# Regional Haze State Implementation Plan

For Kentucky’s Class I Area

Prepared by
Kentucky Energy and Environment Cabinet
Kentucky Division for Air Quality

Final

June 2008

Kentucky Regional Haze SIP

### Preface: This document contains summaries of the t echnical analyses that will be used by the Kentucky Division for Air Qual ity (KYDAQ) to support the regi onal haze State Implementation Plan (SIP) pursuant to §§107(d)(3)(D) and (E) of the Clean Air Act, as amended. KRS 224.10- 100(5) provides the Kentucky Energy and Envir onment Cabinet (formerly the Environmental and Public Protection Cabinet) wi th the statutory authority to a dopt and implement its regional
haze SIP. A link to KRS 224.10-100 is as follows: http://www.lrc.ky.gov/KRS/224-10/100.PDF

.

Kentucky Regional Haze SIP ii

## EXECUTIVE SUMMARY

Introduction
Regional haze is pollution that impairs visibilit y over a large region, including national parks, forests, and wilderness areas (many termed “Cla ss I” areas). Regional ha ze is caused by sources and activities emitting fine particles and their precursors, often transported over large regions. Particles affect visibility thr ough the scattering and absorption of light. Reducing fine particles in the atmosphere is an effective method of im proving visibility. In th e southeast, the most important sources of haze-forming emissions are coal-fired power plants , industrial boilers and other combustion sources, but also include mob ile source emissions, area sources, fires, and
wind blown dust.

An easily understood measure of vi sibility to most people is visu al range. Visual range is the greatest distance, in kilometers or miles, at which a dark object can be viewed against the sky. However, the most useful measure of visibility impairment is li ght extinction, wh ich affects the clarity and color of objects being viewed. The m easure 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 dem onstrate reasonable progress toward meeting the national goal of a re turn to natural visibil ity conditions by 2064. The rule directs states to graphically show what would be a “uniform rate of progress,” al so known as the “glide path,”
toward natural conditions for each Class I area within the State and certain ones outside the
State.

Kentucky’s Class I Area
Kentucky has one Class I area within its bord ers: Mammoth Cave Nati onal Park. The figure below illustrates the location of Mammoth Cave in Kentucky.

Mammoth Cave
National Park

Kentucky Regional Haze SIP iiiVisibility on the worst days at Mammoth Ca ve is an estimated 31dv. Natural background visibility on the worst days is an estimated 11 dv.

State Implementation Plan Requirements
States are required to submit state implemen tation plans (SIPs) to the United States Environmental Protection Agency that set out each state’s plan for meeti ng the national goal of a return to natural visibility conditions by 2064. The plan includes the st ates’ reasonable progress goals, expressed in deciviews, for visibility impr ovement 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, characterizing, and reporti ng of regional haze visibility impairment. The long-term strategy includes enforceable emissi ons limitations, compliance schedules, and other measures as necessary to achieve the reasonabl e progress goals. States must also consider ongoing control programs, measures to mitigate construction 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 du e December 17, 2007. These pl ans 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 ensure
that installed emissions controls are on track with emission reducti on forecasts in each SIP.

Federal and State Control Requirements
There are significant control progr ams being implemented between the baseline period and 2018. These programs will all reduce the particulate emissi ons that affect visibility in the Class I areas, and include: the Clean Air Interstate Rule (CAIR), the NO x SIP Call or state equivalent, one-hour ozone SIPs submitted by Atlanta, Birmingham, and Northern Kentucky, NOx RACT in 8-hour nonattainment area SIPs, heavy duty diesel (2007) engine standard (for on-road trucks and buses), Tier 2 tailpipe standard s for on-road vehicles, large spark ignition a nd recreational vehicle rule, nonroad diesel ru le, and various Federal Maximu m Achievable Control Technology regulations and consent agreements with Tampa Electric, Virginia Electric and Power Company, Gulf Power, East Kentucky Power Cooperati ve, American Electric Power and the PM2.5 attainment demonstrations due in April 2008.

The regional haze rule also requires states to determine be st available retrofit technology (BART) for certain facilities. Twenty-one of Ke ntucky’s twenty-six BART-eligible sources were able to demonstrate that they did not cause or c ontribute to visibility impairment. Further BART determination analysis of five Kentucky Elec tric Generating Units (EGUs) provided for the
installation of controls to redu ce visibility impairing emissions.

Kentucky Regional Haze SIP ivConclusion
For Kentucky’s Class I area, Ma mmoth Cave National Park, visibility improvements on the worst days are expected to be better than th e uniform rate of progr ess glidepath by 2018 based solely on reductions from existing and planned emissions controls. Additionally, the visibility is expected to improve for the best days at Ma mmoth Cave. The table below displays the 2018 reasonable progress goals fo r Kentucky’s Class I area.

Class I Area Baseline Visibility for Worst Days (dv) Uniform Rate of Progress
for Worst

Days (dv) Reasonable Progress Goal
Modeled for Worst Days (dv) Baseline Visibility for

Best Days (dv) Uniform Rate of Progress for Best Days (dv) Reasonable Progress Goal
Modeled for

Best Days (dv)

Mammoth
Cave
National Park, KY 31.37 26.64 25.56 16.51 16.51 15.57

Kentucky Regional Haze SIP vTABLE 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 regional haze SIP requirements ............................................................... 2 1.4 Class I area in Kentucky ........................................................................................................... 4 1.5 State and Federal Land Manager (FLM) coordination ............................................................. 5

2.0 ASSESSMENT OF BASELINE AND CURR ENT CONDITIONS AND ESTIMATE OF
NATURAL BACKGROUND CONDITIONS IN CLASS I AREAS ................................... 7 2.1 Estimating Natural Conditions for Kentucky’s Class I Area .................................................... 8 2.2 Estimating Baseline Conditions for Kentucky’s Class I Area .................................................. 9 2.3 Summary of Natural Background and Baselin e Conditions for Kentucky’s Class I Area ..... 10

2.4 Pollutant Contributions to Visibil ity Impairment (2000-2004 Baseline Data) ....................... 10

3.0 GLIDEPATHS TO NATUR AL CONDITIONS IN 2064 ...................................................... 14 3.1 Glidepaths for the Class I Area in Kentucky .......................................................................... 14

4.0 NATURE OF THE PROBLEM: CHIEF CAUSES OF VISIBILITY IMPAIRMENT IN
KENTUCKY’S CLASS I AREA ......................................................................................... 16 4.1 Baseline Emissions Inventory ................................................................................................. 16 4.1.1 Stationary Point Sources ............................................................................................ 17 4.1.2 Stationary Area Sources ............................................................................................. 18

4.1.3 Off-Road Mobile Sources .......................................................................................... 19 4.1.4 Highway Mobile Sources ........................................................................................... 19 4.1.5 Biogenic Emission Sources ........................................................................................ 19 4.1.6 Summary 2002 Baseline Emissi ons Inventory for Kentucky .................................... 20 4.1.7 Model Performance Improvements thr ough Emissions Inventory Improvements .... 20

4.2 Assessment of Relative Contributions from Specific Pollutants and Sources Categories ...... 21

5.0 REGIONAL HAZE MODELING METHODS AND INPUTS ............................................. 22 5.1 Analysis Method ..................................................................................................................... 22

5.2 Model Selection ...................................................................................................................... 23

5.2.1 Selection of Photochemical Grid Model .................................................................... 23 5.2.2 Selection of Meteorological Model ............................................................................ 24 5.2.3 Selection of Emissions Processing System ................................................................ 25 5.3 Selection of the Modeling Year .............................................................................................. 27 5.4 Modeling Domains .................................................................................................................. 28 5.4.1 Horizontal Modeling Domain .................................................................................... 28 5.4.2 Vertical Modeling Domain ......................................................................................... 31

Kentucky Regional Haze SIP vi6.0 MODEL PERFORMANCE EVALUATION ......................................................................... 31 6.1 Modeling Performance Goals, and Criteria ............................................................................ 32

6.2 VISTAS Domain-Wide Performance ..................................................................................... 33 6.3 Kentucky’s Class I Area Performance .................................................................................... 37

7.0 LONG-TERM STRATEGY FOR KENTUCKY’S CLASS I AREA .................................... 39 7.1 Overview of the Long-Term Strategy Development Process ................................................. 39 7.2 Expected Visibility Results in 2018 for Ke ntucky’s Class I Area under existing and planned
emissions controls ......................................................................................................................... 40 7.2.1 Federal and State Control Requirements .................................................................... 40 7.2.2 Additional State programs to reduce emissions ......................................................... 43 7.2.3 Projected 2009 and 2018 Emissions Inventories ........................................................ 43 7.2.4 Model Results for the 2018 Inventory Comp ared to the Uniform Rate of Progress

Glidepaths for Kentucky’s Class I Area ........................................................................... 45

7.3 Relative Contribution from In ternational Emissions to Visi bility Impairment in 2018 at
VISTAS Class I areas ................................................................................................................... 50 7.4 Relative Contributions to Visibility Impa irment: Pollutants, Source Categories, and Geographic Areas .......................................................................................................................... 54 7.5 What Control Determinations Represent Best Available Retrofit Te chnology for Individual
Sources?

............................................................................................................................... ......... 56 7.5.1 Kentucky BART-Eligible Sources ........................................................................... 57

7.5.2 Determination of Sources Su bject to BART in Kentucky ......................................... 58 7.5.3 Determination of BART Requirem ents for Subject-to-BART Sources ..................... 62 7.6 Relative Contributions to Visibility Impa irment: Geographic Areas of Influence for Kentucky’s Class I Area ............................................................................................................... 65 7.6.1 Back Trajectory Analyses .......................................................................................... 67

7.6.2 Residence Time Plots ................................................................................................. 68

7.6.3 SO2 Areas of Influence .............................................................................................. 69

7.6.4 Emissions Sources within SO2 Areas of Influence .................................................. 70 7.6.5 Specific Source Types in the Area of Influence for Kentucky’s Class I Area ........... 73 7.7 Evaluating the Four Statutory Factors for Specific SO2 Emissions Sources in Each Area of
Influence

............................................................................................................................... ........ 77

7.8 Which Control Measures Represent Reasonable Progres s for Individual Sources? ............... 79

7.9 What Additional Emissions Controls Were Cons idered as part of the Long-Term Strategy for Visibility Improvement by 2018?

................................................................................................. 82

8.0 REASONABLE PROGRESS GOALS ................................................................................... 84

9.0 MONITORING STRATEGY ................................................................................................. 85

10.0 INTERSTATE CONSULTATION ...................................................................................... 88

Kentucky Regional Haze SIP vii11.0 COMPREHENSIVE PERIODIC IM PLEMENTATION PLAN REVISIONS ................... 89

12.0 DETERMINATION OF ADEQUA CY OF THE EXISTING PLAN .................................. 91

LIST OF APPENDICES

Appendix A: VISTAS Memorandu m of Understanding and Bylaws

Appendix B: Conceptual Description

Appendix C: Modeling Analysis Protocol and Quality Assurance Project Plan

Appendix D: Emissions Preparation and Results

Appendix E: Air Quality and Meteor ological Preparations and Results

Appendix F: Model Performance Evaluation

Appendix G: Modeling Results and Supplemental Analysis

Appendix H: Reasonable Progress Evaluation and Long Term Strategy

Appendix I: Data Access

Appendix J: Consultation Process

Appendix K: Recommended Improvements

Appendix L: Best Available Retrofit Technology (BART) Related Documentation

Appendix M: VISTAS Modeling Reports

Appendix N: Public Hearing Notice and Su mmary of Comments Received and Cabinet Responses

Kentucky Regional Haze SIP viii1.0 INTRODUCTION

1.1 What is regional haze?

Regional haze is pollution from di sparate sources that impairs vi sibility over a large region, including national parks, forest s, and wilderness areas (156 of which are termed mandatory Federal “Class I” areas). Regional haze is caused by sources and activities emitting fine particles and their precursors. Those emissions ar e often transported over large regions.

Particles affect visibility thr ough the scattering and absorption of light, and fine particles – particles similar in size to the wavelength of li ght – 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 sulf ur dioxides (SO2) and nitrogen oxides (NOx). Reducing fine particles in the at mosphere is generally considered to be an effective method of reducing regional haze, and thus improving visibility. Fine par ticles also adversely impact human health, especially respiratory and cardiovascular systems. The United States Environmental Protection Agency (USEPA) has se t national ambient air quality standards for daily and annual levels of fine particles with di ameter smaller than 2.5 μ m (PM2.5). In the southeast, the most important so urces of PM2.5 and its precursors are coal-fired power plants,
industrial boilers and other combustion sources. Other significant cont ributors to PM2.5 and visibility impairment include mobile source em issions, area sources, 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 Am endments to the Clean Air Ac t (CAA), Congress set forth a program, for protecting visibility in Class I areas, which calls for the “prevention of any future, and the remedying of any existing, impairment of visibility in mandatory Class I Federal areas which impairment results from manmade air pollution.” Congress a dopted the visibility provisions to protect visibility in these 156 na tional parks, forests and wilderness areas. On December 2, 1980, the USEPA promulgated regula tions to address visibility impairment (45 FR 80084). The 1980 regulations were develope d to address visibility impairment that is “reasonably attributable ” to a single source or small gr oup of sources. These regulations represented the first phase in a ddressing visibility impairment and deferred action on regional haze that emanates from a variety of sour ces until monitoring, modeling and scientific knowledge about the relationships between polluta nts and visibility impairment improved.

In the 1990 Amendments to th e CAA, Congress added section 169B and called on the USEPA to issue regional haze rules. The regional haze ru le that the USEPA promulgated on July 1, 1999 (64 FR 35713), revised the existing visibility regulations in order to integrate provisions addressing regional haze impairment and establish a comprehensive visibility protection program
for Class I Federal areas. States are required to submit state impl ementation plans (SIPs) to the USEPA that set out each states’ plan for comp lying with the regional haze rule, including
consultation and coordination with other states and with Federal Land Managers (FLMs). The timing of SIP submittal is tied to the USEPA’s promulgation of designations for the National Ambient Air Quality Standard (NAAQS) for fine particulate matter. States must submit a

Kentucky Regional Haze SIP 1regional haze implementation plan to the USEPA w ithin three years after the date of designation. Because the USEPA promulgated designation da tes on December 17, 2004, regional haze SIPs must be submitted by December 17, 2007.

The regional haze rule addressed the combined visi bility effects of various pollution sources over a wide geographic region. This wide reaching po llution net meant that many states – even those without Class I areas – would be required to pa rticipate in haze reduction efforts. The USEPA designated five Regional Planning Organizations (RPOs) to assi st with the coordination and cooperation needed to address th e visibility issue. The RPO that makes up the southeastern portion of the contiguous United States is know n as VISTAS (Visibility Improvement – State
and Tribal Association of the Southeast), and includes the east ern band of the Cherokee Indians in addition to the following states: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia.

Figure 1.2-1. Geographical Areas of Re gional Planning Organizations

1.3 General overview of regional haze SIP requirements

The regional haze rule at 40 CFR 51.308(d) requires states to demonstrate reasonable progress toward meeting the national goal of a return to natural visibility condi tions by 2064. As a guide for reasonable progress, the regional haze rule dir ects states to graphically show what would be a “uniform rate of progress” toward natural condi tions for each mandatory Class I Federal area within the State and/or for each mandatory Clas s I Federal area located outside the State, which may be affected by emissions from sources within the State. States are to establish baseline visibility conditions for 2000-2004, natural background visibility c onditions in 2064, and the rate

Kentucky Regional Haze SIP 2of uniform progress between baseline and back ground conditions. The uniform rate of progress is also known as the “glidepath.”

The regional haze rule then requires states to establish reasonable prog ress goals, expressed in deciviews, for visibility improvement at each affected Class I area covering each (approximately) 10-year period until 2064. The goals must provide for reasonable progress towards achieving natural visibi lity conditions, provide for impr ovement in visibility for the most impaired days over the period of the im plementation plan, and ensure no degradation in visibility for the least impa ired days over the same peri od (see 40 CFR 51.308(d)(1)).

In order to ensure that visibility goals are properly met and set, state plans must include determinations, for each Class I area, of the baseline visibility conditions (expressed in deciviews) for the most impaired and least impa ired days. SIPs must also contain supporting documentation for all required analyses used to cal culate 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 in clude a monitoring strategy for measuring, characterizing, and reporting of re gional haze visibility impairment that is representative of all mandatory Class I Federal areas w ithin the state (see 40 CFR 51.308(d)(4)).

This first set of reasonable progress goals must be met through measures c ontained in the state’s long-term strategy covering the period from the present until 2018. The long-term strategy includes enforceable emissions limitations, co mpliance schedules, and other measures as necessary to achieve the reasonable progress goals, including all controls required or expected under all federal and state regulations by 2009 a nd by 2018. During development of the longterm strategy, states are also required to consid er specific factors such as the above mentioned ongoing control programs, measures to mitigate construction activities, source retirement and replacement schedules, smoke management pr ograms for agriculture and forestry, and enforceability of specific meas ures (see 40 CFR 51.308(d)(3)).

In addition, a specific component of each state’ s first long-term strategy is dictated by the specific best available retrofit technology (BART) requiremen ts in 40 CFR 51.308(e) of the regional haze rule. The regional haze rule at 40 CFR 51.308(e) requires states to include a determination of BART for each BART-eligible s ource in the State that emits any air pollutant, which may reasonably be anticipated to cause or contribute to any im pairment of visibility in any mandatory Class I Federal area. The Clean Air Act section 169A(b) defines BART-eligible sources as sources in 26 specific source categori es, in operation within a 15-year period prior to enactment of the 1977 Clean Air Act Amendments and having total potent ial emissions of 250 tons per year or more for any visibility-impairi ng pollutant for all emission units. States must determine BART according to five factors set ou t 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 du e December 17, 2007. These pl ans 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 ensure
that installed emissions controls are on track with emissions reduction forecasts in each SIP. The

Kentucky Regional Haze SIP 3first interim review will be due to the USEPA in December 2012. If emissions controls are not on track to meet SIP forecasts, then states would need to take acti on to ensure emissions controls by 2018 will be consistent with the SIP or to revi se the SIP to be consistent with the revised
emissions forecast.

The USEPA provided several guidance document s listed below to assist the states in implementation of the regiona l haze rule requirements. KYDAQ followed these guidance documents in developing the technica l 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 Visibility Conditions Under the Regional Haze Rule (EPA-454/B-03-005, September 2003).

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

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

1.4 Class I area in Kentucky

Kentucky has one Class I area with in its borders: Mammoth Cave National Park. The Kentucky Energy and Environment Cabine t’s Division for Air Quality (KYDAQ) is responsible for developing the Kentucky’s Regiona l Haze SIP. This SIP establishes reasonable progress goals for visibility improvement at its Class I area, and a long-term strategy that will achieve those reasonable progress goals within the first regional haze planning period.

Mammoth Cave
National Park

Figure 1.4-1. Kentucky Class I area

Kentucky Regional Haze SIP 4In developing this SIP, the KYDAQ has also c onsidered that emission sources outside of Kentucky may affect visibility at Kentucky’s Class I area, and that emission sources within Kentucky may affect visibility at Class I ar eas in neighboring stat es. Through VISTAS, the southeastern states have worked together to assess state-by-state contri butions to visibility impairment in specific Class I areas, including Mammoth Cave in Kent ucky and those affected by emissions from Kentucky. This technical work is discussed further in Sections 5, 6, and 7. Consultations to date between Kentucky and other states are summarized in Section 10; consultations are ongoing.

Prior to VISTAS, the southern states cooperated in a voluntary regional partnership to identify and recommend reasonable measures to remedy exis ting and prevent future adverse effects from human-induced air pollution on th e air quality related values of the Southern Appalachian Mountains. States cooperate d with the FLMs, the USEPA, industry, environmental organizations and academia to complete a technical assessment of the imp acts of acid deposition, ozone, and fine particles on sensitive resources in the Southern Appalachians. The (Southern Appalachian Mountain Initiative) SAMI Final Report was delivered in August 2002. The SAMI
Assessment concluded that ammonium sulfate is th e major contributor to visibility impairment in the Southern Appalachian Mountains and to impr ove visibility, it is mo st important to reduce SO2 emissions. SAMI also concluded that reducing ammonia emissions would be helpful to reduce ammonium nitrate contributions to visibility 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 th e haziest days, much of the benefit of emissions reductions
would occur in the state where emissions redu ctions were made. Emissions in surrounding SAMI states and states outside the SAMI region al so contribute to air qua lity in the SAMI Class I areas. The SAMI states suppor ted strong national multi-pollutant legislation to accomplish its
mission. Emissions reductions to meet national he alth standards for ozone and fine particles were expected to also improve air quality in the Southern Appalachia n Mountains. The SAMI
states committed to consider air quality benefits in the Southern Appalachians as they developed
SIPs for the health standards.

Congress considered several legislative bills to reduce SO2 and NOx from electric generating utilities. In 2004, the USEPA pr omulgated the Clean Air Intersta te Rule (CAIR) to require
emissions reductions for SO2 and NOx from electric generating utilities in 26 eastern states. The CAIR rule allows for interstate trading of em issions to find cost-effec tive reductions. These reductions will improve visibility in the Class I area in Kentucky.

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 States and FLMs on the implementation of the visibility protection program, including development and review of implementation plan revisions and 5-year progress reports, and on the implementation of ot her programs having the pot ential to contribute to impairment of visibility in any mandatory Cl ass I Federal area within the State. The three FLMs are the United States Department of Interi or’s (USDI’s) Fish and Wildlife Service (FWS)

Kentucky Regional Haze SIP 5and National Park Service (NPS) and the United States Department of Agriculture’s (USDA’s) Forest Service (FS).

Successful implementation of a regional haze program will involve long-term regional coordination among states. VISTAS was formed in 2001 to address regional haze and visibility problems in the southeastern United States. Jurisdictions represen ted by VISTAS members include the Eastern Band of Cherokee Indians; the States of Alabama, Florida, Georgia, Kentucky, Mississippi, North Caroli na, South Carolina, Tennessee, Virginia, and West Virginia; and the local air pollution control programs locat ed 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 es tablish natural background visibility conditions across the mandatory Class I Federal areas, identify current visibility impairment levels, analyze
emission control levels that will achieve in terim visibility goals, and provide adequate documentation to member agencies so that they can develop their regional haze State/Tribal Implementation Plans (SIP/TIP). Figure 1.5-1 s hows the 18 mandatory Cla ss I Federal areas in the VISTAS Region, where visibility is an importa nt value. Table 1.5-1 lis ts these Class I areas and the reported acreage associated with the Class I areas.

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

Kentucky Regional Haze SIP 6Table 1.5-1 Mandatory Class I Federal Areas in the VISTAS Region

State Area Name Acreage Federal Land Manager

Alabama Sipsey Wilderness 24,922 USDA-FS

Chassahowitzka Wilderness 23,579 USDI-FWS
Everglades National Park 1,397,429 USDI-NPS Florida

St. Marks Wilderness 17,350 USDI-FWS

Cohutta Wilderness 36,977 USDA-FS

Okefenokee Wilderness 353,981 USDI-FWS Georgia

Wolf Island Wilderness 5,126 USDI-FWS
Kentucky Mammoth Cave National Park 51,303 USDI-NPS
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 North Carolina

Swanquarter Wilderness 8,785 USDI-FWS

South Carolina Cape Romain Wilderness 29,000 USDI-FWS
Great Smoky Mountains National Park 241,207 USDI-NPS Tennessee Joyce Kilmer-Slickrock Wilderness 3,832 USDA-FS

James River Face Wilderness 8,886 USDA-FS Virginia Shenandoah National Park 190,535 USDI-NPS
Dolly Sods Wilderness 10,215 USDA-FS West Virginia Otter Creek Wilderness 20,000 USDA-FS



### 2.0 ASSESSMENT OF BASELINE AND CURRENT CONDITIONS AND

ESTIMATE OF NATURAL BACKGROU ND CONDITIONS IN CLASS I

AREAS

The goal of the Regional Haze Rule is to restore natural visibility condi tions 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 phenom ena that reduce visibility as measured in terms of light extinction, visual rang e, contrast, or colorati on.” The Regional Haze
SIPs must contain measures that make “reas onable progress” toward this goal by reducing anthropogenic emissions that cause haze.

An easily understood measure of vi sibility to most people is visu al range. Visual range is the greatest distance, in kilometers or miles, at which a dark object can be viewed against the sky.

Kentucky Regional Haze SIP 7For evaluating the relative contributions of polluta nts to visibility impairment, however, the most useful measure of visibility impairment is light extinction, which is usually expressed in units of inverse megameters (Mm -1 ). Light extinction affects the cl arity and color of objects being
viewed.

The measure used by the regional haze rule is th e deciview (dv). Deci views are calculated directly from light extinction using a logarithmic scale. The deciview is a useful measure for tracking progress in improving visibility, because each deciview change is an equal incremental change in visibility perceived by the human eye. Most people can detect a change in visibility at
one deciview.

For each Class I area, there are thr ee metrics of visibility that ar e part of the determination of reasonable progress:

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

Each of the three metrics include s the concentration data of the visibility pollutants as different terms in the light extinction algorithm, with respective extinction coefficients and relative humidity factors. Total light extinction when converted to deci views (dv) is calculated for the average of the 20 percent best and 20 percent worst visibility days.

“Natural” visibility is determined by estimating th e natural concentrations of visibility pollutants and then calculating total light extinction. “Baseline” visibility is the starting point for the improvement of visibility conditions. It is the average of the In teragency Monitoring of Protected Visual Environments (IMPROVE) monitoring data for 2000 through 2004 and is equivalent to “current” visibility conditions for this initial review period. The comparison of
initial baseline conditions to na tural visibility conditions indi cates the amount of improvement necessary to attain natural visi bility by 2064. Each state must esti mate natural visibility levels
for Class I areas within its borde rs in consultation with Federa l Land Managers and other states (40 CFR 51.308(d)(2)). “Current conditions” are as sessed 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 Kentucky’s Class I Area

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 fine particle components. The same annual average natural background visibility is assumed fo r all Class I areas in the eastern United States (separate values
are estimated for the western Un ited States). Natural backgr ound visibility for the 20 percent worst days is estimated by assuming that fine particle concentrations for natural background are normally distributed and the 90 th percentile of the annual dist ribution repres ents natural background visibility on the 20 percent worst days.

Kentucky Regional Haze SIP 8In the 2003 guidance, USEPA also provided that st ates may use a “refined approach” to estimate
the values that characte rize the natural visibil ity conditions of the Clas s I areas. The purpose of such a refinement would be to provide more accu rate estimates with changes to the extinction algorithm that may include the concentration va lues, factors to calcul ate extinction from a measured particular species and particle size, the extinction coefficients for certain compounds, geographical variation (by al titude) of a fixed value, and the addition of visibility pollutants.

In 2005 the IMPROVE Steering Committee made r ecommendations for a refined equation that modifies the terms of the origin al equation to account for the mo st recent data. The choice between use of the old or the new equation for ca lculating the visibility metrics for each Class I area is made by the state in which the Class I area is located.

The new IMPROVE equation account s for the effect of partic le size distribution on light extinction efficiency of sulfate, nitrate, and organic carbon. The mass multiplier for organic carbon (particulate organic matter) is increased from 1.4 to 1.8. New terms are added to the equation to account for light ex tinction by sea salt and light absorption by gaseous nitrogen dioxide. Site-specific values ar e used for Rayleigh scattering to account for the site-specific effects of elevation and temperature. Separate relative humidity enhancement factors are used for small and large size distributions of ammoni um sulfate and ammonium nitrate and for sea salt. The elemental carbon (li ght-absorbing carbon), fine soil, and coar se mass terms do not change between the original and new IMPROVE equation.

Natural background conditions using the new IMPR OVE equation are calculated separately for
each Class I area. The calculation starts with the annual average values for natural background for each component of PM2.5 mass from the EP A 2003 guidance (default values). The annual frequency distribution of values of each PM2.5 component for current conditions (2000-2004) is then defined. This species-speci fic frequency distribution is applie d to the default annual average values for that PM2.5 component to calculate natural conditions on the 20% worst days. The current variability in each com ponent is retained while also re taining the same annual average background condition for that component as defi ned in the 2003 guidance. The new calculation of natural background allows Rayleigh scattering to vary with elevation. Current sea salt values
are used for natural back ground levels of sea salt.

The VISTAS states chose to use the new IMPR OVE 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 IMPROVE Steering Committee. For more detailed discussion of the two IMPROVE equations, see Appendix B.

2.2 Estimating Baseline Conditions for Kentucky’s Class I Area

Baseline visibility conditions at Kentucky’s Class I area is estimated using sampling data collected at the IMPROVE monitoring site at Ma mmoth Cave National Park. 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 for Mammoth

Kentucky Regional Haze SIP 9Cave for the period 2000 to 2004 meet USEPA requi rements for data comp leteness (75 percent for the year and 50 percent for each quarter). The light extinction and deciview visi bility values for the 20 percent worst and 20 percent best visibility days at Mammoth Cave are based on data
and calculations included in Appendix B of this SIP.

2.3 Summary of Natural Background and Baselin e Conditions for Kentucky’s Class I Area

Table 2.3-1 presents estimated natural background and baseline visibility metrics for Kentucky’s Class I area. Note that Kentucky is not considering inte rnational emissions to be a component of natural background. Baseline visibi lity on the 20 percent worst days at the Mammoth Cave monitoring site is predicted to be 31 dv. Natural bac kground visibility at the Mammoth Cave monitoring site is predicted to be 11 dv. A list of the 20 percent worst and 20 percent best days used in the technical analyses for Kentucky’s Class I area are available on page 7 of Appendix
F.2.

Table 2.3-1 Natural Background and Baseline Conditions for Kentucky’s Class I Area

Natural Background Conditions
Class I 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)
Mammoth Cave 11.1 5.0 30.7 16.5

Baseline Visibility Conditions 2000-2004
Class I 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
Mammoth Cave 31.4 16.5 241.4 53.0

2.4 Pollutant Contributions to Visibilit y Impairment (2000-2004 Baseline Data)

The 20 percent worst visibility days at Kentucky ’s Mammoth Cave generally occur in the period March through September. The peak hazy days occur in the summer under stagnant weather conditions with high relative humidity, high temp eratures, and low wind speeds. The 20 percent best visibility days at Mammoth Cave generally can occur at any time of year. Figures 2.4-1 and 2.4-2 display the average light extinction for the 20% haziest days and 20% clearest days, respectively.

Kentucky Regional Haze SIP 10VISTAS coastal VISTAS inland Neighboring non-VISTAS 0 50 100 150 200 250 300Swanquarter, NCCae 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 Glade, MOUpper Buffalo, ARCaney Creek, ARExtinction (Mm-1)
Sea Salt
Coarse
Soil
EC
POM
NH4NO3 (NH4)2SO4 Rayleigh

VISTAS coastal VISTAS inland Neighboring non-VISTAS 0 50 100 150 200 250 300Swanquarter, NCCae 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 Glade, MOUpper Buffalo, ARCaney Creek, ARExtinction (Mm-1)
Sea Salt
Coarse
Soil
EC
POM
NH4NO3 (NH4)2SO4 Rayleigh

Figure 2.4-1. Average light extinction for the 20 percent Haziest Days in 2000-2004 at VISTAS and neighboring Class I area s using New IMPROVE equation

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, NCCae 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 Glade, MOUpper Buffalo, ARCaney Creek, AR 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, NCCae 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 Glade, MOUpper Buffalo, ARCaney Creek, AR

Figure 2.4-2. Average light extinction for the 20 percent Clearest Days in 2000-2004 at VISTAS and neighboring Class I a reas using New IMPROVE equation

Kentucky Regional Haze SIP 11Ammonium sulfate , (NH 4 ) 2 SO 4 , is the most important contributor to visibility impairment and fine particle mass on the 20 percent worst (haziest) and 20 percent best (clear est) visibility days at Mammoth Cave. Sulfate levels on the 20 pe rcent worst days account for 78 percent of the visibility impairment at Mammoth Cave. Across the VISTAS region, sulfat e levels are higher at
the inland sites than at the coastal sites (Figure 2.4-1). On the 20 percent clearest days, sulfate levels are more uniform across the region (Figure 2.4-2).

Sulfate particles are formed in the atmosphere from SO2 emissions. Sulf ate 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.

Particulate Organic Matter (POM) is the second most important c ontributor to fine particle mass and light extinction on the 20 percent haziest days at the Kentucky’s Class I area. Elevated levels of POM and Elemental Carbon, EC, indicate impact from wildfires or prescribed fire. Typically, in state significant fi re impacts are infrequent at Ke ntucky’s Class I area. VISTAS collected additional samples of carbon at five sites, including Mammot h Cave, KY; Great Smoky Mountains, NC; Millbrook (Raleigh, NC); Cape Romain, SC; and Shenandoah, VA, to better understand sources contributing to carbon in rural and urban areas. Samples were analyzed to define the amount of carbon-14 isotope as an in dicator of the amount of carbon from modern sources (vegetative emissions, fires) and the am ount of carbon from fossi l sources (gasoline, diesel, oil). For most samples, the ratio of modern carbon to fossil ca rbon was greater than 0.60 throughout the year. In the fall, winter, and spring, more of the m odern carbon is attributable to wood burning while in the summer months more of the modern carbon mass is attributable to biogenic emissions from vegetation. On some days, greater than 90% of the carbon at Great Smoky Mountains is attributable to modern s ources of carbon. Biogenic carbon emissions at Cape Romain, SC, a coastal site similar to Swanquarter, were lower than emissions at the forested mountain sites. Carbon from gasolin e and diesel engines is a relatively small contribution at the rural sites. At Millbrook, carbon from fossil fuel combustion is a larger percentage contribution than at the rural sites, but still less than 50 percent of total carbon measured. These results suggest that controlling an thropogenic sources of ca rbon will have little benefit in improving visibility in Class I area s since the majority of the POM comes from natural, i.e., biogenic, sources. Controlling an thropogenic sources of carbon will likely be more
effective to reduce levels of PM2.5 in urban areas.

Ammonium nitrate , NH 4 NO 3 , is the second most important cont ributor to fine particle mass and light extinction on the 20 percent clearest days at Kentucky’ s Class I area. NH 4 NO 3 is formed in the atmosphere by reaction of NH3 and NOx. In the VISTAS region, nitrate formation is limited by availability of NH3 and by temperature. Ammoni a preferentially reacts with SO2 and sulfate before reacting with NOx. Particle nitrate is formed at lower temperatures; at elevated temperatures nitric 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 concentrations 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 off-road m obile sources. Modeling data (see Section 7) indicate that in the VISTAS region ammo nium nitrate formation is limited by NH3

Kentucky Regional Haze SIP 12concentrations and suggest that for winter da ys, controls of NH3 sources would be more effective in reducing ammonium nitrate levels than contro ls of NOx.

Elemental Carbon, EC, is a comparatively minor contributor to visibility impairment. Sources include agricultural and wildland (wildfire, wild land fire use and prescribed fire) burning, and incomplete combustion of fossil fuels. EC levels are higher at urban monito rs than at the Class I areas and suggest controls of fo ssil fuel combustion sources would be more effective to reduce PM2.5 in urban areas than to improve visibility in Class I areas.

Soil fine particles are minor contri butors to visibility impairment at most southeastern sites on most days. Occasional episodes of elevated fine soil can be attributed to Saharan dust episodes, particularly at Everglades, Florid a, but rarely are seen at the Ke ntucky’s Class I area. No control strategies are indicated for fine soil.

Sea salt , NaCl, is observed at the coastal sites. Sea sa lt contributions to visibility impairment are most important on the 20 percent clearest days wh en sulfate and POM levels are low. Sea salt levels do not contribute significantly to visibility on the 20 percen t worst visibility days. The new IMPROVE equation uses Chloride ion, Cl - , from routine IMPROVE measurements to
calculate sea salt levels. VISTAS used Cl - to calculate sea salt cont ributions to visibility following IMPROVE guidance.

Coarse particle mass (particles with diameters between 2.5 and 10 microns) has a relatively small contribution to visibility impairment because the light extinction efficiency of coarse mass is very low compared to the extinction effi ciency 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 positive or negative and is attributable to water and/or the factors used to calculate molecular weights of the other components.

The New IMPROVE equation generally results in higher calcula ted light extinction on days with higher mass and lower light extinction on days w ith lower mass. This tends to increase calculated light extinction for current 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 POM in the new IMPROVE equation increases light extinction for current conditions more than for natural conditions. The new algorithm does not change the conclusion that in the VISTAS region, and in Kentucky, the most effective means to improve 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 (STN) in ne arby urban areas to understand the similarities and differences in composition of fine particle mass. Several PM2.5 nonattainment areas are in close proximity to the Class I areas in the so utheastern United States , including Atlanta, GA; Birmingham, AL; Charleston, WV; Chattanooga, TN; Knoxville, TN; and Louisville, KY. Ammonium sulfate concentrations are comparab le between urban and nearby Class I areas,

Kentucky Regional Haze SIP 13while organic carbon, elemental carbon, and nitrate c oncentration are generally higher in urban
areas than the Class I areas. Th ese results suggest that sulfate is widely distributed regionally while urban areas see an additional incremental pollu tant loading from local emissions sources.

Role of meteorology in determ ining visibility conditions: Classification and Regression Tree (CART) Analyses were used to characterize the relationship be tween meteorological conditions and visibility conditions at the Class I areas. Days were assigned to one of five visibility classes ranging from poor to good visibilit y. Days were then assigned to bins based on meteorological
conditions. For the Kentuc ky’s Class I area, poor visibility days were most likely to occur on days with high temperatures, hi gh relative humidity, low wind sp eeds, and elevated PM2.5 mass at upwind urban areas. Precipita tion was not a good predictor of visibility condition. Weights 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 NA TURAL CONDITIONS IN 2064

As stated in Section 1.3, the regional haze rule dir ects states to graphically show what would be a “uniform rate of progress” toward natural condi tions for each mandatory Class I Federal area within the State as well as for each mandatory Cl ass I Federal area located outside the State, which may be affected by emissions from sources within the State. The uni form rate of progress is also known as the “glidepath.” The glidepath is simply a straight graphical line drawn from the baseline level of visibility impairment fo r 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 visibility conditions by 2064. Section 51.308(d)(1) of the regional haze rule requires that reasonable pr ogress goals must both: (1) provide for improvement in visibility for th e 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 develope d in accordance with the USEPA’s 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 the Class I Area in Kentucky

The following is the glidepath for the 20 per cent most impaired days for Mammoth Cave National Park assuming uniform rate of pr ogress toward regional haze goals. Natural background visibility for the most impaired days at Mammoth Cave is predicted to be 11 dv.

Kentucky Regional Haze SIP 14

### Uniform Rate of Progress Glidepath Mammoth Cave - 20% Worst Data Days New IMPROVE Equation

31.37 29.68

26.64

23.26

19.87

16.49

13.11

11.08

0 5 10 15 20 25 30 35

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064
YearHaziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation
Figure 3.1-1. Uniform Rate of Progress Glidepat h for 20 percent worst days at Mammoth Cave National Park.

Kentucky Regional Haze SIP 15

### 4.0 NATURE OF THE PROBLEM: CHIEF CAUSES OF VISIBILITY

IMPAIRMENT IN KENT UCKY’S CLASS I AREA

4.1 Baseline Emissions Inventory

The Regional Haze Rule at 51.308(d) (4) (v) requires a statewide em issions inventory of pollutants that are reasonably anticipated to cause or contribute to visibility impairment in any mandatory Class I area. An inventory was developed for the baseline year 2002 and projected to 2009 and 2018. The pollutants inventoried incl ude volatile organic compounds, nitrogen oxides, fine particulate (PM2.5) , coarse particulate (PM10), ammonia and sulfur dioxide. The baseline emissions inventory for 2002 was de veloped for Kentucky following the methods described in Appendix D.

There are five different emission inventory source classifications: stationary point and area sources, off-road and on-road mob ile sources, and biogenic sources. Stationary point sources are those sources that emit greater than a specified tonnage per year, with data provided at the facility level. Electric gene rating utilities and i ndustrial sources are th e major categories for stationary point sources. Stationary area sour ces are those sources whose individual emissions are relatively small, but due to the large number of these sources, the collective emissions from the source category could be signifi cant (i.e., dry cleaners, service stations, agricultural sources, 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 locomotiv es, aircraft, etc. The emissions from these sources, like stationary area sources, are estimated on a countywide level. On-road mobile sources are automobiles, trucks, and motorcycle s that use the roadway system. The emissions from these sources are estimated by vehicle t ype and road type and are summed to the countywide level. Biogenic sources are the natura l 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 classifications , there are also various types of emission
inventories. The first is the actua l base year inventory. This inve ntory is the base year emissions that correspond to the meteorological data used, which for this modeling effort is data from
2002. These emissions are used for evaluati ng the air quality model performance.

The second type of inventory is the typical base y ear inventory. This inventory is similar to the actual base year inventory, excep t 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 for the electric generating units (EGUs) and the wildland fire emissions. The air quality modeling runs using the ty pical base year inve ntory provide results which are then used to calculate relative reduction factors for future y ears. These relative reduction factors for future years are then used to dem onstrate reasonable progress to ward visibility goals.

Below is an overview of the inventories used for each source classification. More detailed discussion of the emissions inventory deve lopment is contained in Appendix D.

Kentucky Regional Haze SIP 164.1.1 Stationary Point Sources

Point source emissions are emissions from indi vidual sources that are in a fixed location. Generally, these sources must have permits in order to construct and/ or operate and their
emissions are inventoried on an annual basis. All sources emitting VOC are inventoried in Kentucky’s ozone nonattainment and maintenance ar eas on an annual basis. Large NOx sources having minimum capacity to emit 100 tons per year (tpy) of a criteria pollu tant, 10 tpy of a single hazardous air pollutant (HAP), or 25 tpy tota l of multiple HAPs are inventoried annually. Smaller sources have been inventoried less frequently. For the purposes of this modeling point source emissions data can be grouped into EGU sources and other non-EGU sources.

Electric Generating Units

The actual base year inventory for the EGU sour ces used 2002 continuous emissions monitoring (CEM) data reported to the USEPA’s Acid Ra in program or 2002 hourly emissions data provided by stakeholders. These data provide hourly emissions profiles for SO2 and NOx that can be used in air quality modeling. Emissions profiles are used to estimate emissions of other pollutants (volatile organic comp ounds, carbon monoxide, ammonia, fine particles, soil) based
on measured emissions of SO2 and NOx.

Emissions from EGUs vary daily and seasonally as a function of variability in energy demand and utilization and outage schedules. To avoid anomalies in future year emissions created by relying on 2002 operations to represent future operat ions, a typical base y ear emissions inventory was developed for EGUs. This approach is co nsistent with the USEPA’s 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 multiplied by the 2002 actual emissions. The heat inputs for the period 2000 through 2004 were used because the modeling of
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 period, the averag e of the 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 qua lity modeling, VISTAS, in coope ration 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 updated with state-specific information . IPM is a dynamic linear optimization model that can be used to exam ine air pollution contro l policies for various pollutants throughout the contiguous United 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 determ ines the least-cost means of meeting electric generation and capacity requireme nts while complying with speci fied constraint s including air pollution regulations, transmission bottlenecks, and plant-specific op erational constraints. The versatility of IPM allows users to specify which constraints to exercise, and to populate IPM with
their own datasets.

Kentucky Regional Haze SIP 17The IPM modeling runs took into consideration the USEPA’s Cl ean Air Interstate Rule ( CAIR ) implementation and North Carolina’s CSA requirements.

Other Industrial Point Sources

For the non-EGU sources, the same inventory is us ed for both the actual and typical base year emissions inventories. The non-EGU category uses annual emissions as reported under the Consolidated Emissions Reporting Rule (CERR) for the year 2002. These emissions are temporally allocated 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 CAIR analyses. This data was supplemented with statespecific growth factors and stakeh older input on growth assumptions.

4.1.2 Stationary Area Sources

Stationary area sources are sources whose individua l emissions are relatively small, but due to the large number of these sources, the collectiv e emissions could be sign ificant (i.e., combustion of fuels for heating, structure fires, service stations, etc.). Emissions are estimated by multiplying an emission factor by some known indi cator of collective activity, such as fuel usage, number of households, or population. Sta tionary area source emissions are estimated at the countywide level.

A small portion of the 2002 area s ource base year inventory for ozone precursors in eleven Kentucky ozone areas was developed by the KYDAQ and provided to the VI STAS contractor. The VISTAS contractor calculated the remaini ng portion of the area source inventory. The sources estimated by the contractor include em issions from animal husbandry, wildland fires, and particulate matter from paved and unpaved road s. For the other stat es within the modeling domain, either state-supplied data or data reported under CERR for 2002 was used.

The actual base year inventory wi ll serve as the typical base y ear inventory for all area source categories except for wildland fires. For wildland fires, a typical year inventory was used to avoid anomalies in wildfire activity in 2002 compar ed to longer term averages. Development of a typical year fire inventory provided the capability of using a comparable data set for both the base year and future years. The VISTAS Fire Sp ecial Interest Work Group used State records to ratio the number of acres burned over a longer te rm period (three or more years, as available from state records) to 2002. Based on these ra tios, the 2002 acreage was then scaled up or down to develop a typica l year inventory.

The VISTAS contractor generated future year emissions inventories for 2009 and 2018 for the regional haze modeling. Growth f actors, supplied either by states or taken from the CAIR emission projections, were applied to project th e controlled emissions to 2018. If no growth
factor was available from either a state or the CAIR growth f actor files, then the USEPA’s Economic Growth and Analysis System Version 5 growth factors were used.

Kentucky Regional Haze SIP 184.1.3 Off-Road Mobile Sources

Off-road (or non-road) mobile sources are equipm ent 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 non-road mobile sources, the emissi ons for 2002 were estimated using the
USEPA’s NONROAD2005c model. For the thr ee source categories not included in the NONROAD model, i.e., aircraft engines, rail road 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 the 2009 and 2018 future year inventories. The NONROAD
model takes into consideration regulations affecting emission s from these source categories

. For the Northern Kentucky Greater Cincinnati In ternational Airport, K YDAQ confirmed to the
VISTAS contractor the number of landings and takeoffs for 2002. For the commercial marine, railroad locomotives and the remaining airport em issions, the VISTAS contractor calculated the future growth in emissions using detailed inventor y data (both before and after controls) for 1996 and 2010, obtained from the CAIR Technical Support Document. When available, state-specific growth factors were used.

4.1.4 Highway Mobile Sources

For onroad 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 road s, the average speeds on the roads, the mix of vehicl es on the roads, any programs in place in an area to reduce emissions for motor vehicles (e.g., emissions inspection programs), and temperature.

The MOBILE model takes into consideration regulat ions that affect emissions from this source sector. The same MOBILE run is used to repres ent the actual and typical year emissions for onroad vehicles using input data reflective of 2002. The MOBILE model then is run for 2018 inventory using input data reflective of th at year. KYDAQ provided 2002 and 2018 VMT and speed information to the VISTAS contractor for a small number of Kentucky counties. For several urban areas in Kentucky that run travel demand models (TDMs), VMT and speed data from TDMs was obtained by KYDAQ and pr ovided to the VISTAS contractor.

4.1.5 Biogenic Emission Sources

Biogenic emissions were prepared with th e SMOKE-BEIS3 (Biogeni c Emission Inventory System 3 version 0.9) preprocessor. SMOKE-BEIS3 is a modified version of the Urban Airshed Model (UAM)-BEIS3 model. Modifications incl ude use of MM5 data, gridded land use data, and improved emissions characterization. The em ission factors that are used in SMOKE-BEIS3
are the same as the emission factors as in UAM-B EIS3. The basis for the gridded land use data used by BEIS3 is the county la nd use data in the Biogenic Emissions Landcover Database version 3 (BELD3) provided by th e USEPA. A separate land classification scheme, based upon satellite (AVHRR, 1 km spatial resolution) and census information, aided in defining the forest, agriculture and urban portions of each county.

Kentucky Regional Haze SIP 194.1.6 Summary 2002 Baseline Emission s Inventory for Kentucky

Table 4.1.6-1 is a summary of the 2002 baseline emission inventory for Kentucky. The complete inventory and discussion of the methodology is contained in Appendix D. The emissions summaries for other VISTAS states can also be found in Appendix D.

Table 4.1.6-1 2002 Emissions Inventory Summa ry for Kentucky (tons per year).
VOC NOx PM2.5 PM10 NH3 SO2
Point 46,315 240,362 14,219 21,421 995 529,182
Area 98,713 40,966 51,763 240,226 51,246 41,941
On-Road Mobile 103,503 156,417 2,697 3,723 5,055 6,308
Non-Road Mobile 44,805 104,571 6,046 6,425 31 14,043 Biogenics 630,506 21,090 0 0 0 0
TOTAL 923,842 563,406 74,725 271,795 57,327 591,474

4.1.7 Model Performance Improvements thro ugh Emissions Inventory Improvements

Since the initial model performance evaluation, VISTAS has made several improvements to the emissions inventory, which in turn improv ed model performance. These inventory improvements are detailed in the VISTAS emissi ons inventory report an d Appendix D, and are
summarized here:

• For electric generating utilities, the Integrated Planning Mode l (IPM) was used to provide estimates of future year utility produc tion and emissions. Continuous Emissions Monitoring data was used to define seasonal variability in production and emissions. The states updated IPM model projections with cont rol data provided by utility companies in late 2006 through early 2007.

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

• For ammonia emissions from agricultural sources, the Carnegie Mellon University ammonia model was used to impr ove annual and monthly estimates.

• For fires, the VISTAS states provided fire ac tivity data for 2002 for wildfires, prescribed fire, land clearing and agricultural burning to develop a 2002 fire inventory. Where data allowed, large fire events were modeled as point sources. In 2006, the United States
Forest Service and Fish and W ildlife Service provided projecti ons of increased prescribed burning in 2009 and 2018; these data were incor porated in the inventory for all states except Florida. Because current prescribed fire activity already reflects the use of fire as a forest management technique, Florida believ ed that there is too much uncertainty to project how future total fire activity (prescri bed plus wildfire) will change. In Florida, prescribed fire in the future years is th e same as 2002 typical for prescribed fire.

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

• For commercial marine emissions in shipping la nes in the Gulf of Mexico and Atlantic Oceans, gridded emissions for the VIST AS modeling domain was created using inventory data newly develope d for the USEPA by Corbett at University of Delaware. These emissions were incorporated in the modeling.

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

Kentucky Regional Haze SIP 204.2 Assessment of Relative Contributions from Specific Pollutants and Sources Categories

Ammonium sulfate is the largest contributor to vi sibility impairment at Kentucky’s Class I area
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 in the VISTAS states are attributable to electric generati ng facilities and industr ial point sources. Similarly, in Kentucky the stationary point sources, cons isting of electric generating f acilities and i ndustrial point sources, contribute 90 percent of the SO2 em issions as illustrated in Figure 4.2-2.



## 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 tons

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



## KY SO2 Emissions 2002

591,474 tons

Utilities
84% Industrial
6%

Utilities

Industrial

On road

Non road

Other

Area

Figure 4.2-1. SO2 emissions in 2002 in Kentucky.

Kentucky Regional Haze SIP 21Since the largest source of SO2 emissions comes fr om the stationary poin t sources, the focus of potential controls and the impacts for those contro ls was on this source sector. In Kentucky, the types of sources emitting SO2, and thus contributing to the visibility impairment of its Class I area, were predominately coal fire d utility and industrial boilers.



### 5.0 REGIONAL HAZE MODELI NG METHODS AND INPUTS

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

5.1 Analysis Method

The modeling analysis is a complex technical eval uation that begins by se lection of the modeling system. VISTAS decided to use the following modeling system:

• Meteorological Model: The Pennsylvania State University/National Center for Atmospheric Research ( PSU/NCAR ) Mesoscale Meteorological Model ( MM5 ) is a nonhydrostatic, prognostic meteorological model r outinely used for urban- and regionalscale photochemical, fine particulate matte r, and regional haze regulatory modeling
studies.

• Emissions Model: The Sparse Matrix Operator Kernel Emissions ( SMOKE ) modeling system is an emissions modeling 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: The USEPA’s Mode ls-3/ Community Multiscale Air Quality (CMAQ) modeling system is an ‘One-Atmosph ere’ photochemical grid model capable of addressing ozone, particulate matte r (PM), visibility and acid deposition at regional scale.

The USEPA’s 2007 modeling guidance recommends m odeling an entire year or at a minimum several days in each quarter of a year to adequately represen t the range of meteorological
conditions that contribute to elevated levels of fine particulate matter. The year 2002 was selected by VISTAS as the mode ling year for this demonstrati on. Meteorologi cal inputs were developed for 2002 using the meteor ological model. Emission inve ntories were also developed for 2002 and processed through the emissions model. These inputs were used in the air quality model to predict fine particle mass and visibi lity. The model results for 2002 were compared with observed meteorological and air quality data to evaluate model performance. Several configurations of the meteorological and ai r quality model were evaluated to select a configuration that gave the best overa ll performance for the VISTAS region.

Once model performance was deemed adequate, th e current and future 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.

Kentucky Regional Haze SIP 22The 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 scie ntifically appropriate for the intended application and be freely accessible to all stakeholders. “S cientifically appropriate” means that the models address important physical and chemical phenomen a in sufficient detail, using peer-reviewed methods. “Freely accessible” means that model fo rmulations and coding are freely available for
review and that the models are available to stak eholders, and their consul tants, for execution and
verification at no or low cost.

The following sections outline the criteria for sel ecting a modeling system that is both defensible and capable of meeting the study's goals. 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 ca ndidate for use in a re gional haze SIP, a State needs to show that it meets the same several ge neral criteria as a model for an attainment demonstration for a 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 evalua tions have shown the model is not biased
toward underestimates or overestimates

• 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 th e model cannot otherwise be proprietary.

Overview of CMAQ

The photochemical model selected for this study was CMAQ version 4.5, with (SOAmods), secondary organic aerosol (SOA) modifications that were devel oped by the VISTAS contractor in order to improve organic carbon under predic tion performance in the VISTAS modeling (See Appendix C for published article re garding the SOA modifications). For more than a decade, the USEPA has been developing the Models-3 CMAQ modeling system with the overarching aim of producing a ‘One-Atmosphere’ air quality modeling system capable of addressing ozone, fine particulate matter, visibility and acid deposition within a co mmon platform. The original justification for the Models-3 development emerged from the challenges posed by the 1990

Kentucky Regional Haze SIP 23CAAA and the USEPA’s desire to develop an advanced modeling fr amework for ‘holistic’ environmental modeling utilizing state-of-science representations of atmospheric processes 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 the initial operating
science model under the Models-3 framework. The most recent rendit ion is CMAQ version 4.5, which was released in September 2005. Please note that VISTAS used a previous version of the CMAQ model (CMAQv4.4_SOAmods) in ea rlier VISTAS sensitivity work.

An advantage of choosing CMAQ as the atmosphe ric model is the ability to do one-atmospheric modeling. The same model configuration is be ing applied for the ozone and PM2.5 attainment demonstrations SIPs, as well as the regional haze SIP. A number of features in CMAQ’s
theoretical formulation and tec hnical implementation make the m odel well suited for annual PM modeling.

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

5.2.2 Selection of Meteorological Model

Criteria

Meteorological models, either through objective, diagnostic, or prognostic analysis, extend
available information about the state of the at mosphere to the grid upon which photochemical grid modeling is to be carried out. The criteria for selecting a meteorological model are based on both the models ability to accurately replicate important meteorological phenomena in the region of study, and the model's ability to interface with th e rest of the modeling systems -- particularly the photochemical grid m odel. With these issues in mind, th e following criteria were established for the meteorological model to be used in this study:

• Non-Hydrostatic Formulation

• Reasonably current, peer reviewed formulation

• 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 re gional air quality model applications. The basic model has been under continuous development, improvement, testing and open peer-review for

Kentucky Regional Haze SIP 24more than 20 years and has been used worldw ide by hundreds of scientists for a variety of
mesoscale studies.

MM5 uses a terrain-following non-dimensionalized pressure, or "sigma", vertical coordinate similar to that used in many ope rational and research models. In the non-hydrostatic MM5, the sigma levels are defined according to the initial hydrostatically-balanced re ference state so that the sigma levels are also time-invariant. Th e gridded meteorological fields produced by MM5 are directly compatible with the input requirements of ‘one atmo sphere’ air-quality models using this coordinate. MM5 fields can be easily used in other regional air quality models with different coordinate systems by performing a vertical in terpolation, followed by a mass-conservation readjustment.

Distinct planetary boundary layer parameterizati ons are available for air-quality applications, both of which represent sub-grid -scale turbulent fluxes of heat , moisture and momentum. One scheme uses a first-order eddy diffusivity formul ation for stable and neutral environments and a
modified first-order scheme fo r unstable regimes. The other scheme uses a prognostic equation for the second-order turbulent kinetic energy, while diagnosing the other key boundary layer

terms.

Initial and lateral boundary conditio ns are specified for real-dat a cases from mesoscale threedimensional analyses performed at 12-hour interv als on the outermost grid mesh selected by the user. Surface fields are analyzed at three-hour intervals. A Cre ssman-based techni que is used to analyze standard surface and radiosonde obse rvations, using the National Meteorological Center's spectral analysis, as a first guess. The lateral boundary data are introduced using a relaxation technique applied in the outermost five rows and columns of the coarsest grid domain.

The MM5 modeling system in regulatory air qualit y application studies ha s been widely reported in the literature (e.g., Emery et al., 1999; Tesche et al., 2000, 2003) and many have involved comparisons with other prognosti c models such as the Regiona l Atmospheric Modeling System ( RAMS ) and the Systems Application International Mesoscale Model. The MM5 enjoys a far richer application history in re gulatory modeling studies compared with RAMS or other models. Furthermore, in evaluations of 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 i nputs than alternative models.

The configuration used for this modeling demonstra tion, as well as a more detailed description of the MM5 model, can be found in the mete orological 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 the phot ochemical grid model being used. The following
list includes clarification of this criterion and add itional desirable criteria for effective use of the system.

Kentucky Regional Haze SIP 25• 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 proposed 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 Protot ype was originally de veloped at the Microcomputing 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 ‘first principles’. This means that, with the exception of mobile and biogenic sources, its purpose 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 emi ssions based on input mobile-source activity data, emission factors and outputs from tr ansportation travel-demand models.

SMOKE was originally designed to allow emissions data processing methods to utilize emergent high-performance-computing as applied to sparse-matrix algor ithms. Indeed, SMOKE is the fastest emissions processing tool currently availa ble to the air quality modeling community. The sparse matrix approach 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 us ed in previous systems. The processing is flexible because the processing steps of temporal projection, controls, chemical speciation, temporal allocation, and spatial allocation have been separate d into independent operations wherever 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 supports a variet y of input formats from other emissions processing systems and models. It supports bot h gridded and county total land use scheme for biogenic emissions modeling. SMOKE can accommodate emissions files from up to 10 countries and any pollutant can be processed by th e system. For additional information about the SMOKE model please refer to Modeling Protocol (Appendix C).

Kentucky Regional Haze SIP 265.3 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 changes in air quality in re sponse 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 Quality Goal s for Ozone, PM2.5, and Regional Haze identifies specific goals to consider when selecting one or more episodes for use in dem onstrating reasonab le progress in attaining 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 concen trations 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 da ys so that the modeled atta inment test applied at each monitor violating the NAAQS is based on multiple days.

For regional haze modeling, the guidance goes furthe r by suggesting that th e preferred approach is to model a full, representative year. Moreover, the required re lative reduction factor values
should be based on model result s averaged over the 20% worst and 20% best visibility days determined for each Class I area based on monitoring data from the 2000 – 2004 baseline period.

The USEPA also lists several other considerations to bear in mind when choosing potential regional haze episodes including: (a) choose pe riods which have already been modeled, (b) choose periods which are drawn from the years u pon which the current design values are based, (c) include weekend days among those chosen, a nd (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 explicit ly recommended in its 2007 modeling guidance to use 2002 as the baseline inventory year.

VISTAS adopted a logical, st epwise approach in implem enting the USEPA’s 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 meteor ological characteristics and data sets in the VISTAS region directly relevant 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 i nputs and model performance evaluation. VISTAS used CART analyses to evalua te visibility conditions for 2000, 2001, and 2002,

Kentucky Regional Haze SIP 27the candidate modeling years. The year 2002 was found to be representative of conditions in the other two years. Subsequent ly, these analyses were repeated with the meteorological 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 a nd PM2.5 mass in 2002 were repr esentative of the five-year baseline period for the VISTAS Class I areas. Th is analysis is discussed in more detail in the project report in Appendix B.

Data Availability: In parallel with the CART analyses , episode characterization analyses, collaborative investigations by VISTAS st ates (e.g., NCDAQ, Geor gia Department of Natural Resources, Florida Department of E nvironmental Protection) intensively studied the availability of PM2.5, me teorological, and emissions data and represen tativeness 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 advantage of the 2002 inventory.

Years to be used by other RPOs: VISTAS also considered what years other RPO would be modeling, and several had already 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 January through 31 December) as the most current, representative, and pragmatic choice for VIST AS regional haze modeling. All of the USEPA criteria for regional haze episode selection were directly considered in this process together with many other considerations (e.g., timing of new emissions or aero metric data de liveries 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 recommends a 12-km modeling grid resolution for PM2.5 modeling while a 36-km grid is considered acceptable for regional haze. For the VISTAS modeling, a coarse 36-km grid re solution was used for modeling th e entire United States and a finer 12-km grid was used to model the eastern United States.

The CMAQ model was run in one-way nested grid mode. This allowed the larger outer domains
to feed concentration data to the inner nested domain. Two-wa y nesting was not considered due to numerical and computational uncerta inty associated with the technique.

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

Kentucky Regional Haze SIP 28The coarse 36-km horizontal grid domain covers the continental United States. This domain was used as the outer grid domain for MM5 modeling with the CM AQ 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.

Kentucky Regional Haze SIP 29Figure 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.

To achieve finer spatial resolution in the VIST AS states, a one-way nested high resolution (12-km grid resolution) 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 results on which the reasonable progress go als will be assessed.

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

Kentucky Regional Haze SIP 305.4.2 Vertical Modeling Domain

The CMAQ vertical structure is primarily de fined by the vertical 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 effects of layer averag ing were evaluated in conjunction with the VISTAS modeling effort and was found to have a relatively minor effect on the model performance metrics when both the 34 layer an d a 19 layer CMAQ models were compared to ambient monitoring data.



### 6.0 MODEL PERFORMA NCE EVALUATION

The initial modeling effort focused on evalua ting previous regional air quality modeling applications and testing candi date model configurations 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 configuration for the a nnual emissions and air quality modeling, which is included as part of the VIST AS Emissions and Air Quality M odeling Protocol. The evaluation of the meteorological modeling configuration ca n be found in Appendix F.1, with a summary of the final meteorological and air quality mode ling configuration in the modeling protocol contained in Appendix E and Appendix C, respectively.

Air quality model performance for the 2002 modeling year was initially test ed in 2004 using an early version of the VISTAS emi ssions inventory. In keeping w ith the one-atmosphere objective of the CMAQ modeling platform, model perfor mance was evaluated based on measured ozone, fine particles, and acid deposition in the Ai r Quality System (AQS), IMPROVE, Speciated Trends Network (STN), Southeastern Aeroso l Research and Characterization (SEARCH), National Acid Deposition Program (NADP) a nd Clean Air Status and Trends Network (CASTNet) monitoring networks (Figure 6.0-1). A detailed examination of the results was published in 2005 in the Journal of Air and Wast e Management (see Appendix B.3) as well as being summarized in Appendix B.1.

Kentucky Regional Haze SIP 31Figure 6.0-1: Monitoring Networks used for VISTAS 2002 model performance evaluation, and their location within the VISTAS 12km domain.

6.1 Modeling Performance Goals, and Criteria

In 2004, VISTAS established model performance goals and criteria for components of fine particle mass (Table 6.1-1) based on previous m odel performance for ozone and fine particles. The USEPA’s 2007 modeling guidance for fine par ticulate matter at the time noted that PM models might not be able to achieve the same le vel of performance as ozone models. VISTAS’s
evaluation considered seve ral statistical performa nce measures and displays. Fractional bias and
mean fractional error were selected as the most appropriate metrics to summarize model performance; other metrics were also calculated and are included for IMPROVE monitors in the full model performance evaluation (Appendix F.2).

Kentucky Regional Haze SIP 32Table 6.1-1: Established model performance goal s and criteria for the component species of fine particle mass.

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.

Several graphic displays of model performance were prepared including:
1. Scatter plots of predicted and observed concentrations and deposition by species, monitoring network, and month
2. Time series plots of predicted and observe d concentrations 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 plots”) that display fractional bias or error as a function of concentration by species, region, monitoring network, and month

The “soccer plots” and “bugle plot s” are relatively new tools in model performance evaluations, and have recently been included as model perf ormance evaluation displays in the USEPA’s 2007 modeling guidance for ozone, PM2.5, and regional haze. Both “soccer plots” and “bugle plots” allow for convenient way to examine model performa nce with respect to se t goals and criteria. The bugle plots have the added benefit of adjus ting the goals and criteria to consider the concentration of the species. Analysis of “bugle plots” generally sugges ts that greater emphasis should be placed on performance of those componen ts with the greatest contribution to PM mass and visibility impairment (e.g. sulfate and organi c carbon) and that greater bias and error could be accepted for components with smaller contribu tions 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 monitors and model output data from the VISTAS 2002 actual annual air quality modeling. Focus is lim ited to the IMPROVE monitoring network as
these sites are the locations used in projecting visibility improvement goals in the Class I areas.

The evaluation will primarily focus on the air quality model’s performance with respect to individual components of PM2.5, as good model performance of the component species will

Kentucky Regional Haze SIP 33dictate good model performance of total or reco nstituted PM2.5. Model performance of the total PM2.5 and the resulting total light extinction will also be provi ded as a means to discuss the overall model performance for this SIP.

In the analyses, mean fractional bias (error) is us ed 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% a nd 60%, respectively, for mean fractional bias and 50% and 75%, respectively, for mean fractional error. Plots have been developed for the average monthly concentrations and the performan ce statistics for all of the most significant light scattering component species (Sul fate, Nitrate, and Organic Car bon) for the 20% best days and 20% worst days. Plots for individual IMPROVE monitors associated with Kentucky’s Class I area are included in Appendix F.2.

The soccer plots of monthly concentrations (Figures . 6.2-1 and 6.2-2) show th at values for nitrate generally fall outside of criteria performance thresholds (red box). Sulf ates 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 thre sholds. 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 summe r, when observed nitrate is quite low and predicted nitrate is even lower. When concentrat ion is factored into performance criteria, nitrate performance improves with respect to mean fractional bias and mean fractional error (Figures 6.2-4 and 6.2-5).

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 fo r all VISTAS sites.

Kentucky Regional Haze SIP 34All 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 plot depicting both the mean fractional error and fractional bias for component co ncentration for all VISTAS sites.

All Sites CMAQ 12km - 2002 Spring 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 Ammon. Organics EC Soils PM2.5
PM10 CM Criteria
Goal All Sites CMAQ 12km - 2002 Summer 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 Ammon. Organics EC Soils PM2.5
PM10 CM Criteria
Goal

All Sites CMAQ 12km - 2002 Fall 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 Ammon. Organics EC Soils PM2.5
PM10 CM Criteria
Goal All Sites CMAQ 12km - 2002 Winter 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 Ammon. Organics EC Soils PM2.5
PM10 CM Criteria
Goal

Figure 6.2-3: Seasonal soccer plots fo r all VISTAS IMPROVE monitors.

Kentucky Regional Haze SIP 35All 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 fraction bias for particulate matter and its component concentrations for all VISTAS sites.

igure 6.2-5: Bugle plot of me an fraction error for particulate matte r and its component

dditionally, performance assessed at the “one atmosphere” level was also deemed acceptable 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

F species for all VISTAS sites.

A for ozone and particulate matter at various m onitoring sites (STN, FRM, CASTNet, etc.). Overall, VISTAS found the modeling results to be representative and acceptable for use in modeling projection for ozone, particul ate matter, and regional haze.

Kentucky Regional Haze SIP 366.3 Kentucky’s Class I Area Performance

The following section provides stack bar char ts comparing observed PM2.5 composition and modeled PM2.5 composition. The charts have been sp lit into two charts, with the first displaying the 20% best days followed by the chart for the 20% worst days. Stacked bar charts have been developed for the IMPROVE monitoring site re levant to Kentucky: Mammoth Cave National Park (for the location of this area see Figure 1.4-1).

The stacked bar chart allows a side by side comparison of the each day’s observed and modeled compositional and 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 observe d (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 the observed. The total height of the bar provides the total reconstructed particulate matte r light extinction value.

Kentucky Regional Haze SIP 37Best 20% Obs (left) vs 2002gt2a (right) at MACA1

0 20 40 60 80 100 120 140 160

14 23 32 35 44 62 68 83 89 113 116 227 266 269 278 281 287 302 308 323 338 347 356 _ _ Avg
Julian Day in Best 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Worst 20% Obs (left) vs 2002gt2a (right) at MACA1

0 100 200 300 400 500 600 700 800

86 149 152 155 170 173 185 191 197 203 206 212 215 218 224 233 236 239 245 251 254 260 263 341 _ Avg
Julian Day in Worst 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 6.3-1: Stacked bar chart for Mammoth Cave 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.

Kentucky Regional Haze SIP 38A cursory view of the stacked bars charts reiterates that sulfates are a large contributor to light extinction in the Kentucky Class I area on both 20% best days and 20% worst days. The bar charts also suggest that organi c carbon and nitrates are important on the 20% best days at the IMPROVE site at Mammoth Cave for Kentucky

. The bar charts for the 20% best days indicate an over prediction of the nitrate and a slight under prediction of the sulfate on many of the 20% best days.

Comparing the 20% best day charts to the 20% wo rst days charts, one notices that the various components of particle pollution play a more promin ent role in the 20% best days than with the 20% worst days. Also, the specie s make up on the 20% best days varies more widely compared to the 20% worst days. This s uggests accurately modeling each species is especially important on the 20% best days.

With the bar chart for the 20% worst days, you can see model performance does improve across the sites. Light extinction due to sulfate predicti on is better, but still falls short on some days. Light extinction due to organic ca rbon also becomes more important to total light extinction.
Much like the sulfate com ponent, the organic carbon component accuracy has improved performance over the 20% best day series, thought some days are st ill under predic ted. Overall, the KYDAQ found model performance to fall within acceptable limits for model performance. The KYDAQ further asserts the one atmosphere modeling performed by the VISTAS contractors is representative of conditions in the southeastern states and is applicable for use in attainment
demonstrations.



### 7.0 LONG-TERM STRATEGY FOR KENTUCKY’S CLASS I AREA

As stated in Section 1.3, the regional haze rule requires a State to establish reasonable progress goals for Class I areas within the State, expre ssed in deciviews, that provide for reasonable progress toward achieving natural visibility by 2064. This first set of reas onable progress goals must be met through measures c ontained in the State’s long-ter m strategy covering the period from the baseline through 2018. Stat es are also to evaluate the e ffects of emissions from their
State on Class I areas in other states. This se ction discusses developm ent of Kentucky’s longterm strategy.

7.1 Overview of the Long-Term Strategy Development Process

The process KYDAQ used to develo p its long-term strategy was to address the following set of questions:

a. Assuming implementation of existing federa l and state air regulatory requirements, how much visibility improvement would be expected at Kentucky’s Class I area between now and 2018 compared to the area’s uniform rate of progress?

b. What additional emission controls represent BART in Kentucky?

Kentucky Regional Haze SIP 39c. If additional emission reductions were needed, from what pollutants and source categories would the greatest visibility bene fits be realized between the baseline and
2018?

d. Based on the pollutants identified in (c) above, determine what are the geographic locations (i.e., area of influence) for the Class I area where these emissions having the greatest impact on visibility are found?

e. What types of emissions sour ces do we find in those geog raphic locations (i.e., area of influence)?

f. Which specific individual sources in those geographic locations (i.e., area of influence) have the greatest visibility impacts at a Class I area?

g. What additional emission controls represen t reasonable control measures for those
sources identifie d in (f) above?

h. Given the additional emission reductions from BART and re asonable control measures identified, how much visibility im provement, compared to the glidepath, is expected at the Class I area in Ke ntucky between the baseline and 2018?

7.2 Expected Visibility Results in 2018 for Kentucky’s Class I Area under existing and planned emissions controls

There are significant control progr ams being implemented between the baseline period and 2018. These programs are describe d in more detail below.

7.2.1 Federal and State Control Requirements

NOx SIP Call or state equivalent. Phase I of the NO x SIP call applies to certain EGUs and large non-EGUs, including large industria l boilers and turbines, and ce ment kilns. Those states affected by the NOx SIP call in the VISTAS regi on have developed rules for the control of NOx emissions that have been approved by the USEP A. The NOx SIP Call has resulted in a 66 percent reduction in summertime NOx emissions from large stationary combustion sources in Kentucky.

CAIR

. CAIR will permanently cap emissions of SO2 and NOx from EGUs in the eastern United States by 2015. When fully implemented, CAIR will reduce SO2 emissions from EGUs in these states by more than 70 percent, and NOx emissions by more than 60 percent, from 2003 levels.

One-hour ozone SIPs (A tlanta / Birmingham / Northern Kentucky). New SIPs have been
submitted to the USEPA to demonstrate attainme nt of the one-hour ozone NAAQS. These SIPs require NOx reductions from specifi c coal fired power plants and address transportation plans in
these cities.

Kentucky Regional Haze SIP 40Heavy Duty Diesel (2007) Engine Standard (for on-road trucks and buses). The USEPA set a PM emissions standard for new heavy-duty engi nes of 0.01 grams per brake-horsepower-hour (g/bhp-hr), to take full effect for diesel engine s in the 2007 model year. This rule also includes standards for NOx and non-methane hydrocarbon s (NMHC) of 0.20 g/bhp-hr and 0.14 g/ bhp-hr, respectively. These NOx and NMHC standards will be phased in t ogether between 2007 and 2010, for diesel engines. Sulfur in diesel fuel must be lowered to enable modern pollutioncontrol technology to be effective on these truc ks and buses. The USEPA requires a 97 percent reduction in the sulfur content of highway diesel fuel from its previous level of 500 parts per million (low sulfur diesel, or LSD) to 15 parts per million (ultra-low sulfur diesel, or ULSD).

Tier 2 Tailpipe (O n-road vehicles) . The USEPA mobile source ru les include the Tier 2 fleet averaging program, modeled afte r the California LEV II standa rds. Manufacturers can produce vehicles with emissions ranging fr om relatively dirty to zero emi ssions, but the mix of vehicles a manufacturer sells each year must have av erage NOx emissions below a specified value

. Tier 2 standards became effective in the 2005 model year.

Large Spark Ignition and Recreational Vehicle Rule . The USEPA has adopted new standards for emissions of NOx, hydrocarbons, and carbon monoxi de from several groups of previously unregulated nonroad engines. Included in thes e are large industrial sp ark-ignition engines and recreational vehicles. Nonroad spark-ignition engines are t hose powered by gasoline, liquid propane gas, or compressed natural gas rated over 19 kilowatts (kW) (25 horsepower). These engines are used in commercia l and industrial applications, including forklifts, electric generators, airport baggage tr ansport vehicles, and a variet y of farm and construction applications. Nonroad recreational vehicles incl ude snowmobiles, off-hi ghway motorcycles, and
all-terrain-vehicles. These rules were initially effective in 2004 and will be fully phased-in by
2012.

Nonroad Diesel Rule . This rule sets standards that will reduce emissions by more than 90 percent from nonroad diesel equipment, and re duce 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 locomotiv es and marine vessels in 2012.

Industrial Boiler/Proces s Heater/RICE MACTs . The USEPA issued final rules to substantially reduce emissions of toxic air pollutants from i ndustrial, commercial a nd institutional boilers, process heaters and from stationary reciproca ting internal combustion engines (RICE). These
rules reduce emissions of a number of toxic air pollutants, including hydrogen chloride, manganese, lead, arsenic and merc ury by 2009. This rule also redu ces emissions of SO2 and PM in conjunction with the toxic air pollutant reductions. The applied Maximum Achievable Control Technology (MACT) control e fficiencies were 4 percent for SO 2 and 40 percent for
PM10 and PM2.5. The USEPA’s industrial boiler MACT rules were vacated on June 8, 2007.
The VISTAS states decided to leave these controls in the modeling since it is believed that by 2018 the USEPA will have re-promulgated a boiler MA CT rule or states will have addressed the issue through state rule making.

Kentucky Regional Haze SIP 41Combustion Turbine MACT . The projection inventor ies do not include the NOx co-benefit effects of the MACT regulations for Gas Turbines or stati onary Reciprocat ing Internal Combustion Engines, which the 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 implemen ted. Reductions occurring befo re 2002 were assumed to be accounted for in the 2002 base year inventory.

Consent Agreements. Under a settlement agreement, by 2008, Tampa Electric will install permanent emissions-control equipment to meet st ringent pollution limits; implement a series of interim pollution-reduction measur es to reduce emissions while the permanent controls are designed and installed; and retire pollution emissi on allowances that Tampa Electric or others could use, or sell to others, to emit additional NOx, SO2, and PM.

Virginia Electric and Power Co. (VEPCO) agreed to spend $1.2 billion by 2013 to eliminate 237,000 tons of SO2 and NOx emissions each year from eight coal-fired electricity generating plants in Virginia and West Virginia.

A 2002 agreement calls for Gulf Power to upgrad e its operation to cut NOx emission rates by 61 percent at its Crist generating plant by 2007, w ith major reductions begi nning in early 2005. The Crist plant is a significant source of NOx emissions in the Pensacola area.

East Kentucky Power Cooperative (EKPC), a coal-f ired electric utility, has agreed to spend approximately $650 million on pollution contro ls and pay a $750,000 penalty to resolve
violations of the Clean Air Act at its three plants in Kentucky known as Spurlock, Dale, and Cooper. The agreement will reduce harmful ai r pollutants by more than 60,000 tons per year according to EPA. The company will install st ate-of-the-art pollution control equipment to reduce emissions of pollutants that cause acid rain and smog. The controls will reduce annual emissions of smog-forming nitrogen oxides by a pproximately 8,000 tons and sulfur dioxide by more than 54,000 tons per year from EKPC’s S purlock, Dale, and Cooper plants when the controls are fully implemented. This consent decree will facilitate that SO2 scrubbers are installed for EKPC’s Spurlock Units 1 and 2 and Cooper Units 1 and 2 for BART. Per IPM, SO2 scrubbers for EKPC Spurlock Units 1 and 2 and Cooper Unit 2 have been included in this SIP’s regional haze modeling results for 2018.

American Electric Power (AEP) has agreed to cut 813,000 tons of air pollutants annually (654,000 tons of SO2 and 159,000 tons of NOx) at an estimated cost of mo re than $4.6 billion, pay a $15 million penalty, and spend $60 million on proj ects to mitigate the a dverse effects of its past excess emissions. The agreement imposes caps on emissions of pollutants from 16 plants
located in five states. Th e facilities are located in Moundsville (2 faciliti es), St. Albans, Glasgow, and New Haven (2 facilities), West Virginia; L ouisa, Kentucky; Glen L yn and Carbo, Virginia; Brilliant, Conesville, Cheshire , Lockburne, and Beverly, Ohio; and Rockport and Lawrenceburg, Indiana. AEP will install pollution control eq uipment to reduce and cap sulfur dioxide and nitrogen oxide emissions by more than 813,000 tons per year when fully implemented. By installing these pollution control measures, the plants will emit 79 percent less sulfur dioxide and

Kentucky Regional Haze SIP 4269 percent less nitrogen oxides, as compared to 2006 emissions. This consent decree will
facilitate that a SO2 scrubber is installed fo r Kentucky’s AEP Big Sandy Unit 2 for BART. Per IPM, a SO2 scrubber for AEP Big Sandy Unit 2 ha s been included in this SIP’s regional haze modeling results.

7.2.2 Additional State programs to reduce emissions

In addition to accounting for specific emission reductions due to ongoing air pollution programs as required under the regional haze rule 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)]. These state measures are discussed in more detail in Appendix H.

7.2.3 Projected 2009 and 2018 Emissions Inventories

The inventories for 2009 and 2018 account for pos t-2002 emission reductions from promulgated and proposed federal, state, local, and site-s pecific control programs as of July 1, 2004. In general, emissions inventor ies were developed for 2009 and 2018 using current control information in Kentucky.

For EGUs, IPM results were adjusted based on state and loca l air agencies knowledge of planned emission controls at specific EGUs. These updates are documented in the MACTEC emissions inventory report “Documentation of the 2002 Base Year and 2009 and 2018 Projection Year
Emission Inventories for VISTAS” dated February 2007 (Appendix D).

For non-EGUs, VISTAS used recently updated growth and control data consistent with the data used in the USEPA’s CAIR analyses (Clean Air Interstate Rule Emissions Inventory Technical Support Document, March 2005) supplemented by state and local air 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 estimates, as well as controls used to project emissions for the USEPA’s Heavy Duty Diesel rule making and for the CAIR rulemaking.

Mobile source controls include d local controls underlying the 2002 baseline inventory (vehicle emission inspection, Stage II vapor recovery, an ti-tampering, etc.) with changes based on specific State input. The future year invent ories were developed by running the MOBILE6.2 model for each year modeled. The future year emissions for the off-road mobile sources
included in the USEPA NONROAD 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 from the USEPA' s CAIR Technical Support Document, and straight-line projections were us ed to estimate 2009 and 2018 levels.

The following bar charts show expected decrea ses in emissions of SO2 and NOx across the VISTAS states from 2002 through 2018. (Similar char ts for other visibility impairing pollutants are contained in Appendix H). Note that for SO 2 emissions in particular , which are the largest

Kentucky Regional Haze SIP 43contributors to haze, emissions from electric generating facilities are expected to decrease dramatically (70 percent) be tween 2002 and 2018. However, ev en after implementation of CAIR, EGU emissions are projected to remain the largest contributor to haze, comprising more than half of remaining SO2 emissions in most states.

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

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Figure 7.2.3-2. Annual NOx emissions for 2002, 2009, and 2018 in the VISTAS states

Kentucky Regional Haze SIP 44Summary of Emission Inventories for 2002, 2009, and 2018

Tables 7.2.3-1, 7.2.3-2, and 7.2.3-3 are summ aries of the 2002, 2009, and 2018 emission inventory, respectively. The complete inventor y and discussion of the methodology is contained in Appendix D.

Table 7.2.3-1. 2002 Emissions Inventory Summa ry for Kentucky (tons per year).
VOC NOx PM2.5 PM10 NH3 SO2
Point 46,315 240,362 14,219 21,421 995 529,182
Area 98,713 40,966 51,763 240,226 51,246 41,941
On-Road Mobile 103,503 156,417 2,697 3,723 5,055 6,308
Non-Road Mobile 44,805 104,571 6,046 6,425 31 14,043 Biogenics 630,506 21,090 0 0 0 0
TOTAL 923,842 563,406 74,725 271,795 57,327 591,474

Table 7.2.3-2. 2009 Emissions Invent ory Summary for Kentucky.
VOC NOx PM2.5 PM10 NH3 SO2
Point 49,154 129,778 15,966 23,637 1,160 326,611
Area 97,379 43,548 52,553 248,844 53,115 43,222
On-Road Mobile 73,942 101,182 1,920 2,976 5,796 759
Non-Road Mobile 38,558 94,752 5,203 5,544 34 9,180 Biogenics 630,506 21,090 NA NA NA NA
TOTAL 889,539 390,350 75,642 281,001 60,105 379,772

Table 7.2.3-3. 2018 Emissions Invent ory Summary for Kentucky.
VOC NOx PM2.5 PM10 NH3 SO2
Point 57,287 105,411 18,172 26,848 1,377 266,745
Area 106,827 45,806 53,955 262,719 55,321 44,322
On-Road Mobile 47,066 52,263 1,272 2,580 7,811 763
Non-Road Mobile 30,920 79,392 4,256 4,556 40 8,592 Biogenics 630,506 21,090 NA NA NA NA
TOTAL 872,606 303,962 77,655 296,703 64,549 320,422

7.2.4 Model Results for the 2018 Inventory Compared to the Uniform Rate of Progress Glidepaths for Kentucky’s Class I Area

Using 2000 - 2004 IMPROVE monitoring data, the deci view values for the 20 percent best days in each year are averaged together, producing a si ngle average deciview value for the best days. Similarly, the deciview values for the 20 percent worst days in each year are averaged together, producing a single average deciview value for the worst days. Th e average values represent the current visibility conditions.

Kentucky Regional Haze SIP 45The predicted visibility improvement is calcula ted by comparing the 2002 typical year modeling results for the 12-km grid to the 2018 12-km m odeling results to devel op 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.

For the 20% worst days in Kentucky’s Class I area, Figure 7.2.4-1 graphically compares the visibility which would result with the area’s unif orm rate of progress (red line) to the predicted visibility from 2004 to 2018 due to modeled emissi on reductions expected by federal and state control programs (purple line) Similarly, fo r the 20% best days in the area, Figure 7.2.4-2 graphically compares visibility wi th no degradation over the first pl anning period (red line) to the predicted visibility from 2004 to 2018 due to modeled emission reductions expected by federal and state programs (purple line).



### Reasonable Progress Assessment Mammoth Cave - 20% Worst Days New IMPROVE Equation, 12 km grid

31.37 29.68

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YearHaziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction
Figure 7.2.4-1. Reasonable progress assessment co mpared to Uniform Rate of Progress for 20% worst days at Mammoth Cave National Park.

Kentucky Regional Haze SIP 46

### Reasonable Progress Assessment Mammoth Cave - 20% Best Days New IMPROVE Equation, 12 km grid

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YearHaziness Index (Deciviews)

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction
Figure 7.2.4-2 Reasonable progress assessment fo r 20% best days at Mammoth Cave NP.

Note that for Mammoth Cave, visibility improve ments on the 20 percent worst days are expected
to be better than the uni form rate of progress glidepath by 2018 based solely on reductions from existing and planned emissions controls

. For Mammoth Cave, a 4.73 dv improvement in visibility would meet uniform rate of progre ss in 2018; expected emi ssions reductions by 2018 are projected to achieve a 5.81 dv improvement. In fact, as illustrated in Figure 7.2.4-3, visibility improvements at all the VISTAS mo untain Class I areas and most of the coastal Class I areas are projected to be better than the uniform rate of progress. In Fi gure 7.2.4-3 the percentage of the target reduction achieved for Kentucky’s Class I area using the new IMPROVE equation is an estimated 123 percent. This means that the rate of improvement is 23 percent greater than the uniform rate of progress by 2018.

In addition to improving visibility on the 20 percent worst visibility days, st ates are also required to protect visibility on the 20 percent best days at the Class I areas. As illustrated in Figure 7.2.4-4, visibility on the 20 percent best days is projected to improve in 2018 at all VISTAS
Class I areas as a result of the 2018 emission reduc tions. In Figure 7.2.4-4 the percentage of the target achieved for the Kentucky’s Class I area is an about -6 pe rcent. Zero percent change would mean no change in visibility ; -6 percent means that visibility is better than no change, or a 6 percent improvement (values lowe r than current conditions).

Kentucky Regional Haze SIP 470% 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 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 Algorithm

Figure 7.2.4-3. Projected visibili ty improvement on 20 percent worst visibility days at VISTAS and neighboring Class I areas for 2018 (12 km grid)

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CMAQ Old IMPROVE Algorithm

Figure 7.2.4-4. Projected visibili ty improvement on 20 percen t best visibility days at VISTAS and neighboring Class I areas for 2018 (12 km grid)

Kentucky Regional Haze SIP 48The expected change in visibility at Mammoth Cave between 2000-2004 baseline conditions on the 20 percent worst days and 2018 projecti ons is illustrated in Figure 7.2.4- 5. In contrast, natural background visibility conditions for the 20 percent worst days are illustrated at Mammoth Cave in Figure 7.2.4-6. These images were generated usi ng WINHAZE, a photographic imaging t ool that accounts for the effect of concentrations of fine partic le components and relative humidity on visibility. These images try to illustrate that notable improvements in visibility are expected by 2018 and that significantly greater improvements are needed to reach natural background conditions.



### 20% Haziest Days at Mammoth Cave

Figure 7.2.4-5. Visibility improvement on 20 percent haziest days at Mammoth Cave National Park between 2000-2004 baseline condi tions (left) and 2018 projected visibility (right). Image generated using WinHaze.

Kentucky Regional Haze SIP 49

### Natural Background Visibility at Mammoth Cave

F igure 7.2.4-6. Projected visibility on 20 p ercent haziest days for natural background visibility conditions at Mammo th Cave National Park. Im age generated using WinHaze.

7.3 Relative Contribution from International Em issions to Visibility Impairment in 2018 at

VISTAS Class I areas

Emissions from Mexico, Canada, Central Amer ica, Asia, and Africa contribute to PM2.5 loadings and visibility impairment at Class I area s in the continental United States. To evaluate the relative contribution of international emissions to visibility at Class I areas in the southeastern United States, VISTAS used a co mbination of modeling results from the global three-dimensional chemical transport model (GEOS-Chem) and CMAQ. VISTAS used the GEOS-Chem global model to generate initia l and boundary conditions for the CMAQ modeling
domain. GEOS-Chem was run for the 2002 modeli ng year using a 4 x 5 degree horizontal grid resolution and a 3-hour temporal resolution. Because emissions were based on monthly averages, the model does not capture the episodic variability in emissions. The GEOS-Chem outputs were used to calculate initial and boundary conditions for the national CMAQ modeling domain. The national CMAQ domain included porti ons of Canada and Mexico, so emissions for
these countries were included within the natio nal CMAQ modeling domain or as part of the boundary conditions outside the national modeling domain, as appropriate.

Two complementary methods were used to calculate the impact of international emissions at
Class I areas. Because the international em issions inventory used in GEOS-CHEM did not distinguish between wildfires a nd anthropogenic fires, all intern ational fire emissions were assumed to be wildfire, and the contributions attr ibutable to international anthropogenic fires are underestimated in these projections.

Kentucky Regional Haze SIP 501) International emissions are represented by th e difference between two GEOS-Chem runs. In the first run United States anthropogenic emissions were removed, and in the second
run both United States and international an thropogenic emissions were removed. The difference represents intern ational anthropogenic emissions, in the absence of United States anthropogenic emissions (e.g. compar ed to 2064 levels). Harvard University provided GEOS-Chem results to VISTAS fo r 2002 international co ntribution on 4 x 5 degree grid. Concurrently Harvard modeling for the Electric Power Research Institute (EPRI) provided GEOS-Chem results for 200 1 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 2018 Base F emissi ons and boundary conditions from GEOSChem. In the first CMAQ run, all global natural and anthropogenic emissions in 2018 are active. In the second CMAQ run, only global (United States and international) natural emissions are active. Here the imp acts of international emissions are compared against 2018 conditions rather th an natural background conditions.

VISTAS has compared its results to results fr om other RPOs on the impact of international emissions and boundary conditions on visibility at Class I areas in 20 02; the results were
similar.

As illustrated in Figure 7.3-1 for annual averag e contributions to sulfate at VISTAS and neighboring Class I areas, the estimated internatio nal contributions are hi gher at Class I areas near the Canadian and Mexican borders and along the eastern coast. The estimated international contribution is higher using CM AQ than in the GEOS-Chem runs because the grid scale is finer (more accurate disper sion of emissions) and because the background atmosphere includes loadings from current Un ited States anthropogenic emissions (greater photochemical activity). Similar charts for nitrate and organic carbon mass, for impacts on 20 percent worst visibility days, and for impact s of international emissions on calculated light extinction are included in Appendix H.

Kentucky Regional Haze SIP 51

## Transboundary Man-Made Impacts

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

GEOS_4x5 GEOS_1x1 CMAQ

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

Kentucky Regional Haze SIP 52In Figure 7.3-2 CMAQ projections of contributions from international emissions to PM mass on 20 percent worst visibility days in 2002 at Mammot h Cave, Kentucky are compared to United States domestic contributions to PM comp onents at the site on those days.

MACA1 Domestic and International Aerosol Mass by Species 20% Worst Days (2002)

0 5 10 15 20 25 30 35 40DomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternational

3/27 5/29 6/1 6/4 6/19 6/22 7/4 7/10 7/16 7/22 7/25 7/31 8/3 8/6 8/12 8/21 8/24 8/27 9/2 9/8 9/11 9/17 9/20 12/7Mass (μg/m3)

Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt
Figure 7.3-2. PM component concentrations from US domestic sources on 20 percent worst visibility days in 2002 (left bars) and CMAQ-s imulated international contributions (right bars) at Mammoth Cave, Kentucky.

Although VISTAS assessed impacts from interna tional emissions at the Class I areas, these modeling runs were to provide information to the states to understand what the potential impacts may be at their Class I areas. The modeling sh owed that for Kentucky’ s Class I area Mammoth Cave the impacts from international emissions were 1.27 dv of the projec ted current conditions. Since good projected emissions for ar eas outside of the continental United States are not readily available, there is no real way to assess what th e impacts from internatio nal emissions would be for 2018, other than to assume th at it would be approximately sa me amount as for the baseline current conditions. Therefore, the KYDAQ will not be accounting fo r international emissions in setting the 2018 reasonable progre ss goals for its Class I areas.

Nevertheless, as the atmosphere becomes closer 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.

Kentucky Regional Haze SIP 537.4 Relative Contributions to Visibility Impa irment: Pollutants, Source Categories, and Geographic Areas

An important step toward identifying potential r easonable control measures is to identify the key pollutants contributing to visibility impairment at each Class I area. To understand the relative benefit of further reducing emissions from differ ent pollutants, source sectors, and geographic areas, VISTAS engaged the Geor gia 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 considered 30 percent reductions from specific pollutants, source categories, and geographic areas . As part of a separate effort, emissions sensitivities were repeated using a prelimin ary 2009 projection inventor y and two, month-long episodes from 2002: Jun 1 – Jul 10 and Nov 19 – Dec 19. The 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 state, other RPOs in the VISTAS 12 km grid, and Boundary Conditions from 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 of f-road plus area) in each VISTAS state and other RPOs. In the VISTAS states, thes e reductions were only applied to specific
counties that were of concern fo r 8-hour ozone nonattainment.

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

• NH3 from all sources in VISTAS and other RPOs

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

• Primary Carbon from all ground level sources in each VISTAS state and other RPOs. In the VISTAS states, these reductions 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

While the 2009 sensitivity analyses cannot be used to judge the ab solute contributions from each state or source sector, the results do indicate relative level of response among pollutants, sectors, and geographic areas. The KYDAQ decided to use the 2009 sensitivities to assess relative contribution to visibility impair ment from various source sect ors and believes this is an appropriate use of this data since the use of the emissions sensitivities is to qualitatively understand how reductions in emissions from various source sectors would impact visibility at
Class I areas.

Results are shown in Figures 7.4-1 below for the av erage of the 20 percent worst visibility days for Kentucky’s Class I area. Responses for 20 per cent worst days were calculated by averaging

Kentucky Regional Haze SIP 54the responses of the 20 percen t worst days that were modele d in the two episodes. For Kentucky’s site, responses on six of the 20 percent worst visibility days were included in these graphics.



# Mammoth_Cave, KY (Worst)

-20.00

-15.00

-10.00

-5.00

0.00

5.00

S

O

2 _EG U

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n d NOx _ P

o i n t NH3 VO C

s

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n d

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_Point PC_ F

i resΔBext(Mm-1) 

### Bio.

Antro.
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M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL 

# Mammoth_Cave, KY (Worst)

-20.00

-15.00

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S

O

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VA

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MS

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GA

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AL

Figure 7.4-1. CMAQ projections of visibili ty responses on 20 percent worst days at Mammoth Cave to 30 percent reductions from a 2009 inventory for visibility-reducing pollutants in different source cate gories and geographic areas.

As Figure 7.4-1 illustrates, the greatest visibility bene fits on the 20 percent worst days for the Kentucky’s Class I area ar e projected to result from furthe r reducing SO2 from EGUs. At the mountain Class I areas, benefits are projected from SO2 reductions from EGUs in several VISTAS states including Alabama, Georgia, Kentucky, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia. Contributions from other RPOs and SO2 coming from outside the boundary are also significant. The gr eatest benefit would likely be from further reductions from other VISTAS states, the MRPO, and outside the boundary. Additional, smaller benefits are projected from additional SO2 emission reductions from non-utility, industrial point

Kentucky Regional Haze SIP 55sources. The pattern of relativ e SO2 contributions from non-E GUs 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.

VOC emissions do contribute to vi sibility impairment, but as s hown in the char ts above, this contribution is from biogenic sources such as vegetative emissions. Controlling anthropogenic sources of VOC emissions has little if any visi bility benefit at the Class I areas. Reducing primary carbon from point sources, ground level sources or fires are projected to have small to no visibility benefit. This is consistent with the monitoring data that shows that most of measured organic carbon is secondary in origin and primary carbon is only a small fraction of the total measured carbon (Appendix B). Reducing car bon from fires was not found to be effective because there was little fire activity at these sites on the days modele d in the sensitivity analyses.

Note that these results from the emission sensiti vity runs are consistent with the conclusions
drawn from the 2000-2004 baseline m onitoring data (see Section 2.4). The results indicate that
sulfate is the dominant contribut or to visibility impairment on th e 20 percent worst days at all sites, and that ammonium nitr ate may be important for sites where the 20 percent worst days occur in the winter. KYDAQ concl udes that reducing SO2 emissions from EGUs in the Midwest RPO would have the greatest visibility benefit for Mammoth Cave. Contributions from other VISTAS states are also significant for this area . These results are consistent with the CMAQ model results indicating that cont ributions from international emi ssions to visibility impairment at VISTAS Class I areas are greater closer to the boundaries of the modeling domain (see summary in Section 7.3 and furthe r discussion in Appendix H).

7.5 What Control Determinations Represent Best Available Retrofit Technology for

Individual Sources?

Section 169A of the CAA directs States to assess certain large emission sources for additional controls in order to address visibility imp acts. States are directed to conduct BART determinations for such sources in specific source categories, and which contribute to visibility impairment in Class I areas. The 1999 regional haze rule includes the BART requirement, and
directs States to include BART in their re gional haze SIPs. On July 6, 2005, the USEPA published a revised final rule, including Appendix Y to 40 CFR Part 51, the Guidelines for BART Determinations Under the Regional Haze Rule (hereinafter referred to as the “BART Guidelines”) that provides dire ction to states on determining wh ich 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 pote ntial to emit 250 tons or more of a visibilityimpairing air pollutant, was put in place be tween August 7, 1962 and August 7, 1977, and whose operations fall within one or more of 26 speci fically listed source categories. Under the CAA, BART is required for any BART-eligible source th at a State determines ‘‘emits any air pollutant

Kentucky Regional Haze SIP 56which may reasonably be anticipated to cause or contribute to any im pairment of visibility 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 making BART determinations: (1) the costs of compliance, (2) the energy and non-air quality environmental imp acts of compliance, (3) any existing pollution control technology in use at the source, (4) the remaining usef ul life of the source, and (5) the degree of improvement in visibil ity which may reasonably be antic ipated to result from the use of such technology.

7.5.1 Kentucky BART-Eligible Sources

The following is a list of BART-eligible sour ces in Kentucky. See Appendix L for detailed information regarding each of the BART-eligible sources:

• American Electric Power Big Sandy Plant

• AK Steel Corporation. - Coke Mfg Plant

• AK Steel Corporation - Steel Plant

• Alcan Primary Products Corporation

• Arch Chemicals Inc.

• Calgon Carbon Corporation

• Century Aluminum

• Commonwealth Aluminum Lewisport LLC

• Duke Energy East Bend Station

• E.ON U.S Brown Station

• E.ON U.S Cane Run Station

• E.ON U.S Ghent Station

• E.ON U.S Mill Creek Station

• East Kentucky Power Coope rative Cooper Station

• East Kentucky Power Cooperative Spurlock Station

• Henderson Power and Light

• Marathon Petroleum Company Refinery

• Martin County Coal Corporation

• NewPage Corporation Wickliffe Paper Company

• Owensboro Municipal Utilities

• Pinnacle Processing Inc.

• TVA Paradise Plant

• Western Kentucky Energy Coleman Station

• Western Kentucky Energy Green Station

• Western Kentucky Energy Reid/Henderson Station

• Westlake Vinyls Inc.

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

Kentucky Regional Haze SIP 57• One or more emissions units at the facility f it 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 potential emissions considering enforceable limits from all emission units identified in the previous two bullets emission units were 250 tons or more per year of any of these visibility-impairing pollutants: SO2, NOx, and PM10.

The BART Guidelines recommend addressing thes e visibility-impairing pollutants: SO2, NOx, and particulate matter, and suggest that States use their best judg ment in determining whether to
address VOC or ammonia emissions. Th e KYDAQ addressed SO2 and NOx, and used particulate matter less than 10 microns in diameter (PM10) as an indicator for particulate matter to identify BART-eligible units, as the BART Guidelines recomme nd. As discusse d in detail in Appendix L, VISTAS modeling demonstrated th at VOCs and ammonia from point sources are not visibility-impairing pollutants. For this reason, the KYDAQ did not evaluate emissions of
VOCs and ammonia in BART determinations. Additional BART modeling information and BART related information regarding KYDAQ BART -eligible sources is available in Appendix
L.

The following five KYDAQ sources were determin ed not to be BART-eligible based on the BART methodology in the BART Guidelines. Thes e sources were discussed with EPA in the January 2006. Documentation and corresponden ce regarding these sources is available in Appendix L.

• Arkema (Formerly Atofina Chemicals)

• E.I. Dupont Inc.

• Cc Metals & Alloys Inc.

• ISP Chemicals Inc.

• Kingsford Manufacturing Co.

7.5.2 Determination of Sources Subject to BART in Kentucky

Under the BART Guidelines, the KYDAQ may consid er exempting some sources from BART if it is determined that they do not cause or contribute to visibility impairment in a Class I area. In
accordance with the BART guidelines, the KYDAQ chose to perf orm source-specific analyses to determine which sources cause or contribute to visibility impairment using the CALPUFF model. The CALPUFF modeling pr otocol used for determining wh ich facilities are subject to BART is included in Appendix L. In accordance with the Guidelines, a contribution threshold of 0.5 dv was used for determining which sources we re subject to BART. Detailed discussions
about how a threshold of 0.5 dv meets the USEPA’s BART guidelines can be found in Appendix L.

All of Kentucky’s twenty-six BART-eligib le sources had BART exemption-modeling demonstrations performed; nine of the sources that had Q/d of less th an 10 for actual 2002 SO2 emissions had exemption modeling performed th rough VISTAS contractor TRC and 17 sources performed the BART exemption modeling with thei r own contractor. Twenty-one of the twentyKentucky Regional Haze SIP 58six sources were able to demonstrate exemption from BART (< 0.5 dv) either with 12 km or 4 km modeling. Results of these demonstrations are summarized in Table 7.5.2-1 as follows. Additional details are available in Appendix L. Facilities found to be s ubject to BART were required to complete a BART determination analysis.

Table 7.5.2-1 represents the facili ties that were able to demonstr ate exemption from BART based on CALPUFF modeling conducted us ing the VISTAS modeling prot ocol and either the old IMPROVE equation or new IMPROVE equation. The KYDAQ is proposing to exempt the units
listed in Table 7.5.2-1. For further details abou t the BART exemption m odeling, please refer to Appendix L.

Table 7.5.2-1. Kentucky BART Exemption Modelin g Results for Sources Exempted From BART

Source Class I Area Impact (Change in DV) Modeling

Duke Energy East Bend Station Mammoth Cave (210 km) 0.242 12 km Max dv value Owensboro Municipal Utilities Mammoth Cave (93 km) 0.432 4km 8th Highest dv value Mingo (289 km) 0.053 4km 8th Highest dv value AK Steel Corporation - Steel Plant Dolly Sods (287 km) 0.346 4km Max dv value James River Face (295 km) 0.386 4km Max dv value Linville Gorge (293) 0.358 4km Max dv value Otter Creek (261) 0.442 4km Max dv value Great Smokey Mt. (308 km) 0.190 4km 8th Highest dv value AK Steel Corp. - Coke Mfg Plant Dolly Sods (282 km) 0.180 12 km Max dv value Great Smokey Mt. 1 (304 km) 0.262 12 km Max dv value James River Face (288 km) 0.182 12 km Max dv value Linville Gorge (287 km) 0.155 12 km Max dv value Otter Creek (257 km) 0.208 12 km Max dv value Martin County Coal Corporation Dolly Sods1 (305 km) 0.068 12 km Max dv value Great Smokey Mt. (226 km) 0.135 12 km Max dv value James River Face (265 km) 0.085 12 km Max dv value Joyce Kilmer-Slickrock (285 km) 0.121 12 km Max dv value Linville Gorge (207 km) 0.131 12 km Max dv value Otter Creek (280 km) 0.077 12 km Max dv value Shining Rock (259 km) 0.103 12 km Max dv value Pinnacle Processing Inc. Great Smokey Mt. (235 km) 0.108 12 km Max dv value James River Face (262) 0.070 12 km Max dv value Joyce Kilmer-Slickrock (294 km) 0.053 12 km Max dv value Linville Gorge (214 km) 0.077 12 km Max dv value Otter Creek (272 km) 0.015 12 km Max dv value Shining Rock (268 km) 0.021 12 km Max dv value
Arch Chemicals Inc. Mammoth Cave (83 km) 0.417 4km 8th Highest dv value Commonwealth Aluminum Lewisport LLC Mammot h Cave (94 km) 0.489 12 km Max dv value Mingo (304 km) 0.052 12 km Max dv value Henderson Power and Light Mammoth Cave (134 km) 0.302 12 km Max dv value Mingo (238 km) 0.084 12 km Max dv value

Kentucky Regional Haze SIP 59Table 7.5.2-1. Kentucky BART Exemption Modelin g Results for Sources Exempted From BART

Source Class I Area Impact (Change in DV) Modeling

Calgon Carbon Corporation Dolly Sods (284 km) 0.133 12 km Max dv value Great Smokey Mt. (290 km) 0.191 12 km Max dv value James River Face (282 km) 0.098 12 km Max dv value Linville Gorge (273 km) 0.103 12 km Max dv value Otter Creek (259 km) 0.157 12 km Max dv value Westlake Vinyls Inc. Mammoth Cave (183 km) 0.150 12 km Max dv value Mingo (156 km) 0.167 12 km Max dv value Sipsey1 (309 km) 0.084 12 km Max dv value Century Aluminum* Mammoth Cave (100 km)* 0.446 4km 8th Highest dv value Alcan Primary Products Corporation Mammoth Cave (118 km) 0.467 4km 8th Highest dv value Mingo (244 km) 0.184 4km 8th Highest dv value NewPage**Corporation Wickliffe PaperCo. Mammoth Cave (250 km)** 0.102 4km 8th Highest dv value Mingo (91 km) 0.291 4km 8th Highest dv value Sipsey (319 km) 0.060 4km 8th Highest dv value Western Kentucky Energy Coleman Station Mammot h Cave (91 km) 0.368 4km 8th Highest dv value Western Kentucky Energy Reid/Henderson
Station*** Mammoth Cave (118 km) 0.464*** ***4km 8th Highest dv
value Mingo (244 km) 0.072 4km 8th Highest dv value Western Kentucky Energy Green Station Mammoth Cave (118 km) 0.217 4km 8th Highest dv value Mingo (244 km) 0.039 4km 8th Highest dv value Marathon Petroleum Company Dolly Sods (287 km) 0.055 12 km Max dv value Great Smokey Mt. (293 km) 0.056 12 km Max dv value James River Face (287 km) 0.079 12 km Max dv value Linville Gorge (276 km) 0.041 12 km Max dv value Otter Creek (261 km) 0.086 12 km Max dv value
E. ON U.S. Brown Station Mammoth Cave (130) km) 0.410 4km 8th Highest dv value Great Smokey Mt. (250 km) 0.210 4km 8th Highest dv value Joyce Kilmer-Slickrock (265 km) 0.153 4km 8th Highest dv value
E. ON U.S. Cane Run Station Mammoth Cave (100) km) 0.378 4km 8th Highest dv value
E. ON U.S. Ghent Station Mammoth Cave (190) km) 0.292 4km 8th Highest dv value *Century Aluminum and **NewPage modeled below 0.5 dv with and without the new improve equation. ***Western Kentucky Energy Reid/Henderson Station BART exemption modeling was based exclusively on the use of the new improve equation. A copy of a request to EPA Region 4 requesting approval of the use of the new improve equation and a letter from EPA granting its approval of the request for these three sources are available in Appendix L.9. The modeled values in the above table for Century Aluminum and NewPage are for the old improve equation. The values for Western Kentucky Energy Reid/Henderson are for the new improve equation.

In Table 7.5.2-2, five of the twenty-six Kentucky BART-eligible sources that were unable to demonstrate exemption from BART based on CA LPUFF modeling conducted using the VISTAS BART Modeling Protocol are provided.

. The five sources found subject to BART are EGUs that are subject to BART because of their inorganic condensible particulate emissions (SO3, H2SO4). These subject BART sources were required to complete BART determination modeling, which included a five factor analysis, to dete rmine appropriate BART controls for PM.

Kentucky Regional Haze SIP 60Table 7.5.2-2. Kentucky BART Exemption Mo deling Results for BART-Subject Sources

Source Class I Areas Impact (Change in DV) Modeling

East Kentucky Power (EKPC) Cooperative Spurlock Station Mammoth Cave (251 km) 1.834 4km 8th Highest dv value

East Kentucky Power (EKPC)

Cooperative Cooper Station Mammoth Cave (130 km) 7.376 4km 8th Highest dv value

Great Smoky Mountains National Park (162 km) 6.763 4km 8th Highest dv value

Joyce Kilmer-Slickrock Wilderness (178 km) 4.974 4km 8th Highest dv value

Cohutta Wilderness Area (221 km) 3.363 4km 8th Highest dv value
Shinning Rock (233 km) 2.022 4km 8th Highest dv value
Linville Gorge Wilderness Area (267 km) 1.885 4km 8th Highest dv value

TVA Paradise Fossil Steam Plant Mammoth Cave (63 km) 3.93 4km 8th Highest dv value
Mingo (283 km) 0.865 4km 8th Highest dv value

American Electric Power (AEP) Big Sandy Plant Dolly Sods (291 km) 1.027 4km 8th Highest dv value

James River Face (279 km) 1.052 4km 8th Highest dv value

Linville Gorge (256 km) 0.835 4km 8th Highest dv value

Otter Creek (266 km) 1.285 4km 8th Highest dv value

E. ON U.S. Mill Creek Station Mammoth Cave (90 km) 2.265 4km 8th Highest dv value

Fourteen of Kentucky’s twenty-six BART-eligible sources are EGUs that are subject to CAIR.

The USEPA has determined that, as a whole, the CAIR cap-and-trade program improves visibility more than implementing BART for indi vidual sources in states affected by CAIR. A State that opts to particip ate in the CAIR program unde r 40 CFR 96.201-.224 (Subpart AAA through EEE) need not require affected BART-eligib le EGUs to install, operate, and maintain
BART for SO2 or NOx emissions. Given that most BART-eligible units have existing or are installing scrubbers and NOx controls, and since Kentucky is participating in CAIR and accepts the USEPA’s overall finding that CAIR “subst itutes” for BART for NOx and SO2, Kentucky’s EGUs were allowed to submit BART exempti on modeling demonstrations for PM emissions only. Nine of the fourteen Kentuc ky EGUs demonstrated that they do not contribute to visibility impairment in any Class I area.

Kentucky Regional Haze SIP 61In total twenty-one of Kentucky’s twenty-six BA RT-eligible sources were able to demonstrate that they did not cause or contribute to visibi lity impairment in any Class I area within 300 km of
the source.

7.5.3 Determination of BART Requi rements for Subject-to-BART Sources

Table 7.5.3-1 presents BART determination modeli ng results for the five Kentucky EGU sources
that were unable to demonstrat e a contribution of less than 0.5 dv at all Class I areas within 300
km from their source location. These five source s are considered to be “subject to BART” and were required to submit BART determination modeling containing their evaluation of potential BART control options and proposed BART determin ations. Each of these sources has agreed to
install emission controls to a ddress inorganic condensible part iculate emissions (SO3/H2SO4), which is causing the sources to be subject to BA RT. The BART determinat ion resulting controls are provided in the Table 7.5.3-1 th at follows and they were taken to public hearing concurrent with the public hearing on Kentucky’s Regional Haze SIP. Table 7.5.3-2 that follows, in addition to the emission controls, provides th e source’s BART emission limits and timeframes for compliance. Applicable BART controls and emission limits will be incorporated into the sources’ Title V permit as appropriate or upon rene wal. In addition, since TVA had previously indicated to the KYDAQ its plans to install hydrated lime inje ction controls on TVA ParadiseUnits 1-3 to mitigate opacity due to SO3 emissions and that additional controls are not costeffective at this time, the KYDAQ has determined BART to be no control for TVA Paradise Units 1-3. However, as related by TVA, the hydr ated lime injection cont rols for TVA Paradise Units 1-3 will be in place well before the BART controls are required; w ill achieve the reduction in visibility impacts listed in the Draft Implementation Plan (Kentucky Regional Haze SIP); and will be included in TVA Paradise’s Title V perm it. Specifically, regarding the installation of hydrated lime injection contro ls for TVA Paradise Units 1-3, TVA has communicated to KYDAQ its proposed plan that provides for permitting activities to proceed in July 2008; for construction to begin in mid-2009 on Unit 3 with c onstruction for Unit 1 a nd 2 to follow; and for controls to be operating on a ll three TVA Paradise units possibly by the fall of 2010. Also, as
indicated in the E.ON U.S. Mill Creek BART de termination submittal, the average cost for installing sorbent controls on al l four Mill Creek units is a bout the same (an estimated 5.1 million $/dv). However, sorbent injection at all four units would mean an additional total capital investment of $8.8 million as compared to controls only on the larger Units 3 and 4. Therefore,
E.ON U.S. concluded that BART should be the installation of sorbent injection controls on the larger Mill Creek Units 3 and 4 since they can ach ieve an estimated 70 percent of the total dv improvement achieved by controlling all four units . Given the extra cost for the lesser additional dv improvement for Units 1 and 2, the Cabinet agreed that BART for Mill Creek is the
installation of sorbent in jection controls on the larger Units 3 and 4. For further details about the BART determination modeling for the five BART -Subject sources, please refer to Appendix L.

Kentucky Regional Haze SIP 62Table 7.5.3-1 Kentucky BART De termination Modeling Results for BART-Subject Sources

Source Class I Areas BART Controls to Be Installed* 98 th

Percentile

Impact Before
BART Controls (Change in dv) 98 th

Percentile Impact with
BART Controls (Change in dv) BART Determination Control Visibility Improvement

From 98 th

Percentile
value (Change in dv)

East Kentucky Power Cooperative (EKPC) Spurlock
Station Mammoth Cave (251 km) EKPC per a consent
decree and for BART
will install a wet FGD
and wet ESP at EKPC Spurlock Units 1 and

2 that will address
condensible particulate emissions

and other visibility impairing pollutants. 1.834 0.213 1.621

East Kentucky Power Cooperative

(EKPC) Cooper Station Mammoth Cave (130) km 7.376 0.252 7.124

Great Smoky Mountains National Park (162 km) 6.763 0.219 6.544

Joyce Kilmer-Slickrock Wilderness (178 km) 4.974 0.122 4.852

Cohutta Wilderness Area (221 km) 3.363 0.087 3.276
Shinning Rock (233 km) 2.022 0.049 1.973 Linville Gorge Wilderness Area (267 km) EKPC per a consent
decree and for BART
will install a wet FGD
and wet ESP at EKPC Cooper Units 1 and 2
that will address

condensible particulate emissions

and other visibility impairing pollutants.

1.885 0.046 1.839

TVA Paradise
Fossil Steam Plant* Mammoth Cave (63 km) U1- 1.285
U2- 1.285
U3- 1.842
4.412

3.930 0.606
0.606
0.836
2.048

2.048 0.679
0.679
1.006
2.364

1.882

Mingo (283 km) *Although not for BART, TVA previously indicated to KYDAQ its plans to install hydrated lime injection controls
on TVA Paradise
Units 1-3 to mitigate opacity due

to SO3 emissions. U1- 0.251
U2- 0.251
U3- 0.381
0.883

0.865 0.116
0.116
0.166
0.398

0.398 0.135
0.135
0.215
0.485

0.467

Kentucky Regional Haze SIP 63Table 7.5.3-1 Kentucky BART De termination Modeling Results for BART-Subject Sources

Source Class I Areas BART Controls to Be Installed* 98 th

Percentile

Impact Before
BART Controls (Change in dv) 98 th

Percentile Impact with
BART Controls (Change in dv) BART Determination Control Visibility Improvement

From 98 th

Percentile
value (Change in dv)

American Electric Power Big Sandy Plant (AEP) Dolly Sods (291 km) 1.027 0.496 0.531

James River Face (279 km) 1.052 0.457 0.595

Linville Gorge (256 km) 0.835 0.364 0.471

Otter Creek (266 km) Per a consent decree and BART, AEP will
install ammonia injection on Unit 1
and a FGD scrubber
on Unit 2 to address
condensible particulate emissions

and other visibility impairing pollutants.

1.285 0.558 0.697

E.ON U.S Mill**

Creek Station Mammoth Cave (90 km) **E.ON U.S. for
BART will install sorbent injection controls on the larger Units 3-4 to mitigate
condensable particulate emissions. 2.265 1.440 0.825

*Since TVA had previously indica ted to the KYDAQ its plans to inst all hydrated lime injection co ntrols on TVA Pa radise Units 1-3 to mitigate opacity due to SO3 emissions and that additi onal controls ar e not cost-effective at this time, the KYDAQ has
determined BART to be no control for TVA Pa radise Units 1-3. **Given the extra cost for the lesser additional dv improvement for Units 1 and 2, the Cabinet agreed that BART for Mill Creek is the installation of sorbent injection controls on the larger Units
3 and 4.

Kentucky Regional Haze SIP 64Table 7.5.3-2 Kentucky BART Controls, Emission Limits, and Compliance Timeframes for BARTSubject Sources

Kentucky BART

Subject Source BART Controls To
Be Installed BART Emission Limits Inclusion in Title V Permit Timeframe for Compliance with BART Emission Limits\Controls East Kentucky Power Cooperative (EKPC)

Spurlock Units 1 and 2 and Cooper
Units 1 and 2 Install wet FGD and wet ESP on Spurlock
Units 1 and 2 and Cooper Units 1 and 2. A 07/02/07 EKPC
consent decree provides a filterable
PM emission rate of 0.030 lb/MMBTU,
which was utilized to
demonstrate modeled visibility improvement. Emission
limits and
controls will
be included in
the source’s
Title V Permit as appropriate
or on renewal. Expeditiously as practicable, but
no later than 5 years after EPA

approves Kentucky’s Regional Haze
SIP.

AEP Big Sandy

Unit 1
Unit 2 Install ammonia injection controls on
Unit 1 and a FGD on
Unit 2. Inorganic
Condensible
Particulate Limits (modeled as sulfates):

101.0 lb/hr (H2SO4) 127.0 lb/hr (H2SO4) Emission
limits and
controls will
be included in
the source’s
Title V Permit as appropriate
or on renewal. Expeditiously as practicable, but
no later than 5 years after EPA

approves Kentucky’s Regional Haze
SIP.

KYDAQ will
work with AEP to
install the FGD
scrubber on AEP Big Sandy Unit 2 as expeditiously as practicable.
TVA Paradise*
Unit 1
Unit 2
Unit 3 *Although not for BART, TVA previously indicated to KYDAQ its plans to install hydrated lime injection
controls on TVA
Paradise Units 1-3 to mitigate opacity due
to SO3 emissions. *NA *Although not for BART,
TVA has
indicated that its planned
SO3 controls
for Paradise
Units 1-3 will
be included in
its Title V
Permit as appropriate or
on renewal. *Although not for BART, TVA in
its BART
Determination
has indicated the
SO3 controls will be in place on
Paradise Units 1-
3 well before
BART controls are required.

Specifically, TVA
has related to

Kentucky Regional Haze SIP 65Table 7.5.3-2 Kentucky BART Controls, Emission Limits, and Compliance Timeframes for BARTSubject Sources

Kentucky BART

Subject Source BART Controls To
Be Installed BART Emission Limits Inclusion in Title V Permit Timeframe for Compliance with BART Emission Limits\Controls KYDAQ its proposed plan to have hydrated lime injection controls operating
on all three TVA
Paradise units possibly by the
fall of 2010.
E.ON U.S.** Mill Creek
Unit 3
Unit 4 **Install sorbent injection controls on larger Units 3 and 4
to control SO3
emissions and
continue to utilize existing ESPs to
control PM emissions for Units 1 through 4. SO3 Limit:

0.015 lb/MMBTU
0.015 lb/MMBTU **Emission
limits and
controls will
be included in
the source’s
Title V Permit as appropriate
or on renewal. **Expeditiously as practicable, but
no later than 5 years after EPA

approves Kentucky’s Regional Haze
SIP.

*Since TVA had previously indica ted to the KYDAQ its plans to inst all hydrated lime injection co ntrols on TVA Pa radise Units 1-3 to mitigate opacity due to SO3 emissions and that additi onal controls ar e not cost-effective at this time, the KYDAQ has
determined BART to be no control for TVA Pa radise Units 1-3. **Given the extra cost for the lesser additional dv improvement for Units 1 and 2, the Cabinet agreed that BART for Mill Creek is the installation of sorbent injection controls on the larger Units
3 and 4.

7.6 Relative Contributions to Visibility Im pairment: Geographic A reas of Influence for Kentucky’s Class I Area

Once it was determined that SO2 emission reduc tions from EGU and non- EGU point sources in
the VISTAS states would be the most effective sources to control to improve visibility at Kentucky’s Class I area, 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 geographic 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 visibility at that Class I area. All SO2 point sources within these Areas of Influence were identified and ranked by their 2018 emissions. Th e 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 Kentucky’s Class I area. The plots that follow are only for Kentucky’s Class I area since KYDAQ’s Q/d times RTMax area of influence analysis identified no Kentucky sources that contribut ed one percent or more to visi bility impairment for any other Class I area examined by VISTAS.

Kentucky Regional Haze SIP 667.6.1 Back Trajectory Analyses

The first step was to generate meteorological back trajectories for IMPROVE monitoring sites in Kentucky 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 masses that arrive at a receptor at a given time. The method essentially follows a parcel of air backward in hourly steps for a specified length of time. Figure 7.6.1-1 is an example back trajectory an alysis for Mammoth Cave National Park for the 20 percent worst days in 2002.



# Back Trajectories for 20% Worst Days for 2002 Mammoth Cave, KY

Figure 7.6.1-1. Example back trajectories for 20 percent worst visibility days in 2002 for Mammoth Cave National Park.

Kentucky Regional Haze SIP 67Trajectories were started at 100 meters and 500 meters above the surface and run backward from
the site for 72-hours. These indi vidual back trajectories for 20 percent worst days in 2002 were also useful in evaluating model performance for individual days at the Class I areas.

7.6.2 Residence Time Plots

The next step was to plot residence time for each Class I area using five years of back trajectories for the 20 percent worst visibility days in 2000- 2004. Residence time is the frequency that winds pass over a specific geographic area on the path to a Class I area. Separate residence time plots were generated using trajector ies with 100m and 500m start he ights. As illustrated in Figure 7.6.2-1, winds influencing Mammoth Cave on the 20 percent worst days come from all directions and there is no singl e predominant wind direction influencing the 20 percent worst visibility days.

Residence Time for 20% Worst Days in 2000-2004

Mammoth Cave, KY

Figure 7.6.2-1 Example residence time plot fo r 20 percent worst visibility days in 2000-2004 for Mammoth Cave National Park. Based on trajectories with 100m start height.

Kentucky Regional Haze SIP 687.6.3 SO2 Areas of Influence

As discussed earlier, the KYDAQ has determined th at reductions in SO2 emissions would have the greatest visibility impact. Therefore, sulf ate extinction-weighted re sidence time plots were developed to define the geogra phic area with highest probability of influencing the receptor on the 20% worst days in the 2000-2004 baseline period th at were dominated by sulfate. Each back trajectory was weighted by sulfate extinction for th at day. This allows the focus to be on the 20 percent worst days that are influenced by sulfat e and places less importance on days influenced by organic carbon from fires. Sulf ate-weighted back trajectories for the 20 percent worst days were combined for 5 years of data. The re sulting sulfate extinction-weighted residence time plots were used to define the geographic Area of Influence for sources of SO2 emissions. In Figure 7.6.3-1 the area representing 10 percent or greater residen ce time is outlined in red and the area representing 5 percent or greater residence time is outlined in gray.

The VISTAS states discussed various options as to what percentage of sulfate extinctionweighted area of influence should be assessed. It was determined that for this planning period that the area of influence defined by 5% or gr eater sulfate extinction-we ighted residence time provided a reasonable universe of s ources that may cause visibility impairment at a Class I area. The VISTAS states recognized that this did not represent 100% of the sources contributing to visibility impairment at Class I areas, but rather a reasonable universe of sources to consider during the first planning period.



### SO2 Area of Influence for Mammoth Cave, KY

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%.

Kentucky Regional Haze SIP 69Figure 7.6.3-1 Example SO2 Area of Influen ce plot for sulfate extinction weighted residence time for 20 percent worst visibi lity days in 2000-2004 for Mammoth Cave National Park based on trajec tories with 100m start height.

7.6.4 Emissions Sources within SO2 Areas of Influence

Residence time plots were then combined with geographically-gridded em ission data based on
the 2002 baseline and 2018 emissi ons inventories. Plots were generated for the Areas of Influence defined by trajectories with 100m and 500 m start heights. As a way of incorporating the effects of transport, deposition, and chemical transformation of point source emissions along the path of the trajectories, these data were weighted by 1/d, where d was calculated as the distance, in kilometers, between the center of the grid cell in which a source is located and the center of the grid cell in which the IMPROVE m onitor is located. The distance-weighted point
source SO2 emissions are then combined with th e gridded extinction-wei ghted back-trajectory residence times at a spatia l resolution of 36-km.

The final step was to combine the residence times and gridded emissions data in plots and data sets. The distance weighted (1/d) gridded poin t source SO2 emissions were multiplied by the total extinction-weighted back-tra jectory 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 using both the 2002 a nd 2018 emissions inventories.

Figure 7.6.4-1 illustrates the 2002 and 2018 distan ce weighted gridded emissions multiplied by sulfate extinction weighted residence time for Ma mmoth Cave 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 s een by comparing emissi ons 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 gr id cells with these sources will show a lighter color gradient than on the 2002 map

. For example, SO2 reductions fr om EGUs from west to east in Kentucky for CAIR can be seen by compar ing the 2002 and 2018 maps . Because the total emissions in the domain are smaller in 2018, a s ource that does not change emissions between 2002 and 2018 may actually appear to increase in importance in 2018 compared to 2002.

Although the sulfate extinction-weighted resi dence times were developed using the 2002 emissions, the 2018 emissions weighted by residence ti me plots still provides useful information. The KYDAQ does not believe that the area of in fluence would have ch anged significantly if sulfate extinction-weighted residence times were developed using the 2018 emissions. However,
if the area of influence would have been sm aller using 2018 emissions due to reductions expected in the EGU source sector, then the area developed to identify potential sources would
be considered conservative.

Kentucky Regional Haze SIP 70

### 2002 vs 2018 SO2 Emissions weighted by Residence Time Mammoth Cave, KY

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 = 19%

Figure 7.6.4-1. Mammoth Cave National Park 2002 (left) and 2018 (right) SO2 distance weighted emissions times SO4 extinc tion-weighted residence time plots.

Figure 7.6.4-2 illustrates similar plots for 2018 fo r Mammoth Cave National Park. These plots illustrate the relative importance of Kentuc ky sources of SO2 compared to sources in neighboring states. Additional analyses, including 2002 and 2018 distance weighted emissions times residence-time plots for the Class I area in Kentucky and neighbori ng states are contained in Appendix H. These analyses are serving as the basis fo r consultation among the VISTAS

states.

Kentucky Regional Haze SIP 71

### 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%. 

## 2018 SO2 Emissions weighted by Residence Time Mammoth Cave, KY

Figure 7.6.4-2. Mammoth Cave 2018 SO2 di stance weighted emissions times SO4 extinction-weighted residence time plot.

Finally, Table 7.6.4-1 shows, in tabular form, the relative contributions of point source SO2 emissions from nearby states to Kentucky’s Class I area. These percentages were estimated by multiplying the maximum residence tim e to the emissions over distance.

Table 7.6.4-1 2018 Point Source SO2 Contribution to Kentucky’s Class I Area by State

State Kentucky Class I Area Mammoth Cave
Alabama 4.33% Arkansas Delaware Florida Georgia 1.79% Illinois 0.53% Indiana 21.22% Iowa Kansas

Kentucky Regional Haze SIP 72State Kentucky Class I Area Mammoth Cave Kentucky 53.60 Louisiana Maryland Michigan Minnesota Mississippi Missouri 0.53 Nebraska New Jersey New York North Carolina Ohio 3.95% Pennsylvania South Carolina
Tennessee 13.46% Virginia West Virginia 0.54

Wisconsin

Total 100.00%

7.6.5 Specific Source Types in the Areas of Influence for Kentucky’s Class I Area

The next step in the analysis was to review the emissions inventories to determine the source categories, as well as specific sources, found to have the greatest imp act on visibility in Kentucky’s Class I area. Lists of SO2 point sources within the Ar eas of Influence for each Class I areas were developed using the VISTAS 2002 ba se year and 2018 future year emissions. For this purpose, the Area of Influence was defined as the counties with maximum sulfate extinction weighted residence time greater than five. Fo r 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 me tric that accounts for the dispersion of
emissions over distance

• Maximum sulfate extinction weighted residence time (RTmax)

Our review was conducted in a top down fashion st arting with an analysis of the major source categories in each SO2 Area of Influence to de termine which major categories had the highest
residual contribution to the area in 2018. It was 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

Kentucky Regional Haze SIP 73controlled under the future year base case ha d the potential for reduction. Once the highest source types were identified, subcategories with in those source types were reviewed. The contributions from major source categories to the 2018 inventory for the SO2 Areas of Influence for the Kentucky Class I area s are listed in Table 7.6.5-1. In th ese tables, the source categories are broken out by the USEPA’s Tier 1 re port categories and are defined below:

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

• Fuel Comb Industrial Emissions from all fuel com bustions at industrial boilers

• Fuel Comb Other Emissions from 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 from metal processing operations

• Petroleum & Related Industries Emissions from petroleum refi neries & related industries

• Other Industrial Processes All other industrial processing not previously mentioned

• Solvent Utilization Emissions from solvent util ization 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 sources

• Off-highway Emissions from off-road mobile sources

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

Kentucky Regional Haze SIP 74Table 7.6.5-1. 2018 SO2 Emission s Contributions from Major Source Categories in the Area of Influence for Mammoth Cave National Park, Kentucky.



### Tier VOC NOX CO 

### SO2 PM-10 PM-2.5 NH3 

### Fuel Comb. Elec. Util. 1% 25% 1% 

### 66% 8% 18% 1% 

### Fuel Comb. Industrial 1% 16% 2% 

### 19% 3% 6% 0% 

### Fuel Comb. Other 4% 7% 3% 

### 5% 3% 8% 0% Chemical & Allied Product Mfg 2% 1% 1% 1% 1% 1% 1% 

### Metals Processing 1% 1% 5% 

### 3% 3% 7% 0%

Petroleum & Related Industries 1% 0% 0% 1% 0% 0% 0% 

### Other Industrial Processes 7% 5% 1% 

### 3% 8% 10% 1%

Solvent Utilization 45% 0% 0% 0% 0% 0% 0% Storage & Transport 6% 0% 0% 0% 1% 1% 0% Waste Disposal & Recycling 3% 1% 2% 0% 3% 7% 0 Highway Vehicles 17% 20% 48% 0% 1% 2% 9% Off-highway 12% 24% 36% 1% 2% 4% 0%
Miscellaneous 1% 0% 3% 0% 69% 35% 87% %

V ISTAS Tota l 100 % 100 % 100 % 100 % 100 % 100 % 100 %

These tables indicate that for Kentucky’s Class I area, EGUs a nd industrial boilers are the two major sources categories contributing to 2018 SO2 emissions in the Areas of Influence, even after implementation of CAIR. T ogether these two source categories contribute 85 percent of the
2018 SO2 emissions for the Areas of Influence for Kentucky’s Class I area. Other fuel combustion and other industrial processes co mprise another 8 percent of the 2018 SO2
emissions.

These tables can also be used to evaluate the major source categories contributing to emissions of NOx, NH3, and PM emissions in 2018. For instan ce, highway vehicles and off road vehicles are major sources of NOx emissions, in addition to electric utilities an d industrial boilers. The source category “miscellaneous” (which includes agricultural sources and fires) is the major contributor to NH3 and primary PM. Howe ver, based upon the 2000 - 2004 reconstructed extinction for the 20% worst visibility days (A ppendix B), these pollutant s are not significant contributors to visibility impairment on most days in the baseline period. Additionally, the
emissions sensitivities discussed in Section 7.4 in dicated very small benefits of controlling NOx, NH3, and primary PM emissions at Kentucky’s Cl ass I area, but if these emissions were of concern, different source categories would need to be addressed.

The contributions to SO2 emissions in 2018 from the three highest sour ce categories, electric utilities, industrial boilers, and other fuel co mbustion have been further broken out into subcategories. Table 7.6.5-2 indicates subcateg ories for the Areas of Influence for Kentucky’s Class I area. Within electric utilities, all the SO 2 emissions are attributable to coal fired power plants. Within industrial boilers, most emissions ar e attributable to coal fi red boilers with lesser

Kentucky Regional Haze SIP 75contributions from oil and gas boilers. Commercial and institutional coal and oil boilers have
smaller contributions.

Table 7.6.5-2. 2018 SO2 Emissions Contributions from Major Source Subcategories in the Area of Influence for Mammoth Cave National Park, Kentucky.



### Tier MACA Fuel Comb. Elec. Util.-Coal 66%

Fuel Comb. Elec. Util.-Oil 0%
Fuel Comb. Elec. Util.-Gas 0%
Fuel Comb. Elec. Util.-Other 0%
Fuel Comb. Elec. Util.-Internal Combustion 0% 

### Fuel Comb. Industrial-Coal 14% Fuel Comb. Industrial-Oil 3% Fuel Comb. Industrial-Gas 2%

Fuel Comb. Industrial-Other 1%
Fuel Comb. Industrial-Internal Combustion 0%

### Fuel Comb. OtherCommercial/Institutional Coal 2%
Fuel Comb. OtherCommercial/Institutional Oil 2%

Fuel Comb. Other-Commercial/Institutional Gas 0% Fuel Comb. Other-Misc. Fuel Comb. (Except Residential) 0%
Fuel Comb. Other-Residential Wood 0%
Fuel Comb. Other-Residential Other 1%

From these analyses, the KYDAQ considered what additional control measures for electric
utilities and industrial bo ilers are reasonable. The lists of i ndividual sources are also being used
to determine if individual sources in other sources categories are major contributors to SO2
emissions in the Areas of Influence.

Kentucky Regional Haze SIP 767.7 Evaluating the Four Statutory Factors fo r Specific SO2 Emissions Sources in Each

Area of Influence

The next step was to identify emission reductions that have already occurred within each source category and at specific units. Unit level tabl es 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 tabl es for non-EGU point sources.

Once emission control profiles for specific units were defined, the next step is to determine what, if any, additional control measures would feasibly be available, and to assign costs to those control measures. For EGUs, the 2018 IPM file used by VISTAS was obt ained and matched to the 2018 base case inventory of EGU sources. This step wa s conducted to ensure that incremental controls assigned to these source types did not duplicat e existing base case assumptions.

VISTAS used the USEPA’s AirControlNET data base, modified for the VISTAS emission inventories, for the non-EGUs. The core of AirC ontrolNET is a relational database system in which control technologies are linked to sources within the USEPA emissions inventories. The system contains a database of control measure a pplicability, efficiency, and cost information for reducing emissions. The control measure data file in AirControlNET includes not only the technology'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 abov e, VISTAS ran every available SO2 control strategy in AirControlNET against the EGU and non- EGU point source inventories to develop a master list of incremental control strategies for each unit in the VI STAS 36 km domain.

For the sources within the Area of Influence fo r Kentucky’s Class I area, the master list of
incremental control measures was sorted to dete rmine the costs of incremental control measures. These data were combined in a master spreadsh eet with the distance from the emission release point to the Class I area IMPR OVE monitor (in km), the 2018 resi dual emissions and distance (Q/d) or squared distance (Q/d^2), and the normalized 2018 SO2 point source emissions times distance-weighted residence time (RTMax) values for the county in which the emission release point was located. Kentucky evaluate d these control measures and cost s as part of their review of the statutory factors for reasonable further progress.

The regional haze rule requires that states cons ider the following factors and demonstrate how
these factors were taken into considerati on in selecting the reasonable 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.

Kentucky Regional Haze SIP 77Cost of Compliance: As defined in Section 7.6.5, coal-fired electric utilities and coal-fir ed industrial boilers were the largest source categories contributing to SO2 in 2018 in the Areas of Influence defined for Kentucky’s Class I area. Industrial boilers usi ng oil and commercial and institutional boilers using coal or oil had small contributions to SO2 in 2018.

Sulfur dioxide emissions from utility, industria l, commercial, or institutional boilers can becontrolled either by switching the fuel source to a lower sulfur fuel content or by installing postcombustion controls. Costs vary by fuel source and boiler type and require source specific analyses for accuracy.

Bituminous coal is commonly bur ned in boilers in the eastern US. Switching to another
bituminous coal with lower sulfur content or blending bituminous coals can reduce SO2 emissions with least impact to boiler performance. While sub-bituminous coal has a lower sulfur content than bituminous coal, it also has a lower heat rate and so more coal has to be burned to generate the same energy output. For boilers th at are designed for bituminous coal, only a small
fraction of the fuel can be switched to low sulf ur sub-bituminous coal without affecting boiler operations. Costs of boiler modifications to acc ommodate low sulfur sub-bituminous coal may make switching to sub-bituminous coal impractical. Contract initiation and termination costs, differential fuel prices and heat rates, transportation costs, a nd modification to boiler operations, fuel handling and waste handling systems will have to be considered specific to each source.

Flue-gas desulfurization is the common post-com bustion control for coal-fired boilers. Flue gas is passed through an absorbent for sulfur dioxide (generally limestone or lime) in either 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 wa ste product handling facilities. Costs per ton vary with boiler size, type, and f acility siting considerations.

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 co sts, 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 necessary to termin ate existing fuel contracts and initiate new contracts needs to be considered. Generally tw o to three years may be required. Installation of post-combustion controls will require 3 or more years depending on market availability of labor and materials and utility system-wide priorities. 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 require additiona l safeguards for fuel handling and waste handling systems to avoid addi tional non-air environmental impacts such as

Kentucky Regional Haze SIP 78increased effluents in waste water discharges and storm water runoff. These factors will need to be considered specific to individual sources. Carbon dioxide is emitted as a by-product 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 Reason able Progress for Individual Sources?

The following summarizes the process for dete rmining reasonable progress for Kentucky
sources. For a detailed discussi on of the reasonable progress assessments for all units with a contribution of greater than one percent to visib ility impairment at the Class I area in Kentucky 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 in the baseline period and concluded that sulfate accounted for great er than 70 percent of th e visibility impairing pollution. The VISTAS States concluded that controlling SO2 emissions was the appropriate step in addressing the reasona ble progress assessment for 2018. The VISTAS findings were consistent with the findings of SAMI. As you recall, SAMI c onfirmed that sulfate particles account for the greatest portion of the haze affecting Class I area s in the Southern Appalachian region and that these sulfates were produced in large part fr om SO2 emissions from coal
combustion.

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

Since the pollutant of primary concern was dete rmined to be SO2, the emissions inventory was assessed to determine the source categories that contribute the most SO2 emissions. Since point source emissions in 2018 are projected to repres ent greater than 95 percent of the total SO2 emissions inventory, the VISTAS 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 is sufficient for reasonable progress for subject
EGUs.

The KYDAQ evaluated the amount of SO2 reducti on from the EGU sector resulting from the implementation of the CAIR. The EGUs in Kentucky are expected to reduce their 2002 SO2 emissions by an estimated 54 percent by 2018. Much of that reducti on is the result of requirements that are predicted by the IPM to meet CAIR

.

To further support EGUs subject to CAIR is sufficient for reas onable progress, a discussion in the CAIR rule highlighted below (See A ppendix H for 70 FR 25197-25198) addresses the reasonable progress factors of cost and time necessary for compliance for these EGUs, and

Kentucky Regional Haze SIP 79provide the necessary support for a State's four factor reasonabl e progress analysis that must accompany a State’s assertion that CAIR is suffi cient for reasonable progress for subject EGU’s during the first planning period.

From past experience in examining multi-pollu tant emissions trading programs for SO2 and NOX, EPA recognized that the air pollution control retrofits that result from a program to achieve highly cost-effective reducti ons are quite signific ant and can not 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 industry 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 requirements to improve the transmission system. Furthermore, allowing pollution control ret rofits to be installed over time enables the industry to take advantage of planned outages at power plants (unplanned outages can lead to lost revenue) and to enable project manage ment to learn from early installations how 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 det ermined in the NPR that the earliest reasonable deadline for compliance with the fin al highly cost-effective control levels for reducing emissions was 2015 (taking into consideration the existing bank of title IV SO2 allowances). First, the Agency confirmed that the levels of SO2 and NOX emissions itbelieved were reasonable to set as annual emissions caps for 2015 lead to highly costeffective controls for the CAIR region.

Once EPA determined the 2015 emissions reductions levels, the Agency determined a proposed first (interim) phase control level that would commence January 1, 2010, the earliest the Agency believed initial pollution controls could be fully operational (in today's final action, the first NOX control phase commences in 2009 instead of in 2010, as explained in detail in section IV.C). Th e first phase would be the initial step on the slope of emissions reductions (the glide-path) leading to the final (second) control phase
to commence in 2015. The EPA determined the first phase based on the feasibility of installing the necessary emission control retrofits, as described in section IV.C.

Although EPA's primary cost-effectiveness de termination is for the 2015 emissions reductions levels, the Agency also evaluated the cost effectiveness of the first phase control levels to ensure that they were also highly cost effective. Throughout this preamble section, EPA reports both the 2015 and 2010 (and 2009 for NOX) costeffectiveness results, although the first phase l evels were determined based on feasibility rather than cost effectiveness. The 2015 emi ssions reductions include the 2010 (and 2009 for NOX) emissions reductions as a subset of the more stringent requirements that EPA is imposing in the second phase.

The KYDAQ intends to re-evaluate the IPM predic tions of SO2 reductions for CAIR at the time of the next periodic report in 2012 to ensure that the reductions currently predicted by IPM for CAIR are in fact taking place where they were expected and needed. If KYDAQ’s assessment for the periodic report indicates th at its emissions are likely not to meet 2018 projections then the KYDAQ may re-evaluate the four factors to re-assess the Long-Term Strategy. Based on the

Kentucky Regional Haze SIP 80controls currently being installed for CAIR, re quired by BART, consent decrees, and predicted by IPM under CAIR, the KYDAQ has concluded that at this time these existing regulatory programs constitute reasonable control measures fo r Kentucky EGUs during this first assessment period (between baseline and 2018).

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

The KYDAQ calculated the fractiona l contribution from all emission units within the SO2 Area of Influence for a given Class I area and identified those emissi on units with a contribution of one percent or more to the visibility impairment at that Class I area. A full description of this process and a list of sources considered in the reasonable pr ogress evaluation can be found in Appendix H.

Step 5: Evaluate the four factors.

Each emission unit identified in Step 4 above wa s considered for evaluation using 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 complia nce, and 4) the remaining useful life of the emissions unit. If any control measure for an emission unit was found reasonable after assessing the four factors, modeling would be performed to determine if the contro ls would result in a visibility improvement at any Class I area.

For the limited purpose of evaluating the cost for the reasonable progress assessment in this first regional haze SIP, the KYDAQ believes it is not equ itable to require Non-EGUs to bear a greater economic burden than EGUs for a given contro l strategy. The KYDAQ used EPA’s CAIR EGU cost analysis to establish a cost/ton of SO2 re moved threshold. During the current reasonable progress assessment, no units in Kentucky were identified for additional control since no measures were found to be below the cost threshold. Below is a summary of the analysis. The detailed analysis is included in Appendix H

.

Results of four-factor analysis

The following is a brief summary of the Non-EGU four-factor analysis. A dditional detail is included in Appendix H. The KYDAQ used the cost of compliance as a screening tool to determine the universe of sources to perform th e full four-factor evalua tion. Therefore, the summary is focused on the cost of control. The dollar per ton of SO2 rem oved threshold that the KYDAQ used to determine if the cost was reas onable was based on EPA’s CAIR cost analysis for implementing CAIR (See EPA’s CAIR cost analysis in 70 FR 25201-25208 12May2005 available in Appendix H). After a review of EP A’s CAIR cost analysis, the KYDAQ determined that the CAIR SO2 control costs vary by year of analysis (2010 vs. 201 5) and may range from $400 to $3,400 per ton of SO2 rem oved. Ultimately, EPA found a consistent marginal cost for both years at $2000 per ton, which KYDAQ believes es tablishes an appropriate threshold against which cost-effectiveness may be evaluated fo r reasonable progress. During the current

Kentucky Regional Haze SIP 81reasonable progress assessment, no Non-EGU units in Kentucky were identified for additional
control because no measures were found to be co st-effective.

For Non-EGUs, KYDAQ found that emissions fr om the following facility contributed one percent or more to visibility impairment in a Class I area, and therefore focused the reasonable progress assessments on specific units at this facility:

• Century Aluminum of Kentucky for impacts at Mammoth Cave.

The KYDAQ also looked at what sources in Kentucky may be im pacting Class I areas located outside of the Kentucky, as well as what source s located outside of Kentucky may be impacting Kentucky’s Class I area. KYDAQ, based on its Q/d times RTMax analysis identified eight EGUs, six from Indiana and two fr om Tennessee, with a one percen t or more contribution for the
Mammoth Cave area of influence. KYDAQ sent letters to Indian a and Tennessee indicating that no additional controls are requeste d at this time since Mammoth Ca ve is currently exceeding the uniform rate of progress and the EGUs are being a ddressed by CAIR (See copies of the letters in Appendix J). In addition, ba sed on the KYDAQ Q/d times RT Max analysis, no Kentucky
sources were identified with a contribution of one percent or more to the visibility impairment at
Class I areas in other states. The list of s ources identified by the KYDAQ’s Q/d times RTMax analysis for given Class I area s are available in Appendix H.

Cost of Compliance

VISTAS contracted with Alpine Geophysics to evaluate control options and costs for sources
within the AoI for the Class I ar eas of concern. Alpine used EPA’s AirControlNet software to evaluate control options and costs for contro ls. The SO2 control suggested by the VISTAS control cost spreadsheet for Century Aluminum is a sulfuric acid pl ant at a cost of $14,207, $23,020, and $43,281 per ton of SO2 removed for potlin es 1-4, potline 5, and the anode baking furnace respectively (See the VISTAS control cost spreadsheet for Century Aluminum in Appendix H). Therefore, since the cost of compliance for the control option ranges from 7 to 22 times greater than the cost-eff ectiveness threshold, the KYDAQ conc ludes that there are no costeffective controls available for these Century Aluminum units at this time within the cost
threshold established for this reasonable progress assessment.

Time Necessary for Compliance, Energy and N on-Air Impacts, and Remaining Useful Life

The three remaining statutory factors: 1) tim e necessary for compliance, 2) the energy and nonair quality environmental impacts of compliance, and 3) the remaining useful life of the emissions unit, while required to be considered, were deemed not applicable, since there were no
cost-effective controls to evaluate.

7.9 What Additional Emissions Controls We re 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

Kentucky Regional Haze SIP 82management, the role of dust and fine soil at Cl ass I areas and status of state plans to mitigate emissions from construction activities, and the ro le of NH3 and potential benefits if emissions from agricultural sources were mitigated.

As discussed in Section 2.4 and demonstrated in Figures 2.4-1 and 2.4-2, elemental carbon (sources include agriculture, prescribed wildland fires, and wildfires) is a relatively minor contributor to visibility impairment at the Class I area in Kentucky. However, KYDAQ has an open burning regulation, 401 KAR 63:005, that esta blishes requirements for the control of open burning in Kentucky. The KYDAQ believes that 401 KAR 63:005, which is already incorporated into Kentucky’s SIP, provides ad ditional support to aid the Commonwealth with meeting its reasonable progress goals (RPGs) for this first planning period. A copy of KYDAQ’s open burning regula tion can be obtained at www.lrc.ky.gov . The exact benefits from the reduction in open burning emissions can not be quantified at this time and will not be included in Final VISTAS modeling.

Also as discussed in Section 2.4 and demonstrated in Figures 2.4-1 and 2.4-2, fi ne soils are a relatively minor contributor to visibility im pairment at the Class I areas in Kentucky. Nevertheless, in regard to construction activit ies, KYDAQ has a fugitive emissions regulation, 401 KAR 63:010. Fugitive emissions, that provides for the control of fugitive emissions in Kentucky

. The KYDAQ believes that 401 KAR 63:010, wh ich is already incorporated into Kentucky’s SIP, provides additional support to aid the Commonwealth with meeting its RPGs for this first planning period. A copy of KYDA Q’s fugitive dust regulation can be obtained at www.lrc.ky.gov

.

In regard to agricultural ammonia, Kentucky, per its CMAQ regional haze modeling with VISTAS, is focused on obtaining additional SO2 emissions reductions to address CAIR, BART, and consent decrees in Kentucky. The reduction in large amounts of SO2 emissions will lessen
the formation of Ammonium sulfate , (NH 4 ) 2 SO 4 emissions.

Additional Kentucky EGU controls for CAIR per IPM, consent decrees, and BART that can be quantified have been included by VISTAS in a final modeling run to address 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 other controls resulting from the states within VISTAS to address reasonable pr ogress that can be quantified. If the final modeling run and analyses are completed within a reasonable period of time prior to the final Regional Haze SIP submittal on December 17, 2007, the KYDAQ would cons ider incorporating these findings in the final SIP, including revising reasonable progre ss goals if needed. If the modeling runs and analyses are not completed in time, the KYDAQ will review the inform ation as it becomes
available and determine if a SIP revision is nece ssary. Furthermore, if the addition of some additional controls or changes in a final VISTAS model run does not significantly change the current VISTAS regional haze modeling results fo r Kentucky, as presented in Section 8, then it is not likely that KYDAQ will modify its regional haze SIP for this reason.

Kentucky Regional Haze SIP 83

### 8.0 REASONABLE PROGRESS GOALS

The regional haze rule at 40 CFR 51.308(d)(1) requi res a State to establish reasonable progress goals for each Class I area within the state (expre ssed in deciviews) that provide for reasonable progress towards achieving natu ral visibility conditions by 2064. In addition, the USEPA released guidance on June 7, 2007 to use in setti ng reasonable progress goa ls. The goals must provide improvement in visibility for the most impaired days, and ensu re no degradation in visibility for the least impair ed days over the SIP period.

In accordance with the requirements of 40 CFR 51.308( d)(1), this Regional Haze SIP establishes reasonable progress goals for th e Class I area in Kentucky. The KYDAQ compared baseline visibility conditions to natural visibility conditions in the Cl ass I area to determine the uniform rate of visibility improvement (in deciviews) that would need to be maintained during each implementation period in order to attain natural visibility conditions by 2064.

Through the VISTAS modeling, the KYDAQ has estimat ed the expected visibility improvements resulting from existing federal and state regulations . As alluded to earlier, VISTAS in December 2007 and also into 2008 was in the process of m odeling 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. The KYDA Q will not include the results of this final modeling run in its final SIP submittal due to ti ming and since the results provide reasonable progress goals that are very similar and as favora ble to the ones presented in this section. The VISTAS baseline modeling has already demonstr ated that the 2018 base control scenario provides for an improvement in visibility better than the uniform rate of progress for Kentucky’s Class I area for the most impaired days over th e period of the implementation plan and ensures no degradation in visibility for the leas t impaired days over the same period.

Table 8.0-1 contains the reasonabl e progress goals for this planni ng period for Kentucky’s Class I area. For the 20% worst days, the reasonable progress goal for the Class I area provides for greater visibility improvement by 2018 than the area’s uniform ra te of progress. For the 20% best days, the reasonable progress goal for the Class I area indicates an improvement of visibility by 2018 than current best day 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 days because the model has gr eater difficulty accurately projecting small concentrations. On the 20% best days, the mo del does not meet VISTAS model performance goals or criteria for sulfate, nitr ate, and coarse mass (under predic ted) and soil (over predicted), however, the organic carbon and elemental carb on do meet performance goals on the 20% best days. Given the larger percent errors of the fractional bias on the 20% best days, the KYDAQ
has less confidence in the absolute values projected for these reasonable progress goals, however, the KYDAQ does expect that visibility on these days will be better than the current conditions 20% best days.

Kentucky Regional Haze SIP 84Table 8.0-1. Kentucky 2018 Reasonable Progress Goals (in decviews)

Class I Area Baseline Visibility for 20% Worst Days (dv) Uniform Rate of Progress

for 20% Worst Days (dv) Reasonable Progress Goal
Modeled for 20% Worst Days (Improvement) (dv) Baseline Visibility for

20% Best

Days (dv) Uniform Rate of Progress for 20% Best Days (dv) Reasonable Progress Goal
Modeled for

20% Best

Days (Improvement) (dv)

Mammoth
Cave
National Park, KY 31.37 26.64 (4.73) 25.56 (5.81) 16.51 16.51 (0.00) 15.57 (0.94)



### 9.0 MONITORING STRATEGY

The State Implementation Plan is to be accomp anied by a strategy for monitoring regional haze visibility impairment. Specifically, the Regi onal 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 plan a monitori ng strategy for measuring, characterizing, and reporting of regional haze visibility imp airment that is representative of all mandatory Class I Federal areas within the State. This monitori ng strategy must be
coordinated with the monito ring strategy required in §51.305 for reasonably attributable visibility impairment. Compliance with this requirement may be met through participation in the IMPROVE network. The im plementation plan must also provide for the following: (i) The establishment of any additional monitoring sites or equipment needed to assess whether reasonable progress goals to address regional haze for all mandatory Class I Federal areas within the State are being achieved. (ii)-(vi) [Other implementation plan requi rements 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 improveme nts 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 ambient 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 models.

Kentucky Regional Haze SIP 85The primary monitoring network for regional h aze, both nationwide and in Kentucky, is the
IMPROVE network. Given that IMPROVE m onitoring data from 2000-2004 serve as the baseline for the regional haze program, the future regional haze monitoring strategy must necessarily be based on, or directly comparab le to, IMPROVE. The IMPROVE measurements provide the only long-term record available for tracking visibility improvement or degradation and therefore Kentucky intends to rely on th e IMPROVE network for complying with the regional haze monitoring requirement in the Regional Haze Rule.

There is currently one IMPROVE site in Kent ucky’s Mammoth Cave Nati onal Park as provided in Table 9.0-1.

Class I Area IMPROVE Site Designation

Mammoth Cave National Park MACA1 (KY)

Table 9.0-1. Kentucky Class I Area a nd Representative IMPROVE Monitor.

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

• Web cameras operated by the National Park Service in Mammoth Cave National Park

KYDAQ and the local air agencies in the State operate a 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 the KYDAQ characterize air pollution levels in areas across the state, and therefore aid in the analysis of visibility impr ovement in and near the Class I

areas.

Kentucky Regional Haze SIP 86Figure 9.0-1 PM2.5 Monitoring Network in Kentucky.

The IMPROVE measurements are central to Ke ntucky’s regional haze monitoring strategy, and it is difficult to visualize how the objectives listed above could be me t without the monitoring provided by IMPROVE. Any reduction in the sc ope of the IMPROVE network in Kentucky would jeopardize the State’s ab ility to demonstrate reasonabl e progress toward visibility improvement in some of its Class I areas. In pa rticular, Kentucky’s regional haze strategy relies
on emission reductions that will re sult from the CAIR, which occur on different time scales and will most likely not be spatially uniform. Monitoring at every Class I area is important to document the different air quality responses to the emissions reductions.

Because each of the current IMPROVE monito r in Mammoth Cave represents a different airshed, reduction of the IMPROVE network by shutting down one of these monitoring sites impedes tracking progress at reducing haze at the a ffected Class I area. In the event this occurs, Kentucky, in consultation with the USEPA and re levant Federal Land Managers, will develop an alternative approach for meeti ng the tracking goal, perhaps by seeking contingency funding to carry out limited monito ring or by relying on data from nearby urban monitoring sites to demonstrate trends in speciated PM 2.5 mass.

Data produced by the IMPROVE monitoring networ k will be used nearly continuously for preparing the 5-year progress re ports and the 10-year SIP revisi ons, each of which relies on

Kentucky Regional Haze SIP 87analysis of the preceding five years of data. Consequently, the monitoring data from the IMPROVE sites needs to be readily accessible and to be kept up to date. Presumably, IMPROVE will continue to process information from its ow n measurements at about the same pace and with the same attention to quality as it has shown in the recent past. The VIEWS web site has been maintained by VISTAS and the other Regional Planning Organiza tions to provide ready access to the IMPROVE data and data analysis tool s. KYDAQ is encouraging VISTAS and the other
RPOs to maintain VIEWS or a similar data mana gement system to facilitate analysis of the
IMPROVE data.



### 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 for each Cla ss 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 Directors held their first formal consultation meeting to review the modeling results and the SO2 Areas of Influence analyses. The Air Directors agreed to look at reasonable control measures for sources on the lists for the SO2 Areas of
Influence. Each state would consider sources w ithin their state and would identify sources in neighboring states that they would like to ha ve that neighboring stat e consider. States acknowledged that the review pr ocess would differ among states since some Class I areas are projected to see visibility improvements near th e uniform rate of progress while most Class I areas are projected to have greater improve ments than uniform rate of progress.

In May 2007, the VISTAS State Air Directors met fo r their second formal interstate consultation.
States shared their lists of sour ces in their state and neighbori ng 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 meeti ng is included in Appendix J.

The KYDAQ has evaluated the imp act of Kentucky sources on Cla ss I areas in neighboring and
other states and determined that there are no ad ditional reasonable control measures that should be implemented to mitigate impacts in Class I areas in neighboring and other states since no Kentucky source was found to contribute one percent or more to visibility impairment for other states’ Class I areas (See Appendix H for KYD AQ’s Q/d times RTMax analysis that identified sources with one percent or more impact for gi ven Class I areas). Analysis of impacts from Kentucky sources and their existin g and expected controls are provided in Appendix H. As discussed in following text, Kentucky received MANE-VU state requests for support in assuring reasonable progress pursuant to certain Kentucky sources. C opies of MANE-VU letters to KYDAQ are available in Appendi x J. KYDAQ’s response to MANE-VU is reflected in its regional haze SIP. In addition, a letter from Tennessee which relates that it does not plan to ask Kentucky for additional emission reductions for this first cycle of the regional haze SIP process is provided in Appendix J.

Kentucky Regional Haze SIP 88The KYDAQ has evaluated the impact of sources in neighboring states and their impact on
Mammoth Cave and determined that there are no additional reasonable co ntrol measures that should be implemented to mitigate impacts on Ma mmoth Cave at this time. The KYDAQ sent letters to the Midwest RPO state of Indiana, the Lake Michigan Air Directors Consortium (LADCO), and to the VISTAS st ate of Tennessee regarding sour ces outside of Kentucky that were determined to contribute one percent or higher to Kentucky’s Class I area. Kentucky
related in its letters that since the units id entified are EGUs subject to CAIR and since Kentucky’s Class I area is currently exceeding the uniform rate of progress, Kentucky would not be requesting additional emission reductions at this time. Copies of the consultation letters can be found in Appendix J.

In addition, KYDAQ did participate in regional haze consultation regarding CENRAP states of Missouri and Arkansas regarding th eir Central Class I areas. Copi es of the consultation letters
with Missouri and Arkansas and conference call minutes are included in Appendix J.

The MANE-VU states of Maine, New Jersey, Ne w Hampshire, and Vermont sent letters to Kentucky in the spring of 2007 stating that ba sed on 2002 emissions Kent ucky contributed to visibility impairment to Class I areas in those states. MANE-VU states asked KYDAQ to participate in further consulta tion with these states, and a m eeting was held in August 2007, in Atlanta, Georgia with VISTAS states.

The MANE-VU states identified 14 EGUs in Kentucky that they would like to see controlled to 90% efficiency. They also requested a cont rol strategy to provide a 28% reduction in SO2 emissions from sources other than EGUs (i.e., N on-EGUs) that would be equivalent to their low sulfur fuel oil strategy. Of the 14 Kentucky EGUs identified by MANE-VU, 93% of those sources have existing SO2 controls or will ha ve SO2 controls by 2015 or sooner. Kentucky EGU existing and expected controls are provided in detail in Appendix H. The KYDAQ believes that the significant Kentucky existing a nd expected EGU emission controls more than adequately addresses MANE-VU’s EGU and Non- EGU emission control requests. The letters
from MANE-VU states and the meeti ng notes are included in Appendix J.



### 11.0 COMPREHENSIVE PERIOD IC IMPLEMENTATION PLAN

REVISIONS

40 CFR section 51.308(f) requires the KYDAQ to revi se 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 Secti on 51.308(f) of the federal rule for regional haze, Kentucky 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) requires peri odic reports evaluating progress towards the reasonable progress goals established for each mandatory Class I area. In accordance with the requirements listed in Section 51.308(g) of th e federal rule for regional haze, the KYDAQ commits to submitting a report on reasonable progress to USEPA every five years following the initial submittal of the SIP. The report will be in the form of a SIP revision. The reasonable

Kentucky Regional Haze SIP 89progress report will evaluate the progress made towards the reasonable progress goal for the mandatory Class I area located within Kentucky and in each mandatory Class I area located outside Kentucky which may be affected by emissions from within Kentucky

.

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 visibi lity conditions at each Class I area (current vs. baseline), expressed as 5-year averages of annual va lues for 20 percent best and worst days;
4. Analysis of emission changes over the 5-year period, identified by source 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 implem entation plan to meet Reasonable progress goals (RPGs); and
7. Review and any modifications to our visibility monitoring plan.

All requirements listed in 51.308(g) shall be addressed in the SIP revision for reasonable progress. In particular, the KYDAQ recognizes that the 2018 proj ections of EGU controls from the IPM runs represent one solution to how the CA IR requirements will be met. By the time of the first periodic report, the KYDAQ anticipates that the actual compliance strategy for the various utility companies will be much more defined. An assessment of those actual compliance plans will be done for the first periodic report.

The KYDAQ believes that its New Source Review (NSR) regulation for nonattainment areas as well as its Prevention of Significant Deteriora tion (PSD) regulation for attainment areas will address emissions from new sources that may locat e near a Class I area or increased emissions from major modifications to existing sources. In addition to the KYDAQ regulations that would govern these sources, consultation with the FLMs is also required fo r sources that are subject to KYDAQ’s NSR/PSD regulations.

KYDAQ also plans for continued consultation with the FLMs throughout the implementation process, including discussion of the implemen tation process and the most recent IMPROVE monitoring and VIEWS data. Consultation be tween KYDAQ and the FLMs will include early involvement of FLMs in the periodic review process and FLMs will receive copies of the revised regional haze SIP for comment prior to finalization.

There are several technical improvements that ar e recommended in the emissions inventory and air quality models that are used to support regulatory deci sions for regional haze. These improvements recommended, as funding is available, to support the next long term strategy. The following is an overall summary; Appendix K contains a fuller di scussion of possible technical improvements.

First and foremost, continued improvements are needed in the integrat ed one-atmosphere air quality models that are used to project air qua lity responses to emissions reductions. As our

Kentucky Regional Haze SIP 90understanding of partitioning between gaseous and aerosol phases improves, this understanding needs to be reflected in the models. Improveme nts can also be made in how the models handle individual pollutants. Sulfate performance for the CMAQ regional air quality model is good overall. However sulfate deposition is frequently overestimated in the mode ls, particularly in the summer months. At the coastal sites, when wi nds are blowing from the Gulf of Mexico or Atlantic Ocean, CMAQ underestim ates measured sulfate at the monitors. CMAQ’s processes
also should be reviewed for sulfate formation ove r water. Nitrate is overestimated by the model
in the winter and underestimated in the summer, although summer monitored values of nitrate are very low. Additional improvements in seasonal allocation of ammonia emissions would improve model estimates of a mmonium nitrate formation. Organic carbon is generally underestimated in the summer months. Improvement s are needed in the characterization of both primary carbon emissions and formation of secondary organic carbon.

Other improvements needed include better t ools for organic carbon source apportionment, and more consistent measurement techniques betwee n rural and urban monitoring networks. To improve our understanding of the contribution of fire from natural forest fires, prescribed burning, land clearing, and agricultural burning, states need improved record keeping. Additional improvements to intern ational emissions inventory are also needed, to improve our understanding of boundary conditions for our modeling domain and of the contributions from international emissions to pollutant concentr ations at the VISTAS Class I areas.



### 12.0 DETERMINATION OF ADEQU ACY OF THE EXISTING PLAN

Depending on the findings of the five-year progress report, KYDAQ commits to taking one of
the actions listed in 40 CFR sec tion 51.308(h). The findings of the five-year progress report will determine which action is appropriate and necessary.

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

1) KYDAQ may determine that the existing SIP re quires no further substantive revision in
order to achieve establishe d goals. KYDAQ would then provi de to the Administrator a negative declaration that furt her revision of the SIP is not needed at this time. 2) KYDAQ may determine that the existing SIP ma y be inadequate to ensure reasonable progress due to emissions from other states which participated in the regional planning process. KYDAQ would then provide notification to the Administrator and the states that participated in regional pla nning. KYDAQ collaborates with states through the regional planning process to address the SIP’s deficiencies. 3) KYDAQ may determine that the current SIP may be inadequa te to ensure reasonable progress due to emissions from anot her country. KYDAQ would then provide s notification, along with available in formation, to the Administrator. 4) KYDAQ may determine that the existing SIP is inadequate to ensu re reasonable progress due to emissions within the state. KYDAQ woul d then take action to revise its SIP to address the plan’s deficiencies within one year.

Kentucky Regional Haze SIP 91