App endix H

i 

# Appendix H Reasonable Progress Evaluation, BART and Long-Term StrategyApp endix H

i 

### TABLE OF CONTENTS
LIST OF APPENDICES ..................................................................................................... iv 

### LIST OF TABLES ...................................................................................................v LIST OF FIGURES ................................................................................................vi 1.0 BASE G CAA CONTROLS ..............................................................................1 1.1 Point Source Controls ........................................................................................................... 2 1.1.1 Electric Generating Units (EGUs) ..................................................................................3 1.1.2 Non-EGUs .....................................................................................................................10 1.2 Area Source Controls .......................................................................................................... 11 1.3 Mobile Sources ................................................................................................................... 12 1.4 Non-Road ............................................................................................................................ 12 1.5 Summary of Emissions Projections .................................................................................... 14 

### 2.0 UNIFORM RATE OF PROGRESS ...............................................................16 3.0 GEORGIA TECH SENSITIVITIES .............................................................19 3.1 Evaluation for Cohutta Wilderness Area, Georgia ............................................................. 19 3.2 Evaluation for Okefenokee Wilderness Area, Georgia ...................................................... 20 

### 4.0 REASONABLE PROGRESS EVALUATION .............................................21 4.1 Area of Influence Analyses ................................................................................................. 21 4.2 Source Type Analyses ........................................................................................................ 23 4.3 Back Trajectory Analyses ................................................................................................... 25 4.4 Area of Influence Data and Displays .................................................................................. 30 4.5 Importance of Pollutants Other Than Sulfate ..................................................................... 42 4.6 Summary of Emission Inventor ies Used in Cost Analysis ................................................. 43 4.7 Process in Preparing Files for Co st Effectiveness Curve Development ............................. 45 4.8 Application of AirControlNET Technologies .................................................................... 47 4.9 Development of AOI-Based Cost Curves ........................................................................... 48 

### 5.0 REASONABLE PROGRESS ASSESSMENT ..............................................49 6.0 FOUR FACTOR ANALYSIS .........................................................................59 6.1 The Four Statutory Factors ................................................................................................. 59 6.1.1 Cost of Compliance .......................................................................................................59 6.1.2 Time Necessary for Compliance ...................................................................................59 6.1.3 Energy and Non-Air Quality Environmental Impacts of Compliance ..........................60 6.1.4 Remaining Useful Life of the Source ...........................................................................60 6.2 Evaluating Four Statutory Factors for Speci fic SO2 Emissions Sources in Each Area of Influence ............................................................................................................................. 60 6.2.1 Cost of Compliance .......................................................................................................61 6.2-2 Time Necessary for Compliance ..................................................................................61 6.2-3 Energy and Non-Air Environmental Impacts ...............................................................62 6.2-4 Remaining Useful Life .................................................................................................62App endix H

ii 6.3 Process of Applying Statutory Factors ............................................................................... 62 6.4 Evaluation of Four-factor Eligible Emissions Units and Recommendations ..................... 64 6.4.1 GA Pacific Brunswick Cellulose, Power Boiler No. 4 (F1) ........................................76 6.4.2 Georgia Pacific Brunswick Cellul ose, Recovery Boiler No. 6 (M24) .........................78 6.4.3 Georgia Pacific Cedar Springs, Power Boiler No. 1 (U 500) ......................................78 6.4.4 Georgia Pacific Cedar Springs, Power Boiler No. 2 (U 501) ......................................80 6.4.5 Georgia Pacific Cedar Springs, Recovery Boiler No. 3 (R402) ..................................80 6.4.6 Georgia Pacific Savannah River Mill, No. 3 Boiler (B001) ........................................82 6.4.7 Georgia Pacific Savannah River Mill, No. 4 Boiler (B002) ........................................84 6.4.8 Georgia Pacific Savannah River Mill, No. 5 Boiler (B003) ........................................84 6.4.9 Georgia Power Plant Kraft, Steam Generator 1 ...........................................................85 6.4.10 Georgia Power Plant Kraft, Steam Generator 2 .........................................................86 6.4.11 Georgia Power Plant Kraft, Steam Generator 3 .........................................................88 6.4.12 Georgia Power Plant McIntosh, Steam Generator 1 ..................................................89 6.4.13 Georgia Power Plant Mitchell, Steam Generator 3 ....................................................91 6.4.14 International Paper Savannah Mill, Power Boiler 13 ................................................92 6.4.15 Temple-Inland Rome Linerboard, No. 4 Power Boiler .............................................96 

### 7.0 BART .................................................................................................................99 7.1 Background ......................................................................................................................... 99 7.2 Approach to Implementation of BART Requirements ..................................................... 100 7.3 Identification of BART-Eligible Sources ......................................................................... 100 7.4 Contribution Threshold ..................................................................................................... 104 7.5 Exemption of Point Source Volatile Organic Compounds for BART Purposes .............. 105 7.5.1 Method ........................................................................................................................106 7.5.2 Conclusions .................................................................................................................107 7.6 Treatment of Ammonia Emissions for BART Purposes .................................................. 107 7.6.1 Method ........................................................................................................................108 7.6.2 Conclusions .................................................................................................................109 

### 8.0 Explanation of BART Exemption Criteria ..................................................109 8.1 Background ....................................................................................................................... 109 8.1.1 Exemption Modeling ..................................................................................................110 8.1.2 Model Plant Criteria ....................................................................................................111 8.1.3 Accepting an Emissions Limit ....................................................................................112 8.2 Use of New IMPROVE Equation ..................................................................................... 115 

### 9.0 BART Determination .....................................................................................116 9.1 BART Subject Sources ..................................................................................................... 116 9.1.1 BART-eligible Units and Existing Control ................................................................116 9.2 BART Determination Analysis and Conclusion ............................................................... 117 

### 10.0 Final Long Term Strategy And Reasonable Progress Goals ..................118 10.1 Long-Term Strategy ........................................................................................................ 118 10.2 What Additional Emissions Controls Were C onsidered as Part of the Long-Term Strategy for Visibility Improvement by 2018? ............................................................................... 119App endix H

iii 10.3 Reasonable Progress Goals ............................................................................................. 119 

### REFERENCES: ...................................................................................................120App endix H

iv 

### LIST OF APPENDICES

Appendix H.1 - Industrial Boiler/Process Heater/RICE MACT
Appendix H.2 - VISTAS Class I Statistics
Appendix H.3 - VISTAS Control Options Spreadsheet
Appendix H.4 - Letters to Facilities
Appendix H.5 - Emissions and Q/d for Georgia's BART Eligible sources
Appendix H.6 - BART VOC, NH3, and Primary PM Sensitivity Modeling
Appendix H.7 - BART Modeling Protocols
Appendix H.8 - BART Determination Submittals
Appendix H.9 - Correspondence Regardi ng Use of New IMPROVE Equation
Appendix H.10 - Basic Smoke Management Pl an and Memorandum of Understanding Among GADNR-EPD, GADNR-WRD, a nd GA Forestry Commission
Appendix H.11 - Co m parison between Original and Revised I MPROVE EquationApp endix H

v 

### LIST OF TABLES

Table 1-1. Adjustments to IPM Control Determin ations Specified by S/L Agencies for the Base G 2009/2018 EGU Inventories. ...................................................................................................... 4 Table 1-2. Other Adjustments to IPM Result s Specified by S/L Agencies for the Base G 2009/2018 EGU Inventories. .......................................................................................................... 6 Table 4-1. Source types 2018 Base G4 modeling COHU – Percentage. ..................................... 23 Table 4-2. Source types 2018 Base G4 modeling OKEF – Percentage. ...................................... 24 Table 4-3. Source types 2018 Base G4 modeling WOLF – Percentage. ..................................... 24 Table 4-4. Back Trajectory Model Parame ters Selected for VISTAS AOI Analysis .................. 25 Table 4-5. VISTAS 2002 Base Year Annual Emissions Summary (tons). ................................. 43 Table 4-6. 2018 Base Case Annual Emissions Summary (tons). ................................................ 44 Table 4-7. IPM Post Processing As signed Device Codes and Applied SO 2 Control Efficiencies.

............................................................................................................................... ........................ 46 Table 4-8. Emissions Weighted NETDC (MW) Association. ...................................................... 47 Table 5-1. Numbers and percentage of 2018 SO2 em ission units that contri bute to Georgia Class I areas.

............................................................................................................................... ............ 50 Table 5-2. Cohutta Wilderness Area Q/d, Q/d^2, RTMax, and fractional c ontribution analysis. 51 Table 5-3. Okefenokee Wilderness Area Q/d, Q/d^2, RTMax, and fractional contribution analysis

............................................................................................................................... ........... 53 Table 5-4. Wolf Island Q/d, Q/d^2, RTMax, and fractional contribution analysis. ..................... 55 Table 5-5. Non-Georgia Class I Areas Q/d, Q/d^ 2, RTMax, and fractional contribution analysis (Georgia sources only) .................................................................................................................. 56 Table 5-6. Units dropped from four-factor analysis ..................................................................... 58 Table 6-1. Summary of Four-factor An alyses – South Georgia Facilities ................................... 67 Table 6-2. Summary of Four-Factor An alyses - North Georgia Facilities ................................... 73 Table 6.3. Explanatory Notes for Summary of Four-Factor Analyses and Metrics ................... 74 Table 7-1. GA BART-Eligible Sources and Distances to Respective Class I Areas .................. 103 Table 7-2. Major BART NH 3 Sources in the VISTAS Region .................................................. 109 Table 8-1. BART Exemption Modeling Results ......................................................................... 112
Table 8.2 Facilities that demonstrated BART exemption based on model plant criteria or by accepting a limit of 250 tpy for visibility causing pollutants ...................................................... 115 Table 9.1 BART determination analysis for both the subject-to-BART sources ....................... 117 Table 10-1 lists Georgia’s Reasonable Progress Goals. ............................................................. 120 Table 10-2. Georgia Reasonable Progr ess Goals – 20 percent Best Days .................................. 120App endix H

vi 

### LIST OF FIGURES

Figure 1-1. Annual NH3 emissions – Base G4. ............................................................................ 14 Figure 1-2. Annual PM2.5 emissions – Base G4. ......................................................................... 15 Figure 1-3. Annual NOx emissions – Base G4. ........................................................................... 16 Figure 1-4. Annual SO2 emissions – Base G4. ............................................................................ 16 Figure 2-1. Uniform Rate of Progress at Cohutta Wilderness ...................................................... 17 Figure 2-2. Uniform Rate of Progress at Okefenokee Wilderness ............................................... 18 Figure 3-1. Sources of various visibility-re ducing pollutants – COHU summer using 2009 Base D emissions.

............................................................................................................................... ... 20 Figure 3-2. Sources of various visibility -reducing pollutants – OKEF summer using 2009 Base D emissions

............................................................................................................................... .... 21 Figure 4-1. Average Extinction for 20% Wors t Visibility Days in 2000-2004 using New IMPROVE Equation (data from VIEWS Sep 2006). ................................................................... 22 Figure 4-2. Example 72-hour back trajectories fo r 20% worst visibility days in 2002 for Cohutta Wilderness Area ............................................................................................................................ 28 Figure 4-3. Example 72-hour back trajectories for 20% worst visibility days in 2002 for Okefenokee Wilderness Area ....................................................................................................... 29 Figure 4-4. Linville Gorge SO4 extinction-weight ed residence time plots for the period 2000- 2004.

............................................................................................................................... ............... 31 Figure 4-5. Shining Rock SO4 extinction-we ighted residence time plots for the period 2000- 2004.

............................................................................................................................... ............... 31 Figure 4-6. Swanquarter SO4 extinction-wei ghted residence time plots for the period 2000- 2004.

............................................................................................................................... ............... 32 Figure 4-7. Great Smoky Mountains SO4 extinction- weighted residence time plots for the period 2000-2004.

............................................................................................................................... ..... 32 Figure 4-8. Okefenokee Wilderness Area SO4 extin ction-weighted residence time plots for the period 2000-2004. ......................................................................................................................... 33 Figure 4-9. Cape Romain SO4 extinction-weight ed residence time plots for the period 2000- 2004.

............................................................................................................................... ............... 33 Figure 4-10. Cohutta SO4 extinction-weighted residence time plots for the period 2000-2004. 34 Figure 4-11. Sipsey SO4 extinction-weighted residence time plots for the period 2000-2004. .. 34 Figure 4-12. Wolf Island SO4 extinction-wei ghted residence time plots for the period 2000- 2004.

............................................................................................................................... ............... 35 Figure 4-13. Annual point source SO2 emissions for the VISTAS region in tons/year. (2002 BaseG)

............................................................................................................................... ............ 36 Figure 4-14. Annual point source SO2 emissions for the VISTAS region in tons/year. (2018 BaseG)

............................................................................................................................... ............ 36App endix H

vii Figure 4-15. Cohutta 2002 SO2 Emissions * SO4 extinction-weighted residence time plots. ... 38 Figure 4-16. Okefenokee 2002 SO2 Emissions * SO 4 extinction-weighted residence time plots.

............................................................................................................................... ........................ 38 Figure 4-17. Wolf Island 2002 SO2 Emissions * SO 4 extinction-weighted residence time plots.

............................................................................................................................... ........................ 39 Figure 4-18. Cohutta 2018 SO2 Emissions * SO4 extinction-weighted residence time plots. ... 39 Figure 4-19. Okefenokee 2018 SO2 Emissions * SO 4 extinction-weighted residence time plots.

............................................................................................................................... ........................ 40 Figure 4-20. Wolf Island 2018 SO2 Emissions * SO 4 extinction-weighted residence time plots.

............................................................................................................................... ........................ 40 Figure 4-21. Cohutta 2002 vs. 2018 SO2 emissions multiplied by residence time. .................... 41 Figure 4-22. Okefenokee 2002 vs. 2018 SO2 emissions multiplied by residence time. ............. 41 Figure 4-23. Wolf Island 2002 vs. 2018 SO2 emissions multiplied by residence time. .............. 42 Figure 4-24. VISTAS Base G2 SO2 Emissi ons Contribution by Major Source Sector. ............. 45 Figure 7-1. GA BART-Eligible Sources and Class I Areas. ...................................................... 102 Figure 7-2. Location of VISTAS BART-Eligible Sources ........................................................ 105 Figure 7-3. Maximum Point VOC Impact ................................................................................. 107 Figure 7-4. Maximum NH3 Impact - Maximu m contributions of all BART-eligible NH 3 point sources in the VISTAS region to haze in Class I areas during the CMAQ-modeled periods. ... 108App endix H

1 of 115 

### 1.0 BASE G CAA CONTROLS

2009 and 2018 emissions projections include the following existing State and Federal regulations:

• Clean Air Interstate Rule (CAIR): Utility projections are based on Integrated Planning Model

. CAIR will permanently cap emissions of sulfur dioxide (SO2) and nitrogen oxides (NOx) in the eastern United States. CAIR achieves large reductions of SO2 and/or NOx emissions across 28 eastern states and the District of Columbia. When fully implemented, CAIR will reduce SO2 emissions in these states by over 70 percent and NOx emissions by over 60 percent from 2003 le vels. Note that on July 11, 2008, the U.S. District Court of Appeals in the District of Columbia vacated the Clean Air Interstate Rule. As of the date of writing of this SIP, the mandate for that decision has not been issued. Should the vacatur of CAIR stand, the Division may need to revise this SIP.

• Georgia Rule 391-3-1-.02 (2) ( sss) (i.e., The Multipollutant Control for Electric Utility Steam Generating Units) was enacted in 2007. Th e effective date of the rule varies for various EGU units, but it will start being effective from December 31, 2008, and will be in place over all the rule-effected units by January 1, 2018. When fully implemented, Rule (sss) will reduce SO2 emissions by about 90 percent, NOx emissions by approximately 85 percent and mercury emissions by approximately 79 percent.

• 1-hr Ozone SIPs (Atlanta/Birmingham/Northe rn Kentucky). The purpose of a State implementation plan (SIP) is to demonstrat e attainment or maintenance of the 1-hour ozone National Ambient Air Quality Standard (NAAQS). States must determine which Clean Air Act requirements are applicable to their area and submit plans to EPA for public comment and approval.

• Georgia Rule 391-3-1-.02(2)(yy) (e.g., Emi ssions of Nitrogen Oxides from Major Sources) based on Section 182 of the Fede ral CAA Amendments of 1990, requires that every major source (i.e., a source with potent ial to emit > 50 tons/yr) of NOx located in the13-county Atlanta non-attainment area should apply reasonably available control technology (RACT) in controlling NOx em issions. Georgia EPD has required full compliance with NOx RACT limits since July 31, 1995.

• Heavy Duty Diesel (2007) Engine Standard. This standard is 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 diesels in the 2007 model year. It also includes standards for NOx and nonmethane 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 lowe red to enable modern pollution-control technology to be effective on these trucks and buses. EPA will require a 97 percent reduction in the sulfur content of highway dies el 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).App endix H

2 of 115 • Tier 2 Tail pipe. Tier 2 is a fleet averaging program mode led after the California LEV II standards. Manufacturers can produce vehicl es with emissions ranging from relatively dirty to zero, but the mix of vehicles a ma nufacturer sells each year must have average NO x emissions below a specified value

.

• Large Spark Ignition and Recreat ional Vehicle Rule. Sets emission standards for these categories of nonroad engines and vehicles.

• Nonroad Diesel Rule. Implements a low sulfur fuel requirement that affects both future (CMV) Commercial Marine Vessels and locomotive emissions.

• Industrial Boiler/Process He ater/RICE MACT. The Envi ronmental Protection Agency (EPA) issued final rules to substantially reduce emissions of toxic air pollutants from industrial, commercial and institutional bo ilers, process heaters and from stationary reciprocating internal combustion engines (R ICE). These rules reduce emissions of a number of toxic air pollutants, including hyd rogen chloride, manganese, lead, arsenic and mercury by 2009. This rule also reduces emi ssions of sulfur dioxi de and particulate matter in conjunction with the toxic air pol lutant reductions. The applied Maximum Achievable Control Technology (MACT) contro l efficiencies were four percent for SO 2 and 40 percent for PM10 and PM2.5. [It should be noted that EPA’s industrial boiler MACT rules were vacated on June 8, 2007. The emissions reductions that were modeled to account for the industria l boiler MACT were 1,343.5 tons /year of SO2, 879.4 tons/year of PM2.5, and 1,304.2 tons/year of PM10. Thes e reductions are insignificantly small (0.69% for SO2, 0.39% for PM2.5, and 0.13% for PM10) compared to the statewide totals (193,665.5 tons/year SO2; 225,104.5 to ns/year PM2.5; and 1,002,873.2 tons/year PM10) and should not impact the conclusi ons made in the SIP (Appendix H.1). If another VISTAS “best and final” modeling run is performed, it will remove control assumptions solely due to this MACT rule.]
VOC 2-, 4-, 7-, and 10-year MACT Standard s. The point source MACTs and associated emission reductions were designed from Federal Register (FR) notices and discussions with EPA’s Emission Standards Division (ESD) sta ff. We did not apply reductions for MACT standards with an initia l compliance date of 2001 or earlier, assuming that the effects of these controls are already account ed for in the 2002 inventor ies supplied by the States.
The following are source category specific cont rols used to projec t future-year emissions inventories:

1.1 Point Source Controls
Different approaches were used for the Electr ic Generating Unit (EGU) and the non-EGU sectors of the point source inventory:App endix H

3 of 115 • For the EGUs, the State relied primarily on the Integrated Planning Model ® (IPM ® ) to project future generation, as well as to calc ulate the impact of future emission control programs, to include CAIR. The IPM result s were adjusted based on State and local (S/L) agency knowledge of planned em ission controls at specific EGUs.

• For non-EGUs, we used recently-updated growth and control data consistent with the data used in EPA’s CAIR analyses and s upplemented these data with available S/L agency input and updated fuel use forecast data for the U.S. Department of Energy.
For both sectors, we generated 2009 and 2018 i nventories for a combined on-the-books (OTB) and on-the-way (OTW) control scenario. The OTB/OTW control scenario accounts for post2002 emission reductions from promulgated and proposed Federal, State, local, and site-specific control programs as of July 1, 2004.
1.1.1 Electric Generating Units (EGUs)
S/L agencies specified a number of changes to the IPM results to better reflect current information on when and where future controls wo uld occur. These changes to the IPM results primarily involved S/L agency addition or subtr action of future emission controls based on the best available data from State rules, enfor cement agreements, compliance plans, permits, and discussions/commitments from individual companies.
Table 1-1 lists adjustments to IPM control determ inations as specified by S/L agencies for the Base G 2009/2018 EGU inventories. Table 1-2 list s other adjustments to IPM results specified by S/L agencies for Base G 2009/2018 inventories.App endix H

4 of 115 Table 1-1. Adjustments to IPM Control De terminations Specified by S/L Agencies for the Base G 2009/2018 EGU Inventories.

State Plant Name Unit IPM State IPM State IPM State IPM State

3 & 4 SCR during

ozone

season SCR year round from

Consent Decree SCR during

ozone

season SCR year round from

Consent Decree None Scrubber None Scrubber

1, 2, 3 None SNCR SCR SNCR None None None None

4 None SNCR SCR SNCR None None Scrubber None 5 None None SCR SCR None None Scrubber None 4-Ja n SCR None SCR None None None Scrubber None

5 SCR SCR SCR SCR Scrubber None Scrubber None 6 & 7 None None None None None None None None 8 & 9 None None None None None Scrubber None Scrubber

10 SCR SCR SCR SCR None Scrubber Scrubber Scrubber 1 None None None None None Scrubber None Scrubber 2 & 3 SCR SCR SCR SCR Scrubber Scrubber Scrubber Scrubber

2BLR SCR SCR SCR SCR None Scrubber Scrubber 3BLR SCR SCR SCR SCR Scrubber Scrubber Scrubber 4BLR SCR SCR SCR SCR Scrubber Scrubber Scrubber

2 SCR SCR SCR SCR None Scrubber Scrubber

1, 2 None None None None None None None None 3 None None SCR None None None None None

1 None None None None Scrubber Scrubber Scrubber Scrubber 2, 3 None None None None None None None None 4 – 7 None None SCR None None None Scrubber None

1 None None SCR SCR None Scrubber Scrubber Scrubber 2 None None SCR SCR None Scrubber Scrubber Scrubber 3 None None SCR SCR None Scrubber Scrubber Scrubber 4 SCR SCR SCR SCR Scrubber Scrubber Scrubber Scrubber 1 None SCR SCR SCR Scrubber Scrubber Scrubber Scrubber 2 None None SCR SCR None Scrubber Scrubber Scrubber 3, 4 None SCR SCR SCR None Scrubber Scrubber Scrubber IPM had scrubbers but little emission reductions

KY Ghent ORISID=1356

James H.

Miller ORISID=6002

None None None GA

Wansley ORISID=6052

Kraft ORISID=733

McIntosh ORISID=6124

Yates ORISID=728

Hammond ORISID=708 Scrubber Scrubber Scrubber

1 None None SCR None None SCR SCR SCR SCR 1 Gorgas ORISID=8

Charles R. Lowman ORISID=56

IPM had retrofit scrubbers but little emission reductions Bowen ORISID=703 None Scrubber Scrubber SCR during

ozone

season SCR probable year round

due to CAIR SCR year round due to CAIR

Barry ORISID=3 None None None

1BLR SCR SCR SCR SCR NO x Emission Controls SO 2 Emission Controls 2009 2018 2009 2018

1 & 2 SCR during

ozone

season None AL

E C Gaston ORISID=26App endix H

5 of 115 Table 1-1 (continued)

State Plant Name and ID Unit

IPM State IPM State IPM State IPM State C1 None None SCR SCR None Scrubber Scrubber Scrubber C2 None None SCR SCR None Scrubber Scrubber Scrubber C3 None None SCR SCR None Scrubber Scrubber Scrubber H1 SCR SCR SCR SCR Scrubber Scrubber Scrubber Scrubber H2 None SCR SCR SCR Scrubber Scrubber Scrubber Scrubber 1 None None None None None Scrubber None Scrubber 2 None None SCR SCR None Scrubber Scrubber Scrubber 3 None None SCR SCR None Scrubber Scrubber Scrubber 3 SCR None SCR None None None None None 4 None None None None None None None None WAT1 SCR SCR SCR SCR None Scrubber None Scrubber WAT2 SCR SCR SCR SCR None Scrubber Scrubber Scrubber CAN1 None None None None None None None None CAN2 None None None None None None None None CAN3 None None None None None Scrubber None Scrubber CT1A None SCR None SCR None None None None CT1B None SCR None SCR None None None None 1 – 8 SCR SCR SCR SCR None None Scrubber Scrubber

9 None SCR SCR SCR None None Scrubber Scrubber 1 – 10 SCR None SCR SCR None None None None

2 SCR None SCR SCR Scrubber None Scrubber Scrubber

3-Ja n SCR None SCR SCR Scrubber None Scrubber Scrubber Jeffries ORISID=3319 Wateree ORISID=3297 Canadys ORISID=3280

Rainey ORISID=7834

Coleman ORISID=1381

HMP&L Station 2 KY

E W Brown ORISID=1355 NO x Emission Controls SO 2 Emission Controls 2018 2009 2018 2009

Kingston ORISID=3407 Johnsonville ORISID=3406 Willow Island ORISID=3946
Kammer ORISID=3947 SC

TN

WVApp endix H

6 of 115 Table 1-2. Other Adjustments to IPM Resu lts Specified by S/L Agencies for the Base G 2009/2018 EGU Inventories.

Stat e Plant Name and ID Units Nature of Update/Correction

FL Central Power and Lime ORISID= 10333 GEN1 Central Power and Lime (ORIS10333) is a duplicate entry. This is point 18 in Florida Crushed Stone (12-053-0530021). Removed IPM emissions for Central Power and Lime. Cedar Bay Generating ORISID=10672 GEN1 FLDEP disagrees with IPM projections - no knowledge of expansion of this facility and the cogeneration facility should not grow faster than the underlying industry. Cedar Bay is connected to Stone Container (12-031-0310067). Replaced IPM emissions with 2002 em issions for Cedar Bay (12-031-0310337) times the growth factors for Stone Container. Indiantown Cogeneration ORISID=50976 GEN1 FLDEP disagrees with IPM projections - no knowledge of expansion of this facility and the cogeneration facility should not grow faster than the underlying industry. Indiantown is connected to Louis Dreyfus Citrus (12-085-0850002). Replaced IPM emissions with 2002 emi ssions for Indiantown (12-085-0850102) times the growth factors for Louis Drefus Citrus.

GA Bowen
ORISID=703 1BLR
2BLR
3BLR 4BLR IPM indicated retrofit scrubbers on all 4 units in 2009, but the IPM emissions showed little reductions from 2002 le vels. Changed emissions to reflect scrubbers on 1BLR and 2BLR by 2009.

Wansley
ORISID=6052 1, 2 IPM indicated retrofit scrubbers on both units in 2009, but the IPM emissions showed little reductions from 2002 leve ls. Changed emissions to reflect one scrubber on Unit 1 by 2009.
Riverside
ORISID=734 4 All of plant Riverside was retired from service June 1, 2005; emissions set to zero in 2009 and 2018.

McIntosh
ORISID=727 CT10A
CT10B
CT11A
CT11B The McIntosh Combined Cycle facility became commercial June 1, 2005. Added 346 tons of NO x and 121 tons of SO 2 per unit to the 2009 and 2018 inventories.

Longleaf Energy Station 1, 2 Longleaf Energy Station is being proposed by LS Power Development, Inc. GA specified that the emissions from this proposed plant be included in the 2018 projections. Boilers 1 and 2 added 1,882 tons of NO x and 3,227 tons of SO 2 per unit to the 2018 inventory.

MS R D Morrow
ORISID=6061 1, 2 Revised the 2018 emissions to reflect controls not indicated by IPM. The SO 2 emissions are much lower than IPM, but their expected NO x emissions are actually higher than IPM. The controls will be coming online 2009 or 2010, so the 2009 inventory did not change. Jack Watson (2049) Victor J Daniel (6073)

Chevron Oil (2047) All MS DEQ specified that the emission projections provided by the Southern Company for their units in Mississippi were to be used instead of the IPM results.App endix H

7 of 115 Table 1-2 (continued)

State Plant Name and ID Unit Nature of Update/Correction

NC G G Allen (2718) Belews Creek (8042)1 Buck (2720) Cliffside (2721) Dan River (2723) Marshall (2727) Riverbend (2732) All Replaced all IPM 2009 results w ith emission projections from Duke Power’s NC Clean Air Compliance Plan for 2006. Used IPM results for 2018

Asheville (2706) Cape Fear (2708) Lee (2709) Mayo (6250) Roxboro (2712) Sutton (2713) Weatherspoon (2716) All Replaced all IPM 2009 results with emission projections from Progress Energy’s NC Clean Smokestacks Act Ca lendar Year 2005 Progress Report. Used IPM results for 2018

Dwayne Collier Battle Cogeneration Facility
ORISID=10384 GEN1
GEN2 Dwayne Collier Battle is a duplicate entry. This is Cogentrix of Rocky Mount (37-065-3706500146, stacks G-26 and G-27). Duplicate entries were removed both the 2009 and 2018 inventories.

Kannapolis Energy Partners
ORISID=10626 GEN2
GEN3 Kannapolis Energy emissions are being used as credits for another facility. IPM emissions from this facility (37-025-ORIS10626) were removed from the EGU inventory for 2009 and 2018. Emissions from Kannapolis Energy (37-025-3702500113) were carried forward in the 2009/2018.

SC Cross
ORISID=130 1, 2 Unit 1: upgrade scrubber from 82 percent to 95 percent removal efficiency by June 30, 2006. Recalculate emi ssions based on upgrade in control efficiency. Unit 2: upgrade scrubber from 70 percent to 87 percent removal efficiency by June 30, 2006. Recalculate emi ssions based on upgrade in control efficiency. Winyah
ORISID=6249 1 – 4 Unit 1: Install scrubber that meets 95 percent removal efficiency by Dec. 31, 2008; Upgrade ESP from 0.38 to 0.03 lb/mmBTU by Dec. 31, 2008 Unit 2: Replace scrubber w ith one that meets 95 percent removal efficiency from 45 percent by Dec. 31, 2008; Upgrade ESP from 0.10 to 0.03 lb/mmBTU by Dec. 31, 2008 Unit 3: Upgrade scrubber from 70 percent to 90 percent removal efficiency by Dec. 31, 2012; Upgrade ESP from 0.10 to 0.03 lb/mmBTU by Dec. 31, 2012 Unit 4: Upgrade scrubber from 70 percent to 90 percent removal efficiency by Dec. 31, 2007; Upgrade ESP from 0.10 to 0.03 lb/mmBTU by Dec. 31,App endix H

8 of 115 2007
Recalculated SO 2 and PM emissions based on upgrade in control efficiencies.

Table 1-2 (continued)

State Plant Name and ID Unit Nature of Update/Correction

SC Dolphus Grainger
ORISID=3317 1, 2 Unit 1: Upgrade ESP from 0.60 to 0.03 lb/mmBTU by Dec. 31, 2012.
Reduced PM 10 and PM25 emissions in 2018 by 95 percent based on change in allowable emission rate
Unit 2: Install low NO x burners that meet 0.46 lb/mmBTU from 0.9 by May

1, 2004. Recalculated NO x emissions using 0.46/lbs/mmBtu and IPM heat input Unit 2: Upgrade ESP from 0.60 to 0.03 lb/mmBTU by Dec. 31, 2012. Reduced PM 10 and PM25 emissions in 2018 by 95 percent based on change in allowable emission rate

SC Jeffries
ORISID=3319 3, 4 Unit 3: Upgrade ESP from 0.54 to 0.03 lb/mmBTU by Dec. 31, 2012. Reduced PM 10 and PM25 emissions in 2018 by 94.44 percent based on change in allowable emission rate Unit 4: Upgrade ESP from 0.54 to 0.03 lb/mmBTU by Dec. 31, 2012. Reduced PM 10 and PM25 emissions in 2018 by 94.44 percent based on change in allowable emission rate

W S Lee
ORISID=3264 1, 2 IPM does not indicate that thes e units are installing SOFA NO x control technology by April 30, 2006 to meet 0.27 lb/mmBTU, down from 0.45

lb/mmBtu. Calculated NO x emissions using IPM heat input and 0.27 lbs/mmBtu
Generic Unit
ORISID=900545 All All predictions for generic units appear reasonable with the exception of Plant ID ORIS900545 Point ID GSC45 which was modeled in Georgetown County. It will be very difficult to add new generation this close to the Cape Romain Class I area. Santee Cooper has no plans for future generation in Georgetown County, but does have plans for new future

generation in Florence County. This unit was moved to coordinates specified in Florence County.

VA AEP Clinch River
ORISID=3775 1, 2, 3 Replaced all 2009/2018 IPM results using VADEQ’s growth and control estimates.

AEP Glen Lyn
ORISID=3776 51, 52, 6 Replaced all 2009/2018 IPM results using VADEQ’s growth and control estimates.

Dominion Clover

ORISID=7213 1, 2 Replaced all 2009/2018 IPM results using VADEQ’s growth and control estimates.

Dominion Bremo

ORISID=3796 3, 4 Replaced all 2009/2018 IPM results using VADEQ’s growth and control estimates.

Dominion Chesterfield 3, 4, Replaced all 2009/2018 IPM results using VADEQ’s growth and controlApp endix H

9 of 115 ORISID=3797 5, 6 estimates.

Dominion Yorktown
ORISID=3809 1, 2, 3 Units 1, 2: Replaced all 2009/201 8 IPM results using VADEQ’s growth and control estimates. Unit 3: IPM predicts zero heat input for this 880 MW #6 oil fired unit. IPM

does not predict a retirement, just that the heat input is zero. Dominion plans to continue to operate Unit 3. Replaced all 2009/2018 IPM results using VADEQ’s growth and control estimates.

Table 1-2 (continued)

State Plant Name and ID Unit Nature of Update/Correction

VA Dominion Chesapeake
ORISID=3803 1 – 4 Replaced all 2009/2018 IPM results using VADEQ’s growth and control estimates.

Dominion Possum Point
ORISID=3804 3 & 4

5
6 Unit 3&4: IPM had 137 tons of NO x for these units in 2009 and 111 tons in 2018. VA DEQ specified that the permitted emission rates should be used, which equates to 3,066 tons in 2009 and 2018. Unit 5: IPM had zero heat input for this unit. Replaced all 2009/2018 IPM results using VADEQ’s growth and control estimates. Unit 6: Replaced all 2009/2018 IPM results using VADEQ’s growth and control estimates.
Potomac River
ORISID=3788 1 - 5 Units 1&2: IPM retired these units. Mirant has no plans at this time to retire any units. Replaced all 2009 /2018 IPM results using VADEQ’s growth and control estimates. Units 3, 4, 5: Replaced all 2009/2018 IPM results using VADEQ’s growth and control estimates.

WV Albright
ORISID=3942 1, 2 IPM predicted early retirement for th ese units. AEP indicated there are no for early retirement. For 2009, used 2 002 emissions as these units are not

likely to retire by 2009. For 2018, used IPM prediction of retirement.
Rivesville
ORISID=3945 7, 8 IPM predicted early retirement for th ese units. AEP indicated there are no for early retirement. For 2009, used 2 002 emissions as these units are not

likely to retire by 2009. For 2018, used IPM prediction of retirement.
Willow Island
ORISID=3946 1, 2 Unit 1: IPM predicted early retirement for these units. AEP indicated there are no for early retiremen t. For 2009, used 2002 emissions as these units are not likely to retire by 2009. For 2018, used IPM prediction of retirement. Unit 2: IPM predicted SCR and scrubb er for 2009. These controls will not be in place by 2009.
North Branch Power Station
ORISID=7537 1A, 1B SO 2 Permit Rate was corrected from 2.7 to 0.678 lb/MMBtu. Used SO 2 Permit Rate of 0.678 lb/MMBtu and IPM predicted total fuel used to calculate SO 2 emissions in 2009 and 2018

Mt. Storm
ORISID=3954 1, 2, 3 SO 2 Permit Rate was corrected from 2.7 to 0.15 lb/MMBtu. Used SO 2 Permit Rate of 0.15 lb/MMBtu and IPM predicted total fuel used to calculate SO 2 emissions in 2009 and 2018App endix H

10 of 115 1.1.2 Non-EGUs
The general approach for assembling future-year data was to use recently-updated growth and control data consistent with the data used in EPA’s Clean Air Interstate Rule analyses, supplement these data with availa ble stakeholder input, and prov ide the results for stakeholder review to ensure accuracy.
MACTEC (MACTEC, 2006) used the same control programs for both the 2009 and 2018 nonEGU point inventory. Two contro l scenarios were developed: on -the-books (OTB) controls and on-the-way (OTW) controls. The OTB cont rol scenario accounts for post-2002 emission reductions from recently-promulgated Federal, St ate, local, and site-s pecific control programs. The OTW control scenario accounts for proposed (but not final) control programs that are reasonably anticipated to result in post-2002 emission reductions.
Maximum achievable control technology (MAC T) requirements were also applied, as documented in the report entitled Control Packet Development and Data Sources, dated July 14, 2004. The point source MACTs and associated emi ssion reductions were derived from Federal Register (FR) notices and disc ussions with EPA’s Emission Sta ndards Division (ESD) staff. We did not apply reductions for MACT standards with an initial compliance date of 2001 or earlier, assuming that the effects of these controls ar e already accounted for in the 2002 inventories supplied by the States. Emission reductions were applied only for MACT standards with an initial compliance date of 2002 or later.

The final Phase II NO x SIP call rule was finalized on April 21, 2004. States had until April 21, 2005, to submit SIPs meeting the Phase II NO x budget requirements. The Phase II rule applies to large IC engines, which are primarily used in pipeline transmission service at compressor stations. We identified affect ed units using the same methodol ogy as was used by EPA in the proposed Phase II rule (i.e., a large IC engine is one that emitted, on average, more than one tonper-day during 2002). The final ru le reflects a control level of 82 percent for natural gas-fired IC engines and 90 percent for diesel or dual fuel cat egories. Several S/L agencies provided more specific information on the anticipated controls at the compressor stations. This information was used instead of the default approach us ed by EPA in the proposed Phase II rule.
A summary of Non-EGU point source control programs included in 2009/2018 projection inventories include:

• Atlanta/Northern Kentucky/Birmingham 1-hr Ozone Non-attainment SIPs;

• Industrial Boiler/Process He ater/RICE MACT. EPA’s i ndustrial boiler MACT rules were vacated on June 8, 2007. The emissions reductions that were modeled to account for the industrial boiler MACT were 1,343.5 tons/year of SO2, 879.4 tons/year of PM2.5, and 1,304.2 tons/year of PM10. These reductions are insignificantly small (0.69% for SO2, 0.39% for PM2.5, and 0.13% for PM10) compared to the statewide totals (193,665.5 tons/year SO2; 225,104.5 tons/yea r PM2.5; and 1,002,873.2 tons/year PM10) and should not impact the conclusions made in the SIP;App endix H

11 of 115 • NO x RACT in 8-hr Ozone NAA SIPs;

• Petroleum Refinery Initiative (October 1, 2003 notice; MS & WV). This initiative addresses the most significant Clean Ai r Act compliance concerns affecting the petroleum refinery industry. Since Decemb er 2000, 17 global refinery settlements have been reached with refiners representing near ly 77 percent of domestic refining capacity. Three refineries in the VISTAS region ar e affected by two October 2003 Clean Air Act settlements under the EPA Petroleum Refinery In itiative. The refineries are: (1) the Chevron refinery in Pascagoula, Mississi ppi; (2) the Ergon refinery in Vicksburg, Mississpipp; and (3) the Ergon refinery in Newell, West Virginia;

• Reasonable Further Progress 3 percent plans where in place for 1-hour plans (Metropolitan Washington, DC, and Atlanta, Georgia);

• VOC 2-, 4-, 7-, and 10-year MACT Standard s. The point source MACTs and associated emission reductions were derived from Fede ral Register (FR) notices and discussions with EPA’s Emission Standards Division (ESD) staff. We did not apply reductions for MACT standards with an initial compliance date of 2001 or earlie r, assuming that the effects of these controls are already accoun ted for in the 2002 inventories supplied by the States;

• Combustion Turbine MACT. The projec tion inventories do not include the NO x cobenefit effects of the MACT regulations for Gas Turbines or stationary Reciprocating Internal Combustion Engines, which EPA esti mates to be small compared to the overall inventory; and

• NOx SIP Call (Phase II – remaining States & IC engines).
For more information on the development of point source inventories, s ee Appendix C of the SIP narrative.

1.2 Area Source Controls
Controls (including control effici ency, rule effectiveness and ru le penetration) provided by the States or originally developed for use in estim ating projected emissions for U.S. EPA’s Heavy Duty Diesel (HDD) rulemaking emission projections and used in the Clean Air Interstate Rule (CAIR) projections were used to calculate controlled emissions.
The controls obtained by MACTEC (MACTEC, 2006) for the HDD rulemaking were controls for the years 2007, 2020, and 2030. Since MACTEC was preparing 2009 and 2018 projections, control values for intermediate years were prepar ed using a straight-line interpolation of control level between 2007 and 2020.
State-submitted controls had precedence over the U.S. EPA-developed controls. Control measures submitted by the Georgia Environmenta l Protection Division (GA EPD) include Stage I controls for gasoline dispensing facilities and open burning controls.App endix H

12 of 115 As part of the air toxics program, Stage I contro ls for gasoline dispensing facilities was adopted by the State and became effective May 1990 with final compliance by January 1, 1994. Stage I is the vapor recovery technology on the underg round storage tanks and reduces the emissions during the tank-filling operati ons at service stations.
The GA EPD is responsible for going to all gasolin e-dispensing facilities and testing the fuels to ensure that they meet the quality standards of the State. The GA EPD checks for Stage I controls and proper maintenance of the control equipment. If a facility is not found to be properly maintaining the control equipment, then the GA EPD sends a notice of violation informing the facility that the controls are required and gives the facility time to correct the violation before fines are accessed. From this information, the rule effectiveness and rule penetration can be estimated. The rule effectiveness is the percen tage of facilities complying with the rule, whereas, the rule penetration is the percentage of facilities requiring Stag e I controls. Control efficiency is the expected percent re duction from this control technology.
Open burning is treated as a means of waste dispos al in rural areas. Ma terials burned generally include household trash, agricult ural refuse, landscaping refu se, or scrap wood. The open burning emissions inventory includes SCC 2610030000, residential open burning of household waste and SCC 2610000100, and open burning of yard trimmings. Since it is illegal to burn within the corporate limits, the rural population in each county is used to estimate emissions.
For more information on the development of area source inventories, see Appendix C of the SIP narrative.

1.3 Mobile Sources

Local controls included vehicle emissions in spection (i.e., I/M) programs, Stage II vapor recovery programs, anti-tampering programs, etc. By nature, the assumptions used for the 2002 initial base-year inventory vary across the VISTAS region, but our presumption is that these data accurately reflected each State’s situation as it existed in 2002. If a State had no plans to change program requirements between 2002 and 2018, we proposed to maintain the 2002 program descriptions without change. Ho wever, if a State planned change s, we requested information on those plans. In the final implemen tation of the inventory, Stage II controls were exercised in the area source component of the inventory, since th e units used to develop Stage II refueling estimates are different between MOBILE6 and the NONROAD models.
For more information on the development of mob ile source inventories, see Appendix C of the SIP narrative.

1.4 Non-Road
Using the 2002 base-year emissions inventory for aircraft, locomotives, and commercial marine vessels (CMV) prepared as described ear lier in this document, corresponding emission projections for 2009 and 2018 were developed. Deta iled inventory data (both before and after controls) for these same emission sources for 1996, 2010, 2015, and 2020 were obtained fromApp endix H

13 of 115 the EPA's Clean Air Interstate Rule (CAIR) Technical Support Document. Using these data, combined growth and control factors fo r the period 2002-2009 and 2002-2018 were estimated using straight-line interpolation betw een 1996 and 2010 (for 2009) and 2015 and 2020 (for 2018). This is done at the Stat e-county-SCC-pollutant level of detail. According to EPA documentation, the CAIR baseline emissions incl ude the impacts of th e Tier 4 (T4) non-road diesel rulemaking, which implements a low-sulfur fuel requirement that affects both future CMV and locomotive emissions.
For more information on the development of non-ro ad source inventories, see Appendix C of the SIP narrative.App endix H

14 of 115 1.5 Summary of Emissions Projections
Figures 1.1-1.4 depict pollutant totals for NH 3, PM2.5, NOx, and SO2 for the various sectors by state for 2002, 2009, and 2018 Base G2 modeling. The totals are presented assuming the anticipated growth and implementation discussed in previous subsections. As can be seen in Figure 1-1 there is a gradual in crease in the ammonia emissions. In Figure1-2, PM 2.5 emissions do not show any relative change in 2009 or 2018 as compared to 2002. Although, in Georgia, they are showing a slightly in creasing trend. NOx and SO2 em issions in Figure 1-3 and 1-4, respectively, show a steep decreas e in their 2018 emissions as compared to 2002. This decrease can be attributed to various Fe deral and State controls that will be in place on or before 2018.

Figure 1-1. Annual NH3 emissions – Base G4. 0 20 40 60 80 100 120 140 160 180 200

Al a bam

a Flo rid

a G e orgi

a Kent u ck

y Mississippi

N o rth

Carol i na

So uth

C

a ro lin

a Tenn e ssee Vi rgi

ni a West VirginiaAnnual NH3 (Thousand Tons) Miscellaneous
Nonroad
Onroad
Industrial
Other Fuel Combustion
EGU

2002 Typical 2009 OTW 2018 OTWApp endix H

15 of 115 Figure 1-2. Annual PM2.5 emissions – Base G4. 0 50 100 150 200 250

Al ab

ama F lorida G eorgi

a Kentuck y Mississippi Nort h C

aroli

na South Car ol ina Tennes s ee V irgini

a West VirginiaAnnual PM-2.5 (Thousand Tons) Miscellaneous
Nonroad
Onroad
Industrial
Other Fuel Combustion
EGU 2002 Typical 2009 OTW

2018 OTW

0 200 400 600 800 1,000 1,200

Alabama Flor i da Geor gi a

K en t uck

y

M iss

i s s i p pi Nor t h Carolina South Caroli n a

T e nnes

se

e V i r gini

a

W e st

V irginiaAnnual NOx (Thousand Tons) Miscellaneous
Nonroad
Onroad
Industrial
Other Fuel Combustion
EGU 2002 Typical

2009 OTW

2018 OTWApp endix H

16 of 115 Figure 1-3. Annual NOx emissions – Base G4.

Figure 1-4. Annual SO2 emissions – Base G4.



### 2.0 UNIFORM RATE OF PROGRESS

The linear rate of improvement sufficient to attain natural conditions by 2064 is referred to as the “uniform rate of progress” or “glide path.” Th e baseline represents the starting point from which reasonable progress will be measured. There are two baseline values for each Class I area. Using 2000 - 2004 Interagency Monitoring of Pr otected Visual Environments (IMPROVE) monitoring data, the deciview valu es for the 20% best days in each year are averaged together, producing a single average deciview value for the be st days. Similarly, the deciview values for the 20% worst days in each year are averaged together producing a single average deciview value for the worst days. Extinction from i ndividual PM components is calculated from the IMPROVE equation (EPA, 2003; Hand and Malm, 2006) . EPA defined target natural conditions in 2064 for each Class I area (EPA, 2003). For each VISTAS Class I area, a baseline visibility using 2000-2004 IMPROVE data and uniform rate of progress was established, using EPA default assumptions for calculating visibil ity and natural background. Most recently, the 0 100 200 300 400 500 600 700

Alabama F l orida Ge o rgia Kentucky Mississippi

N orth

C arolina

S o u t h Ca

rol

ina Ten n essee Virginia

W e s t Vi

rg i n i aAnnual SO2 (Thousand Tons) Miscellaneous
Nonroad
Onroad
Industrial
Other Fuel Combustion
EGU 2002 Typical

2009 OTW

2018 OTWApp endix H

17 of 115 scientific community has reviewed the assumptions used to calculate current and natural background visibility and has agreed to re vised methods (Hand and Malm, 2006, Pitchford, 2006). VISTAS intends to present both the origin al and revised methods to calculate visibility and visibility improvement. Figures 2.1-2.2 illust rate the average 20% worst visibility days in the 2000-2004 baseline period for Cohutta Wilderness Area (COHU), and Okefenokee Wilderness Area (OKEF), the target natural c ondition in 2064 for the 20% worst days, and the incremental deciview changes assuming a uniform rate of progress between current and natural conditions in 2064, using the original method fo r calculating visibilit y. This is known as “glidepath.”

Figure 2-1. Uniform Rate of Progress at Cohutta Wilderness

Uniform Rate of Reasona ble Progress Glide Path Okefenokee

27.13 25.81
23.42

20.76

18.11

15.45

12.80 11.21

0 5 10 15 20 25 30 35

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

Glide Path Natural Condition (Worst Days) Observation Uniform Rate of Reasona ble Progress Glide Path Cohutta

30.25 28.63

25.71

22.46

19.22

15.97

12.73 10.78

0 5 10 15 20 25 30 35

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

Glide Path Natural Condition (Worst Days) ObservationApp endix H

18 of 115 Figure 2-2. Uniform Rate of Prog ress at Okefenokee WildernessApp endix H

19 of 115 

### 3.0 GEORGIA TECH SENSITIVITIES

Georgia Tech performed emission sensitiviti es to examine the impact of emission reductions on regional haze, annual PM2.5, a nd 8-hour ozone concentrations using CMAQv4.4_SOA modifications on the VISTAS 12 km modeling domain, using the 2009 OTW (on the way) BaseD emissions. Once th e key pollutants contributing to visibility impairment at each Class I area were identified, the sources or source categories responsible for emitting these pollutants or pollutant precursors were determined. Emissions sensitivity analyses were conduc ted using CMAQ with 2009 Base D inventory and considering 30% reductions from major pollutants and source categories. The sensitivity runs were initially designed to consider pollutant contributions in 2009 to PM2.5 in urban areas; results were also cons idered for Class I areas even though the full benefit of controls anticipated by 2018 are no t included in these sensitivity analyses. Results are qualitatively similar to previous sensitivity results using an earlier (Base A) 2018 inventory. While the 2009 sensitivity analyses cannot be used to judge absolute contributions from each state or source sector, the results do indicate relative levels of responses among pollutants, s ectors, and geographic areas.

3.1 Evaluation for Cohutta Wilderness Area, Georgia
Figure 3-1 shows the results of emissions sensitivity run for COHU for summer (since summer is when 20% worst days occur). CMAQ was run using 30% reductions from the 2009 Base D inventory assumptions for im plementing existing State and Federal regulations (listed on Page 2) for major source categories of SO2, NOx, VOC, and PM. The results identify geographic origins for visi bility impairing pollutants, and serve as a good starting point when considering potential air quality benefits from additional controls for specific source sectors in VIST AS states or neighboring RPOs. The greatest benefits at COHU in 2009 are projected to co me from further reducing SO2 from electric generating utilities (EGUs). Additional, sma ller benefits are projected from additional SO2 emission reductions from non-utility, i ndustrial point sour ces. Reductions of nitrogen oxide emissions from point sources (EGU and non-EGU) or ground-level sources (on road vehicles, off road engines, and area sources) and reductions of primary carbon from point- or ground-level sources are projected to be significantly less beneficial than reductions in SO2 em issions. Reductions in anthropogenic VOC emissions show no response in visibility, wh ile a 30 percent reduct ion in biogenic VOC emissions result in a measurable improvement. However, controlling the VOCs from vegetation is not possible or appropriate. For this sensitivity run, further reducing SO2 emissions from electricity-generating utilit ies in Alabama, Georgia, West Virginia, Tennessee, and North Carolina would have the greatest benefit for COHU; reductions from electric generating utilities in othe r VISTAS states would have additional but smaller benefits at COHU.App endix H

20 of 115 Figure 3-1. Sources of various visibility-reducing pollutants – COHU summer using 2009 Base D emissions.

3.2 Evaluation for Okefenok ee Wilderness Area, Georgia
Figure 3-2 shows the results of the emissions sensitivity run for OKEF for summer (since summer is when 20% worst days occur). CMAQ was run using 30% reductions from the 2009 Base D inventory assumptions for im plementing existing state and federal regulations (listed on page 4) for major source categories of SO2, NOx, and PM. The results identify geographic origins for visibi lity impairing pollutants, and serve as a good starting point when considering potential air qua lity benefits from additional controls for specific source sectors in VISTAS states or neighboring RPOs. The greatest benefits at OKEF in 2009 are projected to come from furt her reducing SO2 from electric generating utilities (EGUs). Additional, smaller benefi ts are projected from additional SO2 emission reductions from non-utility, industrial point sources. Reductions of nitrogen oxide emissions from point sources (EGU and non-EGU) or ground level sources (on road vehicles, off road engines, and area sources) and reducti ons of primary carbon from point or ground level sources are proj ected to be significantly less beneficial than reductions in SO2 emissions. Reductions in anthropogenic VOC emissions show no response in visibility, while a 30 percent reduction in biogenic VOC emissions result in a measurable improvement. However, controlling the VOCs from vegetation is not possible or appropriate. For this sensitivity run, furt her reducing SO2 emissions from electricity generating utilities in Alabama, Florida, and Georgia, would have the greatest benefit for OKEF.App endix H

21 of 115 Figure 3-2. Sources of various visibility-reducing pollutants – OKEF summer using 2009 Base D emissions



### 4.0 REASONABLE PROGRESS EVALUATION

4.1 Area of Influence Analyses
The objective of the VISTAS Area of Influen ce analysis is to identify the geographic source regions that are contribut ing to visibility impairment at the Class I areas on the 20% worst visibility days. This information will be used by the State as part of the evaluation and demonstration of reasonable progress toward visibility improvement in Class I areas.

VISTAS’ contribution assessment based on IMPROVE monitoring data (Brewer and Adlhoch, 2005) demonstrated that ammonium sulfate is the major contributor to PM2.5 mass and visibility impairment at Class I ar eas in the VISTAS and neighboring states. As illustrated in Figure 4-1, on the 20% worst visibility days in 2000-2004, ammonium sulfate accounted for greater than 70% of the calculated light extinc tion at Class I areas in the Southern Appalachians a nd greater than 60% of the cal culated light extinction forApp endix H

22 of 115 0 50 100 150 200 250 300SWAN1ROMA1OKEF1EVER1CHAS1SAMA1DOSO1SHEN1JARI1LIGO1SHRO1GRSM1COHU1SIPS1MACA1CADI1BRIG1BRET1MING1HEGL1UPBU1CACR1

VISTAS coastal VISTAS inland non-VISTASExtinction (Mm-1) Sea Salt CM Soil EC POM Amm NO3 Amm SO4 Rayleigh

0 50 100 150 200 250 300SWAN1ROMA1OKEF1EVER1CHAS1SAMA1DOSO1SHEN1JARI1LIGO1SHRO1GRSM1COHU1SIPS1MACA1CADI1BRIG1BRET1MING1HEGL1UPBU1CACR1

VISTAS coastal VISTAS inland non-VISTASExtinction (Mm-1) Sea Salt CM Soil EC POM Amm NO3 Amm SO4 Rayleigh

Figure 4-1. Average Extinction for 20% Worst Visibility Days in 2000-2004 using New IMPROVE Equation (data from VIEWS Sep 2006).
coastal sites in the VISTAS states (excepting Everglades). In contrast, ammonium nitrate contributed less than 5% of the calculated li ght extinction at VISTAS Class I areas on the 20% worst visibility days. (Nitrate has a somewhat larger contribution on 20% worst days at Class I areas in nei ghboring states to the VISTAS region.) Particulate organic matter (organic carbon) accounted for 10-20% of light extinction on 20% worst visibility days (except Everglades where organic carbon accounted for 40%).
VISTAS emissions sensitivity analyses using the CMAQ regional air quality model project that reductions in SO2 emissions fr om Electric Generating Utilities (EGU) and non-EGU industrial point sources will result in the greatest improvements in visibility at VISTAS Class I areas. EGU and non-EGU industrial point sources comprise 95+% of the SO2 emissions inventory in the VISTAS states. The emissions sensitivity analyses also indicate that improvements in visibil ity from reductions in NOx or primary carbon emissions from ground-level sources or point sources would be small. Based on these analyses, VISTAS states chose to focus thei r reasonable progress evaluation for the first regional haze SIP on potential controls of SO2 emissions from EGU and non-EGU point sources. To select the specific point source s that would be considered for each Class IApp endix H

23 of 115 area, states first identified the geographic area s that most likely influence visibility in each Class I area and then iden tified the major SO2 point sources in that geographic area.

4.2 Source Type Analyses
The following analyses are illustrative of an initial approach from September 2005 to evaluate potential control strategies. These analyses rely on the 200 km area surrounding the Class I area and use the Base F4 model run.
VISTAS emissions inventory for the simplest area of influence, the 200 km concentric circle, indicated for COHU in 2018 that fuel combustion from electric utilities and industrial processes produces the majority of the remaining SO2 emissions (Table 4-1), with 86% of 2018 SO2 total coming from fuel combustion.

Table 4-1. Source types 2018 Base G4 modeling COHU – Percentage. Annual 2018 BaseG2 Emissions (%) Tier VOC NOX CO SO2 PM-10 PM-2.5 NH3 Fuel Comb. Elec. Util. 0% 21% 1% 52% 9% 17% 1% Fuel Comb. Industrial 1% 19% 2% 28% 3% 5% 0% Fuel Comb. Other 5% 6% 3% 6% 5% 10% 1% Chemical & Allied Product Mfg 2% 1% 0% 2% 1% 1% 1% Metals Processing 1% 1% 1% 3% 2% 5% 0% Petroleum & Related Industries 0% 0% 0% 1% 0% 0% 0% Other Industrial Processes 7% 6% 1% 5% 9% 11% 1% Solvent Utilization 42% 1% 0% 0% 0% 1% 0% Storage & Transport 6% 0% 0% 0% 1% 1% 0% Waste Disposal & Recycling 4% 2% 4% 0% 5% 12% 0% Highway Vehicles 18% 25% 48% 0% 2% 2% 12% Off-highway 11% 18% 34% 1% 2% 4% 0% Miscellaneous 2% 1% 7% 0% 61% 30% 83% VISTAS Total 100% 100% 100% 100% 100% 100% 100%

VISTAS emissions inventory for the simplest area of influence, the 200 km concentric circle, indicated for OKEF in 2018 that fuel combustion from electric utilities and industrial processes produces the majority of the remaining SO2 emissions (Table 4-2), with 82% of 2018 SO2 total coming from fuel combustion.App endix H

24 of 115 Table 4-2. Source types 2018 Base G4 modeling OKEF – Percentage. Annual 2018 BaseG2 Emissions (%) Tier VOC NOX CO SO2 PM-10 PM-2.5 NH3 Fuel Comb. Elec. Util. 0% 22% 1% 51% 7% 12% 1% Fuel Comb. Industrial 1% 16% 2% 25% 3% 5% 0% Fuel Comb. Other 3% 4% 1% 6% 2% 4% 0% Chemical & Allied Product Mfg 0% 0% 0% 4% 0% 1% 0% Metals Processing 0% 0% 0% 4% 0% 0% 0% Petroleum & Related Industries 0% 0% 0% 0% 0% 0% 0% Other Industrial Processes 7% 6% 1% 7% 8% 11% 2% Solvent Utilization 39% 0% 0% 0% 0% 0% 0% Storage & Transport 7% 0% 0% 0% 0% 0% 0% Waste Disposal & Recycling 4% 3% 7% 0% 8% 16% 0% Highway Vehicles 18% 25% 38% 1% 1% 1% 10% Off-highway 14% 19% 29% 1% 2% 3% 0% Miscellaneous 6% 4% 20% 1% 68% 46% 86% VISTAS Total 100% 100% 100% 100% 100% 100% 100%

VISTAS emissions inventory for the simplest area of influence, the 200 km concentric circle, indicated for WOLF in 2018 that fu el combustion from electric utilities and industrial processes produces the majority of the remaining SO2 emissions (Table 4-3), with 84% of 2018 SO2 total coming from fuel combustion. Table 4-3. Source types 2018 Base G4 modeling WOLF – Percentage. Annual 2018 BaseG2 Emissions (%) Tier VOC NOX CO SO2 PM-10 PM-2.5 NH3 Fuel Comb. Elec. Util. 0% 21% 1% 47% 7% 14% 1% Fuel Comb. Industrial 1% 16% 2% 31% 3% 5% 0% Fuel Comb. Other 3% 5% 2% 6% 3% 6% 1% Chemical & Allied Product Mfg 1% 2% 0% 4% 0% 1% 1% Metals Processing 0% 0% 0% 3% 0% 0% 0% Petroleum & Related Industries 0% 0% 0% 0% 0% 0% 0% Other Industrial Processes 6% 5% 1% 5% 7% 8% 2% Solvent Utilization 39% 0% 0% 0% 0% 0% 0% Storage & Transport 7% 0% 0% 0% 0% 1% 0% Waste Disposal & Recycling 4% 3% 7% 1% 7% 16% 0% Highway Vehicles 19% 25% 40% 1% 1% 1% 10% Off-highway 15% 21% 32% 1% 2% 5% 0% Miscellaneous 5% 3% 15% 1% 67% 43% 85% VISTAS Total 100% 100% 100% 100% 100% 100% 100%App endix H

25 of 115 4.3 Back Trajectory Analyses
The GA EPD defined AOIs to identify source re gions for which additional controls might be considered and that are likely to have th e greatest impact on each Class I area. Initial focus on AOIs is on SO2 emissions because SO 4 contributes greater than 60% of the calculated light extinction on the haziest days. In fact, figure 4-1 shows that for Cohutta (COHU), SO4 contributes roughly 80% to the calculated light extin ction on the haziest days. To better understand the sources that s hould be considered in a four factor analysis for reasonable progress, VISTAS contracted ENVIRON to partner with VISTAS’s Data Analysis contractor, Air Resource Specialists (ARS), to provide SO2 Areas of Influence for each VISTAS and six neighboring Class I areas. ARS generated meteorological back trajectories for IMPROVE sites in VISTAS and neighboring states. Back trajectory analyses use interpolated measured or modele d 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. Back trajectories account for the impact of wind direction and wind speed on delivery of emissions to the receptor, but do not account for chemical transformation and dispersion of emissions.
Trajectories were generated using the Hybr id-Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model developed by the National Oceanic and Atmospheric Administration’s (NOAA) Air Resources Labora tory (ARL). HYSPLIT uses archived 3- dimensional meteorological fields generated from observations and short-term forecasts. HYSPLIT can be run to generate forward or b ackward trajectories us ing several available meteorological data archives.
The data archives used in this analysis were the National Weather Service's National Centers for Environmental Prediction Eta Da ta Assimilation System (EDAS). The EDAS fields are archived by the ARL across the c ontinental U.S., including a buffer zone, at a horizontal resolution of 80 km before 2004, and at a horizontal resolution of 40 km starting in 2004. Detailed information regardi ng the trajectory model and these data sets can be found on NOAA’s Web site ( http://www.arl.noaa.gov/ready/hysplit4.html ).
The major model parameters selected for this analysis include thos e listed in Table 4-4.

Table 4-4. Back Trajectory Model Parame ters Selected for VISTAS AOI Analysis

Model Parameter Value Trajectory duration 72 hours backward in time Top of model domain 14,000 meters Vertical motion option used model data Meteorological Field EDAS End Times 0600, 1200, 1800 and 2400 EST End Heights 100 and 500 mApp endix H

26 of 115 The choice of these parameters affects the traj ectories generated and the final attribution analyses. In particular, traj ectories tend to become incr easingly uncertain the further back in time they are used. Vertical moti on in the model is sometimes best represented by following actual vertical motion measuremen ts (represented by model data), surfaces of constant entropy, or surfaces of constant pressure, depending on the meteorological conditions at a given location and time. The impact of receptor height (or end height) on an individual trajectory is also important. Low-ending trajectories represent air parcels nearer to ground-level and high-ending trajectories may represent more accurate boundary layer flow above the local terrain.App endix H

27 of 115 For the years 2000 – 2004, back trajectories were computed for the following IMPROVE sampling sites in and around the VISTAS region:

1. Breton, LA (BRET1) 2. Brigantine NWR, NJ (BRIG1) 3. Cadiz, KY (CADI1) 4. Caney Creek, AR (CACR1) 5. Cape Romain National Wilderness Area, SC (ROMA1) 6. Chassahowitzka National Wilderness Area, FL (CHAS1) 7. Cohutta Wilderness, GA (COHU1) 8. Dolly Sods Wilderness, WV (DOSO1) 9. Everglades NP, FL (EVER1) 10. Great Smoky Mountains NP, TN (GRSM1) 11. Hercules-Glades, MO (HEGL1) 12. James River Face Wilderness, VA (JARI1) 13. Linville Gorge, NC (LIGO1) 14. Mammoth Cave NP, KY (MACA1) 15. Mingo, MO (MING1) 16. Okefenokee Wilderness, GA (OKEF1) 17. Shenandoah NP, VA (SHEN1) 18. Shining Rock Wilderness, NC (SHRO1) 19. Sipsy Wilderness, AL (SIPS1) 20. St. Marks, FL (SAMA1) 21. Swanquarter, NC (SWAN1) 22. Upper Buffalo Wilderness, AR (UPBU1) 23. Wolf Island Wilderness, GA (WOLF)App endix H

28 of 115 Figure 4-2 shows the 72-hour back trajectory analysis perf ormed for Cohutta Wilderness

area.

Figure 4-2. Example 72-hour back trajector ies for 20% worst visibility days in 2002 for Cohutta Wilderness Area 

## Back Trajectories for 2002 20% Worst Days Cohutta Wilderness AreaApp endix H

29 of 115 Figure 4-3. Example 72-hour back trajector ies for 20% worst visibility days in 2002 for Okefenokee Wilderness Area
Based on the five years of individual backtrajectories, ARS generated extinctionweighted residence time data for each IMPROVE site. Each hourly trajectory point was tagged with its associated sample (or end) date. Back trajectories with end dates corresponding to the 20% worst visibility days were weight ed with calculated extinction values and summed into 1-degree horizontal grid cells of latitude and longitude.
Extinction data is based on the IMPROVE Regi onal Haze Rule dataset, as available from the VIEWS website ( http://vista.cira.colostate.edu/vi ews/Web/IMPROVE/SummaryData.aspx ) in November 2005 and is calculated based on the original IMPROVE algorithm. For sites with less than three complete years of data, ARS developed and applied a data substitution protocol to fill missing data . Organic carbon and elemental carbon values were filled based on correlations to hydrogen and sulfate values for the same site. Other components were filled based on data from nearby sites.
Trajectory data was weighted by total ex tinction and by extinction components including ammonium sulfate (AmmSO4), ammonium nitrate (AmmNO3), particulate organic material (POM), Light Absorbing Car bon (LAC), Soil, and Coarse Mass (CM). Weighting trajectories for the 20% worst days by sulfate extin ction gave greater importance to days influenced by SO2 emissions and lesser importance to days influenced by organic carbon emissions. This allowed a geographic area of influence to be identified based on sulfate contributions and not skewed by contributions from fire.
These data files were provided to ENVIR ON. ENVIRON was charged with developing residence time plots to define the geographic area with highest probability of influencingApp endix H

30 of 115 the receptor on the 20% worst days dominated by sulfate. Residence time over an area is indicative of general flow patterns, but since it does not account for emissions and removal processes, it does not necessarily im ply specific areas contributed significantly to haze compounds at a receptor site.

4.4 Area of Influence Data and Displays
The primary objective of the Area of Influence (AOI) displays and data sets are to assist the State in focusing its reasonable progre ss analysis on those geographic regions and source categories which most impact a particul ar Class I Area. The sources of data used in these analyses include back-trajector y residence time data provided by ARS and described above and the VISTAS 2002 and 2018 36-km gridded emissions data (Base G, August 2006 inventory version).
Extinction weighted back-trajectory reside nce time plots were provided by ARS for all Class I Areas in the VISTAS region as we ll as for the surrounding, neighboring states. These trajectories were processed on an un-p rojected (i.e., geodetic lat/lon 1 degree by 1 degree) grid and transferred to ENVIRON for further GIS processing and analysis. Backtrajectories were run for 100-meter and 500-me ter start heights. Because the backtrajectories were to be combined with gridded emissions data, the first step in the analysis and GIS processing involved re-projecting th e data to the Inter-RPO Unified Lambert Conformal Projection domain. Once the data was re-projected, the data was mapped to the 36-km grid cells in the RPO projection us ing an area-weighted mapping routine. The re-projected extinction weighted back-traject ory residence times were then plotted on the VISTAS 12-km modeling domain. As sulfat e is the primary visibility-impairing component of the total extinction in the So utheast, the SO4-weighted residence time trajectories were chosen as the basic data set for display. For clarit y, these data are scaled by the maximum value of the total extinction-weighted residence time within the modeling domain. The 100-km and 200-km distances from the Class I area were labeled.

Using the trajectories with the 100-m start he ight, the State has defined the SO2 Areas of Influence as the area with grid cells showi ng 5% (area shaded in yellow in following plots) or greater residence time (the freque ncy that winds pass over that area on the way to the Class I area on the 20% worst days dominated by sulfate) for the period 2000- 2004. Residence time over an area is indicative of general flow patte rns, but since it does not account for emissions and removal proce sses, it does not necessarily imply specific areas contributed significan tly to haze compounds at a receptor site. The following figures, 4-4 through 4-11, are ex tinction-weighted residence time plots for LIGO, SHRO, SWAN, GRSM, OKEF, COHU, ROMA and SI PS for 100-m and 500-m trajectory start heights.App endix H

31 of 115 

## SO2 Area of Influence for Linville Gorge, NC

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%. M ax B ext V alue = 5 4353 9 M ax V alu e = 84%

Figure 4-4. Linville Gorge SO4 extincti on-weighted residence time plots for the period 2000-2004. 

### SO 2 Area of Influence for S hining Rock, NC

Green circles indicate 100-km and 2 00-km ra dii from Class I area . Red line perimete r indicate Area of Influence with Residence Time > 10%. Orange line perimeter indicate Area of Influe nce with Residence Time > 5%. Max Bext V alue = 79 3133 Max Valu e = 85 %

Figure 4-5. Shining Rock SO4 extinction -weighted residence time plots for the period 2000-2004.App endix H

32 of 115 

## SO2 Area of Influence for Swanquarter, NC

Green circles indicate 100-km and 200-km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Time > 10%. Orange line perimeter indicate Area of Influence with Residence Time > 5%. Max B ext V alue = 26 3144 Max V alue = 75%

Figure 4-6. Swanquarter SO4 extinction-w eighted residence time plots for the period 2000-2004. 

### SO2 Area of Influence for Great Smoky Mountains, NC-TN

Green circles indicate 100-km and 200-km radii f rom Class I area. R ed line perimeter indicate Area of Influence with Residence Time > 10%. Orange line perimeter indic ate Area of Inf luenc e with Residence Time > 5%. M a x B e x t V a l u e = 7 2 4 55 8 M a x V a lu e = 8 4 %

Figure 4-7. Great Smoky Mountains SO4 ex tinction-weighted residence time plots for the period 2000-2004.App endix H

33 of 115 Figure 4-8. Okefenokee Wilderness Area SO4 extinction-weighted residence time plots for the period 2000-2004. 

### SO2 Area of Influence for Cape Romain, SC

Green circles indicate 100-km and 200-km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Time > 10% (weighted by sulfate extinction and normalized by total extinction). Orange line perimeter indicate Area of Influence with Residence Time > 5%. Figure 4-9. Cape Romain SO4 extinction -weighted residence time plots for the period 2000-2004.App endix H

34 of 115 

### SO2 Area of Influence for Cohutta, GA

Green circles indicate 100-km and 200-km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Time > 10% (weighted by sulfate extinction and normalized by total extinction). Orange line perimeter indicate Area of Influence with Residence Time > 5%. Figure 4-10. Cohutta SO4 extinction-weigh ted residence time plots for the period 2000-2004.

Figure 4-11. Sipsey SO4 extinction-weigh ted residence time pl ots for the period 2000-2004.App endix H

35 of 115 Figure 4-12. Wolf Island SO4 extinction-w eighted residence time plots for the period 2000-2004.
As an aid to the analysis, all regions with scaled residence time values greater than 5% and greater than 10% were noted on th e display. Finally, county boundaries were overlaid to identify those counties and 36-km modeling grid cells for which the extinction-weighted residence time back-traj ectories exceeded 5% and 10%. This data was then exported as comma-delimited ASCII data files for further analyses. These residence time displays are used primarily to determine the regions (counties and/or grid cells) impacting each Class I Area. The next step in the analysis is to determine the potential impact from emission sources within the regions identified from the extinctionweighted back-trajectory residence time displays and analyses.

Previous VISTAS contribution assessment, emissions sensit ivity modeling, and inventory analyses have indicated that SO4 is the largest contributor to haze on the 20% worst visibility days and that elec tric generating utilities and i ndustrial point sources are the largest contributors to SO4 levels at the Cl ass I areas. These two source categories make up 95% of the VISTAS SO2 inventory. Th erefore these two source categories were identified as the highest priority for evalua tion of reasonable progress in 2018 at the Class I areas. The potential impacts of SO2 point source emissions for each Class I Area are determined by combining gridded emissions da ta and weighted back -trajectory residence times for the 36-km grid cells. The analys is was carried out w ith both the 2002 and 2018 base-year emission inventories. Figures 4- 13 and 4-14 display the gridded point source SO2 emissions from the VISTAS modeli ng databases, for 2002 and 2018, respectively. This data was processed by summing all vertical layers for each grid column and then summing across all days to obtain a to tal annual emission for each grid cell.App endix H

36 of 115 Figure 4-13. Annual point source SO2 emissions for the VISTAS region in tons/year. (2002 BaseG)

Figure 4-14. Annual point source SO2 emissions for the VISTAS region in tons/year. (2018 BaseG)
As a way of incorporating the effects of transport, deposition, and chemical transformation of point source emissions along th e path of the trajectories, the data wasApp endix H

37 of 115 weighted by 1/d, where d was calculated as the distance between grid cell centers, in kilometers. For the grid cell containing the Cl ass I Area monitor, a weighting of 1/9 was applied. The distance-weighted point source SO2 emissions are then combined with the gridded extinction-weighted b ack-trajectory residence times at a spatial resolution of 36- km.
The final step in the development of the disp lays and datasets involve the combination of the residence times and gridded emission data . The distance weighted (1/d) gridded point source SO2 emissions are multiplied by the total extinction-weighted back-trajectory residence times (SO2 * Bext-weighted RT * (1/d )) on a grid cell by grid cell basis. These results are then normalized by the domain- wide total and displayed as a percentage (Appendix H.2). The analysis is carr ied out for both the 2002 and 2018 base year inventories. Figures 4-15 through 4-23 display th e result of this final analysis step. Note that the outlined regions (in re d and light orange) represent the extent of the >10% and >5% sulfate extinction-weighted residence times determined from the first step in the analysis (see Figures 4-3 through 4-11). As with the residence time analysis, the results of the combined distance-weighted SO2 em issions and extinction-weighted residence times are overlayed with county boundaries and exported to comma-delimited ASCII data files for further analys es of emissions and control options for specific sources.App endix H

38 of 115 Figure 4-15. Cohutta 2002 SO2 Emissions * SO4 extinction-weighted residence time plots.

Figure 4-16. Okefenokee 2002 SO2 Emission s * SO4 extinction-weighted residence time plots.App endix H

39 of 115 Figure 4-17. Wolf Island 2002 SO2 Emissions * SO4 extinction-weighted residence time plots.
The following sets of plots show th e 2018 SO2 Emissions multiplied by the SO4 extinction-weighted residence time plots. These were produced the same way as the previous plots, only using 2018 em issions instead of 2002 emissions.

Figure 4-18. Cohutta 2018 SO2 Emissions * SO4 extinction-weighted residence time plots. 

### Wolf Island, GAApp endix H

40 of 115 Figure 4-19. Okefenokee 2018 SO2 Emission s * SO4 extinction-weighted residence time plots.

Figure 4-20. Wolf Island 2018 SO2 Emissions * SO4 extinction-weighted residence time plots. 

### Wolf Island, GAApp endix H

41 of 115 To understand how emissions have change d by 2018, the following plots, 4-21 – 4-23, compare 2002 and 2018 SO2 multiplied by residence time.

Figure 4-21. Cohutta 2002 vs. 2018 SO2 emi ssions multiplied by residence time.

Figure 4-22. Okefenokee 2002 vs . 2018 SO2 emissions multip lied by residence time.App endix H

42 of 115 Figure 4-23. Wolf Island 2002 vs. 2018 SO2 emi ssions multiplied by residence time.
VISTAS states evaluated these plots of re sidence time weighted by sulfate extinction plots of gridded SO2 emissions and defined the SO2 Areas of Influence as those areas with residence time greater than 10%, based on trajectories with start height of 100m and 4 start times per day.
Extinction-weighted residence plots for Class I areas indicate that the areas of probable influence for sulfate contributi on to visibility impairment ar e similar to those for nitrate contribution even though the visibility impact of nitrate is an order of magnitude smaller than that of sulfate. From these plots we can initially conclude th at it is appropriate to focus on sources that contribute to sulfate concentrations and that sources near a Class I area are more likely to contribute to visibi lity impairment than more distant sources. Gridded SO2 emissions for the sulfate extinc tion-weighted residence time plots indicate that large emissions sources at great dist ance from the Class I area can contribute to visibility impairment on some 20% worst days.

4.5 Importance of Pollutants Other Than Sulfate
For completion, the following is a brief di scussion of the importance of other PM components and contributing source categories. If NOx controls ar e considered, 54% of NOx is from point sources, 23% is from highway vehicles, and 20% is from nonroad engines. Elevated organic carbon (OC) and elemental carbon (EC) are indicators of fire. Some states may need to consider emissions from residential wood burning, forest fires Nitrate (NH4NO3) levels in winter can be si gnificant and are depende nt on availability of NH3; 88% of NH3 is from area sources (e.g. ag riculture). However, given that sulfate contributes almost 60% at OKEF and great er than 70% at COHU on the 20% worstApp endix H

43 of 115 visibility days, the GAEPD believes it is a ppropriate to focus on SO2 reductions in this first Regional Haze SIP.

4.6 Summary of Emission Invent ories Used in Cost Analysis
A necessary component of control strategy design is a thorough review of the emission inventories that are used in th e modeling of the future year base case. These inventories can shed light on the residual emissions from sources or source categories defined to be within areas of transport or impact of a Class I area. We used the most current (BaseG fall 2006) VISTAS future year (2018) base case and 2002 base year emissions to conduct this control cost analysis. Fo r purposes of listing sources within the Area of Influence, data were provided for all sources with emi ssions over distance (Q/d ) greater than 5 and maximum residence time (RTmax) greater th an 5. States independently defined the criteria for selecting which sources from th is list would be considered for reasonable control measures.
The NIF formatted and SMOKE-ready modeling files for both 2002 and 2018 base year and base cases were obtained from VI STAS emissions development and modeling contractors and converted to annual emissions and summed for the geography and domain of interest.
Tables 4-5 and 4-6 present the State breakdow n of these emissions for the entire VISTAS domain. Because the SMOKE-ready files were used in this analysis, the particulate matter transport factor (0.90 times inventor y values) is included in the PM emission summaries. This factor is applied to acc ount for the removal of a substantial portion of fugitive dust emissions near a source by surr ounding vegetation and structures when such emissions are used in regional scale modeling analyses.

Table 4-5. VISTAS 2002 Base Year Annual Emissions Summary (tons).

State VOC NOx CO SO2 PM-10 PM-2.5 NH3 AL 455986.6 508186.18 2639729.125 589958.722 229647.3855 102441.48 68365.2496 FL 1336653 1029414.61 7973550.506 597069.646 394623.2101 194814.88 61707.8367 GA 758324.4 676086.152 4640051.048 653870.886 346056.1024 141583.09 98059.1961 KY 298654.1 553437.753 1887662.518 591941.591 152553.0869 54788.97 57652.5317 MS 310938 315509.534 1283278.075 113148.074 182518.1506 45115.861 63991.004 NC 679631.8 668399.482 4410127.923 549402.424 183096.9552 87817.724 173832.4682 SC 393472.3 356431.368 2186195.351 289105.501 166503.9202 79326.939 35637.6515 TN 490647.5 588121.105 2813567.115 450018.744 168682.1637 78674.286 43281.8017 VA 464505.2 499762.38 3256848.719 421145.767 112841.3165 49843.365 55614.3642 WV 140149.3 378559.602 859397.9753 570885.53 48974.9782 28313.253 12468.711 Total 5328963 5573908.16 31950408.35 4826546.89 1985497.269 862719.84 670610.8147App endix H

44 of 115 Table 4-6. 2018 Base Case Annual Emissions Summary (tons).

State VOC NOx CO SO2 PM-10 PM-2.5 NH3 AL 327,243 269,679 1,976,259 295,593 249,430 106,872 84,746 FL 990183.7 467164.541 6003566.751 318293.487 447956.9962 193995 74420.0253 GA 562557.3 346853.445 3373857.236 212461.783 412358.7798 157692.4 123465.3064 KY 239819.6 281504.202 1422317.011 316508.674 165239.7959 57042 64504.0178 MS 249234.7 186848.555 865445.2111 48997.5324 203073.9879 52586.703 76272.3655 NC 428356 280996.238 2789482.511 175581.317 201461.5264 94657.988 197515.9242 SC 301998.5 196269.879 1585057.47 164409.353 186931.0623 86491.75 43361.5571 TN 389063 253040.352 1802311.755 207667.049 187913.2215 85495.927 48448.8788 VA 316675.2 279807.32 2103008.414 267852.827 129835.5692 56201.147 65077.764 WV 107982.3 152446.79 596152.6817 181085.221 46698.516 25158.089 15034.6498 Total 3,913,114 2,714,610 22,517,458 2,188,450 2,230,899 916,193 792,847
Our review was conducted in a top down fashi on starting with an analysis of the major source categories in the domains of interest to determine which major categories had the highest residual contribution to the area. Once the highest s ource types were identified, subcategories within those source types were reviewed. Again, a ranking of the highest residual sub source types was performed and a dditional analyses on these categories were conducted. Using the extinction weighted analysis, these emission reviews and summaries like those presented in Figure 4-24, VISTAS made a decision to further review only point source cont rols in increment to th e Clean Air Act scenario.
In addition to reviewing the residual emissi on categories in the future-year base, it was important to identify reductions that had already occurred within each category or at specific units. This allowed VISTAS States to determine if certain source categories that had yet to be controlled under the future-yea r base case had the potential for reduction or if source types already reduced have reached the full cost potential.
Finally, once each subcategory was identifie d, unit-level tables of emission comparisons from 2002 to 2018 were developed allowing VIST AS States to review existing emission reductions and providing the abil ity to assign new cost-effective controls to units using the best control for the scenario. These tables presented comparisons of 2002 and 2018 emission levels for SO2 and future-yea r control technology assignment (by IPM forecasting and State modification) for EGUs . Georgia EPD then used the processes described in Section 5.0 to identify additional reasonable controls for these units.
Ultimately, the final control strategy decisi ons will include the application of BART subject source reductions in the future-year base case.App endix H

45 of 115 Figure 4-24. VISTAS Base G2 SO2 Emissions Contribution by Major Source Sector.

Note: Sections 4.7 through 4.9 discuss the us e of AirControlNET to determine cost efficiencies of possible c ontrol measures. Georgia EPD relied upon site-specific cost efficiencies supplied by each source to identify co st-efficient control strategies. Results of AirControlNET analyses were used for comparison with source supplied information when evaluating control strategies.

4.7 Process in Preparing Files for Cost Effectiveness Curve Development
The 2018 IPM file used by VISTAS for EGU sources (RPO 2.1.9) was obtained and matched to the 2018 base-base inventory of EGU sources. This step was conducted to ensure that incremental controls assigned to these source types di d not duplicate existing base-case assumptions. Because IPM does not assign a control efficiency with each control device applied to SO 2 and NO X , we made some assumptions, based on IPM documentation, as to what pollutant-specific le vel of reduction was applied in the future0 100 200 300 400 500 600 700

Alabama Fl o r i d a Geor g ia Kentucky Mississippi Nort h Carol i na

So ut h Car o lina Tennessee Vi r ginia West VirginiaAnnual SO2 (Thousand Tons) Miscellaneous
Nonroad
Onroad
Industrial
Other Fuel Combustion
EGU 2002 Typical

2009 OTW

2018 OTWApp endix H

46 of 115 year base case runs. These assumptions, by primary and secondary control device code combinations for SO 2 , are presented in Table 4-7.
Since many of the control-technology-cont rol-cost equations within AirControlNET require additional unit-level characteristic data, we also made matches of the SMOKE IDA files to VISTAS NIF, EPA NEI, or EPA CAMD CEM data sets to obtain these variables when missing. Unit-level boiler capacity (MMBtu/hr) or NETDC (MW) values are required for capital, operating and maintena nce cost calculations for many of the SO2 applied technologies. In cases where th ese nameplate capacity values could not be identified, emission-weighted capacities (based on the final EPA 2002 NEI) were assigned to boilers using a primary (highest emitting) SCC. Table 4-8 presents these weighted capacities. Additionally, stack flow, sulfur content, and primary SCC assignment were necessary to cross-referen ce available incremental control technologies to the base-case emissions inventory data. These variables were obtained where matches could be found in priority order of VISTAS, CAMD, and other EPA datasets, respectively.

Table 4-7. IPM Post Processing Assigned Device Codes and Applied SO 2 Control Efficiencies.
Primary Device Code Secondary Device Code Description CE RE
0 0 No Control 0 0 119 0 Dry Scrubber 90 100
141 0 Wet Scrubber 90 100App endix H

47 of 115 Table 4-8. Emissions Weighted NETDC (MW) Association.

4.8 Application of AirControlNET Technologies

AirControlNET is a control t echnology analysis tool devel oped to support the US EPA in its analyses of air pollution policies and re gulations (Pechan, 2005). The tool provides data on emission sources, potential polluti on control measures and emission reductions and the costs of implementing those controls.
The core of AirControlNET is a relational database system in which control technologies are linked to sources within EPA emissions i nventories. The system contains a database of control measure applicability, efficien cy, and cost information for reducing the emissions contributing to ambien t concentrations of ozone, PM 10 , PM 2.5 , SO 2 , NO X , as well as visibility impairment (regional haze) from point, area, and mobile sources. PM 10 and PM 2.5 as included in AirCont rolNET represent primary emissions of PM. The control measure data file in AirControlN ET includes not only the technology's control efficiency, and calculated emi ssion reductions for that source, but also estimates the costs (annual and capital) for applica tion of the control measure.

Since available versions of AirControlN ET contain the preprocessed application of control technologies to a predetermined set of EPA emission inventories, direct use of the model in this analysis was not possible. Ho wever, Alpine received approval from EPA’s Innovative Strategies and Economics Group (ISEG) to modify the AirControlNET Version 4.1 source code (Sorrels, 2006) and data tables in order to ma ke it useful to this

SCC Description NETDC (MW)

10100201 External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Pulverized Coal: Wet Bottom (Bituminous Coal) 200

10100202 External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Pulverized Coal: Dry Bottom (Bituminous Coal) 500

10100203 External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Cyclone Furnace (Bituminous Coal) 200

10100212 External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Pulverized Coal: Dry Bottom (Tangential) (Bituminous Coal) 500

10100215 External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Cell Burner (Bituminous Coal) 1300

10100218 External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Atmospheric Fluidized Bed Combustion: Circulating Bed (Bitum. Coal) 200

10100222 External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Pulverized Coal: Dry Bottom (Subbituminous Coal) 400

10100223 External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Cyclone Furnace (Subbituminous Coal) 400

10100226 External Combustion Boilers; Electric Generation; Bituminous/Subbituminous Coal; Pulverized Coal: Dry Bottom Tangential (Subbituminous Coal) 500 10100401 External Combustion Boilers; Electric Generation; Residual Oil; Grade 6 Oil: Normal Firing 400 10100404 External Combustion Boilers; Electric Generation; Residual Oil; Grade 6 Oil: Tangential Firing 500 10100501 External Combustion Boilers; Electric Generation; Distillate Oil; Grades 1 and 2 Oil 400

10100601 External Combustion Boilers; Electric Generation; Natural Gas; Boilers > 100 Million Btu/hr except Tangential 400 10100701 External Combustion Boilers; Electric Generation; Process Gas; Boilers > 100 Million Btu/hr 200 10100801 External Combustion Boilers; Electric Generation; Petroleum Coke; All Boiler Sizes 600 10101204 External Combustion Boilers; Electric Generation; Solid Waste; Tire Derived Fuel : Shredded 200 10300811 External Combustion Boilers; Commercial/Institutional; Landfill Gas; Landfill Gas 200 20100101 Internal Combustion Engines; Electric Generation; Distillate Oil (Diesel); Turbine 200 20100109 Internal Combustion Engines; Electric Generation; Distillate Oil (Diesel); Turbine: Exhaust 200 20100201 Internal Combustion Engines; Electric Generation; Natural Gas; Turbine 200

All other boilers 100App endix H

48 of 115 study. The results of the applica tion of this modified version of the code still retain the applicability, efficiency, and cost informati on from the unmodified version of the source code, but were applied to the VISTAS modeling inventor ies with updated price index scalars to reflect cont rol costs in 2005 dollars.
Using the modified inventories identified above, we ran every available SO2 control strategy in AirControlNET ag ainst the EGU and non-EGU poi nt source inventories to develop a master list of available, incremental control strategies for the entire VISTAS 36 km domain necessary for VISTAS states to design command-and-control or costeffectiveness-based control stra tegies by source or domain.

4.9 Development of AOI-Based Cost Curves
Each Class I area in the VISTAS modeling do main had an associated AOI as identified earlier in this document. In order to be st determine where emi ssion reduction has the greatest benefit, this geography was designed to limit the available source type list from including all sources w ithin the entire domain.
These marginal cost curves include the applica tion of all available co ntrol measures to all applicable point sources within the AoI. The curves will allow VISTAS States to determine if, based on the generic applicat ion of SO2 control technologies from the associations originally de veloped in AirControlNET and improved upon with stakeholder input will help attain reas onable progress emission reducti on objectives while presumably minimizing the number and type of controlled sources in each AOI.
Using a geocoded county list from these AOIs, we parsed the master list of incremental control measures from all point sources locat ed within the boundaries of the AOIs. This parsed list was then sorted by incrementa l cost-effectiveness (marginal cost) basis to determine the most cost-effective control su ite available to attain emission reduction targets for SO2 within each AOI. Each indi vidual source had its own cost-effectiveness curve generated.
In aggregate, the results of these applicati ons are SO2 emission cost curves for all EGU and non-EGU point sources within the geogr aphic domain of the AOI. Incremental controls on area and mobile sources were not c onsidered in this analysis. An illustrative example of the steps involved with the co st-effectiveness curve design can be found in the Attachment H-1a of this document.

With each AOI’s cost curve, additional metr ics, calculated for the distance from the emission release point to the Class I area IMPROVE monitor (in km), the 2018 residual emissions and distance (Q/d) or squared di stance (Q/d^2), and the normalized 2018 SO2 point source emissions times distance-weighted residence time (RTMax) values for the county in which the emission release point wa s located, were calculated for incremental review and consideration. The GA EPD used th ese values to further determine control strategy development for individual Class I areas.App endix H

49 of 115 

### 5.0 REASONABLE PROGRESS ASSESSMENT

The State concluded that SO2 emissions from EGU and non-EGU point sources in geographic Areas of Influence (AOIs) are so urces to consider for further controls. VISTAS developed source matrices for SO2 AOIs for each Class I area, using data delivered by ENVIRON for 2002 and 2018 SO2 emissions multiplied by SO4 extinctionweighted residence time plots.
Spreadsheets were developed for each Class I area with:

• Plant name

• Point ID

• Tons of SO2 projected for 2018

• Q (tons)

• Current control efficiency, CE (%)

• Stationary source distance to Class I area [d (km)]

• Analysis of emissions per distance from source [Q/d and Q/d 2 ]

• RT Max, the maximum percentage of time that winds pass over that area on the way to the Class I area on the 20 worst days
The ranking refers to the source rank by Q/d wh en sources from all states are included.
In an analysis of emissions-per-distance from source, Q/d 2 is more representative when d is less than 20 km because plumes are still dispersing in two dimensions. After about 100 km, plumes only disperse in one dimension. The distance calculation is based on a single stack at a unit (when more than one are repres ented in the inventory) and is calculated in distance in km to the IMPROVE monitor at each Class I area. The residence time for the county where the source is located was review ed. It was generally agreed that states would focus on those sources where RT max wa s equal to or greater than 10%. A second matrix for sources in the SO2 AOI was de veloped for sources with a 2018 Q/d > 5 when d was greater than 100 km, Q/d 2 > 0.03 when d was less than 20 km, and Q/d > 5 or Q/d 2 > 0.03 when d was 20-100 km (these values were chosen to limit the number of sources on the list and focus on those th at are most influential).

Next, the GA EPD elected to focus on those units that contributed at least half a percent to sulfate visibility impairment at a given Cl ass I area for several re asons. First, the units with the larger contribution toward visi bility impairment would likely show an environmental benefit under a control evalua tion, and GA EPD would be able to use that environmental benefit to require controls on a given unit. Sec ondly, there are several regulatory programs that use a higher thres hold than half a percent for evaluation thresholds.
1. The BART rule specifies that a maximum impact of 0.5 dv is an acceptable threshold for establishing significance. This threshol d equates to roughly a fi ve percent change inApp endix H

50 of 115 visible perception. This same significance level is used in the Prevention of Significant Deterioration/New Source Review program for the visibility air quality related value.
2. For the CAIR rule, a PM contribution of 0.2 ug/m3 was used to demonstrate a significant impact, which is 1.3% of the annual PM2.5 standard of 15 ug/m3.
3. Lastly, when human health standa rds are proposed (NAAQS), significant impact levels are assigned which allow sources to determine their significance on air quality in the area around their facilities. Sources that demonstrate that their "contribution" from the new or modified sources is less than thes e significance levels do not have to complete any further modeling. The SILS repr esent a percentage of the NAAQS.
After looking at all averaging periods for the criteria pollutants, GA EPD determined that the one half of one percen t (0.5%) threshold proposed fo r reasonable progress was as protective or more protective than the signif icant impact levels. The most restrictive threshold identified was for NOx, which has a NAAQS of 100 ug/m3 and a significance level of 1 ug/m3, which represen ts one percent of the total.
Finally, GA EPD considered available resources to evaluate the sources in the each Class I area’s sulfate AOI. GA EPD recognized that there was neither sufficient time nor resources available to evaluate all units w ithin a given AOI. Therefore, a threshold needed to be established to determine whic h units would be evaluated. Table 5-1 shows that half a percent contributi on threshold captures greater th an 70% percent of the total point source SO2 contribution to two of Geor gia’s three Class I areas while requiring an evaluation of 30 or more units. At the rema ining area, Cohutta Wilderness Area, the half a percent threshold represen ts 69 percent of the total contribution, while requiring an evaluation of 38 units. In or der to capture a significant percentage of the total contribution, the next increment would i nvolve an evaluation of many more units. GA EPD believes that half percent threshold is appropriate, given the contribution to the total visibility impairment at each Class I area and the limited resource s available to conduct the unit-by-unit evaluation for reasonable progress.

Table 5-1. Numbers and percentage of 2018 SO2 emission units that contribute to Georgia Class I areas. Georgia Class I Area Non-Georgia class I areas COHU OKEF WOLF SAMA JOKI ROMA # Units Contributing > 0.5% 38 31 29 51 17 36
Percentage of total contribution 69% 83.16% 73.39% 86.74% 42.91% 83.61%
# Units Contributing > 0.1%, but < 0.5% 121 90 95 70 46 72
Percentage of total contribution 31.61% 26.09% 29.4% 17.73% 14.2% 22.95%
# Units Contributing < 0.1% 119 43 46 7 26 106
Percentage of total contribution 6.3% 2.57% 2.72 0.48% 1.31% 4.92%App endix H

51 of 115 The following three tables, 5-2 through 5- 4, show the analysis for COHU, OKEF, WOLF, and non-Georgia Class I Areas for source s that met the above criteria. The full spreadsheets containing all VISTAS Class I area results can be found in Attachment H-1

Table 5-2. Cohutta Wilderness Area Q/d, Q/d^2, RTMax, and fractional contribution analysis.
STATE PLANT POINT ID (2018 G) CE (%)* DIST (KM) Q/D Q/D^2 RT MAX** FRACTION

GEORGIA INLAND PAPERBOARD & PACKAGING, INC. - LIN F2 1,148 4 87.55 13.11 0.15 78.87 0.0137

GEORGIA INLAND PAPERBOARD & PACKAGING, INC. - LIN F4 3,696 4 87.55 42.22 0.48 78.87 0.0442

GEORGIA MOHAWK INDUSTRIES - SOUTH HAMILTON STREE BL06 200 0 31.93 6.28 0.20 83.31 0.0069

GEORGIA MOHAWK INDUSTRIES - SOUTH HAMILTON STREE BL07 200 0 31.93 6.28 0.20 83.31 0.0069

GEORGIA MOUNT VERNON MILLS, INC., APPAREL FABRIC EU03 351 4 67.72 5.18 0.08 78.87 0.0054

GEORGIA MOUNT VERNON MILLS, INC., APPAREL FABRIC EU04 828 4 67.72 12.22 0.18 78.87 0.0128

TENNESSEE A.E. STALEY MANUFACTURING COMPANY 005 2,982 4 109.35 27.27 0.25 38.49 0.0139

TENNESSEE ALUMINUM COMPANY OF AMERICA - SOUTH PLAN 16 2,370 0 125.75 18.85 0.15 33.77 0.0084

TENNESSEE ALUMINUM COMPANY OF AMERICA - SOUTH PLAN 17 2,320 0 125.75 18.45 0.15 33.77 0.0083

TENNESSEE BOWATER NEWSPRINT & DIRECTORY -

CALHOUN 015 5,871 4 58.30 100.71 1.73 82.90 0.1107

TENNESSEE E. I. DU PONT DE NEMOURS AND

COMPANY 0002 1,173 0 67.08 17.48 0.26 83.31 0.0193

TENNESSEE EASTMAN CHEMICAL COMPANY 021520 16,729 4 270.21 61.91 0.23 8.43 0.0069

TENNESSEE INTERTRADE HOLDINGS, INC. 001 2,530 4 32.47 77.92 2.40 82.90 0.0857

TENNESSEE U.S. DEPARTMENT OF ENERGY, Y-12 PLANT 002 2,336 4 137.55 16.99 0.12 24.66 0.0056

GEORGIA GEORGIA POWER COMPANY, BOWEN

STEAM-ELECT SG03 3,056 95 78.34 39.01 0.50 44.32 0.0233

GEORGIA GEORGIA POWER COMPANY, HAMMOND STEAM-ELE SG04 1,728 95 88.75 19.47 0.22 78.87 0.0207

GEORGIA GEORGIA POWER COMPANY, BOWEN

STEAM-ELECT SG02 2,432 95 78.34 31.05 0.40 44.32 0.0185App endix H

52 of 115 GEORGIA GEORGIA POWER COMPANY, BOWEN

STEAM-ELECT SG01 2,415 95 78.34 30.83 0.39 44.32 0.0184

ALABAMA TVA - WIDOWS CREEK 008 10,185 90 103.54 98.37 0.95 20.39 0.0270

GEORGIA GEORGIA POWER COMPANY, YATES

STEAM-ELECT SG05 6,643 0 149.41 44.46 0.30 39.91 0.0239

GEORGIA GEORGIA POWER COMPANY, YATES

STEAM-ELECT SG04 6,500 0 149.41 43.50 0.29 39.91 0.0234

TENNESSEE TVA BULL RUN FOSSIL PLANT 001 8,657 90 144.05 60.10 0.42 24.66 0.0199

ALABAMA TVA - WIDOWS CREEK 009 6,712 90 103.54 64.83 0.63 20.39 0.0178

GEORGIA GEORGIA POWER COMPANY, YATES

STEAM-ELECT SG03 3,916 0 149.41 26.21 0.18 39.91 0.0141

ALABAMA TVA - WIDOWS CREEK 005 4,839 0 103.54 46.73 0.45 20.39 0.0128

ALABAMA TVA - WIDOWS CREEK 004 4,828 0 103.54 46.63 0.45 20.39 0.0128

ALABAMA TVA - WIDOWS CREEK 002 4,821 0 103.54 46.56 0.45 20.39 0.0128

GEORGIA GEORGIA POWER COMPANY, YATES

STEAM-ELECT SG02 3,547 0 149.41 23.74 0.16 39.91 0.0127

ALABAMA TVA - WIDOWS CREEK 006 4,707 0 103.54 45.46 0.44 20.39 0.0125

ALABAMA TVA - WIDOWS CREEK 003 4,625 0 103.54 44.67 0.43 20.39 0.0122

ALABAMA TVA - WIDOWS CREEK 007 4,550 0 103.54 43.95 0.42 20.39 0.0121

SOUTH CAROLINA DUKE ENERGY:LEE 003 7,274 0 201.57 36.09 0.18 22.06 0.0107

ALABAMA ALABAMA POWER COMPANY - GORGAS 004 9,854 0 268.85 36.65 0.14 13.76 0.0068

SOUTH CAROLINA DUKE ENERGY:LEE 001 4,347 0 201.61 21.56 0.11 22.06 0.0064

SOUTH CAROLINA DUKE ENERGY:LEE 002 4,140 0 201.57 20.54 0.10 22.06 0.0061

ALABAMA ALABAMA POWER COMPANY - GORGAS 005 8,091 0 268.85 30.09 0.11 13.76 0.0056

ALABAMA ALABAMA POWER COMPANY - E C GASTON 006 7,542 90 240.83 31.32 0.13 11.99 0.0051App endix H

53 of 115 Table 5-3. Okefenokee Wilderness A rea Q/d, Q/d^2, RTMax, and fractional contribution analysis

STATE PLANT POINT ID (2018 G) CE (%)* DISTANCE (KM) Q/D Q/D^2 RT MAX** FRACTION

GEORGIA JESUP MILL, RAYONIER PERFORMANCE PB02 624 4 106.37 5.87 0.06 71.58 0.0094

GEORGIA JESUP MILL, RAYONIER PERFORMANCE PB03 1,597 4 106.27 15.03 0.14 71.58 0.0240

GEORGIA JESUP MILL, RAYONIER PERFORMANCE RF01 333 0 106.22 3.14 0.03 71.58 0.0050

GEORGIA JESUP MILL, RAYONIER PERFORMANCE RF04 334 0 105.88 3.15 0.03 71.58 0.0050

FLORIDA ANCHOR GLASS CONTAINER CORPORATION 3 206 0 59.04 3.49 0.06 65.70 0.0051

FLORIDA ANCHOR GLASS CONTAINER CORPORATION 4 212 0 59.04 3.60 0.06 65.70 0.0053

FLORIDA CEDAR BAY GENERATING COMPANY L P GEN1 2,227 0 61.17 36.41 0.60 65.70 0.0534

FLORIDA GEORGIA-PACIFIC CORP. PULP/PAPER MILL 15 4,329 0 125.55 34.48 0.27 22.94 0.0177

FLORIDA GEORGIA-PACIFIC CORP. PULP/PAPER MILL 16 1,581 4 125.55 12.59 0.10 22.94 0.0064

FLORIDA IFF CHEMICAL HOLDINGS, INC. 3 733 0 56.66 12.94 0.23 65.70 0.0190

FLORIDA JEFFERSON SMURFIT CORPORATION (US) 15 3,639 4 64.58 56.35 0.87 71.58 0.0900

FLORIDA JEFFERSON SMURFIT CORPORATION (US) 6 300 0 64.42 4.66 0.07 71.58 0.0074

FLORIDA MILLENNIUM SPECIALTY CHEMICALS 5 269 0 60.46 4.45 0.07 65.70 0.0065

FLORIDA MILLENNIUM SPECIALTY CHEMICALS 6 590 0 60.46 9.76 0.16 65.70 0.0143

FLORIDA PROGRESS ENERGY FLORIDA, INC.

CRYSTAL RI 1 13,537 0 205.80 65.78 0.32 5.47 0.0080

FLORIDA PROGRESS ENERGY FLORIDA, INC.

CRYSTAL RI 2 15,241 0 205.80 74.06 0.36 5.47 0.0090

FLORIDA RAYONIER PERFORMANCE

FIBERS LLC 6 1,256 0 63.34 19.83 0.31 71.58 0.0317

FLORIDA SAINT JOHNS RIVER 16 5,882 90 65.12 90.33 1.39 65.70 0.1325

FLORIDA SAINT JOHNS RIVER 17 7,420 90 65.12 113.9 5 1.75 65.70 0.1671App endix H

54 of 115 FLORIDA SEMINOLE ELECTRIC COOPERATIVE, INC. 1 6,779 95 121.83 55.64 0.46 22.94 0.0285

FLORIDA SEMINOLE ELECTRIC COOPERATIVE, INC. 2 6,508 95 121.83 53.42 0.44 22.94 0.0274

FLORIDA WHITE SPRINGS AGRICULTURAL CHEMICALS,INC 66 1,496 0 69.96 21.39 0.31 48.81 0.0233

FLORIDA WHITE SPRINGS AGRICULTURAL CHEMICALS,INC 67 1,308 0 69.96 18.69 0.27 48.81 0.0204

GEORGIA SAVANNAH ELECTRIC: MCINTOSH STEAM - ELEC SG01 7,015 0 201.59 34.80 0.17 15.51 0.0124

FLORIDA PROGRESS ENERGY FLORIDA, INC.

CRYSTAL RI 2 15,241 0 205.80 74.06 0.36 5.47 0.0093

GEORGIA SAVANNAH ELECTRIC: KRAFT STEAM - ELECTRI SG03 4,474 0 182.45 24.52 0.13 15.53 0.0088

GEORGIA GEORGIA POWER COMPANY, MITCHELL STEAM-EL SG03 4,930 0 206.79 23.84 0.12 14.43 0.0079

SOUTH CAROLINA SCE&G:CANADYS 001 5,203 0 295.33 17.62 0.06 16.79 0.0068

SOUTH CAROLINA SCE&G:CANADYS 002 5,144 0 295.35 17.42 0.06 16.79 0.0067App endix H

55 of 115 Table 5-4. Wolf Island Q/d, Q/d^2, RTMax, and fractional contribution analysis.

STATE PLANT POIN (2018 G) CE (%)* DISTANCE (KM) Q/D Q/D^2 RT MAX* FRACTION

GEORGIA GEORGIA-PACIFIC BRUNSWIC K F1 1,842 4 28.39 64.89 2.29 73.01 0.1255

GEORGIA GEORGIA-PACIFIC BRUNSWIC K M24 193 0 28.39 6.80 0.24 73.01 0.0131

GEORGIA GEORGIA-PACIFIC CORP SAVANNAH BO01 1,844 90 109.91 16.78 0.15 25.08 0.0111

GEORGIA GEORGIA-PACIFIC CORP SAVANNAH BO02 1,415 90 109.95 12.87 0.12 25.08 0.0086

GEORGIA GEORGIA-PACIFIC CORP SAVANNAH BO03 1,282 90 109.99 11.66 0.11 25.08 0.0077

GEORGIA INTERNATIONAL PAPER - SAVANNAH PB13 8,578 4 85.82 99.96 1.16 25.08 0.0664

GEORGIA INTERSTATE PAPER LLC F1 188 0 44.67 4.20 0.09 46.19 0.0051

GEORGIA SAVANNAH ELECTRIC: KRAFT SG01 691 0 90.18 7.66 0.08 25.08 0.0051

GEORGIA SAVANNAH ELECTRIC: KRAFT SG02 704 0 90.18 7.80 0.09 25.08 0.0052

GEORGIA SAVANNAH ELECTRIC: KRAFT SG03 4,474 0 90.18 49.62 0.55 25.08 0.0330

GEORGIA SAVANNAH ELECTRIC: MCINTOSH SG01 7,015 0 112.72 62.24 0.55 25.08 0.0413

GEORGIA SAVANNAH SUGAR REFINERY U161 997 4 89.65 11.13 0.12 25.08 0.0074

GEORGIA SOUTHERN STATES PHOSPHATE & SA02 808 0 83.92 9.62 0.11 25.08 0.0064

SOUTH CAROLINA GIANT CEMENT CO 005 3,893 0 222.58 17.49 0.08 14.78 0.0068

SOUTH CAROLINA HOLCIM:HOLLY HILL 002 4,074 0 235.40 17.31 0.07 12.19 0.0056

SOUTH CAROLINA MEADWESTVACO CORPORATION INC 006 2,706 0 213.39 12.68 0.06 24.20 0.0081

SOUTH CAROLINA SANTEE COOPER CROSS 002 3,380 87 244.36 13.83 0.06 14.73 0.0054

SOUTH CAROLINA SANTEE COOPER CROSS 3 3,835 0 244.36 15.69 0.06 14.73 0.0061

SOUTH CAROLINA SANTEE COOPER JEFFERIES 003 6,761 0 244.62 27.64 0.11 14.73 0.0108

SOUTH CAROLINA SANTEE COOPER JEFFERIES 004 6,609 0 244.62 27.02 0.11 14.73 0.0105

SOUTH CAROLINA SCE&G:CANADYS 001 5,203 0 201.47 25.83 0.13 24.20 0.0166

SOUTH CAROLINA SCE&G:CANADYS 002 5,144 0 201.47 25.53 0.13 24.20 0.0164

FLORIDA SAINT JOHNS RIVER 17 7,420 90 105.18 70.55 0.67 50.75 0.0976

FLORIDA SAINT JOHNS RIVER 16 5,882 90 105.18 55.92 0.53 50.75 0.0774

FLORIDA CEDAR BAY GENERATING GEN1 2,227 0 107.25 20.77 0.19 50.75 0.0287

FLORIDA SEMINOLE ELECTRIC COOPERATIVE
, INC. 1 6,779 95 182.71 37.10 0.20 15.88 0.0161

FLORIDA SEMINOLE ELECTRIC COOPERATIVE
, INC. 2 6,508 95 182.71 35.62 0.19 15.88 0.0154App endix H

56 of 115 Table 5-5. Non-Georgia Class I Area s Q/d, Q/d^2, RTMax, and fractional contribution analysis (Georgia sources only)

STATE PLANT POINT ID (2018 G) CE (%)* DISTANCE (KM) Q/D Q/D^2 RT MAX** FRACTION

SAINT MARKS

GEORGIA GEORGIA PACIFIC CORPORATION, CEDAR SPRINGS U500 2428 4 149.70 16.22 0.11 12.89 0.0108

GEORGIA GEORGIA PACIFIC CORPORATION, CEDAR SPRINGS U501 2533 4 149.70 16.92 0.11 12.89 0.0113

GEORGIA GEORGIA PACIFIC CORPORATION, CEDAR SPRINGS R402 1726 0 149.78 11.53 0.08 12.89 0.0077

GEORGIA GEORGIA POWER COMPANY, PLANT MITCHELL SG03 4930 0 150.49 32.76 0.22 16.16 0.0274

GEORGIA INTERNATIONAL PAPER SAVANNAH MILL PB13 8578 4 366.31 23.42 0.06 5.81 0.0070

GEORGIA SAVANNAH ELECTRIC, PLANT MCINTOSH SG01 7015 0 380.33 18.44 0.05 5.81 0.0055

GEORGIA MILLER BREWING CO B001 1 1073 0 164.59 6.52 0.04 15.16 0.0054

GEORGIA PACKAGING CORPORATION OF AMERICA 1017 653 0 105.83 6.17 0.06 15.90 0.0051

JOYCE KILMER/SLICKROCK

GEORGIA INLAND PAPERBOARD & PACKAGING F4 3696 4 175.7 21.04 0.12 29.12 0.0099

CAPE ROMAIN

GEORGIA INTERNATIONAL PAPER SAVANNAH MILL PB13 8578 4 165.98 51.68 0.31 31.04 0.0155

GEORGIA SAVANNAH ELECTRIC, PLANT MCINTOSH SG01 7089 0 155.93 44.99 0.29 31.04 0.0135

GEORGIA SAVANNAH ELECTRIC, PLANT KRAFT SG03 3992 0 165.25 27.08 0.16 31.04 0.0081

SHINING ROCK

GEORGIA GEORGIA POWER, PLANT SCHERER SGO3 27,735 0 276.79 100.2 0.36 8.61 0.0071

GEORGIA GEORGIA POWER, PLANT SCHERER SGO4 26,616 0 276.79 96.16 0.35 8.61 0.0068

GEORGIA GEORGIA POWER, PLANT SCHERER SGO1 26,264 0 276.79 94.89 0.34 8.61 0.0067

GEORGIA GEORGIA POWER, PLANT SCHERER SGO2 26,075 0 276.79 94.20 0.34 8.61 0.0067

1 Note that Boiler B002 at Miller Brewing Company is identical to B002 and was originally included in Georgia’ s 4-factor list along with B001.App endix H

57 of 115 GEORGIA GEORGIA POWER, PLANT YATES SG07 16,530 0 290.63 56.88 0.20 12.35 0.0058

GEORGIA GEORGIA POWER, PLANT YATES SG06 16,367 0 290.63 56.31 0.19 12.35 0.0057

SIPSEY

GEORGIA GEORGIA POWER, PLANT YATES SG07 16,530 0 245.30 67.39 0.27 15.03 0.0111

GEORGIA GEORGIA POWER, PLANT YATES SG06 16,367 0 245.30 66.72 0.27 15.03 0.0110

GEORGIA GEORGIA POWER, PLANT SCHERER SGO3 27,735 0 356.99 77.69 0.22 6.86 0.0059

GEORGIA GEORGIA POWER, PLANT SCHERER SGO4 26,616 0 356.99 74.56 0.21 6.86 0.0056

GEORGIA GEORGIA POWER, PLANT SCHERER SGO1 26,264 0 356.99 73.57 0.21 6.86 0.0056

GEORGIA GEORGIA POWER, PLANT SCHERER SGO2 26,075 0 356.99 73.04 0.20 6.86 0.0055

SWANQUARTER

GEORGIA INTERNATIONAL PAPER SAVANNAH MILL PB13 8578 4 587.64 14.60 0.02 24.37 0.0086

GEORGIA SAVANNAH ELECTRIC, PLANT MCINTOSH SG01 7089 0 573.33 12.24 0.02 24.36 0.0072

Note that only units located within Georgia are listed in Table 5.5. Since Georgia EPD is only responsible for Four Factor Analyses fo r sources located in Georgia that impact nonGeorgia Class I areas, units ou tside of Georgia that exceed the threshold value of 0.5% are not included.
Spreadsheets were developed for each Class I area source with Q/d > 3 or Q/d 2 > 0.03, indicating the plant name, industry descri ption, emissions projections for 2018, current control efficiency (CE%), distan ce from Class I area, Q/d, Q/d 2 , RT average, RT maximum, Q/d*RT, the fraction a single s ource contributes to the total Q/d*RT for a Class I area.

The GA EPD reviewed the sources for whic h the Four Factor Analysis was to be conducted. The first review was of existing permit and compliance files, pending permit applications and information provided by EPD permitting staff. As a result, three emissions units were determined to have permanently ceased operation and were thus removed from the list. These three sources were: Inland Paperboard I Packaging Inc. – Unit F2; and Mohawk Industries, South Hamilton Street – Units BL06 and BL07.
GA EPD then requested site-specific informa tion for use in the Four Factor Analyses from each source on March 21, 2007. As a resu lt of this inquiry, updated projected 2018 emissions were received from some facilities. Other facilities requested emission limitsApp endix H

58 of 115 to reduce the percentage of an emission unit’s contribution to sulfate visibility impact on the nearest Class I area to less than the thre shold value of 0.5%. Table 5-6 gives a list of the units that were removed as a result of updated emission projections or emission limitations.

Table 5-6. Units dropped fr om four-factor analysis
STATE PLANT POINT ID REASON FOR REMOVING UNIT FROM FOUR FACTOR ANALYSIS

GEORGIA MOUNT VERNON MILLS, INC., APPAREL FABRIC EU03 Facility updated their 2018 emi ssion information, resulting into lowering of sulfate visibility impact to less than 0.5 %

GEORGIA MOUNT VERNON MILLS, INC., APPAREL FABRIC EU04 Facility updated their 2018 emi ssion information, resulting into lowering of sulfate visibility impact to less than 0.5 %

GEORGIA JESUP MILL, RAYONIER PERFORMANCE FIBERS PB02 Facility took voluntary limit on un its eligible for four factor analyses, resulting into lowering of sulfate visibility impact to less than 0 5 % GEORGIA JESUP MILL, RAYONIER PERFORMANCE FIBERS PB03 Facility took voluntary limit on un its eligible for four factor analyses, resulting into lowering of sulfate visibility impact to less than 0 5 % GEORGIA JESUP MILL, RAYONIER PERFORMANCE FIBERS RF01 Facility took voluntary limit on units eligible for four factor analyses, resulting into lowering of sulfate visibility impact to less than 0 5 % GEORGIA JESUP MILL, RAYONIER PERFORMANCE FIBERS RF04 Facility took voluntary limit on un its eligible for four factor analyses, resulting into lowering of sulfate visibility impact to less than 0 5 % GEORGIA SOUTHERN STATES PHOSPHATE & FERTILIZER C SA02 Facility took voluntary limit on un its eligible for four factor analyses, resulting into lowering of sulfate visibility impact to less than 0 5 % GEORGIA SAVANNAH SUGAR REFINERY U161 Facility updated their 2018 emi ssion information, resulting into lowering of sulfate visibility impact to less than 0.5 %

GEORGIA PACKAGING CORPORATION OF AMERICA CE Facility took voluntary limit on un its eligible for four factor analyses, resulting into lowering of sulfate visibility impact to less than 0 5 % GEORGIA MILLER BREWING B001 Facility updated their 2018 emi ssion information, resulting into lowering of sulfate visibility impact to less than 0.5 %

GEORGIA MILLER BREWING B002 Facility updated their 2018 emi ssion information, resulting into lowering of sulfate visibility impact to less than 0.5 %

GEORGIA INTERSTATE PAPER LLC F1 Facility subject to full BART determination analysis. Exempt from 4-factor analysis per EPA guidance.

Georgia EPD conducted Four Factor Analyses on the remaining Georgia Sources on the lists. For Non-Georgia units whose percentage contribution to sulfate visibility impact on a Georgia Class I area of 0.5% or greater, Ge orgia EPD requested, in writing, that the state that the unit is located in conduct a Four Factor Analysis on the unit to determine and require reasonable control measures. A copy of the letter sent to those states is found in Appendix J of the SIP narrative.App endix H

59 of 115 

### 6.0 FOUR FACTOR ANALYSIS

Section 169A(g)(1) of the Clean Air Act (CAA) and 40 CFR 51.308(d)(1)(i)(A). Four statutory factors that are outlined have been identified as mandatory for purposes of establishing a reasonable progress goal for a ny mandatory Class I area within a State: cost of compliance, time necessary for compliance, energy and non-air quality environmental impacts of compliance, and re maining useful life of any existing source subject to such requirements. These statut ory factors would be sp ecific to the sources being considered for additional controls.

6.1 The Four Statutory Factors
6.1.1 Cost of Compliance
The “cost of compliance” is a factor used to determine whether compliance costs for sources are reasonable compared to the emission reduction and visibility improvement they will achieve. Note that visibility impr ovement is not only related to tons of pollutant removed, but also involves how the pollutant or chemical compound affects the extinction coefficient in each Class I area. For example, on a pound-to-pound comparison, sulfate particulate im pairs visibility greater than coarse particulate. Costs should be determined for one-time capital costs, as well as ongoing annual operation and maintenance.
6.1.2 Time Necessary for Compliance
The “time necessary for compliance” factor may be used to adjust the reasonable progress goals to reflect th e degree of improvement achie vable within the long-term strategy period, as opposed to the improvement expected at full implementation of a control measure, if the time needed for full compliance exceeds the length of the longterm strategy period. For instance, if impl ementation of a control measure could not be completed within the schedule outlined by the control strategy, the RPG should reflect what visibility improvement could be expected within the strategy period.
For example, if construction labor availabi lity constraints preclude the installation of controls at all sources of a particular category within the LTS period, the RPG should reflect the visibility improvement anticipat ed from installation of controls at the percentage of sources that could be controll ed within the strategy period. (The SIP could still include contro l strategies that extend beyond the 2018 milestone. In the above example, the visibility improvement anticipat ed from installation of controls at the percentage of sources that c ould not be controlled within the first strategy period would have to be counted in a later SIP.)App endix H

60 of 115 6.1.3 Energy and Non-Air Quality Envi ronmental Impacts of Compliance
The “energy and non-air quality environmental impacts of compliance” factor is meant to consider whether the energy requirements of the control technology result in energy penalties or benefits. For example, contro ls on diesel engines may decrease the overall efficiency and require a signif icant increase in diesel fuel consumption. The State should also consider any significan t or unusual non-air environmental impacts such as a waste stream that may be generated by a particular control technology.
6.1.4 Remaining Useful Life of the Source
The statutory factor of the “remaining useful life of the source” is applicable only to those measures which would require retr ofitting of control devices (or possibly production changes) at existing sources. In such cases, this factor should be treated as one element of the overall cost analysis. Fo r purposes of this an alysis, the remaining useful life is the difference be tween the year of the reasona ble progress analysis and the date the facility permanently stops operations. The remaining useful life of a source affects the annualized cost s of retrofit controls.

6.2 Evaluating Four Statutory Factors fo r Specific SO2 Emissions Sources in Each Area of Influence
The next step was to identify emission reduc tions 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-yea r control technology from IPM forecasting and State modification for EGU and from c ontrol-efficiency tables for non-EGU point

sources.
Once emission control profiles for specific uni ts were defined, the next step is to determine what, if any, additi onal control measures would f easibly 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 di d not duplicate existing base-case assumptions.

For non-EGUs, VISTAS used EPA’s AirContro NET database, modified for the VISTAS emission inventories. The core of AirCont rolNET is a relational database system in which control technologies are linked to source s within EPA emissions inventories. The system contains a database of control measure applicability, efficiency, and cost information for reducing emissions. The co ntrol measure data file in AirControlNET includes not only the technology's control effi ciency and calculated emission reductionsApp endix H

61 of 115 for that source, but also estimates the cost s (annual and capital) fo r application of the control measure.
Using the modified inventories identified above, VISTAS ran every available SO2 control strategy in AirC ontrolNET against the EGU and non-EGU point source inventories to develop a master list of incremental control strategies for each unit in the VISTAS 36 km domain.

For the sources within the AOIs for each Geor gia Class I area, Georgia requested sitespecific analysis from the affected fac ilities. Data obtained from AirControlNet (Appendix H.3) was used for comparison with site-specific analyses to identify possible control options that need to be evaluate d and to determine possible over-estimation or under-estimation of cost efficiencies.
The Regional Haze Rule requires that stat es consider the following factors and demonstrate how these factors were taken into consideration in selecting the reasonable progress goal:

• 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 a ny potentially affected sources.
The general considerations take n into account in Georgia EP D’s 4-factor evaluations and the evaluations of the specific em issions units are presented below.
6.2.1 Cost of Compliance
Georgia EPD evaluated cost of compliance using a variety of factors. Facilities provided data for various control options for each affect ed unit. The data t ypically included total capital costs and cost-per-ton of pollutant removed ($/ton). Georgia EPD compared this data with similarly-controlled units as well as AirControlNET estimates. Costs were presented in 2007 dollars for consistency. In addition, Georgia EPD conducted CMAQ modeling to determine the amount of visibi lity improvement resulting from each ton of SO2 reduced from each unit subject to analysis. This cost-efficiency metric is expressed in dollar-per-inverse megameter ($/Mm-1) and was considered in addition to cost efficiency expressed in $/ton. This approach is similar to the approach used by Georgia EPD to evaluate cost efficiency for other SIPS (e.g., ozone and PM2.5).
6.2-2 Time Necessary for Compliance
Georgia EPD based the time necessary for compliance on the installation and operation of controls by the beginning of the year 2012. Since the next Regional Haze SIP will be due December 17, 2017, a compliance date of 2012 will result in visibility improvement for all five years of monitoring data (2012, 2013, 2014, 2015, and 2016) that will be assessed to determine conformity with 2018 pr ogress goals at the time the next RegionalApp endix H

62 of 115 Haze SIP is due. Any control options that can be installed and operated prior to 2012 were determined to be timely. Control options that cannot be instal led and operated prior to 2012, but can be prior to 2018, would not re sult in the optimal amount of visibility improvement by the 2018 reasonable progress goa l, but were still considered. Control options that could not be installed and ope rated until 2018 or afte r were not considered for this Regional Haze SIP.
6.2-3 Energy and Non-Air Environmental Impacts

Additional factors that were considered were the energy requirements of the control technology and the non-air impacts (that is , impacts on other environmental media). Energy impacts were generally included in the cost-efficiency estimates. Non-air impacts that were considered included solid wast e disposal, water withdrawal, and wastewater discharge. The review of control strategies that were not eliminated because of excessive cost efficiencies and that involved wate r withdrawal and wastewater discharge were coordinated with Georgia EPD’ s Watershed Protection Branch.
6.2-4 Remaining Useful Life
The remaining useful life of an affect ed emissions unit can potentially impact reasonableness of control strategies in two ways. If the remaining useful life of an affected emission unit did not extend past 2018, no controls would be considered for that unit. If the remaining useful life of a unit was less than the amortization period for a particular control option, the shorter period was used in annualizing the capital costs of the control option when determining the co st efficiency. It should be noted that remaining useful life was not an issue for a ny of the control options reviewed by Georgia EPD.
6.3 Process of Applying Statutory Factors
The Georgia EPD has completed the reasonable progress assessment for the first regional haze SIP. This process was based on U.S. EPA’s “Guidance for Setting Reasonable Progress Goals Under the Regional Haze Pr ogram,” June of 2007 [Reasonable Progress guidance]. The following was Georgia EPD’ s Process on reasonable progress assessment for the current time period (from the present to 2018):
Step 1: Determine pollutants of concern.
VISTAS evaluated the species contribution on the 20 percent worst visibility days and concluded that sulfate account ed for greater than 70 percen t of the visibility-impairing pollution. The VISTAS States concluded that controlling SO2 emissions was the appropriate step in addressing the reas onable progress assessment for 2018. The VISTAS findings were consistent with th e findings of the S outhern Appalachian Mountain Initiative (SAMI). As you may recall, SAMI confirmed that sulfate particles account for the greatest portion of the haze affecting Class I ar eas in the SouthernApp endix H

63 of 115 Appalachian region and that these sulfates were produced in large part from SO2 emissions from coal combustion.
Step 2: Determine which source sectors shoul d be evaluated for reasonable progress.
Since SO2 point source emissions in 2018 are projected to represent greater than 95 percent of the total SO2 emissions inventory, the VISTAS States concluded that the focus should be on electric generating unit (E GU) and non-EGU point sources of SO2 emissions.
Step 3: Consideration of Emissions Reductions from State and Federal Control Measures.

The Georgia EPD evaluated the amount of SO 2 reduction from the EGU sector resulting from the implementation of Georgia Rule 391-3 -1-.02(2)(sss) - Multi pollutant Control for Electric Utility Steam Generating Units, CA IR, and other enforceable mechanisms as reported by other VISTAS states. The EGUs in Georgia are expected to reduce their SO2 emissions by greater than 90 percent by 2018. This reduction is th e result of Georgia Rule 391-3-1-.02(2)(sss). This rule requires the instal lation and operation of specific control equipment on specific EGUs in Georgi a by specific deadlines. In contrast, the SO2 emission reductions outside of Georgia th at are predicted by IPM to meet the CAIR requirements are not as certain due to CAIR ’s reliance on unchecked trading and the use of banked Title IV allowances (with certain exceptions such as North Carolina’s Clean Smokestacks Act and controls required by consent agreements). The Georgia EPD intends to re-evaluate the IPM predictions of SO2 reductions for CAIR outside of Georgia at the time of our next periodic report in 2012 to ensure that the reductions currently predicted by IPM for CAIR are in f act taking place where they are expected and needed.
Based on the controls required by Georgia Rule 391-3-1-.02(2)(sss), and predicted by IPM under CAIR for EGUs outside of Georgia, and the fact that th e expected visibility improvement resulting from these requirements will clearly meet the glide slope (see Figure 7.2.4.1 of narrative) we have concluded that at this time th ese existing regulatory programs constitute reasonable progress for Ge orgia EGUs affecting visibility at Cohutta Wilderness Area. Note, however, that on July 11, 2008, the U.S. District Court of Appeals in the District of Columbia vacated th e Clean Air Interstate Rule. As of the date of writing of this SIP, the mandate for that decision has not been issued. Should the vacatur of CAIR stand, the Division may n eed to revise this conclusion regarding reasonable progress for the Cohutta Wilderne ss Area. However, these same existing controls do not predict that the visibility im provement at Okefenokee or Wolf Island will clearly meet the glide slope (see Figures 7.2.4.2 and 7.2.4.3 of Narrative). Therefore, we have determined that CAIR does not satisfy the requirements for reasonable progress for those EGUs impacting Okefenokee Wilderness Area and Wolf Island.App endix H

64 of 115 Step 4: Determine which emission units would be evaluated based on impact.
Georgia EPD calculated the fractional contribution from all emission units within the SO2 Area of Influence for a given Class I area and identified those emission units with a contribution of half of a percent or more.
Step 5: Evaluate the four factors.
Each emission unit identified in Step 4 a bove 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 imp acts of compliance, and 4) the remaining useful life of the emissions unit. Georgia EPD believes that the cost of compliance is typically the dominant factor in whether a s ource will be required to install controls. Therefore, much emphasis was placed on cost of control as part of this reasonable progress assessment, as compared to the other three statutory factors.

6.4 Evaluation of Four-factor Eligible Emissions Units and Recommendations
The Georgia EPD focused the reasonable prog ress assessments on specific units at the following non-EGU and EGU facilities in the State:
Georgia Pacific Brunswick Cellulose Class I Areas: Wolf Island, Okefenokee Area Units (Wolf Island): F-1 (Power Boiler), M-24 (Recovery Boiler) Units (Okefenokee Wilderness Area): F-1 (Power Boiler)
Georgia Pacific Cedar Springs Class I Area: St. Marks Area, Florida Units: U-500 (Power Boiler), U-501 (Pow er Boiler), R-402 (Recovery Boiler)
Georgia Pacific Savannah River Class I Area: Wolf Island Units: B001, B002, B003
International Paper Savannah Mill Class I Area: Wolf Island, Okefenokee, St . Marks, Florida, Swanquarter, North Carolina, Cape Romain, South Carolina Units: PB-13

Interstate Paper Class I Area: Wolf Island Units: Power Boiler F1
Temple Inland Rome Class I Area: Cohutta Wilderness AreaApp endix H

65 of 115 Units: Power Boiler 4
Georgia Power Plant Kraft Class I Area: Wolf Island, Okefenokee Wilderness Area, and Cape Romain, SC NC Units: SG01, SG02 and SG03
Georgia Power Plant McIntosh Class I Area: Wolf Island, Okefenokee , St. Marks Wilderness Area, FL, Cape Romain, SC, Swanquarter, NC Units: SG01
Georgia Power Plant Mitchell Class I Area: Okefenokee and St. Marks, NC Units: SG03
Table 6.1 and 6.2 show the summary of assessment of 4-factor analysis for each of the emission units. Abbreviations and co lor codes are listed below Table 6.2.
In March of 2007, Georgia EPD requested 4-fact or analyses from affected facilities for each of their emissions units that were d eemed eligible (Appendix H.4). In May 2007, EPD sent a follow-up letter extending the deadline for submission of the 4-factor analyses and providing further guidance on the compliance deadline for any controls determined to be reasonable. As discussed in the narrative, eligible units were those emissions units likely to contribute more th an 0.5% to the total visibility impairment caused by sulfate at any Class I area in 2018. Fa cilities were request ed to evaluate the feasibility of additional SO2 controls on these units based on the four statutory factors required to be analyzed in the se tting of reasonable progress goals:
1) The costs of compliance;
2) The time necessary for compliance;
3) The energy and non air quality environmental impacts of compliance; and
4) The remaining useful life of existing sources that contributes to visibility impairment.
The first step was to screen each unit using the fourth factor: remain ing useful life of the source. If the remaining useful life ex tended beyond 2018, EPD requested that the other three statutory factors be analyzed us ing a “top-down” approach as follows:
Step 1: Identification of all control technologies;
Step 2: Elimination of tec hnically infeas ible options;App endix H

66 of 115 Step 3: Ranking of remaining control techno logies (top down) by control effectiveness;
Step 4: Application of the first three statutor y factors (cost of compliance, time necessary for compliance, energy and non air quality environmental impacts) to the control technologies identified in Step 3 and documentation of the results; and
Step 5: Selection of control technology.
In general, the cost estimates provided in the facilities’ submittals followed the methods outlined in EPA’s Air Pollution Control Cost Manual (EPA/452/B-02-001). Company cost estimates were typically prepared as st udy-level estimates, which are presumed to be accurate to within +/- 30 percen t. Study-level estimates are appropriate for the 4-factor analysis.
EPD reviewed the four-factor analyses receiv ed from the facilities to determine what, if any, controls may be reasonable to implement. Additional information was requested as needed. After reviewing all of the analyses, a list of emi ssions units was developed along with information regarding the four factors fo r all technically-feasible controls identified (see Table FF-1). EPD identified emissions units and associated controls that were deemed to be “reasonable” and then met with staff from affected facilities for discussion of controls, further communication of the r easonable progress goals, and verification of facility data.
Georgia EPD used a number of factors to determine what should be considered “reasonable” for each emissions unit on a case -by-case basis. Incl uded in these factors were the magnitude of the contribution to visibi lity impairment at the nearest Class I area, the number of Class I areas for which the emi ssions unit contributed to sulfate visibility impairment above the 0.5% “threshold,” the impact on PM 2.5 non-attainment areas, existing SO 2 controls, and cross-comparison of facili ty cost estimates to an EPA database (Air Control Net) and to other facility’s estimates (for comparably-sized control equipment).
The results of the 4-factor analysis and review process are summarized for each eligible unit in Tables 6.1 and 6.2. An entry of “bel ow threshold” means that the sulfate-related visibility impairment to the specific Cl ass I area was below 0.5 %. Table 6.3 provides explanatory notes for the various fields (c olumns) in Tables 6.1 and 6.2. The following subsections provide details of the 4-factor analysis and decision process for each eligible unit.App endix H

67 of 115 Table 6-1. Summary of Four-factor An alyses – South Georgia FacilitiesApp endix H

68 of 115App endix H

69 of 115App endix H

70 of 115App endix H

71 of 115App endix H

72 of 115App endix H

73 of 115 Table 6-2. Summary of Four-Factor An alyses - North Georgia FacilitiesAppendix H74 of 115Table 6.3. Explanatory Notes for Summaryof Four-Factor Analyses and MetricsColumnSourceNotesClass I sulfateimpact fractionP&SModelingunitThis is the impact of sulfate emissions fromthe specific emissions unit on visibility in thespecified Class I area, expressedas a fraction of the combined sulfate impacts of all of theimpacting facilities. The impact given isbased on the VISTAS SO2 projected 2018 emissions.Threshold for inclusion in 4-factor analysis was impact of 0.005 (0.5 %) or more.4F baseline SO2companyThe baseline emissions used by the facility in the 4-factor submittal. In the cases of U500 andU501 at GA Pacific Cedar Springs, the number represents emissions after an existing venturiscrubber with water scrubbant (they estimate 30% SO2 control).In the case of GA PacificSavannah River, all 3 emissions units currently perform limestone injection for SO2 removal.Baseline basiscompanyIn many instances the company used a different baseline emissions level than the VISTASprojected value. A blank field means that thecompany did not provide a full evaluation and thatthe VISTAS value has been inserted into the baseline field.ControlTechnologycompanyControl, deemed technically feasible by the facilty, evaluated for reduction of SO2 emissions.ControlEffectivenesscompanyPercent reduction as presented in the facility’s 4-factor analysis. In cases where the facility didnot list a control effectiveness, this was back-calculated from the tons of reductions.Annual cost,companycompanyCompany cost estimates were typically prepared as study-level estimates, accurate to within +/-30 percent.The costs are assumed to be in 2007 dollars(some companies stated this explicitly).The annual cost is the sum of the annual operation and maintenance costand the total installedcost (capital cost), annualized over the life ofthe project. Project lives ranged from 15 to 20years, interest rates from 7% to 10%,and capital recovery factors from 0.094 to 0.117.Cost/toncalculationThe annual cost divided by the tonsreduced from the 4F baseline.Annual Cost,ACNetAir ControlNetThe estimated annual cost from a query of EPA’s Air Control Net database. The costs listedcame from queries which found matches for the applicable technology atthe specific unit andfacility. The queries provide estimates in 2004dollars. This number was then escalated to 2006dollars using the Chemical Engineering Plant Cost Index.Appendix H75 of 115Cost ratio,company toACNetcalculationComparison of company estimate to ACNet estimate.Yellow cell indicates a ratio that is greaterthan 1.3.Statutory factorsCompanyA “0” indicates that the submittal provided no information pertaining to the specific factor. Ablue cell indicates that the company has indicated that the option could be implemented prior toJan. 1, 2012. This would allow the control measure to be in place during all five years thatvisibility data will be collected for the 2018 Regional Haze progress report.The expected usefullife of all of the emissions unitsis 2018 or later. The expected useful life of all of the emissionsunits also exceeds the project life (i.e., amortization period) of all control options considered.VisibilitySensitivity:Tons SO2/Mm-1P&SModelingunitThe tons of SO2 required toeffect a reduction of 1 Mm-1(inverse megameter) in the applicableClass I area. This value is independent of the control technology.VisibilitySensitivity:$106/Mm-1CalculationThe cost, based on the specific technology, to effect a reduction of 1 Mm-1(inverse megameter)in the applicable Class I area. This is the product of $/ton and ton/ Mm-1. This figure, along withdollars per ton ($/ton) was usedin evaluating cost efficiency.VisibilitySensitivity:Tons SO2/Mm-1CalculationThe extinction reduction (visibility improvement) in Mm-1that would be achieved in theapplicable Class I area by implementation of the specific control.An orange cell indicates anextinction reduction ofgreater than 0.010 and less than 0.100.A light green cell indicates anextinction reduction ofgreater than 0.100.App endix H

76 of 115 6.4.1 GA Pacific Brunswick Cellulose, Power Boiler No. 4 (F1)
Georgia Pacific’s Brunswick Cellulose facili ty is located in Glynn County, near the Georgia coast. Power Boiler No. 4 is an 800 MMBtu/hr boiler that burns primarily No. 6 fuel oil and wood waste, including bark. It is also permitted to burn tire-derived fuel and wastewater treatment sludge. The sulfur content of the fuel oil is 3% or less. Particulate emissions are controlled by an electrosta tic precipitator (ESP). The baseline SO2 emissions reported by the company were 1642 tons, based on the 2002 emissions report. In the course of communications with Geor gia Pacific, it was learned that a boiler modification in 2002 had resulted in increa sed production efficiency when burning oil. The increased efficiency had reduced typica l annual oil consumption to less than five million gallons-per-year.
In determining what control options should be considered “reasonabl e” for this unit, EPD took into consideration that th is unit had the highest contribut ion to visibility impact on a Class I area (12.55 at Wolf Island) of any ot her Georgia emission un it subject to the 4- factor analysis. EPD also took into consider ation that this unit si gnificantly contributed to two Class I areas that were predicted to clearly not meet the Uniform Rate of Progress (glide slope) in 2018 based on “on the books” controls.
Georgia Pacific’s original submittal identif ied the following control technologies, from most effective to least effectiv e as being technically feasible:

1. Wet scrubbing with caustic (wet flue gas desulfurization) 2. Dry scrubbing with limestone injection 3. In-duct sorbent injection 4. Cleaner fuels 5. Wet scrubbing with water only

As noted above, a boiler modification in 2002 resulted in increased production efficiency when burning oil. Therefore, EPD added a 5 million gallon oil limit as a control option for evaluation and also combined the oil li mit with other contro l technologies (in-duct sorbent injection and clean er fuels) for evaluation.

Wet flue gas desulfurization. Wet flue gas desulfurization (FGD) would achieve an SO2 reduction of approximately 95% and could be implemented before 2013. The cost metrics are $5449 per ton of SO2 reduced and $31.7 million and $35.7 million per inverse megameter (Mm-1) of visibility improvement (at Wolf Island and Okefenokee Class I areas, respectively). Regarding en ergy and non-air environmental impacts, this option would consume approximately 98 million gallons of water and generate an estimated 34 million gallons per year of wastewater. This control efficiency is projected to achieve light extinction re ductions (which are visibility improvements) of 0.27 and 0.24 Mm-1 at Wolf Island and Okefenokee, resp ectively. The cost e ffectiveness of this control option was determined not to be reasonable.App endix H

77 of 115 Dry scrubbing with limestone injection. Georgia Pacific inst alled dry scrubbing with limestone injection at its Po rt Hudson, LA, facility in 2007. The Port Hudson design was for a unit comparable in size to Brunswick’s Power Boiler No. 4. The total installed cost of the Port Hudson equipment was more than twice the estimated installed cost for wet FGD at Brunswick. Because the typical re moval efficiencies of wet FGD and dry scrubbing are approximately the same, dry sc rubbing was not consider ed further in the Brunswick four-factor submittal due to its high expected cost. The cost effectiveness of this control option was determined not to be reasonable.

Five million gallon oil limit and in-duct sorbent injection. A five million gallon oil limit combined with in-duct sorbent injection was evaluated. This could be implemented before 2013 and would reduce allowable SO2 emissions to approximately 330 tons (accounting for 70% control efficiency appl ied to 1100 tons of emissions). The cost metrics are $3562 per ton of SO2 reduced and $20.7 million and $23.4 million per inverse megameter (Mm-1) of visibility improvement (at Wolf Island and Okefenokee Class I areas, respectively). Regarding en ergy and non-air environmental impacts, this option would generate an estimated 8000 tons per year of solid waste. This control option is projected to achieve light exti nction reductions of 0.13 and 0.12 Mm-1 at Wolf Island and Okefenokee, respectively. This c ontrol option is consider ed to be reasonable due to cost-effectiveness, the relatively high vi sibility impact on two Class I areas and the fact that neither of these Class I areas is clearly meeting the uniform rate of progress.

Five million gallon oil limit and fu el switch to 1.0 % sulfur oil. A five million gallon oil limit combined with switching to 1.0 % sulfur oil was evaluated. This could be implemented before 2012 and would reduce allowable SO2 emissions to approximately 368 tons (accounting for 67% SO2 reduction ap plied to 1100 tons of emissions). The cost metrics are $3228 per ton of SO2 reduced and $18.8 million and $21.2 million per inverse megameter (Mm-1) of visibility improvement (at Wolf Island and Okefenokee Class I areas, respectively). No ener gy or non-air environmental impacts were documented. This control option is projecte d to achieve light ex tinction reductions of 0.13 and 0.11 Mm-1 at Wolf Island and Okefenokee, respectively. This control option is considered to be reasonable due to cost-eff ectiveness, the relatively high visibility impact on two Class I areas and the fact that neither of these Class I areas is clearly meeting the uniform rate of progress.

Five million gallon oil limit. Implementing a five million gallon limit on burning oil with the existing sulfur per centage would mimic typical operation since the improvement to boiler efficiency. This could be implemented immediately and would reduce allowable SO2 emissions to approximatel y 1100 tons, a 33% reduction from 1642 tons. There is no cost associated with this cont rol, and there would be no energy or non-air environmental impacts. This control opti on is projected to ach ieve light extinction reductions of 0.093 and 0.083 Mm-1 at Wolf Island and Okefenokee, respectively. This control option has a lower control efficiency than other options that are considered reasonable.App endix H

78 of 115 Five million gallon oil limit and fu el switch to 2.2% sulfur oil. This could be implemented before 2012 and would reduce allowable SO2 emissions to approximately 805 tons (accounting for 27% SO2 reduction ap plied to 1100 tons of emissions). This control option has a lower control efficiency than other options that are considered reasonable.

Wet scrubbing with water only . This control option has a lower control efficiency (30%) than other options that are considered reasonable.

Conclusion. In-duct sorbent injection and fuel-switc hing to 1% sulfur oil, both including the five million gallon per year oil consumption limit, were identified as reasonable controls. This was based on reasonable costs/ ton, very low costs/visibility improvement, and timeliness of implementation. In addition, the visibility improvements at Wolf Island and Okefenokee would be relatively large. Im plementation of the less restrictive of these two options would reduce SO2 emissions to 368 tons/12-consecutive months, as discussed above.
Supplemental information provided by the facility indicated that the two controls deemed to be reasonable would control emissions fr om oil combustion but would not affect SO2 emissions from combustion of wood waste a nd tire-derived fuel (TDF). The facility requested an allowance for an additional 200 tons of emissions based on calculations of historical emissions from w ood waste and TDF. This request was also supported by the facility’s assertion that the sulfur content of locally available TDF may be above what has been burned historically. EPD concurred with the facility’s request and established an SO2 emissions limit for the power boiler of 568 tons (368 plus 200) for Regional Haze reasonable progress with a compliance date of 2012.
6.4.2 Georgia Pacific Brunswick Cellu lose, Recovery Boiler No. 6 (M24)
Recovery Boiler No. 6 burns black liquor with a 72% solids concentration. Particulate emissions are controlled by an electrosta tic precipitator (ESP). The baseline SO2 emissions estimated by VISTAS were 193 tons per year, contributing about 1.3% to the sulfate visibility impairment at Wolf Island.
No control options were provided by the company.

Conclusion. Due to the fact that this emissi on unit only contribu tes to visibility impairment at one Class I area and the rela tively low baseline emissions level, Georgia EPD will not require any additional contro ls for Regional Haze Reasonable Progress.
6.4.3 Georgia Pacific Cedar Springs, Power Boiler No. 1 (U 500)
Georgia Pacific’s Cedar Springs facility is located in Early County in the southwest corner of the state. Power Boiler No. 1 is a 784 MMBtu/hr boiler that burns primarily bituminous coal, wood waste, and fuel oil. Th e sulfur content of th e fuel oil is typically 1%. A venturi scrubber (water) controls particulate emissions and also provides someApp endix H

79 of 115 control of SO2 emissions estimated at 30%. The baseline SO2 emissions reported by the company were 1976 tons based on 2006 CEMS data.
Georgia Pacific’s original submittal identif ied the following control technologies, from most effective to least effective, as being technically feasible:

1. New wet FGD with caustic; 2. Addition of spray towers and caus tic to existing venturi scrubber; 3. Addition of caustic to existing venturi scrubber; 4. Duct sorbent injection; 5. Cleaner coal; and 6. Coal washing.
In determining what control options should be considered “reasonabl e” for this unit, EPD took into consideration that this unit had a rela tively low visibility impact on only one Class I area (St. Marks, although it is not cl early meeting the uniform rate of progress). EPD also took into consideration that this unit is BART-eligible and that the facility desired to take reductions for th e unit to be exempt from BART.

New wet FGD with caustic. This option would achieve an SO2 reduction of approximately 93% and could be implemen ted before 2013. The cost metrics are $2725 per ton of SO2 reduced and $1391.2 million per inverse megameter (Mm-1) of visibility improvement (at Saint Marks Class I area). Regarding energy and non-air environmental impacts, this option would generate approx imately 15,000 tons per year of solid waste. This control is projected to achieve a light ex tinction reduction of 0.0036 Mm-1 at Saint Marks. This option was no t considered cost effective.

Addition of spray towers and causti c to existing venture scrubber. This option would achieve an SO2 reduction of approximately 86% and could be implemented before 2012. The cost metrics are $2952 per ton of SO 2 reduced and $1497.0 million per inverse megameter (Mm-1) of visibility improve ment (at the Saint Marks Class I area). Regarding energy and non-air environmental impacts, this option would generate approximately 15,000 tons per year of solid waste. This control is projected to achieve a light extinction reduction of 0.0033 Mm-1 at Saint Marks. This option was not considered cost effective.

Addition of caustic to existing venturi scrubber. This option would achieve an SO2 reduction of approximately 79% (total control, in lieu of existing 30%) and could be implemented before 2012. The cost metric s are $1675 per ton of SO2 reduced and $849 million per inverse megameter (Mm-1) of visibility improvement (at Saint Marks Class I area). Regarding energy and non-air envir onmental impacts, this option would generate approximately 15,000 tons per year of solid waste. This control is projected to achieve a light extinction reduction of 0.0031 Mm-1 at Saint Marks. The cost efficiencies (in both $/ton and $/Mm-1), the energy and non-environm ental impacts were determined not to be unacceptable, and the controls could be installe d in time for the full benefit to be realizedApp endix H

80 of 115 during all five years in which the 2018 Re gional Haze progress report will be based. Therefore, this cont rol option is determined to be reasonable.

Duct sorbent injection. This option would achieve an SO2 reduction of approximately 70% (upstream of and in conjunction with the 30% scrubber removal) and could be implemented before 2012. The cost metric s are $1663 per ton of SO2 reduced and $843 million per inverse megameter (Mm-1) of visibility improvement (at the Saint Marks Class I area). No energy or non-air environmental impacts were documented. This control is projected to achieve a light extinction reduction of 0.0027 Mm-1 at Saint Marks. The cost efficiencies (in both $/ton and $/Mm-1), the energy and nonenvironmental impacts were determined not to be unacceptable, and the controls could be installed in time for the full benefit to be realized during all 5 years in which the 2018 Regional Haze progress report will be based. Th erefore, this control option is determined to be reasonable.

Cleaner coal . This control option was not consider ed because the SO2 control efficiency (25%) is lower than other options that are considered reasonable.

Cleaner washing . This control option was not c onsidered because the SO2 control efficiency (15%) is lower than other options that are considered reasonable.

Conclusion. Addition of caustic to the existing venturi scrubber and duct sorbent injection were determined to be reasonable b ecause of their relatively low costs per unit of visibility improvement

. The other options had higher co sts per visibility improvement and coal washing and coal switching also ha d lower total visibility improvements. As part of Georgia Pacific’s BART exemp tion modeling, the company has proposed SO2 emission limits of 135 pounds per hour from Power Boiler. A limit of 135 lb/hr would result in maximum annual emissions of 591 tons/yr of SO2. This is a 70% reduction from baseline emissions. The actual annual reduction should be ev en higher since the boiler would not be expected to emit SO2 at the maximum allowable level for an entire year. Therefore, the 70+% reduction requir ed by the BART exemption limit satisfies the requirement for reasonable progress. No additional limitations will be required.
6.4.4 Georgia Pacific Cedar Springs, Power Boiler No. 2 (U 501)

Power Boiler No. 2 is the same size, burns the same fuels, and has the same estimated SO2 emissions as Power Boiler 1. The 4- factor analysis and recommendations are identical to those for Power Boiler No. 1.
6.4.5 Georgia Pacific Cedar Springs , Recovery Boiler No. 3 (R402)
Recovery Boiler No. 3 burns black liquor and has a capacity of 769 MMBtu/hr. Particulate emissions are controlled by an el ectrostatic precipitator (ESP). The baseline SO2 emissions estimated by VISTAS were 1726 tons per year, which contributes about 0.77 % to the sulfate visibility impairment at Saint Marks.App endix H

81 of 115 Georgia Pacific’s original submittal did not contain any control measures for 4-factor analysis. However, the company did include control scenarios as part of the company’s BART exemption modeling request that incl uded modifications to Recovery Boiler 3. Scenario 1 included repaired concentrator se ts. This should result in a decrease of peak SO2 emissions. However, the company could not estimate the annual reduction in emissions from this control option. Therefor e, it is assumed to be zero. Scenario 2 included new concentrator sets and a new multi-level air system for the recovery boiler. This control measure would reduce SO2 em issions to 919.8 tons/yr. The Division requested cost analysis information for a ne w concentrator/multi-level air air system and received this information on July 31, 2008. This submittal also contained further information regarding SO2 emissions from Recovery Boiler 3 and regarding the cost effectiveness of switching to 1% sulfur fuel oil. After reviewing this information, the Division requested information regarding the cost-effectiveness of switching from no. 6 fuel oil to no. 2 fuel oil and receiv ed this information on August 15, 2008.

Based on the information received from Geor gia-Pacific, the recovery boiler has two operating modes. The normal operating mode is when the recovery boiler is burning black liquor (and at times co-firing fuel oil) at which time the furnace is operating in a reducing mode and thus has relatively lo w SO2 emissions (20-30 ppm). The other operating mode is when there is no black li quor being fired, but the mill is burning fuel oil in the recovery boiler in order to produce steam needed for the mill processes. During these periods, SO2 emissions are emitted uncontrolled (approximately 400-500+ ppm). The company provided information for 2006-2007 regarding SO2 emissions during each of these operating modes. Recovery Boil er 3 emitted 184 and 169 tons of SO2 per year in 2006 and 2007, respectively, when burning bl ack liquor. Recovery Boiler 3 emitted 278 and 571 tons of SO2 per year in 2006 and 2007, respectively, when burning only fuel oil.
The control options reviewed for Recovery Bo iler 3, ranked from most effective to least effective, are:

1. Switching from no. 6 to no. 2 fuel oil with 0.5% sulfur; 2. Switching from no. 6 fuel oil with 1.8 % su lfur (typical max)) to no. 6 fuel oil with 1% sulfur; 3. New concentrator and new multi-level air system for high solids firing
In determining what control options should be considered “reasonabl e” for this unit, EPD took into consideration that this unit had a rela tively low visibility impact on only one Class I area (St. Marks, although it is not clear ly meeting the uniform rate of progress).

New Concentrator and New Multi-Level Air Sytem. This option would only reduce SO2 emissions when black liquor is fired in the recovery boiler. This is the normal operating mode of the recovery boiler. Th is conversion is estimated to reduce SO2 emissions by 22% when firing black liquor . The company provided SO2 emissions information for periods when black liquor was being fired in the recovery boiler. This amount is 184 tons in 2006 and 169 tons in 2007. The cost metrics are $149,000 per tonApp endix H

82 of 115 of SO2 reduced and $75,500 million per inverse megameter (Mm-1) of visibility improvement (at Saint Marks Class I area). These high cost figures are logical considering that this contro l option would only be effective when SO2 emissions from the recovery boiler are already low. The company did not pr ovide any indication that the control measure could not be operated prior to 2012 nor that there would be any energy or non-air quality environmental impacts. This option was not considered cost effective.

Switching to 1% Fuel Oil . This option would only reduce SO2 emissions when only fuel oil is being fired in the recovery boiler. This sulfur content of fuel oil burned in this unit is limited to 3%. The company stated that the actual sulfur content ranged from 0.7% to 1.8%. 1.8% sulfur fuel oil is used as the basis for this cost estimate. Conversion to 1% fuel oil would reduce SO2 emissions by 44% when burning fuel oil. The cost metrics are $12,000 per ton of SO2 reduced and $6116 million per inverse megameter (Mm-1) of visibility improvement (at Sain t Marks Class I area). The company did not provide any indication that the control meas ure could not be operated prior to 2012 nor that there would be any ener gy or non-air quality environmental impacts. This option was not considered cost effective.

Switching from no. 6 to no. 2 Fuel Oil . This option would reduce SO2 emissions only when fuel oil is being fired in the recovery boiler. The recovery boiler currently burns no. 6 fuel oil and the company stated that th e actual sulfur content ranged from 0.7% to 1.8%. 1.8% sulfur fuel oil is used as the basi s for this cost estimate. The sulfur content for no. 2 fuel oil is defined as 0.5%. C onversion to no. 2 fuel oil would reduce SO2 emissions by 72% when burning fuel oil. The cost metrics are $22,900 per ton of SO2 reduced and $11,594 million per inverse megame ter (Mm-1) of visibility improvement (at Saint Marks Class I area). The compa ny did not provide any indication that the control measure could not be operated prior to 2012 nor that there would be any energy or non-air quality environmental impacts. This option was not considered cost effective.

Conclusion. None of the control options were considered reasonable given the high-cost efficiencies (in both $/ton and $/Mm -1 ), and the relatively low-visibility impact on a single Class I area. Georgia EPD, therefore, will not require any additional controls be required for Regional Haze reasonable progress.
6.4.6 Georgia Pacific Savannah River Mill, No. 3 Boiler (B001)
Georgia Pacific’s Savannah River facility is located in Effingham County, northwest of the City of Savannah. The No. 3 Power Bo iler is a re-circulating fluidized bed 422 MMBtu/hr boiler that burns petcoke (6.1 % sulfur 2 ) and bituminous coal (1.55 % sulfur). Under current operation, petcoke is the prim ary fuel. Limestone injection into the combustion chamber provides control (approximately 90%) of SO2 emissions. The facility’s current Title V permit limits SO2 emissions from this unit to 2152 tons. The

2 The sulfur content of petcoke is allowe d to exceed the 3 percent limit set by rule Georgia Air Quality Rule 391-3-1-.02(2)(g)2. because the boilers are equipped with flue gas desulfurization and thus qualifies for the provisions of 391-3-1-.02(2)(g)3.App endix H

83 of 115 baseline level of SO2 emissions reporte d by the company was 1659 tons, based on 2005 CEMS data. This compares with the VISTAS estimated baseline of 1844 tons.
Georgia Pacific’s original submittal identif ied the following control technologies, from most effective to least effective, as being technically feasible:

1. Wet FGD with caustic; 2. Circulating fluidized bed scrubber; 3. Switch to 100 % coal firing; 4. Increased limestone injection; and 5. Rotating opposed fire air combustion control.
In determining what control options should be considered “reasonabl e” for this unit, EPD took into consideration that this unit had a rela tively low visibility impact on only one Class I area (1.1% of the sulfate impact at Wolf Island, although it is not clearly meeting the uniform rate of progress). Another cons ideration was that this unit has an existing control (approximately 90%) for SO2.

Wet FGD with caustic. This option would achieve an SO2 reduction of approximately 95% and could be implemented after 2012 (a sp ecific date was not provided). The cost metrics are $2430 per ton of SO2 reduced and $ 602.2 million per inverse megameter (Mm-1) of visibility improvement (at the Wo lf Island Class I area). Regarding energy and non-air environmental impacts, this option would use 20 million gallons-per-year of water and increased the dissolved solids load to their wastewater treatment system. This control is projected to achieve a light extinction reduction of 0.0006 Mm-1 at Wolf Island. This option was not considered cost effective.

Circulating fluidized bed scrubber. This option would achieve an SO2 reduction of approximately 95% and could be implemen ted after 2012 (a specific date was not provided). The cost metrics are $4504 pe r ton of SO2 reduced and $1116.3 million per inverse megameter (Mm-1) of visibility im provement (at the Wolf Island Class I area). No energy and non-air environmental impacts were reported. This control is projected to achieve a light extinction re duction of 0.0006 Mm-1 at Wolf Island. This option was not considered cost effective.

Switch to 100% coal firing. This option would achieve an SO2 reduction of approximately 65% and could be implemen ted after 2012 (a specific date was not provided). The cost metrics are $4451 pe r ton of SO2 reduced and $1103.0 million per inverse megameter (Mm-1) of visibility im provement (at the Wolf Island Class I area). No energy and non-air environmental impacts were reported. This control is projected to achieve a light extinction re duction of 0.0004 Mm-1 at Wolf Island. This option was not considered cost effective.

Rotating opposed fire air. This option would achieve an SO2 reduction of approximately 30% and could be implemen ted after 2012 (a specific date was not provided). The cost metrics are $2150 pe r ton of SO2 reduced and $532.7 million perApp endix H

84 of 115 inverse megameter (Mm-1) of visibility impr ovement (at Wolf Island Class I area). No energy and non-air environmental impacts were reported. This control is projected to achieve a light extinction re duction of 0.0002 Mm-1 at Wolf Island. This option was not considered cost effective.

Increased limestone injection. Georgia-Pacific conduc ted trial operations with increased limestone injection rates and f ound that overall SO2 removal could only be increased by an additional two percent (from 87% to 89%) resulting in an additional SO2 reduction of approximately 15%. This option could be implemented prior to 2012. The cost metrics are $7223 per ton of SO2 reduced and $1792.4 million per inverse megameter (Mm-1) of visibility improvement (at Wolf Island Class I area). Regarding energy and non-air environmental impacts, this option would result in 24,000 to 60,000 tons per year of solid waste. This contro l is projected to achieve a light extinction reduction of 0.0001 Mm-1 at Wolf Island. This option was not considered cost effective.

Conclusion. None of the control options were cons idered reasonable given the resulting control efficiencies, the time delay to impl ementation (with the exception of increased limestone injection), the high-cost efficiencies (in both $/ton and $/Mm -1 ), and the relatively low-visibility impact on a single Cla ss I area. Georgia EPD, therefore, will not require any additional controls be requi red for Regional Haze reasonable progress.
6.4.7 Georgia Pacific Savannah River Mill, No. 4 Boiler (B002)
The No. 4 Power Boiler is also a re-circulating fluidized bed boiler that is the same size, burns the same fuels, and has the same SO2 c ontrols as the No. 3 Bo iler. The facility’s current Title V permit limits SO2 emissions from this unit to 1671 tons. The baseline level of SO2 emissions reported by the company was 1195 tons based on 2005 CEMS data. This compares with the VI STAS estimated baseline of 1415 tons.
In determining what control options should be considered “reasonabl e” for this unit, EPD took into consideration that this unit had a rela tively low visibility impact on only one Class I area (0.86% of the sulfate impact at Wolf Island although it is not clearly meeting the uniform rate of progress). Another cons ideration was that this unit has a required minimum 90% control efficiency for SO 2 as specified in NSPS Subpart Db.

The four-factor submittal identified the same c ontrol options as for the No. 3 Boiler. For the same reasons as listed for the No. 3 Boiler, Georgia EPD will not require any additional controls be required.
6.4.8 Georgia Pacific Savannah River Mill, No. 5 Boiler (B003)
The No. 5 Power Boiler is also a re-circulating fluidized bed boiler that is the same size, burns the same fuels, and has the same SO2 controls as the No. 3 and No. 4 Boilers. The facility’s current Title V permit limits SO2 emissions from this unit to 1671 tons. The baseline level of SO2 emissions reporte d by the company was 1190 tons based on 2005 CEMS data. This compares with the VISTAS estimated baseline of 1282 tons.App endix H

85 of 115 In determining what control options should be considered “reasonable” for this unit, EPD took into consideration that this unit had a rela tively low visibility impact on only one Class I area (0.77% of the sulfate impact at Wolf Island although it is not clearly meeting the uniform rate of progress). Another cons ideration was that this unit has a required minimum 90% control efficiency for SO 2 as specified in NSPS Subpart Db.

The four-factor submittal identified the same control options as for the No. 3 and No. 4 Boilers. For the same reasons as listed for the No. 3 Boiler, Georgia EPD will not require any additional controls be required.

6.4.9 Georgia Power Plant Kraft, Steam Generator 1
Georgia Power’s Plant Kraft is located in Chatham County, near the Georgia coast. Steam Generator Unit 1 is a 50 Megawatt coal-f ired electric utility boiler. The sulfur content of the coal is limited to 3% or less and currently burns rela tively low-sulfur coal (0.62% S). Particulate emissions are contro lled by an electrosta tic precipitator (ESP). The 2018 baseline SO2 emissions reported by the company were 632 tons which is close to the 2018 emissions projected by VISTAS us ing an IPM of 691 tons. 632 tons per year was used as the baseline SO2 emissions used in this analysis.
In determining what control options should be considered “reasonable” for this unit, EPD took into consideration that this unit significantly contribut ed to only one Class I area (Wolf Island) by a relatively low amount (0.51%; EPD’s “threshold” for significant contribution was 0.5%). EPD also took into consideration that this unit significantly contributed to a Class I area that was predic ted to not clearly meet the Uniform Rate of Progress (glide slope) in 2018 ba sed on “on the books” controls.
Georgia Power’s submittal identified the fo llowing control technologies, from most effective to least effective, as being technically feasible:

1. Wet flue gas desulfurization; 2. Coal swithching; and 3. Coal washing.

Wet flue gas desulfurization. Wet flue gas desulfurization (FGD) would achieve an SO2 reduction of approximately 95% and could be implemented by January 2016. The cost efficiencies are $8161 per ton of SO2 reduced and $144.5 million per inverse megameter (Mm-1) of visibility improve ment (at the Wolf Island Class I area). Regarding energy and non-air environmental impacts, this option would reduce energy production by approximately 2% (1000 k), cons ume approximately 90 gallons of water per Mwh, discharge 30 gallons of wastewater per Mwh, and generate 2.7 tons of gypsum per ton of SO2 removed. This control efficien cy is projected to ach ieve light extinction reductions (which are visibility improvement s) of 0.03 Mm-1 at Wolf Island. The cost effectiveness of this control option was determined not to be reasonable.App endix H

86 of 115 Coal Switching . Switching to 0.38 percent coal would achieve an SO2 reduction of approximately 39%. No data was submitted regarding the time to implement, thus it was assumed that coal washing could be achie ved by January 1, 2012. The cost efficiencies are $4041 per ton of SO2 reduced and $71.5 million per inverse megameter (Mm-1) of visibility improvement (at the Wolf Island Cl ass I area). Regarding energy and non-air environmental impacts, the company indicat ed that this option could possibly reduce boiler efficiency, but did not provide a quantitative amount. Non-air quality environmental impacts were not reported. This control efficiency is projected to achieve light extinction reductions (w hich are visibility improvements) of 0.01 Mm-1 at Wolf Island. The cost effectiveness of this contro l option was determined not to be reasonable.

Coal Washing . Coal washing would achieve an SO2 reduction of approximately 6%. No data was submitted regarding the time to implement, thus it was assumed that coal switching could be achieved by January 1, 2012. The cost efficiencies are $1839 per ton of SO2 reduced and $32.5 million per inverse megameter (Mm-1) of visibility improvement (at the Wolf Island Class I area). The annualized cost of this control was only about 30% of the cost predicted by AirC ontrolNET, indicating that, if anything, the costs provided by the company may be unde restimated. Regarding energy and non-air environmental impacts, the company indicat ed that this option could possibly reduce boiler efficiency, would use 4400 gallons per day of water, would result in acidic wastewater requiring treatment, and would result in coal refuse in the amount of approximately 5% of the total coal consump tion. This control efficiency is projected to achieve light extinction reductions (which ar e visibility improvements) of 0.002 Mm-1 at Wolf Island. Based on the relatively low cont rol efficiency of this option, the negative non-air environmental impacts, and the relativ ely low visibility im pact, this control option was determined not to be reasonable.

Conclusion. N o additional controls will be required for Plant Kraft Steam Generator 1.
6.4.10 Georgia Power Plant Kraft, Steam Generator 2
Georgia Power’s Plant Kraft is located in Chatham County, near the Georgia coast. Steam Generator Unit 1 is a 54 Megawatt coal-f ired electric utility boiler. The sulfur content of the coal is limited to 3% or less and currently burns rela tively low-sulfur coal (0.62% S). Particulate emissions are contro lled by an electrosta tic precipitator (ESP). The 2018 baseline SO2 emissions reported by the company were 889 tons, which is higher, but relatively close, to the 2018 emissions projected by VISTAS using an IPM of 704 tons. 889 tons per year was used as the ba seline SO2 emissions used in this analysis.
In determining what control options should be considered “reasonabl e” for this unit, EPD took into consideration that this unit significantly contribut ed to only one Class I area (Wolf Island) by a relatively low amount (0.52%; EPD’s “threshold” for significant contribution was 0.5%.) EPD also took into consideration that this unit significantly contributed to a Class I area that was predic ted to not clearly meet the Uniform Rate of Progress (glide slope) in 2018 ba sed on “on the books” controls.App endix H

87 of 115 Georgia Power’s submittal identified the fo llowing control technologies, from most effective to least effective, as being technically feasible:

1. Wet flue gas desulfurization; 2. Coal switching; and 3. Coal washing.

Wet flue gas desulfurization. Wet flue gas desulfurization (FGD) would achieve an SO2 reduction of approximately 95% and could be implemented by January 2016. The cost efficiencies are $6039 per ton of SO2 reduced and $106.9 million per inverse megameter (Mm-1) of visibility improve ment (at the Wolf Island Class I area). Regarding energy and non-air environmental impacts, this option would reduce energy production by approximately 2% (1000 k), cons ume approximately 90 gallons of water per Mwh, discharge 30 gal of wastewater pe r Mwh, and generate 2.7 tons of gypsum per ton of SO2 removed. This control efficiency is projected to achieve light extinction reductions (which are visibility improvement s) of 0.05 Mm-1 at Wolf Island. The cost effectiveness of this control option was determined not to be reasonable.

Coal Switching . Switching to 0.38 percent coal would achieve an SO2 reduction of approximately 39%. No data was submitted regarding the time to implement, thus it was assumed that coal switching could be achie ved by January 1, 2012. The cost efficiencies are $4041 per ton of SO2 reduced and $71.5 million per inverse megameter (Mm-1) of visibility improvement (at the Wolf Island Cl ass I area). Regarding energy and non-air environmental impacts, the company indicat ed that this option could possibly reduce boiler efficiency, but did not provide a qua ntitative amount. There were not reported non-air quality environmental impacts. This control efficiency is projected to achieve light extinction reductions (w hich are visibility improvements) of 0.02 Mm-1 at Wolf Island. The cost effectiveness of this contro l option was determined not to be reasonable.

Coal Washing . Coal washing would achieve an SO2 reduction of approximately 6%. No data was submitted regarding the time to implement, thus it was assumed that coal washing could be achieved by January 1, 2012. The cost efficiencies are $1847 per ton of SO2 reduced and $32.7 million per inverse megameter (Mm-1) of visibility improvement (at the Wolf Island Class I area). The annualized cost of this control was only about 30% of the cost predicted by AirC ontrolNET, indicating that, if anything, the costs provided by the company may be unde restimated. Regarding energy and non-air environmental impacts, the company indicat ed that this option could possibly reduce boiler efficiency, would use 4600 gallons per day of water, would result in acidic wastewater requiring treatment, and would result in coal refuse in the amount of approximately 5% of the total coal consumpti on. This control efficiency is projected to achieve light extinction reductions (which ar e visibility improvements) of 0.003 Mm-1 at Wolf Island. Based on the relatively low cont rol efficiency of this option, the negative non-air environmental impacts, and the relativ ely low visibility im pact, this control option was determined not to be reasonable.

Conclusion. N o additional controls will be required for Plant Kraft Steam Generator 2.App endix H

88 of 115 6.4.11 Georgia Power Plant Kraft, Steam Generator 3
Georgia Power’s Plant Kraft is located in Chatham County, near the Georgia coast. Steam Generator Unit 1 is a 104 Megawatt coal-f ired electric utility boiler. The sulfur content of the coal is limited to 3% or less and currently burns rela tively low-sulfur coal (0.62% S). Particulate emissions are contro lled by an electrosta tic precipitator (ESP). The 2018 baseline SO2 emissions reported by the company were 2455 tons, which is significantly lower than the 2018 emissions projected by VISTAS using an IPM of 4474. Georgia Power provided information dated September 7, 2007, indicating that utilization of the boiler will drop significantly in 2015 as a result of new generation coming on-line about that same time. EPD used a baseline SO2 emission rate of 2455 tons in this analysis. EPD can review the actual utiliz ation of Unit 3 when the next Regional Haze SIP is developed to ensure that the assu mption of reduced utilization has actually occurred. If not, analysis of additional controls can be re-analyzed during development of that SIP.
In determining what control options should be considered “reasonabl e” for this unit, EPD took into consideration that th is unit significantly contributed to three Class I areas [Wolf Island (3.3%), Okefenokee (0.9%), and Ca pe Roman (0.8%).] EPD also took into consideration that this unit significantly cont ributed to two Class I areas (Wolf Island and Okefenokee) that were predicted to not clear ly meet the Uniform Rate of Progress (glide slope) in 2018 based on “on the books” controls.
Georgia Power’s original submittal identified the following control technologies, from most effective to least effective, as being technically feasible:

1. Wet flue gas desulfurization; 2. Coal swithching; and 3. Coal washing.

Wet flue gas desulfurization. Wet flue gas desulfurization (FGD) would achieve an SO2 reduction of approximately 95% and could be implemented by January 2016. The cost efficiencies are $3216 per ton of SO2 re duced and $56.9 and $58.2 million per inverse megameter (Mm-1) of visibility improveme nt at Wolf Island and Okefenokee Class I areas, respectively. Regarding energy and non -air environmental impacts, this option would reduce energy producti on by approximately 2% (2000 k), consume approximately 90 gallons of water per Mwh, discharge 30 gal of wastewater per Mwh, and generate 2.7 tons of gypsum per ton of SO2 removed. This control efficiency is projected to achieve light extinction reductions (w hich are visibility improvements) of 0.13 Mm-1 at both Wolf Island and Okefenokee. Taking into account the cost effectiveness and non-air quality environmental impacts, this opti on was determined not to be reasonable.

Coal Switching . Switching to 0.38 percent coal would achieve an SO2 reduction of approximately 39%. No data was submitted regarding the time to implement, thus it was assumed that coal switching could be achie ved by January 1, 2012. The cost efficienciesApp endix H

89 of 115 are $4042 per ton of SO2 reduced and $71.5 and $73.2 million per inverse megameter (Mm-1) of visibility improvement at Wolf Island and Okefenokee Class I areas, respectively. Regarding energy and non-ai r environmental impacts, the company indicated that this option co uld possibly reduce boiler effici ency, but did not provide a quantitative amount. Non-air quality envi ronmental impacts were not reported. This control efficiency is projected to achieve lig ht extinction reductions (which are visibility improvements) of 0.05 Mm-1 at both Wolf Island and Okefenokee. The cost effectiveness of this control option was determined not to be reasonable.

Coal Washing . Coal washing would achieve an SO2 reduction of approximately 6%. No data was submitted regarding the time to implement, thus it was assumed that coal washing could be achieved by January 1, 2012. The cost efficiencies are $1845 per ton of SO2 reduced and $32.7 and $33.4 million per inverse megameter (Mm-1) of visibility improvement at Wolf Island and Okefenokee Cl ass I areas, respectively. The annualized cost of this control was only about 50% of the cost predicted by AirControlNET, indicating that, if anything, the costs provided by the company may be underestimated. Regarding energy and non-air environmental im pacts, the company indicated that this option could possibly reduce boiler efficienc y, would use 7500 gallons per day of water, would result in acidic wastew ater requiring treatment, and w ould result in coal refuse in the amount of approximately 5% of the total co al consumption. This control efficiency is projected to achieve light ex tinction reductions (which are visibility improvements) of 0.01 Mm-1 at both Wolf Island and Okefenokee. Based on the realatively low control efficiency of this option, th e negative non-air environmenta l impacts, and the relatively low visibility impact, this control option was determined not to be reasonable.

Conclusion. No additional controls will be required for Plant Kraft Steam Generator 3. However, this conclusion may be revised s hould the reduction in utilization and SO2 emissions reported by the company not be realized.
6.4.12 Georgia Power Plant McIntosh, Steam Generator 1
Georgia Power’s Plant McIntosh is located in Effingham County, near the Georgia coast about 10-15 miles north of Plant Kraft. St eam Generator Unit 1 is a 178 megawatt coalfired electric utility boiler. The sulfur content of the coal is limited to 3% or less and currently burns relatively low- sulfur coal (0.62% S). Partic ulate emissions are controlled by an electrostatic precipitator (ESP). Th e 2018 baseline SO2 emissions reported by the company were 1860 tons, which is significan tly lower than the 2018 emissions projected by VISTAS using an IPM of 7015. Georgia Power provided information dated September 7, 2007, indicating that utilization of the boiler will drop significantly between 2011 and 2015 as a result of new a generation coming on-line about that same time. EPD used a baseline SO2 emission rate of 1860 tons in this analysis. EPD can review the actual utilization of McIntosh Unit 1 when the next Regional Haze SIP is developed to ensure that the assumption of reduced utilizati on has actually occurred. If not, analysis of additional controls can be re-analy zed during development of that SIP.App endix H

90 of 115 In determining what control options should be considered “reasonabl e” for this unit, EPD took into consideration that th is unit significantly contributed to five class I areas [Wolf Island (4.1%), Okefenokee ( 1.2%), Saint Marks (0.6%), Cape Romain (1.4%), and Swanquarter (0.7%)], the highest number of a ll of the units (excep t International Paper Power Boiler 13, which also impacts the same fi ve class I areas) reviewed by EPD. EPD also took into consideration that this unit si gnificantly contributed to three Class I areas (Wolf Island, Okefenokee, and Saint Marks) th at was predicted to not clearly meet the Uniform Rate of Progress (glide slope) in 2018, based on “on the books” controls. However, should utilization and SO2 emissi ons by approximately 75%, as reported by the company, the visibility impairment will reduce to less than the 0.5% threshold for four of these Class I areas and will be significantly less on the remaining one (Wolf Island (1.1%).
Georgia Power’s submittal identified the fo llowing control technologies, from most effective to least effective, as being technically feasible:

1. Wet flue gas desulfurization; 2. Coal swithching; and 3. Coal washing.

Wet flue gas desulfurization. Wet flue gas desulfurization (FGD) would achieve an SO2 reduction of approximately 95% and could be implemented by January 2016. The cost efficiencies are $7131 per ton of SO2 reduced and $118.5, $132.6 and $411.0 million per inverse megameter (Mm-1) of visibility im provement at Wolf Island, Okefenokee, and St. Marks Class I areas, respectively. Re garding energy and non-air environmental impacts, this option would reduce energy production by approximately 1-2%, consume approximately 80-110 gallons of water per MWh and generate 2.7 tons of gypsum per ton of SO2 removed. This control efficiency is projected to achie ve light extinction reductions (which are visibility improveme nts) of 0.11 Mm-1 at Wolf Island, 0.09 Mm-1 at Okefenokee, and 0.03 Mm-1 at St. Marks. Th e cost effectiveness of this control option was determined not to be reasonable.

Coal Switching . Switching to 0.38 percent coal would achieve an SO2 reduction of approximately 39%. No data was submitted regarding the time to implement, thus it was assumed that coal switching could be achie ved by January 1, 2012. The cost efficiencies are $4306 per ton of SO2 reduced a nd $71.5, $80.1, and $248.2 million per inverse megameter (Mm-1) of visibility improvement at Wolf Island, Okefenokee, and St. Marks Class I areas, respectively. Regarding ener gy and non-air environmental impacts, the company indicated that this option could possibly reduce boiler efficiency, but did not provide a quantitative amount. Non-air quality environmental impacts were not reported. This control efficiency is projected to ach ieve light extinction reductions (which are visibility improvements) of 0.04 Mm-1 at both Wolf Island and Okefenokee, and 0.01 Mm-1 at St. Marks. The cost effectiveness of this control option was determined not to be reasonable.App endix H

91 of 115 Coal Washing . Coal washing would achieve an SO2 reduction of approximately 6%. No data was submitted regarding the time to implement, thus it was assumed that coal washing could be achieved by January 1, 2012. The cost efficiencies are $5534 per ton of SO2 reduced and $91.9, $102.9, and $319.0 million per inverse megameter (Mm-1) of visibility improvement at Wolf Island, Okefenokee, and St. Marks Class I areas, respectively. The annualized cost of this control was only about 25% of the cost predicted by AirControlNET, indicating that , if anything, the costs provided by the company may be underestimated. Regarding energy and non-air environmental impacts, the company indicated that this option would use 163,000 gallons per day of water, would result in acidic wastew ater requiring treatment, and w ould result in coal refuse in the amount of approximately 5% of the tota l coal consumption. Energy impacts were not reported and are thus assumed to be minimal. This control efficiency is projected to achieve light extinction reductions (which ar e visibility improvements) of 0.002 Mm-1 at both Wolf Island and Okefenokee, and 0.0006 Mm-1 at St. Marks. The cost effectiveness of this control option was dete rmined not to be reasonable.

Conclusion. No additional controls will be required for Plant McIntosh Steam Generator 1. However, this conclusion may be revised should the reduction in utilization and SO2 emissions reported by the company not be realized.
6.4.13 Georgia Power Plant Mi tchell, Steam Generator 3
Georgia Power’s Plant Mitchell is locate d in Dougherty County, in southwest Georgia. Steam Generator Unit 3 is a 163 Megawatt coal-f ired electric utility boiler. The sulfur content of the coal is limited to 3% or less and currently burns rela tively low-sulfur coal (1% S) and employs coal washing, which slig htly reduces SO2 emissions. Particulate emissions are controlled by an electrostatic precipitator (ESP). The 2018 baseline SO2 emissions reported by the company were 1189 tons , which is significantly lower than the 2018 emissions projected by VISTAS usi ng IPM of 4930. Georgia Power provided information dated September 7, 2007, indicating that utilization of the boiler will drop significantly between 2008 and 2010 as a result of new a generation coming on-line about that same time. EPD used a baseline SO2 em ission rate of 1189 tons in this analysis. EPD can review the actual utilization of Mc Intosh Unit 1 during the mid-course review to ensure that the assumption of reduced utiliza tion has actually occurre d. If not, analysis of additional controls can be re-analy zed during development of that SIP.
In determining what control options should be considered “reasonabl e” for this unit, EPD took into consideration that th is unit significantly contributed to two Class I areas (Saint Marks (2.7%) and Okefenokee (0.8%). EPD al so took into consider ation that this unit significantly contributed to two Class I areas th at were predicted to not clearly meet the Uniform Rate of Progress (glide slope) in 2018, based on “on the books” controls. However, should utilization and SO2 emissi ons drop by approximately 75%, as reported by the company, the visibility impairment will reduce to less than the 0.5% threshold for Okefenokee and will be significantly less on the remaining one [Saint Marks (0.7%)].App endix H

92 of 115 Georgia Power’s submittal identified the fo llowing control technologies, from most effective to least effective, as being technically feasible:

1. Wet flue gas desulfurization; and 2. Coal switching.

Wet flue gas desulfurization. Wet flue gas desulfurization (FGD) would achieve an SO2 reduction of approximately 95% and could be implemented by January 2016. The cost efficiencies are $9119 per ton of SO2 reduced and $148.5, and $299.4 0 million per inverse megameter (Mm-1) of visibility im provement at Okefenokee and St. Marks Class I areas, respectively. Regarding energy a nd non-air environmental impacts, this option would reduce energy producti on by approximately 1-2% (1250-2500 kw). No non-air quality environmental impacts were reported. This control efficiency is projected to achieve light extinction reductions (which ar e visibility improvements) of 0.07 Mm-1 at Okefenokee and 0.03 Mm-1 at St. Marks. The cost effectiveness of this control option was determined not to be reasonable.

Coal Switching . Switching to 0.38 percent coal would achieve an SO2 reduction of approximately 47%. No data was submitted regarding the time to implement, thus it was assumed that coal switching could be achie ved by January 1, 2012. The cost efficiencies are $2347 per ton of SO2 reduced and $38.2 and $77.1 million per inverse megameter (Mm-1) of visibility improvement at Okefenokee and St. Marks Class I areas, respectively. Regarding energy and non-ai r environmental impacts, the company indicated that this option co uld possibly reduce boiler effi ciency. No non-air quality environmental impacts were reported. This control efficiency is projected to achieve light extinction reductions (which are visibility improvements) of 0.03 Mm-1 at Okefenokee, and 0.02 Mm-1 at St. Marks. Based on the relatively low visibility impairment resulting from significant reductions in SO2 emissions from this unit, the cost effectiveness of this control option was not low enough to be c onsidered reasonable.

Conclusion. No additional controls will be required for Plant Mitchell Steam Generator 3. However, this conclusion may be revised should the reduction in utilization and SO2 emissions reported by the company not be realized.

6.4.14 International Paper Savannah Mill, Power Boiler 13
International Paper’s Savannah Mill is locat ed in Chatham County, on Georgia’s coast at the border with South Carolina. Power Bo iler 13 (PB 13) is a 1280 MMBtu/hr boiler that burns bituminous coal (primary fuel), bark, and wood fines. The sulfur content of the coal is typically 0.69 % or less. In additi on, the boiler controls (by combustion) waste gas emissions from the pulping process: low-volume high-concentration (LVHC) noncondensable gases, high-volume low-concen tration (HVLC) non-condensable gases, and stripper off-gases (SOG). These waste gases contribute approximate ly 35% of the total sulfur load to the boiler. An electrostatic pr ecipitator controls part iculate emissions. The baseline SO2 emissions level used by the company was the VISTAS estimate of 8578App endix H

93 of 115 tons with approximately 1944 tons/yr of th is coming from the combustion of LVHC, HVLC, and SOG.
International Paper provided two 4-factor analyses in separate submittals. The second submittal was in response to a request from EPD and it identified controls specifically for the pulping waste gas emissions (options 6, 7, and 8 below). The submittals identified the following control technologies, from most effective to least effective, as being technically feasible:

1. Wet FGD (packed tower); 2. FGD, wet limestone spray tower; 3. Semi-dry lime spray tower; 4. Fuel switching to natural gas; 5. Dry sorbent injection; 6. Regenerative thermal oxidizer (RTO ) with scrubber: control HVLC ; 7. Regenerative thermal oxidizer (RTO) w ith scrubber: control LVHC and SOG; and 8. Regenerative thermal oxidizer (RTO) with scrubber: control HVLC, LVHC, and SOG.
Also, facility representatives later suggested an SO2 re duction of 2000 tons/year as a control option that would provide maximum flexibility for compliance (i.e., a 6758 ton per year emission limit). This control option could be implemented by 2016.
In determining what control options should be considered “reasonabl e” for this unit, EPD took into consideration that this unit signi ficantly contributes to the sulfate visibility impairment at five Class I areas (approxim ately 6.4% at Wolf Island, approximately 1.7% at Okefenokee, approximately 0.7% at Sa int Marks, approximately 1.6% at Cape Romain, and approximately 0.9% at Swanquarter). This is the highest number of Class I areas significantly impacted by any single emissions unit of all those reviewed.

Wet FGD, packed tower. This option would achieve an SO2 reduction of approximately 95% and could be implemen ted before 2012. The cost metrics are $4896 per ton of SO2 reduced and $72.3 million per inverse megameter (Mm -1 ) of visibility improvement (at Wolf Island, the closest of the Class I areas). Regarding energy and non-air environmental impacts, this op tion would consume 2210 kW of power and approximately 212 million gallons of water per year. It would generate approximately 161 million gallons of wastewater per year. This control is projected to achieve a light extinction reduction of 0.55 Mm-1 at Wolf Island. Based on information received from EPD’s Watershed Protection Branch, the Savannah River (into which this mill discharges) currently had a re striction on increased dissolved oxygen (DO) demand to the river. Although the Division did not receive sufficient information to determine whether this control option would result in actual DO lo ad to the river, the potential for this to happen was sufficient for EPD to consider an option that did not result in increased wastewater load.App endix H

94 of 115 FGD, wet limestone spray tower. This option would achieve an SO2 reduction of approximately 90% and could be implemen ted before 2012. The cost metrics are $4391/ton reduced and $64.8 million/Mm -1 at Wolf Island. Regarding energy and non-air environmental impacts, this option woul d consume 2210 kW of power and generate approximately 22,000 tons of solid waste per year. No additional water use or wastewater discharge was reporte d. This control is projected to achieve a light extinction reduction of 0.52 Mm -1 at Wolf Island. Based on cost estimates from other sources (i.e., the company’s capital cost estimate was 8.37 times that derived from AirControlNet,) the company’s cost estimates may be high. EPD requested that a more precise sitespecific cost analyis be performed by the company, but the company chose not to do so. However, despite the possibly high cost estim ated, this control opt ion is not considered reasonable at this time. This determination may be revisited at the mid-course review or when determining future Regional Haze reas onable progress goals (i.e., future Regional Haze SIPs.)

Semi-dry lime spray tower. A semi-dry lime spray dryer would achieve an SO2 reduction of approximately 85% and could be implemented by before 2012. The cost efficiencies are $4869 per ton of SO2 redu ced and $71.9 million per inverse megameter (Mm -1 ) of visibility improvement at Wolf Island. Regarding energy and non-air environmental impacts, this option w ould reduce energy production by 780 kw, would generate 19,300 tons per year of solid waste and approximately 114 million gallons of wastewater per year. The company reported no water withdrawal for this option. This control is projected to achie ve a light extinction reducti on of 0.49 Mm-1 at Wolf Island. Based on information received from EPD’s Watershed Protection Branch, the Savannah River (into which this mill discharges) curr ently had a restriction on increased dissolved oxygen (DO) demand to the river. Although the Division did not receive sufficient information to determine whether this control option would result in actual DO load to the river, the potential for this to happen wa s sufficient for EPD to consider an option that did not result in increased wastewater load.

Fuel switching to natural gas. Switching from coal to na tural gas woul d achieve an SO2 reduction of approximately 65% and could be implemented before 2012. The cost metrics are $9506/ton reduced and $140.4 million/Mm -1 at Wolf Island. No negative energy or non-air environmental impacts were reported. This control is projected to achieve a light extinction re duction of 0.38 Mm-1 at Wolf Island. This control option results in a lower control efficiency and has a higher cost efficiency than others determined not to be cost effective at this time. Therefore, this option is not considered reasonable.

Dry sorbent injection. Dry sorbent injection woul d achieve an SO2 reduction of approximately 25% and could be implemen ted before 2012. The cost metrics are $5223/ton reduced and $77.1 million/Mm -1 at Wolf Island. Regarding energy and non-air environmental impacts, this option w ould reduce energy production by 280 kw and would generate 7900 tons per year of solid wa ste. No water or wastewater impacts were reported. This control is projected to ach ieve a light extinction reduction of 0.15 Mm-1 at Wolf Island. This control option results in a lower control efficiency and has a higherApp endix H

95 of 115 cost efficiency than others determined not to be cost effective at this time. Therefore, this option is not considered reasonable.

6758 ton per year emission limit: The company has proposed to reduce SO2 emissions from this unit by 2000 tons per year from the projected 2018 baseline emissions of 8578 tons/year. This results in an emission lim it of 6578 tons per year. This reduction could be accomplished by 2016. This control option would achieve SO2 reductions of approximately 23%. The company did not specify how the reductions would be accomplished. Since this control option wa s proposed by the company, it is assumed to have no unacceptable energy or non-air environmen tal impacts. This control is projected to achieve a light extinction reduction of 0.14 Mm-1 at Wolf Island. Although no costeffectiveness information was provided by the company, this control option is determined to be reasonable at this time.

Regenerative thermal oxidizer (RTO) with scrubber controlling HVLC, LVHC, and SOG : This control option would involve r outing a portion of the mill’s non-condensible gases or NCGs [in this case the high-vol ume low-concentration stream (HVLC), lowvolume high-concentration stream (LVHC), and the stripper off-gas stream (SOG)] which contain sulfur-bearing compounds to a stand-alone RTO equipped with a scrubber (95% SO2 removal) to remove resulting SO2 emissions instead of using Power Boiler 13 to combust the NCGs. This control option was not considered because the SO2 control efficiency (approximately 22%) is lower than other options that are considered reasonable.

Regenerative thermal oxidizer (RTO) wi th scrubber controlling LVHC and SOG : This control option would involve routing a portion of the mill’s non-condensible gases or NCGs (in this case LVHC and SOG) wh ich contain sulfur bearing compounds to a stand-alone RTO equipped with a scrubber ( 95% SO2 removal) to remove resulting SO2 emissions instead of using Power Boiler 13 to combust the NCGs. This control option was not considered because the SO2 contro l efficiency (approximately 18%) is lower than other options that are considered reasonable.

Regenerative thermal oxidizer (RTO ) with scrubber controlling HVLC : This control option would involve routing a portion of th e mill’s non-condensible gases or NCGs (in this case HVLC) which contain sulfur-bearing compounds to a stand-alone RTO equipped with a scrubber (95% SO2 removal) to remove resulting SO2 emissions instead of using Power Boiler 13 to combust the NCGs . This control option was not considered because the SO2 control efficiency (approxim ately 4%) is lower than other options that are considered reasonable.

Conclusion. Of the control options considered, an emission limit of 6578 tons per year was considered reasonable. To allow the facility flexibility in selection and implementation of a control, Georgia EPD will establish a permit limit of 6578 tons of SO2 per 12 months, including emissions from the pulping process.App endix H

96 of 115 6.4.15 Temple-Inland Rome Linerboard, No. 4 Power Boiler
Temple-Inland Rome in Floyd County, in northwest Georgia. No. 4 Power Boiler is a 565 MMBtu/hr coal and oil fired boiler. The unit is also permitted to burn a relatively small amount of sawdust. The sulfur conten t of the coal and oil are limited by permit condition to 1.29% and 0.5%, respectively. There are hourly and annual SO2 permit limits of 1,130.0 pounds per hour and 3837 tons pe r 12-month period for the boiler. The permit also limits the combustion of fuel oil to 35,352,857 gallons per 12-month period. The pound per hour SO2 limit is a PSD limit. A ll of the others are for PSD avoidance. Particulate emissions are controlled by a multiclone followed by an electrostatic precipitator (ESP). The 2018 baseline SO2 emissions reported by the company were 3837 tons (the same as the permit limit). The VISTAS 2018 projected emissions were 3696, relatively close to the permit limit. 3837 tons per year was used as the baseline SO2 emissions used in this analysis. Re view of data submitted under the Consolidated Emissions Reporting Rule showed 3293 tons emitted in 2002, 3404 tons in 2003, 3396 tons in 2004, 2533 tons in 2005, and 2839 tons in 2006. Because a maximum permit limit is used as the 2018 baseline emissions in this review, the appropriate 2018 baseline may be over-estimated, but not by a significan t amount. As a result, the amount of SO2 reduced by any particular control stra tegy may be slightly overestimated.
In determining what control options should be considered “reasonabl e” for this unit, EPD took into consideration that this unit significantly cont ributed to two Class I areas [Cohutta (4.4%) and Joyce Kilmer (1.0%)]. EP D also took into consideration that this unit significantly contributed to Class I areas that were predicted to clearly meet the Uniform Rate of Progress (glide slope) in 2018, based on “on the books” controls. EPD also took into consideration that this unit is loca ted within a PM 2.5 non-attainment area (Floyd County) and within a c ounty adjacent to another PM 2.5 non-attainment area (Atlanta.)
Temple-Inland’s submittal identified the fo llowing control technologies, from most effective to least effective, as being technically feasible:

1. Wet flue gas desulfurization – magnesium enhanced lime (wet FGD MEL); 2. Wet flue gas desulfurization – limest one forced oxidation (wet FGD LSFO); 3. Dry flue gas desulfurization (dry FGD); 4. Fuel Switching to Powder River Basin coal (i.e., subbituminous coal); and 5. Duct Lime Sorbent Injection.
The company reported the control efficien cy of both wet FGD MEL and wet FGD LSFO as equivalent. Cost estimates for the five control technologies were prepared as studylevel estimates (which are not site-speci fic) in the company’s original four-factor submittal. The company subsequently prepared and submitted to EPD a site-specific cost estimate for the wet FGD LSFO option. No site-specific analysis was presented for wet FGD MEL, but the submittal stated that the capital and operating costs for MEL technology would be approximately the same as LSFO. Costs from the site-specificApp endix H

97 of 115 analysis are presented in this SIP since these are expected to be more accurate than those from the study-level estimate.

It should also be noted that the co mpany submitted a report dated April 3, 2008, proposing that EPD should establ ish thresholds of direct PM 2.5 emissions within Floyd County and require RACT and/or RACM analys es in lieu of cont rolling SO2 emissions from No. 4 Power Boiler. However, the repo rt did not propose or analyze any specific direct PM 2.5 control measures from either No. 4 Power Boiler or any other source at the mill. Furthermore, EPD has determined for this particular Regional Haze SIP that reducing SO2 emissions from EGU and non-E GU point sources in the VISTAS states would have the greatest visibility be nefits for the Georgia Class I areas.

Wet flue gas desulfurization – magnesium enhanced lime. Wet FGD MEL would achieve an SO2 reduction of approximately 90 % and could be implemented by before 2012. The cost efficiencies are $2027 per ton of SO2 reduced and $39.5 million per inverse megameter (Mm -1 ) of visibility improvement (at the Cohutta Class I area). Regarding energy and non-air environmental impacts, this option would reduce energy production by 450 kw, consume 37 million gall ons of water per year, and generate 16,000 tons of gypsum per year. The electrical usage was taken into account with the economic analysis. EPD’s Watershed Protection Branch has indicated that mill had sufficient capacity within its currently permitte d water withdrawal permit to easily handle the increased water use associated with wet F GD. This control efficiency is projected to achieve light extinction reductions (which are visibility improvements) of 0.17 Mm -1 at Cohutta. The cost effectiveness of this control option was determined not to be reasonable at this time. This determination may be revisited at the mid-course review or when determining future Regional Haze reas onable progress goals (i.e., future Regional Haze SIPs.)

Wet flue gas desulfurization – limestone forced oxidation. Wet FGD LSFO would achieve an SO2 reduction of approximately 90 % and could be implemented by before 2012. The cost efficiencies are $2027 per ton of SO2 reduced and $39.5 million per inverse megameter (Mm -1 ) of visibility improvement (at the Cohutta Class I area). Regarding energy and non-air environmental impacts, this option would reduce energy production by 450 kw, consume 37 million gallons per year, and generate 16,000 tons of gypsum per year. The electrical usage was taken into account with the economic analysis. EPD’s Watershed Protection Bran ch has indicated that mill had sufficient capacity within its currently permitted wate r withdrawal permit to easily handle the increased water use associated with wet FGD. This control efficiency is projected to achieve light extinction reductions (which are visibility improvements) of 0.17 Mm -1 at Cohutta. The cost effectiveness of this control option was determined not to be reasonable at this time.This determination ma y be revisited at the mid-course review or when determining future Regional Haze reas onable progress goals (i.e., future Regional Haze SIPs.)

Dry flue gas desulfurization . Dry FGD would achieve an SO2 reduction of approximately 80% and could be implemente d before 2012. The cost efficiencies areApp endix H

98 of 115 $1857 per ton of SO2 reduced and $36.2 million per inverse megameter (Mm -1 ) of visibility improvement (at the Cohutta Cl ass I area). Regarding energy and non-air environmental impacts, this option w ould reduce energy production by 650 kw and would generate 10,000 tons of gypsum per year. The company reported no water or wastewater impacts. This control efficien cy is projected to ac hieve light extinction reductions (which are visibi lity improvements) of 0.16 Mm -1 at Cohutta. This control option results in a lower control efficiency a nd has a similar cost efficiency than other control opstions determined not to be cost eff ective at this time. Therefore, this option is not considered reasonable.

Fuel switching . Switching to lower sulfur fuel s would achieve an SO2 reduction of approximately 65% and could be implemente d before 2012. The cost efficiencies are $2807 per ton of SO2 reduced and $54.6 million per inverse megameter (Mm -1 ) of visibility improvement (at the Cohutta Clas s I area). Regarding energy impacts, this option would reduce energy production by 650 kw. The company reported no non-air quality environmental impacts from this c ontrol option. This control efficiency is projected to achieve light ex tinction reductions (which are visibility improvements) of 0.13 Mm -1 at Cohutta. This control option results in a lower control efficiency and has a higher cost efficiency than others determin ed not to be cost effective at this time. Therefore, this option is not considered reasonable.

Duct lime sorbent injection . Duct sorbent injection w ould achieve an SO2 reduction of approximately 45% and could be implemente d before 2012. The cost efficiencies are $1943 per ton of SO2 reduced and $37.8 million per inverse megameter (Mm -1 ) of visibility improvement (at the Cohutta Clas s I area). Regarding energy impacts, this option would reduce energy production by 650 kw. The company reported no non-air quality environmental impacts from this c ontrol option. This control efficiency is projected to achieve light ex tinction reductions (which are visibility improvements) of 0.11 Mm -1 at Cohutta. This control option results in a lower control efficiency and has a similar cost efficiency than other control ops tions determined not to be cost effective at this time. Therefore, this op tion is not considered reasonable.

Conclusion. No additional controls will be required for Temple-Inland Rome No. 4 Power Boiler Plant. However, this determ ination may be revisited at the mid-course review or when determining future Regiona l Haze reasonable progress goals (i.e., future Regional Haze SIPs.) Furthermore, Temple-Inland Rome is located within a PM2.5 nonattainment area (Floyd County) and is near two other PM2.5 nonattainment areas (Atlanta and Chattanooga). Further evaluation of controls for this unit may be included in attainment plans for these nonattainment areas.

.App endix H

99 of 115 

### 7.0 BART

7.1 Background
As required by the CAA, EPA included in the final regional haze rule a requirement for Best Available Retrofit Technology (BART) for certain large stationary sources that were placed in operation between 1962 and 1977. The BART-eligible sources are those sources which have the potential to emit 250 tons or more of a visibility-impairing air pollutant, were placed in operation between August 7, 1962, and August 7, 1977, and whose operations fall within one or more of 26 specifically-lis ted source categories. Under the CAA, BART is required for any BA RT-eligible source that a State determines ‘‘emits any air pollutant which may reasonabl y be anticipated to cause or contribute to any impairment of visibility in any such area.’’ Accordingly, for stationary sources meeting these criteria, States must addre ss the BART requirement when they develop regional haze SIPs. EPA has set guidance for identifying BART-eligible sources in 40 CFR Part 51 Appendix Y.
A BART-eligible source that is responsible for a 0.5 deciview (dv) change or more is considered to “cause” visibi lity impairment. Although th e appropriate threshold may vary, the guidelines state that the contri bution threshold used for BART applicability should not be higher than 0.5 deciviews. Thus a BART-eligible sour ce that is responsible for a 0.5 dv change or more is considered to “contribute” to visibility impairment. Any source determined to cause or contribute to visibility impairment in any Class I area is subject to BART.
The guidelines direct that States should in clude SO2, NOX, and direct particulate matter (PM) emissions, including both PM10 and PM 2.5, in determining whether sources cause or contribute to visibility impairment. Stat es may use their best judgment to determine whether VOC or ammonia emissions are likely to have an impact on visibility in an area.
To determine which BART-eligible sources ar e subject to BART, states have several options. A state may consider all BART-eli gible sources subject to BART; may perform analysis showing that the fu ll group of BART-eligible sources in a State cumulatively may not be reasonably anticipated to cause or contribute to any visibility impairment in any Class I areas; or may consider the i ndividualized contribution of a BART-eligible source to determine whether a specific BA RT-eligible source is subject to BART.
Sources that are subject to BART must undergo a BART determination. 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 complia nce, (3) any existing pollution control technology in use at the source, (4) the remain ing useful life of the source, and (5) the degree of improvement in visibility which ma y reasonably be anticipated to result from the use of such technology.App endix H

100 of 115 To demonstrate the degree of improvement in visibility from various BART control options, the States may run CALPUFF or anot her appropriate dispersion model to predict visibility impacts. Scenar ios would be run for the pre-controlled and post-controlled emission rates for each of the BART cont rol options under review. The maximum 24- hour emission rates would be modeled fo r a period of three or five years of meteorological data. States have the flexibil ity to develop their own methods to evaluate model results.

7.2 Approach to Implementation of BART Requirements
In order to meet the SIP submittal deadline, Georgia has used a multi-faceted approach to implementation of the federal regional h aze rule BART requirements. The approach consists of source identification efforts, us e of a permitting proc ess to establish any resulting BART limits for inclusion in the SI P submittal as well as participation in the VISTAS BART subgroup to resolve technical issues spanning st ates in the VISTAS region, and ongoing consultation with aff ected sources, and EPA and FLMs throughout the BART process.
EPA’s Best Available Retrofit Technology (B ART) applies to sources that have the potential to emit 250 tons or more of a visibi lity impairing pollutant, those that were in existence on August 7, 1977 and began operati on after August 7, 1962, and those that fall within one of the 26 industr ial source categories listed in 40 CFR Part 51 Appendix Y guidelines. The rule allows affected so urces to demonstrate that the source does not contribute to visibility impairment and shoul d be exempted. Non-exempt sources are required to undergo a determination of best available retrofit te chnology. BART controls are required to be installed a nd begin operating by January 1, 2012.

7.3 Identification of BART-Eligible Sources
EPA provided guidance for identifying BA RT-eligible sources in 40 CFR Part 51 Appendix Y. Georgia followed this guidan ce in identifying its BART-eligible sources. The Georgia 2002 air emissions inventory was reviewed to determine the population of potential BART-eligible sources. The data was initially queried for any sources emitting SO2, NOx, PM-10, VOC, or ammonia. The da ta set was refined based on emissions, whether emission units fit in any of the twen ty-six listed source categories for BART, and available date information obtained through re view of permit and in spection reports and discussion with permitting and compliance staff familiar with the facilities.
An information packet on BART was sent to and additional information was collected from the remaining sources regarding potentia l emissions and BART eligibility from the standpoint of source category and date criteria . Through this iterative review process, the list of potential BART-eligible sources was pared down to the 24 BA RT-eligible sources listed below. For more details on emissions a nd Q/d at these faciliti es, see Appendix H.5. These include one cement manufacturing faci lity, six pulp and paper facilities, ten electric utilities, two chemi cal manufacturers, two packag ing facilities, one phosphoric acid manufacturer, one asphalt manufacturer, and one nitrogen fertilizer manufacturer.App endix H

101 of 115 Georgia BART Eligible Sources

FACILITY NAME
Georgia Power-Plant Bowen Georgia Power-Plant Branch Georgia Power-Plant Hammond Georgia Power-Plant McDonough Georgia Power-Plant Mitchell Georgia Power-Plant Scherer Georgia Power-Plant Wansley Georgia Power-Plant Yates Savannah Electric-Plant Kraft Savannah Electric-Plant McIntosh Chemical Products Corp DSM Chemicals North America Koch Cellulose (GP Brunswic k, Brunswick Pulp & Paper) Georgia-Pacific Cedar Springs Operations International Paper – Augusta International Paper - Savannah (Union Camp Corp.) Interstate Paper, LLC Kerr-McGee Pigments, Inc. (Kemira) Lafarge Building Materials (Blue Circle Cement – Atlanta Plant) Owens Corning PCA (Tenneco Packaging, Inc.) PCS Nitrogen Fertilizer, LP – Augusta Prayon, Inc. (Solutia, Inc.) Rayonier (ITT Rayonier, Inc.)

Georgia BART-eligible sources were presumed to be subject to BART but were provided the opportunity to submit modeling demonstrati ons showing that they did not contribute to visibility impairment (i.e., had less than 0.5 deciviews (dv) imp act on any Class I area within 300 km and thus could be exempt).App endix H

102 of 115 Figure 7-1. GA BART-Eligible Sources and Class I Areas. 

### Georgia BART-Eligible SourcesAppendix H103 of 115Facility NameWOLFISLANDOkefenokeeCohutttaCapeRomainLinvilleGorgeJoyceKillmerShinningRockSt. MarksGreat SmokyMountainsSipseyBardwellBayChassahowitzkaGeorgia Power-Plant BowenGeorgia Power-Plant Branch274.4254.8218.6247.4242.5250.5Georgia Power-Plant Hammond93.7174.8252.4187.5183Georgia Power-Plant McDonough113.7176.9221.8189.8269Georgia Power-Plant Mitchell265.4163.4141.7Georgia Power-Plant Scherer299.4263.1211.8256.3268.3265.3Georgia Power-Plant Wansley163.8237.5289.9250.9235Georgia Power-Plant Yates156.3228.2278.3241.5240Savannah Electric-Plant Kraft87159161.7Savannah Electric-Plant McIntosh110.5177.1152.6Chemical Products CorpDSM Chemicals North America236.9266288.7219.2266.7281.52276Koch Cellulose (GP Brunswick,Brunswick Pulp & Paper)26100130280Georgia-Pacific Cedar SpringsOperationsInternational Paper – Augusta226254294219275289236International Paper - Savannah (UnionCamp Corp.)83.6156.9162.5Interstate Paper, LLC41103207295Kerr-McGee Pigments, Inc. (Kemira)90180210Lafarge Building Materials (Blue CircleCement – Atlanta Plant)114.3177.3221.9190.2269Owens Corning146.4212.3255.6225.3267PCA (Tenneco Packaging, Inc.)20173.189.1128.5220.8PCS Nitrogen Fertilizer, LP – Augusta238267288220265280225Prayon, Inc. (Solutia, Inc.)Rayonier (ITT Rayonier, Inc.)5876246264Exempt SourceNon-Exempt SourceExempt UtilityNon-Exempt UtilityDistance From Class I AreaTable 7-1. GA BART-Eligible Sources and Distances to Respective Class I AreasApp endix H

104 of 115 7.4 Contribution Threshold
Determining whether a source causes or contribu tes to visibility impairment is one step in the BART review process. The Guidelines for BART Determinations Under the Regional Haze Rule 3 states “a single source that is res ponsible for a 1.0 deciview change or more should be considered to ‘cau se’ visibility impairment.” The guideline document also states that “the appropriate threshold fo r determining whether a source ‘contributes to visibility impairment’ may reasonably differ acr oss states,” but, “as a general matter, any threshold that you use for determining whet her a source ‘contributes’ to visibility impairment should not be higher than 0.5 decivi ews.” The rationale for these instructions is provided in the preamble to the BART guidance in the statement, “if ‘causing’ visibility impairment means causing a human ly perceptible change in visibility in virtually all situations ( i.e. a 1.0 deciview change), then ‘contributing’ to visibility impairment must mean having some lesser im pact on the conditions affecting visibility that need not rise to th e level of human perception.” 4 The guidance document itself also states that, “States remain free to use a threshold lower than 0.5 deciviews 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 threshol ds in the context of the human perception of visibility change. As noted above, the EPA considers a 1.0-deciview change in visibility to be humanly perceptible “in virtually all situations.” Also, the preamble to the BART guidance 5 cites an analysis in an appendix of a NAPAP (National Acid Precipitation Assessment Program) report, which asserts that “changes in light extinction of 5 percent will evoke just noticeable ch anges in most landscapes.” 6 (A 5% change is approximately 0.5 dv.) But, as noted above, the preamble also states that pe rceptibility is not a prerequisite for choosing a cont ribution threshold. Putting a ll this together, it appears that “causing” visibility impairment mean s having a humanly perceptible impact (for which EPA considers the practical thres hold to be 1.0 dv) while “contributing” to visibility impairment means having a sma ller impact (for which EPA considers the threshold to be 0.5 dv or some smaller valu e) that may or may not be perceptible.
The EPA argues that a contribution threshol d of less than 0.5-dv-impact-per-source is appropriate when multiple sources contribute, in order to limit the combined effect of these sources. As an example, EPA asserts th at if there were 100 sources, each affecting visibility by 0.1 dv (presumably an imperceptib le amount), their total impact would be 10 dv (which can be expected to be quite perceptible). 7 The point remains that multiple sources can cause a larger impact than a single one. For BART purposes, visibility

3 40 CFR 51, Appendix Y, Section III.A.1

4 70 FR 39120. Footnote 31

5 70 FR 39119. Footnote 28
6 “The Quadratic Detection Model” by W. Ma lm. Appendix D in Acidic Deposition: State of Science and Technology, Report 24, Visibility: Existing and Historical Conditions -- Causes and Effects. National Acid Precipitation A ssessment Program, Washington, DC. 1990.
7 70 FR 39121. 1st columnApp endix H

105 of 115 impacts are calculated as 24-hour averages of 1-hour plume impacts, so if the plumes from the various sources each impact the poi nt of interest at some time during a 24-hour period (not necessarily all at the same hour) then the 24-hour aver age will reflect their combined impact.
Georgia EPD concluded that the EPA sugge sted contribution threshold of 0.5 dv was appropriate in this situation since there ar e a limited number of out of state sources that impact the various Class I areas in the State.

Figure 7-2. Location of VISTAS BART-Eligible Sources
7.5 Exemption of Point Source Volatile Organic Compounds for BART Purposes
The State of Georgia has determined th rough modeling that volatile organic compounds (VOCs) from point sources are not anticipated to cause or c ontribute significantly to any impairment of visibility in Class I areas and should be exempt for BART purposes.App endix H

106 of 115 7.5.1 Method
The State of Georgia determined through m odeling that VOCs from point sources are not anticipated to cause or contribute significantly to any impairment of visibility in Class I

areas.
Modeling was conducted through The Visibi lity Improvement State and Tribal Association of the Southeast (VISTAS). VI STAS is a collaborative effort of State governments, tribal governments, and various Fe deral agencies establis hed to initiate and coordinate activities associat ed with the management of regional haze, visibility and other air quality issues in the Southeastern United States. VISTAS contracted with Georgia Institute of Technology to perform model sensitivity runs to determine the impact of point source VOCs on vi sibility in Class I areas.
Georgia Tech performed emission sensitiviti es to examine the impact of emission reductions on regional haze, annual PM2.5, a nd 8-hour ozone concentrations using CMAQv4.4_SOAmods on the VISTAS 12-km modeling domain, using the 2009 OTW (on the way) BaseD emissions. One such sensitivity run reduced anthropogenic, point source VOCs by 100%. The purpose was to quan tify the impact of VOC emissions from VISTAS BART sources on Class I areas. Two episodes were examined: June 1-July 10, 2002 and November 19 – December 19, 2002. The approach included calculating the extinction coefficient in dv (deciviews), th en determining the maximum impact of point source VOCs. The chart below shows the im pact on the following twenty-two Class I areas within the VISTAS domain (Appendix H.6).

BRET = Breton, LA BRIG = Brigantine NWR, NJ CACR = Caney Creek, AR CHAS = Chassahowitzka, FL COHU = Cohutta, GA DOSO = Dolly Sods, WV EVER = Everglades, FL GRSM = Great Smoky Mountains National Park HEGL = Hercules Glade, MO JARI = James River Face, VA LIGO = Linville Gorge, GA MACA = Mammoth Cave, KY MING = Mingo, MO OKEF = Okefenokee, GA ROMA = Cape Romain, SC SAMA = Saint Marks, FL SHEN = Shenandoah, VA SHRO = Shining Rock, GA SIPS = Sipsey Wilderness, AL SWAN = Swanquarter, GA UPBU = Upper Buffalo Wilderness, ARApp endix H

107 of 115 Figure 7-3. Maximum Point VOC Impact

7.5.2 Conclusions
The results show that the maximum impact from eliminating all point source VOC emissions in the VISTAS 12 km domain is le ss than a 0.5 dv for all Class I areas in the VISTAS domain. The expected impact of controlling VOCs from all BART sources in Georgia would be much less than the 0.3 dv th reshold. VISTAS and the State of Georgia conclude that VOCs from point sources are not a visibility -impairing pollutant for BART purposes and that BART-eligible sources do not need to consider VOC emissions.

7.6 Treatment of Ammonia Emissions for BART Purposes
Similar to its treatment of VOCs, EPA guida nce allows States the discretion to decide whether or not ammonia emissions are to be considered for BART purposes based on evaluations of the contributions of the emissi ons to haze at Class I areas in their areas of influence. One approach a State can use to determine whether applying BART will be needed is to evaluate the haze impacts of all current emissions from all BART-eligible sources in the State. If the impact from all sources in the state is less than the contribution threshold established by the Stat e, 0.5 dv for Georgia, then source-by-source analysis for BART is not needed.
The State of Georgia has determined th rough modeling that ammonia (NH3) emissions from point sources are not anticipated to cause or contribute significantly to any impairment of visibility in Class I areas and should be exempt for BART purposes. 

# Maximum Point VOC Impact

Maximum VOC Impact

0.0 0.1 0.2 0.3 0.4 0.5 0.6BRETBRIGCACRCHASCOHUDOSOEVERGRSMHEGLJARILIGOMACAMINGOKEFROMASAMASHENSHROSIPSSWANUPBUWOLF∆dVApp endix H

108 of 115 7.6.1 Method
The State of Georgia determined through m odeling that ammonia emissions from point sources are not anticipated to cause or cont ribute significantly to any impairment of visibility in Class I areas with the excepti on of one large point source in close proximity to one Georgia Class I area.
VISTAS contracted with Georgia Institute of Technology to perform model sensitivity runs to determine the impact of point sour ce ammonia on visibility in Class I areas.
Georgia Tech performed emission sensitiviti es to examine the impact of emission reductions on regional haze using CMAQv4.5 with SOA models on the VISTAS 12 km modeling domain using the VISTAS 2009 OTW ( on the way) Base F4 emissions. One such sensitivity run reducedBART-eligible source, ammonia, by 100%. The purpose was to quantify the impact of ammonia emi ssions from VISTAS BART sources on Class I areas. Two episodes were examined: June 1-July 10, 2002, and November 19 – December 19, 2002. The approach included calc ulating the extinction coefficient in dv (deciviews), then determining the maximum impact of BART-eligible source ammonia. The chart below, taken from the VISTAS report BART in the VISTAS Region: Sensitivity to VOC, NH3, and Primary PM Emissions (included as Appendix H.6) shows the impact on the following 22 Class I areas within the VISTAS domain.

Figure 7-4. Maximum NH3 Impact - Maxi mum contributions of all BART-eligible NH 3 point sources in the VISTAS region to haze in Class I areas during the CMAQmodeled periods.
The chart shows the largest haze impact in delta dv over the two tim eframes at each Class I area. These delta dv changes represent the haze contribution of all BART source 

## Maximum NH 3 

## Impact

0.0 0.5 1.0 1.5BRETBRIGCACRCHASCOHUDOSOEVERGRSMHEGLJARILIGOMACAMINGOKEFROMASAMASHENSHROSIPSSWANUPBUWOLF∆dVApp endix H

109 of 115 ammonia emissions relative to assumed natura l haze levels. The na tural haze levels are based on EPA-default annual average natural concentrations for the East and monthlyvarying “climatologically-representative” re lative humidity at each Class I area. During the periods modeled, the ammonia em issions from all BART-eligible sources contributed more than 0.5 dv to haze at Mammoth Cave National Park (MACA), Kentucky; Mingo Wilderness (MING), Missouri, Cape Romain National Wildlife Refuge Area (ROMA) South Carolina, and Swanquart er National Wildlife Refuge (SWAN), North Carolina.
The majority of ammonia emissions in th e VISTAS region come from four BARTeligible sources as shown in Table 7-2 below.

Table 7-2. Major BART NH 3 Sources in the VISTAS Region

Facility State NH 3 Emissions (tpy) Distance to Nearest Class 1 Area (km) Meadwestvaco Corp. SC 174 30 PCS Phosphate GA 206 31 PCS Nitrogen TN 1252 180 PCS Nitrogen GA 1765 220

7.6.2 Conclusions
The BART requirements of the regional haze ru le allow states to determine whether or not ammonia is to be considered a visibil ity-impairing pollutant to be addressed for BART purposes. NH3 emissions from BART sources may impair visibility; however. the majority of NH3 emissions in the VI STAS region are from a few BART sources. Removal of the large NH3 emission sources re sults in minimal impact on visibility at Class I areas in the VISTAS region. Furt hermore, ammonia emissions from all BARTeligible sources contributed less than 0.5 dv to the Class I areas in Georgia (Cohutta Wilderness (COHU), Okefenokee Wilderness (OKEF), and Wolf Island Wilderness (WOLF)). Therefore, the State of Georgi a concludes that ammonia from point sources do not need to be considered for BART purposes.


### 8.0 Explanation of BART Exemption Criteria

8.1 Background
Georgia opted to consider its BART-eligible sources subject to BART unless the source demonstrated exemption via any of the foll owing three options: 1) Exemption modeling 2) Meeting the specifically-l isted model plant criteria for emissions and distance from the; nearest class I area; and 3) accepting an emissions lim it of less than 250 tpy of the visibility-impairing pollutant.App endix H

110 of 115 8.1.1 Exemption Modeling
BART-eligible sources can be excluded from BART determinations by demonstrating that the source cannot be reasonably expect ed to cause or contribute to visibility impairment in a Class I area. The threshold for determining that a source causes visibility impairment is set at 1.0-dv change from natural conditions over a 24-hour averaging period. The BART guidelines also propose th at the threshold at which a source may “contribute” to visibility impairment shoul d not be higher than 0.5 deciviews; however, depending on factors affecting a specific Cl ass I area, it may be set lower than 0.5 deciviews.
As stated in the BART regulation, EPA’s pr eferred approach for determining cause or contribution is an assessment with an air quality model such as CALPUFF or other appropriate model followed by comparison of the estimated 24-hour visibility impacts against a threshold above estim ated natural conditions to be determined by the State. EPA recommends that the 98 th percentile value from the modeling be compared to the State’s chosen contribution th reshold to determine if a source does not contribute to visibility impairment and, thus, is not subject to BART. Comparison of the 98 th percentile value to the threshold must be made for each Class I area. For an annual period, this implies the 8 th highest 24-hour value at a part icular Class I area is compared to the contribution threshold. Fo r a 3-year modeling period, the 98 th percentile value may be interpreted as the highe st of the three annual 98 th percentile values at a particular Class I area or the 22 nd highest value in the combined 3-year record, whichever is more conservative.
Georgia worked with the regional planni ng organization (RPO) VISTAS on development of the VISTAS Protocol for the Application of the CALPUFF Model for Analyses of Best Available Retrofit Technology (BART) (Appendix H.7). The common protocol was established to provide the basis for a common understanding among the organizations performing BART analyses or reviewing BA RT modeling results in the VISTAS region.
The VISTAS protocol describes common procedures for carrying out air quality modeling to support BART determinations th at are consistent with the 40 CFR Part 51 Appendix Y guidelines. The protocol provi des a consistent model, CALPUFF, and modeling guidelines for BART determinations , clearly delineated modeling steps, a common CALPUFF configuration, guidance for site-specific modeling, and common expectations for reporting model results. De tails of the CALPUFF system can be found in Chapter 3 of the VISTAS protocol, specif ic recommendations for its application for BART purposes are found in Chapter 4, and spec ific information that should be included in site-specific protocols is found in Chapter 5.
VISTAS contracted EarthTech to develop a version of the CALPUFF model appropriate for BART purposes. The VISTAS versi on of the CALPUFF model is CALPUFF Version 5.754 and CALMET Version 5.7.App endix H

111 of 115 For BART modeling purposes VISTAS made publicly available 12-km CALMET output files for the entire VISTAS modeling dom ain (eastern United States) and provided CALMET output files for five 4-km grid s ub domains covering the VISTAS states and VISTAS Class I areas. To generate the CALMET input files, the VISTAS contractor used the MM5 databases developed by EPA for 2001, VISTAS for 2002, and Midwest RPO for 2003. For the 12-km grid, large domai n covering the entire VISTAS region, the No-Obs setting was used. For the 4-km gr id, available surface a nd upper air observations were used in addition to MM5 meteorological model outputs. Specific model settings were provided with the CALMET files and via the CALPUFF website so that work could be replicated.
For CALPUFF modeling, source emissions were to be defined using the maximum 24- hour actual emission rate during normal operation for the most recent three or five years. If maximum 24-hour actual emissions were not available, continuous emission data, permit allowable emissions, potential emissions , and emission factors from AP-42 source profiles could be used as available.
Key points on specific CALPUFF, CALPOST, and POSTUTIL configuration that were to be used for BART modeling are:

• After running CALPUFF for an i ndividual facility, NO3 should be repartitioned in POSTUTIL.

• Ozone data from non-urban monitors should be used as the background ozone input.

• The Pasquill-Gifford dispersion method should be used.

• In CALPOST, Method 6 with monthly average RH for calculating extinction as recommended by EPA should be used.

• EPA default calculations of light extinction under current and natural background conditions should be used. In addition a source may also calculate visibility using the recently-revised IMPROVE algorithm.
Additional discussion of the CALPUFF model and VISTAS recommended settings can be found in the VISTAS protocol in Attachment H-7.

8.1.2 Model Plant Criteria
In its final BART rule, EPA included mechanisms for exempting sources from BARTdetermination requirements. Under one appr oach, EPA established specific exemption thresholds based on CALPUFF modeling conduc ted for a model plant. BART eligible sources meeting specifically-listed model pl ant criteria for emissions and distance from the nearest Class I area can be exempted fr om BART determination requirements. The criteria that must be met are that sources mu st have potential emi ssions of sulfur dioxide (SO2) and nitrogen oxides (NOx) combined of less than 500 tons and be greater than 50 km from any Class I area or have combined potential emissions less than 1000 tons and be greater than 100 km from any Class I ar ea. Sources must also have potentialApp endix H

112 of 115 emissions of particulate matter (PM) of less than 15 tons-per-year to be exempted without some form of modeling. In situations where a source’s combined SO2 and NOx emissions are less than 500 or 1000 tons, but PM emissions are greater than 15 tons, the emissions of SO2, NOx, and PM may be co mbined and compared to 500 or 1000 tons.

8.1.3 Accepting an Emissions Limit
If a facility is willing to take a facility-w ide limit of 250 tpy on all BART eligible equipment for each pollutant for which the facili ty is BART eligible, then the facility is exempt from BART requirements. The facility must submit an application to this effect and such limits must be incorporated into an enforceable permit.
Table 8-1 below provides a summary of the exemption modeling results.

Table 8-1. BART Exemption Modeling Results

Facility Class 1 Areas Impacted Delta-Deciviews PCS-Nitrogen Okefenokee Wilderness Area 0.393 Cohutta Wilderness Area 0.409 Wolf Island 0.294 Cape Romain 0.359 Great Smoky Mountains 0.163 Joyce Kilmer 0.175 Shining Rock Wilderness 0.220 Linville Gorge 0.393 Shinning Rock Wilderness 0.220 Rayonier Wolf Island 0.319 Okefenokee Wilderness Area 0.254 Cape Romain 0.132

St. Marks 0.151 Georgia Power-Plant Wansley Cohutta Wilderness Area 0.44 Great Smoky Mountains 0.17 Joyce Kilmer 0.18 Shining Rock Wilderness 0.12 Sipsey 0.14
DSM Chemicals North America Cape Romain 0.094 Cohutta Wilderness Area 0.112 Great Smoky Mountains 0.072 Joyce Kilmer 0.036
Okefenokee Wilderness Area 0.063

Shining Rock Wilderness 0.069App endix H

113 of 115 Wolf Island 0.114 International Paper Agusta Cohutta Wilderness Area 0.114 Cape Romain National Wildlife Refuge 0.211 Great Smoky Mountains 0.093 Joyce Kilmer 0.086 Linville Gorge 0.103
Okefenokee Wilderness Area 0.157

Shining Rock Wilderness 0.127
Wolf Island 0.148 Georgia Power-Plant Branch Cohutta Wilderness Area 0.17 Great Smoky Mountains 0.11 Joyce Kilmer 0.09 Shining Rock Wilderness 0.09
Okefenokee Wilderness Area 0.12
Wolf Island 0.11 Georgia Power-Plant Hammond Great Smoky Mountains 0.08 Joyce Kilmer 0.09
Cohutta Wilderness Area 0.29 Shining Rock Wilderness 0.05 Sipsey 0.06
Savannah Electric-Plant Kraft Cape Romain National Wildlife Refuge 0.18
Okefenokee Wilderness Area 0.18
Wolf Island 0.21 Georgia Power-Plant McDonough Great Smoky Mountains 0.07 Joyce Kilmer 0.07
Cohutta Wilderness Area 0.19 Shining Rock Wilderness 0.05 Sipsey 0.04 Savannah Electric-Plant McIntosh Cape Romain National Wildlife Refuge 0.04
Okefenokee Wilderness Area 0.03
Wolf Island 0.03 Georgia Power-Plant Mitchell Okefenokee Wilderness Area 0.04
Wolf Island 0.02

St.Marks 0.05 Georgia Power-Plant Scherer Okefenokee Wilderness Area 0.08
Wolf Island 0.05 Joyce Kilmer 0.07App endix H

114 of 115

Cohutta Wilderness Area 0.08 Shining Rock Wilderness 0.06 Great Smoky Mountains 0.07 Georgia Power-Plant Yates Cohutta Wilderness Area 0.15 Great Smoky Mountains 0.05 Shining Rock Wilderness 0.03 Joyce Kilmer 0.06 Sipsey 0.05 Koch Cellulose (GP Brunswick, Brunswick Pulp & Paper Okefenokee Wilderness Area 0.282
Wolf Island 0.447

St.Marks 0.055 Cape Romain National Wildlife Refuge 0.092
PCA Valdosta Chassahowitzka (12 km) 0.197 Okefenokee Wilderness Area (12 Km) 0.349 Wolf Island (12 Km) 0.181

St. Marks (4 Km) 0.190 Kerr-McGee (Tronox) Okefenokee Wilderness Area 0.213
Wolf Island 0.246 Cape Romain National Wildlife Refuge 0.134 Prayon Okefenokee Wilderness Area 0.007
Wolf Island 0.005 Cape Romain National Wildlife Refuge 0.009
Cohutta Wilderness Area 0.004 Great Smoky Mountains 0.002 Shining Rock Wilderness 0.003 Joyce Kilmer 0.002 Linville Gorge 0.005 Owens Corning Cohutta Wilderness Area 0.119 Great Smoky Mountains 0.051 Shining Rock Wilderness 0.072 Joyce Kilmer 0.056 Sipsey 0.054 Georgia Pacific Cedar Springs Okefenokee Wilderness Area 0.306
St. Marks 0.499 Table 8-2 below provides a list of facilities exempted from BART on the basis of model plant criteria or by acceptance of a limit of 250 tpy for visibility-causing pollutants.App endix H

115 of 115 Table 8.2 Facilities that demonstrated BART exemption based on model plant criteria or by accepting a limit of 250 tpy for visibility causing pollutants

BART Eligible Sources Basis of Exemption Chemical Products Corp. Facility has taken a 250 tpy limit on BART Eligible Equipment International Paper - Savannah Total potential Emissions of visibility impairing pollutants is lesser than 500 t ons and distance from the nearest Class I area is greater than 50 Km Lafarge Building Materials (Blue Circle Cement –Atlanta Plant) Total potential Emissions of visibility impairing pollutants is lesser than 1000 tons and distance from the nearest Class I area is greater than 100 Km

8.2 Use of New IMPROVE Equation
During the regional haze SIP development process, the IMPROVE Steering Committee made recommendations for a new IMPROVE equation. Among other things, the new equation includes components that take into account the effects ofsea salt and sitespecific Rayleigh scattering (Appendix H.11). Th ese are important issues to consider for coastal and mountainous sites. Additional discussion of the new IMPROVE equation can be found in Appendix B of the SIP narrative.

VISTAS contracted Dr. Ivar Tombach to develop a postprocessor to allow CALPOST outputs to be used with the new IMPROVE equation. Georgia EPD submitted to EPA Region IV a request for approval to use the new IMPROVE equation and postprocessor developed by Dr. Tombach in BART modeling. A discussion of the rationale for use of the new IMPROVE equation follows.
Rationale for Use of New IMPROVE Equation The new IMPROVE equation is a much better re presentation of the e ffects of particulate matter on light extinction than the old equation and takes into account the latest scientific understanding of several parameters.
1. The new algorithm overcomes biases of the old algorithm on the haziest days and the clearest days as demonstrated by co mparing the measured light extinction from nephelometers at Class I areas to light extinction calculated using each of the equations. 2. The new algorithm recognizes spatial and temporal variation in light extinction as size distribution of the aerosol change s by increasing extinction efficiency as sulfate, nitrate and organic concentrations increase. 3. The new algorithm incorporates a term to reflect the contribu tion of fine sea salt and its hygroscopic growth with increasing relative-humidity-recognizing research findings showing th at fine sea salt can be an important contributor to light extinction in coastal areas.App endix H

116 of 115 4. The new algorithm reflects research finding that the mass concentration of particulate organic matter in rural areas is greater than represented by the old equation. 5. The new algorithm includes an NO 2 term to represent times when light absorption by NO 2 is a meaningful contri butor to light extinction. 6. The new algorithm incorporates site-spe cific Rayleigh scattering values to better represent sites close to sea level or with very hot or cold climates.

With this combination of revisions to the IMPROVE equation, the resulting apportionment of extinction to various com ponents is more accurate on the haziest and clearest days. This is important for devel opment of emission control strategies since the benefits of control of concentrations of each species will be represented more correctly with the new algorithm.
GA EPD submitted a request for approval to the use of the new IMPROVE equation instead of the original IMPROVE equation. This request also contains the new IMPROVE postprocessor and its instructions. Region 4 EPA granted this request to use the new IMPROVE equation (Appendix H.9).



### 9.0 BART Determination

For those facilities subject to BART, the st ate must determine what constitutes BART controls for the source by considering vari ous control options and selecting the best alternative taking into consid eration any pollution control equipment in use at the source, the costs of compliance with c ontrol options, the remaining usef ul life of the facility, the energy and non air-quality environmental impacts of compliance, and the degree of improvement in visibility that may reasonabl y be anticipated to result from the use of such technology. Under Georgi a’s approach, facilities subj ect to BART were requested to propose BART considering these factors.

9.1 BART Subject Sources
Only two BART-eligible sources in Georgia emit any air pollutant which may reasonably be anticipated to cause or cont ribute to any impairment of vi sibility in any Class I area. The two facilities, Interstate Paper in Ri ceboro, Georgia, and Georgia Power Company – Plant Bowen, in Cartersville, Georgia, we re unable to demonstrate less than a 0.5 dv impact on one or more Class I ar eas and, thus, are subject to BART

9.1.1 BART-eligible Units and Existing Control
Georgia Power Plant-Bowen has four BART-e ligible emission units that comprise the BART-eligible source. These units are coal fired electric generating units 1,2,3 and 4.App endix H

117 of 115 The above-mentioned units are BART eligible for their PM emissions. Each of these EGU’s PM emissions are already controlled by electrostatic precipitators (ESPs) and wet flue gas desulfurization scrubbers (Wet FGD).
Interstate Paper has three BART-eligible emission units that comprise the BART-eligible source. These units are recovery boiler, po wer boiler and lime kiln. Each of these units is BART eligible for PM 10 , SO 2 and NO x . The recovery boiler has a venturi scrubber as an existing control for PM 10 emissions, staged combustion and combustion control as an existing control strategy for NO x and SO 2 respectively. The lime kiln has a wet scrubber as an existing control for both PM 10 and SO 2 . 9.2 BART Determination Analysis and Conclusion
In accordance to 40 CFR Part 51, Regional Haze Regulations and Guidelines for Best Available Retrofit Technology (BART) Dete rminations; Final Rule the state is responsible for determining BART for all th e subject-to-BART sources. Georgia EPD requested the two facilities to do a detailed BART determination analysis. Interstate Paper in Riceboro, Georgia failed to res pond to the above-men tioned request hence, Georgia EPD did a complete detailed BART analysis for the facilities BART eligible units. Table 9.1 gives a summary of the BART determination analysis for the two facilities. A complete BART determination analysis for both of the subject-to-BART sources are given in Appendix H.8.

Table 9.1 BART determination analysis for both the subject-to-BART sources

Source Unit Required Control Option Control Efficeincy Cost of Control ($/ton) Power Boiler (F1) Combustion of natural gas only except during periods of curtailment 99% 370

Recovery

Boiler No cost effective control options Available Not required Not required Interstate Paper

Lime Kiln No cost effective control options Available Not required Not required

Boiler 1 Boiler 2 Boiler 3 Georgia Power

Company – Plant Bowen
Boiler 4 No cost effective control options Available Not required Not required

The required permit conditions to incorporate the BART de termination for Interstate Paper Power Boiler F1 will be incorporated into the Title V permit as discussed in Section 7.7.2 of the narrative. Additionally, the Power Boiler (and Multi-fuel BoilerApp endix H

118 of 115 MFB1) is permitted to combust TRS containing waste gas streams when the associated Lime Kiln, LM01, is not operating. Operati onal records have demonstrated that this scenario has not occurred; however, Georgia EPD will modify the Title V permit to restrict to combustion of waste gas in the Power Boiler only when both the lime kiln and multi-fuel boiler are not operational.


### 10.0 Final Long Term Strategy And Reasonable Progress Goals

The regional haze rule requires States to es tablish reasonable progr ess goals, expressed in deciviews, for visibility improvement at each affected Class I area covering each (approximately) 10-year period until 2064. This first set of reasonable progress goals must be met through measures contained in the State’s long-term strategy covering the period from the baseline until 2018. This se ction discusses development of Georgia’s long-term strategy.

10.1 Long-Term Strategy

Ammonium sulfate is the most important contributor to visibility impairment and fine particle mass on the 20 percent worst and 20 pe rcent best visibility days at all of the Georgia Class I areas. Sulfate levels on the 20 percent worst days account for 60-70 percent of the visibility impairment. Across the VISTAS region, sulfate levels are higher at the Southern Appalachian sites than at th e coastal sites. On the 20 percent clearest days, sulfate levels are more uniform acr oss the region. Reduction of SO2 emissions would be the most effective means of reduci ng ammonium sulfate. Ninety-five percent of SO2 emissions in Georgia are attributable to electric generating fa cilities and industrial point sources. Thus, the long-term strategy for this first Regional Haze SIP focused on reductions in SO2 emissions. When fully im plemented, Georgia Rule (sss) will reduce SO2 emissions by about 90 percent, NOx em issions by approximately 85 percent and mercury emissions by approximately 79 percent.
Such large SO2 reductions are predicted to result in significant improvements in visibility. Visibility improvements at all of the VISTAS mountain Class I areas and most of the coastal Class I areas are projected to be better than the uni form rate of progress. The percentage of the target reduction achie ved for the Georgia Class I areas using the new IMPROVE equation is 90-160 percent.
In addition to improving visibili ty on the 20 percent worst visi bility days, states are also required to protect visibility on the 20 percent best days at the Class I areas. Visibility on the 20 percent best days is projected to impr ove in 2018 at all VISTAS Class I areas as a result of the 2018 Base G2 emissions reductio ns. The percentage of the target achieved for the Georgia Class I areas is about -10 percent. Zero percent change would mean no change in visibility; -10 percent means that visibility is better than no change, or a 10 percent improvement (values lo wer than current conditions).
The Georgia EPD completed a reasonable progress assessment after modeling the Clean Air Act controls discussed previously. The assessment did not identify any cost-effectiveApp endix H

119 of 115 control measures for the SO2 EGU units that contributed greater th an half a percent to visibility impairment at any of the Class I ar eas in Georgia or in neighboring States. The Georgia EPD did an indepth 3- factor analysis and has propos ed reasonable controls for some non-EGUs that contribute more than half a percent to visibility impairment at any of the Class I areas in Geor gia or in neighboring States.
The Georgia EPD reviewed the BART determ ination from Georgia Power Plant Bowen, which is one of the two Georgi a facilities that were determined to be subject to BART. From its review, Georgia EPD concluded th at no cost-effective BART reductions are possible. Georgia EPD also did a BART dete rmination analysis for Interstate Paper and concluded that burning of natural gas excep t during curtailment periods would be costeffective BART control for the facility.

10.2 What Additional Emissions Controls Were Considered as Part of the LongTerm Strategy for Visibility Improvement by 2018?
Section 308(d)(3)(v) of the regiona l haze rule lists several fact ors that must be addressed in each SIP. These factors include the role of fire at Class I areas and status of State planning for smoke management, the role of dus t and fine soil at Cl ass I areas and status of State plans to mitigate emissions from cons truction activities, and the role of NH3 and potential benefits if emissions from agricultural sources were mitigated.
On July 2008, Georgia EPD entered into a memorandum of understanding with the Georgia Forestry Commission and Georgia De partment of Natural Resources Wildlife Resources Division adopting a smoke management plan that addresses the issues laid out in EPA’s 1998 draft guidance for smoke management plans. The Memorandum, along with the Basic Smoke Management Plan, is given in Appendix H.10.

10.3 Reasonable Progress Goals
The Regional Haze Rule at 40 CFR Section 51.308(d)(1) requires States to establish reasonable progress goals for each Class I ar ea within the state (expressed in deciviews) that provide for reasonable progress towards achieving natural visi bility. In addition, EPA released guidance on June 7, 2007, to use in setting reasonable progress goals. The goals must provide improvement in visibility for the most impaired days, and ensure no degradation in visibility for the least impa ired days over the State Implementation Plan (SIP) period. The state must also provide an assessment of the number of years it would take to attain natural visibili ty conditions if improvement con tinues at the rate represented by the reasonable progress goal.
In accordance with the requirements of 40 CFR §51.308(d)(1), this Regional Haze Implementation Plan establishes reasonabl e progress goals for each Class I area in Georgia. To calculate the rate of progre ss represented by each reasonable progress goal, Georgia EPD compared baseline visibility conditions to natural visibility conditions in each class I area and determined the uniform rate of visibility improvement (in deciviews) that would need to be maintain ed during each implementation period in orderApp endix H

120 of 115 to attain natural visibility conditions by 2064. The Georgia EPD will summarize expected visibility improvements under ex isting Federal and State regulations, BART determinations in Georgia and neighboring states, and any additional control measures found to be reasonable to implement in this review period. These controls will be modeled in CMAQ as part of the long-term strategy. The modeling results will be used to set the reasonable progress goal. At this time, Georgi a EPD knows that the reasonable progress goals will be at least as stringent as the Base G4 modeling results and will likely be more stringent when BART and reasonable measures are taken into account. VISTAS has already demonstrated that the 2018 Ba se G4 control scenario provides for an improvement in visibility bette r than the uniform rate of pr ogress for all Georgia Class I areas for the most impaired days over the pe riod of the implementation plan and ensures no degradation in visibility fo r the least impaired days over the same period. In addition, we expect that IMPROVE monitoring result s for 2018 will, at that time, be used to establish a new baseline for the next planning period.

Table 10-1 lists Georgia’s Reasonable Progress Goals.

Table 10-1. Georgia Reasonable Prog ress Goals – 20 percent Worst Days Class I Area Baseline Visibility (dv, 20 percent Worst Days) Reasonable Progress Goal (Deciview Improvement Expected by 2018, 20 percent Worst Days) Deciview Improvement Needed 2018- 2064 (20 percent Worst Days)

Cohutta Wilderness Area 30.25 7.47 12.00 Okefenokee Wilderness Area 27.13 3.36 12.56 Wolf Island 27.13 3.36 12.56

Table 10-2. Georgia Reasonable Progress Goals – 20 percent Best Days Class I Area Baseline Visibility (dv, 20 percent Best Days) Reasonable Progress Goal (Expected Visibility Improvement (dv) by 2018, 20 percent Best Days) Deciview Improvement Needed 2018- 2064 (20 percent Best Days)

Cohutta Wilderness Area 13.77 2.02 7.43 Okefenokee Wilderness Area 15.23 1.31 8.61 Wolf Island 15.23 1.31 8.61



### REFERENCES:

1. Boylan, J., 2006, VISTAS Joint Tech Analysis-Planning Call. Power Point Presentation: Summary BART_VOC_NH3_PM.ppt.App endix H

121 of 115 2. Brewer, P.F.; and Adloch, J.P., 2005, Trends in Speciated Fine Particulate Matter and Visibility across Monitoring Networks in the Southeastern United States . J Air and Waste Management Association , 55, 1663-1674. 3. Federal Register / Vol. 70, No. 128 / Wednesday, July 6, 2005 / Rules and Regulations. 4. Hand, J.L.; and Malm, W.C.,2006, Review of the IMPROVE Equation for Estimating Ambient Light Extinction Coefficients - Final Report. March 2006. Prepared for Interagency Monitoring of Protected Visual Environments (IMPROVE), Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO. 5. MACTEC, 2006, Documentation of the Base G 2002 Base Year, 2009 and 2018, Emissions Inventory VISTAS. 6. Pechan, E.H., and Associates, In c., 2005, AirControlNET User Guide. 7. Pitchford, Marc., 2006, Natural Haze Leve ls II: Application of the New IMPROVE Alogrithm to Natural Species Concentrations Estimates. Final Report of the Natural Haze Levels II Committee to the RPO Monitoring/Data Analysis Workgroup. Sep 2006. 8. Sorrels, Larry, 2006, AirControlNET 4.1 So ftware; US EPA, OAQPS, Research Triangle Park, NC. 9. U.S. Environmental Protection Agency. (2003). Guidance for Tracking Progress under the Regional Haze Program; EPA-54/B-03-004, 2003.