Regional Haze State Implementation Plan 

For 

The State of Alabama 

Prepared by 

Alabama Department of Environmental Management 

Air Division 

July 2008
TABLE OF CONTENTS 

1.0 INTRODUCTION .................................................................................................................... 1 

1.1 What is Regional Haze?........................................................................................................ 1 

1.2 What are the Requirements under the Clean Air Act for addressing Regional Haze? ......... 1 

1.3 General Overview of Regional Haze SIP requirements ....................................................... 2 

1.4 VISTAS ................................................................................................................................ 3 

1.5 Alabama’s Class I Area ........................................................................................................ 4 

1.6 State and Federal Land Manager (FLM) Coordination ........................................................ 6 

2.0 ASSESSMENT OF BASELINE AND CURRENT 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 Visibility Impairment (2000-2004 Baseline Data)................... 10 

3.0 GLIDEPATHS TO NATURAL CONDITIONS IN 2064...................................................... 14 

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

4.0 TYPES OF EMISSIONS IMPACTING VISIBILITY 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 through 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 

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5.2.2 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 Strategy (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 Inventory Compared to the Uniform Rate of 

Progress Glidepath for the Sipsey Wilderness Area ........................................................ 52 

7.3 Relative Contribution from International Emissions to Visibility Impairment in 2018 at 

VISTAS Class I areas....................................................................................................... 56 

7.4 Relative Contributions to Visibility Impairment: Pollutants, Source Categories, and 

Geographic Areas............................................................................................................. 58 

7.5 Relative Contributions to Visibility Impairment: 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 SO2 Areas of Influence (AoIs) ................................................................................... 63 

7.5.4 Emissions Sources within SO2 Areas of Influence (AoIs)......................................... 64 

7.5.5 Specific Source Types in the Areas of Influence (AoI) for the Sipsey Wilderness... 66 

7.6 Evaluating the Four Statutory Factors for Specific SO2 Emissions Sources in Each Area of 

Influence (AoI)................................................................................................................. 68 

7.7 Which Control Measures Characterize Reasonable Progress for Individual Sources? ...... 78 

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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 considered 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 CONTRIBUTION TO 

VISIBILITY IMPAIRMENT AT CLASS I FEDERAL AREAS IN NEIGHBORING 

STATES ............................................................................................................................... 99 

11.0 COMPREHENSIVE PERIODIC IMPLEMENTATION PLAN REVISIONS ................. 100 

12.0 DETERMINATION OF ADEQUACY OF THE EXISTING PLAN................................ 102 
LIST OF TABLES 

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

Important Value .............................................................................................................................. 4 

Table 2.3-1 Natural Background and Baseline Conditions 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 and 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, RTMax, 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 fractional contribution analysis (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 contribution 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 fractional 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 fractional contribution analysis (Sources that are 

1% or greater of Q/d*RTMax)...................................................................................................... 73 

Table 7.6-6 Breton Q/d, Q/d^2, RTMax, 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 fractional contribution analysis (Sources that 

are 1% or greater of Q/d*RTMax)................................................................................................ 76 

Table 7.8.2-1 Alabama’s VOC Exempt BART sources- remaining VIP emissions totals........... 84 

Table 7.8.3-1 Alabama’s non BART-eligible sources 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 NOx and SO2 Projections for Solutia, Inc...................................................... 89 

Table 7.8.5-2 2018 NOx and SO2 Projections for International Paper Company, Courtland Mill

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

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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 Glidepath for the 20% worst days at the Sipsey 

Wilderness..................................................................................................................................... 15 

Figure 3.1-2 Uniform Rate of Progress Glidepath 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, blue 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 VISTAS 2002 model performance evaluation and their 

location within the VISTAS 12km domain. ................................................................................. 31 

Figure 6.2-1 Soccer plot depicting both the mean 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 concentration 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 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.................................................................................................. 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......................................................................................................................................... 41 
Figure 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 predicted versus observed for the Sipsey Wilderness 

Area for 2002 ................................................................................................................................ 44 

Figure 6.3-10 Time Series plot of PM2.5 predicted 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% worst 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 sulfate 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 international 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 categories 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 2000-2004 for Sipsey. Based on back trajectories with 

100 m start height.......................................................................................................................... 64 

Figure 7.5.4-1 Sipsey 2002 and 2018 SO2 distance weighted emissions * SO4 extinctionweighted residence time plots....................................................................................................... 65 

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Figure 7.5.4-2 Sipsey 2018 SO2 distance weighted 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% worst 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 Visibility for the 20% Worst Days for Natural Background Visibility 

Conditions at the Sipsey Wilderness............................................................................................. 95 

Figure 9-1 PM2.5 Monitoring Network in Alabama .................................................................... 98 

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LIST OF APPENDICES 

Appendix A: VISTAS Memorandum 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 Meteorological 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 large 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 visibility, 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 develop and implement air quality protection plans called State 

Implementation Plans (SIPs) to reduce the pollution that causes visibility impairment. These 

plans establish goals and emission reduction strategies based on trends from various source 

sectors. Under the Regional Haze Rule, states are required to develop SIPs to reduce visibility 

impairment with the express intent that by 2064, visibility in the Class I areas will be returned to 

natural conditions. 

Five multi-state Regional Planning Organizations (RPOs) 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 making "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 Alabama has developed a SIP to address visibility impairment at the 

Sipsey Wilderness Area, located within the Bankhead National Forest in northeast Alabama, as 

well as visibility impairment associated with Alabama source impacts at Class I areas outside of 

Alabama. In developing this SIP, Alabama has 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 visibility at the Sipsey Wilderness by 2018. This goal represents 

progress greater than the uniform rate of progress for the same time period. Likewise, Alabama 

has also adopted a reasonable progress goal for the 20% best days that reflects a 1.4 dv 

improvement in visibility at the Sipsey Wilderness 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.

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ACKNOWLEDGEMENTS 

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 single out Patricia Brewer, VISTAS Technical 

Coordinator and commend her on the many roles she played during this process. 

Air Resource Specialists: Monitoring Data Analysis 

Alpine Geophysics: Emissions and Air Quality Modeling, Technical Advisor for Emissions 

Inventory 

Atmospheric Research and Analysis, Inc.: Operation 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 Spectrometry, source attribution using Chemical 

Mass Balance analyses and Positive Matrix Factorization 

Earth Tech, Inc: BART Modeling, CALPUFF Training 

ENVIRON: Emissions and Air Quality Modeling 

Georgia Institute of Technology: Emissions 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 Inventory and Projections, CALPUFF training 

E.H. Pechan and Associates: 2002 Mobile Inventory 

Research Triangle Institute: Chemical Analysis of Monitoring Samples 

System Applications International: Meteorological Characterization 

Tennessee Valley Authority: Operation 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 

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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 geographic 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 visibility in all directions uniformly. Airborne 

particles are produced by a variety of natural and manmade sources. 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 major cause of reduced visibility (haze) in the 

United States, including many of the national parks, forests and wilderness areas (Federal “Class 

I” areas as defined in 40 CFR Part 81.400). While some haze-causing particles are emitted 

directly to the air, secondary particles can be formed when emitted gases form particles well 

downwind of the emission sources. Nitrates and sulfates are examples of secondary particles that 

contribute to haze. In many Class I areas, the visual range can be substantially reduced by air 

pollution. In eastern Class I areas, 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 air pollution.” Congress adopted the visibility 

provisions to protect visibility in 156 national parks and wilderness areas. On December 2, 

1980, EPA promulgated regulations to address visibility impairment (45 FR 80084). The 1980 

regulations were developed to address visibility impairment that is “reasonably attributable” to a 

single source or small group of sources. These regulations represented the first phase in 

addressing visibility impairment and deferred action on regional haze that emanates from a 

variety of sources until monitoring, modeling, and scientific 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 (from hereinafter referred to as the Rule) that 

EPA promulgated on July 1, 1999 (64 FR 35713), revised the existing visibility regulations in 

order to integrate provisions addressing regional haze impairment and establish a comprehensive 

visibility protection program for 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 coordination with other states and 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 (PM2.5) at 40 CFR 

Part 81. States must submit a Regional Haze SIP to EPA within three years after the date of 

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designation. EPA promulgated PM2.5 designations on December 17, 2004, and thus, Regional 

Haze SIPs must be submitted by December 17, 2007. 

The Rule addressed the combined visibility effects of various pollution 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 efforts. EPA designated five Regional Planning 

Organizations (RPOs) to assist with the coordination and cooperation needed to address the 

visibility issue (Figure 1.2-1). Those states that make up the southeastern portion of the 

contiguous United States are known as VISTAS (Visibility Improvement – State and Tribal 

Association of the Southeast), and include the following states: Alabama, Florida, Georgia, 

Kentucky, Mississippi, North Carolina, 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 demonstrate 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 Federal Class I area within the state and/or for 

each mandatory Federal Class I area located outside the state, which may be affected by 

emissions from sources within the state. States are to establish baseline visibility conditions for 

2000-2004, natural background visibility conditions in 2064, and the rate of uniform progress 

between baseline and background conditions. The uniform rate of progress is also known as the 

“glidepath.” 

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The Rule then requires states to establish Reasonable 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 goals must provide for reasonable progress 

towards achieving natural visibility 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 visibility goals are properly met and set, SIPs must include 

determinations, for each Class I area, of the baseline visibility conditions (expressed in dv) for 

the most impaired and least impaired days. SIPs must also contain supporting documentation for 

all required analyses used to calculate the degree of visibility impairment under natural visibility 

conditions for the most impaired and least impaired days (See 51.308(d)(2)). In addition, states 

must include a monitoring strategy for measuring, characterizing, and reporting of regional haze 

visibility impairment that is representative of all mandatory Federal Class I areas within the state 

(See 51.308(d)(4)).

This first set of RPGs must be met through measures contained in the state’s long-term strategy 

(LTS) covering the period from the present until 2018. The LTS includes enforceable emissions 

limitations, compliance schedules, and other measures as necessary to achieve the RPGs, 

including all controls required or expected under all federal and 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, smoke management programs for agriculture and 

forestry, and enforceability of specific measures (See 51.308(d)(3)). 

In addition, a complementary component to each state’s first LTS is dictated by the specific best 

available retrofit technology (BART) requirements in 40 CFR 51.308(e) of the Rule. The Rule 

at 51.308(e) requires states to include a determination of BART for each BART-eligible source 

in the state that emits any air pollutant, which 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 sources in 26 specific 

source categories, in operation within a 15-year period prior to enactment of the 1977 CAA 

Amendments, whose emissions contribute to visibility impairment in Class I areas. States must 

determine BART according to five factors set out 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 LTS for visibility improvement between baseline 

conditions in 2000-2004 and 2018. States 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 review will be due to EPA in December 2012 and will 

include an evaluation of the emissions since the baseline date as well 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 

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action to assure emissions controls by 2018 will be consistent 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 Association of the Southeast (VISTAS) is one of 

five Regional Planning Organizations (RPOs) that have responsibility for coordinating 

development of SIPs and Tribal Implementation Plans (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 citizen groups established to initiate and coordinate activities 

associated with the management of Regional Haze and other air quality issues within the 

VISTAS states. The VISTAS region includes the states of Alabama, Florida, Georgia, 

Kentucky, Mississippi, North Carolina, 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 interim visibility goals, and provide adequate 

documentation to member agencies so that they can develop their Regional Haze State/Tribal 

Implementation Plans (SIP/TIP). Figure 1.4-1 shows the 18 mandatory Federal Class I areas in 

the VISTAS Region, where visibility is an important value. Table 1.4-1 lists these Class I areas. 

Figure 1.4-1 Class I Areas in the VISTAS Region 

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Table 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 Alabama (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 SIP, emission sources outside of Alabama were 

considered as possibly affecting visibility at the Sipsey Wilderness area, as well as consideration 

that emission sources within Alabama may affect visibility at Class I areas in neighboring states. 

 4 
Through both VISTAS and CENRAP, Alabama has worked 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 partnership “to identify 

and recommend reasonable measures to remedy existing and prevent future adverse effects from 

human-induced air pollution on the air quality related values of the Southern Appalachian 

Mountains”. States cooperated with FLMs, EPA, industry, environmental organizations, and 

academia to complete a technical assessment of the impacts of acid deposition, ozone, and fine 

particles on sensitive resources in the Southern Appalachians. The Southern 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 concluded that reducing ammonia emissions would be 

helpful to reduce ammonium nitrate contributions to visibility impairment. Emissions controls 

for organic carbon, elemental carbon, and soil were expected to be less important for improving 

visibility. SAMI modeling found that on the haziest days, much of the benefit of emissions 

reductions would occur in the state where emissions reductions were made. Emissions in 

surrounding SAMI states and states outside the SAMI region also contribute to air quality in the 

SAMI Class I areas. The SAMI states supported 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 benefits in the Southern Appalachians as they 

developed SIPs for the health standards. 

 5 
 6 

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

Autauga

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 Regional 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 other programs having the potential to contribute 

to impairment of visibility in any mandatory Federal Class I area within the state. Section 11 of 
the 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 Haze 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 Haze SIPs has been prepared cooperatively by 

VISTAS staff and states to characterize Regional Haze in the southeastern United States. The 

report includes a review of the science and situation, calculation of initial baseline visibility, 

review of monitoring data/data gaps, and recommendations for additional monitoring, initial 

emission inventory characterization and projections, and compliance with existing control 

programs. Source contributions to VISTAS mandatory Federal Class I areas are also assessed. 

These technical support documents are included as Appendix N of this SIP. 

2.0 ASSESSMENT OF BASELINE AND CURRENT CONDITIONS AND 

ESTIMATES OF NATURAL BACKGROUND CONDITIONS IN CLASS I 

AREAS 

The goal of the Rule is to restore natural visibility conditions to the 156 Class I areas identified 

in the 1977 Clean Air Act Amendments. Section 51.301(q) defines natural conditions: “Natural 

conditions include naturally occurring phenomena that reduce visibility as measured in terms of 

light extinction, visual range, contrast, or coloration.” The Regional Haze SIPs must contain 

measures that make “reasonable progress” toward this goal by reducing anthropogenic emissions 

that cause haze. 

An easily understood measure of visibility to most people is visual range. Visual range is the 

greatest distance, in kilometers or miles, at which a dark object can be viewed against the sky. 

For evaluating the relative contributions of pollutants to visibility impairment, however, the most 

useful measure of visibility impairment is light extinction, which is usually expressed in units of 

inverse megameters (Mm-1). Light extinction affects the 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 three metrics of visibility that are part of the determination of 

reasonable progress: 

1) natural conditions, 

 7 
2) baseline conditions, and 

3) current conditions. 

 

Each of the three metrics includes the concentration data of the visibility pollutants as different 

terms in the light extinction algorithm, with respective extinction coefficients and relative 

humidity factors. Total light extinction when converted to deciviews (dv) is calculated for the 

average of the 20% best and 20% worst visibility days. 

“Natural” visibility is determined by estimating the 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 Interagency Monitoring of 

Protected Visual Environments (IMPROVE) monitoring data for 2000 through 2004 and is 

equivalent to “current” visibility conditions for this initial review period. The comparison of 

initial baseline conditions to natural visibility conditions indicates the amount of improvement 

necessary to attain natural visibility by 2064. Each state must estimate natural visibility levels 

for the baseline period for Class I areas within its borders (See 51.308(d)(2)). “Current 

conditions” are assessed every five years as part of the SIP review where actual progress in 

reducing visibility impairment is compared to the reductions projected in the SIP.

2.1 Estimating Natural Conditions for the Sipsey Wilderness Area 

Natural background visibility, as defined 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 visibility for the 20% worst 

days is estimated by assuming that fine particle concentrations for natural background are 

normally distributed and the 90th percentile of the annual distribution 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 states 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 accurate estimates with changes to the extinction 

algorithm that may include the concentration values, factors to calculate extinction from a 

measured particular species and particle size, the extinction coefficients for certain compounds, 

geographical variation (by altitude) of a fixed value, and the addition of visibility pollutants. 

In 2005, the IMPROVE Steering Committee made recommendations 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 calculating 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 effect 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 absorption by gaseous nitrogen 

dioxide. Site-specific values are used for Rayleigh scattering to account for the site-specific 

effects of elevation and temperature. Separate relative humidity enhancement factors are used 

 8 
for small and large size distributions of ammonium sulfate and ammonium nitrate and for sea 

salt. The elemental carbon (light-absorbing carbon), fine soil, and coarse mass terms do not 

change between the original and new IMPROVE equation. 

The VISTAS states chose to use the new IMPROVE equation as the basis for the conceptual 

description because it takes into account the most recent review of the science and because it is 

recommended by the IMPROVE Steering Committee. For more detailed discussion of the two 

IMPROVE equations, see Appendix B. 

2.2 Estimating Baseline Conditions for the 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 five year average (2000 to 2004) was calculated 

for each of the 20% worst and 20% best visibility 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 completeness (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 background and baseline visibility metrics for the 

Sipsey Wilderness area. Baseline visibility on the 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 natural background. The data used by ARS were 

downloaded from VIEWS and the data was updated 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 corrected 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 example, the data supplied by Scott 

Copeland (USFS) included a natural background number 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. 

 9 
Table 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 Sipsey 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, the pollutant breakout for 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 Everglades. The 20% best visibility days also show a 

strong sulfate signature, but this is coupled with nitrate and particulate organic matter (Figure 

2.4-2) 

 10 
0

10

20

30

40

50

60

Extinction (Mm-1)

Sea Salt

CM

Soil

EC

POM

NH4NO3

(NH4)2SO4

Rayleigh

VISTAS coastal VISTAS inland Neighboring non-VISTAS

Swanquarter, NC

Cae Romain, SC

Okefenokee, GA

Everglades, FL

Chassahowitzka, FL

St. Marks, FL

Dolly Sods, WV

Shenandoah, VA

James River Face, VA

Linville Gorge, NC

Shining Rock, NC

Great Smoky Mtns., TN

Cohutta, GA

Sipsey, AL

Mammoth Cave, KY

Brigantine, NJ

Breton, LAMingo, MO

Hercules Glade, MO

Upper Buffalo, AR

Caney Creek, AR

0

10

20

30

40

50

60

Extinction (Mm-1)

Sea Salt

CM

Soil

EC

POM

NH4NO3

(NH4)2SO4

Rayleigh

VISTAS coastal VISTAS inland Neighboring non-VISTAS

Swanquarter, NC

Cae Romain, SC

Okefenokee, GA

Everglades, FL

Chassahowitzka, FL

St. Marks, FL

Dolly Sods, WV

Shenandoah, VA

James River Face, VA

Linville Gorge, NC

Shining Rock, NC

Great Smoky Mtns., TN

Cohutta, GA

Sipsey, AL

Mammoth Cave, KY

Brigantine, NJ

Breton, LAMingo, MO

Hercules Glade, MO

Upper Buffalo, AR

Caney Creek, AR

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 

VISTAS coastal VISTAS inland Neighboring non-VISTAS

0

50

100

150

200

250

300

Swanquarter, NC

Cae Romain, SC

Okefenokee, GA

Everglades, FL

Chassahowitzka, FL

St. Marks, FL

Dolly Sods, WV

Shenandoah, VA

James River Face, VA

Linville Gorge, NC

Shining Rock, NC

Great Smoky Mtns., TN

Cohutta, GA

Sipsey, AL

Mammoth Cave, KY

Brigantine, NJ

Breton, LAMingo, MO

Hercules Glade, MO

Upper Buffalo, AR

Caney Creek, AR

Extinction (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

300

Swanquarter, NC

Cae Romain, SC

Okefenokee, GA

Everglades, FL

Chassahowitzka, FL

St. Marks, FL

Dolly Sods, WV

Shenandoah, VA

James River Face, VA

Linville Gorge, NC

Shining Rock, NC

Great Smoky Mtns., TN

Cohutta, GA

Sipsey, AL

Mammoth Cave, KY

Brigantine, NJ

Breton, LAMingo, MO

Hercules Glade, MO

Upper Buffalo, AR

Caney Creek, AR

Extinction (Mm -1)

Sea Salt

Coarse

Soil

EC

POM

NH4NO3

(NH4)2SO4

Rayleigh

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 
Ammonium sulfate, (NH4)2SO4, 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 across 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. Sulfate particles occur as hydrogen sulfate, H2(SO4), 

ammonium bisulfate HNH4SO4, and ammonium sulfate, (NH4)2SO4, depending on the 

availability of ammonia, NH3, in the atmosphere. 

Particulate Organic Matter (POM) is the second most important contributor to fine particle mass 

and light extinction on the 20% worst days. On the 20% best days at the Sipsey Wilderness, 

POM and ammonium nitrate seem to be equally important contributors to visibility 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 understand sources contributing to carbon in rural and urban areas. At 

the Great Smoky Mountains and other 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 months. Biogenic emissions, chiefly emissions of 

volatile organic compounds (VOCs) from vegetation, peak in spring and summer. Carbon from 

gasoline and diesel engines is a relatively small contribution at the rural sites. At Millbrook, 

carbon from fossil fuel combustion is a larger percentage contribution than at the rural sites, but 

still less than 50% of total carbon measured. These results suggest that controlling 

anthropogenic sources of carbon will have little benefit in improving visibility in Class I areas. 

Controlling anthropogenic sources of carbon will likely be more effective to reduce levels of 

PM2.5 in urban areas. 

Ammonium nitrate, NH4NO3, is formed in the atmosphere by the reaction of ammonia and 

nitrogen oxides. In the VISTAS region, nitrate formation is limited by the availability of 

ammonia and by temperature. Ammonia preferentially reacts with sulfur dioxide and sulfate 

before reacting with nitrogen oxides. Particle nitrate is formed at lower temperatures; at elevated 

temperatures nitric acid remains in gaseous form. For this reason, particle nitrate levels are very 

low in the summer and a minor contributor to visibility impairment. Particle nitrate 

concentrations are higher on winter days and are more important for the coastal sites where 20% 

worst days can occur on winter days. Nitrogen oxides are emitted by fossil fuel combustion by 

point, area, on road, and non road sources. Modeling data indicates that in the VISTAS region 

ammonium nitrate formation is limited by ammonia concentrations and suggests that for winter 

days, controls of ammonia sources would be more effective in reducing ammonium nitrate levels 

than control of nitrogen oxides. 

 

Elemental Carbon (EC) is a comparatively minor contributor to visibility impairment. Sources 

include agricultural and wildland (wildfire, wildland fire use and 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 

PM2.5 in urban areas than to improve visibility in Class I areas. 

 12 
Soil fine particles are minor contributors to visibility impairment at most sites on most days. 

Occasional episodes of elevated fine 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 salt contributions to visibility impairment are 

most important on the 20% cleanest days when sulfate 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 uncertainty has greatest implications for calculating 

sea salt levels under natural visibility 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 efficiency for sulfate, nitrate, and carbon. 

An unidentified component is reported by IMPROVE as the difference between the total PM2.5

mass measured on the filter and the sum of the measured components. This unidentified mass 

may be 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 calculated light extinction on days with 

higher mass and lower light extinction on days with lower mass. This tends to increase 

calculated light extinction for current conditions and to decrease calculated light extinction for 

natural visibility conditions. Adding sea salt to the new IMPROVE equation increases light 

extinction for both current and natural visibility conditions. Increasing the mass multiplier for 

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 most effective means to improve visibility is to 

reduce sulfate concentrations. 

PM2.5 Trends in Urban and Class I areas: IMPROVE data were compared to monitoring data 

from the Speciated Trends Network (STN) in nearby urban areas to understand the similarities 

and differences in composition of fine particle mass. Several PM2.5 nonattainment areas are in 

close proximity to the Class I areas in the southeastern U.S., including Atlanta, GA; 

Birmingham, AL; Charleston, WV; Chattanooga, TN; Louisville, KY; and Knoxville, TN. 

Ammonium sulfate concentrations 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 suggest that sulfate is widely distributed regionally 

while urban areas see an additional incremental pollutant loading from local emissions sources. 

 13 
 14 

Role of meteorology in determining visibility conditions: Classification and Regression Tree 

(CART) Analyses were used to characterize the relationship between meteorological conditions 

and visibility conditions at the Class 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 Wilderness Class I area, poor visibility days were most likely to occur 

on days with moderate temperatures, high relative humidity, low wind speeds, and elevated 

PM2.5 mass at upwind urban areas. Precipitation was not a good predictor of visibility 

conditions. Weights were assigned to days based on frequency 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, the Rule directs states to graphically show what would be a 

“uniform rate of progress” toward natural conditions for each mandatory Federal Class I area 

within the state. The uniform rate of progress is also known as the “glidepath.” The glidepath is 

simply a straight graphical line drawn from the baseline level of visibility impairment 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. Section 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 IMPROVE 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 Regional 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 SIP. However, the “Best and Final” run, incorporating all final 

known inventory changes and BART and RP controls, was completed in late January 2008. This 

run (Figure 3.1-2) resulted in new 2018 predictions, and subsequently, revised glidepaths, which 

are also presented here. Using 2018 Base G2 emissions, the model projected 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 the 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 run, it was not possible to revise all of the figures 
and tables in this SIP, including those in the Appendices. For this reason, ADEM has elected to 

retain the older figures and tables. The differences may be small had they been updated, but 

ADEM contends that the results of the Best and Final Runs show a continued improvement over 

previous runs that would not change any of the conclusions presented. Natural background 

visibility at the Sipsey Wilderness 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 Glidepath for the 20% worst days at the Sipsey 

Wilderness 

Note: Method 1 is the EPA method of averaging all 20% worst days in 2002 without 

consideration of model performance. 

Observation

3

29.03

Haziness Index (Deciviews) 3 27.52

24.80

26.43 2

23.69 21.78

18.76 2

15.74

1 12.72

10.9

1

5

0

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

Year

Glide Path Natural Condition Observation Method 1 

 15 
Progress Assessment Base G2 inventory, new

IMPROVE Algorithm

Sipsey - Worst 20% Days- Best and Final

35

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

Year

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064

23.53

26.43

10.90

12.72

15.74

18.76

21.78

24.80

27.52

29.03

20

25

30

Haziness Index Deciviews (dv) 15

10

5

0

Figure 3.1-2 Uniform Rate of Progress Glidepath 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) requires a statewide emissions inventory of pollutants that are 

reasonably anticipated to cause or contribute to visibility impairment in any mandatory Class I 

area. An inventory was developed for the baseline year 2002 and projected to 2009 and 2018. 

The pollutants inventoried include volatile organic compounds (VOCs), nitrogen oxides (NOx), 

fine particulate (PM2.5), coarse particulate (PM10), ammonia (NH3) and sulfur dioxide (SO2). 

The baseline emissions inventory for 2002 was developed for Alabama following the methods 

described in Appendix D. 

There are five different emission inventory source classifications: stationary point and area 

sources, off-road and on-road 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 generating utilities (EGUs) and industrial sources are the major categories 

for stationary point sources. Stationary area 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 significant (i.e., dry cleaners, service stations, 

agricultural sources, fire emissions, etc.). These types of emissions are estimated on a 

countywide level. Non-road (or off-road) mobile sources are equipment that can move but do 

not use the roadways, (i.e., lawn mowers, construction equipment, railroad locomotives, aircraft, 

etc.) The emissions from these sources, like stationary area sources, are estimated on a 

countywide level. On-road mobile sources are automobiles, trucks, and 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 sources like trees, crops, 

grasses and the natural decay of plants. The emissions from these sources are also estimated on a 

countywide level. 

In addition to the various source classifications, there are also various types of emission 

inventories. The first is the actual base year inventory. This inventory 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 evaluating air quality model performance. 

The second type of inventory is the typical base year inventory. This inventory is similar to the 

actual base year inventory, except that for sources whose emissions change significantly from 

year to year, a more typical emission value is used. In this modeling effort, typical emissions 

were developed for the EGUs and the wildland fire emissions. The air quality modeling runs 

using the typical base year inventory provide results which are then used to calculate Relative 

Response Factors (RRFs) for future years. These RRFs for future years are then used to 

demonstrate reasonable progress toward visibility goals. 

Below is an overview of the inventories used for each source classification. A more detailed 

discussion of the emissions inventory development is contained in Appendix D. 

 17 
4.1.1 Stationary Point Sources 

Point source emissions are emissions from individual sources having a fixed 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 tpy of total HAPs are inventoried annually. Smaller 

sources are inventoried less frequently. The point source emissions data can be grouped as EGU 

sources and other industrial point sources, also called non-EGUs. 

4.1.1.1 Electric Generating Units (EGUs) 

The actual base year inventory for the EGU 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 SO2 and NOx that 

can be used in air quality modeling. 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 SO2 and NOx. 

Emissions from EGUs vary daily and seasonally as a function of variability in energy demand 

and utilization and outage schedules. To avoid anomalies in future year emissions created by 

relying on 2002 operations to represent future operations, 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 emissions inventory for EGU sources, each unit’s average CEM heat 

input for 2000 through 2004 was divided by the 2002 actual heat input to generate a unit specific 

normalizing factor. This normalizing factor was then multiplied by the 2002 actual emissions. 

The heat inputs for the period 2000 through 2004 were used because the modeled current design 

values used monitored data from this same 5-year period. If a unit was shut down for an entire 

year during the 2000 through 2004 period, the average 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, VISTAS, in cooperation with the other eastern 

RPOs, contracted with ICF Resources, L.L.C., to generate future year emission inventories for 

the electric generating sector of the contiguous U.S. using the Integrated Planning Model (IPM). 

IPM is a dynamic linear optimization model that can be used to examine air pollution control 

policies for various pollutants throughout the contiguous U.S. for the entire electric power 

system. The dynamic nature of IPM enables projection of the behavior of the power system over 

a specified future period. Optimization logic in IPM determines the least-cost means of meeting 

electric generation and capacity requirements while complying with specified constraints 

including air pollution regulations, transmission 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 both CAIR implementation, state SIPs and other 

initiatives such as North Carolina’s Clean Smokestacks Act (CSA). 

 

 18 
4.1.1.2 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 (CERR) for the year 2002. These emissions are 

temporally allocated to month, day, and hour using Source Category Code (SCC) based 

allocation factors. 

The general approach for assembling future year data was to use recently updated growth and 

control data consistent with EPA’s CAIR analyses. This data was supplemented with state 

specific growth factors and stakeholder input on growth assumptions. 

4.1.2 Stationary Area Sources 

Stationary area sources are sources whose individual 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 indicator of collective activity, such as fuel 

usage, number of households, or population. Stationary area source emissions are estimated at 

the countywide level. 

A portion of the area source 2002 base year inventory for Alabama was developed by ADEM 

and submitted under the CERR, which the VISTAS contractor obtained. The VISTAS 

contractor calculated the remaining portion of the area source inventory. The sources estimated 

by the contractor include emissions from animal husbandry, wildland fires, and particulate matter 

from paved and unpaved roads. For the other states within the modeling 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 inventory for all area source 

categories except for wildland fires. For wildland fires, a typical year inventory was used to 

avoid anomalies in wildfire activity in 2002 compared to longer term averages. Development of 

a typical year fire inventory provided the capability of using a comparable data set for both the 

base year and future years. The VISTAS Fire Special Interest Work Group used state records to 

ratio the number of acres burned over a longer term period (three or more years, as available 

from state records) to 2002. Based on these ratios, 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, supplied 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 factor 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. 

 19 
4.1.3 Off-Road Mobile Sources 

Off-road (or non-road) mobile sources 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 three source categories not included in the 

NONROAD model, (i.e., aircraft engines, railroad locomotives and commercial marine, etc.) 

more traditional methods of estimating the emissions 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 regulations affecting emissions from these source categories. For 

the commercial marine, railroad locomotives and the remaining airport emissions, the VISTAS 

contractor calculated the future growth in emissions using detailed inventory data (both before 

and after controls) for 1996 and 2010, obtained from the CAIR Technical Support 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 consideration regulations that affect emissions from this source 

sector. The same MOBILE run is used to represent the actual and typical year emissions for 

onroad vehicles using input data reflective of 2002. The MOBILE model then is run for the 

2018 inventory using input data reflective of that year. The 2002 vehicle miles traveled (VMT), 

was obtained from the Alabama Department of Transportation (ALDOT). 

4.1.5 Biogenic Emission Sources 

Biogenic emissions were prepared with the SMOKE-BEIS3 (Biogenic Emission Inventory 

System 3 version 0.9) preprocessor. SMOKE-BEIS3 is a modified version of the Urban Airshed 

Model (UAM)-BEIS3 model. Modifications include use of MM5 data, gridded land use data, 

and improved emissions characterization. The emission factors that are used in SMOKE-BEIS3 

are the same as the emission factors as in UAM-BEIS3. 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 separate land classification scheme, based upon 

satellite (AVHRR, 1 km spatial resolution) and census information, aided in defining the forest, 

agriculture and urban portions of each county. 

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 methodology is contained in Appendix D. The 

emissions summaries for other VISTAS states can also be found in Appendix D. 

 20 
 21 

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

VOC NOx SO2 PM10 PM2.5 NH3

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 through Emissions Inventory Improvements 

Since the initial model performance evaluation, VISTAS has made several improvements to the 

emissions inventory, which in turn improves model performance. These inventory 

improvements are detailed in the VISTAS emissions inventory report and in Appendix D, and 

are summarized here: 

• For electric generating utilities (EGUs), the Integrated Planning Model (IPM) was used to 

provide estimates of future year utility production 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 provided by utility companies in 2006 through early 2007. 

• For on-road vehicle emissions, agencies provided updated MOBILE model input and 

VMT data. 

• For ammonia emissions from agricultural sources, the Carnegie Mellon University 

ammonia model was used to improve annual and monthly estimates. 

• For fires, the VISTAS states provided fire activity data for 2002 for wildfires, prescribed 

fire, land clearing and agricultural burning and MACTEC developed a 2002 fire 

inventory. Where data allowed, Alpine Geophysics modeled fire events as point sources. 

In 2006, the U.S. Forest Service and Fish and Wildlife Service provided projections of 

increased prescribed burning in 2009 and 2018; these data were incorporated in the Base 

G inventory for all states except Florida. 

• For non-road engines, the updated EPA NONROAD2005 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 Corbett at the University of Delaware. 

These emissions were incorporated in the Base G modeling. 

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

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

Ammonium sulfate is the largest contributor to visibility impairment at the Sipsey Wilderness 

area, and the reduction of SO2 emissions would be the most effective means of reducing 

ammonium sulfate. As illustrated in Figure 4.2-1, 96% of SO2 emissions in the VISTAS states 

are attributable to electric generating facilities and industrial point sources. As shown previously 

in Table 4.1.6-1, approximately 88% of SO2 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

Industrial: 20% Utilities: 76%

4.8 Million tons

VISTAS States SO2 Emissions - 2002 

Utilities

Industrial

On road

Non road

Other

Industrial: 20% Utilities: 76%

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 methods 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 evaluation that begins by selection of the modeling 

system. VISTAS decided to use the following modeling systems: 

• Meteorological Model: The Pennsylvania State University/National Center for 

Atmospheric Research (PSU/NCAR) Mesoscale Meteorological Model (MM5) is a 

nonhydrostatic, prognostic meteorological model routinely used for Urban and Regional 

scale photochemical, fine particulate matter, and Regional Haze regulatory modeling 

studies. 

• Emissions Model: The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling 

system is an emissions modeling system that generates hourly gridded speciated emission 

 22 
• Air Quality Model: EPA’s Models-3/Community Multiscale Air Quality (CMAQ) 

modeling system is an ‘One-Atmosphere’ photochemical grid model capable of 

addressing ozone, particulate matter (PM), visibility and acid deposition at Regional 

scales for periods up to one year. 

EPA Modeling Guidance recommends modeling an entire year or, at a minimum, several days in 

each quarter of a year to adequately represent 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 also developed for 2002 and processed through 

SMOKE. These inputs were used in the air quality 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. Several configurations of the meteorological and air 

quality model were evaluated to select 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 quality modeling results are used to determine a 

relative reduction in future visibility impairment, which is used to determine reasonable progress. 

The complete modeling protocol used for this 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 stakeholders. The models should address important 

physical and chemical phenomena in sufficient detail, 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 execution and verification at no or low cost. 

The following sections outline the criteria for selecting a modeling system that is both defensible 

and capable of meeting the study's 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 candidate for use in a Regional Haze SIP, a state 

needs to show that it meets the same several general criteria as a model for an attainment 

demonstration as for a national ambient 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, and the model cannot otherwise 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 deposition within a common platform. The original 

justification for the Models-3 development emerged from the challenges posed by the 1990 

CAAA and EPA’s desire to develop an advanced modeling framework for ‘holistic’ 

environmental modeling utilizing state-of-science representations of atmospheric processes in a 

high performance computing environment. EPA completed the initial stage of development with 

Models-3 and released the CMAQ model in mid-1999 as the initial operating science model 

under the Models-3 framework. 

 

An advantage of choosing CMAQ as the atmospheric model is the ability to do one atmosphere 

modeling. The same model configuration is being applied for the ozone and PM2.5 attainment 

demonstrations SIPs, as well as the Regional Haze SIP. A number of features in CMAQ’s 

theoretical formulation and technical implementation make the model well suited for annual PM 

modeling. 

The configuration used for this modeling demonstration, as well as a more detailed description of 

the CMAQ model, can be found in the Modeling Protocol located in Appendix C. 

5.2.2 Selection of the Meteorological Model 

5.2.2.1 Selection Criteria 

Meteorological models, either through objective, diagnostic, or prognostic 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 accurately 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-dimensional, limited-area, primitive equation, 

prognostic model that has been used widely in Regional air quality model applications. The 

basic model has been under continuous development, improvement, testing and open 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 operational 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. The gridded meteorological fields produced by MM5 

are directly compatible with the input requirements of ‘one atmosphere’ air-quality models using 

this coordinate. MM5 fields can be easily used in other Regional air quality models with 

different coordinate systems by performing a vertical interpolation, followed by a mass 

conservation readjustment. 

Distinct planetary boundary layer (PBL) parameterizations are available for air quality 

applications, both of which represent sub-grid-scale turbulent fluxes of heat, moisture and 

momentum. One scheme uses a first-order eddy diffusivity 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 diagnosing 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 intervals on the outermost grid mesh selected by the 

user. Surface fields are analyzed at three hour intervals. A Cressman based technique is used to 

analyze standard surface and radiosonde observations, using the National Meteorological 

Center's spectral analysis, as a first guess. The lateral boundary data are introduced using a 

relaxation technique applied in the outermost five rows and columns of the coarsest grid domain. 

The MM5 modeling system in regulatory air quality application studies has been widely reported 

in the literature (e.g., Emery et al., 1999; Tesche et al., 2000, 2003) and many studies have 

involved comparisons with other prognostic models such as the Regional Atmospheric Modeling 

System (RAMS) and the Systems Application International Mesoscale Model. The MM5 enjoys 

a far richer application history in regulatory modeling studies compared with RAMS or other 

models. Furthermore, in evaluations of these models in over 60 recent Regional scale air quality 

 25 
application studies since 1995, it has generally been found that the MM5 model tends to produce 

somewhat better photochemical model inputs than alternative models. 

The configuration used for this modeling demonstration, as well as a more detailed description of 

the MM5 model 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 processing system is that it accurately prepares 

emissions files in a format suitable for the photochemical grid model being used. The following 

list includes clarification of this criterion and additional desirable criteria for effective use of the 

system. 

• File System Compatibility with the I/O API 

• File Portability 

• Ability to grid emissions on a Lambert Conformal projection 

• Report Capability 

• Graphical Analysis Capability 

• MOBILE6 Mobile Source Emissions 

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

• Ability to process emissions for the 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 Prototype 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 transportation travel demand models. 

 

SMOKE was originally designed to allow emissions data processing methods to utilize emergent 

high performance computing as applied to sparse matrix algorithms. Indeed, SMOKE is the 

fastest emissions processing tool currently available to the air quality modeling community. The 

sparse matrix approach utilized throughout SMOKE permits both rapid and flexible processing 

 26 
of emissions data. The processing is rapid because SMOKE utilizes a series of matrix 

calculations instead of less efficient algorithms used in previous systems. The processing is 

flexible because the processing steps of temporal projection, controls, chemical speciation, 

temporal allocation, and spatial allocation have been separated into independent operations 

wherever possible. The results from these steps are merged together at a final stage of 

processing. 

 

SMOKE contains a number of major features that make it an attractive component of the 

modeling system. The model supports a variety of input formats from other emissions 

processing systems and models. It supports both 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 the 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 project changes in air quality in response 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 Other Analyses for Demonstrating 

Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze identifies specific goals 

to consider when selecting one or more episodes for use in demonstrating reasonable progress in 

attaining the Regional Haze air quality goals. EPA recommends that episode selection derive 

from several principal criteria: 

• Simulate a variety of meteorological conditions; 

• Model time periods in which observed concentrations are close to the appropriate baseline 

design value or visibility impairment; 

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

• Model a sufficient number of days so that the modeled attainment test applied at each 

monitor violating the NAAQS is based on multiple days. 

For Regional Haze modeling, the guidance goes further by suggesting that the preferred 

approach is to model a full, representative year. Moreover, the required Relative Response 

Factor (RRF) values should be based on model results averaged over the 20% worst and 20% 

best visibility days determined for each Class I area based on monitoring data from the 2000–

2004 baseline period. 

EPA also lists several other considerations to bear in mind when choosing potential Regional 

Haze episodes including: (a) choose periods which have already been modeled, (b) choose 

periods which are drawn from the years upon which 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 possible in the maximum number of nonattainment or Class I areas 

 27 
as possible. Finally, EPA explicitly recommended in its 2007 guidance to use 2002 as the 

baseline inventory year. 

VISTAS adopted a logical, stepwise approach in implementing the guidance in order to identify 

the most preferable, representative year for Regional Haze modeling, including the following 

steps: 

Representativeness of Meteorological Conditions: The VISTAS meteorological 

contractor (BAMS) identified important meteorological 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 inputs and model performance evaluation. 

VISTAS used Classification and Regression Tree (CART) Analyses 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 meteorological and air quality monitoring data for 2000 

to 2004 to evaluate how well the 2002 modeling year represented the full 2000-2004 

baseline period. This analysis confirmed that visibility and PM2.5 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 PM2.5, meteorological, and emissions data 

and representativeness of alternative baseline modeling periods from a regulatory 

standpoint. Additionally, since 2002 was the year that EPA required states to provide 

emissions inventory data for the Comprehensive 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 consistency between inputs and outputs across 

RPOs, especially in areas that straddle multiple RPOs. 

After a lengthy process of integrated studies, the episode selection process culminated in the 

selection of calendar year 2002 (1 January through 31 December) as the most current, 

representative, and pragmatic choice for VISTAS’ Regional Haze modeling. All of the EPA 

criteria for Regional Haze episode selection were directly considered in this process together 

with many other considerations (e.g., timing of new emissions or aerometric data deliveries by 

EPA or the states to the modeling teams). 

 28 
5.4 Modeling Domains 

5.4.1 Horizontal Modeling Domain 

EPA’s modeling guidance recommends a 12-km modeling grid resolution for PM2.5 modeling 

while a 36-km grid is considered acceptable for Regional Haze. For the VISTAS modeling, a 

coarse 36-km grid 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-way nesting was not considered due 

to the numerical and computational uncertainty 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 location 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. 

 29 
To achieve finer spatial resolution in the VISTAS states, a one-way nested 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 domain 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 vertical grid used in the MM5 

modeling. The MM5 model employed a terrain following coordinate system defined by 

pressure, using 34 layers that extend from the surface to the 100 mb. A layer-averaging scheme 

was used to generate 19 vertical layers for CMAQ to reduce the computational cost of the 

CMAQ simulations. The effects of layer averaging were evaluated in conjunction with the 

VISTAS modeling effort and were found to have a relatively minor effect on the model 

performance metrics when both the 34 layer and a 19 layer CMAQ models were compared to 

ambient monitoring data. This is discussed in more detail in Appendix E. 

6.0 MODEL PERFORMANCE EVALUATION 

The initial modeling effort focused on evaluating previous regional air quality modeling 

applications and testing candidate model configurations for the SMOKE emissions and CMAQ 

model for the VISTAS 36-km and 12-km modeling domains. This effort resulted in a report 

recommending the model configuration for the annual 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 modeling configuration in the modeling protocol 

contained in Appendix E and Appendix C, respectively. 

 30 
Air 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 Air Quality System (AQS), IMPROVE, Speciated 

Trends Network (STN), Southeastern Aerosol Research and Characterization (SEARCH), 

National Acid Deposition Program (NADP) and Clean Air Status and Trends Network 

(CASTNet) monitoring networks (Figure 6.0-1). 

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 model performance for ozone and fine particles. 

EPA’s 2007 modeling guidance for fine particulate matter at the time noted that PM models 

might not be able to achieve the same level of performance as ozone models. VISTAS’ 

evaluation considered several statistical performance measures and displays. Fractional bias and 

mean 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 evaluation (Please see Appendix N). 

 31 
Table 6.1-1 Established model performance goals and criteria for the component species of fine 

particle mass. 

Fractional Bias Mean Fractional 

Error 

Comment 

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

model performance, considered excellent performance 

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

performance 

<60 percent <75 percent Criteria for PM model performance, considered 

average performance. Exceeding this level of 

performance indicates fundamental concerns with the 

modeling system and triggers diagnostic evaluation. 

Several graphic displays of model performance were prepared including: 

1. Scatter plots of predicted and observed concentrations and deposition by species, 

monitoring network, and month 

2. Time series plots of predicted and observed concentrations and deposition by species, 

monitoring site, and month 

3. Spatially averaged time series plots 

4. Time series plots of monthly fractional bias 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 plots” 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, PM2.5, and Regional Haze. Both “soccer plots” and “bugle plots” 

allow for a convenient way to examine model performance with respect to set goals and criteria. 

The bugle plots have the added benefit of adjusting the goals and criteria to consider the 

concentration of the species. Analysis of “bugle plots” generally suggests that greater emphasis 

should be placed on performance of those components with the greatest contribution to PM mass 

and visibility impairment (e.g. sulfate and organic carbon) and that greater bias and error could 

be accepted for components with smaller contributions to total PM mass (e.g. elemental carbon, 

nitrate, and soil). 

The initial model performance evaluation in 2004 demonstrated that 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 consistently over predicted on 

winter days and under predicted in summer. This result was attributed to high uncertainty in 

ammonia emissions and, in particular, the seasonal allocation of ammonia emissions. In 2005, 

the ammonia inventory was updated with more recent emissions data and emissions were 

 32 
 33 

reallocated seasonally (lower percentage of annual emissions assigned to winter and higher 

percentage to summer). These ammonia inventory 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 major contributor to PM mass and visibility 

impairment in the southeastern U.S. Evaluation of the components of the CMAQ Secondary 

Organic Aerosol (SOA) module indicated that several processes potentially important to 

formation of biogenic SOA were not included. ENVIRON modified the SOA module in CMAQ 

to incorporate additional SOA formation from isoprenes 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 modifications to the SOA module. With the release of CMAQ Version 4.5 

in October 2005, the SOAmods enhancement was added to the AERO3 aerosol module for use in 

the VISTAS regulatory modeling. 

Both fine soil and coarse matter were over predicted by CMAQ in the 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 components in addition to metal oxides in the 

inventory category assigned to fine soils. Modeled metal oxides agreed well with observed 

metal oxides. Coarse matter is likely over predicted because the transport of primary particulate 

matter has been over estimated. In 2006, EPA recommended that the transport 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 PM2.5, as good model performance of the component species will 

dictate good model performance of total or reconstituted PM2.5. Model performance of the total 

PM2.5 and the resulting total light extinction will also be provided as a means to discuss the 

overall model performance for this SIP. 

In the analyses, mean fractional bias (error) is used in lieu of mean bias (error), to prevent low 

observations and model predictions from skewing the metrics. A full list of model performance 

statistics is found in Appendix E. The soccer and bugle plots for the all of the VISTAS 
IMPROVE monitors are included here for summary purposes. The goal and criteria levels used 

for regional haze model performance were 30% and 60%, respectively, for mean fractional bias 

and 50% and 75%, respectively, for mean fractional error. Plots have been developed for the 

average monthly concentrations and the performance 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 individual IMPROVE monitors associated 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 (Figures 6.2-1 and 6.2-2) show that values for nitrate 

generally fall outside of criteria performance thresholds (red box). Sulfates and organic carbon 

generally fall within goal thresholds (green box), with a couple of months falling just outside the 

goal thresholds but well within the criteria thresholds. In these figures, each point represents a 

month. Figure 6.2-3 contains separate soccer plots for each season. The seasonal plots 

emphasize poorer nitrate performance in the summer, when observed nitrate is quite low and 

predicted nitrate is even lower. When concentration is factored into performance criteria, nitrate 

performance improves with respect to mean fractional bias and mean fractional error 

(Figures 6.2-4 and 6.2-5). 

All Sites CMAQ 12km - 2002 Monthly

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

180.0

200.0

-180.0 -120.0 -60.0 0.0 60.0 120.0 180.0

Fractional Bias

Mean 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.

 34 
All Sites CMAQ 12km - 2002 Monthly

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

-75.0 -25.0 25.0 75.0

Fractional Bias

Mean 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 concentration 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 Bias

Mean 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 Bias

Mean 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 Bias

Mean 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 Bias

Mean 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. 

 35 
All Sites CMAQ 12km - 2002 Monthly

0.0

50.0

100.0

150.0

200.0

0.0 1.0 2.0 3.0 4.0 5.0 6.0

Average Concentration (μg/m3)

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 (μg/m3)

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 monitoring sites (STN, FRM, CASTNet, etc.). 

Overall, VISTAS found the modeling results to be representative and acceptable for use in 

modeling projection for ozone, particulate 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 VISTAS Class I areas. These plots are illustrated 

for each pollutant species and PM2.5 mass in Appendix C of the VISTAS Technical Support 

 36 
 37 

Document (Please see Appendix N). Model performance at each Class I area individually is 

illustrated in Appendix C of the VISTAS Technical 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 region, 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 particulate 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 (bextSO4) 

• Red = light extinction due to nitrates (bextNO3) 

• 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 order 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 matter light extinction value. 

In reviewing the stacked bar charts, the fact that sulfates are a large contributor to light extinction 

at the Sipsey Wilderness area on both 20% worst days and 20% best days is reinforced. The bar 

charts also suggest that organic carbon and nitrates are important 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 fall 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 organic 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. The 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 the 20% worst days. This suggests accurately 

modeling each species is especially important on the 20% best days. 

Figure 6.3-1 Sipsey Wilderness Area 

 38 
Figure 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 Wilderness are listed in Tables 6.3-1 and 6.3-2 

below. These tables demonstrate how well multiple variables performed for the Sipsey 

Wilderness Class I area. 

 39 
Table 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 plots (Figures 6.3-3 – 6.3-10) of predicted and observed NO3, SO4, 

OMC, CM, EC, Soil, Sea Salt and PM2.5 mass across all monitoring days at the Sipsey 

Wilderness provide information on the performance across each season and the year in total. 

 40 
Figure 6.3-3 Time Series plot of NO3 predicted versus observed for the Sipsey Wilderness Area for 

2002 

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

2002 

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

2002 

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 

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 predicted versus observed for the Sipsey Wilderness Area 

for 2002

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

for 2002 

 44 
 45 

Overall, ADEM found that model performance fell within the accepted limits. As such, ADEM 

contends that the one atmosphere modeling 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 (LTS) FOR ALABAMA’S SIPSEY 

WILDERNESS CLASS I AREA 

As stated previously in Section 1.3 above, the Rule requires states 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 period until 2064. This first set of RPGs must 

be met through measures contained in the State’s long-term strategy (LTS) covering the period 

from the baseline (2000-2004) until 2018. This section 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 regional haze in the baseline timeframe were large 

stationary sources of sulfur dioxide emissions. Keeping that key conclusion in mind, this section 

addresses the following questions: 

a. Assuming only existing and planned emissions controls will take place in Alabama 

and the VISTAS region, how much visibility 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 realized between the baseline and 

2018 at the Sipsey Wilderness? 

c. Narrowing down further, in what geographic locations are the emissions found which 

have the greatest impact on visibility 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 greatest 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 improvement, compared to the glidepath, is 

expected to be seen at the Sipsey Wilderness between the 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 implemented 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 Clean Air Interstate Rule (CAIR) and the NOx SIP 

Call will be realized regionally. The implementation of these programs in Alabama will 

significantly reduce emissions of sources 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 SO2 and NOx from EGUs in the eastern United 

States by 2015. When fully implemented, CAIR will reduce SO2 emissions from EGUs in these 

states by more than 70%, and NOx emissions by more than 60%, from 2003 levels. 

 

NOx SIP Call. Phase I of the NOx SIP call applies to certain EGUs and large non-EGUs, 

including large industrial boilers and turbines, and cement kilns. Those states affected by the 

NOx SIP call in the VISTAS region, including Alabama, have developed rules for the control of 

NOx emissions that have been approved by EPA. The NOx SIP Call has resulted in a 68% 

reduction in NOx emissions from large stationary combustion sources in Alabama. For this 

analysis, emissions were capped for NOx 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 NOx emissions by 2009 and a 73% cut in SO2 emissions by 2013. 

This legislation establishes annual caps on both SO2 and NOx emissions for the two primary 

utility companies in North Carolina, Duke Energy and Progress Energy. These reductions must 

be made in North Carolina, and allowances are not saleable. 

Consent Agreements (TECO, VEPCO, Gulf Power Crist 7, Santee Cooper, EKPC and APCO 

Plant Miller).

• Under a settlement agreement, by 2008, Tampa Electric Company will install permanent 

emissions-control equipment to meet stringent pollution limits; implement a series of 

interim pollution reduction measures to reduce emissions while the permanent controls 

are designed and installed; and retire pollution emission allowances that Tampa Electric 

or others could use, or sell to others, to emit additional NOx, SO2, and PM. 

 46 
• Virginia Electric and Power Co. (VEPCO) agreed to spend $1.2 billion by 2013 to 

eliminate 237,000 tons of SO2 and NOx emissions each year from eight coal-fired 

electricity generating plants in Virginia and West Virginia. 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 Power to upgrade its operation to cut NOx

emission rates by 61% at its Crist generating plant by 2007, with major reductions 

beginning in early 2005. The Crist plant is a significant source of NOx emissions in the 

Pensacola, Florida area. 

• A 2004 consent agreement calls for Santee Cooper to install and commence operation of 

continuous emissions control equipment for PM/SO2/NOx emissions; comply with 

system-wide annual PM/SO2/NOx emissions limits; agree not to buy, sell or trade 

PM/SO2/NOx allowances allocated to Santee Cooper System as a result of said 

agreement; and to comply with emission unit 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 the utility to reduce its emissions by 62,000 tpy (including 

54,000 tons of SO2 and 8,000 tons of NOx) by installing/operating selective catalytic 

reduction (SCR) technology; low-NOx burners, and PM and Mercury Continuous 

Emissions Monitoring at the utility’s Spurlock, Dale and Cooper Plants. According to 

EPA, the plant’s total emissions 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 Interstate 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 (Jefferson County) On April 25, 2006, the U.S. 

EPA and the Department of Justice announced a settlement with Alabama Power 

Company (APC) to reduce emissions of sulfur dioxide (SO2) and nitrogen oxides (NOx) 

from Boiler Units 3 and 4 at its Plant Miller in Quinton, Alabama. The plant is required 

to reduce SO2 by installing flue gas desulfurization (FGD) and is required to reduce NOx

by installing selective catalytic reduction (SCR) equipment. The Consent Decree 

requires APC to commence continuous year-round operation of SCR equipment for 

control of NOx emissions by May 1, 2008. APC is also required to begin year-round 

operation of the FGD equipment for control of SO2 emissions by December 31, 2011. 

The installation and operation of these controls are expected to result in the reduction of 

 47 
4,953 tons per year of NOx and 22,788 tons per year of SO2. APC is also required to 

install and operate by December 31, 2008, a mercury continuous emission monitoring 

system. APC is precluded from using the emissions reductions required under other 

programs, such as the Clean Air Interstate Rule (CAIR). In addition, the Consent Decree 

requires APC to purchase and permanently retire $4.9 million worth of 2007 SO2

emissions allowances. 

Cargill, Inc. Consent Decree

On March 3, 2006, the Federal District Court in Minnesota entered a Consent Decree 

between Cargill, Incorporated, U.S. EPA, the Iowa Department of Natural Resources and 

other participating agencies. Under this agreement, Cargill shall implement a program of 

enforceable emissions reductions of SO2, CO, NOx, and VOCs from its corn processing and 

oilseed processing plants of at least 40,000 tons per year. This includes a reduction of SO2 of 

15,000 tons per year, CO of 16,000 tons per year, NOx 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 Consent 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 tons per year on a 12-month rolling sum. This represents a 

reduction of 15,067 tons of SO2 per year from the current allowable emissions from these 

sources. Also, Cargill is required submit a permit application within three years from entry 

of the Consent Decree that will include individual 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 SO2

emission rate of 1.2 lb/MMBtu. The Cargill Sweetners Decatur Facility is required by 3/3/09 

to control and comply with SO2 emissions limitations for its coil fired boilers. 

One-hour Ozone SIPs (Atlanta / Birmingham / 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 NOx reductions from specific coal fired 

power plants and address transportation plans in these cities. 

Heavy Duty Diesel (2007) Engine Standard (for onroad trucks and buses). EPA set a PM 

emissions standard for new heavy-duty engines of 0.01 grams per brake-horsepower-hour 

(g/bhp-hr), to take full effect for diesel engines in the 2007 model year. This rule also 

includes standards for NOx and non-methane hydrocarbons (NMHC) of 0.20 g/bhp-hr and 

0.14 g/bhp-hr, respectively. These NOx and NMHC standards will be phased in together 

between 2007 and 2010, for diesel engines. Sulfur in diesel fuel must be lowered to enable 

modern pollution-control technology to be effective on these trucks and buses. 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 LSD) 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 California LEV II standards. Manufacturers can produce vehicles 

with emissions ranging from relatively dirty to zero, but the mix of vehicles a manufacturer 

sells each year must have average NOx emissions below a specified value. Tier 2 standards 

became effective in the 2005 model year. 

 48 
 49 

Large Spark Ignition and Recreational Vehicle Rule. EPA has adopted new standards for 

emissions of oxides of nitrogen (NOx), hydrocarbons (HC), and carbon monoxide (CO) from 

several groups of previously unregulated nonroad engines. Included in these are large 

industrial spark-ignition engines and recreational vehicles. Nonroad spark-ignition engines 

are those powered by gasoline, liquid propane gas, or compressed natural gas rated over 19 

kilowatts (kW) (25 horsepower). These engines are used in commercial and industrial 

applications, including forklifts, electric generators, airport baggage transport vehicles, and a 

variety of farm and construction applications. 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 will reduce emissions by more than 90% 

from nonroad diesel equipment, and reduce sulfur levels by 99% from current levels in 

nonroad diesel fuel starting in 2007. This rule will apply to most nonroad diesel fuel in 2010 

and to fuel used in locomotives and marine vessels in 2012. 

Combustion Turbine MACT. The projection inventories do not include the NOx co-benefit 

effects of the MACT regulations for Gas Turbines 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 Programs to Reduce Emissions 

In addition to accounting for specific emission reductions due to ongoing air pollution programs 

as required under Section 308 (d)(3)(v)(A), States are also required to consider the air quality 

benefits of measures to mitigate the impacts of construction activities [Section 308(d)(3)(v)(B)]

and agricultural and forestry smoke management [Section 308(d)(3)(v) (E)]. 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 account for post 2002 emission reductions from 

promulgated and proposed federal, state, local, and site-specific control programs as of July 1, 

2004. In general, emissions 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) analyses supplemented by state and local agency data 

and updated forecasts from the Department of Energy (DOE). 

Area source controls were estimated using known state level Stage I controls on gasoline 

dispensing facilities and open burning 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 underlying 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 Technical 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 

SO2 and NOx across the VISTAS states from 2002 through 2018. (Similar charts for other 

visibility impairing pollutants are contained in Appendix N). Note that for SO2 emissions in 

particular, which are the largest contributors to haze, emissions from electric generating facilities 

are expected to decrease dramatically (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, comprising more than half of remaining SO2 emissions in most states. 

West Virginia

Alabama

Florida

Kentucky

Georgia

Mississippi

South Carolina

North Carolina

Tennessee

Virginia

100

200

300

400

500

600

700

Annual SO2 (Thousand Tons)

Miscellaneous

Nonroad

Onroad

Industrial

Other Fuel Combustion

EGU

2002

2009

2018

West Virginia

Alabama

Florida

Kentucky

Georgia

Mississippi

South Carolina

North Carolina

Tennessee

Virginia

100

200

300

400

500

600

700

Annual SO2 (Thousand Tons)

Miscellaneous

Nonroad

Onroad

Industrial

Other Fuel Combustion

EGU

2002

2009

2018

Alabama

Florida

Kentucky

Georgia

Mississippi

South Carolina

North Carolina

Tennessee

Virginia

100

200

300

400

500

600

700

Annual SO2 (Thousand Tons)

Miscellaneous

Nonroad

Onroad

Industrial

Other Fuel Combustion

EGU

Miscellaneous

Nonroad

Onroad

Industrial

Other Fuel Combustion

EGU

2002

2009

2018

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.

0

100

200

300

400

500

600

700

800

900

1,000

Alabama

Florida

Georgia

Kentucky

Mississippi

North Carolina

South Carolina

Tennessee

Virginia

West Virginia

Annual NOx (Thousand Tons)

Miscellaneous

Nonroad Onroad

Industrial Other Fuel Combustion EGU

2002

2009

2018

0

100

200

300

400

500

600

700

800

900

1,000

Alabama

Florida

Georgia

Kentucky

Mississippi

North Carolina

South Carolina

Tennessee

Virginia

West Virginia

Annual NOx (Thousand Tons)

Miscellaneous

Nonroad Onroad

Industrial Other Fuel Combustion EGU

Miscellaneous

Nonroad Onroad

Industrial Other Fuel Combustion EGU

2002

2009

2018

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 develop the inventory is contained 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 develop the inventory is contained in Appendix D. 

 51 
 52 

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 Inventory Compared to the Uniform Rate of 

Progress Glidepath for the Sipsey Wilderness Area 

Using 2000 - 2004 IMPROVE monitoring data, the deciview (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 predicted visibility improvement on the 20% worst days in 2018 

that represents implementation of existing federal 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 shows 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, for 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

Year

Haziness 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 below 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”, commonly known as the “Best and Final” inventory, circa 2007. 

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

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

220%

240%

COHU1

DOSO1GRSM1

JARI1

LIGO1

CADI1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1OKEF1

ROMA1

SAMA1

SWAN1

BRET1

BRIG1

CACR1

HEGL1

MING1

UPBU1

Percent 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 visibility days, states are 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 projected 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 fact, visibility should improve beyond that level. 

 54 
CMAQ 2018g4a vs 2018g2b Visibility Projection at VISTAS+ sites on Best 20% 

days -50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

COHU1

DOSO1

GRSM1

JARI1

LIGO1

CADI1

MACA1

SHEN1

SHRO1

SIPS1

CHAS1

EVER1

OKEF1

ROMA1

SAMA1

SWAN1

BRET1

BRIG1

CACR1

HEGL1

MING1

UPBU1

Percent 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

Year

Haziness 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

 55 
7.3 Relative Contribution from International Emissions to Visibility Impairment in 2018 

at VISTAS Class I areas 

Emissions from Mexico, Canada, Central America, Asia, and Africa contribute to PM2.5 loadings 

and visibility impairment at Class I areas in the Continental U.S. To evaluate the relative 

contribution of international emissions to visibility at Class I areas in the southeastern U.S., 

VISTAS used a combination of modeling results 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 modeling domain. GEOS-Chem was run 

for the 2002 modeling year using a 4 x 5 degree horizontal grid resolution and a 3-hour temporal 

resolution. Because emissions were based on monthly averages, the model does not capture the 

episodic variability in emissions. The GEOS-Chem outputs were then used to calculate initial 

and boundary conditions for the national CMAQ modeling domain. The national CMAQ 

domain included portions of Canada and Mexico, so emissions for these countries were included 

within the national CMAQ modeling domain or as part of the boundary conditions outside the 

national modeling domain, as appropriate. 

Two complementary methods were used to calculate the impact of international emissions at 

Class I areas, with the intention of isolating the visibility effects on Class I areas from non-U.S. 

anthropogenic emissions. All international fires are treated as natural emissions 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 international anthropogenic emissions, in the 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 Electric Power Research Institute (EPRI) 

provided GEOS-Chem results for the 2001 international contribution on a 1 x 1 degree 

grid scale. 

2) International emissions are represented by the difference between two CMAQ 36-

km simulations, both using 2018 Base F emissions and boundary conditions from 

GEOS-Chem. In the first CMAQ run, all global natural and anthropogenic emissions in 

2018 are active. In the second CMAQ run, only global (U.S. and International) natural 

emissions are active. Here the impacts of international emissions are compared against 

2018 conditions rather than natural background conditions. 

Separate from VISTAS, the CENRAP RPO used PM Source Apportionment Technology 

(PSAT) in the CAMx Regional air quality model to calculate the impact of Canadian and 

Mexican emissions and boundary conditions on visibility at Class I areas in 2002. 

As illustrated in Figure 7.3-1 for annual average contributions to sulfate at VISTAS and 

neighboring Class I areas, the estimated international contributions are higher at Class I areas 

near the Canadian and Mexican borders and along the eastern coast. The estimated international 

 56 
contribution is higher using CMAQ and PSAT than in the GEOS-Chem runs because the grid 

scale is finer (providing a more accurate dispersion 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 international 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

1.2

BRIG

HEGL

CACR

MING

UPBU

BRETEVER

CHAS

SAMA

COHU

OKEF

ROMA

SIPS

SHRO

GRSM

LIGO

MACASWAN

JARI

DOSO

SHEN

(NH

4)

2SO4 Conc. (

μg/m

3

)

GEOS_4x5 GEOS_1x1 PSAT CMAQ

Figure 7.3-1 Estimated international emissions contributions 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 components at the site on those days. 

 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 international contributions (right bars) at Sipsey.

For the Sipsey Wilderness area, accounting for the international contribution 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 atmosphere becomes closer to natural 

background conditions in the future, the incremental 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 Impairment: Pollutants, Source Categories, and 

Geographic Areas 

An important step toward identifying further reasonable progress measures is to identify the key 

pollutants contributing to visibility 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 sensitivities were initially performed for three 

episodes representing winter and summer conditions: January 2002, July 2001, and July 2002. 

These runs used the initial 2018 projection inventory and considered 30% reductions from 

specific pollutants, source categories, and geographic areas. Emissions sensitivities were 

repeated using the 2009 Base D projection inventory and two month-long episodes from 2002: 

 58 
June 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: 

• SO2 from EGU sources in each VISTAS state, other RPOs in the VISTAS 12 km grid, 

and Boundary Conditions from outside the 12 km domain. 

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

Conditions 

• NOx from ground level (on-road plus non-road plus area) sources in each VISTAS state 

and other RPOs 

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

• NH3 from all sources in VISTAS and other RPOs 

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

modeling domain 

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

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

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

Results are shown below in Figure 7.4-1 for the average of the 20% worst visibility days at the 

Sipsey Wilderness area. Responses for 20% worst 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

-8.00

-7.00

-6.00

-5.00

-4.00

-3.00

-2.00

-1.00

0.00

1.00

SO2_EGU

SO2_nonEGU

NOx_Ground

NOx_Point

NH3

VOCs

PC_Ground

PC_Point

PC_Fires

Δ

Bext (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 SO2 from EGUs. Reductions in 

SO2 from Alabama’s EGUs would have the greatest benefit; contributions from other VISTAS 

states, other RPOs and from the boundary conditions are smaller but not insignificant. 

Additionally, smaller benefits are projected from additional SO2 emission reductions from nonutility, industrial point sources. 

Because ammonium nitrate is a contributor to PM2.5 mass and visibility impairment on the 20% 

worst days that occur in the winter at the Sipsey Wilderness, the benefits of reducing NOx and 

NH3 emissions could be beneficial in improving visibility on the 20% worst days that occur in 

winter and have elevated NO3 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 any, 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 with the monitoring data that shows that most of measured organic 

carbon is secondary in origin and primary carbon is only a small fraction of the total measured 

carbon (for more information please see Appendix B). 

 60 
 61 

Note that these results from the emission sensitivity runs are consistent with the conclusions 

drawn from the 2000-2004 baseline monitoring data (Please see Section 2.4). The results 

indicate that sulfate is the dominant contributor to visibility impairment on the 20% worst days at 

all sites, and that ammonium nitrate may be important for sites where the 20% worst days occur 

in the winter. It can be concluded that reducing SO2 emissions from EGU and non-EGU point 

sources in the VISTAS states would have the greatest visibility benefits for the Sipsey 

Wilderness area.

7.5 Relative Contributions to Visibility Impairment: Geographic Areas of Influence (AoI) 

at Class I areas affected by Alabama Sources 

Once it was determined that SO2 emission reductions from EGU and non-EGU point sources in 

the VISTAS states would be the most effective sources to control to improve 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 SO2 point sources located in that geographic area. An SO2

Area of Influence (AoI) was defined for all the VISTAS 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 SO2 point sources within this AoI were identified and ranked by their 2018 Base G 

emissions. The following sections contain a broad overview of the steps in the AoI analysis, 

including tables providing information on Alabama 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 meteorological back trajectories for the Sipsey Wilderness 

IMPROVE monitoring site and neighboring Class 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 arrive at a receptor at a given time. The method 

essentially follows a parcel of air backward in hourly steps for a specified length of time. Figure 

7.5.1-1 is an example of a back trajectory analysis for the Sipsey Wilderness 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 trajectories for the 20% worst days in 2002 were 

also useful in evaluating model performance for individual days at the Sipsey Wilderness. 

7.5.2 Residence Time Plots 

The next step was to plot residence time using five years of back trajectories for the 20% worst 

visibility days in 2000-2004. Residence time is the frequency that winds pass over a specific 

geographic area on the path to a Class I area. Separate residence time plots were generated using 

trajectories with 100 meter and 500 meter start heights. 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. 

 62 
Residence 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 SO2 Areas of Influence (AoIs) 

The next step was to develop sulfate extinction weighted residence time plots to define the 

geographic area with the highest probability of influencing the receptor 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 the 20% worst days that are influenced by sulfate 

and places less importance on days influenced by organic carbon from fires. 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 SO2 emissions. In Figure 7.5.3-1, the area representing 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 AoIs 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 for Sipsey. Based on back trajectories with 100 m 

start height.

7.5.4 Emissions Sources within SO2 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 inventories. Plots were generated for each AoI 

defined by trajectories with 100 m and 500 m start heights. As a way of incorporating the effects 

of transport, deposition, and chemical transformation of point source emissions along the path of 

the trajectories, these data were weighted by 1/d, where d was calculated as the distance, in 

kilometers, between the center of the grid cell in which a source is located and the center of the 

grid cell in which the IMPROVE monitor is located. The distance weighted point source SO2

emissions are then combined with the gridded extinction weighted back-trajectory residence 

times at a spatial resolution of 36 km. 

The final step was to combine the residence times and gridded emissions data in plots and data 

sets. The distance weighted (1/d) gridded point source SO2 emissions were multiplied by the 

total extinction weighted back trajectory residence times on a grid cell by grid cell basis. These 

results were then normalized by the domain wide total and displayed as a percentage. The 

analysis was done using both the 2002 and 2018 base year inventories. 

 64 
Figure 7.5.4-1 illustrates the 2002 and 2018 distance weighted gridded emissions * sulfate 

extinction weighted residence time plots for the Sipsey Wilderness. These maps help visualize 

where the emissions reductions will be occurring between 2002 and 2018. The change in SO2

emissions between 2002 and 2018 can be seen by comparing emissions source strengths in the 

two plots. Note the emissions from each source are normalized by the total emissions in the 

domain. Sources that reduce SO2 emissions by 2018 will show a lower contribution to emissions 

in the domain. On the 2018 map the grid cells with these sources will 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 SO2 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 SO2 compared to sources in neighboring 

states. Additional analyses, including 2002 and 2018 distance weighted emissions * residence 

time plots for the Sipsey Wilderness Area in Alabama and neighboring Class I areas are 

contained in Appendix H. 

 65 
These analyses served as the basis for 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 Influence (AoI) for the Sipsey Wilderness 

The next step in the analysis was to review the emissions inventories to determine what source 

categories, as well as specific sources, are found to have the greatest impact on visibility in the 

Sipsey Wilderness area. Lists of SO2 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 defined as the counties with maximum sulfate 

extinction weighted residence time greater than 5%. For SO2 sources within the AoI, the 

following attributes were defined for each individual unit: 

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

• SO2 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 metric that accounts for the dispersion of 

emissions over distance 

• Maximum sulfate extinction weighted residence time (RTmax) 

 66 
The review was conducted in a top down fashion starting with an analysis of the major source 

categories in each SO2 AoI to determine which major categories had the highest residual 

contribution to the area in 2018. It was also important to identify reductions that are projected to 

occur between 2002 and 2018 within each category or at specific units. This allowed VISTAS 

states to determine if certain source categories or units that had yet to be controlled under the 

future year base case had the potential for reduction. Once the highest source types were 

identified, subcategories within those source types were reviewed. The contributions from major 

source categories to the 2018 Base G2 inventory for the SO2 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 Wilderness area, EGUs and industrial boilers are the two 

major source categories contributing to the bulk of the 2018 SO2 emissions in the AoI, even after 

implementation of CAIR. Together these two source categories contribute 79% of the 2018 SO2 

emissions for the AoI for the Sipsey Wilderness area. Other fuel combustion and other industrial 

processes comprise another 19% of the 2018 SO2 emissions. 

These tables can also be used to evaluate the major source categories contributing to emissions 

of NOx, NH3, and PM emissions in 2018. For instance, highway vehicles and off road vehicles 

are major sources of NOx emissions, in addition to electric utilities and industrial boilers. The 

source category “miscellaneous” (which includes agricultural sources and fires) is the major 

contributor to NH3 and primary PM. The emissions sensitivities discussed in Section 7.4 

indicated very small benefits of controlling NOx, and primary PM emissions at Sipsey, but if 

these emissions were of concern, different source categories would need to be addressed. 

The contributions to SO2 emissions in 2018 from the three highest source categories, electric 

utilities, industrial boilers, and other fuel combustion have been further broken out into 

subcategories. Table 7.5.5-2 indicates these categories for the AoI for Sipsey. Within electric 

utilities, all the SO2 emissions are attributable to coal fired power plants. Within industrial 

 67 
 68 

boilers, most emissions are attributable to coal fired boilers with lesser contributions from oil and 

gas boilers. Commercial and institutional coal and oil boilers have 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 other source categories are major contributors to SO2

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/Institutional 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/Institutional 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 within Major Source Categories in the AoI- Sipsey

7.6 Evaluating the Four Statutory Factors for Specific SO2 Emissions Sources in Each 

Area of Influence (AoI) 

The next step was to identify emission reductions that have already occurred within each source 

category and at specific units. Unit level tables of emission comparisons from 2002 to 2018 

were developed, allowing VISTAS states to review existing emission reductions. These tables 

assigned future year control technology from IPM forecasting and state modification for EGUs 

and from control efficiency tables 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 for 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 source types did not duplicate 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 monitor (in km), the 2018 

residual emissions and distance (Q/d) or squared distance (Q/d^2), and the normalized 2018 SO2

point source emissions times distance-weighted residence time (RTMax) values for the county in 

which the emission release point was located. In addition, a new metric 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 the total. Given this information, ADEM chose 

to further evaluate those sources with Q/d*RTMax percentages of 1% or greater. 

Tables 7.6-1 through 7.6-7 below show, in tabular form, the SO2 units within the AOI whose 

percent contribution was 1% or greater than the total contribution at both the Sipsey Wilderness 

Class I area and neighboring Class I areas. Please note in Table 7.6-6 for the Breton Wildlife 

Class I area, the Akzo Nobel sulfuric 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 SO2

emission rate exceeded the allowable emission rate for that unit. When the table was revised to 

incorporate the allowable emission rate, the contribution from the sulfuric 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

 69 
Table 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 

 70 
Table 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 2002G 2018G CE Dist Q/d BART RTMax RTMax RTMax Number (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 002 153 200 0 3.97 50.32 83.87 4220.34 3.87 NO 470090021 TN/HARRISON 

U.S. DOE TN 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 

 71 
Table 7.6-4 Joyce Kilmer Q/d, Q/d^2, RTMax, 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 for the 2018 progress goal, the 

CAIR is equivalent to Reasonable Progress for Class I areas projected to meet or exceed their 2018 progress goal, as 

is the case for the Joyce Kilmer Class I area. 

 72 
 73 

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 0001 
Table 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 

 74 
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 

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 

 75 
Table 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 

 76 
The Rule requires that states consider the following factors and demonstrate how these factors 

were taken into consideration in selecting reasonable progress goals: 

• the costs of compliance 

• the time necessary for compliance 

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

• the remaining useful life of any potentially affected sources. 

Cost of Compliance: 

As defined in Section 7.5.5, coal-fired electric utilities and coal-fired industrial boilers were the 

largest source categories contributing to SO2 in 2018 in the AoI defined for the Sipsey 

Wilderness area. 

Sulfur dioxide emissions from utility, industrial, commercial, or institutional boilers can be 

controlled either by switching the fuel source to a lower sulfur fuel content or by installing post 

combustion controls. Costs vary by fuel source and boiler type and require source specific 

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 SO2

emissions with least impact to boiler performance. While sub-bituminous coal has a lower sulfur 

content than bituminous coal, it also has a lower heat rate and so more coal has to be burned to 

generate the same energy output. For boilers that are designed for bituminous coal, only a small 

fraction of the fuel can be switched to low sulfur sub-bituminous coal without affecting boiler 

operations. Costs of boiler modifications 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, transportation costs, and modification to boiler operations, 

fuel handling and waste handling systems will have to be considered specific to each source. 

Flue gas desulfurization is the common post-combustion control for coal-fired boilers. Flue gas 

is passed through an absorbent for sulfur dioxide (generally limestone or lime) in either a wet 

scrubber, dry scrubber, or spray dryer. A calcium sulfate by-product is produced that may be 

further processed to produce gypsum as a commercial byproduct. Costs of flue gas 

desulfurization include initial construction costs and ongoing operational and maintenance costs 

for the absorber tower, sorbent handling, and waste product handling facilities. Costs per ton 

vary with boiler size, type, and facility 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 necessary to terminate existing fuel contracts and initiate new 

contracts needs to be considered. Generally two to three years may be required. Installation of 

post-combustion controls will require three or more years depending on market availability of 

 77 
 78 

labor and materials and utility system wide priorities. Time necessary for compliance will need 

to be refined for specific sources. 

Energy and Non-Air Environmental Impacts: 

Switching to lower sulfur fuel or installing post-combustion controls may reduce boiler heat rate 

and energy output. Scrubbers and spray dryers will require additional safeguards for fuel 

handling and waste handling systems to avoid additional 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 individual sources. Carbon dioxide may be emitted as a by-product of 

flue gas desulfurization, especially if limestone is used. Therefore, 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 Reasonable Progress for Individual Sources? 

ADEM’s Process on Reasonable Progress Assessment

Step 1: Determine pollutants of concern. 

VISTAS evaluated the species contribution on the 20% worst visibility days and concluded that 

sulfate accounted for the greatest contribution of visibility impairing pollution. The VISTAS 

states concluded that controlling SO2 emissions was the appropriate step in addressing the 

reasonable progress assessment for 2018. The findings were consistent with the findings of 

SAMI where it was confirmed that sulfate particles account for the greatest portion of the haze 

affecting Class I areas in the Southern Appalachian region and that these sulfates were produced 

in large part from SO2 emissions from coal combustion. 

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

Since SO2 point source emissions in 2018 are projected to represent the majority of the total SO2

emissions inventory, the VISTAS states concluded that the focus should be on electric generating 

unit (EGU) and non-EGU point sources of SO2 emissions. 

Step 3: Determine if the Clean Air Interstate Rule (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, Alabama, as part of its long term reasonable 

progress analysis to consider potential sources contributing to visibility impairment, examined 

other CAA requirements such as CAIR. ADEM evaluated the amount of expected SO2 reduction 

from EGUs due to CAIR. Since the EGUs will be reducing their SO2 emissions by 

approximately 70% through these programs, ADEM concluded that additional EGU control 

during this time period (between present and 2018) was not reasonable. 
The preceding section (Section 7.6) discusses a general four factor analysis 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 State concludes that CAIR controls satisfy 

reasonable progress for SO2 for the first planning period ending in 2018. 

Under the Rule, each “state must identify the uniform rate of progress over the 60 year period 

that would be needed to attain natural background conditions by the year 2064” (64 FR 35732). 

To calculate the rate of progress represented by each reasonable progress goal, Alabama 

compared baseline visibility conditions to natural visibility conditions at the Sipsey Wilderness 

area and determined the uniform rate of visibility (in dv) that would need to be maintained 

during each implementation period in order to attain natural visibility conditions by 2064. SO2

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 SO2 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 concluded that, at this time, these existing regulatory programs 

constitute reasonable measures for Alabama’s EGUs 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 SO2 AoI for a 

given Class I area and identified those emission units with a contribution 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. Therefore, a threshold needed to be established to determine which 

units would be evaluated. ADEM chose to adopt 1% as the contribution threshold used to 

consider a source for a four factor analysis of reasonable further controls based on the 

precedence established in other Federal programs that used an equal to or higher threshold for 

evaluation (For a full explanation of the rationale behind the selection 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 representative of a 5% change in 

visible perception. 

• The NOx SIP call established a significance level for 126 petitions (the level at which a 

state’s contribution to another 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, which was the standard at the time. 

 79 
• The CAIR rule established a PM contribution threshold of 0.2 μg/m3 as significant. This 

threshold represents 1.3% of the Annual PM2.5 standard of 15 μg/m3. 

• The NAAQS assign significant impact levels for human health standards based on a 

source’s contribution to air quality. These significance levels represent a percentage of 

the NAAQS. Those facilities able to demonstrate that a new or modified source’s 

contribution is less than a particular significance level is relieved of further modeling. 

ADEM examined all the averaging periods for the criteria pollutants and determined that 

the 1% threshold contribution chosen for reasonable progress was as stringent as any 

significant impact level, the most restrictive being NO2 at 1 μg/m3 or 1%. (NO2 NAAQS: 

100 μg/m3) 

For the Sipsey Wilderness area, six units were determined to have impacts greater than 1% of the 

total impact. These included 1 unit at the International Paper- Courtland Mill, 4 units at the 

Solutia, Inc. facility and 1 unit at the Cargill, Inc. facility. 

At both the International Paper- Courtland 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 these 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 greater than 1% of the total impact. This unit 

took a permit limit of approximately 7963 tons of actual SO2 emissions (equivalent to a ~ 40% 

reduction in emissions). This limit precluded the 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 this 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 Louisiana 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 impact. The Florida reasonable progress methodology 

used to identify sources for a four factor analysis 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 ratio 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 Florida are using different 

methodologies, ADEM feels it is the responsibility of the state in which the Class I area resides 

to establish procedures for evaluating 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 this reason, ADEM contends that no further evaluation is needed for 

these sources. 

 80 
 81 

Step 5: Evaluate the four factors. 

The emission unit identified in Step 4 above was evaluated using the statutory and regulatory 

factors of 1) cost of compliance, 2) time necessary for compliance, 3) the energy and non-air 

quality environmental impacts of compliance, and 4) the remaining useful life of the emissions 

unit. For the limited purpose of evaluating the cost for the reasonable 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 SO2 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 equipment. “The availability of skilled labor--

specifically, boilermakers--is an important constraint for the installation of significant amounts 

of emission controls. Boilermakers are skilled steel workers who are specially trained to install 

both NOX controls, such as SCR, and SO2 controls, such as scrubbers” (69 FR 4617). During the 

current reasonable progress assessment, no units in Alabama were identified for additional 

control since no measures were found to be below the $2,000 cost threshold. 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 greater; therefore, a four factor analysis was 

necessary. The unit is permitted to burn coal, natural gas, or No. 2 fuel oil. Coal is the primary 

fuel source. The unit is equipped with a Lear Sigler Continuous Opacity Monitor and Nitrogen 

Oxide/Oxygen Emissions Monitor. The boiler exhaust is controlled by a Carborundum 

baghouse. The coal handling system is controlled by a Griffin baghouse. The pneumatic flyash 

conveying system is controlled by an Ultra Industries Baghouse, while air displaced by the 

flyash to the ash silo is controlled by a National Conveyor Baghouse. A summary of control 

options considered is listed below: 

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

Lower sulfur content coal could not be used because of its availability. Also, lower sulfur content 

coal would require significant modification to the boiler to burn it and would challenge the 

combustion integrity due to the higher dust content nature of lower sulfur coal. Therefore, lower 

sulfur coal is technically infeasible. As for the add-on controls (wet and dry scrubbers), Cargill 

estimated that it would cost $2,946/ton to control SO2 with these technologies. As such, no 

control was deemed feasible for this unit. 

7.8 The Identification and Evaluation of Best Available Retrofit Technology (BART) for 

Individual Sources 

Section 169A of the CAA directs states to assess certain emission sources which meet specific 

criteria for additional controls in order to address visibility impacts. States are directed to 

conduct BART determinations for such sources in specific source categories, whose emissions 
contribute to visibility impairment in Class I areas. The 1999 Regional Haze rule includes the 

BART requirement, and directs states to include BART in their Regional Haze SIPs. On July 6, 

2005, EPA published a revised final rule, including Appendix Y of 40 CFR Part 51, the 

Guidelines for BART Determinations 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 potential 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 contribute 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 determinations: (1) the costs of compliance, (2) 

the energy and non-air quality environmental impacts 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 reasonably 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 description 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 

 82 
The 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 units 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 visibility-impairing pollutants: SO2, NOx, and PM10. 

The BART Guidelines recommend addressing these visibility-impairing pollutants: SO2, NOx, 

and PM, and suggest that states use their best judgment in determining whether to address VOC 

or ammonia (NH3) emissions. ADEM addressed SO2 and NOx, and used PM10 as an indicator 

for particulate matter to identify BART-eligible units, as the BART Guidelines recommend. As 

discussed in detail in subsequent sections as well as in Appendix H, VISTAS modeling 

demonstrated that VOCs and NH3 from point sources do not contribute to visibility impairment 

at Class I areas within the VISTAS region. As such, ADEM did not evaluate emissions of VOCs 

and NH3 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 BART-eligible emissions units have potential 

emissions equal to or greater than 250 tons per year of volatile organic 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 impairment at Class I areas within the 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 that cumulative VOC emissions (from all sectors) 

do not contribute to visibility impairment 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 thus determined to be less than 0.5 dv 

(compared to annual average natural background) at every Class I area in the VISTAS 

domain and in adjacent States. It follows that the impact of any one BART eligible 

source would be much less that the 0.5 dv threshold. Based on these analyses, the 

VISTAS States have concluded that VOC emissions from BART sources do not cause or 

contribute to visibility impairment and do 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 potential emissions across all 

BART eligible sources at these facilities, ADEM determined that the following facilities’ 

visibility impairing pollutant (VIP) emissions no longer exceed 250 tons per year (Table 7.8.2-1). 

 83 
Therefore, 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 must 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 affected emission units considering all federally enforceable permit 

limits, were greater than 250 tons or more per year of any of these visibility-impairing pollutants: 

SO2, NOx, and PM10. Alabama had three sources which chose 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 Mobile Energy Services Company no longer have 

BART-eligible sources. Additionally, the Escambia Operating Company’s Big Escambia Creek 

facility also took an enforceable limit that resulted in an insignificant impact, (i.e. less than 0.5 

dv) thereby relieving the source from completing a BART determination. These limits have 

been written into and are enforced through the permits for each applicable source at the facility. 

Table 7.8.3-1 provides information on these sources. 

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

Facility Namea Pollutant

Potential 

Emissions 

Pre limits 

(tpy) 

Potential 

Emissions 

Post limits 

(tpy) 

Effective 

Date of 

Permit 

Changed 

PM10 10.3 10.3 

SO2 210.0 210.0 Gulf States Paper #2 Power Boiler 

(now known as Rock Tenn) NOx 455.0 245.0 

07/13/2007

PM10 552.0 0 

SO2 455.0 0 

Gulf States Paper #2 Recovery 

Furnacec

(now known as Rock Tenn) NOx 248.0 0 

07/13/2007

PM10 131.0 99.0 

SO2 663.0 99.0 

Kerr McGee Chemical 

(now known as Tronox, LLC) 

(source is a synthetic minor for all 

pollutants) 

NOx 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 #6 494.0 240.0b 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 cause or contribute to visibility impairment in a Class I area. In 

accordance with the BART Guidelines, ADEM chose to require source-specific analyses to 

determine which sources cause or contribute to visibility impairment using the CALPUFF 

model. The CALPUFF modeling protocol used for determining which facilities are subject to 

BART is included in 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 Guidelines1 state that “A single source that is responsible for 

a 1.0 deciview (dv) change or more should be considered to ‘cause’ visibility impairment.” The 

guideline document also states that “the appropriate threshold for determining whether a source 

‘contributes to visibility impairment’ may reasonably differ across states,” but, “As a general 

matter, any threshold that you use for determining whether a source ‘contributes’ to visibility 

impairment should not be higher than 0.5 dv.” The rationale for these instructions is provided in 

the preamble to the BART Guidance, in the statement, “If ‘causing’ visibility impairment means 

 

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

 85 
causing a humanly perceptible change in visibility in virtually all situations (i.e. a 1.0 dv 

change), then ‘contributing’ to visibility impairment must mean having some lesser impact on 

the conditions affecting visibility 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 sources 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 the human perception of 

visibility change. As noted above, EPA considers a 1.0 dv change in visibility to be humanly 

perceptible “in virtually all situations.” Also, the preamble to the BART Guidance3 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 contribution threshold. Putting all 

this together, it appears that “causing” visibility impairment means having a humanly perceptible 

impact (for which EPA considers the practical threshold to be 1.0 dv) while “contributing” to 

visibility impairment means having a smaller impact (for which EPA considers the threshold to 

be 0.5 dv or some smaller value) that may or may not be perceptible. 

ADEM retained the threshold of 0.5 dv for evaluating significance for several reasons. First, 

there is a clear spatial variability of sources across the state. Additionally, ADEM is electing to 

use half of the threshold established by EPA for 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. Facilities found to be subject to BART must complete a BART 

determination analysis and these 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 using the VISTAS modeling protocol 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. Appendix D in Acidic Deposition: State of Science and 

Technology, Report 24, Visibility: Existing and Historical Conditions -- Causes and Effects. National 

Acid Precipitation Assessment Program, Washington, DC. 1990. 

 86 
Table 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 

 87 
 88 

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 determined that, as a whole, the 

CAIR cap-and-trade program improves visibility more than implementing BART for individual 

sources in states affected by CAIR. A state that opts to participate in the CAIR program under 

part 96 AAA-EEE need not require affected BART-eligible EGUs to install, operate, and 

maintain BART for SO2 or NOx emissions. Given that many of the BART-eligible units are 

installing scrubbers and NOx controls, and since Alabama is participating in CAIR and accepts 

EPA’s overall finding that CAIR “substitutes” for BART for NOx and SO2, 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 sources were able to demonstrate 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 Appendix H. All BART modeling 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 Class I areas within 300 km 

from their BART-eligible sources. These two sources are considered to be “subject to BART” 

and were required to submit permit applications containing their evaluation of potential BART 

options and proposed BART determinations. These facilities submitted permit applications 

including their proposed BART determinations in the spring and summer of 2007. The proposed 

BART determinations have been concluded and the 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 International Paper Courtland 

BART determinations are listed below. 
Solutia BART Summary 

The Department has concluded that no additional particulate 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 SO2, Solutia has 

proposed to utilize low sulfur coal for Boiler 5 and Boiler 6, and the installation of a flue solvent 

injection (FSI) system for Boiler 7. It was concluded 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. 

NOx

• 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 emission limit of 0.36 lb NOx/MMBTU with the 

installation of a ROFA system or a comparable technology. 

SO2

• Solutia has proposed to limit Boilers 5 and 6 to burning coal with a sulfur content 

resulting in SO2 emissions less than 1.40 lbs/MMBTU. 

• Boiler 7 would be required to meet a limitation of 0.47 lb SO2/MMBTU with the 

installation of the FSI system or a comparable technology. 

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

PM10

• 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 NOx and SO2 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 

 89 
International Paper Courtland BART Summary 

The Department has concluded that additional controls for SO2 would not be cost effective at the 

Courtland Mill and that the control of NOx is only cost effective for the No. 2 Combination 

Boiler and that no other add-on controls would be cost 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. 

NOx

• Install low NOx Burners or only operate the No. 1 Combination Boiler when one of the 

following emissions units is either not operating or in periods of start-up of shutdown: the 

No. 2 Combination Boiler the No. 2 Recovery Furnace, the No. 3 Combination Boiler, or 

the No. 3 Recovery Furnace. 

• Install low NOx Burners, which will give a 30% baseline reduction in emissions, on the 

No. 2 Combination Boiler. 

• IP has proposed using existing combustion control techniques for the control of NOx on 

the Package Boiler. 

• The No. 2 Recovery Furnace will utilize existing combustion control techniques for the 

control of NOx. 

• IP proposed the existing Low NOx burners for the No. 1 and No. 2 Lime Kilns for control 

of NOx emissions for these units. 

SO2

• IP has proposed no add on controls for the No. 1 Combination Boiler, No. 2 Recovery 

Furnace, No. 2 Smelt Tank, No. 1 Lime Kiln, and No. 2 Lime Kiln for the control of SO2

emissions. 

• The existing Wet Scrubber will be utilized to control SO2 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 

PM10

• IP has proposed utilizing the existing Venturi Scrubber for the control of PM emissions 

from the No. 1 Combination Boiler. 

• The No. 2 Combination Boiler will utilize the 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 the 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. 

 90 
Table 7.8.5-2 2018 NOx and SO2 Projections for International 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 “Best and Final” VISTAS modeling 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 provisos, please see Attachment H5 to Appendix 

H. 

7.9 What Additional Emissions Controls were 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 emissions from construction activities and the role of 

NH3 and potential benefits if emissions from agricultural sources are mitigated. 

As discussed in Section 2.4, elemental carbon (sources include agriculture, 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 forestry management purposes include plans as 

they currently exist within the state for these purposes. The Alabama Forestry Commission 

(“AFC”) has developed The State of Alabama Smoke Management Program, which serves to 

regulate vegetative debris burning for forestry, agriculture, and wildlife purposes in the State 

of Alabama. ADEM’s Air Division has developed a state air pollution control regulation 

(ADEM Admin. Code 335-3-3-.01, Open Burning) that lists the specific circumstances in 

which open burning is permissible. ADEM has 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 techniques practiced include Class I areas as a sensitive receptor, 

and ADEM contends that the current 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 sources generated within plant boundaries. ADEM 

Admin. Code 335-3-4-.02, reads as follows: 

(1) No person shall cause, suffer, allow, or permit 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 include, 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, the grading of roads, or the clearing of land; 

(b) Application of asphalt, oil, water, 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 materials. 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 matter, 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 that the building or equipment in which processing, 

handling, and storage are done be tightly closed and 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 currently 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) emissions during a two-year study, while at the 

same time protecting participants through a “safe harbor” from liability under certain provisions 

of federal environmental laws. The purpose of the 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, and 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 early 2008 to account for the cumulative benefits 

of the Base G2a emissions controls, BART controls, and additional measures to achieve 

reasonable progress by 2018. This SIP contains the revised RPGs for the Sipsey Wilderness 

Area reflecting these final decisions. 

 92 
7.10 Post BART Visibility Improvement Expected at the Sipsey Wilderness by 2018 

compared to the Glidepath 

As discussed earlier in Section 7.0, visibility on the 20% worst visibility days is expected to 

improve to levels better than the glidepath by 2018 based on reductions from existing and 

planned emissions controls as well as BART and RP controls. Figure 7.10-1 shows the predicted 

visibility (in dv) in 2018 on the worst days at the Sipsey Wilderness. 

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

Year

Haziness 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 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

Year

Haziness 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 the Sipsey Wilderness between 2000-2004 baseline 

conditions on the 20% worst days and 2018 projections is illustrated in Figure 7.10-3. In 

contrast, natural background visibility conditions for the 20% worst days are depicted in Figure 

7.10-4. These images were generated using WINHAZE 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 improvements in visibility are expected by 2018 and that 

significantly greater improvements are needed to reach natural background conditions. 

 94 
Figure 7.10-3 Visibility Improvements on 20% worst 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 PROGRESS GOALS (RPGs) 

The Rule at 40 CFR Section 51.308(d)(1) requires states to establish RPGs for each Class I area 

within the state (in dv) that provide for reasonable progress towards achieving 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 the RPG provides for a slower rate 

of visibility improvement than the uniform rate of progress, the state must also provide an 

assessment of the number of years it would take to attain natural visibility conditions if 

improvement continues at the rate represented by the RPGs. 

In accordance with the requirements of Section 51.308(d)(1), this Regional Haze SIP establishes 

RPGs for the Sipsey Wilderness area. To calculate the rate of progress represented by each goal, 

ADEM compared baseline visibility conditions to natural visibility conditions at the Sipsey 

Wilderness area and determined the uniform rate of visibility improvement (in dv) that would 

need to be maintained during each implementation period in order to attain natural visibility 

conditions by 2064. These RPGs provide for an 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. 

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 implementation plan requirements. The State must 

submit with the SIP a monitoring strategy for measuring, characterizing, and reporting of 

regional haze visibility impairment that is representative of all mandatory Federal Class I 

areas within the state. This monitoring strategy must be coordinated with the monitoring 

strategy required in §51.305 for reasonably 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 monitoring 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 requirements that pertain to reporting and use 

of monitoring data and an emission inventory.]” 

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

1. Track the expected visibility improvements resulting from emissions reductions 

identified in this SIP. 

2. Better understand the atmospheric processes of importance to haze. 

3. Identify chemical species in ambient particulate matter and relate them to emissions 

from sources. 

4. Evaluate Regional air quality models for haze and construct Relative Response 

Factors (RRFs) for using those models. 

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 future Regional Haze monitoring strategy must 

necessarily be based on, or directly comparable to, IMPROVE. The IMPROVE measurements 

provide the only long-term record available for tracking 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 State, at the Sipsey Wilderness area within the 

Bankhead National Forest. The State supplements the IMPROVE sampling by operating 

additional collocated monitoring at the Sipsey, including: 

• A PM2.5 FRM monitor 

 97 
PM2.5 Monitor Locations

Montgomery

PM2.5 Monitors

Fairhope

Huntsville

Bay Road

Chickasaw

Decatur

Providence

McAdory

Sumter

Jasper Corner

Wylam 

Pelham

Hoover

Pinson

North 

Birmingham

Ashland

Phenix City

Muscle Shoals

Childersburg

Crossville

Gadsden

Brewton

Dothan

Tuscaloosa

Sipsey 

Figure 9-1 PM2.5 Monitoring Network in Alabama 

 98 
The IMPROVE measurements are central to Alabama’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 the scope of the IMPROVE network in Alabama 

and neighboring Class I areas would jeopardize the state’s ability to demonstrate reasonable 

progress toward visibility improvement in its Class I areas and plan for appropriate future 

programs. In particular, Alabama’s Regional Haze strategy relies on emission reductions that 

will result from CAIR and reductions in neighboring 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 Alabama represents a unique airshed and a significant 

component of the contributions are regional, reduction of the IMPROVE network by shutting 

down these monitoring sites impedes tracking progress or planning improvements at the affected 

Class I areas. If this occurs, Alabama, in consultation with EPA and the relevant Federal Land 

Managers, will develop an alternative approach for meeting the tracking goal, perhaps by 

seeking contingency funding to carry out limited monitoring, or by relying on data from nearby 

urban monitoring sites to demonstrate trends in speciated PM2.5 mass. 

Data produced by the IMPROVE monitoring network 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 available and up to date. Presumably, IMPROVE will 

continue to process information from its own measurements 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 CONSULTATION ON ALABAMA’S 

CONTRIBUTION TO VISIBILITY IMPAIRMENT 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 implementation of the visibility protection program.

The VISTAS states have jointly developed the technical analyses that define the visibility 

improvement by 2018 under existing federal and state regulations compared to the uniform rate 

of progress, SO2 Areas of Influence (AoI) analyses for each Class I area, and methods to 

prioritize contributions from individual sources within 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 modeling results and the SO2 AoI analyses. AoIs were developed for each 

 99 
Class I area to identify source regions 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 meteorological data on the 20% worst days over the 2000-2004 period where 

SO2 was the biggest contributor to visibility impairment. The Air Directors agreed to look at 

reasonable control measures for sources on the lists for reasonable progress. Each state would 

consider sources within their state and would identify sources in neighboring states that they 

would like to have that neighboring state consider. States acknowledged 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 Class I areas are projected to have improvements 

greater than the uniform rate of progress. 

In May 2007, the VISTAS state Air Directors met for their second formal interstate consultation. 

States shared their lists of sources in their state and neighboring states for each Class I area, as 

well as their criteria for listing sources 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 reasonable 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 meeting between the states of Florida, Alabama, Mississippi and 

Louisiana in January 2007 to discuss issues specific to the Breton Wildlife Refuge, located on 

the Chandeleur Island chain. Additionally, Louisiana participated in a June 2007 FLM/EPA 

meeting hosted by VISTAS in Asheville, North Carolina, 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 the 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 Section 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) requires periodic reports evaluating 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 Rule, 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. 

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

1. Description of the status of implementation; 

2. Summary of emission reductions 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 actual compliance strategy 

for the various utility companies will be much 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 (current vs. baseline), 

expressed as 5-year averages of annual 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 addressed in the SIP revision for reasonable 

progress. 

ADEM also commits to ongoing consultation with the FLMs throughout the implementation 

process, including discussions on the implementation process and the most recent IMPROVE 

monitoring data and VIEWS data. 

Relating to the New Source Review/Prevention of Significant Deterioration 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 emissions from major modifications to existing 

sources. In addition to the regulations that would govern these sources, consultation with the 

FLMs is also required for sources that are subject to the Class I provisions of the regulations. 

There are several technical improvements that are recommended in the emissions inventory and 

air quality models that are used to support regulatory decisions for Regional Haze. These 

improvements are recommended, as funding is available, to support the next Long Term Strategy 

(LTS). The following is an overall summary; Appendix K contains a more detailed discussion of 

possible technical improvements. 

First and foremost, continued improvements are needed in the integrated one-atmosphere air 

quality models that are used to project air quality responses to emissions reductions. As our 

understanding of partitioning between gaseous and aerosol phases improves, this understanding 

needs to be reflected in the models. Improvements can also be made in how the models handle 

individual pollutants. Sulfate performance for the CMAQ regional air quality model is good 

overall; however, sulfate deposition is frequently overestimated in the models, particularly in the 

summer months. At the coastal sites, when winds 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 overestimated by the model 

in the winter and underestimated in the summer, although summer monitored values of nitrate 

are very low. Additional improvements in seasonal allocation of ammonia emissions would 

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improve model estimates of ammonium nitrate formation. Organic carbon is generally 

underestimated in the summer months. Improvements are needed in the characterization 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 between rural and urban monitoring networks. To 

improve our understanding of the contribution of fire from natural forest fires, prescribed 

burning, land clearing, and agricultural burning, states need improved record keeping. 

Additional improvements to the international emissions inventory are also needed, to improve 

our understanding of boundary conditions for our modeling domain and of the contributions 

from international emissions to pollutant concentrations 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). The 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 requires 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 participated in the regional planning process. 

ADEM provides notification to the Administrator and the states that participated in 

regional planning. ADEM collaborates with states through the regional 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.