# Appendix H.6 BART Modeling Protocols

### Vistas BART Modeling Protocol...........................................................................................3

Bowater Final BART Modeling Protocol..............................................................................80 FLM comments on Bowater Modeling Protocol ...................................................................185 Bowater Final BART Modeling Analysis..............................................................................191

DAK Final BART Modeling Protocol...................................................................................259 EPA comments on DAK Modeling Protocol.........................................................................381 DAK Final BART Modeling Analysis ..................................................................................383

VISTAS Final BART Modeling Report (to in clude INVISTA Final Modeling Protocol) ...459 U.S. EPA Region IV comments on INVISTA Modeling Protocol........................................493 INVISTA Final BART Modeling Analysis ...........................................................................494

IP Georgetown Final BART Modeling Protocol ...................................................................841 IP Georgetown Addendum to Final BART Modeling Analysis............................................935 U.S. EPA Region IV comments on IP Georgetown Modeling Protocol ...............................946 IP Georgetown response to SC DHEC comments on Addendum .........................................951

ISG Georgetown Final BART Modeling Protocol ................................................................957 VISTAS Final BART Modeling Report ................................................................................1057

MeadWestvaco Final BART Modeling Protocol...................................................................1063 U.S. EPA Region IV comments on M eadWestvaco Modeling Protocol...............................1190 MeadWestvaco Final BART Modeling Analysis ..................................................................1191

SC DHEC comments on Santee Cooper Modeling Protocol.................................................1332 Santee Cooper Final BART Modeling Analysis....................................................................1334

SCE&G Canadys Final BART Modeling Protocol ...............................................................1540 SCE&G Canadys Final BART Modeling Analysis...............................................................1559 SCE&G Wateree Final BART Modeling Protocol................................................................1609 SCE&G Wateree Final BART Modeling Analysis ...............................................................1628 SCE&G Williams Final BA RT Modeling Protocol...............................................................1665 SCE&G Williams Final BART Modeling Analysis ..............................................................1684

Stone Container Final BA RT Modeling Protocol..................................................................1715 FLM Comment on Stone Container Final BART Modeling Protocol...................................1730 VISTAS Final BART Modeling Report (t o include Stone Container Final BART Modeling Analysis)

Wellman Final BART Modeling Protocol.............................................................................1730 VISTAS Final BART Modeling Report (t o include Wellman Final BART Modeling Analysis)

# Protocol for the Application of the CALPUFF Model for Analyses of Best Available Retrofit Technology (BART)

December 22, 2005

(Revision 3.2

– 8/31/06)



## Visibility Improvement State and Tribal Association of the Southeast (VISTAS)Table of Contents iv 

## TABLE OF CONTENTS

Page

SUMMARY 1

1. INTRODUCTION AND PRO TOCOL OBJECTIVES 1 1.1 Background 1 1.2 Objective of this Protocol 2

2. REVIEW OF EPA’S GUIDANCE FOR B ART MODELING 5 2.1 Overview of the Regional Haze BART Process 5 2.2 Model Recommendations for the BART Analysis 7 2.3 Performance of a Cap and Trade Program 8

3. OVERVIEW OF THE CALP UFF MODELING SYSTEM 10 3.1 Capabilities and features of CALPUFF 10 3.1.1 Major Features of CALMET 12 3.1.2 Major Features of CALPUFF 14 3.1.3 Major Features of Postprocessors (CALPOST and POSTUTIL) 18 3.2 Discussion of CALPUFF Applicability and Limitations 19 3.2.1 Transport and Diffusion 19 3.2.2 Aerosol Constituents 22 3.2.3 Regional Haze 30

4. VISTAS’ COMMON MODEL ING PROTOCOL 34 4.1 Overview of Co mmon Modeling Approach 34 4.1.1 BART Exemption Analysis 34 4.1.2 BART Control Evaluation 36 4.1.3 VISTAS’ Treatment of VOC, NH 3 , and PM 36 4.2 Optional Source

- Specific Modeling 38 4.3 Initial Procedure for BART Exemption 38 4.3.1 Overview of Initial Approach 38 4.3.2 Discussion of 12

- km Initial Exempt ion Modeling 39 4.3.3 Model Configuration and Settings for Initial Analysis 41 4.4 Finer Grid Modeling Procedures 46 4.4.1 Rationale for and Overview of Finer Grid Mo deling Approach 46 4.4.2 Model Configuration and Settings for Finer Grid Modeling 47 4.5 Presentation of Modeling Results 49 4.6 VISTAS Contribution to CALPUFF Modeli ng of BART Eligible Sources 54

5

. SOURCE

- SPECIFIC MODELING PR OTOCOL 55Table of Contents iv 6. QUALITY ASSURANCE 58 6.1 Scope and Purpose of the QA program 58 6.2 QA Procedures for Common Protocol Modeling 59 6.2.1 Quality Control of Input Data 59 6.2.2 Quality Control of Application of CALMET 60 6.2.3 Quality Control of Application of CALPUFF 61 6.2.4 Quality Control of Application of CALPOST and POSTUTIL 62 6.3 Additional QA Issues for Alternative Source

- Specific Modeling 63 6.4 Ass essment of Uncertainty in Modeling Results 64

7. REFERENCES 65Summary S

- 1 

## SUMMARY

This Protocol for the Application of the CALPUFF Model for Analyses of Best Available Retrofit Technology (BART) for the VI STAS Regional Planning Organization (RPO) describes common procedures for carrying out air quality modeling to support BART determinations that are consistent with guidelines of the U.S. Environmental Protection Agency in 40 CFR Part 51 Appendix W and Appe ndix Y. The Protocol is intended to serve as the basis for a common understanding among the organizations that will be performing BART analyses or reviewing the BART modeling results in the VISTAS region.


### Background

Best Available Retrofit Technology is required for any BART

- eligible source that ‘‘emits any air pollutant which may reasonably be anticipated to cause or contribute to any impairment of visibility” in any mandatory Class I federal area. According to 40 CFR Part 51 Appendix Y, “ You can use dis persion modeling to determine that an individual source cannot reasonably be anticipated to cause or contribute to visibility impairment in a Class I area and thus is not subject to BART .” In the “individual source attribution approach,” a BART

- eligible so urce that is responsible for a 1.0 deciview (dv) change or more is considered to “cause” visibility impairment. A BART

- eligible source that is responsible for a 0.5 dv change or more is considered to “contribute” to visibility impairment in a Class I area. Any source determined to cause or contribute to visibility impairment in any Class I area is subject to BART.

The member states of the VISTAS RPO agreed to develop a common BART Modeling Protocol to guide them, their sources, and reviewers in the BART de termination and review effort. The Protocol has been in preparation within VISTAS since January 2005. The original authors are Pat Brewer, VISTAS Technical Coordinator, and Ivar Tombach, VISTAS Technical Advisor. The VISTAS state BART contacts, particula rly Tom Rogers, FL, Chris Arrington, WV, Leigh Bacon, AL, and Michael Kiss, VA, have directed and extensively reviewed the Protocol. The Protocol was enhanced and completed with the assistance of Joseph Scire, Christelle Escoffier

- Czaja and Jelena Popovic of Earth Tech, Inc. and it has received extensive contributions and review from the VISTAS federal partners: Federal Land Managers and US EPA. The VISTAS RPO held a meeting on September 21, 2005 in Research Triangle Park, NC to discuss the Protocol with participants before starting a public comment period. The Protocol underwent formal external review during the period between September 26, 2005 and October 31, 2005. Numerous comments were received. All comments were carefully considered and discussed with VISTAS participants and federal partners. VISTAS gratefully acknowledges the very useful contributions of those that provided comments. On November 1 st , 2005 VISTAS held another meeting with its participants in Nashville, TN to present and discuss t he comments being considered for inclusion in the Protocol. No formal document will be prepared to address all the comments received on the Protocol.Summary S

- 2 

### Objectives

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

 A consistent appro ach to determine if a source is subject to BART

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

 Clearly delineated modeling steps

 A common CALPUFF configuration

 Guidance for site

- specific modeling

 Common expectations for report ing model results

The Protocol is not intended to define the engineering analyses required by the US EPA’s BART Guidance, nor address model alternatives to the CALPUFF model, nor address emissions trading.

Chapter 2 is intended to provide summary backgr ound on EPA’s guidance for BART modeling. The CALPUFF model system is reviewed in Chapter 3, while specific recommendations for applying the CALPUFF model for BART purposes appear in Chapter 4. Chapter 5 describes the specific information that should be included in site

- specific protocols. Chapter 6 describes the quality assurance requirements for BART analyses in the VISTAS RPO.



### Recommendations

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

I. Process

Follow the BA RT process steps discussed in Chapter 2 :

1. Identify BART eligible sources

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

3. Identify sources that are subject to BART

4. Identify baseline visibility impact of each BART source

5. Identify feasib le controls and emission changes

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

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

- 3 II. CALPUFF Model Configurat ion

Use the CALPUFF dispersion modeling system, as described in Chapter 4, to determine if a single source is subject to BART. VISTAS will use CALPUFF Version 5.754 and CALMET Version 5.7. These versions contain enhancements funded by the Minerals Manage ment Service (MMS) and VISTAS. They were developed by Earth Tech, Inc. and are maintained on the CALPUFF website ( www.src.com ) for public access.

VISTAS is making publicly available 12

- km CALMET output files for the en tire VISTAS modeling domain (eastern United States) and intends to also provide CALMET output files for five 4

- km grid subdomains covering the VISTAS states and VISTAS Class I areas. To create the CALMET input files, Earth Tech used the MM5 databases deve loped by EPA for 2001, VISTAS for 2002, and Midwest RPO for 2003. For the 12 km grid large domain covering the entire VISTAS region, Earth Tech used the No

- Obs setting (i.e., did not include additional surface and upper air observations beyond those incorp orated in the MM5 calculations). For finer resolution subdomains (4 km grid or less), available surface and upper air observations will be used in addition to MM5 meteorological model outputs. The specific model settings will be provided with the CALMET f iles and via the CALPUFF website so that users can review or replicate the work.

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

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

- hr actual emissions are not available, continuous emissions data, permit allowable emissions, potential emissions, and emissions factors from AP

- 42 source profiles may be used as available.

Key points from comments received on the specific CALPUFF, C ALPOST, and POSTUTIL configurations are highlighted below.

• After running CALPUFF for an individual facility, repartition NO 3 in POSTUTIL

. 1

• Use ozone data from non

- urban monitors as the background ozone input.

• Use the Pasquill

- Gifford dispersion method. 2

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

- derived background data for SO 2 , NO 3 and NH 3 in POSTUTIL. After extensive discussion with the EPA and FLMs in early 2 006, EPA did not approve the recommended approach so background gaseous concentrations from CMAQ 2002 modeling will not be provided by VISTAS for use in POSTUTIL. Rather the standard default NH 3 concentrations specified on page 14 of the IWAQM Phase 2 repo rt (IWAQM, 1998) will be used.

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

- based AERMOD dispersion methods, citing EPA’s Revision to the Guideline on Air Quality Models: Adoption of a Preferred General Purpose (Fl at and Complex Terrain) Dispersion Model and Other Revisions; Final Rule. 70 FR 68218

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

Summary S

- 4 • In CALPOST, use Method 6 with monthly average RH for calculating extinction, as recommended by the EPA.

• Use EPA default calculations of light extinction under current and natural background conditions. In addition to the default assumptions, a source may choose to also calculate visibility using the recently revised IMPROVE algorithm described by Pitchford, et al., (2005).

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

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

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

• Number of Class I areas with impacts > 0.5 dv

III. CALPUFF Application for BART

For determining if a BART

- eligible source is subject to BART CALPUFF modeling, use a two

tier approach. For the initial exemption modeling use CALPUFF with 12

- km grid CALMET. For finer resolution of meteorological fields, use CALPUFF with CALMET of 4

- km or smaller grid size.

VISTAS States are accepting EPA guidance that the thresh old value to establish that a source contributes to visibility impairment is 0.5 deciview.

VISTAS States are using emissions (tons per year) divided by distance (km) from a Class I area boundary (Q/d) as a presumptive indicator that a BART

- eligible sour ce is subject to BART. If Q/d for SO 2 is greater than 10 for 2002 actual annual emissions, then the State presumes that the source is subject to BART and no exemption modeling will be performed using VISTAS funds. If the source agrees with this presumptio n, then the source can proceed to the BART determination using CALPUFF to evaluate impacts of control options and perform the engineering analyses. If a source disagrees, the source may perform fine grid modeling to determine if its impact is <0.5 dv.

Fo r sources with Q/d less than or equal to 10, VISTAS intends to fund TRC Environmental Corp. 3 to assist States with the initial CALPUFF exemption modeling. Each State will prioritize which sources will be offered modeling by VISTAS. Modeling of these sour ces will be conducted in priority order to first accommodate States with nearer term timing constraints in their SIP development process. To conserve VISTAS resources, modeling will begin with sources at lower Q/d values and continue with sources with hig her Q/d values until a Q/d value

based dispersion methods would be considered a non

- guideline applica tion of CALPUFF. Thus this Protocol has been revised to indicate Pasquill

- Gifford dispersion coefficients should be used.

3 In April 2006, Earth Tech’s CALPUFF modeling staff became part of TRC Environmental Corporation. References to Earth Tech and to TRC in this protocol refer to the same technical staff, just at different times.Summary S

- 5 that consistently results in a greater than 0.5 dv impact is identified. Chapter 4 addresses the number of VISTAS sources eligible for BART based on Q/d analysis.

Note that VISTAS does not propose to use Q/d to exempt BAR T

- eligible sources, but only to prioritize sources for modeling purposes. Thus this application is consistent with EPA guidance not to use Q/d for exemption purposes.

For the 12

- km initial modeling exemption test, compare the highest single 24

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

- hr average value is below 0.5 dv at all Class I areas, then the source is not subject to BART. If the highest 24

- hr average value is greater than 0.5 d v, then the source may choose to perform finer grid modeling for exemption purposes or may accept determination that the source is subject to BART and proceed to establish visibility impacts prior to and after BART controls. If using the single highest 24

- hr average value proves, after initial 12

- km grid CALPUFF modeling, to be too conservative a screening level, VISTAS may allow some exceedances of the threshold value for exemption purposes, up to no more than the 98 th percentile value.

The 12

- km model ing results can be used to focus finer grid modeling for exemption purposes on only those Class I areas where impacts greater than 0.5 dv were projected in the 12

- km modeling.

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

- hr average. Use either the 8 th highest day in each year or the 22 nd highest day in the 3

- year period, whichever is more conservative, for comparison to the exemption threshold.

Use the same model assumptions for pre

- BART visibility impact and for B ART control options modeling: establish baseline visibility from the pre

- BART run; change one control at a time; and evaluate the change in visibility impact, i.e. the delta

- deciview. Note that “no control” may constitute BART.

Visibility impact is one of the five factors considered in the engineering analysis required under the USEPA BART guideline. If a source accepts to institute the most stringent control, the engineering analyses are not required.

This common VISTAS Protocol consistently recommends c onservative assumptions. Individual States ultimately have responsibility to determine which, if any, BART controls are recommended in their State Implementation Plans (SIPs).Introduction and Protocol Objectives 1 

## 1. INTRODUCTION AND PRO TOCOL OBJECTIVES

1.1 Background

Under regional haze regul ations, the Environmental Protection Agency (EPA) has issued final guidelines dated July 6, 2005 for Best Available Retrofit Technology (BART) determinations (70 FR 39104

- 39172). The regional haze rule includes a requirement for BART for certain large
sta tionary sources. Sources are BART

- eligible if they meet three criteria including potential emissions of at least 250 tons per year of a visibility

- impairing pollutant, were put in place between August 7, 1962 and August 7, 1977, and fall within one of the 26 listed source categories in the guidance. A BART engineering evaluation using five statutory factors

-- 1) existing controls; 2) cost; 3) energy and non

- air environmental impacts; 4) remaining useful life of the source; 5) degree of visibility improvem ent expected from the application of controls

-- is required for any BART

- eligible source that can be reasonably expected to cause or contribute to impairment of visibility in any of the 156 federal parks and wilderness (Class I) areas protected under the regional haze rule. (Note that, depending on the five factors, the evaluation may result in no control.) Air quality modeling is an important tool available to the States to determine whether a source can be reasonably expected to contribute to visibility impairment in a Class I

area.
Throughout this document the term “BART

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

- eligible source” is defined as the total of all BART

- eligible emission un its at a single facility. If a source has several emission units, only those that meet the BART

- eligible criteria are included in the definition “BART

eligible source”.

One of the listed categories is steam electric plants of more than 250 million BTU/hr heat input. To determine if such a plant has greater than 250 million BTU/hr heat input and is potentially subject to BART, the boiler capacities of all electric generating units (EGUs) should be added together regardless of construction date. In this cat egory, electric generating sources greater than 750 MW have presumptive SO 2 and NO x emission limits. States may presume the same limits for EGU sources between 250

- 750 MW. However, units at those sources constructed after the BART

- eligibility dates are n ot subject to a BART engineering evaluation. EPA, in the Clean Air Interstate Rule (CAIR), determined that an EGU participating in the CAIR trading program satisfies the BART requirements for SO 2 and NO x . VISTAS states are tentatively accepting this guid ance. CAIR does not cover PM so EGUs would still need to evaluate impacts of PM if PM emissions are above de minimis values.

As illustrated in Table 1

- 1, as of December 5, 2005, VISTAS States had identified a total of 274 BART

- eligible sources that fall i nto 20 of the 26 BART source categories. Of the 274 sources with BART

- eligible units, 84 sources are utility EGUs and 190 are non

- EGU industrial sources. (Note that these numbers are not final and are subject to slight adjustments and refinements.) No
B ART sources are located on Tribal lands.Introduction and Protocol Objectives 2 Table 1

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



### State Total Number of Sources EGU Sources Non

- EGU Sources

AL 48 8 40

FL 50 23 27

GA 24 10 14

KY 29 12 17

MS 18 8 10

NC 16 5 11

SC 3 1 6 25

TN 13 2 11

VA 18 3 15

WV 26 7 19


### Total 273 84 189



### 1.2 Objective of this Protocol

The objective of this VISTAS’ BART Modeling Protocol is to describe common procedures for air quality modeling to support BART determinations that are consistent with the EPA guidelines. The protocol will serve as the basis for establishing a common understanding among the organizations who will be performing the BART analyses or reviewing the BART modeling results, including VISTAS State and Local air regulatory agencies, EPA, Federal Land Managers (FLMs), source operators, and contractors for the sources. This final protocol incorporates EPA final guidance and comments that were received on VISTAS’ draft protocol 4 and provides additional description of modeling procedures. The original final protocol of 22 December 2005 has been revised since then to clarify items, resolve technical issues, and reflect decisions by the EPA and FLMs. This document is the third revision.

The VISTAS States have accepted EPA’s guida nce to use the CALPUFF modeling system to comply with the BART modeling requirements of the regional haze rule. A BART

- eligible source will be required to submit a site

- specific modeling protocol to the State for review and approval prior to performing CA LPUFF modeling. States will consult with FLMs and the EPA when evaluating the site

- specific BART protocols. The site

- specific protocol will include the

4 Draft Protocol for the Application of the CALPUFF Model for Analyses of Best Available Retrofit Technology (BART). VISTAS , March 22, 2005 and September 20, 2005.Introduction and Protocol Objectives 3 source

- specific data on source location, stack parameters, and emissions. The methods of the VISTAS co mmon modeling protocol will be followed in the site

- specific protocol unless the source proposes to the State, and the State approves, alternative methods or assumptions.

Each VISTAS State or Local agency retains responsibility for the specific procedure s and processes it will follow in working with the BART sources under its jurisdiction, the FLMs, EPA, and public to determine BART controls for sources in the State. Nothing in the VISTAS process replaces States’ responsibility to determine BART control s.

The remainder of this document describes the CALPUFF modeling system and the application of CALPUFF to two situations:

• Air quality modeling to determine whether a BART

- eligible source is “subject to BART” and therefore the BART analysis process must b e applied to its operations.

• Air quality modeling of emissions from sources that have been found to be subject to BART, to evaluate regional haze benefits of alternative control options and to document the benefits of the preferred option.

Chapters 2 and 3 of this document are intended to provide background information on EPA’s guidance for BART analysis modeling and on the CALPUFF modeling system. Subsequent chapters include more specific recommendations. Chapter 2 of this document reviews EPA’s guidance f or regional haze BART analysis modeling, as outlined in the 6 July 2005 Federal Register notice. The CALPUFF model is the preferred model recommended by the EPA for BART modeling analyses and its characteristics and limitations are discussed in Chapter 3. The specific steps to determine whether a BART

- eligible source is subject to BART and to evaluate BART controls are described in Chapter 4. The procedures include initial modeling of BART

eligible sources using CALPUFF run in a conservative mode with regi onal meteorological datasets. For sources determined to be subject to BART based on these first modeling analyses, further finer grid CALPUFF analyses would be performed. The model configuration for the common modeling protocol is described in Chapter 4. Details of the source

- specific protocol are described in Chapter 5. A quality assurance plan is outlined in Chapter 6.

EPA’s guidance allows for the use of appropriate alternative models, however VISTAS will not develop a protocol for alternative mode ls. This protocol focuses on guidance for the application of the preferred CALPUFF modeling approach. If a source wants to use an alternative model in its BART demonstration, the source will need to submit a detailed written justification to the State for review and approval. The State will provide the documentation to the EPA and Federal Land Managers for their review.

Also, this protocol does not address a preferred modeling approach to demonstrate the effectiveness of an optional emissions cap and tr ade program. Such a cap and trade program is not required, but can be implemented in lieu of BART if desired by the VISTAS States. VISTASIntroduction and Protocol Objectives 4 States are not pursuing a regional trading alternative under the proposed EPA trading guidance (70 FR 44154

- 44175) th at is to be promulgated in 2006.Review of EPA’s Guidance 5 

## 2. REVIEW OF EPA’S GUID ANCE FOR BART MODELI NG

The final guidance for regional haze BART determinations was published in the Federal Register on 6 July 2005 (70 FR 39104 to 39172). It prescribes the m odeling approaches that are to be used for various stages of the BART analysis process.

This chapter provides a summary of EPA’s guidance for BART modeling. It is not intended to provide a comprehensive review of the guidance. Nor does this chapter addre ss specific recommendations for VISTAS’ approach to CALPUFF BART modeling. Those recommendations appear in Chapter 4.


### 2.1 Overview of the Regional Haze BART Process

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

1) Ident ify whether a source is “BART

- eligible” based on its source category, when it was put in service, and the magnitude of its emissions of one or more “visibility

- impairing” air pollutants. The BART guidelines list 26 source categories of stationary sources t hat are BART

- eligible. Sources must have been put in service between August 7, 1962 and August 7, 1977 in order to be BART

eligible. Finally, a source is eligible for BART if potential e missions of visibility

- impairing air pollutants are greater than 250 tons per year. Qualifying pollutants include primary particulate matter (PM 10 ) and gaseous precursors to secondary fine particulate matter, such as SO 2 and NO x . Whether ammonia or volatile organic compounds (VOCs) should be included as visibility

impairi ng pollutants for BART eligibility is left for the States to determine on a case

- by

- case basis. The guidance states that high molecular weight VOCs with 25 or more carbon atoms and low vapor pressure should be considered as primary PM 2.5 emissions and not VOCs for BART purposes.

(Note: If the source is subject to BART because one visibility impairing pollutant has potential emissions > 250 TPY, the State may determine that other visibility impairing pollutants are not subject to BART if their potential em issions are less than the de minimis levels (40 TPY for SO 2 and NO x and 15 TPY of PM 10 or PM 2.5 . This assumes that the other BART

- eligibility criteria are met.)

2) Determine whether a BART

- eligible source can be excluded from BART controls by demonstrating that the source cannot be reasonably expected to cause or contribute to visibility impairment in a Class I area. The preferred approach is an assessment with an air quality model such as CALPUFF or other appropriate model followed by comparison of the es timated 24

- hr visibility impacts against a threshold above estimated natural conditions to be determined by the States. 5 The threshold to determine whether a single source “causes” visibility impairment is set at

5 A recent draft settlement agreement with the EPA (to be published in the Federal Register for public comment) provides that a State has the discretion to decide whether annual average or 20% best natural conditions are to be used as the reference. This ruling resol ves an ambiguity in EPA’s BART guidance, where t he BART guidelineReview of EPA’s Guidance 6 1.0 deciview change from natural conditions over a 24

- hour averaging period in the final BART rule (70 FR 39118). The guidance also states that the proposed threshold at which a source may “contribute” to visibility impairment should not be higher than 0.5 deciviews although, depending on factors a ffecting a specific Class I area, it may be set lower than 0.5 deciviews. The test against the threshold is “driven” by the contribution level, since if a source “causes”, by definition it “contributes”.

EPA recommends that the 98 th percentile value fro m the modeling be compared to the contribution threshold of 0.5 deciviews (or a lower level set by a State) to determine if a source does not contribute to visibility impairment and therefore is not subject to BART. Whether or not the 98 th percentile valu e exceeds the threshold must be determined at each Class I area. Over an annual period, this implies the 8 th highest 24

- hr value at a particular Class I area is compared to the contribution threshold. Over a 3

- year modeling period, the 98 th percentile va lue may be interpreted as the highest of the three annual 98 th percentile values at a particular Class I area or the 22 nd highest value in the combined three year record, whichever is more conservative.

Alternatively, States have the option of considering that all BART

- eligible sources within the State are subject to BART and skipping the initial impact analysis. In rare cases, a State might be able to do exactly the opposite, and use regional modeling to conclude that all BART

- eligible sources in the Stat e do not cumulatively contribute to “measurable” visibility impairment in any Class I areas. Also, the States have an option to exempt individual sources based on model plant analysis conducted by EPA in finalizing the BART rule. Under this option, sourc es with potential emissions of SO 2 plus NO x of less than 500 tons and a distance from any Class I area greater than 50 kilometers or sources with SO 2 plus NO x potential emissions of less than 1000 tons and a distance from any Class I area greater than 100 kilometers can be exempted. PM emissions are not specifically addressed in the model plant analysis, but subsequent discussions with EPA staff indicate that PM may be considered along with SO 2 and NO x , so that a plant could be exempted if the combined pot ential emissions of SO 2 , NO x , plus PM meet the criteria above.

3) Determine BART controls for the source by considering various control options and selecting the “best” alternative, taking into consideration:

a) Any pollution control equipment in use at the source (which affects the availability of options and their impacts),

b) The costs of compliance with control options,

c) The remaining useful life of the facility,

d) The energy and non air

- quality environmental impacts of compliance, and

text says “natural conditions” at 70 FR 39162, col. 3, while the preamble to the BART rule says “natural visibility baseline for the 20% best visibility days” at 70 FR 39125, col. 1.Review of EPA’s Guidance 7 e) The degree of improvement in visibility that may reasonably be anticipated to result from the use of such technology.

Note that if a source agrees to apply the most stringent controls available to BART

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

4) Incorporate the BART determination into the State Implementation Plan for Regional Haze, which is due by December 2007.

Instead of applying BART on a source

- by

- source basis, a State (or a group of Sta tes) has the option of implementing an emissions trading program that is designed to achieve regional haze improvements that are greater than the visibility improvements that could be expected from BART. If the geographic distributions of emissions under t he two approaches are similar, determining whether trading is “better than BART” may be possible by simply comparing emissions expected under the trading program against the emissions that could be expected if BART was applied to eligible sources. If the g eographic distributions of emissions are likely to be different, however, air quality modeling comparing the expected improvements in visibility from the trading program and from BART would be required. (See the proposed BART Alternative rule, at 70 FR 441 60.) EPA suggests that regional modeling using a photochemical grid model may be more appropriate than CALPUFF for this purpose.

Note that EPA has indicated in the BART rule (70 FR 39138

- 39139) that emissions reductions under the Clean Air Interstate Rule (CAIR) meet the BART requirement for SO 2 and NO x control for those EGUs subject to BART. However, PM emissions from EGUs are not addressed by CAIR and therefore a BART analysis may still be required for PM.


### 2.2 Model Recommendations for the BART Analysi s

To evaluate the visibility impacts of a BART

- eligible source at Class I areas beyond 50 km from the source, the EPA guidance recommends the use of the CALPUFF model as “the best regulatory modeling application currently available for predicting a single source’s contribution to visibility impairment” (70 FR 39162). The use of another “appropriate model” is allowed although the EPA prefers the use of CALPUFF. If a source wants to use an alternative model, the source needs to submit a written justificati on and source

- specific modeling protocol to its State for review and approval. As part of the consultation process, the State will provide documentation to EPA and FLM.

For modeling the impact of a source closer than 50 km to a Class I area, EPA’s BART guidance recommends that expert modeling judgment be used, “giving consideration to both CALPUFF and other methods.” The PLUVUE

- II plume visibility model is mentioned as a possible model to consider instead of CALPUFF for a source within less than 50 km of a Class I area

.
The EPA guidance notes that “regional scale photochemical grid models may have merit, but such models have been designed to assess cumulative impacts, not impacts from individual sources” andReview of EPA’s Guidance 8 they are “very resource intensive and time consuming relative to CALPUFF”, but States may consider their use for SIP development in the future as they may be adapted and “demonstrated to be appropriate for single source applications” (70 FR 39123). Photochemical grid models may be more appropriate for cumulative modeling options such as in the determination of the aggregate contribution of all

- BART

- eligible sources to visibility impairment, but such use should involve consultation with the appropriate EPA Regional Office (70 FR 39163).

According to the BART guidance, a modeling protocol should be submitted for all modeling demonstrations regardless of the distance from the BART

- eligible source to the Class I area. EPA’s role in the development of the protocol is only advisory as the “States better understand the BART

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

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

- hour average actual emission rate for the most rec ent three to five

- year period of record. Emissions on days influenced by periods of start

- up, shutdown and malfunction are not to be considered in determining the appropriate emission rates. (70 FR 39129).

If a source is found to be subject to BART, CALP UFF or another appropriate model should be used to evaluate the improvement in visibility resulting from the application of BART controls. Visibility improvements may be evaluated on a pollutant

- specific basis in the BART determination (70 FR 39129).

For evaluating the improvement in visibility resulting from the application of BART, the EPA guidelines state that States are “encouraged to account for the magnitude, frequency, and duration of the contributions to visibility impairment caused by the source b ased on the natural variability of meteorology” (70 FR 39129).


### 2.3 Performance of a Cap and Trade Program

If a State or States elect to pursue an optional cap and trade program, they are required to demonstrate greater “reasonable progress” in reducing haze than would result if BART were applied to the same sources. In some cases, a State may simply be able to demonstrate that a trading program that achieves greater progress at reducing emissions will also achieve greater progress at reducing haze. Such would be the case if the likely geographic distribution of emissions under the trading program would not be greatly different from the distribution if BART was in place.

If the expected distribution of emissions is different under the two approaches, then “dispersion modeling” of all sources must be used to determine the difference in visibility at each impacted Class I area, in order to establish that the optional trading program will result in visibility improvements aggregated over all Class I areas tha t are “better than BART” (70 FR 39137

- 39138). The BART guidance does not specify the method to be used for this modeling. From a technical perspective, either applying CALPUFF to every source or using a regional photochemical model would satisfy the need.Review of EPA’s Guidance 9 A rulemaking procedure is currently underway to establish final guidance for such alternatives to BART (70 FR 44154

- 44175). The rule is expected to be finalized in 2006.Overview of the CALPUFF Modeling System 10 

## 3. OVERVIEW OF THE CALP UFF MODELING SYSTEM

This chapter conta ins a general description of the CALPUFF modeling system and its capabilities and limitations. It does not include specific recommendations regarding the use of the model for BART analysis in the VISTAS region. These specific recommendations can be found in Chapter 4.


### 3.1 Capabilities and features of CALPUFF

The CALPUFF modeling system (Scire et al., 2000a, b) is recommended as the preferred modeling approach for use in the BART analyses. CALPUFF and its meteorological model, CALMET, are designed to ha ndle the complexities posed by the complex terrain, the large

source

- receptor distances, chemical transformation and deposition, and other issues related to Class I visibility impacts. The CALPUFF modeling system has been adopted by the EPA as a Guideline Model for source

- receptor distances greater than 50 km, and for use on a case

- by

- case basis in complex flow situations for shorter distances (68 FR 18440

- 18482). CALPUFF is recommended for Class I impact assessments by the Federal Land Managers Workgroup ( FLAG 2000) and the Interagency Workgroup on Air Quality Modeling (IWAQM) (EPA 1998). The final BART guidance recommends CALPUFF as “the best modeling application available for predicting a singe source’s contribution to visibility impairment” (70 FR 39122 ). As a result of these recommendations, the VISTAS modeling protocol is based on the use of CALPUFF for its BART determinations.

The main components of the CALPUFF modeling system are shown in Figure 3

- 1. CALMET is a diagnostic meteorological model that is used to drive the CALPUFF dispersion model. It produces three

- dimensional wind and temperature fields and two

- dimensional fields of mixing heights and other meteorological fields. It contains slope flow effects, terrain channeling, and kinematic effect s of terrain. CALMET includes special algorithms for treating the overwater boundary layer and coastal interaction effects. CALMET can use meteorological observational data and/or three

- dimensional output from prognostic numerical meteorological models su ch as MM5 (Grell et al., 1995) or RUC (Benjamin et al., 2004) in the developments of its fine

- scale meteorological fields.

CALPUFF is a non

- steady

- state Lagrangian puff transport and dispersion model that advects Gaussian puffs of multiple pollutants from modeled sources. CALPUFF’s algorithms have been designed to be applicable on spatial scales from a few tens of meters to hundreds of kilometers from a source. It includes algorithms for near

- field effects such as building downwash, stack tip downwash and transitional plume rise as well as processes important in the far

- field such as chemical transformation, wet deposition, and dry deposition. CALPUFF contains an option to allow puff splitting in the horizontal and vertical directions, which extends the dis tance range of the model. The primary outputs from CALPUFF are hourly concentrations and hourly deposition fluxes evaluated at user

- specified receptor locations.Overview of the CALPUFF Modeling System 11 Figure 3

- 1. CALPUFF modeling system components.

A set of postprocessing programs associat ed with CALPUFF computes visibility effects and allows cumulative source impacts to be assessed, including potential non

- linear effects of ammonia limitation on nitrate formation. The CALPOST postprocessor contains several options for computing change in extinction and deciviews for visibility assessments. The POSTUTIL postprocessor includes options for summing contributions of individual sources or groups of sources to assess cumulative impacts. POSTUTIL also contains CALPUFF’s nitric acid

- nitrate chemi cal equilibrium module, which allows the cumulative effects of ammonia consumption by background sources to be assessed in the postprocessor. In addition, the combination of CALPUFF and POSTUTIL allows the effects of source emissions of ammonia to be incr ementally added to background ammonia levels when determining nitrate formation.

The rest of this chapter summarizes the capabilities and features of the CALPUFF modeling components in more detail.Overview of the CALPUFF Modeling System 12 3.1.1 Major Features of CALMET

The CALMET meteorological m odel consists of a diagnostic wind field module and micrometeorological modules for overwater and overland boundary layers. When modeling a large geographical area, as would be necessary for the regional VISTAS domain, the user has the option to use a Lamb ert Conformal Projection coordinate system to account for Earth’s curvature.

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

- 1. The techniques used in the CALMET model are briefly described below.

Table 3

- 1. Major F eatures of the CALMET Meteorological Model

• Boundary Layer Modules of CALMET

- Overland Boundary Layer

- Energy Balance Method

- Overwater Boundary Layer

- Profile Method

- - COARE algorithm

-- OCD

- based method

- Produces Gridded Fields of:

-- Surface Friction Velocity

-- Convective Velocity Scale

-- Monin

- Obukhov Length

-- Mixing Height

-- PGT Stability Class

-- Air Temperature (3

- D)

-- Prec ipitation Rate

• Diagnostic Wind Field Module of CALMET

- Slope Flows

- Kinematic Terrain Effects

- Terrain Blocking Effects

- Divergence Minimization

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

- Inputs Include Domain

- S cale Winds, Observations, and (optionally) Coarse

- Grid Prognostic Model Winds

- Lambert Conformal Projection Capability

CALMET Boundary Layer Models

The CALMET model contains two boundary layer models for application to overland and
overwater grid cells.

Overland Boundary Layer Model : Over land surfaces, the energy balance method of Holtslag and van Ulden (1983) is used to compute hourly gridded fields of the sensible heat flux, surfaceOverview of the CALPUFF Modeling System 13 friction velocity, Monin

- Obukhov length, and convective v elocity scale. Mixing heights are determined from the computed hourly surface heat fluxes and observed temperature soundings using a modified Carson (1973) method based on Maul (1980). The model also determines gridded fields of Pasquill

- Gifford

- Turner (PG T) stability class and hourly precipitation rates.

Overwater Boundary Layer Model : The aerodynamic and thermal properties of water surfaces suggest that a different method is best suited for calculating the boundary layer parameters in the marine environme nt. A profile technique, using air

- sea temperature differences, is used in CALMET to compute the micro

- meteorological parameters in the marine boundary layer. The version of CALMET being used by VISTAS contains improvements in the overwater boundary layer parameterizations (Fairall et al., 2003) based on the Coupled Ocean Atmosphere Response Experiment (COARE) and enhancements in the calculation of overwater mixed layer heights (Batchvarova and Gryning, 1991, 1994). Further details and the results of an e valuation of the model containing these enhancements are described in Scire et al. (2005). An upwind

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

- dimensional temperature fields in order to account for important adv ective effects.

Diagnostic Wind Field Module

The diagnostic wind field module uses a two

- step approach to the computation of the wind fields (Douglas and Kessler, 1988). In the first step, an initial

- guess wind field is adjusted for kinematic effects of te rrain, slope flows, and terrain blocking effects to produce a Step 1 wind field. Gridded MM5 can be used to define the initial guess field. The second step consists of an objective analysis procedure to introduce observational data into the Step 1 wind fie ld to produce a final wind field.

Step 1 Wind Field. Development of the Step 1 wind field begins with the initial guess field defined by the MM5 prognostic meteorological model. Normally, the CALMET computational domain is specified to be at finer grid res olution than the MM5 dataset used to initialize the initial guess field. For example, 36

- km MM5 data available for VISTAS modeling may be used to develop the initial guess field on a 12

- km or even a 1

- km CALMET grid. The Step 1 algorithms in CALMET descr ibed below apply terrain adjustments to the initial guess field on the fine

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

- scale terrain features not necessarily seen by the coarser scale MM5 model.

Kinematic Effects of Terrain : The approach of Liu and Yocke (1980) is used to evaluate the effects of the terrain on the wind field. The initial guess field winds are used to compute a terrain

forced vertical velocity, subject to an exponential, stability

- dependent decay function. The effects of terrain on the horizontal wind components are evaluated by applying a divergence

minimization scheme to the initial guess wind field. The divergence minimization scheme is applied iteratively until the three

- dimensional divergence is less than a threshold value.

Slope Flows : The original slope flow algorithm in CALMET has been upgraded (Scire and Robe, 1997) based on the shooting flow algorithm of Mahrt (1982). This scheme includes bothOverview of the CALPUFF Modeling System 14 advective

- gravity and equilibrium flow regimes. At night, the slope flow model parameterizes the flow down the sides of the valley walls into the floor of the valley, and during the day, upslope flows are parameterized. The magnitude of the slope flow depends on the local surface sensible heat flux and local terr ain gradients. The slope flow wind components are added to the wind field adjusted for kinematic effects.

Blocking Effects : The thermodynamic blocking effects of terrain on the wind flow are parameterized in terms of the local Froude number (Allwine and Wh iteman, 1985). If the Froude number at a particular grid point is less than a critical value and the wind has an uphill component, the wind direction is adjusted to be tangent to the terrain.

Step 2 Wind Field. The wind field resulting from the above adju stments of the initial

- guess wind is the Step 1 wind field. The second step of the procedure may involve introduction of observational data into the Step 1 wind field through an objective analysis procedure. An inverse

distance squared interpolation scheme is used which weights observational data heavily in the vicinity of the observational station, while the Step 1 wind field dominates the interpolated wind field in regions with no observational data. The resulting wind field is subject to smoothing, an
op tional adjustment of vertical velocities based on the O’Brien (1970) method, and divergence minimization to produce a final Step 2 wind field.

The introduction of observational data in the Step 2 calculation is an option. It is also possible to run the model in “no observations” (No

- Obs) mode, which involves the use only of MM5 gridded data for the initial guess field followed by fine

- scale terrain adjustments by CALMET. In No

Obs mode, observational data are not used in the Step 2 calculations. The No

- Obs mode is appropriate when the MM5 simulations adequately characterize the regional wind patterns and when local observations, especially surface observations, reflect local conditions on a scale smaller than that of the CALMET domain and hence their spa tial representativeness may be limited. Such situations are most likely to occur when the CALMET grid scale is relatively large i.e., coarser than the scale of variation of the true wind field, which is particularly likely to occur in complex terrain or al ong the seashore,

3.1.2 Major Features of CALPUFF

By its puff

- based formulation and through the use of three

- dimensional meteorological data developed by the CALMET meteorological model, CALPUFF can simulate the effects of time

and space

- varying meteorolo gical conditions on pollutant transport from sources in complex terrain. The major features and options of the CALPUFF model are summarized in Table 3

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

Complex Terrain: The effects of complex terrain on puff transport are derived from the CALMET winds. In addition, puff

- terrain interactions at gridded and discrete receptor locations are simulated using one of two algorithms that modify the puff

- height (either th at of ISCST3 or a general “plume path coefficient” adjustment), or an algorithm that simulates enhanced vertical dispersion derived from the weakly

- stratified flow and dispersion module of the Complex TerrainOverview of the CALPUFF Modeling System 15 Dispersion Model (CTDMPLUS) (Perry et al., 1989 ). The puff

- height adjustment algorithms rely on the receptor elevation (relative to the elevation at the source) and the height of the puff above the surface. The enhanced dispersion adjustment relies on the slope of the gridded terrain in the direction of transport during the time step.

Subgrid Scale Complex Terrain (CTSG): An optional module in CALPUFF, CTSG treats terrain features that are not resolved by the gridded terrain field, and is based on the CTDMPLUS (Perry et al., 1989). Plume impingement on subgrid

- scale hills is evaluated at the CTSG subgroup of receptors using a dividing streamline height (H d ) to determine which pollutant material is deflected around the sides of a hill (below H d ) and which material is advected over the hill (above
H d ). The local flow (near the feature) used to define H d is taken from the gridded CALMET fields. As in CTDMPLUS, each feature is modeled in isolation with its own set of receptors.

Puff Sampling Functions: A set of accurate and computationally efficient puf f sampling routines is included in CALPUFF, which solve many of the computational difficulties encountered when applying a puff model to near

- field releases. For near

- field applications during rapidly

- varying meteorological conditions, an elongated puff ( slug) sampling function may be used. An integrated puff approach may be used during less demanding conditions. Both techniques reproduce continuous plume results under the appropriate steady state conditions.

Building Downwash: The Huber

- Snyder and Schu lman

- Scire downwash models are both incorporated into CALPUFF. An option is provided to use either model for all stacks, or make the choice on a stack

- by

- stack and wind sector

- by

- wind sector basis. Both algorithms have been implemented in such a way as to allow the use of wind direction specific building dimensions. The PRIME building downwash model (Schulman et al., 2000) is also included in CALPUFF as an option.

Dispersion Coefficients: Several options are provided in CALPUFF for the computation of disp ersion coefficients, 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

- Gifford (PG) or McElroy

- Pooler (MP) dispersion coefficie nts, or dispersion equations based on the CTDM. Options are provided to apply an averaging time correction or surface roughness length adjustments to the PG coefficients. In version 5.754 of CALPUFF being used by VISTAS, an option is provided to use the A ERMOD turbulence profiles for determining dispersion rates, which is the most recent approach to dispersion in EPA

- approved regulatory modeling. In addition, turbulence advection is included. For additional details on these features, see Scire et al. (20 05).

Overwater and Coastal Interaction Effects: Because the CALMET meteorological model contains both overwater and overland boundary layer algorithms, the effects of water bodies on plume transport, dispersion, and deposition can be simulated with CALPUFF . The puff formulation of CALPUFF is designed to handle spatial changes in meteorological and dispersion conditions, including the abrupt changes that occur at the coastline of a major body of water.Overview of the CALPUFF Modeling System 16 Dry Deposition: A resistance model is provided in CALPU FF for the computation of dry deposition rates of gases and particulate matter as a function of geophysical parameters, meteorological conditions, and pollutant species. For particles, source

- specific mass distributions may be provided for use in the resi stance model. Of particular interest for BART analyses is the ability to separately model the deposition of fine particulate matter (< 2.5 μ m diameter) from coarse particulate matter (2.5

- 10 μ m diameter).

Wind Shear Effects: CALPUFF contains an optional p uff splitting algorithm that allows vertical wind shear effects across individual puffs to be simulated. Differential rates of dispersion and transport among the “new” puffs generated from the original, well

- mixed puff can substantially increase the effec tive rate of horizontal spread of the material. Puffs may also be split in the horizontal when the puff size becomes large relative to the grid size, to account for wind shear across the puffs.

Wet Deposition: An empirical scavenging coefficient approac h is used in CALPUFF to compute the depletion and wet deposition fluxes due to precipitation scavenging. The scavenging coefficients are specified as a function of the pollutant and precipitation type (i.e., frozen vs. liquid precipitation).

Chemical Tran sformation: CALPUFF includes options for parameterizing chemical transformation effects using the five species scheme (SO 2 , SO 4 = , NO x , HNO 3 , and NO 3 - ) employed in the MESOPUFF II model or a set of user

- specified, diurnally

- varying transformation
rates. T he MESOPUFF II scheme is recommended by IWAQM. It produces secondary fine particulate matter (sulfate and nitrate) from emissions of SO 2 and NO x and thus allows analyses of visibility impacts. Ambient ozone concentrations are used in the parameterized che mical transformation module as a surrogate for OH radicals during daylight hours. Ambient ammonia concentrations are used together with a temperature and relative humidity

- dependent equilibrium relationship to partition nitric acid and nitrate on an hour

- by

- hour and receptor

- by

- receptor basis.Overview of the CALPUFF Modeling System 17 Table 3

- 2. Major Features of the CALPUFF Dispersion Model

• Source types

- Point sources (constant or variable emissions)

- Line sources (constant or variable emissions)

- Volume sources (constant or v ariable emissions)

- Area sources (constant or variable emissions)

• Non

- steady

- state emissions and meteorological conditions

- Gridded 3

- D fields of meteorological variables (winds, temperature)

- Spatially

- variable fields of mixing height, fri ction velocity, convective velocity scale, Monin

- Obukhov length, precipitation rate

- Vertically and horizontally

- varying turbulence and dispersion rates

- Time

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

- Tempora l or wind

- dependent scaling factors for emission rates, for all source types

• Interface to the Emissions Production Model (EPM)

- Time

- varying heat flux and emissions from controlled burns and wildfires

• Efficient sampling functions

- Integrate d 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

-- AERMOD turbulence profiles

-- Original turbu lence profiles

- Pasquill

- Gifford (PG) dispersion coefficients (rural areas)

- McElroy

- Pooler (MP) dispersion coefficients (urban areas)

- CTDM dispersion coefficients (neutral/stable)

• Vertical wind shear

- Puff splitting

- Differential advection and dispersion

• Plume rise

- Buoyant and momentum rise

- Stack tip effects

- Building downwash effects

- Partial penetration

- Vertical wind shear

• Building downwash

- Huber

- Snyder method

- Schulman

- Scire method

- PRIME method

• Complex terrain

- Steering effects in CALMET wind field

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

- Optional enhanced vertical dispersion (neutral/weakly stable flow in CTDMPLUS)Overview of the CALPUFF Modeling System 18 Table 3

- 2. Major F eatures of the CALPUFF Dispersion Model (Cont’d)

• 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 defl ects around the hill, splits, and wraps around the hill

• Dry Deposition

- Gases and particulate matter

- Three options:

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

- User

- specified diurnal cycles fo r each pollutant

- No dry deposition

• Overwater and coastal interaction effects

- Overwater boundary layer parameters (COARE algorithm or OCD

- based method)

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

- Plume fumigation

• Chemical transformation options

- Pseudo

- first

- order chemical mechanism for 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 precipitation intensity and precipitation type

3.1.3 Major Features of Postprocessors (CALPOST and POSTUTIL)

The two main postprocessors of interest for BART applications are the CALPOST and POSTUTIL programs. CALPOST is used to process the CALPUFF outputs, producing tabulations that summarize the results of the simulations, identifying, for example, the highest and second

- highest hourly

- average concentrations at each receptor. When performing visibility

related modeling, CALPOST uses concentrations from CALPUFF to compute light extinction and relat ed measures of visibility (haze index in deciviews), reporting these for a 24

- hour averaging time.

The CALPOST processor contains several options for evaluating visibility impacts, including the method described in the BART guidance, which uses monthly av erage relative humidity values. CALPOST contains implementations of the IWAQM

- recommended and FLAG

- recommended visibility techniques and additional options to evaluate the impact of natural weather events (fog, rain and snow) on background visibility and visibility impacts from modeled sources.

The POSTUTIL processor is a program that allows the cumulative impacts of multiple sources from different simulations to be summed, can compute the difference between two sets ofOverview of the CALPUFF Modeling System 19 predicted impacts (useful for evalua ting the benefits of BART controls), and contains a chemistry module to evaluate the equilibrium relationship between nitric acid and nitrate aerosols. This capability allows the potential non

- linear effects of ammonia scavenging by sulfate and nitrate

so urces to be evaluated in the formation of nitrate from an individual source. CALPUFF makes the full ambient ammonia concentration available to each puff without regard for any scavenging by other puffs. POSTUTIL corrects for such scavenging when the puffs generated by the CALPUFF model overlap, as could be the case for a single source when the wind speed is low, or when nitrate formation is to be attributed to each of several sources that are in a cluster and whose plumes overlap,

POSTUTIL will also comput e the impacts of individual sources or groups of sources on sulfur and nitrogen deposition into aquatic, forest and coastal ecosystems. The postprocessor allows the changes in deposition fluxes resulting from changes in emissions to be quantified. For ex ample the output of POSTUTIL and CALPOST can be used as input into an Acid Neutralizing Capacity (ANC) analysis, or for comparison to Deposition Analysis Thresholds (DATs).


### 3.2 Discussion of CALPUFF Applicability and Limitations

3.2.1 Transport and Diffu sion

According to the IWAQM Phase 2 report (page 18), “CALPUFF is recommended for transport distances of 200 km or less. Use of CALPUFF for characterizing transport beyond 200 to 300 km should be done cautiously with an awareness of the likely problems inv olved.” 6

IWAQM’s 200

- km limitation derives from the observation that, when compared to the data of the Cross Appalachian Tracer Experiment (CAPTEX), the basic configuration of CALPUFF overestimated inert tracer concentrations by factors of 3 to 4 at recep tors that were 300 to 1000 km from the source. The apparent reason was insufficient horizontal dispersion of the simulated plume, presumably because an actual large plume does not remain coherent in the presence of vertical wind shears that typically occur , especially during the night, and of horizontal wind shears over the large puffs that arise over long transport distances.

To better represent such situations, an optional puff splitting algorithm has since been added to CALPUFF to simulate wind shear ef fects across a well

- mixed individual puff by dividing the puff horizontally and vertically into two or more pieces. Differential rates of transport among the new puffs thus generated can increase the horizontal spread of the material in the plume due to

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

6 The IWAQM presentation at EPA’s 6 th Modeling Conference provides the background for this recommendation: “The IWAQM concludes that CALPUFF be recommended as providing unbiased estimates of concentration impacts for transport distances of order 200 km and less, and for transport times of order 12 hours or less. For larger transport times and distances, our experience thus far is that CALPUFF tends to underestimate the horizontal extent of the dispersion and hence tends to overestimate the surface

- level concentra tion maxima. This does not preclude the use of CALPUFF for transport beyond 300 km, but it does suggest that results in such instances be used cautiously and with some understanding.” (From page D

- 12 of the IWAQM Phase 2 report.)Overview of the CALPUFF Modeling System 20 designed to allow large puffs that may grow to be several grid cells or more in size to split into smaller puffs that can then more accurately respond to variatio ns in the local wind field across the original large puff. This will also tend to increase horizontal dispersion of the plume. Since the creation of additional puffs via puff splitting will increase the computational requirements of the model, possibly s ubstantially, puff splitting is not enabled by default, but can be turned on at the option of the user. Puff splitting may be appropriate for transport distances over 200 to 300 km, or possibly over shorter distances in complex terrain.

Turning to the shor ter distance end of the transport range, the CALPUFF section of Appendix A of the Guideline on Air Quality Models (40 CFR 51, Appendix W) states, “CALPUFF is intended for use on scales from tens of meters from a source to hundreds of kilometers.” This is s upported by the IWAQM Phase 2 report, which indicates that the diffusion algorithms in CALPUFF were designed to be suitable for both short and long distances. In this regard, CALPUFF does contain algorithms for such near

- field effects as plume rise, buildi ng downwash, and terrain impingement and includes routines that deal with the computational difficulties encountered when applying a puff model in the field near to a source.

The recommendations for regulatory use in Appendix A of the Guideline on Air Qual ity Models state, “CALPUFF is appropriate for long range transport (source

- receptor distance of 50 to several hundred kilometers)”, but provisions for using CALPUFF in the near

- field in “complex flow” situations are also included in the regulatory guidance . Complex flow situations may include complex terrain, coastal areas, situations where plume fumigation is likely, and areas where stagnation, flow reversals, recirculation or spatial variability in wind fields (e.g., as due to changes in valley orientati on) are important.

The tracer studies with which CALPUFF transport and diffusion capabilities were evaluated in the IWAQM Phase 2 report were generally over distances greater than 50 km. More recently, additional studies of model performance have been per formed at shorter distances, including at a power plant in New York state in complex terrain (at source

- receptor distances of 2 to 8.5 km) and a second power plant in Illinois in simple terrain (at source

- receptor distances in arcs ranging from 0.5 km to 5 0 km from the stack) (Strimaitis et al., 1998). Other CALPUFF evaluation studies over short

- distances include ones by Chang et al. (2001) and Morrison et al. (2003). These studies demonstrate good model performance over source

- receptor distances from a f ew hundred meters to 50 km.

An important factor in the performance of CALPUFF is the choice of dispersion coefficients. The EPA has defined the "regulatory default" option in CALPUFF to allow either Pasquill

- Gifford (PG) or turbulence

- based dispersion coef ficients. CALPUFF has been evaluated and shown to perform better using turbulence

- based dispersion for tall stacks (Strimaitis et al, 1998). CALPUFF with turbulence

- based dispersion has also been evaluated for overwater transport and coastal situations (Sc ire et al., 2005). In many other studies, including AERMOD evaluation studies conducted by EPA, the use of PG

- dispersion, or more specifically the lack of a convective probability density function (pdf) module, has been demonstrated to result in underpredi ction of peak concentrations.Overview of the CALPUFF Modeling System 21 In November 2005, EPA approved the AERMOD model, which relies on turbulence

- based dispersion, as a regulatory Guideline Model 7 . The ISCST3 model and its PG dispersion coefficients are being phased out as an acceptable regulat ory approach. However, EPA Region IV has indicated that the application of turbulence

- based dispersion coefficients in CALPUFF needs to be further demonstrated before they are approved for BART application. They will consider accepting the use of turbul ence dispersion coefficients on a case

- by

- case basis for sources that are close to Class I areas.

For regional haze light extinction calculations, use of a plume

- simulating model such as CALPUFF is appropriate only when the plume is sufficiently diffuse that it is not visually discernible as a plume per se , but nevertheless its presence could alter the visibility through the background haze. The IWAQM Phase 2 report states that such conditions occur starting 30 to 50 km from a source. In this light, the B ART guidance strongly recommends using CALPUFF for

source

- receptor distances greater than 50 km but also presents CALPUFF as an option that can be considered for shorter transport distances.

As discussed above, there do not appear to be any scientific reas ons why CALPUFF cannot be used for even shorter transport distances than 30 km, though, as long as the scale of the plume is larger than the scale of the output grid so that the maximum concentrations and the width of the plume are adequately represented a nd so that the sub

- grid details of plume structure can be ignored when estimating effects on light extinction. The standard 1

- km output grid that has been established for Class I area analyses should serve down to source

- receptor distances somewhat under 3 0 km; how much closer than 30 km will depend on the topography and meteorology of the area and should be evaluated on a case

- by

- case basis. For extremely short transport distances, depiction of the concentration distribution will require a grid that is fin er than 1 km. (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.)

As an additional consideration, if the plume width is small compared to the visual range, the atmospheric extinction along a typical sight path of tens of kilometers through the plume will be inhomogeneous and the simple CALPOST point estimate of regional light extinction at a receptor point will not be correct. However, the effect of averaging light extinction estimates for 24 hours, during which the plume location shifts over various receptor points, is likely to mitigate this problem to some degree and suggests that using CALPUFF at distances under 30 km will often be
appro priate. For the narrow plumes that result from short transport distances, though, the modeled peak 24

- hr average extinction at a receptor will tend to overstate the effect of the source on regional haze.

7 Revision to the Guidelin e on Air Quality Models: Adoption of a Preferred General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions; Final Rule. 70 FR 68218

- 68261. 9 November 2005.Overview of the CALPUFF Modeling System 22 The U.S. EPA has suggested that the plume visibilit y model, PLUVUE

- II, could be used in lieu of CALPUFF for simulating visibility effects at such short distances. 8 PLUVUE

- II is a Gaussian model that simulates the dispersion, chemical conversion, and optical effects of emissions of particles, SO 2 , and NO x f rom a single source. Its outputs include the discoloration of the sky by the plume (so called “plume blight”) and the effect of the plume on visibility along user

- selected sight paths that pass through the plume. The impacts of the plume on visibility depe nd not only on the plume composition, but also on the sight path chosen and its direction relative to the axis of the plume and the location of the sun. It isn’t clear how such sight

- path dependent results could be compared to the 0.5 and 1.0 deciview thre sholds in the BART guidance. Since CALPUFF is designed to be useful for short transport distances (with features such as the simulation of plume downwash caused by structures at the source), CALPUFF seems more appropriate than
PLUVUE

- II for evaluating sour ce impact at short distances for BART assessment purposes.

3.2.2 Aerosol Constituents

Primary PM 2.5
Appendix A of the Guideline on Air Quality Models (40 CFR 51, Appendix W) states that CALPUFF can treat primary pollutants such as PM 10 . In actuality, CALPU FF can simulate PM 10 or PM 2.5 or some other size range, because the assumed size distribution of the particles is a user input. The smaller the particles, the more they disperse like an inert gas. In most cases, the dispersion of inert PM 2.5 particles will be only minutely different from that of an inert gas, but the behavior of larger particles will differ.

A particularly important contributor to PM concentrations is the rate of deposition to the surface.
PM 2.5 particles, which have a mass median diameter around 0.5 μ m, have an average net deposition velocity of about 1 cm/min (or about 14 m/day) and thus the deposition of fine particles is usually not significant except for ground

- level emissions. On the other hand, coarse particles (those PM 10 particles l arger than PM 2.5 ) have an average deposition velocity of more than 1 m/min (or 1440 m/day), which is significant, even for emissions from elevated stacks.

CALPUFF includes parametric representations of particle and gas deposition in terms of atmospheric, deposition layer, and vegetation layer “resistances” and, for particles, the gravitational settling speed. Gravitational settling, which is of particular importance for the coarse fraction of PM 10 , is accounted for in the calculation of the deposition velo city. 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 addressed. The BART guidance recommends that fine particulate matter (l ess than 2.5 μ m diameter), which has higher light extinction efficiency than coarse particulate matter (2.5

- 10 μ m diameters), should be treated separately in the model. CALPUFF allows for user

- specified size categories to be treated as

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

- II for BART applicationOverview of the CALPUFF Modeling System 23 separate species, w hich includes calculating size

- specific dry deposition velocities for each size category.

A primary PM 2.5 emission from coal

- fired electric generating units (EGUs) that is of relevance to visibility calculations is that of primary sulfate. Although primary sulfate emissions account for only a small fraction of the total sulfur emissions from such sources, it may be important to simulate their effect with CALPUFF, especially at shorter distances before significant formation of secondary sulfate conversion fr om SO 2 has taken place.

Sulfur Dioxide and Secondary Particulate Sulfate

The MESOPUFF

- II chemistry algorithm used in CALPUFF 9 simulates the gas phase oxidation of sulfur dioxide to sulfate by a linear transformation rate that was developed using regressi on relationships derived from the analysis of chemical conversion rates produced by a complex photochemical box model (see Scire et al., 1984, for a description of the development of the chemical module). As in all empirically

- derived models, the relation ships are based on easily

computed or observed parameters that are used as surrogates for the factors that control SO 2 oxidation.

The surrogate factors included in the parameterized chemistry during the daytime hours include solar radiation intensity, am bient ozone concentration, and atmospheric stability class. For example, gas phase SO 2 oxidation is a function of OH radical concentrations. Ozone concentrations are correlated with OH radical concentrations during daytime hours, and their use in the day time SO 2 conversion rate in CALPUFF is based on this correlation relationship. The philosophy is that OH radical measurements are not available and cannot easily be computed within a model like CALPUFF, but ozone is commonly measured throughout the countr y, so the use of the well

- known surrogate variable (ozone) is more useful in the empirical relationship than factors that are unknown or have a high degree of uncertainty. The same logic applies to the other variables in the relationship. They are surrog ates for factors that the regression analysis has shown to be important in SO 2 oxidation rates. At night, the SO 2 conversion is set to a constant low value (default is 0.2%/hr). Aqueous phase oxidation of SO 2 is represented by an additive term that varies with relative humidity and peaks at 3%/hr at 100% relative humidity. CALPUFF represents the chemical conversion as a linear process because it requires linear independence between puffs, although as explained below, non

- linear behavior in nitrate formati on can be modeled.

9 CALPUFF offers two options for parameterizing chemical transformations: the 5 species (SO 2 , SO 4 = , NO x , HNO 3 , and NO 3 - ) MESOPUFF

- II system and the 6 species RIVAD system (which treats NO and NO 2 separately). IWAQM recommends using the MESOPUFF

- II system with CALPUFF. The RIVAD system is believed to be more appropriate for clean environments, however, and therefore was used in the Southwest Wyoming Regional CALPUFF Air Quality Modeling Study in 2001. For the VISTAS region, the IWAQM

- and FLM

- recommended MESOPUFF

- II chemistry is most appropriate.Overview of the CALPUFF Modeling System 24 The IWAQM Phase 2 report concludes that this chemistry algorithm is adequate for representing the gas phase sulfate formation but that it does not adequately account for the aqueous phase oxidation of SO 2 . Actual aqueous phase oxidation in clouds or fog can proceed at rates much greater than 3% per hour, leading IWAQM to suggest that sulfate might be underestimated in such situations. However, aqueous phase oxidation depends on liquid water content, not relative humidity. In reality, liqu id water does not exist in the atmosphere at relative humidity much below 100%, while the CALPUFF aqueous reaction term produces sulfate at lower relative humidity. This can lead CALPUFF to overestimate sulfate concentrations when the humidity is high but the cloud water that enables aqueous conversion is not present. Therefore, the direction of the bias in the aqueous chemistry simulation of sulfate formation can vary.

Other potential sources of error in the sulfate formation mechanism of CALPUFF include (1) overestimation of sulfate formation when NO x concentrations in the plume are high and in actuality they deplete the local availability of ozone and hydrogen peroxide for oxidizing the SO 2 ; and (2) lack of direct consideration of the effect of temperat ure on the conversion rates, which may cause the model to overstate sulfate formation on cold days (below 10C or 50 ° F) (Morris et al., 2003). However, in CALPUFF, the effects of temperature are, to some degree, compensated for indirectly by the use of the solar radiation surrogate variable in the empirical conversion equations.

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

- 2 shows a comparison of predicted and observed 24

- hour sulfate concentrations, due to a large number of SO 2 sources, at the Pinedale IMPROVE site in Wyoming for the 1995 period (Scire et al., 2001). Overall, in this case there was very little bias in the sulfate predictions. Whether CALPUFF predictions would compare as well with measurem ents in the Southeast remains to be

seen.
CALPUFF does not identify the chemical form of the sulfate compound that results from its reactions, which will generally be some form of ammoniated sulfate whose degree of neutralization will depend on the availa bility of ammonia in the atmosphere. This consideration, which has been found to be relevant for calculating light extinction in the VISTAS region, is not addressed by CALPUFF or CALPOST.

In most applications, the ozone concentrations required for the sulf ate formation calculations are derived from ambient measurements, although concentrations simulated by regional models can be used.Overview of the CALPUFF Modeling System 25 Figure 3

- 2. Observed vs. CALPUFF

- predicted 24

- hour sulfate concentrations at the IMPROVE monitoring site in Pinedale, Wyoming for 1995.

NO x and Secondary Ammonium Nitrate

The MESOPUFF

- II chemistry algorithm used in CALPUFF simulates the oxidation of NO x to nitric acid and organic nitrates (both gases) by transformation rates that depend on NO x concentration, ambient ozon e concentration, and atmospheric stability class during the day. The conversion rate at night is set at to a constant value (default is 2.0 %/hr). The temperature

- and humidity

- dependent equilibrium between nitric acid gas and ammonium nitrate particles is taken into account when estimating the ammonium nitrate particle concentration, an equilibrium that depends on the ambient concentration of ammonia. The user supplies the value of the ambient concentration of ammonia. CALPUFF assumes that the sulfate reac ts preferentially with that ammonia to form ammonium sulfate and the left over ammonia is available to form ammonium nitrate.

The IWAQM Phase 2 report considers that this mechanism is adequate for representing nitrate chemistry. Potential situations where this assumption may not be correct, however, include (1) plumes with high concentrations of NO x that deplete the ambient ozone and thus limit theOverview of the CALPUFF Modeling System 26 transformation of NO x to nitric acid in the plume; and (2) when ambient temperature is below 10 C, and thus t he transformation rate is much slower and the nitrate concentration may be lower than that simulated by CALPUFF (Morris et al., 2003). In both cases, CALPUFF may overestimate the amount of nitrate that is produced. In particular, the impact of ammonium nit rate concentrations on visibility at Class I areas in the VISTAS region is greatest in the winter, when temperatures are lowest, the nitrate concentrations are the greatest, and the sulfate concentrations tend to be the least. CALPUFF may overstate the imp acts of NO x emissions at those times, especially in the colder northern states

. This potential overestimate of nitrate was not evident, however, in an evaluation of CALPUFF

- modeled nitrate against actual observational data in the Wyoming study, as shown in Figure 3

- 3a (Scire et al., 2001),

Another factor in the calculation of nitrate is that CALPUFF makes the full amount of the background concentration of ammonia available to each puff, and that amount is scavenged by the sulfate in the puff. If puffs over lap, then that approach could overstate the amount of ammonium nitrate that is formed in total if, in reality, the combined scavenging by the overlapping puffs at a location would deplete the available ammonia enough that the combined nitrate formation was limited by the availability of ammonia. This effect of such ammonia limiting can be large in summer; for a source 75 km west of Mammoth Cave National Park, one modeling analysis found the maximum light extinction impact of the source to be 7.4% (roughly 0 .74 deciviews) at the park when CALPUFF was used without consideration of ammonia limiting and about 30% less, between 5.5 and 5.8% (roughly 0.55 to 0.58 dv), when the effect of ammonia limiting was considered (Escoffier

- Czaja and Scire, 2002).

To address the issue, since 1999 (i.e., after the IWAQM Phase 2 report) the CALPUFF system has included the optional POSTUTIL postprocessing program, which repartitions the ammonia and nitric acid concentrations estimated by CALPUFF to reflect potential ammonia

- limit ing effects on the development of nitrate

. This allows non

- linearity associated with ammonia limiting effects to be included in the CALPUFF model estimates. POSTUTIL computes the total sulfate concentrations from all sources (modeled sources plus inflow b oundary conditions) and estimates the amount of ammonia available for total nitrate formation after the preferential scavenging of ammonia by sulfate. That is, as new sulfate, nitrate or ammonia from the source of interest is added to an existing mix of p ollutants, POSTUTIL will estimate both the nitrate formed from the new source and the change in background nitrate as a result of the incremental depletion of ammonia (due to the new sulfate and nitrate) or addition of ammonia (from a new source of ammonia ).

Reliable estimates of the ambient concentrations of ammonia, especially with the temporal and spatial resolution that would be optimal for use with CALPUFF, are needed to take full advantage of the increased accuracy provided by POSTUTIL. The processor requires estimated concentrations of ammonia throughout the modeling domain and period. Such estimates can be inferred from CASTNet measurements, which are integrated over a week, from 24

- hr SEARCH measurements, or from the output of a regional photochemic al model such as CMAQ or CAMx. The CASTNet network is fairly sparse and the uncertainty in the ammonia measurements is large,Overview of the CALPUFF Modeling System 27 so defining the ammonia concentration throughout the Southeast would require extensive interpolation or extrapolation from the mea sured values. The quality of the SEARCH measurements is much better, but there are only 8 sites and they do not cover the entire VISTAS domain. Modeled concentrations have the advantage of being resolved in space and time, but their accuracy should be eval uated by comparison with measurements wherever possible.

Benefit is obtained by considering seasonal trends of ammonia and using POSTUTIL to determine the diurnal variability in available ammonia due to the daily cycle of nitrate formation associated wi th temperature and relative humidity effects. For example, results of the Wyoming study (see Figure 3

- 3a) show that POSTUTIL adjustments produced daily average nitrate concentrations well within the factor of two lines and with very little mean bias. On the other hand, analysis of the same results with use of constant ammonia of 0.5 ppb or 1.0 ppb produced consistent overpredictions of nitrate by factors of 2

- 3 and 3

- 4, respectively, as shown in Figure 3

- 3b (Scire et al., 2003).

Figure 3

- 3a. Observed vs. CALPUFF

- predicted 24

- hour nitrate concentrations at the IMPROVE monitoring site in Pinedale, Wyoming for 1995 using the ammonia limiting method. (Scire et al., 2001)Overview of the CALPUFF Modeling System 28 Figure 3

- 3b. Observed vs. CALPUFF

- predicted 24

- hour nitrate concentrations at the IMPROVE monitoring site in Pinedale, Wyoming for 1995 using the ammonia limiting method (blue), constant ammonia at 0.5 ppb (pink) and constant ammonia at 1.0 ppb (green). (Scire et al., 2003)

Secondary Organic Aerosol

Ongoing research studies at several Class I areas throughout the country (Fallon and Bench, 2004) and at SEARCH sites in the Southeast (Edgerton et al., 2004) are finding that, typically, 90 to 95% of the rural organic carbon fine particle concentration consists of modern carbon (e.g., that from the burning of vegetation and deriving from VOC emissions from vegetation) and only 5 to 10% is attributable to man’s burning of fossil fuels. In addition, a field study at Great Smoky Mountains National Park in August 2002 (Tanner, et al., 2005) fou nd that an average of 83% of the fine carbon was modern carbon

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

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

o rganic carbon particles derived from anthropogenic fossil fuel burning emissions are unlikely to have a large impact (around 1%) on current visibility. (Man

- caused burning of vegetation can have significant localized, short

- term impacts, however.)

Current organic fine particle concentrations in the Southeast are typically within a factor of 2 of the 1.4 μ g/m 3 concentration assumed for natural conditions by the EPA, which means that current fossil fuel burning would contribute less than 2% to visibility in a n atmosphere that represents natural conditions. Thus, it is unlikely that VOC and organic particle contributions from BART [NO3] Q-Q Plot for CALPUFF Predictions w ith Different NO3 and HNO3 Repartition and Bridger IM PROVE Observations

0 0.2 0.4 0.6 0.8

1 1.2

0 0.2 0.4 0.6 0.8 1 1.2

Bridger IMPROVE [NO3]. Units: ug/m**3 of NO3 CALPUFF Summed Repartition (Seasonal NH3) CALPUFF Separated Repartition (NH3=0.5ppb)
CALPUFF Separated Repartition (NH3=1.0ppb) Factor 1 Factor 2 Factor 3 Factor 4

Factor 2

Factor 3

Factor 4 [NO3] Q-Q Plot for CALPUFF Predictions w ith Different NO3 and HNO3 Repartition and Bridger IMPROVE Observations

0 0.2 0.4 0.6 0.8

1 1.2

0 0.2 0.4 0.6 0.8 1 1.2 Bridger IMPROVE [NO3]. Units: ug/m**3 of NO3 CALPUFF Summed Repartition (Seasonal NH3) CALPUFF Separated Repartition (NH3=0.5ppb) CALPUFF Separated Repartition (NH3=1.0ppb) Factor 1 Factor 2 Factor 3 Factor 4

Factor 2

Factor 3

Factor 4Overview of the CALPUFF Modeling System 29 sources will cause a large impact to visibility at Class I areas, but a 5% (0.5 dv) localized impact from a particularly large VOC s ource cannot be dismissed out of hand.

CALPUFF has only rudimentary capabilities for addressing formation of visibility

- impairing organic particles from some forms of volatile organic carbon (VOC). The capabilities that do exist include the following.

Fi rst, PM 10 emissions (such as from power plants) are often divided into filterable and condensable components, with the condensable mass being 100

- 200% of the filterable mass. For purposes of visibility analyses with CALPUFF, a fraction of the condensable part is typically treated as organic particles, i.e., it is assumed that a fraction of the condensable components in the
PM 10 emissions condense into organic PM 2.5 particles. The size of this organic fraction varies with process and process equipment, and can range from 20 to 100% of the condensable mass. These fine organic particles can be readily modeled by CALPUFF. (The remaining condensable material may be sulfuric, hydrochloric, or hydrofluoric acid.)

Second, a module that treats the formation of seco ndary organic particles from organic emissions was recently developed and is now part of the CALPUFF system. (Scire et al., 2001). This simplified secondary organic aerosol (SOA) module is a linear, parameterized representation that is currently considered best suited for biogenic organics. It relies on the conventional wisdom that only hydrocarbons with more than six carbon atoms can form significant SOA (Grosjean and Seinfeld, 1989). For example, according to this rule, isoprene (C 5 H 8 ) does not make SOA b ut terpenes do, making pine trees more important biogenic contributors to SOA than oak trees. 10

Limited evaluation of the performance of CALPUFF at simulating SOA with its biogenic SOA module at one IMPROVE site in a regional modeling study in Wyoming found that 95% of 101 estimated 24

- hr SOA concentrations were within 2% of the measured values (Scire et al., 2001). This performance seems promising, although the developers view the SOA module as needing more testing and evaluation.

Thus, CALPUFF includes app roaches for dealing with condensable VOC emissions that are characterized as condensable PM 10 and with biogenic VOCs, although the soundness of concentration estimates by these approaches when modeling a plume from a single source is largely untested. 11 The CALPUFF simulation of VOC emissions from sources whose VOC emissions are predominantly anthropogenic is problematic, however. Perhaps the approach used for the simplified biogenic SOA module may be extended to anthropogenic VOCs when speciated VOC emissio ns information is available. If only those VOCs with more than six carbon atoms are presumed to be of importance, this eliminates many anthropogenic sources of VOC emissions. For example, the fugitive emissions of butane and ethane during petroleum process ing

10 Recent research suggests that isoprene may be a SOA precursor, however.

11 Note that neither of these VOC

- related simulation approaches is described in the current (Version 5) CALPUFF User’s Guide dated January 2001. See the Wyoming report referenced above for a description of this module.Overview of the CALPUFF Modeling System 30 are not important, while aromatic emissions (such as of toluene and xylene) are considered by the SOA module’s mechanism. Development, testing, and evaluation would be needed before one could rely on such a module for estimating SOA from anthropogenic SOA emissions, though.

Therefore, to demonstrate the visibility impacts of VOC emissions from BART

- eligible sources, means other than CALPUFF will be needed. A technical approach using a regional photochemical model to evaluate visibility impacts of VOC e missions is presented in Section 4.1.3. CALPUFF can be used to estimate the contribution from the primary condensable fraction of PM 10 emissions, though.

3.2.3 Regional Haze

Calculation of the impact of the simulated plume particulate matter component con centrations on light extinction is carried out in the CALPOST postprocessor. The formula used is the usual IMPROVE/EPA formula, which is applied to determine a change in light extinction due to changes in component concentrations. Using the notation of CAL POST, the formula is the following:

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

- 1)

The concentrations, in square brackets, are in μ g/m 3 and b ext is in units of Mm - 1 . The Rayleigh

s cattering term (b Ray ) has a default value of 10 Mm - 1 , as recommended in EPA guidance for tracking reasonable progress (EPA, 2003a).

There are a few important differences in detail and in notation between the CALPOST formula for estimating light extinction (i.e., Equation 3

- 1) and that of IMPROVE and EPA. First, the OC in the formula above represents organic carbonaceous matter (OMC in IMPROVE’s notation), which is 1.4 times the OC (i.e., organic carbon alone) in the IMPROVE formula. The EC above is
synonymo us with LAC in the IMPROVE formula. CALPOST now offers the option of using the old IMPROVE f(RH) curve, whose values are documented in the December 2000 FLAG report, or the f(RH) now used by IMPROVE and EPA (as documented in EPA’s regional haze guidance do cuments). Also, CALPOST sets the maximum RH at 98% by default (although the user can change it), while the EPA’s guidance now caps it at 95%.

The haze index (HI) is calculated from the extinction coefficient via the following formula:

HI = 10 ln (b ext /1 0) (3

- 2)

where HI is in units of deciviews (dv) and b ext is in Mm - 1 . The impact of a source is determined by comparing HI for estimated natural background conditions with the impact of the source and without the impact of the source.Overview of the CALPUFF Modeling System 31 CALPOST Meth ods

CALPOST uses Equation 3

- 1 to calculate the extinction increment due to the source of interest and provides various methods for estimating the background extinction against which the increment is compared in terms of percent or deciviews.

For background extinction, the CALPOST processor contains seven techniques for computing the change in light extinction due to a source or group of sources (called Methods 1

- 7). These are usually reported as 24

- hour average values, consistent with EPA and FLM guidance. In addition, there are two techniques for computing the 24

- hour average change in extinction (i.e., as the ratio of 24

- hour average extinctions, or as the average of 24

- hour ratios). A brief summary of the techniques is provided below. Method 2 is the c urrent default, recommended by both IWAQM (EPA, 1998) and FLAG (2000) for refined analyses. Method 6 is recommended by EPA’s BART guidance (70 FR 39162).

Methods 4 and 5 use optically measured hourly background extinctions, which represent current actual levels of extinction and thus are not consistent with the “natural conditions” the BART proposal says should be used as a baseline. Methods 1 through 3 and 6 and 7 allow for user inputs of estimated (e.g., natural conditions) background extinction or compo nent concentrations, and thus are consistent with the BART proposal.

Method 1 allows the user to specify a single value of a “dry” background extinction coefficient for each receptor, specify that a certain fraction of that coefficient is due to hygroscopi c species, and use relative humidity measurements to vary the extinction hourly via a 1993 IWAQM f(RH) curve or, optionally, the EPA regional haze f(RH) curve (EPA, 2003b). The RH is capped at 98%

or a user

- selected value (95% for the EPA curve). The same f(RH) is applied to both the modeled sulfate and nitrate.

For an example of the use of Method 1, one could use the dry particle extinction coefficient of 9.09 Mm - 1 that results from EPA’s default natural conditions concentrations, together with an assumpt ion that for natural conditions, say, 0.9 Mm

– 1 (or 10%) of this amount results from hygroscopic ammonium sulfate and ammonium nitrate, and then apply f(RH) to this 10%.

In Method 2, user

- specified, speciated monthly concentration values are used to descri be the background. When applied to natural conditions, for which EPA’s default natural conditions concentrations are annual averages, the same component concentrations would have to be used throughout the year (unless potential refinements to those default values resulted in concentrations that vary during the year). Hourly background extinction is then calculated using these concentrations and hourly, site

- specific f(RH) from a 1993 IWAQM curve (a different oneOverview of the CALPUFF Modeling System 32 than that in Method 1) or, optionally, the EP A regional haze f(RH) curve. 12 Again the RH is capped at either 98% (default) or a user

- selected value (most commonly at 95%).

Method 3 is the same as Method 2, except that any hour in which the RH exceeds 98% (or the selected maximum) is dropped from the analysis. When 24

- hr extinction is computed, no fewer than 6 valid hours are accepted at each receptor; otherwise the value for the day is tabulated as “missing”.

Method 6 is similar to Method 2, except monthly f(RH) values (e.g., EPA’s monthly climatologi cally representative values in EPA (2003a, b)) are used in place of hourly values for calculating both the extinction impact of the source emissions and the background conditions extinction. Hourly source impacts, with the effect on extinction due to sulfa tes and nitrates calculated using the monthly

- average relative humidity in f(RH), are compared against the monthly default natural background concentrations. Thus the monthly

- averaged relative humidity is applied to the hygroscopic components (i.e., sulfat e and nitrate) of both the source impact and the background extinction with Method 6

.
Method 7 is a new variant of Method 2 that was developed as a result of a ruling by the Assistant Secretary of the Interior for Fish and Wildlife and Parks, in response to a New Source Review case in Montana, that “natural conditions” should reflect the visibility impairment caused by significant meteorological events such as fog, precipitation, or naturally occurring haze (DOI, 2003). 13 Under Method 7, during hours when v isibility is obscured by meteorological conditions, the actual measured visibility is used to represent natural conditions instead of the value that is calculated from EPA’s default natural conditions concentrations under Method 2. A recent modification de veloped in response to FLM comments on Method 7, in which the daily average natural extinction is calculated somewhat differently, is called Method 7’, i.e., “7 prime”.

Refined Estimates of Extinction and Natural Background Visibilit y
Separate from the BAR T discussions, IMPROVE, EPA, and the Regional Planning Organizations are evaluating whether refinements are warranted to the methods recommended in EPA’s guidance to calculate default estimates of natural background visibility. In particular, IMPROVE has r ecently approved an alternative to the formula (Eq. 3

- 1) it uses to estimate extinction from particle concentration measurements (Pitchford et al., 2005).

Refinements in the revised IMPROVE formula include the following:

- Adding a sea salt term, including a growth factor due to relative humidity

12 Note that the hourly

- varying natural background extinction in this method is not consistent with that prescribed by the EPA’s natural conditions guidance (EPA, 2003b), for which a “climatologically

- representative” f(RH) that only varies monthly is to be used. Method 6 uses these monthly average humidity values.

13 The Secretary’s guidance applies only to Federal Land Managers. EPA’s position on this interpretation of natural conditions is unknown.Overview of the CALPUFF Modeling System 33 - Increasing the factor used to calculate the mass of particulate organic matter (OC in Eq. 3

- 1) from organic carbon measurements

- Modifying the relative humidity growth formula, f(RH), for sulfates and nitrates

- Revisi ng the extinction efficiencies (the numerical constants in Equation 3

- 1) for sulfates, nitrates, and organic carbon so that they vary with concentration

- Adding a site

- specific Rayleigh scattering term to the formula. Values will be calculated by IMPROVE fo r all Class I areas.

For the purposes of calculating current, future, and natural background visibility at VISTAS Class I areas as part of the reasonable progress analyses, VISTAS intends to present regional air quality modeling results using both the cur rent EPA recommended assumptions and the newly revised aerosol extinction formula. If a BART

- eligible source chooses to consider its projected impacts using the newly revised formula as well as the current formula, then modifications would need to be made to CALPOST to carry out calculations with the new algorithm.VISTAS’ Common Modeling Protocol 34 

### 4. VISTAS’ COMMON MODEL ING PROTOCOL
4.1 Overview of Common Modeling Approach

In this section, guidance is provided on the use of the CALPUFF modeling system for two
purposes:

1) Evaluating whether a BART

- eligible source is exempt from BART controls because it is not reasonably expected to cause or contribute to impairment of visibility in Class I areas, and

2) Quantifying the visibility benefits of BART control opti ons.

For purpose 1), States must determine whether a source emits any air pollutant (SO 2 , NO x , PM, and in certain cases VOC and NH 3 ) that “may reasonably be anticipated to cause or contribute to any impairment of visibility” in a Class I area. The States have 3 options to accomplish this:

A) Conclude that all BART

- eligible sources in State are subject to BART.

B) Demonstrate that all BART

- eligible sources in the State together do not cause or contribute to any visibility impairment

C) Determine if the impact from each individual BART

- eligible source is greater than a threshold value.

VISTAS States intend to follow Option C (determine if the visibility impact from individual sources exceeds a contribution threshold) for SO 2 and NO x emissions. The meth ods for Option C are described in Section 4.1.1. In early 2006, VISTAS pursued Option B (demonstrate that all BART eligible sources in a State do not impact visibility) for VOC, NH 3 and PM emissions. The approach and results for Option B are described in Section 4.1.3. As a result of this exercise, the VISTAS States have determined that the Option C exemption analyses should also include PM emissions and, for sources with large NH 3 emissions, NH 3 . The States determined that anthropogenic VOC emissions do not cause or contribute to visibility impairment at VISTAS Class I areas and that VOC emissions do not need to be considered in BART analyses.

4.1.1 BART Exemption Analysis

As illustrated in Figure 4

- 1, three steps will evaluate whether a BART

- eligible s ource of SO 2 , NO x , or PM is subject to BART:

1) VISTAS plans to use Q/d as a presumptive indicator that a source is subject to BART. If Q/d for SO 2 > 10 for 2002 actual emissions, then the State presumes that the source is subject to BART. If the sour ce agrees with this presumption, then no exemption modeling is required and the source can proceed to the BART determination using CALPUFF to evaluate impacts of control options and can perform the engineering analyses. If a source disagrees, the sourceVISTAS’ Common Modeling Protocol 35 ma y perform fine grid modeling as described in Section 4.4 to determine if its impact is < 0.5 dv.

Figure 4

- 1. Flow chart showing the components of the VISTAS common modeling protocol. Assessment should be made for each Class I Area. (If a so urce agrees to install the most stringent controls then the modeling steps indicated above and engineering analyses and visibility impact modeling would not be required.)

2) An optional initial modeling assessment using the CALPUFF model with the coarse scale 12

- km regional VISTAS domain can be used to answer questions whether (a) a particular source may be exempted from further BART analyses and (b) if finer grid CALPUFF analysis were to be undertaken, which Class I areas should be included. Assumptions for the initial modeling assessment are conservative so that a source that contributes to visibility impairment is not exempted in error. If a source is shown not to contribute to visibility impairment using the initial modeling assessment, the source wo uld not be subject to BART and would be exempted from further BART analyses. If a source is shown to contribute to visibility impairment using the initial modeling assessment, the source has the option to undertake finer grid CALPUFF modeling to evaluate further whether it is subject to BART.

3) A finer grid CALPUFF modeling analysis using a subregional CALMET domain will be the definitive test as to whether a source is subject to BART.VISTAS’ Common Modeling Protocol 36 For large sources that will clearly exceed the initial screening t hresholds, this step can be skipped and the analysis may proceed directly to the finer grid modeling analysis, which is described in Section 4.4.

4.1.2 BART Control Evaluation

For sources that are determined to be subject to BART controls, part of the BA RT review process involves evaluating the visibility benefits of different BART control measures. These benefits will be determined by making additional CALPUFF simulations using the same CALMET and CALPUFF configuration as those used in the finer grid ana lysis of Step 2. The only exception is that the source and emissions data used in the CALPUFF control evaluation simulations will reflect the BART control measures being evaluated. Using the same model configuration will produce an “apples

- to

- apples” com parison, where differences in impacts are due to the effectiveness of the controls rather than model configuration differences. For example, a control scenario evaluation that uses more conservative assumptions than the base case simulation may produce re sults showing no or little improvement in visibility impacts. That control scenario run with the same model configuration as the base case may show significant visibility improvement. Therefore, in order to not obscure the response to predicted visibilit y improvements by differences in the modeling approach, the same model configuration should be used in the BART control evaluation simulation as in the base case simulation.

The base case to which the effectiveness of BART controls is to be compared is the “current emissions” scenario for which the finer grid Step 2 modeling was performed. The postprocessing steps and procedures are the same as in the BART eligibility simulation. Side

- by

- side comparison of the visibility impacts will be tabulated to quant ify the effectiveness of each control scenario relative to the base case.

The modeling evaluation is a unit

- by

- unit evaluation and can be conducted on a pollutant specific basis. Modeling results are used with the other four statutory factors mentioned in Section 2.1 to decide which control technology, if any, is appropriate. Finally, if a source decides to use the most stringent control technology available, the BART control analysis, including modeling, is not necessary.

4.1.3 VISTAS’ Treatment of VOC, NH 3 , and PM

Volatile Organic Compounds

CALPUFF is currently not recommended for addressing visibility impacts from VOC because its capability to simulate secondary organic aerosol formation from VOC emissions is not adequately tested, especially for anth ropogenic emissions. (Separately, condensable organic carbon can be calculated from PM 10 .)

VISTAS has performed a weight of evidence analysis to demonstrate, using the CMAQ regional air quality model, that the combined VOC emissions from all point sour ces (BART

- eligible and

non

- BART) in each State do not contribute to visibility impairment. Emissions sensitivityVISTAS’ Common Modeling Protocol 37 simulations run for VISTAS by Georgia Institute of Technology using VISTAS’ 12 x 12 km grid and CMAQ v 4.3 for episodes in July 2001 and Janu ary 2002 demonstrated very low to no response of organic carbon levels and light extinction at Class I areas to changing VOC emissions from all anthropogenic sources in the VISTAS 12

- km modeling domain (eastern US). Georgia Tech repeated the sensitivity an alyses using the VISTAS 12

- km domain and CMAQ v 4.4 with a refined SOA module for summer (Jun 1

- Jul 10) and winter (Nov 19

- Dec 19) periods in 2002. VOC emissions from all anthropogenic point sources in every VISTAS State were reduced by 100% (i.e., elimi nated). The maximum 24

- hr impact of all VOC emissions from all point sources throughout the VISTAS domain was thus determined to be less than 0.5 dv (compared to annual average natural background) at every Class I area in the VISTAS domain and in adjacent States. It follows that the impact of any one BART

- eligible source would be much less than 0.5 dv. Based on these analyses, the VISTAS States have concluded that VOC emissions from BART sources do not cause or contribute to visibility impairment and do n ot need to be included in BART analyses.

Ammonia

EPA has given states the option to address ammonia (NH 3 ) emissions from BART

- eligible sources. VISTAS also contracted with Georgia Tech to calculate NH 3 emissions sensitivities using CMAQ v 4.4 with a re fined SOA module and the same Jun

- Jul and Nov

- Dec periods in 2002 that were used for the VOC sensitivity evaluation. The NH 3 emissions from all point sources (BART

- eligible and not

- BART) in every State were reduced by 100% for these analyses. This sensiti vity evaluation showed that the collective impact of all VISTAS region point NH 3 emissions is greater than 0.5 dv (compared to annual average natural background) at several Class I areas. When the NH 3 emissions were scaled to represent 100% reduction from only the BART

eligible sources in each State, then the maximum impact of those sources was under 0.5 dv at most, but not all Class I areas. The high values appear to result primarily from emissions from 13 large NH 3 sources. In the absence of those 13 fac ilities, the scaled NH 3 emissions peak impacts at Class I areas were 0.3 dv or less. Based on these analyses, the VISTAS States recommended that, except for these 13 facilities, NH 3 emissions not be included in BART modeling. States will provide instructio ns to those 13 sources as to how to evaluate contributions of their NH 3 emissions to visibility impairment. For documentation purposes, in summer 2006 VISTAS is repeating the NH 3 emissions sensitivity calculations, using CMAQ v4.5 with Base F emissions

an d reducing 100% of NH 3 emissions from only the BART

- eligible sources in the VISTAS states.

Primary Particulate Matter

Primary particulate matter is considered a visibility impairing pollutant. However, the extent to which primary PM from BART

- eligible sources contributes to impairment at Class I areas in the southeastern US is not clear. For EGUs, the EPA has determined that emissions reductions of SO 2 and NO x under the CAIR rule meet the BART requirements, but these EGUs may still be subject to BART f or primary PM. To determine the potential impacts of PM from EGU and non

EGU sources in the VISTAS states, two CMAQ sensitivity runs for the first and third quarters of 2002 were carried out by VISTAS’ CMAQ modeling team of ENVIRON, UCR, and AlpineVISTAS’ Common Modeling Protocol 38 Geophy sics In one run, all primary PM from EGUs was removed while in the other run all primary PM from non

- EGU sources was removed. All other CMAQ modeling components were held constant. At almost all Class I areas in the VISTAS region, primary PM emissions

co ntribute to regional haze, with the collective impact of all EGU and non

- EGU point primary PM emissions being greater than 0.5 dv compared to annual average natural background. In fact, the impacts of EGU PM emissions alone or of non

- EGU PM emissions alone were each mostly greater than 0.5 dv. Although the impacts of BART sources alone would be smaller, the VISTAS States have concluded that all BART

- eligible sources need to consider the impacts of their PM emissions.


### 4.2 Optional Source

- Specific Modeling

I n some circumstances, a source may want to apply techniques designed to evaluate the impacts in a more detailed way than the standard VISTAS common protocol. A source may propose

source

- specific modeling procedures to address special issues to the State f or State review. For example, sources very close to Class I areas may be better treated by a finer grid resolution that the generic Step 2 “fine” grid resolution meteorological fields provided by VISTAS. In some situations, higher resolution MM5 or other prognostic meteorological datasets may be available than the standard 12

- km or 36

- km MM5 datasets provided by VISTAS. Because it is not possible to anticipate all of the situations where there would be a benefit to conducting more detailed

source

- specifi c analyses, the option to pursue this option is left as an open issue, to be resolved and justified based on specific factors relevant for the source in question.

A source

- specific modeling protocol is required for each source. This document should describ e the data sources and model configuration, and provide rationale for any changes in the model approach from the common protocol. This source

- specific protocol must be provided for review and approval by the State. The State will share the protocol with EPA and the Federal Land Managers for their review. Discussion of approaches to source

- specific modeling and an outline of the typical contents of the source

- specific protocol are presented in Chapter 5. Discussions with the regulatory authorities should be conducted prior to development of a source

- specific protocol to ensure all of the relevant issues are included in the protocol.


### 4.3 Initial Procedure for BART Exemption

4.3.1 Overview of Initial Approach

The first step in the common protocol, the in itial assessment in Figure 4

- 1, is a simple procedure to evaluate whether a source can be exempted from BART controls using a consistent set of meteorological and dispersion options. A pre

- computed set of meteorological files and a pre

defined CALPUFF inp ut option configuration, based on guidance in the final BART rule (70 FR 39104

- 39172) and other EPA and FLAG model guidance, will allow relatively simple initial simulations. The regional initial domain is designed to allow any Class I areas within the VI STAS area to be evaluated with a single meteorological database and consistent CALPUFF modeling options. The second important question that this first screening step will answer is, ifVISTAS’ Common Modeling Protocol 39 initial modeling indicates a source may impact visibility significantl y, what Class I areas should be included in a finer grid analysis? Due to the multitude of factors affecting the contribution of a source to visibility in a Class I area, simple screens or rules of thumb alone (such as that the closest Class I area will pr oduce the controlling visibility impacts) are not likely to be universally reliable.

4.3.2 Discussion of 12

- km Initial Exemption Modeling

Meteorological Fields

A regional initial domain and a set of pre

- computed regional CALMET meteorological files will be prepared for VISTAS, to allow any Class I areas within the VISTAS area to be evaluated with a consistent meteorological database and consistent CALPUFF modeling options.

The following three years of MM5 meteorological data have been assembled by VISTAS for use in the regional CALPUFF modeling effort:

- 2001 MM5 dataset at 12 km and 36 km grid (developed for EPA)

- 2002 MM5 dataset at 12 km and 36 km grid (developed by VISTAS)

- 2003 MM5 dataset at 36 km grid (developed by the Midwest Regional Planning Organization).

These data sets have been provided to Earth Tech by VISTAS, and from them Earth Tech has produced annual CALMET meteorological files at 12

- km grid resolution for the domain shown in Figure 4

- 2. The CALMET modeling output files in the form of CALPUFF

- ready three

dimensional meteorological files will be available on external hard drives to the States and other parties.

The initial procedure to determine if a BART

- eligible source is subject to BART uses the pre

computed CALMET meteorological f ields for the years 2001

- 2003 on the 12

- km CALMET domain in Figure 4

- 2 and simulates with CALPUFF any BART

- eligible source to be screened. The CALMET simulations will be developed using the highest resolution MM5 data available for each year (i.e., 36

- km MM5 data for 2003, 12

- km MM5 data for 2001 and 2002).

The development of the regional CALMET meteorological fields from MM5 data will be conducted in No

- Observations (“No

- Obs”) mode. The MM5 data already reflect assimilation of observational data and are likely to adequately characterize regional wind patterns that are consistent with the 12

- km grid scale. Blending of MM5 data with local observations (which are mainly at the surface) could lead to wind structures that may not be realistic under some condit ions and may result in poorer characterization of the regional winds. Thus, the effort required to prepare observational data sets for CALMET for the large regional domain involves considerable effort that may not provide corresponding improvement of the w ind field.VISTAS’ Common Modeling Protocol 40 Figure 4

- 2. VISTAS Regional 12

- km Resolution CALMET Modeling Domain (color area with terrain contours). The locations of the 36

- km resolution MM5 grid points are shown on the plot.

For 2003, the 36

- km MM5 data will be used as CALMET’s initial guess field and then the CALMET diagnostic terrain adjustments (see Section 3.1.1) will be applied to reflect terrain on the scale of the CALMET grid (i.e., 12

- km). When the 12

- km MM5 (2001 and 2002) data are used, the di agnostic CALMET terrain adjustments will be turned off since the grid resolution of the MM5 data is the same as the CALMET grid and the terrain adjustments on the 12

- km grid scale will already be reflected in the MM5 dataset. In this case, the MM5 winds w ill be interpolated by CALMET to the CALMET layers and CALMET’s boundary layer modules will compute mixing heights, turbulence parameters and other meteorological parameters that are required by CALPUFF.VISTAS’ Common Modeling Protocol 41 Impact Threshold

The final BART guidance recommends that the threshold value to define whether a source “contributes” to visibility impairment is 0.5 dv change from natural conditions 14 (although States may set a lower threshold). The 98 th percentile (8 th highest annual) 24

- hr average predicted impact at th e Class I area, as calculated using CALPOST Method 6 (monthly average relative humidity values), is to be compared to this contribution threshold value. For this comparison, the predicted impact at the Class I area on any day is taken to be the highest 24

- hr average impact at any receptor in the Class I area on that day. (Note that the receptor where the highest impact occurs can change from day to day.) According to clarification of the BART guidance received from EPA, for a three

- year simulation the model ing values to be compared with the threshold are the greatest of the three annual 8 th highest values or the 22 nd highest value over all three years combined, whichever is greater.

For the purposes of the initial analysis, however, the highest value over the three

- year period (not the 98 th percentile value) is to be compared to the contribution threshold. This ensures a significant measure of conservatism in the initial approach. VISTAS will evaluate the initial CALPUFF results to determine if using the single highest value provides too conservative a screen for exemption purposes. If so, VISTAS may increase the number of exceedances of the contribution threshold that would be allowed and still qualify to exempt a source.

4.3.3 Model Configuration and Settings for Initial Analysis

VISTAS will use CALPUFF Version 5.754 and CALMET Version 5.7. These versions contain enhancements funded by the Minerals Management Service (MMS) and VISTAS. They were developed by Earth Tech, Inc. and they are maintained on the CALPUFF website ( www.src.com ) for public access. This version includes CALMET, CALPUFF, CALPOST, CALSUM, and POSTUTIL as well as CALVIEW.

The initial analysis uses a CALPUFF computational domain that includes all Class I areas within 300 km of a source. These Class I areas are specified in the CALPUFF control file for analysis. States could decide to require a different value for the maximum distance threshold for the CALPUFF domain, depending on the locations of the Class I areas in their states and other factors such as meteorological conditions and the magnitudes of the emissions from BART

eligible sources. The regional CALMET domain will be unchanged by these adjustments.

Also, the initial approach is design ed to significantly reduce the CALPUFF simulation time by restricting the CALPUFF computational domain size to include only areas where significant impacts are feasible rather than the entire regional domain. CALPUFF allows its computational domain to be specified as a subset of the CALMET meteorological domain by settings within the

14 As described in Footnote 5 on page 6, States have the option of defining natural conditions as either the annual average default conditions or the average of the 20% best natural condition days.VISTAS’ Common Modeling Protocol 42 CALPUFF input file. The advantage of selecting a smaller CALPUFF computational domain in the regional CALPUFF simulations is that CALPUFF run time is proportional to the numb er and residence time of the puffs on the domain (and other factors such as the number of receptors and the internal time step computed by the model). A CALPUFF domain covering an area 300 km from a source in all directions would involve only 50 x 50 12

- k m grid cells, which will require modest computational resources.

CALMET output files for the VISTAS regional domain shown in Figure 4

- 2 will be provided to VISTAS by Earth Tech. These files will be in CALPUFF

- ready format, and as such, no CALMET user inpu ts will be required. An option in CALMET allows finer grid CALMET input files to be calculated from the 12

- km CALMET files.

The basic characteristics of the CALMET, CALPUFF and CALPOST configurations for the initial analyses are listed below.

CALMET Model ing Configuration (12

- km initial exemption modeling)

The CALMET model configuration for the regional CALMET simulations will be defined by Earth Tech in collaboration with the VISTAS States. The basic model configuration will follow the recommended IWAQM guidance (EPA, 1998; Pages A

- 1 through A

- 6), except as noted below.

The basic features of the modeling simulation are the following:

- Modeling period: 3 years (2001

- 2003)

- Meteorological inputs: MM5 data provide initial guess fields in CALMET

- CALMET grid resolution: 12

- km (same Lambert Conformal coordinate system and grid cells as the 12

- km 2001/2002 MM5 simulations)

- CALMET vertical layers: 10 layers. Cell face heights (meters): 0, 20, 40, 80, 160, 320, 640, 1200, 2000, 3000, 4000.

- CALM ET mode: No

- Observations mode including option to read overwater data directly from MM5.

- Diagnostic options: IWAQM default values, except as follows: diagnostic terrain blocking and slope flow algorithms used for 2003 simulations (using 36

- km MM5 dat a), but no diagnostic terrain adjustments in 2001 and 2002 simulation (using 12

- km MM5 data)

- CALMET options dealing with radius of influence parameters (R1, R2, RMAX1, RMAX2, RMAX3), BIAS, ICALM parameters are not used in No

- Observations mode.VISTAS’ Common Modeling Protocol 43 - TERR AD (terrain scale) is required for runs with diagnostic terrain adjustments (i.e., the 2003 simulations). Values of ~10

- 20 km will be tested, and an appropriate value determined.

- Land use defining water: JWAT1 = 55, JWAT2 = 55 (large bodies of water). This feature allows the temperature field over large bodies of water such as the Atlantic Ocean and the Great lakes to be properly characterized by buoy observations.

- Mixing height averaging parameter (MNMDAV) will be determined by Earth Tech for the

r egional simulations based on sensitivity tests. The purpose of the testing is to optimize the variable to allow spatial variability in the mixing height field, but without excessive noise.

- Geophysical data for regional runs: SRTM

- GTOPO30 30

- arcsec terr ain data, Composite Theme Grid (CTG) USGS 200m land use dataset. References for these and other CALMET datasets can be found on the CALPUFF data page of the official CALPUFF site ( www.src.com ).

CALPUFF Modeling Configu ration (Initial exemption modeling)

The CALPUFF model configuration for the regional CALPUFF initial simulations will follow the recommended IWAQM guidance (EPA, 1998; Pages B

- 1 through B

- 8), except as noted below:

- CALPUFF domain configured to include the source and all Class I areas within 300km of the source plus 50km buffer zone in each direction. CALPUFF is recommended for all

source

- receptor distances to be considered in the BART analyses.

- Chemical mechanism : MESOPUFF II module

- Species modele d : SO 2 , SO 4 , NO x , HNO 3 , NO 3 and particulate matter in size categories of <0.625 μ m, 0.625

- 1.0 μ m, 1.0

- 1.25 μ m, 1.25

- 2.5 μ m, 2.5

- 6.0 μ m and 6

- 10 μ m aerodynamic diameters. As noted below, the particulate matter emissions by size category will be combined in to the appropriate species for the visibility analysis (i.e., elemental carbon (EC), fine PM or “soil” (< 2.5 μ m in diameter), coarse PM (between 2.5

- 10 μ m in diameter) and organics (called secondary organic aerosols (SOA) in the CALPOST postprocessor).

- Emission rates for modeling based on EPA BART guidance, i.e., maximum 24

- hour actual emission rate with normal operations from the highest emitting day of the meteorological period modeled (excluding days where start

- up, shutdown or malfunctions occurred

s ometime during the day.) Note that potential emissions are used to determine if a source is BART

- eligible, but 24

- hour average maximum emissions are used for modeling purposes (70 FR 39162). Pollutants considered include SO 2 , H 2 SO 4 , NO x and PM 10 .

Conde nsable emissions are considered as primary fine particulate matter and allocated equally to the two submicrometer

- particle size classes. If actual source emissions data are not available, the modeling should be based on permit limits. If source

- specific sizeVISTAS’ Common Modeling Protocol 44 categories are not available, then AP

- 42 factors may be used for sources where AP

- 42 factors are available. For sources where AP

- 42 factors are not available, alternative approaches to speciation are given below.

Excluded from the modeling are pollu tants with plant

- wide emissions less than de minimis levels (40 tons per year for SO 2 and NO x and 15 tons per year for PM 10 ). De minimis levels are plant wide for each visibility

- impairing pollutant, so individual units may be modeled even if they have emi ssions below de minimis if the plant total is greater than de minimis

.

- Particulate emissions speciation : Break down, as appropriate, filterable and condensable particulate matter into the following species categories: elemental carbon (soot), “soil” (fi ne PM < 2.5 μ m diameter), coarse particulate matter (2.5

- 10 μ m diameter) and organics. The process is illustrated in Figure 4

- 3. If source

- specific speciated emissions factors are not available, AP

- 42 factors or speciation information developed by the Nati onal Park Service ( http://www2.nature.nps.gov/air/permits/ect/index.cfm ) can be used to estimate the PM speciation for many source sectors.

Figure 4

- 3. Speciation of PM

- 10 Emissio ns. (PMC is coarse particulate matter

-- 2.5 to 10 μ m diameter.) PM

- 10

Filterable Condensabl e

EC PM

- 2.5 PMC PM
(including

H 2 SO 4 ) As Prim. SO4) Organic PM

(SOA)

EC Soil PMC SO 4
f(RH) Non

- SO 4 Inorganic OCVISTAS’ Common Modeling Protocol 45 Otherwise, assumptions will need to be proposed by the source, and reviewed and approved by the State. Possible acceptable alternative approaches to estimating speciation include the follow ing:

 Speciation profiles developed by the SMOKE emissions model for use in VISTAS’ CMAQ regional air quality modeling (available at http://www.vistas

sesarm.org/BART/calpuff.asp ).

 The approach described in a memo available at http://www.vistas

sesarm.org/BART/calpuff.asp , which provides reasonably conservative estimates in situations where data are incomplete.

- Class I receptors : Us e FLM Class I receptor list with receptor elevations provided (available from the NPS).

- CALPUFF model options : Use IWAQM (EPA, 1998) default guidance, including Pasquill

- Gifford dispersion coefficients.

- Ozone dataset

– use observed ozone data for 200 1

- 2003 from CASTNet and AIRS stations. Only non

- urban ozone stations should be used in the OZONE.DAT file. Monthly average ozone (backup) background values are to be computed based on daytime average ozone concentrations from the OZONE.DAT file (6am

- 6pm average ozone concentrations computed by month).

- Background ammonia concentration: In CALPUFF, use constant (0.5 ppb) value for ammonia.

- Puff representation: integrated puff sampling methodology.

- Building downwash: Ignore building downwash unle ss source is within 50

- km of a Class I area and the State instructs the source to specifically consider building downwash.

CALPOST and POSTUTIL Configuration (Initial exemption modeling)

- Use Visibility Method 6 in CALPOST

- Species considered in visibil ity analysis: SO 4 , NO 3 , EC, SOA (i.e., condensable organic emissions), soil, coarse PM

- Natural background light extinction: Several options are acceptable at the discretion of the State: (1) A single annual average natural background extinction for each Class I area, as presented in Appendix B of EPA’s natural conditions guidance (EPA, 2003b); (2) A single value that represents the average haze index on the 20% best natural conditions days, again as presented in the same Appendix B; or (3) A monthly aver age natural background as calculated by CALPOST under Method 6, based on annual average default naturalVISTAS’ Common Modeling Protocol 46 conditions component concentrations and monthly average f(RH) values for the centroid of the Class I area, from Table A

- 3 in the natural conditions guid ance document,.

A special procedure is needed for options 1 and 2, since CALPOST requires input of natural background concentrations of PM components while the backgrounds for options 1 and 2 are expressed in EPA’s guidance document as extinction coeffici ents or haze indices (in deciviews). In order to produce the appropriate natural background in CALPOST for these options, use Equation 3

- 2 to calculate the extinction coefficient that corresponds to EPA’s haze index value for the Class I area (if necessar y), subtract the Rayleigh scattering value of 10 Mm - 1 , and enter a soil concentration (in μ g/m 3 ) into CALPOST that is numerically equal to this result. (Since the extinction efficiency of soil is 1 m 2 /g, Equation 3

- 1 shows that this process produces a back ground extinction that equals the EPA’s value.) Leave the concentrations of all other species blank, since the number that is entered represents extinction by all components.

- Light extinction efficiencies: Use EPA (2003a) values. If a source chooses, th e new IMPROVE algorithm for calculating light extinction (see Section 3.2.3) may be used in addition to the default IMPROVE algorithm. (Calculations would need to be performed outside CALPOST or CALPOST would need to be modified to accommodate the new alg orithm.)

- Nitrate repartitioning in POSTUTIL: Do not use for the initial modeling.

The initial run results will be based on the highest change in light extinction (deciviews) from natural conditions over the three

- year modeling period for each Class I a rea considered. Predicted changes exceeding the “contribution” threshold (0.5 deciviews) will trigger a finer grid CALPUFF modeling analysis.


### 4.4 Finer Grid Modeling Procedures
4.4.1 Rationale for and Overview of Finer Grid Modeling Approach

There are t wo potential applications for finer grid CALPUFF modeling:

BART Exclusion Modeling. First, finer grid CALPUFF modeling can be used to demonstrate that a source does not cause or contribute to visibility impairment in any Class I areas, and thus can be ex cluded from BART controls. As shown in Figure 4

- 1, if the initial regional modeling results are not below the threshold for visibility impacts, the next step is to conduct modeling using a finer grid resolution for the meteorological fields and the treatm ent of terrain effects and land use variability. In the finer grid modeling the predicted visibility impairment that is compared to the threshold is based on the BART guidance of the 98 th percentile change in deciviews value rather than the more conservat ive highest value used in the initial analysis.

The BART guidance indicates that the emissions rate to be used for such modeling is the highest 24

- hr rate during the modeling period. Depending on the availability of source data, the followingVISTAS’ Common Modeling Protocol 47 emissions inf ormation (listed in order of priority) should be used with CALPUFF for BART exclusion modeling:

- 24 hr maximum value emissions for the period 2001

- 2003 (Continuous Emission Monitor, CEM data)

- 24 hr maximum value from continuous emissions monitoring d ata

- facility stack test emissions

- potential to emit

- permit allowable emissions, if available

- emissions factors from AP

- 42 source profiles

Quantify Benefits of BART

. The second application of refined modeling is to quantify the visibility benefits from the BART control options. This is accomplished by running CALPUFF with the baseline emissions rates and again with emissions after BART controls. It is important that emission reductions be evaluated in the postprocessing step rather than b y using “negative” emission rates in the CALPUFF model. The chemical scheme requires that emission rates always be positive.

For any of these applications, a source

- specific modeling protocol that defines source properties and the specific model configur ation is required. As discussed in Section 5, the source specific protocol should include source

- specific emissions data and can refer to this document for all methods and assumptions that follow this common protocol.

4.4.2 Model Configuration and Setti ngs for Finer Grid Modeling

Grid resolution substantially better than 12

- km is needed for a finer grid CALPUFF assessment of visibility impacts in most cases involving Class I areas in complex terrain or coastal areas. Thus, the CALMET fine grid resolutio n in the subregional modeling domains used for finer grid modeling will depend on the terrain, land use (especially coastal boundaries), location of the source, distance of the source from Class I areas, and total size of the subregional modeling domain.

VISTAS States have 2001

- 2003 CALMET files for five 4

- km sub

- regional domains as illustrated in Figure 4

- 4. The subdomains are designed to address all BART eligible sources within each VISTAS states and all Class I areas within 300 km of the BART

- eligible sources. For application for a single source, a smaller domain of roughly 200

- 300 km by 200

- 300 km is recommended. Requests to obtain the 4

- km CALMET files should be made to the State BART representatives.VISTAS’ Common Modeling Protocol 48 Figure 4

- 4. The five subregional domains for 4

- km CALMET modeling.

In some instances, as part of the source

- specific protocol, a source may propose to the State to use an even finer grid simulation to properly characterize the flow fields and land use changes that affect dispersion. An application f or source

- receptor distances within about 50 km may require a grid resolution less than 1 km if complex terrain effects are likely to be important. This determination should be made on a case

- by

- case basis. There is not a single distance at which a parti cular grid size is appropriate. It depends on factors such as the complexity of the terrain, the

source

- receptor distances involved, the location of the source relative to the terrain features, the physical stack parameters (e.g., a tall stack in complex t errain may be unaffected by the terrain

forced flow), proximity of the source and Class I area to a coastline, and other factors including availability of representative observational data.

The finer grid CALMET simulations were run in hybrid mode, using b oth MM5 data to define the initial guess fields and meteorological observational data in the Step 2 calculations. Overwater (buoy) data will be provided in addition to the hourly surface meteorological observations, precipitation observations and twice

- da ily upper air sounding data.VISTAS’ Common Modeling Protocol 49 A domain

- specific set of modeling parameters will be defined for each subregional domain. The proper selection of the CALMET diagnostic wind field parameters that are used to blend observations with the Step 1 wind field dep ends on factors such as the locations of the meteorological stations relative to terrain and coastal features (which affects the representativeness of the observational data), the terrain length scale, and the quality (resolution) of the MM5 data used to d efine the initial guess field and its ability to properly resolve wind flows on the fine

- scale CALMET domain. The definition of the proper CALMET parameters is done as part of sensitivity testing where model performance is evaluated against available obse rvations and expected terrain effects, such as channeling of flows within a valley.

In addition to the better grid resolution and the introduction of observational data in the finer grid simulations, several other modeling refinements can enhance the acc uracy of the finer grid modeling. These include use of the higher resolution terrain DEM data (~3 arc sec USGS data) in defining the gridded terrain fields and application of the ammonia limiting method in the POSTUTIL post

- processor. Otherwise, the sour ce configuration, emissions, pollutant speciation, Class I receptors, ozone datasets and CALPUFF model options will be the same as in the initial runs. Similarly, CALPOST will be used in the same manner as for the initial analyses. However, POSTUTIL can b e used to repartition nitrate in the finer grid modeling, using background ammonia concentrations according to the IWAQM Phase 2 report (IWAQM, 1998).

For the finer grid BART exclusion analysis, the test for evaluating whether a source is contributing to v isibility impairment is based on the 98 th percentile modeled value (rather than the highest predicted value used for the initial evaluation), which is consistent with EPA’s BART guidance.


### 4.5 Presentation of Modeling Results

The CALPOST processing compute s the daily maximum change in deciviews. A sample of the summary table produced by CALPOST is shown in Table 4

- 1. For evaluating compliance with the VISTAS screening threshold, the highest change in extinction value, located at the bottom of the CALPOST list file is compared to the threshold value (e.g., 0.5 dv). For example, in the sample shown in Table 4

- 1, the summary at the bottom shows that the highest visibility impact is 1.219 dv, with 9 days over the year showing values greater than 0.5 dv. Ther efore this source would not pass the initial analysis, and finer grid modeling would be required.

In addition to the highest change in deciview value on each day over all the receptors in a particular Class I area, the CALPOST summary table in Table 4

- 1 c ontains the coordinates of the receptor, receptor type (D indicates discrete receptors), the total haze level (background + source, in dv), the background haze in deciviews, the change in haziness (delta dv), the humidity term applied to hygroscopic aeroso ls (f(RH)), and the contribution of each species to light extinction (in percent of the total source contribution) for SO 4 , NO 3 , organics, elemental carbon, coarse and fine particulate matter.VISTAS’ Common Modeling Protocol 50 Table 4

- 1. Example of CALPOST Output, Showing Maximum Daily Impacts of Source and Locations of Those Impacts.

YEAR DAY HR RECEPTOR COORDINATES (km) TYPE DV(Total) DV(BKG) DELTA DV F(RH) %_SO4 %_NO3 %_OC %_EC %_PMC %_PMF

2001 2 0 3 20.540 79.782 D 5.397 5 .358 0.039 4.314 44.33 47.22 3.07 1.07 0.00 4.30

2001 3 0 9 31.680 79.822 D 4.566 4.421 0.145 1.767 40.75 33.89 9.19 3.24 0.00 12.94

2001 4 0 1 24.723 77 .951 D 4.540 4.540 0.000 2.076 0.00 0.00 0.00 0.00 0.00 0.00

2001 5 0 77 30.228 94.571 D 4.950 4.939 0.011 3.144 43.13 44.74 4.64 1.45 0.00 6.05

2001 6 0 1 24.723 77.951 D 5.181 5.166 0.015 3.772 38.58 56.05 1.90 0.70 0.00 2.76

2001 7 0 3 20.540 79.782 D 6.366 5.745 0.620 5.439 44.98 44.99 3.69 1.26 0.00 5.0 8

.

.

.

2001 363 0 113 27.414 103.782 D 5.725 5.652 0.073 5.164 53.49 35.51 4.03 1.39 0.00 5.58

2001 364 0 113 27.414 103.782 D 6.554 6.521 0.033 7.826 48.1 2 47.09 1.67 0.64 0.00 2.48

2001 365 0 1 24.723 77.951 D 6.499 6.499 0.000 7.757 0.00 0.00 0.00 0.00 0.00 0.00

--- Number of days with Delta

- Deciview => 0.50: 9

-- - Number of days with Delta

- Deciview => 1.00: 2

--- Largest Delta

- Deciview = 1.219VISTAS’ Common Modeling Protocol 51 For the finer grid analysis, the data in the table can be imported into a spreadsheet and sorted on the delta dv column. Table 4

- 2 shows an example of the ranked visibility impacts (change in dv) for each of three years at six different Class I areas. The 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 e xample shown in this table, the source passes the finer grid analysis because the highest 98 th percentile visibility impact is below the contribution threshold of 0.5 dv.

The Results section of the CALPUFF modeling report should contain the following info rmation:

1. Map of source location and Class I areas within 300 km of the source

2. For the VISTAS 12

- km CALPUFF initial exemption modeling domain, a table listing all Class I areas in the VISTAS domain and those in neighboring states and impacts at those Class I areas within 300 km of the source, as illustrated in Table 4

- 3

.

3. A discussion of the number of Class I areas with visibility impairment from the source on 98 th percentile days in each year greater than 0.5 dv (total visibility impairment minus impairme nt on 20% best days for natural background visibility equals delta

- dv, the visibility impact attributed to the source).

4. For the Class I area with the maximum impact, discussion of the number of days below the 98 th percentile that the impact of the source exceeds 0.5 dv, the number of receptors in the Class I area where the impact exceeds 0.5 dv, and the maximum impact.

5. For finer grid CALPUFF exemption modeling, results for those Class I areas for which impacts of the source exceeded 0.5 dv in the 12

- km in itial exemption modeling. Report same results as provided for 12

- km initial exemption modeling.

6. For control option modeling, each control option tested should be listed in tabular format. For each control option and for each Class I area where the impact of the source exceeded 0.5 dv, report the change in pollutant emissions and the change in visibility impact from the source as a result of the control option. The effectiveness of candidate control options are to be compared to each other, not to a speci fic target improvement.

States will provide further guidance on graphic presentation of results to simplify evaluation of effectiveness of control measures. For example, a temporal plot of the change in deciviews between the controlled and uncontrolled cases could be developed for the receptor with the maximum modeled impact in each Class I area.

7. Copies of all input files and input data in electronic format for the CALMET, CALPUFF, CALPOST and POSTUTIL runs should be archived and provided to the State.VISTAS’ Common Modeling Protocol 52 Table 4

- 2. Example of Visibility Impact Rankings at Six Class I Areas

Class I Area 2001 2002 2003
Delta

Deciview Ranks 1

- 8 Delta

Deciview Ranks 1

- 8 Delta

Deciview Ranks 1

- 8

Great Smoky NP 0.99 0.88 0.62 0.59 0.55 0.52 0.48 0.47 0.95 0.63 0.51 0.50 0 .46 0.42 0.37 0.36 1.20 0.90 0.73 0.72 0.59 0.47 0.45 0.42

Linville Gorge 0.67 0.45 0.43 0.33 0.29 0.27 0.25 0.23 0.81 0.69 0.65 0.50 0.45 0.33 0.31 0.29 0.76 0.47 0.37 0.35 0.31 0.30 0.28 0.28

Shining Rock 0.66 0.43 0.41 0.35 0.26 0.24 0.23 0.22 0.73 0. 69 0.63 0.52 0.46 0.34 0.29 0.26 0.75 0.45 0.36 0.34 0.28 0.27 0.26 0.25

Cohutta 0.26 0.23 0.22 0.21 0.20 0.19 0.18 0.16 0.54 0.47 0.43 0.37 0.37 0.31 0.31 0.30 0.61 0.42 0.30 0.29 0.28 0.28 0.25 0.25

Joyce Kilmer

- Slickrock 0.34 0.33 0.31 0.26 0.24 0.20 0.18 0.17 0.52 0.43 0.32 0.31 0.30 0.28 0.24 0.24 0.27 0.24 0.23 0.20 0.14 0.13 0.11 0.10

Mammoth Cave NP 0.56 0.44 0.38 0.29 0.25 0.24 0.22 0.21 0.57 0.56 0.53 0.35 0.33 0.33 0.30 0.29 0.50 0.37 0.36 0.35 0.31 0.24 0.21 0.19VISTAS’ Common Modeling Protocol 53 Table 4

- 3. Format of Summa ry of Results for CALPUFF Modeling in VISTAS’ 12

- km Modeling Domain to Determine if a BART Eligible Source is Subject to BART.

Class I area Distance (km) from source to Class I

area boundary # of days 1 and # of receptors with impact > 0.5 dv in Class I area: 2001 # of days 1 and # of receptors with impact > 0.5 dv in Class I area: 2002 # of days 1 and # of receptors with impact > 0.5 dv in Class I area: 2003 # of days 1 and # of receptors with impact > 1.0 dv in Class I area for 3

- yr period Ma x. 24

- hr impact over 3

- yr period

Dolly Sods, WV
Shenandoah, VA

James River Face, VA Mammoth Cave, KY Sipsey, AL Great Smoky Mtns, TN Cohutta, GA Shining Rock, NC

Linvi lle Gorge, NC Swanquarter, NC Cape Romain, SC Okefenokee, GA
Saint Marks, FL
Chassahowitzka, FL Everglades, FL Brigantine, NJ
Breton Island, LA Caney Creek, AR Upper Buffalo, AR Mingo, MO
Hercules Glade, MO

1 Days below the 98 th percentile of days in each year or the three

- year modeling period, as appropriateVISTAS’ Common Modeling Protocol 54 

### 4.6 VISTAS Contribution to CALPUFF Modeling of BART Eligib le Sources

VISTAS will provide updates and supporting information concerning the Common Modeling Protocol (this document) on the VISTAS website. In addition, VISTAS will make publicly available the following data bases developed by Earth Tech:

• VISTAS versi on of the CALPUFF modeling system, maintained on the CALPUFF website. Version 5.754 includes CALMET, CALPUFF, CALPOST, and POSTUTIL files, updated in December 2005. The last update in this VISTAS version is a CALMET update that addresses over water disper sion, which was developed for the Minerals Management Service (MMS) in fall 2005. This VISTAS version of CALPUFF will not be updated further unless errors are found in the code, except that a new one

- step POSTUTIL procedure will be incorporated. BART

- el igible sources in the VISTAS states will be able to use this VISTAS version throughout the BART modeling exercise.

• 12

- km CALMET output files for 2001, 2002, and 2003 produced as described in previous sections. Further detail on model configuration and s ettings will be provided with the output files and will be made available on the CALPUFF website.

• CALMET will include a software modification to allow the meteorological data inputs into CALMET to be used to generate finer grid CALMET files without having to go back to the original MM5 output files

• Five 4

- km CALMET subdomains for 2001, 2002, and 2003, produced as described in previous sections. Further detail on model configuration and settings will be provided with the output files and will be made availa ble on the website.

• File with CALPUFF model configuration and settings sufficient to replicate CALPUFF modeling done for VISTAS using 12 km CALMET, including

o Ozone data used to run CALPUFF

o Ammonia concentrations used to run CALPUFF.

o All other set up files used in VISTAS 12

- km CALPUFF run

Samples of these data files and examples of their application with CALPUFF for BART screening analyses can be found on the CALPUFF web site at ( http://www.s rc.com/verio/download/sample_files.htm ).Source

- Specific Modeling Protocol 55 

## 5

. SOURCE

- SPECIFIC MODELING PR OTOCOL

Sources are required to submit a source

- specific protocol to the State for review and approval prior to source

- specific modeling. States will provide t he documentation to EPA and FLM for their review. An outline of the typical contents of the site

- specific protocol is provided in Table 5

- 1.

If a source

- specific modeling approach is proposed that differs from the common approach in Chapter 4, a more

- deta iled modeling protocol than that required under the common procedures is required. This protocol must explain the data sources, model configuration, and rationale for changes in the model approach from the common protocol and must be approved by the State.

Unit

- specific source data include the following parameters:

- Location (e.g., UTM coordinates, UTM zone and datum)

- Stack height above the ground

- Stack diameter

- Exit velocity

- Exit temperature

- Emission rates (SO 2 , H 2 SO 4 , NO x and PM 10 ).

Addit ional building dimension information (building width, length, height and corner locations) is needed for short stacks that are less than Good Engineering Practice (GEP) height. This information is used in providing effective structure dimensions for build ing downwash calculations. (The requirement to conduct building downwash modeling may be waived by individual States or if the transport distance is greater than 50 km.)

The source coordinates must be expressed in the coordinate system used to define the CALMET and CALPUFF modeling domains. For the regional screening simulations, a Lambert Conformal Conic (LCC) coordinate system will be used. The required parameters to define an LCC coordinate include two matching parallels, latitude/longitude of the pro jection origin, coordinate datum, and false Easting and Northing (if used) of the projection origin. Subregional and source

specific domains may be using either an LCC or UTM projection.

The CALPUFF Graphical User Interface (GUI) system provides softwar e (called COORDS) to compute to/from latitude/longitude, LCC and UTM coordinates for a large number of datums. In addition, the CALVIEW graphics feature allows the use of georeferenced satellite or aerial photographs to be used as base maps to confirm sou rce locations. Links to sources of suitable base maps can be found on the CALPUFF data site ( www.src.com ) in the section on “Aerial Photos”.Source

- Specific Modeling Protocol 56 Table 5

- 1

. Sample Table of Contents of a Sourc e - Specific Fine

- Scale Modeling Protocol.

1. INTRODUCTION
1.1 Objectives
1.2 Location of Source vs. Relevant Class I Areas
1.3 Source Impact Evaluation Criteria
2. SOURCE DESCRIPTION
2.1 Unit

- specific Source Data
2.2 Boundary Conditions
3. GEOPHYSICAL AND METEOROLOGICAL DATA
3.1 Modeling Domain and Terrain
3.2 Land Use
3.3 Meteorological Data Base
3.3.1 MM5 Simulations
3.3.2 Measurements and Observations
3.4 Air Quality Data Base
3.4.1 Ozone Concentrations

– Measured or Modeled
3.4.2 Ammonia Co ncentrations

– Measured or Modeled
3.4.3 Concentrations of Other Pollutants

– Measured or Modeled
3.5 Natural Conditions at Class I Areas
4. AIR QUALITY MODELING METHODOLOGY
4.1 Plume Model Selection
4.1.1 Major Relevant Features of CALMET
4.2.2 Ma jor Relevant Features of CALPUFF
4.2 Modeling Domain Configuration
4.3 CALMET Meteorological Modeling
4.4 CALPUFF Computational Domain and Receptors
4.5 CALPUFF Modeling Option Selections
4.6 Light Extinction and Haze Impact Calculations
4.7 Modelin g Products
5. REVIEW PROCESS
6.1 CALMET Fields
6.2 CALPUFF, CALPOST, and POSTUTIL Results
6. REFERENCES
APPENDICES
A.1 VISTAS BART MODELING PROTOCOL A.2 ... other appendices as neededSource

- Specific Modeling Protocol 57 An example of the data that need to be reported is provided in Table 5

- 2. More detail on the stack data, emissions species, and particulate size fractions to be reported will be made available on the CALPUFF website, www.src.com , Check with your State for the more detailed for mat of Table 5

- 2 that is to be used.

Discussions with the regulatory authorities should be conducted prior to development of a protocol to ensure all of the relevant issues are included in the protocol.

Table 5

- 2. Example of Source Documentation fo r BART Eligible Source.

Unit name and/or description Start

- up dates SO 2 potential emissions (tpy) NO x potential emissions (tpy) Total PM potential emissions (tpy)

Emissions source

name

... Total emissions

Potential BART

eligible emissionsQuality Assurance 58 

## 6. QUALITY ASSURANCE

6.1 Scope and Purpose of the QA program

Air quality modeling covered under this protocol is an important tool for use in determining whether a BART

- eligible source can be reasonable expected to cause or c ontribute to visibility impairment in a Class I area, and therefore whether this source should be subject to BART controls, and if so, to determine the relative benefits of various BART controls. The purpose of the quality assurance (QA) program is to esta blish procedures for ensuring that products produced by the application of the modeling techniques for BART studies satisfy the regulatory objectives of the BART program.

The scope of the QA program affects different users differently. Common features of m ost applications will be the setup and execution of the CALPUFF air quality model and processing of modeling results to determine if a source contributes to visibility impairment at a Class I area. In many cases, users will be provided meteorological datas ets that have been developed with VISTAS funding under a suitable QA program for use in the BART modeling. Other users will be involved in site

- specific or source

- specific analyses that will use additional datasets and potentially different modeling option s and/or tools. More extensive quality assurance will be required in these latter types of applications. It is the responsibility of the modeler to ensure that an adequate QA protocol is in place for a particular application.

The CALPUFF modeling system c ontains built

- in features to facilitate quality assurance of the modeling results. These include the automatic production of “QA” files for various datasets, including geophysical fields, sources and receptors, and imbedded tracking of model options and

s witches within the output files from the major modeling units of the modeling system. The Graphical User Interface system (GUI) provided as part of the latest CALPUFF modeling system allows these QA files to be displayed graphically.

In addition, a detail ed software management system is in place to track version and level numbers associated each program and utility within the CALPUFF modeling system. This information is carried forward in all of the output files to create an audit trail of software versio ns and major model options used that can be retrieved and displayed from the model output files.

Because the required QA procedures will depend heavily on the exact application, there will be differences among different users and different applications.

In addition, the BART modeling process involves multiple organizations. The States have overall responsibility for the process and may also execute some or all of the modeling. VISTAS is contributing general guidance via this protocol and is preparing meteor ological fields and performing modeling under the guidance of the States. The sources that are BART

- eligible need to provide process information and emissions data for use in the analyses. In addition, those sources that are involved in BART assessments wi ll need to be actively involved in controlQuality Assurance 59 technology decisions and assessments. Finally, some of the modeling steps may be carried out by contractors on behalf of VISTAS, a State, or a source.

Each of these organizations has a responsibility to ensure tha t it is providing correct information to others and to evaluate the quality of any analyses it is performing, whether with data of its own or from others. This chapter provides general guidance and information on those aspects of quality assurance that are specific to the CALPUFF modeling effort, irrespective of which organization is carrying out the effort. The focus is on the common protocol efforts described in Chapter 4. As described in Section 6.3, more comprehensive QA may be needed for the unique
asp ects of the source

- specific modeling described in Chapter 5.


### 6.2 QA Procedures for Common Protocol Modeling

The VISTAS common protocol (Section 4) describes the methods and procedures for use in conducting regional scale screening modeling to determine th e whether a particular source or group of sources is subject to BART controls. In the initial application, the regional CALPUFF

ready meteorological data files will be provided by VISTAS. The amount of effort for end

- users performing QA of these pre

- defi ned meteorological fields will be reduced from what is required in developing source

- specific meteorological fields, as described below. Also, VISTAS is planning to provide five subregional CALMET meteorological datasets in a CALPUFF

- ready format. The de velopment of these CALMET datasets will be subject to a QA program as part of their development, so the necessary quality assurance activity of end

- users is again reduced from what would be required in the development of the dataset. It is not expected th at the quality assurance steps in the development will be repeated in each application. The VISTAS

- provided regional and subregional meteorological fields will include a test case simulation for demonstrating that expected modeling results are obtained on the user’s computer platform. This test should be repeated by every user.

Although the CALPUFF modeling system is recommended by the U.S. Environmental Protection Agency for application to BART analyses, a considerable amount of expertise and modeling ju dgment is needed at certain stages of the analysis. The modeling is not a “cookbook” exercise, a fact that was recognized by the U.S. EPA in describing the expertise needed for CALMET modeling (EPA, 1998; pp. 9

- 10,). Current methods for performing refine d chemistry calculation also require an understanding of the chemical and meteorological processing affecting ammonium nitrate formation. VISTAS has committed to provide appropriate CALPUFF training to assist States in obtaining the necessary expertise wi th the latest CALPUFF modeling tools and techniques. An appropriate level of knowledge of the model formulation, technical approach and assumptions is essential for successful BART modeling.

6.2.1 Quality Control of Input Data

The input data required by the model depends on the application. At a minimum, source data is required by CALPUFF (see Section 6.2.3) along with a list of choices made about model options and switches. Most of the modeling option choices are specified or recommended by regulatoryQuality Assurance 60 guidance and default values (see references in Section 4.3.3). However, remodeling of the boundary conditions is not required for VISTAS

- provided finer grid domains so the expertise level is not as high as it would be for development of the boundary condi tions files from scratch.

To the extent that modeling applications are using pre

- defined CALMET files and CALPUFF templates, the quality assurance will be straightforward. More detailed steps are needed for the setup of modeling files for source

- specific applications of subregional domains finer than 4 km.

The basic procedures that will apply to all CALPUFF model applications will include a confirmation of the source data, including units, verification of the correct source and receptor locations, includi ng datum and projection, confirmation of the switch selections relative to modeling guidance, checks of the program switches and file names for the various processing steps, and confirmation of the use of the proper version and level of each model program. It is a common and recommended procedure for an independent modeler not involved in the setup of the modeling files to independently confirm the model switches and data entry in the actual model input files and to conduct an independent run of the worst case event as a confirmation check.

In addition, common practice requires that a model project CD (or DVD or set of DVDs) be created that contains all of the data and program files needed to reproduce the model results presented in a report. The model lis t files from each step are included on the project CD. This information allows independent checking and confirmation of the modeling process.

6.2.2 Quality Control of Application of CALMET

For users of the VISTAS CALPUFF

- ready CALMET meteorological files , a number of large datafiles will be provided by VISTAS on external USB2 or Firewire hard drives in a format ready for use with the CALPUFF model. The QA steps associated with the development of the VISTAS common datasets will be provided separately as p art of the modeling documentation. It is not expected that the QA steps conducted in the development of the meteorological datasets will be repeated in each application, although tests to confirm that the dataset is suitable for the application for which it is being used should be performed as part of the QA. This is discussed in more detail below.

The regional screening CALMET grid is defined in Chapter 4 on a 12

- km Lambert Conformal Conic (LCC) grid system. The subregional and source

- specific domains ma y be defined in either LCC or Universal Transverse Mercator (UTM) coordinates. In the case of the LCC projection, two matching parallels, latitude/longitude of the projection origin, coordinate datum, and false Easting and Northing (if used) of the projec tion origin must also be defined. For any domains in UTM coordinates, the UTM zone (see Appendix D of the CALMET User’s Guide) and datum must be defined. The appropriate projection and map factors are provided as part of the definition of the VISTAS regi onal grid system. For a source

- specific domain, the grid parameters will be provided as part of the source

- specific protocol.Quality Assurance 61 Appendix A of the IWAQM report (EPA, 1998) contains a list of recommended CALMET switch settings. Except as modified in Chapter 4 of this protocol or in a source

- specific protocol, the IWAQM guidance should be used in setting up the CALMET simulations. The CALMET model obtains the switch settings from an ASCII “control file” with a default name of CALMET.INP. Whether the model is run using a GUI or from the control line in a DOS, Linux, or Unix window, it is essential that the control file be reviewed as part of the CALMET QA analysis. The CALMET GUI retains all of the input descriptive information that is part of the standard CA LPUFF.INP file structure. This includes the default value for each variable, a text description of the variable, the meaning of each variable option, the units of the variable and inter

relationships among variables indicating if/when the variable is used . Some third

- party commercial GUIs strip out this descriptive information, which makes the QA step more difficult, although it is essential for perform nonetheless using the variable names as references for the variables in the file.

Part of the CALPUFF mo deling system’s built

- in QA capabilities is a variable tracking system that retains the control file inputs for CALMET and CALPUFF in the output files create by the models. This information includes the Version and Level numbers of the processor codes and main model codes used in the simulations as well as the control files from the main models (CALMET and CALPUFF). The information from the preprocessing steps and the CALMET and CALPUFF model simulations is all carried forward and saved in the CALPUFF/pos tprocessor output files so that the final concentration/flux files contain a history of the model options and switch settings. This allows a user or reviewing agency to confirm the switch settings provided in a control file with that actually used in the m odel simulations. An optional switch in the CALPOST processor creates a complete listing of the QA data. This step requires access to the output CALPUFF concentration and/or flux files, which are normally practical to store on CDs or DVDs and to provide a part of the Project CD/DVD set.

6.2.3 Quality Control of Application of CALPUFF

The quality assurance of the source and emissions data is a major component of the CALPUFF modeling. Also, many errors are found in source coordinates and related projection /datum parameters, so confirmation of the source location is an important part of the modeling QA.

The locations of the Class I area receptors are another important CALPUFF input. The use of
pre

- defined receptors as provided by the National Park Service ( NPS) receptor dataset is recommended in the VISTAS common protocol. However, although the latitude and longitude of each receptor point is provided, it is necessary to ensure that the proper UTM or LCC coordinates have been computed for computational doma in 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 v arious datums.

Most of the CALPUFF input variables contain default values. Appendix B of the IWAQM report contains a list of recommended CALPUFF switch settings. Except as modified in Chapter 4 ofQuality Assurance 62 this protocol or in a source

- specific protocol, the IWAQM guidance should be used in setting up the CALPUFF simulations. The CALPUFF model obtains the switch settings from an ASCII “control file” with a default name called the CALPUFF.INP file. As is the case with the comparable CALMET file, it is essential tha t the control file be reviewed manually as part of the CALPUFF QA analysis. To facilitate this process, as was the case with the CALMET GUI, the CALPUFF GUI retains all of the input descriptive information that is part of the standard CALPUFF.INP file str ucture. Some third

- party commercial GUIs strip out this descriptive information, which makes the QA step more difficult, although it is essential for perform nonetheless using the variable names as references for the variables in the file.

6.2.4 Quality C ontrol of Application of CALPOST and POSTUTIL

CALPOST is run separately for each Class I area in order to obtain the necessary visibility statistics for evaluating compliance with the BART screening and finer grid modeling thresholds. The inputs to CALPOS T involve selection of the visibility method (Method 6 in the standard EPA BART guidance), entry of Class I area

- specific data for computing background extinction (either average or best 20% natural conditions, as prescribed by the State) and monthly rela tive humidity factors for hygroscopic aerosols. CALPOST contains a receptor screening that allow subsets of a receptor network modeling in CALPUFF to be selected for processing in a given CALPOST run. This is how receptors within a single Class I area ar e selected for processing from a CALPUFF output file that may contain receptors from several Class I areas. CALPOST contains options for creating plot files that will help in the confirmation that the proper receptor subset is extracted.

The CALPOST outpu t file contains a listing of the highest visibility impact each day of the model simulation over all receptors included in CALPOST analysis. Receptors will normally be selected in each CALPOST run so that each CALPOST run represents the impacts at a singl e Class I area. The table includes the data shown in the example in Table 4

- 1. For a screening assessment, the peak value of the change in extinction is shown at the bottom of the visibility table (see Table 4

- 1). For a finer grid simulation, the 98 th p ercentile value (8 th highest day) is used for comparison against the BART threshold of 0.5 deciviews. It is necessary to import the results of the CALPOST table into a sorting program such as a spreadsheet to rank the daily change in extinction values suc h as is presented in Table 4

- 2.

The CALPOST inputs that need to be carefully checked as part of the CALPOST quality

assurance are:

- Visibility technique (Method 6 in the common VISTAS protocol)

- Monthly Class I

- specific relative humidity factors for Me thod 6

- Background light extinction valuesQuality Assurance 63 - Inclusion of all appropriate species from modeled sources (e.g., sulfate, nitrate, organics, (as SOA), coarse and fine particulate matter and elemental carbon.

- Appropriate species names for coarse PM used

- Extinction efficiencies for each species

- Appropriate Rayleigh scattering term (10 Mm - 1 for screening modeling but Class I area specific value for finer grid modeling)

- Screen to select appropriate Class I receptors for each CALPOST simulation.

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

POSTUTIL allows the user to sum the contributions of sources from different CALPUFF simulati ons into a total concentration file. In addition, it contains options to scale the concentrations from different modeled species (e.g., different particle sizes) into species

dependent size distributions for the particulate matter. For example, PM is of ten simulated with unit emission rates for each particle size category and, in the POSTUTIL stage, the contributions of each size category based on the species being considered (e.g., elemental carbon, coarse particulate matter, etc.) are combined to form the species concentrations for input into CALPOST. This process, although simple, requires a careful review of the weighting factors for each source. POSTUTIL also allows a repartitioning of nitric acid and nitrate to account for the effects of ammonia li miting conditions.

If source

- specific modeling is performed using different sources of data or different techniques, the source

- specific modeling protocol should provide justification for deviations from the VISTAS common protocol, and a QA plan specific for the application provided to address the quality assurance of the data used.


### 6.3 Additional QA Issues for Alternative Source

- Specific Modeling

The level of QA required for application of source

- specific protocols will be substantially higher than for t he use of datasets that have already been subject to a QA procedure. For example,

source

- specific protocols may include the use of on

- site meteorological datasets, the use of higher resolution prognostic meteorological (e.g., MM5) datasets, alternative vi sibility calculations, different extinction coefficients, or other changes to the common protocol. In addition to providing a source

- specific modeling protocol describing and justifying the changes to the modeling approach from the VISTAS common protocol, the site

- specific applications should include the development of a QA plan to properly evaluate the data used in the site

- specific modeling.

The critical CALMET input parameters depend on the mode in which the model is run (observations mode, hybrid mode or no

- observations mode), and the location and spatialQuality Assurance 64 representativeness of any observational data. In a site specific protocol involving the development of a meteorological dataset, the elements of the QA process include preparation of wind rose (using observed, MM5 and CALMET

- derived data), including examination of the data as a function of season and time of day (e.g., 4am, 10am, 4pm wind roses), time series analyses, and presentation of 2

- D vector plots illustrating terrain effects/sea breeze circulat ion or other features of the flow expected to occur within the domain. For example, 2

- D vector plots produced during light wind speed stable conditions (e.g., early morning such as 4 am) are good for assessing the performance of the CALMET model configura tion and switches in reproducing terrain effects because these conditions are likely to maximize the terrain impacts in the model. Season wind roses at 4 am, 10 am and 4 pm would be expected to show the development of sea breeze circulations that may be i mportant for certain applications. Customization of the QA process for the individual site

- specific domain based on the availability of data and the physical processes expected to be important at that location should be conducted as part of the site

- speci fic QA plan development.

If site

- specific CALPUFF simulations involving the Ammonia Limiting Method are conducted, performance of the model in reproducing observed CASTNet or IMPROVE sulfate and nitrate concentrations at measurement sites within the site

- s pecific modeling domain should be evaluated. The use of alternative ammonia concentration data (e.g., CMAQ output rather than derived ammonia based on aerosol measurements) will require an evaluation of the model performance relative to the techniques in the VISTAS common protocol.

In any site

- specific protocol a site

- specific QA plan should be prepared.


### 6.4 Assessment of Uncertainty in Modeling Results

Chapter 3 discussed the uncertainties and known limitations in CALPUFF. The source specific modeling report does not need to repeat the uncertainties listed in Chapter 3, but the reviewer should interpret results in light of these limitations. It is expected that the performance of the model will be better in predicting changes in visibility impacts due to BART controls than in predicting absolute visibility values. This is because uncertainties in meteorological conditions transport and dispersion are expected to be less important in evaluating a change in impact, since a comparable effect will be inclu ded in both the base and sensitivity simulations.References 65 

## 7. REFERENCES

Allwine, K.J. and C.D. Whiteman, 1985: MELSAR: A Mesoscale Air Quality Model for Complex Terrain: Volume 1

-- Overview, Technical Description and User's Guide. Pacific N orthwest Laboratory, Richland, Washington.

Batchvarova, E. and S.

- E. Gryning, 1991: Applied model for the growth of the daytime mixed layer. Boundary

- Layer Meteorol
., 56 , 261

- 274.

Batchvarova, E. and S.

- E. Gryning, 1994: An applied model for the height of the daytime mixed layer and the entrainment zone. Boundary

- Layer Meteorol
., 71 , 311

- 323.

Benjamin, S.G, D. Devenyi, S.S. Weygandt, K.J. Brundage, J.M. Brown, G.A. Grell, D. Kim, B.E. Schwartz, T.G. Smirnova, T.L. Smith, and G.S. Manikin, 2004: An Hourl y Assimilation Forecast Cycle: the RUC, M on. Wea. Rev
., 132 , 495

- 518.

Carson, D.J., 1973: The development of a dry, inversion

- capped, convectively unstable boundary layer. Quart. J. Roy. Meteor. Soc
., 99 , 450

- 467.

Chang, J.C. P. Franzese, K. Chayantrakom and S.R. Hanna, 2001: Evaluations of CALPUFF, HPAC and VLSTRACK with Two Mesoscale Field Datasets. J. Applied Meteorology
, 42 (4): 453

- 466.

Department of the Interior, 2003: Letter from the Assistant Secretary of the Interior to the Director of the
Montan a Department of Environmental Quality.

Douglas, S. and R. Kessler, 1988: User's Guide to the Diagnostic Wind Model. California Air Resources Board, Sacramento, California.

Edgerton, E., 2004: Natural Sources of PM2.5 and PMcoarse Observed in the SEARCH Net work. Presented at the A&WMA Specialty Conference on Regional and Global Perspectives in Haze: Causes, Consequences and Controversies, Asheville, NC, October 2004.

Environmental Protection Agency, 2003a: Guidance for Tracking Progress under the Regional H aze Rule; EPA

- 54/B

- 03

- 004, U.S. Environmental Protection Agency, Research Triangle Park, NC.
Environmental Protection Agency, 2003b: Guidance for Estimating Natural Visibility Conditions under the Regional Haze Rule. EPA

- 454/B

- 03

- 005. U.S. Environmental Pr otection Agency, Research Triangle Park, NC.

Environmental Protection Agency, 1998: Phase 2 Summary Report and Recommendations for Modeling Long Range Transport and Impacts. Interagency Workgroup on Air Quality Modeling (IWAQM). EPA

454/R

- 98

- 019, U.S. Envi ronmental Protection Agency, RTP, NC.References 66 Escoffier

- Czaja, C., and J. Scire. 2002: The Effects of Ammonia Limitation on Nitrate Aerosol Formation and Visibility Impacts in Class I Areas. Paper J5.13, 12 th AMS/A&WMA Conference on the Applications of Air Polluti on Meteorology, Norfolk, VA. May 2002.

Fairall, C.W., E.F. Bradley, J.E. Hare, A.A. Grachev, and J.B. Edson, 2003: Bulk parameterization of air

- sea fluxes: Updates and verification for the COARE algorithm. Journal of Climate
, 16 , 571

- 591.

Fallon, S. and G. Bench, 2004: IMPROVE Special Study: Hi

- Vol Sampling for Carbon

- 14 Analysis of PM 2.5 Aerosols. Draft Report, Lawrence Livermore National Laboratory.

FLAG, 2000. Federal Land Managers’ Air Quality Related Values Workgroup (FLAG). Phase I Report. U.S. F orest Service, National Park Service, U.S. Fish and Wildlife Service.

Grell, G.A., J. Dudhia, and D.R. Stauffer, 1995: A Description of the Fifth Generation Penn State/NCAR MM5, NCAR TN

- 398

- STR, NCAR Technical Note.

Grosjean, D., and J. Seinfeld, 1989: Par ameterization of the Formation Potential of Secondary Organic Aerosols

. Atmos. Environ.
, 23 , 1733

- 1747.

Holtslag, A.A.M. and A.P. van Ulden, 1983: A simple scheme for daytime estimates of the surface fluxes from routine weather data. J. Clim. and Appl. Met eor ., 22 , 517

- 529.

Irwin, J.S., J.S. Scire and D.G. Strimaitis, 1996: A Comparison of CALPUFF Modeling Results with CAPTEX Field Data Results. In Air Pollution Modeling and Its Applications , XII. Edited by S.E. Gryning and F.A. Schiermeier. Plenum Pres s, New York, NY.

IWAQM, 1998: Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary Report and Recommendations for Modeling Long Range Transport Impacts. EPA

- 454/R

- 98

- 019.

Liu, M.K. and M. A. Yocke, 1980: Siting of wind turbine generators i n complex terrain. J. Energy
, 4
, 10:16.

Mahrt, L., 1982: Momentum Balance of Gravity Flows. J. Atmos. Sci
., 39 , 2701

- 2711.

Maul, P.R., 1980: Atmospheric transport of sulfur compound pollutants. Central Electricity Generating Bureau MID/SSD/80/0026/R. Notti ngham, England.

Morris, R., C. Tang and G. Yarwood, 2003: Evaluation of the Sulfate and Nitrate Formation Mechanism in the CALPUFF Modeling System. Proceedings of the A&WMA Specialty Conference

– Guideline on Air Quality Models: The Path Forward. Mystic, C T, 22

- 24 October 2003.

Morrison, K., Z

- X Wu, J.S. Scire, J. Chenier and T. Jeffs

- Schonewille, 2003: CALPUFF

- Based Predictive and Reactive Emissions Control System. 96 th A&WMA Annual Conference & Exhibition, 22

- 26 June 2003, San Diego, CA.References 67 O'Brien, J.J., 1970: A note on the vertical structure of the eddy exchange coefficient in the planetary boundary layer. J. Atmos. Sci
., 27 , 1213

- 1215.

Pitchford, M., W. Malm, B. Schichtel, N. Kumar, D. Lowenthal, and J. Hand, 2005: Revised IMPROVE Algorithm for Estimatin g Light Extinction from Particle Speciation Data. Report to IMPROVE Steering Committee, November 2005

Perry, S.G., D.J. Burns, L.H. Adams, R.J. Paine, M.G. Dennis, M.T. Mills, D.G. Strimaitis, R.J. Yamartino, E.M. Insley, 1989: User’s Guide to the Complex Terrain Dispersion Model Plus Algorithms for Unstable Situations (CTDMPLUS) Volume 1: Model Description and User Instructions. EPA/600/8

- 89/041, U.S. EPA, Research Triangle Park, North Carolina.

Schulman, L.L., D.G. Strimatitis, and J.S. Scire, 2000: Devel opment and Evaluation of the PRIME Plume Rise and Building Downwash Model, J. Air & Waste Manage. Assoc., 50 , 378

- 390.

Scire, J.S., F.W. Lurmann, A. Bass and S.R. Hanna, 1984: Development of the MESOPUFF II Dispersion Model. EPA

- 600/3

- 84

- 057, U.S. Envir onmental Protection Agency, Research Triangle Park, NC.

Scire, J.S. and F.R. Robe, 1997: Fine

- Scale Application of the CALMET Meteorological Model to a Complex Terrain Site. Paper 97

- A1313. Air & Waste Management Association 90th Annual Meeting & Exhibiti on, Toronto, Ontario, Canada. 8

- 13 June 1997.

Scire, J.S., D.G. Strimaitis, and R.J. Yamartino, 2000a: A User’s Guide for the CALPUFF Dispersion Model (Version 5). Earth Tech, Inc., Concord, Massachusetts

Scire, J.S., F.R. Robe, M.E. Fernau, and R.J. Yamar tino, 2000b: A User’s Guide for the CALMET Meteorological Model (Version 5). Earth Tech, Inc., Concord, Massachusetts.

Scire, J.S., Z

- X Wu, D.G. Strimaitis and G.E. Moore, 2001: The Southwest Wyoming Regional CALPUFF Air Quality Modeling Study

– Volume I. Prepared for the Wyoming Dept of Environmental Quality. Available from Earth Tech, Inc., 196 Baker Avenue, Concord, MA.

Scire, J.S., Z.

- X. Wu, 2003: Evaluation of the CALPUFF Model in Predicting Concentration, Visibility and deposition at Class I Areas in Wyoming. Presented at the A&WMA Specialty Conference, Guideline on Air Quality Models: The Path Forward. Mystic, CT, 22

- 24 October 2003.

Scire, J.S., D.G. Strimaitis and F.R. Robe, 2005: Evaluation of enhancements to the CALPUFF model for offshore and coastal applications. Proceedings of the Tenth International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes. Sissi (Malia), Crete, Greece. 17

- 20 October 2005.

Strimaitis, D.G., J.S. Scire and J.C. Chang, 1998: Evaluation of the CALPUFF Dispersion Model with Two Power Plant Data Sets. Tenth Joint Conference on the Applications of Air Pollution Meteorology, Phoenix, Arizona. American Meteorological Society. 11

- 16 January 1998.References 68 Tanner, R., M. Zheng, K. Lim, J. Schauer, and A. P. McNichol, 2005. Contributions to the Organic Aerosol Fraction in the Tennessee Valley: Seasonal and Urban

- Rural Differences. Paper presented at the AAAR International Specialty Conference, PM: Supersite Program and Related Studies, Atlan ta, 7

- 11 February 2005.

# Protocol for the Application of the CALPUFF Model in Support of the Best Available Retrofit Technology (BART) Regulations – 40 CFR 51.300 and Appendix Y

Bowater Coated and Specialty Papers Division

Catawba, South Carolina

Prepared by:

URS Corporation Morrisville, North Carolina Greenville, South Carolina

March 17, 2006

# Protocol for the Application of the CALPUFF Model for Analyses of Best Available Retrofit Technology (BART)

Prepared for:



## Bowater Coated and Specialty Papers Division Catawba, South Carolina

March 17, 2006

Prepared by: URS Corporation Morrisville, North Carolina Greenville, South CarolinaCALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

March 17, 2006 Page ii 

# Table of Contents

1.0 Introduction and Background ......................................................................................... 1 1.1 Objective of Protocol .............................................................................................. 3 1.2 Facility Location and Relevant Class I Areas......................................................... 4 1.3 Source Impact Evaluation Criteria.......................................................................... 6 1.4 General Overview of the CALPUFF Modeling System......................................... 6
2.0 BART Source Descriptions .............................................................................................. 7 2.1 Unit Specific Source Data....................................................................................... 7 2.2 Tabulated Source Data.......................................................................................... 10
3.0 Geophysical and Meteorological Data .......................................................................... 16

4.0 CALPUFF Modeling Methodology ............................................................................... 16 4.1 Methodology......................................................................................................... 17 4.2 CALMET Model Configura tion and Application................................................. 19 4.3 CALPUFF Model Configura tion and Application ............................................... 19 4.3.1 Model Codes............................................................................................ 19 4.3.2 Domain Definition................................................................................... 19 4.3.3 Model Set-Up .......................................................................................... 21 4.3.4 Emissions Input Development................................................................. 21 4.3.5 Additional CALPUFF Input Information and Settings ........................... 22
5.0 POSTUTIL PROCESSING ........................................................................................... 24

6.0 CALPOST PROCESSING............................................................................................. 24 6.1 Visibility Assessment............................................................................................ 24
7.0 ALTERNATIVE MODELI NG METHODOLOGY ................................................... 27

8.0 REPORTING .................................................................................................................. 33 8.1 Presentation of Modeling Results ......................................................................... 33 8.2 Reporting of CALPUFF Modeling Results .......................................................... 33
9.0 REFERENCES................................................................................................................ 34

Tables 2-1 BART Eligible Emission Units – Stack Parameters (English Units) ................................10 2-2 BART Eligible Emission Units – Stack Parameters (Metric Units)..................................11 2-3 BART Eligible Emission Units – Emission Rates (English Units) ...................................12 2-4 BART Eligible Emission Units – Emission Rates (Metric Units).....................................13 2-5 BART Eligible Emission Units – Sp eciated Emissions (English Units) ...........................14 2-6 BART Eligible Emission Units – Speciated Emissions (Metric Units).............................15CALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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### 7-1. Example of LOS Modeling Results ...................................................................................26
Figures 1-1 Facility Location Relative to Class I Areas .........................................................................4 4-1. BART Modeling Flow Chart .............................................................................................13 4-2. VISTAS CALMET Domains with Proposed CALPUFF Sub-domains............................15 7-1. Example of LOS Modeling Approach ...............................................................................25

Appendices

Appendix A CALPUFF Configuration Appendix B POSTUTIL Screening Configuration Appendix C CALPOST Sc reening Configuration Appendix D Class I Receptors in Lambert Conformal Coordinates Appendix E North Dakota Background Procedure Appendix F EPA Natural Background Values Appendix G EPA Monthly F(RH) Values Appendix H Paper Describing the Deciview Metric Appendix I CALPUFF Plots Showing Plume DistributionCALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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## 1.0 Introduction and Background

Bowater Coated and Specialty Papers Division (Bowater) Pulp and Paper mill has retained URS Corporation (URS) to prepare this Protocol for the Applicatio n of the CALPUFF Model in Support of the Best Available Retrofit T echnology (BART) Regulation – 40 CFR 51.300 and Appendix Y . This protocol was prepar ed at the request of the S outh Carolina Department of Health and Environmental Contro l (SCDHEC) in order to document the modeling procedures that will be used to perform CALPUFF disp ersion modeling in support of the EPA BART regulation. Bowater is proposi ng to perform modeling analyses to determine whether air emissions from potentially BART eligible em ission units at the Bowater Catawba, South Carolina Pulp and Paper mill cause or contribute to regional haze in any Class I area within 300 kilometers of the mill as defined by 40 CFR 51, Appendix Y. The following provides a regulatory background of the BART regulation and a summary of the modeling procedures detailed in this protocol.

The Clean Air Act established goals for visibility in many national parks and wilderness areas. Through the 1977 amendments to the Clean Air Act, Congress set a national goal for visibility as “the prevention of any future, and the remedying of any existing, impairment of visibility in mandatory Class I Federal areas which impairment results from manmade air pollution.” The Amendments required EPA to issue regulations to assure “reasonable progress” toward meeting the national goal.

In 1980, EPA promulgated regulations to address the visibility issues that is “reasonably attributable” to a single source or small gr oup of sources. In 1988, the States, Federal Land Managers (e.g., National Park Service, U.S. Fore st Service, U.S. Fish and Wildlife Service, Bureau of Land Management), and EPA began mon itoring of fine particle concentrations and visibility in 30 national parks and wi lderness areas across the country.

The Clean Air Act Amendments of 1990 required EPA to take regulatory action on regional haze and they proposed the Regional Haze Regulations in July 1997 in conjunction with issuing new national ambient air quality standards for fine particulate matter.

On July 1, 1999, EPA promulgated the final Regi onal Haze Regulation. The final Regional Haze Regulation calls for state and fede ral agencies to work together to improve visibility in 156 national parks and wilderness areas in the Un ited States by developing and implementing longterm air quality protection plans to reduce the pollution that causes visibility impairment in these protected areas.

The Regional Haze regulation provides States fl exibility in determin ing reasonable progress goals for protected areas by conducting certain an alyses to ensure that they consider the possibility of setting an ambitious reasonable progress goal, one that is aimed at reaching natural background conditions by the year 2064. The regulat ion requires States to establish goals for each affected area to (1) improve visibility on the haziest days, and (2) ensure no degradation occurs on the clearest days over the period of each implementation plan.CALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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### The Regional Haze regulation also requires States to develop long-term strategies including enforceable measures designed to meet reasonabl e progress goals. The first long-term strategy will cover 10 to 15 years, with reassessment a nd revision of those goals and strategies in 2018 and every 10 years thereafter. State’s strategi es will address their cont ribution to visibility problems in Class I areas both w ithin and outside the State.

One of the principal elements of the visibili ty protection provisions of the Clean Air Act addresses installation of best av ailable retrofit technology (BART) for certain existing sources. “BART-eligible” sources are th ose sources built between 1962 a nd 1977 that have the potential to emit more than 250 tons per year of one or more visibility-impairing compounds including sulfur dioxide (SO 2 

### ), nitrogen oxides (NO x 

### ), particulate matter (P M), and volatile organic compounds (VOCs), and that fall within 26 indus trial source categories (i ncluding Kraft pulp and paper manufacturing).

Soon after the Regional Haze Regulation was fina lized, several parties filed petitions to challenge the rule with the U.S. Court of App eals for the D.C. Circuit. In April 2004, EPA Administrator Mike Leavitt signed a propos ed amendment to the 1999 Regional Haze Regulation. The proposed rule satisfied the te rms of a May 2002 ruling by the U.S. Court of Appeals for the D.C. Circuit, which vacated pa rts of the BART provisions of the 1999 Regional Haze Regulation ( American Corn Growers et. al. v. EPA , 291 F. 3d 1 (D.C. cir. 2002).

The rule requires states to consider the visibi lity impacts of an indi vidual facility when determining whether they have to install controls, and what thos e controls would be. The final BART implementation and guidance rule (40 CF R Part 51, Appendix Y) was published on July 6, 2005 and it allows for a BART evaluation for a ny BART-eligible source that ‘‘emits any air pollutant which may reasonably be anticipated to cause or contribute to any impairment of visibility” in any mandatory Class I federal area.

Pursuant to the rule, States have the option of exempting a BART-eligible source from the BART requirements based on dispersion mode ling demonstrating that the source cannot reasonably be anticipated to cause or contribute to visibility impairment in a Class I area. Regional Planning Organizations (RPOs), such as the Visibility Improvement State and Tribal Association of the Southeast (V ISTAS) have prepared guidance for performing the dispersion modeling analyses. According to 40 CFR Pa rt 51, Appendix Y, a BART-eligible source is considered to “contribute” to visibility impa irment in a Class I area if the modeled 98 th 

### percentile change in deciviews (dv) is equal to or grea ter than the “contribution threshold.” Any BARTeligible source determined to caus e or contribute to visibility impa irment in any Class I area is subject to a BART evaluation.

The Application of the CALPUFF Model for Analys es of Best Available Retrofit Technology (BART), December 22, 2005, was prepared by the VISTAS RPO to provide some common guidance for performing conservative BART exem ption and determination modeling evaluations as allowed by the regulation.CALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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### It should be noted that the final BART rule de fines a “contribution threshold” of 0.5 dv as the value where a modeled BART eligible source may “contribute” to visi bility impairment and the threshold to determine whether a single source “causes” visibility impairment is set at a 1.0 dv change from natural conditions (background visual range) over a 24-hour averaging period in the final BART rule (70 FR 39118). An approximate 1.0 deciview change was defined by Pitchford and Malm (1992) as a “just noticeable change” to the observer when the background visual range equals the line of sight of the ob server. According to L. Willard Richards in “Use of the Deciview Haze Index as an Indicator for Regional Haze” if a shorter line of sight distance than the background visual range (natural conditions) is us ed in performing the calculations then a higher extinction value, or deciview, is needed to cause a “just noticeable change.” In other words, when the line of sight is less than the background visual range, then it would require a higher deciview value in order to be a “just no ticeable change.” However, this protocol uses a conservative modeling approach as defined by the VISTAS protocol by compar ing the extinction changes from a single source at Class I area receptors only (not along the line of sight) to the background visual range (natural conditions) in order to compare to the 0.5 deciview change thres hold. We are also proposing in this protocol to use the refined line-of -sight modeling approach if needed in order to better determine the actual impact that a single source would have on natural visibility conditions in a Class I area (Section 7.0).



## 1.1 Objective of Protocol

The objective of this BART mode ling protocol is to obtain appr oval, from the SCDHEC, for the modeling procedures that will be used to s upport the BART exemption modeling for the Bowater Pulp and Paper mill located in Catawba, South Carolina. This protocol incorporates guidance developed by VISTAS for conducting a BART modeling analysis and, where needed, expands on that final modeling guidance.

The VISTAS States have accepted EPA’s guidan ce to use the CALPUFF modeling system to comply with the BART modeling requirements of the regional haze rule. This protocol is intended to satisfy the BART requirement thata BART-eligible source must submit a sitespecific modeling protocol to the State for re view and approval prior to performing CALPUFF modeling. Many of the methods and procedures recommended in the VISTAS common modeling protocol will be followed for modeling and the results will be presented in the standard recommended format.

Bowater is also proposing to conduct more re fined CALPUFF modeling to better quantify the estimated impact of the mill’s potentially BART-eligible units on regional haze as necessary. The refined modeling procedures, including examples, and the justif ication for these procedures are presented in this protocol. Bowater requ ests that the SCDHEC carefully evaluate and approve these modeling procedures or provide detailed comments to Bowater of why these procedures are not acceptable. Bowater and our environmental consultant, URS Corporation is willing to discuss these procedur es in greater detail with th e SCDHEC and supply additional material demonstrating why thes e modeling procedures are appr opriate for calculating changes in regional haze from individual point sources.CALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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### The remainder of this document describes the CALPUFF modeling system and the application of CALPUFF to two situations: 1) Air Quality modeling to determine whethe r a potentially BART-e ligible source is exempt from a BART control technology evaluation. 2) Air Quality modeling of emissions from a ll BART-eligible sources that are required to perform a BART control technology eval uation for the purpose of determining the relative benefits of potentia l alternative control options as it pertains to reducing regional haze in Class I areas.


## 1.2 Facility Location and Relevant Class I Areas

The Bowater Catawba, South Carolina pulp and paper mill is located at 5300 Cureton Ferry Road in Catawba, South Carolina. The Universal Transverse Merc ator (UTM) coordinates, in kilometers (km), for the mill are Zone 17, 510.014 East and 3855.589 North. The approximate Lambert Conformal Conic (LCC) coor dinates are 1458.14516 km East and -441.09012 km North.

There are several Class I areas near the Bowate r mill: Linville Gorge, Shining Rock, and Joyce Kilmer-Slickrock Wilderness Areas; Great Smok y Mountains National Park; and Cape Romain National Wildlife Refuge. Figure 1-1 displays th e location of the mill and the Class I areas. Linville Gorge, Shining Rock, and Joyce K ilmer-Slickrock Wilderness Areas are located approximately 140 km, 180 km, and 280 km west-northwest of the mill, respectively. Great Smoky Mountains National Park is located approximately 216 km west-northwest of the mill at its’ nearest boundary. Cape Romain National W ildlife Refuge is located approximately 230 km southeast of the mill.CALPUFF Modeling ProtocolBART Exemption Modeling AnalysisBowater Coated and Specialty Papers DivisionMarch 17, 2006Page 5-86 -85 -84 -83 -82 -81 -80 -79 -78 -77 -76Longitude32333435363738
LatitudeLinville GorgeGreat Smoky Mts.Joyce Kilmer SlickrockShining RockCape RomainBowaterFILE NO.15266083FIG. NO.SCALE:DRAWN BY:SCLDATE:2/10/061-1AS SHOWNCHECKED BY:DATE:Bowater – Catawba, SCFacility LocationRelative to Class IAreasCALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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## 1.3 Source Impact Evaluation Criteria

To assess whether the Bowater Mill is exempt from performing a BA RT control technology evaluation, a two-tiered modeling approach is being pr oposed. For the initial exemption modeling, the CALPUFF model w ill be used with 12-km grid CALMET data in a screening mode. If the initial modeling results show h aze impacts less than the recommended visibility threshold value of 0.5 dv, no further modeling woul d be necessary. If the initial 12-km grid modeling results show haze impacts greater than the recommended visibi lity threshold of 0.5 dv, then a finer resolution 4-km CALMET meteorol ogical grid will be used in the CALPUFF modeling. If the finer resolution modeling results show haze impacts less than the visibility threshold on 0.5 dv, then no furthe r modeling would be necessary. All modeling results will be presented in detail in a report th at will be submitted to the SCDHEC for review and approval.

If the initial 12-km grid m odel greatly exceeds the recommended threshold or finer grid modeling results exceed the recommended thre shold then BART determination CALPUFF modeling will be conducted using finer resoluti on CALMET data. It is expected that the SCDHEC will accept EPA guidance that the threshold value to establish that a source contributes to visibility im pairment is 0.5 deciview.

Since the EPA BART guidance rule specifically st ates that modeling resu lts should be based on the 98 th 

### percentile value, we are proposing that the 98 th 

### percentile be applied to all CALPUFF modeling results. Thus, for the 12-km initial modeling exemption analysis, the 24-hour 98 th 

### percentile value across all receptors in the Cla ss I area will be compared to the threshold value of 0.5 dv. If the 24-hour 98 th 

### percentile value is below 0.5 dv, then Bowater will be exempt from performing a BART evaluation. If the 24-hour 98 th 

### percentile value is greater than 0.5 dv, then Bowater may choose to perform finer grid modeling for exemption purposes or use the alternative modeling approach es described in Section 7 of this protocol.

Bowater will use the 12-km modeling results to focus finer grid modeling for exemption purposes on only those Class I areas where impact s greater than 0.5 dv are projected in the 12- km modeling.

For finer grid (4 km or less) analyses, the 98 th 

### percentile value for the 24-hour average will also be used.



## 1.4 General Overview of the CALPUFF Modeling System

The CALPUFF modeling system consists of four main processors: CALMET, CALPUFF, POSTUTIL and CALPOST. CALMET is the meteor ological model that generates hourly threedimensional meteorological fields of variable s such as wind and temperature. CALPUFF simulates the transport, dispersion, and transf ormation of compounds emitted from a source and calculates hourly concentration values for visibility impairing compounds at each receptor located in the modeling domain. POSTUTIL can perform many post processing tasks on the CALPUFF output data file. CALP OST calculates time-averaged concentration values from theCALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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### CALPUFF predictions and perfor ms regional haze calculations like those described in the Section 6.1 of this protocol. 

## 2.0 BART Source Descriptions

The Bowater Catawba Mill is located in Catawba, South Carolina, along the Catawba River. The primary activities at Catawba Mill are pulp a nd paper production (SIC code 2611, NAICS Codes 32211 and 322121). The mill began operations in 1959. Primary operations at the mill include: kraft pulping, bleaching, and associated chemi cal recovery operations; thermo-mechanical pulping and associated bleaching; coated pape r production and market pulp drying; wood and wood waste receiving, storing, and handling; wa stewater treatment operations; process steam generation; and addition al operations and equipment necessary to support these operations.


## 2.1 Unit Specific Source Data

According to VISTAS, the emission estimates used in the CALPUFF model are intended to reflect steady-state operating conditions du ring periods of high capacity util ization. As suggested in the VISTAS common protocol, modeled emissions shoul d not reflect periods of start-up, shutdown, and malfunction since this would produce higher than normal impact s on visibility impairment. VISTAS recommends that modeling be based on th e 24-hour average actual emission rate from the highest emitting day of the meteorological peri od modeled. VISTAS and the SCDHEC will accept emission estimates based on maximum actual emission rates during the most recent 3 to 5 year period. VISTAS has supplied the followi ng prioritization for developing the emission estimates:

• Continuous Emissions Monitoring (CEM) data;

• Facility emissions tests;

• Emissions factors;

• Permit limits; or lastly,

• Potential to emit.

The Catawba Mill has developed emission estim ates based on source testing and accepted emission factors used for routine annual emissi ons reporting. Should Bowa ter elect to develop emission rates for future subject-to-BART mode ling, case-by- case procedures will be developed in consultation with the SCDHEC . In general, the following emission rates are considered acceptable:

• Short-term (24-hours) allowable emission ra tes (e.g., emission rates calculated using the maximum rated capacity of the source).

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

• Peak 24-hour actual emission rates (or calculate d emission rates) from the most recent 3 to 5 years of operation that account for “h igh capacity utiliz ation” during normal operating conditions and fuel/material flexibil ity allowed under a source’s permit. InCALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

March 17, 2006 Page 8 

### situations where a unit is allowed to use more than one fuel, the fuel resulting in the highest emission rates will be used for the modeling.

Short-term emission rates (24-hours) for SO 2 

### , NO x 

### , H 2 

### SO 4 

### mist and PM 10 

### (including condensible and filterable direct PM 10 

### ) will be modeled since visibility ch anges are calculated for a 24-hour averaging period. All BART-eligible emission units at the mill will be modeled together in the CALPUFF model.

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

• No. 2 Recovery Furnace (RF2): This r ecovery furnace primarily fires spent kraft cooking liquor (black liquor) at approximately 68% solids. No. 6 fuel oil is also used during startup and as auxili ary fuel. The No. 2 recove ry furnace production capacity is 1,129 tons of black liquor solids per day, and 360 MM Btu per hour when firing 2.1% sulfur No. 6 fuel oil. The unit is controlled by an electr ostatic precipitator.

• No. 2 Smelt Dissolving Tank (ST2): This smelt dissolving tank processes the molten smelt from the bottom of the No. 2 recove ry furnace. The No. 2 smelt dissolving tank production capacity is 1,129 tons of black liquor solids per day. The No. 2 smelt dissolving tank does not burn any fuels, but emissions are evolved from the smelt when being processed. This unit is controlled by a wet scrubber.

• No. 2 Evaporator Set (vents to NCG1 or NCG2): This evaporator set concentrates spent kraft cooking liquor prior to firing in the No. 2 recovery furnace. This unit is controlled by the No. 1 or No. 2 co mbination boilers, which generates SO 2 

### emissions as a result of burning non condensable ga ses (NCG’s) from the evaporator bodies that contain total reduced sulfur (TRS) compounds.

• No. 2 Combination Boiler (CB2): This multi-fuel boiler primarily fires biomass (bark, wood, fines, etc.), along with No. 6 fuel oil, natural gas, and tire derived fuel (TDF). The No. 2 Combination Boiler heat input capacity 498 MM Btu per hour firing biomass, 700 MM Btu per hour firing 2.1% sulfur No. 6 fuel oil, and 720 MM Btu per hour firing natural gas. The boiler is also capable of burning up to 1.5 tons per hour of TDF.

• No. 3 coated paper machine (PM3). This paper machine was originally built as a newsprint machine, and was converted to coated paper production in August 2003. The coated paper production capacity is 1,049 air dried short tons per day. The coating drying section installed in 2003 burns natural gas as the primary fuel, with propane and kerosene as back-up fuels. Th e design heat input capacity to the coating section is 48 MM Btu per hour, however no fuels were burned prior to August 2003.

• Chlorine Dioxide Plant (CLO2): This unit generates chlorine di oxide for use in the kraft bleaching system by r eacting sodium chlorate, methanol, and sulfuring acid. There are no fuels burned in this unit, and only nominal emissions of VOC. The production capacity is 33 tons per day of ClO 2 

### . The unit is contro lled using a series of two wet scrubbers.CALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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### The following BART-eligible units do not emit any significant levels of SO 2 

### , NO x 

### , H 2 

### SO 4 

### mist, or PM 10 

### and will not be modeled.

• Chlorine Dioxide Plant

Table 2-1 and 2-2 provide stack parameters for all BART-eligible sources at the facility in English and metric units, respectively. Tables 2- 3 and 2-4 provide emissi on rates for all BARTeligible sources in English and metric units, resp ectively. Tables 2-5 and 2-6 provide particulate speciation information for all BART eligible so urces in English and metric units, respectively.CALPUFF Modeling ProtocolBART Exemption Modeling AnalysisBowater Coated and Specialty Papers DivisionMarch 17, 2006Page 102.2Tabulated Source DataTABLE 2-1BART ELIGIBLE EMISSION UNITS – STACK PARAMETERS (English Units)STACK SPECIFIC DETAILSLambert ConformalConicU.S.G.SEquipment Description Stack ID UTM East (km) UTM North (km) UTM East (km) UTM North (km) UTM Zone DATUM Stack Height (feet) Base Elevation (feet above MSL) Stack Diameter or Equivalent (feet) Gas Exit Velocity (ft/sec) Stack Gas Exit Temp ( 0 F) Stack Orientation (Horizontal or Vertical) Rain Cap Present?Chlorine Dioxide Plant CLO2 1459.0480 -440.6843510976 3855638 17 NAD 27 81 532 1 34 140 vertical noNo. 3 Paper Machine PM3 1457.7943 -441.0181 509677 3855529 17 NAD 27 i 532 i i i i iNo. 2 Smelt Tank ST2 1458.1175 -440.9312 510012 3855558 17 NAD 27 218 532 6 34.4 170.4 vertical noNo.2 Evaporator Set NCG1c1458.1131 -441.0314 509990 3855460 17 NAD 27 228 532 10 47.2 363.8 vertical noNo. 2 Recovery Furnace RF2 1458.1776 -440.9621 510066 3855517 17 NAD 27 195 532 7 99.1 364.7 vertical noNo. 2 Combination Boiler CB2 1458.0964 -441.0456 509971 3855449 17 NAD 27 228 532 10 62.3 405.2 vertical noNOTES:cNo. 2 evaporator set is controlled using No.1 and No. 2 combination boilers. CB1 stack parameters shown.dHighest average 24-hour emissions shown, except NOXfrom CB2 is maximum emissions burning natural gas at capacity (720 MM Btu/hr).eST2, RF2, and CB2 AP-42 PM6size distribution multiplied by ratio of NCASI TB 884 PM10to AP-42 PM10.fST2, RF2, and CB2 AP-42 PM1.25, PM1.0, and PM0.625size distributions multiplied by ratio of NCASI TB 884 PM2.5to AP-42 PM2.5.gBased on emission factors for PM10emissions.hBased on representative CMAQ speciation data.IEmissions through multiple stacks, historically modelled as area source 80 feet high, 50 feet wide, and 100 feet long.CALPUFF Modeling ProtocolBART Exemption Modeling AnalysisBowater Coated and Specialty Papers DivisionMarch 17, 2006Page 11TABLE 2-2BART ELIGIBLE EMISSION UNITS – STACK PARAMETERS (Metric Units)STACK SPECIFIC DETAILSLambert ConformalConicU.S.G.SEquipment Description Stack ID UTM East (km) UTM North (km) UTM East (km) UTM North (km) UTM Zone DATUM Stack Height (meters) Base Elevation (meters above MSL) Stack Diameter or Equivalent (meters) Gas Exit Velocity (m/sec) Stack Gas Exit Temp (K) Stack Orientation (Horizontal or Vertical) Rain Cap Present?Chlorine Dioxide Plant CLO2 1459.0480 -440.6843510976 3855638 17 NAD 27 24.7 162.2 0.3 10.4 333.2 vertical noNo. 3 Paper Machine PM3 1457.7943 -441.0181 509677 3855529 17 NAD 27 i 162.2 i i i i iNo. 2 Smelt Tank ST2 1458.1175 -440.9312 510012 3855558 17 NAD 27 66.4 162.2 1.8 10.5 350.0 vertical noNo.2 Evaporator Set NCG1c1458.1131 -441.0314 509990 3855460 17 NAD 27 69.5 162.2 3.0 14.4 457.5 vertical noNo. 2 Recovery Furnace RF2 1458.1776 -440.9621 510066 3855517 17 NAD 27 59.4 162.2 2.1 30.2 458.0 vertical noNo. 2 Combination Boiler CB2 1458.0964 -441.0456 509971 3855449 17 NAD 27 69.5 162.2 3.0 19.0 480.5 vertical noNOTES:cNo. 2 evaporator set is controlled using No.1 and No. 2 combination boilers. CB1 stack parameters shown.dHighest average 24-hour emissions shown, except NOXfrom CB2 is maximum emissions burning natural gas at capacity (720 MM Btu/hr).eST2, RF2, and CB2 AP-42 PM6size distribution multiplied by ratio of NCASI TB 884 PM10to AP-42 PM10.fST2, RF2, and CB2 AP-42 PM1.25, PM1.0, and PM0.625size distributions multiplied by ratio of NCASI TB 884 PM2.5to AP-42 PM2.5.gBased on emission factors for PM10emissions.hBased on representative CMAQ speciation data.IEmissions through multiple stacks, historically modelled as area source.CALPUFF Modeling ProtocolBART Exemption Modeling AnalysisBowater Coated and Specialty Papers DivisionMarch 17, 2006Page 12TABLE 2-3BART ELIGIBLE EMISSION UNITS – EMISSION RATES (English Units)SHORT TERM EMISSIONS, g/sec(highest 24hr average actual emission ratefor the years 2001-2003)dEquipment DescriptionSO2H2SO4NOXPM10PM2.5NH3VOCChlorine Dioxide Plant nonenonenonenonenonenone0.1No. 3 Paper Machine 0.00.03.40.50.5none15.4No. 2 Smelt Tank0.20.00.96.35.25.51.2No.2 Evaporator Set 49.01.5nonenonenonenone0.3No. 2 Recovery Furnace 627.727.989.515.011.3none6.4No. 2 Combination Boiler 969.343.1201.646.123.6none15.9NOTES:cNo. 2 evaporator set is controlled using No.1 and No. 2 combination boilers. CB1 stack parameters shown.dHighest average 24-hour emissionsshown, except NOx from CB2 is maximum emissions burning natural gas at capacity (720 MM Btu/hr).eST2, RF2, and CB2 AP-42 PM6size distribution multiplied by ratio of NCASI TB 884 PM10to AP-42 PM10.fST2, RF2, and CB2 AP-42 PM1.25, PM1.0, and PM0.625size distributions multiplied by ratio of NCASI TB 884 PM2.5to AP-42 PM2.5.gBased on emission factors for PM10emissions.hBased on representative CMAQ speciation data.IEmissions through multiple stacks, historically modelled as area source 80 feet high, 50 feet wide, and 100 feet long.CALPUFF Modeling ProtocolBART Exemption Modeling AnalysisBowater Coated and Specialty Papers DivisionMarch 17, 2006Page 13TABLE 2-4BART ELIGIBLE EMISSION UNITS – EMISSION RATES (Metric Units)SHORT TERM EMISSIONS, g/sec(highest 24hr average actual emission ratefor the years 2001-2003)dEquipment DescriptionSO2H2SO4NOXPM10PM2.5NH3VOCChlorine Dioxide Plant nonenonenonenonenonenone0.0No. 3 Paper Machine 0.00.00.40.10.1none1.9No. 2 Smelt Tank0.00.00.10.80.70.70.1No.2 Evaporator Set 6.20.2nonenonenonenone0.0No. 2 Recovery Furnace 79.13.511.31.91.4none0.8No. 2 Combination Boiler 122.15.425.45.83.0none2.0NOTES:cNo. 2 evaporator set is controlled using No.1 and No. 2 combination boilers. CB1 stack parameters shown.dHighest average 24-hour emissionsshown, except NOx from CB2 is maximum emissions burning natural gas at capacity (720 MM Btu/hr).eST2, RF2, and CB2 AP-42 PM6size distribution multiplied by ratio of NCASI TB 884 PM10to AP-42 PM10.fST2, RF2, and CB2 AP-42 PM1.25, PM1.0, and PM0.625size distributions multiplied by ratio of NCASI TB 884 PM2.5to AP-42 PM2.5.gBased on emission factors for PM10emissions.hBased on representative CMAQ speciation data.IEmissions through multiple stacks, historically modelled as area source 80 feet high, 50 feet wide, and 100 feet long.CALPUFF Modeling ProtocolBART Exemption Modeling AnalysisBowater Coated and Specialty Papers DivisionMarch 17, 2006Page 14TABLE 2-5BART ELIGIBLE EMISSION UNITS – SPECIATED EMISSIONS (English Units)Speciated Emissions for BART CALPUFF Modeling AnalysesParticle Size Distribution Emission Rate SummaryParticle Speciation(%)Equipment Description Stack ID Geometric Mass Mean Diameter 6-10 um (lbs/hr) 2.5-6 um e (lbs/hr) 1.25-2.5 um (lbs/hr) 1.0-1.25 um f (lbs/hr) 0.625-1.0 um f (lbs/hr) 0.5-0.625 um f (lbs/hr) Filterable PM10 g Condensible PM10 g Elemental Carbon h

fraction of fine (PM2.5) (filterable) Organic Condensible h

Inorganic Condensible hChlorine Dioxide Plant CLO2 no data n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/aNo. 3 Paper Machine PM3 no data 0.5 0.5 0.5 0.5 0.5 0.5 86.8 13.2 0.0 60.0 20.6No. 2 Smelt Tank ST2 no data 6.3 6.3 5.2 4.4 3.9 3.2 81.0 19.0 1.5 6.3 55.2No.2 Evaporator Set NCG1cno data n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/aNo. 2 Recovery Furnace RF2 no data 15.0 14.5 11.3 9.3 8.2 6.6 80.7 19.3 1.5 6.3 55.2No. 2 Combination Boiler CB2 no data 46.1 43.6 23.6 22.6 21.9 20.3 81.9 18.1 14.0 39.0 47.0NOTES:cNo. 2 evaporator set is controlled using No.1 and No. 2 combination boilers. CB1 stack parameters shown.dHighest average 24-hour emissionsshown, except NOx from CB2 is maximum emissions burning natural gas at capacity (720 MM Btu/hr).eST2, RF2, and CB2 AP-42 PM6size distribution multiplied by ratio of NCASI TB 884 PM10to AP-42 PM10.fST2, RF2, and CB2 AP-42 PM1.25, PM1.0, and PM0.625size distributions multiplied by ratio of NCASI TB 884 PM2.5to AP-42 PM2.5.gBased on emission factors for PM10emissions.hBased on representative CMAQ speciation data.IEmissions through multiple stacks, historically modelled as area source 80 feet high, 50 feet wide, and 100 feet long.CALPUFF Modeling ProtocolBART Exemption Modeling AnalysisBowater Coated and Specialty Papers DivisionMarch 17, 2006Page 15TABLE 2-6BART ELIGIBLE EMISSION UNITS – SPECIATED EMISSIONS (Metric Units)Speciated Emissions for BART CALPUFF Modeling AnalysesParticle Size Distribution Emission Rate SummaryParticle Speciation(%)Equipment Description Stack ID Geometric Mass Mean Diameter 6-10 um (g/sec) 2.5-6 um e (g/sec) 1.25-2.5 um (g/sec) 1.0-1.25 um f (g/sec) 0.625-1.0 um f (g/sec) 0.5-0.625 um f (g/sec) Filterable PM10 g Condensible PM10 g Elemental Carbon h

fraction of fine (PM2.5) (filterable) Organic Condensible h

Inorganic Condensible hChlorine Dioxide Plant CLO2 no data n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/aNo. 3 Paper Machine PM3 no data 0.1 0.1 0.1 0.1 0.1 0.1 86.8 13.2 0.0 60.0 20.6No. 2 Smelt Tank ST2 no data 0.8 0.8 0.7 0.6 0.5 0.4 81.0 19.0 1.5 6.3 55.2No.2 Evaporator Set NCG1cno data n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/aNo. 2 Recovery Furnace RF2 no data 1.9 1.8 1.4 1.2 1.0 0.8 80.7 19.3 1.5 6.3 55.2No. 2 Combination Boiler CB2 no data 5.8 5.5 3.0 2.9 2.8 2.6 81.9 18.1 14.0 39.0 47.0NOTES:cNo. 2 evaporator set is controlled using No.1 and No. 2 combination boilers. CB1 stack parameters shown.dHighest average 24-hour emissionsshown, except NOx from CB2 is maximum emissions burning natural gas at capacity (720 MM Btu/hr).eST2, RF2, and CB2 AP-42 PM6size distribution multiplied by ratio of NCASI TB 884 PM10to AP-42 PM10.fST2, RF2, and CB2 AP-42 PM1.25, PM1.0, and PM0.625size distributions multiplied by ratio of NCASI TB 884 PM2.5to AP-42 PM2.5.gBased on emission factors for PM10emissions.hBased on representative CMAQ speciation data.IEmissions through multiple stacks, historically modelled as area source 80 feet high, 50 feet wide, and 100 feet long.CALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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## 3.0 Geophysical a nd Meteorological Data

URS will use the geophysical and meteorological data developed by VISTAS for initial 12-km and 4-km BART exemption modeling. The devel opment of this information is discussed in detail in the VISTAS co mmon protocol.

In the event that source-specific fine-scale (<4- km grid) modeling is need ed, a revised protocol would be submitted to the SCDHEC for review a nd comment. Since the scope of this modeling cannot be determined, the exact configuration of any refined m odeling domain also cannot be determined. Since domain placement and grid si ze selection cannot be de termined until either 12-km or 4-km grid modeling has been complete d a detailed discussion cannot be presented at this time describing a more refined modeling dom ain and the interactions of terrain, land use, MM5 data, observational data, monitoring data a nd other parameters. In the event a more refined modeling domain is developed a revised prot ocol will be prepared and submitted to the SCDHEC for review and comment using the follo wing outline to discuss the use of geophysical and meteorological data.

3.1 Modeling Domain and Terrain 3.2 Land Use 3.3 Meteorological Data Base 3.3.1 MM5 Simulations 3.3.2 Measurements and Observations 3.4 Air Quality Data Base 3.4.1 Ozone Concentrations-Measured 3.4.2 Ammonia Concentrations – Measured 3.4.3 Concentration of Other Pollutants – Measured 3.5 Natural Conditions at Class I Areas


## 4.0 CALPUFF Modeling Methodology

Initial CALPUFF modeling will be performed using a screening level approach in order to efficiently and conservatively determine whet her the Bowater Mill can be exempted from a BART evaluation. The screening method described in this section will he lp identify specific Class I areas that might be most affected by emissions from BART eligible emission units located at the Bowater Mill. It will also supply useful in formation on whether performing a more time consuming and refined “source-specific” analysis would be beneficial. Should source-specific modeling become necessary, this information will assist URS in tailoring the modeling domain to focus on the areas of greatest concerns.

CALPUFF modeling will be performed using a standard set of default meteorological, air quality and dispersion conditions that have been deve loped by VISTAS for a 12- and 4-km gridded CALMET domain. These data were developed to be consistent with reco mmendations developed by the Interagency Workgroup on Air Quality Modeling (IWAQM, 1998) and FLAG (2000).CALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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### As mentioned earlier, the results from the CA LPUFF screening analysis has a high degree of conservatism (i.e., systematic tendency to over-pre dict visibility impacts) as compared to a source-specific methodology. Therefore, predicte d impacts on visibility impairment will be overstated by using these screen ing modeling methodologies.


## 4.1 Methodology

The screening level methodology will use th e CALPUFF model with three years of meteorological data with the standard complime nt of model algorithms invoked and will use the most conservative of all the c onditions likely to be examined for the source in question. For example, there are many factors that influen ce the contribution of a source beyond just the distance to the Class I area. The frequency of winds transporting the compounds toward the Class I area may often be important to include fo r a reliable screening analysis. Also, a more distant Class I area downwind in the predomin ant wind direction from a source may receive a higher visibility impact than a closer Class I area that is infrequently downwind of the source. Further, there may be correlations between winds from certain directions and meteorological conditions conducive for higher visibility impacts. Such effects and re lationships will be addressed in the sc reening approach.

If initial screening level 12- km CALPUFF modeling is too c onservative, a less conservative modeling approach (finer grid) can and will be performed by URS using a standard set of 4-km gridded CALMET data developed by VISTAS. URS will use of the 98 th 

### percentile impacts when using the 12 and 4-km data as recomme nded under 40 CFR 51, Appendix Y. We may also develop even more refined gridded data (< 4 km) depending on the results from standard 4-km grid modeling. All modeling will be based on using a 98 th 

### percentile value when comparing to thresholds. As discussed in Section 3.0 a deta iled site-specific protocol would need to be developed and approved before pr oceeding with this step.

The regional haze impacts at each Class I area will be calculated from the daily visibility values for each receptor by determining the change in deciviews compared against natural visibility conditions. EPA’s “Guidance for Estimating Na tural Visibility Conditions Under the Regional Haze Rule,” EPA-454/B03-005 (September 2003) lists recommended natural visibility conditions. To determine whether Bowater may r easonably be anticipated to cause or contribute to visibility impairment at a nearby Class I area, the impacts predicted by CALPUFF will be compared against the pertinent natural visibi lity background and the threshold that has been selected. URS is also proposing refinements to the natural visibility background values as discussed in Section 7.

Figure 4-1 presents the BART modeling pr ocess flow chart that will be followed.CALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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FIG. NO. SCALE: DRAWN BY: SCL DATE: SCL
AS SHOWN CHECKED BY: DATE: 

### BART Modeling Flow Chart

4-1CALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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## 4.2 CALMET Model Configuration and Application

Sections 4.3.2 and 4.4.2 within the VISTAS comm on protocol discuss in detail the model configurations used to generate the common CALMET meteorological files for modeling BART eligible sources. The configur ation is reported to follow th e IWAQM recommendations (EPA, 1998, Appendix A), except as noted in the protoc ol. For CALPUFF screening and initial fine grid assessments, there is no need to comp ile CALMET inputs, run the CALMET model or evaluate the outputs.

The model-ready meteorological data sets have been developed by VISTAS for one large 36 (12)-km regional domain and five smaller sub-regional domains. The Bowater Catawba Mill is located in sub-regional domain number 5 depicted in the VISTAS common protocol. Figure 4-2 displays the configuration of the regional domain and the potential location of a smaller CALPUFF modeling domain.


## 4.3 CALPUFF Model Configuration and Application

4.3.1 Model Codes

Bowater will use CALPUFF Version 5.8 and CALMET Version 5.7. These versions contain enhancements funded by the Minerals Management Service (MMS) and VISTAS. This version includes CALMET, CALPUFF, CALPOST, CALS UM, and POSTUTIL as well as CALVIEW and will be obtained from the CALPUFF website.

Should these recommended versions not be availa ble in time for final modeling then the latest ‘beta’ versions of the CALPUFF mode ling system (CALPUFF ver 5.711a, Level 040716, POSTUTIL ver 1.4, Level 040818, CALPOST ver 5.51, Level 030709) will be used. It should be noted that this model is not the EPA guide line codes but rather upda ted versions containing science improvements and bug fixes. (The guide line CALPUFF code is ver 5.7, level 030402). This substitution results from EPA phasing out the legacy Pasquill-Gifford (P-G) dispersion parameters with the introduction of AERMOD as a new guideline model.

CALPUFF employs the AERMOD turbulence-based dispersion coefficients and probability density function (pdf) dispersion methods scheme instead of P-G. The appropriate model codes will be obtained and used. The sequence of model processors for all modeling is CALPUFF, POSTUTIL and finally CALPOST. CALMET and associated preprocessors are not discussed since VISTAS performed these model runs.

4.3.2 Domain Definition

The meteorological modeling data sets cover three contiguous years (2001, 2002, and 2003) and were resolved to a 12 and 4 km horizontal resolution grid usin g MM5 data. Details of the modeling domains and the meteorological databases for 2001, 2002, and 2003 are discussed in detail in the VISTAS common protocol.CALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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### 200 400 600 800 1000 1200 1400 1600 1800 2000
LCC East ( km ) -1600 -1400 -1200 -1000

-800 -600 -400 -200 0 200 400LCC North (km) 

### Mill

Cape Romain Linville Gorge

Shining Rock Great Smoky Mts.

Joyce Kilmer/ Slickrock

FILE NO.

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FIG. NO. SCALE: DRAWN BY: SCL DATE: SCL
AS SHOWN CHECKED BY: DATE: 

### Bowater – Catawba, SC VISTAS CALMET DOMAINS with Proposed

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### Receptor Network and Class I Receptors. Discrete receptor coordinate data for the Federal Class I areas within 300 km of the source were de veloped using the Nationa l Park Service (NPS) Convert Class I Areas (NCC) comp uter program. The receptor elevations provided by the NPS will be used for modeling. All receptors for ea ch Class I area will be included in single CALPUFF simulations. Appendix D contains a listing of the Lamb ert Conformal coordinates for each Class I area with the asso ciated receptors heights.

4.3.3 Model Set-Up

The modeling will use a CALPUFF computational domain that includes all applicable Class I areas within 300 km of the source and will includ e a large buffer around applicable Class I areas and the mill. The size and location of the potential 12- and 4-km CALPUFF computational domains are shown in Figure 4-2.

As depicted in Figure 4-2, the CALPUFF modeling domains are a s ubset of the larger regional CALMET meteorological domain. A smaller CALP UFF domain is being used to reduce the CALPUFF run times. It is expected that the CALPUFF 12-km sub domain will be approximately 900 kilometers in the east/west di rection and 756 kilomete rs in the north/south direction. Using a 12-km grid spacing from the CA LMET files, this would relate to 75 x 63 grid squares. It is expected that any potential CALP UFF 4-km sub domain will be the same size with more grid cells unless one dominant Class I area is identified. Should this occur then a smaller sub-domain maybe developed which only includes the critical Class I area. Appendix A contains a summary of the input options, wh ich will be selected when perf orming CALPUFF modeling.

4.3.4 Emissions Input Development

Stack Parameters. Point source stack parameters requi red for modeling BART-eligible units include: height of the stack opening from ground, inside diameter, exit velocity, exit gas temperature, elevation of ground, location coordi nates of the stack and emission rates.

Stack Emission Rates. Emission rates for the CALPUFF modeling analyses will be developed following EPA’s BART guidance and recent staff r ecommendations. Source terms in the initial modeling will be based on periods of high capacity util ization associated with normal operating conditions. If more accurate short-term data beco me available that reflects the 24-hour average actual emission rate with normal operations from the highest emitting day of the meteorological period it will be used for the modeling. Periods of start-up, shutdown or malfunctions will not be included in the modeling.

The compounds that will be included in the model include SO 2 

### , NO x 

### , H 2 

### SO 4 

### mist, and PM 10 

### (including condensable a nd filterable direct PM 10 

### ). Compounds with emi ssions that are less than the de minimis levels (40 tons per year for SO 2 

### and NO x 

### and 15 tons per year for PM 10 

### ) will be excluded from the modeling.

In cases where a unit may burn more than one fuel , the fuel resulting in the highest short-term emission rates will be used for the modeling.CALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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### Emissions Speciation. Defining an applicable PM speci ation profile for th e highest 24-hour average actual emissions will prove particularly challenging to Bowater. However, Bowater understands that reliable estimates are necessary given the widely varying effects of different types of particulate matter on visibility. For ex ample, 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). Thus, generalized, conservative, or arbitrary assignments of partic ulate emissions to different pollutant categories can have a considerable influence on modeled visibility impacts attr ibutable to a single facility.

For this reason, all PM 10 

### emissions have been initially cate gorized using representative industry and EPA CMAQ speciation profiles to quantify fine particulate ma tter (PMF), coarse particulate matter (PMC), soot or elemental carbon (EC) , organic aerosols (SOA), and sulfate (SO 4 

### ).

Bowater will evaluate the rela tive contribution of a ll visibility impairing compounds including SO 4 

### and NO 3 

### . Should these components by themselves pr event the facility from being exempted then no additional exemption modeling will be conducted at the screening level.

Condensable Emissions . Condensable emissions will be considered primary fine particulate matter. For the screening assessment all condens able mass will be assigned to the < 0.625 μm category. Using this single category maintains c onservatism in the analysis where there may be uncertainty regarding the exact si ze of condensable PM mass.

Size Classification of Primary PM Emissions . When modeling PM size classes, an appropriate “mass mean diameter” will be used that is with in the specified particle size range. Bowater understands that the use of a mass mean diameter equal to the top of the range is inappropriate since it will overestimate PM deposition and possibly underestimate PM concentrations and visibility impacts.

This section discusses the procedures and input assumptions that Bowater will follow in applying the CALPUFF model for BART exemp tion and determination modeling.

4.3.5 Additional CALPUFF Input Information and Settings

CALPUFF Model Options . The model options, parameter setti ngs, and ‘switches’ for exercising CALPUFF for BART modeling are di scussed below. Appendix A c ontains tables that list the recommended configurations for VISTAS BART modeling. The tabl es also identify the default recommendations from the IWAQM Phase 2 Report (EPA, 1998).

Visibility Modeling Domain . The CALPUFF domain will be configured to include the source and all Class I areas within 300 km. An additional large buffer zone will be established in each cardinal direction from the sour ce and class I area. Bowater acknow ledges the fact that VISTAS recommends CALPUFF for a ll source-receptor distances.

Building Downwash . Bowater will include building/stru cture information for point sources subject to plume downwash.CALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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### Puff Dispersion . The EPA (1998) guidance for plume disp ersion modeling will be followed with the important exception that the turbulence-based dispersion coefficients and probability density function (pdf) dispersion methods (i.e., th e AERMOD dispersion method in the CALPUFF ‘beta’ version) will be used instead of the legacy Pasquill-Gifford curves.

Puff Representation . Bowater will use the default inte grated puff sampling methodology in CALPUFF.

Puff Splitting . There is no quantitative evidence that the horizontal and vertical puff-splitting algorithms in CALPUFF yield improved accuracy a nd precision in model estimates of inert or linearly reactive pollutants although conceptually the methods are appealing because they mimic lateral and vertical wind speed a nd direction shears. Therefore puff splitting will not be invoked.

Chemical Mechanism . The MESOPUFF II module will be used for BART modeling. For the aqueous phase conversion of SO 2 

### to sulfate (important when the plume interacts with clouds and fog), the IWAQM defaults will be used, i.e., nighttime SO 2 

### loss rate (RNITE1) is assumed to be 0.2 percent per hour. The nighttime NOx loss rate (RNITE2) and HNO3 formation rate (RNITE3) are both set to 2.0 percent per hour.

Species Modeled . Species to be modeled in CALPUFF include SO 2 

### , SO 4 

### , NO x 

### , NO 3 

### and particulate matter. Initially all PM will be modeled in the < 0.625 μm size category. Bowater will evaluate the relative cont ribution of all visibility im pairing compounds including SO 4 

### and NO 3 

### . Should these components, by themselves, prev ent the facility from being exempted then no additional exemption modeling will be needed or conducted. Once BART determination modeling begins a more detailed investigation into PM speciation and size distribution may begin.

Should initial dv predictions exceed the contri bution threshold by a small amount then absent more detailed speciation and size distribution data, PM will be modeled in five (5) size categories:

> < 0.625 μm, > 0.625-1.0 μm, > 1.0-1.25 μm, > 2.5-6.0 μm, and > 6-10 μm aerodynamic diameters.

Particulate matter emissions by si ze category will be combined wherever possible into the appropriate species for the visibi lity analysis. Thes e species include (a) elemental carbon (EC), (b) fine PM or “soil” (< 2.5 μm in diameter), (c ) coarse PM (between 2.5-10 μm in diameter) and (d) organics, referred to as s econdary organic aerosols in th e CALPOST postprocessor. If source-specific emissions factors are not available, AP-42 factors will be used to estimate the PM speciation for those source sectors for which AP-42 emissions factors have been developed. Otherwise assumptions will need to be pr oposed by Bowater and approved by SCDHEC.

Background Ozone Concentrations . Ozone concentration data for 2001-2003 from ambient AIRS/CASNET/SCDHEC monitors located within th e particular domain being modeled will be used to develop background estimates. Only non-urban ozone stations will be used in theCALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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### OZONE.DAT file. Monthly average ozone back ground values will be computed from daytime average ozone concentrations (6 am to 6 pm average).

Background Ammonia Concentrations . Bowater will use a constant (0.5 ppb) value for ammonia. For each applicable Class I area, CMAQ NH 3 

### data will be used in POSTUTIL to repartition HNO 3 

### and NO 3 

### . Other Background Concentrations . Concentrations of SO 4 

### and TNO 3 

### (HNO 3 

### + NO 3 

### ) from CMAQ 2001-2003 will be used for modeling.



## 5.0 POSTUTIL PROCESSING

POSTUTIL Parameters. User-selected options, para meter settings, and ‘switches’ for exercising POSTUTIL are presented in Appendix B. This a ppendix contains tables that list the proposed screening and default configurations for the BART modeling. The ammonia-limiting method (ALM) in CALPUFF (Escoffier-Czaja and Scire, 2002, 2005) repartitions nitr ic acid and nitrateon a receptor-by-receptor and hour-by-hour basis to account for the models systematic overprediction due to overlapping puffs. Bowater will set the parameter MNIRATE=1 in POSTUTIL to implement this approximate correction in its simplest form. Bowater will use ammonia 

### from CMAQ to define NH3 for each Class I area. Bo water will choose ammonia from either the CMAQ grid cell where the IMPROVE monitor is located or the grid cell of the centroid of the Class I area (the later in the cas e that the IMPROVE monitor is located outside the Class I area or there is no IMPROVE monitor.)



## 6.0 CALPOST PROCESSING

CALPOST Parameters . Appendix C summarizes the CA LPOST post-processor options, parameters, and switches. Tables are pres ented containing the proposed and default configurations for the BART modeling. While all receptors w ill be included in a single CALPUFF simulation, Bowater will calculate the visibility impact s in CALPOST for each Class I area separately using the NDREC P parameter. It specifies the r eceptor range to be processed in CALPOST. Given the importance of the CALPOS T processor to the entire BART visibility estimation a brief overview of how CALPOST calculates visibility impacts is presented in the following section.


## 6.1 Visibility Assessment

The recommended procedure for quantifying visibili ty impacts is described in detail in the VISTAS common protocol. The key point is that the light extinc tion coefficient (bext) 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] + 0.6[Coarse Mass] + 10[EC] + b rayCALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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### The monthly site-specific f(RH) values will be obtained for each mandatory Federal Class I Area from Table A-3 in the EPA (2003) guidance documen t. Then, the haze index (HI), in deciviews, will be calculated in terms of the extinction coefficient via:

HI = 10 ln (b ext 

### /10)

The change in visibility (measured in terms of ‘delta-deciviews’) will then be compared against background conditions. The delta-deciview, .dv, va lue will be calculated from the Catawba Mill’s contribution to extinction, b source 

### , and background extinction, b background 

### , as follows:

dv = 10 ln ({b background 

### + b source 

### }/ b background 

### )

If the dv value is greater than the 0.5 dv threshol d, then Bowater could cont ribute to visibility impairment and maybe ‘subject to BART’ controls . If not, Bowater will be BART-exempt.

Visibility Impacts from BART-Eligible Sources

Visibility Impact Method. CALPOST will be r un initially using Method 6 (MVISBK=6) for calculating extinction. That is, monthly f(RH) adjust ment factors will be a pplied directly to the background and modeled sulfate and nitrate conc entrations, as recommended in the BART guidelines. Note that the RHMAX parameter (the maximum relative humidity factor used in the particle growth equation) is not used when Met hod 6 is selected. Similarly, the relative humidity adjustment factor (f(RH)) curves in CA LPOST (e.g., IWAQM growth curve and the 1996 IMPROVE curve) are not used wh en MVISBK is equal to 6.

Monthly average Class I area-specific relative humidity values will be employed in the extinction analysis (EPA, 2003, Table A-3) . Species to be considered include SO 4 

### , NO 3 

### , EC, SOA (i.e., condensable or ganic emissions), soil, and coarse PM. With Method 6, background extinction coefficients are comput ed from EPA (2003) monthly estimates of concentrations of ammonium sulfate (BKSO4), a mmonium nitrate (BKNO3), coarse particulates (BKPMC), organic carbon (BKOC), soil (BKSOIL), and elemental carbon (BKEC). Values for these coefficients are listed in CALPOST input group 2 contained in Appendix C. In screening analyses, the extinction due to Rayleigh scattering (i.e., the scattering of li ght by natural particles much smaller than the wavelength of the light) will be set to 10 Mm -1 

### (BEXTRAY = 10.0) for all modeled Class I areas.

Natural Background Light Extinction. The A ppendix Y BART guidance recommends that visibility impacts should be evaluated against ‘natural’ background conditions. EPA (2003) describes the calculation of the annual averag e 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 ex tinction values (in 1/Mm) are converted to annual average Haze Index (HI) values (in deciview or dv). The average HI value is for the 20% best visibility days (B est 

### Days (dv)) is estimated from 10th percentile of the annual average HI value for a Class I area assuming a normal distribu tion. Thus, no average natural concentrations are provided for determining extinction for the 20% best visibility days. EPA maintains that the above definition of natural visibility baseline as the 20% best visibility days is likely to be reasonably conservative and cons istent with the Regional Haze Ru le goal of natural conditions.CALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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### 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 concentrations assumed to occur every month of the year, and (c) fine sea salt and associated wa ter is not included. Also, in the calculation of 20% best visibility days, the sa me frequency distribution is assu med for every Class I area in the East or in the West. In other words, ‘one size fi ts all’ (Tombach, 2004). But this really is not the

case.
The background extinction computation with Method 6 in CALPOST involves user-supplied monthly concentrations of SO4, NO3, PM coarse, organic carbon, soil, and elemental carbon species. In practice, concen trations for only 2 species, SO4 ([BKSO4]) and soil ([BKSOIL]), are supplied in the CALPOST input file to represent hygroscopic and nonhygroscopic portions of background extinction, respectively. Furthermore, the species concentrations are held consta nt over the annual cycle (i.e., no daily, monthly, or seasonal variation). Finally, the EPA natural background de fault values are define d separately for the eastern and western U.S. result in natural bac kground extinction values that vary spatially and temporally only in response to the spatial dist ribution and monthly variat ion of climatologicallyrepresentative relative humidity values (EPA, 2003, Table A-3). Thus, the default definition of natural conditions does not take into account meteorologically caused visibility impairment.

For CALPUFF analyses, these EPA (2003) default procedures for calculation of light extinction will be used for current and natural background conditions.

To determine background extinc tion for the BART analysis with CALPOST, average natural concentrations that represent av erage natural background visibility for the best 20% days need to be determined. Bowater will use the approach developed by the North Dakota Department of Health. This approach is discussed in Appendix F.

Impact Threshold. The EPA BART guidance reco mmends that the threshold value for defining whether a source “contributes” to visibility impair ment is 0.5 dv change from natural conditions.

BART determinations are based upon the 98th percentile of the predicted 24-hour average deciview change obtained from the CALPOST postprocessor. When 98 th 

### percentile modeling is conducted the highest modeled delta deciview value for each modeling day for each modeled receptor will be determined. The value is then compared to the 0.5 dv contribution threshold value. If the value exceeds the “contribution” th reshold of 0.5 dv the source will be subject to a BART evaluation. If the value is less than th e “contribution” threshol d 0.5 dv, the source is exempted from the BART requirements.

To conserve computational and analysis resour ces, the CALPUFF modeli ng will be performed sequentially for calendar years 2001, 2002, and 2003. URS understands that evaluation of all three years will be required to exclude a BART eligible source from the BART requirements.

Since the current regulatory version of CALPOST does not ge nerate 98 percentile results, Bowater will use a modified vers ion of CALPOST that generates a file with a full distribution of daily delta-deciview values for each receptor should source-specific modeling be performed. TheCALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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### Colorado Department of Public Health and Environment (CDPHE, 2005) has developed a FORTRAN processor to generate 98 th 

### percentile results and it is available upon request.



## 7.0 ALTERNATIVE MOD ELING METHODOLOGY

The Bowater Mill is located 140 k ilometers from the nearest Cla ss I Area. Over the years the accepted practice for modeling a single point sour ce for regional haze was to use the CALPUFF model and follow guidance developed by the Fe deral Land Manager. These FLM modeling procedures have always include d many highly conservative modeli ng assumptions. One of these assumptions is that the delta dv value at a singl e “worst-case” receptor location within the Class I area is the value used to compare to the just- noticeable-change haze th reshold of 1.0 dv or a 10 percent change in extinction.

URS researched the formulation of the deciview metric and discovered that it is based on a line of sight (LOS) concept. Appendix H contai ns a technical paper on the development and application of the dv. The LOS concept is not new and was originally discussed by the VISTAS technical consultant in the in itial draft of the VISTAS Mode ling Protocol dated January 31, 2005 under section 4.1.4 (Additional Technical Considerations ). The draft protocol actually gave an example of how an analysis could be conducte d using CALPUFF by averaging predicted change in dv along a LOS.

The following paragraphs have been extracted fr om the Initial Draft VISTA Modeling Protocol regarding the LOS modeling approach when usi ng CALPUFF to estimate regional haze impacts from a single source.

Statement 1 from Initial VISTAS Draft Protocol:

“A more difficult issue to address is that CALPOST calculates the extinction coefficient at each receptor point. That value represents extinction in the vicinity of that point, but does not necessarily represent the impact of the source on extinction over any sight path, particularly over the longer sight paths that are likely under natural conditions. The human perception of visibility takes place over si ght paths, not at points.

For example, consider a 40-km sight path that represents the visual range and includes 10 CALPUFF receptor points on a 4 km grid. If ther e is a 10% change in extinction (a 1 dv change) at two receptors and no change at the others, the actual change in haziness over the sight path is 2% (or 0.2 dv). If, as the EPA asserts in it s BART proposal, a 0.5 dv change is barely perceptible, then this change in haziness would be wholly imperceptible even though two receptor points exceed the 0.5 dv threshold!

Thus, if the scale of the plume is small compared to the visual range under natural conditions, which is likely to be the case at all but the longest transport distances (and will be especially true for the small plumes very close to the source), th e change in extinction at any receptor point is not representative of the effect of the source on the ability to see thro ugh the haze. Rather, in such cases, a sight path must be selected and the aver age change in extincti on over that sight path calculated.”CALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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### Statement 2 from Initial VISTAS Draft Protocol:

“... Also, when the plume is narrow, the aerosol concentrations across it may vary considerably from one CALPUFF receptor to the next, and the c oncentration field is unlikely to be uniform over the distance one can see (the visual range), especially if th e background is assumed to be at default natural conditions. Therefore the ligh t extinction impact of the source will vary depending on the sight path. In su ch situations, this variabili ty of the aerosol from one CALPUFF receptor to the next should be ta ken into account by averaging the CALPOSTcalculated light extinction over al l receptors along each of the sight paths of interest within a Class I area.”
After our further review of the fo rmulation of the deciview metric, URS agrees that this approach should be applicable for BART exclusion and de termination analyses and URS proposes to use the LOS approach for BART mode ling if initial predic ted values suggest this approach would provide a more accurate representation and comparis on to the 0.5 contribution threshold value.

After reviewing many ground level plume footprint plots from CALPUFF for a point source near a Class I area it was verified that a plum e is not widely disp ersed under “worst-case” meteorological conditions (see Appendix I). Therefore using one “worst-case” receptor location to determine if a “just noticeable change” in regional haze is occurring does not properly represent a facility’s impact on the change in de ciview based on the formul ation of the deciview metric.

It should be noted that the deci view is properly applied for am bient monitoring studies or one atmosphere modeling that include a homogene ous mix of visibility impairing compounds resulting from a wide variety of emission s ources over a large geographical region thereby causing relatively small changes in deciview s over the length of the visual range.

Therefore in order to properly apply the 0.5 deciview threshold for BART modeling, URS is proposing to conduct (if needed) additional regional haze modeling based on averaging the predicted change in deciviews along a line of r eceptors extending from a worst-case receptor location within the Class I area extending to a dist ance equal to the visual range for that day.

It should be noted, as a preliminary modeling step for either screening level or refined level modeling, URS will still conduct the standard “worst-case” recepto r modeling and present results in a standard format. However if initial mode ling indicates that a 0.5 deciview threshold is exceeded by a factor of 2 or less then the more refined LOS modeling approach will be used for screening and refined level CALP UFF modeling. These modeling steps have been discussed in this protocol An example of how the modeli ng approach will work is presented below assuming that a visual range approach is acceptable.

Maximum hourly emissions of SO 2 

### , NO x 

### , filterable and condensable PM 10 

### will be input to the CALPUFF model. Results from the initial VI STAS modeling will be used to determine the “worst-case” LOS receptor locations within th e Class I area. CALPUFF will be used to determine the highest delta dv receptor location along the front and back edges of the Class I area. The worst-case receptor location along the front and back edges will be considered theCALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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### starting points for the LOS analysis. It will be assumed that observers lo ok into the Class I area and the LOS ends at the visu al range for the 24-hour time period being investigated.

Once the worst-case receptors have been identified several other analyses will be conducted. The initial maximum modeling results for each time period will be evaluated to determine if any of the predicted “worst-c ase” extinctions occurred during periods of fog, rain or other naturally occurring haze. Should any of these conditions actually occur then a new 24-hour natural background value will be determined using the Method 7 modeling approach as discussed in a previous draft of the VISTAS protocol. Th e CALPOST model currently has the ability to address this issue using Method 7 for a 24 hour time period.

For the 24-hour averaging period the top 50 delta dv values will be studied with respect to the default monthly F(rh) values to determine whether using actual relative humidity data and the EPA F(rh) curve for the time period results in a higher F(rh) value. This type of analysis was discussed in the December 2005 draft VISTAS protocol. If a higher F(rh) value is determined then the CALPOST program will be run using the actual relative humidity and associated EPA F(rh) value to determine a new initial maximum delta dv value. To limit the number of times CALPOST is executed, only pe riods with higher EPA F(rh) values will be recalculated to determine the fina l worst-case receptor locations.

Two additional CALPUFF analyses will be conduc ted in order to produce a final 24-hour LOS visibility impairment estimate. A string of LOS receptors will be developed that extend backwards towards the Bowater Mill and another set of receptors that extends downwind alone the same line-of-sight (forward). The backward facing analysis will begin at a receptor location directly downstream but on the opposite boundary from the “worst-case” receptor located nearest the Bowater Mill. The LOS will extend through the Class I area and over the worst-case receptor on the front side of the Class I area and end at the actual visual range for the time period under investigation. The forward facing LOS will begin at the nearest worst-case receptor on the front side of the Class I area (near est the Bowater Mill) and look th rough the Class I area and extend downwind ending at the applicable visual range. The length of each string of receptors will vary depending on the actual visual range for the 24-hour time pe riod under study and each string will end at the most sensitive recepto r location required by the deciview metric. A total of 50 evenly spaced receptors will be placed along each string . This limit is dictated by CALPOST printed output information. The time specific natural ba ckground visual range can be provided from CALPOST and will be based on using actual EPA adjusted F(rh) data.

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

VR = 3912/b ext(NC)


### VR = visual range (km) b ext(NC) 

### = background extinction for natural conditions (Mm -1 

### )

Figure 7-1 is an illustration of how the modeling would be conducted using the LOS modeling approach for two receptor strings. A 75 km LOS was assumed for illustration.CALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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### Tabulated results from the analysis would be prepared and presented in the simple format presented in Table 7-1. This example assumes a 170-kilometer visual range is applicable for both time periods. The actual table would include 50 receptors.CALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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### Swanquarter NWR 75 km visual range north

visual range south Demo Source

860 880 900 920 940 960 980 UTM East (km) 3860 3880 3900 3920 3940 3960 3980UTM North (km)

Observer location looking north > <Observer location looking south



### 75 km Observer locations obtained from 5-year CALPUFF modeling with receptors placed on northern & sourthern boundary <50 evenly spaced receptors located on arrow

50 evenly spaced receptors > placed along arrow

FILE NO.

15266083

FIG. NO. SCALE: DRAWN BY: SCL DATE: SCL
AS SHOWN CHECKED BY: DATE: Bowater – Catawba, South Carolina 

### Example of LOS Modeling Approach

7-1CALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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### Table 7-1: Example of LOS Modeling Results

FORWARD LOOKING LOS 24-Hour Period Delta Distance (km) Deciview 0 1.647 10 1.524 20 1.246 30 0.911 40 0.631 50 0.475 60 0.39 70 0.328 80 0.279 90 0.244 100 0.217 110 0.194 120 0.174 130 0.156 140 0.137 150 0.12 160 0.102 170 0.085

Average 0.492222 

### BACKWARD LOOKING LOS

24-Hour Period Delta Distance (km) Deciview

0 1.246 -10 1.524 -20 1.647 -30 2.003 -40 1.466 -50 0.872 -60 0.036 -70 0.007 -80 0.001 -90 0 -100 0 -110 0 -120 0 -130 0 -140 0 -150 0 -160 0 -170 0

Average 0.489CALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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### The default for Rayleigh scatteri ng associated with higher elevati ons will be incorporated for all modeling. Initial modeling will be conducted us ing the standard IMPROVE coefficients. Again, should initial modeling results only slightly exceed ambient thresholds then the SCDHEC will be contacted to gain approval for using the EPRI coefficients.



## 8.0 REPORTING

8.1 Presentation of Modeling Results

The CALPOST processing computes the daily maximum change in deciviews. For evaluating compliance with the VISTAS screening threshold, the 8 th 

### highest change in extinction value for each year will be compared to the threshol d value (e.g., 0.5 dv). At a minimum, tabular presentation of the following results will be provided:

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

For evaluating compliance using all grid mode ling results from exemption and determination modeling the 98th percentile of the predicted 24- hour averaged deciview impact deduced from the CALPOST postprocessor will be compared to the threshold valu e (e.g., 0.5 dv). At a minimum, tabular presentation of the following results will be provided:

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

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


## 8.2 Reporting of CALPUFF Modeling Results

The report accompanying the CALPUFF modeling will provide a clear description of the modeling procedures followed and the results of the analysis. Any departures from the approved modeling protocol will be discussed and justified. The report will also include a discussion of the uncertainty in the modeling results and the likeli hood that the modeling pr ocess was effective in its determination. Any needs for source-specific or alternative modeling will be identified.

Accompanying the modeling report will be an electronic archive (CDs, DVDs, or removable USB2/IEEE 1394 hard drives as appropriate) th at includes the full set of CALPUFF inputs and model output fields as we ll as any pre- or post-processo r codes used to generate the results. The VISTAS 12/4 km regional CALPUF F-ready meteorological fields will not be included in the archive. The modeling data arch ive will be sufficiently complete as to allow an independent modeler to fully corr oborate the CALPUFF screening results.CALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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## 9.0 REFERENCES

CDPHE, 2005. CALMET/CALPUFF BART Protocol for Class I Federal Area Individual Source Attribution Visibility Impairme nt Modeling Analysis”, prepar ed by the Air Pollution Control Division, Colorado Department of Public Health and Environment, enver, CO.

Earth Tech, Inc., 2002. “Application of CALM ET/CALPUFF and MESOPUFF II to Compare Regulatory Design Concentrations for a Typical Long-Range Transport Analysis”, prepared for U.S. EPA, prepared by Earth Tech, Inc, Concord, MA.

EPA, 1995. “Interagency Workgroup on Air Qual ity Modeling (IWAQM): Assessment of Phase 1 Recommendations Regarding the Use of ME SOPUFF II”, EPA- 54/R-95-006. Office of Air Quality Planning and Standards, U.S Environmen tal Protection Agency, Research Triangle Park, NC 27711.

EPA, 1998b. “An Analysis of the CALMET/CALPU FF Modeling System in a Screening Mode.” EPA-454/R-98-010. Office of Air Quality Planni ng and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711.

EPA, 1998c. “A Comparison of CALPUFF with ISC3.” EPA-454/R-98-020. Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711.

EPA, 1998d. Phase 2 Summary Report and Recommendations for Modeling Long Range Transport and Impacts. Interagency Workgr oup on Air Quality Modeling (IWAQM). EPA454/R98-019, U.S. Environmental Protection Agency, RTP, NC.

EPA. 1998e. “Response to Peer Review Comments of CALMET/CALPUFF Modeling System”. Research Triangle Park, NC. November.

EPA. 1999. “Response to Peer Review Comments of the Interagency Workgroup on Air Quality Modeling Phase 2 Summary Report and Recomme ndations for Modeling Long Range Transport Impacts”. Research Triangle Park, NC. February.

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

EPA, 2003. Guidance for Estimating Natural Vi sibility Conditions under the Regional Haze Rule. EPA-454/B-03-005. U.S. Environmental Prot ection Agency, Research Triangle Park, NC.

EPA, 2005. Regional Haze Regulations and Guidelines for Best Available Retrofit Technology (BART) Determinations. Federal Register, 70 (128), Wednesday, July 6, 2005.

Escoffier-Czaja, C., and J. Scire. 2002: The E ffects of Ammonia Limitation on Nitrate Aerosol Formation and Visibility Impacts in Class I Areas. Paper J5.13, 12 th 

### AMS/A&WMA Conference on the Applications of Air Polluti on Meteorology, Norfolk, VA. May 2002.CALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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### Escoffier-Czaja, C., and J. Scir e. 2005: Comments on the Computation of Nitrate Using the Ammonia Limiting Method in CALPUFF”, Earth Tech, Inc., Concord, MA

Federal Register. 2003. 40 CFR Part 51. “Revisions to the Guidelines on Air Quality Models: Adoption of a Preferred Long Range Transport M odel and Other Revisions; Final Rule. Federal Register/Vol. 68, No 72/Tuesday April 15, 2003

FLAG 2000. “Federal Land Managers’ Air Qualit y Related Values Workgroup (FLAG)”: Phase I Report. USDI – National Park Servi ce, Air Resources Division, Denver, CO.

Garrison, M., A. Gray, S.T. Rao, M. Scruggs, 19 99. “Peer Review of the Interagency Workgroup On Air Quality Modeling Phase 2 Summary Report and Recommendations For Modeling Long Range Transport Impacts”, report compiled by: John S. Irwin Air Policy Support Branch, Atmospheric Sciences Modeling Division U.S. Environmental Protection Agency Research Triangle Park, NC 27711.

Irwin, J.S., 1998b. “Interagency Workgroup on Air Quality Modeling (IWAQM): Phase 2 Summary Report and Recommendations for Mode ling Long Range Transport Impacts”. EPA454/R-98-019, Office of Air Qualit y Planning and Standards, Re search Triangle Park, NC, 151 pp. (NTIS Accession Number PB 99-121089).

Irwin, J.S. and J.P. Notar. 2001. Long-range-tra nsport screening techni que using CALPUFF. Proceedings of Guideline on Air Quality Modeling: A New Beginning AW&MA Specialty Conference. April 4-6, 2001, Newport, RI.

Grell, G.A., J. Dudhia, and D.R. Stauffer, 1995: A Description of the Fifth Generation Penn State/NCAR MM5, NCAR TN-398 -STR, NCAR Technical Note.

IWAQM. 1998. Interagency Workgroup on Air Qu ality Modeling (IWAQM) Phase 2 Summary Report and Recommendations fo r Modeling Long-Range Transport and Impacts on Regional Visibility. EPA-454/R-98-019. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Re search Triangle Part, NC.

Tonnesen G.S., R.E. Morris, M. Uhl, K. Briggs , J. Vimont and T. Moore. 2003. “The WRAP Regional Modeling Center – Application and Evaluation of Regional Visibility Models” presented at 96 th 

### Annual Conference and Exhibition of the Air and Waste Management Association, San Diego, California

Tombach, I., 2004. “Options for Estimating Natu ral Background Visibility in the VISTAS Region”, prepared for the VISTAS Technica l Analysis Work Group (TAWG), 15 January.

Tombach, I., and P. Brewer, 2005. Natural backgr ound visibility and regi onal haze goals in the Southeastern United States. Journal of the Air & Wa ste Manage. Association. (in press). Tombach, I., P. Brewer, T. Rogers, and C. Arrington, 2005a. “BART mo deling protocol for VISTAS: Frst Draft”, 22 March.CALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

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### Tombach, I., P. Brewer, T. Rogers, C. Arrington, J. Scire 2005b. “Protocol for the application of the CALPUFF model for analyses of Best Av ailable Retrofit Tec hnology (BART): VISTAS second draft”, 22 August.

Scire, J., I. Tombach, et al . 2005. Protocol for the applicat ion of the CALPUFF model for analyses of Best Available Re trofit Technology (BART): (VISTAS third draft), Prepared for the Visibility Improvement State and Tribal Associ ation of the Southeast (VISTAS), Prepared by Earth Tech, Inc., VISTAS Technical Analysis Work Group (TAWG), the Florida Department of Environmental Protection, and the West Virgin ia Department of Environmental Protection.

Scire, J.S., D.G. Strimaitis, and R.J. Yama rtino, 2000a: A User’s Guide for the CALPUFF Dispersion Model (Version 5). Ea rth Tech, Inc., Concord, MA.

Scire, J.S., F.R. Robe, M.E. Fernau, and R.J. Yamartino, 2000b: A User’s Guide for the CALMET Meteorological Model (Version 5). Earth Tech, Inc., Concord, M.A

Scire, J.S., Z-X Wu, D.G. Strimaitis and G.E. Moore, 2001: The Southwest Wyoming Regional CALPUFF Air Quality Modeling Study – Volume I. Prepared for the Wyoming Dept of Environmental Quality. Available from Earth Tech, Inc., 196 Baker Avenue, Concord, M.A.

McNally, D. E., 2003. “Annual App lication of MM5 for Calendar Year 2001”. Prepared for the U.S. EPA, Office of Air Qualit y Planning and Standards, Prepared by Alpine Geophysics, LLC, Arvada, CO. 178 pp.

McNally, D. E., and T. W. Tesche, 2002. “Annua l Meteorological Modeling Protocol: Annual Application of the MM5 to the Continental United States”, prepared for the EPA Office of Air Quality Planning and Standards, Research Triangle Park, NC.

Ryan, P., D. Lowenthal and N. Kumar, 2004: Light Extinction Reconstruction in IMPROVE. Presented at the A&WMA Specialty Conference on Regional and Global Perspectives in Haze: Causes, Consequences and Controvers ies, Asheville, NC, 26-29 October 2004.

Johnson, M. T., 2003b. Iowa DNR 2002 Annual MM5 Modeling Project. Presented at the August 11 th 

### , 2003 CENRAP Workgroup Meeti ng in Bloomington, Minnesota.

Johnson, M. T., 2005. CALPUFF Modeling Protocol in Support of Best Available Retrofit Technology Determinations”, Prepared by the Io wa Department of Natural Resources, Air Quality Bureau, Des Moines, IA

LADCo, 2005. “Single Source Modeling to Support Regional Haze BART: Version 3”, Prepared by the Lake Michigan Air Directors Consortium (LADCO), Des Plaines, IL. Levy, J., J. Spengler, D, Hlinka, D. Sulliva n, and D. Moon, 2002. Using CALPUFF to evaluate the impacts of power plant emissions in Il linois: model sensitivity and implications. Atmospheric Environment. 36 (6). 1063-1075.CALPUFF Modeling Protocol BART Exemption Modeling Analysis Bowater Coated and Specialty Papers Division

March 17, 2006 Page 37 

### Morris, R., C. Tana, and G. Yarwood, 2003. “Evalu ation of the Sulfate and Nitrate Formation Mechanism in the CALPUFF Modeling System”, Proceedings of the A&WMA Specialty Conference --Guideline on Air Qual ity Models: The Path Forward. Mystic, CT. 22-24 October.

Morris, R.E., B. Koo, T.W. Tesche, C. Loomis , G. Stella, G. Tonnesen, and Z. Wang. 2004a. Modeling protocol for the VISTAS Phase II regional haze modeling. (http://pah.cert.ucr.edu/vistas/vistas2/)

MPCA, 2005. “Best Available Retrofit Technology (BART) Modeling Protocol to Determine Sources Subject to BART in the State of Mi nnesota”, prepared by the Minnesota Pollution Control Agency, St. Paul, MN.

NDDH, 2005, “Proto col for BART-Related Visibility Impa irment Modeling Analyses in North Dakota”, prepared by the North Dakota Department of Health, Division of Air Quality, Bismark, ND.

NPS, 1993. “Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 1 Report: Interim Recommendation for Modeling Long Range Transport And Impacts On Regional Visibility”, EPA-454/R-93-015, U. S. Environmental Protection Agency, Technical Support Division, Research Triangle Park, NC.

Scire, J.S. and F.R. Robe, 1997: Fine-Scale A pplication of the CALM ET Meteorological Model to a Complex Terrain Site. Paper 97-A1313. Air & Waste Management Association 90th Annual Meeting & Exhibition, Toronto, Ontario, Canada. 8-13 June 1997.

Scire, J.S., F.W. Lurmann, A. Bass and S.R. Hanna, 1983: Development of the MESOPUFF II Dispersion Model. EPA-600/3-84-057, U.S. En vironmental Protection Agency, Research Triangle Park, NC.

### APPENDIX A - CALPUFF Configuration

The tables below identify the recommended CALPUFF configurations for VISTAS BART modeling. Also identified are the default recommendations from the IWAQM Phase 2 Report (EPA, 1998).

Input Groups in the CALPUFF Control File.

Input Group Description Applicable to VISTAS BART
0 Input and output file names Yes 1 General run contro l parameters Yes 2 Technical options Yes 3 Species list Yes 4 Grid control parameters Yes 5 Output options Yes 6 Sub grid scale complex terrain inputs No
7 Dry deposition parameters for gases Yes 8 Dry deposition parameters for particles Yes 9 Miscellaneous dry depositi on for parameters Yes 10 Wet deposition parameters Yes 11 Chemistry parameters Yes 12 Diffusion and computational parameters Yes 13 Point source parameters Yes 14 Area source parameters Yes 15 Line source parameters No 16 Volume source parameters Yes 17 Discrete receptor information Yes

### CALPUFF Model Input Group 1: General Run Control Parameters

Parameter Default VISTAS Comments METRUN 0 0 All model periods in met file(s) will be run IBYR - 2001 Starting year IBMO - 1 Starting month IBDY - 1 Starting day IBHR - 1 Starting hour XBTZ - 5 Base time zone (6 = CST) IRLG - 8760 Length of run NSPEC 5 6 Number of MESOPUFF I I chemical species NSE 3 4 Number of chemical species to be emitted ITEST 2 2 Program is executed after SETUP phase MRESTART 0 0 Do not read or write a restart file during run NRESPD 0 0 File written only at last period METFM 1 1 CALMET binary file (CALMET.MET) AVET 60 60 Averaging time in minutes PGTIME 60 60 PG Averaging time in minutes

### CALPUFF Model Input Group 2: Technical Options

Parameter Default VISTAS Comments MGAUSS 1 1 Gaussian distribution used in near field MCTADJ 3 3 Partial plume path terrain adjustment MCTSG 0 0 Sub-grid-scale complex terrain not modeled MSLUG 0 0 Near-field puffs not modeled as elongated MTRANS 1 1 Transitional plume rise modeled MTBowater 1 1 Stack tip downwash used MSHEAR 0 0 (0, 1) Vertical wind shear (not modeled, modeled) MSPLIT 0 0 Puffs are not split MCHEM 1 1 MESOPUFF I I chemical parameterization Scheme MAQCHEM 0 0 Aqueous phase transformation not modeled MWET 1 1 Wet removal modeled MDRY 1 1 Dry deposition modeled MDISP 3 2 dispersion coefficients from internally calculated sigma v, sigma w using micrometeorological calculated variables

### CALPUFF Model Input Group 2: Technical Options

MTURBVW 3 3 Use both σ 

### v and σ 

### w from PROFILE.DAT to compute σ 

### y and σ 

### z (n/a) MDISP2 3 2 dispersion coefficients from internally calculated sigma v, sigma w using micrometeorological calculated variables MROUGH 0 0 PG σ 

### y and σ 

### z not adjusted for roughness MPARTL 1 1 No partial plume pen etration of elevated inversion MTINV 0 0 Strength of temperatur e inversion computed from default gradients MPDF 0 1 PDF not used for dispersion under convective conditions MSGTIBL 0 0 Sub-grid TIBL module not used for shoreline MBCON 0 0 Boundary concentration conditions not modeled MFOG 0 0 Do not configure for FOG model output MREG 1 0 NO checks are made

### CALPUFF Model Input Group 3: Speci es List-Chemistry Options.

CSPEC Modeled 1 

### Emitted 2 

### Dry Deposition 3 

### Output Group Number
SO 2 

### 1 1 1 0 SO 4 -2 

### 1 1 2 0 NOx 1 1 1 0
HNO 3 

### 1 0 1 0 NO 3 

### - 1 0 2 0
NH 3 

### 1 0 1 0 PMC 1 1 2 0 PMF 1 1 2 0 EC 1 1 2 0 SOA 1 1 2 0 Notes: 1 0=no, 1=yes 2 0=no, 1=yes 3 0=none; 1=computed-gas; 2=comp uted-particle; 3=user-specified

### CALPUFF Model Input Group 4: Map Projec tion and Grid Control Parameters.



### Parameter Default VISTAS Comments PMAP UTM UTM Map Projection NX - 160 Number of X grid cells in meteorolog ical grid NY - 172 Number of Y grid cells in meteorolog ical grid NZ

- 10 Number of vertical layers in meteorological Grid DGRIDKM - 12 Grid spacing (km) ZFACE - 0, 20 40, 80, 160, 320, 640,

1200, 2000, 3000, 4000 Cell face heights in meteorological grid (m)

XORIGKM - 137.973 Reference X coordinate for SW corner of grid cell (1,1) of me teorological grid (km) YORIGKM - -1625.974 Reference Y coordinate for SW corner of grid cell (1,1) of me teorological grid (km) IUTMZN - 17 UTM zone of coordinates IBCOMP

- 70 X index of lower left corner of the computational grid JBCOMP

- 67 Y index of lower left corner of the computational grids IECOMP

- 145 X index of the upper right corner of the computational grid JECOMP

- 130 Y index of the upper right corner of the computational grid LSAMP T F Sampling grid is not used IBSAMP

- 0 X index of lower left co rner of the sampling Grid JBSAMP

- 0 Y index of lower left co rner of the sampling Grid IESAMP

- 0 X index of upper righ t corner of the sampling grid JESAMP

- 0 Y index of upper right corner of the sampling grid MESHDN 1 1 Nesting factor of the sampling grid

### CALPUFF Model Input Group 5: Output Options.



### Parameter Default VISTAS Comments ICON 1 1 Output file CONC.DAT co ntaining concentrations is created IDRY 1 1 Output file DFLX.DAT containing dry fluxes is Created IWET 1 1 Output file WFLX.DAT c ontaining wet fluxes is created IVIS 1 1 Output file containing re lative humidity data is created LCOMPRS T T Perform data compre ssion in output file IMFLX 0 0 Do not calculate mass fl uxes across specific boundaries IMBAL 0 0 Mass balances for each species not reported hourly ICPRT 0 1 Print concentration fields to the output list file IDPRT 0 0 Do not print dry flux fi elds to the output list file IWPRT 0 0 Do not print wet flux fields to the output list file ICFRQ 1 1 Concentration fields are printed to output list file every hr IDFRQ 1 1 Dry flux fields are printed to output list file every 1 hour IWFRQ 1 1 Wet flux fields are prin ted to output list file every 1 hour BowaterR TU 1 3 Units for line printer output are in g/m3 for concentration and g/m2/s for deposition IMESG 2 2 Messages tracking the pr ogress of run written to

screen LDEBUG F F Logical value for debug output BowaterF 1 1 First puff to track NPFDEB 1 1 Number of puffs to track NN1 1 1 Meteorological peri od to start output NN2 10 10 Meteorological period to end output

### CALPUFF Model Input Group 6: Sub-Grid Scale Complex Terrain Inputs.

Parameter Default VISTAS Comments NHILL 0 0 Number of terrain features NCTREC 0 0 Number of special complex terrain receptors MHILL - 2 Input terrain and receptor data for CTSG hills input in CTDM format XHILL2M 1 1 Conversion factor for changi ng horizontal dimensions to meters ZHILL2M 1 1 Conversion factor for changing vertical dimensions to meters XCTDMKM - 0.0 E+00 X origin of CTDM system relative to CALPUFF coordinate system (km) YCTDMKM - 0.0 E+00 Y origin of CTDM system relative to CALPUFF coordinate system (km)

CALPUFF Model Input Group 7: Dry De position Parameters for Gases.
Species Default VISTAS Comments
SO 2 

### 0.1509 0.1509 Diffusivity 1000. 1000. Alpha star 8.0 8.0 Reactivity 0.0 0.0 Mesophyll resistance 0.04 0.04 Henry’s Law coefficient
NO x 

### 0.1656 0.1656 Diffusivity 1.0 1.0 Alpha star 8.0 8.0 Reactivity 5.0 5.0 Mesophyll resistance 3.5 3.5 Henry’s Law coefficient
HNO 3 

### 0.1628 0.1628 Diffusivity 1.0 1.0 Alpha star 18.0 18.0 Reactivity 0.0 0.0 Mesophyll resistance 8.0E-8 8.0E-8 Henry’s Law coefficient 0.000359 0.000359 Henry’s Law coefficient

### CALPUFF Model Input Group 8: Dry Depo sition Parameters for Particles.

Species Default VISTAS Comments SO 4 

### 0.48 0.48 Geometric mass mean diameter of SO4 -2 

### [μm] NO 3 

### 2.0 0.48 Geometric mass mean diameter of NO3 - 

### [μm] PMC 2.0 0.48 Geometric mass me an diameter of PMC [μm] PMF 2.0 0.48 Geometric mass me an diameter of PMF [μm]

EC 2.0 0.48 Geometric mass me an diameter of EC [μm] SOA 0.48 0.48 Geometric mass me an diameter of SOA [μm] (Geometric Standard Deviation for all species assumed to be 2.0 μm).

CALPUFF Model Input Group 9: Miscella neous Dry Deposition Parameters.

Parameter Default VISTAS Comments RCUTR 30 30 Reference cuticle resistance (s/cm) RGR 10 10 Reference ground resistance (s/cm) REACTR 8 8 Reference pollutant reactivity NINT 9 9 Number of particle size intervals for effective particle deposition velocity IVEG 1 1 Vegetation in non-irrigat ed areas is active and unstressed

### CALPUFF Model Input Group 10: Wet Deposition Parameters.

Default VISTAS Comments
SO 2 

### 3.21E-05 3.21E-05 Scavenging coeffi cient for liquid precipitation [s -1 

### ] 0.0 0.0 Scavenging coefficien t for frozen precipitation [s -1 

### ]

-2 

### 1.0E-04 1.0E-04 Scaven g in g coefficient for li q uid p reci p itation [ s - 1 

### ] SO 4 

### 3.0E-05 3.0E-05 Scavenging coeffi cient for frozen precipitation [s -1 

### ]

HNO 3 

### 6.0E-05 6.0E-05 Scavenging coeffi cient for liquid precipitation [s -1 

### ] 0.0 0.0 Scavenging coefficien t for frozen precipitation [s -1 

### ]
- 1.0E-04 1.0E-04 Scaven g in g coefficient for li q uid p reci p itation [ s - 1 

### ] NO 2 

### 3.0E-05 3.0E-05 Scavenging coeffi cient for frozen precipitation [s -1 

### ]
NH 3 

### 8.0E-05 8.0E-05 Scavenging coeffi cient for liquid precipitation [s -1 

### ] 0.0 0.0 Scavenging coefficien t for frozen precipitation [s -1 

### ] PMC 1.0E-04 1.0E-04 Scavenging coeffi cient for liquid precipitation [ s-1 

### ] 3.0E-05 3.0E-05 Scavenging coeffi cient for frozen precipitation [s -1 

### ] PMF 1.0E-04 1.0E-04 Scavenging coeffi cient for liquid precipitation [ s-1 

### ] 3.0E-05 3.0E-05 Scavenging coeffi cient for frozen precipitation [s -1 

### ] EC 1.0E-04 1.0E-04 Scavenging coeffi cient for liquid precipitation [ s-1 

### ] 3.0E-05 3.0E-05 Scavenging coeffi cient for frozen precipitation [s -1 

### ]

OC 1.0E-04 1.0E-04 Scavenging coeffi cient for liquid precipitation [ s-1 

### ] 3.0E-05 3.0E-05 Scavenging coeffi cient for frozen precipitation [s -1 

### ]

### CALPUFF Model Input Group 11: Chemistry Parameters.

Parameter Default VISTAS Comments MOZ 1 1 Read ozone backgr ound concentrations from ozone.dat file (measured values). BCKO3 12*80 12*40 Background ozon e concentration (ppb) BCKNH3 12*10 12*0.5 Background amm onia concentration (ppb) RNITE1 0.2 0.2 Nighttime NO 2 

### loss rate in percent/hour RNITE2 2 2 Nighttime NOX loss rate in percent/hour RNITE3 2 2 Nighttime HNO 3 

### loss rate in percent/hour MH202 1 1 Background H2O2 

### concentrations (Aqueous phase transformation s not modeled) BCKH202 1 1 Background monthly H2O2 

### concentrations (Aqueous phase transf ormations not modeled) BCKPMF 1. 1. Fine particulate conc entration for SOA Option (micrograms per cubic meter) OFRAC .2 .2 Organic fraction of fine particulate for SOA Option VCNX 50. 50. VOC/NOx rati o for SOA Option

CALPUFF Model Input Group 12: Disp ersion/Computational Parameters.

Parameter Default VISTAS Comments

SYDEP 550 550 Horizontal size of a puff in meters beyond which the time dependant dispersion equation of Heffter (1965) is used
MHFTSZ 0 0 Do not use Heffter formulas for sigma z JSUP 5 5 Stability class used to determine dispersion rates for puffs above boundary layer CONK1 0.01 0.01 Vertical dispersion constant for stable Conditions CONK2 0.1 0.1 Vertical dispersi on constant for neutral/stable conditions

### Parameter Default VISTAS Comments

TBD 0.5 0.5 Use ISC transition point for determining the transition poi nt between the Schulman-Scire to Huber-Snyder Building Downwash scheme
IURB1 10 10 Lower range of land use categories for which urban dispersion is assumed IURB2 19 19 Upper range of land use categories for which urban dispersion is assumed
ILANDUIN 20 * Land use category for modeling domain XLAIIN 3.0 * Leaf area index fo r modeling domain ZOIN -0.25 * Roughness length in meters for modeling domain
ELEVIN 0.0 * Elevation above sea level XLATIN -999 - North latitude of station in degrees XLONIN -999 - South latitude of station in degrees ANEMHT 10 10 Anemometer height in meters ISIGMAV 1 1 Sigma-v is read fo r lateral turbulence data IMIXCTDM 0 0 Predicted mixi ng heights are used XMXLEN 1 1 Maximum length of emitted slug in meteorological grid units XSAMLEN 1 10 Maximum travel distanc e of slug or puff in meteorological grid units during one sampling unit

MXNEW 99 60 Maximum number of puffs or slugs released from one source during one time step MXSAM 99 60 Maximum number of sampling steps during one time step for a puff or slug NCOUNT 2 2 Number of iterations used when computing the transport wind for a sampling step that includes transitional plume rise

### Parameter Default VISTAS Comments
SYMIN 1 1 Minimum sigma y in me ters for a new puff or slug SZMIN 1 1 Minimum sigma z in me ters for a new puff or slug SVMIN .50 .50 Minimum lateral turbulence velocities (m/s) SWMIN 0.20, 0.12, 0.08, 0.06, 0.03, 0.016 0.20, 0.12, 0.08, 0.06, 0.03, 0.016 Minimum vertical turbulence velocities (m/s)

WSCALM 0.5 0.5 Minimum non-calm wind speeds (m/s) XMAXZI 3000. 3000. Maximum mixing height (m) XMINZI 50. 20. Minimum mixing height (m) SL2PF 10. 10. Maximum Sy/puff length PLXO 0.07, 0.07, 0.10, 0.15, 0.35, 0.55 0.07, 0.07, 0.10, 0.15, 0.35, 0.55 Wind speed power-law exponents

WSCAT 1.54, 3.09, 5.14, 8.23, 10.80 1.54, 3.09, 5.14, 8.23, 10.80 Upper bounds 1 st 

### 5 wind speed classes

PGGO 0.020, 0.035 0.020, 0.035 Potential temp gradients PG E & F (deg/km) CDIV 0.01 0.01 Divergence criterion for dw/dz (1/s) PPC 0.5, 0.5, 0.5, 0.5, 0.35, 0.35 0.5, 0.5, 0.5, 0.5, 0.35, 0.35 Plume path coefficients (only if MCTADJ=3)

NSPLIT 3 3 Number of puffs when puffs split IRESPLIT - 1900 Hour(s) when puff is eligible to split ZISPLIT 100 100 Previous hour’s minimum mixing height, m ROLDMAX 0.25 0.25 Previous Max mixing height/current mixing height ratio, must be less than this value to allow puff to split
NSPLITH 5 5 Number of puffs re sulting from a split SYSPLITH 1.0 1.0 Minimum sigma-y of pu ff before it may split

### Parameter Default VISTAS Comments
SHSPLITH 2.0 2.0 Minimum puff elongation rate from wind shear before pu ff may split CNSPLITH 1.0E-07 1.0E-07 Minimum species conc entration before a puff may split EPSSLUG 1.0E-04 1.0E-04 Criter ion for SLUG sampling EPSAREA 1.0E-06 1.0E-06 Cr iterion for area so urce integration DSRISE 1.0 1.0 Trajectory step length for numerical rise algorithm Note: Values indicated by an asterisk (*) were allowed to vary spatially across the domain and were ob tained from CALMET

CALPUFF Model Input Group 13: Point Source Parameters.

Parameter Default VISTAS Comments NPT1 - 7 Number of point sources with constant stack parameters or variable emission rate scale factors
BowaterTU 1 1 Units for point source emission rates are g/s NSPT1 0 0 Number of source-speci es combinations with variable emissions scaling factors NPT2 - 0 Number of point sources with variable emission parameters prov ided in external file

### CALPUFF Model Input Group 14: Area Source Parameters.

Parameter Default VISTAS Comments NAR1 Varies by scenario Number of polygon area sources

IARU 1 1 Units for area source emission rates are g/m2/s NSAR1 0 - Number of source species combinations with variable emissions scaling factors NAR2 - - Number of buoyant polygon area sources with variable location and emission parameters

CALPUFF Model Input Group 15: Line Source Parameters.

Parameter Default VISTAS Comments

NLN2 - - Number of buoyant line sources with variable location and emission parameters NLINES - - Number of buoyant line sources ILNU 1 - Units for line source emission rates is g/s NSLN1 0 - Number of source-species combinations with variable emissions scaling factors MXNSEG 7

- Maximum number of segments used to model each line NLRISE 6

- Number of distance at wh ich transitional rise is computed
XL - - Average line source length (m) HBL - - Average height of line source height (m) WBL - - Average building width (m) WML - - Average line source width (m) DXL - - Average separation between buildings (m) FPRIMEL - - Average buoyancy parameter (m4/s3)

### CALPUFF Model Input Group 16: Volume Source Parameters.

Parameter Default VISTAS Comments
NVL1 - 4 Number of volume sources

IVLU 1

- Units for volume source emission rates is grams per second NSVL1 0 - Number of source-species combinations with variable emissions scaling factors IGRDVL - - Gridded volume source data is not used VEFFHT - - Effective height of emissions (m) VSIGYI - - Initial sigma y value (m) VSIGZI - - Initial sigma z value (m)

Table B-18. CALPUFF Model Input Group 17: Discrete Receptor Information.

Parameter Default VISTAS Comments NREC - 194 Number of non-gridded receptors

### APPENDIX B - POSTUTIL Screening Configuration

The tables below identify the recomm ended POSTUTIL processor screening configurations for CENRAP BART modeling.

Input Groups in the POSTUTIL Processor Control File.

Sub Group Description Applicable to VISTAS BART
0a Input and output file names Yes 1 NMET – Number of CALM ET data files (365) Yes 2 NFILES – Number of CA LMET data files (1) Yes

POSTUTIL Processor Input Group 1: General Run Control Parameters

Parameter DEFAULT VISTAS Comments ISYR -- 2001 Starting year ISMO -- 1 Starting month ISDY -- 1 Starting day ISHR -- 0 Starting hour NPER -- 8760 Number of peri ods to process NSPECINP -- 6 Number of CALPUFF species to process NSPECOUT -- 6 Number of species to output NSPECCMP -- 0 Number of species to derive MDUPLCT -- 1 Stop run if duplicate name NSCALED -- 0 Number of CALPUFF files to ‘scale’ MNITRATE -- 1 Recompute the HNO 3 

### /NO 3 

### partition for CALPUFF modeled concentrations? 1 = yes for all sources combined BCKNH3 10. CMAQ. Default NH 3 

### concentration (ppb) for HNO 3 

### /NO 3 

### partitioning

### POSTUTIL Processor Input Group 2: Species Processing Information

Parameter DEFAULT VISTAS Comments ASPECI -- SO 2 

### , SO 4 

### , NO x 

### , HNO 3 

### , NO 3 

### , PM 10 

### Species to post-process

ASPECO -- SO 2 

### , SO 4 

### , NO x 

### , HNO 3 

### , NO 3 

### , PM 10 

### Species to output

CSPECCMP -- CSPECCMP = N Nitrogen species to be SO 2 

### = 0.0 computed by scaling and SO 4 

### = 0.291667 summing one or more of the NO = 0.466667 proces sed input species using NO 2 

### = 0.304348 the scaling factors for each HNO 3 

### = 0.222222 of the NSPECINP input NO 3 

### = 0.451613 species PM 10 

### = 0.0 CSPECCMP -- CSPECCMP = S Sulfur species to be SO 2 

### = 0.50 computed by scaling and SO 4 

### = 0.333333 summing one or more of the NO = 0.0 processe d input species using NO 2 

### = 0.0 the scaling factors for each HNO 3 

### = 0.0 of the NSPECINP input NO 3 

### = 0.0 species PM 10 

### = 0.0 MODDAT -- A (Default=1.0) Each species in NSCALED SO 2 

### = 1.1 CALPUFF data files may be SO 4 

### = 1.5 scaled bef ore processing HNO3 = 0.8 (e.g., to change the emission NO 3 

### = 0.1

B (Default=0.0) rate for all sources modeled in the run that produced a data file). For each scaled
SO 2 

### = 0.0 species the scaling factors SO 4 

### = 0.0 are A and B where x' = Ax + HNO 3 

### = 0.0 B. NO 3 

### = 0.0

### APPENDIX C – CALPOST Screening Configuration

The tables below identify the recommended CALPOST processor screening configurations for CENRAP BART modeling.

Input Groups in the CALPOST Processor Control File.

Group Description Applicable to VISTAS BART
0 Input and output file names Yes 1 General Run Control Parameters Yes 2 Visibility Parameters Yes 3 Output Options Yes

### CALPOST Processor Input Group 1: General Run Control Parameters

Parameter DEFAULT VISTAS Comments ISYR -- 2001 Starting year ISMO -- 1 Starting month ISDY -- 1 Starting day ISHR -- 0 Starting hour NPER -- 8760 Number of periods to process NREP 1 1 Process every hour of data? Yes = 1 ASPEC -- VISIB Process species for visibility ILAYER 1 1 Layer/deposition code; 1 for CALPUFF concentrations
A 0.0 0.0 Scaling factor, slope B 0.0 0.0 Scaling factor, intercept LBACK F F Add hourly background concentrations or fluxes?
LG F F Process gridded receptors? LD F T Process discrete receptors? LCT F F Process complex terrain receptors? LDRING F F Report receptor ring results? NDRECP -1 -1 Select all discrete receptors
IBGRID -1 -1 

### X index of LL corner of receptor grid JBGRID -1 -1 

### Y index of LL corner of receptor grid IEGRID -1 -1 

### X index of UR corner of receptor grid JEGRID -1 -1 

### X index of UR corner of receptor grid NGONOFF 0 0 Number of gri dded receptor rows NGXRECP 1 0 Exclude specific gr idded receptors, Yes = 0

### CALPOST Processor Input Group 2: Species Processing Information



### Parameter DEFAULT VISTAS Comments RHMAX 98 95 Maximum RH (%) used in particle gr owth curve LVSO4 T T Compute light ex tinction for sulfate? LVNO3 T T Compute light ex tinction for nitrate? LVOC T F Compute light exti nction for organic carbon? LVMPC T F Compute light extincti on for coarse particles? LVMPF T T Compute light extinc tion for fine particles? LVEC T F Compute light extinc tion for elemental carbon? LVBK T T Include background in extinction calculation? SPECPMC PMC PMC Coarse particulate species SPECPMF PMF PM10 Fine particulate species EEPMC 0.6 0.6 Extinction efficienc y for coarse particulates EEPMF 1.0 1.0 Extinction efficienc y for fine particulates EEPMCBK 0.6 0.6 Extinction efficiency for coarse part. background EESO4 3.0 3.0 Extinction efficiency for ammonium sulfate EENO3 3.0 3.0 Extinction effici ency for ammonium nitrate EEOC 4.0 4.0 Extinction effi ciency for organic carbon EESOIL 1.0 1.0 Extinction efficiency for soil EEEC 10.0 10.0 Extinction effici ency for elemental carbon MVISBK 2 6 Method 6 for backgro und light extinction: Compute extinction from speciated PM measurements. FLAG RH adjustment factor applied to observed & model ed sulfate and nitrate
BEXTBTBK -- 10 Background extinction for MVISBK=1 (1/Mm) RHFRAC -- 10 Percentage of partic les affected by RH RHFAC 12*value Depends on Class

I Area Extinction coefficien ts for modeled and background hygroscopic species computed using EPA (2003) monthly RH adjustment factors
Parameter DEFAULT VISTAS Comments BKSEC 0.02 0.02 Background elem. ca rbon extinct. coeff – east BKSO4 0.23 0.23 Background sulfate extinction coeff – east BKNO3 0.10 0.10 Background nitrate extinction coeff – east BKPMC 3.00 3.00 Background coarse pa rt. extinction coeff – east BKSOC 1.40 1.40 Background organic carb on extinct. coeff – east

### BKSSOIL 0.50 0.50 Background soil ex tinction coeff – east BKSEC 0.02 0.02 Background elem. ca rbon extinct. coeff – east BEXTRAY 10.0 10.0 Extinction due to Rayleigh scattering (1/Mm)

CALPOST Processor Input Gr oup 3: Output Options

Parameter DEFAULT VISTAS Comments LDOC F F Print documentation image? BowaterRT U 1 3 Print output units (μg/m 3 

### ) for concentr ations and (μg/m 2 

### /sec) for deposition
L1HR T F Report 1 hr averaging times L3HR T F Report 3 hr averaging times L24HR T T Report 24 hr averaging times LRUNL T F Report run-length (a nnual) averaging times LT50 T F Top 50 table LTOPN F F Top ‘N’ table NTOP 4 4 Number of ‘Top-N’ va lues at each receptor ITOP 1,2,3,4 1,2,3,4 Ranks of ‘Top-N’ values at each receptor LEXCD F F Threshold exceedances counts THRESH1 -1.0 -1.0 Averaging time threshold for 1 hr averages THRESH3 -1.0 -1.0 Averaging time threshold for 3 hr averages THRESH24 -1.0 -1.0 Averaging time th reshold for 24 hr averages THRESHN -1.0 -1.0 Averaging time th reshold for NAVG-hr averages NDAY 0 0 Accumulation period, days NCOUNT 1 1 Number of exceedances allowed LECHO F F Echo option LTIME F F Time series option LPLT F F Plot file option LGRD F F Use grid format in stead of DATA format LDEBUG F F Output information for debugging?APPENDIX D – CLASS I RECEPTORS IN LAMBERT CONFORMAL COORDINATES 

### Great Smoky Mountains Lambert Conforma

l X (km) Lambert Conforma

l Y (km) Height (m) Lambert Conformal

X (km) Lambert Conformal

Y (km) Height (m) Lambert Conformal

X (km) Lambert Conformal

Y (km) Height (m)
1201.214 -417.246 521 1197.428 -412.206 944 1183.507 -410.517 1072 1204.191 -416.805 521 1198.916 -411.986 1281 1184.994 -410.3 1199 1189.038 -417.173 521 1200.403 -411.766 1427 1186.481 -410.083 1097 1190.527 -416.954 522 1201.891 -411.546 1221 1187.968 -409.865 720 1192.015 -416.736 523 1203.378 -411.325 1030 1189.455 -409.647 640 1193.503 -416.517 521 1204.865 -411.105 983 1190.942 -409.429 873 1194.992 -416.298 538 1206.352 -410.883 894 1192.429 -409.21 852 1196.48 -416.079 550 1207.84 -410.662 675 1193.915 -408.991 978 1197.968 -415.859 640 1209.327 -410.44 950 1195.402 -408.772 809 1199.456 -415.639 607 1210.814 -410.219 915 1196.889 -408.553 761 1200.944 -415.419 523 1212.301 -409.996 732 1198.376 -408.333 987 1202.432 -415.199 548 1213.788 -409.774 980 1199.863 -408.113 874 1203.92 -414.978 524 1216.762 -409.328 1020 1201.349 -407.893 1147 1205.408 -414.757 608 1218.249 -409.105 886 1202.836 -407.673 1323 1208.384 -414.315 546 1219.736 -408.881 615 1204.322 -407.452 1221 1209.871 -414.093 566 1221.222 -408.657 680 1205.809 -407.231 1097 1211.359 -413.871 541 1176.335 -413.427 767 1207.296 -407.01 800 1212.847 -413.649 530 1177.823 -413.211 919 1208.782 -406.788 942 1181.33 -416.432 683 1179.311 -412.995 905 1210.268 -406.566 1167 1182.818 -416.216 711 1180.798 -412.778 911 1211.755 -406.344 1052 1184.307 -415.999 583 1182.286 -412.561 965 1213.241 -406.122 1012 1185.795 -415.781 599 1183.773 -412.344 837 1214.728 -405.899 1002 1187.283 -415.564 561 1185.261 -412.127 1055 1216.214 -405.676 1143 1188.771 -415.346 582 1186.748 -411.91 716 1217.7 -405.453 998 1190.259 -415.128 703 1188.235 -411.692 825 1219.186 -405.23 710 1191.747 -414.909 707 1189.723 -411.474 609 1220.672 -405.006 998 1193.235 -414.69 522 1191.21 -411.256 814 1222.158 -404.782 907 1194.723 -414.471 760 1192.697 -411.037 1006 1223.644 -404.558 1200 1196.21 -414.252 846 1194.184 -410.818 1036 1225.131 -404.333 1173 1197.698 -414.033 952 1195.672 -410.599 732 1226.616 -404.108 1067 1199.186 -413.813 765 1197.159 -410.379 746 1228.102 -403.883 896 1200.674 -413.593 1067 1198.646 -410.16 929 1229.588 -403.658 799 1202.161 -413.372 915 1200.133 -409.94 1008 1231.074 -403.432 683 1203.649 -413.152 730 1201.62 -409.72 1288 1169.857 -410.632 330 1205.137 -412.931 822 1203.107 -409.499 1266 1171.344 -410.418 523 1206.624 -412.71 748 1204.594 -409.278 1218 1172.831 -410.203 599 1208.112 -412.488 544 1206.081 -409.057 959 1174.318 -409.988 769 1209.599 -412.267 706 1207.568 -408.836 681 1175.805 -409.772 831 1211.087 -412.045 627 1209.054 -408.614 920 1177.292 -409.556 941 1212.574 -411.822 845 1210.541 -408.392 1170 1178.779 -409.34 1150 1214.061 -411.6 772 1212.028 -408.17 956 1180.266 -409.124 1194 1215.549 -411.377 836 1213.515 -407.948 818 1181.753 -408.907 1084 1220.01 -410.707 665 1215.001 -407.725 1223 1183.24 -408.69 1206 1175.112 -415.469 554 1216.488 -407.502 1270 1184.727 -408.473 1225

1176.6 -415.254 760 1217.974 -407.279 872 1186.213 -408.256 987 1178.088 -415.038 805 1219.461 -407.055 694 1187.7 -408.038 1063 1179.576 -414.822 703 1220.947 -406.832 963 1189.187 -407.82 873 1181.064 -414.605 729 1222.434 -406.608 859 1190.673 -407.602 921 1182.552 -414.388 699 1223.92 -406.383 1000 1192.16 -407.384 1233 1184.04 -414.171 1109 1170.121 -412.46 365 1193.646 -407.165 1006 1185.528 -413.954 1001 1171.608 -412.245 406 1195.133 -406.946 1024 1187.015 -413.737 811 1173.096 -412.03 600 1196.619 -406.726 792 1188.503 -413.519 522 1174.583 -411.815 591 1198.106 -406.507 1015 1189.991 -413.301 581 1176.07 -411.599 644 1199.592 -406.287 1094 1191.478 -413.082 629 1177.558 -411.383 1036 1201.079 -406.067 996 1192.966 -412.864 634 1179.045 -411.167 1309 1202.565 -405.846 1148 1194.453 -412.645 582 1180.532 -410.951 1262 1204.051 -405.626 1368 1195.941 -412.426 612 1182.019 -410.734 1261 1205.537 -405.405 1052

### Great Smoky Mountains Lambert Conforma

l X (km) Lambert Conforma

l Y (km) Height (m) Lambert Conformal

X (km) Lambert Conformal

Y (km) Height (m) Lambert Conformal

X (km) Lambert Conformal

Y (km) Height (m)
1207.024 -405.184 958 1229.034 -400.007 802 1177.982 -403.859 634 1208.51 -404.962 988 1230.519 -399.781 756 1179.468 -403.643 662 1209.996 -404.74 1019 1239.43 -398.422 1213 1180.954 -403.427 567 1211.482 -404.518 1344 1240.915 -398.195 1312 1182.44 -403.21 579 1212.968 -404.296 1246 1169.33 -406.978 365 1183.926 -402.993 660 1214.454 -404.073 1377 1170.816 -406.763 393 1185.411 -402.776 878 1215.94 -403.85 1135 1172.303 -406.548 455 1186.897 -402.558 907 1217.426 -403.627 1196 1173.789 -406.333 590 1188.383 -402.34 955 1218.911 -403.404 851 1175.275 -406.118 775 1189.868 -402.122 1048 1220.397 -403.18 883 1176.762 -405.902 690 1191.354 -401.904 1056 1221.883 -402.956 1060 1178.248 -405.686 697 1192.84 -401.685 1333 1223.369 -402.732 1207 1179.734 -405.47 678 1194.325 -401.466 1223 1224.854 -402.508 1245 1181.22 -405.253 607 1195.81 -401.247 1411 1226.34 -402.283 1045 1182.707 -405.037 824 1197.296 -401.028 1106 1227.826 -402.058 1068 1184.193 -404.82 802 1198.781 -400.808 1347 1229.311 -401.832 870 1185.679 -404.602 1033 1200.267 -400.588 1222 1230.797 -401.607 782 1187.165 -404.385 1241 1201.752 -400.368 1524 1241.194 -400.02 1326 1188.651 -404.167 1297 1203.237 -400.147 1386 1169.593 -408.805 453 1190.137 -403.949 1503 1204.722 -399.926 1198 1171.08 -408.59 439 1191.623 -403.73 1477 1206.208 -399.705 1242 1172.567 -408.376 582 1193.109 -403.512 1533 1207.693 -399.484 1415 1174.054 -408.16 664 1194.594 -403.293 1242 1209.178 -399.262 1358 1175.54 -407.945 770 1196.08 -403.073 1270 1210.663 -399.04 1355 1177.027 -407.729 853 1197.566 -402.854 1198 1212.148 -398.818 1403 1178.514 -407.513 810 1199.052 -402.634 1372 1213.633 -398.596 1712

1180 -407.297 979 1200.537 -402.414 1394 1215.118 -398.373 1431 1181.487 -407.08 809 1202.023 -402.194 1187 1216.603 -398.15 1422 1182.973 -406.864 818 1203.508 -401.973 1517 1218.087 -397.927 1236 1184.46 -406.646 1027 1204.994 -401.752 1490 1219.572 -397.703 1228 1185.946 -406.429 1372 1206.48 -401.531 1518 1221.057 -397.479 1492 1187.432 -406.211 1203 1207.965 -401.31 1632 1222.542 -397.255 1372 1188.919 -405.994 975 1209.45 -401.088 1692 1224.026 -397.031 1181 1190.405 -405.775 1077 1210.936 -400.866 1888 1225.511 -396.806 792 1191.891 -405.557 1343 1212.421 -400.644 1951 1226.996 -396.581 939 1193.378 -405.338 1463 1213.906 -400.422 1603 1228.48 -396.356 737 1194.864 -405.119 1193 1215.392 -400.199 1543 1229.965 -396.131 1213 1196.35 -404.9 932 1216.877 -399.976 1292 1231.449 -395.905 1173 1197.836 -404.68 1059 1218.362 -399.752 1067 1235.902 -395.226 1111 1199.322 -404.461 1144 1219.847 -399.529 928 1237.387 -395 1138 1200.808 -404.24 1231 1221.332 -399.305 1190 1238.871 -394.773 1362 1202.294 -404.02 1197 1222.817 -399.081 1415 1240.355 -394.545 1709 1203.78 -403.799 1220 1224.302 -398.856 1066 1241.839 -394.318 1585 1205.266 -403.579 1342 1225.787 -398.632 899 1243.324 -394.09 1298 1206.752 -403.357 1350 1227.272 -398.407 714 1244.808 -393.862 1224 1208.237 -403.136 1251 1228.757 -398.182 703 1246.292 -393.633 1150 1209.723 -402.914 1467 1237.666 -396.825 1097 1247.776 -393.404 1348 1211.209 -402.692 1407 1239.15 -396.598 1224 1171.774 -402.894 383 1212.694 -402.47 1671 1240.635 -396.37 1526 1173.26 -402.679 482 1214.18 -402.247 1369 1242.119 -396.142 1566 1174.746 -402.464 533 1215.666 -402.025 1412 1243.604 -395.915 1311 1176.231 -402.248 559 1217.151 -401.802 1279 1245.088 -395.686 1283 1177.717 -402.032 598 1218.637 -401.578 863 1246.573 -395.458 1482 1179.202 -401.816 532 1220.122 -401.355 823 1169.066 -405.151 290 1180.688 -401.6 525 1221.608 -401.131 1103 1170.552 -404.936 389 1182.173 -401.383 533 1223.093 -400.906 1432 1172.038 -404.721 506 1183.659 -401.166 543 1224.578 -400.682 1448 1173.524 -404.506 644 1185.144 -400.949 562 1226.064 -400.457 1352 1175.01 -404.291 578 1186.63 -400.731 602 1227.549 -400.232 1094 1176.496 -404.075 539 1188.115 -400.514 736

### Great Smoky Mountains Lambert Conformal

X (km) Lambert Conformal

Y (km) Height (m) Lambert Conformal

X (km) Lambert Conformal

Y (km) Height (m) Lambert Conformal

X (km) Lambert Conformal

Y (km) Height (m)
1189.6 -400.296 817 1198.241 -397.155 768 1209.844 -393.563 1203 1191.085 -400.077 809 1199.725 -396.935 983 1211.328 -393.341 1212 1192.571 -399.859 920 1201.21 -396.715 1281 1212.812 -393.118 1380 1194.056 -399.64 1017 1202.695 -396.495 1378 1214.296 -392.896 1300 1195.541 -399.421 1006 1204.179 -396.274 1371 1215.78 -392.673 1206 1197.026 -399.201 956 1205.664 -396.053 1135 1217.263 -392.45 1581 1198.511 -398.982 1137 1207.148 -395.832 884 1218.747 -392.227 1457 1199.996 -398.762 982 1208.632 -395.61 907 1220.231 -392.003 1699 1201.481 -398.541 1187 1210.117 -395.389 1249 1221.715 -391.779 1543 1202.966 -398.321 1365 1211.601 -395.167 1528 1223.198 -391.555 1640 1204.451 -398.1 1245 1213.085 -394.944 1552 1224.682 -391.33 1476 1205.936 -397.879 1074 1214.57 -394.722 1473 1226.166 -391.106 1036 1207.42 -397.658 1365 1216.054 -394.499 1677 1227.649 -390.881 1315 1208.905 -397.436 1401 1217.538 -394.276 1553 1229.133 -390.655 1504 1210.39 -397.214 1080 1219.022 -394.052 1572 1230.616 -390.43 1469 1211.874 -396.992 1260 1220.506 -393.828 1526 1232.1 -390.204 1242 1213.359 -396.77 1660 1221.99 -393.604 1261 1233.583 -389.978 1453 1214.844 -396.547 1703 1223.474 -393.38 1217 1235.066 -389.751 1414 1216.328 -396.324 1525 1224.958 -393.156 1247 1236.55 -389.525 1012 1217.813 -396.101 1299 1226.442 -392.931 947 1238.033 -389.298 1127 1219.297 -395.878 1389 1227.926 -392.706 1163 1239.516 -389.071 1352 1220.782 -395.654 1084 1229.41 -392.48 1434 1240.999 -388.843 1189 1222.266 -395.43 1048 1230.894 -392.255 1373 1242.482 -388.615 1257 1223.75 -395.206 1002 1232.378 -392.029 1170 1243.966 -388.387 1048 1225.235 -394.981 1374 1233.861 -391.803 1293 1245.449 -388.159 856 1226.719 -394.756 910 1235.345 -391.576 1077 1246.932 -387.931 825 1228.203 -394.531 1109 1236.829 -391.35 953 1248.415 -387.702 1060 1229.687 -394.306 1002 1238.312 -391.123 1202 1249.898 -387.473 1125 1231.172 -394.08 1469 1239.796 -390.895 1602 1175.435 -396.767 429 1232.656 -393.854 1136 1241.279 -390.668 1433 1176.92 -396.551 537 1234.14 -393.628 1324 1242.763 -390.44 1106 1178.404 -396.335 459 1235.624 -393.401 912 1244.246 -390.212 966 1179.889 -396.119 575 1237.108 -393.175 1427 1245.73 -389.984 1077 1181.373 -395.903 564 1238.592 -392.948 1487 1247.213 -389.755 884 1182.858 -395.686 646 1240.076 -392.72 1681 1248.696 -389.526 1096 1188.795 -394.816 609 1241.559 -392.493 1310 1174.216 -398.81 459 1190.279 -394.598 535 1243.043 -392.265 1080 1175.701 -398.594 516 1191.764 -394.379 489 1244.527 -392.037 1029 1177.186 -398.378 579 1193.248 -394.161 408 1246.011 -391.808 989 1178.67 -398.162 576 1194.732 -393.942 669 1247.494 -391.58 1038 1180.155 -397.946 656 1196.216 -393.722 675 1248.978 -391.351 1337 1181.64 -397.729 718 1197.7 -393.503 789 1172.995 -400.852 368 1183.125 -397.513 760 1199.184 -393.283 834 1174.481 -400.637 411 1184.61 -397.295 994 1200.668 -393.063 852 1175.966 -400.421 485 1187.579 -396.86 782 1202.152 -392.843 817 1177.451 -400.205 480 1189.064 -396.642 618 1203.636 -392.622 823 1178.936 -399.989 569 1190.548 -396.424 489 1205.12 -392.401 750 1180.422 -399.773 610 1192.033 -396.206 549 1206.603 -392.18 930 1181.907 -399.556 585 1193.517 -395.987 598 1208.087 -391.959 1127 1183.392 -399.339 596 1195.002 -395.768 577 1209.571 -391.737 726 1184.877 -399.122 591 1196.486 -395.549 809 1211.055 -391.515 1065 1186.362 -398.905 681 1197.97 -395.329 899 1212.538 -391.293 1323 1187.847 -398.687 787 1199.455 -395.109 1006 1214.022 -391.07 1448 1189.332 -398.469 528 1200.939 -394.889 1262 1215.505 -390.847 1548 1190.817 -398.251 700 1202.423 -394.669 988 1216.989 -390.624 1772 1192.302 -398.032 615 1203.907 -394.448 1120 1218.472 -390.401 1416 1193.786 -397.813 905 1205.392 -394.227 1054 1219.956 -390.177 1198 1195.271 -397.594 836 1206.876 -394.006 860 1221.439 -389.954 1225 1196.756 -397.375 827 1208.36 -393.785 1013 1222.922 -389.729 1580

### Great Smoky Mountains Lambert Conformal

X (km) Lambert Conformal

Y (km) Height (m) Lambert Conformal

X (km) Lambert Conformal

Y (km) Height (m) Lambert Conformal

X (km) Lambert Conformal

Y (km) Height (m)
1224.406 -389.505 1529 1246.369 -384.282 971 1222.094 -384.254 877 1225.889 -389.28 1165 1247.851 -384.053 945 1223.577 -384.029 892 1227.372 -389.055 1418 1249.333 -383.824 1058 1225.059 -383.805 910 1228.855 -388.83 1635 1192.709 -390.508 492 1226.541 -383.58 1134 1230.339 -388.605 1459 1194.193 -390.289 640 1228.023 -383.355 1358 1231.822 -388.379 1310 1195.676 -390.07 587 1229.506 -383.13 1512 1233.305 -388.153 1441 1197.159 -389.851 517 1230.988 -382.904 1674 1234.788 -387.926 1391 1198.643 -389.631 876 1232.47 -382.678 1806 1236.271 -387.7 1337 1200.126 -389.411 983 1233.952 -382.452 1678 1237.754 -387.473 1455 1201.609 -389.191 850 1235.434 -382.225 1725 1239.236 -387.246 1336 1203.092 -388.97 776 1236.916 -381.999 1256 1240.719 -387.018 1223 1204.576 -388.749 518 1238.397 -381.772 1160 1242.202 -386.791 1226 1206.059 -388.528 555 1239.879 -381.544 1382 1243.685 -386.563 979 1207.542 -388.307 487 1241.361 -381.317 1289 1245.168 -386.335 1079 1209.025 -388.085 626 1242.843 -381.089 1584 1246.65 -386.106 1104 1210.508 -387.864 794 1244.325 -380.861 1317 1248.133 -385.877 858 1211.991 -387.641 824 1245.806 -380.633 1170 1249.616 -385.648 1030 1213.474 -387.419 1004 1211.444 -383.99 548 1178.139 -394.509 475 1214.957 -387.196 1101 1212.926 -383.768 730 1179.623 -394.292 440 1216.439 -386.973 1435 1214.408 -383.545 699 1181.107 -394.076 640 1217.922 -386.75 1039 1215.89 -383.323 749 1191.495 -392.553 369 1219.405 -386.527 765 1217.372 -383.099 624 1192.979 -392.334 478 1220.888 -386.303 961 1218.854 -382.876 608 1194.462 -392.115 570 1222.37 -386.079 963 1220.336 -382.652 530 1195.946 -391.896 552 1223.853 -385.855 1391 1221.818 -382.428 797 1197.43 -391.677 843 1225.336 -385.63 1485 1223.3 -382.204 1103 1198.913 -391.457 604 1226.818 -385.405 1332 1224.782 -381.98 1110 1200.397 -391.237 605 1228.301 -385.18 1520 1226.264 -381.755 1130 1201.881 -391.017 652 1229.783 -384.955 1705 1227.746 -381.53 1499 1203.364 -390.796 770 1231.266 -384.729 1572 1229.228 -381.305 1742 1204.848 -390.575 834 1232.748 -384.503 1802 1230.71 -381.079 1769 1206.331 -390.354 725 1234.23 -384.277 1371 1232.191 -380.853 1639 1207.814 -390.133 608 1235.713 -384.05 1674 1233.673 -380.627 1342 1209.298 -389.911 656 1237.195 -383.823 1676 1235.155 -380.401 1247 1210.781 -389.689 1126 1238.677 -383.596 1586 1236.636 -380.174 1168 1212.265 -389.467 1115 1240.159 -383.369 1727 1238.118 -379.947 1012 1213.748 -389.245 1387 1241.641 -383.142 1516 1239.599 -379.72 959 1215.231 -389.022 1569 1243.124 -382.914 1359 1241.081 -379.492 1366 1216.714 -388.799 1595 1244.606 -382.686 1097 1242.562 -379.265 1348 1218.197 -388.576 1195 1246.088 -382.457 1022 1244.044 -379.037 1526 1219.68 -388.352 961 1247.57 -382.229 1016 1245.525 -378.808 1042 1221.163 -388.128 1084 1249.051 -382 732 1214.134 -381.72 488 1222.646 -387.904 1324 1198.372 -387.805 652 1215.616 -381.497 515 1224.129 -387.68 1732 1199.855 -387.585 1164 1217.097 -381.274 491 1225.612 -387.455 1720 1201.338 -387.365 1129 1218.579 -381.051 550 1227.095 -387.23 1512 1202.821 -387.144 1070 1220.061 -380.827 579 1228.578 -387.005 1680 1204.304 -386.924 942 1221.543 -380.603 781 1230.061 -386.78 1528 1205.786 -386.703 668 1223.024 -380.379 900 1231.544 -386.554 1452 1207.269 -386.481 425 1224.506 -380.155 1059 1233.026 -386.328 1535 1208.752 -386.26 532 1225.987 -379.93 1123 1234.509 -386.102 1401 1210.235 -386.038 696 1227.469 -379.705 1307 1235.992 -385.875 1483 1211.717 -385.816 673 1228.95 -379.48 1166 1237.474 -385.648 1605 1213.2 -385.594 775 1230.432 -379.254 1302 1238.957 -385.421 1574 1214.682 -385.371 1061 1231.913 -379.028 1672 1240.439 -385.194 1577 1216.165 -385.148 939 1233.394 -378.802 1533 1241.922 -384.966 1326 1217.647 -384.925 817 1234.876 -378.576 1429 1243.404 -384.738 1393 1219.13 -384.701 763 1236.357 -378.349 1457 1244.887 -384.51 1134 1220.612 -384.478 616 1237.838 -378.122 1391

### Great Smoky Mountains Lambert Conformal

X (km) Lambert Conformal

Y (km) Height (m) Lambert Conformal

X (km) Lambert Conformal

Y (km) Height (m) Lambert Conformal

X (km) Lambert Conformal

Y (km) Height (m)
1239.319 -377.895 937 1240.8 -377.668 1015 1242.282 -377.44 1056 1243.763 -377.212 1071 1245.244 -376.984 1060 1218.304 -379.225 427 1219.785 -379.002 480 1221.267 -378.778 488 1222.748 -378.554 624 1224.229 -378.33 761 1225.71 -378.105 732 1227.191 -377.88 881 1228.673 -377.655 821 1230.154 -377.429 1000 1231.635 -377.204 1094 1233.116 -376.978 1208 1234.597 -376.751 860 1236.078 -376.525 857 1237.558 -376.298 1391 1239.039 -376.071 1265 1240.52 -375.843 1067 1242.001 -375.616 921 1243.482 -375.388 611 1226.914 -376.055 608 1229.876 -375.604 699 1231.356 -375.379 785 1232.837 -375.153 892 1234.318 -374.927 622 1235.798 -374.7 762 1237.279 -374.473 1130 1238.759 -374.246 1458 1240.24 -374.019 1097 1241.72 -373.791 823 1243.201 -373.563 603 1232.558 -373.328 534 1234.039 -373.102 545 1235.519 -372.875 582 1236.999 -372.649 735 1238.479 -372.422 885 1239.96 -372.195 873 1239.319 -377.895 937

### Shining Rock Lambert Conformal

X (km) Lambert Conformal

Y (km) Height (m) Lambert Conformal

X (km) Lambert Conformal

Y (km) Height (m) Lambert Conformal

X (km) Lambert Conformal

Y (km) Hei ght (m)
1273.44 -416.507 1629 1275.842 -412.391 1161 1274.184 -416.39 1586 1265.287 -413.11 968 1274.928 -416.274 1370 1266.03 -412.994 1198 1275.672 -416.157 1274 1266.774 -412.878 1362 1276.416 -416.04 1181 1267.518 -412.762 1470 1268.832 -416.294 1183 1268.262 -412.646 1248 1269.576 -416.178 1416 1269.006 -412.529 1058 1270.321 -416.062 1541 1269.749 -412.413 1170 1271.065 -415.945 1677 1270.493 -412.296 1415 1271.809 -415.828 1640 1271.237 -412.18 1689 1272.553 -415.712 1770 1271.981 -412.063 1547 1273.297 -415.595 1679 1272.724 -411.946 1550 1274.041 -415.478 1585 1273.468 -411.83 1437 1274.785 -415.361 1529 1274.212 -411.713 1508 1275.529 -415.245 1309 1274.955 -411.596 1300 1276.273 -415.128 1128 1275.699 -411.479 1176 1267.946 -415.498 1097 1268.863 -411.617 1012 1268.69 -415.382 1217 1269.607 -411.501 1285 1269.434 -415.266 1536 1270.35 -411.384 1366 1270.178 -415.149 1463 1271.094 -411.268 1566 1270.922 -415.033 1436 1271.837 -411.151 1451 1271.666 -414.916 1629 1272.581 -411.034 1359 1272.41 -414.8 1771 1273.325 -410.918 1273 1273.154 -414.683 1622 1274.068 -410.801 1274 1273.898 -414.566 1425 1274.812 -410.684 1280 1274.642 -414.449 1312 1275.555 -410.567 1155 1275.385 -414.332 1362 1270.207 -410.472 1179 1276.129 -414.215 1067 1270.951 -410.355 1348 1276.873 -414.098 1162 1271.694 -410.239 1488 1277.617 -413.981 1399 1270.808 -409.443 1442 1267.059 -414.702 1029 1271.551 -409.327 1565 1267.803 -414.586 1227 1272.295 -409.21 1505 1268.547 -414.47 1505 1273.038 -409.093 1409 1269.291 -414.354 1347 1273.782 -408.977 1380 1270.035 -414.237 1317 1274.525 -408.86 1303 1270.779 -414.121 1536 1275.269 -408.743 1104 1271.523 -414.004 1675 1269.922 -408.648 1500 1272.267 -413.887 1729 1271.408 -408.415 1678 1273.011 -413.771 1523 1272.152 -408.298 1707 1273.754 -413.654 1544 1272.895 -408.181 1515 1274.498 -413.537 1429 1273.638 -408.065 1321 1275.242 -413.42 1315 1274.382 -407.948 1219 1275.986 -413.303 1068 1271.265 -407.503 1394 1276.73 -413.186 1066 1272.009 -407.386 1522 1277.474 -413.069 1352 1272.752 -407.269 1411 1266.173 -413.906 1024 1273.495 -407.152 1234 1266.917 -413.79 1296 1271.122 -406.59 1189 1267.661 -413.674 1404 1271.865 -406.474 1343 1268.404 -413.558 1373 1272.609 -406.357 1265 1269.148 -413.441 1198 1270.979 -405.678 1045 1269.892 -413.325 1198 1271.722 -405.562 1235 1270.636 -413.208 1419 1272.465 -405.445 1066 1271.38 -413.092 1571 1272.124 -412.975 1741 1272.867 -412.859 1717 1273.611 -412.742 1616 1274.355 -412.625 1569

### Joyce Kilmer Slickrock Lambert Conformal

X (km) Lambert Conformal

Y (km) Height (m) Lambert Conformal

X (km) Lambert Conformal

Y (km) Height (m) Lambert Conformal

X (km) Lambert Conformal

Y (km) Height (m)
1174.116 -429.607 1154 1168.724 -423.854 1065 1174.861 -429.499 1056 1169.468 -423.747 836 1175.607 -429.391 903 1170.213 -423.64 700 1176.352 -429.283 820 1170.958 -423.532 782 1177.097 -429.175 818 1171.703 -423.425 961 1173.239 -428.8 1278 1173.937 -423.102 809 1173.984 -428.693 1069 1174.682 -422.995 941 1174.729 -428.585 913 1167.847 -423.047 991 1175.475 -428.477 776 1168.592 -422.94 817 1176.22 -428.369 788 1169.337 -422.833 751 1176.965 -428.262 750 1170.081 -422.726 635 1172.362 -427.994 1327 1170.826 -422.619 772 1173.107 -427.887 1085 1171.571 -422.511 882 1173.852 -427.779 985 1168.461 -422.026 954 1174.597 -427.671 934 1169.205 -421.919 739 1175.342 -427.563 978 1169.95 -421.812 620 1176.088 -427.456 982 1170.694 -421.705 635 1176.833 -427.348 824 1171.439 -421.597 700 1169.995 -427.402 1317 1172.184 -421.49 865 1170.74 -427.295 1394 1168.329 -421.113 943 1171.485 -427.188 1487 1169.074 -421.005 742 1172.23 -427.08 1308 1169.818 -420.898 505 1172.975 -426.973 1237 1170.563 -420.791 558 1173.72 -426.865 1336 1171.307 -420.684 538 1174.465 -426.757 1290 1172.052 -420.576 686 1175.21 -426.65 1304 1168.198 -420.199 946 1175.955 -426.542 1111 1168.942 -420.092 760

1176.7 -426.434 1066 1169.686 -419.984 644 1169.863 -426.489 1189 1170.431 -419.877 491 1170.608 -426.381 1127 1171.175 -419.77 442 1171.353 -426.274 1299 1171.92 -419.662 491 1172.098 -426.166 1380 1168.066 -419.285 959 1172.843 -426.059 1553 1168.81 -419.178 752 1173.588 -425.951 1336 1169.555 -419.071 528 1174.333 -425.844 1219 1170.299 -418.963 598 1175.078 -425.736 1109 1171.043 -418.856 515 1175.823 -425.628 1133 1171.788 -418.749 400 1176.568 -425.52 1226 1172.532 -418.641 523 1168.987 -425.682 1280 1167.935 -418.371 712 1169.731 -425.575 1136 1170.167 -418.05 743 1170.476 -425.467 906 1170.912 -417.942 684 1171.221 -425.36 999 1171.656 -417.835 538 1171.966 -425.253 1170 1172.4 -417.727 361 1172.711 -425.145 1376 1275.099 -412.508 1422 1173.456 -425.037 1318 1174.201 -424.93 1369 1174.946 -424.822 1152 1175.691 -424.714 924 1176.436 -424.607 882 1168.11 -424.875 1151 1168.855 -424.768 1116

1169.6 -424.661 1044 1170.345 -424.554 772 1171.09 -424.446 866 1171.834 -424.339 1090 1173.324 -424.124 1020 1174.069 -424.016 1067 1167.979 -423.961 1224

### Linville Gorge Lambert Conformal

X (km) Lambert Conformal

Y (km) Height (m) Lambert Conformal

X (km) Lambert Conformal

Y (km) Height (m) Lambert Conformal

X (km) Lambert Conformal

Y (km) Height (m)
1349.498 -350.859 468 1346.472 -337.331 924 1350.236 -350.735 457 1347.209 -337.207 901 1349.345 -349.949 478 1347.946 -337.084 1114 1350.083 -349.825 576 1348.683 -336.96 1219 1347.717 -349.286 550 1345.583 -336.544 946 1348.455 -349.162 472 1346.32 -336.421 983 1349.193 -349.039 721 1347.056 -336.297 1142 1349.931 -348.915 759 1346.167 -335.511 1063 1350.669 -348.791 600 1351.406 -348.667 517 1347.564 -348.376 641 1348.302 -348.252 439 1349.04 -348.129 607 1349.778 -348.005 881 1350.516 -347.881 758 1347.412 -347.466 920 1348.15 -347.342 665 1348.887 -347.219 458 1349.625 -347.095 585 1350.363 -346.971 870 1347.997 -346.432 826 1348.735 -346.308 518 1349.473 -346.185 525 1350.21 -346.061 695 1350.948 -345.937 892 1347.845 -345.522 874 1348.582 -345.398 581 1349.32 -345.275 573 1350.057 -345.151 664 1348.43 -344.488 728 1349.167 -344.364 694 1349.905 -344.241 914 1348.277 -343.578 756 1349.015 -343.454 631 1349.752 -343.331 944 1348.125 -342.668 826 1348.862 -342.544 624 1349.599 -342.42 965 1347.972 -341.758 732 1348.709 -341.634 685 1349.446 -341.51 889 1347.819 -340.848 890 1348.557 -340.724 697 1349.294 -340.6 827 1346.93 -340.061 1096 1347.667 -339.938 749 1348.404 -339.814 816 1349.141 -339.69 1106 1346.777 -339.151 1055 1347.514 -339.027 762 1348.251 -338.904 909 1348.988 -338.78 1092 1345.888 -338.364 941 1346.625 -338.241 813 1347.362 -338.117 953 1348.099 -337.994 1097 1348.835 -337.87 1083 1345.735 -337.454 923

### Cape Romain Class I Area Lambert Conformal X (km) Lambert Conformal Y (km) Height (m) 1609.918 -633.619 0 1610.682 -633.471 1 1610.506 -632.561 1 1611.27 -632.414 1 1611.094 -631.504 1 1611.858 -631.356 1 1610.154 -630.742 1 1610.918 -630.594 1 1611.682 -630.446 1 1612.446 -630.298 1 1610.742 -629.684 1 1611.506 -629.537 1 1612.27 -629.389 1 1610.566 -628.775 0 1611.33 -628.627 1 1612.093 -628.479 1 1612.857 -628.331 1 1610.39 -627.865 1 1611.154 -627.717 1 1610.214 -626.955 1 1610.978 -626.807 1 1611.741 -626.66 1 1610.802 -625.898 1 1611.565 -625.75 1 1610.626 -624.988 1 1611.389 -624.84 2 1612.152 -624.693 1 1615.968 -623.952 0 1611.213 -623.931 1 1611.976 -623.783 1 1616.554 -622.895 0 1611.037 -623.021 1

1611.8 -622.873 1 1612.387 -621.816 1 1613.149 -621.668 1 1617.727 -620.779 0 1612.973 -620.758 1 1613.736 -620.61 1 1621.949 -618.069 0 1622.712 -617.92 1 1623.474 -617.772 1