# Regional Haze State Implementation Plan

for Tennessee Class I Areas

Prepared by 

### Tennessee Department of Environment and Conservation

Division of Air Pollution Control

April 4, 2008i 

### Preface: This document contains summaries of the tec hnical analyses that will be used by the Tennessee Department of Environment and Cons ervation’s Division of Air Pollution Control (TDEC-APC) to support the regional haze state implementation plan pursuant to §§107(d)(3)(D) and (E) of the Clean Air Act, as amended.ii 

### EXECUTIVE SUMMARY

Introduction

Regional haze is pollution that im pairs visibility over a large re gion, including national parks, forests, and wilderness areas (many termed “Cla ss I” areas). Regional haze is caused by sources and activities emitting fine particles and their pr ecursors, often transported over large regions. Particles affect visibili ty through the scattering an d absorption of light. Re ducing fine particles in the atmosphere is an effective method of impr oving visibility. In the southeast, the most important sources of haze-forming emissions are coal-fired power plants, industrial boilers and other combustion sources, but also include m obile source emissions, area sources, fires, and wind blown dust.

An easily understood measure of visibility to mo st people is visual range . Visual range is the greatest distance, in kilometers or miles, at wh ich a dark object can be viewed against the sky. However, the most useful measure of visibility impairment is light extinction, which affects the clarity and color of objects being viewed. The measure used by the regional haze rule is the deciview (dv), calcula ted directly from light extinction using a logarithmic scale.

The regional haze rule requires states to demonstrate reasonable progress toward meeting the national goal of a return to natural visibility conditions by 2064. The ru le directs states to graphically show what would be a “uniform rate of progress”, also known as the “glide path”, toward natural conditions for each Class I area within the State and certain ones outside the State.

Tennessee’s Class I areas

Tennessee has two Class I areas within its bord ers: Great Smoky Mountains National Park and Joyce Kilmer-Slickrock Wilderness Area. The Great Smoky Mountains National Park and Joyce Kilmer – Slickrock Wilderness Area are each loca ted in both North Carolina and Tennessee. The figure below illustrates the location of these Class I areas.

Visibility on the worst days at Tennessee’s Cl ass I areas is generally 30 dv. Natural background visibility on the worst da ys is between 11 and 12 dv. Great Smoky Mountains National Park

Joyce Kilmer - Slickrockiii State Implementation Plan Requirements

States are required to submit state implement ation plans (SIPs) to the United States Environmental Protection Agency th at set out each states’ plan fo r meeting the national goal of a return to natural visibility conditions by 2064. The plan includes the states’ reasonable progress goals, expressed in deciviews, fo r visibility improvement at each affected Class I area for each 10-year period until 2064.

SIPs must include determinations of the baselin e visibility conditions (e xpressed in deciviews) for the most impaired and least impaired days . In addition, states must include a monitoring strategy for measuring, characterizi ng, and reporting of regional h aze visibility impairment. The long-term strategy includes enforceable emissi ons limitations, complian ce schedules, and other measures as necessary to achieve the reasonabl e progress goals. States must also consider ongoing control programs, measures to mitigate c onstruction activities, source retirement and replacement schedules, smoke management pr ograms for agriculture and forestry, and enforceability of specific measures.

The SIPs for the first review period are due December 17, 2007. These plans will cover longterm strategies for visibility improvement between baseline conditions in 2000-2004 and 2018. States are required to evaluate progress toward reasonable progress goals every 5 years to assure that installed emissions controls are on track with emissions re duction forecasts in each SIP.

Federal and State Control Requirements

There are significant control programs being impl emented between the baseline period and 2018. These programs will all reduce the part iculate emissions that affect visibility in the Class I areas, and include: the Clean Air Interstate Rule (C AIR), the NOx SIP Call, the North Carolina Clean Smokestacks Act, Georgia Multi-Pollutant Rule , consent agreements with Tampa Electric, Virginia Electric and Power Company, American Electric Power, and Gulf Power, one-hour ozone SIPs submitted by Atlanta, Birmingham, and Northern Kentucky, NOx RACT in 8-hour nonattainment area SIPs, heavy dut y diesel (2007) engine standa rd (for on-road trucks and buses), Tier 2 tailpipe standards for on-road vehicles, large spark ig nition and recreational vehicle rule, nonroad diesel ru le, and various Federal Maximu m Achievable Control Technology regulations.

The regional haze rule also requires states to determine best available retrofit technology (BART) for certain facilities. Ei ght of Tennessee’s twelve BART-e ligible sources were able to demonstrate that they did not cause or contribute to visibility impairment at any Class I areas. Four BART sources in Tenness ee are being subjected to BART limitations. Permit conditions for the facilities appear in Section 7.5.3, and upon approval by USEPA, will be incorporated into the facility’s Part 70 permit as a new applicable requirement in keeping with the reopening requirements of 40 CFR Part 70. The four BART sources and a brief description of their BART control obligations are presented below. The actual permit conditions presented in Section 7.5.3 are the ruling limitations. The summary below is presented merely for ease of reference and should in no way be interpreted to supercede the permit conditions pres ented in Section 7.5.3:iv 1. Eastman Chemical Company in Kingsport, Sullivan County, Tennessee – This facility has an SO 2 emission limitation/removal efficiency limitation such that post control combustion controls are a certainty. 2. Alcoa in Alcoa, Blount County, Tennessee – This facility is a primary aluminum smelting operation that uses pe troleum coke to manufacture aluminum from alumina ore. The sulfur content of the petroleum coke is being limited to 3 pe rcent by weight. The limitation of sulfur in the coke will limit the formation of sulfur oxides. 3. TVA Cumberland Fossil Plan t in Cumberland City, Stewart County, Tennessee – This facility is already controlled by a fl ue gas desulfurizat ion scrubber for SO 2 . Because of trading provisions allowed under the USEPA’ s Acid Rain Program and its Clean Air Interstate Rule, an emission limitation of 0.5 pounds of SO 2 per million British Thermal Units of heat input is being established to li mit the “importation” of control credits from other facilities to satisfy the f acility’s BART c ontrol obligations. 4. Dupont in Old Hickory, Davidson County, Tennessee – This facili ty is being subjected to seasonal emission limitations for both SO 2 and NO x .

Conclusion

At both Class I areas in Tennessee, visibility improvements on the wo rst days are expected to be better than the uniform rate of progress g lidepath by 2018 based solely on reductions from existing and planned emissions cont rols. Additionally, the visibility is expected to improve for the best days for all of the Tennessee Class I ar eas. The table below displays the 2018 reasonable progress goals for the Te nnessee Class I areas.

Class I Area Baseline Visibility Worst Days Reasonable Progress Goal – Worst Days Baseline Visibility Best Days Reasonable Progress Goal – Best Days Great Smoky Mountains National Park 30.28 23.50 13.58 12.11

Joyce – Kilmer Slickrock Wilderness Area 30.28 23.50 13.58 12.11v TABLE OF CONTENTS

1.0 INTRODUCTION ................................................................................................................... 1 1.1 What is regional haze? .................................................................................................... ...... 1 1.2 What are the requirements under the Clean Air Act for addressing regional haze? ............. 1 1.3 General overview of regiona l haze SIP requirements .......................................................... 3
1.4 Class I areas in Tennessee ................................................................................................ .... 4
1.5 State and Federal Land Mana ger (FLM) coordination ......................................................... 7

2.0 ASSESSMENT OF BASELINE AND CU RRENT CONDITIONS AND ESTIMATE OF NATURAL BACKGROUND CONDIT IONS IN CLASS I AREAS ................................. 9 2.1 Estimating Natural Conditions for Tennessee Class I Areas .............................................. 10 2.2 Estimating Baseline Conditions for Tennessee Class I Areas ............................................ 12 2.3 Summary of Natural Background and Baseline Conditions for Tennessee Class I Areas . 12 2.4 Pollutant Contributions to Visibility Impairment (2000-2004 Baseline Data) ................... 13

3.0 GLIDEPATHS TO NATURAL CONDITIONS IN 2064 ................................................... 17 3.1 Glidepaths for Class I Areas in Tennessee ......................................................................... 17

4.0 TYPES OF EMISSIONS IMPACTING VISIBILITY IMPAIRM ENT IN TENNESSEE CLASS I AREAS ................................................................................................................ 19 4.1 Baseline Emissions Inventory ............................................................................................. 19 4.1.1 Stationary Point Sources ............................................................................................ 20 4.1.2 Stationary Area Sources ............................................................................................. 21
4.1.3 Off-Road Mobile Sources .......................................................................................... 22 4.1.4 Highway Mobile Sources ........................................................................................... 22 4.1.5 Biogenic Emission Sources ........................................................................................ 22 4.1.6 Summary 2002 Base G2 (February 2007 vers ion) Baseline Emissions Inventory for

Tennessee ..................................................................................................................... .... 23 Source Category ............................................................................................................... ......... 23
Point ......................................................................................................................... ................. 23 4.1.7 Model Performance Improvements through Emissions Inventory Improvements .... 23 4.2 Assessment of Relative Contributions from Sp ecific Pollutants and S ources Categories . 24

5.0 REGIONAL HAZE MODELING METHODS AND INPUTS ........................................... 25 5.1 Analysis Method ........................................................................................................... ...... 25
5.2 Model Selection ........................................................................................................... ....... 26
5.2.1 Selection of Photochemical Grid Model .................................................................... 26 5.2.2 Selection of Meteorological Model ............................................................................ 27 5.2.3 Selection of Emissions Processing System ................................................................ 29vi 5.3 Selection of the Modeling Year .......................................................................................... 3 0 5.4 Modeling Domains .......................................................................................................... ... 32 5.4.1 Horizontal Modeling Domain .................................................................................... 32 5.4.2 Vertical Modeling Domain ......................................................................................... 33

6.0 MODEL PERFORMANCE EVALUATION ....................................................................... 33 6.1 Modeling Performance Goals, and Criteria ........................................................................ 35
6.2 VISTAS Domain-Wide Performance ................................................................................. 36
6.3 Tennessee Class 1 Areas Performance ............................................................................... 39 6.3.1 Great Smoky Mountain National Park ....................................................................... 41

7.0 LONG-TERM STRATEGY FOR TENNESSEE CLASS I AREAS ............................... 42 7.1 Overview of the Long-Term Strategy Development Process ............................................. 42 7.2 Expected Visibility Results in 2018 for TN Class I Areas under existing and planned emissions controls ............................................................................................................ 43 7.2.1 Federal and State Control Requirements .................................................................... 43 7.2.2 Additional State programs to reduce emissions ......................................................... 47 7.2.3 Projected 2009 and 2018 Emissions Inventories ........................................................ 47 7.2.4 Model Results for the 2018 Inventory Comp ared to the Uniform Rate of Progress

Glidepaths for Tennessee Class I Areas ........................................................................... 49
7.3 Relative Contribution from In ternational Emissions to Visi bility Impairment in 2018 at VISTAS Class I areas ....................................................................................................... 54 7.4 Relative Contributions to Visibility Impa irment: Pollutants, Source Categories, and Geographic Areas ............................................................................................................. 57 7.5 What Control Determinations Represent Be st Available Retrofit Technology (BART) for Individual Sources? .......................................................................................................... 60 7.5.1 BART-Eligible Sources in Tennessee ...................................................................... 60 7.5.2 Determination of Sources Subj ect to BART in Tennessee ........................................ 62 7.5.3 Determination of BART Requiremen ts for Subject-to-BART Sources ..................... 66 7.6 Relative Contributions to Visibility Impa irment: Geographic Areas of Influence for Tennessee Class I Areas ................................................................................................... 70 7.6.1 Back Trajectory Analyses .......................................................................................... 71
7.6.2 Residence Time Plots ................................................................................................. 72
7.6.3 SO2 Areas of Influence .............................................................................................. 72
7.6.4 Emissions Sources within SO2 Areas of Influence .................................................. 73 7.6.5 Specific Source Types in the Areas of Influence for Tennessee Class I Areas ......... 75 7.7 Evaluating the Four Statutory Factors for Specific SO2 Emissions Sources in Each Area of Influence ..................................................................................................................... ...... 79
7.8 Which Control Measures Re present Reasonable Progress fo r Individual Sources? .......... 81vii 8.0 REASONABLE PROGRESS GOALS .............................................................................. 86

9.0 MONITORING STRATEGY ............................................................................................... 88

10.0 INTERSTATE CONSULTATION .................................................................................... 91

11.0 COMPREHENSIVE PERIODIC IM PLEMENTATION PLAN REVISIONS ............... 92

12.0 DETERMINATION OF ADEQUACY OF THE EXISTING PLAN ............................... 94

13.0 IMPACT OF TENNESSE E SOURCES ON CLASS I AREAS OUTSIDE OF TENNESSEE ...................................................................................................................... 95viii Appendix A: VISTAS Memorandu m of Understanding and Bylaws Appendix A - VISTASBylaws\Appendix A.doc

Appendix B: Conceptual Description Appendix B - ConceptDesc\Appendix B - Cover & Intro.doc

Appendix C: Modeling Analysis Protocol and Quality Assurance Project Plan Appendix C - ModProtocols&QAPP-Revi sed\Appendix C Header and Intro.doc

Appendix D: Emissions Preparation and Results Appendix D - Emissions Preparati on and Results\Appendix D header.doc

Appendix E: Air Quality and Meteor ological Preparations and Results Appendix E - AQ&MetPrep&Result\A ppendix E. - Header and Intro.doc

Appendix F: Model Performance Evaluation Appendix F - MODPerformanceEval\Appendix F header.doc

Appendix G: Modeling Results and Supplemental Analysis Appendix G- MODResults&SuppAnalysis\Appendix G header.doc

Appendix H: Reasonable Progress Evaluation/Long Term Strategy Appendix H - RPG and LTS\Appendix H header.doc

Appendix I: Data Access Appendix I - Data Access\Appendix I header.doc

Appendix J: Consultation Process Appendix J - Consultation Process\Appendix J - header.doc

Appendix K: Recommended Improvements Appendix K - Improvements\Appendix K - header.doc

Appendix L: BART Related Documentation Appendix L - TN BART Related Documents\Appendix L header.doc

Appendix M: VISTAS Modeling Reports Appendix M - VISTAS Modeling\Appendix M - header.doc

Appendix N: Public Hearing Comment Summary & Agency Responses Appendix N - Public Record\Appendix N - Header.doc1 

### 1.0 INTRODUCTION

1.1 What is regional haze?

Regional haze is pollution from disparate sour ces that impairs visibility over a large region, including national parks, forests, a nd wilderness areas (156 of which are termed mandatory Federal “Class I” areas). Regi onal haze is caused by sources and activities emitting fine particles and their precursors. Those emissions are often transported over large regions.

Particles affect visibi lity through the scatteri ng and absorption of light, and fine particles – particles similar in size to the wavelength of light – are most efficient, per unit of mass, at reducing visibility. Fine particles may either be emitted directly or formed from emissions of precursors, the most important of which are sulfur dioxides (SO2) and nitrogen oxides (NOx). Reducing fine particle s in the atmosphere is generally considered to be an effective method of reducing regiona l haze, and thus improving visibility. Fine particles also adversely impact human health, especially respirator y and cardiovascular systems. The United States Environmental Protection Agency (USEPA) has set national ambient air quality standards for daily and annua l levels of fine particles with a diameter smaller than 2.5 μ m (PM2.5). In the southeast, the most important sources of PM2.5 and its precursors are coal-fired power plants, industrial bo ilers and other combustion sources. Other significant contributors to PM2.5 and visibility impairment include mobile source emissions, area source s, fires, and wind blown dust.

1.2 What are the requirements under the Clean Air Act for addressing regional
haze?

In Section 169A of the 1977 Amendments to the Clean Air Act (CAA), Congress set forth a program for protecting visibility in Federal Class I areas which calls for the “prevention of any future, and the remedying of any ex isting, impairment of visibility in mandatory Class I Federal areas which impairme nt results from manm ade air pollution.” Congress adopted the visibility provisions to pr otect visibility in th ese 156 national parks, forests and wilderness areas. On Decembe r 2, 1980, the USEPA promulgated regulations to address visibility impairment (45 FR 80084). The 1980 regulations were developed to address visibility impairment that is “reasonab ly attributable” to a single source or small group of sources. These regula tions represented the first pha se in addressing visibility impairment and deferred action on regional haze that emanates from a variety of sources until monitoring, modeling and scientific know ledge about the relationships between pollutants and visibility impairment improved.

In the 1990 Amendments to the CAA, Congress added section 169B and called on USEPA to issue regional haze rules. The regional haze rule that USEPA promulgated on July 1, 1999 (64 FR 35713), revised the existing vi sibility regulations in order to integrate2 provisions addressing regional haze impairment and establish a comprehensive visibility protection program for Class I Federal areas . States are required to submit state implementation plans (SIPs) to the USEPA that set out each states’ plan for complying with the regional haze rule, including consulta tion and coordination with other states and with Federal Land Managers (FLMs). The timing of SIP submittal is tied to USEPA’s promulgation of designations for the Nati onal Ambient Air Quality Standard (NAAQS) for fine particulate matter. States must s ubmit a regional haze implementation plan to the USEPA within three years after the date of designation. Because the USEPA promulgated designation dates on Decembe r 17, 2004, regional haze SIPs must be submitted by December 17, 2007.

The regional haze rule addressed the combined visibility effects of various pollution sources over a wide geographic region. Th is wide reaching pollution net meant that many states – even those without Class I Areas – would be required to participate in haze reduction efforts. The USEPA designated fi ve Regional Planning Organizations (RPOs) to assist with the coordination and cooperation needed to address the visibility issue. The RPO that makes up the southeastern por tion of the contiguous United States is known as VISTAS (Visibility Improvement – State and Tribal Association of the Southeast), and include the following stat es: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia.

Figure 1.2-1. Geographical Areas of Regional Planning Organizations3 1.3 General overview of regional haze SIP requirements

The regional haze rule at 51.308(d) requires st ates to demonstrate reasonable progress toward meeting the national goal of a return to natural visibility conditions by 2064. As a guide for reasonable progress, the regional haze rule directs states to graphically show what would be a “uniform rate of progress” toward natural conditions for each mandatory Class I Federal area within the State and/or for each mandatory Class I Federal area located outside the State, which may be aff ected by emissions from sources within the State. States are to esta blish baseline visibility conditions for 2000-2004, natural background visibility conditions in 2064, a nd the rate of uniform progress between baseline and background conditi ons. The uniform rate of progress is also known as the “glidepath.”

The regional haze rule then requires States to establish reasonable progress goals, expressed in deciviews, for vi sibility improvement at each affected Class I area covering each (approximately) 10-year period until 2064. The goals must provide for reasonable progress towards achieving natu ral visibility conditions, pr ovide for improvement in visibility for the most impaired days ove r the period of the implementation plan, and ensure no degradation in visibility for the le ast impaired days over the same period [see 40 CFR 51.308(d)(1)].

In order to ensure that visi bility goals are prop erly met and set, SIPs must include determinations, for each Class I area, of the baseline visibility conditions (expressed in deciviews) for the most impaired and least impaired days. SIPs must also contain supporting documentation for all required an alyses used to calculate the degree of visibility impairment under natural visibility conditions for the most impaired and least impaired days [see 40 CFR 51.308(d)(2)]. In addition, states must include a monitoring strategy for measuring, characterizing, a nd reporting of regional haze visibility impairment that is representative of all ma ndatory Class I Federal areas within the state [see 40 CFR 51.308(d)(4)].

This first set of reasonable progress goals must be met through measur es contained in the state’s long-term strategy covering the peri od from the present until 2018. The long-term strategy includes enforceable emissions lim itations, compliance schedules, and other measures as necessary to achieve the reasona ble progress goals, in cluding all controls required or expected under all federal and state regulations by 2009 and by 2018. During development of the long-term strate gy, states are also re quired to consider specific factors such as the abovementione d ongoing control progr ams, measures to mitigate construction activities, source retirement and replacement schedules, smoke management programs for agriculture and forestry, and enforceability of specific measures [see 40 CFR 51.308(d)(3)].

In addition, a specific component of each state’ s first long-term strate gy is dictated by the specific best available retr ofit technology (BAR T) requirements in 40 CFR 51.308(e) of the regional haze rule. The regional haze ru le at 40 CFR 51.308(e) requires states to include a determination of BART for each BART-eligible source in the State that emits4 any air pollutant, which may reasonably be an ticipated to cause or contribute to any impairment of visibility in any mandatory Class I Federal area. The Clean Air Act at section 169A(b) defines BART-eligible s ources as sources in 26 specific source categories, in operation within a 15-year pe riod prior to enactmen t of the 1977 Clean Air Act Amendments. States must determine BA RT according to five factors set out in section 169A(g)(7) of the Clean Air Act. Emission limitations representing BART and schedules for compliance with BART for each source subject to BART must be included in the long-term strategy.

The SIPs for the first review period are due December 17, 2007. These plans will cover long-term strategies for visibility improve ment between baseline conditions in 2000-2004 and 2018. States are required to evaluate progress toward reasonable progress goals every 5 years to assure that installed emi ssions controls are on track with emissions reduction forecasts in each SIP. The first interim review would be due to USEPA in December 2012. If emissions controls are not on track to meet SIP forecasts, then states would need to take action to assure emissions controls by 2018 will be consistent with the SIP or to revise the SIP to be consistent with the revised emi ssions forecast.

The USEPA provided several guidance documents listed below to assist the states in implementation of the regional haze rule requirements. Tennessee followed these guidance documents in developing the techni cal analyses reported in this plan.

• Guidance for Tracking Progress Under the Regional Haze Rule (EPA-454/B-03- 004, September 2003).

• Guidance for Estimating Natural Visibil ity Conditions Under the Regional Haze Rule (EPA-454/B-03-005, September 2003).

• Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5 and Regional Haze (EPA, 2007).

• Guidance for Setting Reasonable Progr ess Goals Under the Regional Haze Program (EPA, June 2007).

1.4 Class I areas in Tennessee

Tennessee has two Class I areas within its borders: Great Smoky Mountains National Park and Joyce Kilmer-Slickrock Wilderne ss Area. TDEC-APC is responsible for developing the Regional Haze SIP for Tenne ssee and submitting it to the Tennessee Air Pollution Control Board for approval. This SIP establishes reasonable progress goals for visibility improvement at each of these Cla ss I areas, and a long-term strategy that will achieve those reasonable progress goals with in the first regional haze planning period. The Great Smoky Mountains and Joyce Kilmer – Slickrock are located in both Tennessee and North Carolina. For the Great Smoky Mountai ns, both states are sharing the lead for setting goals and for Joyce Kilmer – Slickr ock, North Carolina is the lead. These two5 Class I Areas for Tennessee are described at 40 CFR 81.428 and are shown graphically in Figure 1.4-1.

Per the aforementioned federal regulation and data from the National Park Service regarding the Great Smoky Mountains Nationa l Park, Tennessee has 244,742 acres of the Park and North Carolina has 276,344 acres of the Park for a total of 521,086 acres.
Great Smoky Mountains National Park TN & NC Acreage Percentages

NC 276,344 Acres 53% TN 244,742 Acres 47%

With respect to the Joyce Kilmer Slickr ock Wilderness Area, Tennessee has 3,832 acres of the Area and North Carolina has 13,562 acr es of the Area for a total of 17,394 acres.

Joyce Kilmer Slickrock Wilderness Area TN & NC Acreage Percentages

NC 13,562

Acres 78% TN 3,832

Acres 22%

Because both of Tennessee’s Class I Areas ar e also shared with the State of North Carolina, it is fitting that the two states’ SIPs for the common Class I Areas are similar. Tennessee’s Regional Haze SIP follows the general outline of the North Carolina Regional Haze SIP with the particularities of Tennessee appearing in the narrative and parts of the Appendices. The data in the Appe ndices was jointly developed by the State of Tennessee and the State of North Carolin a working in conjunc tion with VISTAS.6 Figure 1.4-1. Tennessee Class I areas

In developing this SIP, TDEC-APC has also considered that emissi on sources outside of Tennessee may affect visibility at these Tennessee Class I areas, and that emission sources within Tennessee may affect visibili ty at Class I areas in neighboring states. Through VISTAS, the southeastern states have worked together to assess state-by-state contributions to visibility impairment in specific class I areas, including those in Tennessee and those affected by emissions fr om Tennessee. This technical work is discussed further in Sections 5, 6, and 7 belo w. Consultations to date between Tennessee and other states are summarized in S ection 10; consultati ons are ongoing.

Prior to VISTAS, the southern states coopera ted in a voluntary re gional partnership “to identify and recommend reasonable measures to remedy existing and prevent future adverse effects from human-induced air polluti on on the air quality related values of the Southern Appalachian Mountains ”. States cooperated with FLMs, the USEPA, industry, environmental organizations and academia to complete a technical assessment of the impacts of acid deposition, ozone, and fine particles on sensitive resources in the Southern Appalachians. The (Southern A ppalachian Mountain Init iative) SAMI Final Report was delivered in August 2002. The SA MI Assessment concluded that ammonium sulfate is the major contributor to visibili ty impairment in the Southern Appalachian Mountains and to improve visibility, it is most important to reduce sulfur dioxide emissions. SAMI also concluded that redu cing ammonia emissions would be helpful to reduce ammonium nitrate contributions to visi bility impairment. Emissions controls for organic carbon, elemental carbon, and soil were expected to be less important for improving visibility. The SAMI modeling found that on the haziest days, much of the benefit of emissions reductions would occur in the state where emi ssions reductions were made. Emissions in SAMI states and states outside the SAMI regi on also contribute to air quality in the SAMI Cla ss I areas. The SAMI states supported strong national multipollutant legislation to accomplish its missi on. Emissions reductions to meet national health standards for ozone and fine particles we re expected to also improve air quality in the Southern Appalachian Mountains. The SAMI states committed to consider air quality benefits in the Southern Appalachians as they developed State Implementation Plans for the health standards. Great Smoky Mountains National Park

Joyce KilmerSlickrock7 The Tennessee Valley Authority (TVA) operates several coal-fired electric utility power plants in Tennessee. The Tennessee Air Pollu tion Control Board has been overseeing the efforts of the TVA to control both its nitrog en oxide and sulfur dioxide emissions through formal updates from its senior management to the Board. The Board has made it clear to TVA that it expects the reductions to be si gnificant and targeted to most effectively address nonattainment and the protection of Cl ass I areas. If TVA fails to satisfy the Board’s expectations, rulemaking will follow. To TVA’s credit, thousands of tons of emissions have been or will shortly be reduced in areas where it was needed most. Their reductions are also consistent with the SAMI analysis and recommendations.

Congress considered several legi slative bills to reduce sulfur dioxide and nitrogen oxides from electric generating utilities. In 2004, USEPA promulgated the Clean Air Interstate Rule (CAIR) to require emissions reductions fo r sulfur dioxide and ni trogen dioxide from electric generating utilities in 26 eastern states. The CAIR rule allows for interstate trading of emissions to find cost effectiv e reductions. These reductions will improve visibility in Class I areas in Tennessee.

1.5 State and Federal Land Manager (FLM) coordination

As required by 40 CFR §51.308(i), the regional haze SIP must include procedures for continuing consultation between the Stat es and Federal Land Managers on the implementation of the visibility protection program, including development and review of implementation plan revisi ons and 5-year progress repor ts, and on the implementation of other programs having the potential to cont ribute to impairment of visibility in any mandatory Class I Federal area within the St ate. The three FLMs are the United States Department of Interior’s (USDI’s) Fish a nd Wildlife Service (FWS) and National Park Service (NPS) and the United States Depart ment of Agriculture’s (USDA’s) Forest Service (FS).

Coordination of Tennessee’s continuing obliga tions to periodically revise its regional haze SIP with the Federal Land Managers is also discussed in Section 11.0 –COMPREHENSIVE PERIODIC IMPLEMENTATION PLAN REVISIONS. TDECAPC formally commits to follow the Federal Land Manager consultation procedures as prescribed in 40 CFR Secti on 51.308(i) in making these future plan reviews and revisions.

Pursuant to 40 CFR 51.308(i)(4), procedures are required to be declared that detail the coordination with the Federal Land Managers and TDEC-APC with respect to regional haze matters. In addition to complying w ith 40 CFR 51.308(i), TDEC-APC declares that it will offer the Federal Land Managers an opportunity to meet on a yearly basis to discuss the progress being made to improve visibility and any Federal Land Manager suggested improvements to the process that ar e relevant to the efforts of TDEC-APC to comply with the regulations governing the de velopment and implementation of regional haze SIPs. The timing of these yearly meeti ngs will be approximately 60 days after the IMPROVE monitoring data is finalized for the preceding year.8 The Federal Land Managers were extensively in volved in the preparation of this Regional Haze SIP. Documentation of the formal co mments made by the Federal Land Managers and TDEC-APC’s response appears in Appe ndix N – Public Hearing Comment Summary and Agency Responses.

Successful implementation of a regional h aze program will involve long-term regional coordination among States. VI STAS was formed in 2001 to address regional haze and visibility problems in the southeastern Un ited States. Jurisdic tions represented by VISTAS members include the Eastern Band of Cherokee Indians; the States of Alabama, Florida, Georgia, Kentucky, Mississippi, No rth Carolina, South Carolina, Tennessee, Virginia, and West Virginia; and the local ai r pollution control progr ams located in these States. A copy of the VISTAS Memorandum of Agreement and Bylaws is enclosed as Appendix A.

The objectives of the VISTAS project are to establish natural b ackground visibility conditions across the mandatory Class I Fede ral areas, identify current visibility impairment levels, analyze emission control le vels that will achieve interim visibility goals, and provide adequate documentation to me mber agencies so that they can develop their regional haze State/Tribal Implementation Plans (SIP/TIP). Figure 1.5-1 shows the 18 mandatory Class I Federal areas in th e VISTAS Region, where visibility is an important value. Table 1.5- 1 lists these Class I areas.

Figure 1.5-1. Class I Areas in the VISTAS Region9 Mandatory Class I Federal Areas in the VISTAS Region

State Area Name Acreage Federal Land Manager Alabama Sipsey Wilderness 24,922 USDA-FS

Florida Chassahowitzka Wilderness 23,579 USDI-FWS Everglades National Park 1,397,429 USDI-NPS St. Marks Wilderness 17,350 USDI-FWS

Georgia Cohutta Wilderness 36,977 USDA-FS Okefenokee Wilderness 353,981 USDI-FWS Wolf Island Wilderness 5,126 USDI-FWS Kentucky Mammoth Cave National Park 51,303 USDI-NPS

North Carolina Great Smoky Mountains National Park 273,551 USDI-NPS Joyce Kilmer-Slickrock Wilderness 13,562 USDA-FS Linville Gorge Wilderness 11,786 USDA-FS Shining Rock Wilderness 18,483 USDA-FS Swanquarter Wilderness 8,785 USDI-FWS South Carolina Cape Romain Wilderness 29,000 USDI-FWS Tennessee Great Smoky Mountains National Park 241,207 USDI-NPS Joyce Kilmer-Slickrock Wilderness 3,832 USDA-FS Virginia James River Face Wilderness 8,886 USDA-FS Shenandoah National Park 190,535 USDI-NPS West Virginia Dolly Sods Wilderness 10,215 USDA-FS Otter Creek Wilderness 20,000 USDA-FS

Table 1.5-1. Mandatory Class I Federal Areas in the VISTAS Region



### 2.0 ASSESSMENT OF BASELI NE AND CURRENT CONDITIONS
AND ESTIMATE OF NATURAL B ACKGROUND CONDITIONS IN
CLASS I AREAS

The goal of the Regional Haze Rule is to rest ore natural visibility conditions to the 156 Class I areas identified in the 1977 Clean Air Act Amendments. 40 CFR 51.301(q) defines natural conditions: “Natural conditions include naturally occurring phenomena that reduce visibility as measured in terms of light extinction, visu al range, contrast, or coloration.” The Regional Haze SIPs must contain measures that make “reasonable progress” toward this goal by reducing anth ropogenic, i.e., manmade, emissions that cause haze.10 An easily understood measure of visibility to most people is visual range. Visual range is the greatest distance, in kilometers or miles, at which a dark object can be viewed against the sky. For evaluating the relative contributi ons of pollutants to visibility impairment, however, the most useful measure of visibili ty impairment is light extinction, which is usually expressed in units of inverse megameters (Mm -1 ). Light extinction affects the clarity and color of objects being viewed.

The measure used by the regional haze rule is the deciview (dv). Deciviews are calculated directly from light extinction usi ng a logarithmic scale. The deciview is a useful measure for tracking progress in im proving visibility, because each deciview change is an equal incremental change in vi sibility perceived by the human eye. Most people can detect a change in visibility at one deciview.

For each Class I area, there ar e three metrics of visibil ity that are part of the determination of reasonable progress:

1) natural conditions, 2) baseline conditions and 3) current conditions.

Each of the three metrics includes the concentr ation data of the visibility pollutants as different terms in the light extinction algorith m, with respective ex tinction coefficients and relative humidity factors. Total light ex tinction when converted to deciviews (dv) is calculated for the average of the 20 percent be st and 20 percent worst visibility days.

“Natural” visibility is determined by estima ting the natural concentrations of visibility pollutants and then calculating total light extinction. “Baselin e” visibility is the starting point for the improvement of visibility conditio ns. It is the averag e of the Interagency Monitoring of Protected Visual Environm ents (IMPROVE) mon itoring data for 2000 through 2004 and is equivalent to “current” visi bility conditions for this initial review period. The comparison of initial baseline c onditions to natural visibility conditions indicates the amount of improvement necessary to attain natural visibility by 2064. Each state must estimate natural visibility levels for Class I areas within its borders in consultation with Federal Land Managers and other states [40 CFR 51.308(d)(2)]. “Current conditions” are assessed every five years as part of the SIP review where actual progress in reducing visibility impairment is compared to the reductions committed to in the SIP.

2.1 Estimating Natural Conditions for Tennessee Class I Areas

Natural background visibility, as defined in Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Program, EPA-454/B-03-005, September 2003, is based on annual average concentrations of fi ne particle components. The same annual average natural background visi bility is assumed for all Cl ass I areas in the eastern United States (separate values are estimate d for the western United States). Natural background visibility for the 20 percent worst days is estimated by assuming that fine particle concentrations for natural ba ckground are normally distributed and the 90 th11 percentile of the annual distri bution represents natural b ackground visibility on the 20 percent worst days (i.e, Method 1 predicti on per EPA default estimate guidance). For detailed information on the 20 percent worst da ys and 20 percent best days for TN Class I areas for the baseline year 2002, please refer to Appendix F-2.

In the 2003 guidance, USEPA also provided that states may use a “r efined approach” to estimate the values that characterize the natura l visibility conditions of the Class I areas. The purpose of such a refinement would be to provide more accurate estimates with changes to the extinction algorithm that may in clude the concentration values, factors to calculate extinction from a measured particul ar species and particle size, the extinction coefficients for certain compounds, geographical variation (by altitude ) of a fixed value, and the addition of visibility pollutants.

In 2005 the IMPROVE Steering Committee ma de recommendations for a refined equation that modifies the terms of the orig inal equation to account for the most recent data. The choice between use of the old or the new equation for calculating the visibility metrics for each Class I area is made by the st ate in which the Class I area is located.

The new IMPROVE equation accounts for the e ffect of particle size distribution on light extinction efficiency of sulfate, nitrate, and organic carbon. The mass multiplier for organic carbon (particulate or ganic matter) is increased from 1.4 to 1.8. New terms are added to the equation to account for light extinction by sea salt and light absorption by gaseous nitrogen dioxide. Site-specific values are used for Rayleigh scattering to account for the site-specific effects of elevation a nd temperature. Separate relative humidity enhancement factors are used for small and la rge size distributions of ammonium sulfate and ammonium nitrate and for sea salt. Th e elemental carbon (lig ht-absorbing carbon), fine soil, and coarse mass terms do not change between the original and new IMPROVE equation.

Natural background conditions using the new IMPROVE equation are calculated separately for each Class I area. The calculati on starts with the a nnual average values for natural background for each component of PM2.5 mass from the EPA 2003 guidance (default values). The annual frequency dist ribution of values of each PM2.5 component for current conditions (2000-2004) is then de fined. This species-specific frequency distribution is applied to the default annual average values for that PM2.5 component to calculate natural conditions on the 20% wors t days. The current variability in each component is retained while also reta ining the same annual average background condition for that component as defined in the 2003 guidance. The new calculation of natural background allows Raylei gh scattering to vary with elevation. Current sea salt values are used for natural background levels of sea salt.

The VISTAS states chose to use the ne w IMPROVE equation as the basis for the conceptual description because it takes into account the most recent review of the science and because it is recommended by the IM PROVE Steering Committee. For more detailed discussion of the two IMPROVE equations, see Appendix B.12 2.2 Estimating Baseline Conditions for Tennessee Class I Areas

Baseline visibility conditions at each Tenne ssee Class I area are estimated using sampling data collected at the IMPROVE monitoring si te at one of the two Class I areas in Tennessee. A five year average (2000 to 2004) was calculated for each of the 20 percent worst and 20 percent best visibility days in accordance with 40 CFR 51.308(d)(2) and Guidance for Tracking Progress Under the Regional Haze Rule , EPA-454-03-004, September 2003. IMPROVE data records fo r Great Smoky Mountains for the period 2000 to 2004 meet the USEPA requirements fo r data completeness (75 percent for the year and 50 percent for each quarter). IMPROVE does not operate a monitor at Joyce Kilmer Wilderness and considers the IMPR OVE monitor at Great Smoky Mountains to be representative of visibili ty in the Joyce Kilmer area . The light extinction and deciview visibility values fo r the 20 percent worst and 20 per cent best visibility days at the Class I areas are based on data and calculati ons included in Appendix B of this SIP.

2.3 Summary of Natural Background and Baseline Conditions for Tennessee Class I
Areas

Table 2.3-1 presents estimated natural back ground and baseline visibility metrics for Tennessee Class I areas. Note that Tennessee is not considering international emissions to be a component of natural background. Baseline visibility on the 20 percent worst days at Great Smoky Mountains National Park is approximately 30 dv. Natural background visibility at Great Smoky M ountains National Park is pred icted to be between 11 and 12 dv. For detailed information, see Appendi x F-2 (MPE for GRSM) and Appendix G (Modeling Results & Supplemental An alyses including 2002 PM2.5 species concentration data).

Table 2.3-1 Natural Background and Baseline Conditions for Tennessee Class I Areas

Natural Background Conditions

Class 1 area Average for 20 percent Worst Days (deciviews) Average for 20 percent Best Days (deciviews) Average for 20 percent Worst Days Bext (Mm-1) Average for 20 percent Best Days Bext (Mm-1) Great Smoky Mountains 11.1 4.5 30.8 15.8 Joyce Kilmer Wilderness 11.1 4.5 30.8 15.8

Baseline Visibility Conditions 2000-2004

Class 1 area Average for 20 percent Worst Days (deciviews) Average for 20 percent Best Days (deciviews) Bext (Mm-1) Average for 20 percent Worst Days Bext (Mm-1) Average for 20 percent Best Days Great Smoky Mountains 30.3 13.6 216.3 40.2 Joyce Kilmer Wilderness 30.3 13.6 216.3 40.213 2.4 Pollutant Contributions to Visibilit y Impairment (2000-2004 Baseline Data)

The 20 percent worst visibility days at Gr eat Smoky Mountains National Park generally occur in the period April to September. Th e peak hazy days occur in the summer under stagnant weather conditions with high rela tive humidity, high temperatures, and low wind speeds (see Figure 2.4.1). The 20 percent be st visibility days at the Southern Appalachian sites can occur at any time of year. See Fi gures 2.42, and 2.4.3 for visibility impairing pollutant attribution on these days by Class I area.

0 50 100 150 200 250 300 3503/85/176/36/106/287/17/87/228/58/168/198/238/2610/1510/1810/2110/2410/2711/212/115/15/76/96/126/216/277/37/127/187/217/247/308/28/58/88/118/148/178/238/269/79/139/1911/182/255/25/85/176/16/136/196/227/17/77/107/167/318/38/68/98/128/158/219/59/89/119/179/233/164/275/245/276/56/116/206/266/297/57/177/207/268/78/108/168/198/228/258/289/39/69/129/211/102/216/86/116/206/266/297/87/177/207/298/48/108/168/198/258/288/319/39/129/159/3010/310/30

2000 2001 2002 2003 2004Extinction (Mm-1) ECM ESoil ELAC EOMC EAmm_NO3 EAmm_SO4 Rayleigh Great Smoky Mtns., TN 2000-2004 Reconstructed Extinction New IMPROVE Algorithm 20% Worst Days

Figure 2.4-1. Reconstructed Extinction for the 20 percent Haziest Days in 2000- 2004 at Great Smoky Mountains Class I area using Ne w IMPROVE equation

Ammonium sulfate , (NH 4 ) 2 SO 4 , is the most important contributor to visibility impairment and fine particle mass on the 20 percent worst an d 20 percent best visibility days at both Tennessee Class I areas. Sulfate levels on the 20 percent worst days account for 60-70 percent of the visibility impairment. Across the VISTAS region, sulfat e levels are higher at the Southern Appalachian sites than at the coastal sites (Figure 2.4-2). On the 20 percent clearest days, sulfate levels are mo re uniform across the region (Figure 2.4-3). [Note that in these two figures, levels at Great Smoky Mountains National Park should be considered to be representative of levels at Joyce Kilmer-Slickrock Wilderness.] Sulfate particles are formed in the atmosphere from sulfur dioxide emissions . Sulfate particles occur as hydrogen sulfate, H 2 (SO 4 ), ammonium bisulfate HNH 4 SO 4 , and ammonium sulfate, (NH 4 ) 2 SO 4 , depending on the availability of ammonia, NH3, in the atmosphere.14 Figure 2.4-2. Average light extinction fo r the 20 percent Haziest Days in 2000-2004 at VISTAS and neighboring Class I a reas using New IMPROVE equation

Figure 2.4-3. Average light extinction fo r the 20 percent Clearest Days in 2000-2004 at VISTAS and neighboring Class I areas using New IMPROVE equation VISTAS coastal VISTAS inland Neighboring non-VISTAS 0 50 100 150 200 250 300Swanquarter, NCCape Romain, SCOkefenokee, GAEverglades, FLChassahowitzka, FLSt. Marks, FLDolly Sods, WVShenandoah, VAJames River Face, VALinville Gorge, NCShining Rock, NCGreat Smoky Mtns., TNCohutta, GASipsey, ALMammoth Cave, KYBrigantine, NJBreton, LAMingo, MOHercules Glades, MOUpper Buffalo, ARCaney Creek, ARExtinction (Mm-1) Sea Salt Coarse Soil EC
POM NH4NO3 (NH4)2SO4 Rayleigh Average Extinction for 20% Haziest Days in 2000-2004 New IMPROVE Algorithm

0 10 20 30 40 50 60Extinction (Mm-1)

Sea Salt CM Soil EC
POM NH4NO3 (NH4)2SO4 Rayleigh

VISTAS coastal VISTAS inland Neighboring non-VISTASSwanquarter, NCCape Romain, SCOkefenokee, GAEverglades, FLChassahowitzka, FLSt. Marks, FLDolly Sods, WVShenandoah, VAJames River Face, VALinville Gorge, NCShining Rock, NCGreat Smoky Mtns., TNCohutta, GASipsey, ALMammoth Cave, KYBrigantine, NJBreton, LAMingo, MOHercules Glades, MOUpper Buffalo, ARCaney Creek, AR Average Extinction for 20% Clearest Days in 2000-2004 New IMPROVE Algorithm15 Particulate Organic Matter (POM) is the second most important contributor to fine particle mass and light extinction on the 20 percent haziest and the 20 percent clearest days at the Tennessee Class I areas. Elevat ed levels of POM and Elemental Carbon, EC, indicate impact from wildfires or prescribed fire. Significant fire impacts are infrequent at Class I areas in TN. VISTAS collected additional samples of carbon at five sites, including Great Smoky Mountains and Millbro ok, (Raleigh, NC), to better understand sources contributing to carbon in rural and urba n areas. Samples were analyzed to define the amount of carbon-14 isotope as an indicator of the am ount of carbon from modern sources (vegetative emissions, or fires) and the amount of carbon from fossil sources (gasoline, diesel or oil). For most sample s, the ratio of modern carbon to fossil carbon was greater than 0.60 throughout the year. In the fall, winter and spring, more of the modern carbon is attributable to wood burni ng while in the summer months, more of the modern carbon mass is attribut able to biogenic emissions from vegetation. On some days, greater than 90% of the carbon at Great Smoky Mountains is attributable to modern sources of carbon. Carbon from gasoline a nd diesel engines is a relatively small contribution at the rural sites. These re sults suggest that co ntrolling anthropogenic sources of carbon will have little benefit in improving visibility in Class I areas since the majority of the POM comes from natura l, i.e., biogenic sources. Controlling anthropogenic sources of carbon wi ll likely be more effectiv e to reduce levels of PM2.5 in urban areas.

Ammonium nitrate , NH 4 NO 3 , is formed in the atmosphere by reaction of ammonia and nitrogen oxides. In the VISTAS region, nitr ate formation is limited by availability of ammonia and by temperature. Ammonia prefer entially reacts with sulfur dioxide and sulfate before reacting with nitrogen oxides. Particle nitrate is formed at lower temperatures while at elevated temperatures n itric acid remains in gaseous form. For this reason, particle nitrate levels are very low in the summer and a minor contributor to visibility impairment. Particle nitrate con centrations are higher on winter days and are more important for the coastal sites where 20 percent worst days can occur on winter days. Nitrogen oxides are emitted by fossil fuel combustion by point, area, on road, and non-road sources. Modeling data (see section 7) indicate that in the VISTAS region ammonium nitrate formation is limited by a mmonia concentrations and suggest that for winter days, control of ammonia sources woul d be more effective in reducing ammonium nitrate levels than contro l of nitrogen oxides.

Elemental Carbon, EC, is a comparatively minor contri butor to visibility impairment. Sources include agriculture, prescribed, wildland, and wild fires and incomplete combustion of fossil fuels. EC levels are hi gher at urban monitors than at the Class I areas and suggest controls of fossil fuel co mbustion sources would be more effective to reduce PM2.5 in urban areas than to impr ove visibility in Class I areas.

Soil fine particles are minor contributors to visibility impairment at most southeastern sites on most days. Occasional episodes of elevat ed fine soil can be attributed to Saharan dust episodes, particularly at Everglades, FL, but rarely are seen at the TN Class I areas. No control strategies are i ndicated for fine soil.16 Sea salt , NaCl, is observed at the coastal sites. Sea salt contributions to visibility impairment are most important on the 20 per cent clearest days when sulfate and POM levels are low. Sea salt le vels do not contribute significan tly to visibility on the 20 percent worst visibility days. The new IMPROVE equation uses Chloride ion, Cl - , from routine IMPROVE measurements to calculate sea salt levels. Cl - may react with nitrate and volatize off the filter, so that Cl - may underestimate sea salt levels. VISTAS used Cl - to calculate sea salt contributions to vi sibility following IMPROVE guidance.

Coarse particle mass (particles with diameters be tween 2.5 and 10 microns) has a relatively small contribution to visibility impairment because the light extinction efficiency of coarse mass is very low compar ed to the extinction efficiency for sulfate, nitrate, and carbon.

An unidentified component is reported by IMPROVE as the difference between the total PM2.5 mass measured on the filter and the sum of the measured components. This unidentified mass may be positiv e or negative and is attribut able to water and/or the factors used to calculate molecular weights of the other components.

The New IMPROVE equation generally results in higher ca lculated light extinction on days with higher mass and lower light extinction on days with lower mass. This tends to increase calculated light extinction for curr ent conditions and to decrease calculated light extinction for natural visibility conditions . Adding sea salt to the new IMPROVE equation increases light extinction for both current and natural visibility conditions. Increasing the mass multiplier for partic ulate organic matter in the new IMPROVE equation increases light extinction for current co nditions more than for natural conditions. The new algorithm does not change the conclu sion that in the VISTAS region and in Tennessee, the most effective means to im prove visibility is to reduce sulfate concentrations.

PM2.5 trends in urban and Class I areas: IMPROVE data were compared to monitoring data from the Speciated Trends Network (S TN) in nearby urban areas to understand the similarities and differences in compositi on of fine particle mass. Several PM2.5 nonattainment areas are in close proximity to th e Class I areas in the southeastern United States, including Atlanta, GA; Birmi ngham, AL; Charleston, WV; Chattanooga, TN; Louisville, KY; and Knoxville, TN. Ammoni um sulfate concentrations are comparable between urban and nearby Class I areas, while organic carbon, elemental carbon, and nitrate concentration are generally higher in urban areas than the Class I areas. These results suggest that sulfate is widely di stributed regionally while urban areas see additional incremental pollutant loadi ngs from local emissions sources.

Role of meteorology in determ ining visibility conditions: Classification and Regression Tree (CART) Analyses were used to characteri ze the relationship be tween meteorological conditions and visibility conditi ons at the Class I areas. Days were assigned to one of five visibility classes rangi ng from poor to good visibility. Days were then assigned to bins based on meteorological conditions. For the Tennessee Class I areas poor visibility days were most likely to occur on days w ith high temperatures, high relative humidity,17 low wind speeds, and elevated PM2.5 mass at upwind urban areas. Precipitation was not a good predictor of visibility condition. We ights were assigned to days based on frequency of occurrence of days with similar meteorological conditions.

The above analyses are further discussed in Appendix B.



### 3.0 GLIDEPATHS TO NATURAL CONDITIONS IN 2064

As stated in section 1.3 above, the regional ha ze rule directs states to graphically show what would be a “uniform rate of progress” toward natural conditions for each mandatory Class I Federal area within the State as well as for each mandatory Class I Federal area located outside the State, which may be aff ected by emissions from sources within the State. The uniform rate of progress is also known as the “glidepath.” The glidepath is simply a straight graphical line drawn from th e baseline level of visibility impairment for 2000-2004 to the level representing no manmade impairment in 2064.

Each state must set goals for each Class I area that provide for reasonable progress towards achieving natural vi sibility conditions by 2064. Section 51.308(d)(1) of the regional haze rule requires that reas onable progress goals must both: (1) provide for improvement in visibility for the most impaired days over the period of the implementation plan; and (2) ensure no degradation in visibility for the least impaired days over the same period.

Uniform rate of progress graphs (glidepaths), were developed for each Class I area in the VISTAS region. The glidepaths were de veloped in accordance with the USEPA guidance for tracking progress and used data collected from the IMPROVE monitoring sites as described in section 2 of this document. The glidepath is one of the indicators used in setting reasonable progress goals.

3.1 Glidepaths for Class I Areas in Tennessee

The following are glidepaths for the 20 per cent most impaired days for Great Smoky Mountains National Park and Joyce Kilm er-Slickrock Wilderness Area, assuming uniform rate of progress toward regional h aze goals. Natural background visibility at both sites is predicted to be between 11 and 12 dv. Natural background visibility calculations are based on EPA’s default na tural conditions estimates (Method 1) as explained in Appendix B-4 (page 31).18 (Method 1 Prediction = Default Calculation per EPA guidance)

Figure 3.1-1. Uniform Rate of Progress Glidepath for 20 percent worst days at Great Smoky Mountains National Park

(Method 1 Prediction = Default Calculation per EPA guidance) Figure 3.1-2. Uniform Rate of Progress Glidepath for 20 percent worst days at Joyce Kilmer Wilderness Area. Uniform Rate of Reasonable Progress Glide Path Great Smoky Mountains - W20% Data Days

30.28 28.67 25.79
22.59
19.38
16.18
12.97 11.05 26.72
23.50



### 0 5 10 15 20 25 30 35

200 0 200 4 200 8 201 2 201 6 202 0 202 4 202 8 203 2 203 6 204 0 204 4 204 8 205 2 205 6 206 0 206 4

YearHaziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction

Uniform Rate of Reasonable Progress Glide Path Joyce Kilmer-Slickrock - W20% Data Days

30.28 28.67 25.79
22.59
19.38
16.18
12.97 11.05 26.72
23.50



### 0 5 10 15 20 25 30 35

200 0 200 4 200 8 201 2 201 6 202 0 202 4 202 8 203 2 203 6 204 0 204 4 204 8 205 2 205 6 206 0 206 4

YearHaziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction19 

### 4.0 TYPES OF EMISSIONS IMPACTING VISIBILITY IMPAIRMENT
IN TENNESSEE CLASS I AREAS

4.1 Baseline Emissions Inventory

The Regional Haze Rule at 51.308(d) (4) (v) re quires a statewide emissions inventory of pollutants that are reasonably anticipated to cause or contribute to visibility impairment in any mandatory Class I area. An inventor y was developed for the baseline year 2002 and projected to 2009 and 2018. The pollutants inventoried include volatile organic compounds, nitrogen oxides, fine particul ate (PM2.5), coarse particulate (PM10), ammonia and sulfur dioxide. The baselin e emissions inventory for 2002 was developed for Tennessee following the methods described in Appendix D.

There are five different emissi on inventory source classifica tions: stationary point and area sources, off-road and on-road mobile so urces, and biogenic s ources. Stationary point sources are those sources that emit great er than a specified tonnage per year, with data provided at the facility level. Electri c generating utilities a nd industrial sources are the major categories for stationary point s ources. Stationary area sources are those sources whose individual emissions are relativ ely small, but due to the large number of these sources, the collective emissions from the source category coul d be significant (i.e., dry cleaners, service stations, agricultural so urces, fire emissions, etc.). These types of emissions are estimated on a countywide level. Non-road (or off -road) mobile sources are equipment that can move but do not use the roadways, i.e., lawn mowers, construction equipment, railroad locomotives, aircraft, etc. The emissions from these sources, like stationary area sources, are es timated on a countywide level. On-road mobile sources are automobiles, trucks, and motorcycles that use the roadway system. The emissions from these sources are estima ted by vehicle type and road type and are summed to the countywide level. Biogenic s ources are the natural sources like trees, crops, grasses and natural decay of plants. The emissions from these sources are estimated on a countywide level.

In addition to the various source classificati ons, there are also various types of emission inventories. The first is the actual base year inventory. This invent ory is the base year emissions that correspond to the meteorological data used, which for this modeling effort is data from 2002. These emissions are us ed for evaluating the air quality model performance.

The second type of inventory is the typical base year inventor y. This inventory is similar to the actual base year inventory, except that for sources whose emissions change significantly from year to year, a more typical emission value is used. In this modeling effort, typical emissions were developed fo r the electric generating units (EGUs) and the wildland fire emissions. The air quality modeling is run using the typical base year inventory and the future year inventory. The re sults from these two runs are then used to calculate relative reduction fact ors. These relative reduction factors for future years are then used to demonstrate reasonabl e progress toward visibility goals.20 Below is an overview of the inventories used for each source classification. More detailed discussion of the emissions inventor y development is contained in Appendix D.

4.1.1 Stationary Point Sources

Point source emissions are emissions from individual sources havi ng a fixed location. Generally, these sources must have permits to operate, and their emissions are inventoried on a regular schedule. Large sour ces emitting at least 100 tons per year (tpy) of a criteria pollutant, 10 tpy of a single hazardous air pollu tant (HAP), or 25 tpy total HAP are inventoried annually. Smaller sources have been inventoried less frequently. The point source emissions data can be gr ouped as EGU sources and other industrial point sources, also called non-EGUs.

Electric Generating Units

The actual base year inventory for the E GU sources used 2002 continuous emissions monitoring (CEM) data reported to the US EPA’s Acid Rain program or 2002 hourly emissions data provided by stakeholders. Th ese data provide hourly emissions profiles for SO2 and NOx that can be used in air quali ty modeling. Emissions profiles are used to estimate emissions of other pollutants ( volatile organic compounds, carbon monoxide, ammonia, fine particles, soil) based on measured emissions of SO2 and NOx.

Emissions from EGUs vary daily and seasonall y as a function of va riability in energy demand and utilization and outage schedules. To avoid anomalies in future year emissions created by relying on 2002 operations to represent future operations, a typical base year emissions inventory was developed for EGUs. This approach is consistent with the USEPA’s 2007 modeling guidance. To develop a typical year 2002 emissions inventory for EGU sources, each unit’s average CEM heat input for 2000 through 2004 was divided by the 2002 actual heat input to generate a unit specific normalizing factor. This normalizing factor was then multiplie d by the 2002 actual emissions. The heat inputs for the period 2000 through 2004 were used because the modeling current conditions use monitored data from this same 5-year period. If a unit was shut down for an entire year during the 2000 through 2004 peri od, the average of th e years the unit was operational was used. If a unit was shut down in 2002, but not permanently shutdown, the emissions and heat inputs from 2001 (or 2000) were used in the normalizing calculations.

As part of the VISTAS air quality modeling, VISTAS, in cooperation with the other eastern RPOs, contracted with ICF Resources, L.L.C., to generate future year emission inventories for the electric generating sector of the contiguous United States using the Integrated Planning Model (IPM) version 2.1.9 u pdated with state specific information. IPM is a dynamic linear optimization model th at can be used to examine air pollution control policies for various pollutants thr oughout the contiguous Un ited States for the entire electric power system. The dynamic nature of IPM enables projection of the behavior of the power system over a specified future period. Optimization logic in IPM determines the least-cost mean s of meeting electric generation and capacity requirements21 while complying with specified constrai nts including air pollution regulations, transmission bottlenecks, and plant-specific ope rational constraints. The versatility of IPM allows users to specify which constraint s to exercise, and to populate IPM with their own datasets. TDEC-APC worked closely w ith the TVA in terms of an IPM “reality check” to gain a more accurate understanding of its plans for future generation capacity and air pollution controls.

Other Industrial Point Sources

For the non-EGU sources, the same inventory is used for both the actual and typical base year emissions inventories. The non-EGU cat egory uses annual em issions as reported under the Consolidated Emissions Reporting Rule (CERR) for the year 2002. These emissions are temporally a llocated to month, day, and hour using source category code (SCC)-based allocation factors.

The general approach for assembling future year data was to use recently updated growth and control data consistent with USEPA’s CA IR analyses. This data was supplemented with state-specific growth factors and stakeholder input on growth assumptions.

4.1.2 Stationary Area Sources

Stationary area sources are sources whose indi vidual emissions are relatively small, but due to the large number of these sources, th e collective emissions could be significant (i.e., combustion of fuels for heating, structure fires, service stations, etc.). Emissions are estimated by multiplying an emission factor by some known indicator of collective activity, such as fuel usage, number of hous eholds, or population. Stationary area source emissions are estimated at the countywide level.

A portion of the area source 2002 base year i nventory for Tennessee was developed by TDEC-APC in conjunction with its contractor, the University of Tennessee at Knoxville, and provided to the VISTAS contractor. The VISTAS contractor calculated the remaining portion of the area source inventory. The sources estimated by the contractor include emissions from animal husbandry, w ildland fires, and particulate matter from paved and unpaved roads. For the other stat es within the modeling domain, either statesupplied data or data report ed under CERR for 2002 was used.

The actual base year inventory will serve as th e typical base year in ventory for all area source categories except for wildland fires. For wildland fires, a ty pical year inventory was used to avoid anomalies in wildfire activity in 2002 compared to longer-term averages. Development of a typical year WI LDfire inventory provide d the capability of using a comparable data set for both the base ye ar and future years. Thus, fire emissions remain the same for air quality modeling in both the base and any future years. The VISTAS Fire Special Interest Work Group used State records to ratio the number of acres burned over a longer term period (three or more y ears, as available from state records) to 2002. Based on these ratios, the 2002 acreage wa s then scaled up or down to develop a typical year inventory.22 The VISTAS contractor generated future year emissions inventories for 2009 and 2018 for the regional haze modeling. Growth factor s, supplied either by states or taken from the CAIR emission projections, were applied to project the controll ed emissions. If no growth factor was available from either a stat e or the CAIR growth f actor files, then the USEPA’s Economic Growth and Analysis Syst em Version 5 growth factors were used.

4.1.3 Off-Road Mobile Sources

Off-road mobile sources are equipment that can move but do not use the roadways, such as construction equipment, aircraft, railroad locomotives, lawn and garden equipment, etc. For the majority of the off-road mobile sources, the emissions for 2002 were estimated using the USEPA’s NONROAD2005c m odel. For the three source categories not included in the NONROAD model, i.e., airc raft engines, railroad locomotives and commercial marine, more traditional methods of estimating the emissions were used. The same inventory is used for both the actual and typical base year emissions inventories.

For the source categories estimated using the USEPA’s NONROAD model, the model growth assumptions were used to create th e 2009 and 2018 future year inventories. The NONROAD model takes into consideration re gulations affecting emissions from these source categories. For the commercial ma rine, railroad locomotives and airport emissions, the VISTAS contract or calculated the future growth in emissions using detailed inventory data (both before and after controls) for 1996 and 2010, obtained from the CAIR Technical Support Docu ment. When available, stat e-specific growth factors were used.

4.1.4 Highway Mobile Sources

For on-road vehicles, the newest version of the MOBILE model, MOBILE6.2, was used. Key inputs for MOBILE include information on the age of vehicles on the roads, the average speeds on the roads, the mix of vehicles on the roads, any programs in place in an area to reduce emissions for motor vehicl es (e.g., emissions inspection programs), and temperature.

The MOBILE model takes into consideration regu lations that affect emissions from this source sector. The same MOBILE run is used to represent the act ual and typical year emissions for on-road vehicles using input data reflectiv e of 2002. The MOBILE model then is run for 2018 inventory using input data reflective of that year.

4.1.5 Biogenic Emission Sources

Biogenic emissions were prepared with the SMOKE-BEIS3 (Biogenic Emission Inventory System 3 version 0.9) preprocessor. SMOKE-BEIS3 is a modified version of the Urban Airshed Model (UAM)-BEIS3 mode l. Modifications include use of MM5 data, gridded land use data, and improved emissions characterization. The emission factors that are used in SMOKE-BEIS3 are th e same as the emission factors as in UAM-23 BEIS3. The basis for the gridded land use data used by BEIS3 is the county land use data in the Biogenic Emissions Landcover Data base version 3 (BELD3) provided by the USEPA. A separate land classification scheme, based upon satellite (AVHRR, 1 km spatial resolution) and census information, ai ded in defining the forest, agriculture and urban portions of each county.

4.1.6 Summary 2002 Base G4 (March 2008 version) Best and Final Baseline Emissions Inventory for Tennessee

Table 4.1.6-1 is a summary of the 2002 base line actual Base G4 (Best and Final) emission inventory for Tennessee. Table 4.1.6-2 is a summary of the 2002 baseline typical emission inventory. The complete inventory and disc ussion of the methodology is contained in Appendix D. The emissions summa ries for other VISTAS states can also be found in Appendix D.

Table 4.1.6-1 2002 Actual Base G4 (B&F) Em issions Inventory Summary for TN in tons per year.

Source Category VOC NOx PM2.5 PM10 NH3 SO2 Point 85,254 221,651 39,973 49,814 1,817 413,755 Area 153,509 17,936 42,925 212,972 34,412 29,942 On-Road Mobile 179,807 238,577 3,949 5,371 6,625 9,226 Non-Road Mobile 66,450 96,827 6,458 6,819 43 10,441 Biogenics 894,214 18,081 0 0 0 0 TOTAL 1,379,234 593,072 93,305 274,976 42,897 463,364

Table 4.1.6-2 2002 Typical Base G4 (B&F) Em issions Inventory Summary for TN in tons per year.

Source Category VOC NOx PM2.5 PM10 NH3 SO2 Point 85,218 216,481 39,298 49,040 1,810 399,750 Area 153,783 18,061 43,410 213,538 34,439 29,977 On-Road Mobile 179,807 238,577 3,949 5,371 6,625 9,226 Non-Road Mobile 66,450 96,827 6,458 6,819 43 10,441 Biogenics 894,214 18,081 0 0 0 0 TOTAL 1,379,472 588,027 93,115 274,768 42,917 449,394

4.1.7 Model Performance Improvements through Emissions Inventory Improvements

Since the initial model performance evalua tion, VISTAS has made several improvements to the emissions inventory, which in turn im prove model performance. These inventory improvements are detailed in the VISTAS em issions inventory re port and Appendix D, and are summarized here:

• For electric generating utilities, the Inte grated Planning Model (IPM) was used to provide estimates of future year util ity production and emissions. Continuous Emissions Monitoring data was used to define seasonal variability in production24 and emissions. The states updated IPM model projections with control data provided by utility companies in late 2006 through winter 2007.

• For on-road vehicle emissions, states and local agencies provided updated MOBILE model input and vehi cle-miles-traveled data.

• For ammonia emissions from agricult ural sources, the Carnegie Mellon University ammonia model was used to improve annual and monthly estimates.

• For fires, the VISTAS states provided fi re activity data for 2002 for wildfires, prescribed fire, land clea ring and agricultural burni ng to develop a 2002 fire inventory. Where data allowe d, large fire events were modeled as point sources. In 2006, the United States Forest Service and Fish and Wildlife Service provided projections of increased prescribed bu rning in 2009 and 2018; these data were incorporated for all states except Florida. Because current prescribed fire activity already reflects the use of fire as a fo rest management technique, Florida believed that there is too much uncertainty to project how future total fire activity (prescribed plus wildfire) will change. In Florida, prescribed fire in the future years is the same as 2002 t ypical for prescribed fire.

• For non-road engines, the updated USEPA NONROAD2005 emissions model was used.

• For commercial marine emissions in shi pping lanes in the Gu lf of Mexico and Atlantic Oceans, gridded emissions fo r the VISTAS modeling domain were created using inventory data newly developed for USEPA by Corbett at University of Delaware. These emissions were incorporated in the modeling.

• Updated inventories from the neighbor ing RPOs, Mexico, and Canada were incorporated as available.

4.2 Assessment of Relative Contribution s from Specific Pollutants and Sources Categories

Ammonium sulfate is the largest contributor to visibility impairment at the Tennessee Class I areas, and reduction of SO2 emissions would be the most effective means of reducing ammonium sulfate. As illustrated in Figure 4.2-1, 96 percent of SO2 emissions 

## VISTAS States SO2 Emissions - 2002

Utilities Industrial On road Non road Other Utilities: 76% Industrial: 20% 4.8 Million tons



## VISTAS States SO2 Emissions - 2002

Utilities Industrial On road Non road Other Utilities: 76% Industrial: 20% 4.8 Million tons25 in the VISTAS states are attributable to el ectric generating faciliti es and industrial point sources. Similarly, in Tennessee, the stati onary point and area sources consisting of electric generating facilities and industrial po int sources, contribute 95.7 percent of SO2 emissions in the state (see Table 4.2-1).

Table 4.2-1 2002 SO2 Emissions for Tennessee (tons/year) Source Category SO2 Emissions Percent Point 399,730 89.0 % Area 29,977 6.7 % On-Road Mobile 9,127 2.0 % Off-Road Mobile 10,441 2.3 % Biogenics 0 0 % TOTAL 449,275 100 %

Figure 4.2-1. SO2 emissions in 2002 in the VISTAS States.



### 5.0 REGIONAL HAZE MODELING METHODS AND INPUTS

Modeling for regional haze was performed by VISTAS for the ten southeastern states, including Tennessee. The sections below outline the methods and inputs used by VISTAS for the regional modeling. Additional details are provided in Appendices C, D and M.

5.1 Analysis Method

The modeling analysis is a complex technical evaluation that begins by selection of the modeling system. VISTAS decided to use the following modeling system:

• Meteorological Model: The Pennsylvania St ate University/National Center for Atmospheric Research (PSU/NCAR) Meso scale Meteorological Model (MM5) is a nonhydrostatic, prognostic meteorological model routinely used for urban- and regional-scale photochemical, fine particul ate matter, and regional haze regulatory modeling studies.

• Emissions Model: The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system is an emissions modeli ng system that generates hourly gridded speciated emission inputs of mobile, nonroad mobile, area, point, fire and biogenic emission sources for photochemical grid models.

• Air Quality Model: USEPA’s Models-3/ Community Multiscale Air Quality (CMAQ) modeling system is an ‘One-A tmosphere’ photochemical grid model capable of addressing ozone, particul ate matter (PM), visibility and acid deposition at regional scale.

The USEPA’s 2007 Modeling Guidance recommends modeling an entire year or at a minimum several days in each quarter of a y ear to adequately represent the range of meteorological conditions that contribute to elev ated levels of fine particulate matter.26 The year 2002 was selected by VISTAS as th e modeling year for this demonstration. Meteorological inputs were developed fo r 2002 using the meteorological model. Emission inventories were also developed for 2002 and processed through the emissions model. These inputs were used in the air qu ality model to predict fine particle mass and visibility. The model results for 2002 were compared with observed meteorological and air quality data to evaluate model perfor mance. Several configurations of the meteorological and air quality model were eval uated to select a conf iguration that gave the best overall performance for the VISTAS region.

Once model performance was deemed adequate , the current and futu re year emissions were processed through the emissions model. The air quality modeling results are used to determine a relative reduction in future visibility impairment, which is used to determine reasonable progress.

The complete modeling protocol used for th is analysis can be found in Appendix C.

5.2 Model Selection

To ensure that a modeling study is defensible, care must be taken in the selection of the models to be used. The models selected must be scientifically appropriate for the intended application and be freely accessible to all stakeholders. Scientifically appropriate means that the models addre ss important physical and chemical phenomena in sufficient detail, using peer-reviewed me thods. Freely accessible means that model formulations and coding are free ly available for review and that the models are available to stakeholders, and their consultants, for execution and verification at no or low cost.

The following sections outline the criteria for selecting a modeling system that is both defensible and capable of meeting the study's goa ls. These criteria were used in selecting the modeling system used for this modeling demonstration.

5.2.1 Selection of Photochemical Grid Model

Criteria

For a photochemical grid model to qualify as a candidate for use in a regional haze SIP, a State needs to show that it meets the same several general criteria as a model for an attainment demonstration for a national ambient air quality standard (NAAQS):

• The model has received a scientific peer review

• The model can be demonstrated applicable to the problem on a theoretical basis

• Data bases needed to perform the analysis are available and adequate

• Available past appropriate performance ev aluations have shown the model is not biased toward underestimates or overestimates27 • A protocol on methods and procedures to be followed has been established

• The developer of the model must be willing to make the source code available to users for free or for a reasonable cost, and the model cannot otherwise be proprietary.

Overview of CMAQ

The photochemical model selected for this st udy was CMAQ version 4.5. For more than a decade, the USEPA has been developing the Models-3 CMAQ modeling system with the overarching aim of producing a ‘One-A tmosphere’ air quality modeling system capable of addressing ozone, fine particulate matter, visibility and acid deposition within a common platform. The original justifica tion for the Models-3 development emerged from the challenges posed by the 1990 CAAA and the USEPA’s desire to develop an advanced modeling framework for ‘holistic’ environmental modeling utilizing state-ofscience representations of atmospheric pr ocesses in a high performance computing environment. The USEPA completed the initial stage of development with Models-3 and released the CMAQ model in mid-1999 as th e initial opera ting science model under the Models-3 framework. The most recent rendition is CMAQ version 4.5, which was released in September 2005.

An advantage of choosing CMAQ as the atmo spheric model is the ability to do oneatmospheric modeling. The same model config uration is being applied for the ozone and PM 2.5 attainment demonstrations SIPs, as well as the regional haze SIP. A number of features in CMAQ’s theoretical formulati on and technical implementation make the model well suited for annual PM modeling.

The configuration used for this modeling demonstration, as well as a more detailed description of the CMAQ model, can be f ound in the Modeling Protocol (Appendix C).

5.2.2 Selection of Meteorological Model

Criteria

Meteorological models, either through object ive, diagnostic, or prognostic analysis, extend available information about the state of the atmosphere to the grid upon which photochemical grid modeling is to be carri ed out. The criteria for selecting a meteorological model are based on both the model’s ability to accurately replicate important meteorological phenomena in the re gion of study, and the model's ability to interface with the rest of the modeling syst ems -- particularly the photochemical grid model. With these issues in mind, the fo llowing criteria were established for the meteorological model to be used in this study:

• Non-Hydrostatic Formulation

• Reasonably current, peer reviewed formulation28 • Simulates Cloud Physics

• Publicly available at no or low cost

• Output available in I/O API format

• Supports Four Dimensional Data Assimilation (FDDA)

• Enhanced treatment of Planetary Bo undary Layer heights for AQ modeling

Overview of MM5

The non-hydrostatic MM5 model is a three-dime nsional, limited-area, primitive equation, prognostic model that has been used widely in regional air quality model applications. The basic model has been under continuous development, improvement, testing and open peer-review for more than 20 years and has been used worldwide by hundreds of scientists for a variety of mesoscale studies.

MM5 uses a terrain-following non-dimensiona lized pressure, or "sigma", vertical coordinate similar to that used in many ope rational and research models. In the nonhydrostatic MM5, the sigma levels are defined according to the ini tial hydros taticallybalanced reference state so that the sigma le vels are also time-invariant. The gridded meteorological fields produced by MM5 are directly compatible with the input requirements of ‘one atmosphere’ air-quality models using this coor dinate. MM5 fields can be easily used in other re gional air quality models with different coordinate systems by performing a vertical inte rpolation, followed by a mass-conservation re-adjustment.

Distinct planetary boundary layer paramete rizations are available for air-quality applications, both of which repr esent sub-grid-scale turbulent fluxes of heat, moisture and momentum. One scheme uses a first-order eddy diffusivity formulation for stable and neutral environments and a modified first-orde r scheme for unstable regimes. The other scheme uses a prognostic equation for the se cond-order turbulent kinetic energy, while diagnosing the other key boundary layer terms.

Initial and lateral bounda ry conditions are specified for real-data cases from mesoscale three-dimensional analyses performed at 12- hour intervals on the outermost grid mesh selected by the user. Surface fields are anal yzed at three-hour intervals. A Cressmanbased technique is used to analyze standa rd surface and radiosonde observations, using the National Meteorological Center's spectral analysis, as a first guess. The lateral boundary data are introduced using a relaxati on technique applied in the outermost five rows and columns of the coarsest grid domain.

MM5 modeling system in regulatory air quality application studies have been widely reported in the literature (e.g., Emery et al., 1999; Tesche et al., 2000, 2003) and many have involved comparisons with other prognostic models such as the Regional Atmospheric Modeling System (RAMS) and the Systems Application International Mesoscale Model. The MM5 enjoys a far richer application hi story in regulatory modeling studies compared with RAMS or othe r models. Furthermore, in evaluations of29 these models in over 60 recent regional scale air quality application studies since 1995, it has generally been found that the MM5 model tends to produce somewhat better photochemical model inputs than alternative models.

The configuration used for this modeling demonstration, as well as a more detailed description of the MM5 model, can be f ound in the meteorological modeling protocol (Appendix E).

5.2.3 Selection of Emissions Processing System

Criteria

The principal criterion for an emissions processi ng system is that it accurately prepares emissions files in a format suitable for th e photochemical grid model being used. The following list includes clarification of this criterion and additional desirable criteria for effective use of the system.

• File System Compatibility with the I/O API

• File Portability

• Ability to grid emissions on a Lambert Conformal projection

• Report Capability

• Graphical Analysis Capability

• MOBILE6 Mobile Source Emissions

• Biogenic Emissions Inventory System version 3 (BEIS-3)

• Ability to process emissions for the propos ed domain in a reasonable amount of time.

• Ability to process control strategies

• No or low cost for acquisition and maintenance

• Expandable to support other species and mechanisms

Overview of SMOKE

The SMOKE Emissions Processing System Prot otype was originally developed at the Micro-computing Center of North Carolina. As with most ‘emissions models’, SMOKE is principally an emission processing system and not a true emissions modeling system in which emissions estimates are simulated from ‘fir st principles’. This means that, with the exception of mobile and biogenic sources, its pur pose is to provide an efficient, modern tool for converting emissions inventory data into the formatted emission files required by an air quality simulation model. For mobile sources, SMOKE actually estimates emissions rates based on input mobile-source activity data, emission factors and outputs from transportation travel-demand models.30 SMOKE was originally designed to allow em issions data processing methods to utilize emergent high-performance-computing as appl ied to sparse-matrix algorithms. Indeed, SMOKE is the fastest emissions processing t ool currently available to the air quality modeling community. The sparse matrix a pproach utilized throughout SMOKE permits both rapid and flexible processing of emissions data. The processing is rapid because SMOKE utilizes a series of matrix calculations instead of less efficient algorithms used in previous systems. The processing is flexib le because the processing steps of temporal projection, controls, chemical speciation, tempor al allocation, and spatial allocation have been separated into independent operations wh erever possible. The results from these steps are merged together at a final stage of processing.

SMOKE contains a number of ma jor features that make it an attractive component of the modeling system. The model s upports a variety of input fo rmats from other emissions processing systems and models. It suppor ts both gridded and county total land use scheme for biogenic emissions modeling. SMOKE can accommodate emissions files from up to 10 countries and any polluta nt can be processed by the system. For additional information about the SMOKE model please refer to Modeling Protocol (Appendix C).

5.3 Selection of the Modeling Year

A crucial step to SIP modeling is the selection of the period of time to model to represent current air quality conditions and to project ch anges in air quality in response to changes in emissions. The year 2002 was selected as the base year for several reasons.

The USEPA’s April 2007 Guidance on the use of Models and Other Analyses for Demonstrating Attainment of Air Qua lity Goals for Ozone, PM2.5, and Regional Haze identifies specific goals to consider when selecting one or more episodes for use in demonstrating reasonable progress in attaini ng the regional haze air quality goals. The USEPA recommends that episode selection derive from three principal criteria:

• Simulate a variety of me teorological conditions;

• Model time periods in which observed con centrations are close to the appropriate baseline design value or visibility impairment;

• Model periods for which extensive air qual ity/meteorological data bases exist; and

• Model a sufficient number of days so that th e modeled attainment te st applied at each monitor violating the NAAQS is based on multiple days.

For regional haze modeling, the guidance goes further by suggesting that the preferred approach is to model a full, representative year. Moreover, the required relative reduction factor values should be based on model results averaged over the 20% worst and 20% best visibility days determined for each Class I are based on monitoring data from the 2000 – 2004 baseline period.31 The USEPA also lists several other consid erations to bear in mind when choosing potential regional haze episode s including: (a) choose period s which have already been modeled, (b) choose periods which are draw n from the years upon which the current design values are based, (c) include weeke nd days among those chosen, and (d) choose modeling periods that meet as many episode selection criteria as possible in the maximum number of nonattainment or Class I areas as possible. Finally, the USEPA explicitly recommended in its 2007 guidance to use 2002 as the baseline inventory year.

VISTAS adopted a logical, stepwise a pproach in implementing the USEPA 2007 modeling guidance in order to identify the most preferable, representative year for regional haze modeling. These steps include the following:

Representativeness of Meteorological Conditions: The VISTAS meteorological contractor (BAMS) identified important meteorological characteristics and data sets in the VISTAS region directly releva nt to the evaluation of candidate annual modeling episodes.

Initial Episode Typing: At the time of selection in 2003, meteorological and air quality data were available for 2002 for model inputs and model performance evaluation. VISTAS used CART Analyses to evaluate visi bility conditions for 2000, 2001, and 2002, the candidate modeling ye ars. The year 2002 was found to be representative of conditions in the other two years. Subsequently, these analyses were repeated with the meteorol ogical and air quality monitoring data for 2000 to 2004 to evaluate how well the 2002 modeling year represented the full 2000-2004 baseline period. This analysis confirmed that visibility and PM2.5 mass in 2002 were representative of the fi ve-year baseline period for the VISTAS Class I areas. This analysis is discussed in more detail in the project report in Appendix B.

Data Availability: In parallel with the CART an alysis, episode characterization analyses, collaborative investigations by VISTAS states (e.g., North Carolina Division of Air Quality, Ge orgia Department of Natural Resources, Florida Department of Environmental Protection) intensively studied the availability of PM2.5, meteorological, and emissions data and representativeness of alternative baseline modeling periods from a regulatory standpoint. Additionally, 2002 was the year that the USEPA was requiring stat es to provide emissions inventory data for the Comprehensive Emissions Reporting Rule. It made sense to use 2002 as the modeling year to take a dvantage of the 2002 inventory. Years to be used by other RPOs: VISTAS also considered what years other RPOs would be modeling, and several had alre ady chosen calendar year 2002 as the modeling year.

After a lengthy process of integrated studies, the episode selection process culminated in the selection of calendar year 2002 (1 Janua ry through 31 December) as the most current, representative, and pragmatic choice for VISTAS regional haze modeling. All of the USEPA criteria for regional haze episode select ion were directly considered in this32 process together with many other consider ations (e.g., timing of new emissions or aerometric data deliveries by the USEPA or the states to the modeling teams).

5.4 Modeling Domains

5.4.1 Horizontal Modeling Domain

The USEPA’s 2007 modeling guidance recomme nds a 12-km modeling grid resolution for PM2.5 modeling while a 36-km grid is c onsidered acceptable for regional haze. For the VISTAS modeling, a coarse 36-km grid re solution was used for modeling the entire United States and a finer 12-km grid was us ed to model the eastern United States.

The CMAQ model was run in the one-way nested grid mode. This allowed the larger outer domains to feed concentration data to the inner nested domain. One-way nesting is believed to be appropriate for the generally stagnant conditions e xperienced during North Carolina ozone episodes. Two-way nesting was not considered due to numerical and computational uncertainty asso ciated with the technique.

The horizontal coarse grid modeling doma in boundaries were determined through a national effort to develop a common grid pr ojection and boundary. A smaller 12-km grid modeling domain was selected in an attempt to balance location of areas of interest, such as ozone and fine particulat e matter nonattainment areas, as well as Class 1 areas for regional haze. Processing time was also a factor in choosing a smaller 12-km grid, modeling domain.

The coarse 36-km horizontal grid domain cove rs the continental United States. This domain was used as the outer grid doma in for MM5 modeling with the CMAQ domain nested within the MM5 domain. Figure 5.4.1- 1 shows the MM5 horizontal domain as the outer most, blue grid with the CMAQ 36-km domain nested in the MM5 domain.

Figure 5.4.1-1. The MM5 horizontal domain is the outer most, blue grid, with the CMAQ 36-km domain nested in the MM5 domain.33 To achieve finer spatial resolution in th e VISTAS states, a one-way nested high resolution (12-km grid reso lution) was used. Figure 5.4.1-2 shows the 12-km grid, modeling domain for the VISTAS region. The modeling results from this modeling domain are the basis for which the reasonable progress goals will be assessed.

Figure 5.4.1-2. A more detailed view of th e 12-km grid over the VISTAS region.

5.4.2 Vertical Modeling Domain

The CMAQ vertical structure is primarily defined by the vert ical grid used in the MM5 modeling. The MM5 model employed a terrain following coordinate system defined by pressure, using 34 layers that extend from the surface to the 100 mb. A layer-averaging scheme was used to generate 19 vertical layers for CMAQ to reduce the computational cost of the CMAQ simulations. The effect s of layer averaging were evaluated in conjunction with the VISTAS modeling effort a nd found to have a relatively minor effect on the model performance metrics when bot h the 34 layer and a 19 layer CMAQ models were compared to ambient monitoring data.



### 6.0 MODEL PERFORMANCE EVALUATION

The initial modeling effort focused on evalua ting previous regional air quality modeling applications and testing candidate model c onfigurations for the SMOKE emissions and CMAQ model for the VISTAS 36-km and 12-km modeling domains. This effort resulted in a report recommending the model confi guration for the annual emissions and air quality modeling, which is included as part of the VISTAS Emissions and Air Quality Modeling Protocol. The evaluation of the me teorological modeling configuration can be found in Appendix F.1, with a summary of the final meteorological and air quality modeling configuration in the modeling prot ocol contained in A ppendix E and Appendix C, respectively.

Air quality model performance for the 2002 mo deling year was initially tested in 2004 using an early version of the VISTAS emi ssions inventory. In keeping with the one-34 atmosphere objective of the CMAQ mode ling platform, model performance was evaluated based on measured ozone, fine partic les, and acid deposition in the Air Quality System (AQS), IMPROVE, Speciated Trends Network (STN), Southeastern Aerosol Research and Characterization (SEARCH), National Acid Deposition Program (NADP) and Clean Air Status and Trends Network (CASTNet) monitoring networks (Figure 6.0- 1). A detailed examination of the results was published in 2005 in th e Journal of Air and Waste Management (see Appendix B.3) as well as being summarized in Appendix B.1.

Figure 6.0-1: Monitoring Networks used for VISTAS 2002 model performance evaluation, and their location wi thin the VISTAS 12km domain.35 6.1 Modeling Performance Goals, and Criteria

In 2004, VISTAS established model performa nce goals and criteria for components of fine particle mass (Table 6.1-1) based on prev ious model performance for ozone and fine particles. The USEPA’s 2007 modeling guidanc e for fine particulate matter at the time noted that PM models might not be able to achieve the same level of performance as ozone models. VISTAS’ evaluation considered several statistical performance measures and displays. Fractional bias and mean frac tional error were selected as the most appropriate metrics to summarize model pe rformance. Other metrics were also calculated and are included for IMPROVE monitors in the full model performance evaluation (Appendix F.2).

Fractional Bias Mean Fractional Error Comment

< 15 percent < 35 percent Goal for PM model performance based on ozone model performance, considered excellent performance

< 30 percent < 50 percent Goal for PM model performance, considered good performance

< 60 percent < 75 percent Criteria for PM model performance, considered average performance. Exceeding this level of performance indicates fundamental concerns with the modeling system and trigge rs diagnostic evaluation.

Table 6.1-1: Established model performan ce goals and criteria for the component species of fine particle mass.

Several graphic displays of model perf ormance were prepared including: 1. Scatter plots of predicted and observed c oncentrations and deposition by species, monitoring network, and month 2. Time series plots of predicted and ob served concentrati ons and deposition by species, monitoring site, and month 3. Spatially average time series plots 4. Time series plots of monthly fractional bias and error for a species, region, and network 5. Performance goal plots (“soccer plots” ) that summarize model performance by species, region, season 6. Concentration performance plots (“bugle pl ots”) that display fractional bias or error as a function of co ncentration by species, region, monitoring network, and month

The “soccer plots” and “bugl e plots” are relatively new tools in model performance evaluations, and have recently been include d as model performance evaluation displays in the USEPA’s 2007 modeling guidance for Ozone, PM2.5, and Regional Haze. Both “soccer plots” and “bugle plots” allow for a convenient way to examine model performance with respect to set goals and crit eria. The bugle plots have the added benefit of adjusting the goals and criter ia to consider the concentratio n of the species. Analysis36 of “bugle plots” generally suggests that greater emphasis should be placed on performance of those components with th e greatest contribution to PM mass and visibility impairment (e.g. su lfate and organic carbon) and that greater bias and error could be accepted for components with sm aller contributions to total PM mass (e.g. elemental carbon, nitrate, and soil).

6.2 VISTAS Domain-Wide Performance

Further discussion of model performance in this document will focus on the comparison of observational data from the IMPROVE m onitors and model output data from the 2002 VISTAS actual annual air quality modeli ng. Focus is limited to the IMPROVE monitoring network as these site s are the locations used in pr ojecting attainment visibility improvement goals in the Class I areas.

The evaluation will primarily focus on the ai r quality model’s performance with respect to individual components of fine particulate matter (PM 2.5 ), as good model performance of the component species will dictate good model performance of total or reconstituted fine particulate matter. Model performance of the total fine particulate matter and the resulting total light extinction will also be provided as a means to discuss the overall model performance for this Implementation Plan.

In the analyses, mean fractional bias (error) is used in lieu of mean bias (error), to prevent low observations and model predictions from skewing the metrics. A full list of model performance statistics is found in Appendix F .2. The soccer and bugle plots for the all of the VISTAS IMPROVE monitors are included here for summary purposes. The goal and criteria levels used for regional haze model performance were 30% and 60%, respectively, for mean fractional bias and 50% and 75%, respectivel y, for mean fractional error. Plots have been developed for th e average monthly concentrations and the performance statistics for all of the most significant light scattering component species (Sulfate, Nitrate, and Organic Carbon) for th e 20% best days and 20% worst days. Plots for individual IMPROVE monitors associated with Tennessee Class I areas are included in Appendix F.2.

The soccer plots of monthly concentrations (Figures. 6.2-1 and 6.2-2) show that values for nitrate generally fall outside of criteria performa nce thresholds (red box). Sulfates and organic carbon generally fall within goal thresholds (green box), with a couple of months falling just outside the goal thresholds but well within the criteria thresholds. In these figures, each point represents a month. Figure 6.2-3 contains separate soccer plots for each season. The seasonal plots emphasize poorer nitrate performance in the summer (does not even appear on the plots provided because performance is off scale with other constituents), when observed nitrate is quite low and predicted nitrate is even lower. When concentration is factored into perfor mance criteria, nitrate performance improves with respect to mean fractiona l bias and mean fractional erro r (Figures 6.2-4 and 6.2-5)

Additionally, performance asse ssed at the “one atmosphere” level was also deemed acceptable for ozone and particulate matter at various monitoring sites (STN, FRM,37 CASTNet, etc.). Overall, VISTAS fo und the Base G2 modeling results to be representative and acceptable for use in mode ling projection for ozone, particulate matter, and regional haze.

All Sites CMAQ 12km - 2002 Monthly

0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0 180.0 200.0

-180.0 -120.0 -60.0 0.0 60.0 120.0 180.0 Fractional BiasMean Fractional Error Sulfate

Nitrate

Organics

Criteria

Goal

Figure 6.2-1: Soccer plot depicting both the mean fractional error and fractional bias for component concentration for all VISTAS sites.

All Sites CMAQ 12km - 2002 Monthly

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

-75.0 -25.0 25.0 75.0 Fractional BiasMean Fractional Error Sulfate

Nitrate

Organics

Criteria

Goal

Figure 6.2-2: A zoomed view of the soccer pl ot depicting both the mean fractional error and fractional bias for component concentration for all VISTAS sites.38 Figure 6.2-3: Seasonal soccer plots fo r all VISTAS IMPROVE monitors.

All Sites CMAQ 12km - 2002 Monthly

0.0 50.0 100.0 150.0 200.0

0.0 1.0 2.0 3.0 4.0 5.0 6.0 Average Concentration ( μ g/m 3 )Mean Fractional Error Sulfate

Nitrate

Organics

Criteria

Goal

Figure 6.2-4: Bugle plot of the mean fracti on bias for particulate matter and its component concentrations for all VISTAS sites. V ISTAS IMPROVE Ga2a 12km - 2002 Spring (Apr May Jun)

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

-75.0 -25.0 25.0 75.0 Fractional BiasFractional Error Sulfate Nitrate Ammon. Organics EC Soils PM25 PM10 CM Criteria Goal V ISTAS IMPROVE Ga2a 12km - 2002 Summer (Jul Aug Sep)

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

-75.0 -25.0 25.0 75.0 Fractional BiasFractional Error Sulfate Nitrate Ammon. Organics EC Soils PM25 PM10 CM Criteria Goal

V ISTAS IMPROVE Ga2a 12km - 2002 Fall (Oct Nov Dec)

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

-75.0 -25.0 25.0 75.0 Fractional BiasFractional Error Sulfate Nitrate Ammon. Organics EC Soils PM25 PM10 CM Criteria Goal V ISTAS IMPROVE Ga2a 12km - 2002 Winter (Jan Feb Mar)

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

-75.0 -25.0 25.0 75.0 Fractional BiasFractional Error Sulfate Nitrate Ammon. Organics EC Soils PM25 PM10 CM Criteria Goal39 All Sites CMAQ 12km - 2002 Monthly

-200.0 -150.0 -100.0

-50.0

0.0 50.0 100.0 150.0 200.0

0.0 1.0 2.0 3.0 4.0 5.0 6.0 Average Concentration ( μ g/m 3 )Mean Fractional Bias Sulfate

Nitrate

Organics

(+) Criteria

(+) Goal

(-) Goal

(-) Criteria

Figure 6.2-5: Bugle plot of mean fraction e rror for particulate matter and its component species for all VISTAS sites.

6.3 Tennessee Class 1 Areas Performance

The following section provides stack bar char ts comparing observed fine particulate matter composition and modeled fine particul ate matter composition. The charts have been split into two charts, with the first displaying the 20% best days followed by the chart for the 20% worst days. Stacked bar ch arts have been developed for the IMPROVE monitoring site located in the Great Smoky Mountains Natio nal Park (GSMNP1). (See Figure 1.4-1). The Great Smoky Mountains Nati onal Park IMPROVE monitor is used to represent the Joyce Kilmer-Slickrock Wilderness Area.

The stacked bar chart allows a side-by-side comparison of the each day’s observed and modeled compositional a nd total light extinction. Within each bar the color codes are:

• Yellow = light extinction due to sulfates (bextSO4)

• Red = light extinction due to nitrates (bextNO3)

• Green = light extinction due to organic carbon (bextOC)

• Black = light extinction due to elemental (bextEC)

• Brown = light extinction due to soil (bextSoil)

• Grey = light extinction due to coarse mass (bextCM)

The components are presented in the same or der for both the observed (left hand bar) and modeled bar (right hand bar), so it easy to identify days when the prediction light extinction for the component differs from th e observed. The total height of the bar provides the total reconstructed particulate matter light extinction value. Note the40 differences in scale of the two charts; on the 20% best days modeled extinction is generally less than 20 Mm-1 different from measured values. For 20% worst days total differences between modeled and measured ex tinction varies from near zero to greater than 200 Mm-1.

A cursory view of the stacked ba rs charts reiterates that sulf ates are a large contributor to light extinction in the Great Smoky Mountains National Park on both 20% best days and 20% worst days. The bar charts also sugge st that organic carb on and nitrates are important on the 20% best days. The bar ch arts for the 20% best days reiterate the general under prediction seen in previous sections. Th e under prediction of sulfate on most of the 20% best days appears to be th e crux of the under pred iction, with the under prediction of nitrate observed on some days. On most 20% best days there is a small under prediction of SO4 and on some 20% be st days there is a large nitrate under prediction.

Comparing the 20% best day charts to the 20% worst days charts, one notices that the various components of particle pollution play a more prominent role in the 20% best days than with the 20% worst days. Also, the sp ecies make up on the 20% best days varies more widely compared to the 20% worst da ys. This suggests accurately modeling each species is especially impor tant on the 20% best days.

With the bar chart for the 20% worst days, it can be seen that model performance does improve across the sites. Light extinction due to sulfate prediction is better, but still falls short on some days. Light extinction due to organic carbon also becomes more important to total light extinction. Mu ch like the sulfate component, the organi c carbon component accuracy has improved performance over the 20 % best day series, though some days are still under predicted. Overall, TDEC-APC found model performa nce to fall within acceptable limits for model performance. TD EC-APC further asserts the one atmosphere modeling performed by the VISTAS contractors is representa tive of conditions in the southeastern states and is a pplicable for use in setting reasonable progress goals for the Class I areas.41 6.3.1 Great Smoky Mountain National Park

Best 20% Obs (left) vs 2002gt2a (right) at GRSM1

0 10 20 30 40 50 60 70 80 90 100

8 11 14 23 32 38 50 59 62 68 89 116 194 257 269 299 302 308 320 326 341 347 356

Julian Datebext (1/Mm) bextCM

bextSOIL

bextEC

bextOC

bextNO3

bextSO4

Worst 20% Obs (left) vs 2002gt2a (right) at GRSM1

0 50 100 150 200 250 300 350 400

56 122 128 137 152 164 170 173 182 188 191 197 203 212 215 218 221 224 227 233 248 251 254 260

Julian Datebext (1/Mm) bextCM

bextSOIL

bextEC

bextOC

bextNO3

bextSO4

Figure 6.3-1: Stacked bar char t for GSMNP1 on the 20% best days (top) and 20% worst days (bottom). Observed composition is presented in the left hand bar, with modeled composition represented by the right hand bar.42 

### 7.0 LONG-TERM STRATEGY FOR TENNESSEE CLASS I AREAS

As stated in section 1.3 above, the regiona l haze rule requires States to establish reasonable progress goals, expressed in deci views, for visibility improvement at each affected Class I area in the State coveri ng each (approximately) 10-year period until 2064. This first set of reasonable progress goa ls must be met through measures contained in the state’s long-term strategy covering the period from the baseline until 2018. States are also to evaluate the effects of emissions fr om that state on Class I areas in other states. This section discusses development of Tennessee’s long-term strategy.

7.1 Overview of the Long-Term Strategy Development Process

a. The monitored data and modeling analys es cited in sections 2 and 5 above establish that for the VISTAS region, the key contributors to regional haze in the 2000-2004 baseline timeframe were la rge stationary sources of sulfur dioxide emissions. Keeping that ke y conclusion in mind, this section addresses the following questions:

b. Assuming implementation of existing federal and state air regulatory requirements in Tennessee and the VI STAS region, how much visibility improvement, compared to the glidepath, would we expect to see at Class I areas in Tennessee between now and 2018?

c. What additional emission controls represent BART in Tennessee?

d. If additional emission reductions were needed, from what pollutants and source categories would the gr eatest visibility benefits be realized between the baseline and 2018?

e. Narrowing down further, what are the geographic locations of the emissions which have the greatest impact on visi bility in specific Class I areas?

f. What types of emissions sources do we find in those geographic locations?

g. Which specific individual sources in those geographic locations have the greatest visibility impacts at a given Class I area?

h. What additional emission controls re present reasonable progress for those specific sources?

i. Given the additional emission reductions expected from reasonable progress and BART, how much additional visibil ity improvement, compared to the glidepath is expected at Class I areas in Tennessee between the baseline and 2018?43 7.2 Expected Visibility Results in 2018 for TN Class I Areas under existing and planned emissions controls

There are significant control programs being implemented between the baseline period and 2018. These programs are described in more detail below.

7.2.1 Federal and State Control Requirements

CAIR

. CAIR will permanently cap emissions of SO2 and NOx from EGUs in the eastern United States by 2015. When fu lly implemented, CAIR will reduce SO2 emissions from EGUs in these states by mo re than 70 percent, and NOx emissions by more than 60 percent, from 2003 levels. For TVA, the electric utili ty that operates coalfired EGUs in Tennessee, the following ta bles reflect the re ductions expected:

Emissions are aggregated at the facility level, rather than the unit level, as presented in the VISTAS emission inventories.

Facility Name 2006 Heat Input (mmBtu) 2006 Net Output (GWH) Allen 55,883,612 5,291 Bull Run 45,085,485 4,696 Cumberland 185,663,408 18,743 Gallatin 75,755,960 7,524 John Sevier 49,978,425 5,044 Johnsonville 88,835,413 7,555 Kingston 107,167,323 10,378 2006 heat inputs were obtained from Acid Rain Program monitoring data, and 2006 net outputs were obtained from TVA.

Facility Name PM 10 Emissions (tons) SO 2 Emissions (tons) NO X Emissions (tons)
2002 2009 2018 2002 2009 2018 2002 2009 2018 Allen 866 981 981 19,030 21,263 21,263 14,510 2,061 2,061 Bull Run 1,919 2,267 2,267 42,188 8,657 8,657 17,909 2,078 2,078 Cumberland 1,831 1,690 1,690 16,661 24,009 24,009 49,939 5,762 5,762 Gallatin 611 594 594 34,157 47 ,391 2,157 11,782 11,461 11,456 John Sevier 160 151 163 35,488 32,327 1,846 10,234 10,109 1,574 Johnsonville 5,561 2,661 2,661 108,789 22,216 22,216 23,879 13,329 1,333 Kingston 3,574 3,766 4,056 77,571 71,120 4,037 26,089 3,238 3,633

Emissions from 2002, 2009, and 2018 are from the VISTAS Base G/G2 emission inventory. 2002 emissions are based on the VISTAS 2002 actual emissions inventory.44 TVA Facility Existing SO 2 Controls (2006) Projected SO 2 Controls**

Allen None. Plant burns low-sulfur fuel. 2006 emission rate was 0.62 lb/mmBtu SO 2 . None planned prior to 2015.

Bull Run None Wet Scrubber. Completion projected in 2009.
Cumberland Wet Scrubber No additional controls planned.

Gallatin None. Plant burns low-sulfur fuel.2006 emission rate was0.63 lb/mmBtu SO 2. None planned prior to 2015.

John Sevier None Scrubber. Completion projected in 2012.

Johnsonville None Testing is underway to determine the viability of reducing SO 2 emissions from 1.5 lb/mmBtu to 0.9 lb/mmBtu by 2015, based on combustion of lower sulfur coal.

Kingston None Wet scrubber. Completion projected in 2010. ** Phase II CAIR requiremen ts will require additional SO 2 reductions within the TVA system by installing additional controls or by repowering or shutting down units.

NOx SIP Call . Phase I of the NOx SIP call applie s to certain EGUs and large non-EGUs, including large industrial boilers and turbines, and cement kilns. Those states affected by the NOx SIP call in the VISTAS region have developed rules for the control of NOx emissions that have been approved by the US EPA. The NOx SIP Call has resulted in a 66 percent reduction in summertime NOx emi ssions from large stationary combustion sources in Tennessee. For this analysis , we capped the emissions for NOx SIP callaffected sources at 2007 levels, and carried forward the capped levels for the 2009 and 2018 future year inventories.

North Carolina CSA . Under the act, enacted in 2002, co al-fired power plants in North Carolina must achieve a 77-percent cut in NOx emissions by 2009 and a 73-percent cut in SO2 emissions by 2013. This legislation es tablishes annual caps on both SO2 and NOx emissions for the two primary utility compan ies in North Carolina, Duke Energy and Progress Energy. These reducti ons must be made in Nort h Carolina, and allowances cannot be sold.

Georgia Multipollutant Bill. Georgia enacted regulations in summer 2007 to require coal fired power plants in Georgia to reduce SO2 by approximately 90%, NOx by approximately 50%, and mercury by an estimated 75-85% by 2015. Reductions will affect 21 units at seven facilities. The rule requires specific contro ls on specific units according to a specific schedule. This is not a cap and trade rule, thus there are no allowances to be banked, sold or traded.

Consent Agreements. Under a settlement agreement, by 2008, Tampa Electric will install permanent emissions-control equipment to me et stringent pollution limits; implement a series of interim pollution-re duction measures to reduce emissions while the permanent45 controls are designed and insta lled; and retire pollution em ission allowances that Tampa Electric or others could use, or sell to others, to emit additional NOx, SO2, and PM.

Virginia Electric and Power Co. agreed to spend $1.2 billion by 2013 to eliminate 237,000 tons of SO2 and NOx emissions each y ear from eight coal-fired electric generating plants in Virgin ia and West Virginia.

AEP [US District Court for the Southern Distri ct of Ohio, Eastern Di vision]: In October 2007 American Electric Power agreed to sp end $4.6 billion dollars to eliminate 72,000 tons of NOx emissions each year by 2016 a nd 174,000 tons of SO2 emissions each year by 2018 from sixteen coal-fired power plants located in Indiana, Kentucky, Ohio, Virginia and West Virginia.

A 2002 agreement calls for Gulf Power to upgrade its operation to cut NOx emission rates by 61 percent at its Cris t generating plant by 2007, with major reductions beginning in early 2005. The Crist plant is a significan t source of NOx emissions in the Pensacola

area.

One-hour ozone SIPs (Atlanta / Birmingham / Northern Kentucky). New SIPs have been submitted to the USEPA to demonstrate attainment of the one-hour ozone National Ambient Air Quality Standard (NAAQS). These SIPs require NOx reductions from specific coal fired power pl ants and address transporta tion plans in these cities.

NOx RACT in 8-hour Nonattainment Area SIPs . North Carolina’s SIP for the Charlotte / Rock Hill / Gastonia nonattain ment area includes Reasonable Achievable Control Technology (RACT) for NOx for two f acilities located in the nonattainment area: Philip Morris USA and Norandal USA. These controls were also modeled in 2018. Additional RACT controls may be re alized as other companies subject to RACT complete the determination, but RACT -level controls were assumed for just these two sources.

Heavy Duty Diesel (2007) Engine Sta ndard (for on-road trucks and buses). USEPA set a PM emissions standard for new h eavy-duty engines of 0.01 grams per brakehorsepower-hour (g/bhp-hr), to take full eff ect for diesel engines in the 2007 model year. This rule also includes sta ndards for NOx and non-methane hydrocarbons (NMHC) of 0.20 g/bhp-hr and 0.14 g/ bhp-hr , respectively. These NOx and NMHC standards will be phased in together be tween 2007 and 2010, for diesel engines. Sulfur in diesel fuel must be lowered to enable modern pollution-control technology to be effective on these trucks and buses. USEPA will require a 97 percent reduction in the sulfur content of highway diesel fu el from its current level of 500 parts per million (low sulfur diesel, or LSD) to 15 pa rts per million (ultra-low sulfur diesel, or ULSD).

Tier 2 Tailpipe (On-road vehicles) . USEPA mobile rules include the Tier 2 fleet averaging program, modeled af ter the California LEV II stan dards. Manufacturers can46 produce vehicles with emissions ranging from relatively dirty to zero, but the mix of vehicles a manufacturer sells each year must have average NO x emissions below a specified value. Tier 2 standards became effective in the 2005 model year.

Large Spark Ignition and Recreational Vehicle Rule . USEPA has adopted new standards for emissions of oxides of nitr ogen (NOx), hydrocarbons (HC), and carbon monoxide (CO) from several groups of pr eviously unregulated nonroad engines. Included in these are large i ndustrial spark-ignition engine s and recreational vehicles. Nonroad spark-ignition engines are those pow ered by gasoline, liquid propane gas, or compressed natural gas rated over 19 kilowa tts (kW) (25 horsepower). These engines are used in commercial a nd industrial applications, in cluding forklifts, electric generators, airport baggage transport vehicl es, and a variety of farm and construction applications. Nonroad recreational vehi cles include snowmobiles, off-highway motorcycles, and all-terrain-vehicles. Thes e rules were initially effective in 2004 and will be fully phased-in by 2012.

Nonroad Diesel Rule . This rule sets standards th at will reduce emissions by more than 90 percent from nonroad diesel e quipment, and reduce sulfur levels by 99 percent from current levels in nonroad diesel fuel starting in 2007. This step will apply to most nonroad diesel fuel in 2010 and to fuel used in locomotives and marine vessels in 2012.

Industrial Boiler/Proces s Heater/RICE MACTs . The USEPA issued final rules to substantially reduce emissions of toxic air polluta nts from industrial, commercial and institutional boilers, proce ss heaters and from stationa ry reciprocating internal combustion engines (RICE). These rules re duce emissions of a number of toxic air pollutants, including hydrogen chloride, manganese, lead, arsenic and mercury by 2009. This rule also reduces emissions of SO2 and PM in conjunction with the toxic air pollutant reductions. The applied Maximum Achievable Control Technology (MACT) control efficiencies were 4 pe rcent for SO2 and 40 percent for PM10 and PM2.5. The USEPA’s industrial boiler MA CT rules were vacated on June 8, 2007. The VISTAS states decided to leave these co ntrols in the modeling since it is believed that by 2018, the USEPA will have re-promulgat ed a boiler MACT rule or states will have addressed the issue through state rule making.

Combustion Turbine MACT . The projection inventorie s do not include the NOx cobenefit effects of the MACT regulations for Gas Turbines or stationary Reciprocating Internal Combustion Engines, which USEPA estimates to be small compared to the overall inventory.

VOC 2-, 4-, 7-, and 10-year MACT Standards . Various point source MACTs and associated emission reductions were impl emented. Reductions occurring before 2002 were assumed to be accounted for in the 2002 base year inventory.47 7.2.2 Additional State program s to reduce emissions

In addition to accounting for specific emissi on reductions due to ongoing air pollution programs as required under the regional haze rule’s section 308 (d)(3)(v)(A), states are also required to consider the air quality benefits of measures to mitigate the impacts of construction activities [section 308(d)(3)(v)(B)] and agricultural and forestry smoke management [section 308(d)(3)(v) (E)]. Thes e state measures are discussed in more detail in Section 7.9 and Appendix H.

7.2.3 Projected 2009 and 2018 Emissions Inventories

The inventories for 2009 and 2018 account for post-2002 emission reductions from promulgated and proposed federal, state, local , and site-specific control programs as of July 1, 2004. In general, emissions inve ntories were developed for 2009 and 2018 using current control information in Tennessee.

For EGUs, IPM results were adjusted based on state and local agencies’ knowledge of planned emission controls at specific E GUs. These updates are documented in the MACTEC emissions inventory report “Documen tation of the Base G2 and Best & Final 2002 Base Year and 2009 and 2018 Projection Year Emission Inventories for VISTAS” dated March 2008. (See Appendix D-5b)

For non-EGUs, VISTAS used recently updated gr owth and control data consistent with the data used in USEPA’s CAIR analyses s upplemented by state and local agencies’ data and updated forecasts from the Department of Energy (DOE).

Area source controls were estimated using know n state level Stage I controls on gasoline dispensing facilities and open burning estimat es, as well as controls used to project emissions for the USEPA’s Heavy Duty Diesel rulemaking and for the CAIR rulemaking.

Mobile source controls included local cont rols underlying the 2002 baseline inventory (vehicle emission inspection, Stage II vapor recovery, anti-tampering, etc.) with changes based on specific State input. The future ye ar inventories were developed by running the MOBILE 6.2 model for each year modeled. The future year emissions for the off-road mobile sources included in the USEPA NONR OAD model were estimated by running the model for each future year. For the other off -road mobile source categories, control data and projections for 1996, 2010, 2015, and 2020 were obtained form the USEPA’s CAIR Technical Support Document and straight-lin e projections were used to estimate 2009 and 2018 levels.

The following bar charts show expected decr eases in emissions of SO2 and NOx across the VISTAS states from 2002 through 2018. (Sim ilar charts for other visibility impairing pollutants are contained in Appendix H). No te that for SO2 emissions in particular, which are the largest contributors to haze, em issions from electric generating facilities are expected to decrease dramatically (70 pe rcent) between 2002 and 2018. However, even after implementation of CAIR, EGU emissions are projected to remain the largest48 contributor to haze, comprising more than half of remaining SO2 emissions in every

state.

Annual SO 2 Emissions by Source Sector

0 100,000 200,000 300,000 400,000 500,000 600,000 700,000

AL F L G A KY MS NC S C TN VA WVTons per year FIRES

AREA
NONROAD
ONROAD
NONEGU
EGU 2002 Actual EGU and Fires

2002 Typical EGU and Fires

2009

2018

Figure 7.2.3-1. Annual SO2 emissions for 2002, 2009, and 2018 in the VISTAS States.

Annual NOx Emissions by Source Sector

0 200,000 400,000 600,000 800,000 1,000,000 1,200,000

AL F L GA KY M S NC S C TN VA WVTons per year FIRES

AREA
NONROAD
ONROAD
NONEGU
EGU 2002 Actual EGU and Fires
2002 T yp ical EGU and Fires 2009

2018

v

Figure 7.2.3-2. Annual NOx emissions in 2002, 2009, and 2018 in the VISTAS States.49 Summary of Emissions Inventories for 2009 and 2018

Tables 7.2.3-1 and 7.2.3-2 are summaries of the 2009 and 2018 Base G4 (Best and Final) emission inventory, respectively. The comp lete inventory and discussion of the methodology is contained in Appendix D.

Table 7.2.3-1. 2009 Base G4 (B&F) Emi ssions Inventory Summary for TN. Source Category VOC NOx PM2.5 PM10 NH3 SO2 Point 74,430 123,919 40,459 50,189 2,067 320,374 Area 155,194 18,872 45,573 227,787 35,331 30,679 On-Road Mobile 115,181 151,912 2,751 4,206 7,782 1,076 Non-Road Mobil e 55,358 86,641 5,557 5,877 48 5,651 Biogenics 894,214 18,081 0 0 0 0 TOTAL 1,294,377 399,425 94,340 288,059 45,228 357,780

Table 7.2.3-2. 2018 Base G4 (B&F) Emi ssions Inventory Summary for TN. Source Category VOC NOx PM2.5 PM10 NH3 SO2 Point 93,432 94,234 46,680 57,940 2,454 169,354 Area 183,110 20,002 48,265 248,086 36,376 32,073 On-Road Mobile 67,324 69,385 1,544 3,092 9,021 948 Non-Road Mobil e 45,084 70,226 4,403 4,672 55 5,207 Biogenics 894,214 18,081 0 0 0 0 TOTAL 1,283,164 271,928 100,892 313,790 47,906 207,582

7.2.4 Model Results for the 2018 Inventory Compared to the Uniform Rate of Progress Glidepaths for Tennessee Class I Areas

Using 2000 - 2004 IMPROVE monitoring data, th e deciview values for the 20 percent best days in each year are averaged together , producing a single average deciview value for the best days. Similarly, the deciview va lues for the 20 percent worst days in each year are averaged together, producing a single average deciview value for the worst days. The average values represent the current visibility conditions.

The predicted visibility improvement is ca lculated by comparing the 2002 typical year modeling results for the 12-km grid to th e 2018 12–km modeling results to develop a relative reduction factor. This factor is then applied to the current visibility condition values to estimate the future visibility. Detailed discussions about how the relative reduction factors are calculated can be found in Appendix G.

Figures 7.2.4-1 through 7.2.4- il lustrate the predicted visibility improvement by 2018 resulting from the Best & Fi nal inventory that represents implementation of existing federal and state regulations, compared to the reasonable progress assessment for Great Smoky Mountains National Park and Joyce Kilm er-Slickrock Wilder ness Area. In the figures, the red lines show the uniform rate of progress to natural conditions in 2064 for the 20 percent worst days or 20 percent best days, and the incremental deciview changes resulting form a uniform rate of progress be tween current and natura l conditions in 2064.50 The purple lines show the improvement expe cted from existing and planned emission controls during the period of the first long term strategy. As previously stated, the visibility improvement for Joyce Kilmer – Slickrock Wilderness Area is represented by the results for the Great Smoky Mountains Na tional Park. Natural background visibility calculations are based on EPA’s default na tural conditions estimates (Method 1) as explained in Appendix B-4 (page 31).

(Method 1Prediction = Default Calculation per EPA Guidance) Figure 7.2.4-1. Reasonable progress assessment compared to Uniform Rate of Progress for 20% worst days at Great Smoky Mountains National Park.

(Method 1 Prediction = Default Calculation per EPA Guidance) Figure 7.2.4-2. Reasonable progress assessment compared to Uniform Rate of Progress for 20% worst days at Joyce Kilmer-Slickrock Wilderness Area. Reasonable Progress Assessment Great Smoky Mountains - 20% Worst Data Days NEW IMROVE EQUATION, 12 km grid

30.28 28.67

25.79

22.59

19.38

16.18

12.97 11.05 26.72

23.50



### 0 5 10 15 20 25 30 35

200 0 200
4 200 8 201
2 201 6 202 0 202
4 202 8 203
2 203 6 204 0 204
4 204 8 205
2 205 6 206 0 206
4

Year

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction Uniform rate of p ro g ress = 4.5 dv b y 2018

Reasonable Progress Assessment Joyce Kilmer-Slickrock - 20% Worst Data Days NEW IMROVE EQUATION, 12 km grid

30.28 28.67 25.79 22.59 19.38 16.18 12.97 11.05 26.72 23.50



### 0 5 10 15 20 25 30 35

200 0 200
4 200 8 201
2 201 6 202 0 202
4 202 8 203
2 203 6 204 0 204
4 204 8 205
2 205 6 206 0 206
4

YearHaziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction Uniform rate of p ro g ress = 4.5 dv b y 201851 (Method 1 Prediction = Default Calculation per EPA Guidance) Figure 7.2.4-3 Reasonable progress assessment for 20 % best days at Great Smoky Mountains National Park

Visibility Improvement at Joyce Kilmer Wilderness is represented by Great Smoky Mountains.

Figure 7.2.4-4 CMAQ 2018 results at Joy ce Kilmer-Slickrock Wilderness Area.

Note that at both Class I areas in Tenness ee, visibility improvements on the 20 percent worst days are expected to be better than the uniform rate of progress glidepath by 2018 based solely on reductions from existing and pl anned emissions controls. For example, at Great Smoky Mountains, a 4.5 dv improvement in visibility would meet uniform rate of progress in 2018; expected em issions reductions by 2018 are pr ojected to achieve a 6.78 dv improvement. In fact, as illustrated in Figure 7.2.4-5, visibility improvements at all the VISTAS mountain Class I area s and most of the coastal Cl ass I areas are projected to be better than the uniform rate of progress. In Figure 7.2.4-5, the percentage of the target reduction achieved for the Great Smoky Mountains National Park using the new IMPROVE equation is 150 percen t. This means that the rate of improvement is 50 percent greater than the unifo rm rate of progress by 2018.

In addition to improving visibility on the 20 pe rcent worst visibility days, states are also required to protect visibility on the 20 per cent best days at the Class I areas. As illustrated in Figure 7.2.4-6, visibility on the 20 percent best days is projected to improve in 2018 at all VISTAS Class I areas as a re sult of the 2018 emissions reductions. In Figure 7.2.4-6 the percentage of the target achieved for the Great Smoky Mountains National Park is about -10 percent. Zero percent change would mean no change in visibility; -10 pe rcent means that visibility is be tter than no change, or a 10 percent improvement (values lower than current condit ions). Reasonable Progress Assessment Great Smoky Mountains - 20% Best Days New Improve Equation, 12 km grid

13.58 13.58 13.58

13 .11 12 .11 4.54



## 0 5 10 15 20 25 30 35

20 00 20 04 20 08 20
12 20 16 20 20 20
24 20 28 20 32 20 36 20 40 20
44 20 48 20 52 20 56 20 60 20 64

Year

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction52 Figure 7.2.4-5. Projected visibi lity improvement on 20 percent worst visibility days at VISTAS and neighboring Cla ss I areas for 2018 (12 km grid)

Figure 7.2.4-6. Projected visibili ty improvement on 20 percen t best visibility days at VISTAS and neighboring Class I a reas for the 2018 Base G2a CMAQ run 2018 Base G2a CMAQ Visibility Projection for 20% Best Days

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%COHUDOSOGRSMJARILIGOMACASHENSHROSIPSCHASEVEROKEFROMASAMASWANBRETBRIGCACRHEGLMINGUPBUPercent of target reduction achieved

VISTAS non-VISTAS CMAQ New IMPROVE Algorithm

CMAQ Old IMPROVE Algorithm 2018 Base G2a CMAQ Visibility Projection for 20% Best Days

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%COHUDOSOGRSMJARILIGOMACASHENSHROSIPSCHASEVEROKEFROMASAMASWANBRETBRIGCACRHEGLMINGUPBUPercent of target reduction achieved

VISTAS non-VISTAS CMAQ New IMPROVE Algorithm

CMAQ Old IMPROVE Algorithm CMAQ Base G2a Visibility Improvement between 2002 and 2018

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%COHUDOSOGRSMJARILIGOMACASHENSHROSIPSCHASEVEROKEFROMASAMASWANBRETBRIGCACRHEGLMINGUPBUPercent of target reduction achieved

VISTAS non-VISTAS CMAQ New IMPROVE Algorithm CMAQ Old IMPROVE Algorithm CMAQ Base G2a Visibility Improvement between 2002 and 2018

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%COHUDOSOGRSMJARILIGOMACASHENSHROSIPSCHASEVEROKEFROMASAMASWANBRETBRIGCACRHEGLMINGUPBUPercent of target reduction achieved

VISTAS non-VISTAS CMAQ New IMPROVE Algorithm CMAQ Old IMPROVE Algorithm

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%COHUDOSOGRSMJARILIGOMACASHENSHROSIPSCHASEVEROKEFROMASAMASWANBRETBRIGCACRHEGLMINGUPBUPercent of target reduction achieved

VISTAS non-VISTAS CMAQ New IMPROVE Algorithm CMAQ Old IMPROVE Algorithm53 The expected change in visibility at Great Smoky Mountai ns between 2000-2004 baseline conditions on the 20 percent worst days, and 2018 projections is illustrated in Figure 7.2.4-7 (upper picture). In contrast , natural background visibility for the 20 percent worst days is illustrated in the bo ttom picture in Figure 7.2.4-7. These images were generated using WINHAZE, a photographi c imaging tool that accounts for the effect of concentrations of the fine pa rticle components and relative humidity on visibility. These images illustrate that notab le improvements in visibility are expected by 2018 and that significan tly greater improvements are need ed to reach natural background conditions.

Figure 7.2.4-7. Visibility improvement on 20% haziest days at Great Smoky Mountains between 2000-2004 baseline condi tions (upper left) and 2018 projected visibility (upper right). (Bottom) Project ed visibility on 20% haziest days for natural background 20% Haziest Days at Great Smoky Mtns

Natural Background Visibility Great Smoky Mtns54 7.3 Relative Contribution from International Emissions to Visibili ty Impairment in
2018 at VISTAS Class I areas

Emissions from Mexico, Canada, Central Am erica, Asia, and Africa contribute to PM2.5 loadings and visibility impairme nt at Class I areas in the continental United States. To evaluate the relative cont ribution of international emissions to visibility at Class I areas in the southeastern United States, VISTAS used a combination of modeling results from the global three-dimensional chemical transpor t model (GEOS-Chem) and CMAQ. VISTAS used the GEOS-Chem global model to generate initial and boundary conditions for the CMAQ modeling domain. GEOS-Chem was run for the 2002 modeling year using a 4 x 5 degree horizontal grid reso lution and a 3-hour temporal re solution. Because emissions were based on monthly averages, the model doe s not capture the episodic variability in emissions. The GEOS-Chem outputs were used to calculate initial and boundary conditions for the national CMAQ modeli ng domain. The national CMAQ domain included portions of Canada and Mexico, so emissions for these countries were included within the national CMAQ modeling domain or as part of the boundary conditions outside the national modeli ng domain, as appropriate.

Two complementary methods were used to calculate the impact of international emissions at Class I areas. Because th e international emissions inventory used in GEOS-Chem did not distinguish between wildfires and anthropogenic fires, all international fire emissions were assume d to be wildfire, and the contributions attributable to international anthropogenic fires are underestimated in these projections.

1) International emissions are represented by the difference between two GEOSChem runs. In the first run United States anthropogenic emissions were removed, and in the second run both United States and international anthropogenic emissions were removed. Th e difference represen ts international anthropogenic emissions, in the absence of United States anthropogenic emissions (e.g. compared to 2064 levels). Harvard University provided GEOS-Chem results to VISTAS for 2002 internati onal contribution on 4 x 5 de gree grid. Concurrently Harvard modeling for the Electric Powe r Research Institute (EPRI) provided GEOS-Chem results for 2001 international contribution on a 1 x 1 degree grid scale.

2) International emissions are represented by the difference between two CMAQ 36- km simulations, both using an earlier ve rsion of the 2018 emissions and boundary conditions from GEOS-Chem. In the first CMAQ run, all global natural and anthropogenic emissions in 2018 are ac tive. In the second CMAQ run, only global (United States and international) natural emissions are active. Here the impacts of international em issions are compared against 2018 conditions rather than natural background conditions.55 VISTAS compared its results to results from other RPOs on the impact of international emissions and boundary conditions on visibility at Cl ass I areas in 2002. The results were similar.

As illustrated in Figure 7.3-1 for annual aver age contributions to sulfate at VISTAS and neighboring Class I areas, the estimated international contribu tions are higher at Class I areas near the Canadian and Mexican borders and along the eastern coast. The estimated international contribution is higher using CMAQ than in the GEOSChem runs because the grid scale is finer (more accurate dispersion of emissions) and because the background atmosphere includes loadings from current United States anthropogenic emissions (greater photochemical activity). Similar charts for nitrate and organic carbon mass, for impacts on 20 pe rcent worst visibility days, and for impacts of international emissions on calcu lated light extinction are included in Appendix G.

Figure 7.3-1. Estimated international emission s contributions to sulfate at VISTAS and neighboring Class I areas.

In Figure 7.3-2 CMAQ projections of contributions from inte rnational emissions to PM mass on 20 percent worst visibility days in 2002 at Great Smoky Mountains National Park are compared to United States domestic contributions to PM components at the site on those days. 

### Transboundary Man-Made Impacts

0.0 0.2 0.4 0.6 0.8 1.0 1.2BRIGHEGLCACRMINGUPBUBRETEVERCHASSAMACOHUOKEFROMASIPSSHROGRSMLIGOMACASWANJARIDOSOSHEN(NH4)2SO4Conc. (μg/m3)

GEOS_4x5 GEOS_1x1 CMAQ56 Figure 7.3-2. PM component concentrations from US domestic sources on 20 percent worst visibility days in 2002 (left bars) and CMAQ-simulated international contributions (right bars) at Great Smoky Mountains Na tional Park.

Although VISTAS assessed impacts from intern ational emissions as the Class I areas, these modeling runs were to provide informa tion to the states to understand what the potential impacts may be at their Class I ar eas. Since good projecte d emissions for areas outside of the continental Unite d States are not readily availa ble, there is no real way to assess what the impacts from international emissions would be for 2018, other than to assume that it would be approximately sa me amount as for the baseline current conditions. Therefore, TDEC-APC will not be accounting for international emissions in setting the 2018 reasonable progre ss goals for its Class I areas.

Nevertheless, as the atmosphere becomes cl oser to natural background conditions in the future, the incremental contribution from international emissions will become more important. The information is included in this SIP documentation to provide reference for future assessments of reasonable progress. GRSM1 Domestic and International Aerosol Mass by Species 20% Worst Days (2002)

0 5 10 15 20 25 30 35DomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternational

2/25 5/2 5/8 5/17 6/1 6/13 6/19 6/22 7/1 7/7 7/10 7/16 7/22 7/31 8/3 8/6 8/9 8/12 8/15 8/21 9/5 9/8 9/11 9/17Mass (μg/m3)

Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt57 7.4 Relative Contributions to Visibility Im pairment: Pollutants, Source Categories, and Geographic Areas

An important step toward identifying furthe r reasonable progress measures is to identify the key pollutants contributing to visibility impairment at each Class I area. To understand the relative benefit of further redu cing emissions from different pollutants, source sectors, and geographi c areas, VISTAS engaged the Georgia Institute of Technology to perform emission sensitivity model runs using CMAQ. Emissions sensitivities were initially performed for three episodes representing winter and summer conditions: Jan 2002, July 2001, and July 2002. These runs used the initial 2018 projections inventory and c onsidered 30 percent reductions from specific pollutants, source categories, and geographic areas. Emi ssions sensitivities were repeated using a preliminary 2009 projection inventory and tw o month-long episodes from 2002: Jun 1- Jul 10 and Nov 19 – Dec 19. Emissions in 2009 were reduced by 30 percent for each pollutant sensitivity run. The pollutant contributions that were evaluated were:

• SO2 from EGU sources in each VISTAS st ate, other RPOs in the VISTAS 12 km grid, and Boundary Conditions fr om outside the 12 km domain.

• SO2 from non-EGU point sources in each VISTAS state, other RPOs, and Boundary Conditions

• NOx from ground level sources (on-road plus off-road plus area) in each VISTAS state and other RPOs. In the VI STAS states, these reductions were only applied to specific counties that were of concern for 8-hour ozone nonattainment.

• NOx from point (EGU plus non-EGU) sour ces in each VISTAS state and other RPOs

• NH3 from all sources in VISTAS and other RPOs

• Volatile Organic Compounds from anth ropogenic and biogenic sources in the 12 km modeling domain

• Primary Carbon from all ground level sour ces in each VISTAS state and other RPOs. In the VISTAS states, these re ductions were only applied to specific counties that were of concern for PM2.5 nonattainment.

• Primary Carbon from all point sources in each VISTAS state and other RPOs

• Primary Carbon from all fires in ea ch VISTAS state and other RPOs

Figure 7.4-1 below shows the average of the 20 percent worst visibility days for Great Smoky Mountains National Park. Responses fo r 20 percent worst days were calculated by averaging the responses of the 20 percent worst days th at were modeled in the two episodes. These results were used to unde rstand the relative importance of pollutant contributions at this site.58 Figure 7.4-1. CMAQ projections of visibili ty responses on 20 percent worst days at Great Smoky Mountains to 30 percent reductions from a 2009 inventory for visibility-reducing pollutants in different source categories and geographic areas.

As Figure 7.4-1 illustrates the greatest visibi lity benefits on the 20 percent worst days for the Great Smoky Mountains National Park are projected to result from further reducing SO2 from EGUs. Benefits ar e projected from SO2 reductions from EGUs in several VISTAS states including Alabama, Georgia, Kentucky, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia. C ontributions from other RPOs and from the boundary conditions are comparatively small a nd the greatest benefits would likely be from further EGU reductions within the VISTAS states. Additional, smaller benefits are projected from additional SO2 emission reducti ons from non-utility, industrial point sources. The pattern of relative SO2 cont ributions from non-EGUs among the various VISTAS states is similar to the pattern of relative SO2 contributions from EGUs.

Because ammonium nitrate is a small contributor to PM2.5 mass and visibility impairment on the 20 percent worst days at the mountain Class I areas, the benefits of reducing NOx and NH3 emissions at these sites are small.

VOCs do contribute to visibility impairment , but as shown in the chart above, this contribution is from biogenic sources such as vegetative emissions. Controlling anthropogenic sources of VOC emissions has litt le if any visibility benefit at the Great Smoky Mountains National Park. Reducing pr imary carbon from point sources, ground level sources or fires are proj ected to have small to no visibility benefit. This is consistent with the monitoring data that show s that most of measur ed organic carbon is secondary in origin and primary carbon is only a small fraction of the total measured 

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AL59 carbon (Appendix B). Reducing carbon from fi res was not found to be effective because there was little fire activity at these sites on th e days modeled in the sensitivity analyses.

Note that these results from the emission se nsitivity runs are consistent with the conclusions drawn from the 2000-2004 baseline monitoring data (see section 2.4). The results indicate that sulfate is the dominant contributor to visibility impairment on the 20 percent worst days at all sites, and that ammonium nitrate may be important for sites where the 20 percent worst days occur in the winter. TDEC-APC concludes that reducing SO2 emissions from EGU and non-E GU point sources in the VISTAS states would have the greatest visibi lity benefits for the Tennessee Class I areas. Contributions from the Midwest RPO and MANE-VU are grea ter at the Class I areas bordering these RPOs. Contributions from outside the VISTAS 12-km modeling domain are more important for the coastal Class I areas. These results are consistent with the CMAQ model results indicating that contributions from internati onal emissions to visibility impairment at VISTAS Class I areas are great er closer to the bounda ries of the modeling domain (see summary in section 7.3 a nd further discussion in Appendix G).

Emissions sensitivities were also run for SO2 emissions from EGU point sources in VISTAS states, non-EGU industr ial point sources in VISTAS states, point sources in non-VISTAS states, and boundary conditions for the VISTAS modeling domain. The 2018 Base G inventory was run for the entire annual modeling year and SO2 emissions were zero-ed out in separate CMAQ runs for each SO2 source category listed in the previous sentence. These 2018 annual emission sensitivity runs conf irm the conclusions from the 2009 episode runs. As illustrated in Figure 7.4.2, on the 20% worst days, even after implementation of the Clean Air Interstate Rule, in 2018 further reduction in emissions from the utility sector would have greater benefit to visi bility at Great Smoky Mountains than reductions from the industrial sectors. These conclu sions apply the total source category and to determine relative benefi ts of reductions from specific utility or industrial sources, additional source-s pecific modeling would be required.

Figure 7.4-2. 2018 Annual Base G Emissi ons: Responses to SO2 Zero-out Sensitivity Runs. -80

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--------------------- VISTAS -----------------------------------∆Bext (Mm-1)Bext (Mm-1) Boundary MMS MEX& CAN non-VISTAS VISTAS non-EGU VISTAS EGU 2018-g1 Boundary MMS MEX& CAN non-VISTAS VISTAS non-EGU VISTAS EGU 2018-g160 7.5 What Control Determinations Represen t Best Available Retrofit Technology (BART) for Individual Sources?

Section 169A of the CAA directs States to asse ss certain large, olde r emission sources for additional controls in order to address visibility impacts. States are directed to conduct BART determinations for such sources in specific source categories, and which contribute to visibility impair ment in Class I areas. The 1999 regional haze rule includes the BART requirement, and directs States to include BART in their regional haze SIPs. On July 6, 2005, USEPA published a revised fi nal rule, including Appendix Y to 40 CFR Part 51, the Guidelines for BART Determinati ons Under the Regional Haze Rule (hereinafter referred to as the “BART Guidelin es”) that provides direction to states on determining which of these sources should be subject to BART, and how to determine BART for each source.

A BART-eligible source is one which has the potential to emit 250 t ons or more of a visibility-impairing air pollutant, was put in place between August 7, 1962 and August 7, 1977, and whose operations fall within one or more of 26 specifically listed source categories. Under the CAA, BART is required for any BART-eligible source that a State determines ‘‘emits any air pollutant which may reasonably be anticipated to cause or contribute to any impairment of vi sibility in any such area.’’

For those sources subject to BART, Section 169A(g)(7) of the CAA requires that States must consider the following factors in ma king BART determinati ons: (1) the costs of compliance, (2) the energy and non-air quality environmental impacts of compliance, (3) any existing pollution control t echnology in use at the source , (4) the remaining useful life of the source, and (5) th e degree of improvement in vi sibility which may reasonably be anticipated to result from th e use of such technology.

7.5.1 BART-Eligible Sources in Tennessee

The following is a list of facilities with BA RT-eligible sources in Tennessee. TDEC-APC developed this list by examining the 2002 NEI data summary. The list was electronically sorted to retrieve sources th at had emissions of visibility impairing pollutants that were greater than or equal to 250 tons per year. The pollutants included sulfur dioxide, nitrogen oxides, particulate matter, and volatile organic compounds as set forth in the proposed BART rule in the FR Vol. 69, No. 87, May 5, 2004 on page 25216. Per the final BART rule in the FR Vol. 70, No.128 on page 39112 and page 39160, the list was expanded to include ammonia.

Next, the sources of the aforementioned emi ssion magnitude were examined by SIC code and NAICS code to determine if they fell under one of the BART 26 source categories. Most large VOC sources were excluded from BART because they were not in the BART specific source categories. Using this electr onically reduced list, a file review and dialogue with the identified source was used to determine if the source construction date was in the 1962 to 1977 BART period. That final list was then evaluated under BART exemption modeling that is already discussed in the Regional Haze SIP that was taken61 out for public comment. See Appendix L for a detailed description of each BARTeligible emission unit:

FACILITY NAME Aluminum Company of America (Alcoa) – South Plant DuPont White Pigment and Mineral Products (Humphreys County) Eastman Chemical Compa ny – Tennessee Operations E I DuPont de Nemours and Co mpany, Inc. (Old Hickory) E I DuPont de Nemours and Company, Inc. (Shelby County) Holston Army Ammunition Plant Intertrade Holdings, Inc. Liberty Fibers Corporation Lucite International Owens Corning Packaging Corporation of America (PCA) PCS Nitrogen Tennessee Valley Authority (TVA) – Bull Run Fossil Plant Tennessee Valley Authority (TVA) – Cumberland Fossil Plant Zinifex Weyerhaeuser Corporation (now Domt ar Paper Company) - Sullivan County

The BART-eligible sources were identif ied using the methodology in the BART Guidelines.

• One or more emissions units at the faci lity fit within one of the 26 categories listed in the BART Guidelines;

• The emission unit(s) were in existence on August 7, 1977 and began operation at some point on or after August 7, 1962; and

• The limited potential emissions from all emi ssion units identified in the previous two bullets emission units were greater than 250 tons or more per year of any of these visibility-impairing pollutants: SO2, NOx, and PM10.

The BART Guidelines recommend addressing these visibility-impairing pollutants: SO 2 , NO x , and particulate matter, and suggest th at States use their best judgment in determining whether to address VOC or ammonia emissions. TDEC-APC addressed SO2 and NOx, and used particulate matter less than 10 microns in diameter (PM 10 ) as an indicator for particulate matter to identify BA RT-eligible units, as the BART Guidelines recommend. As discussed in detail in Appendix L (BART related documentation narrative), VISTAS modeling demonstrated th at VOCs and ammonia from point sources are not visibility-impairing pollutants. Fo r this reason TDEC-APC did not evaluate emissions of VOCs. However, ammonia emi ssions were evaluated only from one major source of ammonia (i.e., PCS Nitroge n) in the BART determinations.62 7.5.2 Determination of Sources Subject to BART in Tennessee

Under the BART Guidelines, TDEC-APC may consider exempting some sources from BART if we find that they do not cause or cont ribute to visibility impairment in a Class I area. In accordance with the BART guide lines, TDEC-APC chos e to perform sourcespecific analyses to determine which sources cau se or contribute to visibility impairment using the CALPUFF model. The CALPUFF modeling protocol used for determining which facilities are subject to BART is included in Appendix L.

TDEC-APC also examined the appropriatene ss of the 0.5 deciview threshold for potential “clusters” of BART sources that might be impacting a Class I area in a cumulative manner, and did not find evidence that an ex emption threshold less than the 0.5 deciview threshold should be used. Appendix H’s ma in document describing reasonable progress has additional information in Section 5.0 re garding individual s ource impacts upon Class I areas. In accordance with the Guidelines, a contribution th reshold of 0.5 deciviews was used for determining which sources were subject to BART.

Tennessee initially identified sixteen (16) f acilities with BART-eligible sources. Since the time that the BART-eligible sources were identified, Liberty Fibers Corporation has permanently shut down, and the BART-eligible bo ilers located at the facility have been dismantled. Additionally, Intertrade Holdings , Inc. has permanently shut down the acid plant that was determined to be BART-elig ible (a reduction of sulfur dioxide of approximately 374 tons per year). Holst on Army Ammunition Plant has requested a permit limit of 249 tons per year for the emissi ons units that make up their acid plant The Division of Air Pollution Control issued a federally enforceable permit restricting the acid plant emissions to 249 tons per y ear on February 25, 2008 (See Appendix L-12, pages 3-4). The subject acid pl ant consists of eight source s, with combined potential emissions of 638.2 tons of nitrogen oxides ( NOx) before the issuance of the federally enforceable permit. However, this part of the facility has not been operated since 1997. The remaining sources with potential emissi ons above 250 TPY at this facility were either built before August 1962 or were built or modified after the promulgation of major NSR rules in 1977. The power boiler (#7) at the Weyerhaeuser facility (formerly Willamette Industries) in Sullivan county has been retired and the facility is no longer BART eligible. Appendix L-12 provides sp ecific permit information on these BART exempted sources.

The BART listing of the remaining twelve (12) operational sources is as follows:

Aluminum Company of America (Alcoa) – South Plant DuPont White Pigment and Mineral Products (Humphreys County) Eastman Chemical Compa ny – Tennessee Operations E I DuPont de Nemours and Co mpany, Inc. (Old Hickory) E I DuPont de Nemours and Company, Inc. (Shelby County) Lucite International Owens Corning Packaging Corporation of America (PCA) PCS Nitrogen63 Tennessee Valley Authority (TVA) – Bull Run Fossil Plant Tennessee Valley Authority (TVA) – Cumberland Fossil Plant Zinifex

Eight (8) of Tennessee’s BART-eligible sources submitted exemption-modeling demonstrations. Results of these demons trations are summarized in Tables 7.5.2-1 through 7.5.2-2 below. Additional details are av ailable in Appendix L. Facilities found to be subject to BART must complete a BART analysis (Table 7.5.2-3).

Table 7.5.2-1 represents the six (6) facilities that were able to demonstrate exemption from BART based on CALPUFF modeling conducted using the VISTAS modeling protocol, VISTAS CALMET screening 12-km regional domain, and the old IMPROVE equation. No significant adverse comments were received during the public comment period.

Table 7.5.2-1. BART Exemption Modelin g Results for Sources using Old IMPROVE Equation – 12 Km. CALMET

Facility Class 1 Areas Distance To Class I Area (km) Impact (Change in dv)

DuPont – Humphreys County (for SO 2 , NOx, and PM10) Mammoth Cave NP 203 0.338 Mingo Wildlife Refuge 225 0.128 Sipsey Wilderness Area 200 0.369 DuPont – Shelby County (for SO2, NOx and PM10) Mingo Wildlife Refuge 218 0.151 Sipsey Wilderness Area 216 0.147
Lucite International (for SO 2 , NOx, and PM10) Mingo Wilderness Area 187 0.053 Sipsey Wilderness Area 253 0.040
Owens Corning (for SO 2 , NOx,and PM10) Mammoth Cave NP Mingo Wildlife Refuge Sipsey Wilderness Area 297 187 200 0.045 0.064 0.040 PCS Nitrogen (for SO 2 , NOx, NH 3 and PM10) Mingo Wildlife Refuge 186 0.166
Sipsey Wilderness Area 253 0.074

Zinifex – Clarksville (for SO 2 , NOx, and PM10) Cohutta Wilderness Area 312 0.099
Mammoth Cave NP 131 0.465 Mingo Wildlife Refuge 256 0.125 Sipsey Wilderness Area 247 0.237

Table 7.5.2-2 represents the two (2) facilities that were able to demonstrate exemption from BART based on the VISTAS modeling protocol, VISTAS CALMET refined 4-km resolution (domain 3), an d the old IMPROVE equa tion (PCA and TVA).64 Table 7.5.2-2. BART Exemption Modelin g Results for Sources using Old IMPROVE Equation – 4 Km. CALMET

Facility Class 1 Areas Distance To Class I Area (km) Impact (Change in dv)

TVA – Bull Run Fossil Plant (for PM10) Cohutta Wilderness Area 118 0.126 Great Smoky Mountains NP 48 0.445
Joyce Kilmer-Slickrock WA 63 0.226
Linville Gorge Wilderness Area 200 0.063

Mammoth Cave National Park 209 0.033

Shinning Rock WA 131 0.081

Packaging Corp. of America (for SO 2 , NOx and PM10 ) Mammoth Cave National Park 299 0.101

Mingo Wilderness Area 273 0.064 Sipsey Wilderness Area 100 0.247

Table 7.5.2-3 represents the f our (4) facilities that were not able to demonstrate exemption from BART based on the VISTAS modeling protocol. These sources are subject to the BART controls modeling demonstrations.

Table 7.5.2-3. Modeling Determination Results for BART-Subject Sources

Facility Class 1 Areas Distance To Class I Area (km) Impact (Change in dv)

Alcoa – South Plant (1) (for SO 2 , NOx, and PM10) (New Improve Equation except MACA w/Old Improve Equation) Cohutta Wilderness Area Great Smoky Mountains Joyce Kilmer-Slickrock Linville Gorge WA Mammoth Cave NP (2) Shinning Rock WA 99 18 16 185 244 108 0.335 1.310 0.686 0.370 0.350 0.463

DuPonrt – Old Hickory (for SO2, NOx and PM10) (New Improve Equation) Mammoth Cave NP (3)
Cohutta Wilderness Area Great Smoky Mountains Joyce Kilmer-Slickrock Sipsey Wilderness Area 105 233 249 254 220 0.504 0.160 0.220 0.190 0.31065 Facility Class 1 Areas Distance To Class I Area (km) Impact (Change in dv)

Eastman Chemical (4) (for SO 2 , NOx, and PM10 )(New Improve Equation) Cohutta Wilderness Area Great Smoky Mountains James River WA Joyce Kilmer-Slickrock Linville Gorge WA Shinning Rock WA 245 98 289 174 85 124 0.292 0.992 0.289 0.470 0.875 0.543

TVA-Cumberland (5) (for PM10) (Old Improve Equation) Mammoth Cave NP Mingo Wildlife Refuge Sipsey Wilderness Area 153 233 223 1.359 0.404 0.557

(1) Alcoa BART base case, refined 1-km runs, highest-1 st to 8 th -high value, 3% sulfur coal using New Improve Equation.

(2) Alcoa exemption modeling, screening 4-km runs, 1 st highest value, using Old Improve Equation only for MACA.

(3) DuPont BART base case Mammoth Cave (MACA) mode ling with New Improve Equation, refined 4-km runs, 8 th highest value (occurs only 7 days in 2003 at 24 hour maximum firing rate). A maximum delta DV of 0.36 is obtained at only 3 days occurrence in 2003 at monthly averaged maximum emission ra te, which is more representative of the firing patterns for the Boilers. Revised su mmer time 24-hour maximum emission rates resulted in a 0.474 DV at MACA. This source is still subject to BART control assessment.

(4) Eastman BART control case, refined 4-km runs, 8 th highest value, SDA/FF Controls, using New Improve Equation.

(5) TVA-Cumberland BART base case, refined 4-km runs, 8 th highest value, hydrated lime , using Old Improve Equation.

Two of Tennessee’s BART-eligible sources are EGUs. These are TVA-Bull Run and TVA- Cumberland Fossil Plants. These two sources have already installed or are installing scrubbers to reduce SO2 and Sele ctive Catalytic Reduc tion (SCRs) to reduce NOx.

The USEPA has determined that, as a whol e, the CAIR cap-and-trade program improves visibility more than implementing BART fo r individual sources in states affected by CAIR. A State that opts to participat e in the CAIR program under 40 CFR 96.201-.224 (Subpart AAA through EEE) need not require a ffected BART-eligible EGU to install, operate, and maintain BART for SO2 or NOx emissions. Given that all BART-eligible units are installing scrubbers and NOx contro ls, and since Tennessee is participating in CAIR and accepts USEPA’s overall finding that CAIR “substitutes” for BART for NOx and SO2, Tennessee’s EGUs were allowe d to submit BART exemption modeling demonstrations for PM emissions only. TVA-Bull Run demonstrated that it does not contribute to visibility impair ment in any Class I area.

In total, eight (8) of Tennessee’s twelve (12) BART-eligible sources were able to demonstrate that they did not cause or contri bute to visibility impairment in any Class I area within 300 km of the source. One source did exceed the threshold on only one Class I area out of four. Three sources used th e refined 4-km CALMET resolution and one source used the new improve equation in effo rts to demonstrate exemption. For further details about the BART exemption m odeling, please refer to Appendix L.66 7.5.3 Determination of BART Requi rements for Subject-to-BART Sources

Four (4) sources, Eastman Chemical in Ki ngsport, Tennessee, Alcoa-South Plant in Alcoa, Tennessee, TVA – Cumberland Foss il Plant in Cumberland City, Tennessee and DuPont in Old Hickory, Tenness ee were unable to demonstrate a contribution of less than 0.5 dv at all Class I areas with in 300 km from their BART-eli gible sources. These four sources are considered to be “subject to BART” and have submitted analyses containing their evaluation of potential BART opti ons and proposed BART determinations.

The proposed BART determinations have b een reviewed by the TDEC-APC. In addition, comments from USEPA regarding the source’s self-analysis we re received and discussed with the sources. The source self-analysis, USEPA’s comments, the source’s response to those comments and TDEC-APC’s position on those comments appear in Appendix L.

From those determinations, permit conditions have been developed for each BART facility. Upon USEPA’s final approval of the Tennessee Re gional Haze SIP as announced in the Federal Register thereby constituting approval the following BART permit conditions, said conditions will be inse rted into the facili ty’s Part 70 Title V Operating Permit as a new applicable requireme nt. The timing of adding these federally approved permit conditions will be done consis tent with the permit reopening provisions of the Tennessee rules that implement Part 70. Any reference to Part 70 or Tennessee’s rules that implement Part 70 are merely to explain the vehicle and timing of the permit action and not to offer them for inclusion into the Regional Haze SIP. The permit conditions listed below will become applicable requirements and are offered for federal approval into Tennessee’s Regional Haze SIP. Pursuant to Division Rule 1200-3-23-.05, actual operating permits for the sources ar e presented in Appendix L-12. Like the aforementioned permit conditions, these pe rmits will become effective upon USEPA’s final approval of the Tennessee Regional Haze SIP and serve until such time that they can be added to the facilities’ Part 70 permits as a new applicable requirement as described above.

For Eastman, BART will include a more stringe nt sulfur dioxide lim itation on its boilers that necessitates the addition of control equipment.

Eastman Chemical Company BART Perm it Condition for inclusion into the Tennessee Regional Haze SIP

Powerhouse B-253-1, Boilers #25-#29, BART Requirements

Emission Limitation: No later than five (5) years after publication in the Federal Register of U. S. EPA’s approval of Tennessee’s Regional Haze State Implementation Plan67 revision, sulfur dioxide (SO 2 ) emissions from Boilers 25-29 shall comply with the less stringent of the following limits:

1. 0.20 pounds of SO 2 per million British Thermal Units (lb/MMBtu) of heat input; or 2. Reduce uncontrolled SO 2 emissions by 92%.

Compliance with these emission limits shall be determined on a thirty (30) calendar day rolling average basis as the average emission rate, or average SO 2 reduction, from either each boiler individually while combusting coal, or averaged across all of the boilers that are combusting coal.

Compliance Assurance Monitoring Requirements:

Measurement of SO 2 emissions: SO 2 emissions shall be measured through the use of continuous in-stack monitoring fo r sulfur dioxide, as specified below:

The source owner or operator shall insta ll, maintain, operate, and submit quarterly reports of excess emissions and sulfur dioxide (SO 2 ) removal efficiency (if applicable) from continuous in-stack moni toring systems for sulfur dioxide (SO 2 ). The sulfur dioxide monitoring systems shall meet all the requirements of 40 CFR 60 Appendices A and B, or 40 CFR Part 75 Appendices A and F.

Compliance will be determined on a 30 calendar day rolling average basis. Each 30 calendar day average shall be the aver age of the valid daily averages during the previous thirty (30) calendar days

Operational requirements for Sulfur Dioxide (SO 2 ) Monitoring Systems : For this fuel burning installation to demonstrate c ontinual compliance with the BART sulfur dioxide emission limitation, each sulfur di oxide monitoring system for boilers #25- #29 shall be fully operational for at l east ninety five percent (95%) of the operational time (during which coal is co mbusted) of the monitored units during each calendar quarter. Operational availab ility levels of less than these amounts may be considered the basis for declaring the fuel burning installation in noncompliance with the applicable monitori ng requirements, unless the reasons for the failure to maintain these levels of operational availability are accepted by the Technical Secretary as being legitimate malfunctions of the instruments. Data recorded during periods of monitoring sy stem breakdown, repairs, calibration checks, and zero and span adjustments s hall not be included in the data averages.

Quality Assurance requirements for the Sulfur Dioxide (SO 2 ) Monitoring Systems: The continuous in-stack sulfur dioxide m onitoring systems shall meet all of the requirements of 40 CFR Part 60 Appendix B (Performance Specification 2) and 40 CFR Part 60 Appendix F; or 40 CFR Part 75 Appendices A and B.

Monitoring Plan: Monitoring shall be conducted as specified in an approved sitespecific monitoring plan. The monitoring plan must be submitted to the Technical Secretary at least ninety (90) days prior to the startup of the control device.

Recordkeeping: All records required to demonstrate compliance with this condition shall be maintained at the source location and kept available for inspection by the Technical Secretary or his representative. Records shall be maintained for five (5) years.

For Alcoa, the final BART determination re sulted in an agreed upon limitation of 3% sulfur in the petroleum coke used in the facility’s electrode pr oduction operations and68 thus, used in their aluminum potlines. Alcoa e nvisions that it will still be in operation in 2064. Alcoa will continue to evaluate long-term options for reducing SO2 emissions including pollution controls and process cha nges. Progress updates on emerging options will be provided to TDEC-APC as pertinent wi th a formal update to be provided no later than five years after the US EPA’s approval of the SIP. The progress updates are not intended to be an enforceable commitment. Instead, they will be used to keep TDECAPC apprised of the company’s efforts. Those reports may also form the basis for determining whether or not more stringent c ontrols are warranted in the future progress reviews.

Alcoa BART Permit Condition for inclusion into the Tennessee Regional Haze SIP

Emission Limitation: No later than five (5) years after public ation in the Federal Register of U.S. EPA’s approval of Tennessee’s Regional Haze Stat e Implementation Plan revision, the sulfur content of coke used to produce anodes shall not exceed 3 percent by weight on a monthly average.

Compliance Methodology: Compliance with this limitation shall be assured by maintaining records of the sulfur content of petroleum coke used to make anodes. All records required to demonstrate compliance with this condition shall be maintained at the source location and kept available for inspection by the Technical Secr etary or his representat ive. Records shall be maintained for five (5) years.

For TVA Cumberland Fossil Plant, TDEC-APC has determined that no additional controls for visibility impairi ng pollutants will be required as the facility is currently well controlled with flue gas de sulfurization technology and oxides of nitrogen reduction technology.

TVA Cumberland Fossil Plant BART Perm it Condition for inclusion into the Tennessee Regional Haze SIP

BART Requirements

Emission Limitation for BART: Sulfur dioxide (SO 2 ) emissions from Boilers No. 1 and 2 shall comply with the following limit:

0.5 pounds of SO 2 per million British Thermal Units (lb/MMBtu) of heat input

Compliance with this emission limit shall be determined on a thirty (30) calendar day rolling average basis averaged across both boilers.

Compliance Assurance Monitoring Requirements:

Measurement of SO 2 emissions: SO 2 emissions shall be measured through the use of continuous in-stack monitoring fo r sulfur dioxide, as specified below:

The source owner or operator shall insta ll, maintain, operate, and submit quarterly reports of excess emissions and sulfur dioxide (SO 2 ) removal efficiency (if applicable) from continuous in-stack moni toring systems for sulfur dioxide (SO 2 ).69 The sulfur dioxide monitoring systems shall meet the availability and quality assurance requirements specified in this permit.

Compliance will be determined on a 30 calendar day rolling average basis. Each 30 calendar day average shall be calculated from the valid daily averages during the previous thirty (30) calendar days

Recordkeeping: All records required to demonstrate compliance with this condition shall be maintained at the source location and kept available for inspection by the Technical Secretary or his representative. Records shall be maintained for five (5) years.

For Dupont Old Hickory, the facility proposes limiting the total combined daily emissions for Boilers 20 and 24 based upon the fo llowing operating limits stated below.

Dupont Old Hickory BART Permit C ondition for inclusion into the Tennessee Regional Haze SIP

1. Name of source facility and the speci fic emission units a nd pollutants being controlled. (40 CFR 51.308(e)(1)(i)&(ii)).

• E. I. du Pont de Nemours and Company, Old Hickory Site

• Units 47-037-00002-009 (Boiler 20) and 47-037-00002-011 (Boiler 24)

• Pollutants being controlled: Su lfur dioxide and Nitrogen oxide

2. Specifics of the controls, control effi ciency(ies), emission reductions expected.

E. I. du Pont de Nemours is reducing allowabl e sulfur dioxide emissions from the affected units by 20,834 pounds/day in the summer (May – September) and 14,522 pounds/day in the winter (October – April). The facility is reducing allowable nitrogen oxide emissions from these units by 3,978 pound s/day in the summer and 3,330 pounds/day in the winter.

3. Enforceable emission limitations re presenting BART (40 CFR 51.308(d)(3);(e)).

Allowable emission limitations* Sulfur dioxide Nitrogen oxides (pounds/day) (pounds/day) Summer (May – September): 32,256 6,120 Winter (October – April): 38,568 6,768 * Combined emissions from both units

4. Schedules for compliance with BART (40 CFR 51.308(d)(3);(e))

E. I. du Pont de Nemours and Company, Old Hickory Site will be in compliance with the applicable emission limitations as expeditiously as practicable, but in no event later than five (5) years after publication in the Fe deral Register of U.S. EPA’s approval of Tennessee’s Regional Haze State Implementation Plan revision.70 5. A requirement that each source subject to BART maintains the control equipment and establish procedures to ensure such equipment is properly operated and maintained. (40 CFR 51.308(e)(v))

N/A

6. Reporting, monitoring, and recordkeepi ng requirements adequate to determine the source’s compliance (40 CFR Part 51, Appendix Y-V).

The source will be required to maintain adequat e records of fuel burning rates and fuel sulfur contents to demonstrate compliance with the applicable daily emission standards using the following equation:

Where:

M C20 = Mass of coal burned in Boiler No. 20, tons per day M C24 = Mass of coal burned in Boiler No. 24, tons per day V NG20 = Volume of natural gas burned in Boiler No. 20, 10 6 cubic feet per day V NG24 = Volume of natural gas burned in Boiler No. 24, 10 6 cubic feet per day V FO20 = Volume of fuel oil burned in Boiler No. 20, 1,000 gallons per day V FO24 = Volume of fuel oil burned in Boiler No. 24, 1,000 gallons per day EF C20 = Emission factor for coal burning in Boiler No. 20, pounds per ton EF C24 = Emission factor for coal burning in Boiler No. 24, pounds per ton EF NG20 = Emission factor for natural gas burning in Boiler No. 20, pounds per 10 6 cubic feet EF NG24 = Emission factor for natural gas burning in Boiler No. 24, pounds per 10 6 cubic feet EF FO20 = Emission factor for fuel oil burning in Boiler No. 20, pounds per 1,000 gallons EF FO24 = Emission factor for fuel oil burning in Boiler No. 24, pounds per 1,000 gallons EL = Applicable emission limit, pounds of pollutant per day

7. All records must be maintained on site and made available for inspection upon request. Records must be retained for at least five years. The source will be required to report any excess emissions, including those attributed to upset conditions, the probable cause of such de viations, and any corrective actions or preventative measures taken, to the permitting authority within one (1) to (10) days from the date on which the deviation occurs. Averaging times and reference methods to determine compliance (40 CFR Part 51, Appendix Y – V).

The allowable emission limitations are based on a maximum 24-hour standard beginning at midnight each calendar day. For a more detailed discussion of the afor ementioned BART determinations, please see Appendix L.

7.6 Relative Contributions to Visibility Impairment: Geographic Areas of Influence
for Tennessee Class I Areas

Once it was determined that SO2 emission reductions from EGU and non-EGU point sources in the VISTAS states would be the mo st effective sources to control to improve 

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⋅ 24 24 24 24 24 24 20 20 20 20 20 2071 visibility at the TN Class I areas, the next step was to identify the specific geographic areas that most likely influence visibility in each Class I area, and then to identify the major SO2 point sources located in those ge ographic areas. An SO2 Area of Influence was defined for each Class I area to represent the geographic area containing sources that would likely have the greatest impact on visibi lity at that Class I area. All SO2 point sources within these Areas of Influen ce were identified a nd ranked by their 2018 emissions. The following sections contain a broad overview of the steps in the Area of Influence analyses. See Appendix H for a more detailed discussion of these analyses and plots for additional Class I areas.

7.6.1 Back Trajectory Analyses

The first step was to generate meteorologi cal back trajectories for IMPROVE monitoring sites in Tennessee and neighboring Class I areas for the 2000-2004 20 percent worst days baseline period. Back trajectory analyses use interpolated measured or modeled meteorological fields to estimate the most likely central path of air ma sses that arrive at a receptor at a given time. The method essent ially follows a parcel of air backward in hourly steps for a specified length of time. Figure 7.6.1 is an example of a back trajectory analysis for Great Smoky Mountai n National Park for the 20 percent worst days in 2002.

Trajectories were started at 100 meters and 500 meters above the surface and run backward from the site for 72-hours. Thes e individual back traj ectories for 20 percent worst days in 2002 were also useful in eval uating model performance for individual days at the Class I areas.72 7.6.2 Residence Time Plots

The next step was to plot residence time fo r each Class I area using five years of back trajectories for the 20 percent worst visibili ty days in 2000-2004. Residence time is the frequency that winds pass over a specific geogr aphic area on the path to a Class I area. Separate residence time plots were generate d using trajectories with 100m and 500m start heights. As illustrated in Figure 7.6.2, wi nds influencing Great Smoky Mountains on the 20 percent worst days come from all direct ions and there is no single predominant wind direction influencing the 20 per cent worst visibility days.

Figure 7.6.2. Example residence time plot for 20 percent worst visibility days in 2000-2004 for Great Smoky Mountains National Park. Based on trajectories with 100m start height.

7.6.3 SO2 Areas of Influence

Consistent with the objective to identify geographic area of influence for SO2 point source emissions, the next step was to devel op sulfate extinction weighted residence time plots to define the geographic area with highest probability of influencing the receptor on73 the 20 percent worst days in 2000-2004 that were dominated by sulfate. Each back trajectory was weighted by sulfate extinction for that day. This allows the focus to be on the 20 percent worst days that are influen ced by sulfate and place less importance on days influenced by organic carbon from fires. Analyses of monitoring data (see section 2.4) indicate that few of the 20% worst days at GRSM and Class I areas in neighbori ng states were influenced by fire. Sulfate-weighted back trajec tories for the 20 percent worst days were combined for 5 years of data. The resulting sulfate extinction-weighted resi dence time plots were used to define the geographic Area of Influence fo r sources of SO2 emissions. In Figure 7.6.3 the area representing 10 percent or greater residence time is outlined in red and the area representing 5 percent or greater residence ti me is outlined in gray. The VISTAS states focused their analyses on the Area of Influe nce defined by 5 percent or greater sulfate extinction-weighted residence time.

Figure 7.6.3. Example SO2 Area of Influence plot for sulfate extinction weighted residence time for 20 percent worst visibi lity days in 2000-2004 for Great Smoky Mountains. Based on trajector ies with 100m start height.

7.6.4 Emissions Sources within SO2 Areas of Influence

Residence time plots were then combined w ith geographically gridded emission data based on the 2002 baseline and 2018 emissions inventories. Plots were generated for the Areas of Influence defined by trajectories with 100m and 500 m start heights. As a way of SO2 Area of Influence for Great Smoky Mountains

Green circles indicate 100-km and 200-km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Time > 10% Orange line perimeter indicate Area of Influence with Residence Time > 5%. SO2 Area of Influence for Great Smoky Mountains

Green circles indicate 100-km and 200-km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Time > 10% Orange line perimeter indicate Area of Influence with Residence Time > 5%.74 incorporating the effects of transport, depos ition, and chemical transformation of point source emissions along the path of the trajectori es, these data were weighted by 1/d, where d was calculated as the distance, in kilometers, be tween the center of the grid cell in which a source is located and the center of the grid ce ll in which the IMPROVE monitor is located. The distance-weighted point s ource SO2 emissions are then combined with the gridded extinction-weighted back-traje ctory residence times at a sp atial resoluti on of 36-km.

The final step was to combine the residence tim es and gridded emissions data in plots and data sets. The distance weighted (1/d) gridde d point source SO2 emissions were multiplied by the total extinction-weighted back-trajector y residence times on a grid cell by grid cell basis. These results were then normalized by the domain-wide total and displayed as a percentage. The analysis was done usi ng both the 2002 and 2018 inventories.

Figure 7.6.4 illustrates 2002 and 2018 distance weighted gridded emissions multiplied by sulfate extinction weighted residence time plots for the Great Smoky Mountains National Park. These maps help visualize where the emissions reductions will be occurring between 2002 and 2018. The change in SO2 emissions between 2002 and 2018 can be seen by comparing emissions source strengths in the two plots. Note the emissions from each source are normalized by the total emissions in the domain. Sources that reduce SO2 emissions by 2018 will show a lower contribution to emissions in the domain. On the 2018 map the grid cells with these sources will s how a lighter color gradient than on the 2002 map. Because the total emissions in the domain are smaller in 2018, a source th at does not change emissions between 2002 and 2018 may actually appear to increase in importance in 2018 compared to 2002.

Figure 7.6.4. Great Smoky Mountains 2002 (left) and 2018 (right) SO2 distance weighted emissions multiplied by SO4 ex tinction-weighted residence time plots. 2002 vs 2018 SO2 Emissions weighted by Residence Time Great Smoky Mtn., TN

2002 SO2 emissions 2018 SO2 emissions

Green circles indicate 100-km and 200-km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Time > 10%. Orange line perimeter indicate Area of Influence with Residence Time > 5%. Max Value = 46% 2002 vs 2018 SO2 Emissions weighted by Residence Time Great Smoky Mtn., TN

2002 SO2 emissions 2018 SO2 emissions

Green circles indicate 100-km and 200-km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Time > 10%. Orange line perimeter indicate Area of Influence with Residence Time > 5%. Max Value = 46% 2002 vs 2018 SO2 Emissions weighted by Residence Time Great Smoky Mtn., TN

2002 SO2 emissions 2018 SO2 emissions

Green circles indicate 100-km and 200-km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Time > 10%. Orange line perimeter indicate Area of Influence with Residence Time > 5%. Max Value = 46%75 The plot for Great Smoky Mount ain National Park also repr esents nearby Joyce KilmerSlickrock Wilderness, which does not have its own monitor. Additional analyses, including 2002 and 2018 distance weighted emissions multiplied by residence time plots for the Class I areas in Tennessee and neighbor ing states are contai ned in Appendix H. These analyses are serving as the basis for consultation among the VISTAS states.

Finally, Table 7.6.4 shows, in tabular form, the relative contributions of point source SO2 emissions from nearby states to Tennessee Class I areas. These percentages were estimated by multiplying the maximum residence time to the emissions over distance.

Table 7.6.4 2018 Point Source SO2 Contribution to Tennessee Class I Areas by State
State GRSM JOKI Alabama 3.1% 6.53% Delaware Florida Georgia 7.7% 20.46% Kentucky 1.0% 1.38% Maryland New Jersey North Carolina 7.4% 9.08% Ohio 0.9% Pennsylvania South Carolina 2.4% 5.14% Tennessee 74.5% 55.87% Virginia 2.3% 1.53% West Virginia 0.5%

Total 100.00% 100.00%

7.6.5 Specific Source Types in the Areas of Influence for Tennessee Class I Areas

The next step in the analysis was to review the emissions inventories to determine the source categories, as well as specific s ources, found to have the greatest impact on visibility in Tennessee Class I areas. List s of SO2 point sources within the Areas of Influence for each Class I area were deve loped using the VISTAS 2002 base year and 2018 future year emissions. For this purpos e the Area of Influence was defined as the counties with maximum sulfate extinction weight ed residence time greater than five. For SO2 sources within each Area of Influence, the following attributes were defined for each individual unit:

• State, county, and source (plant), and industry iden tification codes

• SO2 emissions for 2002 and 2018

• 2018 control efficiency

• Distance to Class I areas (defined by dist ance to the monitor at the Class I area)

• Emissions divided by distance (Q/d), a metr ic that accounts for the dispersion of emissions over distance

• Maximum sulfate extinction weighted residence time (RTmax)76 The review was conducted in a top down fashi on starting with an analysis of the major source categories in each SO2 Area of Influence to determine which major categories had the highest residual contributi on to the area in 2018. It wa s also important to identify reductions that are projected to occur between 2002 and 2018 within each category or at specific units. This allowed VISTAS States to determine if certain source categories or units that had yet to be contro lled under the future year ba se case had the potential for reduction. Once the highest source types were identified, subcate gories within those source types were reviewed. The contributi ons from major source categories to the 2018 inventory for the SO2 Areas of Influence fo r the Tennessee Class I areas are listed in Tables 7.6.5-1 and 7.6.5-2. In these tables, the source categories are broken out by the USEPA’s Tier 1 report categor ies and are defined below:

• Fuel Comb Electric Utility Emissions from all fuel combustions at utility boilers

• Fuel Comb Industrial Emissions from all fuel combustions at industrial boilers

• Fuel Comb Other Emissions fr om all fuel combustions from commercial/institutional and residential sources (i.e., fireplaces, natural gas stoves, oil heaters, etc.)

• Chemical & Allied Product Mfg Emissions from chemical manufacturing processes

• Metal Processing Emissions fr om metal proce ssing operations

• Petroleum & Related Industries Emissions from petroleum refineries & related industries

• Other Industrial Processes All other industrial processing not previously mentioned

Solvent Utilization Emissions from so lvent utilization such as degreasing operations, surface coating operations, etc.

• Storage & Transport Emissions from storage and transport of petroleum, organic and inorganic products

• Waste Disposal & Recycling Emissions from open burning, incineration, landfills, publicly owned treatment works, treatment storage and/or disposal facilities, wa stewater treatment facilities

• Highway Vehicles Emissions from on-road mobile sources77 • Off-highway Emissions from off-road mobile sources

• Miscellaneous (Ag, Fires) Emissions from agricultural operations, wildland fires, and other emissions sources not previously mentioned

Table 7.6.5-1. 2018 Emissions Contributions from Major Source Categories in the Area of Influence for Great Sm oky Mountains Na tional Park.

Table 7.6.5-2. 2018 Emissions Contributions fr om Major Source Categories in the Area of Influence for Joyce Kilmer – Slickrock Wilderness Area

These tables indicate that for the two Te nnessee Class I Areas, EGUs and industrial boilers are the two major sour ces categories contributing to 2018 SO2 emissions in the 86% 26% 59% 0% 5% 1% 1% Miscellaneous (Ag, Fires) 0% 4% 2% 1% 35% 18% 11% Off-highway 11% 2% 2% 0% 48% 25% 18% Highway Vehicles 0% 11% 5% 0% 3% 2% 4% Waste Disposal & Recycling 0% 1% 0% 0% 0% 0% 6% Storage & Transport 0% 1% 0% 0% 0% 1% 44% Solvent Utilization 0% 11% 8% 3% 1% 5% 6% Other Industrial Processes 0% 0% 0% 1% 0% 0% 0% Petroleum & Related Industries 0% 5% 2% 3% 2% 1% 1% Metals Processing 0% 1% 1% 2% 0% 0% 2% Chemical & Allied Product Mfg 1% 12% 5% 6% 3% 6% 5% Fuel Comb. Other 0% 5% 3% 30% 2% 18% 1% Fuel Comb. Industrial 1% 21% 11% 54% 1% 22% 0% Fuel Comb. Elec. Util. NH3 PM-2.5 PM-10 SO2 CO NOX VOC Tier

86% 26% 59% 0% 5% 1% 1% Miscellaneous (Ag, Fires) 0% 4% 2% 1% 35% 18% 11% Off-highway 11% 2% 2% 0% 48% 25% 18% Highway Vehicles 0% 11% 5% 0% 3% 2% 4% Waste Disposal & Recycling 0% 1% 0% 0% 0% 0% 6% Storage & Transport 0% 1% 0% 0% 0% 1% 44% Solvent Utilization 0% 11% 8% 3% 1% 5% 6% Other Industrial Processes 0% 0% 0% 1% 0% 0% 0% Petroleum & Related Industries 0% 5% 2% 3% 2% 1% 1% Metals Processing 0% 1% 1% 2% 0% 0% 2% Chemical & Allied Product Mfg 1% 12% 5% 6% 3% 6% 5% Fuel Comb. Other 0% 5% 3% 30% 2% 18% 1% Fuel Comb. Industrial 1% 21% 11% 54% 1% 22% 0% Fuel Comb. Elec. Util. NH3 PM-2.5 PM-10 SO2 CO NOX VOC Tier

85% 28% 61% 0% 5% 1% 1% Miscellaneous (Ag, Fires) 0% 4% 2% 1% 35% 18% 11% Off-highway 12% 2% 2% 1% 48% 26% 18% Highway Vehicles 0% 12% 5% 1% 4% 2% 4% Waste Disposal & Recycling 0% 1% 0% 0% 0% 0% 6% Storage & Transport 0% 1% 0% 0% 0% 1% 44% Solvent Utilization 1% 11% 9% 3% 1% 5% 6% Other Industrial Processes 0% 0% 0% 1% 0% 0% 0% Petroleum & Related Industries 0% 5% 2% 3% 1% 1% 1% Metals Processing 0% 1% 1% 3% 0% 0% 2% Chemical & Allied Product Mfg 1% 12% 5% 7% 3% 6% 5% Fuel Comb. Other 0% 5% 3% 30% 2% 18% 1% Fuel Comb. Industrial 1% 17% 9% 51% 1% 21% 0% Fuel Comb. Elec. Util. NH3 PM-2.5 PM-10 SO2 CO NOX VOC Tier

85% 28% 61% 0% 5% 1% 1% Miscellaneous (Ag, Fires) 0% 4% 2% 1% 35% 18% 11% Off-highway 12% 2% 2% 1% 48% 26% 18% Highway Vehicles 0% 12% 5% 1% 4% 2% 4% Waste Disposal & Recycling 0% 1% 0% 0% 0% 0% 6% Storage & Transport 0% 1% 0% 0% 0% 1% 44% Solvent Utilization 1% 11% 9% 3% 1% 5% 6% Other Industrial Processes 0% 0% 0% 1% 0% 0% 0% Petroleum & Related Industries 0% 5% 2% 3% 1% 1% 1% Metals Processing 0% 1% 1% 3% 0% 0% 2% Chemical & Allied Product Mfg 1% 12% 5% 7% 3% 6% 5% Fuel Comb. Other 0% 5% 3% 30% 2% 18% 1% Fuel Comb. Industrial 1% 17% 9% 51% 1% 21% 0% Fuel Comb. Elec. Util. NH3 PM-2.5 PM-10 SO2 CO NOX VOC Tier78 Areas of Influence, even after implementati on of the North Carolina Clean Smokestacks Act and CAIR. Together, these two source categories contribu te more than 80 percent of the 2018 SO2 emissions for the Areas of Influe nce for the Tennessee Class I areas. Other fuel combustion and other industrial processe s comprise another 6- 7 percent of the 2018 SO2 emissions.

These tables can also be used to evaluate the major source cate gories contributing to emissions of NOx, NH3, and PM emissions in 2018. For instance, highway vehicles and off road vehicles are major sources of NOx em issions, in addition to electric utilities and industrial boilers. Th e source category “miscellaneous” (which includes agricultural sources and fires) is the major contributor to NH3 and primary PM. The emissions sensitivities discussed in s ection 7.4 indicated very small benefits of controlling NOx, NH3, and primary PM emissions at the Tennesse e Class I areas, but if these emissions were of concern, different source cate gories would need to be addressed.

The contributions to SO2 emissions in 2018 from the three highest source categories, electric utilities, industrial boilers, and other fuel combus tion have been further broken out into subcategories. Table 7.6.5-3 indica tes subcategories for the Areas of Influence for the Tennessee Class I areas. Within el ectric utilities, all the SO2 emissions are attributable to coal fired pow er plants. Within industrial boilers, most emissions are attributable to coal fired boilers with le sser contributions from oil and gas boilers. Commercial and institutional coal and oil boilers have smaller contributions.

Table 7.6.5-3 2018 Emissions Contributions fr om Major Source Categories in the Area of Influence Subcategorized by Fuel Types

Tier GRSM JOKI Fuel Combustion Elect ric Utility - Coal 54% 51%
Fuel Combustion Electric Utility - Oil 0% 0% Fuel Combustion Electric Utility - Gas 0% 0% Fuel Combustion Electric Utility - Other 0% 0% Fuel Combustion Electric Utility - Internal Combustion 0% 0% Fuel Combustion Industrial - Coal 22% 21% Fuel Combustion Industrial – Oil 5% 6% Fuel Combustion Industrial - Gas 2% 2% Fuel Combustion Industrial - Other 1% 1% Fuel Combustion Industrial - Internal Combustion 0% 0%

Fuel Combustion Other - Co mmercial/Institutional Coal 2% 2%

Fuel Combustion Other - Commercial/Institutional Oil 3% 3% Fuel Comb. Other-Commercial/Institutional Gas 0% 0%

Fuel Comb. Other-Residential Wood 0% 0%

Fuel Comb. Other-Residential Other 1% 1%79 From these analyses, TDEC-APC considered what additional control measures for electric utilities and industrial boilers are reasonable. The li sts of individual sources are also being used to determine if individual so urces in other sources categories are major contributors to SO2 emissions in the Areas of Influence.

7.7 Evaluating the Four Statutory Factor s for Specific SO2 Emissions Sources in
Each Area of Influence

The next step was to identify emission reduction s that have already occurred within each source category and at specific units. Unit level tables of emission comparisons from 2002 to 2018 were developed, allowing VISTAS States to review existing emission reductions. These tables assigned future year control technology from IPM forecasting and State modification for EGU and from control efficiency tables for Non-EGU point

sources.

Once emission control profiles for specific uni ts were defined, the next step is to determine what, if any, additional control meas ures would feasibly be available, and to assign costs to those control measures. For EGUs, the 2018 IPM file used by VISTAS for EGU sources was obtained and matched to the 2018 base case inventory of EGU sources. This step was conducted to ensure that incremental contro ls assigned to these source types did not duplicate ex isting base case assumptions.

VISTAS used the USEPA’s AirControlNET database, modified for the VISTAS emission inventories for non-EGUs,. The core of AirControlNET is a relational database system in which control technologies are linked to sources within USEPA emissions inventories. The system cont ains a database of control m easure applicability, efficiency, and cost information for reducing emissions . The control measure data file in AirControlNET includes not only the technol ogy's control efficiency, and calculated emission reductions for that source, but also estimates the costs (annual and capital) for application of the control measure.

Using the modified inventories identified above, VISTAS ran every available SO2 control strategy in AirControlNET agai nst the EGU and non-EGU point source inventories to develop a master list of incremental control strategies for each unit in the VISTAS 36 km domain.

For the sources within the AOIs for each Te nnessee Class I area, the master list of incremental control measures was sorted to determine the costs of incremental control measures. These data were combined in a ma ster spreadsheet with the distance from the emission release point to the Class I area IMPROVE monitor (in km), the 2018 residual emissions and distance (Q/d) or squared di stance (Q/d^2), and the normalized 2018 SO2 point source emissions times distance-weight ed residence time (RTMax) values for the county in which the emission release point was located. Tennessee evaluated these control measures and costs as part of their re view of the statutory factors for reasonable further progress.80 The regional haze rule requires that states consider the following factors and demonstrate how these factors were taken into considerati on in selecting the reas onable progress goal:

• the costs of compliance

• the time necessary for compliance

• the energy and non-air quality environm ental impacts of compliance, and

• the remaining useful life of any potentially affected sources.

Cost of Compliance: As defined in Section 7.6.5, coal-fired electric u tilities and coal-fired in dustrial boilers were the largest source catego ries contributing to SO2 in 2018 in the Areas of Influence defined for the Tennessee Class I areas. Industrial bo ilers using oil and commercial and institutional boilers using coal or oil had sma ll contributions to SO2 in 2018.

Sulfur dioxide emissions from utility, industria l, commercial, or inst itutional boilers can be controlled either by switching the fuel source to a lower sulfur fuel content or by installing post-combustion controls. Costs vary by fuel source and boiler type and require source specific analyses for accuracy.

Bituminous coal is commonly burned in boilers in the eastern United States. Switching to another bituminous coal with lower sulfur c ontent or blending bituminous coals can reduce SO2 emissions with least impact to boiler pe rformance. While sub-bituminous coal has a lower sulfur content than bitumi nous coal, it also has a lower h eat rate and so more coal has to be burned to generate the same energy output. For boilers that are designed for bituminous coal, only a small fraction of the fuel can be switched to low sulfur subbituminous coal without affecting boiler opera tions. Costs of boiler modifications to accommodate low sulfur sub-bituminous coal may make switching to sub-bituminous coal impractical. Contract initiation and termination costs, differentia l fuel prices and heat rates, transportation costs, and modification to boile r operations, fuel handling and waste handling systems will have to be consid ered specific to each source.

Flue-gas desulfurization is the common post-com bustion control for coal-f ired boilers. Flue gas is passed through an absorbent for sulfur di oxide (generally limestone or lime) in a wet scrubber, dry scrubber, or spray dryer. A cal cium sulfate by-product is produced that may be further processed to produce gypsum as a commercial byproduct. Costs of flue-gas desulfurization include initial construction costs and ongoing operational and maintenance costs for the absorber tower, sorbent handling, and waste product handlin g facilities. Costs per ton vary with boiler size, type, an d facility siting c onsiderations.

For oil-fired boilers, lower sulfur oil may be an option. Costs need to consider differential fuel prices and heat rate, boiler modifications , fuel handling costs, and maintenance costs. Conceptually, post-combustion controls can be used for oil boilers, but there is little precedence for such installations.

Time Necessary for Compliance: For fuel switching, the time n ecessary to terminate existing fu el contracts and initiate new contracts needs to be considered. Generally two to three years may be required. Installation81 of post-combustion controls will require 3 or more years depending on market availability of labor and materials and utility system-wide pr iorities. Time necessary for compliance will need to be refined for specific sources.

Energy and Non-Air Environmental Impacts:

Switching to lower sulfur fuel or installing post-combustion controls may reduce boiler heat rate and energy output. Scrubbers and spray dryers will requir e additional safeguards for fuel handling and waste handling systems to avoid ad ditional non-air environmental impacts such as increased effluents in waste water discharges and storm water runoff. These factors will need to be considered specifi c to individual sources. Carbon dioxide is emitted as a byproduct of flue gas desulfurization, therefore im pacts of increased carbon emissions will need to be considered, particularly if carbon emissions are limited in the future under climate change mitigation strategies.

Remaining Useful Life

The useful remaining life is specific to the unit for which controls are considered.

7.8 Which Control Measures Represent Reasonable Progress for Individual
Sources?

The following summarizes the process for de termining reasonable progress for Tennessee sources. For a detailed discussion of the reasonable progress assessments for all units contribution greater than one percent to any Class I area in Tennessee or in neighboring states, please see Appendix H.

Step 1: Determine pollutants of concern.

VISTAS evaluated the species contribution on the 20 percent worst visibility days and concluded that sulfate accounted for greater than 70 percent of the visibility impairing pollution. The VISTAS States concluded that controlling SO2 emissions was the appropriate step in addressing the reasonable progress as sessment for 2018. The VISTAS findings were consistent with the findings of SAMI. As men tioned previously, SAMI confirmed that sulfate particles account for the greatest portion of the haze a ffecting Class I areas in the Southern Appalachian region and that these sulfates were produced in large part from SO2 emissions from coal combustion.

Step 2: Determine which source sectors s hould be evaluated for reasonable progress.

Since SO2 point source emissions in 2018 are proj ected to represent greater than 95 percent of the total SO2 emissions inventory, the VIST AS States concluded that the focus should be on electric generating unit (EGU) and non- EGU point sources of SO2 emissions.

Step 3: Determine if the Clean Air Interstate Rule (CAIR) is sufficient for reasonable progress for subject EGUs.82 TDEC-APC evaluated the am ount of SO2 reduction from the EGU sector based upon consultations with TVA. The EGUs in Te nnessee are expected to reduce their SO2 emissions by approximately 75% between 2002 and 2018. TVA reduced significant (~58% from the 1994 to 2002 time period) SO2 emission tonnage prior to that under Phase I acid rain reductions as a part of the Title IV Program for an overall 2018 reduction of >85%. TDEC-APC believes it has an accurate unders tanding of where emission reductions will occur in the Tennessee EGU sector based upon fixed installations of post combustion FGD controls.

To further support EGUs subject to CAIR is su fficient for reasonable progress, a discussion in the CAIR rule highlighted below (70 FR 25197) addresses the reas onable progress factors of cost and time necessary for compliance fo r these EGUs, and provide the necessary support for a State's four factor reasonable progress an alysis that must accom pany a State’s assertion that CAIR is sufficient for reasonable progr ess for subject EGUs during the first planning period.

From past experience in examining multi-pollutant emissions trading programs for SO2 and NOX, EPA recognized that the air polluti on control retrofits that result from a program to achieve highly cost-effective redu ctions are quite signi ficant and cannot be immediately installed. Such retrofits require a large pool of specialized labor resources, in particular, boilermakers, the availability of which will be a major limiting factor in the amount and timing of reductions.

Also, EPA recognized that the regulated indus try will need to secure large amounts of capital to meet the control requirements wh ile managing an already large debt load, and is facing other large capital requirement s to improve the transmission system. Furthermore, allowing pollution control retrof its to be installed over time enables the industry to take advantage of planned outag es at power plants (unplanned outages can lead to lost revenue) and to enable project management to learn from early installation show to deal with some of the engineering challenges that will exist, especially for the smaller units that often present space limitations.

Based on these and other considerations, EPA determined in the NPR that the earliest reasonable deadline for compliance with the fi nal highly cost-effectiv e control levels for reducing emissions was 2015 (taking into c onsideration the existing bank of title IV SO2allowances). First, the Agency confirme d that the levels of SO2 and NOX emissions it believed were reasonable to set as annua l emissions caps for 2015 lead to highly cost effective controls for the CAIR region.

Once EPA determined the 2015 emissions reduc tions levels, the Agency determined a proposed first (interim) phase control le vel that would commence January 1, 2010, the earliest the Agency believed initial polluti on controls could be fully operational (in today's final action, the first NOX control ph ase commences in 2009 instead of in 2010,as explained in detail in section IV.C). The first phase would be the initial step on the slope of emissions reductions (the glide-path) lead ing to the final (sec ond) control phase to83 commence in 2015. The EPA determined the first phase based on the feasibility of installing the necessary emission control retrof its, as described in section IV.C. Although EPA's primary cost-effectiveness determina tion is for the 2015 emissions reductions levels, the Agency also evaluated the cost eff ectiveness of the first phase control levels to ensure that they were also highly cost e ffective. Throughout this preamble section, EPA reports both the 2015 and 2010 (and 2009 for NOX) cost effectiveness results, although the first phase levels were determined based on feasibility rather than cost effectiveness. The 2015 emissions reductions include the 2010 (and 2009for NOX) emissions reductions as a subset of the more stringent requirements that EPA is imposing in the second phase.

We have concluded that at this time that CAIR constitutes reasonable measures for Tennessee EGUs during this first assessment period (between baseline and 2018). While reasonable progress applies to existing sources, TDEC-APC will follow the visibility analysis requirements as required in the federal NSR/ PSD rules as part of all new major source NSR/PSD permitting actions. These rules appear in Chapter 1200-3-9 and are periodically updated to conform to federal re quirements. Because these rules are periodically updated, TDEC-APC declares that this statement is simply a recitation of the rules and not an inclusion of the current version of the ru le into the Tennessee Regional Haze SIP.

Step 4: Determine which emission units would be evaluated based on impact.

TDEC-APC calculated the fractional contributio n from all emission units within the SO2 Area of Influence for a given Class I area and identified those emission units with a contribution of one percent or more to the visibil ity impairment at that Class I area. A list of sources considered in the reasonable progre ss evaluation can be found in Section 5.0 of Appendix H in the area of influence discussion. Some sources had grea ter than one percent impacts, but TDEC-APC did not request a f our-factor analysis from the source due to information already in the possession of the agen cy. A brief description of these sources is as follows:

• Intertrade Holdings in Polk County – The ma jority of the source is being dismantled and being shipped out of th e country. The remaining ope rations have very little sulfur dioxide emissions.

• TVA’s Bull Run Fossil Plant in Anderson Count y is being fitted out with a new flue gas desulfurization unit that should be operational in 2009.

• Tate & Lyle (formerly A. E. Staley) in Loudon County significantly reduced its sulfur dioxide emissions and that reduction was not recognized in the inventories used to perform the VISTAS modeling. The comp any was issued a construction permit on March 9, 2007, that reduced SO2 emissions from the boilers by approximately 62 percent. The lower SO2 emissions (1,145 t ons) are not reflected in the VISTAS inventory for 2018. Because the predicted 2018 future visibility levels were well below the glide slopes for the uniform rate of progress for nearby Class I areas, and the modeled 2018 emissions from Tate & Ly le were significantly overstated, TDEC-84 APC determined that no further analysis was re quired at this facility for this review period. As predicted visibility levels are replaced with future measured visibility levels, a reassessment of the facility’s SO 2 emissions in light of those measured visibility levels can be conducted.

• Aluminum Company of America (ALCOA) in Blount County was a BART source for its sulfur dioxide emissions a nd under review in that program

• Eastman Chemical Company in Sullivan County was a BART source installing significant emission control technology to reduce its sulfur dioxide emissions

• APAC-TN Harrison Construction Compa ny in Blount County was erroneously modeled by VISTAS as a ready mix concre te plant with a 2018 future projected source emission strength of 200 tons per year of SO 2 . At this level, there was a resulting 3.9 percent impact at the Gr eat Smoky Mountains Na tional Park. In actuality, the facility is an asphalt drum mix plant with a current SO 2 allowable of 20.3 TPY. For illustration purposes only, if this value were doubled to a 2018 emission rate of 40 TPY, the predicte d impact would be 0.0077 percent, well below the impact evaluation level of one percent. For these reasons, no further attempts were made to evaluate the facility under the four-factor analysis.

• United States Department of Energy Y-12 Pl ant is in the process of repowering its coal-fired boilers. Construction Permit # 960947F has been issued to the USDOE’s Y-12 Plant to replace the coal-fired boilers with natural gas fired boilers. A copy of this permit with the timing of the repoweri ng appears in Appendix H-7. Compared to coal-fired emissions, the reduction in sulfur dioxide emissions is very significant, as natural gas has essentially no sulfur. For this reason, no further attempts were made to evaluate the facility u nder the four-factor analysis.

• TVA’s Cumberland Fossil Plant already has fl ue gas desulfurizat ion units and it is also a BART source.

Step 5: Evaluate the four factors.

Each emission unit identified in Step 4 above is being evaluated us ing the statutory and regulatory factors of 1) cost of compliance, 2) time necessary for compliance, 3) the energy and non-air quality environmental impacts of compliance, and 4) the remaining useful life of the emissions unit.

Results of the fou r-factor analysis:

TDEC-APC determined that two non-EGU sources, Bowater Newsprint in Calhoun, Tennessee and INVISTA in Hixson, Tennessee s hould be subjected to the four-factor analysis because those facilities contributed one percent or more to visibility impairment in a Class I area. Each facility was requested to perform an analysis and submit that to TDECAPC for review. These facility analyses appear in Appendix H.85 Bowater Newsprint Bowater Newsprint operates three coal-fired po wer boilers that constitute the bulk of its facility SO2 emissions. Bowater presented info rmation and data in its four-factor analysis that lead TDEC-APC to concl ude that it should not have to install SO2 post combustion controls nor switch to lower su lfur fuels during this first re gional haze SIP review period. The information in their four factor analysis states that the emissions in the VISTAS modeling inventory are overstated a nd that the growth projections for that particular facility are in excess of what is likely to occur for a paper mill that age. TDEC-APC is making a formal declaration that additional SO2 reduc tions at Bowater Newsprint are not needed during this first regional haze SIP review period. This decl aration does not constitute a determination that additional SO2 reductions at Bowater Newsprint will not be required for any subsequent regional haze SIP review pe riod beyond 2018, nor does it constitute such a determination for any other regu latory program or requirement.

INVISTA INVISTA operates three coal-fired boilers that constitute the bulk of its SO2 emissions. INVISTA presented an argument that switching to lower sulfur coal at a cost of $1,225 per ton control costs was un-justifia ble as the cost estimates were based upon the current cost of low sulfur coal. INVISTA argued, and TDEC-A PC agreed, that with fuel-switching becoming a favored option among a number of s ources for a number of air pollution control reasons, the future cost of a coal switch coul d be potentially cost prohibitive. For this reason, TDEC-APC deferred requiring the comp any to switch during this review period until future low sulfur fuel costs were more finite in the next review period. TDEC-APC notes that 2018 predicted visibil ity impairment values for the Cla ss I areas in the vicinity of INVISTAS are well below the uniform rate of progress glide slopes as further justification for deferring fuel switching in this current review period.

7.9 What Additional Emissions Controls w ere considered as part of the Long-Term Strategy for Visibility Improvement by 2018?

Section 308(d)(3)(v) of the regional haze rule lists several factors that must be addressed in each SIP. These factors include the role of fire at Class I areas and status of state planning for smoke management, the role of dust and fine soil at Class I areas and status of state plans to mitigate emissions from construction activities, and the role of NH3 and potential benefits if emissions from agricultural sources were mitigated.

As discussed in Section 2.4 and demonstr ated in Figures 2.4-1 and 2.4-2, elemental carbon (sources include agricu lture, prescribed wildland fi res, and wildfires) is a relatively minor contributor to visibility impa irment at the Class I areas in Tennessee. However, TDEC-APC is currently working with the TN Division of Forestry to develop a smoke management program that addresses the issues laid out in USEPA’s 1998 draft guidance for smoke management plans. TDEC-APC will not make any smoke management plan a part of its SIP due to fl exibility reasons. It would simply be too cumbersome to formally revise the SIP for minor changes to a smoke management program or plan. Any resulting plan would simply be offered as additional assurances that the emission reductions in other source categories would “carry the day” in effectively solving regional haze issues.86 Also as discussed in Section 2.4 and demonstr ated in Figures 2.4-1 and 2.4-2, fine soils are a relatively minor contribu tor to visibility impairment at the Class I areas in Tennessee. TDEC-APC has an effective rela tionship with the Tennessee Department of Transportation (TDOT) in working on air qua lity action days, di esel retrofits of construction equipment, distribution of alte rnative fuels and the innovative use of the Congestion Mitigation and Air Quality Im provement Program (CMAQ) money to improve air quality in nonattainment ar eas. TDEC-APC will engage TDOT in discussions to control road construction project dust emissions as part of its contract bid specifications. As with the above-describ ed smoke management plan, TDEC-APC will not place this in the SIP nor rely upon it to r each an air quality or emission related target. Instead, it will be used as an assurance that the proven plan has more than adequate measures with these incidental meas ures present as an unrelied upon backup.

In addition, TDEC-APC’s Rule 1200-3-8-.03 requires additi onal control measures on source operating permits to cont rol dust emissions. That rule and the entire Chapter 1200-3-8 for controlling fugitive dust may be seen at: http://tennessee.gov/sos/ rules/1200/1200-03/1200-03-08.pdf . The citation of Rule 1200-3-8-.03 is offered only for reference purpos es. It is not offered for inclusion into the Tennessee Regional Haze SIP Note that benefits from the rule have not been included in the VISTAS modeling runs.

Copies of Chapter 1200-3-8 rule are attached to Appendix H.

In regard to agricultural ammonia, TDEC -APC will await the emission sampling and Best Management Practices arising from EP A’s CAFO Consent Order Agreements prior to initiating and control measures for agricultural ammonia. VISTAS is working on a modeling run that wi ll include the cumulative benefits from the emission controls discussed in Section 7.2.1, any controls resulting from BART determinations within the VISTAS states and any controls resulting from the states within VISTAS to address reasonable progress. If the modeling runs and analyses are completed within a reasonable period of time prior to the final Regional Haze SIP submittal on December 17, 2007, TDEC-APC will incorporat e these findings in the final SIP, including revising reasonable pr ogress goals if needed. If th e modeling runs and analyses are not completed in time, TDEC-APC w ill review the information as it becomes available and determine if a SIP revision is necessary.



### 8.0 REASONABLE PROGRESS GOALS

The Regional Haze Rule at 40 CFR section 51.308(d)(1) requires States to establish reasonable progress goals for each Class I area with in the state (expressed in deciviews) that provide for reasonable progress towards achievi ng natural visibility. In addition, USEPA released guidance on June 7, 2007 to use in se tting reasonable progress goals. The goals must provide improvement in visibility fo r the most impaired days, and ensure no degradation in visibility for the least impaired days over the SIP period. The state must also87 provide an assessment of the number of years it would take to attain natural visibility conditions if improvement continues at the rate represented by the re asonable progress goal.

In accordance with the requirements of 40 CFR §51.308(d)(1), this Regional Haze SIP establishes reasonable progress goals for each Cl ass I area in Tennessee. To calculate the rate of progress represented by each reas onable progress goal, TDEC-APC compared baseline visibility conditions to natural vi sibility conditions in each Class I area and determined the uniform rate of visibility impr ovement (in deciviews) th at would need to be maintained during each implementation period in order to attain natural visibility conditions by 2064.

TDEC-APC has summarized expected visibi lity improvements under existing federal and state regulations. As stated above, VISTAS is in the process of modeling additional control measures found to be reasonable to implement in this review period by the VISTAS states, as well as the results of VISTAS states’ BART determinations. TDEC-APC will include the results of this modeling run in its final SIP s ubmittal if the results and analyses are completed in time for inclusion and the results provide r easonable progress goals different from the ones presented in this section. VISTAS has alrea dy demonstrated that the 2018 base control scenario provides for an improvement in visibili ty better than the uniform rate of progress for both Tennessee Class I areas for the most impaired days over the period of the implementation plan and ensures no degradation in visibility for the least impaired days over the same period.

North Carolina and Tennessee are sharing the lead for the Great Smoky Mountains National Park since it is located in both states. The TDEC-APC and North Caro lina’s Division of Air Quality have agreed to work together in se tting reasonable progress goals and both agencies have been working together, through VIST AS, throughout the regiona l haze SIP planning
process.

Table 8.0-1 indicates reasonable progress goals for the 20% best and 20% worst visibility days for this planning period. The goals for th e 20% worst days assume greater than the uniform rate of progress by 2018 and for the 20% best days the modeling results indicate an improvement over visibility on the 20% best da ys current conditions. These goals are based on the modeling results discussed in Section 7.2.4. The model performance for the 20% best days is not as good as for the 20% worst da ys because the model has greater difficulty accurately projecting small concentrations. On the 20% best days, the model does not meet VISTAS model performance goals or criteria for sulfate, ni trate, and coarse mass (under predicted) and soil (over pred icted), however, the organic carbon and elemental carbon do meet performance goals on the 20% best days . Given the larger pe rcent errors of the fractional bias on the 20% best days, TDEC-APC has less confidence in the absolute values projected for these reasonable progress goals, however, the TDEC-APC does expect that visibility on these days will be better th an the current conditions 20% best days.88 Table 8.0-1. Tennessee 2018 Reasonabl e Progress Goals (in deciviews)

Class I Area Baseline Visibility for 20%

Worst Days Reasonable Progress Goal – 20% Worst Days (Improvement) Baseline Visibility For 20% Best Days Reasonable Progress Goal 20% Best Days

(Improvement)

Great Smoky Mountains National Park 30.28 23.50 (6.78) 13.58 12.11

Joyce KilmerSlickrock Wilderness 30.28 23.50 (6.78) 13.58 12.11



### 9.0 MONITORING STRATEGY

The State Implementation Plan is to be accompanied by a strategy for monitoring regional haze visibility impairment. Specifi cally, the Regional Haze Rule states at 40 CFR 51.308(d)(4):

“(4) Monitoring strategy and other im plementation plan requirements. The State must submit with the implementation pl an a monitoring strategy for measuring, characterizing, and reporting of regional haze visibility impairment that is representative of all manda tory Class I Federal areas within the State. This monitoring strategy must be coordinated w ith the monitoring strategy required in §51.305 for reasonably attributable visibili ty impairment. Compliance with this requirement may be met through partic ipation in the IMPROVE network. The implementation plan must also provide for the following: (i) The establishment of any addition al monitoring sites or equipment needed to assess whether reasonable progress goals to address regional haze for all mandatory Class I Federa l areas within the State are being achieved. (ii)-(vi) [Other implementation plan requirements that pertain to reporting and use of monitoring data and an emission inventory.]”

Such monitoring is intended to provide th e data needed to satisfy four objectives:

1. Track the expected visibility impr ovements resulting from emissions reductions identified in this SIP. 2. Better understand the atmospheric pr ocesses of importance to haze 3. Identify chemical species in the ambien t particulate matter and relate them to emissions from sources 4. Evaluate regional air quality models for haze and construct relative response factors (RRFs) for using those models89 The primary monitoring network for regional ha ze, both nationwide and in Tennessee, is the IMPROVE network. Given that IMPROVE monitoring data from 2000-2004 serve as the baseline for the regional haze program, th e future regional haze monitoring strategy must necessarily be based on, or direc tly comparable to, IMPROVE. The IMPROVE measurements provide the only long-term r ecord available for tracking visibility improvement or degradation and therefore Tennessee intends to rely on the IMPROVE network for complying with the regional h aze monitoring requirement in the Regional Haze Rule.

There is currently 1 IMPROVE site in the Stat e. As Table 1 shows, an IMPROVE site in the Great Smoky Mountains National Park in Te nnessee serves as the monitoring site for both the Great Smoky Mountains National Park and Joyce Kilmer – Slickrock Wilderness Area, both of which lie partly in Tennessee and partly in North Carolina.

Class I Area IMPROVE Site Designation Great Smoky Mountains National Park GRSM1 (TN) Joyce Kilmer – Slickrock Wilderness Area GRSM1 (TN)

Table 1. Class I areas in Tennessee a nd IMPROVE monitors that represent conditions at each of them.

In addition to the IMPROVE measurements , some ongoing long-term limited monitoring supported by Federal Land Managers provides additional insight into progress toward regional haze goals. Tennessee benefits from the data from these measurements, but is not responsible for the funding decisions to maintain these measurements into the future. Such measurements include:

• Web cameras operated by the National Park Service at Look Rock, TN at the Great Smoky Mountains National Park

• An integrating nephelometer for continuously measuring light sc attering, operated by the National Park Service at Look Rock, TN.

• A Tapered Element Oscillating Microbal ance (TEOM) for continuously measuring PM 2.5 mass concentration, operated by the Natio nal Park Service at Look Rock, TN.

In addition, TDEC-APC and the lo cal air agencies in the State operate a fairly comprehensive PM2.5 network of the filter based Federal reference method monitors, continuous mass monitors (TEOMs) and filter based speciated monitors. A map of the various locations around the State is included in Figure 9.0.1. These PM2.5 measurements help TDEC-APC characterize air pollution levels in areas across th e state, and therefore aid in the analysis of visibility improvement in and near the Class I areas.90 Figure 9.0.1 PM2.5 Monitoring Network in Tennessee

The IMPROVE measurements are central to Tennessee’s regional haze monitoring strategy, and it is difficult to visua lize how the objectives listed a bove could be met without the monitoring provided by IMPROVE. Any reduction in the scope of the IMPROVE network in Tennessee would jeopardize the State’s ability to demonstrate reasonable progress toward visibility improvement in its Class I areas.

Data produced by the IMPROVE monitoring netw ork will be used nearly continuously for preparing the 5-year progr ess reports and the 10-year SI P revisions, each of which relies on analysis of the preceding five year s of data. Consequentl y, the monitoring data from the IMPROVE sites needs to be readily accessible and to be kept up-to-date. Presumably, IMPROVE will continue to proce ss information from its own measurements at about the same pace and with the same atte ntion to quality as it has shown in the recent past. The VIEWS web site has been main tained by VISTAS and the other Regional Planning Organizations to provi de ready access to the IMPROVE data and data analysis tools. TN is encouraging VISTAS and the ot her RPOs to maintain VIEWS or a similar data management system to facilita te analysis of the IMPROVE data.91 

### 10.0 INTERSTATE CONSULTATION

The VISTAS states have jointly developed the technical analyses to define the visibility improvement by 2018 under existing federal and st ate regulations compared to the uniform rate of progress, SO2 Areas of Influence fo r each Class I area, and methods to prioritize contributions from individual sources within the Areas of Influence. The states collectively accept the conclusions of these analyses.

In December 2006 the VISTAS State Air Direct ors held their first formal consultation meeting to review the modeling results and th e SO2 Areas of Influence analyses. The Air Directors agreed to look at re asonable control measures for so urces on the lists for the SO2 Areas of Influence. Each stat e would consider sources within their state and would identify sources in neighboring states th at they would like to have th at neighboring state consider. States acknowledged that the review process would differ among states since some Class I areas are projected to see visibility improveme nts near the uniform rate of progress while most Class I areas are projected to have greater improvements than uniform rate of progress.

On June 12-13, 2007, the VISTAS State Air Direct ors met for their second formal interstate consultation. States shared their lists of sour ces in their state and neighboring states for each Class I area. They also shared their criteria for listing sources and their plans for further interstate consultation. A summary of this meeting ma y be found in Appendix J.

The MANE-VU states of Maine, New Jersey, Ne w Hampshire, and Vermont sent letters to Tennessee in the spring of 2007 stating that based on 2002 emissions, Tennessee contributed to visibility impairment to Class I areas in those states. While M ANE-VU identified TVAJohnsonville and TVA-Gallatin, th ey did not identify TVA- Bull Run, which is getting scrubbers and is located closer to Great Smoky Mountains than J ohnsonville and Gallatin. MANE-VU states asked TDEC-APC to participate in further consultation with these states, and a meeting was held in August 2007, in Atlanta, Georgia. TDEC–APC participated in these consultation meetings. The MANE-VU states identified 5 EGU stacks in Tennessee that they would like to see controlled to 90% efficiency. They also re quested a control strategy to provide a 28% reduction in SO2 emissions from sources other th an EGUs that would be equivalent to their low sulfur fuel oil strategy. Wo rking with Tennessee, TVA has controlled or is expecting to control three of the stacks - Kingston 1 & 2 a nd John Sevier. The cons truction schedule for these three stacks appears in Section 13. The remaining two stacks – Gallatin and Johnsonville have been discussed with TVA. TVA has indicated that it will ultimately control Gallatin if needed to meet its CAIR oblig ations or more stringent controls to meet the increasingly stringent NAAQS that are proposed, and either repower or shut down the Johnsonville facility by the next review period in 2018.

With respect to MANE-VU’s request for a 28% non-EGU SO 2 emissions reduction, TDECAPC does not believe MANE-VU’s request, which was based upon the VISTAS regional92 emissions, (not necessarily just those of Tenne ssee) is justified for Tennessee’s emissions. The rationale for this is based upon Tennesse e’s belief that MANE -VU’s own 2018 modeling that appeared in their technical support doc ument for the August 2007 meeting did not prove that Tennessee’s non-EGU emissions were advers ely impacting any of the Class I areas in the MANE-VU RPO.

TDEC-APC believes that the EGU 5 stack re ductions discussed above satisfy MANE-VU’s request. The letters from these states and th e meeting notes are in cluded in Appendix J.

As discussed in Section 7.7, TDEC-APC has evaluated the impact of Tennessee sources on Class I areas in neighboring states and consulted with neighbori ng states to seek their input on the matter. TDEC-APC determined that only two sources, Bowater Paper Company in Calhoun, Tennessee and INVISTA in Hixson, Te nnessee, have impacts large enough to warrant further evaluation and analysis using the four factors for reasonable progress. Analyses of impacts from Tennessee sources are discussed in greater detail in Appendix H. Consultation letters to neighboring states may be found in Appendix J.

TDEC-APC believes that its regional haze SI P adequately protects the best days and improves the worst days for the Class I Areas in Tennessee and those in other states during this first planning period. Nei ghboring states are still in the process of evaluating BART and reasonable progress for affected sources in their state. TDEC-A PC will place primary reliance upon EPA and the FLMs to ensure that those states are adequately protecting their Class I Areas.



### 11.0 COMPREHENSIVE PERIODIC IMPLEMENTATION PLAN
REVISIONS

40 CFR section 51.308(f) requires TDEC-APC to revise its regional haze implementation plan and submit a plan revision to USEPA by July 31, 2018 and every ten years thereafter. In accordance with the requirements listed in Section 51.308(f) of the federal rule for regional haze, Tennessee commits to revising and submitting this regional haze implementation plan by July 31, 2018 and every ten years thereafter.

In addition, Section 51.308(g) re quires periodic reports ev aluating progress towards the reasonable progress goals established for each mandatory Class I area. In accordance with the requirements listed in S ection 51.308(g) of the federal ru le for regional haze, TDEC-APC commits to submitting a report on reasonable progress to the USEPA every five years following the initial submittal of the SIP. The re port will be in the form of a SIP revision. The reasonable progress report will evaluate the progress made towards the reasonable progress goal for each mandatory Class I area located within Tennessee and in each mandatory Class I area located outside Tennesse e which may be affected by emissions from within Tennessee

.93 The requirements listed in 51.308( g) include the following: 1. Description of the stat us of implementation; 2. Summary of emission reductions achieved thus far, including especially the status of implementation of the CAIR compliance plans for EGUs compared to the control assumed in the modeling. 3. Assessment of changes in visibility cond itions at each class I area (current vs. baseline), expressed as 5-year averages of annual values fo r 20 percent best and worst days; 4. Analysis of emission changes over the 5-year period, identified by s ource or activity; 5. Analysis of any significant changes in or out of the State which have impeded
progress; 6. Assessment of the sufficiency of the implementation plan to meet reasonable progress goals; 7. Review any modifications to TDEC-A PC’s visibility monitoring plan.

All requirements listed in 51.308(g) shall be ad dressed in the SIP revision for reasonable
progress.

As mentioned earlier in Section 7.8, Step Three, TDEC-APC will consider visibility as part of its NSR/PSD program. TDEC-APC believes that its current ne w source review regulations for both nonattainment areas as well as the pr evention of significan t deterioration will address emissions from new major sources that may be located near a Class I area, or increased emissions from major modifications to existing sources. In addition, under the TDEC-APC regulations that govern these sour ces, consultation with the FLMs is also required for sources that are subject to the new source review regulations.

TDEC-APC also commits to ongoing cons ultation with the FLMs throughout the implementation process, including annual disc ussion of the implementation process and the most recent IMPROVE monito ring data and VIEWS data.

There are several technical improvements that are recommended in the emissions inventory and air quality models that are used to suppor t regulatory decisions for regional haze. These improvements will be considered as recommended, and as funding is available, to support the next long term strategy. Anot her factor to consider in using these upgraded techniques during the next review period will be its harmony with what other states in the RPO will be doing. The following is an overall summary of these recommendations; Appendix K contains a more detailed discussion of possible technical improvements.

First and foremost, continued improvements are n eeded in the integrated one-atmosphere air quality models that are used to project air qua lity responses to emissi ons reductions. As our understanding of partitioning between gase ous and aerosol phases improves, this understanding needs to be reflec ted in the models. Improvements can also be made in how the models handle individual pollutants. Su lfate performance for the CMAQ regional air quality model is good overall. However sulfate deposition is frequently overestimated in the models, particularly in the summer months. At the coastal sites, when winds are blowing from the Gulf of Mexico or Atlantic Ocean, CMAQ underestimat es measured sulfate at the94 monitors. CMAQ’s processes also should be re viewed for sulfate formation over water. Nitrate is overestimated by the model in th e winter and underestimated in the summer, although summer monitored values of nitrate ar e very low. Additional improvements in seasonal allocation of ammonia emissions w ould improve model estimates of ammonium nitrate formation. Organic carbon is genera lly underestimated in the summer months. Improvements are needed in the characteriz ation of both primary carbon emissions and formation of secondary organic carbon.

Other improvements needed include better tool s for organic carbon source apportionment, and more consistent measurement techniques be tween rural and urban monitoring networks. To improve our understanding of the contribution of fire from natu ral forest fires, prescribed burning, land clearing, and agricultural burning, states need improved record keeping. Additional improvements to international emissi ons inventory are also needed, to improve our understanding of boundary conditions for our modeling domain and of the contributions from international emissions to pollutant con centrations at the VISTAS Class I areas.



### 12.0 DETERMINATION OF ADEQUA CY OF THE EXISTING PLAN

Depending on the findings of the five-year progr ess report, Tennessee commits to taking one of the actions listed in 40 CFR section 51.308( h). The findings of the five-year progress report will determine which action is appropr iate and necessary. Tennessee formally commits to the FLM that it will involve thos e agencies in the disc ussions leading up to finalization of the five -year progress reports.

List of Possible Actions – 40 CFR section 51.308(h)

1) TDEC-APC determines that the existing SI P requires no further substantive revision in order to achieve estab lished goals. TDEC-APC provi des to the Administrator a negative declaration that furt her revision of the SIP is not needed at this time. 2) TDEC-APC determines that the existing SIP may be inadequate to ensure reasonable progress due to emissions from other stat es which participated in the regional planning process. TDEC-APC provides notif ication to the Administrator and the states that participated in regional pla nning. TDEC-APC collaborates with states through the regional planni ng process to address the SIP’s deficiencies. 3) TDEC-APC determines that the current SIP may be inadequate to ensure reasonable progress due to emissions from another country. TDEC-APC provides notification, along with available information, to the Administrator. 4) TDEC-APC determines that the existing SIP is inadequate to ensure reasonable progress due to emissions within the state. TDEC-APC will revise its SIP to address the plan’s deficiencies within one year.95 

### 13.0 IMPACT OF TENNESSEE SOURCES ON CLASS I AREAS
OUTSIDE OF TENNESSEE

The following table details Tennessee BART-eligible source impacts on the seven Class I areas outside of Tennessee. Since the BART determination takes into consideration the same basic factors as for reas onable progress, the BART subject units were not re-evaluated in the reasonable progress re view. No other non-BART subject units at a BART source in Tennessee were shown to have an adverse im pact upon Class I areas outside of Tennessee.96Table 13.1 Impacts of TN BART-EligibleSources on Class I Areas Outside TNTN BARTEligibleSources w/in300 kmClass I Areas

COHU Fraction of AOI JARI Fraction of AOI LIGO Fraction of AOI MACA Fraction of AOI SHRO Fraction of AOI SIPS Fraction of AOI MING Fraction of AOIAlcoa-South Plant0.3350.00640.3700.00430.35000.4630.068DuPont –Humphreys0.338 0.0055 0.369 0.0004 0.128 0DuPont –OldHickory0.16000.5040.00850.3100.0024DuPont –Shelby 0.147 0 0.151 0EastmanChemical0.2920.00540.2890.00980.8750.24290.5430.0445LuciteInternational0.04000.0530.0002Owens Corning0.04500.04000.0640Packaging Corp.of America0.1010.00070.2470.00180.0640.0004PCS -Nitrogen 0.074 0 0.166 0TVA Bull Run0.1260.01520.0630.01220.03300.0810.0132TVA -Cumberland1.3590.01730.5570.00560.4040.0068Zinifex 0.0990.4650.00400.2370.00100.1250.0004Table 13.2 Impacts of TN non-BART Sourceson Class I Areas Outside TN – COHU OnlySO2 TonsCEDistanceFractionStatePlantIDPlantPointID(2002G)(2018G)(%)*(km)Q/dQ/d^2RTMax**Q/d*RTMaxContributionTN0012BOWATERNEWSPRINT &DIRECTORY -CALHOUN0155,2325,871458.30100.711.7382.908348.860.0858TN0004INTERTRADEHOLDINGS, INC.0011,8372,530432.4777.922.4082.906459.570.0664TN2730E. I. DU PONT DENEMOURS ANDCOMPANY00021,2861,173067.0817.480.2683.311456.260.0150TN0081A.E. STALEYMANUFACTURINGCOMPANY0053,0052,9824109.3527.270.2538.491049.620.0108NOTE:E.I DUPONT ( now INVISTA); A.E. STALEY ( now TATE & LYLE).97 Sulfur oxide emissions from coal-fired EGUs an d large coal-fired industr ial fuel burning have been demonstrated to be the most effective s ources to control in br inging about visibility improvement in all Class I areas of the Southeas t. This section of the Tennessee SIP describes the efforts underway in the state to reduce sulf ur oxide emissions from the coal-fired EGU and large industrial sources in the state.

The following text is to provide a general over view of what TDEC-APC is doing to improve visibility. It is not offered for inclusion in to the Tennessee Regional Haze SIP as enforceable commitments. Some of the general overview text will refer to activities described elsewhere in the Tennessee Regional Haze SIP as enforceable co mmitments. The general text that follows does not nullify those enforceable commitments made elsewhere in this document.

TVA Scrubber (FGD) Improvement Program

Along the Appalachian Mountains of the State, TVA is installing FGD at its Kingston Steam Plant in Roane County, Tennessee, Bull Run St eam Plant in Anderson County, Tennessee and John Sevier Steam Plant in Hawkins County, Tennessee. The construction schedule above describes the timing of the contro l installation on a plant-by-plan t basis. The Cumberland Steam Plant in Stewart County, Tennessee is already co ntrolled with FGD and Gallatin Steam Plant in Sumner County, Tennessee is already using low- sulfur coal at a 0.67# SO2/MM Btu Heat Input

rate.

TVA is currently testing lower sulfur fuels at its New Johnsonville Steam Plant in Humphreys County, Tennessee. TDEC-APC w ill apply SO2 emission limits at these plants sufficient to ensure that the emission source strengths used as a modeling input to predict the regulatory prescribed successful and timely attainment of th e regional haze SIP visibility improvement and protection requirements are enforceable.

Eastman Chemical Company in Sullivan County Tennessee is installing FGD on several of its boilers as part of BART and will significantly reduce sulfate emissions along the Appalachians. Project
Paradise Unit 3
Bull Run Kingston Units 6-9 Kingston Units 1-5
John Sevier Units 1-2
John Sevier Units 3-4 Calendar Year 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012