## 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.comi 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 Ba ckground 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.......................................................................................4-4ii 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..........................................................................................................4-1 2 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 CAL PUFF 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-Re ady 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-SPECIFI C 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 ......................................7-2iii 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

REFERE NCES .................................................................................................................... ...............R-1iv 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 Monito ring 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 Gr owth 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 SO 2 Emissions Source at 5 Hours after Release .....................................4-18

Figure 4-3. Aerosol Sulfate (SO 4 ) 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 (SO 4 ) 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 (NO 3 ) 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 SO 2 and Sulfate Over the Upper Midwest on 14 June 2002: Plot on Right Reveals the SO 2 and SO 4 Increments

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

Figure 4-8. CAMx PM PiG Simulation of NO 3 and NH 4 Over the Upper Midwest on 14 June 2002: Plot on Right Reveals the NO 3 and NH 4 Increments

Associated with Sub-Grid-Scale Treatment of Reactive Plumes............................4-21v 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 Si tes 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 Relati ve 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 ......................................................................................................5-23vi 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/m 3 ) for Eastern and Western

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

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

NO 3 /HNO 3 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 .................................................................. 8-8vii 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 PM 2.5 /8-hr Ozone Study Using CAMx for

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

Figure 8-7. Incremental 24-hr Average SO 2 , Sulfate, Nitrate, and NH 4 Concentrations Fields on 14 June 2002 from the CAMx PM PiG Simulation Over the Upper Midwest ...................................................................... 8-14viii 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 Nitrat e Formation Algorithms...........................................3-22

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

Table 5-2. Vertical Layer Stru cture 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 Moni toring 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........................................................................................................6-1 3

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

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

Table 7-2. Example of Hypothetical Impact Ranking at Six CENRAP Class I Areas ...........7-13P-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 Regi ster, including Appendix Y, the final guidance for Best Available Retrofit Technology (BART) determ inations (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 imp airment in any Class I area. Air quality modeling is the preferred method for determin ing which emission sources cause or contribute to visibility impairment.

This document establishes guidelines for perf orming dispersion modeling analyses in support

of BART determinations for sources located within the CENRAP domain. Largely consistent with EPA guidelines, these CENRAP BART Modeling Guid elines cover the many assumptions, data base elements, model configurations an d analytical methods that CENRAP states and/or source operators should consider using in their asse ssments of whether BART eligible sources are indeed ‘subject to BART’, or may be exempted. For those sources dete rmined to be subject to BART, these guidelines provide for a common and consistent methodol ogy for estimating the visibility improvements

associated with BART emissions reductions.

Each state is required to develop a BART Mode ling Protocol setting forth the required steps in assessing the levels of c ontrols needed on sources subject to BA RT. At the same time, the five (5) Regional Planning Organizations (RPO s) 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 themse lves will take the lead in the BART modeling, but CENRAP will provide assistance in the form ofmodeling guidance (this document) and readilyavailable modeling data bases (deliver ed 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 w ill 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 fram ework for performing the analyses. It is up to the states and RPO’s to develop the modeling protocols governing the BA RT modeling activities within their jurisdictions.

Several BART modeling protocols have been developed within th e past year prior to and in parallel with these CENRAP BART Modeling Guidelines . In the interest of providing consistency in the selection, use, and interpreta tion of models and analytical methods, we have attempted to synthesize key unifying elements of these protocols and weave them in to a consistent set of modeling procedures for the CENRAP region, paying attention to the unique ch aracteristics of this region and the sources located within. At the same time, EP A guidance on how the BART modeling analyses will be judged has evolved and many prot ocols as well as this guideline do cument 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 Associati on of the Southeast (VISTAS) Technical Analysis Work Group (TAWG), and Earth Tech, Inc.

> “CALPUFF Modeling Protocol in Support of Best Avail able Retrofit Technology
Determinations”, (Johnson, 2005). Prepared by the Iowa Department of Natural Resources, Air Quality Bu reau, 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 Cl ass 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 co mmon subject; thus, there is substant ial overlap in the discussions of numerous topics such as Federal require ments, EPA guidance, CALMET/CALPUFF model descriptions, and recommended mode ling procedures and so on. Ther e are differences, of course, due to local meteorology, topography, location of Class I areas, the mix of BART-eligible sources, and established modeling procedures w ithin the state, local and fede ral land manager (FLM) groups.

To promote consistency in BART modeling applications across th e central and eastern U.S., in preparing these CENRAP Guidelines , we have incorporated common elements of existing protocols where possible. The VISTAS protocol deserves sp ecial mention. It was prepared jointly by members of the VISTAS Technical Analysis Work Group (T AWG) 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 ve rsions and considerable public discussion and input. At least two public workshops were convened in the autumn of 2005 to solicit comm ents and suggestions on draft versions and to receive input fr om EPA staff on evolving agency gu idance. Details of these public discussions may be found at: http://www.vistas-sesarm.or g/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 Guidel ines presented in this document represent the integrated writing efforts of many groups, especi ally 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 contributi ons of these groups, and assumeP-3 responsibility for any misstatements or errors in incorporating written descriptions from the

aforementioned documents.

ACKNOWLEDGMENTS

The authors wish to acknowledge specifically the assi stance and, in several cases, written contributions by the following individuals in the preparation of th is guidance document:

Mr. Chris Arrington, West Virginia Departme nt of Environmental Protection; Mr. Bret
Anderson, U.S. EPA; Dr. Kirk Baker, Lake Michigan Air Directors Consortium; Ms. Pat Brewer, VISTAS Technical Coor dinator; Mr. Chad Daniels, Iowa Department of Natural Resources; Dr. Bright Dor nblaser, Texas Commission on E nvironmental Quality; Mr. Todd Hawes, EPA/OAQPS; Ms. Cyndi Huber, U.S. Forest Service; Mr. Matthew Johnson, Iowa Department of Natural Resour ces; Ms. Margaret Mc Courtney, Minnesota Pollution Control Agency; Mr. Bruce Pol kowsky, National Park Service; Mr. Tom Rodgers, Florida Department of Environmen tal 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 Interna tional Corporation who served as an independent technical reviewer throughout the course of this study and provided many useful suggestions, insightful reco mmendations 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 Fe deral Register, including Appendix Y, the final guidance for Best Available Retr ofit Technology (BART) determ inations (70 FR 39104-39172). The rule applies to any BART-eligible source that ‘‘e mits 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 cer tain BART-eligible sources based on dispersion modeling demonstrati ng that the source cannot reasonabl y be anticipate d 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 emissi ons limits on those sources for which controls are required. To assist the states, the EPA has o ffered guidelines for how BART modeling should be conducted (EPA, 2005). Although the Fe deral 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 disper sion 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 wi th the overall aim of the RHR.

States have the responsibility for preparing m odeling protocols that de scribe both the modeling analyses for the ‘subject to BART’ test and the visi bility 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), Regiona l Planning Organizations (RPOs) and the various source operato rs. Ultimately, EPA has the auth ority to approve or disapprove a state’s State Implementation Plan (SIP). A fl awed BART modeling analys is, including deviations from an agreed upon protocol, would be c onsidered in the SIP approval process.

At least three CENRAP States (Iowa, Minneso ta, and Texas) have already developed BART modeling protocols:

> “CALPUFF Modeling Protoc ol in Support of Best Ava ilable Retrofit Technology
Determinations”, (Johnson, 2005). Prepared by the Iowa Department of Natural Resources, Air Quality Bu reau, Des Moines, IA

> “Best Available Retrofit Technology (BART ) Modeling Protocol to Determine Sources Subject to BART in the State of Minnesota”, (MPCA, 2005), prepar ed 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 consiste ncy between states in the development of BART modeli ng protocols and to harmonize the approaches between adjacent RPOs, CENRAP commissioned the deve lopment 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 s ource-specific modeling protocols sp ecifically tailored to the needs and requirements of a given f acility 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 (VIS TAS, 2005), the Midwest RPO (Baker, 2005;1-2 LADCo, 2005) have also develope d BART protocols. Since they all address a common subject, there is substantial overlap in the discussions of nume rous topics such as Federal requirements, EPA guidance, CALMET/CALPUFF mode l descriptions, and recommended modeling procedures. Differences among the protocols exis t, of course, due to local mete orology, topography, the location of Class I areas, the mix of BART-eligible sources, and established modeling proced ures within the state, local and FLM groups. For consistency in BART m odeling applications acro ss 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 versi ons and considerable public discussi on and input. At least two public workshops were convened in the autumn of 2005 to solicit comments and sugges tions on draft versions and to receive input from EPA and FLM staff on e volving agency guidance. See full comments at: http://www.vistas-sesarm.or g/BART/BARTComments.asp . Thus, element of the VISTAS protocol, reflecting the work of a very large number of organizations, were inco rporated 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, Colora do 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 guide lines mandatory for all BART determinations.

Sources are BART-eligible if they meet three specific criteria in cluding (a) emissions of at least 250 tons per year of a visibil ity-impairing pollutant, (b) begin operation between 7 August 1962 and 7 August 1977, and (c) are listed as one of the 26 listed source catego ries 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 mode ling is an important tool available to the States in determining whether a source can be reasonably exp ected 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 “BAR T-eligible source” is defined as the total of all BART-eligible emission units at a single facilit y. If a source has seve ral emission units, only those that meet the BART-eligible criteria are included in th e definition “BART-eligible source”.

1.2. Objectives of These Guidelines

Overall compliance with RHR visibility improveme nt goals in the CENRAP region entails four phases of visibility modeling:1-3 > Single-source modeling to determine wh ich BART-eligible sources are ‘subject to
BART’;

> Single-source modeling to determine the degr ee of visibility impr ovement attributable to proposed BART controls for each source subject to BART;

> Cumulative modeling to determine the combin ed 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 ultimatel y satisfy the RHR visibility improvement goals.

The responsibility for the first th ree modeling analyses belong to th e individual states. CENRAP’s mandate is to assess the aggregate effects of contro ls on progress toward attaining the visibility goals

across the domain. Tables 1-1 a nd 1-2 identify the Class I areas in and adjacent to the CENRAP domain. Figures 1-2 through 1-4 indi cate the location of Class I areas by federal land manager. Figure 1-5 identifies where visibility monitoring is currently occu rring in the CENRAP region.

This document applies only to the first tw o phases involving single-source modeling (i.e., screening to determine which BART-eligible sources are subject to BART, a nd 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 thei r 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 mo st 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 be tween state and source operators wi ll likely vary widely across the CENRAP states. Ultimately, it is the state’s res ponsibility 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 deve lop individual protocols. This issue must be worked out with the state and cognizant FLM.

Given the focus on the first tw o visibility assessment phases , these guidelines focus on common procedures for air quality modeling in support of BART determ inations, consistent with the EPA guidelines and harmonized with the methods em ployed by other RPOs. (Figure 1-6 depicts this process). The guidelines are intended to pr ovide a common understanding among the organizations performing or reviewing BART anal yses – CENRAP state and local ai r agencies, EPA, FLMs, source operators, and contractors. Each state retains res ponsibility 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. Ther efore, this document describes suitable BART modeling systems and their app lication to two situations:

> Air quality modeling to determine whether a BART-eligible source is “subject to BART” and therefore the BART analysis proc ess must be applied to its operations.1-4 > Air quality modeling of emissions from s ources 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 propos ed by several organizations.

1.3 Overview of Recommended BART Modeling Procedures

States must determine whether a source in th eir jurisdiction emits any air pollutant (SO 2 , NO

x , PM, VOC) that “may reasonably be anticipated to cause or contribute to any im pairment 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 vi sibility 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 op tion, 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 visibili ty 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 de scribed in these guidelines are grouped into th ree categories:

> Screening Modeling : The screening analysis uses the CALP UFF modeling system in default mode with pre-prepared modeling domains and model-ready meteorological inputs. This simple methodol ogy, 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 imp acts 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 in troduce greater source specificity into the modeling through the use of variable backgr ound 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-sp ecific approaches will satisfy the BART regulatory requirements. Separate ch apters of this protocol are devoted to each of1-5 these BART modeling approaches. Re gardless of the approach, exemp tion 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 scr eening assessment with CALPUFF

should be considered to assess whet her a BART-eligible source is subj ect to BART. As described in Chapter 6, a screening CALPUFF di spersion modeling analysis is performed to assess whether a particular source is exem pt from BART, obviously ‘subject to BA RT’, or should consider additional source-specific modeling analyses to explore further its exemp tion 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 assessmen t, the state may choose to exempt the source from BART or to require additiona l source-specific modeling to confirm it is not subject to BART.

If a source is shown to contribut e 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-specif ic 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 woul d typically include exerci sing 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 oper ator may wish to apply full-science modeling

methods to overcome CALPUFF’s inherent limitations (see Chapter 8). For exam ple, sources close to Class I areas, sources located in proximity to ot her large industrial or ur ban source complexes, or sources located great distances from a Class I ar ea 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 mu st 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 conduc ted prior to development of an alternative modeling protocol to en sure 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 es timating the visibility benefits of possible BART control measures. Th ese 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 th at the source terms and/or emissions data are

modified to reflect the BART control measures being evaluated. The ba se case to which the effectiveness of BART controls is compared is the “current emissions” scenario for which the exemption modeling analysis was performed. The pos t processing steps and procedures are the same. Side-by-side comparison of the visibility impacts provi des 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 modeli ng approach (e.g., screening, sour ce-specific, or alternative modeling), a formal modeling protocol is require d. A modeling protocol should be submitted for all modeling demonstrations re gardless of the distance from the BART-e ligible source to the Class I area. The protocol is typically written by the state or the source operator an d then reviewed with state, EPA, and the FLM. EPA’s role in the development of th e protocol is advisory only as the “States better understand the BART-eligible source configurations” a nd 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 mode ling 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 hei ght, temperature, exit ve locity, elevation, and emission rates, speciation and size fractioni zation of applicable pollutants); and

> Receptor data from appropriate Class I areas.

The protocol guides the technical details of a modeling study and pr ovides a formal framework for reviewing technical assumptions , operational details, commitment s and expectations of the participants. It also provides means for resoluti on of potential differences of technical and policy opinion in an open process within pres cribed time and budge t constraints.

Modeling protocols all too often fa ll short of providing sufficient de tail 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 a pplication to sour ce-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 a pplication. Chapter 2 reviews EP A’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 characteri stics and limitations discussed. EPA’s guidance allows for the use of appropriate al ternative models and Chapter 4 gives an overview of pertinent state-of-science regiona l photochemical/aerosol models and cu rrently available data bases. However, these CENRAP guidelines focus prim arily on the applicati on the CALPUFF modeling system. Chapter 5 identifies ex isting data bases developed by CENR AP and others to support BART modeling. Then, Chapters 6, 7 and 8 presen t 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 th e 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 descri bed 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 re garding protocol development is provided as a template. Finally, a quality assurance me thodology for CALMET/CALPUFF BART modeling is outlined in Chapter 9.

Figure 1-1. Federal Class I A reas 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 Wildli fe Service Class I Areas.1-11 Figure 1-5. Location of Vi sibility Monitoring Site s 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

Y ES

NO Y ES

Y ES1-13 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 Regula tions was published in the Federal Register on 6 July 2005 (70 FR 39104). One of the provisions of the program is th e requirement that certain existing stationary sources emitting visibility-impairing air pollutants install and opera te 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 su mmarizes 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 nece ssary for sources shown to cause or contribute to visibility impairment at fede rally-protected Class I areas.

2.1 Regional Haze Rule and BART Guidelines

Section 169A of the Clean Air Act (CCA) sets fo rth a national goal for vi sibility 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 polluti on.” In 1999, EPA published a final rule to address a type of visibility impairment known as regi onal 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 wild erness areas (i.e., the Class I scenic areas identified in the Clean Air Act). The 1999 rule was issued to fulfill a long-st anding 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 Ava ilable Retrofit Technology (B ART) for certain large stationary sources that were put in place between 1962 and 1977. Th e 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 h aze is believed to be caused in large measure by long-range transport of emissions from multiple sour ces, 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 vi sibility impairment in the Class I area.

The BART guidelines were written primarily for th e benefit of state, local and Tribal agencies, and describe a process for making the BART determ inations and establishing the emission limitations

that must be included in their SIPs or Tribal implementation plans (T IPs). Because the individual states have the authority to require source operators to assume part of th e 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 da ta collection, analysis, and rule development may be performed by Regional Planni ng Organizations, for adoption w ithin 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 requir ed 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 crit eria: (a) emissions of at least 250 tons per year of a visibility-impairing pollutant, (b) in operation between 7 A ugust 1962 and 7 August 1977, and (c) one of 26 liste d source categories in the guidance. BART controls are required for any BARTeligible source which can be reasonab ly 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 contri bute to visibility impair ment in a Class I area.

In EPA’s recent 1 August 2005 proposed rulema king (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 ge nerating capacity in excess of 750 MW. These presumptive levels, deve loped through a formal rulemaking process, are to be applied on a source-specific basis. EPA believe s that it appropriate to apply th em 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 presumpti ons are not appropriate at particular units.

2.2 Role of Air Quality Models

The EPA guidelines present severa l options for assessing whether or not sources are subject to BART. The options, relying on different modeling a nd/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 dispersi on 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 visibi lity as a result of its emissions of SO 2 , NOx and

direct PM emissions. Because EPA’s reco mmended CALPUFF dispersion model cannot currently be used to reliably estimate the pr edicted impacts on visibilit y 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 so urces of VOC or ammonia emissions may be likely to impair visibility and should theref ore be subject to BART review. EPA approved models should be used to predic t 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 approa ch. The plume and stack characteristics of the model plants were developed considering a broa d 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 mete orology and terrain in their regi on. Alternatively, a state could determine that the assumptions in EPA’s m odel plant analyses accurately reflect the

characteristics of the sources a nd region at issue and no further analysis would be necessary. Because PM was not evaluated in EPA’s model pl ant analysis, states must determine whether PM emissions are significant enoug h to warrant further analysis.2-3 It may be most useful to use this type of anal ysis to identify the types of small sources that do not cause or contribute to visibility impairmen t for purposes of BART, and thus should not be subject to a BART review. Diffe rent Class I areas may have di fferent characteristics, however,

so care must be taken to ensure that the criter ia developed are appropria te for the applicable cases. States could use modeling analyses of re presentative plants to reflect groupings of specific sources with important common characte ristics. 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 categorica lly 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 vi sibility-impairing pollutants to determine if

emissions from these sources contri bute 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 di spersion modeling. Air quality m odeling allows a state or source operator to analyze an individua l source’s impact on visibility as a result of its gaseous SO 2 , NOx and

direct PM emissions.

EPA assumes in the BART guidance that disp ersion modeling cannot currently be used to estimate the predicted impacts on vi sibility from an individual sour ce’s emissions of VOC or ammonia owing to uncertainties in the ammonia inventories 1 . Presumably, EPA believes that the uncertainty in current biogenic and anthropogenic ammonia inve ntories are sufficiently large that dispersion modeling of such sources would not provide meaningful results. Howe ver, this view does not comport with EPA’s CAIR modeling or ongoing RPO region al haze visibility modeling in which existing

ammonia inventories are indeed used to calculate daily and annual PM 2.5 and visibility impact assessments. EPA suggests a more qualitative asse ssment to determine on a case-by-case basis which sources of VOC or ammonia emissions may be likel y to impair visibility and should therefore be subject to BART review. CENRAP is consider ing 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 ar e 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 qua ntify the impact of source

emissions of SO 2 , NOx, and direct PM (PM 2.5 and/or PM 10 ) on 24-hr visibility impairment at receptors

located within downwind Cla ss I areas. Since visibility is define d in the context of light extinction, which itself is determined by atmospheric concentr ations of specific fine particulate species --

1 We believe this viewpoint is outdated, give recent improvem ents 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 simula ting 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) stat es “Because the air quali ty model CALPUFF is currently the best application available to predict th e 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 th e BART determination process.

CALPUFF can be used to estimate not on ly the effects of directly emitted PM 2.5 emissions from a source, but also to predict the visibility impacts fro m 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 simula ting aerosol chemistry, transport, and deposition of airborne pollutant s, including particulate matter and ozone. Regional scale air quality models are generally applied for geographic scales ra nging 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 a ppropriate EPA Regional Office prior to carrying out any such modeling”.

In contrast, EPA’s “Guidance for Demonstratin g Attainment of the Air Quality Goals for PM 2.5 and Regional Haze” (EPA, 2001) sets forth the type s 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 eff ects 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

PM 2.5 SIP modeling guidance, EPA indicates that CA LPUFF (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 CA LPUFF is the “best regulatory modeling application currently available for predicting a singl e source’s contribution to visibility impairment” and notes that “it is the only EP A-approved model for use in esti mating single source pollutant concentrations resulting from the long range transport of primary po llutants”. On th e other hand, only regional grid models with appropriate ch emistry are to be used in developing PM 2.5 and Regional Haze SIPs. EPA attempted to reconcile these two positions in the Final BART Rule by asserting that (a) regional models were not develope d to treat individual point sources and (b) CALPUFF’s secondary aerosol chemistry is adequate for estimating relative benefits of controls on BA RT sources. The first point ignores the significant amount of advanced pl ume-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 gu idance stands and CALPUFF is

the recommended model for BART determinations. EP A does allow for the use of alternative models on a case-by-case basis. Thus, th is guidance addresses both appr oaches 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 wa s put in service, and the magnitude of its emissions of one or more “visibility-impairin g” air pollutants. The BART guidelines list 26 source categories of stationary sources that are BART-eligible. Sources mu st 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 vi sibility-impairing air pollutant are required to make a source eligible for BART. Qualifying pollutants include primary particulate matter (PM 10 ) and gaseous precursors to secondary fine particulate matter such as SO 2 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 ba sis. The guidance states that high molecular weight VOCs with 25 or more carbon atoms and low vapor pressure should be considered as primary PM 2.5 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 a ll 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 re sult in impairment above the state-determined threshold, the source may be deemed subject to BART.

To determine whether a BART-eligible sour ce can be excluded from BART controls, a modeling demonstration is required. EPA’s pref erred 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 determin ed by the states. The threshold to determine whether a single source “causes” visibility impa irment is set at 1.0 deciview change from natural conditions over a 24-hour averaging pe riod. Any exceedance of this threshold would trigger a BART review. The guidance also states that the proposed thresh old at which a source may “contribute” to visibility impairment shoul d not be higher than 0.5 deciviews, although depending on factors affecting a sp ecific Class I area, it may be se t 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 ex tinction will evoke a just noticeable change in most landscapes. Converting the 5% change in li ght 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. Sin ce visibility degradat ion 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 determini ng that an individual source is causing visibility impairment at a Clas s I area. This level would be calculated by comparing the air quality model’s results for an individual source against’ natu ral visibility’ conditions. A source’s impact is asse ssed by comparing the 98 th percentile modeled value (8 th highest day annually at a receptor or 22 nd highest over 3-years) with the 0.5 deciview threshold. An impact

is declared if the source contribut es 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 eligib le sources at th e facility are greater th an 0.5 dv, the source is ‘subject to BART’. In other words, if the comb ined impact of all units is greater than the threshold for contribution determined by the st ate (such as 0.5 dv), the source is declared2-6 ‘subject to BART’. This does not imply that c ontrols 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 opti ons and selecting the “bes t” alternative, taking into consideration: (a) any pollu tion control equipment in use at the source which affects the availability of options and thei r impacts, (b) the costs of comp liance with control options, (c) the remaining useful life of th e facility, (d) the energy a nd non air-quality environmental impacts of compliance, and (e) the degree of impr ovement 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 s ource-by-source basis, a state (or a group of states) has the option of impl ementing an emissions trading program designed to achieve regional haze improvements that are greater than the visibility improvements that could be expected from BART 2 alone. If the geographic distri butions of emissions under the two approaches are similar, determining whether trad ing 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 eligib le sources. If the geographic distributions of emissions are likely to be different, however , regional air quality modeling comparing the expected improvements in visibility from th e 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 th e use of the CALPUFF m odel. 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 additi on 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 determin ation 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 satis fy the BART requirements for affected EGUs, which could affect the modeling efforts a State would need to undertake.2-7 For both BART exemption an d BART determination modelin g with CALPUFF, emissions reflecting periods of start-up, shutdo wn and malfunction are not to be considered in determining the appropriate emission rates. The EPA recommends th at the states use the highe st 24-hour average actual emission rate for the most recent five-year peri od (excluding periods with start-up, shutdown and malfunctions). Visibility improvem ents may be evaluated on a polluta nt-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 aeroso l formation from VOC emissions is deficient, especially for anthropogenic em issions. Currently, VISTAS is performing a weight-of-evidence analysis using EPA’s CMAQ regiona l air quality model to assess whether VOC emissions from all BART-eligible and non-BART point s ources in each VISTAS State cont ribute 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 CENR AP States might argue similarly that VOC emissions should not be subject to BART.

EPA has given states the option to address ammonia (NH 3 ) 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 NH 3 emissions in a state is less than 0.5 dv, th en the impact of a single BART eligible

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

role of direct NH 3 emissions on Class 1 area visibility, the CENRAP States might argue similarly that VOC emissions should not be subject to BART. CE NRAP is presently cons idering similar modeling

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

NH 3 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 foundati on: 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, CM AQ) or neglected (CALPUFF) in the governing equations and the choice of coordi nate system (Lagrangian or Eule rian). As shown in the next Chapter, much of the simplicity of the CALPUFF m odel 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 photoc hemical reaction schemes and to provide more realistic simulations than simpler approaches such as CALPUFF. Full-science re gional models such as CAMx and CMAQ treat the various physical and chemical processes in detail, al beit 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 formulati on. Because EPA allows the use of alternative models on a case-by-case basis, th ese CENRAP guidelines include desc riptions of these models and how they can be used.2-8 EPA essentially dismisses the use of regional sc ale models because they are “generally applied for geographic scales ranging from a multi-state to th e continental scale and that by design they may not be appropriate for simulating in dividual point sources”. This pos ture 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 (Karaman chandani et al., 2002), are fully applicable to the analysis of point sour ce plumes, most especially when reactive atmospheric chemistry occurs. If they were not, then they woul d not be reliable in simulating the combined effects of the wide array of anthropogenic and biogenic emi ssions that cause gas phase, particulate, secondary aerosol, and visibility air polluti on problems—precisely the problem cu rrently being addressed by all five RPOs. Furthermore, the convergence of fast commodity-based Linux computer clusters and the recently-developed RPO regional modeling emissions, me teorological, 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 st udy are today quite comparable wi th and occasionally less that a traditional PSD modeling study using ISC, CALPU FF, or AERMOD. Given grid nesting and plumein-grid technology, modern regional m odels 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 de termination of the aggregate contribution of all-BART-eligible sources to visibility impairment.

States may elect to perform cumulative mode ling in order to examine the feasibility of exempting BART-eligible sources, but this is certa inly 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 tec hnology, 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-eligib le sources in the CENR AP domain, application of the CALPUFF modeling sy stem 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-scie nce modeling systems and supporting RP O data bases are available. Their broad use by the five RPOs in the past thre e years amply demonstrate that these comprehensive models are no longer arcane acade mic/research institution project s 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 (b ext ) which is a measure of light scattering and absorption due to atmospheric constituents. Values for b ext are estimated using an empirically derived equation which relates the extin ction coefficient to relative humidity and the following components of particulate matter mass: (a) sulfates (SO 4 ); (b) nitrates (NO 3 ); (c) organic carbon (OC); (d) elemental carbon (EC); (f) particulat e matter (IP) (“crustal material”); and (g) coarse mass (CM) (i.e., PM 10 – PM 2.5 ). The BART guidance requires the use of modeled concentrations of these components, together with a “humidity correction factor”, to estimate values for b ext on all days within a three year period. These estimates, wh en 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 particulat e matter constitutes an important fraction of PM 2.5 and that
the modeling requirements for secondary and primary pa rticulate matter differ in their need to consider atmospheric chemistry and in the degree of spatia l resolution needed for the modeling (EPA, 2001, pg 22).

This chapter introduces the formulation of th e CALPUFF modeling syst em. 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 screeni ng and/or source-specific applicati ons of CALPUFF are appropriate.

In most cases, we expect that application of th e CALPUFF system will be sufficient to meet the BART Rule requirements. For that subset of conditions requiring adva nced methods, Chapter 5 provides details on full-science alte rnative models and available data bases for BART modeling. Such conditions might include a situation where the default modeling shows th at a source just barely causes or contributes to visibility degr adation or in negotiations over the final BART determination that weighs technical and economic feasibility against exp ected 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 Re sources 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 app lication of the two. CALGRID was the urban- scale photochemical grid model resulting from the project (Yamartino et al., 1992) comparable in sc ience and capabilities to the Urban Airshed Model (UAM-IV) (Scheffe and Mo rris, 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 SO 2 ) from elevated point sources. Thus, the CALGRID model was designed to treat the complexities of ur ban-scale photochemical processes while CALPUFF was formulated to treat the non-steady state tran sport, diffusion, linear reaction, and deposition of primar y pollutants from point sources. CALPUFF was not designed to address photochemical oxidants or and secondary aerosol forma tion 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 studie s to address point source issues that include complexities posed by complex terrain, large source -receptor distances, parameterized chemical transformation and3-2 deposition, and issues related to Class I visibility im pacts. 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 assessmen ts 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 regi on. Thus, examination of the model’s formulation provides the context for assessing th e extent to which it suitable for simulating the various physical processes and gas-phase, aerosol, an d aqueous-phase chemical processe s 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/interpolati on model that provides meteorological inputs to

CALPUFF. These fields include hourly-averaged three-dimensional wind and temperat ure 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 (G rell 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 esti mated concentrations of ambient pollu tants 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 f acilitate cumulative source impacts (POSTUTIL) and estimates of light extinction and visibility attenuation at Class I re ceptors of intere st (CALPOST). In particular, CALPOST contains several options fo r computing change in extinction and deciviews for visibility assessments while the POSTUTIL postpr ocessor includes options fo r summing contributions of individual sources or groups of sources to assess cumulative impact s. POSTUTIL also contains an empirical nitric acid-nitr ate chemical equilibrium module to es timate 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 Lagr angian. Consistent with the original ARB design criteria, the Lagrangian (moving puf f) reference system was chosen for CALPUFF. In the Eulerian approach, the behavior of pollutants is described rela tive 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 mathema tical relationships for pollutant concentrations that are equally valid . The choice of which appr oach to adopt depends upon the specific design goals of the modeling system.

The advantages and drawbacks of each approa ch are thoroughly discussed in the literature (Tesche, 1983; Seinfeld and Pandis, 1998; Jaco bson, 1999; Russell and Dennis, 2000). One of the criticisms of early Eulerian grid models was their ‘over-dilution’ of point so urce 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 conc eptually simple, flexible, and computationally inexpensive, the governing equations are not directly applicable to situations involving non-linear chemical reactions (Seinfeld a nd 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 indi vidual puffs carried downwind by the threedimensional fields generated by CA LMET. The model’s implementati on 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-dimens ional hourly meteorological data, allow CALPUFF to simulate the effects of time- and space-varyi ng 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 di rection specific building dimensi ons. 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 co efficients, including the use of turbulence measurements ( σ v and σ w ), the use of similarity theory to estimate σ v and σ w from modeled surface heat and momentum fluxes, or the use of Pasquill-G ifford (PG) or McElroy- Pooler (MP) dispersion coefficients, or dispersion equati ons 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 in cluded 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-fie ld 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 condi tions. Both techniques reproduce continuous plume results under th e appropriate steady state conditions.

Wind Shear Effects : A key underpinning of the Lagrangian c oncept is that the mo deled puffs retain their identity over the time- and spatial-scale associ ated 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 di ffused and dispersed by the wind. The point where significant puff shredding occu rs is difficult to define since it depends substantially upon the complexity of the meteorol ogical 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 m odel will systematically over-predict pollutant

concentrations.3-4 To deal with this conceptual limitation, CALP UFF contains an optional puff splitting algorithm

to simulate vertical wind shear effects across individual puffs. Diff erential 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 si ze becomes large relative to the grid size to account for wind shear across the puffs. Detailed guida nce on when and how the puff-splitting algorithm

should be used and actual verification studies demons trating 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 trans port 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 th e puff-height (either that of IS CST3 or a general “plume path coefficient” adjustment), or an algorithm that simu lates enhanced vertical dispersion derived from the weakly-stratified flow and di spersion module of the Comp lex Terrain Dispersion Model (CTDMPLUS) (Perry et al., 1989). The puff-height adjustment algorithms rely on the receptor elevation (relative to the elevati on 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 th e 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 ba sed on the CTDMPLUS (Pe rry et al., 1989). Plume impingement on subgrid-scale hills is evaluated at the CTSG subgroup of receptors using a dividing streamline height (H d ) to determine which pollutant material is deflected around the sides of a hill (below H d ) and which material is a dvected over the hill (above H d ). The local flow (near the feature)

used to define H d is taken from the gridded CALMET fiel ds. As in CTDMPLUS, each feature is modeled in isolation with its own set of receptors.

Overwater and Coastal Interaction Effects : The CALMET processor cont ains 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 meteor ology 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 com putation of dry depositi on rates of gases and particulate matter as a function of geophysical para meters, meteorological c onditions, and pollutant species. For particles, source-spec ific mass distributions may be prov ided for use in the resistance model. Of particular intere st for BART analyses is th e ability to separately m odel the deposition of fine particulate matter (< 2.5 μ m diameter) from coarse particulate matter (2.5-10 μ m diameter).

Wet Deposition : An empirical scavenging coefficient appro ach 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., fro zen vs. liquid precipitation).

3.2.3 Primary Particulates

CALPUFF is designed to simulate PM 10 or PM 2.5 or other user define d size distributions of particles. The smaller the particles, the more they disperse like an inert gas. In most cases, the dispersion of inert PM 2.5 particles will differ only slightly from that of an inert gas. A key primary3-5 PM 2.5 emission from coal-fired electric generating units (EGUs) of releva nce to visibility calculations is particulate sulfate. Although primary sulfate emi ssions 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 availabl e. Treating primary sulfate emissions is likely to be most important at short distances from the stack before significant SO 2 to secondary sulfate conversion has taken place.

3.2.4 Gas-Phase Chemistry

Chemical reactions in the gas-phase play an im portant role in secondary aerosol formation by generating radical concentrations (e.g., the hydroxyl radical). These radical species oxidize SO 2 and NOx, providing the precursors to aqueous–phase chem istry (i.e., chemistry in liquid water droplets)

that convert SO 2 to sulfate (e.g., H 2 O 2 and O 3 ), and form condensable gases from some volatile organic compounds (VOCs) that can then condense into par ticulate secondary organi c aerosols (SOA). The levels of NOx, VOC, and O 3 concentrations along with the re activity 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 proces ses entirely. The chemistry in CALPUFF parameterizes chemical transformati on effects using five species (SO 2 , SO 4 = , NO

x , HNO 3 , and NO 3 - ) via a set of user-specified, diurna lly-varying transformation rates. The model estimates secondary fine particulate matter (sulfate and nitrat e) from emissions of gas-phase SO 2 and NO

x . Rather than simulating important non-linear gas phase oxidant chemistry, the model employs a user-supplied hourly ozone concentration as a su rrogate 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 ha ve been attempted in the past, correct representation of the gas-pha se photochemistry and the radical cycles are critically important in order to properly characterize sulfate and nitrate formati on in the real atmosphere. Seigneur et al., (2000)

demonstrated this fact in their evaluation of fu ll-science representations of photochemistry against simplified representations (but more advanced th an CALPUFF). They concluded that simplified linearized transformation schemes are inadequate for describing sulfate and nitrate formation

processes:

“These results indicate that the accurate pr ediction of source-recepto r relationships for PM 2.5 requires a comprehensive treatment of PM 2.5 formation from gaseous precursors for the secondary components of PM 2.5 and a spatially resolved treatment of transport processes for primary PM 2.5 . Simplified treatments of either atmospheric chemistry or transport are appropriate only when the seconda ry or primary components of PM 2.5 , respectively, are not significant. Therefore, the developmen t of source-receptor relationships for PM 2.5 should be based on air quality models that provide compre hensive 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 mode l incorporating a simplified empirical chemical mechanism developed in a manner similar to the mech anism in CALPUFF. Eval uating the full-science and empirical chemistry models against observed con centrations, 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 ar e 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 de pendent on the rate of mixing with ambient (backgr ound) air and the chemical composition of this background. The rates of oxidation of sulfur dioxide (SO 2 ) and nitrogen dioxide (NO 2 ) to sulfate and nitric acid can be very diffe rent within a power plant or industrial plume compared to that in the bac kground 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 in troduces 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 out side of the plume must be treate d realistically. If the chemical

interactions between thes e two fundamentally different and inte ractive chemical environments are overly-simplified or neglected alto gether, the ability of the mode l to correctly calculate plume concentrations, deposition, or vi sibility impacts is lost.

Sulfate and Nitrate Formation. Two SO 2 and NOx chemical transforma tion schemes are available in CALPUFF: the MESOPUFF-II algor ithm (Scire et al., 1983; Atkinson et al., 1982) and the RIVAD algorithm (Latimer et al., 1986). Th ese algorithms calculate sulfate and nitrate formation rates based on the puff concentrations, background environm ental parameters provided by CALMET, and background ozone and ammonia concentr ations 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

• SO 2 concentration

Environmental Parameters (from CALMET)

• Temperature

• Surface Relative Humidity (RH)

• Atmospheric Stability

• Solar Radiation

Background Concentrations (User Input)

• Ozone (O 3 )

• Ammonia (NH 3 )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 fi ve active pollutants (SO 2 , SO 4 , NOx,

HNO 3 , and NO 3 ) as follows:

k 1 SO 2 Æ SO 4 k 2 NOx Æ HNO 3 (+RNO 3 )

k 3 NOx Æ HNO 3 NH 3 HNO 3 (g) ÅÆ NO 3 (PM)

where,

SO 2 is the puff average sulfur dioxide concentration; NOx is the puff average oxides of nitrogen concentrations;

SO 4 is sulfate concentrations formed from the SO 2 ; HNO 3 is the nitric acid formed from the NOx;

NO 3 is the particulate nitrate that is in equilibrium with the nitric acid; and

NH 3 is the background ammonia concentration.

Daytime Rates

k 1 = 36 x R 0.55 x [O 3 ] 0.71 x S -1.29 + k 1(aq)

k 1(aq) = 3 x 10 -8 x RH 4 (added to k 1 above during the day)

k 2 = 1206 x [O 3 ] 1.5 x S -1.41 x [NOx] -0.33

k 3 = 1261 x [O 3 ] 1.45 x S -1.34 x [NOx] -0.12

Nighttime Rates

k 1 = 0.20 (%/hr)

k 2 = 0.00 (%/hr)

k 3 = 2.00 (%/hr)

with,

k 1 is the SO 2 to SO 4 gas-phase transformation rate (%/hr)

k 1(aq) is the SO 2 to SO 4 aqueous-phase transformation rate (%/hr)

k 2 is the NOx to HNO 3 +RNO 3 transformation rate (%/hr)

k 3 is the NOx to HNO 3 (only) transforma tion 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/m 2 ) RH is the relative humidity (%) [O 3 ] is the user provided backgr ound ozone concentrations (ppm) [NOx] is the plume average NOx concentration (ppm)

NH 3 is the user provided bac kground ammonia concentrations3-8 Daytime chemical transformations are based on statistically analyzed hourly transformation rates (Scire et al., 1983) obtai ned from box model simulations us ing the Atkinson et al., (1982) photochemical mechanism. In this scheme, gas-phase oxidation of SO 2 and NOx depends on the hydroxyl (OH) radical concentrations for which bac kground ozone, solar intens ity (R), and stability index are used as surrogates. At night, OH concentrations are much lower and default SO 2 and NOx

oxidation rates of 0.2 %/hr and 2.0 %/hr are assumed. The k 1(aq) sulfate formation rate is added to the

k 1 rate during the day as a surroga te for aqueous-phase sulfate form ation 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 forma tion rate equations used in the MESOPUFF II scheme were originally generated by developing regression equati ons for a few key variables on the results of 144 box model simulations that used the 1982 photochemi cal 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 nitr ate transformation equations were extremely limited. For example, the transformati on rates did not cover temperatures below 10 deg C (50 deg F) or cleaner rural atmospheric condit ions with VOC concentr ations 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 NO X /SO 2 emissions source plumes. In many cases these defici encies lead to an overestimation bias of the source’s sulfate and nitrate impacts. F actors 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 con centrations tend to occur on hot summer days. Lower temperatures produce lower OH and othe r radical concentrations and consequently

lower sulfate and nitrate formation rates. Th e CALPUFF sulfate and n itrate formation rates, however, do not adequately incorporate temper ature effects. The MESOPUFF-II chemical transformation algorithm was deve loped under conditions with a minimum temperature of only

10 ° C (50 ° F). Thus, under cond itions 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 spri ng and winter months; th ese 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 syst ematically overpredict nitrate impacts.

Effects of NOx Emissions on Sulfate Chemistry : Downwind of a point s ource with significant

NOx/SO 2 emissions, high NOx and SO 2 concentrations co-exist. Under high NOx concentrations, radical concentra tions are greatly reduced, resulting in very low ozone, sulfate,

and nitrate formation rates. This is due to the NOx inhibition ef fect on photochemistry whereby: (1) the titration of NO with ozone eliminates ozone and its source as a radical generator; and (2) the high NO 2 concentrations eliminate the OH radical via the NO 2 + OH reaction thereby effectivel y shutting down photochemistry. Thus, in a NOx/SO 2 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/SO 2 point source plume3-9 near the source, which in turn leads to overstating the sulfate formation rate. Because

NOx/SO 2 point sources are typically buoyant, they ar e frequently be emitted aloft in a stable layer where the high NOx concentrations and inhi bited sulfate and nitrate formation rates could persist 100 km or more downwind.

Aqueous-Phase Sulfate Formation Algorithm. CALPUFF’s MESOPUFF-II chem istry treats aqueousphase sulfate formation solely as a function of re lative humidity (RH), which actually has no direct affect on aqueous-phase sulfate formation chem istry. 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 cont ent of water vapor in the atmosphere. However, in reality aqueous-phase sulfate formation will depend on the am ount of atmospheric liquid water content (LWC) in cloud or fog droplets, the pH of the water droplets, and the level of H 2 O 2 , ozone, and

SO 2 concentrations. Accordingly, in the atmosphere, aqueous-phase su lfate 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 f og can be quite frequent.

In a liquid water droplet, the reaction of SO 2 with H 2 O 2 to form sulfate is essentially instantaneous and is usually limited by the amount of H 2 O 2 present (i.e., oxidant limited) for a

NOx/SO 2 point source. Once the H 2 O 2 is reacted away within the water droplet, sulfate formation via this pathway slows to the rate of H 2 O 2 formation, which would be extremely slow to nonexistent in a large point source plume due to th e scavenging of radicals by the high NOx concentrations. This introduces an inaccurate representa tion of sulfate formation in CALP UFF 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 a pproach is scientifically invalid. Under conditions of high RH and little clouds or little plume interac tion with clouds, it will cl early overstate sulfate formation. However, under conditions of cloudy c onditions with available photochemical oxidants (i.e., H 2 O 2 and O 3 ) and a dilute NOx/Sox point source plume, it may understate sulfate formation. Near large NOx/SO 2 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 a ccount for many environmental parameters that are necessary to simulate sulfate and nitrate fo rmation rates, including VOCs and their reactivity, temperature, liquid water content, and NOx concentra tions. 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 PM 2.5 and Regional Haze SIP modeling guidance (EPA, 2001) that recomme nds against using Lagrangian models such as CALPUFF for simulating secondary PM.

From the foregoing, it is clea r that the CALPUFF chemical tr ansformation algorithms neglect important chemical processes necessary to accurate ly estimate the sulfate and nitrate impacts due to

SO 2 and NO X emissions. Given that EPA recommends the model for BART determinations, a key question is “What is the influence of the simplif ied chemistry on modeled estimates of visibility impacts from BART sources? In some cases, the in adequacies in the CALPUFF chemistry algorithms may simply introduce broader uncerta inties 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 estimat ed concentrations and visi bility impacts due to SO 2 and NO X emissions sources. For larg e point sources that emit SO 2 and NOx emissions, such as EGUs,3-10 petrochemical process heaters, cement plant kiln s, etc., many of the limitations in the CALPUFF

MESOPUFF-II SO 2 and NOx transformation algorithms would re sult in an overestimation bias. While models that are systematically bi ased 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 contro l of emissions from some sour ces. Thus, the tradeoff between simplicity and conservativism on the one hand and t echnical credibility and unbiased answers on the other is a key element in the ne gotiation of modeling protocols de veloped 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. PM 2.5 particles, which have a mass median di ameter around 0.5 μm, ha ve an average net deposition velocity of about 1 cm/m in (or about 14 m/day) and thus th e deposition of fine particles is not usually significant except for ground-level emissions. On the othe r hand, coarse particles (those

PM 10 particles larger than PM 2.5 ) have an average deposition veloc ity of more than 1 m/min (or 1440 m/day), which is significant, even fo r emissions from elevated stacks.

CALPUFF includes parametric re presentations of particle and gas deposition in terms of atmospheric, deposition layer, and vegetation layer “r esistances” and, for particles, the gravitational settling speed. Gravitational settli ng, which is of particular importa nce for the coarse fraction of PM 10 , is accounted for in the calculation of the deposition ve locity. Effects of inertial impaction (important for the upper part of the PM 10 distribution) and Brownian motion (important for small, sub-micron particles) and wet scavenging are also addre ssed. 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 diameter s), 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 velo cities for each size category.

3.3 CALMET Meteorological Preprocessor

The CALMET meteorological m odel consists of a diagnos tic wind field module and micrometeorological modules for over-water an d 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 feat ures 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 laye r modules for applicat ion to overland and overwater grid cells.

Overland Boundary Layer Module : Over land surfaces, the energy ba lance 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 c onvective velocity scale. Mixing heights are determined from the computed hourly surface heat fluxes and observe d temperature soundings using a modified Carson (1973) method based on Maul (1980) . The module also determines gr idded fields of PGT stability class and hourly pr ecipitation rates.3-11 Overwater Boundary Layer Module: The aerodynamic and thermal pr operties of water surfaces suggest that a different method is needed for es timating 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 fo r important advective effects.

3.3.2 CALMET Diagnostic Wind Field Module

The CALMET wind model was constructed from tw o other meteorological models used in

California in the late 1970s. On e 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 Syst ems 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 spr ead and avalanche forecasting in m ountainous regions of California. Various heuristic algorithms were developed to approximate down slope drainage flows, terrain blocking and channeling (Geiger, 1965), thermal heat islands (S tern and Malkus, 1953), surface friction retardation, capping by an elevated invers ion 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 (Def ant, 1933). Later work by Tesche et al., (1986), Kessler et al., (1987) and Douglas and Kessler (1988) inte grated the CIT and CTWM mo deling system into a single meteorological model that included algorithms to blend observational data with prognostic meteorological model output. The combined mode l was used extensively for urban-scale ozone studies throughout the U.S. prior to the switch to MM5 as the pr eferred meteorological model for SIP

studies in the mid-1990s.

The CALMET model development incorporated the main features of th e CTWM and CIT wind model and significantly updated the physical parameterizations and im proved 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’ wi nd field is constructed and then adjusted to approximate the kinematic effects of te rrain, slope flows, and terrain blocking. Currently, the gridded MM5 field is used as the initial gue ss prior to terrain-pertur bation. The second step consists of an objective analysis procedure to blend the MM5 field w ith observational data to produce

a final wind field. This introduct ion of observational data in the second step of the CALMET wind field development is optional. It is also possi ble 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 th e scale of the CALMET domain.

Normally, the CALMET computational domain is sp ecified to be at smaller grid spacing than

the MM5 dataset used to initiali ze the initial guess field. For ex ample, 36/12 km MM5 data sets

available for 2000-2003 over the CENRAP domain ha ve 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 de velopment, 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 initi al guess field winds are used to compute a terrain-forced vertical velocity, subj ect to an exponential, st ability-dependent decay

function. The effects of terrain on the horizontal wind compone nts are evaluated by applying a divergence-minimization scheme to the initial gue ss 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 al gorithm (Defant, 1933) has been upgraded (Scire and Robe, 1997) based on the shooting flow al gorithm of Mahrt (1982). This scheme includes both advectivegravity and equilibrium flow regime s. At night, the slope flow mode l parameterizes the flow down the sides of the valley walls into the floor of th e valley, and during the day, upslope flows are parameterized. The magnitude of the slope flow de pends on the local surface sensible heat flux and local terrain gradients. The slope flow wind components are ad ded to the wind field adjusted for

kinematic effects.

Blocking Effects : The thermodynamic blocking effects of te rrain 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 Region al 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). Generall y, ‘visibility’ may be quan tified 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 dist ance due to scattering a nd absorption by gases and particles. Visibility is impaired when light is s cattered in and out of the line of sight and by light absorbed along the line of sight. Th e light extinction coefficient (b ext ) considers light extinction by scattering (b scat ) and absorption (b abs ):

b

ext = b

scat + b

abs

The scattering components of extinction (b scat ) are represented by light scattering due to air molecules (i.e., Rayleigh scattering, b rayleigh ) and light scattering due to particles, b sp . The absorption components of extinction (b abs ) include light absorp tion due to gases (b ag ) and particles (b ap ). Furthermore, particle scattering, b sp , 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 [(NH

4 )

2 SO

4 ] f(RH)

b

NO3 = 3 [NH

4 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 equa tion is the dry scattering or absorption efficiency in meters-squared per gram. The f(RH) term is a monthly-average relative hum idity adjustment factor.

The terms in the brackets are the estimated c oncentrations fro CALPUFF (or other model) in micrograms per cubic meter (μg/m 3 ).

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,

b ext = 3 f(RH) [(NH 4 ) 2 SO 4 ] + 3 f(RH) [NH 4 NO 3 ] + 4[OC] + 1[Soil] + (3-1) + 0.6[Coarse Mass] + 10[EC] + b Ray

This is the so-called IMPROVE extinction equation currently recommended by EPA (2003). Note that the sulfate (SO 4 ) and nitrate (NO 3 ) components are hygroscopic becaus e their extinction coefficients depend upon relative humidity. The concentrations, in square brackets, are in μg/m 3 and b ext is in units

of Mm -1 . The Rayleigh scattering term (b Ray ) 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 SO 4 and NO 3 can be estimated in severa l 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 exti nction coefficient. Th e change in visibility (measured in terms of ‘deciviews’) is compared against background conditions. The delta-deciview,

Δ dv, value is calculated from the s ource’s contribution to extinction, b

source , and background extinction,

b background , as follows:

Δ dv = 10 ln ((b background + b

source )/ b background )

The impact of a source is determined by comparing the Δ dv, or haze index (HI), for estimated natural background conditions with the impact of the source and without the impact of the source . If the Δ 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.

CALPOST uses a previous IMPROVE f(RH) curve (FLAG, 2000) which differs slightly from

the f(RH) now used by IMPROVE and EPA (2003), main ly at high relativ e 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 modi fied in the CALPUFF input file).3-14 For regional haze light extinc tion calculations, use of a plume-simulating model such as CALPUFF is appropriate only when the plume is suffici ently diffuse that it is not visually discernible as a plume per se , but nevertheless its presence could alte r 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 recomme ndation for using CALPUF F 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 prev iously, there do not appear to be any major reasons why CALPUFF cannot be used for even shorte r 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 a nd so that the sub-grid de tails of plume structure can be ignored when estimating effects on light extin ction. 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 topograp hy and meteorology of the area

and should be evaluated on a case- by-case basis with individual CE NRAP State modelers. (For reference, the width of a Gaussian plume, 2 σ y , 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 pl ume 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 out puts, producing tabulations that summarize the results of the simulations, identifying for exampl e, the highest and second-highest hourly-average concentrations at each receptor. When perfor ming visibility-related modeling, CALPOST uses concentrations from CALPUFF to compute light extinction and related m easures of visibility (deciviews), reporting these for a 24-hour averaging time. The CALPOST processor contains several options for evaluating visibility impacts, including th e method described in th e 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 backgrou nd 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 th e background extinction against which the increment is compared in terms of percent or deciviews.

For background extinction, the CALPOST processo r 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, consis tent with EPA and FLM guidance. In addition, there are two techniques for computing the 24-hour av erage change in extincti on (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 c oncentration values are used to describe the background. When applied to natu ral conditions, for which EPA’ s default natural conditions concentrations are annual averages, the same co mponent concentrations would have to be used throughout the year (unless potential re finements 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, optiona lly, 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 m onthly 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 extincti on 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-av eraged 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 ch emistry 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 depositi on 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 overcom e the bias introduced when CALPUFF assumes that the full background ammonia concen tration is entrained into each disc rete puff. For a single puff, this may be satisfactory, but the model overest imates 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 approxi mate post-processing method is fundamentally dependent on reliable estimates of ambient NH 3 at the Class I receptor of interest.

3.4.3 Refined Extinction and Back ground Visibility Estimates

EPA, the IMPROVE Steering Committee, and the RPOs are evaluating whether refinements

are warranted to the methods recommended for cal culating extinction and th e default estimate of natural background visibility. Whether EPA will appr ove 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 th ese methods are recommended by EPA, CENRAP is encouraged to adopt them. Howeve r, 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 CALPUF F system should be addressed in the State’s or source operators modeling protocol.

3.5 Model Availability

The EPA-approved version of th e 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 adde d to the Download BETA-Test page. This version of CALMM5 processes MM5 Vers ion 3 output data directly.

Earth Tech offers CALPUFF training courses th at include a description of the technical formulation of the models, overviews of each of th e processor programs, and hands-on application of the models to several case study data sets. Attendees of the cour se receive a training notebook, a workbook of case study problems, exercises, and data sets, updates on rece nt and future model enhancements, and the latest (proprietary) versi ons of the models and Graphical User Interfaces (GUIs). Other third-party training cour ses 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 mode l evaluation information for CALP UFF 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 dist ances of about 200 km from a source. This level or uncertainty in a 200 km radius ar ound a source is increased if one examines CALPUFF’s predictions in a particular m odeling cell (e.g., one containing a population center) at a specific hour as opposed to consid ering 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 sugge st that the accuracy a nd 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 spar se model evaluation information, EPA’s suggestion that the model can be used fo r scales beyond 200 km, even with case-bycase approval, is not based on model evaluation data; and

> For chemically reactive pollutants such as SO 2 , NO

x , sulfate, nitrate, nitric acid, and other secondary reaction produc ts, 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 simultaneousl y, sulfate and nitrate predictions at the CASTNet monitoring site in Pinedale, Wyom ing were evaluated agains t observations, and light extinction predictions were evaluated using transmissometer measurements. Wet sulfur and nitrogen predictions were compared to observations at seve ral acid deposition monitori ng sites. This study is especially relevant because it eval uates the performance of the model to predict variables of direct interest in Class I visibility analyses, such as su lfate and nitrate concentra tions and light extinction

coefficients

More recently, Chang et al., (2003) reported an intercomparison of CALPUFF with two other transport and dispersion m odels with high resolution fi eld data. CALPUFF predictions for inert SF 6 were compared using two recent mesoscale fi eld datasets: the Dipole Pride 26 (DP26) and the Overland Along-wind Dispersion (OLAD). Both fi eld experiments involved in stantaneous releases of

sulfur hexa fl uoride tracer gas in a mesoscale region w ith desert basins and mountains. Tracer concentrations were observed along lines of samplers at distances up to 20 km. CALPUFF predictions were evaluated using the maximu m 3-h dosage (concentration integr ated over time) along a sampling line. At the DP26 sample r 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 w ithin a factor of 2 of the observations. At the OLAD site, the model underp redicted 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 SF 6 were within a factor of 2 of observations.

The tracer studies with which CALPUFF transport and diffusion cap abilities were evaluated in the IWAQM Phase 2 report were generally over dist ances 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 fr om 0.5 km to 50 km from the stack (Strimaitis et al., 1998). Another CALPUFF evaluation study over s hort-distances is repor ted by Morrison et al. (2003). These studies address model performance over source-receptor distan ces from a few hundred

meters to 50 km.3-18 Figure 3-1. CALPUFF Modeling Syst em Components. (Scire et al., 2000a)3-19 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 Fiel d 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 Capability3-20 Table 3-2. Major Features of the CALPU FF Dispersion Model (Scire et al., 2000a)

• Source types - Point sources (constant or variable emissions) - Line sources (consta nt 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 meteorol ogical variables (winds, temperature) - Spatially-variable fields of mixing hei ght, friction velocity, convective velocity scale,

Monin-Obukhov le ngth, precipitation rate - Vertically and horizontally-var ying turbulence and dispersion rates - Time-dependent source and emissions data for point, area, and volume sources - Temporal or wind-dependent scaling fact ors for emission rates, for all source types

• Interface to the Emission s Production Model (EPM) - Time-varying heat flux and emissi ons from controlled burns and wildfires

• Efficient sampling functions - Integrated puff formulation - Elongated puff (slug) formulation

• Dispersion coefficient ( σ y , σ z ) options

- Direct measurements of σ v and σ w - Estimated values of σ v and σ w based on similarity theory - Pasquill-Gifford (PG) disp ersion coefficients (rural areas) - McElroy-Pooler (MP) dispersion coefficients (urban areas) - CTDM dispersion coe fficients (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 method3-21 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, H d , as in CTDMPLUS:

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

- Below H d , 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 vari ations of depositi on with a resistance

model - User-specified diur nal cycles for each pollutant - No dry deposition

• Overwater and coastal interaction effects - Overwater boundary layer parameters - Abrupt change in meteorological c onditions, plume dispersion at coastal boundary - Plume fumigation

• Chemical transformation options

- Pseudo-first-order chemical mechanism for SO 2 , SO = 4 , NO

x , HNO 3 , and NO 3 (MESOPUFF II method)

- Pseudo-first-order chemical mechanism for SO 2 , SO = 4 , NO, NO 2 HNO 3 , and NO 3 (RIVAD/ARM3 method) - User-specified diurnal cycles of transformation rates

- No chemical conversion

• Wet Removal - Scavenging coefficient approach - Removal rate a function of preci pitation intensity and precipitation type3-22 Table 3-3. Parameter Variations in Box Mo del Simulations Used to Develop the

CALPUFF Sulfate and Nitrate Formatio n 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 concentr ations of 7, 350 and 1400 ppb were used.4-1 4.0 ALTERNATIVE MODELING

In some situations, the modeling of a BA RT-eligible source may require more rigorous simulation tools in order to adequately estimate th e 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 st rong discrete sources of visibility-reducing emissions (e.g., SO 2 , NOx);

> Co-mingling of the sources plume with emi ssions from one or more urban or metropolitan

areas;

> Complex meteorological flow s entailing significant lateral a nd/or vertical wind shear or stagnation; and

> Extensive transport distance between source location and the neares t 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 Cla ss I area given the propensity of CALPUFF to overpredict fine particulate aerosol co ncentrations 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 reasone d BART control requirements may prompt some states or BART source ope rators to pursue alternative modeling.

This chapter summarizes these tools and pr ovides 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 discussi ons with the state, EPA and FLM.

4.1 Overview

All source-oriented models, in cluding CALPUFF, are derived fr om the fundamental atmospheric species conservation equation (Tesche, 1983). Much of the difference between simple and comprehensive models stems from the extent to wh ich the various chemical and physical atmospheric processes are treated: ri gorously, heuristica lly, in a highly parameteri zed fashion, or neglected altogether. Model developers must choose what pollu tants 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 sw iftly on a PC with minimal data requirements.

At the other end of the spectru m, full-science photochemical disp ersion models rigorously treat the major processes that govern the formation, tran sport, gas-phase and secondary aerosol chemical transformation and removal of atmospheric gas phase and aerosol species and their precursors. These4-2 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 su rface, and a set of numerical algo rithms 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 re cent annual report by the National Oceanic and Atmospheric Administrati on (NOAA, 2005) to the EPA. Other in -depth reviews of photochemical and meteorological models, chemical kinetic mechanis ms, emissions inventorying techniques, deposition modules, numerical methods, and data base preparation procedures appear extens ively in the literature (see, for example: http://www.cmascenter.org/index.html ).

One-atmosphere photochemical dispersion mode ls employ nested, stati onary three-dimensional arrays of grid cells within which pollutants are emitted, transported from cell to cell, diffused by turbulence, undergo chemical reactions, and are re moved from the grid region by precipitation, adsorption on the ground, and other means. This Eu lerian grid model con cept has been usefully applied and tested across a large range of domain scales, from microscale to regional and continental scales. Current generation ‘one-atmosphere’ mode ls estimate concentrations of VOCs, NO, NO 2 , NOx, O 3 , fine particulate aerosol includi ng sulfate, nitrate, ammonium, elemental and organic species, and product species such as HNO 3 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 monitori ng stations and as two-dimensional ‘tile’ plots that show the hourly modeled concentrations for each species acro ss the full domain. Comparisons between hourly model estimates of gas phase (e.g., ozone, NOx, VOCs, PAN) and particulate (sulfate, nitrate, am monium, organic and elemental car bon, etc.) species and observations are readily made.

One-atmosphere models offer numerous a dvantages for certain BART analyses:

> They include detailed char acterization of all relevant ma n-made and natural sources of NOx, VOCs, SOx, and primary particulates (e.g., sulfate, nitrate, ammonia, carbon) across the entire modeling domain on an hour ly basis for the entire year.

> They include state-of-sci ence 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 specifi cally 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). El imination 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;4-3 > As the result of detailed mulsti-scale nestin g or plume-in-grid trea tment and accounting for

all manmade and natural source emissions in th e 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 BA RT-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 so urce 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 back ground estimates can be rigorously evaluated using existing CASTNet, IMPROVE, SEAR CH, and related aerosol data sets;

> They have been widely applied by stat e and federal regulatory agencies, industrial organizations and academic/consulting organizations worldwide;

> They are accompanied by in-depth EPA re gulatory guidance documents (for 8-hr ozone,

PM 2.5 , and Regional Haze) specifying regarding proc edures for justifying model usage in a protocol; developing emissions, meteorologi cal, and air quality inputs; evaluating model performance; and usage in cont rol strategy and SIP development:

> There now exist up-to-date, detailed regional da te bases for the entire U.S. (plus northern Mexico and southern Canada) to support mo del applications, including high resolution 36/12 km data bases over the CENRAP region;

While these science advantages over CALPUFF ar e 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 draw backs may not dissuade source operators from considering their use. When compared to the like ly cost of BART controls, the marginal increased cost of an alternative modeling study is modest in deed. In fact, recent experience in the RPO modeling programs suggests that application of ‘one-atmosphe re’ 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 clus ters of Linux PCs. Furt hermore, 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 ba ses (e.g., the CMAS clearing house) and so on.

Several questions need to be addressed in de termining whether use of a one-atmosphere model is preferred over EPA’s defa ult CALPUFF modeling system:

> What alternative models are available?

> How do the models treat the sub-grid-sca le interaction of BART plumes with the surrounding atmosphere containing emissi ons 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 pro cess for an alternative model?

> What are the costs of using an alternative model instead of CALPUFF?

Each of these questions is a ddressed 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 m odeling 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 de position at regional scale for periods up to one year (Byun and Ching, 1999). Devel oped by EPA over a 12 year period, the CMAQ modeling system is designed to addr ess air quality as a whole by including state-of-the-science capabilities for mode ling 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 re gional scale air quality modeling. The CMAQ modeling system contains th ree types of modeling compone nts: (a) a meteorological module for the description of atmospheric st ates and motions, (b) an emission models

for man-made and natural emissions that ar e injected into the atmosphere, and (c) a chemistry-transport modeling system for simulation of the chemical transformation and

fate.

> CAMx: The Comprehensive Air Quality Mode l with Extensions (CAMx) modeling system is a second state-of-science ‘one-a tmosphere’ photochemical grid model capable of addressing ozone, particulate matter (PM), visibility and acid de position 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 PM 2.5 and PM 10 and mercury and toxics, (c) provide source-receptor, sensitivity, and proc ess analyses and (d) be computationally efficient and easy to use. EPA has approve d the use of CAMx for numerous ozone and4-5 PM SIPs throughout the U.S, and has used both CAMx and CMAQ to evaluate regional ozone and fine particulate st rategies (e.g., CAIR, CAMR).

Each of these models have been shown to be signi ficantly more accurate an d free from systematic bias in estimating visibility re ducing air pollutants when compar ed with CALPUFF (Tesche, 2002; Morris et al., 2003, 2005a,b; Te sche et al., 2005).

A potential compromise between the CMAQ/ CAMx comprehensive three-dimensional photochemical grid grids with full-science that si mulates 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 trea t a single source but uses full-science chemistry:

> SCICHEM: SCICHEM is the reactive chemistry version of SCIPUFF, which stands for Second-Order-Closure (SOC) Integrat ed Puff, and includes comprehensive treatment of gas-phase and aqueous-phase chemistry and aerosol processes. SCIPUFF simulates plume transport and dispersion us ing a second-order closure approach to

solve the turbulent diffusi on equations (Sykes et al., 1988, 1993; Sykes and Henn, 1995). SCIPUFF represents a plume by a collect ion of three-dimensional puffs that are advected and dispersed according to the lo cal 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 characteri stics 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), pr oviding a direct connection between measurable velocity sta tistics 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 scal es 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 simi larity theory to relate the dispersion coefficients to micrometeorological variables (e.g., as us ed 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 significan tly, thereby leading to an excessive computational burden. A puff merging algorit hm 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 ri se and initial dispersion are simulated by solving the conservation equations for ma ss, 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 SO 2 to sulfate. A4-6 pathway is also included for the hete rogeneous production of nitrate from N 2 O 5 hydrolysis in the presence of cloud or fog droplets. For PM 2.5 calculations, SCICHEM includes modules to simulate the partitio ning 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 ``ben chmarks'' for controls on sources subject to BART. For example, regional sc ale 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 i ndividual pollutant species that co mbine to reduce vi sibility. 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 im pact 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 Intersta te Rule (CAIR) and of the most-stringent-case

BART for EGUs.

What EPA has not addressed, bot h 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 el sewhere 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 organiza tions 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-pha se and aerosol species concentrations in isolated, major point source pl umes 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 EP A to improve the character ization 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 modeli ng domains, chemical processes and pollutant concentration levels from point source emissions are greatly impacted. Consequently, the PinG approach models the meteorological, photochemical, a nd aerosol processes at the appropriate spatial dimension by allowing for the realistic, gradual hori zontal/vertical growth of each subgrid scale plume. The PinG treatment has been fully integrated into the CMAQ chemical transport model (CTM)4-7 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 wa s to provide a realistic treatment of the dynamic and chemical/aerosol processes impacting pollutant c oncentrations in major point source plumes. They formulated the CMAQ/PinG model with an imbedde d Lagrangian plume tec hnique to simulate the relevant atmospheric dispersion processes governing vertical and ho rizontal plume expansion. This more realistic treatment of plume growth allows photochemistry and ae rosol 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 AE 3 aerosol algorithm in CMAQv4.4 was incorporated and successfully tested in the PinG module. Th e current 2005 public rele ase (CMAQ4.5) contains

EPA’s most recent PinG module that has significantly improved capabil ity to simulate aerosol species

and PM 2.5 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 plum e 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 potentia l 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 in to EPA’s CMAQ model and evaluated with monitoring program data is desc ribed by Vijayaraghavan, et al., ( 2004). CMAQ-APT-PM consists of the EPA CMAQ model with an embedded reactive plum e model, the Second-order Closure Integrated puff model (SCIPUFF) with CHEMistry (SCICH EM). 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 disper sed 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 dist ribution of these concentra tions, 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 inho mogeneous velocity field. The effects of buoyancy on plume rise and initial di spersion are simulated by solving the conservation equations for mass, heat,

and momentum. The formulation of nonlinear chem ical kinetics within the puff framework is described by Karamchandani et al. (2000). Chemical species concentrati ons 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 a ny chemical kinetic mechanism to be treated. The4-8 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 anot her 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 Modeli ng and Analysis System (CMAS) center. CMAQMADRID-APT utilizes the same emissions, meteorol ogical and air quality files as the other CMAQ

versions discussed above. It also ha s 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: IR ON 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 av erage output concentration fields for postprocessing and analysis.

Implementation of PM chemistry into the IRON Pi G 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 (t o determine the evoluti on 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 pl ume chemistry and physics takes advantage of the ‘flexi-nesting’ capability of CA Mx. In this method, a series of nested grids ar e defined with successively smaller horizontal grid spacing, provid e 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 define d that directly overcomes the pl ume over-dilution problem of older Eulerian models while retaining manageable com putational 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 th e modeler to ensure that the fine grid mesh is

used to best effect.4-9 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 alternativ e model with representatives from the state, EPA, and FLM. Since decisions on accep tability of an altern ative model is a joint responsibility, it is prudent to engage the EPA an d 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 pr otocol (see Chapter 7), additi onal technical details would be required in the description of th e model configuration, model-set up procedures, data bases to be employed, model evaluation and sensitivity te sts, and procedures for application.

Once the alternative modeling protocol is approv ed, the state or source operator (or consultant) would implement the procedures set forth in the do cument. Typically, these w ould include: selection of appropriate emissions, meteorological, and air quality models; selection of m odeling 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; mode l performance evaluation and sensitivity testing; modeling of BART source(s) with appropriate sub-grid-scale pl ume technology; calculation of background visibility; comparison of BART source plume increment re lative to background visibility; uncertainty analysis; reporting; and m odeling 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 fi ne particulate aerosols

than CALPUFF. While this like lihood is easily seen in compara tive 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 particulat e 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, ther e simply are no detailed atmospheric plume data bases that record the chemical evolution of point sour ce 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 meas ured.) If such data bases existed, it would be a simple matter to compare alternative modeling platfo rms. 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 chemi cal transformation of point source emissions. Thus, one cannot evaluate CALPUFF pred ictions against downwind second ary 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 elucidat ing the model’s ability to simulate the transport and dispersion of non-reactive materi als. In contrast, there is a rich and evolving performance evaluation history of regional on e-atmosphere models, stimulate d in large measure by the CMAQ and/or CAMx modeling being performed by WR AP, 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, a nd 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 an alyses. For the CENRAP modeling (Morris et al., 2005d), these evaluations, for NO, NO 2 , ozone, sulfate, nitrate, ammonium, ammonia, EC, OC, secondary organic aerosol, PM 2.5 , extinction coefficient, and vari ous 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 B ugle plot as a func tion of the species concentration. This helps characterize di fferences 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. Pe rformance 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 o ccurs because the model mis-locates the high concentrations by just a few grid cells from the obs erved high concentrations.

> Stacked bar time-series plots for each site : These plots show c ontributions 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 & wo rst 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 m odel 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 clea n to polluted conditions.

Three recent studies summarize the current pe rformance capabilities of the CMAQ and CAMx regional models for aerosol species and visibility . Morris et al., (2005d) summa rize the performance of CAMx and CMAQ over the 36 km CENRAP domain fo r the full calendar ye ar 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) a nd criteria (the level of accuracy that is considered to be acceptable for re gulatory applications) that vary as a function of concentration and extinction. Spec ifically, 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. Additiona lly, 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 recommendati ons are based upon an anal ysis of numerous PM and visibility modeli ng studies performed throughout the country.

In particular, Boylan and Russell (2005) tabulated the mean fract ional 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 frac tional error for each component of

PM as well as PM 10 and PM 2.5 . Overall model performance for PM 2.5 is fairly good with about 50% of the points meeting the goals and a large majority m eeting the criteria. Sulfat e performance is better than most components of PM 2.5 that were examined, with a majority of the points meeting the goal and only two points falling outside the criteria. This was attrib uted to good estimates of SO 2 emissions, sulfate chemistry being less complex than other co mponents 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 th e 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 performa nce falls between that of sulfate and nitrate. Overall, the performance is fairly good because th e majority of the ammonium is associated with sulfate which performs well. Less than 40% of the organic performan ce 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 urba n 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 out side 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) perfor med an identical analysis for components of light extinction. They report similar performance patte rns 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 impr oved over that of their PM mass performance. In fact, only a handful of soils and co arse mass points fall outside the cr iteria lines. Using the identified metrics, sulfate and EC components of PM 2.5 and light extinction are generally the most accurately simulated, while nitrate and organic carbon performan ce are poor. Poor soil performance is of concern

for PM 2.5 , but not visibility. Poor coarse ma ss performance does not impact total PM 2.5 and has minimal impact on total light exti nction. Detailed model evaluati on 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 curre nt one-atmosphere models such as CMAQ and CAMx reveals that for some species (sulfate, PM 2.5 ) the models do quite well while for certain others the models exhibit bias and large un certainty (e.g., nitrate concentratio ns, 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 con tinuous years (2001, 2002, and 2003). This performance record clearly surpasses – in rigor, scope, and statistics – any evaluati on 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 inform ation would be quite helpful in supporting discussions with EPA Re gional 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 a nd 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 gr id scales are available over the CENRAP states and may be obtaine d from one or more RPOs. Deta ils 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 tech nical 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, st aff, 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 ha rdware requirements are still well within the range of modern scientific computing set-ups. Give n the availability of m odel-ready meteorological, emissions, and air quality data bases from the various RPOs, the only significant computational cost in alternative modeling is in runni ng 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 n eed to develop new meteorology. Unlike CALPUFF, there is no need to develop estim ates of background emissions , or air quality; these are already compiled in the existi ng RPO data bases. The only da ta input development need, common to CALPUFF too, is the characteriza tion 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 c onfiguration for applying CMAQ in multi-processor mode might include 3 dual Athlon MP 2800+ computers (6 total proces sors), 1 to 2.5 Gybte RAM for each node, a GigE switch connection, and 1-3 Tbyte ex ternal hard disk storage for model input and output files. With four such clus ters, 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-proce ssor Athlon MP machines would be employed with the ‘master" node4-13 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 rath er 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 proj ects do not seem to vary significantly between CMAQ and CAMx platforms.

Finally, EPA provides a website for the comm unity to share different experiences in acquiring, configuring, and appl ying different hardware soluti ons 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 tran scends the skill level normally associated with operating EPA guideline models. Particular ar eas of modeling expertis e involve meteorology, atmospheric chemistry, emissions inventory developm ent, 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 agen cies, regional EPA offices, governmental laboratories,

and academic institutions. There is al so a fairly large consultant commu nity 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 av ailable 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 BA RT application. Of course, so me studies can be performed more quickly and others may ta ke 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 regi onal 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, th ere are some important decisions to be ratified (calculati on 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. S o, depending on the number of years modeled and the number of BART control strategies examined, com puter simulations alone may necessitate two months

or more of calendar time. 1 Third, unlike CALPUFF model applica tions, the alternativ e model protocol development and annual visibility simulations can not realistically be carried out in parallel. Negotiation of the protocol must first be comple ted 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.4-14 CAMx simulations is actually implemented. Based on th ese factors, it is reasonable to expect that an alternative BART modeling applica tion 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 mu st follow the procedures set forth in the Guideline on Air Quality Models for obtain ing 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 alternativ e model is a Regional Office responsibility. If it can be demonstrated that in a sp ecific 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 St ate or the source show th at 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 performa nce evaluation has been conducte d using measured air quality data and the results of that evaluation indicate the alternative model performs better for the application than a comparab le model in appendix A; and

> If there is no preferred m odel 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 th e acceptability of a m odel 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 bot h control agencies and regulated industry is an important element in

such an evaluation. If it can be shown that CALP UFF is not applicable to the BART source-specific modeling problem at hand, an al ternative refined model may be used provided that:

> The model can be demonstrated to be applic able to the problem on a theoretical basis;

> The data bases which are necessary to perf orm 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 evalua ted 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 consiste nt 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 object ive decision-making on the acceptability of an alternative model for a given re gulatory application. These doc uments contain procedures for conducting both the technical evaluati on of the model and the field te st or performance evaluation.

Ultimately, the Regional Administrator has the au thority to determine whether an alternative model is acceptable for a source-specific BART an alysis. However, recognizing the need for assistance and guidance in the select ion process so that fairness and consistency in modeling decisions is fostered among the various Regional Offices a nd 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 modelin g study for a source can vary depending upon

several factors includin g: (a) availability of meteor ological and air quality data sets, (b) availability of emissions source characterization (especially particul ate 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 su ccessfully negotiate the modeling protocol with the state, EPA and FLM, and various other factors. Computer cost s are minimal since the CALPUFF system runs on personal computers equipped with ine xpensive 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 source 2 is in the range of $20,000 to $50,000 depending upon the nature of the problem, the expertise and cost structure of th e organization performing the modeling, the modeling experience of the state or source operator, and the extent of interaction re quired 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 applicab le regional data sets. Key cost elements in an alternativ e modeling study include: (a) protocol development and negotiation with the reviewing agen cies, (b) obtaining and setting up one or more RPO data bases on the host computer network 3 , (c) running the base case regiona l model simulation to establish ‘background visibility conditions, (d) running the regi onal 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 modeli ng is whether the state, EPA, and/or FLM will require that more than one calendar year be mode led 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 d ouble. However, even if the reviewing agencies require a full three years of CMAQ or CAMx modeling, there are still several opportunities for cost reducti on. Opportunities include:

2 This exercise would includes analyses to determine whet her the source is ‘subject to BART’, followed by several CALPUFF model runs to estimate the visibility impacts of different levels of BA RT 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 mode ling can likely focus ju st 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 gr id nesting SGS technologies in the regional model only during those episodic periods wh ere 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 alternativ e models can very likely reduce the overall program cost. The greatest variable in estimating the resources requ ired 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-Sca le 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 Concentratio ns in the Cross Section from a High NOx/Low

SO2 Emissions Source at 5 Ho urs After Release. Ozone (O 3 ) is the solid red line. Units: SO4 (μg/m 3 ), OH (ppt), SO 2 and HNO 3 (ppb). (Source: Godowitch, 2004).

Figure 4-3. Aerosol Sulfate (SO 4 ) Difference Field Determined by Subtracting the CMAQNoPinG Concentrations from the CMAQ-P inG Concentrations at 2 pm EDT on 7 July 1995. (Source: Godowitch, 2004).4-19 Figure 4-4. Change in the Co ntributions of Fourteen (14) EGUs to Local Sulfate (SO 4 ) Concentrations When the CMAQ-APT-P M 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 (NO 3 ) Concentrations When the CMAQ-APT-P M 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 Asso ciated with Sub-Grid-Scale Treatment of Reactive Plumes. (Sou rce: Yarwood et al., 2005).

Figure 4-7. CAMx PM PiG Simulation of SO 2 and Sulfate Over the Upper Midwest on 14 June 2002: Plot on Right Reveals the SO 2 and SO 4 Increments Associated with Sub-GridScale Treatment of Reactive Plumes . (Source: Yarwood et al., 2005).4-21 Figure 4-8. CAMx PM PiG Simulation of NO 3 and NH 4 Over the Upper Midwest on 14 June 2002: Plot on Right Reveals the NO 3 and NH 4 Increments Associated with SubGrid-Scale Treatment of Reactive Pl umes. (Source: Yarwood et al., 2005).4-22 (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 CA LPUFF MODELING

To support BART modeling by the states and source operators, both meteorological and aerometric data sets are required. Regional meteor ological data sets generated by the CALMET model suitable for direct input to the CALPUFF modeling sy stem have been developed and archived. These data sets cover calendar years 2001, 2002, and 2003 fo r three sub-regional grid domains shown in Figures 5-1 through 5-4. The proc edures used in developing the CALMET data sets generally follow the IWAQM recommendations (EPA, 1998), ex cept for a few notable refinements. The processed CALMET files, in CALPUFF-ready input format, ar e available from CENRAP on hard disk drives to

interested states and stakeholders.

This chapter describes how these meteorological modeling sets were deve loped 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 appli cability. In addition, for those states or source operators who elect to conduct more source-specific CALMET/CALPU FF modeling, the information in th is chapter may be helpful in guiding specification of revised CA LMET model inputs and generation of revised CALMET data sets.

Also included in Sectio n 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 se t of CALMET regional meteorological modeling data sets for use by the CENRAP States, BART elig ible sources within the region and others. These meteorological modeling data sets were constructe d through the joint use of the CALMET processor and results from existing annual three-dimensional MM5 meteorologi cal simulations. The specific annual prognostic model simulations availa ble for CENRAP BART modeling included:

> 2001 MM5 data set at 36/12 km resolu tion 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 eval uation of the MM5 genera ted data sets against surface meteorological observat ions and the results of these evaluati ons 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 i ndependent CENRAP modeling centers and these data sets have not

been concatenated into one master data base. Mo re importantly, there has b een no systematic, rigorous model performance evaluation performed on the CENR AP 2002 12 km MM5 data yet. Accordingly,

until such time as the 2002 12 km data set has b een 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 cons truct meteorological i nputs 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-d escribed in the CALMET User’s Guid e (Scire et al., 2000a) and in the CALMET input file to the executabl e code. Appendix A of this pr otocol 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 ar e the default CALMET options and parameter settings recommended in the IWAQM Phase 2 Report (EPA, 1998).

CALMET Domain. Three slightly overla pping 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 aver ages terrain features to the modeling grid resolution; TERREL constructs the basic pr operties of the gridded domain and defines the coordinates upon which meteorological da ta are stored. Key parameters include specification of grid type, location, resolution and terrain elevation.

> CTGPROC computes the fractional land use fo r the modeling grid resolution. Land use characteristics for each grid cell are assigned using CT GPROC. The primary variable adjustment associated with CTGRPOC is sel ection of an appropriate land use database.

Version 2.0 of the North American Land Cove r Characteristics database is used.

> MAKEGEO is the final pre-processor that co mbines the terrain and land use data for input to CALMET. Generating the appropria te MAKEGEO.INP control file requires only minimal alteration of the default assignments. Key modifications include specifying domain attributes and ensuri ng input files are correctly referenced.

Terrain . CALMET requires both terrain height and la nd use/land cover for the application region. These are generated using the CALMET CTGP ROC, TERREL and MAKEGEO processors. The

terrain data were created usi ng the TERREL (version 3.311 , level 030709) processor and the Shuttle Radar Topography Mission (SRTM)-GTOPO 30 second (~1 km) re solution dataset.

Land Use . The landuse data set was created using th e 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 ot her 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 sugg estions of IWAQM. The CE NRAP vertical structure was designed to reduce the need for vertical inte rpolation while simultane ously 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 exer cises. The three annual MM5 simulations (2001, 2002, and 2003) will be used as the sole source fo r meteorological data within CALMET. Blending

observational data with the MM5 data within CALM ET (i.e., use of the “OBS” option is essentially a redundant use of the same data. Substantial improveme nt in the MM5 initializat ion 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 a nd 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 Se rvice surface and upper air data, GOES satellite precipitable water; VAD wind profile s from NEXRAD; ACARS aircraft temperature data; SSM/I oceanic surface winds; daily NESDIS snow cover a nd 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 complexi ty, resolution, and accuracy of the ETA data that is used to initialize and ‘nudge’ th e MM5 forecasts is extensive indee d. Particularly at the 12-36 km horizontal grid scales over the fl at to modestly rolling topography of the CENRAP domain, there is no need to introduce local meteorologi cal observations in order to retrieve local terrain effects, for example. Thus, mesoscale wind patterns are likel y to be adequately ch aracterized by the MM5

simulations.

Many observations, especially surface observations , reflect local conditions on a scale smaller

than the 6 km CENRAP CALMET fiel ds. The introduction of the local observations into the regional modeling domain may extend the infl uence of the observational data beyond its true re presentativeness and result in internally inconsistent flow features. In particular the time interpolation of the 12-hourly upper air sounding data may wash out st ructure in the MM5 fields that ar e 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 weights 1 that allows significant influence of the observations where appropriate, wi ll 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 observa tional data into the regi onal 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 resoluti on and the smaller domain size, more control over the region of influence of the me teorological 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 simu lations 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 meteorologi cal data sets. These are summari zed in Appendix A, compared to the default CALMET settings, a nd 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 MM 5-derived (IPROG=14; IT PROG=2), surface layer winds were not extrapolated to the upper layers (IEXTRP = -1);

> The IWAQM recommendation for disabling th e computation of kinematic effects in the wind field options and parameters was selected. This was selected in light of the very

modest elevated terra in in the CENRAP domain, relativ e to the mountainous regions in the U.S. and Alps where the kinematic parame terizations were originally developed. Thus, the option for computing kinematic effects was disabled (IKINE = 0).

> The BIAS array was set to 0. in the CALM ET 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 extrapol ation 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 assigne d 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 f eatures is comparable to the resolution of the processed terrain data: 12 km.

> The radius of influence for temperatur e interpolation is set to 36 km (TRADKM), a value considered appropriate given th e 6 km CALMET domain and 36/12 km MM5

domain.

> The beginning/ending land use categories fo r temperature interpolation over water are assigned category 55: (JWAT1 = JWAT2 = 55).

> SIGMAP was set to 50 km, while the IW AQM recommendation is 100 km, but with no supporting documentation. Because precipitatio n rates are explicitly incorporated from the MM5 data, a lower radius of in fluence was deemed appropriate.

> Diagnostic options: IWAQM default values were used (see Appendix A);

> TERRAD (terrain scale) is required for runs with diagnostic terra in 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, JW AT2 = 55 (large bodies of water). This feature allows the temperature field over la rge bodies of water such as the Gulf of Mexico and the Great lakes to be pr operly characterized by buoy observations.

> Mixing height averaging parameter (MNMDAV) we re 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 a dvanced and/or recently developed procedures for constructing the CALMET fields have been used compared with the IWAQ M (1998) guidance. For example, one agency expressed concern about th e 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 sc ience 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 gridde d moisture fields in the MM5 mode l 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, th e States, source operators, and regulatory agencies have an opportunity to re-exami ne 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”) m ode. 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-rea dy meteorological fields entails a two-step process. First, the MM5 prognostic model outpu t fields are extracted and processed for input to CALMET. This step entails running various extraction software routin es followed by the CALMM5 code. Then, CALMET is exercised for the full three year peri od over each sub-regional CENRAP domain.

CALMM5. Previous applications of the prognostic Me soscale Meteorological model version 5 (MM5) served as the source of the gridded meteorologi cal fields for calendar years 2001, 2002, and 2003. The actual CALMM5 configuration enta iled modification of a few user-s pecified variables. However, two setting are of primary importance:

> All vertical layers from MM5 were extracted, providing CALMET configuration flexibility, and

> Vertical velocity, relative humidity, cl oud/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 co nsistent with CENRAP’s recommendation that the 6 km be generated using the ‘No-Obs’ op tion. The specific options used have been discussed above and are summarized in Appendix A.5-6 5.1.4 Evaluation of the CALPUFF-Re ady Meteorological Data Sets

In typical applications the ad equacy of the CALMET fields is seldom evaluated using independent measurements. Often, onl y cursory visual examination of wind vector plots or time series is considered. This evaluation is important becau se the CALMET performance analysis gives direct insight into the adequacy of the mo del-processed fields on a subregional basis. It also serves as an independent quality assurance tool . Alpine’s MAPS evaluation softwa re 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 temperat ures for 2001-2003 across all three domains. Since only a small portion of the meteorologi cal content of these data were ingested in the MM5 data assimilation routines (see Johnson, 2003a), th ese data sets are essentially an independent, quantitative means for evaluating the adequacy of the meteorological fiel ds 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 bot h hourly and daily time frames over the full annual cycle. For this study, evaluation meas ures were calculated for wind, temp erature, and relative humidity because these parameters are the principal meteor ological inputs to CALPU FF. The full set of

CALMET evaluation statistics an d 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 re lative terms (percent erro r) as is commonly done for air quality assessments. Obviously, a very differe nt 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 ridiculous ly large error. On the other hand, pollutant concentration errors of 10% at 1 ppb or 10 ppm carry prac tically the same significance.

Three key meteorological metrics include the bi as, 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 gi ven 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 fo r a given time period (hourly or daily).

Note that the bias and gross error for winds are cal culated from the predicted-observed residuals in speed and direction (not from vector components u and v ). The direction erro r for a given predictionobservation pairing is limited to range from 0 to ± 180 °

. 

# ( ) ∑∑

= = − = J

j I

i i j i j O P IJ B

1 1 1



## ∑∑

= = − = J

j I

i i j i j O P IJ E

1 1 15-7 Index of Agreement (IOA) : calculated following the approach of Willmont (1981). This metric

condenses all the differences betwee n 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 pr edicted 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 suggesti ng perfect agreement.

CALMET Evaluation Results

Table 5-5 summarizes the statistical measures, av eraged over the month, for temperature, wind speed, and relative humidity fo r all three years. The CALM ET 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 su rface temperature, relative humidity, and wind across the three CENRAP subdomains for the three-y ear period 2001-2003. Figures 5-28 through 5-31 provide convenient summaries of the bias and erro r in the relative humidit y, temperature, and wind speed fields across the conti nuous 36 month period by subdomain.

Thorough discussion of the performance findings is beyond the scope of these guidelines. However, a few key findings of th e evaluation are worth noting here. From Table 5-5, the wind speed index of agreement, a general measure of corre lation between measured and observed winds, is systematically greater than a value of 0.8 for virtually every mont h. These values are typically better than those generally achieved in urban- and regional-scale model a pplications for ozone SIPs. For example, the statistical benchmar k 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 appear s quite good relative to other MM5/RAMS model applications. From Figure 5-11, th e 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 temperatur e bias results for the 36 month are generally quite

close to the + 0.5 deg C performance goal. As shown in Fi gure 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 result s into an historical context.

In summary, we find that:

 Relative Humidity

 Bias over three-year peri od 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 j o i j M O M P RMSE

IJ IOA

1 1 2 15-8  Errors typically diminish from 2001 thr ough 2003, and are genera lly < 12% after 1st quarter of 2001.

 Surface Temperatures

 Monthly averaged temperatures are system atically biased low (cooler) by 0.25 to 1.25 deg C.

 The errors in monthly averaged temperatures typically range betw een 1.8 and 2.6 deg C

 Average error over all mont hs 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 pot entially useful information for diagnosing BART visibility modeling analyses

 MM5/CALMET fields exhibit good statistical ag reement with observations, in part because observations figure prominently in the constr uction of the interpolated CALMET fields.

 MM5/CALMET fields for the three CENRAP s ubdomains are quite sufficient for use in CALPUFF modeling.

5.1.5 Meteorological Data Archive and Distribution

All models, scripts and CALMET data (excepti ng MM5 outputs) are ava ilable from CENRAP on appropriate external combin ation Firewire/USB drives.

5.2 Aerometric Monitoring Networks

Data from ambient monitoring networks for bot h gas-phase and aerosol species are available

for use in CENRAP BART modeli ng analyses. Table 5-4 summari zes 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 CE NRAP. Figures 5-5 and 5- 6 display the locations of monitoring s ites 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.)

L AYER

NUMBER L AYER
H EIGHT L AYER

NUMBER L AYER
H EIGHT
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 th e CALMET Meteorological Fields for 2001-2003.

Jan Feb Mar Apr May Jun Jul A ug 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 IO A

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 A ug 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 IO A

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 A ug 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 IO A

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 2003.5-11Table 5-5. Overview of Ambient Data Monitoring Networks Covering the CENRAP Domain.Monitoring NetworkChemical Species MeasuredSamplingPeriodData Availability/SourceThe Interagency Monitoring ofProtected Visual Environments(IMPROVE)Speciated PM25 and PM10 (seespecies mappings)1 in 3 days; 24hr averagehttp://vista.cira.colostate.edu/improve/Data/IMPROVE/improve_data.htmClean Air Status and TrendsNetwork (CASTNET)Speciated PM25, Ozone (seespecies mappings)Approximately 1-week averagehttp://www.epa.gov/castnet/data.htmlNational Atmospheric DepositionProgram (NADP)Wet deposition (hydrogen (acidity aspH), sulfate, nitrate, ammonium,chloride, and base cations (such ascalcium, magnesium, potassium andsodium)), Mercury1-week averagehttp://nadp.sws.uiuc.edu/Air Quality System (AQS) AkaAerometric Information RetrievalSystem (AIRS)CO, NO2, O3, SO2, PM25, PM10,PbTypically hourlyaveragehttp://www.epa.gov/air/data/Speciation Trends Network (STN) Speciated PM 24-hour averagehttp://www.epa.gov/ttn/amtic/amticpm.htmlSoutheastern Aerosol Researchand Characterization (SEARCH)(Southeastern US only)24-hr PM25 (FRM Mass, OC, BC,SO4, NO3, NH4, Elem.); 24-hr PMcoarse (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 onparameter.Electric Power Research Institute (EPRI),Southern Company, and other companies.http://www.atmospheric-research.comEPA Particulate Matter Supersites(Includes St. Louis in the CENRAPregion)Speciated PM25http://www.epa.gov/ttn/amtic/supersites.htmlPhotochemical AssessmentMonitoring Stations (PAMS)Varies for each of 4 station types.http://www.epa.gov/ttn/amtic/pamsmain.htmlNational Park Service GaseousPollutant Monitoring NetworkAcid deposition (Dry; SO4, NO3,HNO3, NH4, SO2), O3,meteorological dataHourlyhttp://www2.nature.nps.gov/ard/gas/netdata1.htm5-12 Figure 5-1. CENRAP North, Central, a nd 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, CASTN et, 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

AQS5-17 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

AQS5-18 Figure 5-7. Spatial Mean Relative Humidi ty (%) 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 Agreemen t over the Central Domain: July 2002.

Figure 5-10. Standard Deviation in Wind Sp eed (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 Hu midity (%) 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 Te mperature (%) over the Central Domain: July 2002.5-24 Figure 5-19. Normalized Error in Surface Te mperature over the Central Domain: July 2002.

Figure 5-20. Surface Temperature (deg C) at Kenosha, WI: July 2002.5-25 Figure 5-21. Surface Temperatu re at Topeka, KS: July 2002.5-26 Figure 5-22. MM5/CALMET Relative Humidity Bi as (%) 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

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nRelative 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

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nRelative 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

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nRelative Humidity Bias,%

North Central South5-27 Figure 5-23. MM5/CALMET Relative Humidi ty 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

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nRelative 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

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nRelative 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

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nRelative Humidity Error,%

North Central South5-28 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

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nTemperature 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

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nTemperature Bias, degC

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

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nTemperature Bias, deg C

North Central South5-29 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

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nTemperature Error, degC

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

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nTemperature Error, degC

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

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nTemperature Error, degC

North Central South5-30 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

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nWind 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

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nWind 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

J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a nWind Speed Index of Agreement

North Central South5-31Figure 5-27. MM5/CALMET Relative Humidity Bias(%) over Three Years in All CENRAP Domains.Monthly Average Relative Humidity Bias, %-15.0-10.0-5.00.05.010.0 J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a n

Relative Humidity Bias, %NorthCentralSouth5-32Figure 5-28. MM5/CALMET Relative Humidity Error(%) over Three Years inAll CENRAP Domains.Monthly Average Relative Humidity Error, %0.02.55.07.510.012.515.017.520.0 J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a n

Relative Humidity Error, %NorthCentralSouth5-33Figure 5-29. MM5/CALMET Surface Temperature Bias (degC) over Three Years in All CENRAP Domains.Monthly Average Temperature Bias, deg C-2.00-1.75-1.50-1.25-1.00-0.75-0.50-0.250.000.250.50 J a n M a r M a y J u l S e p N o v J a n M a r M a y J u l S e p N o v J a n M a r M a y J u l S e p N o v

Temperature Bias, deg CNorthCentralSouth5-34Figure 5-30. MM5/CALMET Surface Temperature Error (degC) over Three Years in All CENRAP Domains.Monthly Average Temperature Errror, deg C0.00.51.01.52.02.53.03.54.0 J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c M e a n

Temperature Error, deg CNorthCentralSouth5-35Figure 5-31. MM5/CALMET Wind Speed Index of Agreement over Three Years in All CENRAP Domains.Monthly Average Wind Speed Index of Agreement0.600.650.700.750.800.850.900.951.00 J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c J a n F e b M a r A p r M a y J u n J u l A u g S e p O c t N o v D e c

M e a n

Wind Speed Index of AgreementNorthCentralSouth6-1 6.0 CALPUFF SCREENING APPLICATIONS

The objective of the CALPUFF screening appr oach 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 chapte r will also help identif y 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 va riability in wind and disp ersion 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 disper sion conditions are assumed that, fo r the most part, are consistent with the IWAQM (1998) and FLAG (2000) recomme ndations. 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 fo r 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 c onservatism (i.e., systematic tendency to over-predict visibility impacts) compared w ith 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 s ources 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 freque ncy 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 direc tion from a source may receive a higher visibility impact than a closer Class I area that is infreque ntly downwind of the sour ce. Further, correlations

between winds from certain directions and mete orological 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 determinatio n of no contribution to visibility impairment and also by using conservative model settings.

The impact of the source or sources is calculate d from the daily visibility values for each receptor by determining the change in deciviews comp ared against natural visibility conditions. EPA’s “Guidance for Estimating Natural Visibility Cond itions 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 cont ribute to visibility impairm ent at Class I area, the impacts predicted by CALPUFF are compared against the pertinent natural vi sibility 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 negotiati on of a modeling protoc ol 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 Mode ling Guidelines may be adopted or referenced directly in a specific screeni ng 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 visibili ty screening of BART eligible sources followed the IWAQM recommendations (EPA, 1998, Appendi x A), except as noted. For CA LPUFF screening assessments, there is no need to compile CALMET inputs, run the model or process the outpu ts. 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, th e 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 wr iting) science improvements and bug fixes. (The current guideline CALPUFF code is versio n 5.7, level 030402). This s ubstitution results from EPA phasing out the use of the le gacy Pasquill-Gifford (P-G) dispersion parameters with the introduction of AERMOD as a new guideline m odel. 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 orde r in which the programs are typically run.

6.4.2 Domain Definition

Three sub-regional modeling domains and associ ated CALMET meteorological data bases have been developed for CENRAP and are available to the states a nd source operators for performing CALPUFF screening analyses for any Class I areas within the domain. The m odeling 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 mete orological data bases for 2001, 2002, and 2003 were discussed in Chapter 5.6-3 Receptor Network and Class I Receptors . Experience with the CALPUF F modeling system has shown

that sizeable emissions sources can have disc ernible 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 impair ment 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 substan tially over predicts concentrations of visibility-reduci ng fine particulates under various re al-world conditions at the larger downwind distances when compared to one-atmosphe re 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 s hould not necessarily be neglected because there may be some validity to a pa rticular CALPUFF model simula tion 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 lo cated no closer than 500-600 km

to their nearest Class I area. The sc ience-based approach to this dilemm a 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 necessa ry to employ CALPUFF notwithstanding the uncertainties associated with its bias at large trans port 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 measurem ents demonstrating that puff splitting actually reduces CALPUFF model overpredict ion at large scales. Implemen tation of puff splitting, coupled with fine grid resolution (~ 12 km or less) might be c onsidered as an approach for distant Class I receptors, but this strategy will li kely 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 Na tional Park Service (NPS) Convert Class I Areas (NCC) computer program. Whether a ll Class I receptors need to be modeled is an issue to be resolved in the modeling protocol process in cooperation w ith the regulatory agencies and land managers. Receptor elevations provided by th e 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 sour ce. 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 CA LPUFF domain, depending on the locations of the

Class I areas in their states and other factors such as meteorological conditi ons 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 comput ational domain size to include only areas where significant impacts are f easible 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 a6-4 smaller CALPUFF computational domain is that th e model run time is propor tional to the number and residence time of the puffs on the domain (and othe r 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 BA RT-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 sour ce term parameters (needed to calculate localized impacts) such as building heights and widths for calcu lating 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 norma l 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 occu rring 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 CALPUF F screening modeling purposes). Pollutants

considered include SO 2 , H 2 SO 4 , NO

x and PM 10 . Excluded from the modeling are pollutants with

emissions less than de minimis levels (40 tons per year for SO 2 and NO

x and 15 tons per year for

PM 10 ).

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. Cau tion is urged when estimating emissions rates in cases where abnormal and infrequent fuel usage ove rstate 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 year s). Thus emissions rates should so lely be based upon data from the most recent years, in order to accurately charac terize 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 type s of particulate matter on visibili ty. For example, the extinction coefficient ranges in value from 0.3 to 0.6 m 2 /g for coarse particles, to 1.0 to 1.25 m 2 /g for fine inorganic particulate matter, to 1.5 to 4.0 m 2 /g for sulfate and nitrate precursors, to 1.8 to 4.7 m 2 /g for organic aerosols, and up to 8-12 m 2 /g for elemental carbon (Tombach and McDonald, 2003). Thus,6-5 generalized, conservative, or arbi trary assignments of particulate emissions to different pollutant categories can have a considerable influence on m odeled visibility impacts attributable to a single facility. Currently, data are qu ite limited on appropriate specia tion of organic/inorganic andfilterable/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 facilitie s operate continuous emission monitors on all affected equipment, there is likely to be limite d information of regarding actual emissions on the requisite time resolution ( 24-hour average), much less speciation pr ofiles 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 pa rticulate matter speciation measurements may be used to assign PM 10 to its components (e.g., assume pr imary sulfate is a fraction of SO 2 emissions, primary sulfate is a fraction of PM 10 emissions, all PM 10 emissions are PM 2.5 ). Absent this information, default speciation profiles may be used as agreed upon in the protocol.

Condensable Emissions. Condensable emissions are considered primary fine partic ulate 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 testi ng or other reliable

information. This maintains conservatism in the anal ysis where there may be uncertainty regarding the

exact size of condensable PM mass. If actual sour ce emissions data are not available, the modeling should be based on permit limits. If source-specifi c size categories are not available, then AP-42 factors may be used for sources wh ere 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 se gregated by size category. NPS has developed PM 10 emissions speciation for certain sour ce 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 a ppropriate “mass mean diamet er” 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 depositio n and possibly underestimate PM concentrations and visibility impacts.

6.4.5 CALPUFF Model Configuration

This section discusses the proced ures and input assumptions that States or source operators should follow in applying the CALPUFF model in a sc reening mode to assess whether a particular source is subject to BART (i .e., BART exemption modeling).

CALPUFF Model Options. The model options, parameter setti ngs, 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 IWAQ M Phase 2 Report (EPA, 1998).

Visibility Modeling Domain. The CALPUFF domain should be conf igured to include the source and

all Class I areas within 300 km. An additional 50 km buffer zone should be established in each6-6 cardinal direction from the source. For screenin g applications, CENRAP recommends CALPUFF for all source-receptor distances unless otherw ise 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 di spersion modeling should be followed, including the use of the Pasquill-G ifford curves. The use of turbul ence-based dispersion coefficients and probability density function dispersion methods ha ve 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 coefficien ts is outside the current CENRAP guidance and will require a caseby-case determination by the appropriate regulatory review ing authorities.

Puff Representation. Use the default integrated pu ff sampling methodology in CALPUFF.

Puff Splitting. There is no quantitative evidence that the horizontal and ve rtical puff-splitting algorithms in CALPUFF yield improved accuracy and pr ecision in model estimates of inert or linearly reactive pollutants although conceptually the methods ha ve appeal in that they attempt to mimic lateral and vertical wind speed and direc tion shears. Since there is no direct evidence confirming the correctness of the puff-splitting algorithms, the d ecision whether to invoke th e algorithms should be addressed in the modeling protocol. For screening a pplications, an added degree of conservatism at large down wind dist ances (> 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 mode l 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 SO 2 to sulfate (important when the plume interacts with clouds and fog), the IWAQM defaults are recommended, i.e., nighttime

SO 2 loss rate (RNITE1) is assumed to be 0.2 percen t per hour. The nighttim e NOx loss rate (RNITE2)

and HNO 3 formation rate (RNITE3) are bot h set to 2.0 percent per hour.

Species Modeled. Species to be modeled in the screening assessment include SO 2 , SO 4 , NO

x , HNO 3 , NO 3 and particulate matter. Absent detailed sp eciation and size distribution data from the source, PM should be modeled in two (2) size ca tegories, fine (0.0-2.5 μm) and coarse (2.5-10.0 μm),

consistent with the IMPROVE reconstructed ma ss equation. Particulate matter emissions by size category should be combined wherever possible into th e appropriate species for the visibility analysis. These species include (a) elemental carbon (EC), (b) fi ne PM or “soil” (< 2.5 μm in diameter), (c) coarse PM (between 2.5-10 μm in diameter) and (d) organics, re ferred to as secondary organic6-7 aerosols in the CALPOST postprocessor. If source-sp ecific emissions factors are not available, AP-42

factors can be used to estimate th e 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 a pplications, the background ammonia concentration is assumed to be temporal ly and spatially invariant. Background ammonia estimates should be developed from CENRAP’s mo st recent CMAQ or CAMx simulation for the 2002 base year. Because CMAQ/CAMx modeled and obser ved monthly averaged ammonia concentrations exhibit wide spatial variability, we recomme nd obtaining separate mont hly-averaged ammonia concentrations from CMAQ or CAMx for the CENR AP 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 sett ings, and ‘switches’ for exercising POSTUTIL in a screening mode are presented in Appe ndix C. This appendix contains tables that list the recommended screening and defa ult configurations for CENRAP BART modeling. The ammonialimiting method (ALM) in CALPUFF (Escoffier-Czaja a nd Scire, 2002, 2005) repar titions 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 screeni ng applications, the user should set the parameter MNIRATE=1 in POSTUTIL to implement this appr oximate correction in its simplest form. The background ammonia concentration obtained from CAMx or CMAQ re gional 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. Wh ile all receptors should be included in a single CALPUFF simulation, one may calculate the visibility impact s in CALPOST for each Class I area separately using the NDRECP parameter. It specifies the recepto r range to be processed in CALPOST. Given the importance of the CALPOST processo r to the entire BART visibility estimation, process, we focus specifically on the parameter settings and input a ssumptions 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 (b ext ) can be calculated from the IMPROVE Equation as:

b ext = 3 f(RH) [(NH 4 ) 2 SO 4 ] + 3 f(RH) [NH 4 NO 3 ] + 4[OC] + 1[Soil] + (6-1)6-8 + 0.6[Coarse Mass] + 10[EC] + b Ray

The monthly site-specific f(RH) values are obtaine d 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 (b ext /10) (6-2)

The change in visibility (measured in terms of ‘d elta-deciviews’) is then compared against background conditions. The delta-deciview, ∆ dv, value is calculated from the source’s contribution to extinction,

b

source , and background extinction, b background , as follows:

∆ dv = 10 ln ({b background + b

source }/ b background ) (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 tw enty 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, CALP OST 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 m odeled sulfate and nitrate concen trations, as recommended in the BART guidelines. Note that the RHMAX parameter (the maximum relative humidity factor used in the particle growth equation) is no t used when Method 6 is selecte d. Similarly, the relative humidity adjustment factor (f(RH)) cu rves in CALPOST (e.g., IWAQM gr owth curve and the 1996 IMPROVE curve) are not used when MV ISBK 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 SO 4 , NO 3 , EC, SOA (i.e., condensable organic emissions), soil, and co arse PM. With Method 6, background extinction coefficients are computed from EPA (2003) monthly estimates of concentrati ons of ammonium sulfate (BKSO4), ammonium nitrate (BKNO3), coarse part iculates (BKPMC), orga nic 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 th e 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 annua l average Haze Index (HI) values (in deciview or6-9 dv). Then, the average HI value for the 20% best vi sibility days (Best Days (dv)) is estimated from 10th percentile of the annual aver age HI value for a Class I area assuming a normal distribution. Thus, no average natural concentrations ar e provided for determining extinction for the 20% best visibility days. EPA maintains that the above definition of natura l visibility baseline as the 20% best visibility days is likely to be reasonably conservative and c onsistent with the Regional Haze Rule goal of natural

conditions.

There are major technical issues with this appro ach: (a) the same concentrations assumed at all Class I areas in the East or West, (b) the same c oncentrations 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 Cl ass I area in the East or in the West. In other words, ‘one size fits all’ (T ombach, 2004). But this really is not the case.

The background extinction computation with Method 6 in CALPOST involves user-supplied monthly concentrations of SO 4 , NO 3 , PM coarse, organic carbon, soil , and elemental carbon species. In practice, concentrations for only 2 species, SO 4 ([BKSO4]) and soil ([BKSOIL]), are supplied in the CALPOST input file to represent hygroscopic an d 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 variatio n). Finally, the EPA natural back ground 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 spa tial distribution and mo nthly 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 proced ures for calculation of light

extinction should be used for current and natu ral background conditions. Table 6-3 provides the species concentrations representing natural backgr ound conditions for the western and eastern Class I areas. As shown in Figure 6-1, EPA’s east-west division cuts through th e middle of the CENRAP domain. Accordingly, in the modeling protocols, the states and source operators will need to determine whether receptors in a par ticular Class I area fall within the East or West domains as this determines the appropriate default background ligh t extinction values to use from Table 6-3.

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

whether a source “contributes” to vi sibility impairment is 0.5 dv cha nge from natural conditions. States may set a lower threshold. BART determinations are based upon the 98 th percentile of the predicted 24 hour averaged deciview impact deduced from the CALPOST postprocessor. More specifically, to determine if a source may be ex empted 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 cons ervative (i.e., highest) 98 % impact may come from

the maximum 8 th highest value for each of the three years or the 22 nd highest value for all years combined (if three years of data and 365 values fo r each year are calculated). States and source operators should use both methods in order to identi fy the higher value. The peak impact is then compared to the 0.5 dv contribution th reshold value. If the maximum 8 th highest value for each of the three years (or the 22 nd highest value for all years combined) exceeds the “contribu tion” threshold of

0.5 deciviews the source is declar ed ‘subject to BART’, potential ly triggering a source-specific CALPUFF modeling analysis (Chapter 7). Otherwise, the source is BART-excluded. In a cooperative agreement with EPA Regions VI a nd VII and federal land managers, CENRAP guidance deviates from6-10 use of the 98 th percentile. The CALMET datasets as describe d in this protocol were processed with the ‘NO-OBs’ options, (i.e., surface obser vations were not used in the CA LMET wind field interpolation). Aware that exercising CALMET with ‘NO-OBs’ may lead, in some applications, to potentially less conservatism in the CALPUFF visibility results comp ared with the use of CA LMET with observations, CENRAP has agreed to EPA’s reco mmendation that the maximum visibili ty impact rather than the 98 th
percentile value should be used for screening analyses using the CENRAP developed CALMET

datasets.

To conserve computational and analysis reso urces, the CALPUFF screening modeling may be performed sequentially for cale ndar years 2001, 2002, and 2003. If a BART source is found to be subject to BART following the first or second annua l evaluation, the additiona l year(s) need not be processed. Evaluation of all three years will be required to exclud e a BART eligible source from the BART determination process.

Since the current regulatory vers ion of CALPOST does not generate 98 percentile results, states or source operators may wish to us e a modified version of CALPOST th at 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 FORTRA N processor to generate 98 th
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 so urce responsible for a 1.0 dv change or more should be considered to “cause” visibility impa irment 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 mode ling is needed to assist in identifying various control options and selecting the “best” alternative. The BART guidelines iden tify 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 (whi ch affects the availability of options and their impacts), (b) the costs of compliance with control opti ons, (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 antic ipated to result from the use of such technology.

For the visibility analysis (the fifth factor in determining what BART shoul d 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 ma ximum change in deciviews. As noted above,

the maximum 8 th highest value for each of the three years or the 22 nd highest value for all years must be compared to the 0.5 deciviews threshold. At a minimum, tabular presen tation 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 with in each Class I area with impacts > 0.5 dv;

> Number of Class I areas with impacts > 0.5 dv; and > At each receptor, the magnit ude 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 scr eening 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 discusse d and justified. The report should also include a discussion of the uncertainty in the modeling result s and the likelihood that the screening process was effective in its determination. Any needs for s ource-specific or alternative mode ling should be identified. Accompanying the modeling report should be an el ectronic archive (CDs, DVDs, or removable USB2/IEEE 1394 hard drives as ap propriate) 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 su fficiently 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 Inputs6-13 Table 6-2. CALMET/CALPUFF Model Code s for Screening Applications.

P ROCESSOR V ERSION L EVEL

TERREL 3.311 030709

CTGCOMP 2.42 030709

CTGPROC 2.42 030709

MAKEGEO 2.22 030709

CALMM5 a 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, leve l 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 Back ground Concentrations (μg/m 3 ) for Eastern and Western U.S. Class I areas. (Source: EPA, 2003, Table 2-1)

Average Natural

Concentration West ( μ g/m 3 ) Average Natural

Concentration

East ( μ g/m 3 ) Error

Factor Dry Extinction Efficiency (m 2 /g)

Ammonium sulfate b 0.12 0.23 2 3

Ammonium nitrate 0.10 0.10 2 3

Organic carbon mass c 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/m 3 and 0.2 μg/m 3 of ammonium bisulfate. c: Values adjusted to represent chemical species in current IMPROVE light extinction algorithm; Trijonis estimates were 0.5 μg/m 3 and 1.5 μg/m 3 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 WIMO16-15 Figure 6-1. Division of the U.S. for the Pu rpose of Assigning Default Natural Background Concentrations (μg/m 3 ) for Eastern and Western U.S. Class I areas. Depicted are

the Estima tes of the De fault 10% Natural Ha z e Index Va lues (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 re duce the uncertainties in assumed background ammonia, hydrogen peroxide , and ozone concentrations. Th ese and other refinements in model inputs may help to clarify whether a source should be excluded from BA RT and/or to quantify the expected visibility impact s 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 m odeling does not pass the screening threshold for visibility impacts, the next step is to conduct source-specific modeling, perh aps using a finer grid resolution for the meteorological fields, improved trea tment of terrain effects and land use variability, and the inclusion of more refined model parameteri zations such as consider ation of time and spacevarying ammonia and ozone concentration fields. As with the screening met hod, in the source-specific modeling, the visibility threshold is based on the BART guidance of the 98 th percentile change in

deciview value.

The BART guidance indicates that the emissions ra tes to be used for ex emption modeling is the highest 24-hr rate during the modeling period. De pending on the availability of source data, the following 24-hr maximum emissions rate information (listed in order of prio rity) 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 ra tes 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 alterna tive approach to consider in addi tion to CALPUFF within 50 km of a source, the CALPUFF model appears to offer grea ter realism, provided sufficiently small grid resolution (< 4 km) is used. As discussed in Chapte r 3, there are no funda mental reasons why

CALPUFF cannot be used for much s horter 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 plum e are adequately repres ented (and the sub-grid details of plume structure can be ignored when estim ating effects on light exti nction). 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 fo r 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 sy stem 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 requi res 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 pr otocol 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 protoc ol 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 modeli ng 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 simulatio ns to better characterize the flow fields and land use changes. A typical source-specific CALMET me teorological 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 co mputers. Domains with up to 600 + x 600 + grid cells are sometimes necessary (for long tran sport distances), while domains with fewer than 100 x 100 cells generally are not used in a source-specific analys es because they cover insufficient territory.

For source-receptor distances < 50km, even fine r grid resolution than 1 km may be needed, particularly if complex terrain or coastal meteor ological effects are likely to be important. This determination should be made on a case-by-case ba sis. There is no si ngle 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 relativ e to the terrain features, the physical stack parameters (e.g., a ta ll stack in complex te rrain may be unaffected by the terrain-forced flow), proximity of the source and Class I area to a co astline, and other factors including availability of representative observational data.

7.3 CALMET Configuration for Source-Specific Assessments

The CALMET processor accepts ob servational data in the form of hourly surface observations of winds, temperature, pressure and cloud data, hourly precipitati on observations, and twice-daily upper air sounding data of winds, pre ssure and temperature. In add ition, 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), pr ognostic data only (No-Obs mode) or both sets of data (hybrid mode). For example, one possible a pproach is to consider using the CALMET “hybrid”7-3 mode which incorporates surface observations with progno stic 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 lo cated in complex terrain or coastal regions, it may be desirable to develop mete orological fields over fine r 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 ev aluate the following factor s in the specific context

of the source and the affected Class I area.

Time and space resolution of observational data . Upper air observational da ta are available at roughly 12-hourly intervals and the spac ing between sounding locations is typically several hundred kilometers. Because CALMET performs a time interpolation of wind da ta between the sounding intervals, this linear time-interpolat ed 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 va riation 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 whethe r one has confidence that the spatial and temporal representativeness of the local measurement stations is sufficient to favor measurements over prognostic modeled fields at th e local scale. While use of measurements is attractive in many situations, too great a reliance m easurements 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 cons ervation. This is partic ularly an issue where there are measurement sites of questionable repres entativeness 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 discrepa ncies between the prognostic model- derived initial guess fields and the observations by giving greater we ight to the observations in the “vicinity’ of the observations and greater weight to the prognostic wi nd fields in portions of the domai n 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, blendi ng 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 te mperatures may contain biases relative to the real atmosphere. In partic ular, MM5 sometimes underestimates the frequency of light wind and calm wind conditions and possibly underestimates the strength of the nocturnal surface temperature7-4 inversion. These biases may be mo st important for near surface sour ces when relative coarse grid resolution is used. If the adequacy of the MM5 fiel ds becomes a concern, the st ate or source operator may wish to examine the detailed MM5 model evalua tion reports prepared by the original modelers (see Chapter 5).

Level of effort vs. benefit . The preparation of observational da tasets 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 regiona l winds over the three CENRAP su b-domains (Figure 5-1) may be better characterized by the three-dimensional MM5 m odel dataset than time- a nd space-interpolated of observational data, especially above the surface where sounding data is fairly sparse spatially and poorly-resolved in time. For source-specific mode ling, the decision whether to exercise CALMET in the hybrid mode is left to the stat e or source operator who are in the best position to a ssess 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 patter ns are likely to be adequately characterized at the 6 km scale via th e 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 intr oduction of the local obser vations into the CALMET processing does not extend the influence of the obser vational data beyond its true representativeness and result in internally inconsistent flow featur es. On smaller domains with higher CALMET grid resolution, more control over the region of influe nce of the meteorological observations can be achieved and it may be easier for the diagnostic mode l to allow the local flow observations to have appropriate influence in the vicinity of the observati on, yet still allow terrain-ad justed flow to dominate away from the observations. Given that the sub-re gional 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 th e introduction observational data ma y indeed yield improvements in the resulting meteorological fields.

The decision on how the MM5/CALMET processi ng 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 meteorologica l observations into the CALMET processing at the finer grid scal es (i.e., below 6 km) should be contingent upon commitment to a thorough analysis of the instrument location, qua lity 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 resu ltant CALMET meteor ological 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 a ddition to finer grid spacing in th e 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 el evation model data (~3 arcsec USGS data) in defining the gridded7-5 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 obtaine d 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. NH 3 concentrations required as input to source-specific CALPUFF modeling should be deri ved from regional model simulations obtained from CENRAP/s CAMx or CMAQ modeling or the modeling by other RP Os in preference to the use of CASTNet or

other network ammonia measurements at discrete m onitors (see discussion in section 7.4.3 below). In addition to representing a better-science appro ach, this has the advant age 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 oxida tion of sulfur dioxide (SO 2 ) to sulfate (SO 4 ) and nitric oxides (NO

x ) to nitric acid (HNO 3 ) and nitrate (NO 3 ). The equilibrium between nitr ic acid and nitrate that is a nonlinear function of temperature and relative humi dity and the equilibrium constant can vary substantially over a typical diurnal cycle, as the temperature and rela tive humidity change. In addition, the availability of ammonia (to form ammonium nitr ate) affects ambient concen trations of nitrate. Because sulfate preferentially scavenges ammoni a, the amount of ammonia remaining for the formation of nitrate may be limited. Thus, amm onia-limiting effects can be important under certain conditions in assessing the impacts of nitrate aer osols on visibility (B lanchard 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 th e 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 NH 3 concentrations may be imposed as boundary conditions to each modeled puff, while cumula tive ammonia consumption is modeled in the POSTUTIL processor for conditions at the final endpoi nt 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 NO 3 predictions somewhat in source-specific analyses.

In CALPUFF, a nominal background ammonia con centration is used in conjunction with the

HNO 3 /NO 3 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 comp etition for ambient ammonia by indivi dual puffs and by background sources is neglected. ALM was developed to tr eat this limitation. The post-proces sing 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 incrementa l 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 co ntribution of an individual s ource or facility to nitrate formation is determined via a four-step procedure:

Step 1. The total background ammonia (ambient fr ee ammonia + ammonia associated with sulfate/nitrate aerosols) is estimated either th rough (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 (TNO 3 = HNO 3 + NO 3 ) into HNO 3 and NO 3 , leading to the following relationship,

[NH 3 ] total = [NO 3 ] + 2 [SO 4 ] + NH 3 (g)

where [NO 3 ] is the observed nitrate concentration (ppb), [SO 4 ] is the observed sulfate concentration (ppb), and NH 3 (g) is the estimated free ammonia concentration (ppb) derived by applying the chemical equilibrium relationshi p. Using a 5-year monthly average of NO 3 and

TNO 3 ( = NO 3 + HNO 3 ) and the hourly temperature and re lative humidity measured at a CASTnet site, an iterative scheme is used to estimate the free component of the NH 3 and the estimation of background sulfate and nitrate.

Step 2. The project sources are modeled in the normal way by CALPUFF to predict TNO 3 from those sources.

Step 3. The ‘boundary condition’ CALPUFF m odel is used to simulate pollutant concentrations (SO 2 , SO 4 , NO

x , HNO 3 and NO 3 ) from distant sources (outside the modeling domain) at the receptors of interest. The bac kground sulfate concentrations are used with the monthly averaged total NH 3 in Step 1 to compute availabl e 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 su lfate concentrations are varying.

Step 4. POSTUTIL is then used to develop the total concentrations of sulfat e, 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 refl ect the total concentrati ons 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 e quilibrium relationship.

In a separate run of POSTUTIL, the contributi on 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 NO 3 to any individual source (Step 3 in Figure 7-1). Once the

total nitrate from all sources is determined with ammonia limiting e ffects evaluated (i.e., Steps 1 and 2 in Figure 7-1), a separate POSTUTIL run computes the TNO 3 from the project source alone, and attributes the nitrate formed from that pa rticular source as a fraction of the total NO 3 formed based on

that source’s contribution to total TNO 3

.7-7 The distribution of CASTNet a nd IMPROVE monitoring sites in the central United States is shown in Figure 5-5. Most Class I areas in this region include eith er 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 produ ce boundary conditions of sulfate, nitrate and precursors and to derive empirical estimates of ammonia concentrations. However, CASTNet

measurements have inherent errors a nd biases that tend to overestimate HNO 3 and underestimate NO 3 (some NO 3 will volatilize to HNO 3 within the sample after collection and before laboratory analysis). Thus, applying CASTNet data to estimate background NH 3 would tend to underestimate the ambient

NH 3 and would therefore underest imate visibility impacts.

One-atmosphere regional modeling results are an alternative to the use of CASTNet or other monitoring network observations. CENRAP’s gri dded, 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

SO 2 , SO 4 , NO 3 , HNO 3 and NO

x 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 inputto the ALM method in CALPUFF. Monthly averaged, BCs and NH 3 concentrations can easily be derived from CENRAP’s 2002 annual CMAQ or CAMx modeling files.

> Sulfate, Nitrate and Ammoni a at Class I Receptors . One may extract SO 4 , NO 3 and

NH 3 concentrations at the Class I recepto rs from regional model simulations on an hourly or longer time-average at the Class I r eceptors as direct i nput 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 re sults include the avai lability of hourly concentrations of all relevant gas pha se 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.e du/aqm/cenrap/index.shtml ).

Considering the various sources of uncertainty (e.g., inadequate aerosol chemistry in CALPUFF, uncertainties in regional ammonia emissions invent ories; 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 opti on over reliance on one or more CASTNet sites for some source-specific analyses.

For source-specific modeling, we recommend usi ng 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 m easurements based on the AIRS/AQS data. Straightforward processing of the modeled CMAQ or CA Mx nitrate and sulfate fi elds can be used to generate the inputs needed for CALPUFF boundary c onditions. Gridded model out put fields should be used to generate daily average ozone and NH 3 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 th rough which BART plumes traverse compared to7-8 point measurements that likely do not coincide with the plume path and certainly do not characterize the full chemical history of the plume. Gridde d 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 ou tlined in the BART guidance constitutes the default approach presently, but ongoing research s uggests that improved methods are or soon will be available and recommended for use by the IMPR OVE 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 justif ied 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 Region al 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 bei ng 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 al so 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 consta nts in Equation 3-1), including making those for sulfates, nitrates, and or ganic carbon vary with

concentration

Presumably any changes that are made by the IM PROVE Steering Committee would also be reflected in the EPA’s approach. If this is the case, then CENRAP source-sp ecific modeling protocol s should adopt the emergent recommendations unless the sour ce can justify, and the state approves, a different approach. Thus, for example, in the source-specifi c modeling, consideration of sea salt in the natural background, elevation effects on Rayleigh scattering (s ee below) and natural weather effects such as7-9 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 fo r 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 sour ce on haze would be overstated by us ing the default Ra yleigh value. Publicly available codes are avai lable for making the elevation-depe ndent adjustments to the high

elevation default value of 10 Mm -1 . For the source-specifi c analyses, an effort should be made to be consistent with the approach ta ken by the CENRAP and the other RPOs in defining the Rayleigh scattering coefficient for thei r 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 sp ecified in EPA guidance documents. These values are held as constant throughout the East, which result in natural background extincti on that varies spatially and temporally only in response to the spatial dist ribution and monthly varia tion 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 natura l 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 ra nge of 1.8 to 2.1; (b) changing the estimates of natural conditions extincti on 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. Th e organic carbon multiplier adjustment applies to all

BART-related calculations. Since mo st of the extinction under natura l conditions is attributed to organic carbon, the first change w ill substantially increase the natu ral background level of extinction from the default level. This, in turn, will reduce th e modeled percentage or deciview impacts of most sources, which may affect their BART status.

The statistical adjustment applies for the calc ulation of conditions on the 20% clearest and haziest days. The clearest days adjustment is releva nt if the baseline for ev aluating 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 adju stment is of relevance for the assessment of the benefits of the application of BART, for which th e 20% haziest days are the baseline. It slightly

increases the default haziness on th ose days and therefore reduces the absolute deciview impact of a source’s emissions. Finally, the sea salt adjustment a pplies to the description of natural conditions, and has effect mainly near the seacoast.

Other refinements to natural conditions estimates may be appropriate at sp ecific 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 natura l 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 im pairment is based on the 98 th percentile modeled va lue (rather than the highest predicted value used for the screening evalua tion). States or sources can accomplish this in one of two ways. First, a coding change can be ma de to the CALPOST postprocessor to allow the 98 th
percentile change in extinction to be computed. Th is may be accomplished by using a specific program that post-processes the CALPOS T output (CDPHE, 2005). Altern atively, because the CALPOST processor produces visibility imp acts in deciviews, these impacts can be externally processed in a spreadsheet to calculate the cha nges in deciviews. Table 7-2 s hows a hypothetical example of the ranked visibility impacts (change in dv) for each of three years at si x different Class I areas. The 98 th
percentile (8 th 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 sour ce passes the refined analys is because the highest

98 th percentile visibility impact is below the contribu tion threshold of 0.5 dv. The actual procedure selected by the State or source should be set fo rth clearly in the protoc ol. Any CALPUFF code changes should be documented and copies of the s ource code and input/outpu t files provided to the reviewing agency for corroboration.

7.5 Assessment of Modeling Results

The report accompanying the source-specific CALPUFF modeling s hould provide a clear description of the modeling procedur es followed and the results of the analysis. Any departures from the approved modeling protocol should be specifica lly discussed and justifie d. The report should also include a discussion of uncertainty in the modeling re sults. There are many sources of uncertainty in modeling due to factors such as errors and approxima tions used in the calculation of emission rates, speciation of the emissions, particle size distribu tions, meteorological data inputs including the MM5 data sets used for determining the wind fields, th e representativeness of me teorological observations used in the analysis, uncertainties in the mete orological and dispersion model itself due to its parameterizations of transport, di spersion and chemical transformati on, and in the methods used to compute light extinction from particulate matter c oncentrations predictions. In addition, the use of monthly average relative humidity in the hygroscopi c 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 re spond to geophysical features su ch 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 cita tions 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 app licability of the CALPUFF (or alternative model) results.

Accompanying the modeling report should be an electronic archive (C Ds, DVDs, or removable USB/IEEE 1394 hard drives as appropriate) that in cludes the full set of CA LPUFF inputs and model output fields and well as any pre- or post-processor codes used to genera te the results. While it is not necessary to archive the MM5 data files used to generate the CALMET fi elds, the CALPUFF-ready meteorological fields should be included in the archive. The m odeling data archive should be sufficiently complete as to allow an independent group to corroborate the m odeling 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 an d 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 Aff ecting 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 Stations7-12 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 NH 3 Concentrations 5.3.9 Background H 2 O 2 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 Inputs7-13 Table 7-2. Example of Hypothetical Visibili ty 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

0.107-14 Figure 7-1. POSTUTIL Processing of the Ammonia Limiting Method (ALM), NO 3 /HNO 3 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

NO 3 _1; HNO 3 _1,

TNO 3 _1=NO 3 _1+HNO 3 _1 CALPUFF Incoming Boundary:

BOUNDARY.CON

NO 3 _2; HNO 3 _2;
TNO3_2=NO 3 _2+HNO 3 _2

POSTUTIL Step 1

TNO 3 _1+TNO 3 _2 are repartitioned in

new NO 3 _total/HNO 3 _total using

NH 3 available, hourly Temperature and Relative Humidity

POSTUTIL Step 2

NO 3 _total and HNO 3 _total are renamed

NO 3 ALL and HNO 3 ALL

POSTUTIL Step 3

NO 3 ALL/(NO 3 ALL+HNO 3 ALL)=

NO 3 src/(NO 3 _1 +HNO 3 _1)

NO 3 src; HNO 3 src are the new output

TNO 3 src=NO 3 src+HNO 3 src=TNO 3

_ 18-1 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 demonstr ate that CALPUFF substantially overpredicts concentrations of visibility-re ducing fine particulates under va rious real-world at the larger downwind distances when compared to one-atmosphe re models. Perhaps onl y a few BART eligible sources in the CENRAP region will consider seri ously the use of alternative models to EPA’s

CALPUFF model. But for those sources whose an ticipated control costs are substantial, the application of comprehensive, full-science regional visibility assessment tools will yield more realistic BART control requirements than those genera ted by a puff model. In this chapter, the steps required to select and apply a state-of-science regiona l model and attendant data bases are presented.

While there are other alternatives to CALP UFF (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, an d 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 operato rs interested in conducting truly re fined visibility modeling of BARTeligible sources have several options for using on e-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, MP RO), 36 km (CENRAP, MPRO, VISTAS, CENRAP, WRAP)

Figures 8-1 thorough 8-6 pr esent the various grid domains used by the RPOs and the Five States Study 1 (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 domain s as well. Depending on the source’s location and the Class I area(s) of con cern, there are likely to be several mode l/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 PM 2.5 modeling study in the Upper Midwest aimed at identifying effective SO 2 , 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 PM 2.5 simulations are being carried out for 2002 and 2009 to

address emissions controls needed for atta inment of the 8-hr ozone and annual PM 2.5 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 year s 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 Ru ssell, 2005; Tesche et al., 2005). Over the CENRAP domain, annual fine partic ulate and visibility modeling da ta 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 opera tors 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 availabil ity of local information. The elements of the CENRAP modeling inventory we ll-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 (NH 3 ) 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 dur ing February 2005 were updated to incorporate comments provided by the CENRAP State, Local, a nd Tribal (S/L/T) agencies and the Emissions Inventory (EI) and Modeling Workgr oups. As a result of the updates, the updated Base_B inventory was developed and is now being used in CENR AP’s 2002 and 2018 regional haze modeling. The CENRAP modeling inventorie s and supporting data prepar ed include the following:

> Comprehensive, county-level, mass emissi ons and modeling inventories for point, area,

and nonroad sources of 2002 emissions for the CAPs and NH 3 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 allo cated (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 (SO 2 ), oxides of nitrogen (NO

x ), volatile organic compounds ( VOC), carbon monoxide (CO), NH 3 , and particles with an aerodynamic diameter less than or equal to a nominal 10 and 2.5 microm eters (i.e., primary PM 10 and PM 2.5 ). The inventories included summer day, wi nter 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, particul ate, and regional haze

precursors.

Meteorological Data Bases The MM5 model has been used excl usively by the RPOs in support of the regional haze modeling and the modeling files used in the CENRAP and MRPO haze modeling are the8-3 same ones that have been processe d to yield the CALMET fi elds discussed in Chap ter 5. In particular, the following annual MM5 simulations have been us ed to construct CAMx and CAMQ modeling data

bases over the U.S.

> 2001 MM5 data set at 36/12 km resolu tion developed for EPA by Alpine Geophysics (McNally and Tesche, 2002; McNally 2003);

> 2002 MM5 data set ad 36/12 km reso lution developed for VISTAS by Baron Advanced Meteorological Servi ces (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 evalua tion of the MM5 against surface meteorological

observations and the results of these evaluations ar e contained in the reports or presentations cited

above.

Regional Haze Modeling Data Bases Regional modeling data base s 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 ba se development activities and associated model performance evaluations are thoroughly de scribed in the literature references.

8.1.2 Obtaining Regional Modeling Data Bases

As discussed in Chapter 4, the aggregate file si zes of the regional haze modeling data sets are substantial, requiring several 300-500 Gbyte external di sk drives to store the information. In addition to the CENRAP CMAQ and CAMx modeling data ba ses, the MRPO and VISTAS have indicated a willingness to provide their data sets to CENRAP for use by CENRAP states or s ource operators. It is anticipated that CENRAP w ill serve as the central repository, co llecting 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 se ts are the modeling contractors pe rforming the regional haze modeling for CENRAP, VISTAS, and WRAP.

8.1.3 Protocol Development

EPA’s BART guidance clearly indi cates the need for a detailed modeling protocol to support any application of alternative mode ls for BART analyses. An exampl e 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 devel oped with CALPUFF (Tables 6-1 and 7-2) would be appropriate. The alternative modeling protocol shoul d be submitted to the state, regional EPA office8-4 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 conf igurations” 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 re gional 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 re ported by the RPO from whom the CMAQ or CAMx modeling files were obtained. The purpose of this a nnual simulation is two-fol d. First, it establishes proper operation of the modeling system on the hos t computer via comparison with the RPO model output files. Second, it provides the gridded 24-hourly estimates of (NH 4 ) 2 SO 4 , NH 4 NO 3 , 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. (Furth er 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 approximat ely two weeks to complete execution.

An important question to be addressed in the modeling protocol is whether one annual

simulation of 2002 would be suffici ent to provide the necessary a ssessment of a BART source’s visibility impact. Recent annual CAMx si mulations 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 variabil ity, 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 a ll regional modeling studies, needs additional improvement.

The performance levels achieved in the MRPO CAMx modeling are gene rally consistent with that of

the other RPOs. These results ar e included in Figure 4-9, presented previously. Because the regional models include all of the important known gas-pha se, 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 appr oach remains nonetheless with the 2001-2003 CAMx data sets developed by the MPRO.8-5 8.2.2 BART Source Simulati on 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 pl ume-in-grid or nested grid options should be used to provide near s ource resolution of the SO 2 , NOx, NH 3 , 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 additi on 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 mode l 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 simu lation and plotting the residual aerosol species concentrations and HI values in daily tile plot s. Figure 8-7 provides an example of such an incremental 24-hr average concentr ation tile plot. From the CAMx PM PinG modeling by Yarwood et al, (2005) using the 2002 MPRO data base, incremental 24-hr average SO 2 , sulfate, nitrate, and NH 4 concentration fields on 14 June 2002 are shown. This plume reveals the individual plume signatures downwind from their origin. Simila r plots of delta-deciviews can be determined using the IMPROVE extinction equation with the modeled aerosol fields from both model simulati ons. 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 stat es must develop a strategy for reducing regional haze in Class I areas during the following d ecade. 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 thos e 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 vari ability and local geogra phic and meteorological conditions. Consequently, the default natural visibili ty 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). R ecognizing that the “default” concentrations (see Table 6-3) are not appropriate fo r 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 re fined approach to es timating natural visibility conditions for several reasons, including:

> The default estimates are shown, through mo re 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 conditi ons are close to the current8-6 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 explic itly between man-made and natural sources.

EPA identifies several possible refined approach es which can be adopted for developing ore

realistic estimates of natural vi sibility conditions and states may identify others that are more appropriate for their own situations. Approach es suggested by EPA include: (a) develop refined

estimates of the constant values of one or more na tural species, (b) estimate natural visibility using species concentrations th at 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 latt er 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 wish ing to employ a refined approach should supply demonstrations that the refined approach is tec hnically sound and provides regionally representative

estimates of natural vi sibility 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 CA LPUFF, this necessity arises because puff models cannot calculate the three-dimensional, hourly varyi ng gas phase and particulat e concentration fields that contain the various visibility -limiting pollutant species. Conse quently, some independent estimate of natural visibility conditions is required. Hist orical measurements of pris tine conditions measured in

the 1980s have been used to fill this void.

One-atmosphere models, by design, eliminat e the need to estimate natural background conditions against which individual plume contributions are assessed. This is done di rectly via the simulation of the hour-by-hour, day-by-day emissions (including sea salt), meteorology (rain, snow, fog), transport, chemical transf ormation and removal of gaseous a nd particulate emissions from all sources (natural and manmade) in the region of inte rest 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 entir e U.S. at grid scales of 36 km and 12 km using the CMAQ and/or CAMx models (depending upon RPO). Importantly, the MRPO ha s even extended the CAMx visibility modeling to in clude the same three year peri od (2001-2003) being use in the screening and source-specific CALPUFF modeling. The 24-hour av eraged modeled ground level concentration pred ictions of (NH 4 ) 2 SO 4 , NH 4 NO 3 , OC, EC, soil, and coarse mass (i.e., all components in the IMPROVE extinction equation) from any of these regional simulati ons provide a direct quantification on a day-to-day basi s 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 then8-7 subtracting the outputs of the two si mulations, a direct, gridded estimate is produced of the change in visibility (measured in terms of ‘delta-deciviews’) compared agai nst realistic, current background

conditions. As with the screen ing CALPUFF modeling, if the Δ dv value is greater than the 0.5 dv threshold, the source is said to co ntribute to visibility impairment and is thus ‘subject to BART’

controls.

This approach to calculating a realistic natu ral 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 visi bility background conditions over th e Class I areas simulated by the CAMx/CMAQ models in the 2002 base cases will in all likelihood be less ‘pristin e’ then those estimated by Trijonis and others in the late 1980s. But, we maintain, they will be more representative

of the actual current co nditions in the Class I ar eas against which the BA RT 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 disc ussed 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 a nnual 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 perf ormed 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 re quired to carry out four types of visibility modeling under the Regional Haze Rule: (a) single-source mode ling to determine which BARTeligible sources are ‘subject to BART’, (b) single-s ource 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 co mbined effect of proposed BART contro ls for sources subj ect 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 source s are expected to seriously examine the merits of using full-science models as an alternative CALPUFF. But fo r 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 asso ciated data bases that are ideally suited to rigorous BART visibility impact modeling. While the resource re quirements of an alternative modeling study – using existing models and data bases-- exceed those of a typical CALPUFF8-8 analysis, the potential to devel op more reasonable control limits on certain BART sources may easily eclipse the additional cost s 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 Si mulations 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 Us ed 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 oute r grid; 12 km domain is colored green (Source: Koerber, 2005).8-11 Figure 8-4. VISTAS 36/12 km CMAQ/CAMx Do main 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 Doma in 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 Oz one Study Using CAMx for the 2002 Annual Period. (Source: Tesche, et al., 2005b).8-14 24-hr SO 2 Concentrations 24-hr SO 4 Concentrations

24-hr NO 3 Concentrations 24-hr NH 4 Concentrations

Figure 8-7. Incremental 24-hr Average SO 2 , Sulfate, Nitrate, and NH 4 Concentrations Fields on

14 June 2002 from the CAMx PM PiG Si mulation 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 applicat ions satisfy the regulatory objec tives 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 ba se development, setup and execu tion of the CALPUFF air quality model and processing of modeling results to determin e compliance with visibili ty 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 sour ce-specific or alternat ive modeling that use different datasets, modeling options or tools will need to ensure th at an appropriate QA program is defined in their protocols. More extensive qua lity assurance will be required in these latter applications. It is the re sponsibility of the state ensure that an adequa te QA program, defined in the protocol, is imple mented faithfully.

The CALPUFF modeling system contains features to facilitate quality assurance. These include the automatic production of “QA” files fo r various datasets, incl uding geophysical fields, sources and receptors, and imbedded tracking of mode l options and switches w ithin the output files from the major modules. The CALPUFF Graphical Us er 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 utili ty. This information reproduced in all of the output files, creating an aud it trail of software versions and major model options.

The BART modeling process involves multiple or ganizations. As mentioned earlier, the states have overall responsibility for the process and may al so execute some or all of the modeling. CENRAP is contributing general guidance via these gui delines and has funded the development of meteorological fields for use in screening applicati ons. 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 car ried out by contractors on behalf of a state or a

source operator.

Each of these organizations has a responsibili ty 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 ge neral guidance and information on those aspects of quality assurance that are specific to the CALPU FF modeling effort, regardle ss of which organization is carrying out the effort. The focus is on the CALP UFF screening analyses de scribed 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 conducti ng 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 da ta files developed for CE NRAP are available to modelers for the screening assessments. The devel opment of these CALMET datasets was subject to a QA program. Thus, the amount of effort for modelers perf orming QA of these pre-defined9-2 meteorological fields is less than that required when developing sour ce-specific meteorological fields

at finer scale and/or ove r 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 specifi cation of the model configuration (choices of options and switches). Most of the modeli ng option choices are specified or recommended by regulatory guidance and default values (see Appendi x A through Appendix D). If an application uses

CENRAP’s CALMET files and the CALPUFF screeni ng configuration recommended in Chapter 6, the quality assurance of input data will be straightforw ard. More detailed steps ar e needed for the setup of modeling files for CALPUFF source-specific applica tions 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, ve rification of the correct source and receptor locations, including datum and projec tion, confirmation of the switch se lections 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 inde pendent reviewer should also exer cise the CALPUFF modeling system

for one or a few corroboration simu lations (e.g., run of the worst case event) as a confirmation check.

The protocol should stipulate that a set of DVD s be created that contain all of the data and program files needed to reproduce th e results presented in the final re port. The model list files from each step should be included on the project DVD. Th is information allows independent checking and confirmation of the modeling process and f acilitates 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 ar e described in the docum entation attending the distribution to CENRAP. While these QA steps n eed not be repeated, some testing should be performed to show that the data ar e 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 simu lations. The CALMET model obtains the switch settings from an ASCII control f ile 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 stru cture. This includes the default value for each variable, a text descripti on of the variable, the m eaning of each variable option, the units of the

variable and inter-relatio nships among variables indi cating if/when the variable is used. If CALMET is set up and run from the command line or if othe r 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 prepro cessing steps and the CA LMET 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 confir m the switch settings provided in a control file with that actually used in the model simulations. An optional switc h 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 CD s 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 (SO 2 , H 2 SO 4 , NO

x and PM 10 ).

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 fo r building downwash calculations. The requirement to conduct build ing 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 use d. The required paramete rs include two matching parallels, latitude/l ongitude of the projection origin, coordina te datum, and false Easting and Northing (if used) of the projection origi n. For source-specific CALMET/CALP UFF domains, either an LCC or UTM projection may be used. The CALPUFF user interface includes softwa re (e.g., COORDS) to compute to/from latitude/longitude, LCC and UTM coordinates for a large number of datum. In addition, the CALVIEW graphics f eature allows the use of georeferenced satellite or aerial photographs to be used as base maps to confirm s ource 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 relate d projection/datum parameters, so

confirmation of the source location is an important part of the model QA.

The PM 10 emissions should be broken into filterable and condensable components. The

filterable PM 10 emissions should be speciated into elem ental carbon (EC), fine particulate matter (PMF) less than 2.5 μm diameter and coarse partic ulate matter (PMC) between 2.5 to 10 μm diameter. The condensable emissions should be divided into a primary H 2 SO 4 component and organic and non-9-4 H 2 SO 4 inorganic components. (The sources of this in formation, such as source-specific data and AP42 tables, are not addressed in this protocol.) The speciation of the PM 10 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, hygros copic aerosols are subject to a humidity growth factor (f(RH)), which can increase the light scatteri ng from dry conditions by more than an order of magnitude in highly humid conditi ons. 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 re ceptor point is provided, it is necessary to ensure that the proper UTM or LCC c oordinates 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 CENR AP screening applications. CALPUFF is configured using the ASCII “c ontrol 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 manua lly as part of the QA an alysis. The CALPUFF GUI retains all of the input desc riptive information that is part of the standard CALPUFF.INP file structure. This includes the default value for each variable, a te xt description of the vari able, the meaning of each variable option, the units of the variable and inte r-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 ar ea in order to obtain th e necessary visibility statistics for evaluating compliance with the BART screening and refined m odeling thresholds. The inputs to CALPOST involve selecti on of the visibility method (Met hod 6), entry of Class I areaspecific data for computing background extincti on and monthly relative humidity factors for hygroscopic aerosols. CALPOST c ontains a receptor screening that allow subsets of a receptor network modeling in CALPUFF to be selected for pr ocessing in a given CALP OST run. This is how receptors within a single Class I ar ea are selected for processing from a CALPUFF output file that may contain receptors from several Clas s I areas. CALPOST contains optio ns for creating plot files that will help in the confirmation that th e 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 CALPOS T 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 98 th percentile value (8 th highest day) is used for comparison against the BART th reshold 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 pro cessing manually. That is, one may import the results of the CALPOST table into a sorting program su ch as a spreadsheet to rank the daily change in extinction values in order to identify the 98 th percentile or 8 th highest day visibility increment.9-5 CALPOST inputs that require pa rticular QA review include:

> Visibility technique (Method 6);

> Monthly Class I-specific rela tive 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 Raylei gh scattering term (10 Mm -1 for screening modeling but Class I area specific value for source-specific modeling); and

> Screen to select appropriate Clas s I receptors for each CALPOST simulation.

The CALPOST program produces plot files compatib le with CALVIEW that allow confirmation of receptor locations that is useful in evaluating the receptor screening step.

POSTUTIL allows the user to sum the cont ributions of sources from different CALPUFF

simulations into a total concentratio n file. In addition, it contains op tions to scale the concentrations from different modeled species (e.g., different partic le sizes) into species- depe ndent 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 inpu t into CALPOST. This process, although simple, requires a careful review of the weighting factors for each source.

POSTUTIL also allows a repart itioning of nitric acid and nitrat e to account for the effects of ammonia limiting conditions. The four-step pro cedure for applying the AL M method is summarized in Chapter 7 and described in detail in Appendix A of the VISTAS protocol 1 . If source-specific modeling is performed using different sources of ammonia data or di fferent 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 so urce-specific or altern ative 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 CALP UFF protocols may includ e the use of on-site meteorological datasets, the use of higher resolution prognostic me teorological (e.g., MM5) datasets, alternative visibility calculations, different extincti on 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 pl an to properly evaluate the data used in the modeling.

The critical CALMET input parameters depe nd on the mode in which the model is run (observations mode, hybrid mode or no-observ ations mode), and the location and spatial representativeness of any observational data. In a source specific protocol involving the development of a meteorological dataset, the el ements 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 an alyses, and presentation of 2-D vector plots illustrating terrain effects/sea breeze circul ation or other features of the flow expected to occur within the domain. For example, 2-D vect or plots produced during light wind speed stable conditions (e.g., early morning such as 4am) ar e good for assessing the performance of the CALMET model configuration and switches in reproducing terr ain effects because these conditions are likely to maximize the terrain impacts in the model. Seas onal 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 physic al processes expected to be impor tant at that location should be conducted as part of the source- specific QA plan development.

If source-specific CALPUFF simulations i nvolving the ALM are conducted, the procedure summarized in Chapter 7 should be used. This include s an evaluation of the performance of the model in reproducing observed CASTNet or IMPROVE sulfat e and nitrate concentrations at measurements sites within the source-specific modeling domain. Th e use of alternative amm onia concentration data (e.g., one-atmosphere model output rather than deri ved ammonia based on aerosol measurements) will require a review of the CMAQ or CAMx model performance resu lts developed for CENRAP by the regional modeling contractor (Morris et al., 2005d)

9.3 Quality Assessment Summary

Each BART application requires a modeling repor t that includes a discussion of the datasets, modeling assumptions and a general discussion of limitations and uncertainties of the modeling

exercise. Where CALPUFF screeni ng analyses are performed followe d 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 BA RT 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 sin ce the errors are included in both the base and sensitivity simulation cases.R-1 REFERENCES

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