better 

than those generally achieved in urban- and regional-scale model applications for ozone SIPs. For 

example, the statistical benchmark for IOA suggested by Emery et al., (2001) is IOA > 0.6. Thus, the 

wind speed agreement for all three domains and all three years appears quite good relative to other 

MM5/RAMS model applications. From Figure 5-11, the wind speed root mean square error for the 

Central domain for 2002 is generally below 2.0 m/s, the performance goal for this parameter. From 

Figure 5-29 (as well as in Table 5-5), the temperature bias results for the 36 month are generally quite 

close to the + 0.5 deg C performance goal. As shown in Figure 5-30 the temperature error results are 

slightly poorer than the 2 deg C performance goal for 2001 and 2003, but are below the 2.0 deg C 

threshold for 2002. Note that the benchmarks were developed not to provide a pass/fail standard to 

which all modeling results should be held, but rather to put the results into an historical context. 

In summary, we find that: 

 Relative Humidity 

 Bias over three-year period near zero all domains 

 For some months over- and under-prediction (up to 10% or more) is evident – no 

discernable trend 

⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎣

⎡

− + −

⋅ = −

∑∑= =

J

j

I

i

o

i

o j

i Pj M O M

IJ RMSE IOA

1 1

2

1
 

5-8

 Errors typically diminish from 2001 through 2003, and are generally < 12% after 1st 

quarter of 2001. 

 Surface Temperatures 

 Monthly averaged temperatures are systematically biased low (cooler) by 0.25 to 1.25 

deg C. 

 The errors in monthly averaged temperatures typically range between 1.8 and 2.6 deg C 

 Average error over all months is about 2.2 deg C. 

 Surface Wind Speeds 

 IOA typically between 0.8 0-0.9 

 Seasonally variable 

 Central subdomain gives best correlation 

 Results from MM5/CALMET evaluation provide potentially useful information for diagnosing 

BART visibility modeling analyses 

 MM5/CALMET fields exhibit good statistical agreement with observations, in part because 

observations figure prominently in the construction of the interpolated CALMET fields. 

 MM5/CALMET fields for the three CENRAP subdomains are quite sufficient for use in 

CALPUFF modeling. 

 5.1.5 Meteorological Data Archive and Distribution 

All models, scripts and CALMET data (excepting MM5 outputs) are available from CENRAP 

on appropriate external combination Firewire/USB drives. 

5.2 Aerometric Monitoring Networks 

Data from ambient monitoring networks for both gas-phase and aerosol species are available 

for use in CENRAP BART modeling analyses. Table 5-4 summarizes ambient monitoring networks. 

Data for 2002 have been compiled for all networks covering the CENRAP domain with the exception 

of the PAMS and PM Supersites. These data sets may be obtained from CENRAP. Figures 5-5 and 5-

6 display the locations of monitoring sites in and near the CENRAP States. 
 

5-9

Table 5-1. CENRAP Lambert Conic Conformal Modeling Domain Specifications (40.97 degree 

projection origin; 33 and 45 degree matching parallels). 

Domain Southwest 

Coordinate (km)

Number 

of X 

grid cells

Number 

of Y 

grid cells 

Horizontal

Resolution 

CALMET 

 South -1008, -1620 306 246 6 km 

 Central -1008, -864 388 234 6 km 

 North -1008, 0 300 193 6 km 

Table 5-2. Vertical Layer Structure in CALMET Fields. (Heights are in meters.) 

LAYER 

NUMBER

LAYER 

HEIGHT

LAYER 

NUMBER

LAYER 

HEIGHT

0 0. 6 640. 

1 20. 7 1200. 

2 40. 8 2000. 

3 80. 9 3000. 

4 160. 10 4000. 

5 320. 

Table 5-3. Meteorological Model File Sizes for CENRAP BART Modeling. 

Domain Monthly Annual 3 Years Domain Grid 3 years

North 4.6 55.2 165.6 2001 12 km 1370

Central 6.6 79.2 237.6 2002 36 km 430

South 6.0 72.0 216.0 2003 36 km 430

total 17.2 206.4 619.2 total 2230

CALMET 6 km File Sizes, (Gbytes) MM5 File Sizes, (Gbytes)
 

5-10

Table 5-4. Statistical Evaluation of the CALMET Meteorological Fields for 2001-2003. 

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mean

RH Bias (%)

 North 4.54 3.19 0.17 -14.55 -12.09 -4.35 -0.62 1.17 -2.07 -7.98 -6.62 -4.22 -3.62

 Central -2.60 -7.28 -11.38 -10.69 -8.62 -2.90 0.66 1.07 -1.44 -5.46 -6.16 -7.78 -5.21

 South -10.23 -11.53 -13.78 -4.24 -2.08 0.99 4.12 3.16 -0.12 -2.12 -3.44 -9.76 -4.09

RH Error (%)

 North 10.06 10.31 14.03 18.77 16.28 12.39 11.82 11.76 13.26 15.54 13.53 12.89 13.39

 Central 13.32 15.86 17.45 17.05 14.50 11.67 11.52 11.32 12.26 15.52 14.79 14.95 14.18

 South 16.22 18.37 18.17 13.26 12.15 11.51 12.09 12.40 11.82 14.85 14.73 16.19 14.31

Temp Bias (0

C)

 North -1.63 -1.23 -1.23 -0.24 0.08 -0.29 -0.23 -0.54 -0.55 -0.09 -0.40 -1.27 -0.64

 Central -0.99 -0.65 -0.54 -0.16 0.13 -0.23 -0.43 -0.54 -0.36 -0.34 -0.30 -0.74 -0.43

 South -0.47 -0.42 0.03 -0.31 -0.33 -0.63 -0.99 -0.85 -0.52 -0.36 -0.19 -0.21 -0.44

Temp Error (0

C)

 North 3.10 2.88 2.54 2.49 2.44 2.43 2.42 2.49 2.58 2.48 2.89 2.55 2.61

 Central 2.38 2.25 1.99 2.18 1.99 2.01 2.07 2.11 2.21 2.52 2.61 2.42 2.23

 South 2.31 2.28 1.92 2.13 2.01 2.17 2.19 2.21 2.19 2.70 2.49 2.50 2.26

Wind Speed IOA

 North 0.79 0.83 0.83 0.87 0.86 0.85 0.81 0.84 0.84 0.82 0.81 0.79 0.83

 Central 0.85 0.87 0.88 0.88 0.89 0.86 0.84 0.86 0.87 0.86 0.85 0.84 0.86

 South 0.81 0.80 0.85 0.79 0.83 0.83 0.78 0.80 0.82 0.82 0.80 0.82 0.81

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mean

RH Bias (%)

 North 8.33 9.52 6.63 0.95 -2.42 1.25 2.43 1.60 0.57 0.47 4.47 7.73 3.46

 Central 7.43 5.13 4.60 1.65 -1.02 1.52 2.50 1.88 -0.27 -1.40 -0.01 4.35 2.20

 South 3.08 -1.19 2.53 2.32 1.26 1.98 2.51 2.62 -0.80 -2.42 -4.45 -1.03 0.53

RH Error (%)

 North 11.85 13.18 11.61 11.13 11.90 10.04 9.54 9.08 10.26 10.26 11.55 11.61 11.00

 Central 12.21 12.43 11.26 10.58 10.72 9.89 9.55 9.54 10.22 10.25 11.42 11.26 10.78

 South 11.24 11.76 10.34 8.95 9.30 9.49 9.46 9.61 9.68 9.33 11.63 10.95 10.14

Temp Bias (0

C)

 North -0.70 -0.82 -0.96 -0.52 -0.25 -0.36 -0.53 -0.49 -0.44 -0.67 -0.76 -0.69 -0.60

 Central -0.57 -0.65 -0.79 -0.62 -0.41 -0.68 -0.81 -0.74 -0.49 -0.54 -0.55 -0.52 -0.61

 South -0.23 -0.13 -0.52 -0.61 -0.61 -0.94 -0.94 -1.07 -0.65 -0.47 0.04 -0.13 -0.52

Temp Error (0

C)

 North 2.15 2.07 2.04 1.89 1.86 1.83 1.86 1.80 1.95 1.78 1.99 2.15 1.95

 Central 2.12 2.05 2.14 1.95 1.91 1.93 1.93 1.92 2.02 1.77 2.00 2.00 1.98

 South 2.18 2.05 2.17 1.83 1.89 1.91 1.88 2.00 1.92 1.68 2.06 1.93 1.96

Wind Speed IOA

 North 0.82 0.84 0.86 0.88 0.86 0.85 0.85 0.83 0.85 0.85 0.81 0.78 0.84

 Central 0.87 0.88 0.90 0.90 0.88 0.87 0.84 0.84 0.87 0.88 0.85 0.85 0.87

 South 0.86 0.86 0.85 0.85 0.84 0.82 0.79 0.80 0.83 0.83 0.83 0.82 0.83

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mean

RH Bias (%)

 North 10.15 7.40 6.01 0.93 -3.76 -0.38 1.38 2.04 -1.66 -1.99 2.96 7.68 2.56

 Central 6.94 4.76 4.15 0.42 -2.18 0.17 2.08 2.13 -2.05 -4.13 0.00 5.47 1.48

 South 0.00 0.00 0.47 -1.10 -0.37 0.54 1.77 2.89 -3.31 -6.01 -3.66 -0.33 -0.76

RH Error (%)

 North 13.30 11.21 12.32 11.70 11.65 10.03 9.70 9.57 11.13 12.68 11.53 11.85 11.39

 Central 12.77 10.95 11.61 11.18 10.33 9.91 9.49 9.50 10.70 12.69 12.10 12.43 11.14

 South 11.18 10.00 9.85 10.17 9.20 9.54 8.90 9.91 10.21 12.12 12.15 12.39 10.47

Temp Bias (0

C)

 North -1.24 -0.99 -0.63 -0.29 -0.11 -0.10 -0.22 -0.49 -0.34 0.29 -0.85 -1.34 -0.53

 Central -0.84 -0.80 -0.64 -0.47 -0.27 -0.36 -0.60 -0.66 -0.32 0.30 -0.54 -0.89 -0.51

 South -0.17 -0.27 -0.36 -0.43 -0.46 -0.62 -0.91 -0.98 -0.28 0.53 0.00 -0.03 -0.33

Temp Error (0

C)

 North 2.31 2.15 2.14 2.02 1.81 1.77 1.91 1.98 2.25 2.57 2.30 2.67 2.16

 Central 2.14 2.03 2.15 2.13 1.80 1.81 1.96 1.99 2.16 2.54 2.31 2.45 2.12

 South 2.10 1.90 2.00 2.08 1.84 1.81 1.88 2.06 1.94 2.40 2.28 2.48 2.06

Wind Speed IOA

 North 0.79 0.81 0.83 0.86 0.87 0.85 0.86 0.87 0.84 0.82 0.80 0.82 0.83

 Central 0.85 0.88 0.87 0.89 0.90 0.87 0.87 0.86 0.87 0.87 0.86 0.86 0.87

 South 0.83 0.83 0.85 0.83 0.85 0.81 0.83 0.82 0.85 0.84 0.82 0.82 0.83

CALMET Model Evaluation Statistics for 2001. 

CALMET Model Evaluation Statistics for 2002. 

CALMET Model Evaluation Statistics for 2003. 
5-11

Table 5-5. Overview of Ambient Data Monitoring Networks Covering the CENRAP Domain. 

Monitoring Network Chemical Species Measured Sampling Period Data Availab

The Interagency Monitoring of Protected Visual Environments (IMPROVE) 

Speciated PM25 and PM10 (see species mappings) 

1 in 3 days; 24 

hr average 

http://vista.cira.colostate.

VE/improve_data.htm

Clean Air Status and Trends Network (CASTNET) 

Speciated PM25, Ozone (see species mappings) 

Approximately 1-

week average 

http://www.epa.gov/castn

National Atmospheric Deposition Program (NADP) 

Wet deposition (hydrogen (acidity as pH), sulfate, nitrate, ammonium, chloride, and base cations (such as calcium, magnesium, potassium and sodium)), Mercury 

1-week average http://nadp.sws.uiuc.edu/

Air Quality System (AQS) Aka 

Aerometric Information Retrieval System (AIRS) 

CO, NO2, O3, SO2, PM25, PM10, Pb 

Typically hourly 

average 

http://www.epa.gov/air/da

Speciation Trends Network (STN) Speciated PM 24-hour average http://www.epa.gov/ttn/amSoutheastern Aerosol Research and Characterization (SEARCH) (Southeastern US only) 

24-hr PM25 (FRM Mass, OC, BC, SO4, NO3, NH4, Elem.); 24-hr PM coarse (SO4, NO3, NH4, elements); Hourly PM2.5 (Mass, SO4, NO3, NH4, EC, TC); Hourly gases (O3, NO, NO2, NOy, HNO3, SO2, CO) 

Hourly or 24-

hour average, 

depending on 

parameter. 

Electric Power Research Southern Company, and http://www.atmospheric-r

EPA Particulate Matter Supersites (Includes St. Louis in the CENRAP region) 

Speciated PM25 http://www.epa.gov/ttn/am

Photochemical Assessment Monitoring Stations (PAMS) 

Varies for each of 4 station types. http://www.epa.gov/ttn/am

National Park Service Gaseous Pollutant Monitoring Network 

Acid deposition (Dry; SO4, NO3, HNO3, NH4, SO2), O3, meteorological data 

Hourly http://www2.nature.nps.g
5-12

Figure 5-1. CENRAP North, Central, and South 6 km Meteorological Domains. 
5-13

Figure 5-2. CENRAP South Domain. 
5-14

Figure 5-3. CENRAP Central Domain. 
5-15

Figure 5-4. CENRAP North Domain. 
5-16

Figure 5-5. Locations of IMPROVE, CASTNet, SEARCH, STN and NADP Monitoring Sites 

in and Near the CENRAP States. 

-1000 -750 -500 -250 0 250 500 750 1000

-1500

-1250

-1000

-750

-500

-250

0

250

500

750

1000

1250

IMPROVE

CASTNet

SEARCH

STN

NADP

AQS
5-17

Figure 5-6. Locations of AQS Monitoring Sites in and Near the CENRAP States. 

-1000 -750 -500 -250 0 250 500 750 1000

-1500

-1250

-1000

-750

-500

-250

0

250

500

750

1000

1250

IMPROVE

CASTNet

SEARCH

STN

NADP

AQS
5-18

Figure 5-7. Spatial Mean Relative Humidity (%) over the Central Domain: July 2002. 

Figure 5-8. Spatial Mean Surface Temperature (deg C) over the Central Domain: July 2002. 
5-19

Figure 5-9. Wind Speed Index of Agreement over the Central Domain: July 2002. 

Figure 5-10. Standard Deviation in Wind Speed (m/s) over the Central Domain: July 2002. 
5-20

Figure 5-11. Root Mean Square Error in Wind Speed (m/s) over the Central Domain: July 2002. 

Figure 5-12. Scalar Mean Wind Speed (m/s) over the Central Domain: July 2002. 
5-21

Figure 5-13. Vector Mean Wind Speed (m/s) over the Central Domain: July 2002. 

Figure 5-14. Normalized Bias in Relative Humidity (%) over the Central Domain: July 2002. 
5-22

Figure 5-15. Normalized Error in Relative Humidity (%) over the Central Domain: July 2002. 

Figure 5-16. Relative Humidity (%) at Kenosha, WI: July 2002. 
5-23

Figure 5-17. Relative Humidity (%) at Topeka, KS: July 2002. 

Figure 5-18. Normalized Bias in Surface Temperature (%) over the Central Domain: July 2002. 
5-24

Figure 5-19. Normalized Error in Surface Temperature over the Central Domain: July 2002. 

Figure 5-20. Surface Temperature (deg C) at Kenosha, WI: July 2002. 
5-25

Figure 5-21. Surface Temperature at Topeka, KS: July 2002. 
5-26

Figure 5-22. MM5/CALMET Relative Humidity Bias (%) by Month for Three BART Modeling 

Years (2001, 2003, and 2003). 

Monthly Average Mean Bias in Relative Humidity for 2001

-20.0

-15.0

-10.0

-5.0

0.0

5.0

10.0

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Relative Humidity Bias, 

%

North Central South

Monthly Average Mean Bias in Relative Humidity for 2002

-20.0

-15.0

-10.0

-5.0

0.0

5.0

10.0

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Relative Humidity Bias, 

%

North Central South

Monthly Average Mean Bias in Relative Humidity for 2003

-20.0

-15.0

-10.0

-5.0

0.0

5.0

10.0

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Relative Humidity Bias, 

%

North Central South
5-27

Figure 5-23. MM5/CALMET Relative Humidity Error (%) by Month for Three BART 

Modeling Years (2001, 2003, and 2003). 

Monthly Average Mean Errror in Relative Humidity for 2002

0.0

2.5

5.0

7.5

10.0

12.5

15.0

17.5

20.0

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Relative Humidity Error, 

%

North Central South

Monthly Average Mean Errror in Relative Humidity for 2001

0.0

2.5

5.0

7.5

10.0

12.5

15.0

17.5

20.0

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Relative Humidity Error, 

%

North Central South

Monthly Average Mean Errror in Relative Humidity for 2003

0.0

2.5

5.0

7.5

10.0

12.5

15.0

17.5

20.0

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Relative Humidity Error, 

%

North Central South
5-28

Figure 5-24. MM5/CALMET Temperature Bias (deg C) by Month for Three BART Modeling 

Years (2001, 2003, and 2003). 

Monthly Average Temperature Bias for 2001 (deg C)

-2.00

-1.75

-1.50

-1.25

-1.00

-0.75

-0.50

-0.25

0.00

0.25

0.50

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Temperature Bias, deg C

North Central South

Monthly Average Temperature Bias for 2002 (deg C)

-2.00

-1.75

-1.50

-1.25

-1.00

-0.75

-0.50

-0.25

0.00

0.25

0.50

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Temperature Bias, deg 

C

North Central South

Monthly Average Temperature Bias for 2003 (deg C)

-2.00

-1.75

-1.50

-1.25

-1.00

-0.75

-0.50

-0.25

0.00

0.25

0.50

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Temperature Bias, deg C

North Central South
5-29

Figure 5-25. MM5/CALMET Temperature Error (deg C) by Month for Three BART Modeling 

Years (2001, 2003, and 2003). 

Monthly Average Temperature Errror for 2001 (deg C)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Temperature Error, deg 

C

North Central South

Monthly Average Temperature Errror for 2002 (deg C)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Temperature Error, deg 

C

North Central South

Monthly Average Temperature Errror for 2003 (deg C)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Temperature Error, deg 

C

North Central South
5-30

Figure 5-26. MM5/CALMET Wind Speed Index of Agreement by Month for Three BART 

Modeling Years (2001, 2003, and 2003). 

Monthly Average Wind Speed Index of Agreement for 2001

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Wind Speed Index of Agreement

North Central South

Monthly Average Wind Speed Index of Agreement for 2002

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Wind Speed Index of Agreement

North Central South

Monthly Average Wind Speed Index of Agreement for 2003

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Mean

Wind Speed Index of Agreement

North Central South
5-31

Figure 5-27. MM5/CALMET Relative Humidity Bias (%) over Three Years in All CENRAP Domains. 

Monthly Average Relative Humidity Bias, %

-15.0-10.0

-5.0

0.05.0

10.0

JanFebMarAprMayJunJul AugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJul AugSepOctNovD

Relative Humidity Bias, %

North Central South
5-32

Figure 5-28. MM5/CALMET Relative Humidity Error (%) over Three Years in All CENRAP Domains. 

Monthly Average Relative Humidity Error, %

0.02.55.07.5

10.012.515.017.520.0

JanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDec

Relative Humidity Error, %

North Central South
5-33

Figure 5-29. MM5/CALMET Surface Temperature Bias (deg C) over Three Years in All CENRAP Domains. 

Monthly Average Temperature Bias, deg C

-2.00-1.75-1.50-1.25-1.00-0.75-0.50-0.25

0.000.250.50

JanMarMayJulSepNovJanMarMayJulSepNovJanMarMayJulSepNov

Temperature Bias, deg C

North Central South
5-34

Figure 5-30. MM5/CALMET Surface Temperature Error (deg C) over Three Years in All CENRAP Domains. 

Monthly Average Temperature Errror, deg C

0.00.51.01.52.02.53.03.54.0

JanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDe

Temperature Error, deg C

North Central South
5-35

Figure 5-31. MM5/CALMET Wind Speed Index of Agreement over Three Years in All CENRAP Domains. 

Monthly Average Wind Speed Index of Agreement

0.600.650.700.750.800.850.900.951.00

JanFebMarApr MayJunJul AugSepOct NovDecJanFebMarApr MayJunJul AugSepOct NovDecJanFebMarApr MayJunJul AugSepOct NovDecMea

Wind Speed Index of AgreementNorth Central South
 

6-1

6.0 CALPUFF SCREENING APPLICATIONS 

The objective of the CALPUFF screening approach is to efficiently and conservatively 

determine whether a source can be exempted from BART controls or whether source-specific modeling 

is needed. The screening method described in this chapter will also help identify specific Class I areas 

that might be affected by the source. Should source-specific modeling become necessary, this 

information will assist the state or source in tailoring the analysis domain and modeling procedures to 

focus on the area(s) of greatest likely interest. Due to the nonlinearities in the physical and chemical 

processes governing visibility impairment and the variability in wind and dispersion transport patterns, 

simple notions about source-receptor relationships (e.g., the closest Class I area will produce the 

controlling visibility impacts) may not be universally reliable. So the screening analysis may offer 

some insight here. 

CALPUFF screening modeling should be performed consistent with the procedures set forth in 

this chapter, adapted as necessary to the source or sources of interest. A standard set of default 

meteorological, air quality and dispersion conditions are assumed that, for the most part, are consistent 

with the IWAQM (1998) and FLAG (2000) recommendations. For many sources in the CENRAP 

region, the screening approach may be sufficient to show whether it is a contributor to visibility 

problems in a particular Class I area. To support screening applications of CALPUFF, model-ready 

meteorological data sets have been developed for three CENRAP sub-regional domains (Figures 5-1 

through 5-4). These data sets, described in Chapter 5, expedite the screening analysis and provide for 

consistency of application across the CENRAP region. 

The CALPUFF screening methodology possesses a higher degree of conservatism (i.e., 

systematic tendency to over-predict visibility impacts) compared with the source-specific methodology 

in Chapter 7. For large sources that will clearly exceed the screening thresholds, the screening step 

may be skipped as the analysis proceeds directly to source-specific modeling. 

6.1 Methodology 

The screening methodology recommended for sources in the CENRAP region uses the 

CALPUFF model with three years of meteorological data and the standard compliment of model 

algorithms invoked. To ensure that no sources pass the screening test when they should fail, the 

simple approach, by its nature, must be the most conservative of all the conditions likely to be 

examined for the source in question. For example, many factors influence the contribution of a source 

to the Class I area other than distance. The frequency of winds transporting the pollutants toward the 

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

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

impact than a closer Class I area that is infrequently downwind of the source. Further, correlations 

between winds from certain directions and meteorological conditions may be conducive for higher 

visibility impacts. Such effects and relationships are addressed in the screening approach. It is 

designed to ensure conservatism by comparing the maximum visibility impact with the 0.5 dv 

threshold in making a determination of no contribution to visibility impairment and also by using 

conservative model settings. 

The impact of the source or sources is calculated from the daily visibility values for each 

receptor by determining the change in deciviews compared against natural visibility conditions. EPA’s 

“Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule,” EPA-454/B-
 

6-2

03-005 (September 2003) lists recommended natural visibility conditions. To determine whether a 

source may reasonably be anticipated to cause or contribute to visibility impairment at Class I area, the 

impacts predicted by CALPUFF are compared against the pertinent natural visibility background and 

the threshold that has been selected. The steps in the screening approach are outline below. It is 

described diagrammatically in Figure 1-1 in the introductory chapter. 

6.2 Protocol Development and Negotiation 

The first step is the development and negotiation of a modeling protocol with the state and 

FLM. An outline of a typical protocol is presented in Table 6-1, containing the basic elements of such 

a document. Portions of these CENRAP BART Modeling Guidelines may be adopted or referenced 

directly in a specific screening protocol as appropriate. 

6.3 CALMET Model Configuration and Application 

 

As discussed in Chapter 5, the model configurations used to generate the common CALMET 

meteorological files for visibility screening of BART eligible sources followed the IWAQM 

recommendations (EPA, 1998, Appendix A), except as noted. For CALPUFF screening assessments, 

there is no need to compile CALMET inputs, run the model or process the outputs. As discussed in 

Chapter 7, for source-specific modeling, these activities with CALMET may indeed be appropriate.

6.4 CALPUFF Model Configuration and Application 

6.4.1 Model Codes 

For screening applications, the latest ‘beta’ versions of the CALMET/CALPUFF modeling 

system should be used as given in Table 6-2. Note that these are not the EPA guideline codes but 

rather updated versions containing recent (as of this writing) science improvements and bug fixes. 

(The current guideline CALPUFF code is version 5.7, level 030402). This substitution results from 

EPA phasing out the use of the legacy Pasquill-Gifford (P-G) dispersion parameters with the 

introduction of AERMOD as a new guideline model. CALPUFF employs the AERMOD turbulencebased dispersion coefficients and probability density function (pdf) dispersion methods scheme instead 

of P-G. 

The appropriate model codes may be downloaded from www.src.com or purchased with the 

latest graphical user interface (GUI) from the model developer. The sequence of model processors 

listed in Table 6-2 corresponds to the order in which the programs are typically run. 

6.4.2 Domain Definition 

Three sub-regional modeling domains and associated CALMET meteorological data bases have 

been developed for CENRAP and are available to the states and source operators for performing 

CALPUFF screening analyses for any Class I areas within the domain. The modeling data sets cover 

three contiguous years (2001, 2002, and 2003) and are resolved to a 6 km horizontal resolution grid. 

Details of the modeling domains and the meteorological data bases for 2001, 2002, and 2003 were 

discussed in Chapter 5. 
 

6-3

Receptor Network and Class I Receptors. Experience with the CALPUFF modeling system has shown 

that sizeable emissions sources can have discernible impacts beyond 300 km. Given CALPUFF's 

propensity towards overprediction, one could easily expect CALPUFF to predict that a source causes 

or contributes to visibility impairment at Class I areas greater than 300 km. As discussed in Chapter 8, 

recent model inter comparisons between CALPUFF and full science models such as CAMx and 

CMAQ (Morris et al., 2003) amply demonstrate that CALPUFF substantially over predicts 

concentrations of visibility-reducing fine particulates under various real-world conditions at the larger 

downwind distances when compared to one-atmosphere models. Thus, a dilemma arises in that 

experience with CALPUFF indicates a tendency to overpredict at great downwind distances yet 

sources having a modeled effect beyond, say, 300km should not necessarily be neglected because there 

may be some validity to a particular CALPUFF model simulation of far downwind impacts. 

There are a significant number of BART eligible sources in the CENRAP region with no Class 

I areas within 300 km. Additionally, numerous BART sources are located no closer than 500-600 km 

to their nearest Class I area. The science-based approach to this dilemma is to use a regional model to 

address far downwind impacts. However, for most screening analyses, even out to 500-600km, States 

and source operators will likely find it necessary to employ CALPUFF notwithstanding the 

uncertainties associated with its bias at large transport scales. If CALPUFF is viewed to be a suitable 

model for use over such distances (say 300 – 600 km) States may wish to consider the use of puff 

splitting despite the lack of quantitative measurements demonstrating that puff splitting actually 

reduces CALPUFF model overprediction at large scales. Implementation of puff splitting, coupled 

with fine grid resolution (~ 12 km or less) might be considered as an approach for distant Class I 

receptors, but this strategy will likely lead to computer run times approaching and even exceeding 

those of one-atmosphere models. 

In sum, discrete receptor coordinate data for all Class I federal areas within 600 km of the 

source should be developed, preferably using the National Park Service (NPS) Convert Class I Areas 

(NCC) computer program. Whether all Class I receptors need to be modeled is an issue to be resolved 

in the modeling protocol process in cooperation with the regulatory agencies and land managers. 

Receptor elevations provided by the NPS conversion program should be used. All receptors should be 

included in a single CALPUFF simulation. (http://www2.nature.nps.gov/air/Maps/Receptors/index.cfm).

6.4.3 Model Set-Up 

Following the procedures set forth in the protocol, the agreed to version of CALPUFF is set up 

on one or more of the three CENRAP domains. The screening analysis should use 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 BART-eligible sources. The regional CALMET domain will be unchanged by 

these adjustments. 

At the election of the state or source operator, the screening approach may be designed to 

significantly reduce the CALPUFF simulation time by restricting the 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 CALPUFF input file. The advantage of selecting a 
 

6-4

smaller CALPUFF computational domain is that the model 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). This technique, if used, must be fully described in the 

modeling protocol beforehand. 

6.4.4 Emissions Input Development 

 

Stack Parameters. Stack parameters required for modeling BART-eligible units include: height of the 

stack opening from ground, inside diameter, flow-rate, exit gas temperature, ground elevation of the 

stack base, and location coordinates of the stack. Because the BART modeling focuses on mesoscale 

transport to Class I areas, other source term parameters (needed to calculate localized impacts) such as 

building heights and widths for calculating downwash may not be needed.

Emission Rates. Emission rates for CALPUFF screening analyses follow EPA’s BART guidance and 

recent staff recommendations. Source terms in the modeling should be based on emissions during 

periods of high capacity utilization during normal operating conditions. Specifically, the 24-hour 

average actual emission rate with normal operations from the highest emitting day of the year should 

be modeled. Excluded from consideration are days where start-up, shutdown or malfunctions occurred 

unless these activities are regular, frequently occurring components of the source’s operation cycle. 

(Note that while potential emissions are used to determine if a source is BART-eligible, 24-hour 

average maximum emissions are used for CALPUFF screening modeling purposes). Pollutants 

considered include SO2, H2SO4, NOx and PM10. Excluded from the modeling are pollutants with 

emissions less than de minimis levels (40 tons per year for SO2 and NOx and 15 tons per year for 

PM10). 

Identification of the maximum 24-hr actual emissions rates should be made for the most recent 

3 or 5 years, according to the following prioritization: 

> Continuous Emissions Monitoring (CEM) data; 

> Facility emissions tests; 

> Emissions factors; 

> Permit limits; or lastly, 

> Potential to emit. 

In cases where a unit is permitted to burn more than one fuel, the fuel resulting in the highest 24-hr 

emission rates should be used for the modeling. Caution is urged when estimating emissions rates in 

cases where abnormal and infrequent fuel usage overstate the potential for visibility improvement. 

Typically BART controls are expected to include the utilization of cleaner fuels (such as those burned 

by a facility in the more recent years). Thus emissions rates should solely be based upon data from the 

most recent years, in order to accurately characterize potential visibility improvements from BART 

controls. 

Emissions Speciation. Definition of the PM speciation profile for the highest 24-hour average actual 

emissions may prove particularly challenging but reliable estimates are necessary given the widely 

varying effects of different types of particulate matter on visibility. For example, the extinction 

coefficient ranges in value from 0.3 to 0.6 m2

/g for coarse particles, to 1.0 to 1.25 m2/g for fine 

inorganic particulate matter, to 1.5 to 4.0 m2

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

organic aerosols, and up to 8-12 m2

/g for elemental carbon (Tombach and McDonald, 2003). Thus, 
 

6-5

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

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

facility. Currently, data are quite limited on appropriate speciation of organic/inorganic and 

filterable/condensable emissions by source category. While speciation profiles are available for gasand oil-fired combustion turbines and coal combustion processes, detailed profiles for the full range of 

BART-eligible sources is lacking. 

In practice, except in cases where facilities operate continuous emission monitors on all 

affected equipment, there is likely to be limited information of regarding actual emissions on the 

requisite time resolution (24-hour average), much less speciation profiles for PM species. Thus it is 

particularly important for state/local agencies to work with BART-eligible sources to collect this 

information. Where available, facility-specific particulate matter speciation measurements may be 

used to assign PM10 to its components (e.g., assume primary sulfate is a fraction of SO2 emissions, 

primary sulfate is a fraction of PM10 emissions, all PM10 emissions are PM2.5). Absent this 

information, default speciation profiles may be used as agreed upon in the protocol. 

Condensable Emissions. Condensable emissions are considered primary fine particulate matter. For 

screening assessments all condensable mass should be assigned to the < 0.625 μm category unless the 

source operator has evidence of a different value based on emissions testing or other reliable 

information. This maintains conservatism in the analysis where there may be uncertainty regarding the 

exact size of condensable PM mass. If actual source emissions data are not available, the modeling 

should be based on permit limits. If source-specific size 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, assumptions for partitioning should be resolved with the reviewing agencies during 

the protocol development process. 

Size Classification of Primary PM Emissions. PM emissions should be segregated by size category. 

NPS has developed PM10 emissions speciation for certain source groups and this information can be 

used where appropriate. Modelers using information from AP-42 or other ‘reference’ documents must 

remember that the PM size classification should be applied only to the “filterable” PM mass. 

Furthermore, when modeling PM size classes, an appropriate “mass mean diameter” must be used that 

is within the specified particle size range. Use of a mass mean diameter equal to the top of the range is 

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

and visibility impacts. 

6.4.5 CALPUFF Model Configuration 

This section discusses the procedures and input assumptions that States or source operators 

should follow in applying the CALPUFF model in a screening mode to assess whether a particular 

source is subject to BART (i.e., BART exemption modeling). 

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

CALPUFF in the BART screening mode are discussed below. Appendix B contains tables that list the 

recommended screening configurations for CENRAP BART modeling. The tables also identify the 

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

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

all Class I areas within 300 km. An additional 50 km buffer zone should be established in each 
 

6-6

cardinal direction from the source. For screening applications, CENRAP recommends CALPUFF for 

all source-receptor distances unless otherwise negotiated in the modeling protocol.

Dispersion

Building Downwash. In the unlikely case where a source is immediately upwind of a Class I 

area (say, within 20 km), the IWAQM-recommended algorithms for sources subject to downwash 

should be used. For consistency across all CENRAP regional modeling studies, the CALPUFF 

building downwash algorithms should be used for all source-receptor distances if building data are 

available. 

 Puff Dispersion. The EPA (1998) guidance for plume dispersion modeling should be followed, 

including the use of the Pasquill-Gifford curves. The use of turbulence-based dispersion coefficients 

and probability density function dispersion methods have not been extensively evaluated for use in 

long range transport evaluations. Until such an evaluation has been completed and documented, the 

use of turbulence-based coefficients is outside the current CENRAP guidance and will require a caseby-case determination by the appropriate regulatory reviewing authorities. 

Puff Representation. Use the default integrated puff sampling methodology in CALPUFF. 

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

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

reactive pollutants although conceptually the methods have appeal in that they attempt to mimic lateral 

and vertical wind speed and direction shears. Since there is no direct evidence confirming the 

correctness of the puff-splitting algorithms, the decision whether to invoke the algorithms should be 

addressed in the modeling protocol. For screening applications, an added degree of conservatism at 

large down wind distances (> 200 km) may result if puff splitting is not be invoked. For sourcespecific applications (Chapter 7), puff splitting may be used as set forth in the protocol subject to 

concurrence by the cognizant reviewing agency. In sum, until verified with atmospheric data, the puff 

splitting remains an ad hoc procedure whose real impact on model reliability is unknown. In some 

cases, it may be prudent to use full-science regional models to circumvent this limitation of Lagrangian 

models. 

Chemistry

Chemical Mechanism. The MESOPUFF II module should be used for all screening (and 

source-specific) BART applications. For the aqueous phase conversion of SO2 to sulfate (important 

when the plume interacts with clouds and fog), the IWAQM defaults are recommended, i.e., nighttime 

SO2 loss rate (RNITE1) is assumed to be 0.2 percent per hour. The nighttime NOx loss rate (RNITE2) 

and HNO3 formation rate (RNITE3) are both set to 2.0 percent per hour. 

Species Modeled. Species to be modeled in the screening assessment include SO2, SO4, NOx, 

HNO3, NO3 and particulate matter. Absent detailed speciation and size distribution data from the 

source, PM should be modeled in two (2) size categories, fine (0.0-2.5 μm) and coarse (2.5-10.0 μm), 

consistent with the IMPROVE reconstructed mass equation. Particulate matter emissions by size 

category should be combined wherever possible into the appropriate species for the visibility analysis. 

These species include (a) elemental carbon (EC), (b) fine PM or “soil” (< 2.5 μm in diameter), (c) 

coarse PM (between 2.5-10 μm in diameter) and (d) organics, referred to as secondary organic 
 

6-7

aerosols in the CALPOST postprocessor. If source-specific emissions factors are not available, AP-42 

factors can be used to estimate the PM speciation for those source sectors for which AP-42 emissions 

factors have been developed. Otherwise assumptions will need to be proposed by the source operator 

and approved by the state, EPA and FLM. 

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

AIRS/AQS monitors located within the particular domain being modeled should be used to develop background 

estimates. Only non-urban ozone stations should be used in the OZONE.DAT file. Monthly average 

ozone background values should be computed from daytime average ozone concentrations (6 am to 6 

pm average). 

Background Ammonia Concentrations. In CALPUFF screening applications, the background 

ammonia concentration is assumed to be temporally and spatially invariant. Background ammonia 

estimates should be developed from CENRAP’s most recent CMAQ or CAMx simulation for the 2002 

base year. Because CMAQ/CAMx modeled and observed monthly averaged ammonia concentrations 

exhibit wide spatial variability, we recommend obtaining separate monthly-averaged ammonia 

concentrations from CMAQ or CAMx for the CENRAP north, central and south modeling domains, 

respectively. These would then be used as input to CALPUFF. The CENRAP 2002 monthly domain 

average ammonia for each CENRAP subdomain should also be used for the corresponding months in 

2001 and 2003. 

6.4.6 Post Processing 

 

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

POSTUTIL in a screening mode are presented in Appendix C. This appendix contains tables that list 

the recommended screening and default configurations for CENRAP BART modeling. The ammonialimiting method (ALM) in CALPUFF (Escoffier-Czaja and Scire, 2002, 2005) repartitions nitric acid 

and nitrate on a receptor-by-receptor and hour-by-hour basis to account for the models systematic 

over-prediction due to overlapping puffs. For screening applications, the user should set the parameter 

MNIRATE=1 in POSTUTIL to implement this approximate correction in its simplest form. The 

background ammonia concentration obtained from CAMx or CMAQ regional simulations on the three 

CENRAP subdomains should be used for consistency. 

CALPOST Parameters. Appendix D contains summarizes the CALPOST post-processor options, 

parameters, and switches. Tables are presented containing recommended and default configurations 

for CENRAP BART modeling. While all receptors should be included in a single CALPUFF 

simulation, one may calculate the visibility impacts in CALPOST for each Class I area separately 

using the NDRECP parameter. It specifies the receptor range to be processed in CALPOST. Given the 

importance of the CALPOST processor to the entire BART visibility estimation, process, we focus 

specifically on the parameter settings and input assumptions for CALPOST in the following section. 

6.5 Visibility Assessment 

The recommended procedure for quantifying visibility impacts was described in Chapter 3. The key 

point is that the light extinction coefficient (bext) can be calculated from the IMPROVE Equation as: 

bext = 3 f(RH) [(NH4)2SO4] + 3 f(RH) [NH4NO3] + 4[OC] + 1[Soil] + (6-1) 
 

6-8

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

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

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

calculated in terms of the extinction coefficient via: 

 

HI = 10 ln (bext/10) (6-2) 

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

conditions. The delta-deciview, ∆dv, value is calculated from the source’s contribution to extinction, 

bsource, and background extinction, bbackground, as follows:

 

∆dv = 10 ln ({bbackground

+ bsource}/ bbackground) (6-3) 

If the ∆dv value is greater than the 0.5 dv threshold, the source is said to contribute to visibility 

impairment and is thus ‘subject to BART’ controls. If not, it is BART-exempt. 

 

6.5.1 Visibility Impacts from BART-Eligible Sources 

Class I Receptors. Within the CENRAP domain, there are twenty three (23) Federally mandated Class 

I areas. These areas are listed in Table 6-4. For each area, the National Park Service has developed a 

receptor file containing specific coordinates and elevation data. 

(http://www2.nature.nps.gov/air/Maps/Receptors/index.cfm). 

Visibility Impact Method. In the screening approach, CALPOST should be run using Method 6 

(MVISBK=6) for calculating extinction. That is, monthly f(RH) adjustment factors are applied 

directly to the background and modeled sulfate and nitrate concentrations, as recommended in the 

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

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

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

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

 

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

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

condensable organic emissions), soil, and coarse PM. With Method 6, background extinction 

coefficients are computed from EPA (2003) monthly estimates of concentrations of ammonium sulfate 

(BKSO4), ammonium nitrate (BKNO3), coarse particulates (BKPMC), organic carbon (BKOC), soil 

(BKSOIL), and elemental carbon (BKEC). Values for these coefficients are listed in Table 6-3 and 

Appendix B. In screening analyses, the extinction due to Rayleigh scattering (i.e., the scattering of 

light by natural particles much smaller than the wavelength of the light) should be set to 10 Mm-1

(BEXTRAY = 10.0) for all Class I areas. 

Natural Background Light Extinction. EPA’s BART guidance recommends that visibility impacts 

should evaluated against ‘natural’ background conditions. EPA (2003) describes the calculation of the 

annual average background extinction (in 1/Mm) for a Class I area using the area's annual f(RH) and 

average natural concentrations based on the area's geographic location. Annual average background 

extinction values (in 1/Mm) are converted to annual average Haze Index (HI) values (in deciview or 
 

6-9

dv). Then, the average HI value for the 20% best visibility days (Best Days (dv)) is estimated from 

10th percentile of the annual average HI value for a Class I area assuming a normal distribution. Thus, 

no average natural concentrations are provided for determining extinction for the 20% best visibility 

days. EPA maintains that the above definition of natural visibility baseline as the 20% best visibility 

days is likely to be reasonably conservative and consistent with the Regional Haze Rule goal of natural 

conditions. 

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

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

year, and (c) fine sea salt and associated water is not included. Also, in the calculation of 20% best 

visibility days, the same frequency distribution is assumed for every Class I area in the East or in the 

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

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

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

In practice, concentrations for only 2 species, SO4 ([BKSO4]) and soil ([BKSOIL]), are supplied in the 

CALPOST input file to represent hygroscopic and non-hygroscopic portions of background extinction, 

respectively. Furthermore, the species concentrations are held constant over the annual cycle (i.e., no 

daily, monthly, or seasonal variation). Finally, the EPA natural background default values are defined 

separately for the eastern and western U.S. result in natural background extinction values that vary 

spatially and temporally only in response to the spatial distribution and monthly variation of 

climatologically-representative relative humidity values (EPA, 2003, Table A-3). Thus, the default 

definition of natural conditions does not take into account meteorologically caused visibility 

impairment. 

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

extinction should be used for current and natural background conditions. Table 6-3 provides the 

species concentrations representing natural background conditions for the western and eastern Class I 

areas. As shown in Figure 6-1, EPA’s east-west division cuts through the middle of the CENRAP 

domain. Accordingly, in the modeling protocols, the states and source operators will need to 

determine whether receptors in a particular Class I area fall within the East or West domains as this 

determines the appropriate default background light extinction values to use from Table 6-3. 

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

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

States may set a lower threshold. BART determinations are based upon the 98th percentile of the 

predicted 24 hour averaged deciview impact deduced from the CALPOST postprocessor. More 

specifically, to determine if a source may be exempted from BART, the highest modeled deltadeciview value for each modeling day for each modeled receptor should be determined. Depending on 

the yearly distribution of the results, the most conservative (i.e., highest) 98 % impact may come from 

the maximum 8th highest value for each of the three years or the 22nd highest value for all years 

combined (if three years of data and 365 values for each year are calculated). States and source 

operators should use both methods in order to identify the higher value. The peak impact is then 

compared to the 0.5 dv contribution threshold value. If the maximum 8th highest value for each of the 

three years (or the 22nd highest value for all years combined) exceeds the “contribution” threshold of 

0.5 deciviews the source is declared ‘subject to BART’, potentially triggering a source-specific 

CALPUFF modeling analysis (Chapter 7). Otherwise, the source is BART-excluded. In a cooperative 

agreement with EPA Regions VI and VII and federal land managers, CENRAP guidance deviates from 
 

6-10

use of the 98th percentile. The CALMET datasets as described in this protocol were processed with the 

‘NO-OBs’ options, (i.e., surface observations were not used in the CALMET wind field interpolation). 

Aware that exercising CALMET with ‘NO-OBs’ may lead, in some applications, to potentially less 

conservatism in the CALPUFF visibility results compared with the use of CALMET with observations, 

CENRAP has agreed to EPA’s recommendation that the maximum visibility impact rather than the 98th

percentile value should be used for screening analyses using the CENRAP developed CALMET 

datasets. 

To conserve computational and analysis resources, the CALPUFF screening modeling may be 

performed sequentially for calendar years 2001, 2002, and 2003. If a BART source is found to be 

subject to BART following the first or second annual evaluation, the additional year(s) need not be 

processed. Evaluation of all three years will be required to exclude a BART eligible source from the 

BART determination process. 

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

or source operators may wish to use a modified version of CALPOST that generates a file with a full 

distribution of daily delta-deciview values for each receptor. The Colorado Department of Public 

Health and Environment (CDPHE, 2005) has developed a FORTRAN processor to generate 98th

percentile results and it is available upon request. 

6.5.2 Change in Visibility Due to BART Controls 

According to the BART guidelines, a single source responsible for a 1.0 dv change or more 

should be considered to “cause” visibility impairment and a source that causes less than a 1.0 dv 

change may still contribute to visibility impairment and thus be subject to BART. For sources 

determined to be subject to BART, additional modeling is needed to assist in identifying various 

control options and selecting the “best” alternative. The BART guidelines identify five factors to be 

considered when determining what BART should be for a specific source. These include: (a) any 

pollution control equipment in use at the source (which affects the availability of options and their 

impacts), (b) the costs of compliance with control options, (c) the remaining useful life of the facility, 

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

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

For the visibility analysis (the fifth factor in determining what BART should be), it is up to the 

state to determine how to weigh the impacts. The 1.0 ∆dv threshold might be one of the parameters 

that is used although it is not required. The 98% value at this step in the analysis is also not required. 

For example, visibility improvement of 1.0 ∆dv may be predicted on 4 days out of the year, and the 

state may determine that is significant rather than judging it to be insignificant because it does not 

occur on 8 or more days. 

6.6 Presentation of Modeling Results 

 The CALPOST processor computes the daily maximum change in deciviews. As noted above, 

the maximum 8th highest value for each of the three years or the 22nd highest value for all years must 

be compared to the 0.5 deciviews threshold. At a minimum, tabular presentation of the following 

results should be provided: 

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

6-11

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

 > Number of Class I areas with impacts > 0.5 dv; and 

 > At each receptor, the magnitude of the change in extinction. 

A variety of other tabular and graphical summaries may be desirable as well. 

6.7 Reporting of CALPUFF Screening Assessment Modeling Results 

The report accompanying the screening CALPUFF modeling should provide a clear description 

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

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

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

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

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

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

output fields and well as any pre- or post-processor codes used to generate the results. The CENRAP 6 

km regional CALPUFF-ready meteorological fields do not need to be included in the archive. The 

modeling data archive should be sufficiently complete as to allow an independent modeler to fully 

corroborate the CALPUFF screening results. 
 

6-12

Table 6-1. Contents of a Typical Screening BART Modeling Protocol. 

1.0 INTRODUCTION 

1.1 Objectives 

1.2 Location of Source and Relevant Class I Areas 

1.3 Source Impact Evaluation Criteria 

1.4 Modeling Study Participants 

1.5 Protocol Review Process 

1.6 Schedule 

2.0 SOURCE DESCRIPTION 

 2.1 Unit-specific Source Data 

 2.2 Nearby Sources Affecting Same Class I Areas 

3.0 MODEL INPUT DATA 

3.1 Modeling Domain 

3.2 Terrain and Land Use 

3.3 Emissions Data Base 

 3.3.1 Stack Parameters 

 3.3.2 Emissions Rates 

 3.3.3 Condensable Emissions 

 3.3.4 Speciation and Size Distributions 

3.4 Meteorological Data Base 

 3.4.1 CENRAP CALMET Data Sets 

 3.4.2 Observational Data 

3.5 Air Quality Data Base 

 3.5.1 Ozone Concentrations 

 3.5.2 Ammonia Concentrations 

 3.5.3 Concentrations of Other Pollutants 

 3.5.5 CENRAP Regional Modeling Data Sets 

3.6 Natural Conditions at Class I Areas 

4.0 CALPUFF MODELING METHODOLOGY 

4.1 Model Selection 

 4.2 Domain Configuration and Receptors 

4.3 CALPUFF Configuration 

4.4 Light Extinction and Haze Impact Calculations 

4.5 Modeling Results 

4.6 Uncertainty Analysis 

5.0 EXEMPTION MODELING 

6.0 BART CONTROL MODELING RESULTS 

 7.0 REPORTING 

 REFERENCES 

APPENDIX A: CALMET Inputs 

APPENDIX B: CALPUFF Inputs 

APPENDIX C: POSTUTIL Inputs 

APPENDIX D: CALPOST Inputs
 

6-13

Table 6-2. CALMET/CALPUFF Model Codes for Screening Applications. 

PROCESSOR VERSION LEVEL

TERREL 3.311 030709 

CTGCOMP 2.42 030709 

CTGPROC 2.42 030709 

MAKEGEO 2.22 030709 

CALMM5a 2.4 050413 

CALMET 5.53a 040716 

CALPUFF 5.711a 040716 

POSTUTIL 1.4 040818 

CALPOST 5.51 030709 

a: The versions of CALMM5 (version 2.4, level 050413) and of CALMET (version 5.53a, level 

040716) are not compatible as published. Alpine Geophysics developed software 

modifications to reconcile the two. The code is available at: www.alpinegeophysics.com. 

Table 6-3. Default Natural Background Concentrations (μg/m3) for Eastern and Western U.S. 

Class I areas. (Source: EPA, 2003, Table 2-1) 

Average Natural 

Concentration West 

(μg/m3)

Average Natural 

Concentration 

East (μg/m3)

Error 

Factor

Dry Extinction 

Efficiency (m2/g)

Ammonium sulfateb 0.12 0.23 2 3 

Ammonium nitrate 0.10 0.10 2 3 

Organic carbon massc 0.47 1.40 2 4 

Elemental carbon 0.02 0.02 2-3 10 

Soil 0.50 0.50 11⁄2 - 2 1 

Coarse Mass 3.0 3.0 11⁄2 - 2 0.6 

a: After Trijonis (1990) 

b: Values adjusted to represent chemical species in current IMPROVE light extinction 

algorithm; Trijonis estimates were 0.1 μg/m3

 and 0.2 μg/m3 of ammonium bisulfate. 

c: Values adjusted to represent chemical species in current IMPROVE light extinction 

algorithm; Trijonis estimates were 0.5 μg/m3

 and 1.5 μg/m3 of organic compounds. 
 

6-14

Table 6-4. Federal Class I Areas in the CENRAP Domain. 

Federal Class I Areas in the CENRAP Domain 

Class I Area ST Name 

Big Bend National Park TX BIBE 

Boundary Waters Canoe Area MN BOWA1 

Breton LA BRET1 

Caney Creek AR CACR1 

Guadalupe Mountains Nat'l Park TX GUMO1 

Hercules Glades MO HEGL1 

Mingo MO MING1 

Upper Buffalo Wilderness AR UPBU1 

Voyageurs National Park MN VOYA2 

Wichita Mountains OK WIMO1 
 

6-15

Figure 6-1. Division of the U.S. for the Purpose of Assigning Default Natural Background 

Concentrations (μg/m3) for Eastern and Western U.S. Class I areas. Depicted are 

the Estimates of the Default 10% Natural Haze Index Values (in dv). (Source: EPA,

2003).
 

7-1

7.0 CALPUFF SOURCE-SPECIFIC APPLICATIONS 

The BART process provides an option for source-specific modeling to be performed. There are 

a number of reasons why a more detailed approach than that discussed previously may be desired. For 

example, sources very close to Class I areas may elect to use finer grid resolution than the 6 km 

gridded fields provided by CENRAP. Also, it may be desirable to reduce the uncertainties in assumed 

background ammonia, hydrogen peroxide, and ozone concentrations. These and other refinements in 

model inputs may help to clarify whether a source should be excluded from BART and/or to quantify 

the expected visibility impacts of alternative BART controls on an affected source. 

7.1 Methodology 

Source-specific CALPUFF modeling may be helpful in demonstrating more convincingly that a 

source does not cause or contribute to visibility impairment in any Class I areas, and thus can be 

exempted from BART controls. If the initial modeling does not pass the screening threshold for 

visibility impacts, the next step is to conduct source-specific modeling, perhaps using a finer grid 

resolution for the meteorological fields, improved treatment of terrain effects and land use variability, 

and the inclusion of more refined model parameterizations such as consideration of time and spacevarying ammonia and ozone concentration fields. As with the screening method, in the source-specific 

modeling, the visibility threshold is based on the BART guidance of the 98th percentile change in 

deciview value. 

The BART guidance indicates that the emissions rates to be used for exemption modeling is the 

highest 24-hr rate during the modeling period. Depending on the availability of source data, the 

following 24-hr maximum emissions rate information (listed in order of priority) should be used with 

CALPUFF for BART exclusion modeling: 

> Emissions for the period 2001-2003 (CEM data); 

> Emissions for 2002 (CEM data); 

> Actual 2002 emissions rates in state CERR inventory; 

> Emissions factors from AP-42 source profiles; 

> Permit allowable emissions, if available; or finally 

> Potential to emit. 

A source-specific modeling analysis will likely be required for all sources located within 50 km 

of a Class I area. EPA’s BART guidance recommends that expert modeling judgment be used, “giving 

consideration to both CALPUFF and other methods.” While the PLUVUE-II plume visibility model 

is mentioned as a possible alternative approach to consider in addition to CALPUFF within 50 km of a 

source, the CALPUFF model appears to offer greater realism, provided sufficiently small grid 

resolution (< 4 km) is used. As discussed in Chapter 3, there are no fundamental reasons why 

CALPUFF cannot be used for much shorter transport distances than 50 km, as long as the scale of the 

plume is larger than the scale of the output grid. This requirement must be met in order that the 

maximum concentrations and the width of the plume are adequately represented (and the sub-grid 

details of plume structure can be ignored when estimating effects on light extinction). Use of a 1 km 

CALPUFF modeling grid for Class I area receptors should be sufficient for source-receptor distances 

in the 25-50 km range; a finer grid may be needed for transport distances much smaller than 25-30 km. 
 

7-2

If source-specific modeling shows that a particular source causes or contributes to visibility 

impairment at a Class I area, the CALPUFF modeling system can then be used to quantify the visibility 

benefits from various BART control options. This is accomplished by running CALPUFF with the 

baseline emissions rates and with emissions after BART controls. It is important that emission 

reductions be evaluated in the post-processing step rather than by using “negative” emission rates in 

the CALPUFF model. The chemical scheme requires that emission rates always be positive. 

7.2 Source-Specific Considerations 

7.2.1 Protocol Development and Negotiation. 

For any source-specific application, a modeling protocol that defines source properties and the 

specific model configuration and application approach is required. An example of typical elements of a 

source-specific protocol is given in Table 7-1. This protocol should be reviewed with the state, EPA, 

and FLM. 

7.2.2 Domain Definition. 

While source-specific modeling can be carried out on one or more of the three CENRAP 

domains (Figures 5-1 through 5-3) with the 6 km MM5/CALMET fields for 2001-2003, it may be 

more appropriate to develop a specialized modeling domain and with finer resolution meteorological 

fields. Because CENRAP has not developed fine-scale CALMET files, the state or source operator 

will have to do this. Particularly in rough terrain or in coastal areas, finer horizontal grid spacing may 

be useful in source-specific simulations to better characterize the flow fields and land use changes. A 

typical source-specific CALMET meteorological domain might consist of a 300 x 300 array of 1-2 km 

grid cells covering the domain of interest. This produces a reasonable number of grid points that can 

easily be accommodated on personal computers. Domains with up to 600+

 x 600+ grid cells are 

sometimes necessary (for long transport distances), while domains with fewer than 100 x 100 cells 

generally are not used in a source-specific analyses because they cover insufficient territory. 

For source-receptor distances < 50km, even finer grid resolution than 1 km may be needed, 

particularly if complex terrain or coastal meteorological effects are likely to be important. This 

determination should be made on a case-by-case basis. There is no 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 terrain-forced 

flow), proximity of the source and Class I area to a coastline, and other factors including availability of 

representative observational data. 

7.3 CALMET Configuration for Source-Specific Assessments

 The CALMET processor accepts observational data in the form of hourly surface observations 

of winds, temperature, pressure and cloud data, hourly precipitation observations, and twice-daily 

upper air sounding data of winds, pressure and temperature. In addition, CALMET allows the use of 

three-dimensional gridded fields of data from prognostic models such as MM5. CALMET can be run 

in a mode using observations only (Obs-mode), prognostic data only (No-Obs mode) or both sets of 

data (hybrid mode). For example, one possible approach is to consider using the CALMET “hybrid” 
 

7-3

mode which incorporates surface observations with prognostic data, but does not include the use of 

upper air data (option NOOBS = 1). 

 In preparing the regional MM5/CALMET fields for the CALPUFF screening applications 

(Chapter 6), CALMET was exercised on a regional 6 km resolution domain (Figure 5-1) in NoObservations mode using the highest resolution MM5 data available for each year modeled. For 

source-specific modeling of Class I areas located in complex terrain or coastal regions, it may be 

desirable to develop meteorological fields over finer resolution grids (i.e.,. 1-4 km scale). In these 

cases there may be reasons to invoke the hybrid mode of CALMET option. In determining which 

method to use, the State or source operator should evaluate the following factors in the specific context 

of the source and the affected Class I area. 

Time and space resolution of observational data. Upper air observational data are available at roughly 

12-hourly intervals and the spacing between sounding locations is typically several hundred 

kilometers. Because CALMET performs a time interpolation of wind data between the sounding 

intervals, this linear time-interpolated wind data will not be able to retrieve important patterns of wind 

variation at scales less than 12 hours. 

Time and space resolution of 3-D prognostic data. The MM5 output fields provide hourly “soundings” 

over a 36 km or 12 km grid at hourly intervals. This hourly time resolution of the prognostic model 

fields is likely to better characterize the time variation of wind flows across the domain. However, on 

scales smaller than the MM5 grid nest (say below 12 km) the MM5 fields may miss potentially 

important local flow features that might be reflected in local surface measurement networks. 

Representativeness of observational data. A key question, then is whether one has confidence that the 

spatial and temporal representativeness of the local measurement stations is sufficient to favor 

measurements over prognostic modeled fields at the local scale. While use of measurements is 

attractive in many situations, too great a reliance measurements introduces the possibility of spurious 

wind or thermodynamic variable variations because the CALMET data interpolation methods does not 

obey the combined laws of momentum and energy conservation. This is particularly an issue where 

there are measurement sites of questionable representativeness or data quality. Therefore, careful 

inspection of the actual surface measurement sites to be used in the Obs-mode is needed before the 

method is used. 

Discrepancies between prognostic winds and observations. The CALMET diagnostic wind field 

module attempts to reduce discrepancies between the prognostic model-derived initial guess fields and 

the observations by giving greater weight to the observations in the “vicinity’ of the observations and 

greater weight to the prognostic wind fields in portions of the domain away from the observations. 

Although the prognostic model simulation reflects observational data through the use of fourdimensional data assimilation, there will sometimes be discrepancies between the observations (which 

may reflect a very localized flow) and the 36 km or 12 km resolved MM5 winds. Sometimes the 

differences are due to poor time resolution of the observational data; at other times they may result 

from poor instrument placement or faulty observations. In such cases, blending of the MM5 data with 

the observations (i.e., the hybrid mode) can lead to wind structures which may not be realistic. 

Model biases. The MM5 model predictions of winds and temperatures may contain biases relative to 

the real atmosphere. In particular, MM5 sometimes underestimates the frequency of light wind and 

calm wind conditions and possibly underestimates the strength of the nocturnal surface temperature 
 

7-4

inversion. These biases may be most important for near surface sources when relative coarse grid 

resolution is used. If the adequacy of the MM5 fields becomes a concern, the state or source operator 

may wish to examine the detailed MM5 model evaluation reports prepared by the original modelers 

(see Chapter 5). 

Level of effort vs. benefit. The preparation of observational datasets for CALMET on large regional 

domains involves a considerable effort due to the large number of surface, precipitation and upper air 

stations involved. The question is whether the effort associated with the preparation of this dataset is 

worthwhile given that the regional winds over the three CENRAP sub-domains (Figure 5-1) may be 

better characterized by the three-dimensional MM5 model dataset than time- and space-interpolated of 

observational data, especially above the surface where sounding data is fairly sparse spatially and 

poorly-resolved in time. For source-specific modeling, the decision whether to exercise CALMET in 

the hybrid mode is left to the state or source operator who are in the best position to assess the relative 

strengths and drawbacks of the two modes of operation in the specific context of the source they are 

modeling. Given the orography of the CENRAP domain, the regional wind patterns are likely to be 

adequately characterized at the 6 km scale via the use of MM5/CALMET with No-obs. However, at 

the finer grid scales that may be considered in source specific modeling (1-4 km), introduction of 

particularly well sited monitors – especially in key impact areas – may be appropriate. 

 Care must be taken to ensure that the introduction of the local observations into the CALMET 

processing does not extend the influence of the observational data beyond its true representativeness 

and result in internally inconsistent flow features. On smaller domains with higher CALMET grid 

resolution, more control over the region of influence of the meteorological observations can be 

achieved and it may be easier for the diagnostic model to allow the local flow observations to have 

appropriate influence in the vicinity of the observation, yet still allow terrain-adjusted flow to dominate 

away from the observations. Given that the sub-regional domains will be used especially in complex 

flow situations (complex terrain and coastal regions), the relatively coarse-scale MM5 simulations are 

less likely to be adequate, and the introduction observational data may indeed yield improvements in 

the resulting meteorological fields. 

 The decision on how the MM5/CALMET processing should be performed for source-specific 

modeling requires consultation with the State, regional EPA office and the FLM overseeing the Class I 

area of concern. A decision to allow ingestion of meteorological observations into the CALMET 

processing at the finer grid scales (i.e., below 6 km) should be contingent upon commitment to a 

thorough analysis of the instrument location, quality assurance record, and spatial/temporal 

representativeness of the each monitoring station to be used in the CALMET interpolation. For the 

reasons discussed above, absent this assessment of the measurement data representativeness, ingestion 

of observational data may in fact degrade the integrity of the resultant CALMET meteorological fields 

and the modeler/decision-maker may not be aware of it. 

 

7.4 CALPUFF Configuration for Source-Specific Assessments 

 Domain-specific CALPUFF input parameters must be defined for each source-specific 

modeling analysis. In addition to finer grid spacing in the CALMET and CALPUFF models and the 

possible introduction of observational data in the CALMET simulations, several other modeling 

enhancements can serve to increase the realism of the source-specific modeling. These include use of 

higher resolution terrain digital elevation model data (~3 arcsec USGS data) in defining the gridded 
 

7-5

terrain fields, and the use of time-varying ammonia and ozone concentrations rather than constant 

values. 

7.4.1 Background Conditions 

Background Ozone Concentrations. Ozone concentrations required as input to source-specific 

CALPUFF should be derived from regional model simulations obtained from CENRAP or the other 

RPOs in preference to the use of AIRS/AQS ozone measurements at discrete monitors (see discussion 

in section 7.4.3 below). 

Background Ammonia Concentrations. NH3 concentrations required as input to source-specific 

CALPUFF modeling should be derived from regional model simulations obtained from CENRAP/s 

CAMx or CMAQ modeling or the modeling by other RPOs in preference to the use of CASTNet or 

other network ammonia measurements at discrete monitors (see discussion in section 7.4.3 below). 

In addition to representing a better-science approach, this has the advantage of making the BART 

analyses more consistent with the CENRAP’s reasonable progress modeling for regional haze. 

 7.4.2 Ammonia Limiting Method (ALM) 

CALPUFF calculates linear oxidation of sulfur dioxide (SO2) to sulfate (SO4) and nitric oxides 

(NOx) to nitric acid (HNO3) and nitrate (NO3). The equilibrium between nitric acid and nitrate that is a 

nonlinear function of temperature and relative humidity and the equilibrium constant can vary 

substantially over a typical diurnal cycle, as the temperature and relative humidity change. In addition, 

the availability of ammonia (to form ammonium nitrate) affects ambient concentrations of nitrate. 

Because sulfate preferentially scavenges ammonia, the amount of ammonia remaining for the 

formation of nitrate may be limited. Thus, ammonia-limiting effects can be important under certain 

conditions in assessing the impacts of nitrate aerosols on visibility (Blanchard and Hidy; 2003; 

Escoffier-Czaja and Scire, 2002). 

In the screening approach (Chapter 6), the ALM was recommended using a constant 

background ammonia concentration relevant to the dominant land use in the Class I area being 

modeled was assumed (i.e., 0.5 ppb for forest, 1.0 ppb for arid lands, 10 ppb for grasslands.) For 

source-specific analyses, time-varying, user-supplied NH3 concentrations may be imposed as boundary 

conditions to each modeled puff, while cumulative ammonia consumption is modeled in the 

POSTUTIL processor for conditions at the final endpoint of the puff (i.e., the Class 1 receptor). In 

addition, the boundary concentration module within CALPUFF can be invoked to allow for time- and 

space-varying background concentrations. Use of the ALM method in conjunction with background 

source boundary conditions may improve the modeled NO3 predictions somewhat in source-specific 

analyses. 

In CALPUFF, a nominal background ammonia concentration is used in conjunction with the 

HNO3/NO3 equilibrium relationship to estimate the amount of nitrate formed. However, the model’s 

Lagrangian puff formulation causes an overestimation of the amount of ammonia available to form 

nitrate because the competition for ambient ammonia by individual puffs and by background sources is 

neglected. ALM was developed to treat this limitation. The post-processing method repartitions nitric 

acid and nitrate on a receptor-by-receptor and hour-by-hour basis and approximates the influence of 

other background sources of sulfate and nitrate in computation of incremental nitrate formation. 

Details of the ALM, incorporated into POSTUTIL are described by Escoffier-Czaja and Scire (2005). 
 

7-6

Here we provide a summary of their description of the method as it might be used in CENRAP sourcespecific modeling. 

With the ALM procedure, the contribution of an individual source or facility to nitrate 

formation is determined via a four-step procedure: 

Step 1. The total background ammonia (ambient free ammonia + ammonia associated with 

sulfate/nitrate aerosols) is estimated either through (a) use of observational data at existing 

monitoring networks such as CASTNet, (b) from the output of one-atmosphere models such as 

CMAQ or CAMx. For example, assume weekly average sulfate, nitrate and nitric acid 

concentrations at CASTNet stations can be used to estimate the ammonia in the domain. An 

iterative scheme is used split total nitrate (TNO3 = HNO3 + NO3) into HNO3 and NO3, leading 

to the following relationship, 

 [NH3]total = [NO3] + 2 [SO4] + NH3(g) 

where [NO3] is the observed nitrate concentration (ppb), [SO4] is the observed sulfate 

concentration (ppb), and NH3(g) is the estimated free ammonia concentration (ppb) derived by 

applying the chemical equilibrium relationship. Using a 5-year monthly average of NO3 and 

TNO3 ( = NO3 + HNO3) and the hourly temperature and relative humidity measured at a 

CASTnet site, an iterative scheme is used to estimate the free component of the NH3 and the 

estimation of background sulfate and nitrate. 

Step 2. The project sources are modeled in the normal way by CALPUFF to predict TNO3

from those sources. 

Step 3. The ‘boundary condition’ CALPUFF model is used to simulate pollutant 

concentrations (SO2, SO4, NOx, HNO3 and NO3) from distant sources (outside the modeling 

domain) at the receptors of interest. The background sulfate concentrations are used with the 

monthly averaged total NH3 in Step 1 to compute available ammonia (i.e., after consumption 

by sulfate). The available ammonia will vary hourly and spatially even though the total 

ammonia may be constant, because the sulfate concentrations are varying. 

Step 4. POSTUTIL is then used to develop the total concentrations of sulfate, nitrate and nitric 

acid from all sources (project source, boundary conditions and background in-domain sources 

if any), and to re-partition the nitrate to reflect the total concentrations of ammonia-scavenging 

pollutants from all sources. In this step, the ammonia consumed by background sources of 

sulfate and nitrate as well as due to puff overlap from modeled sources is all accounted for in 

the re-application of the equilibrium relationship. 

In a separate run of POSTUTIL, the contribution of any individual source or group of sources 

to the total nitrate can be determined. The flow chart diagram in Figure 7-1 shows the POSTUTIL 

process to attribute a fraction of total NO3 to any individual source (Step 3 in Figure 7-1). Once the 

total nitrate from all sources is determined with ammonia limiting effects evaluated (i.e., Steps 1 and 2 

in Figure 7-1), a separate POSTUTIL run computes the TNO3 from the project source alone, and 

attributes the nitrate formed from that particular source as a fraction of the total NO3 formed based on 

that source’s contribution to total TNO3. 
 

7-7

 The distribution of CASTNet and IMPROVE monitoring sites in the central United States is 

shown in Figure 5-5. Most Class I areas in this region include either a CASTNet and/or an IMPROVE 

monitoring site and many others are in the vicinity. CALPUFF’s ALM procedure described above 

uses the CASTNet monitoring network to produce boundary conditions of sulfate, nitrate and 

precursors and to derive empirical estimates of ammonia concentrations. However, CASTNet 

measurements have inherent errors and biases that tend to overestimate HNO3 and underestimate NO3

(some NO3 will volatilize to HNO3 within the sample after collection and before laboratory analysis). 

Thus, applying CASTNet data to estimate background NH3 would tend to underestimate the ambient 

NH3 and would therefore underestimate visibility impacts. 

One-atmosphere regional modeling results are an alternative to the use of CASTNet or other 

monitoring network observations. CENRAP’s gridded, hourly CAMx or CMAQ data bases for 2002 

may be used in the CALPUFF ALM methodology in two ways: 

> Boundary Conditions and Background Ammonia. Regional model concentrations of 

SO2, SO4, NO3, HNO3 and NOx can be used to define the boundary conditions for the 

CALPUFF boundary module run. Regional model ammonia concentration fields over 

the Class I receptors of interest can also be used directly as input to the ALM method in 

CALPUFF. Monthly averaged, BCs and NH3 concentrations can easily be derived 

from CENRAP’s 2002 annual CMAQ or CAMx modeling files. 

> Sulfate, Nitrate and Ammonia at Class I Receptors. One may extract SO4, NO3 and 

NH3 concentrations at the Class I receptors from regional model simulations on an 

hourly or longer time-average at the Class I receptors as direct input into the CALPUFF 

ALM procedure. Here, the CALPUFF boundary condition module is replaced by the 

direct use of CMAQ-predicted concentrations. 

The value in using CMAQ or CAMx modeling results include the availability of hourly 

concentrations of all relevant gas phase and fine particulate aerosol species from all sources in the U.S. 

The models have been evaluated against virtually all of the individual monitoring stations identified in 

Figure 5-5 and these results are easily obtained from the UCR/CENRAP website: 

(http://pah.cert.ucr.edu/aqm/cenrap/index.shtml). 

Considering the various sources of uncertainty (e.g., inadequate aerosol chemistry in CALPUFF, 

uncertainties in regional ammonia emissions inventories; inconsistency in measurement protocols 

among the monitoring networks in Figure 5-5, and uncertainties in regional models), use of CMAQ or 

CAMx modeling data bases is an attractive option over reliance on one or more CASTNet sites for 

some source-specific analyses. 

 For source-specific modeling, we recommend using regional model ozone, nitrate, sulfate, and 

ammonia concentration fields available over the central U.S. for 2001, 2002, 2003 from the CENRAP, 

VISTAS, and MRPO modeling studies instead of ammonia concentrations based on CASTNet, 

IMPROVE, and STN measurements and ozone measurements based on the AIRS/AQS data. 

Straightforward processing of the modeled CMAQ or CAMx nitrate and sulfate fields can be used to 

generate the inputs needed for CALPUFF boundary conditions. Gridded model output fields should be 

used to generate daily average ozone and NH3 concentrations (averaged for each calendar month) 

along the trajectory path from source to Class I area. This approach offers an improved 

characterization of the atmospheric environment through which BART plumes traverse compared to 
 

7-8

point measurements that likely do not coincide with the plume path and certainly do not characterize 

the full chemical history of the plume. Gridded CMAQ or CAMx output files are available from 

CENRAP and the other RPOs. 

7.4.3 Computing Light Extinction 

The methodology for calculating light extinction outlined in the BART guidance constitutes the 

default approach presently, but ongoing research suggests that improved methods are or soon will be 

available and recommended for use by the IMPROVE committee, EPA, and other knowledgeable 

groups. Thus, any changes to the default method (e.g., deviation from the use of the EPA monthly 

f(RH) CALPOST Method 6) will have to be justified in the source-specific modeling protocol and 

evaluated on a case-by-case basis. 

Currently, the IMPROVE Steering Committee, EPA, the Electric Power Research Institute, the 

Federal Land Managers, the Regional Planning Organizations and other scientists are discussing 

whether refinements are warranted to the method recommended in EPA’s guidance for calculating 

light extinction (i.e., the IMPROVE equation). Among the topics being considered are: 

 > Increase OC to OC Mass multiplier from 1.4 to 2; 

> Use formula to provide for varying sulfate and nitrate extinction efficiencies depending 

on concentration; 

> Accounting for anthropogenic transboundary pollution’ 

> Corrections to the statistical assumptions used to estimate 20% best and 20% worst 

days; and 

> Use actual IMPROVE derived relative humidity data rather than EPA’s 

“climatological” values 

Other refinements under consideration, which also apply to the CALPOST extinction formula 

(Equation 3-1), include the following: 

> Adding a sea salt term to the formula, which would include a growth factor due to 

relative humidity; 

> Increasing the factor used to calculate the mass of particulate organic matter from 

organic carbon measurements; 

> Slightly modifying the relative humidity growth formula, f(RH), for sulfates and 

nitrates; and 

> Revising some of the extinction efficiencies (the numerical constants in Equation 3-1), 

including making those for sulfates, nitrates, and organic carbon vary with 

concentration 

Presumably any changes that are made by the IMPROVE Steering Committee would also be reflected 

in the EPA’s approach. If this is the case, then CENRAP source-specific modeling protocols should 

adopt the emergent recommendations unless the source can justify, and the state approves, a different 

approach. Thus, for example, in the source-specific modeling, consideration of sea salt in the natural 

background, elevation effects on Rayleigh scattering (see below) and natural weather effects such as 
 

7-9

fog, rain and snow might be appropriate. Specific configuration settings for these and other model 

features should be proposed in the source-specific modeling protocol and negotiated with the state, 

regional EPA office and the FLM. 

7.4.4 Estimating Rayleigh Scattering. 

The Rayleigh scattering coefficient for clear air has a default value 10 Mm-1 (see Equation 3-1). 

This default value is appropriate for an elevation of 1600 m (about 5000 ft). Since the correct value at 

sea level is about 12 Mm-1, the default value could never be attained at low altitude sites and therefore 

the relative impact (in dv) of a source on haze would be overstated by using the default Rayleigh value. 

Publicly available codes are available for making the elevation-dependent adjustments to the high 

elevation default value of 10 Mm-1. For the source-specific analyses, an effort should be made to be 

consistent with the approach taken by the CENRAP and the other RPOs in defining the Rayleigh 

scattering coefficient for their regional analyses. 

7.4.5 Natural Baseline Conditions. 

The visibility impacts of BART-eligible sources must be evaluated against natural conditions. 

Default values of natural conditions have been specified in EPA guidance documents. These values are 

held as constant throughout the East, which result in natural background extinction that varies spatially 

and temporally only in response to the spatial distribution and monthly variation of climatologicallyrepresentative relative humidity values. (This default definition of natural conditions does not take 

into account meteorologically caused visibility impairment such as fog and rain.) 

The EPA allows refinements to the default natural conditions values, with adequate scientific 

justification. Three changes seem to have wide support: (a) increasing the multiplier of the measured 

organic carbon concentration from 1.4 to a larger value, probably in the range of 1.8 to 2.1; (b) 

changing the estimates of natural conditions extinction on the 20% clearest and haziest days to better 

reflect the statistical distribution of extinction; and (c) including sea salt in the IMPROVE/EPA 

formula, as discussed in the preceding section. The organic carbon multiplier adjustment applies to all 

BART-related calculations. Since most of the extinction under natural conditions is attributed to 

organic carbon, the first change will substantially increase the natural background level of extinction 

from the default level. This, in turn, will reduce the modeled percentage or deciview impacts of most 

sources, which may affect their BART status. 

The statistical adjustment applies for the calculation of conditions on the 20% clearest and 

haziest days. The clearest days adjustment is relevant if the baseline for evaluating source impacts in 

the BART exclusion modeling is based on the 20% clearest days. It would increase the deciview 

impact of a source’s emissions. The haziest days adjustment is of relevance for the assessment of the 

benefits of the application of BART, for which the 20% haziest days are the baseline. It slightly 

increases the default haziness on those days and therefore reduces the absolute deciview impact of a 

source’s emissions. Finally, the sea salt adjustment applies to the description of natural conditions, and 

has effect mainly near the seacoast. 

Other refinements to natural conditions estimates may be appropriate at specific locations or at 

specific times of the year. Also, any refinements to the light extinction estimation formula of Equation 

3-1 would be reflected in natural conditions extinction estimates. 
 

7-10

7.4.6 Visibility Impact Assessment. 

As recommended in the final BART guidance, the test for evaluating whether a source is 

contributing to visibility impairment is based on the 98th percentile modeled value (rather than the 

highest predicted value used for the screening evaluation). States or sources can accomplish this in one 

of two ways. First, a coding change can be made to the CALPOST postprocessor to allow the 98th

percentile change in extinction to be computed. This may be accomplished by using a specific program 

that post-processes the CALPOST output (CDPHE, 2005). Alternatively, because the CALPOST 

processor produces visibility impacts in deciviews, these impacts can be externally processed in a 

spreadsheet to calculate the changes in deciviews. Table 7-2 shows a hypothetical example of the 

ranked visibility impacts (change in dv) for each of three years at six different Class I areas. The 98th

percentile (8th highest value) in the sorted table would be compared to the contribution threshold (e.g., 

0.5 dv). In the example shown in Table 7-2, the source passes the refined analysis because the highest 

98th percentile visibility impact is below the contribution threshold of 0.5 dv. The actual procedure 

selected by the State or source should be set forth clearly in the protocol. Any CALPUFF code 

changes should be documented and copies of the source code and input/output files provided to the 

reviewing agency for corroboration. 

7.5 Assessment of Modeling Results 

The report accompanying the source-specific CALPUFF modeling should provide a clear 

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

the approved modeling protocol should be specifically discussed and justified. The report should also 

include a discussion of uncertainty in the modeling results. There are many sources of uncertainty in 

modeling due to factors such as errors and approximations used in the calculation of emission rates, 

speciation of the emissions, particle size distributions, meteorological data inputs including the MM5 

data sets used for determining the wind fields, the representativeness of meteorological observations 

used in the analysis, uncertainties in the meteorological and dispersion model itself due to its 

parameterizations of transport, dispersion and chemical transformation, and in the methods used to 

compute light extinction from particulate matter concentrations predictions. In addition, the use of 

monthly average relative humidity in the hygroscopic aerosol growth equations results in error. 

Furthermore, grid resolution affects the ability of the model to resolve terrain features and for wind and 

other meteorological fields to respond to geophysical features such as terrain, land-sea boundaries, 

surface characteristics such as roughness, albedo and other parameters. 

As appropriate, the main sources of uncertainty in the BART modeling should be addressed in 

the final report. Good discussions of uncertainty in air quality modeling are readily available, e.g., 

Russell and Dennis (2000) and the citations in the reference section of this document. The uncertainty 

analysis is not intended to be exhaustive but rather shed light on important areas of model uncertainty 

and the likely range of applicability of the CALPUFF (or alternative model) results. 

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

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

output fields and well as any pre- or post-processor codes used to generate the results. While it is not 

necessary to archive the MM5 data files used to generate the CALMET fields, the CALPUFF-ready 

meteorological fields should be included in the archive. The modeling data archive should be 

sufficiently complete as to allow an independent group to corroborate the modeling results developed 

in the source-specific analysis. 
 

7-11

Table 7-1. Contents of a Typical Source-Specific BART Modeling Protocol. 

1.0 INTRODUCTION 

1.1 Objectives 

1.2 Location of Source and Relevant Class I Areas 

1.3 Source Impact Evaluation Criteria 

1.4 Modeling Study Participants 

1.5 Protocol Review Process 

1.6 Schedule 

2.0 SOURCE DESCRIPTION 

 2.1 Unit-specific Source Data 

 2.2 Nearby Sources Affecting Same Class I Areas 

3.0 MODEL INPUT DATA 

3.1 Modeling Domain 

3.2 Terrain and Land Use 

3.3 Emissions Data Base 

 3.3.1 Stack Parameters 

 3.3.2 Emissions Rates 

 3.3.3 Condensable Emissions 

 3.3.4 Speciation and Size Distributions 

3.4 Refined Meteorological Modeling Base 

 3.4.1 MM5 Simulations 

 3.4.2 CALMET Data Sets 

 3.4.2 Observational Data 

3.5 Air Quality Data Base 

 3.5.1 Ozone Concentrations 

 3.5.2 Ammonia Concentrations 

 3.5.3 Concentrations of Other Pollutants 

 3.5.5 CENRAP Regional Modeling Data Sets 

3.6 Natural Conditions at Class I Areas 

4.0 CALMET MODELING METHODOLOGY 

4.1 Domain Configuration 

4.2 MM5 Extraction Procedures 

4.3 CALMET Configuration 

 4.3.1 Terrain 

 4.3.2 Land Use 

 4.3.3 Vertical Layer Structure 

 4.3.4 Diagnostic Model Settings 

 4.3.5 BIAS, RMIN2, IXTERP settings 

 4.3.6 R1, R2, RMAX1, RMAX2, RMAX3 settings 

 4.3.7 Surface Stations 

4.3.8 Upper Air Stations 
 

7-12

4.3.9 Precipitation Stations 

4.4 Evaluation of CALMET Datasets 

5.0 CALPUFF MODELING METHODOLOGY 

5.1 Model Selection 

 5.2 Domain Configuration and Receptors 

5.3 CALPUFF Configuration 

5.3.1 CALPUFF model options 

 5.3.2 Visibility Modeling Domain 

 5.3.3 Dispersion 

- Building downwash 

- Puff representation 

- Puff Splitting 

 5.3.4 Chemistry 

5.3.5 Chemical Mechanism 

5.3.6 Speciation 

 5.3.7 Background Ozone Concentrations 

5.3.8 Background NH3 Concentrations 

5.3.9 Background H2O2 Concentrations 

5.4 Post Processing Methodology 

5.4.1 POSTUTIL Parameters 

5.4.2 CALPOST Parameters 

5.5 Light Extinction and Haze Impact Calculations 

 5.5.1 Natural Background Conditions 

5.5.2 Visibility Impact Methodology 

5.5.3 Ammonia Limiting Method 

 

5.6 Modeling Results 

5.7 Uncertainty Analysis 

 6.0 EXEMPTION MODELING (if performed) 

7.0 BART CONTROL MODELING RESULTS 

 8.0 REPORTING 

REFERENCES 

APPENDIX A: CALMET Inputs 

APPENDIX B: CALPUFF Inputs 

APPENDIX C: POSTUTIL Inputs 

APPENDIX D: CALPOST Inputs
 

7-13

Table 7-2. Example of Hypothetical Visibility Impact Rankings at Five CENRAP Class I 

Areas. 

Class I Area 2001 2002 2003 

DeltaDeciview 

Ranks 1-8 

DeltaDeciview 

Ranks 1-8 

DeltaDeciview 

Ranks 1-8 

Caney Creek 

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 

Upper Buffalo 

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 

Boundary Waters 

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 

Voyageurs 

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 

Hercules Glade 

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 
 

7-14

Figure 7-1. POSTUTIL Processing of the Ammonia Limiting Method (ALM), NO3/HNO3

Repartitioning for an Individual Source, and Estimating Ammonia Consumption 

from Modeled Sources and Boundary Conditions. (Source: Escoffier-Czaja and 

Scire. 2005.) 

CALPUFF new source(s): 

CALPUFF.CON 

NO3_1; HNO3_1, 

TNO3_1=NO3_1+HNO3_1 

CALPUFF Incoming Boundary: 

BOUNDARY.CON 

NO3_2; HNO3_2; 

TNO3_2=NO3_2+HNO3_2 

POSTUTIL Step 1 

TNO3_1+TNO3_2 are repartitioned in 

new NO3_total/HNO3_total using 

NH3 available, hourly Temperature 

and Relative Humidity 

POSTUTIL Step 2 

NO3_total and HNO3_total are renamed 

NO3ALL and HNO3ALL 

POSTUTIL Step 3 

NO3ALL/(NO3ALL+HNO3ALL)= 

NO3src/(NO3_1 +HNO3_1) 

NO3src; HNO3src are the new output 

TNO3src=NO3src+HNO3src=TNO3_

1 
8-1

8.0 ALTERNATIVE MODEL APPLICATIONS 

Recent model inter-comparisons between CALPUFF and full science models such as CAMx 

and CMAQ (Morris et al., 2003) amply demonstrate that CALPUFF substantially overpredicts 

concentrations of visibility-reducing fine particulates under various real-world at the larger 

downwind distances when compared to one-atmosphere models. Perhaps only a few BART eligible 

sources in the CENRAP region will consider seriously the use of alternative models to EPA’s 

CALPUFF model. But for those sources whose anticipated control costs are substantial, the 

application of comprehensive, full-science regional visibility assessment tools will yield more 

realistic BART control requirements than those generated by a puff model. In this chapter, the steps 

required to select and apply a state-of-science regional model and attendant data bases are presented. 

While there are other alternatives to CALPUFF (i.e., PLUVUE-II, SCICHEM), only full-science 

models have the requisite chemistry and physics formulations to surmount the many limitations 

associated with CALPUFF (discussed in Chapter 3). In addition, we briefly summarize the scope and 

availability of the emissions, meteorological, and air quality modeling data bases for the CENRAP 

region that can be used to support alternative BART modeling. 

8.1 Use of One-Atmosphere Models in Refined Assessments 

States or source operators interested in conducting truly refined visibility modeling of BARTeligible sources have several options for using one-atmosphere models supported by current RPO data 

bases. Options available include: 

 > Modeling Systems: CMAQ4.5 or CAMx4.2 

> Sub-Grid-Plume Treatment: CMAQ-PinG, CMAQ-APT-PM, CMAQ-MADRID-APT, 

CAMx PM IRON PiG, and CAMx multi-scale flexi-nesting; 

 > Modeling Years: 2001 (MRPO), 2002 (CENRAP, MRPO, VISTAS, WRAP), 2003 

(MRPO) 

 > Modeling Resolutions: 12 km (VISTAS, WRAP, MPRO), 36 km (CENRAP, MPRO, 

VISTAS, CENRAP, WRAP) 

Figures 8-1 thorough 8-6 present the various grid domains used by the RPOs and the Five States 

Study1

 (Tesche et al., 2005b). Clearly, each CENRAP state is covered by all of the 36 km domains 

and by many of the higher resolution 12 km domains as well. Depending on the source’s location and 

the Class I area(s) of concern, there are likely to be several model/data base configurations available. 

All of the modeling performed on these domains has been rigorously evaluated and publicly 

disseminated. 

 

 

1 The Five States Study is an integrated 8-hr ozone and annual PM2.5 modeling study in the Upper Midwest 

aimed at identifying effective SO2, NOx, and VOC controls to address the residual nonattainment beyond 

imposition of CAIR controls. Using augmented versions of the MRPO 2002 regional haze data sets, a suite of 

nested 36/12/4 km ozone and 36/12 km annual PM2.5 simulations are being carried out for 2002 and 2009 to 

address emissions controls needed for attainment of the 8-hr ozone and annual PM2.5 NAAQS. These data sets 

will be publicly available. 
8-2

 8.1.1 Regional Haze Modeling Data Bases 

The readiness of one-atmosphere regional models for visibility impact assessment has been 

substantially strengthened in the past three years as the result of the RPO efforts to address the 

Regional Haze Rule. CENRAP and the other four RPOs have developed and evaluated 

comprehensive data sets for regional haze model applications (Morris et al., 2004, 2005a,b,d; Baker, 

2005a-c; Brewer and Adlhoch, 2005; Boylan and Russell, 2005; Tesche et al., 2005). Over the 

CENRAP domain, annual fine particulate and visibility modeling data bases now exist for calendar 

years 2001, 2003, and 2003 (Baker, 2005c). Availability of these public data bases significantly 

reduces the resource burden on that subset of states or source operators who may elect to carry out 

alternative BART modeling. 

Emission Inventories. Comprehensive modeling inventories for 2002 have been developed by all of 

the RPOs using common national data sets (2002 NEI) together with local and state-specific emissions 

information. Generally, all of these inventories are consistent with on another although there are 

naturally some differences stemming from availability of local information. The elements of the 

CENRAP modeling inventory well-characterize the work performed by all the RPOs. 

As part of the CENRAP regional haze modeling program, a gridded, hourly emissions 

inventory for 2002 of criteria air pollutants (CAPs) and ammonia (NH3) emissions inventories for 

point, area, and nonroad sources has been developed (Pechan and CEP, 2005; UCR, 2005). The 

original Base_A 2002 inventory files completed during February 2005 were updated to incorporate 

comments provided by the CENRAP State, Local, and Tribal (S/L/T) agencies and the Emissions 

Inventory (EI) and Modeling Workgroups. As a result of the updates, the updated Base_B inventory 

was developed and is now being used in CENRAP’s 2002 and 2018 regional haze modeling. The 

CENRAP modeling inventories and supporting data prepared include the following: 

> Comprehensive, county-level, mass emissions and modeling inventories for point, area, 

and nonroad sources of 2002 emissions for the CAPs and NH3 for the state, local, and 

tribal agencies included in the CENRAP region; 

> Modeling inventory files containing 2018 projection year emissions for EGUs; and 

> A modeling inventory for Ontario fires during 2002. 

The mass emissions inventory files were prepared in the National Emissions Inventory (NEI) Input 

Format Version 3.0 (NIF 3.0) and then converted to the SMOKE/IDA format. These point, area, 

mobile and nonroad inventories were temporally allocated (hourly), chemically speciated (for the CB4 

mechanism) and spatially distributed (on a 36/12 km grid) over the CENRAP domain for the full 

calendar year 2002. The inventories include emissions rates for sulfur dioxide (SO2), oxides of 

nitrogen (NOx), volatile organic compounds (VOC), carbon monoxide (CO), NH3, and particles with 

an aerodynamic diameter less than or equal to a nominal 10 and 2.5 micrometers (i.e., primary PM10

and PM2.5). The inventories included summer day, winter day, and average day emissions. The 

temporal profiles developed by Pechan and CEP (2005) were used by the CENRAP modeling team to 

generate gridded, speciated, hourly emissions of photochemical, particulate, and regional haze 

precursors.

Meteorological Data Bases The MM5 model has been used exclusively by the RPOs in support of the 

regional haze modeling and the modeling files used in the CENRAP and MRPO haze modeling are the 
8-3

same ones that have been processed to yield the CALMET fields discussed in Chapter 5. In particular, 

the following annual MM5 simulations have been used to construct CAMx and CAMQ modeling data 

bases over the U.S. 

> 2001 MM5 data set at 36/12 km resolution developed for EPA by Alpine Geophysics 

(McNally and Tesche, 2002; McNally 2003); 

> 2002 MM5 data set ad 36/12 km resolution developed for VISTAS by Baron 

Advanced Meteorological Services (Olerud and Sims, 2004); 

> 2002 MM5 data set at 36 km resolution developed for CENRAP by Iowa DNR 

(Johnson, 2003a,b); 

> 2003 MM5 data set at 36 km resolution developed for the Midwest RPO (Baker, 

2005; Baker et al., 2004; Kembell-Cook et al., 2005); and 

> 2004 MM5 data set at 36 km resolution developed for the Midwest RPO (Baker, 

2005). 

Each of these studies included a performance evaluation of the MM5 against surface meteorological 

observations and the results of these evaluations are contained in the reports or presentations cited 

above. 

Regional Haze Modeling Data Bases Regional modeling data bases using the emissions and MM5 

fields have been developed and evaluated by VISTAS (CAMx/CMAQ for 2002 at 36/12 km); MRPO 

(CAMx for 2001, 2002, 2003 at 36 km [and some 12 km]); CENRAP (CMAQ/CAMx for 2002 at 36 

km [and some 12 km]). Details of these date base development activities and associated model 

performance evaluations are thoroughly described in the literature references. 

 8.1.2 Obtaining Regional Modeling Data Bases 

 As discussed in Chapter 4, the aggregate file sizes of the regional haze modeling data sets are 

substantial, requiring several 300-500 Gbyte external disk drives to store the information. In addition 

to the CENRAP CMAQ and CAMx modeling data bases, the MRPO and VISTAS have indicated a 

willingness to provide their data sets to CENRAP for use by CENRAP states or source operators. It is 

anticipated that CENRAP will serve as the central repository, collecting one complete distribution of 

the pertinent MRPO and/or VISTAS data bases on a set of high-capacity disk drives and then making 

them available to the states or source operators on an as requested basis for advanced modeling. 

Parallel sources of these data sets are the modeling contractors performing the regional haze modeling 

for CENRAP, VISTAS, and WRAP. 

 8.1.3 Protocol Development 

EPA’s BART guidance clearly indicates the need for a detailed modeling protocol to support 

any application of alternative models for BART analyses. An example of the content of such a oneatmosphere modeling protocol would be the CMAQ/CAMx modeling protocols developed for 

CENRAP (Morris et al., 2004c) and VISTAS (Morris et al, 2004a). In addition, certain components of 

the screening and source-specific protocols developed with CALPUFF (Tables 6-1 and 7-2) would be 

appropriate. The alternative modeling protocol should be submitted to the state, regional EPA office 
8-4

and FLM for review and negotiation. Note that EPA’s role in the development of the protocol is only 

advisory as the “states better understand the BART-eligible source configurations” and factors 

affecting their particular Class I areas (70 FR 39126). 

8.2 Model Applications 

Assessment of BART sources with regional models is a straightforward, albeit computationally 

intensive exercise. Two types of regional model simulations are required. 

 8.2.1 Regional Base Case Simulation 

 

The first step is to re-run one of the existing regional simulations for the 2002 annual basecase to 

establish comparability with the base case run reported by the RPO from whom the CMAQ or CAMx 

modeling files were obtained. The purpose of this annual simulation is two-fold. First, it establishes 

proper operation of the modeling system on the host computer via comparison with the RPO model 

output files. Second, it provides the gridded 24-hourly estimates of (NH4)2SO4, NH4NO3, OC, EC, 

soil, and coarse mass needed to calculate daily extinction coefficients from the IMPROVE extinction 

equation for every grid cell in the domain, including the Class I areas. (Further discussion of natural 

visibility conditions is given in the following section). On a modern Linux cluster of 4-6 nodes, a 36 

km simulation would require approximately two weeks to complete execution. 

 An important question to be addressed in the modeling protocol is whether one annual 

simulation of 2002 would be sufficient to provide the necessary assessment of a BART source’s 

visibility impact. Recent annual CAMx simulations of 2001, 2002, and 2003 by the MRPO (Baker, 

2005c) indicated that for these three years: 

 > CAMx model performance by specie was very consistent from one year to the next; 

> Sulfate predictions exhibited the greatest year to year variability, likely due to the 

interannual variability of atmospheric water content; 

 > CAMx performance was best for sulfate and elemental carbon; and 

> Organic carbon predictions during the summer months were the poorest of the 

secondary particulate species; and 

 > Nitrate performance, a challenge in all regional modeling studies, needs additional 

improvement. 

The performance levels achieved in the MRPO CAMx modeling are generally consistent with that of 

the other RPOs. These results are included in Figure 4-9, presented previously. Because the regional 

models include all of the important known gas-phase, aerosol, and aqueous-phase chemistry producing 

visibility-impairing particulates, a strong case can be made that only one year of modeling would be 

needed instead of the three years of CALPUFF modeling. However, if the reviewing agencies require 

a full three years of modeling data, a valid approach remains nonetheless with the 2001-2003 CAMx 

data sets developed by the MPRO. 

 
8-5

 8.2.2 BART Source Simulation with SGS Technology 

 

 Once the base case is established, the CMAQ or CAMx simulation is repeated with the 

emissions from the BART source are included in the point source emissions file. For this application, 

depending upon the host model chosen, one of several plume-in-grid or nested grid options should be 

used to provide near source resolution of the SO2, NOx, NH3, and VOC emissions of the BART 

source. For most single point BART simulations conducted with a full-science photochemical grid 

model, the plume-in-grid technique is generally recommended over the use of multi-scale grid nesting 

although the latter may still be appropriate in addition to the plume-in-grid approach for other reasons, 

including replication of meteorology and transport phenomena. Prescription of how the sub-grid-scale 

(SGS) technology is used within the regional model is a key point to be addressed in the modeling 

protocol. The use of SGS will allow the simulation of BART plume impacts down to very small scales 

(on the order of 1 km or less) depending upon the need. 

8.2.3 Assessing Modeled BART Source Impacts 

 The direct impacts of the BART sources on 24-hr visibility is quantified by subtracting the 2002 

Base Case simulation from the BART source simulation and plotting the residual aerosol species 

concentrations and HI values in daily tile plots. Figure 8-7 provides an example of such an 

incremental 24-hr average concentration tile plot. From the CAMx PM PinG modeling by Yarwood et 

al, (2005) using the 2002 MPRO data base, incremental 24-hr average SO2, sulfate, nitrate, and NH4

concentration fields on 14 June 2002 are shown. This plume reveals the individual plume signatures 

downwind from their origin. Similar plots of delta-deciviews can be determined using the IMPROVE 

extinction equation with the modeled aerosol fields from both model simulations. Tabular summaries 

of the visibility impacts in all grid cells, but especially the Class I areas are easily produced. 

8.3 Estimation of Natural Visibility Conditions 

8.3.1 Flexibility Suggested by EPA 

Under the Regional Haze Rule, by 2008, the states must develop a strategy for reducing 

regional haze in Class I areas during the following decade. The rate of improvement of visibility is 

intended to be at a rate sufficient to reach “natural conditions” in 2064, and thus depends on 

quantifying what the visibility would be under those conditions. The initial, “default” estimate of 

natural conditions recommended by EPA (2003) – based on pre-1990 research (Trijonis, 1990) does 

not take into account spatial and temporal variability and local geographic and meteorological 

conditions. Consequently, the default natural visibility conditions are out-of-date and do not reflect the 

very substantial theoretical, observational, and modeling data bases now available for estimating 

natural background concentrations (Tombach and Brewer, 2005). Recognizing that the “default” 

concentrations (see Table 6-3) are not appropriate for every Class I area, EPA allows states the option 

to develop "refined" estimates of natural conditions that reflects local conditions. 

States might wish to adopt a refined approach to estimating natural visibility conditions for 

several reasons, including: 

> The default estimates are shown, through more recent Class I area-specific observations 

and/or modeling analyses to be out-dated and/or incorrect; 

> If the default estimates of the natural background conditions are close to the current 
8-6

visibility conditions, small uncertainties can have significant impacts on states’ ability 

to meet SIP goals; 

> In some regions, natural sources are known to exhibit predictable seasonal influences on 

visibility; therefore, states might wish to use refined estimates of natural visibility 

conditions to account for these influences; 

> States which receive significant visibility impacts from biomass smoke might wish to 

distinguish more explicitly between man-made and natural sources. 

EPA identifies several possible refined approaches which can be adopted for developing ore 

realistic estimates of natural visibility conditions and states may identify others that are more 

appropriate for their own situations. Approaches suggested by EPA include: (a) develop refined 

estimates of the constant values of one or more natural species, (b) estimate natural visibility using 

species concentrations that vary (e.g., seasonally, monthly, or climatologically), or (c) adjust the 

estimated constant species to account for infrequent natural events, such as forest fires or wind-blown 

dust, as major influences on visibility. This latter approach would require estimating the frequency 

and magnitude of the natural contribution to particle concentrations during the events. EPA encourages 

flexibility in these approaches so that default and refined annual average, seasonal, monthly, and 

event-specific species concentrations provide the best estimates of natural visibility for each of the 

mandatory Federal Class I areas. States wishing to employ a refined approach should supply 

demonstrations that the refined approach is technically sound and provides regionally representative 

estimates of natural visibility conditions. 

8.3.2 Refined Procedures for One-Atmosphere Modeling 

The whole purpose of estimating natural visibility conditions is to develop a reasonable 

background against which a human observer might be able to discern the added presence of a BART 

source plume. With Lagrangian models such as CALPUFF, this necessity arises because puff models 

cannot calculate the three-dimensional, hourly varying gas phase and particulate concentration fields 

that contain the various visibility-limiting pollutant species. Consequently, some independent estimate 

of natural visibility conditions is required. Historical measurements of pristine conditions measured in 

the 1980s have been used to fill this void. 

One-atmosphere models, by design, eliminate the need to estimate natural background 

conditions against which individual plume contributions are assessed. This is done directly via the 

simulation of the hour-by-hour, day-by-day emissions (including sea salt), meteorology (rain, snow, 

fog), transport, chemical transformation and removal of gaseous and particulate emissions from all 

sources (natural and manmade) in the region of interest for one or more calendar years. Today, the 

CENRAP, WRAP, MRPO, and VISTAS regional haze programs have all developed base year 2002 

fine particulate and visibility estimates for the entire U.S. at grid scales of 36 km and 12 km using the 

CMAQ and/or CAMx models (depending upon RPO). Importantly, the MRPO has even extended the 

CAMx visibility modeling to include the same three year period (2001-2003) being use in the 

screening and source-specific CALPUFF modeling. The 24-hour averaged modeled ground level 

concentration predictions of (NH4)2SO4, NH4NO3, OC, EC, soil, and coarse mass (i.e., all components 

in the IMPROVE extinction equation) from any of these regional simulations provide a direct 

quantification on a day-to-day basis of current background visibility conditions in not only the Class I 

areas, but in every 12 km or 36 km grid cell in the U.S. By simply re-running one or more of these 

regional one-atmosphere model simulations with the BART source emissions included, and then 
8-7

subtracting the outputs of the two simulations, a direct, gridded estimate is produced of the change in 

visibility (measured in terms of ‘delta-deciviews’) compared against realistic, current background 

conditions. As with the screening CALPUFF modeling, if the Δdv value is greater than the 0.5 dv 

threshold, the source is said to contribute to visibility impairment and is thus ‘subject to BART’ 

controls. 

 

 This approach to calculating a realistic natural visibility background condition departs from 

EPA’s suggestions in two respects. First, it is far more rigorous, and thus is more resource intensive to 

carry out. Second, the natural visibility background conditions over the Class I areas simulated by the 

CAMx/CMAQ models in the 2002 base cases will in all likelihood be less ‘pristine’ then those 

estimated by Trijonis and others in the late 1980s. But, we maintain, they will be more representative 

of the actual current conditions in the Class I areas against which the BART plume impacts should 

realistically be compared. 

 Clearly, the acceptability of this refined one-atmosphere modeling procedure for calculating 

natural visibility background conditions in the Class I areas will need to be discussed with state, EPA, 

and FLM staff in order to reach consensus on the merits and acceptability of the approach. The 

fallback is to use the default (EPA, 2003) numbers. 

8.4 Cumulative Impact Assessments 

While the use of regional models incorporating plume-in-grid or multi-scale nesting allow for 

rigorous assessment of 24-hr average point source annual visibility impacts over one or more years, 

where one-atmosphere models are most useful is in cumulative impact assessments. The example 

shown in Figure 8-7 is one such application. The modeling performed by Yarwood et al., (2005) 

treated several dozen point sources with plume-in-grid technology allowing for the opportunity to 

assess the cumulative impact of all of these sources in the context of all anthropogenic and biogenic 

sources in the central U.S. 

 As noted in Chapter 1, each CENRAP state is required to carry out four types of visibility 

modeling under the Regional Haze Rule: (a) single-source modeling to determine which BARTeligible sources are ‘subject to BART’, (b) single-source modeling to determine the degree of visibility 

improvement attributable to proposed BART controls for each source subject to BART, (c) cumulative 

modeling to determine the combined effect of proposed BART controls for sources subject to BART in 

each CENRAP state; and (d) regional-scale modeling to determine if the combined effect of proposed 

BART controls for all CENRAP states ultimately satisfy the RHR visibility improvement goals. The 

third responsibility, cumulative impact assessment of all state sources subject to BART, is best 

addressed through the use of regional models. 

8.5 Concluding Remarks 

Only a few states and/or BART eligible sources are expected to seriously examine the merits 

of using full-science models as an alternative CALPUFF. But for those who do, the review of oneatmosphere modeling capabilities (Chapter 4) and the procedures one would follow in their 

application to BART sources (this chapter) will hopefully provide useful information to inform 

decision-making. Fortuitously, the recent work by the various RPOs and model developers has 

produced a suite of state-of-science models and associated data bases that are ideally suited to 

rigorous BART visibility impact modeling. While the resource requirements of an alternative 

modeling study – using existing models and data bases-- exceed those of a typical CALPUFF 
8-8

analysis, the potential to develop more reasonable control limits on certain BART sources may easily 

eclipse the additional costs of full-science modeling. 

-2000 -1500 -1000 -500 0 500 1000 1500 2000 2500

-2000

-1500

-1000

-500

0

500

1000

1500

12 km Domain

36 km Domain

Figure 8-1. CENRAP 36/12 km CMAQ/CAMx Domain Used for 2002 Annual OneAtmosphere Regional Haze Simulations. 36 km domain is outer grid; 12 km 

domain is colored red. (Note: Model Simulations on 12 km domain may not be 

performed for full annual cycle). (Source: Morris et al., 2005d). 
8-9

MM5 CAMx 

Figure 8-2. MRPO 36 km CAMx Domain Used for 2001, 2002, 2003 Annual One-Atmosphere 

Regional Haze Simulations (Source: Baker, 2005c). 
8-10

\ 

Figure 8-3. MRPO 36/12 km CAMx Domain Used for 2002 Annual One-Atmosphere Regional 

Haze Simulations. 36 km domain is outer grid; 12 km domain is colored green 

(Source: Koerber, 2005). 
8-11

Figure 8-4. VISTAS 36/12 km CMAQ/CAMx Domain Used for 2002 Annual One-Atmosphere 

Regional Haze Simulations. 36 km domain is outer grid; 12 km domain is colored 

green (Source: Tesche et al., 2005). 
8-12

Figure 8-5. WRAP 36 km CMAQ/CAMx Domain Used for 2002 Annual One-Atmosphere 

Regional Haze Simulations. (Source: Morris et al., 2004b). 
8-13

Figure 8-6. Five States Study 36/12/4 km PM2.5/8-hr Ozone Study Using CAMx for the 2002 

Annual Period. (Source: Tesche, et al., 2005b). 
8-14

24-hr SO2 Concentrations 24-hr SO4 Concentrations

24-hr NO3 Concentrations 24-hr NH4 Concentrations

Figure 8-7. Incremental 24-hr Average SO2, Sulfate, Nitrate, and NH4 Concentrations Fields on 

14 June 2002 from the CAMx PM PiG Simulation Over the Upper Midwest. 

Apparent in this figure are the individual plume footprints downwind from their 

origin. The modeled plumes result from full chemistry interaction between the 

background regional atmosphere and the chemical and physical dynamics of the 

individual plumes. (Adapted from Yarwood et al., 2005). 
 

9-1

9.0 QUALITY ASSURANCE 

 

9.1 Objective of the QA Program 

 A quality assurance (QA) program is needed to ensure that products produced in the BART 

exemption or BART control applications satisfy the regulatory objectives of the program. The scope 

of the QA program for a particular application should be defined in the protocol. Common elements 

include the configuration, data base development, setup and execution of the CALPUFF air quality 

model and processing of modeling results to determine compliance with visibility thresholds. In most 

if not all screening cases (see Chapter 6), states or source operators will use the CALMET datasets 

provided by CENRAP. Applications involving source-specific or alternative modeling that use 

different datasets, modeling options or tools will need to ensure that an appropriate QA program is 

defined in their protocols. More extensive quality assurance will be required in these latter 

applications. It is the responsibility of the state ensure that an adequate QA program, defined in the 

protocol, is implemented faithfully. 

 The CALPUFF modeling system contains features to facilitate quality assurance. 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 modules. The CALPUFF Graphical User Interface system (GUI) allows these QA files 

to be displayed graphically. In addition, a software management system is available to track version 

and level numbers associated each program and utility. This information reproduced in all of the 

output files, creating an audit trail of software versions and major model options. 

 The BART modeling process involves multiple organizations. As mentioned earlier, the states 

have overall responsibility for the process and may also execute some or all of the modeling. CENRAP 

is contributing general guidance via these guidelines and has funded the development of 

meteorological fields for use in screening applications. BART-eligible source operators will need to 

provide process information and emissions data for use in the analyses. Furthermore, ‘subject to 

BART’ sources will need to be actively involved in control technology decisions and assessments. 

Finally, some or all of the modeling steps may be carried out by contractors on behalf of a state or a 

source operator. 

 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, regardless of which organization 

is carrying out the effort. The focus is on the CALPUFF screening analyses described in Chapter 6. 

More comprehensive QA is needed for the unique aspects of the CALPUFF source-specific or the use 

of alternative models described in Chapters 7 and 8, respectively. 

9.2 Quality Assurance Procedures 

 Chapter 6 recommends procedures for conducting CALPUFF screening modeling to determine 

the whether a particular source or group of sources is subject to BART controls. For initial 

applications, the regional 6 km resolution CALMET data files developed for CENRAP are available to 

modelers for the screening assessments. The development of these CALMET datasets was subject to a 

QA program. Thus, the amount of effort for modelers performing QA of these pre-defined 
 

9-2

meteorological fields is less than that required when developing source-specific meteorological fields 

at finer scale and/or over tailored domains. 

 9.2.1 Input Data 

 The input data required by the model depends on the application. At a minimum, source data is 

required by CALPUFF (see Section 6.3.4) along with specification of the model configuration (choices 

of options and switches). Most of the modeling option choices are specified or recommended by 

regulatory guidance and default values (see Appendix A through Appendix D). If an application uses 

CENRAP’s CALMET files and the CALPUFF screening configuration recommended in Chapter 6, the 

quality assurance of input data will be straightforward. More detailed steps are needed for the setup of 

modeling files for CALPUFF source-specific applications using domains and meteorological fields 

that are not already developed. 

 The basic procedures that will apply to all CALPUFF model applications 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. An independent 

modeler should review the CALPUFF input files to confirm the switches and data entry in the model 

input files are correct. The independent reviewer should also exercise the CALPUFF modeling system 

for one or a few corroboration simulations (e.g., run of the worst case event) as a confirmation check. 

 The protocol should stipulate that a set of DVDs be created that contain all of the data and 

program files needed to reproduce the results presented in the final report. The model list files from 

each step should be included on the project DVD. This information allows independent checking and 

confirmation of the modeling process and facilitates archival of the study results. 

 9.2.2 MM5 and CALMET Applications 

 The CALMET meteorological data sets are available on external USB2 or IEEE 1394 

(Firewire) hard drives in a format ready for use with CALPUFF. The QA steps used in the 

development of the CENRAP common datasets are described in the documentation attending the 

distribution to CENRAP. While these QA steps need not be repeated, some testing should be 

performed to show that the data are indeed suitable for the application for which it is being used. This 

is discussed in more detail below. 

 Appendix A contains a list of recommended CALMET switch settings for screening and 

source-specific modeling. Except as modified in a source-specific protocol, these configurations 

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 DOS or Linux, 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 inter-relationships among variables indicating if/when the variable is used. If CALMET is 

set up and run from the command line or if other user interfaces are employed, this QA step may 

become somewhat more difficult. It is essential nonetheless. 
 

9-3

 Part of the CALPUFF modeling system’s built-in QA capability 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. 

 9.2.3 CALPUFF Applications 

 The minimum source data required by CALPUFF includes: 

 - Number of sources 

 - Locations (e.g., UTM coordinates, UTM zone and datum) 

 - Stack heights above the ground 

 - Stack diameters 

 - Exit velocities 

 - Exit temperatures 

 - Emission rates (SO2, H2SO4, NOx and PM10). 

There are additional requirements for building dimension information (building width, length, height 

and corner locations) 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 where 

the pertinent transport distance to the nearest Class I area is large. 

 The source coordinates must be expressed in the coordinate system used to define the 

CALMET and CALPUFF modeling domains. For CENRAP screening applications, a Lambert 

Conformal Conic (LCC) coordinate system is used. The required parameters include two matching 

parallels, latitude/longitude of the projection origin, coordinate datum, and false Easting and Northing 

(if used) of the projection origin. For source-specific CALMET/CALPUFF domains, either an LCC or 

UTM projection may be used. The CALPUFF user interface includes software (e.g., COORDS) to 

compute to/from latitude/longitude, LCC and UTM coordinates for a large number of datum. 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”. The 

CALPUFF QA files provide the source coordinates used by the model in a CALVIEW-ready format 

for plotting. Many errors are found in source coordinates and related projection/datum parameters, so 

confirmation of the source location is an important part of the model QA. 

 The PM10 emissions should be broken into filterable and condensable components. The 

filterable PM10 emissions should be speciated into elemental carbon (EC), fine particulate matter 

(PMF) less than 2.5 μm diameter and coarse particulate matter (PMC) between 2.5 to 10 μm diameter. 

The condensable emissions should be divided into a primary H2SO4 component and organic and non-
 

9-4

H2SO4 inorganic components. (The sources of this information, such as source-specific data and AP42 tables, are not addressed in this protocol.) The speciation of the PM10 emissions is important 

because the light extinction efficiency varies by more than a factor of 16 for different species, from 0.6 

for coarse particulate matter to 10.0 for elemental carbon. Plus, hygroscopic aerosols are subject to a 

humidity growth factor (f(RH)), which can increase the light scattering from dry conditions by more 

than an order of magnitude in highly humid conditions. Thus, careful QA of the source and emissions 

data is an important component of the modeling. 

 The locations of the Class I area receptors may be obtained from the National Park Service 

(NPS) receptor dataset. 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 contains a list of 

recommended CALPUFF switch settings for CENRAP screening applications. CALPUFF is 

configured using the ASCII “control file”, CALPUFF.INP file. As with CALMET, whether 

CALPUFF is exercised GUI or from the command line in a DOS, Linux, or Unix window, it is 

essential that the control file be reviewed manually as part of the QA analysis. The CALPUFF 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 inter-relationships among variables indicating if/when the 

variable is used. 

 9.2.4 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 refined modeling thresholds. The 

inputs to CALPOST involve selection of the visibility method (Method 6), entry of Class I areaspecific data for computing background extinction 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. For a screening assessment, the peak value of the change in extinction is presented near the end 

of the CALPOST output file. For a source-specific studies where the 98th percentile value (8th highest 

day) is used for comparison against the BART threshold of 0.5 deciviews, the standard CALPOST 

output must be additionally processed. One may either obtain special-purpose processing software 

(see, for example CDPHE, 2005) or perform the processing manually. That is, one may import the 

results of the CALPOST table into a sorting program such as a spreadsheet to rank the daily change in 

extinction values in order to identify the 98th percentile or 8th highest day visibility increment. 

 
 

9-5

 CALPOST inputs that require particular QA review include: 

 

 > Visibility technique (Method 6); 

 > Monthly Class I-specific relative humidity factors for Method 6; 

 > Background light extinction values; 

> 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 source-specific modeling); and 

 

 > 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 species- dependent 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) is 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. The four-step procedure for applying the ALM method is summarized 

in Chapter 7 and described in detail in Appendix A of the VISTAS protocol1

. If source-specific 

modeling is performed using different sources of ammonia data or different techniques, the protocol 

should provide justification for all assumptions and a QA plan specific for the application and the data 

bases used. 

 9.2.5 QA Issues for Alternative Source-Specific or Alternative Modeling 

 The level of QA required for application of source-specific or alternative modeling protocols 

will be substantially higher than for the use of datasets that has already been subject to a QA 

procedure. For example, source-specific CALPUFF 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 screening 

 

1

 “Comments on the Computation of Nitrate Using the Ammonia Limiting Method in CALPUFF”, (Scire et al., 2005) 
 

9-6

approach. Source-specific must therefore include the development of a QA plan to properly evaluate 

the data used in the 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 

representativeness of any observational data. In a source 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 4am) 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. Seasonal wind roses at 4am, 10am and 4pm would be 

expected to show the development of seas breeze circulations that may be important for certain 

applications. Customization of the QA process for the individual source-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 source-specific QA plan development. 

 If source-specific CALPUFF simulations involving the ALM are conducted, the procedure 

summarized in Chapter 7 should be used. This includes an evaluation of the performance of the model 

in reproducing observed CASTNet or IMPROVE sulfate and nitrate concentrations at measurements 

sites within the source-specific modeling domain. The use of alternative ammonia concentration data 

(e.g., one-atmosphere model output rather than derived ammonia based on aerosol measurements) will 

require a review of the CMAQ or CAMx model performance results developed for CENRAP by the 

regional modeling contractor (Morris et al., 2005d) 

 

9.3 Quality Assessment Summary 

 

 Each BART application requires a modeling report that includes a discussion of the datasets, 

modeling assumptions and a general discussion of limitations and uncertainties of the modeling 

exercise. Where CALPUFF screening analyses are performed followed by source-specific modeling, it 

will be possible to assess the relative conservatism of the screening approach. This may be helpful in 

assessing the impact of uncertainties in screening sources from BART controls. The reliability of 

source-specific CALPUFF modeling is expected to be somewhat better in predicting changes in 

visibility impacts due to BART controls than in predicting absolute visibility values. This is because 

errors and uncertainties in defining meteorological conditions, real-world atmospheric chemistry, and 

plume transport and dispersion rates may be diminished somewhat since the errors are included in both 

the base and sensitivity simulation cases. 
 

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REFERENCES 

Allwine, K.J., W. F. Dabberdt, and L. L. Simmons, 1998. “Peer Review of CALMET/CALPUFF 

Modeling System” compiled by Veronica A. Hanzel, The KEVRIC Company Inc., Durham, NC (EPA 

Contract No. 68-D-98-092, Work Assignment No. 1-03, Dated April 29, 1998). 

Arnold, J. R., R. L. Dennis, and G. S. Tonnesen, 2003. Diagnostic evaluation of numerical air quality 

models with specialized ambient observations: testing of the Community Multiscale Air Quality 

modeling system (CMAQ) at selected SOS 95 ground sites. Atmospheric Environment, 37, 1185-1198. 

Atkinson, Lloyd, and Winges. 1982. An Update Chemical Mechanism for Hydrocarbon/NOx/SO2

Photooxidations Suitable for Inclusion in Atmospheric Simulation Models. Atmospheric Environment. 

16, 1341-1355. 

Baker, K. and M. Koerber, 2004. Modeling to Support BART, Prepared by the Midwest Regional 

Planning Organization and the Lake Michigan Air Directors Consortium, Des Plaines, IL. 

Baker, K., M. Johnson, et al. 2004. Meteorological modeling performance summary for application to 

PM2.5/haze/ozone modeling projects, Prepared by the Lake Michigan Air Directors Consortium and the 

Midwest Regional Planning Organization, Des Plaines, IL. 

Baker, K., 2005a. MM5 Model Performance: Great Lakes Region: 2002-04. Ad-Hoc Meteorological 

Modelers Group Meeting, Lakewood, Colorado. 11 June. 

Baker, K., 2005b. Modeling to support BART. National RPO Meeting, Denver, CO, 8-10 June. 

Baker, K., 2005c. “Model Performance for Three Applications Supporting PM2.5/Haze”, Air & Waste 

Management Association's 98th Annual Conference, June 23-27, 2005; Minneapolis, MN. 

Barna, M., B. Lamb, 2000. Improving ozone modeling in regions of complex terrain using observational 

nudging in a prognostic meteorological model. Atmospheric Environment. 34, 4889-4906. 

Barna, M., B. Lamb, S. O'Neil, H. Westberg, C. Figueroa-Kaminsy, S. Otterson, C. Bowman, and J. 

DeMay, 2000. Modeling Ozone formation and transport in the Cascadian Region of the Pacific 

Northwest. J. Appl. Meteorol. 39, 349-366. 

Barna, M.G., and Gimson, N.R., 2002. Dispersion modeling of a wintertime pollution episode in 

Christchurch, New Zealand. Atmospheric Environment. 36 (21), 3531-3544. 

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. 

Bennett, M.J, M.E. Yansura, I.G. Hornyik, J.M. Nall, D.G. Caniparoli and C.G. Ashmore, 2002, 

“Evaluation of the CALPUFF Long-range Transport Screening Technique by Comparison to Refined 

CALPUFF Results for Several Power Plants in Both the Eastern and Western United States”. 

Proceedings of the Air & Waste Management Association's 95th Annual Conference, June 23-27, 2002; 

Baltimore, MD. Paper #43454. 
 

R-2 

Binkowski, F.S. and S.J. Roselle, 2003. Models-3 Community Multiscale Air Quality (CMAQ) Model 

Aerosol Component -- 1. Model Description. J. Geophysical Research, 108, (D6), 4163, 

DOI:10.1029/2001JD001409. 

Boylan, J.W., and Russell, A.G., 2005. PM and light extinction model performance metrics, goals, and 

criteria for three-dimensional air quality models. Atmospheric Environment, (submitted). 

Brewer, P. and J. Adlhoch, 2005. Trends in Speciated PM2.5 and Visibility across Monitoring Networks 

in the Southeastern U. S., Journal of the Air & Waste Manage. Association (in press). 

Byun, D.W. and J.K.S. Ching. 1999. Science Algorithms of the EPA Models-3 Community Multiscale 

Air Quality (CMAQ) Modeling System. U.S. Environmental Protection Agency, Research Triangle 

Park, NC. EPA/600/R-99/030. 

CARB. 2003. Staff Report: Public Hearing to Consider Approval of Proposed 2003 State 

Implementation Plan Revisions for the South Coast Air Basin and Cochella Valley – Hearing Date 

September 23, 2003. Posted at http://www.arb.ca.gov/planning/sip/sip.htm 

Carizi G., Gariazzo C., Polla Mattiot F., Volta M. (2001). UAM-V/CALGRID nesting approach to an 

ozone episode in the Milan metropolitan area, Proc. Int. Conf. HARMO7, Belgirate (I) pp:360-364. 

Carson, D.J., 1973: The development of a dry, inversion-capped, convectively unstable boundary layer. 

Quart. J. Roy. Meteor. Soc., 99, 450-467. 

Carter, W. P. L., 2000. “Documentation of the SAPRC-99 Chemical Mechanism for VOC Reactivity 

Assessment”, Report to the California Air Resources Board, Contracts 92-329 and 95-308. Available at 

http://cert.ucr.edu/~carter/absts.htm#saprc99. 

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

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

Colorado Department of Public Health and Environment, Denver, CO. 

Chang, J.C. P. Franzese, K. Chayantrakom and S.R. Hanna, 2003: Evaluations of CALPUFF, HPAC 

and VLSTRACK with Two Mesoscale Field Datasets. J. Applied Meteorology, 42(4): 453-466. 

Defant, A., 1933: Der Abfluss schwerer Luftmassen auf geneigtem Boden, nebst einigen B 

Bemerkungen zur Theorie stationarer Luftstrome. Sitzungsberichte Preusissche Akademie der 

Wissenschaften Physikalische-Mathematikalische Klasse, 18, 624-635. 

Dhaniyala, S. and A. S. Wexler. 1996. Numerical schemes to model condensation and evaporation of 

aerosols. Atmospheric Environment. 30, 919-928. 

Douglas, S. and R. Kessler, 1988: User's Guide to the Diagnostic Wind Model. California Air Resources 

Board, Sacramento, California. 

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

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

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

R-3 

E. H. Pechan and CEP, 2005. “Refinement of CENRAP’s 2002 Emissions Inventories”, Schedule 9, 

Work Item 3, prepared for the Central Regional Air Planning Association, prepared by E. H. Pechan, 

Durham, NC, and Carolina Environmental Programs, Chapel Hill, NC. 

Emery, C., E. Tai, and G. Yarwood, 2001. Enhanced Meteorological Modeling and Performance 

Evaluation for Two Texas Episodes, Work Assignment No. 31984-11. Prepared for the Texas natural 

Resource Conservation Commission, Prepared by ENVIRON International Corporation, Novato, CA. 

Emery, C. and G. Yarwood, 2005. Implementing PM chemistry in the CAMx "IRON PiG" plume-ingrid module, Prepared for the Lake Michigan Air Directors Consortium, Prepared by ENVIRON 

International Corporation, Novato, CA. 

 

EPA, 1995. “Interagency Workgroup on Air Quality Modeling (IWAQM): Assessment of Phase 1 

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

Planning and Standards, U.S Environmental Protection Agency, Research Triangle Park, NC 27711. 

EPA, 1998a. “A Comparison of CALPUFF Modeling Results To Two Tracer Field Experiments”, EPA454/R-98-009. Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, 

Research Triangle Park, NC 27711. 

EPA, 1998b. “An Analysis of the CALMET/CALPUFF Modeling System In A Screening Mode”. EPA454/R-98-010. Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, 

Research Triangle Park, NC 27711. 

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

Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711. 

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

Impacts. Interagency Workgroup on Air Quality Modeling (IWAQM). EPA454/R-98-019, U.S. 

Environmental Protection Agency, RTP, NC. 

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

Research Triangle Park, NC. November. 

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

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

Impacts”. Research Triangle Park, NC. February. 

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

EPA, 2001. “Draft Guidance for Demonstrating Attainment of Air Quality Goals for PM2.5 and Regional 

Haze”, U. S. EPA, Office of Air Quality Planning and Standards, Research Triangle Park, NC. 

EPA, 2003. Guidance for Estimating Natural Visibility Conditions under the Regional Haze Rule. EPA454/B-03-005. U.S. Environmental Protection Agency, Research Triangle Park, NC. 

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

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

R-4 

ENVIRON. 2005. “User’s Guide – Comprehensive Air-quality Model with extensions, Version 4.20.” 

ENVIRON International Corporation, Novato, California. (Available at http://www.camx.com). 

ENVIRON, 2005. “Screening Analysis of Potentially BART-eligible Sources in Texas”, prepared for 

the Texas Commission on Environmental Quality, prepared by ENVIRON International Corp., Novato, 

CA. 

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. 

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

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

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

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

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

Finzi, G. C. Gariazzo, F. Polla Mattiot, and M. Volta, (2001). B.C. nesting approach harmonization: 

UAM-V/CALGRID model exercise on Northern Italy. Subproject Saturn. 

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

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

Gabusi, V., C. Pertot, G. Finzi. 2002. Performance Assessment of Long-Term Photochemical 

Modelling System, 8th Int. Conf. On Harmonisation within Atmospheric Dispersion Modelling for 

Regulatory Purposes, October 14-17, Sofia, Bulgaria. 

Garrison, M., S. Gill, J. Sherwell. 1999. “Further Evaluations of the CALMET/CALPUFF Modeling 

System for the Estimation of the Fate of Atmospheric Nitrogen”. Presented at the 92nd Annual Air and 

Waste Management Association Annual Meeting and Exhibition. Maryland Power Plant Research 

Program. Annapolis, MD. 

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

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

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

Sciences Modeling Division U.S. Environmental Protection Agency Research Triangle Park, NC 27711. 

Garrison, M., and D. Riley, et al., 2005. Development of parallel CALPUFF dispersion modeling 

platforms for sulfate source attribution studies in the Northeast U.S.--Appendix D, Prepared by the 

Maryland Department of Environment, Maryland Department of Natural Resources, Baltimore, MD and 

the Vermont Department of Environmental Conservation, Waterbury, VT: 91. 

Geiger, R. (1965). The Climate Near the Ground, Harvard University Press, Cambridge, Mass. 

 

Gillani, N.V., and J.M. Godowitch. 1999. Plume-in-grid treatment of major point source emissions. In 

Science algorithms of the EPA Models-3 Community Multiscale Air Quality modeling system. Chapter 

9. D.W. Byun, and J.K.S. Ching (Eds.). EPA-600/R-99/030, National Exposure Research Laboratory, 

U.S. Environmental Protection Agency, Research Triangle Park, NC, 40 pp.
 

R-5 

Gimson, N.R. 2000. Applications of CALMET/CALGRID to Auckland, New Zealand. Proceedings of 

the 11th Joint Conf. On Applications of Air Pollution Meteorology. Amer. Meteor. Soc. Boston, MA. 

Godfrey, J.J., 1998. “Air quality modeling in a stable polar environment - Ross Island, Antarctica”. 

Atmospheric Environment. 32 (17), 2899-2911. 

Godowitch, J.M., 2001: Results of photochemical simulations of subgrid scale point source emissions 

with the Models-3 CMAQ modeling system. Preprints, AMS Millennium Symposium on Atmos. Chem., 

14-19 Jan. 2001, Albuquerque, NM, p. 43-49. 

Godowitch, J. M. 2004. Simulations of Aerosols and Photochemical Species with the CMAQ Plume-inGrid Modeling System. 3rd Annual CMAS Models-3 User's Conference, Chapel Hill, NC, 18-20 

October. 

Godowitch, J. M., and J. O. Young, 2005: Modeling photochemistry and aerosol formation in point 

source plumes with the CMAQ plume-in-grid system. 7th Conference on Atmospheric Chemistry, 85th 

Annual Meeting of the American Meteorological Society, San Diego, CA 10-14 January. 

Gooden, W. R., G. J. McRae, and J. H. Seinfeld, 1980: An objective analysis technique for constructing 

three-dimensional urban-scale wind fields. Journal of Applied Meteorology, 19, 96-106. 

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. 

Honaganahalli, Puttanna S; Seiber, James N, 2000. “Measured and predicted airshed concentrations of 

methyl bromide in an agricultural valley and applications to exposure assessment”. Atmospheric 

Environ;ment. 34 (21), 3511-3523. 

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. 

Irwin, J.S. 1998a. A comparison of CALPUFF modeling results with 1977 INEL field data results. Air 

Pollution Modeling and Its Application XII, (Edited by S-E Gryning and N. Chammerhac). Plenum 

Press, NY., pp 143-153. 

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

Report and Recommendations for Modeling Long Range Transport Impacts”. EPA-454/R-98-019, 

Office of Air Quality Planning and Standards, Research Triangle Park, NC, 151 pp. (NTIS Accession 

Number PB 99-121089). 
 

R-6 

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

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

April 4-6, 2001, Newport, RI. 

Irwin, J. S., J. Niedzialek, , and J. Burzynski, 2001. A comparison of CALPUFF air quality simulation 

results with monitoring data for Krakow Poland. Proceedings of the 7th International Conference on 

Harmonisation Within Atmospheric Dispersion Modelling for Regulatory Purposes, March 28-31, 2001, 

Beligrate, Italy, International Journal of Environment and Pollution. 

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

and Recommendations for Modeling Long-Range Transport and Impacts on Regional Visibility. EPA454/R-98-019. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, 

Research Triangle Part, NC. 

Jacobson, M. Z. (1999). Fundamentals of Atmospheric Modeling. New York, NY, Cambridge 

University Press. 

 

Jang C., Timins, B., Dolwick, P., Braverman, T., Tickvart, J.. 2001. Annual Application of U.S. EPA’s 

Third-Generation Air Quality Modeling System over the Continental United States. Presented at the 

Annual Meeting of the Air & Waste Management Association. 

Jiang, W., D. L. Singleton, R. McLaren, M. Hedley. 1997a. Sensitivity of ozone concentrations to rate 

constants in a modified SAPRC90 chemical mechanism used for Canadian Lower Fraser Valley Ozone 

Studies. Atmospheric Environment, 31 (8) 1195-1208. 

Jiang, W., D.L. Singleton, M. Hedley, and R. McLaren. 1997b. Sensitivity of ozone concentrations to 

VOC and NOx emissions in the Canadian Lower Fraser Valley. Atmospheric Environment, 31, 627-638. 

Jiang, W., M. Hedley, and D. Singleton. 1998. Comparison of the MC2/CALGRID and SAIMM/UAMV photochemical modeling systems in the Lower Fraser Valley, British Columbia. Atmospheric 

Environment, 32, 2969-2980. 

Johnson, M. T., 2003a. Meteorological modeling protocol: IDNR 2002 annual MM5 application, 

Prepared by the Iowa Department of Natural Resources, Air Quality Bureau, Des Moines, IA 

Johnson, M. T., 2003b. Iowa DNR 2002 Annual MM5 Modeling Project. Presented at the August 11th, 

2003 CENRAP Workgroup Meeting in Bloomington, Minnesota. 

Johnson, M. T., 2005. CALPUFF Modeling Protocol in Support of Best Available Retrofit Technology 

Determinations”, Prepared by the Iowa Department of Natural Resources, Air Quality Bureau, Des 

Moines, IA 

Karamchandani, P.; Santos, L; Sykes, I; Zhang, Y; Tonne, C; Seigneur, C. Development and evaluation 

of a state-of the-science reactive plume model, Environ. Sci. Technol., 2000, 34, 870–880. 

Karamchandani, P, C. Seigneur, K. Vijayaraghavan, and S-Y Wu, 2002. Development and Application 

of a state-of-science plume-in-grid model. J. Geophysical Research, 107, (D19), 4403, DOI: 

10.1029/2002JD002123. 
 

R-7 

Karamchandani, P., K. Vijayaraghaavan, S.-Y. Chen, and C. Seigneur, 2005: Development and 

application of advanced plume-in-grid PM models. 4th Annual CMAS Models-3 User's Conference, 

Chapel Hill, NC, 26-28 September. 

Kemball-Cook, S., Y. Jia, C. Emery, R. Morris, Z. Wang and G. Tonnesen, 2005. Comparison of 

CENRAP, VISTAS and WRAP 36 km MM5 Model Runs for 2002, Task 3: Met Gatekeeper Report. 

http://pah.cert.ucr.edu/aqm/cenrap/ppt_files/CENRAP_VISTAS_WRAP_2002_36km_MM5_eval.ppt) 

Kessler, R., M. A. Yocke, et al. (1987). Designing a windfield module, Report No. SYSAPP-87/052, 

Systems Applications, Inc., San Rafael, CA. 

Koerber, M. 2005. “Regional Air Quality Planning in the Upper Midwest: Attainment Strategy 

Options”, Prepared by the Lake Michigan Air Directors Consortium and the Midwest Regional Planning 

Organization, Des Plaines, IL. 

Kumar, N., A. G. Russell, and G. J. McRae. 1992. A Project to add the Carbon-Bond 4 Chemistry to the 

California Air Resources Board Model (CALGRID). Sigma Research Agreement No. A974-222. PTSD, 

California Air Resources Board. Sacramento, CA. 

Kumar, N., A. G. Russell, T. W. Tesche, and D. E. McNally. 1994. Evaluation of CALGRID using two 

different ozone episodes and comparisons to UAM. Atmospheric Environment, 28 (17), 2823-2845. 

Kumar, N., F. Lurmann, and W.P.L. Carter. 1995. Development of the Flexible Chemical Mechanism 

Version of the Urban Airshed Model. Final Report STI-94470-1508-FR. PTSD, California Air 

Resources Board. Sacramento, CA. 

Kumar, N., and A. G. Russell, 1996. Multiscale air quality modeling of the northeastern United States. 

Atmospheric Environment, 30, 1099-1116. 

LADCo, 2005. “Single Source Modeling to Support Regional Haze BART: Version 3”, Prepared by the 

Lake Michigan Air Directors Consortium (LADCO), Des Plaines, IL. 

Lamb, R. G. and J. H. Seinfeld (1973). "Mathematical modeling of urban air pollution--general theory." 

Environmental Science and Technology 7: 253-261. 

Latimer D. A., M. W. Gery and H. Hogo. 1986. A Theoretical Evaluation of the Role of Nighttime 

Nitrate Formation in the Formation of Layered Haze. Systems Applications, Inc., San Rafael, California 

(SYSAPP-86/012). 

Levy, J., J. Spengler, D, Hlinka, D. Sullivan, and D. Moon, 2002. Using CALPUFF to evaluate the 

impacts of power plant emissions in Illinois: model sensitivity and implications. Atmospheric 

Environment. 36 (6). 1063-1075. 

 

Liu, M.-K. and J. H. Seinfeld (1974). "On the validity of grid and trajectory models of urban air 

pollution." Atmospheric Environment 9: 555-574. 

Liu, M.K. and M. A. Yocke, 1980: Siting of wind turbine generators in complex terrain. J. Energy, 4, 

10:16. 

 
 

R-8 

Lyons, W. A., and H. S. Cole, 1992. Photochemical oxidant transport: Mesoscale Lake Breeze and 

synoptic-scale aspects”, Journal of Applied Meteorology, 15, 733-743. 

Lyons, W. A., et al., 1995. Applications of the Regional Atmospheric Modeling System (RAMS) to 

provide input to photochemical grid models for the Lake Michigan Ozone Study (LMOS), Journal of 

Applied Meteorology, 34 (8), 1762-1786. 

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. 

Mayes, P., G. E. Moore, L. L. Schulman, 1996. Comparison of CALGRID, UAM-IV and UAM-V 

Ozone Predictions in New England. 96-TA23A.01, Preprints of the Ninth Joint Conference on 

Applications of Air Pollution Meteorology with A&WMA, American Meteorological Society, Atlanta, 

GA, 28 January-2 February 1996. 

McNally, D. E., 2003. “Annual Application of MM5 for Calendar Year 2001”. Prepared for the U.S. 

EPA, Office of Air Quality Planning and Standards, Prepared by Alpine Geophysics, LLC, Arvada, CO. 

178 pp. 

McNally, D. E., and T. W. Tesche, 1994. "MAPS2.3 User's Guide", Alpine Geophysics, LLC, Golden, 

CO. 

McNally, D. E., and T. W. Tesche, 2002. “Annual Meteorological Modeling Protocol: Annual 

Application of the MM5 to the Continental United States”, prepared for the EPA Office of Air Quality 

Planning and Standards, Research Triangle Park, NC. 

McNally, D. E., C. F. Loomis, T. W. Tesche, R. E. Morris, and E. Tai, 2003. “Assessment of the 

Potential 8-Hr Ozone and PM2.5/PM10 Impacts From the WE Energy ‘Power the Future’ Proposal”, 

prepared for WE Power, LLC, prepared by Alpine Geophysics, LLC, Ft. Wright, KY and ENVIRON 

International, Corp., Novato, CA. 

Moore, G.E., M.E. Fernau, R.J. Yamartino, L.L. Schulman and P.R. Mayes, 1996. Recent Improvements 

in Ozone Modeling in New England During the July 7-8 1988 Ozone Episode. Preprints of the Ninth 

Joint Conference on Applications of Air Pollution Meteorology with A&WMA, American Meteorological 

Society, Atlanta, GA, 28 January-2 February. 

Moore, G. E.; Londergan, R. J.; Fernau, M. E., 1998. CALGRID Photochemical Modeling of Air 

Quality Impacts of Alternative Transportation Fuel Use in Los Angeles. NREL/SR-540-25204, National 

Renewable Energy Lab., Golden, CO. 

Moore, G.E., R.J. Londergan, M.E. Fernau, 1999. A Comparison of Ozone Predictions From UAM-IV 

CB-4 vs. CALGRID CB-4, SAPRC90, and SAPRC93 for the Los Angeles 27-28 August 1987 Episode. 

Air Waste Management Association’s 1999 Annual Meeting and Exhibition, June 20-24, St. Louis, MO. 

Morris, R. E. and T. C. Myers. 1990. “User's Guide for the Urban Airshed Model. Volume I: User's 

Manual for UAM (CB-IV)” prepared for the U.S. Environmental Protection Agency (EPA-450/4-90-

007a), Systems Applications International, San Rafael, CA. 
 

R-9 

Morris, R.E., R.C. Kessler, S.G. Douglas, and K.R. Styles. 1987. “The Rocky Mountain Acid 

Deposition Model Assessment Project – Evaluation of Mesoscale Acid Deposition Models for Use in 

Complex Terrain. SYSAPP-87/218. Prepared for U.S. EPA, Research Triangle Park, NC. December 

Morris, R.E., C. Emery, N. Kumar, F. Lurmann, and H. Feldman. 1998. “Performance Evaluation of 

Two Particulate Matter Grid Models using the 1995 Enhanced Measurement Database in the South 

Coast Air Basin” presented at the 91st Annual Meeting of the Air and Waste Management Association, 

San Diego, CA. June 14-18, 1998. 

Morris, R. E., et al., 2002a. “Model Evaluation and Sensitivity of the Models-3/CMAQ and CAMx 

Modeling Systems for the July 1995 NARSTO-Northeast Episode, Models-3/CMAS Workshop, 

Research Triangle Park, NC, 21 October. 

Morris, R. E., et al., 2002b, “Recent Development and Applications of the Comprehensive Air Quality 

Model with Extensions (CAMx)”, Presented at the Bi-Annual EPA Modeling Conference, prepared by 

ENVIRON International Corporation, Novato, CA. 

Morris, R. E., et al., 2002c. “Recent Advances in Photochemical Air Quality Modeling Using the CAMx 

Model: Current Update and Ozone Modeling of Point Source Impacts”, Presented at the Air and Waste 

Management Association Meeting, Paper # 43180, prepared by ENVIRON International Corporation, 

Novato, CA. 

Morris, R. E. et al., 2003. “Development and Application of the PMCAMx and CAMx4 Models to Treat 

Fine Particulate and Visibility Issues”, Presented at the American Association of Aerosol Research 

(AAAR), prepared by ENVIRON International Corporation, Novato, CA. 

Morris, R., C. Tana, and G. Yarwood, 2003. “Evaluation of the Sulfate and Nitrate Formation 

Mechanism in the CALPUFF Modeling System”, Proceedings of the A&WMA Specialty Conference --

Guideline on Air Quality Models: The Path Forward. Mystic, CT. 22-24 October. 

Morris, R.E., B. Koo, T.W. Tesche, C. Loomis, G. Stella, G. Tonnesen, and Z. Wang. 2004a. Modeling 

protocol for the VISTAS Phase II regional haze modeling. (http://pah.cert.ucr.edu/vistas/vistas2/)’ 

Morris, R.E., B. Koo, G. Tonnesen, and Z. Wang. 2004b. “WRAP Multi-Model Evaluation using the 

1996 36 km Section 309 Database”, National RPO Modeling Meeting, Denver, CO, 24-25 May. 

Morris, R.E., G. Mansell, B. Koo, G. Tonnesen, M. Omary, and Z. Wang. 2004c. Modeling Protocol for 

the CENRAP 2002 Annual Emissions and Air Quality Modeling”, prepared for CENRAP, prepared by 

ENVIRON International Corp, Novato, CA, and the University of California, Riverside, CA. 

Morris, R.E., B. Koo, A. Guenther, G. Yarwood, D. McNally, T. W. Tesche, G. Tonnesen, J. Boylan, P. 

Brewer. 2005a. Model sensitivity evaluation for organic carbon using two multi-pollutant air quality 

models that simulate regional haze in the southeastern United States. Atmospheric Environment. (in 

press). 

Morris, R. E., D. E. McNally, T. W. Tesche, G. Tonnesen, J. W. Boylan, and P. Brewer. 2005b. 

Preliminary Evaluation of the CMAQ Model for 2002 and the Southeastern U.S. Journal of the Air & 

Waste Manage. Association. (in press). 
 

R-10 

Morris, R., G. Wilson, and G. Yarwood, 2005c: Use of a full-science 3-D photochemical grid model to 

address the BART visibility modeling requirements. 8th Annual Electric Utilities Environmental 

Conference, Tucson, AZ, 24-25 January. 

Morris et al., 2005d, “Air Quality Modeling Analysis for CENRAP: Preliminary 2002 Base Case 

CMAQ and CAMx Modeling of the Continental U.S. 36 km Domain and Model Performance 

Evaluation”, prepared for the Central Regional Air Planning Association, prepared by ENVIRON 

International Corporation, and the University of California, Riverside. 

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. 

MPCA, 2005. “Best Available Retrofit Technology (BART) Modeling Protocol to Determine Sources 

Subject to BART in the State of Minnesota”, prepared by the Minnesota Pollution Control Agency, St. 

Paul, MN. 

NARSTO Synthesis Team. 2000. An Assessment of Tropospheric Ozone Pollution: A North 

American Perspective. Available at http://narsto.owt.com/Narsto/assess.doc.html. July. 

NAS, 1992. “Rethinking the Ozone Problem in Urban and Regional Air Pollution” National Academy of 

Sciences, National Academy Press 

Nenes, A., S. N. Pandis, and C. Pilinis, 1998. “ISORROPIA: A new thermodynamic equilibrium model 

for multiphase multicomponent inorganic aerosols”, Aquatic Geochemistry, Vol. 4, pg. 123-152. 

NOAA (2005). Fiscal year 2004 summary report of the NOAA Atmospheric Sciences Modeling 

Division to the U.S. Environmental Protection Agency. NOAA Technical Memorandum OAR ARL255, Prepared by the National Oceanic and Atmospheric Administration, Office of Oceanic and 

Atmospheric Research, Prepared for the U.S. Environmental Protection Agency. 

http://www.epa.gov/asmdnerl/Library/AnnualReports/2004.html

NDDH, 2005, “Protocol for BART-Related Visibility Impairment Modeling Analyses in North Dakota”, 

prepared by the North Dakota Department of Health, Division of Air Quality, Bismark, ND. 

NPS, 1993. “Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 1 Report: Interim 

Recommendation For Modeling Long Range Transport And Impacts On Regional Visibility”, EPA454/R-93-015, U.S. Environmental Protection Agency, Technical Support Division, Research Triangle 

Park, NC. 

NRC. 2002. “Estimating the Public Health Benefits of Proposed Air Pollution Regulations”. National 

Research Council of the National Academies. Washington D.C. (www.nap.edu). 

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. 

Odum, J.R.; Hoffman, T.; Bowman, F.; Collins, D.; Flagan, R.C.; and Seinfeld, J.H. 1996. Gas/particle 

partitioning and secondary aerosol formation. Environ. Sci. and Technol. 30, 2580-2585. 
 

R-11 

Olerud, D. and A. Sims. 2004. MM5 2002 Modeling in Support of VISTAS (Visibility Improvement – 

State and Tribal Association). Baron Advanced Meteorological Systems, LLC, Research Triangle Park, 

NC. (http://www.baronams.com/projects/VISTAS/). 

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. 

Pilinis, C., P. Kassomenos, and G. Kallos. 1993. Modeling of photochemical pollution in Athens, 

Greece. Application of the RAMS-CALGRID modeling system. Atmospheric Environment, 27B, 353-

370. 

Pleim, J. D., and J. S. Chang, 1992. A non-local closure model for vertical mixing in the convective 

boundary layer. Atmospheric Environment, 26A, 965-981. 

Pleim, J. E., et al., 2001. A coupled land-surface and dry deposition model and comparison to field 

measurements of surface heat, moisture, and ozone fluxes. Water, Air, and Soil Pollution: Focus, 1, 243-

252. 

Roth, P. M., S. D. Reynolds, and T. W. Tesche, 2005. Air quality modeling and decisions for ozone 

reduction strategies. Journal of the Air & Waste Manage. Association, 55, (10), 1-16. 

Ryan, P., D. Lowenthal and N. Kumar, 2004: Light Extinction Reconstruction in IMPROVE. Presented 

at the A&WMA Specialty Conference on Regional and Global Perspectives in Haze: Causes, 

Consequences and Controversies, Asheville, NC, 26-29 October 2004. 

SCAQMD, 2003. South Coast Air Quality Management Plan: Attachment-6 – Proposed Modification to 

Draft Appendix V: Modeling and Attainment Demonstration. Diamond Bar, CA. 

Scheffe, R. D. and R. E. Morris (1993). "A review of the development and application of the Urban 

Airshed Model." Atmospheric Environment 27B: 23-29. 

Scire, J.S. and F.R. Robe, 1997: Fine-Scale Application of the CALMET Meteorological Model to a 

Complex Terrain Site. Paper 97-A1313. Air & Waste Management Association 90th Annual Meeting & 

Exhibition, Toronto, Ontario, Canada. 8-13 June 1997. 

 

Scire, J.S., F.W. Lurmann, A. Bass and S.R. Hanna, 1983: Development of the MESOPUFF II 

Dispersion Model. EPA-600/3-84-057, U.S. Environmental Protection Agency, Research Triangle Park, 

NC. 

Scire, J. S., R. J. Yamartino, G. R. Carmichael, and Y.S. Chang. 1989. CALGRID: A mesoscale 

Photochemical Grid Model. Volume II: User's Guide. Sigma Research Report No. A6-215-74. PTSD, 

California Air Resources Board. Sacramento, CA. 

Scire, J.S., D.G. Strimaitis, and R.J. Yamartino, 2000a: A User’s Guide for the CALPUFF Dispersion 

Model (Version 5). Earth Tech, Inc., Concord, MA. 
 

R-12 

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, MA. 

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. 

Seigneur, C., T. W. Tesche, P. M. Roth, and M. K. Liu 1981. “On the Treatment of Point Source 

Emissions in Urban Air Quality Modeling”, Atmospheric Environment, 17 (9), 1655-1676. 

Seigneur, C., C. Tonne, K. Vijayaraghavan, P. Pai, and L. Levin, 2000. The sensitivity of PM2.5 sourcereceptor relationships to atmospheric chemistry and transport in a three-dimensional air quality model. 

Journal of the Air & Waste Management Association. 42 (8), 40-50. 

Seigneur, C. et al., 2000. “Guidance for the Performance Evaluation of Three-Dimensional Air Quality 

Modeling systems for Particulate Matter and Visibility”, Journal of the Air and Waste Management 

Association, 50. 588-599. 

Seigneur, C. et al., 2002. “Development of New Science In Models-3/CMAQ”, Models-3/CMAS 

Workshop, Research Triangle Park, NC. 

Seinfeld, J. H., 1986. Atmospheric Chemistry and Physics of Air Pollution. New York, NY, John Wiley 

and Sons. 

Seinfeld, J. H. and S. N. Pandis, 1998. Atmospheric Chemistry and Physics: From Air Pollution to 

Climate Change. New York, NY, John Wiley and Sons. 

Silibello, C., G. Calori, G. Brusasca, G. Catenacci, and G. Finzi. 1998. Application of a photochemical 

grid model to Milan metropolitan area. Atmospheric Environment 32, 2025-2038. 

Stern, M. E., and J. S. Malkus, 1953: The flow of a stable atmosphere over a heated island, Part II. 

Journal of Meteorology, 10, 105-120. 

Stern, R. M., 2002, Development and first results of a new photochemical model for simulating ozone 

and PM-10 over extended periods. Prepared for the 12th AMS/AWMA Conf. on the Applications of Air 

Pollution Meteorology, Norfolk, Virginia May 20-24, 2002. 

Stern, R., and R. J. Yamartino, 2001. Dispersion Modeling within the European Community’s Air 

Quality Framework Directive: Long Term Modeling of PM10. Proceedings of the Fifth GLOREAM 

Workshop, Wengen, Switzerland, September 24-26, 2001. 

Strader, R, et al., 1988. “Development of a Gas-Phase Chemistry, Secondary Organic Aerosol, and 

Aqueous Chemistry Modules for PM Modeling”, Final Report STI-997510-1822-FR. Coordinating 

Research Council, Atlanta, GA. 

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. 
 

R-13 

Sun, Q. and A.S. Wexler. 1988. Modeling urban and regional aerosols near acid neutrality - Application 

to the June 24-25 SCAQS episode. Atmospheric Environment. 32, 3533-3545. 

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. 

Tesche, T. W., and M. A. Yocke, 1978: Numerical modeling of wind fields over mountainous regions in 

California. Proceedings of the Conference on Sierra Nevada Meteorology, South Lake Tahoe, CA 19-

21 June, American Meteorological Society. 

Tesche, T. W., 1983. Photochemical dispersion modeling: Review of model concepts and applications 

studies. Environment International, 9, (6), 465-489. 

Tesche, T. W., T. C. Meyers, and G. E. Moore, 1986: Use of dynamic meteorological models as input to 

photochemical dispersion models. 79th Annual Meeting of the Air Pollution Control Association, 

Minneapolis, MN. 

Tesche, T. W., et al., 1990. “Improvement of Procedures for Evaluating Photochemical Models”, 

Prepared for the California Air Resources Boards, prepared by Alpine Geophysics, LLC, Ft. Wright, 

KY. 

Tesche, T. W., and D. E. McNally, 1991a. "Photochemical Modeling of Two 1984 SCCCAMP Ozone 

Episodes", Journal of Applied Meteorology. 30, 745-763. 

Tesche, T. W. and D. E. McNally, 1991b. “A Three-Dimensional Photochemical-Aerosol Model for 

Episodic and Long-Term Simulation: Formulation and Initial Application for the Los Angeles Basin”, in 

Atmospheric Chemistry: Models and Predictions for Climate and Air Quality, C. S. Sloane and T. W. 

Tesche, Eds, Lewis Publishers, Chelsea, MI. 

Tesche, T. W. and D. E. McNally, 1997a. “Refined Modeling Approaches to the Identification of 

Onshore/Offshore NOx and SOx Emissions Sources Beyond 100 Km of the Breton National Wilderness 

Area”, prepared for Walk Haydel Environmental, prepared by Alpine Geophysics, LLC, Ft. Wright, KY. 

Tesche, T. W., and D. E. McNally, 1997b. “Modeling and Analysis in Support of the Design of the 

Breton Aerometric Monitoring Network”, U.S. Minerals Management Service 17th Information Transfer 

Meeting, New Orleans, LA. 16-18 December. 

Tesche, T. W., and D. E. McNally, 1999. “Comparative Evaluation of the MM5 and RAMS3c 

Prognostic Meteorological Models over the Midwestern U.S. for Two 1999 LMOS Intensive 

Measurement Episodes”, prepared for the Coordinating Research Council, prepared by Alpine 

Geophysics, LLC, Ft Wright, KY. 

Tesche, T. W., and D. E. McNally, 2002. “Annual Application of MM5 to Support 1994 CALPUFF Air 

Quality Modeling”, prepared for the U. S. EPA, Region VIII, prepared by Alpine Geophysics, LLC, 

Arvada, CO.. 
 

R-14 

Tesche, T. W., 2002a. “Limitations and Uncertainties in CALPUFF Modeled Visibility Impacts from 

Electric Generating Stations”, prepared for WE Energies, prepared by Alpine Geophysics, LLC, Ft. 

Wright, KY. 

Tesche, T. W., 2002b. Limitations of regulatory ozone models in Houston, Prepared for the Business 

Coalition for Clean Air, Prepared by Alpine Geophysics, LLC, Ft. Wright, KY. 

Tesche, T. W., J. L. Haney, and C. Seigneur, 1981. Comparison between Airshed and Plume-AirshedReactive-Interactive-System (PARIS) model oxidant predictions for the South Coast Air Basin, Prepared 

for the Electric Power Research Institute, Prepared by Systems Applications, Inc., San Rafael, CA. 

 

Tesche, T. W., R. E. Morris, G. S. Tonnesen, D.E. McNally, P. Brewer and J. Boylan. 

2005a.CMAQ/CAMx Annual 2002 Performance Evaluation over the Eastern United States. 

Atmospheric Environment (in press). 

Tesche, T. W., D. E. McNally, C. F. Loomis, G. Stella, J. G. Wilkinson, and R. E. Morris, 2005b. 

“Modeling Analyses in Support of 8-hr Ozone and PM2.5 Attainment Demonstrations in the Five-State 

Upper Midwest” , prepared for the Midwest Ozone Group, prepared by Alpine Geophysics, LLC, Ft. 

Wright, KY. 

Tirabassi, T., M. Deserti, L. Passoni, A. Kerschbaumer (2001) Photochemical Smog Evaluation in an 

Urban Area for Environmental Management, Fifth International Conference of the International Society 

for Ecological Economics Russian Chapter, Institute of Control Sciences, Russian Academy of Sciences, 

Moscow, Russia, September 26-29, 2001. 

Tombach, I., P. and K. McDonald, 2003. “Visibility and Radiative Balance Effects”, Chapter 9 in 

Particulate Matter Science for Policy Makers, NARSTO Phase II Assessment, prepared for the Electric 

Power Research Institute, Palo Alto, CA. 

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

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

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

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

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

Frst Draft”, 22 March. 

Tombach, I., P. Brewer, T. Rogers, C. Arrington, J. Scire 2005b. “Protocol for the application of the 

CALPUFF model for analyses of Best Available Retrofit Technology (BART): VISTAS second draft”, 

22 August. 

Tonnesen, G. et al., 2001. “Modeling Protocol and Quality Assurance Plan for the WRAP Visibility 

Modeling Project”, prepared for the Western Regional Air Partnership Modeling Forum, prepared by 

University of California, Riverside and ENVIRON International Corporation. 

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

Modeling Center – Application and Evaluation of Regional Visibility Models” presented at 96th Annual 

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

R-15 

Trijonis, J. C. Characterization of Natural Background Aerosol Concentrations. Appendix A in Acidic 

Deposition: State of Science and Technology Report 24. Visibility: Existing and Historical Conditions-- 

Causes and Effects. National Acid Precipitation Assessment Program, Washington, DC.1990. 

 

Turner, D.B. 1970. “Workbook of Atmospheric Dispersion Estimates.” Prepared for U.S. EPA, Office 

of Air Programs, Research Triangle Park, NC. 

Vaughan, J; and B. Lamb. 2002. AIRPACT: A real-time air quality forecast system for the Pacific 

Northwest. 82nd Annual Meeting of the American Meteorological Society. AMS, Boston, MA. 02108. 

January 13-17. 

Vijayaraghavan, K., P. Karamchandani, and C. Seigneur, 2004: Application of CMAQ-APT to the 

Central California Ozone Study. 3rd Annual CMAS Models-3 User's Conference, Chapel Hill, NC, 18-

20 October. 

VISTAS, 2005. VISTAS Protocol for the Application of the CALPUFF Model for Analyses of Best 

Available Retrofit Technology (BART)” Prepared by the Visibility Improvement State and Tribal 

Association of the Southeast (VISTAS) Technical Analysis Work Group (TAWG), and Earth Tech, Inc. 

Wilmont, C. J. 1981. “On the Validation of Models”, Phys. Geogr., 2, 168-194. 

Wexler A. S., F. W. Lurmann, and J. H. Seinfeld. 1994. Modelling urban and regional aerosols: I. 

Model development. Atmospheric Environment. 28, 531-546. 

Wexler, A.S. and S.L. Clegg. 2002. Atmospheric aerosol models for systems including the ions H+, 

NH4+, Na+

, SO4

2-, NO3-

, Cl-, Br- and H2O. J. Geophys. Res. 107(D14), doi:10.1029/2001JD000451. 

Xiu, A., and J. E. Pleim, 2001. Development of a land surface model. Part I: Application in a mesoscale 

meteorological model, Journal of Applied Meteorology, 40, 192-209. 

Yamartino, R.J., J. S. Scire, S. R. Hanna, G. R. Carmichael, and Y. S. Chang. 1989. CALGRID: A 

Mesoscale Photochemical Grid Model. Volume I: Model Formulation Document Sigma Research 

Report No. A6-215-74. PTSD, California Air Resources Board. Sacramento, CA 94814. September, 

1989. 

Yamartino, R. J., J. S. Scire, G. R. Carmichael, and Y. S. Chang, 1992. The CALGRID mesoscale 

photochemical grid model – I. Model formulation. Atmospheric Environment, 26A, (8), 1493-1512. 

Yamartino, R. J., J. S. Scire, G. E. Moore, D. McNaughton, and M. Fernau, (1996). Development of the 

Regional CALGRID Ozone Model. Sigma Research Division, Earth Tech, Concord, MA 01742. 96-

TA23A.04. 

Yarwood, G., C. Emery, et al., 2005. Recent CAMx updates and current MRPO projects. MRPO Fall 

Modeling Meeting, Des Plaines, IL 12-13 September. 

Yu, S., et al. (2005), An assessment of the ability of three-dimensional air quality models with current 

thermodynamic equilibrium models to predict aerosol NO3, Journal of Geophysical Research, 110, 

DO7S13, 1-22. 
 

R-16 

Zhang, K.M. and A.S. Wexler. 2002. Modeling the number distributions of urban and regional aerosols. 

Atmospheric Environment. 36, 1863-1874. 

 

Zhou, Y, J. Levy, I. Hammitt, and J. S. Evans, 2003. Estimating population exposure to power plant 

emissions using CALPUFF: a case study in Beijing, China. Atmospheric Environment. 37 (6). 815-826.