# Regional Haze State Implementation Plan

For

The State of Alabama

Prepared by Alabama Department of En vironmental Management

Air Division

July 2008TABLE OF CONTENTS

1.0 INTRODUCTION .................................................................................................................... 1 1.1 What is Regional Haze?........................................................................................................ 1 1.2 What are the Requirements under the Clea n Air Act for addressing Regional Haze? ......... 1 1.3 General Overview of Regional Haze SIP requirements ....................................................... 2
1.4 VISTAS

............................................................................................................................... . 3 1.5 Alabama’s Class I Area ........................................................................................................ 4 1.6 State and Federal Land Ma nager (FLM) Coordination ........................................................ 6

2.0 ASSESSMENT OF BASELINE AND CURR ENT CONDITIONS AND ESTIMATES OF NATURAL BACKGROUND CONDITIONS IN CLASS I AREAS ................................... 7 2.1 Estimating Natural Conditions for the Sipsey Wilderness Area ........................................... 8 2.2 Estimating Baseline Conditions for the Sipsey Wilderness Class I Area ............................. 9 2.3 Summary of Natural Background and Baseline Conditions for the Sipsey Wilderness Area

............................................................................................................................... ............. 9 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 Glidepath for the Sipsey Wilderness Class I Area in Alabama .......................................... 14

4.0 TYPES OF EMISSIONS IMPACTING VISI BILITY IMPAIRMENT AT THE SIPSEY WILDERNESS CLASS I AREA ......................................................................................... 17 4.1 Baseline Emissions Inventory ............................................................................................. 17 4.1.1 Stationary Point Sources ............................................................................................ 18

4.1.1.1 Electric Generating Units (EGUs) ........................................................................ 18 4.1.1.2 Other Industrial Point Sources (non-EGUs) ......................................................... 19 4.1.2 Stationary Area Sources ............................................................................................. 19 4.1.3 Off-Road Mobile Sources .......................................................................................... 20 4.1.4 Highway Mobile Sources ........................................................................................... 20 4.1.5 Biogenic Emission Sources ........................................................................................ 20 4.1.6 Summary 2002 Base G2 (August 2007 version) Baseline Emissions Inventory for

Alabama ........................................................................................................................... 20 4.1.7 Model Performance Improvements thr ough Emissions Inventory Improvements .... 21 4.2 Assessment of Relative Contributions from Specific Pollutants and Sources Categories . 22

5.0 REGIONAL HAZE MODELING METHODS AND INPUTS ............................................. 22 5.1 Analysis Methods ............................................................................................................... 22 5.2 Model Selection .................................................................................................................. 23 5.2.1 Selection of the Photochemical Grid Model .............................................................. 23

5.2.1.1 Selection Criteria .................................................................................................. 23 5.2.1.2 Overview of CMAQ ............................................................................................. 24

ii5.2.2 Selection of the Meteorological Model ...................................................................... 24 5.2.2.1 Selection Criteria .................................................................................................. 24 5.2.2.2 Overview of MM5 ................................................................................................ 25 5.2.3 Selection of the Emissions Processing System .......................................................... 26

5.2.3.1 Selection Criteria .................................................................................................. 26 5.2.3.2 Overview of SMOKE ........................................................................................... 26 5.3 Selection of the Modeling Year.......................................................................................... 27 5.4 Modeling Domains ............................................................................................................. 29 5.4.1 Horizontal Modeling Domain .................................................................................... 29 5.4.2 Vertical Modeling Domain ......................................................................................... 30

6.0 MODEL PERFORMANCE EVALUATION ......................................................................... 30 6.1 Modeling Performance Goals and Criteria ......................................................................... 31 6.2 VISTAS Domain-Wide Performance ................................................................................. 33 6.3 Sipsey Wilderness Area Class I Performance ..................................................................... 37

7.0 LONG-TERM STRATEGY (LTS) FOR ALABAMA’S SIPSEY WILDERNESS CLASS I AREA

............................................................................................................................... .... 45 7.1 Overview of the Long Term St rategy (LTS) Development Process .................................. 45 7.2 Expected Visibility Results in 2018 for the Sipsey Wilderness Area under Existing and Planned Emissions Controls ............................................................................................. 46 7.2.1 Federal and State Control Requirements .................................................................... 46 7.2.2 Additional State Programs to Reduce Emissions ....................................................... 49 7.2.3 Projected 2009 and 2018 Base G2 Emissions Inventories ......................................... 49 7.2.4 Model Results for the 2018 Base G2 Invent ory Compared to the Uniform Rate of

Progress Glidepath for the Sipsey Wilderness Area ........................................................ 52
7.3 Relative Contribution from In ternational Emissions to Visi bility Impairment in 2018 at VISTAS Class I areas....................................................................................................... 56 7.4 Relative Contributions to Visibility Impa irment: Pollutants, Source Categories, and Geographic Areas ............................................................................................................. 58 7.5 Relative Contributions to Visibility Impair ment: Geographic Areas of Influence (AoI) at Class I areas affected by Alabama Sources ...................................................................... 61 7.5.1 Back Trajectory Analyses .......................................................................................... 61 7.5.2 Residence Time Plots ................................................................................................. 62
7.5.3 SO 2 Areas of Influence (AoIs) ................................................................................... 63
7.5.4 Emissions Sources within SO 2 Areas of Influence (AoIs) ......................................... 64 7.5.5 Specific Source Types in the Areas of In fluence (AoI) for the Sipsey Wilderness ... 66 7.6 Evaluating the Four Statut ory Factors for Specific SO 2 Emissions Sources in Each Area of Influence (AoI) ................................................................................................................. 68 7.7 Which Control Measures Characterize R easonable Progress for Individual Sources? ...... 78

iiiiv 7.8 The Identification and Evaluation of Best Available Retrofit Technology (BART) for Individual Sources ............................................................................................................ 81 7.8.1 BART-Eligible Sources in Alabama .......................................................................... 82 7.8.2 VOC Evaluation of BART Eligible Sources in Alabama .......................................... 83 7.8.3 Sources that took Limits to Exempt Units from BART ............................................. 84 7.8.4 Determination of Sources Subject to BART in Alabama .......................................... 85 7.8.5 Alabama’s Evaluation of Subject-to-BART Sources ................................................. 88 7.9 What Additional Emissions Controls were c onsidered as part of the Long-Term Strategy (LTS) for Visibility Improvement by 2018? .................................................................... 91 7.10 Post BART Visibility Improvement Expected at the Sipsey Wilderness by 2018 compared to the Glidepath ............................................................................................... 93

8.0 REASONABLE PROGRESS GOALS (RPGs) ...................................................................... 96

9.0 MONITORING STRATEGY ................................................................................................. 96

10.0 INTERSTATE AND FLM CONSULTATION ON ALABAMA’S C ONTRIBUTION TO VISIBILITY IMPAIRMENT AT CLASS I FEDERAL AREAS IN NEIGHBORING STATES ............................................................................................................................... 99

11.0 COMPREHENSIVE PERIODIC IM PLEMENTATION PLAN REVISIONS ................. 100

12.0 DETERMINATION OF ADEQUA CY OF THE EXISTING PLAN ................................ 102LIST OF TABLES Table 1.4-1 Mandatory Class I Federal Areas in the VISTAS Region Wher e Visibility is an Important Value .............................................................................................................................. 4 Table 2.3-1 Natural Background and Baseline Co nditions for the Sipsey Wilderness Area ....... 10 Table 4.1.6-1 2002 Actual Emissions Inventory Summary for Alabama in tons per year. ......... 21 Table 6.1-1 Established model performance goals a nd criteria for the component species of fine particle mass.

............................................................................................................................... .. 32

Table 6.3-1 Model Performance Statistics for the Sipsey Wilderness Area for the 20% best days

............................................................................................................................... ........................ 40

Table 6.3-2 Model Performance Statistics for the Sipsey Wilderness Area for the 20% worst days

............................................................................................................................... ........................ 40 Table 7.2.3-1 2009 Emissions Inventory Summary for Alabama. ............................................... 51 Table 7.2.3-2 2018 Emissions Inventory Summary for Alabama. ............................................... 52

Table 7.5.5-1 2018 Emissions Contributions from Major Source Categories in the AoI- Sipsey 67

Table 7.5.5-2 2018 Emissions Contributions within Major Source Categories in the AoI- Sipsey

............................................................................................................................... ........................ 68 Table 7.6-1 Sipsey Q/d, Q/d^2, RT Max, and fractional contribution analysis (Sources that are 1% or greater of Q/d*RTMax) ...................................................................................................... 69 Table 7.6-2 Cohutta Q/d, Q/d^2, RTMax, and fractio nal contribution analys is (Sources that are 1% or greater of Q/d*RTMax) ...................................................................................................... 70 Table 7.6-3 Great Smokies Q/d, Q/d^2, RTMax, and fractional c ontribution analysis (Sources that are 1% or greater of Q/d*RTMax) ......................................................................................... 71 Table 7.6-4 Joyce Kilmer Q/d, Q/d^2, RTMax, and fr actional contribution analysis (Sources that are 1% or greater of Q/d*RTMax)................................................................................................ 72 Table 7.6-5 St. Marks Q/d, Q/d^2, RTMax, and fracti onal contribution analysis (Sources that are 1% or greater of Q/d*RTMax) ...................................................................................................... 73 Table 7.6-6 Breton Q/d, Q/d^2, RTMa x, and fractional contribution analysis (Sources that are 1% or greater of Q/d*RTMax) ...................................................................................................... 74 Table 7.6-7 Okefenokee Q/d, Q/d^2, RTMax, and frac tional contribution anal ysis (Sources that are 1% or greater of Q/d*RTMax)................................................................................................ 76 Table 7.8.2-1 Alabama’s VOC Exempt BART sources- remaining VI P emissions totals ........... 84 Table 7.8.3-1 Alabama’s non BART-eligible so urces by virtue of emissions limitations .......... 85 Table 7.8.4-1 BART Exemption Modeling Results-Round 1- Old IMPROVE Equation ........... 87 Table 7.8.4-2 BART Exemption Modeling Results–Round 2- New IMPROVE Equation ......... 88

Table 7.8.5-1 2018 NO x and SO 2 Projections for Solutia, Inc ...................................................... 89

Table 7.8.5-2 2018 NO x and SO 2 Projections for International Paper Company, Courtland Mill

............................................................................................................................... ........................ 91

vvi Table 8-1 Sipsey Wilderness Reasonable Progress Goals ............................................................ 96

TABLE OF FIGURES Figure 1.2-1 Geographical Areas of Regional Planning Organizations ......................................... 2 Figure 1.4-1 Class I Areas in the VISTAS Region ......................................................................... 3 Figure 1.5-1 Alabama Class I area- Sipsey Wilderness Area ......................................................... 6 Figure 2.4-1 Average light extinction for the 20% Cleanest Days in 2000-2004 at VISTAS and neighboring Class I areas using the New IMPROVE equation .................................................... 11 Figure 2.4-2 Average light extinction for the 20% Haziest Days in 2000-2004 at VISTAS and neighboring Class I areas using the New IMPROVE equation .................................................... 11 Figure 3.1-1 Uniform Rate of Progress Glidepat h for the 20% worst days at the Sipsey Wilderness

............................................................................................................................... ...... 15 Figure 3.1-2 Uniform Rate of Progress Glidepat h for the 20% worst days at the Sipsey Wilderness

............................................................................................................................... ...... 16 Figure 4.2-1 SO2 emissions in 2002 in the VISTAS States. ........................................................ 22 Figure 5.4.1-1 The MM5 horizontal domain is the outer most, bl ue grid, with the CMAQ 36-km domain nested in the MM5 domain. ............................................................................................. 29 Figure 5.4.1-2 Resolved view of the 12-km grid over the VISTAS region. ................................. 30 Figure 6-1 Monitoring Networks used for VIST AS 2002 model performance evaluation and their location within the VISTAS 12km domain. ................................................................................. 31 Figure 6.2-1 Soccer plot depicting both the m ean fractional error and fractional bias for component concentration for all VISTAS sites. ........................................................................... 34 Figure 6.2-2 A zoomed view of the soccer plot depicting both the mean fractional error and fractional bias for component conc entration for all VISTAS sites. .............................................. 35 Figure 6.2-3 Seasonal soccer plots for all VISTAS IMPROVE monitors. .................................. 35 Figure 6.2-4 Bugle plot of the mean fraction bias for particulate matter and its component concentrations for all VISTAS sites. ............................................................................................ 36 Figure 6.2-5 Bugle plot of mean fraction error for particulate matter and its component species for all VISTAS sites. ..................................................................................................................... 36 Figure 6.3-1 Sipsey Wilderness Area ........................................................................................... 38 Figure 6.3-2: Stacked bar chart for SIPS1 on th e 20% worst days (top) and 20% best days (bottom). Observed composition is presented in the left hand bar, with modeled composition represented by the right hand bar. ................................................................................................. 39 Figure 6.3-3 Time Series plot of NO3 predicted versus observed for the Sipsey Wilderness Area for 2002

............................................................................................................................... .......... 41 Figure 6.3-4 Time Series plot of SO4 predicted versus observed for the Sipsey Wilderness Area for 2002

............................................................................................................................... .......... 41Figure 6.3-5 Time Series plot of OMC predicted versus observed for the Sipsey Wilderness Area for 2002

............................................................................................................................... .......... 42 Figure 6.3-6 Time Series plot of CM predicted versus observed for the Sipsey Wilderness Area for 2002

............................................................................................................................... .......... 42 Figure 6.3-7 Time Series plot of EC predicted versus observed for the Sipsey Wilderness Area for 2002

............................................................................................................................... .......... 43 Figure 6.3-8 Time Series plot of Soil predicted versus observed for the Sipsey Wilderness Area for 2002

............................................................................................................................... .......... 43 Figure 6.3-9 Time Series plot of Sea Salt predic ted versus observed for the Sipsey Wilderness Area for 2002

............................................................................................................................... . 44 Figure 6.3-10 Time Series plot of PM2.5 predicte d versus observed for the Sipsey Wilderness Area for 2002

............................................................................................................................... . 44 Figure 7.2.3-1 Base G2 Annual SO2 emissions for 2002, 2009, and 2018 in the VISTAS States.

............................................................................................................................... ........................ 50 Figure 7.2.3-2 Base G2 Annual NOx emissions in 2002, 2009, and 2018 in the VISTAS States.

............................................................................................................................... ........................ 51 Figure 7.2.4-1 CMAQ 2018 results on the 20% wo rst days at the Sipsey Wilderness Area. ....... 53 Figure 7.2.4-2 Projected visibility improvement on the 20% worst visibility days at VISTAS and neighboring Class I areas. ............................................................................................................. 54 Figure 7.2.4-3 Projected visibility improvement on the 20% best visibility days at VISTAS and neighboring Class I areas. ............................................................................................................. 55 Figure 7.2.4-4 CMAQ 2018 results on the Best 20% days at the Sipsey Wilderness .................. 55 Figure 7.3-1 Estimated international emissions contributions to sulf ate at VISTAS and neighboring Class I areas. ............................................................................................................. 57 Figure 7.3-2 PM component concentrations from U. S. domestic sources on 20% worst visibility days in 2002 (left bars) and CMAQ-simulated inte rnational contributions (right bars) at Sipsey.

............................................................................................................................... ........................ 58 Figure 7.4-1 CMAQ projections of visibility responses on 20% worst days at the Sipsey Wilderness to 30% reductions from the 2009 Base D inventory for visibility-reducing pollutants in different source catego ries and geographic areas. .................................................................... 60 Figure 7.5.1-1 Example back trajectories for 20% worst visibility days in 2002 for the Sipsey Wilderness Area. ........................................................................................................................... 62 Figure 7.5.2-1 Example residence time plot for 20% worst visibility days in 2000-2004 for Sipsey. Based on trajectories with 100m start height. .................................................................. 63 Figure 7.5.3-1 Example SO2 Area of Influence plot for sulfate extinction weighted residence time for the 20% worst visibility days in 2000-20 04 for Sipsey. Based on back trajectories with 100 m start height. ......................................................................................................................... 64 Figure 7.5.4-1 Sipsey 2002 and 2018 SO2 distan ce weighted emissions * SO4 extinctionweighted residence time plots. ...................................................................................................... 65

viiFigure 7.5.4-2 Sipsey 2018 SO2 distance weight ed emissions * SO4 extinction-weighted residence time plots. ...................................................................................................................... 66 Figure 7.10-1 Predicted Visibility for the 20% Worst Days in 2018 at the Sipsey Wilderness using the New IMPROVE Equation............................................................................................. 93 Figure 7.10-2 Predicted Visibility for the 20% Best Days in 2018 at the Sipsey Wilderness using the New IMPROVE Equation. ...................................................................................................... 94 Figure 7.10-3 Visibility Improvements on 20% wo rst days at the Sipsey Wilderness between 2000-2004 Baseline Conditions (left side) and the 2018 Base G2a projected visibility (right side)

............................................................................................................................... ........................ 95 Figure 7.10-4 Projected Visibil ity for the 20% Worst Days fo r Natural Background Visibility Conditions at the Sipsey Wilderness. ............................................................................................ 95 Figure 9-1 PM2.5 Monitoring Network in Alabama .................................................................... 98

viiiix LIST OF APPENDICES

Appendix A: VISTAS Memorandu m of Understanding and Bylaws

Appendix B: Alabama’s 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 Analyses

Appendix H: Reasonable Progress Evaluation/Long Term Strategy

Appendix I: Data Access

Appendix J: Consultation Process

Appendix K: Recommended Improvements

Appendix L: WINHAZE Figures

Appendix M: CART Analysis

Appendix N: VISTAS’ Technical Support Documents

Appendix O: Cargill RP Analysis

Appendix P: FLM Comments on Alabama’s Draft RH SIP

## EXECUTIVE SUMMARY

Haze is defined as visibility impairment caused by the absorption and scattering of light by fine particles. Regional Haze is caused by sources and activities that emit these fine particles and their precursors over a la rge region, including areas of concern, such as national parks, forests, and wilderness areas (defined as “Class I” Federal areas). The Clean Air Act (CAA) mandates requirements to protect visibilit y, especially in Class I areas. In 1999, the U.S. Environmental Protection Agency (EPA) finalized the Regional Haze Rule, which called for state, tribal, and federal agencies to work together to improve visibility in 156 national parks and wilderness areas across the nation.

States are required to devel op and implement air quality protection plans called State Implementation Plans (SIPs) to reduce the polluti on that causes visibility impairment. These plans establish goals and emission reduction stra tegies based on trends from various source sectors. Under the Regional Haze Rule, states ar e required to develop SIPs to reduce visibility impairment with the express intent that by 2064, visibi lity in the Class I areas will be returned to natural conditions.

Five multi-state Regional Planning Organizations (R POs) have been working together to develop the technical basis for these SIPs. The products of the RPOs will be used by states to establish monitoring strategies for evaluating visibility conditions, baselines, and trends, and to develop long-term (10 to 15 years) strategies for maki ng "reasonable progress" toward eliminating all manmade visibility impairment from mandatory Class I areas by 2064.

With the help of the Visibility Improvement - State and Tribal Association of the Southeast (VISTAS) RPO, the State of Alab ama has developed a SIP to addre ss visibility impairment at the Sipsey Wilderness Area, located within the Bankhead National Forest in northeast Alabama, as well as visibility impairment associated with Al abama source impacts at Class I areas outside of Alabama. In developing this SIP, Alabama ha s sought to develop reasonable progress goals and long-term strategies for the Sipsey Wilderness.

For the 20% worst days, Alabama has adopted a reasonable progress goal that reflects a 5.5 deciview (dv) improvement in vi sibility at the Sipsey Wilderne ss by 2018. This goal represents progress greater than the uniform rate of progres s for the same time period. Likewise, Alabama has also adopted a reasonable progress goal fo r the 20% best days that reflects a 1.4 dv improvement in visibility at the Sipsey Wilder ness by 2018. This goal represents no degradation above the uniform rate of progress for the same period. The aforementioned improvements in visibility are expected to result from the implementation of existing and planned emission controls that form the basis for this SIP.

xACKNOWLEDGEMENTS

ADEM would like to acknowledge the assistance and support of the many contractors that played a crucial role in the VISTAS initiative. ADEM appreciates greatly their dedication to the project. In particular, ADEM would like to si ngle out Patricia Brew er, VISTAS Technical Coordinator and commend her on the many ro les she played during this process.

Air Resource Specialists: Monitoring Data Analysis

Alpine Geophysics: Emissions and Air Quality Modeling, Tec hnical Advisor for Emissions Inventory

Atmospheric Research and Analysis, Inc.: Oper ation of continuous monitors at Millbrook, NC

Baron Applied Meteorology: Meteorological Modeling

Desert Research Institute: Carbon Source Attribution including sample filter preparation, sample analysis using Gas Chromatography and Mass Sp ectrometry, source attribution using Chemical Mass Balance analyses and Pos itive Matrix Factorization

Earth Tech, Inc: BART Modeling, CALPUFF Training

ENVIRON: Emissions a nd Air Quality Modeling

Georgia Institute of Technology: Emissi ons and Air Quality modeling sensitivities

Harvard University: GEOS-Chem global model

ICF Consulting: Integrated Planning Model for future electric utility generation

MACTEC: 2002, 2009, and 2018 Emissions Invent ory and Projections, CALPUFF training

E.H. Pechan and Associates: 2002 Mobile Inventory

Research Triangle Institute: Chemi cal Analysis of Monitoring Samples

System Applications International: Meteorological Characterization

Tennessee Valley Authority: Op eration of continuous monitors at Great Smoky Mountain National Park and preparation of final project report and draft manuscript

Ivar Tombach, Environmental Consultant

TRC, Inc: BART Modeling

University of California, Riverside: Emissions and Air Quality Modeling

xixii VISTAS Technical Coordinator, Pat Brewer

Woods Hole: Analysis of Carbon 14 isotope in carbon samples as part of carbon source attribution project

### 1.0 INTRODUCTION

1.1 What is Regional Haze?

Regional Haze is defined as visibility impairment that is caused by the emission of air pollutants from numerous sources located over a wide geog raphic area. Haze is caused when sunlight is absorbed or scattered by airborne particles which, in turn, reduces the clarity and color of what is seen. Regional Haze refers to haze that impairs vi sibility in all directio ns uniformly. Airborne particles are produced by a variety of natural and manmade s ources. Windblown dust and soot from wildfires contribute to haze, as do motor vehicles, electric generating facilities, industrial fuel burning, and manufacturing operations.

Pollution from particulate matter (PM) is the majo r cause of reduced visibility (haze) in the United States, including many of the national park s, forests and wilderne ss areas (Federal “Class I” areas as defined in 40 CFR Part 81.400). Wh ile some haze-causing particles are emitted directly to the air, s econdary particles can be formed when emitted gases form particles well downwind of the emission sources. Nitrates and sulf ates are examples of secondary particles that contribute to haze. In many Class I areas, the vi sual range can be substantially reduced by air pollution. In eastern Class I ar eas, average visual range has decreased significantly.

1.2 What are the Requirements under the Clean Air Act for addressing Regional Haze?

In Section 169A of the 1977 Amendments to the Clean Air Act (CAA), Congress set forth a program for protecting visibility in Federal Class I areas which calls for the “prevention of any future, and the remedying of any existing, impairment of visibility in mandatory Federal Class I areas which impairment results from manmade ai r pollution.” Congress a dopted the visibility provisions to protect visibility in 156 nationa l parks and wilderness areas. On December 2, 1980, EPA promulgated regulations to address vi sibility impairment (45 FR 80084). The 1980 regulations were developed to addr ess visibility impairment that is “reasonably attributable” to a single source or small group of sources. Thes e regulations represente d the first phase in addressing visibility impairment and deferred action on regional haze that emanates from a variety of sources until monito ring, modeling, and scie ntific knowledge about the relationships between pollutants and visibility impairment improved.

In the 1990 Amendments to the CAA, Congress added Section 169B and called on EPA to issue Regional Haze rules. The Regional Haze Rule (fro m hereinafter referred to as the Rule) that EPA promulgated on July 1, 1999 (64 FR 35713), revi sed the existing visib ility regulations in order to integrate provisions addressing regional haze impairment and establish a comprehensive visibility protection program fo r Federal Class I areas. States are required to submit State Implementation Plans (SIPs) to EPA that set out each state’s plan for complying with the Rule, including consultation and coor dination with other states an d with Federal Land Managers (FLMs). The timing of the SIP submittal is tied to EPA’s promulgation of designations for the National Ambient Air Quality Standard (NAAQS) for fine particulate matter (PM 2.5 ) at 40 CFR Part 81. States must submit a Regional Haze SIP to EPA within three ye ars after the date of

1designation. EPA promulgated PM 2.5 designations on December 17, 2004, and thus, Regional Haze SIPs must be submitted by December 17, 2007.

The Rule addressed the combined visibility e ffects of various polluti on sources over a wide geographic region. This meant that many states – even those without Class I Areas – would be required to participate in haze reduction effo rts. EPA designated five Regional Planning Organizations (RPOs) to assist with the coordination and coope ration needed to address the visibility issue (Figure 1.2-1). Those states that make up th e southeastern portion of the contiguous United States are know n as VISTAS (Visibility Impr ovement – State and Tribal Association of the Southeast), and include the following states: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolin a, South Carolina, Tennessee, Virginia, and West Virginia.

Figure 1.2-1 Geographical Areas of Regional Planning Organizations

1.3 General Overview of Regional Haze SIP requirements

The Rule at 51.308(d) requires states to demons trate reasonable progress toward meeting the national goal of a return to natural visibility conditions by 2064. As a guide for reasonable progress, the Rule directs states to graphically show what would be a “uniform rate of progress” toward natural conditions for each mandatory Fede ral Class I area within the state and/or for each mandatory Federal Class I area located out side 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 visibili ty conditions in 2064, and th e rate of uniform progress between baseline and background conditions. The unifo rm rate of progress is also known as the “glidepath.”

2The Rule then requires states to establish R easonable Progress Goals (RPGs), expressed in deciviews (dv), for visibility improvement at each affected Class I area covering each (approximately) 10-year period until 2064. The goa ls must provide for reasonable progress towards achieving natural visibil ity conditions, provide for improvement in visibility for the most impaired days over the period of the implementation plan, and ensure no degradation in visibility for the least impaired days over the same period (See 51.308(d)(1)).

In order to ensure that visi bility goals are prop erly met and set, SIPs must include determinations, for each Class I area, of the base line visibility conditions (expressed in dv) for the most impaired and least impaired days. SIPs must also contain supp orting documentation for all required analyses used to calculate the degree of visibility impairment under natural visibility conditions for the most impaired and least impair ed days (See 51.308(d)(2)). In addition, states must include a monitoring strategy for measuri ng, characterizing, and repor ting of regional haze visibility impairment that is representative of al l mandatory Federal Class I areas within the state (See 51.308(d)(4)).

This first set of RPGs must be met through meas ures contained in the st ate’s long-term strategy (LTS) covering the period from the present un til 2018. The LTS includes enforceable emissions limitations, compliance schedules, and other meas ures as necessary to achieve the RPGs, including all controls required or expected under all federal a nd state regulations by 2009 and by 2018. During development of the LTS, states are also required to consider specific factors such as the abovementioned ongoing control programs, measures to mitigate construction activities, source retirement and replacement schedules, sm oke management programs for agriculture and forestry, and enforceability of specific measures (See 51.308(d)(3)).

In addition, a complementary component to each st ate’s first LTS is dictat ed by the specific best available retrofit technology (BART) requirement s in 40 CFR 51.308(e) of the Rule. The Rule at 51.308(e) requires states to include a determ ination of BART for each BART-eligible source in the state that emits any air pollutant, whic h may reasonably be anticipated to cause or contribute to any impairment of visibility in any mandatory Federal Class I area. Section 169A(b) of the Clean Air Act (CAA) defines BART -eligible sources as s ources in 26 specific source categories, in operation within a 15-year period prior to enac tment of the 1977 CAA Amendments, whose emissions contri bute to visibility impairment in Class I areas. States must determine BART according to five factors set ou t in Section 169A(g)(7) of the CAA. Emission limitations representing BART and schedules for compliance with BART for each source subject to BART must be included in the LTS.

SIPs for the first review period will cover the LT S for visibility improvement between baseline conditions in 2000-2004 and 2018. Stat es are required to evaluate progress toward RPGs every 5 years to evaluate whether installed emissions controls are on track with emissions reduction forecasts in each SIP. The first interim revi ew will be due to EPA in December 2012 and will include an evaluation of the emissi ons since the baseline date as we ll as a review of the visibility improvement to date. If emissions controls are not on track to meet SIP forecasts, or the visibility improvement is not consistent with what is expected, then states would need to take

2action to assure emissions controls by 2018 will be c onsistent with the SIP or to revise the SIP to be consistent with the revised emissions forecast.

1.4 VISTAS

The Visibility Improvement State and Tribal Asso ciation of the Southeast (VISTAS) is one of five Regional Planning Organiza tions (RPOs) that have re sponsibility for coordinating development of SIPs and Tribal Implementation Pl ans (TIPs) in selected areas of the U.S. to address the requirements of the Rule. VISTAS is a regional partnership of states, tribes, federal agencies, stakeholders and citi zen groups establishe d to initiate and coordinate activities associated with the management of Regional Ha ze and other air quality issues within the VISTAS states. The VISTAS region includes the states of Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolin a, South Carolina, Tennessee, Virginia, and West Virginia. The objectives of the VISTAS project are to establish natural background visibility conditions across the mandatory Federal Class I areas, identify current visibility impairment levels, analyze emission control levels that will achieve inte rim visibility goals, and provide adequate documentation to member agencies so that they can develop their Regi onal Haze State/Tribal Implementation Plans (SIP/TIP). Figure 1.4-1 sh ows the 18 mandatory Federal Class I areas in the VISTAS Region, where visibility is an important value. Table 1.4-1 list s these Class I areas.

Figure 1.4-1 Class I Areas in the VISTAS Region

3Table 1.4-1 Mandatory Class I Federal Areas in the VISTAS Region Where Visibility is an Important Value

Mandatory Class I Federal Areas in the VISTAS Region Where Visibility is an Important Value

State Area Name Acreage Federal Land Manager 40 CFR §81.401 Alabama Sipsey Wilderness 24,922 USDA-FS 40 CFR §81.407 Florida Chassahowitzka Wilderness 23,360 USDI-FWS Everglades National Park 1,397,429 USDI-NPS St. Marks Wilderness 17,745 USDI-FWS 40 CFR §81.408 Georgia Cohutta Wilderness 36,977 USDA-FS Okefenokee Wilderness 343,850 USDI-FWS Wolf Island Wilderness 5,126 USDI-FWS 40 CFR §81.411 Kentucky Mammoth Cave National Park 51,303 USDI-NPS 40 CFR §81.422 North Carolina Great Smoky Mtns National Park 273,551 USDI-NPS

Joyce Kilmer-Slickrock Wilderness 17,394 USDA-FS Linville Gorge Wilderness 11,786 USDA-FS Shining Rock Wilderness 18,483 USDA-FS Swanquarter Wilderness 9,000 USDI-FWS 40 CFR §81.426 South Carolina Cape Romain Wilderness 28,000 USDI-FWS

40 CFR §81.428 Tennessee Great Smoky Mtns National Park 241,207 USDI-NPS Joyce Kilmer-Slickrock Wilderness 17,394 USDA-FS 40 CFR §81.433 Virginia James River Face Wilderness 8,886 USDA-FS Shenandoah National Park 190,535 USDI-NPS 40 CFR §81.435 West Virginia Dolly Sods Wilderness 10,215 USDA-FS Otter Creek Wilderness 20,000 USDA-FS

1.5 Alabama’s Class I Area

Alabama has one Class I area within its borders, the Sipsey Wilderness area located within the Bankhead National Forest in Northeastern Alabam a (Figure 1.5-1). The Alabama Department of Environmental Management (ADEM) is responsible for developing the Regional Haze SIP. This SIP establishes Reasonable Progress Goals (RPGs) for visibility improvement at the Sipsey Wilderness, and a Long Term Strategy (LTS) that will achieve those RPGs within the first Regional Haze planning period.

As required by the Rule, in developing this SI P, emission sources outsi de of Alabama were considered as possibly affecting vi sibility at the Sipsey Wilderness area, as well as consideration that emission sources within Alabama may affect visibility at Class I ar eas in neighboring states.

4Through both VISTAS and CENRAP, Alabama has wo rked to assess state-by-state contributions to visibility impairment in specific Class I areas, including those in Alabama and those potentially affected by emissions from Alabama.

Prior to VISTAS, the southern states cooperated in a voluntary regional pa rtnership “to identify and recommend reasonable measures to remedy exis ting and prevent future adverse effects from human-induced air pollution on the air quality re lated values of the Southern Appalachian Mountains”. States c ooperated with FLMs, EPA, industr y, environmental organizations, and academia to complete a technical assessment of the impacts of acid deposition, ozone, and fine particles on sensitive resources in the Southern Appalachians . The Southern Appalachian Mountains Initiative (SAMI) Final Report was delivered in August 2002. The SAMI Assessment concluded that ammonium sulfate is the major contributor to visibility impairment in the Southern Appalachian Mountains and, to improve visibility, it is most important to reduce sulfur dioxide emissions. SAMI also concl uded that reducing ammoni a 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. SAMI modeling found that on the hazi est days, much of the benefit of emissions reductions would occur in the state where emi ssions reductions were made. Emissions in surrounding SAMI states and states outside the SAMI region also cont ribute to air quality in the SAMI Class I areas. The SAMI states supporte d strong national multi-pollutant legislation to accomplish its mission. Emissions reductions to meet national health standards for ozone and fine particles were expected to also improve air quality in the Southern Appalachian Mountains. SAMI states committed to consider air quality be nefits in the Southern Appalachians as they developed SIPs for the health standards.

56 Baldwin Barbour Chambers Chilton

Choctaw Cleburne

Coffee Butler Coosa

Covington Cullman

Dale Elmore

Escambia Etowah Cherokee

Fayette

Geneva Henry

Houston Jackson

De

Lamar Lauderdale

Limestone

Lowndes Macon Lee

Russell Madison

Marion

Colbert

Franklin Lawrence

Mobile Washington Clarke

Monroe

Conecuh Montgomer y Perry

Dallas Pickens

Pike Crenshaw Bullock Blount

Shelby Talladega St Clair

Clay Calhoun

Sumter

Marengo Hale

Wilcox Tuscaloosa

Bibb Walker

Jefferson Marshall Morgan Sipsey Wilderness

A utauga

Figure 1.5-1 Alabama Class I area- Sipsey Wilderness Area

1.6 State and Federal Land Manager (FLM) Coordination

As required by 40 CFR 51.308(i)(4), the Regiona l Haze SIP must include procedures for 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 Fede ral Class I area within the state. Section 11 ofthe narrative discusses plans to continue consultation as well as integrating existing programs to insure further progress towards the 2064 goal.

Successful implementation of a Regional H aze program will involve long-term regional coordination among states, as with the VISTAS planning organization. A copy of the VISTAS Memorandum of Agreement and Bylaws is enclosed as Appendix A.

A technical support document for the Regional H aze SIPs has been prepared cooperatively by VISTAS staff and states to ch aracterize Regional Haze in the s outheastern United States. The report includes a review of the science and situ ation, calculation of initial baseline visibility, review of monitoring data/data gaps, and recommendations for additional monitoring, initial emission inventory characterization and projec tions, and compliance with existing control programs. Source contributions to VISTAS mand atory Federal Class I areas are also assessed. These technical support documents are in cluded as Appendix N of this SIP.



### 2.0 ASSESSMENT OF BASELINE A ND CURRENT CONDITIONS AND
ESTIMATES OF NATURAL BACKGROU ND CONDITIONS IN CLASS I
AREAS

The goal of the Rule is to restore natural visib ility conditions to the 156 Class I areas identified in the 1977 Clean Air Act Amendments. Section 51.301(q) defines natural c onditions: “Natural conditions include naturally occurring phenomena that reduce visibility as measured in terms of light extinction, visual range, contrast, or colo ration.” The Regional Haze SIPs must contain measures that make “reasonable progress” towa rd this goal by reducing anthropogenic emissions that cause haze.

An easily understood measure of visibility to mo st people is visual range . Visual range is the greatest distance, in kilometers or miles, at wh ich a dark object can be viewed against the sky. For evaluating the relative contributions of polluta nts to visibility impairment, however, the most useful measure of visibility impa irment is light extinction, which is usually expressed in units of inverse megameters (Mm -1 ). Light extinction affects the clarity and color of objects being viewed.

The measure used by the Rule is the deciview (dv) . Deciviews are calculated directly from light extinction using a logarithmic scale. The dv is a useful measure for tracking progress in improving visibility, because each dv change is an equal incremental change in visibility perceived by the human eye. In most situations, most people can detect a change in visibility at one dv.

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,

72) baseline conditions, and 3) current conditions.

Each of the three metrics includes the concentrati on 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 c onverted to deciviews (dv) is calculated for the average of the 20% best and 20% 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 Inte ragency Monitoring of Protected Visual Environments (IMPROVE) monitoring data for 2000 through 2004 and is equivalent to “current” visibility conditions for this initial review peri od. The comparison of initial baseline conditions to natural visibility conditions indicates the amount of improvement necessary to attain natural visi bility by 2064. Each state must estimate natural visibility levels for the baseline period for Class I areas with in its borders (See 51.308(d)(2)). “Current conditions” are assessed every five years as part of the SIP re view where actual progress in reducing visibility impairment is compared to the reductions pr ojected in the SIP.

2.1 Estimating Natural Conditions fo r the Sipsey Wilderness Area

Natural background visibility, as de fined in the 2003 EPA document Guidance for Estimating Natural Visibility Conditions Under the Regional Haze Rule

, is based on annual average concentrations of fine particle components. Natural background visibi lity for the 20% worst days is estimated by assuming that fine partic le concentrations for natural background are normally distributed and the 90 th percentile of the annual di stribution represents natural background visibility on the 20% worst days. The 20% worst and 20% best days for the Sipsey Wilderness can be found in Section 3.0 of Appendix G.

In the 2003 guidance, EPA also provided that stat es may use a “refined approach” to estimate the values that characterize the natural visibility conditions of the Class I areas. The purpose of such a refinement would be to provide more accura te estimates with changes to the extinction algorithm that may include the concentration va lues, factors to calculate extinction from a measured particular species and particle size, the extinction coefficien ts 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 original equation to account for the most recent data. The choice between use of the old or the new equation for calc ulating 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 accounts for the e ffect of particle size distribution on light extinction efficiency of sulfate, nitrate, and organic carbon. The mass multiplier for organic carbon (particulate organic matter) is increased from 1.4 to 1.8. New terms are added to the equation to account for light extinction by sea salt and light absorpti on by gaseous nitrogen dioxide. Site-specific values are used for Rayl eigh scattering to account for the site-specific effects of elevation and temperat ure. Separate relative humidity enhancement factors are used

8for small and large size distribut ions of ammonium sulfate and ammonium nitrate and for sea salt. The elemental carbon (light-absorbing carb on), fine soil, and co arse mass terms do not change between the original and new IMPROVE equation.

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 th e Sipsey Wilderness Class I Area

Baseline visibility conditions at the Sipsey Wilderness area are estimated using sampling data collected at the IMPROVE monitoring site. A fi ve year average (2000 to 2004) was calculated for each of the 20% worst and 20% best visibil ity days in accordance with 40 CFR 51.308(d)(2) and the 2003 EPA Guidance

. IMPROVE data records for the Sipsey Wilderness for the period 2000 to 2004 meet EPA requirements for data comp leteness (75% for the year and 50% for each quarter).

2.3 Summary of Natural Background and Baseline Conditions for the Sipsey Wilderness
Area

Table 2.3-1 presents the estimated natural bac kground and baseline visibility metrics for the Sipsey Wilderness area. Baseline visibility on th e 20% worst days at Sipsey is 29.0 dv; natural background visibility at Sipsey is predicted to be 10.9 dv. Note that Alabama did not consider non-U.S. anthropogenic emissions as part of na tural background. The data used by ARS were downloaded from VIEWS and the data was update d in September 2007 using revised data for natural background provided by the United States Forest Service (USFS) to correct data with errors previously provided by CIRA. This co rrected data approved by the USFS was used by VISTAS. The USFS files are provided in Appendix B. There may be slight differences in the dv numbers presented in the graphs in this document. For exam ple, the data supplied by Scott Copeland (USFS) included a natural background nu mber of 10.99 dv. However, in tabulating an average across individual days, the number was truncated. Therefore, the VISTAS modeling contractor used a natural background number for Sipsey of 10.9 dv. This difference of 0.09 dv is not expected to change any conclusions for the Sipsey Wilderness area. A similar argument can be made for the 2018 rate of progress dv level.

9Table 2.3-1 Natural Background and Baseline Conditions for the Sipsey Wilderness Area

Natural Background Conditions Class I area Average for 20% Worst Days (dv) Average for 20% Best Days (dv) Average for 20% Worst Days Bext (Mm-1) Average for 20% Best Days Bext (Mm-1) Sipsey Wilderness 10.9 5.00 30.1 16.6

Baseline Visibility Conditions 2000-2004 Class I area Average for 20% Worst Days (dv) Average for 20% Best Days (dv) Average for 20% Worst Days Bext (Mm-1) Average for 20% Best Days Bext (Mm-1) Sipsey Wilderness 29.0 15.6 187.3 48.3

The 20% worst and 20% best days for the Sips ey Wilderness can be found in Section 3.0 of Appendix G.

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

As can be seen in Figure 2.4-1, th e pollutant breakout fo r all the VISTAS Class I areas paints a similar picture. On the 20% worst days sulfates , shown in the figure in yellow, play a dominate role all at the sites with the exception of Evergl ades. The 20% best visibility days also show a strong sulfate signature, but this is coupled with nitrate and pa rticulate organic matter (Figure 2.4-2)

100 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 2.4-1 Average light extinction for th e 20% Cleanest Days in 2000-2004 at VISTAS and neighboring Class I areas using the New IMPROVE equation

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

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 2.4-2 Average light extinction for th e 20% Haziest Days in 2000-2004 at VISTAS and neighboring Class I areas using the New IMPROVE equation

11Ammonium sulfate , (NH 4 ) 2 SO 4 , is the most important contributor to visibility impairment and fine particle mass on the 20% worst and 20% best visibility days at the Sipsey Wilderness. Sulfate levels on the 20% worst days account for 75% of the visibility impairment. On the 20% best days, sulfate levels are more uniform acr oss the region. On the best days at Sipsey, ammonium sulfate accounts for 50% of visibility impairment. Sulfate particles are formed in the atmosphere from sulfur dioxide emissions. Su lfate 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, NH 3 , in the atmosphere.

Particulate Organic Matter (POM) is the second most important c ontributor to fine particle mass and light extinction on the 20% worst days. On th e 20% best days at the Sipsey Wilderness, POM and ammonium nitrate seem to be equally impor tant contributors to vi sibility impairment. Elevated levels of POM and Elemental Carbon (EC) indicate impact from wildfires or prescribed fire. Significant fire impacts are infrequent at the Sipsey Wilderness. VISTAS collected additional samples of carbon at five sites, including Great Smoky Mountains and Millbrook, (Raleigh, NC), to better understa nd sources contributing to carbon in rural and urban areas. At the Great Smoky Mountains and ot her mountain Class I areas, the majority of carbon is from either biogenic emissions from vegetation or from wood burning. Wood burning is more important in the fall, winter, and early spring mo nths. Biogenic emissions, chiefly emissions of volatile organic compounds (VOCs) from vegeta tion, peak in spring and summer. Carbon from gasoline and diesel engines is a relatively sma ll contribution at the rura l sites. At Millbrook, carbon from fossil fuel combustion is a larger percen tage contribution than at the rural sites, but still less than 50% of total carbon measure d. These results sugge st that controlling anthropogenic sources of carbon will have little be nefit in improving visibility in Class I areas. Controlling anthropogenic sources of carbon will likely be more e ffective to reduce levels of PM 2.5 in urban areas.

Ammonium nitrate , NH 4 NO 3 , is formed in the atmosphere by the reaction of ammonia and nitrogen oxides. In the VISTAS region, nitrat e formation is limited by the availability of ammonia and by temperature. Ammonia preferentia lly reacts with sulfur dioxide and sulfate before reacting with nitrogen oxides. Particle nitr ate is formed at lower te mperatures; 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 wint er days and are more important for the coastal sites where 20% worst days can occur on winter days. Nitrogen oxides are emitted by fossil fuel combustion by point, area, on road, and non road sources. Modeli ng data indicates that in the VISTAS region ammonium nitrate formation is limited by ammoni a concentrations and s uggests that for winter days, controls of ammonia sources would be more effective in reducing a mmonium nitrate levels than control of nitrogen oxides.

Elemental Carbon (EC) is a comparatively minor contribut or to visibility impairment. Sources include agricultural and wildland (wildfire, wildland fire use a nd prescribed fire) burning, and incomplete combustion of fossil fuels. EC levels are higher at urban monitors than at the Class I areas and suggest controls of fossil fuel combustion sources would be more effective to reduce PM 2.5 in urban areas than to improve visibility in Class I areas.

12Soil fine particles are minor contributors to visibility impairment at most sites on most days. Occasional episodes of elevated fi ne soil can be attributed to Saharan dust episodes, particularly at Everglades, Florida, but rarely are seen at the Sipsey Wilderness areas. No control strategies are indicated for fine soil.

Sea salt , NaCl, is observed at the coastal sites. Sea sa lt contributions to visi bility impairment are most important on the 20% cleanest days when su lfate and POM levels are low. Sea salt levels do not contribute significantly to visibility on the 20% worst visibility days. The new IMPROVE equation uses Chloride ion, Cl - , from routine IMPROVE measurements to calculate sea salt levels. Cl - may react with nitrate and volatize off the filter, so Cl - may underestimate sea salt levels. VISTAS funded sodium ion, Na + , analyses of the IMPROVE filters from October 2003 to December 2004 for nine sites. Na + measures may overestimate sea salt levels because sodium nitrate is an additional source of sodium ion that cannot be distinguished from sodium chloride. The best understanding to date suggests that using Cl - may underestimate sea salt levels by as much as a factor of two. This uncer tainty has greatest implications for calculating sea salt levels under natural visi bility conditions, but is not much of a concern at the Sipsey Wilderness.

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 PM 2.5 mass measured on the filter and th e sum of the measured compone nts. 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 calculat ed light extinction on days with higher mass and lower light extinction on days with lower mass. This tends to increase calculated light extinction for cu rrent conditions and to decrease calculated light extinction for natural visibility conditions. Adding sea salt to the new IM PROVE equation increases light extinction for both current and natural visibility conditions. Increasing the mass multiplier for particulate organic matter 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 Alabama, the mo st effective means to improve visibility is to reduce sulfate concentrations.

PM 2.5 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 PM 2.5 nonattainment areas are in close proximity to the Class I areas in the southeastern U.S., including Atlanta, GA; Birmingham, AL; Charleston, WV; Chattanooga, TN; Louisville, KY; and Knoxville, TN. Ammonium sulfate concen trations are comparable between urban and nearby Class I areas, while organic carbon, elemental carbon, and nitrate concentrations are generally higher in urban areas than in the Class I areas. These results sugg est that sulfate is wide ly distributed regionally while urban areas see an additional incremental po llutant loading from local emissions sources.

1314 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 Cla ss I areas. Days were assigned to one of five visibility classes ranging from poor to good visibility. Days were then assigned to bins based on meteorological conditions. For the Sipsey Wilder ness Class I area, poor visibility days were most likely to occur on days with moderate temperat ures, high relative humidity, low wind speeds, and elevated PM 2.5 mass at upwind urban areas. Precipitation was not a go od predictor of visibility conditions. Weights were assigned to days based on freque ncy of occurrence of days with similar meteorological conditions.

The above analyses are further discussed in Appendix M.



### 3.0 GLIDEPATHS TO NATURAL CONDITIONS IN 2064

As stated in Section 1.3 above, th e Rule directs states to grap hically show what would be a “uniform rate of progress” toward natural c onditions for each mandatory Federal Class I area within the state. The uniform rate of progress is also known as th e “glidepath.” The glidepath is simply a straight graphical line drawn from the baseline level of visibility impairment for 2000- 2004 to the level representing no manmade impairment in 2064.

Each state must set goals for each Class I area that provide for reasonable progress towards achieving natural visibility conditions by 2064. Sec tion 51.308(d)(1) of the Rule requires that Reasonable Progress Goals (RPGs) must both:

(1) provide for improvement in visibility for the most impaired days over the period of the implementation plan; and (2) ensure no degradation in visibility for the least impaired days over the same period.

Glidepaths were developed for each Class I area in the VISTAS region in accordance with EPA guidance and used data collected from the IMPR OVE monitoring sites as described in Section 2.0 above. The glidepath is one of the indicators used in setting the RPGs.

3.1 Glidepath for the Sipsey Wilderness Class I Area in Alabama

The following glidepaths represent the 20% most impaired days for the Sipsey Wilderness, assuming uniform rate of progress toward Regiona l Haze goals. The first glidepath (Figure 3.1- 1) represents work prior to January 2008. This work was the basis for the majority of the conclusions presented in this SI P. However, the “Best and Fina l” run, incorporating all final known inventory changes and BART and RP contro ls, was completed in la te January 2008. This run (Figure 3.1-2) resulted in new 2018 predictions, and subsequent ly, revised glidepaths, which are also presented here. Using 2018 Base G2 em issions, the model projec ted visibility at the Sipsey Wilderness on the 20% worst days would be 23.69 dv. Using 2018 Best and Final emissions, the model projected visibility at th e Sipsey Wilderness on the 20% worst days would be 23.53 dv. For the remainder of this narrative, the most recent “Best and Final” glidepaths will be included. Due to the lateness of the final r un, it was not possible to re vise all of the figuresand tables in this SIP, including those in the Appendices. For this r eason, ADEM has elected to retain the older figures and tables. The diffe rences may be small had they been updated, but ADEM contends that the results of the Best a nd Final Runs show a continued improvement over previous runs that would not change any of the conclusions presented. Natural background visibility at the Sipsey W ilderness is predicted to be slightly less than 11 dv (10.9).



## Progress Assessment Base G2 inventory, new IMPROVE Algorithm-Sipsey- Worst 20% Days- On the Books

Figure 3.1-1 Uniform Rate of Progress Glidepat h for the 20% worst days at the Sipsey Wilderness

Note: Method 1 is the EPA method of aver aging all 20% worst days in 2002 without consideration of m odel performance. Observation 3

29.03Haziness Index (Deciviews) 27.52 3
24.80
26.43 2 23.69 21.78 18.76 2 15.74 1 12.72 10.9 1

5

0 2020 2048 2052

2 000 2 00 4 2 008 2 0 12 2 0 1 6 2024 2028 2032 2036 2040 2044 2056 2060 2064 Yea r

Glide Path Natural Condition Observation Method 1

15

### Progress Assessment Base G2 inventory, new IMPROVE Algorithm Sipsey - Worst 20% Days- Best and Final
35

Method 1 Prediction Observation Glide Path Natural Condition (Worst Days) Yea r 2064 2060 2056 2052 2048 2044 2040 2036 2032 2028 2024 2020 2016 2012 2008 2004 2000 23.53 26.43

10.90 12.72 15.74 18.76 21.78 24.80 27.52 29.03

20 25 30Haziness Index Deciviews (dv)

15

10

5

0

Figure 3.1-2 Uniform Rate of Progress Glidepat h for the 20% worst days at the Sipsey Wilderness

16

### 4.0 TYPES OF EMISSIONS IMPACTING VISIBILITY IMPAIRMENT AT
THE SIPSEY WILDERNESS CLASS I AREA

4.1 Baseline Emissions Inventory

The Rule at 51.308(d)(4)(v) requir es a statewide emissions inve ntory of pollutants that are reasonably anticipated to cause or contribute to visibility impa irment in any mandatory Class I area. An inventory was developed for the ba seline year 2002 and projected to 2009 and 2018. The pollutants inventoried include volatile organic compounds (VOCs), nitrogen oxides (NO x ), fine particulate (PM 2.5 ), coarse particulate (PM 10 ), ammonia (NH 3 ) and sulfur dioxide (SO 2 ). The baseline emissions inventory for 2002 was developed for Alabama following the methods described in Appendix D.

There are five different emissi on inventory source classificati ons: stationary point and area sources, off-road and on-road mobile 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 genera ting utilities (EGUs) and industrial sources are the major categories for stationary point sources. Stationary ar ea sources 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 sign ificant (i.e., dry clea ners, service stations, agricultural sources, fire emissions, etc.). These types of emissions are estimated on a countywide level. Non-road (or off-road) mob ile sources are equipment that can move but do not use the roadways, (i.e., lawn mowers, constr uction equipment, railroad locomotives, aircraft, etc.) The emissions from these sources, like stationary area sources, are estimated on a countywide level. On-road mobile sources are au tomobiles, trucks, and motorcycles that use the roadway system. The emissions from these sources are estimated by vehicle type and road type and are summed to the countywide level. Biogenic sources are natural sour ces like trees, crops, grasses and the natural decay of plants. The emi ssions from these sources are also estimated on a countywide level.

In addition to the various source classificati ons, there are also various types of emission inventories. The first is the 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 eval uating air quality model performance.

The second type of inventory is th e typical base year inventory. Th is inventory is similar to the actual base year inventory, except that for sour ces 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 EGUs and the wildland fi re emissions. The air quality modeling runs using the typical base year inve ntory provide results which are th en used to calculate Relative Response Factors (RRFs) for future years. Th ese RRFs for future years are then used to demonstrate reasonable progre ss toward visibility goals.

Below is an overview of the inventories used fo r each source classifica tion. A more detailed discussion of the emissions inventory deve lopment is contained in Appendix D.

174.1.1 Stationary Point Sources

Point source emissions are emissions from indivi dual sources having a fixe d location. Generally, these sources must have permits to operate, and their emissions are inventoried on a regular schedule. Large sources emitting at least 100 tons per year (tpy) of a criteria pollutant, 10 tpy of a single hazardous air pollutant (HAP), or 25 tp y of total HAPs are inventoried annually. Smaller sources are inventoried less frequently. The poi nt source emissions data can be grouped as EGU sources and other industrial poin t sources, also called non-EGUs.

4.1.1.1 Electric Generating Units (EGUs)
The actual base year inventory for the E GU sources used 2002 Continuous Emissions Monitoring (CEM) data reported to EPA’s Acid Rain program or 2002 hourly emissions data provided by stakeholders. These data provide hourly emissions profiles for SO 2 and NO x that can be used in air quality mode ling. Emissions profiles are used to estimate emissions of other pollutants (volatile organic compounds, carbon monoxide, ammonia, fine particles and soil) based on measured emissions of SO 2 and NO x . Emissions from EGUs vary daily and seasonally as a function of variab ility in energy demand and utilization and outage schedules. To avoid anomalies in future year emissions created by relying on 2002 operations to represent future opera tions, a typical base year emissions inventory was developed for the EGUs. This approach is consistent with EPA’s modeling guidance. To develop a typical year 2002 emi ssions inventory for EGU sources, each unit’s average CEM heat input for 2000 through 2004 was divided by the 2002 act ual heat input to gene rate 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 we re used because the modeled current design values used monitored data from this same 5-y ear 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 quality modeling, VI STAS, in cooperation with the other eastern RPOs, contracted with ICF Resources, L.L.C., to generate future year emission inventories for the electric generating sect or of the contiguous U.S. using the Integrated Planning Model (IPM). IPM is a dynamic linear optimization model that can be used to examin e air pollution control policies for various pollutants throughout the contiguous U.S. for the entire electric power system. The dynamic nature of IPM enables projec tion of the behavior of the power system over a specified future period. Optim ization logic in IPM determines the least-cost means of meeting electric generation and capacity requirements while complying with specified constraints including air pollution regulations, transmissi on bottlenecks, and plant-specific operational constraints. The versatility of IPM allows users to specify which constraints to exercise, and to populate IPM with their own datasets.

The IPM modeling runs took into consideration bo th CAIR implementation, state SIPs and other initiatives such as North Carolin a’s Clean Smokestacks Act (CSA).

184.1.1.2 Other Industrial Point Sources (non-EGUs)
For the non-EGU sources, the same inventory is used 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 (CE RR) 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 EPA’s CAIR anal yses. This data was supplemented with state specific 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 collective emissions could be significant (i.e., combustion of fuels for heating, structure fires, service stations, etc.). Emissions are estimated by multiplying an emission factor by some known i ndicator of collective activity, such as fuel usage, number of households, or population. Stat ionary area source emissions are estimated at the countywide level.

A portion of the area source 2002 base year i nventory for Alabama was developed by ADEM and submitted under the CERR, which the VIST AS contractor obtained. The VISTAS contractor calculated the rema ining portion of the area source i nventory. The sources estimated by the contractor include emissions from animal husbandry, wildland fires, and particulate matter from paved and unpaved roads. For the other st ates within the modeli ng domain, either statesupplied data or data reported under the CERR for 2002 was used.

The actual base year inventory will serve as the typical base year invent ory for all area source categories except for wildland fires. For wildland fires, a typical year inventory was used to avoid anomalies in wildfire activ ity in 2002 compared to longer te rm averages. Development of a typical year fire inventory provided the capabil ity of using a comparable data set for both the base year and future years. The VISTAS Fire Sp ecial Interest Work Grou p 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 rati os, the 2002 acreage was then scaled up or down to develop a typical year inventory.

Future year emissions The VISTAS contractor generated future year emissions inventories for 2009 and 2018 for the regional haze modeling. Growth factors, supplie d either by states or taken from the CAIR emission projections, were applied to project the controlled emissions to 2018. If no growth factor was available from either a state or the CAIR growth f actor files, then EPA’s Economic Growth and Analysis System (EGAS) v.5 growth factors were used. The fire inventories for 2009 and 2018 retain the same fire locations as indicated in the 2002 inventory; only the emissions changed for 2009 and 2018.

194.1.3 Off-Road Mobile Sources
Off-road (or non-road) mobile s ources are equipment that can move but do not use the roadways, such as construction equipment, aircraft, railroad locomotives and lawn and garden equipment. For the majority of the non-road mobile sources, the emissions for 2002 were estimated using EPA’s NONROAD2005c model. For the thr ee source categories not included in the NONROAD model, (i.e., aircraft engines, railroad locomotives and commercial marine, etc.) more traditional methods of estimating the emissi ons were used. The same inventory was used for both the actual and typical base year emissions inventories.

For the source categories estimated using EPA’s NONROAD model, the model growth assumptions were used to create the 2009 and 2018 future year inventories. The NONROAD model takes into consideration re gulations affecting emissions from these source categories. For the commercial marine, railroad locomotives a nd the remaining airport emissions, the VISTAS contractor calculated the future growth in emissions using deta iled inventory data (both before and after controls) for 1996 and 2010, obtained fr om the CAIR Technical Support Document. Where 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. The MOBILE model takes into consider ation regulations 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 da ta reflective of 2002. The MOBI LE model then is run for the 2018 inventory using input data re flective of that year. The 2002 vehicle miles traveled (VMT), was obtained from the Alabama Departme nt of Transportation (ALDOT).

4.1.5 Biogenic Emission Sources
Biogenic emissions were prepared with th e SMOKE-BEIS3 (Biogenic 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 land use data in the Biogenic Emissions Landcover Database version 3 (BELD3) provided by EPA. A sepa rate land classification scheme, based upon satellite (AVHRR, 1 km spatial re solution) and census information, aided in defining the forest, agriculture and urban portions of each county.

4.1.6 Summary 2002 Base G2 (August 2007 version) Baseline Emissions Inventory for
Alabama
Table 4.1.6-1 is a summary of the 2002 baseline actual emission inventory for Alabama. The complete inventory and discussion of the me thodology is contained in Appendix D. The emissions summaries for other VISTAS st ates can also be found in Appendix D.

2021 Table 4.1.6-1 2002 Actual Emissions Inventory Summary for Alabama in tons per year.

VOC NO x SO 2 PM 10 PM 2.5 NH 3 Point 49,332 244,348 544,309 32,886 23,291 2,200 EGU 2,295 161,038 447,828 7,646 4,113 317 Non-EGU 47,037 83,310 96,481 25,240 19,178 1,883 Area 207,952 34,172 54,462 440,663 98,671 60,007 Nonroad 60,487 65,366 7,584 4,949 4,526 33 Mobile 127,295 158,212 6,900 3,903 2,799 5,588 Total 445,065 502,098 613,255 482,402 129,287 67,827

4.1.7 Model Performance Improvements throug h Emissions Inventory Improvements
Since the initial model performa nce evaluation, VISTAS has made several improvements to the emissions inventory, which in turn improve s model performance. These inventory improvements are detailed in the VISTAS emissi ons inventory report a nd in Appendix D, and are summarized here:

• For electric generating utilities (EGUs), the Integrated Planning Mode l (IPM) was used to provide estimates of future year utility pr oduction and emissions. Continuous Emissions Monitoring (CEM) data was used to define seasonal variability in production and emissions. For Base G2 emissions, states updated IPM model projections from 2005 with control data provid ed by utility companies in 2006 through early 2007.

• For on-road vehicle emissions, agencies provided updated MOBI LE model input and VMT 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 act ivity data for 2002 for wildfires, prescribed fire, land clearing and agricultural burn ing and MACTEC developed a 2002 fire inventory. Where data allowed, Alpine Geophysics modeled fire ev ents as point sources. In 2006, the U.S. Forest Service and Fish a nd Wildlife Service provided projections of increased prescribed burning in 2009 and 2018; th ese data were incorporated in the Base G inventory for all states except Florida.

• For non-road engines, the updated EPA N ONROAD2005 emissions model was used in Base G.

• For commercial marine emissions in shipping lanes in the Gulf of Mexico and Atlantic Oceans, ENVIRON created gridded emissions for the VISTAS modeling domain using inventory data newly developed for EPA by Co rbett at the University of Delaware. These emissions were incorporat ed in the Base G modeling.

• Updated inventories from the neighboring RP Os, Mexico, and Canada were incorporated as available.4.2 Assessment of Relative Contri butions from Specific Pollutant s and Sources Categories

Ammonium sulfate is the largest contributor to visibility impairment at the Sipsey Wilderness area, and the reduction of SO 2 emissions would be the most effective means of reducing ammonium sulfate. As illust rated in Figure 4.2-1, 96% of SO 2 emissions in the VISTAS states are attributable to electric gene rating facilities and indus trial point sources. As shown previously in Table 4.1.6-1, approximately 88% of SO 2 emissions in Alabama are attributable to electric generating facilities and industrial point sources.

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

## 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



### 5.0 REGIONAL HAZE MODELING METHODS AND INPUTS

Regional Haze modeling was performed by VISTAS for ten southeastern states, including Alabama. The sections below outline the met hods and inputs used by VISTAS for the regional modeling. Additional details are provided in Appendices C and E.

5.1 Analysis Methods

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 systems:

• Meteorological Model: The Pennsylvania St ate University/National Center for Atmospheric Research (PSU/NCAR) Meso scale Meteorological Model (MM5) is a nonhydrostatic, prognostic meteorological model routinely used for Urban and Regional scale photochemical, fine particulate matte r, and Regional Haze regulatory modeling studies.

• Emissions Model: The Sparse Matrix Oper ator Kernel Emissions (SMOKE) modeling system is an emissions modeling system that generates hourly gridded speciated emission

22• Air Quality Model: EPA’s Models-3/Com munity Multiscale Air Quality (CMAQ) modeling system is an ‘One-Atmosphere’ photochemical grid model capable of addressing ozone, particulate matter (PM), vi sibility and acid deposition at Regional scales for periods up to one year.

EPA Modeling Guidance recommends modeling an enti re 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 modeling year for this demonstration. Meteorological inputs were developed for 2002 using MM5. Emission inventories were al so developed for 2002 and processed through SMOKE. These inputs were used in the air qua lity model to predict fine particle mass and visibility. The model results for 2002 were compared with observed meteorological and air quality data to evaluate model performance. Se veral configurations of the meteorological and air quality model were evaluated to se lect a configuration that gave the best overall performance for the VISTAS region.

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

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

5.2 Model Selection

To ensure that a modeling study is defensible, the selection of the models to be used should be carefully scrutinized. The models selected must be scientifically appropriate for the intended application and be freely accessible to all stakeh olders. The models should address important physical and chemical phenomena in sufficient deta il, using peer reviewed methods. The model formulations and coding must be freely available for review and the model should be available to stakeholders, and their consultants, for ex ecution and verification at no or low cost. The following sections outline the criteria for select ing 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 the Photochemical Grid Model

5.2.1.1 Selection Criteria
For a photochemical grid model to qualify as a ca ndidate for use in a Regional Haze SIP, a state needs to show that it meets the same several general criteria as a model for an attainment demonstration as for a national ambien t air quality standard (NAAQS):

• The model has received a scientific peer review

23• 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 previous performance evaluations that 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, a nd the model cannot othe rwise be proprietary

5.2.1.2 Overview of CMAQ
The photochemical model selected for this study was CMAQ version 4.5.1. For more than a decade, EPA 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 depos ition within a common pl atform. The original justification for the Models-3 development emerged from the challenges posed by the 1990 CAAA and EPA’s desire to develop an a dvanced modeling framework for ‘holistic’ environmental modeling utilizing st ate-of-science representations of atmospheric processes in a high performance computing environment. EPA co mpleted the initial stage of development with Models-3 and released the CM AQ model in mid-1999 as the in itial operating science model under the Models-3 framework.

An advantage of choosing CMAQ as the atmospheri c model is the ability to do one atmosphere modeling. The same model configuration is being applied for the ozone and PM 2.5 attainment demonstrations SIPs, as well as the Regional Haze SIP. A number of features in CMAQ’s theoretical formulation and tec hnical implementation make the model 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 Modeli ng Protocol located in Appendix C.

5.2.2 Selection of the Meteorological Model

5.2.2.1 Selection Criteria
Meteorological models, either through objectiv e, diagnostic, or pr ognostic analysis, extend available information about the state of the atmosphere 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 accura tely replicate important meteorological phenomena in the region of study, and the model's ability to interface with the rest of the modeling systems -- particularly the photochemical grid model. With these issues in mind, the following criteria were established for the meteorological model to be used in this study:

• Non-Hydrostatic Formulation

• Reasonably current, peer reviewed formulation

24• Simulates Cloud Physics

• Publicly available on no or low cost

• Output available in I/O API format

• Supports Four Dimensional Data Assimilation (FDDA)

• Enhanced treatment of Planetary Boundary Layer heights for Air Quality modeling

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

MM5 uses a terrain-following non-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 reference 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 us ed in other Regional air quality models with different coordinate systems by performing a vertical interpolation, followed by a mass conservation readjustment.

Distinct planetary boundary layer (PBL) para meterizations are available for air quality applications, both of which repr esent sub-grid-scale turbulent fluxes of heat, moisture and momentum. One scheme uses a first-order eddy diffusivity formulation for stable and neutral environments and a modified first-order scheme for unstable regimes. The other scheme uses a prognostic equation for the second-order turbulent kinetic energy, while di agnosing the other key boundary layer terms.

Initial and lateral boundary conditions are specified for real data cases from mesoscale three dimensional 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 technique is used to analyze standard surface and radiosonde observations, usi ng 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 quality application studies has been widely reported in the literature (e.g., Emery et al., 1999; Tesc he et al., 2000, 2003) an d many studies have involved comparisons with other prognostic mode ls such as the Regional Atmospheric Modeling System (RAMS) and the Systems Application Inte rnational Mesoscale Model. The MM5 enjoys a far richer application history in regulatory modeling studies compared with RAMS or other models. Furthermore, in evaluations of these mo dels in over 60 recent Regional scale air quality

25application studies since 1995, it has generally be en 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 is located in Appendix E.

5.2.3 Selection of the Emissions Processing System

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

• File System Compatibility with the I/O API

• File Portability

• Ability to grid emissions on a Lambert Conformal projection

• Report Capability

• Graphical Analysis Capability

• MOBILE6 Mobile Source Emissions

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

• Ability to process emissions for the 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

5.2.3.2 Overview of SMOKE
The SMOKE Emissions Processing System Protot ype was originally developed 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 simulates emission rates 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 a pplied to sparse matr ix algorithms. Indeed, SMOKE is the fastest emissions processing tool currently available to the air quality modeling community. The sparse matrix approach utilized throughout SM OKE permits both rapid a nd flexible processing

26of emissions data. The processing is rapid because SMOKE utilizes a series of matrix calculations instead of less effici ent algorithms used in previous systems. The processing is flexible because the processing steps of tem poral projection, controls, chemical speciation, temporal allocation, and spatial allocation have been separated into independent operations wherever possible. The results from these st eps are merged together at a final stage of processing.

SMOKE contains a number of ma jor features that make it an attractive component of the modeling system. The model s upports a variety of input fo rmats from other emissions processing systems and models. It supports bot h a gridded and a 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 the Modeling Protocol in Appendix C.

5.3 Selection of the Modeling Year

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

The April 2007 Guidance on the Use of Models and Ot her 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 demonstrating reasonable progress in attaining the Regional Haze air quality goals. EP A recommends that episode selection derive from several principal criteria:

• Simulate a variety of me teorological conditions;

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

• Model periods for which extensive air quality/e missions/meteorological data bases exist; and

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

For Regional Haze modeling, the guidance goes further by suggesting that the preferred approach is to model a full, representative year. Moreover, the re quired Relative Response Factor (RRF) values should be based on mode l results averaged over the 20% worst and 20% best visibility days determined for each Cl ass I area based on monitoring data from the 2000– 2004 baseline period.

EPA also lists several other c onsiderations to bear in mind when choosing potential Regional Haze episodes including: (a) choose periods wh ich have already been modeled, (b) choose periods which are drawn from the years upon wh ich the current design values are based, (c) include weekend days among those chosen, and (d ) choose modeling periods that meet as many episode selection criteria as po ssible in the maximum number of nonattainment or Class I areas

27as possible. Finally, EPA e xplicitly recommended in its 2007 guidance to use 2002 as the baseline inventory year.

VISTAS adopted a logical, stepwise approach in implementing the guidanc e in order to identify the most preferable, representative year fo r Regional Haze modeling, including the following steps:

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 Classification a nd Regression Tree (CART) Anal yses to evaluate visibility conditions for 2000, 2001, and 2002, as candidate modeling years. The year 2002 was found to be representative of conditions in the other two years. Subsequently, these analyses were repeated with the meteorologi cal and air quality monitoring data for 2000 to 2004 to evaluate how well the 2002 m odeling year represented the full 2000-2004 baseline period. This analysis confirmed that visibility and PM 2.5 mass in 2002 was representative of the five year baseline period for the VISTAS Class I areas. This analysis is discussed in more detail in the project report in Appendix M.

Data Availability: In parallel with the CART analysis, episode characterization analyses, collaborative investigations by VISTAS states (e.g., NCDAQ, Georgia DNR, and FL DEP) intensively studied the availability of PM 2.5 , meteorological, and emissions data and representativeness of a lternative baseline modeling periods from a regulatory standpoint. Additionally, since 2002 was the ye ar that EPA required states to provide emissions inventory data for the Comprehe nsive Emissions Reporting Rule (CERR), 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 RPOs would be modeling, and several had already chosen calendar year 2002 as the modeling year. Choosing a common base year allowed cons istency between inputs and outputs across RPOs, especially in areas that straddle multiple RPOs.

After a lengthy process of integrated studies, th e 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 VI STAS’ Regional Haze modeling. All of the EPA criteria for Regional Haze episode selection were directly consider ed in this process together with many other considerations (e.g., timing of new emissions or aeromet ric data deliveries by EPA or the states to the modeling teams).

285.4 Modeling Domains

5.4.1 Horizontal Modeling Domain
EPA’s modeling guidance recommends a 12- km modeling grid resolution for PM 2.5 modeling while a 36-km grid is consider ed acceptable for Regional Haze. For the VISTAS modeling, a coarse 36-km grid resolution was used for modeling the entire U. S. and a finer 12-km grid was used to model the eastern U.S.

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 the numerical and computational uncer tainty 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 areas, as well as Class I areas for Regional Haze. Processing time was also a factor in choosing a smaller 12-km grid modeling domain.

The coarse 36-km horizontal grid domain covers the continental U.S. This domain was used as the outer grid domain for MM5 modeling with the CMAQ domain nested within the MM5 domain. Figure 5.4.1-1 shows the MM5 horizontal domain as the outer most, blue grid with the CMAQ 36-km domain nested in the MM5 domain.

Figure 5.4.1-1 The MM5 horizontal domain is the outer most, blue grid, with the CMAQ 36-km domain nested in the MM5 domain.

29To achieve finer spatial resolution in the VISTAS states, a one-way nest ed high resolution grid (12-km grid resolution) was used. Figure 5.4.1-2 shows the 12-km grid modeling domain for the VISTAS region. This is the modeling doma in for which the RPGs will be assessed.

Figure 5.4.1-2 Resolved view of the 12-km grid over the VISTAS region.

5.4.2 Vertical Modeling Domain
The CMAQ vertical structure is primarily defined by the vert ical grid used in the MM5 modeling. The MM5 model employed a terrain following coordinate system defined by pressure, using 34 layers that extend from the surface to the 100 mb. A layer-averaging scheme was used to generate 19 verti cal layers for CMAQ to reduce th e computational cost of the CMAQ simulations. The effects of layer averag ing were evaluated in conjunction with the VISTAS modeling effort and were found to ha ve a relatively minor effect on the model performance metrics when both the 34 layer a nd a 19 layer CMAQ models were compared to ambient monitoring data. This is disc ussed in more detail in Appendix E.



### 6.0 MODEL PERFORMANCE EVALUATION

The initial modeling effort focused on evalua ting previous regional air quality modeling applications and testing candidate model conf igurations for the SMOKE emissions and CMAQ model for the VISTAS 36-km and 12-km modeling do mains. This effort resulted in a report recommending the model configuration for the an nual emissions and air quality modeling, which is included as part of the VISTAS Emissions and Air Quality Modeling Protocol. The evaluation of the meteorological modeling configuration can be found in Appendix E, 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.

30Air quality model performance for the 2002 modeling year was initially tested in 2004 using an early version of the VISTAS emissions inventory. In keeping with the one atmosphere objective of the CMAQ modeling platform , model performance 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 netw orks (Figure 6.0-1).

Figure 6-1 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 mode l performance for ozone and fine particles. EPA’s 2007 modeling guidance for fine particulat e matter at the time noted that PM models might not be able to achieve the same leve l of performance as ozone models. VISTAS’ evaluation considered several sta tistical performance measures a nd 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 all IMPROVE monitors in the full model performance evaluati on (Please see Appendix N).

31Table 6.1-1 Established model performance goals a nd 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 c oncentrations and deposition by species, monitoring network, and month 2. Time series plots of predic ted and observed concentrati ons and deposition by species, monitoring site, and month 3. Spatially averaged time series plots 4. Time series plots of monthly fractional bi as and error by species , region, and network 5. Performance goal plots (“soccer plots”) that summarize model performance by species, region and 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 pl ots” are relatively new tools in model performance evaluations, and have recently been included as model performance evaluation displays in EPA’s 2007 modeling guidance for Ozone, PM 2.5 , and Regional Haze. Both “soccer plots” and “bugle plots” allow for a convenient way to examine model perfor mance with respect to set goals and criteria. The bugle plots have the added benefit of adjus ting the goals and criter ia to consider the concentration of the species. An alysis of “bugle plots” generally suggests 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 contri butions to total PM mass (e.g. elemental carbon, nitrate, and soil).

The initial model performance ev aluation in 2004 demonstrated th at sulfate and elemental carbon met VISTAS goals and criteria while performance for the other components of fine particulate matter did not meet VISTAS goals and criteria . Nitrate was consiste ntly over predicted on winter days and under predicted in summer. This result was attributed to high uncertainty in ammonia emissions and, in particular, the seas onal allocation of ammonia emissions. In 2005, the ammonia inventory was updated with more recent emissions data and emissions were

3233 reallocated seasonally (lower pe rcentage of annual emissions assigned to winter and higher percentage to summer). These ammonia i nventory improvements improved nitrate model performance, especially in the winter. Organic carbon was significantly under predicted in the summer in the initial model performance evaluation. Comparison of model projections of primary and secondary carbon with measurements made by the SEARCH network in Birmingham and Atlanta indicated that the model was under predicting secondary organic carbon. This under prediction was deemed to be unacceptable because organic carbon is a majo r contributor to PM mass and visibility impairment in the southeastern U.S. Evalua tion of the components of the CMAQ Secondary Organic Aerosol (SOA) module indicated that several processes poten tially important to formation of biogenic SOA were not included. ENVIRON modified th e SOA module in CMAQ to incorporate additional SOA formation from is oprenes and sesquiterpenes and to account for polymerization of SOA so that particles do not revolatize. These modifications to the SOA formation in CMAQ were reviewed by EPA Office of Research and Development, published in Atmospheric Environment in 2006, and are documented in the VISTAS Technical Support Document. (Please see Appendix N) Model performance for SOA improved with the incorporation of these modificat ions to the SOA module. With the release of CMAQ Version 4.5 in October 2005, the SOAmods enhancement was a dded to the AERO3 aerosol module for use in the VISTAS regulatory modeling.

Both fine soil and coarse matter were over predicted by CMAQ in th e initial model performance. The discrepancy for fine soil appears to be more related to nomenclature than model performance. The monitoring networks include only metal oxides in the definition of fine soil while emissions inventories include other co mponents in addition to metal oxides in the inventory category assigned to fine soils. M odeled metal oxides agreed well with observed metal oxides. Coarse matter is likely over predic ted because the transport of primary particulate matter has been over estimated. In 2006, EPA reco mmended that the transpor t factor for primary PM emissions be revised to account for near field deposition of particles. Model performance improved when the revised transport factors were incorporated into the emissions inventory.

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 annual air quality modeling. Focus is limited 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 PM 2.5 , as good model performance of the component species will dictate good model performance of total or reconstituted PM 2.5 . Model performance of the total PM 2.5 and the resulting total light extinction will al so be provided as a means to discuss the overall model performance for this SIP.

In the analyses, mean fractional bi as (error) is used in lieu of m ean bias (error), to prevent low observations and model predictions from skewing the metrics. A full list of model performance statistics is found in Appendix E. The socce r and bugle plots for the all of the VISTASIMPROVE 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 (Sulfate, Nitrate, and Organic Carbon) for the 20% best days and 20% worst days. Plots for indi vidual IMPROVE monitors associat ed with the Sipsey Wilderness Area as well as all the VISTAS Class I areas are included in Appendix N.

The soccer plots of monthly concentrations (Figur es 6.2-1 and 6.2-2) show that values for nitrate generally fall outside of criteria performance th resholds (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 thresh olds. 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 summ er, when observed nitrate is quite low and predicted nitrate is even lower. When concentrat ion is factored into perf ormance 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 for all VISTAS sites.

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 con centration 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 for all VISTAS IMPROVE monitors.

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.

All Sites CMAQ 12km - 2002 Monthly

-200.0 -150.0 -100.0

-50.0

0.0 50.0 100.0 150.0 200.0

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

Nitrate

Organics

(+) Criteria

(+) Goal

(-) Goal

(-) Criteria

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

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

Model performance spatially and temporally can be evaluated using the PAVE spatial plots for all the 20% best and 20% worst days for the VIST AS Class I areas. These plots are illustrated for each pollutant species and PM 2.5 mass in Appendix C of the VISTAS Technical Support

3637 Document (Please see Appendix N). Model perfor mance at each Class I area individually is illustrated in Appendix C of the VISTAS Techni cal Support Document (Please see Appendix N) using scatter plots, soccer plots, bugle plots, and time series. Performance for Sipsey is similar to that for other Class I areas in the VISTAS regi on, and is generally better at the interior Class I areas such as Sipsey than at the coastal areas.

6.3 Sipsey Wilderness Area Class I Performance

The following section provides stack bar charts (Figures 6.3-1 and 6.3-2) for the Sipsey Wilderness, comparing observed fine partic ulate matter composition and modeled fine particulate matter composition. The first chart presents observed versus modeled composition for the 20% worst days, and the second presents observed versus modeled composition for the 20% best days.

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 (bextSO 4 )

• Red = light extinction due to nitrates (bextNO 3 )

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

• Black = light extinction due to elemental carbon (bextEC)

• Brown = light extinction due to soil (bextSoil)

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

The components are presented in the same or der for both the observed (left hand bar) and modeled bar (right hand bar), so it is easy to identify days when the predicted 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.

In reviewing the stacked bar charts , the fact that sulfates are a la rge contributor to light extinction at the Sipsey Wilderness area on bo th 20% worst days and 20% best days is reinforced. The bar charts also suggest that orga nic carbon and nitrates are impor tant on the 20% best days at Sipsey. For the Sipsey Wilderness area, the model varies in performance throughout 2002 for the best days. The model from winter into the spring seems to display the best model performance on the 20% best days. During the spring and summer months, the model tends to under predict nitrates. During summer into fa ll the model tends to over predict nitrates

. For the most part, sulfates and all other constituents seem to be accurately portrayed on the 20% best days. The model also shows good performance for sulfates and orga nic carbon throughout 2002 for the 20% worst days. Nitrates can play a large role on the 20 % worst days, but this is not a

common occurrence.
Comparing the 20% best day charts to the 20% worst day charts, it can be seen that sulfates play a much larger role on the 20% worst days. Th e model does a fairly good job replicating all components on the 20% worst days. The breakdown of pollutants shows that the next largest contributor on the 20% worst days is organic carbon. Also, the species composition on the 20%best days varies more widely compared to th e 20% worst days. This suggests accurately modeling each species is especially important on the 20% best days.

Figure 6.3-1 Sipsey Wilderness Area

38Figure 6.3-2: Stacked bar chart for SIPS1 on the 20% worst days (top) and 20% best days (bottom). Observed composition is presented in the left hand bar, with modeled composition represented by the right hand bar.

Model Performance statistics for the Sipsey W ilderness are listed in Tables 6.3-1 and 6.3-2 below. These tables demonstrate how well mu ltiple variables performed for the Sipsey Wilderness Class I area.

39Table 6.3-1 Model Performance Statistics for the Sipsey Wilderness Area for the 20% best days

Table 6.3-2 Model Performance Statistics for the Sipsey Wilderness Area for the 20% worst days

Additionally, time series plot s (Figures 6.3-3 – 6.3-10) of predicted and observed NO 3 , SO 4 , OMC, CM, EC, Soil, Sea Salt and PM 2.5 mass across all monitoring days at the Sipsey Wilderness provide information on the performan ce across each season and the year in total.

40Figure 6.3-3 Time Series plot of NO3 predicted ve rsus observed for the Sip sey Wilderness Area for 2002

Figure 6.3-4 Time Series plot of SO4 predicted ve rsus observed for the Sip sey Wilderness Area for 2002

41Figure 6.3-5 Time Series plot of OMC predicted ve rsus observed for the Sip sey Wilderness Area for 2002

Figure 6.3-6 Time Series plot of CM predicted ve rsus observed for the Sipsey Wilderness Area for 2002

42Figure 6.3-7 Time Series plot of EC predicted versus observed for the Sipsey Wilderness Area for 2002

Figure 6.3-8 Time Series plot of Soil predicted ve rsus observed for the Sipsey Wilderness Area for 2002

43Figure 6.3-9 Time Series plot of Sea Salt predic ted versus observed for the Sipsey Wilderness Area for 2002

Figure 6.3-10 Time Series plot of PM2.5 predic ted versus observed for the Sipsey Wilderness Area for 2002

4445 Overall, ADEM found that model performance fe ll within the accepted limits. As such, ADEM contends that the one atmosphere modeli ng performed by the VISTAS contractors is representative of conditions in Alabama and across the southeastern states and is applicable for use as the basis for this SIP. Additional information on model performance across the VISTAS region, including the Sipsey Wilderness can be found in Appendix N.



### 7.0 LONG-TERM STRATEGY (LT S) FOR ALABAMA’S SIPSEY
WILDERNESS CLASS I AREA

As stated previously in Secti on 1.3 above, the Rule requires st ates to establish Reasonable Progress Goals (RPG’s), expressed in deciviews (dv), for visibility improvement at each affected Class I area covering each (approximate) 10-year pe riod until 2064. This first set of RPGs must be met through measures contained in the Stat e’s long-term strategy ( LTS) covering the period from the baseline (2000-2004) until 2018. This sect ion discusses the development of Alabama’s LTS.

7.1 Overview of the Long Term Strategy (LTS) Development Process

The monitored data and modeling analyses cited in Sections 2.0 and 5.0 above establish that, for the VISTAS region, the key contributors to region al haze in the baseline timeframe were large stationary sources of sulfur di oxide emissions. Keeping that ke y conclusion in mind, this section addresses the following questions:

a. Assuming only existing and pla nned emissions controls wi ll take place in Alabama and the VISTAS region, how much visibi lity improvement, compared to the glidepath, is expected to be seen at the Sipsey Wilderness between now and 2018?

b. If additional emission reductions were needed, from what pollutants and source categories would the greatest visibility benefits be reali zed between the baseline and 2018 at the Sipsey Wilderness?

c. Narrowing down further, in what geographi c locations are the emissions found which have the greatest impact on visibi lity at the Sipsey Wilderness?

d. What types of emissions sources are found in those geographic locations?

e. Which specific individual sources in those locations have the gr eatest visibility impacts at the Sipsey Wilderness?

f. What additional emission controls achieve reasonable progress for those specific sources?

g. What additional emission controls represent BART in Alabama and what are the resulting benefits?h. Given the additional emission reductions expected from reasonable progress and BART, how much additional visibility impr ovement, compared to the glidepath, is expected to be seen at the Sipsey Wilderness between th e baseline and 2018?

7.2 Expected Visibility Results in 2018 for the Sipsey Wilderness Area under Existing and
Planned Emissions Controls

There are significant control programs being impl emented both nationally as well as across the southeast between the baseline period and 2018. These programs are described in more detail below. The impact of programs such as the Cl ean Air Interstate Rule (CAIR) and the NOx SIP Call will be realized regiona lly. The implementation of these programs in Alabama will significantly reduce emissions of s ources not only within the state that these reductions occur, but these reductions will also improve visibility at surrounding Class I areas outside Alabama. A case in point is Table 7.6-1 that clearly illustrates that overwhelmingly Alabama sources impact the Sipsey Wilderness area. These programs will surely benefit visibility and air quality in Alabama.

7.2.1 Federal and State Control Requirements

CAIR

. CAIR will permanently cap emissions of SO 2 and NO x from EGUs in the eastern United States by 2015. When fully implemented, CAIR will reduce SO 2 emissions from EGUs in these states by more than 70%, and NO x emissions by more than 60%, from 2003 levels.

NO x SIP Call . Phase I of the NO x SIP call applies to certain EGUs and large non-EGUs, including large industrial boilers and turbines, and cement kilns. Those states affected by the NO x SIP call in the VISTAS region, including Alabam a, have developed rule s for the control of NO x emissions that have been approved by EPA. The NO x SIP Call has resulted in a 68% reduction in NO x emissions from large stationary com bustion sources in Alabama. For this analysis, emissions were capped for NO x SIP call-affected sources at 2007 levels, and carried forward for the 2009 and 2018 future year inventories.

North Carolina CSA . Under the Act, enacted in 2002, coal -fired power plants in North Carolina must achieve a 77% cut in NO x emissions by 2009 and a 73% cut in SO 2 emissions by 2013. This legislation establis hes annual caps on both SO 2 and NO x emissions for the two primary utility companies in North Carolina, Duke En ergy and Progress Energy. These reductions must be made in North Carolina, and allowances are not saleable.

Consent Agreements (TECO, VEPCO, Gulf Po wer Crist 7, Santee Cooper, EKPC and APCO Plant Miller).

• Under a settlement agreement, by 2008, Tamp a Electric Company will install permanent emissions-control equipment to meet stringent pollution limits; implement a series of interim pollution reduction measures to reduc e emissions while the permanent controls are designed and installed; and retire pollu tion emission allowances that Tampa Electric or others could use, or sell to others, to emit additional NO x , SO 2 , and PM.

46• Virginia Electric and Power Co. (VEPCO) agreed to spend $1.2 billion by 2013 to eliminate 237,000 tons of SO 2 and NO x emissions each year from eight coal-fired electricity generating plants in Virginia a nd West Virginia. VEPCO has installed the following controls/fuel switches already.

o Chesterfield Power Station:

 Unit #4-SCR by 01/01/13;

 Unit #5-SCR by 01/01/12;

 Unit #6 SCR by 01/01/11 & FGD by 01/01/10

o Chesapeake Power Station:

 Unit #3-SCR by 01/01/13;

 Unit #4-SCR by 01/01/13

o Clover Power Station:

 Unit #1 and Unit #2: Upgrade of the existing FGD to meet 95% removal.

o Possum Point Power Station

 Unit #3 and Unit #4: Conversion of these coal fired units to natural gas. Coal fired operations must cease by 5/1/13.

• A 2002 voluntary agreement calls for Gulf Po wer to upgrade its operation to cut NO x emission rates by 61% at its Crist generating plant by 2007, with major reductions beginning in early 2005. The Crist plan t is a significant source of NO x emissions in the Pensacola, Florida area.

• A 2004 consent agreement calls for Santee C ooper to install and commence operation of continuous emissions cont rol equipment for PM/SO 2 /NO x emissions; comply with system-wide annual PM/SO 2 /NO x emissions limits; agree no t to buy, sell or trade PM/SO 2 /NO x allowances allocated to Santee Co oper System as a result of said agreement; and to comply with emission un it limits of said agreement. Specific plants/units were not identified in the consent agreement.

• A July 2, 2007 consent agreement between the U.S. EPA and East Kentucky Power Cooperative (EKPC) requires th e utility to reduce its emissions by 62,000 tpy (including 54,000 tons of SO 2 and 8,000 tons of NO x ) by installing/operating selective catalytic reduction (SCR) technology; low-NO x burners, and PM and Mercury Continuous Emissions Monitoring at the ut ility’s Spurlock, Dale and C ooper Plants. According to EPA, the plant’s total emissi ons will drop from between 50 – 75% from 2005 levels. As with all Federal Consent Decrees, EKPC is precluded from using reductions required under other programs, such as the Clean Air In terstate Rule (CAIR). EKPC is expected to spend over $600 million on pollution controls.

o FGD controls for EKPC Spurlock Units 1 and 2

o EKPC Cooper Unit 2

• Alabama Power Company – Plant Miller (Je fferson County) On April 25, 2006, the U.S. EPA and the Department of Justice announ ced a settlement with Alabama Power Company (APC) to reduce emissions of sulfur dioxide (SO 2 ) and nitrogen oxides (NO x ) from Boiler Units 3 and 4 at its Plant Miller in Quinton, Alabama. The plant is required to reduce SO 2 by installing flue gas desulfurizati on (FGD) and is required to reduce NO x by installing selective catalytic reducti on (SCR) equipment. The Consent Decree requires APC to commence continuous year-round operation of SCR equipment for control of NO x emissions by May 1, 2008. APC is al so required to begin year-round operation of the FGD equipment for control of SO 2 emissions by December 31, 2011. The installation and operation of these controls are expected to result in the reduction of

474,953 tons per year of NO x and 22,788 tons per year of SO 2 . APC is also required to install and operate by December 31, 2008, a me rcury continuous emission monitoring system. APC is precluded from using th e emissions reductions required under other programs, such as the Clean Air Interstate Ru le (CAIR). In addition, the Consent Decree requires APC to purchase and permanently retire $4.9 million worth of 2007 SO 2 emissions allowances.

Cargill, Inc. Consent Decree On March 3, 2006, the Federal District Cour t in Minnesota entered a Consent Decree between Cargill, Incorporated, U.S. EPA, the Iowa Department of Natural Resources and other participating agencies. Under this agre ement, Cargill shall implement a program of enforceable emissions reductions of SO 2 , CO, NO x , and VOCs from its corn processing and oilseed processing plants of at least 40,000 t ons per year. This includes a reduction of SO 2 of 15,000 tons per year, CO of 16,000 tons per year, NO x of 2,500 tons per year, and VOCs of 6,500-11,500 tons per year. The Consent Decree requires permit applications to be submitted within three years from entry of the Cons ent Decree (March 3, 2009) containing annual limits for boilers and facilities in Appendix B of the Consent Decree, which includes Cargill Sweeteners, to less than 15,355 t ons per year on a 12-month rolling sum. This represents a reduction of 15,067 tons of SO 2 per year from the current allowable emissions from these sources. Also, Cargill is required submit a perm it application within three years from entry of the Consent Decree that will include indi vidual emission limits for boilers at various facilities, including the Cargill Sweeteners Stoker Coal-fired boiler, (which was evaluated for reasonable progress (RP)) that in aggregate will not exceed a capacity weighted average SO 2 emission rate of 1.2 lb/MMBtu. The Cargill Sweet ners Decatur Facility is required by 3/3/09 to control and comply with SO 2 emissions limitations for its coil fired boilers.

One-hour Ozone SIPs (Atlanta / Bi rmingham / Northern Kentucky). SIPs have been submitted to EPA to demonstrate attainment of the one-hour ozone National Ambient Air Quality Standard (NAAQS). These SIPs require NO x reductions from specific coal fired power plants and address transp ortation plans in these cities.

Heavy Duty Diesel (2007) Engine St andard (for onroad trucks and buses). EPA set a PM emissions standard for new heavy-duty e ngines of 0.01 grams per brake-horsepower-hour (g/bhp-hr), to take full effect for diesel e ngines in the 2007 model year. This rule also includes standards for NO x and non-methane hydrocarbons (NMHC) of 0.20 g/bhp-hr and 0.14 g/bhp-hr, respectively. These NO x and NMHC standards will be phased in together between 2007 and 2010, for diesel engines. Sulfur in diesel fuel must be lowered to enable modern pollution-control technology to be e ffective on these trucks and buses. EPA will require a 97% reduction in the sulfur content of highway diesel fuel from its current level of 500 parts per million (low sulfur diesel, or LS D) to 15 parts per million (ultra-low sulfur diesel, or ULSD).

Tier 2 Tailpipe (Onroad vehicles) . EPA Mobile rules include the Tier 2 fleet averaging program, modeled after the Calif ornia LEV II standards. Manuf acturers can produce vehicles with emissions ranging from relati vely dirty to zero, but the mi x of vehicles a manufacturer sells each year must have average NO x emissions below a specified value

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

4849 Large Spark Ignition and Recreational Vehicle Rule . EPA has adopted new standards for emissions of oxides of nitrogen (NO x ), hydrocarbons (HC), and carbon monoxide (CO) from several groups of previously unregulated nonroad engines. Included in these are large industrial spark-ignition engine s and recreational vehicles. Nonroad spark-ignition engines are those powered by gasoline, liquid propane ga s, or compressed natural gas rated over 19 kilowatts (kW) (25 horsepower). These engine s are used in commer cial and industrial applications, including forklifts, electric genera tors, airport baggage transport vehicles, and a variety of farm and construction applicati ons. Nonroad recreational vehicles include snowmobiles, off highway 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 wi ll reduce emissions by more than 90% from nonroad diesel equipment, and reduce sulf ur levels by 99% from current levels in nonroad diesel fuel starting in 2007. This rule will apply to mo st nonroad diesel fuel in 2010 and to fuel used in locomotives and marine vessels in 2012.

Combustion Turbine MACT . The projection inventor ies do not include the NO x co-benefit effects of the MACT regulations for Gas Turb ines or stationary Reciprocating Internal Combustion Engines, which EPA 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 implemented. Reductions occurring before 2002 were not considered.

7.2.2 Additional State Program s to Reduce Emissions
In addition to accounting for specific emission re ductions due to ongoing air pollution programs as required under Section 308 (d)(3)(v)(A), States are also re quired 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)] . Please see Section 7.9 for a discussion of Alabama’s Smoke Management Plan.

7.2.3 Projected 2009 and 2018 Base G2 Emissions Inventories

The Base G inventories for 2009 and 2018 acc ount for post 2002 emission reductions from promulgated and proposed federal, state, local, and site-specific control programs as of July 1, 2004. In general, emissions inventories were developed for 2009 and 2018 using current control information in Alabama, and growing the 2002 inventory using EGAS5, MOBILE6, and IPM for electric generating units (EGUs). For EGUs, IPM results were adjusted based on agency knowledge of planned emission controls at specific EGUs. These updates for Base G2 are documented in the MACTEC emissions inventory report “Documentation of the Base G 2002 Base Year, 2009 and 2018 Emission Inventories for VISTAS” dated August 2007 in Appendix D.For non-EGUs, recently updated growth and control data were used consistent with the data used in EPA’s Clean Air Interstate Rule (CAIR) anal yses supplemented by state and local agency 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 burni ng estimates, as well as controls used to project emissions for EPA’s Heavy Duty Diesel rulemaking and for the CAIR rulemaking.

Mobile source controls included local controls underl ying the 2002 baseline inventory (vehicle emission inspection, Stage II vapor recovery, anti-tampering, etc.) with changes based on specific state input. Nonroad control data and projections for 1996, 2010, 2015, and 2020 were obtained from EPA's CAIR Techni cal Support Document, and straight line projections were used to estimate 2009 and 2018 levels.

The following bar charts (Figures 7.2.3-1 and 7.2.3- 2) show expected decreases in emissions of SO 2 and NO x across the VISTAS states from 2002 through 2018. (Similar charts for other visibility impairing polluta nts are contained in Appendi x N). Note that for SO 2 emissions in particular, which are the largest contributors to haze, emissions fr om electric generating facilities are expected to decrease drama tically (approximately 70% in Alabama) between 2002 and 2018. However, even after implementation of CAIR, EGU emissions are projected to remain the largest contributor to haze, comprisi ng more than half of remaining SO 2 emissions in most states.

W

e s t V

i r g i n i a
A

l a b

a m

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a

K e n t u cky G

e o r g i a M i s s i s s i p p i

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a r o l i n a

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o r t h

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a r o l i n a

T e n n e s s e e V i r g i n i a 100 200 300 400 500 600 700Annual SO2 (Thousand Tons) Miscellaneous Nonroad Onroad Industrial Other Fuel Combustion EGU 2 0 0 2
2 0 0 9
2 0 1 8

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T e n n e s s e e V i r g i n i a 100 200 300 400 500 600 700Annual SO2 (Thousand Tons) Miscellaneous Nonroad Onroad Industrial Other Fuel Combustion EGU 2 0 0 2
2 0 0 9
2 0 1 8

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T e n n e s s e e V i r g i n i a 100 200 300 400 500 600 700Annual SO2 (Thousand Tons) Miscellaneous Nonroad Onroad Industrial Other Fuel Combustion EGU Miscellaneous Nonroad Onroad Industrial Other Fuel Combustion EGU 2 0 0 2
2 0 0 9
2 0 1 8

Figure 7.2.3-1 Base G2 Annual SO2 emissions fo r 2002, 2009, and 2018 in the VISTAS States.

50Figure 7.2.3-2 Base G2 Annual NOx emissions in 2002, 2009, and 2018 in the VISTAS States. 0 100 200 300 400 500 600 700 800 900 1,000

Alabama Florida Georgia

K e nt

uck y
Mi s sissippi North Carolina Sout h

Carol in

a

Ten nes see Virgin ia West VirginiaAnnual NOx (Thousand Tons) Miscellaneous Nonroad Onroad Industrial Other Fuel Combustion EGU 2 0 0

2
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9
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Alabama Florida Georgia

K e nt

uck y
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Carol in

a

Ten nes see Virgin ia West VirginiaAnnual NOx (Thousand Tons) Miscellaneous Nonroad Onroad Industrial Other Fuel Combustion EGU Miscellaneous Nonroad Onroad Industrial Other Fuel Combustion EGU 2 0 0

2
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1 8

Summary of Final Base G2 Emissions Inventories for 2009 and 2018

Table 7.2.3-1 is a summary of the 2009 Base G2 emission inventory. The complete inventory and discussion of the methodology used to devel op the inventory is cont ained in Appendix D.

Table 7.2.3-1 2009 Emissions Inventory Summary for Alabama.

VOC NOx SO2 PM10 PM2.5 NH3 Point 49,117 151,714 479,297 32,389 23,151 2,491 EGU 2,440 82,046 378,052 6,959 3,916 359 Non-EGU 46,677 69,668 101,245 25,430 19,235 2,132 Area 170,956 35,831 50,910 465,666 105,211 66,318 Nonroad 50,249 56,862 3,471 4,231 3,807 36 Mobile 76,990 101,831 810 3,171 2,032 6,364 Total 347,312 346,238 534,488 505,457 134,201 75,209

Table 7.2.3-2 is a summary of the 2018 Base G2 emission inventory. The complete inventory and discussion of the methodology used to devel op the inventory is cont ained in Appendix D.

5152 Table 7.2.3-2 2018 Emissions Inventory Summary for Alabama.

VOC NOx SO2 PM10 PM2.5 NH3 Point 57,243 142,676 249,075 37,746 27,366 3,536 EGU 2,952 64,358 135,851 7,822 4,768 1,072 Non-EGU 54,291 78,318 113,224 29,924 22,598 2,464 Area 181,116 36,945 52,950 497,924 108,892 73,969 Nonroad 40,407 43,799 2,818 3,300 2,874 42 Mobile 49,175 47,298 720 2,410 1,192 7,298 Total 327,941 270,719 305,563 541,380 140,324 84,845

7.2.4 Model Results for the 2018 Base G2 Invent ory Compared to the Uniform Rate of Progress Glidepath for the Sipsey Wilderness Area
Using 2000 - 2004 IMPROVE monitoring data, the deci view (dv) values for the 20% best days in each year were averaged together, producing a single average dv value for the best days. Similarly, the dv values for the 20% worst days in each year were averaged together, producing a single average dv value for the worst days.

Figure 7.2.4-1 illustrates the pr edicted visibility improvement on the 20% worst days in 2018 that represents implementation of existing fede ral and state regulations as well as BART and Reasonable Progress controls, compared to the Uniform Rate of Progress glidepath for the Sipsey Wilderness (SIP1). The fuchsia line s hows the target natural condition in 2064 for the 20% worst days, and the incremental dv changes resulting from a uniform rate of progress between current and natural conditions in 2064, fo r the average 20% worst visibility days in the 2000-2004 baseline period. The purple lines show the improvement expected during the period of the first long term strategy.

### Progress Assessment Base G2 inventory, new IMPROVE Algorithm Sipsey - Worst 20% Days

29.03 27.52

24.80

21.78

18.76

15.74

12.72 10.90 26.43

23.53

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 Method 1 Prediction
Figure 7.2.4-1 CMAQ 2018 results on the 20% worst days at the Sipsey Wilderness Area.

Please note that for the Sipsey Wilderness area in Alabama, visibility is expected to improve to levels better than the glidepath by 2018 based on reductions from existing and planned emissions controls alone and even more so with BART and RP controls on the worst days. This is alternately demonstrated in Figure 7.2.4-2 belo w which provides the 2018 estimates across all the VISTAS Class I areas. The inventories used to develop this figure as well as the next are the VISTAS Base G 2018 inventory version “ 2b” circa 2006 and the VISTAS Base G 2018 inventory version “4a”, comm only known as the “Best and Final” inventory, circa 2007.

53CMAQ 2018g4a vs 2018g2b Method1 Prediction at VISTAS+ sites on W20% Days

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200% 220% 240%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent of target reduction achieved 2018g4a 12k
2018g2b 12k

VISTAS non-VISTAS Figure 7.2.4-2 Projected visibility improvement on the 20% worst visibility days at VISTAS and neighboring Class I areas.

In addition to improving visibility on the 20% worst visibi lity days, states ar e also required to protect visibility on the 20% best days at Class I areas. As illustrated in Figure 7.2.4-3, visibility on the 20% best days is projecte d to improve in 2018 at all VISTAS Class I areas as a result of emissions reductions. Specifically, Figure 7.2.4-4 demonstrates that no degradation should occur for the Sipsey Wilderness area, and that, in f act, visibility should im prove beyond that level.

54CMAQ 2018g4a vs 2018g2b Visibility Project ion at VISTAS+ sites on Best 20% days -50%

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50%COHU1DOSO1GRSM1JARI1LIGO1CADI1MACA1SHEN1SHRO1SIPS1CHAS1EVER1OKEF1ROMA1SAMA1SWAN1BRET1BRIG1CACR1HEGL1MING1UPBU1Percent of target reduction achieved 12km 2018g4a

12km 2018g2b

VISTAS non-VISTAS Figure 7.2.4-3 Projected visibility improvement on the 20% best visibility days at VISTAS and neighboring Class I areas.



### Progress Assessment Base G2 inventory, new IMPROVE Algorithm Sipsey - Best 20% Days

15.57 15.57 15.57
15.20 14.22

5.03

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)

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction
Figure 7.2.4-4 CMAQ 2018 results on the Best 20% days at the Sipsey Wilderness

557.3 Relative Contribution from International Emissions to Visibili ty Impairment in 2018
at VISTAS Class I areas

Emissions from Mexico, Canada, Central Am erica, Asia, and Africa contribute to PM 2.5 loadings and visibility impairment at Class I areas in the Continental U.S. To evaluate the relative contribution of international emissions to visibili ty at Class I areas in the southeastern U.S., VISTAS used a combination of modeling result s from the global three-dimensional chemical transport model (GEOS-Chem) and CMAQ. VISTAS used the GEOS-Chem global model to generate initial and boundary conditions for the CMAQ modeli ng domain. GEOS-Chem was run for the 2002 modeling year using a 4 x 5 degree horiz ontal grid resolution a nd a 3-hour temporal resolution. Because emissions were based on monthly averages, the model does not capture the episodic variability in emissions. The GEOS-Che m outputs were then used to calculate initial and boundary conditions for the national CM AQ modeling domain. The national CMAQ domain included portions of Canada and Mexico, so emissions for these countries were included within the national CMAQ modeli ng domain or as part of th e boundary conditions outside the national modeling domain, as appropriate.

Two complementary methods were used to calcul ate the impact of inte rnational emissions at Class I areas, with the intention of isolating the visibility eff ects on Class I areas from non-U.S. anthropogenic emissions. All international fires ar e treated as natural em issions in these runs; thus both cases underestimate the contributions from international fires that are due to anthropogenic burning.

1) International emissions are represented by the difference between two GEOS-Chem

runs . In the first run, U.S. anthropogenic emissions were removed, and in the second run both U.S. and international anthropogenic emissions were removed. The difference represents internati onal anthropogenic emissions, in th e absence of U.S. anthropogenic emissions (e.g. compared to 2064 levels). Harvard University provided GEOS-Chem results to VISTAS for the 2002 international contribution on a 4 x 5 degree grid scale. Concurrently, Harvard modeling for the El ectric Power Research Institute (EPRI) provided GEOS-Chem results for the 2001 inte rnational 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 em issions and boundary conditions from GEOS-Chem. In the first CMAQ run, all global na tural and anthropogenic emissions in 2018 are active. In the second CMAQ run, only global (U.S. and Inte rnational) natural emissions are active. Here th e impacts of international em issions are compared against 2018 conditions rather than natural background conditions.

Separate from VISTAS, the CENRAP RP O used PM Source Apportionment Technology (PSAT) in the CAMx Regional air quality mode l to calculate the impact of Canadian and Mexican emissions and boundary conditions on visibi lity at Class I areas in 2002.

As illustrated in Figure 7.3-1 for annual averag e contributions to sulfate at VISTAS and neighboring Class I areas, the estim ated international c ontributions are higher at Class I areas near the Canadian and Mexican borders and along the eastern coast. The estimated international

56contribution is higher using CMAQ and PSAT than in the GEOS-Chem runs because the grid scale is finer (providing a more accurate disp ersion of emissions) and because the background atmosphere includes loadings from current U.S. anthropogenic emissions (due to greater photochemical activity). Similar charts for nitrate and organic carbon mass, for impacts on 20% worst visibility days, and for impacts of intern ational emissions on calculated light extinction are included in Appendix N.



## Transboundary Man-Made Impacts

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

GEOS_4x5 GEOS_1x1 PSAT CMAQ

Figure 7.3-1 Estimated international emissions cont ributions to sulfate at VISTAS and neighboring Class I areas.

In Figure 7.3-2 CMAQ projections of the contribution from international emissions to PM mass on the 20% worst visibility days in 2002 at the Sipsey Wilderness, are compared to the U.S. domestic contributions of PM comp onents at the site on those days.

57Figure 7.3-2 PM component concentrations from U.S. domestic sources on 20% worst visibility days in 2002 (left bars) and CMAQ-simulated inte rnational contributions (right bars) at Sipsey.

For the Sipsey Wilderness area, accounting for the international contributi on does not change the conclusion that reductions of U.S. anthropogenic emissions will result in visibility improvement greater than the uniform rate of progress by 2018. As the atmosphe re becomes closer to natural background conditions in the future, the incremen tal contribution from international emissions will become more important. This information is included in the SIP documentation to provide reference for future assessments of reasonable progress.

7.4 Relative Contributions to Visibility Impa irment: Pollutants, Source Categories, and Geographic Areas

An important step toward identi fying further reasonable progress measures is to identify the key pollutants contributing to visibili ty impairment at the Sipsey Wilderness area. To understand the relative benefit of further reducing emissions from different pollutants, source sectors, and geographic areas, VISTAS engaged the Georgia Institute of Technology to perform emission sensitivity model runs using CMAQ. Emissions se nsitivities were initially performed for three episodes representing winter and summer condi tions: January 2002, July 2001, and July 2002. These runs used the initial 2018 projection i nventory and considered 30% reductions from specific pollutants, source categories, and ge ographic areas. Emissions sensitivities were repeated using the 2009 Base D projection inventory and two m onth-long episodes from 2002:

58June 1 - July 10 and November 19 – December 19. Emissions in 2009 were reduced by 30% for each pollutant sensitivity run. The pollutant contributions that were evaluated were:

• SO 2 from EGU sources in each VISTAS state, other RPOs in the VISTAS 12 km grid, and Boundary Conditions from outside the 12 km domain.

• SO 2 from non-EGU point sources in each VI STAS state, other RPOs, and Boundary Conditions

• NO x from ground level (on-road plus non-road pl us area) sources in each VISTAS state and other RPOs

• NO x from point (EGU plus non-EGU) sources in each VISTAS state and other RPOs

• NH 3 from all sources in VISTAS and other RPOs

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

• Primary Carbon from all ground level source s in each VISTAS state and other RPOs

• 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

Results are shown below in Figure 7.4-1 for the average of the 20% worst vi sibility days at the Sipsey Wilderness area. Responses for 20% wo rst days were calculated by averaging the responses of the 20% worst days that were modeled in the two episodes.

59

## Sipsey, AL (20% Worst Days)

-9.00

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O2 _ nonEGU

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x _Ground NO x _Po in

t NH3 VOCs
PC _ Gr

o und PC _ P

o int PC_Fir e sΔBext(Mm-1) Bio.
Antro.
BCs
MRPO
M-VU
CEN
VISTAS
WV
VA
TN
SC
NC
MS
KY
GA
FL
AL

Figure 7.4-1 CMAQ projections of visibility responses on 20% worst days at the Sipsey Wilderness

to 30% reductions from the 2009 Base D inventory for visibility-reducing pollutants in different source categories and geographic areas.

As Figure 7.4-1 shows, the greatest visibility benefits on the 20% worst days for the Sipsey Wilderness area are projected to result from further reducing SO 2 from EGUs. Reductions in SO 2 from Alabama’s EGUs would have the greatest benefit; contributions from other VISTAS states, other RPOs and from the boundary condi tions are smaller but not insignificant. Additionally, smaller benefits ar e projected from additional SO 2 emission reductions from nonutility, industrial point sources.

Because ammonium nitrate is a contributor to PM 2.5 mass and visibility impairment on the 20% worst days that occur in the winter at the Sipsey Wilderness, the benefits of reducing NO x and NH 3 emissions could be beneficial in improving visibility on the 20% worst days that occur in winter and have elevated NO 3 contributions.

VOCs do contribute to visibility impairment, but as shown in the charts above, this contribution is from biogenic sources such as vegetative emissions. Controlling anthropogenic sources of VOC emissions has little, if a ny, visibility benefit at 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 w ith 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 (for more information please see Appendix B).

6061 Note that these results from the emission sensitiv ity runs are consistent with the conclusions drawn from the 2000-2004 baseline monitoring data (Please s ee Section 2.4). The results indicate that sulfate is the domin ant contributor to visibility imp airment on the 20% worst days at all sites, and that ammonium nitrate may be im portant for sites where the 20% worst days occur in the winter. It can be concluded that reducing SO 2 emissions from EGU and non-EGU point sources in the VISTAS states would have the greatest visibility bene fits for the Sipsey Wilderness area.

7.5 Relative Contributions to Visibility Impa irment: Geographic Areas of Influence (AoI) at Class I areas affected by Alabama Sources

Once it was determined that SO 2 emission reductions from E GU and non-EGU point sources in the VISTAS states would be the most effective s ources to control to impr ove visibility, the next step was to identify the specific geographic areas that most likely influence visibility at those areas, and then to identify the major SO 2 point sources located in th at geographic area. An SO 2 Area of Influence (AoI) was defined for all the VI STAS Class I areas to represent the geographic area containing sources that would likely have the greatest impact on visibility at that Class I area. All SO 2 point sources within this AoI were id entified and ranked by their 2018 Base G emissions. The following sections contain a broad overview of th e steps in the AoI analysis, including tables providing information on Alabam a source impacts at neighboring Class I areas. Please see Appendix H for a more detailed discussion of these analyses and plots.

7.5.1 Back Trajectory Analyses
The first step was to generate meteorologica l back trajectories for the Sipsey Wilderness IMPROVE monitoring site and neighboring Cla ss I areas for the 2000-2004 baseline period. Back trajectory analyses use interpolated measured or modeled meteorological fields to estimate the most likely central path of air masses that ar rive at a receptor at a given time. The method essentially follows a parcel of air backward in h ourly steps for a specified length of time. Figure 7.5.1-1 is an example of a back trajectory analys is for the Sipsey Wild erness for the 20% worst days in 2002.

### Back Trajectories for 20% Worst Days for 2002 Sipsey , AL

Figure 7.5.1-1 Example back trajectories for 20% worst visibility days in 2002 for the Sipsey Wilderness Area.

Trajectories were started at 100 meters and 500 meters above the surface and run backward from the site for 72 hours. These individual back tr ajectories for the 20% worst days in 2002 were also useful in evaluating model performance fo r individual days at the Sipsey Wilderness.

7.5.2 Residence Time Plots
The next step was to plot residence time using fi ve years of back trajectories for the 20% worst visibility days in 2000-2004. Re sidence time is the frequency that winds pass over a specific geographic area on the path to a Cl ass I area. Separate residence time plots were generated using trajectories with 100 meter and 500 meter start hei ghts. As illustrated in Figure 7.5.2-1, winds influencing Sipsey on the 20% worst days come from all directions and there is no single predominant wind direction influencing the 20% worst visibility days.

62Residence Time for 20% Worst Days in 2000-2004 Sipsey. AL

Figure 7.5.2-1 Example residence time plot for 20% worst visibility days in 2000-2004 for Sipsey. Based on trajectories with 100m start height.

7.5.3 SO 2 Areas of Influence (AoIs)
The next step was to develop sulfate extinction weighted residence time plots to define the geographic area with the highest pr obability of influencing the r eceptor on the 20% worst days in 2000-2004 that were dominated by sulfate . Each back trajectory was weighted by sulfate extinction for that day. This allows focus on th e 20% worst days that are influenced by sulfate and places less importance on days influenced by organic carbon from fire s. Sulfate-weighted back trajectories for the 20% worst days were then combined for all 5 years. The resulting sulfate extinction weighted residence time plots were used to define the geographic AoI for sources of SO 2 emissions. In Figure 7.5.3-1, the area repr esenting 10% or greater residence time is outlined in red and the area representing 5% or greater residence time is outlined in gray. The VISTAS states focused their analyses on the Ao Is defined by 5% or greater sulfate extinctionweighted residence time.

63

### SO2 Area of Influence for Sipsey, AL

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%.

Figure 7.5.3-1 Example SO2 Area of Influence plot for sulfate extinction weighted residence time for the 20% worst visibility days in 2000-2004 fo r Sipsey. Based on back trajectories with 100 m start height.

7.5.4 Emissions Sources within SO 2 Areas of Influence (AoIs)
Residence time plots were then combined with geographically gridded emission data based on the 2002 baseline and 2018 Base G emissions invent ories. Plots were generated for each AoI defined by trajectories with 100 m and 500 m start heights. As a wa y of incorporating the effects of transport, deposition, and chem ical transformation of point s ource emissions along the path of the trajectories, these data were weighted by 1/d, where d was calculate d as the distance, in kilometers, between the center of th e grid cell in which a source is located and the center of the grid cell in which the IMPROVE monitor is located. The dist ance weighted point source SO 2 emissions are then combined with the gridded extinction weighted back -trajectory residence times at a spatial re solution 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 point source SO 2 emissions were multiplied by the total extinction weighted back trajectory residenc e times on a grid cell by gr id 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 and 2018 base year inventories.

64Figure 7.5.4-1 illustrates the 2002 and 2018 distan ce weighted gridded emissions * sulfate extinction weighted residence time plots for the Sipsey Wilderness. These maps help visualize where the emissions reductions will be occurr ing between 2002 and 2018. The change in SO 2 emissions between 2002 and 2018 can be seen by comparing emissions source strengths in the two plots. Note the emissions from each sour ce are normalized by the total emissions in the domain. Sources that reduce SO 2 emissions by 2018 will show a lower contribution to emissions in the domain. On the 2018 map the grid cells w ith these sources will show a lighter color gradient than on the 2002 map. Because the total emissions in the domain are smaller in 2018, a source that does not change emissions between 2002 and 2018 may actually appear to increase in importance in 2018 compared to 2002.

Figure 7.5.4-1 Sipsey 2002 and 2018 SO2 distance weighted emissions * SO4 extinction-weighted residence time plots.

Figure 7.5.4-2 illustrates the gridded SO 2 distance weighted emissions * sulfate extinction weighted residence time plots for 2018 emissions for the Sipsey Wilderness. These plots illustrate the relative importance of Alabama sources of SO 2 compared to sources in neighboring states. Additional analyses, including 2002 and 2018 distance we ighted emissions * residence time plots for the Sipsey Wilderness Area in Alabama and neighboring Class I areas are contained in Appendix H.

65These analyses served as the basis fo r consultation among the VISTAS states.

2018 SO2 Emissions weighted by Residence Time Sipsey, AL

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%.

Figure 7.5.4-2 Sipsey 2018 SO2 distance weighted emissions * SO4 extinction-weighted residence time plots.

7.5.5 Specific Source Types in the Areas of In fluence (AoI) for the Sipsey Wilderness
The next step in the analysis was to review th e emissions inventories to determine what source categories, as well as specific sources, are found to have the great est impact on visibility in the Sipsey Wilderness area. Lists of SO 2 point sources within the AoI for each Class I area were developed using the most current (Base G) VISTAS 2002 base year and 2018 future year emissions. For this purpose the AoI was defi ned as the counties with maximum sulfate extinction weighted residence ti me greater than 5%. For SO 2 sources within the AoI, the following attributes were defined for each individual unit:

• State, source (plant), and industry identification codes

• SO 2 emissions for 2002 and 2018

• 2018 control efficiency

• Distance to Class I areas (defined by centroid of the Class I area)

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

• Maximum sulfate extinction weighted residence time (RTmax)

66The review was conducted in a top down fashion st arting with an analysis of the major source categories in each SO 2 AoI to determine which major cate gories 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 controlled under the future year base case had the potential for reduction. Once the highest source types were identified, subcategories within those source type s were reviewed. The contributions from major source categories to the 2018 Ba se G2 inventory for the SO 2 AoI for the Sipsey Wilderness area are listed in Table 7.5.5-1.


### Annual 2018 BaseG2 Emissions (%) within the AoI- Sipsey
Tier VOC NOX CO SO2 PM-10 PM-2.5 NH3 Fuel Comb. Elec. Util. 1% 24% 1% 58% 8% 16% 1% Fuel Comb. Industrial 1% 18% 2% 21% 3% 5% 0% Fuel Comb. Other 5% 6% 2% 6% 3% 7% 0% Chemical & Allied Product Mfg 1% 1% 1% 2% 1% 1% 0% Metals Processing 1% 1% 3% 5% 3% 6% 0% Petroleum & Related Industries 0% 0% 0% 1% 0% 0% 0% Other Industrial Processes 8% 6% 1% 5% 9% 10% 1% Solvent Utilization 43% 0% 0% 0% 0% 1% 0% Storage & Transport 6% 0% 0% 0% 1% 1% 0% Waste Disposal & Recycling 3% 1% 4% 0% 6% 14% 0% Highway Vehicles 17% 22% 44% 0% 1% 2% 9% Off-highway 11% 19% 32% 1% 2% 4% 0% Miscellaneous 3% 1% 9% 0% 64% 33% 87% VISTAS Total 100% 100% 100% 100% 100% 100% 100%

Table 7.5.5-1 2018 Emissions Contributions from Major Source Categories in the AoI- Sipsey

This table indicates that for the Sipsey Wilderne ss area, EGUs and industrial boilers are the two major source categories contribu ting to the bulk of the 2018 SO 2 emissions in the AoI, even after implementation of CAIR. Together these two source categories contribute 79% of the 2018 SO 2 emissions for the AoI for the Sipsey Wilderness ar ea. Other fuel combustion and other industrial processes comprise another 19% of the 2018 SO 2 emissions.

These tables can also be used to evaluate th e major source categories contributing to emissions of NO x , NH 3 , and PM emissions in 2018. For instance, highway vehicles and off road vehicles are major sources of NO x emissions, in addition to electric utilities and industrial boilers. The source category “miscellaneous” (which includes agricultural sources and fires) is the major contributor to NH 3 and primary PM. The emissions sensitivities discusse d in Section 7.4 indicated very small benefits of controlling NO x , and primary PM emissions at Sipsey, but if these emissions were of concern, different so urce categories would need to be addressed.

The contributions to SO 2 emissions in 2018 from the three hi ghest source categories, electric utilities, industrial boilers, and other fuel combustion have been further broken out into subcategories. Table 7.5.5-2 i ndicates these categories for the AoI for Sipsey. Within electric utilities, all the SO 2 emissions are attributable to coal fired power plants. Within industrial

6768 boilers, most emissions are attribut able to coal fired boilers with lesser contributions from oil and gas boilers. Commercial and inst itutional coal and oil boilers ha ve smaller contributions.

These analyses indicate that ADEM should consider what additional control measures for electric utilities and industrial boilers are reasonable. Lists of individual sources are also being used to determine if individual sources in othe r source categories are major contributors to SO 2 emissions in the AoI. 

### Annual 2018 BaseG2 Emissions (%) within the AoI- Sipsey

Tier VO C NO X CO SO2 PM10 PM2.5 NH3 Fuel Comb. Elec. Util.-Coal 0% 23% 1% 58% 8% 15% 0% Fuel Comb. Elec. Util.-Oil 0% 0% 0% 0% 0% 0% 0% Fuel Comb. Elec. Util.-Gas 0% 0% 0% 0% 0% 0% 0% Fuel Comb. Elec. Util.-Other 0% 0% 0% 0% 0% 0% 0% Fuel Comb. Elec. Util.-Internal Combustion 0% 1% 1% 0% 0% 1% 1% Fuel Comb. Industrial-Coal 0% 4% 0% 15% 2% 2% 0% Fuel Comb. Industrial-Oil 0% 1% 0% 4% 0% 0% 0% Fuel Comb. Industrial-Gas 0% 6% 1% 2% 1% 2% 0% Fuel Comb. Industrial-Other 0% 2% 1% 1% 1% 1% 0% Fuel Comb. Industrial-Internal Combustion 0% 5% 0% 0% 0% 0% 0% Fuel Comb. Other-Commercial/Inst itutional Coal 0% 1% 0% 2% 0% 0% 0% Fuel Comb. Other-Commercial/Institutional Oil 0% 0% 0% 3% 0% 0% 0% Fuel Comb. Other-Commercial/Inst itutional Gas 0% 1% 0% 0% 0% 0% 0% Fuel Comb. Other-Misc. Fuel Comb. (Except Residential) 0% 0% 0% 0% 0% 0% 0% Fuel Comb. Other-Residential Wood 4% 0% 2% 0% 2% 6% 0% Fuel Comb. Other-Residential Other 0% 3% 0% 0% 0% 1% 0% Top Tier Total 4% 47% 6% 85% 14% 28% 1%

Table 7.5.5-2 2018 Emissions Contributions with in Major Source Categories in the AoI- Sipsey

7.6 Evaluating the Four Statutory Factors for Specific SO 2 Emissions Sources in Each Area of Influence (AoI)

The next step was to identify emission reductions th at have already occurred within each source category and at specific units. Unit level tables of emission comp arisons from 2002 to 2018 were developed, allowing VISTAS states to re view existing emission reductions. These tables assigned future year control technology from IP M forecasting and state modification for EGUs 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 fo r EGU sources was obtained and matched to the 2018 base case inventory of EGU sources. This step was conducted to ensure that incremental controls assigned to these sour ce types did not duplic ate existing base case assumptions.For the sources within the AoIs for each Class I area, a master spreadsheet was created with the distance from the emission release point to the Class I area IMPROVE m onitor (in km), the 2018 residual emissions and distance (Q/d) or squa red distance (Q/d^2), and the normalized 2018 SO 2 point source emissions times distance-weighted residence time (RTMax) values for the county in which the emission release point was located. In addition, a new me tric Q/d*RTMax was created. Based on the sum of the Q/d*RTMax across all contributing sources, each source was rank ordered by their individual contribution to th e total. Given this information, ADEM chose to further evaluate those sources with Q/d*RTMax per centages of 1% or greater.

Tables 7.6-1 through 7.6-7 below show, in tabular form, the SO 2 units within the AOI whose percent contribution was 1% or gr eater than the total contributi on at both the Sipsey Wilderness Class I area and neighboring Cla ss I areas. Please note in Tabl e 7.6-6 for the Breton Wildlife Class I area, the Akzo Nobel sulf uric acid unit is retained on the table even though its individual impact is less than 1%. During review of this unit, it was determined that the 2018 projected SO 2 emission rate exceeded the allowable emission rate for that unit. When the table was revised to incorporate the allowable emission rate, the co ntribution from the sulf uric acid unit dropped below 1%. The record is provided in the table for informational purposes.

Table 7.6-1 Sipsey Q/d, Q/d^2, RTMax, and fractional contribution analysis (Sources that are 1% or greater of Q/d*RTMax)
SIPSEY > =1% of Q/d*RTMax

State Plant Name Pt ID 2002G 2018G CE Dist(km) Q/d RTMax Q/d* RTMax % of Q/d RTMax BART Facility Number

AL AL POWER CO- GORGAS 004 8,150 9,854 0 78.80 125.05 74.85 9359.99 10.28 NO 0112700001

AL AL POWER CO- GORGAS 005 9,443 8,091 0 78.80 102.67 74.85 7684.85 8.44 NO 0112700001

AL SOLUTIA, INC 009 1,831 2,244 0 44.06 50.93 74.85 3812.11 4.19 YES 0110300010

AL TVA COLBERT 014 31,769 4,598 90 64.99 70.75 46.90 3318.18 3.64 YES 0103300010

AL SOLUTIA, INC 015 1,863 1,849 4 44.06 41.95 74.85 3139.96 3.45 YES 0110300010

AL SOLUTIA, INC 013 1,686 1,673 4 44.06 37.97 74.85 2842.05 3.12 YES 0110300010

AL SOLUTIA, INC 014 1,622 1,610 4 44.06 36.53 74.85 2734.27 3.00 YES 0110300010

AL AL POWER CO-GORGAS 008 22,082 2, 798 90 78.80 35.51 74.85 2657.92 2.92 YES 0112700001

AL AL POWER CO-GASTON 006 52,378 7, 542 90 147.15 51.25 45.10 2311.38 2.54 YES 0111700005

AL INTER. PAPER CO 006 1,059 1,238 0 43.78 28.27 74.85 2116.01 2.32 YES 0107900001

AL CARGILL, INC 020 966 1,101 0 42.82 25.72 74.85 1925.14 2.11 NO 0110300039

AL TVA - WIDOWS CREEK 008 9,181 10, 185 90 157.42 64.70 29.13 1884.71 2.07 NO 0107100008

AL TVA - WIDOWS CREEK 009 8,588 6, 712 90 157.42 42.64 29.13 1242.10 1.36 YES 0107100008

AL AL POWER CO MILLER 001 11,834 1, 284 90 83.24 15.43 74.85 1154.94 1.27 YES 010730011

AL AL POWER CO MILLER 002 11,375 1, 284 90 83.24 15.42 74.85 1154.19 1.27 YES 010730011

AL AL POWER CO MILLER 004 10,590 1, 256 90 83.24 15.09 74.85 1129.49 1.24 YES 010730011

AL AL POWER CO MILLER 005 12,056 1, 236 90 83.34 14.83 74.85 1110.03 1.22 YES 010730011

GA GA POWER CO YATES SG07 14,024 16, 530 0 245.30 67.39 15.03 1012.87 1.11 YES 0001

GA GA POWER CO YATES SG06 15,175 16, 367 0 245.30 66.72 15.03 1002.80 1.10 YES 0001

AL TVA - WIDOWS CREEK 005 4,122 4, 839 0 157.42 30.74 29.13 895.46 0.98 NO 0107100008

AL TVA - WIDOWS CREEK 004 4,667 4, 828 0 157.42 30.67 29.13 893.42 0.98 NO 0107100008

AL TVA - WIDOWS CREEK 002 4,031 4, 821 0 157.42 30.63 29.13 892.25 0.98 NO 0107100008

69Table 7.6-2 Cohutta Q/d, Q/d^2, RTMax, and fractional contribution analysis (Sources that are 1% or greater of Q/d*RTMax)

Cohutta >=1% of total Q/dRTMAx

State Plant Name Pt ID 2002G 2018G CE Dist (km) Q/d RTMax Q/d* RTMax % of Q/d RTMax BART Facility Number

TN BOWATER CALHOUN 015 5,232 5,871 4 58.30 100.71 82.90 8348.86 8.59 NO 471070012
TN INTERTRADE HOLDINGS 001 1,837 2, 530 4 32.47 77.92 82.90 6459.57 6.65 YES 471390004
GA GA POWER CO YATES SG07 14,024 16, 530 0 149.41 110.63 39.91 4415.24 4.54 YES 0001
GA GA POWER CO YATES SG06 15,175 16, 367 0 149.41 109.54 39.91 4371.74 4.50 YES 0001
GA GA POWER CO BOWEN SG04 42,634 6, 294 90 78.34 80.35 44.32 3561.11 3.67 YES 0011
GA GA POWER CO BOWEN SG03 42,900 6, 111 90 78.34 78.01 44.32 3457.40 3.56 YES 0011
GA INLAND PBRD/PKG -LIN F4 3,293 3,696 4 87.55 42.22 78.87 3329.89 3.43 NO 0021

GA GA POWER CO HAMMOND SG04 18,089 3,455 90 88.75 38. 93 78.87 3070.41 3.16 YES 0003
GA GA POWER CO BOWEN SG02 35,266 4, 865 90 78.34 62.10 44.32 2752.27 2.83 YES 0011
GA GA POWER CO BOWEN SG01 33,050 4, 831 90 78.34 61.67 44.32 2733.21 2.81 YES 0011
AL TVA - WIDOWS CREEK 008 9,181 10, 185 90 103.54 98.37 20.39 2005.76 2.06 YES 010210008
GA GA POWER CO YATES SG05 5,009 6,643 0 149.41 44.46 39.91 1774.40 1.83 NO 0001
GA GA POWER CO YATES SG04 4,842 6,500 0 149.41 43.50 39.91 1736.09 1.79 NO 0001
TN TVA BULL RUN 001 36,729 8,657 90 144.05 60.10 24.66 1482.07 1.53 YES 470010009

TN EI DUPONT DENEMOURS 0002 1,286 1,173 0 67.08 17. 48 83.31 1456.26 1.50 NO 470652730

GA GA POWER CO SCHERER SG03 18,149 27,735 0 206. 59 134.25 10.72 1439.16 1.48 YES 0008

GA GA POWER CO SCHERER SG04 20,771 26,616 0 206. 59 128.84 10.72 1381.16 1.42 YES 0008

GA GA POWER CO SCHERER SG01 25,286 26,264 0 206.59 127. 14 10.72 1362.94 1.40 YES 0008

GA GA POWER CO SCHERER SG02 24,966 26,075 0 206. 59 126.22 10.72 1353.08 1.39 YES 0008
AL TVA - WIDOWS CREEK 009 8,588 6, 712 90 103.54 64.83 20.39 1321.88 1.36 YES 0107100008
TN A.E. STALEY MANUFAC, 005 3, 005 2,982 4 109.35 27.27 38.49 1049.62 1.08 NO 471050081
GA GA POWER CO YATES SG03 3,237 3,916 0 149.41 26.21 39.91 1046.04 1.08 NO 0001
GA INLAND PBRD/PKG -LIN F2 1,023 1,148 4 87.55 13.11 78.87 1033.99 1.06 NO 0021

70Table 7.6-3 Great Smokies Q/d, Q/d^2, RTMax, and fractional contribution analysis (Sources that are 1% or greater of Q/d*RTMax)

Great Smokies > =1% of total Q/dRTMax

State Plant Name Pt ID Q/d* % Q/d Facility Dist 2002G 2018G CE Q/d BART RTMax RTMax Number RTMax (km)

TN TVA BULL RUN 001 36,729 8,657 90 47.31 182.97 84.37 15437.18 14.14 YES 470010009

TN ALUMINUM CO OF AMERICA-SOUTH 16 1,856 2,370 0 16.49 143.71 83.87 12052.96 11.04 YES 470090008

TN ALUMINUM CO OF AMERICA-SOUTH 17 1,817 2,320 0 16.49 140.69 83.87 11799.67 10.81 YES 470090008

TN A.E. STALEY MANUF CO 005 3,005 2,982 4 36.00 82. 83 79.99 6625.57 6.07 NO 471050081

TN EASTMAN CHEMICAL CO 0215 20 16,855 16,729 4 160.39 104.30 41.30 4307.59 3.95 YES 471630003

APAC TN 0 02 1 53 2 00 0 3 .97 50.32 83.87 4 220.34 3 .87 N O 470090021 TN/HARRISON U .S. DOE T N Y -12 PLANT 002 2,354 2,336 4 48.58 48. 09 84.37 4057.35 3.72 NO 470011020

TN BOWATER NEWS DIR CALHOUN 015 5,232 5,871 4 82.82 70. 89 57.01 4041.44 3.70 NO 471070012

TN ALUMINUM CO OF AMERICA - SOUTH 09 424 541 0 16.49 32.80 83.87 2750.94 2.52 YES 470090008

NC BLUE RIDGE PAPER PRO-CANTON G-25 2,195 2,565 0 100.17 25.61 50.35 1289.46 1.18 NO 370870015 9

71Table 7.6-4 Joyce Kilmer Q/d, Q/d^2, RTM ax, and fractional contribution analysis (Sources that are 1% or greater of Q/d*RTMax)
Joyce Kilmer > =1% of total Q/dRTMax

State Plant Name Pt ID 2002G 2018G CE Dist (km) Q/d RTMax Q/d* RTMax % Q/d RTMax BART Facility Number

TN BOWATER NEWS &DIR-CALHOUN 015 5,232 5,871 4 71.46 82. 16 65.67 5395.45 8.77 NO 471070012

TN TVA BULL RUN FOSSIL PLANT 001 36,729 8,657 90 73.92 117.12 44.66 5230.58 8.50 YES 470010009

TN A.E. STALEY MANUFAC. CO 005 3,005 2,982 4 50.89 58. 59 64.11 3756.20 6.11 NO 471050081

TN ALUMINUM CO OF AMERICA - SOUTH 16 1,856 2,370 0 45.21 52.42 64.11 3360.65 5.46 YES 470090008

TN ALUMINUM CO OF AMERICA-SOUTH 17 1,817 2,320 0 45.21 51.32 64.11 3290.13 5.35 YES 470090008

TN INTERTRADE HOLDINGS, INC. 001 1,837 2,530 4 55.89 45.26 65.67 2972.22 4.83 YES 471390004

TN U.S. DOE Y-12 PLANT 002 2,354 2,336 4 72.75 32. 12 44.66 1434.48 2.33 NO 470011020

SC DUKE ENERGY LEE 003 4,384 7,274 0 164.50 44.22 27.54 1217.82 1.98 NO 0200-0004

GA GA POWER CO YATES SG07 14,024 16,530 0 229.18 72. 13 16.05 1157.69 1.88 YES 0011

GA GA POWER CO YATES SG06 15,175 16,367 0 229.18 71. 42 16.05 1146.29 1.86 YES 0011

TN EASTMAN CHEMICAL CO. 021520 16,855 16,729 4 181.93 91.95 12.09 1111.68 1.81 YES 471630003

GA GA POWER CO SCHERER SG03 18,149 27,735 0 258.20 107. 42 8.94 960.33 1.56 YES 0008

GA GA POWER CO SCHERER SG04 20,771 26,616 0 258.20 103. 09 8.94 921.62 1.50 YES 0008

GA GA POWER CO SCHERER SG01 25,286 26,264 0 258.20 101. 72 8.94 909.38 1.48 YES 0008

GA GA POWER CO SCHERER SG02 24,966 26,075 0 258.20 100. 99 8.94 902.85 1.47 YES 0008

TN ALUMINUM CO OF AMERICA-SOUTH 09 424 541 0 45.21 11.97 64.11 767.40 1.25 YES 470090008

SC DUKE ENERGY LEE 001 2,386 4,347 0 164.54 26.42 27.54 727.61 1.18 NO 0200-0004

SC DUKE ENERGY LEE 002 2,643 4,140 0 164.50 25.17 27.54 693.18 1.13 NO 0200-0004

GA GA POWER CO BOWEN SG04 42,634 6,294 90 163.76 38. 44 17.63 677.70 1.10 YES 0011

NC BLUE RIDGE PAPER PRO-CANTON G-25 2,195 2,565 0 104.38 24. 58 26.88 660.71 1.07 NO 3708700159

GA GA POWER CO BOWEN SG03 42,900 6,111 90 163.76 37. 32 17.63 657.95 1.07 YES 0011

AL TVA WIDOWS CRK* 008 9,181 10,185 90 170.64 59.69 10.74 641.07 1.04 YES 10210008

GA INLAND PPRBRD PKG -LIN F4 3,293 3,696 4 175.70 21. 04 29.12 612.68 1.00 NO 0021

*-The TVA Widow’s Creek Facility was identified as having a unit with an impact of greater than 1% of the total impact at the Joyce Kilmer Class I area. It is ADEM’s interpretation, however, that fo r the 2018 progress goal, the CAIR is equivalent to Reasonable Progress for Class I area s projected to meet or exceed their 2018 progress goal, as is the case for the Joyce Kilmer Class I area.

7273 Table 7.6-5 St. Marks Q/d, Q/d^2, RTMax, and fractional contribution analysis (Sources that are 1% or greater of Q/d*RTMax)

SAMA > =1% of Q/d*RTMax

State Plant Name Pt ID 2002G 2018G CE Dist (km) Q/d RTMax Q/d* RTMax % of Q/d RTMax BART Facility Number

FL PROGRESS ENERGY CRYSTAL RI 2 20,728 15,241 0 189.58 80.39 17.27 1388.34 6.50 YES 628, 0170004

FL PROGRESS ENERGY CRYSTAL RI 1 18,998 13,537 0 189.58 71.40 17.27 1233.08 5.78 YES 628, 0170004
FL BUCKEYE FL, LTD 4 736 860 4 61.48 13.99 75.55 1056.94 4.95 NO --, 1230001
FL BUCKEYE FL, LTD 7 621 726 0 61.48 11.80 75.55 891.49 4.18 YES --, 1230001
FL BUCKEYE FL, LTD 6 554 647 0 61.48 10.53 75.55 795.54 3.73 NO --, 1230001
FL BUCKEYE FL, LTD 2 449 524 0 61.48 8.53 75.55 644.44 3.02 NO --, 1230001

FL ST. MARKS POWDER, INC. 11 107 86 0 11.65 7.40 75.55 559.07 2.62 NO --, 1290003

FL PROGRESS ENERGY CRYSTAL RI 4 24,635 6,120 90 189.58 32.28 17.27 557.48 2.61 NO 628, 0170004
FL BUCKEYE FL, LTD 11 385 450 0 61.48 7.32 75.55 553.03 2.59 YES --, 1230001

GA GA POWER CO MITCHELL SG03 4,173 4,930 0 150. 49 32.76 16.16 529.40 2.48 YES 0002

FL GULF POWER CO SCHOLZ 1 320 1,980 0 95.55 20.72 25.02 518.41 2.43 NO 642, 0630014

FL GULF POWER CO SCHOLZ 2 465 1,980 0 95.55 20.72 25.02 518.41 2.43 NO 642, 0630014

AL ESCAMBIA OPERATING CO 014 15,420 14,695 0 325.51 45.14 10.91 492.48 2.31 YES 010530000 7

FL GULF POWER CO - CRIST 6 10,889 12,421 0 298.97 41. 55 11.11 461.62 2.16 YES 641 0330045

GA GA POWER CO SCHERER SG03 18,149 27,735 0 331.91 83. 56 5.17 432.01 2.02 YES 0008

FL STONE CONTAINER CORP 16 1,871 2,108 4 142.53 14. 79 28.59 422.85 1.98 YES --, 0050009

GA GA POWER CO SCHERER SG04 20,771 26,616 0 331.91 80. 19 5.17 414.58 1.94 YES 0008

GA GA POWER CO SCHERER SG01 25,286 26,264 0 331.91 79. 13 5.17 409.10 1.92 YES 0008

GA GA POWER CO SCHERER SG02 24,966 26,075 0 331. 91 78.56 5.17 406.16 1.90 YES 0008

AL SANDERS LEAD CO 003 4,048 6, 404 0 256.39 24.98 12.50 312.22 1.46 YES 011090000 5

AL SANDERS LEAD CO 008 3,948 5, 488 0 256.39 21.40 12.50 267.56 1.25 YES 011090000 5

FL PROGRESS ENERGY CRYSTAL RI 3 26,436 3,634 90 189.58 19.17 17.27 331.07 1.55 NO 628, 0170004

GA LONGLEAF ENERGY STAT BOIL 1 0 3,227 0 154.52 20.88 12.89 269.14 1.26 NO Not Yet Permitted

GA LONGLEAF ENERGY STAT BOIL 2 0 3,227 0 154.52 20.88 12.89 269.14 1.26 NO Not Yet Permitted

GA GA POWER CO YATES SG07 14,024 16,530 0 381.77 43. 30 5.07 219.53 1.03 YES 0001

GA GA PACIFIC CORP CEDAR SPRIN U501 2,257 2,533 4 149.70 16.92 12.89 218.10 1.02 YES 0009

GA GA POWER CO YATES SG06 15,175 16,367 0 381.77 42. 87 5.07 217.35 1.02 YES 0001Table 7.6-6 Breton Q/d, Q/d^2, RTMax, and fractional contribution analysis (Sources that are 1% or greater of Q/d*RTMax)

Breton > =1% of Q/d*RTMax

State Plant Name Pt ID 2002G 2018G CE Dist (km) Q/d RTMax Q/d * RTMax % Q/d RTMax BART Facility Number

MS MS POWER CO PLANT JACK WA 004 7,266 7,640 0 148.07 51.60 28.09 1449.44 5.22 YES 2804700055

LA CONOCOPHILLIPS ALLIANCE 11 1,388 1,664 0 97.80 17.02 76.94 1309.52 4.72 YES 2418

LA CII CARBON CHALMETTE 81 2,235 2,792 0 117.99 23.66 55.27 1307.69 4.71 NO 84724

LA CONOCOPHILLIPS ALLIANCE 10 1,386 1,662 0 97.80 16.99 76.94 1307.21 4.71 YES 2418

FL GULF POWER CO- CRIST 6 10,889 12,421 0 250.15 49.65 18. 70 928.46 3.35 YES 641- 0330045

AL AL POWER CO - BARRY 004 7,801 9,329 0 239.94 38.88 22.80 886.46 3.19 NO 0109701001

LA DUPONT CHEM. BURNSIDE 01 9,747 13,275 0 199.52 66.54 12. 20 811.79 2.93 YES 67572

LA CHALMETTE REFINERY 29 1,246 1, 494 0 117.60 12.70 55.27 701.93 2.53 YES 1376

LA CHALMETTE REFINERY 28 1,219 1, 462 0 117.60 12.43 55.27 687.01 2.48 YES 1376

AL AL POWER CO - BARRY 006 24,676 6,790 90 239.94 28.30 22. 80 645.24 2.33 YES 0109701001

AL ESCAMBIA OPERATING CO (prvs.Exxon Mobile Co.) 014 15,420 14,695 0 279.40 52.59 11.97 629.50 2.27 YES 105300007

LA PCS NITROGEN GEISMAR 01 7,929 10, 799 0 215.29 50.16 12.20 611.95 2.21 YES 3732

LA MOTIVA ENTERPRISES CONVENT 16 7,193 9,459 0 197.24 47.96 12.20 585.11 2.11 YES 1406

LA COLUMBIAN CHEM NORTH BEND 81 6,595 11,825 0 226.92 52. 11 10.76 560.70 2.02 NO 4998

LA MURPHY OIL MERAUX REFINERY 28 831 1,093 0 115.38 9.47 55.27 523.41 1.89 YES 1238

MS CHEVRON PROD. PASCAGOULA REF 051 1,780 157 4 123.85 18.08 28.09 507.87 1.83 YES 2805900058

AL AL POWER CO- BARRY 003 3,970 5,235 0 239.94 21.82 22.80 497.50 1.79 NO 0109701001

AL AL POWER CO - BARRY 002 3,550 5,145 0 239.94 21.44 22.80 488.83 1.76 NO 0109701001

FL GULF POWER CO- CRIST 7 21,546 4,648 90 250.15 18.58 18. 70 347.45 1.25 YES 641

LA CHEVRON ORONITE OAK POINT 06 291 492 0 109.57 4.49 76.94 345.46 1.24 NO 1708

LA LA GENERATING BIG CAJUN2 PPLNT 01 19,851 17,918 0 275.00 65.15 5.27 343.34 1.24 YES 38867

MS DUPONT DELISLE FACILITY 024 1,657 1,645 4 137.98 11. 92 28.09 334.83 1.21 NO 2804700115

LA LA GENERATING BIG CAJUN2 PPLNT 02 17,018 17,354 0 275.14 63.07 5.27 332.38 1.20 YES 38867

MS DUPONT DELISLE FACILITY 025 1,639 1,627 4 137.98 11. 79 28.09 331.18 1.19 NO 2804700115

LA LA GENERATING BIG CAJUN 2 PPLNT 03 15,136 16,691 0 275.28 60.63 5.27 319.52 1.15 YES 38867

AL AL POWER CO - BARRY 005 10,185 3,267 90 239.94 13.62 22. 80 310.54 1.12 YES 0109701001

MS CHEVRON PRODUCTS CO PASCAGOULA REF 017 1,902 263 45 123.85 10.95 28.09 307.59 1.11 YES 2805900058

LA CHALMETTE REFINERY 84 480 576 0 117.60 4.89 55.27 270.27 0.995 YES 1376

74Breton > =1% of Q/d*RTMax

State Plant Name Pt ID 2002G 2018G CE Dist (km) Q/d RTMax Q/d * RTMax % Q/d RTMax BART Facility Number

LA MURPHY OIL MERAUX REFINERY 11 419 551 0 115.29 4.78 55.27 264.19 0.972 YES 1238

AL AKZO NOBEL CHEMICALS INC 003 3,688 2030 0 235.58 8.62 22.80 196.47 0.71 NO 0109705009

75Table 7.6-7 Okefenokee Q/d, Q/d^2, RTMax, and fractional contribution analysis (Sources that are 1% or greater of Q/d*RTMax)

OKEFENOKEE > =1% of total Q/dRTMax

State Plant Name Pt ID 2002G 2018G CE Dist (km) Q/d RTMax Q/d* RTMax % of Q/d RTMax BART Facility Number

FL SAINT JOHNS RIVER 17 10,185 7,420 90 65.12 113.95 65.70 7486.52 15.43 NO 207, 0310045

FL SAINT JOHNS RIVER 16 11,076 5,882 90 65.12 90.33 65.70 5934.68 12.23 NO 207, 0310045

FL JEFFERSON SMURFIT CORP 15 3, 242 3,639 4 64.58 56.35 71.58 4033.53 8.32 NO 0890003

FL CEDAR BAY GENERATING CO GEN1 0 2,227 0 61. 17 36.41 65.70 2392.14 4.93 NO 10672, 0310337

GA GA-PACIFIC BRUNSWICK F1 1,642 1,842 4 75.48 24.41 71.47 1744.58 3.60 NO 0003

FL RAYONIER PRFORMCE FIBERS 6 1,075 1,256 0 63. 34 19.83 71.58 1419.43 2.93 NO 0890004

FL SEMINOLE ELECTRIC COOP 1 10, 912 6,779 95 121.83 55.64 22.94 1276.38 2.63 NO 136, 1070025

FL SEMINOLE ELECTRIC COOP 2 12, 775 6,508 95 121.83 53.42 22.94 1225.45 2.53 NO 136, 1070025

GA RAYONIER PRFORMCE FIBERS-JESUP PB03 1,423 1,597 4 106.27 15.03 71.58 1075.85 2.22 YES 0001

FL WHITE SPRINGS AGRI. CHEM 66 1, 140 1,496 0 69.96 21.39 48. 81 1044.05 2.15 YES 0470002

FL WHITE SPRINGS AGRI. CHEM 67 996 1,308 0 69.96 18.69 48. 81 912.26 1.88 YES 0470002

FL IFF CHEMICAL HOLDINGS, INC. 3 624 733 0 56.66 12. 94 65.70 850.16 1.75 YES 0310071

GA GA POWER CO SCHERER SG03 18,149 27,735 0 302.93 91.56 9. 11 834.11 1.72 YES 0008

GA GA POWER CO SCHERER SG04 20,771 26,616 0 302.93 87.86 9. 11 800.40 1.65 YES 0008

FL GA-PACIFIC CORP. PULP/PAPER MILL 15 3,703 4,329 0 125.55 34.48 22.94 790.97 1.63 YES 1070005

GA GA POWER CO SCHERER SG01 25,286 26,264 0 302.93 86.70 9. 11 789.84 1.63 YES 0008

GA GA POWER CO SCHERER SG02 24,966 26,075 0 302.93 86.07 9. 11 784.10 1.62 YES 0008

GA INTERNATIONAL PAPER SAVANNAH PB13 7,643 8,578 4 179.54 47. 78 15.53 742.02 1.53 NO 0007

FL MILLENNIUM SPECIALTY CHEM. 6 505 590 0 60.46 9.76 65.70 641.23 1.32 NO 0310039

GA SAVANNAH ELECTRIC MCINTOSH SG01 7,089 7,015 0 201.59 34. 80 15.51 539.75 1.11 YES 0003

76The Rule requires that states consider the fo llowing factors and demons trate how these factors were taken into consideration in se lecting reasonable progress goals:

• the costs of compliance

• the time necessary for compliance

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

• the remaining useful life of any potentially affected sources.

Cost of Compliance: As defined in Section 7.5.5, coal-fir ed electric utilities and coal-fired industrial boilers were the largest source categories contributing to SO 2 in 2018 in the AoI defined for the Sipsey Wilderness area.

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

Bituminous coal is commonly burned in boilers in the eastern U.S. Switching to another bituminous coal with lower sulfur content or blending bituminous coals can reduce SO 2 emissions with least impact to boiler performance. While sub-b ituminous coal has a lower sulfur content than bituminous coal, it al so has a lower heat rate and so more coal has to be burned to generate the same energy output. For boilers that are designed for bituminous coal, only a small fraction of the fuel can be switched to low sulf ur sub-bituminous coal without affecting boiler operations. Costs of boiler modi fications to accommodate low sulfur sub-bituminous coal may make switching to sub-bituminous coal impractical . Contract initiation and termination costs, differential fuel prices and heat rates, transpor tation costs, and modifica tion 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-combus tion control for coal-fir ed 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 a nd 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 costs, and maintenance costs. Conceptually, post-combustion controls can be used for oil boilers, but there is little precedence for such installations.

Time Necessary for Compliance: For fuel switching, the time n ecessary to terminate existing fu el contracts and initiate new contracts needs to be considere d. Generally two to three years may be required. Installation of post-combustion controls will require three or more years depending on market availability of

7778 labor and materials and utility system wide prio rities. 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-combus tion controls may reduce boiler heat rate and energy output. Scrubbers and spray dryers will require additional safeguards for fuel handling and waste handling systems to avoid add itional non-air environmental impacts such as increased effluents in waste water discharges and storm water runoff. These factors will need to be considered specific to indivi dual sources. Carbon dioxide may be emitted as a by-product of flue gas desulfurization, especia lly if limestone is used. Ther efore, impacts 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 is specific to the unit for which controls are considered.

7.7 Which Control Measures Characterize Re asonable Progress for Individual Sources?

ADEM’s Process on Reasonable Progress Assessment

Step 1: Determine pollutants of concern.

VISTAS evaluated the species c ontribution on the 20% worst visibi lity days and concluded that sulfate accounted for the greates t contribution of visibility im pairing pollution. The VISTAS states concluded th at controlling SO 2 emissions was the appropriate step in addressing the reasonable progress assessment for 2018. The findi ngs were consistent with the findings of SAMI where it was confirmed that sulfate par ticles account for the greatest portion of the haze affecting Class I areas in the Southern Appalach ian region and that these sulfates were produced in large part from SO 2 emissions from coal combustion.

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

Since SO 2 point source emissions in 2018 are projected to represent the majo rity of the total SO 2 emissions inventory, the VISTAS st ates concluded that the focus should be on electric generating unit (EGU) and non-EGU point sources of SO 2 emissions.

Step 3: Determine if the Clean Air Interstate Rule (CAIR) is sufficient for reasonable progress.

“In determining the emissions and visibility improvement achieved during each implementation period, states should include all air quality improvements that will be achieved by other programs and activities under the CAA and any state air pollution control requirements” (64 FR 35733). In keeping with this recommendation, Al abama, as part of its long term reasonable progress analysis to consider po tential sources contri buting to visibility impairment, examined other CAA requirements such as CAIR. ADEM evaluated the am ount of expected SO 2 reduction from EGUs due to CAIR. Since the EGUs will be reducing their SO 2 emissions by approximately 70% through these programs, ADE M concluded that additional EGU control during this time period (between pres ent and 2018) was not reasonable.The preceding section (Section 7.6) discusses a general four factor an alysis for coal fired units. Additionally, for EGUs subject to CAIR, EPA evaluated a number of factors, including the cost of compliance and time necessary for compliance. In the CAIR rule, EPA determined "that the earliest reasonable deadline for compliance with the final highly cost effective control levels for reducing emissions was 2015..." (70 FR 25197- 25198, May 12, 2005) The State believes the cost of compliance and time necessary for compliance are the dominant factors for determining if additional reductions would be reasonable from CAIR subject EGUs. Based on the detailed analyses in the May 12, 2005, CAIR rule, the St ate concludes that CA IR controls satisfy reasonable progress for SO 2 for the first planning period ending in 2018.

Under the Rule, each “state must identify the uni form rate of progress over the 60 year period that would be needed to atta in natural background conditions by the year 2064” (64 FR 35732). To calculate the rate of pr ogress represented by each reas onable progress goal, Alabama compared baseline visibility conditions to natu ral visibility conditions at the Sipsey Wilderness area and determined the uniform rate of visibil ity (in dv) that would need to be maintained during each implementation period in order to at tain natural visibility conditions by 2064. SO 2 emissions reductions that are predicted by the IPM to meet the CAIR requirements are not certain due to the current rule’s reliance on trading and the use of banked Title IV allowances. ADEM intends to review the SO 2 reductions from CAIR at the time of the periodic report in 2012 to ensure that the reductions are in fact taking place. Based on the controls predicted by IPM under CAIR, ADEM has concl uded that, at this time, thes e existing regulatory programs constitute reasonable measures for Alabama’s E GUs during this first assessment period (baseline to 2018). It is important to note that the IPM model was used extensively by EPA when performing analyses for CAIR.

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

ADEM calculated the fractional contribution from all emission units within the SO 2 AoI for a given Class I area and identified those emission units with a contri bution of 1% or more to the visibility impairment at that Class I area.

ADEM recognized that there was neither sufficient time nor resources available to evaluate all units within a given AoI. Ther efore, a threshold needed to be established to determine which units would be evaluated. ADEM chose to adop t 1% as the contributi on threshold used to consider a source for a four factor analysis of reasonable further controls based on the precedence established in other Federal programs th at used an equal to or higher threshold for evaluation (For a full explanati on of the rationale behind the se lection of the 1% threshold, please refer to Appendix H, Section 5.0) . These programs are listed below:

• The BART rule specified that a maximum impact of 0.5 dv is an acceptable threshold for establishing source contribution. This threshold is represen tative of a 5% change in visible perception.

• The NO x SIP call established a significance level for 126 petitions (the level at which a state’s contribution to anothe r state’s ozone was considered significant) at 4 parts per billion. This threshold is representative of approximately 3.2% of the 1 hour ozone standard of 125 parts per billion, wh ich was the standard at the time.

79• The CAIR rule established a PM contribution threshold of 0.2 μg/m 3 as significant. This threshold represents 1.3% of the Annual PM 2.5 standard of 15 μg/m 3

.

• The NAAQS assign significant impact levels for human health standards based on a source’s contribution to air quali ty. These significance levels represent a percentage of the NAAQS. Those facilities able to demons trate that a new or modified source’s contribution is less than a pa rticular significance level is re lieved of further modeling. ADEM examined all the averaging periods for the criteria pollutants and determined that the 1% threshold contribution chosen for reasonable progre ss was as stringent as any significant impact level, the most restrictive being NO 2 at 1 μg/m 3 or 1%. (NO 2 NAAQS: 100 μg/m 3 )

For the Sipsey Wilderness area, six units were dete rmined to have impacts greater than 1% of the total impact. These included 1 unit at the Inte rnational Paper- Courtland Mill, 4 units at the Solutia, Inc. facility and 1 unit at the Cargill, Inc. facility.

At both the International Paper- C ourtland Mill and the Solutia, Inc. facilities, all units identified in the Sipsey AoI table were subject to BART and subsequently were evaluated for BART controls. It is ADEM’s contention that, for th ese units, BART is equivalent to Reasonable Progress (RP).

For the Cargill, Inc, facility, a four factor analysis was performed for the Keeler Stoker Boiler (Unit S-407), a summary of which is included in Step 5 below, as well as in Appendix O.

For the Breton Wildlife Refuge, a sulfur recovery unit at the Escambia Operating Company- Big Escambia Creek was determined to have impacts gr eater than 1% of the total impact. This unit took a permit limit of approximately 7963 tons of actual SO 2 emissions (equivalent to a ~ 40% reduction in emissions). This limit precluded th e source from BART subjectivity and a permit was issued enforcing this limit in mid 2006. As such, ADEM contends that this unit has, in effect been through the BART process, and that th is reduction is sufficient for RP. The State of Louisiana used a number of tools including CENRAP source apportionment modeling and the VISTAS AOI analyses in developing their source list for the Breton Wildlife Refuge. Discussions with the State of L ouisiana yielded no request for a four factor analysis for this source. For these reasons, ADEM contends that no further evaluation is needed for this source.

For the St. Marks Wilderness area, two units at the Sanders Lead Company were determined to have impacts greater than 1% of the total im pact. The Florida reasonable progress methodology used to identify sources for a four factor an alysis for the St. Marks Wilderness Area involved assessing emissions units having sulfur dioxides (SO2) emissions of at least 250 tons per year, and emissions (Q) to distance rati o of greater than or equal to 50 (Q/d>=50), and being within 300 km of the Class I area. Although the States of Alabama and Florid a are using different methodologies, ADEM feels it is the responsibility of the state in which the Class I area resides to establish procedures for eval uating reasonable progress for that Class I area. In consultation with the Florida Department of Environmental Protection, Florida did not request a four factor analysis of these units. For th is reason, ADEM contends that no further evaluation is needed for these sources.

8081 Step 5: Evaluate the four factors.

The emission unit identified in Step 4 above wa s evaluated using the st atutory and regulatory factors of 1) cost of compliance, 2) time n ecessary for compliance, 3) the energy and non-air quality environmental impacts of compliance, and 4) the remaining useful life of the emissions unit. For the limited purpose of ev aluating the cost for the reasonabl e progress assessment in this first Regional Haze SIP, ADEM believes it is not equitable to require non-EGUs to bear a greater economic burden than EGUs for a given control strategy. ADEM used the capital and operating costs provided in the CAIR rule to establish a $2,000 cost/ton of SO 2 removed threshold. This limit acts as an inherent barrier to non-EGUs considering additional controls. The cost of controls is significant--not just in terms of capital cost, but also the cost of labor needed to efficiently and effectively maintain the equipm ent. “The availability of skilled labor-- specifically, boilermakers--is an important constr aint for the installati on of significant amounts of emission controls. Boilermakers are skilled steel workers who ar e specially trained to install both NO X controls, such as SCR, and SO 2 controls, such as scrubbers” (69 FR 4617). During the current reasonable progress asse ssment, no units in Alabama we re identified for additional control since no measures were found to be be low the $2,000 cost threshol d. The analysis of Cargill Unit S-407 is included in Appendix O and a summary of the Cargill facility is provided below.

CARGILL, Inc. Four Factor Analysis

Cargill Unit S-407 showed an impact of 1% or gr eater; therefore, a four factor analysis was necessary. The unit is permitted to burn coal, natura l gas, or No. 2 fuel oil. Coal is the primary fuel source. The unit is equipped with a Lear Sigler Continuous Opac ity Monitor and Nitrogen Oxide/Oxygen Emissions Monitor. The boile r exhaust is controlled by a Carborundum baghouse. The coal handling system is controlle d by a Griffin baghouse. The pneumatic flyash conveying system is controlled by an Ultra I ndustries Baghouse, while air displaced by the flyash to the ash silo is controlled by a Na tional Conveyor Baghouse. A summary of control options considered is listed below:

Cargill evaluated 3 options: lower sulfur cont ent coal, wet scrubbers and dry scrubbers.

Lower sulfur content coal could no t be used because of its availabi lity. Also, lower sulfur content coal would require significant modification to the boiler to burn it a nd would challenge the combustion integrity due to the higher dust content nature of lower sulfur coal. Therefore, lower sulfur coal is technically infeas ible. As for the add-on controls (wet and dry scrubbers), Cargill estimated that it would cost $2,946/ton to control SO 2 with these technologies. As such, no control was deemed feasible for this unit.

7.8 The Identification and Evaluation of Be st Available Retrofit Technology (BART) for
Individual Sources

Section 169A of the CAA directs states to assess certain emissi on sources which meet specific criteria for additional controls in order to addres s visibility impacts. States are directed to conduct BART determinations for such sources in specific source categories, whose emissionscontribute to visibility impair ment in Class I areas. The 1999 Regional Haze rule includes the BART requirement, and directs states to include BART in their Regional Haze SIPs. On July 6, 2005, EPA published a revised fi nal rule, including Appendix Y of 40 CFR Part 51, the Guidelines for BART Determinati ons Under the Regional Haze Rule (hereinafter referred to as the “BART Guidelines”) that provides direction to states on determining which sources should be subject to BART, and how to determine BART for these sources.

A BART-eligible source is one which has the pote ntial to emit 250 tons or more per year of a visibility-impairing air pollutant; was put in place between August 7, 1962 and August 7, 1977; and whose operations fall within one or more of 26 specifically named source categories. Under the CAA, BART is required for any BART-eligible source that a state determines ‘‘emits any air pollutant which may reasonably be anticipated to cause or cont ribute to any impairment 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 de terminations: (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 useful life of the source, and (5) the degree of improvement in visibility which may r easonably be anticipated to result from the use of such technology.

7.8.1 BART-Eligible Sources in Alabama
The following is a list of facilities with BART-eligible sources in Alabama. Please see Attachment H3 to Appendix H for a descri ption of each BART-eligible emission unit:

Alabama BART Eligible Facility List:

3M Company, Decatur Plant Hunt Refining Co, Tuscaloosa Alabama Electric Coop Lowman International Paper Co Prattville Alabama Power Co Barry International Paper Co Riverdale Alabama Power Co EC Gaston International Paper Co Courtland Alabama Power Co Gorgas JSC Brewton (Smurfit Stone) Alabama Power Co Greene Co Lehigh Cement – JCDH Alabama Power Co Miller- JCDH MeadWestvaco Mahrt Mill American Cast Iron Pipe- JCDH National Cement Co of Alabama Boise White Paper Oak Grove Resources- JCDH Bowater Inc.- Alabama Sanders Lead Co BP Amoco Chemicals Shell Chemical Co Saraland Carmeuse Lime & Stone Solutia- Decatur CEMEX, Inc. Sloss Industries- JCDH Chemical Lime Co Alabaster TVA, Colbert Chemical Lime Co Montevallo TVA, Widows Creek Ciba Specialty Chemicals Corp US Pipe & Co- Bessemer- JCDH ConocoPhillips Co Chatom US Steel- Fairfield- JCDH Degussa Corporation Weyerhaeuser Escambia Operating Co Big Escambia Creek Wise Alloys LLC, Alloys Plant Ft Jms-Pennington, Naheola Vintage Petroleum, Inc, Flomaton

82The BART-eligible sources were identified using the methodology in the BART Guidelines:

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

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

• The potential emissions from all emission uni ts identified in the previous two bullets, considering all federally enforceable permit limits, were greater than 250 tons or more per year of any of these visi bility-impairing pollutants: SO 2 , NO x , and PM 10
. The BART Guidelines recommend addressing these visibility-impairing pollutants: SO 2 , NO x , and PM, and suggest that states use their best judgment in dete rmining whether to address VOC or ammonia (NH 3 ) emissions. ADEM addressed SO 2 and NO x , and used PM 10 as an indicator for particulate matter to identify BART-eligible units, as the BART Guidelines recommend. As discussed in detail in subse quent sections as well as in Appendix H, VISTAS modeling demonstrated that VOCs and NH 3 from point sources do not cont ribute to visibility impairment at Class I areas within the VISTAS region. As such, ADEM di d not evaluate emissions of VOCs and NH 3 in BART determinations. VISTAS did evaluate a few large ammonia sources, none of which were located in Alabama.

7.8.2 VOC Evaluation of BART Eligible Sources in Alabama
Several sources preliminarily identified as BA RT-eligible emissions units have potential emissions equal to or greater than 250 tons per year of volatile or ganic compounds (VOCs) alone, or are eligible by virtue of the addition of VOC emissions to other visibility impairing pollutants. ADEM and VISTAS have determined that point source emissions of VOCs do not contribute to visibility impairme nt at Class I areas within th e VISTAS region. Section 4.1.3 of the VISTAS BART Modeling Protocol , found in Appendix C, describes the Regional modeling analyses that were performed to demonstrate th at cumulative VOC emissions (from all sectors) do not contribute to visibility impa irment within the VISTAS region:

Emissions sensitivity simulations run for VISTAS by the Georgia Institute of Technology using VISTAS’ 12 X 12 km grid and CMAQ v 4.3 for episodes in July 2001 and January 2002 demonstrated very low to no response of organic carbon levels and light extinction at Class I areas to changing VOC emissions from all anthropogenic sources in the VISTAS 12 km modeling domain (Eastern US)...VOC emissions from all anthropogenic point sources in every VISTAS domain was t hus determined to be less than 0.5 dv (compared to annual average natural backgroun d) at every Class I area in the VISTAS domain and in adjacent States. It follows that the impac t of any one BART eligible source would be much less that the 0.5 dv threshold. Based on these analyses, the VISTAS States have concluded that VOC emi ssions from BART sources do not cause or contribute to visibility impairment and do not need to be included in BART analyses.

Of the original 40 sources considered by ADEM as part of its BART source list, 3 sources were given exemption status solely for this reason. After estimating the potenti al emissions across all BART eligible sources at these facilities, ADEM determined that the following facilities’ visibility impairing pollutant (V IP) emissions no longer exceed 250 tons per year (Table 7.8.2-1).

83Therefore, these sources are no longer considered “BART-eligible” for evaluation of visibility impairment at Federal Class I areas.

Alabama’s VOC Exemption Sources

Facility Name SO2 Potential Emissions (tpy) NOx Potential Emissions (tpy) PM Potential Emissions (tpy) 3M Company, Decatur Plant 0.0 0.0 0.0 Ciba Specialty Chemicals Corp. 10.07 78.12 8.08 (TSP) Wise Alloys LLC, Alloys Plant 1.24 212.3 229.6
Table 7.8.2-1 Alabama’s VOC Exempt BART sources- remaining VIP emissions totals

7.8.3 Sources that took Limits to Exempt Units from BART
The BART Guidelines list specific criteria that mu st be met in order for sources to be considered BART-eligible. One of the criteria for a source to be considered BART-eligible was the potential emissions from all aff ected emission units considering all federally enforceable permit limits, were greater than 250 tons or more per year of any of these visibility-impairing pollutants: SO 2 , NO x , and PM 10 . Alabama had three sources which chos e to take emissions limits in order to be exempt from the entire BART process. By taking an emissions limit, Gulf States Paper Corporation, Kerr-McGee Chemical LLC, and Mob ile Energy Services Company no longer have BART-eligible sources. Additionally, the Esca mbia Operating Company’s Big Escambia Creek facility also took an enforceable limit that result ed in an insignificant impact, (i.e. less than 0.5 dv) thereby relieving the source from completi ng a BART determination. These limits have been written into and are enforced through the perm its for each applicable source at the facility. Table 7.8.3-1 provides information on these sources.

84Table 7.8.3-1 Alabama’s non BART-eligible sources by virtue of emissions limitations

Facility Name a Pollutant Potential Emissions Pre limits (tpy) Potential Emissions Post limits (tpy) Effective Date of Permit Changed PM 10 10.3 10.3 SO 2 210.0 210.0 Gulf States Paper #2 Power Boiler (now known as Rock Tenn) NO x 455.0 245.0 07/13/2007

PM 10 552.0 0 SO 2 455.0 0 Gulf States Paper #2 Recovery Furnace c (now known as Rock Tenn) NO x 248.0 0 07/13/2007

PM 10 131.0 99.0 SO 2 663.0 99.0 Kerr McGee Chemical (now known as Tronox, LLC) (source is a synthetic minor for all pollutants) NO x 211.0 99.0 11/30/2006

Escambia Operating Co-Big Escambia Creek- Sulfur Recovery Unit SO2 43,839 7963 7/6/2006

Facility Name Pre limit Maximum Heat Input (MMBTU/hr) Post limit Maximum Heat Input (MMBTU/hr) Date Permit Changed Mobile Energy Services Co., Unit #6 494.0 240.0 b 11/16/2006

a These units represent all the BART eligible units at the facility b As per the BART rule, EGU boilers with Heat Input less than 250 MMBTU/hr are not BART eligible

c Title V Permit issued without this source

7.8.4 Determination of Sources Subject to BART in Alabama
Under the BART Guidelines, ADEM may consider exempting some sources from BART if it can be shown that they do not cau se or contribute to visibility impa irment in a Class I area. In accordance with the BART Guidelines, ADEM chos e to require 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 Attachment H4 to Appendix H.

Determining whether a source causes or contributes to visibility impairment is one step in the BART review process. The BART Guidelines 1 state that “A single source that is responsible for a 1.0 deciview (dv) change or more should be co nsidered to ‘cause’ visibility impairment.” The guideline document also states that “the appr opriate threshold for dete rmining whether a source ‘contributes to visibility impai rment’ may reasonably differ acro ss states,” but, “As a general matter, any threshold that you use for determinin g whether a source ‘contributes’ to visibility impairment should not be higher than 0.5 dv.” The ra tionale for these instru ctions is provided in the preamble to the BART Guidance, in the statem ent, “If ‘causing’ visibility impairment means

1 40 CFR 51, Appendix Y, Section III.A.1

85causing a humanly perceptible change in vi sibility in virtually all situations ( i.e. a 1.0 dv change), then ‘contributing’ to visibility impa irment must mean having some lesser impact on the conditions affecting visibili ty that need not rise to the level of human perception.” 2 The Guidance itself also states that, “States remain free to use a threshold lower than 0.5 dv if they conclude that the location of a large number of BART-eligible s ources within the state and in proximity to a Class I area justify this approach.”

EPA’s documents strive to set these thresholds in the context of th e human perception of visibility change. As noted above, EPA considers a 1.0 dv change in visi bility to be humanly perceptible “in virtually all situations.” Also, the preamble to the BART Guidance 3 cites an analysis in an Appendix of a NAPAP (National Acid Precipitation Assessment Program) report, which asserts that “changes in light extinction of 5% will evoke just noticeable changes in most landscapes.” 4 (A 5% change is approximately 0.5 dv.) But, as noted above, the preamble also states that perceptibility is not a prerequisite for choosing a contribu tion threshold. Putting all this together, it appears that “causing” visibil ity impairment means having a humanly perceptible impact (for which EPA consider s the practical threshold to be 1.0 dv) while “contributing” to visibility impairment means having a smaller imp act (for which EPA considers the threshold to be 0.5 dv or some smaller value) th at may or may not be perceptible.

ADEM retained the threshold of 0.5 dv for evalua ting significance for several reasons. First, there is a clear spatial variability of sources ac ross the state. Additi onally, ADEM is electing to use half of the threshold established by EPA fo r causing visibility impairment. Lastly, the BART rule is a source specific rule and ADEM does not have a technical justification for lowering the threshold based on concerns of multiple plume interaction. As such, ADEM concluded that EPA’s suggested contribution threshold of 0.5 dv was appropriate for evaluating facility contributions at Class I areas that Alabama sources impact.

Of Alabama’s 37 final BART-eligible sources that submitted exemption modeling demonstrations, 35 of the 37 sources were able to exempt using the 0.5 dv threshold. Results of these demonstrations are summarized in Tables 7.8.4-1 and 7.8.4-2 below. Additional details are available in Appendix H. Faci lities found to be subject to BART must complete a BART determination analysis and thes e analyses can be found in the Attachments to Appendix H as well.

Table 7.8.4-1 lists the facilities that were able to demonstrate exemption from BART based on CALPUFF modeling conducted usin g the VISTAS modeling prot ocol and the old IMPROVE equation.

2 70 FR 39120. Footnote 31 3 70 FR 39119. Footnote 28 4 “The Quadratic Detection Model” by W. Malm. A ppendix D in Acidic Deposition: State of Science and Technology, Report 24, Visibility: Existing and Hist orical Conditions -- Causes and Effects. National Acid Precipitation Assessment Program, Washington, DC. 1990.

86Table 7.8.4-1 BART Exemption Modeling Results-Round 1- Old IMPROVE Equation Impact Class I Grid Facility Name Change(DV) Area Spacing Alabama Electric Coop Lowman 0.34 Breton 4 km Alabama Power Co Barry 0.22 Breton 4 km Alabama Power Co EC Gaston 0.17 Sipsey 4 km Alabama Power Co Gorgas 0.25 Sipsey 4 km Alabama Power Co Greene Co 0.13 Sipsey 4 km Alabama Power Co Miller 0.34 Sipsey 4 km American Cast Iron Pipe- JCDH 0.082 Sipsey 12 km Boise White Paper 0.162 Breton 12 km Bowater Inc.- Alabama 0.166 Sipsey 12 km Carmeuse Lime & Stone 0.215 Sipsey 12 km CEMEX, Inc. 0.308 Breton 4 km Chemical Lime Co Alabaster 0.188 Sipsey 4 km ConocoPhillips Co Chatom 0.303 Breton 4 km Degussa Corporation 0.466 Breton 12 km Escambia Oper. Co Big Escambia Crk (ExxonMobile Co) 0.498 Breton 4 km Ft Jms-Pennington, Naheola 0.241 Breton 4 km Hunt Refining Co, Tuscaloosa 0.327 Sipsey 4 km International Paper Co Prattville 0.271 Sipsey 4 km International Paper Co Riverdale 0.246 Sipsey 12 km International Paper Co Courtland 2.392 Sipsey 4 km JSC Brewton (Smurfit Stone) 0.295 Breton 12 km Lehigh Cement - JCDH 0.301 Sipsey 4 km MeadWestvaco Mahrt Mill 0.229 St. Marks 4 km National Cement Co of Alabama 0.192 Sipsey 4 km Oak Grove Resources- JCDH 0.021 Sipsey 12 km Sanders Lead Co 0.223 St. Marks 4 km Shell Chemical Co Saraland 0.066 Breton 12km Sloss Industries- JCDH 0.088 Sipsey 12 km Solutia, Inc, Decatur Plant 4.435 Sipsey 4 km TVA, Colbert 0.476 Sipsey 4 km TVA, Widows Creek 0.183 Cohutta 4 km US Pipe & Co- Bessemer- JCDH 0.036 Sipsey 12 km US Steel Corporation Fairfield- JCDH 0.205 Sipsey 12 km Vintage Petroleum, Inc, Flomaton (Escambia Operating Co- Flomaton) 0.179 Breton 4 km Weyerhaeuser 0.247 Breton 4 km

8788 Table 7.8.4-2 lists the facilities that were able to demonstrate exemption from BART based on the VISTAS modeling protocol and the new IMPROVE equation.

Table 7.8.4-2 BART Exemption Modeling Results–Round 2- New IMPROVE Equation Impact Class I Grid Facility Name Change (DV) Area Spacing BP Amoco Chemicals 0.486 Sipsey 4 km Chemical Lime Co Montevallo 0.484 Cohutta 4 km

Eight of Alabama’s BART-eligible sources are EGUs. EPA has dete rmined that, as a whole, the CAIR cap-and-trade program improves visibility more than implementing BART for individual sources in states affected by CA IR. A state that opts to partic ipate in the CAIR program under part 96 AAA-EEE need not require affected BART-eligible EGUs to install, operate, and maintain BART for SO 2 or NO x emissions. Given that many of the BART-eligible units are installing scrubbers and NO x controls, and since Alabama is participating in CAIR and accepts EPA’s overall finding that CAIR “substitutes” for BART for NO x and SO 2 , Alabama’s EGUs were allowed to submit BART exemption modeling demonstrations for PM emissions only. All eight EGUs demonstrated visibility impacts below the exemption threshold for all Class I areas.

In total, 35 of Alabama’s 37 BART-eligible source s were able to demonstr ate that they did not cause or contribute to visibility impairment in any Class I area within 300 km of the source. Two sources used the new IMPROVE equation in efforts to demonstrate exemption. The BART exemption modeling results can be found in Appe ndix H. All BART mode ling analyses will be archived and maintained by ADEM, and are accessible to any interested parties.

7.8.5 Alabama’s Evaluation of Subject-to-BART Sources

Two sources, Solutia Inc. in Decatur, Alabama and International Paper in Courtland, Alabama, were unable to demonstrate a contribution of less than 0.5 dv at all Cla ss I areas within 300 km from their BART-eligible sources. These two sour ces are considered to be “subject to BART” and were required to submit perm it applications containing their evaluation of potential BART options and proposed BART determinations. Th ese facilities submitted permit applications including their proposed BART determinations in the spring and summer of 2007. The proposed BART determinations have been concluded and th e requirements have been incorporated into Air Permits. The Air Permits were issued on February 5, 2008. A public comment period was held on the Regional Haze SIP. Summaries of the Solutia and Intern ational Paper Courtland BART determinations are listed below.Solutia BART Summary

The Department has concluded that no additional pa rticulate controls would be cost effective for the BART units at Solutia. Each BART unit is currently equipped with an electrostatic precipitator, which range in efficiency from 96% to 99%. The addition of NOx controls would not be cost effective for the Solutia BART units . However, Boiler 7 would be required to install a ROFA (rotating opposed fired-air) system or comparable technology for NOx since Solutia assume the control system as an existing control for the unit. For the control of SO 2, Solutia has proposed to utilize low sulfur coal for Boiler 5 an d Boiler 6, and the installation of a flue solvent injection (FSI) system for Boiler 7. It was concl uded that the addition of any controls for the coking units would negate the viability of the coking units; therefore, no additional controls were proposed for these units. The BART limits for each unit are listed below.

NO x

• Solutia has proposed no add on controls for Boiler 5, Boiler 6, and the Cokers for the control of NOx emissions.

• Boiler 7 would be required to meet an em ission limit of 0.36 lb NOx/MMBTU with the installation of a RO FA system or a comparable technology.

SO 2

• Solutia has proposed to limit Boilers 5 and 6 to burning coal with a sulfur content resulting in SO 2 emissions less than 1.40 lbs/MMBTU.

• Boiler 7 would be required to meet a limitation of 0.47 lb SO 2 /MMBTU with the installation of the FSI system or a comparable technology.

• Solutia has proposed no add on controls for the Cokers.

PM 10

• The existing electrostatic precipitators will be utilized to control PM emissions for each BART unit (Boiler 5-7, Cokers 1-2).

Table 7.8.5-1 2018 NO x and SO 2 Projections for Solutia, Inc

NOx (TPY) SO2 (TPY)

PAFS Unit ID Unit Description 2002 2018 Base G 2018 B&F 2002 2018 Base G 2018 B&F

0110300010 013 290 MMBTU/hr Coal Fired Boiler with ESP - No. 4 Boiler 354.8 120 120 1685.5 1672.88 1672.88

0110300010 014 290 MMBTU/hr Coal Fired Boiler with ESP - No. 5 Boiler 341.3 129.6 129.6 1621.7 1609.56 836.97

0110300010 015 320 MMBTU/hr Coal Fired Boiler with ESP - No. 6 Boiler 392 124.8 124.8 1862.5 1848.55 1020.34

0110300010 016 Coker Boiler No.1 with ESP 80.3 83.02 83.02 425.7 422.51 422.51

0110300010 017 Coker Boiler No.2 with ESP 80.3 83.02 83.02 425.7 422.51 422.51

89International Paper Courtland BART Summary

The Department has concluded that additional controls for SO 2 would not be cost effective at the Courtland Mill and that the control of NO x is only cost effective for the No. 2 Combination Boiler and that no other add-on controls would be co st effective for all other sources. The current add-on controls which control particles at the Courtland mill have efficiencies of greater than 90% therefore no additional controls would be cost effective. The BART limits for each unit are listed below.

NO x

• Install low NO x Burners or only operate the No. 1 Combination Boiler when one of the following emissions units is either not operating or in periods of star t-up of shutdown: the No. 2 Combination Boiler the No. 2 Recovery Furnace, the N o. 3 Combination Boiler, or the No. 3 Recovery Furnace.

• Install low NO x Burners, which will give a 30% ba seline reduction in emissions, on the No. 2 Combination Boiler.

• IP has proposed using existing combustion control techniques for the control of NO x on the Package Boiler.

• The No. 2 Recovery Furnace will utilize ex isting combustion control techniques for the control of NO x .

• IP proposed the existing Low NO x burners for the No. 1 and N o. 2 Lime Kilns for control of NO x emissions for these units.

SO 2

• IP has proposed no add on controls for the No. 1 Combination Boil er, No. 2 Recovery Furnace, No. 2 Smelt Tank, No. 1 Lime Kiln, an d No. 2 Lime Kiln for the control of SO 2 emissions.

• The existing Wet Scrubber will be utilized to control SO 2 emissions from the No. 2 Combination Boiler.

• Since the Package Boiler only fires natural gas, no add-on controls are warranted. The firing of only natural gas will be a federally enforceable limit

PM 10

• IP has proposed utilizing the existing Vent uri Scrubber for the cont rol of PM emissions from the No. 1 Combination Boiler.

• The No. 2 Combination Boiler will utilize th e existing multi tube dust collectors with electrostatic precipitator for the control of PM emissions.

• IP had proposed no controls for the Package Boiler due to the low particulate emissions associated with the firing of natural gas.

• The existing electrostatic precipitator will be utilized to control PM emissions for the No. 2 Recovery Furnace.

• IP has proposed to utilize the existing wet scrubber which is required by 40, CFR Part 63, Subpart MM, as the BART control device for th e control of PM emissions from the No. 2 Smelt Tank

• The existing Venturi Scrubbers will be utilized to control PM emissions from both Lime Kilns No. 1 and No. 2.

90Table 7.8.5- 2 2018 NO x and SO 2 Projections for Internatio nal Paper Company, Courtland Mill

91 NOx (TPY) SO2 (TPY)

PAFS Unit ID Unit Description 2002 2018 Base G 2018 B&F 2002 2018 Base G 2018 B&F
0107900001 001 No. 1 Recovery Furnace 24.31 28.42 28.42 0.05 0.06 0.06

0107900001 003 No. 1 Lime Kiln 10.77 12.59 12.59 0.34 0.4 0.4

0107900001 004 No. 1 Power Boiler 256.94 300.36 0 123.48 138.57 0

0107900001 005 Package Boiler 101 24 24 0.3 0.3507 0.3507

0107900001 006 No. 2 Recovery Furnace 550.37 643.38 643.38 1058.7 1237.57 1237.57

0107900001 007 No. 2 Smelt Tank 0.75 0.88 0.88

0107900001 009 No. 2 Lime Kiln 30.23 35.34 35.34 0.71 0.83 0.83

0107900001 011 No. 2 Combination Fuel Boiler 1332.2 1557.28 1090.1 109.8 123.22 123.22

The BART limits established for these sources , as well as any other limits taken during the BART process were included as part of the “Bes t and Final” VISTAS mo deling run which is the basis for the RPGs discussed in Section 8.0. For a more detailed discussion of the BART determinations including a listing of the permit pr ovisos, please see Attachment H5 to Appendix H.

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

Section 308(d)(3)(v) of the Rule addresses several factors, including the role of fire at Class I areas and status of state planning for smoke management, the role of dust and fine soil at Class I areas and status of state plans to mitigate emissi ons from construction activities and the role of NH 3 and potential benefits if emissions from agricultural sources are mitigated.

As discussed in Section 2.4, elemental car bon (sources include agri culture, prescribed wildland fires, and wildfires) is a relatively minor contributor to visibility impairment at the Sipsey Wilderness Area in Alabama. Section 51.308(d)(3)(iv)(E) of the Rule requires that each state must consider, at a minimum, the following factors in developing the LTS: Smoke Management techniques for agricultural and fore stry management purposes include plans as they currently exist within the state for thes e purposes. The Alabama Forestry Commission (“AFC”) has developed The State of Alabama Smoke Management Program, which serves to regulate vegetative debris burni ng for forestry, agriculture, and wildlife purposes in the State of Alabama. ADEM’s Air Division has develo ped a state air polluti on control regulation (ADEM Admin. Code 335-3-3-.01, Open Burning) that lists the specific circumstances in which open burning is permissible. ADEM ha s an approved smoke management program that addresses the issues laid out in EPA’ s 1998 draft guidance “Interim Air Quality Policy on Wildland and Prescribed Fires” for smoke management plans. The voluntary prescribed fire smoke management techniqu es practiced include Class I ar eas as a sensitive receptor, and ADEM contends that the cu rrent smoke management plan is sufficient to satisfy the directive in Section 308(d)(3)(iv)(E).In regard to construction activities and fugitive dust emission sources, ADEM has issued regulations to address the control of fugitive dust from construction activities and to control particulates from fugitive dust emission sour ces generated within plant boundaries. ADEM Admin. Code 335-3-4-.02, reads as follows:

(1) No person shall cause, suffer, allow, or perm it any materials to be handled, transported, or stored; or a building, its appurtenances, or a road to be used, constructed, altered, repaired, or demolished without taking reasonable precautions to prevent particulate matter from becoming airborne. Such reasonable precautions shall in clude, but not be limited to, the following:

(a) Use, where possible, of water or chemicals for control of dust in the demolition of existing buildings or structures, construction operations, th e grading of roads, or the clearing of land;

(b) Application of asphalt, oil, wa ter, or suitable chemicals on dirt roads, materials stock piles, and other surfaces which create airborne dust problems;

(c) Installation and use of hoods, fans, and fabric filters (or other suitable control devices) to enclose and vent the handling of dusty materi als. Adequate containment methods shall be employed during sandblasting or other similar operations.

(2) Visible Emissions Restrictions Beyond Lot Line. No person shall cause or permit the discharge of visible fugitive dust emissions beyond the lot line of the property on which the emissions originate.

(3) When dust, fumes, gases, mist, odorous ma tter, vapors, or any combination thereof escape from a building or equipment in such a manner and amount as to cause a nuisance or to violate any rule or regulation, the Director may order th at the building or equipment in which processing, handling, and storage are done be tightly closed a nd ventilated in such a way that all air and gases and air or gas-borne material leaving the building or equipment are treated by removal or destruction of air contaminants before discharge to the open air.

In regards to agricultural ammonia, ADEM curre ntly has no regulations. EPA’s Safe Harbor Agreement, called the Air Compliance Agreement, is intended to produce air quality monitoring data on Animal Feeding Operations (AFOs) em issions during a two-ye ar study, while at the same time protecting participants through a “safe harbor” from lia bility under ce rtain provisions of federal environmental laws. The purpose of th e monitoring is to allow development of valid emission factors for equivalent ( but unmonitored) farms. At this time, the study is ongoing to develop the emissions factors, a nd as such, there are currently no federal regulations related to the control of ammonia from AFOs. Once EPA has proposed regulations for these sources, ADEM will commit to evaluating potential controls on applicable sources in Alabama.

A final CMAQ modeling run was performed in ear ly 2008 to account for the cumulative benefits of the Base G2a emissions controls, BART c ontrols, and additional measures to achieve reasonable progress by 2018. This SIP contains the revised RPGs for the Sipsey Wilderness Area reflecting these final decisions.

927.10 Post BART Visibility Improvement Ex pected at the Sipsey Wilderness by 2018 compared to the Glidepath

As discussed earlier in Section 7.0, visibility on the 20% worst vi sibility days is expected to improve to levels better than the glidepat h by 2018 based on reducti ons from existing and planned emissions controls as well as BART an d RP controls. Figure 7.10-1 shows the predicted visibility (in dv) in 2018 on the wors t days at the Sipsey Wilderness.


### Progress Assessment Ba se G2 inventory, new IMPROVE Algorithm Sipsey - Worst 20% Days

29.03 27.52

24.80

21.78

18.76

15.74

12.72 10.90 26.43

23.53

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 Method 1 Prediction

Figure 7.10-1 Predicted Visibility for the 20% Worst Days in 2018 at the Sipsey Wilderness using the New IMPROVE Equation.

In addition to improvement in visibility on the 20% worst visibility days , visibility on the 20% best days is also projected to improve in 2018 at the Sipsey Wilderness. Figure 7.10-2 shows the predicted visibility (dv) in 2018 on the 20% best days at the Sipsey Wilderness.

93

### Progress Assessment Ba se G2 inventory, new IMPROVE Algorithm Sipsey - Best 20% Days

15.57 15.57 15.57
15.20 14.22

5.03

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)

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction

Figure 7.10-2 Predicted Visibility for the 20% Best Days in 2018 at the Sipsey Wilderness using the New IMPROVE Equation.

The expected change in visibility at th e Sipsey Wilderness between 2000-2004 baseline conditions on the 20% worst days and 2018 proj ections is illustrate d in Figure 7.10-3. In contrast, natural background visibi lity conditions for the 20% wors t days are depicted in Figure 7.10-4. These images were generated using WI NHAZE for each of the VISTAS Class I areas and are included in Appendix L. WINHAZE is a photographic imaging tool that accounts for the effect of concentrations of the fine particle components and relative humidity on visibility. These images illustrate that notable improveme nts in visibility are expected by 2018 and that significantly greater improvements are needed to reach natural background conditions.

94Figure 7.10-3 Visibility Improvements on 20% wo rst days at the Sipsey Wilderness between 2000- 2004 Baseline Conditions (left side) and the 2018 Base G2a projected visibility (right side)

Figure 7.10-4 Projected Visibility for the 20% Worst Days for Natural Background Visibility Conditions at the Sipsey Wilderness.

95

### 8.0 REASONABLE PROGRE SS GOALS (RPGs)

The Rule at 40 CFR Section 51.308(d)(1) requires st ates to establish RPGs for each Class I area within the state (in dv) that provide for reasonab le progress towards achievi ng natural visibility. In addition, EPA released guidance on June 7, 2007 to use in setting RPGs. The goals must provide improvement in visibility for the most impaired days, and ensure no degradation in visibility for the least impaired days over the SIP period. If th e RPG provides for a slower rate of visibility improvement than the uniform rate of progress, th e state must also provide an assessment of the number of years it would take to attain natural vi sibility conditions if improvement continues at the rate represented by the RPGs.

In accordance with the requirements of Section 5 1.308(d)(1), this Regional Haze SIP establishes RPGs for the Sipsey Wilderness area. To calculate the rate of progress re presented by each goal, ADEM compared baseline visibility conditions to natural visibil ity conditions at the Sipsey Wilderness area and determined the uniform rate of visibility improvement (in dv) that would need to be maintained during each implementati on period in order to atta in natural visibility conditions by 2064. These RPGs provide for an impr ovement in visibility for the most impaired days over the period of the implementation plan a nd ensure no degradation in visibility for the least impaired days over the same period.

Alabama hereby adopts the following RPGs for the Sipsey Wilderness Area.

Table 8-1 Sipsey Wilderness Reasonable Progress Goals

Class I Area 2000- 2004 Baseline Visibility (dv) RPG (dv Improvement Expected by 2018, 20% Worst Days) RPG (dv Improvement Expected by 2018, 20% Best Days) Dv Improvement Needed 2018- 2064 (20% Worst Days) Sipsey Wilderness Area 29.03 5.5 1.4 12.63



### 9.0 MONITORING STRATEGY

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

“(4) Monitoring strategy and other im plementation plan requirements. The State must submit with the SIP a monitoring strategy fo r measuring, characteriz ing, and reporting of regional haze visibility impairment that is re presentative of all ma ndatory Federal Class I areas within the state. This monitoring stra tegy must be coordinated with the monitoring strategy required in §51.305 for reasonabl y attributable visibility impairment. Compliance with this requirement may be met through participation in the IMPROVE network. The implementation plan must also provide for the following:

96(i) The establishment of any additional m onitoring sites or equipment needed to assess whether reasonable progress goals to address Regional Haze for all mandatory Federal Class I 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 improvement s resulting from emissions reductions identified in this SIP. 2. Better understand the atmospheric pr ocesses of importance to haze. 3. Identify chemical species in ambient partic ulate 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.

The primary monitoring network for regional haze , both nationwide and in Alabama, is the IMPROVE network. Given that IMPROVE monitoring data from 2000-2004 serve as the baseline for the Regional Haze program, the fu ture Regional Haze mon itoring strategy must necessarily be based on, or di rectly comparable to, IMPROV E. The IMPROVE measurements provide the only long-term record available for tr acking visibility improvement or degradation. Therefore, Alabama intends to rely on the IMPROVE network for complying with the Regional Haze monitoring requirement in the Rule.

There is currently one IMPROVE site in the St ate, at the Sipsey Wilderness area within the Bankhead National Forest. The State suppl ements the IMPROVE sampling by operating additional collocated monitoring at the Sipsey, including:

• A PM 2.5 FRM monitor

97

## PM 2.5 

## Monitor Locations

Montgomery



### PM 2.5 

### Monitors

Fairhope Huntsville

Bay Road Chickasaw Decatur

Providence

McAdory

Sumter Jasper Corner

Wylam

Pelham Hoover Pinson

North Birmin g ham

Ashland

Phenix City Muscle Shoals

Childersbur g Crossville

Gadsden

Brewton Dothan Tuscaloosa Sipsey

Figure 9-1 PM2.5 Monitoring Network in Alabama

98The IMPROVE measurements are central to Al abama’s Regional Haze Monitoring Strategy, and it is difficult to visualize how the objectives listed above could be met without the monitoring data provided by IMPROVE. Any reduction in th e scope of the IMPROVE network in Alabama and neighboring Class I areas woul d jeopardize the state’s ability to demonstrate reasonable progress toward visibility improve ment in its Class I areas and plan for appropriate future programs. In particular, Alabama’s Regional Haze strategy relies on emi ssion reductions that will result from CAIR and reductions in neighbori ng states, which occur on different time scales and will most likely not be spatially uniform. Monitoring at Class I areas is important to document the different air quality responses to the emissions reductions.

Because the current IMPROVE monitor in Alabam a represents a unique airshed and a significant component of the contributions are regional, reduction of the IMPROVE network by shutting down these monitoring sites impede s tracking progress or planning improvements at the affected Class I areas. If this occurs, Alabama, in cons ultation with EPA and the relevant Federal Land Managers, will develop an alternative appro ach for meeting the tracking goal, perhaps by seeking contingency funding to carry out limited monitoring, or by relyin g on data from nearby urban monitoring sites to demons trate 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 reports and the 10- year SIP revisions, each of which relies on analysis of the preceding five years of data . Consequently, the monitoring data from the IMPROVE sites needs to be readily availabl e and up to date. Presumably, IMPROVE will continue to process information from its own m easurements at about the same pace and with the same attention to quality as it has shown to date. The Visibility Information Exchange Web System (VIEWS) website has been maintained by VISTAS and the other Regional Planning Organizations (RPOs) to provide ready access to the IMPROVE data and data analysis tools. Alabama is encouraging VISTAS and the other RPOs to maintain VIEWS or a similar data management system to facilitate analysis of the IMPROVE data.



### 10.0 INTERSTATE AND FLM CONS ULTATION ON ALABAMA’S
CONTRIBUTION TO VISIBILITY IM PAIRMENT AT CLASS I FEDERAL
AREAS IN NEIGHBORING STATES

As required by Section 51.308(i), each state’s Regional Haze SIP must include interstate consultation between the states on the implemen tation of the visibili ty protection program.

The VISTAS states have jointly developed the te chnical analyses that define the visibility improvement by 2018 under existing federal and state regulations compared to the uniform rate of progress, SO 2 Areas of Influence (AoI) analyses for each Class I area, and methods to prioritize contributions from individual sources with in the AoI. 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 Base G m odeling results and the SO 2 AoI analyses. AoIs were developed for each

99Class I area to identify source regi ons for which additional controls might be considered and that are likely to have the greatest impact on each Class I area. These analyses looked at back trajectories, using meteorologi cal data on the 20% worst days over the 2000-2004 period where SO 2 was the biggest contributor to visibility impairment. The Ai r Directors agreed to look at reasonable control measures for sources on the list s for reasonable progress. Each state would consider sources within their st ate and would identify sources in neighboring states that they would like to have that neighbori ng state consider. States acknow ledged that the review process would differ among states since some Class I areas are projected to see visibility improvements near the uniform rate of progress while most Cl ass I areas are projected to have improvements greater than the uniform rate of progress.

In May 2007, the VISTAS state Air Directors met fo r their second formal in terstate consultation. States shared their lists of sources in their st ate and neighboring states for each Class I area, as well as their criteria for listing s ources and their plans for further interstate consultation.

ADEM evaluated the impact of Alabama sources on Class I areas in neighboring states and determined that there are no additional reasonabl e control measures that should be implemented to mitigate impacts in Class I areas in neighboring states. Analyses of the impacts from Alabama sources and potential controls are discussed in greater detail in Appendix H. Consultation documentation may be found in Appendix J.

Additionally, ADEM hosted a meetin g between the states of Flor ida, Alabama, Mississippi and Louisiana in January 2007 to di scuss issues specific to the Br eton Wildlife Refuge, located on the Chandeleur Island chain. Additionally, L ouisiana participated in a June 2007 FLM/EPA meeting hosted by VISTAS in Asheville, North Ca rolina, where each state discussed the process used to evaluate sources for reasonable progress. Lastly, ADEM has been a participant in CENRAP meetings over the last 3 years to ensure awareness of th e RPO’s intentions for Breton. Documentation of the January and June meetings can be found in Appendix J.



### 11.0 COMPREHENSIVE PERIODIC IMPLEMENTATION PLAN
REVISIONS

Section 51.308(f) requires ADEM to revise its Regional Haze State Implementation Plan (SIP) and submit a plan revision to EPA by July 31, 2018 and every ten years thereafter. In accordance with the requirements listed in Se ction 51.308(f) of the Rule, Alabama commits to revising and submitting this Regional Haze SIP by July 31, 2018 and every ten years thereafter.

In addition, Section 51.308(g) re quires periodic reports ev aluating progress towards the reasonable progress goals (RPGs) established for each mandatory Class I area. In accordance with the requirements listed in Section 51.308(g) of the Ru le, ADEM commits to submitting a report on reasonable progress to EPA every five years following the initial submittal of the SIP. The report will be in the form of a SIP revision. The reasonable progress report will evaluate the progress made towards the RPG for the Sipsey Wilderness Area located within Alabama and in each mandatory Federal Class I area located outside Alabama which may be affected by emissions from within Alabama

.

100The requirements listed in 51.308( g) include the following:

1. Description of the stat us of implementation; 2. Summary of emission reducti ons achieved thus far, including the status of implementation of the CAIR compliance plans for EGUs compared to the control assumed in the modeling. [ADEM recognizes that the 2018 projections of EGU controls from the IPM runs represent one solution to how the CAIR requirements will be met. By the time of the first periodic report, ADEM anticipates that the act ual compliance strategy for the various utility companies will be mu ch more defined. An assessment of those actual compliance plans will be completed for the first periodic report.] 3. Assessment of changes in visibility conditions at each Class I area (cur rent vs. baseline), expressed as 5-year averages of annua l values for 20% 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 implementation plan to meet the RPGs; 7. Review and any modifications to the visibility monitoring plan.

All requirements listed in 51.308(g) shall be ad dressed in the SIP revision for reasonable
progress.
ADEM also commits to ongoing consultation w ith the FLMs throughout the implementation process, including discussions on the implemen tation process and the most recent IMPROVE monitoring data and VIEWS data.

Relating to the New Source Review/Prevention of Significant Deterior ation Programs, ADEM believes that its New Source Review regulations will address emissions from new sources that may locate near a Class I area, or increased em issions from major modifications to existing sources. In addition to the regulations that w ould govern these sources, consultation with the FLMs is also required for sources that are subjectto the Class I provisi ons of the regulations.

There are several technical improvements that ar e recommended in the emissions inventory and air quality models that are us ed to support regulatory decisions for Regional Haze. These improvements are recommended, as funding is availa ble, to support the ne xt Long Term Strategy (LTS). The following is an overall summary; Appe ndix K contains a more detailed discussion of possible technical improvements.

First and foremost, continued improvements are n eeded in the integrated one-atmosphere air quality models that are used to project air qua lity responses to emissi ons reductions. As our understanding of partitioning between gaseous a nd aerosol phases improves, this understanding needs to be reflected in the models. Improvement s 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 ove restimated in the models, particularly in the summer months. At the coastal sites, when wi nds are blowing from the Gulf of Mexico or Atlantic Ocean, CMAQ underestimates measured sulfate at the monitors. CMAQ’s processes also should be reviewed for sulfate formation over water. Nitrate is over estimated by the model in the winter and underestimated in the summ er, although summer monitored values of nitrate are very low. Additional improvements in se asonal allocation of ammonia emissions would

101102 improve model estimates of ammonium nitrat e formation. Organic carbon is generally underestimated in the summer months. Improvement s are needed in the ch aracterization of both primary carbon emissions and formation of secondary organic carbon.

Other improvements needed include better tools 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 agricu ltural burning, states need imp roved record keeping.

Additional improvements to the international emi ssions inventory are also needed, to improve our understanding of boundary conditions for our modeling domain and of the contributions from international emissions to pollutant con centrations at the VISTAS Class I areas.



### 12.0 DETERMINATION OF ADEQUACY OF THE EXISTING PLAN

Depending on the findings of the 5-year progress report, Alabama commits to taking one of the actions listed in 40 CFR Section 51.308(h). Th e findings of the 5-year progress report will determine which action is appropriate and necessary.

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

1) ADEM determines that the existing SIP require s no further substantive revision in order to achieve established goals. ADEM provides to the Administrator a negative declaration that further revision of the SIP is not needed at this time. 2) ADEM determines that the existing SIP may be inadequate to ensure reasonable progress due to emissions from other states which pa rticipated in the regi onal planning process. ADEM provides notification to the Administrato r and the states that participated in regional planning. ADEM collaborates with st ates through the region al planning process to address the SIP’s deficiencies. 3) ADEM determines that the current SIP may be inadequate to ensure reasonable progress due to emissions from another country. ADEM provides notification, along with available information, to the Administrator. 4) ADEM determines that the existing SIP is inadequate to ensure reasonable progress due to emissions within the state. ADEM will revise its SIP to address the plan’s deficiencies within one year.