# Regional Haze Plan

for Florida Class I Areas

Prepared by Florida Department of Environment Protection Division of Air Resource Management

March 17, 2010 (Amended August 31, 2010)i 

### Preface: This document contains summaries of the technical analyses used by Florida’s Department of Environmental Protection (DEP ), Division of Air Resource Management (DARM), to support the regional haze state im plementation plan pursuant to §§107(d)(3)(D) and (E) of the Clean Air Act, as amended.ii TABLE OF CONTENTS

EXECUTIVE SUMMARY ........................................................................................................... v i

1.0 INTRODUCTION .............................................................................................................. ...... 1 1.1 What is regional haze? .................................................................................................... ...... 1 1.2 What are the requirements under the Clean Air Act for addressing regional haze? ............. 1 1.3 General overview of regiona l haze SIP requirements .......................................................... 2
1.4 Class I areas in and near Florida ......................................................................................... .. 4
1.5 State and Federal Land Mana ger (FLM) coordination ......................................................... 5

2.0 ASSESSMENT OF BASELINE AND CURR ENT CONDITIONS AND ESTIMATE OF NATURAL BACKGROUND CONDITI ONS IN CLASS I AREAS ................................... 8 2.1 Estimating Natural Conditions for Florida Class I Areas ..................................................... 9 2.2 Estimating Baseline Conditions for Florida Class I Areas ................................................. 10 2.3 Summary of Natural Background and Baseline Conditions for Florida Class I Areas ...... 10 2.4 Pollutant Contributions to Visibility Impairment (2000-2004 Baseline Data) ................... 11

3.0 GLIDEPATHS TO NATURAL CONDITIONS IN 2064 ...................................................... 15 3.1 Glidepaths for Class I Areas in Florida and Nearby States ................................................ 15

4.0 NATURE OF THE PROBLEM: CHIEF CAUSES OF VISIBILITY IMPAIRMENT IN FLORIDA CLASS I AREAS ............................................................................................... 21 4.1 Baseline Emissions Inventory ............................................................................................. 21 4.1.1 Stationary Point Sources ............................................................................................ 22 4.1.2 Stationary Area Sources ............................................................................................. 23
4.1.3 Off-Road Mobile Sources .......................................................................................... 24 4.1.4 Highway Mobile Sources ........................................................................................... 24 4.1.5 Biogenic Emission Sources ........................................................................................ 25 4.1.6 Summary 2002 Base G2 (February 2007 vers ion) Baseline Emissions Inventory for

FL ............................................................................................................................ ......... 25 4.1.7 Model Performance Improvements through Emissions Inventory Improvements .... 25 4.2 Assessment of Relative Contributions from Sp ecific Pollutants and S ources Categories . 26

5.0 REGIONAL HAZE MODELING METHODS AND INPUTS ............................................. 27 5.1 Analysis Method ........................................................................................................... ...... 27
5.2 Model Selection ........................................................................................................... ....... 28
5.2.1 Selection of Photochemical Grid Model .................................................................... 28 5.2.2 Selection of Meteorological Model ............................................................................ 29 5.2.3 Selection of Emissions Processing System ................................................................ 30iii 5.3 Selection of the Modeling Year .......................................................................................... 3 1 5.4 Modeling Domains .......................................................................................................... ... 33 5.4.1 Horizontal Modeling Domain .................................................................................... 33 5.4.2 Vertical Modeling Domain ......................................................................................... 35

6.0 MODEL PERFORMANCE EVALUATION ......................................................................... 35 6.1 Modeling Performance Goals, and Criteria ........................................................................ 37
6.2 VISTAS Domain-Wide Performance ................................................................................. 38
6.3 Florida Class 1 Areas Performance .................................................................................... 41

7.0 LONG-TERM STRATEGY FOR FLORIDA CLASS I AREAS .......................................... 48 7.1 Overview of the Long-Term Strategy Development Process ............................................. 48

a. Assuming implementation of existing federal and state air regulatory requirements in

Florida and the VISTAS region, how much vi sibility improvement, compared to the glidepath, would we expect to see at Class I areas in or near Florida between now and 2018? ......................................................................................................................... ....... 48
b. If additional emission reductions were needed, from what pollutants and source

categories would the greatest visibility benefits be reali zed between the baseline and 2018? ......................................................................................................................... ....... 48
d. What is the process for identifying additional emission controls or reductions that

represent reasonable progress fo r those specific sources? ............................................... 48
f. Given the additional emission reductions expected from BART and reasonable progress

improvements, how much additional visibility improvement, compared to the glidepath, will we expect to see at Class I areas in Florida between the baseline and 2018? ........... 48 7.2 Expected Visibility Results in 2018 for Fl orida Class I Areas under existing and planned emissions controls (G2 and, Best and Final Inventory (G4)) ........................................... 49 7.2.1 Federal and State Control Requirements .................................................................... 49 7.2.2 Additional State programs to reduce emissions ......................................................... 51 7.2.3 Projected 2009 and 2018 G2 and Best and Final (G4) Emissions Inventories .......... 52
7.2.4 Model Results for the 2018 Base Best and Final (G4) Inventor y Compared to the Uniform Rate of Progress Glidepaths for FL Class I Areas ............................................. 55
7.3 Relative Contribution from In ternational Emissions to Visi bility Impairment in 2018 at VISTAS Class I areas ....................................................................................................... 73 7.4 Relative Contributions to Visibility Impa irment: Pollutants, Source Categories, and Geographic Areas ............................................................................................................. 75 7.5 Relative Contributions to Visibility Impa irment: Geographic Areas of Influence for Florida Class I Areas ........................................................................................................ 78 7.5.1 Back Trajectory Analyses .......................................................................................... 79
7.5.2 Residence Time Plots ................................................................................................. 79
7.5.3 SO 2 Areas of Influence ............................................................................................... 80
7.5.4 Emissions Sources within SO 2 Areas of Influence .................................................. 81iv 7.5.5 Specific Source Types in the Areas of Influence for Florida and Nearby Class I

Areas ......................................................................................................................... ....... 86 7.6 Reasonable Progress Requirement ...................................................................................... 90

7.7 Florida’s Reasonable Progress Analysis ................................................................................. 91

7.8 Best Available Retrofit Technology (B ART) for Individual Sources .................................. 100 7.8.1 BART-Eligible Sources in Florida .............................................................................. 100
7.8.2 Florida’s BART Rule ..................................................................................................... 102 7.8.3 Determination of Sources Subj ect to BART in Florida ................................................. 103 7.8.4 Determination of BART Requiremen ts for Subject-to-BART Sources ........................ 105
7.8.4.1 CEMEX ................................................................................................................ 105 7.8.4.2 FPL Turkey Point ................................................................................................. 109 7.8.4.3 Progress Energy Crystal River ............................................................................. 111 7.8.4.4 PCS White Springs ............................................................................................... 113 7.8.4.5 City of Tallahassee Purdom ................................................................................. 119

8.0 REASONABLE PR OGRESS GOALS................................................................................. 120

9.0 MONITORING STRATEGY ............................................................................................... 121

10.0 INTERSTATE CONSULTATION .................................................................................... 123

11.0 COMPREHENSIVE PERIODIC IMP LEMENTATION PLAN REVISIONS ................. 124

12.0 DETERMINATION OF ADEQUACY OF THE EXISTING PLAN ................................ 126v LIST OF EXHIBITS

Exhibit 1: Rules Adopted by the State

Exhibit 2: BART Determinations and Permits , and Permits Necessary for BART Exemption

Exhibit 3: Consu ltation Materials

LIST OF APPENDICES

Appendix A: VISTAS Memorandu m of Understanding and Bylaws

Appendix B: Conceptual Description

Appendix C: Modeling Analysis Protocol and Quality Assurance Project Plan

Appendix D: Emissions Inventory Documentation

Appendix E: Air Quality and Meteor ological Preparations and Results

Appendix F1: Technical Support Document – Emissions and Air Quality Modeling

Appendix F2: Meteorological Modeling Documentation

Appendix G: Global Modeling Documentation

Appendix H: Air Quality Modeling Sens itivity Analysis and Source Apportionment

Appendix I: Data Access

Appendix J: Analysis of Consen t Decree and Voluntary Reductions

Appendix K: Recommended Improvements

Appendix L: Best Available Retrofit Technology (BART) Supporting Materials

Appendix M: Supporting Materials for Rule Development

Appendix N: Florida Smoke Management Planvi 

### EXECUTIVE SUMMARY

Pursuant to the Clean Air Act requirements contained in sections 169A and 169B, and the subsequent implementing regul ations contained in 40 CFR 51.308, the State of Florida, Department of Environmental Protection (DEP) has developed a proposed State Implementation Plan (SIP) revision for regional haze. The SI P revision represents commitments and actions taken by the state affecting reasonable progres s during the ten-year period from 2008 to 2018 towards the goal of attaining na tural visibility conditions by 2064. It also provides for the implementation of Best Availa ble Retrofit Technology (BART).

To develop this proposed SIP revision, the state has relied heavily on the work of the Southeast regional planning group VISTAS (Vis ibility Improvement States a nd Tribal Association of the Southeast). VISTAS is directed by the state air directors of 10 southeastern states, including all of the eight U.S. Environmental Protection Agen cy (EPA), Region 4 states plus Virginia and West Virginia. One staff person is directly conn ected to VISTAS – the te chnical coordinator. All other personnel resources are within the st ate programs. Funding for VISTAS was through grants from the U.S. EPA.

The data and analysis necessary to meet the requ irements of the federal regional haze regulations are considerable. The 10 stat es, through VISTAS, comp leted most of the t echnical requirements using contracted resources, managed by the technical coordinator. To help direct the technical work, the state directors created three workgroups. To effect an open and inclusive direction of work, the workgroups consisted of federal, state, and private stakeholder representatives. The federal representatives included EPA regions 3 and 4, and federal la nd managers (or their representatives) from the Nationa l Park Service, the Fish and W ildlife Service, and the Forest Service. Private stakeholders included industry representatives with sources within the 10-state planning region. These workgr oups discussed and reviewed the work completed by the contractors used by VISTAS. These data and an alyses produced by VISTAS form the technical basis for Florida’s proposed SIP revision.

The Florida regional haze SIP consists of a set of rules, commitments, and plans addressing the requirements of the federal regulations. It al so contains the admini strative and technical documentation supporting these rules, commitme nts, and plans. The documents to be incorporated into the SIP incl ude the Regional Haze Plan (i.e., the main body of this report), Exhibit 1, Exhibit 2 (excluding those elements of the permits not specifically related to the BART emission limits), and Exhibit 3. The prim ary elements of the Florida regional haze SIP are the following:

1. Rules 62-296.340, Florida Administrative Code (F.A.C.) (new), and 62-204.800, F.A.C. (amended) – These state rules implement th e Best Available Retrofit Technology (BART) requirements. The state rule adopts the federa l rule by reference (62- 204.800), and adds specific state-determined criteria as needed or required by the federal rule (62-296.340). For example, the state BART rule is implemented through the state’s air construction permit program, and the rule includes required applicati on submission dates and BART compliance dates. The rule also makes certain decisions required of the federal regulations such as allowing BART to equalvii CAIR (Clean Air Interstate Rule) for electr ic generating units and excluding ammonia and volatile organic compounds from BART review.

The department has evaluated 46 facilities havi ng units that meet the BART applicability requirements. Of these, 39 meet the exemption criteria in the rule, 2 have been or soon will be shut down, and 5 have completed BART determinations.

2. Reasonable Progress Requirements – The state is required to consider f our factors (cost, time to comply, energy and non-air impacts, and remain ing useful life) in determining whether further reductions in visibility-impairing pollutants would be reasonable for any sour ces in the state. To limit the scope of this requirement, and based on VISTAS analysis, the de partment focused its response to reasonable progr ess on sulfur dioxide (SO 2 ) emissions from large energy generating and industrial sources. Based on criteria to identify, from among these sources, those that are most affecting visibility in Class I areas, 32 emissions units from 15 major facilities were targeted for four-factor review. The state then considered that 31 of these units were subject to the CAIR rule, and the other un it is subject to the proposed boiler MACT rule. The emissions reduction requirements under both of these rule s were developed in a manner generally consistent with the four factors prescribed for reasonable progress (see section 7.7). As such, we assert that the reasonable progre ss requirements are satisfied for Fl orida’s identified sources with the federal CAIR and the boiler MACT rules.

3. Reasonable Progress Goals – The state is required to set reasona ble progress goals in units of deciviews applicable for 2018. These goals re present the progress (visibility improvement) expected as a result of implementation of the long -range plan presented in this regional haze SIP. Two goals are set for each Class I area, one fo r the 20 percent worst days and one for the 20 percent best days.

Visibility (dv) Baseline (00-04) Natura l Conditions 2018 Reasonable Progress Goals Class I Area Best 20% Worst 20% Best 20% Worst 20% Best 20% Worst 20%

Chassahowitzka 15.51 25.75 5.91 11.03 13.91 22.27 Everglades 11.69 22. 30 5.21 12.09 11.46 19.80 Saint Marks 14.37 26.31 5.39 11.67 12.80 22.92

These goals are based upon predicted visibility re sponse to the expected emissions reductions of visibility-impairing pollutants usi ng air quality models. As a resu lt, a fair amount of uncertainty surrounds each of these goals, but they represent the state’s best estimate at this time.

4. Rate of Progress Needed to Attain Natural Visibility Conditions by 2064 – Using the current estimate of natural conditions for the worst days, the uniform and predicted rates of progress for each of the Florida Class I areas is:viii Worst 20 Percent Days Class I Area Uniform Rate of Progress (dv/yr) 2018 Uniform Rate of Progress (dv) 2018 Predicted Rate of Progress (dv/yr) 2018 Predicted Visibility (dv)

Chassahowitzka 0.25 22.31 0.25 22.27 Everglades 0.17 19.92 0.18 19.80 Saint Marks 0.24 22.89 0.24 22.92

The regulatory requirement for the best 20 percent visibility days is th at these days not get

worse.

Best 20 Percent Days Class I Area Uniform Rate of Progress (dv/yr) 2018 Uniform Rate of Progress (dv) 2018 Predicted Rate of Progress (dv/yr) 2018 Predicted Visibility (dv)

Chassahowitzka 0.00 15.51 0.11 13.91 Everglades 0.00 11.69 0.02 11.46 Saint Marks 0.00 14.37 0.11 12.80

Each of the three Florida Class I areas are pred icted to progress at approximately the nominal uniform rate of progress for the initial planning period. Rates of progress needed to meet the natural visibility goal for subsequent planning pe riods will be dependent on the actual progress made in the previous planning periods.

5. Long-Term Strategy for Regional Haze – The st ate has developed a long-term strategy that largely relies on existing rules and programs that will result in significant improvement of visibility in Class I areas. The most significant of these existing rules is the Clean Air Interstate Rule (CAIR), which in conjunction with other exis ting rules results in visi bility improvement in all the Class I areas equal to the nominal uniform rate of progress towa rd the national goal of natural conditions. But the long-term strate gy also includes new reductions of visibility precursor emissions resulting from the Best Available Retrofit Technology (BART) requirements of the regional haze rule and th e proposed new federal rule for Industrial, Commercial, and Institutional Boilers and Proces s Heaters (Boiler MACT rule). Initial compliance with these rules will occur at vari ous times throughout the planning period, but no later than the end of 2013.

Through the Southeast regional pl anning organization VISTAS, st ate-of-the-art air quality modeling was completed to analyze the regional, na tional, and global contri butions to visibility in each Class I area. Emissions of visibility -impairing pollutants were included from all known source sectors and locations, including boundary conditions derived from a global model. Current visibility conditions were evaluated using data from public and private monitoring networks, and these and other as sociated data were used to validate model performance. Projected emissions were developed for 2018, c onsidering growth and known or estimated emissions changes due to existing regulations. S ubstantial analysis was completed to determineix visibility sensitivity to specifi c pollutant reductions, and to parse-out the source-sector contributions.

The conclusion of this analysis is that the long-term strategy for attaining the 2064 visibility goal can rely on existing and proposed new regulations to provide reasonable progress toward this goal during the first ten-year pla nning period ending in 2018.

6. Commitments – The state is committing to co mplete a mid-point review of the long-range plan as required in the regiona l haze rule (§51.308(g)). The time of the review will be coordinated with the other VISTAS states to ta ke advantage of common analyses, but will be no later than five years after submission of the in itial implementation pla n. We will review the progress of the projected emissions changes to ju dge the necessity of making any revisions to the plan. The state is also committing to comp lete comprehensive periodic revisions of the implementation plan for regional haze. The fi rst revision is due to EPA by July 31, 2018 and every ten years thereafter.1 

### 1.0 INTRODUCTION

1.1 What is regional haze?

Regional haze is pollution from disparate sources that impairs visibility over a large region, including national parks, forests, and wilderness areas (federal “Cla ss I” areas). Regional haze is caused by sources and activi ties emitting fine particles and thei r precursors. Those emissions are often transported ove r large regions.

Particles affect visibili ty through the scattering and absorption of light, and fine particles – particles similar in size to the wavelength of li ght – are most efficient, per unit of mass, at reducing visibility. Fine particles may either be emitted directly or formed from emissions of precursors, the most important of which are sulfur dioxides (SO 2 ) and nitrogen oxides (NOx). Reducing fine particles in the atmosphere is gene rally considered to be an effective method of reducing regional haze, and thus improving visibil ity. Fine particles also adversely impact human health, especially respiratory and car diovascular systems. The United States Environmental Protection Agency (USEPA) has se t national ambient air quality standards for daily and annual levels of fine par ticles with diameter smaller than 2.5  m (PM2.5). The most important sources of PM2.5 and its precursors are coal-fired power plants , industrial boilers and other combustion sources. Other significant co ntributors to PM2.5 and visibility impairment include mobile source emissions, area s ources, fires, and wind blown dust.

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

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

In the 1990 Amendments to the CAA, Congress added section 169B and called on USEPA to issue regional haze rules. The Regional Haze ru le (RHR) that USEPA promulgated on July 1, 1999 (64 FR 35713), revised the existing visibility re gulations in order to integrate provisions addressing regional haze impairment and establish a comprehensive visibility protection program for Class I Federal areas. States are required to submit state implementation plans (SIPs) to USEPA that set out each states’ plan for comp lying with the regional haze rule, including consultation and coordination with other states and with federal land managers. The timing of2 the SIP submittal is tied to USEPA’s promulgati on of designations for the National Ambient Air Quality Standard (NAAQS) for fine particulate ma tter (PM2.5) at 40 CFR Part 81. States must submit a regional haze implementation plan to US EPA within three years after the date of designation. Because USEPA promulgated de signation dates on December 17, 2004, regional haze SIPs must be submitted by December 17, 2007, which is specified at 40 CFR 51.308(b).

The regional haze rule addressed the combined visi bility effects of various pollution sources over a wide geographic region. This wide reaching po llution net meant that many states – even those without Class I Areas – would be required to participate in h aze reduction efforts. USEPA designated five Regional Planni ng Organizations (RPO) to assi st with the coordination and cooperation needed to addre ss the visibility issue. Those states that make up the southeastern portion of the contiguous United States are known as VISTAS (Visibility Improvement – State and Tribal Association of the Southeast), and include the following states: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia.

Figure 1.2-1. Geographical Areas of Regional Planning Organizations

1.3 General overview of regional haze SIP requirements

The regional haze rule (RHR) at 51.308(d) requires states to demonstrate reasonable progress toward meeting the national goal of a return to natural visibility conditions by 2064. As a guide for reasonable progress, the RHR directs states to graphically show what would be a “uniform rate of progress” toward natura l conditions for each mandatory Class I Federal area within the State and/or for each mandatory Class I Federal area located outside the State, which may be affected by emissions from sources within the State. States are to estab lish baseline visibility3 conditions for 2000-2004, natural background visibi lity conditions in 2064, and the rate of uniform progress between baseline and background conditions. The uniform rate of progress is also known as the “glidepath.”

The RHR then requires States to establish r easonable progress goals (RPGs), expressed in deciviews, for visibility improvement at each affected Class I area covering each (approximately) 10-year period until 2064. The goa ls must provide for reasonable progress towards achieving natural visibil ity conditions, provide for improvement in visibility for the most impaired days over the period of the impl ementation plan, and ensure no degradation in visibility for the least impaired days over the same period (see §51.308(d)(1)).

In order to ensure that visibil ity goals are properly met and set, state implementation plans must include determinations, for each Class I area, of the baseline visibility conditions (expressed in deciviews) for the most impaired and least impair ed days. SIPs must also contain supporting documentation for all required analyses used to calculate the degree of visibility impairment under natural visibility conditions for the most impaired and least impaired days (see §51.308(d)(2)). In addition, states must in clude a monitoring strategy for measuring, characterizing, and reporting of regional haze visibil ity impairment that is representative of all mandatory Class I Federal areas within the stat e (see §51.308(d)(4)).

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

In addition, a specific component of each state’ s first long-term strate gy is dictated by the specific best available retrof it technology (BART) requireme nts in 40 CFR 51.308(e) of the RHR. The RHR at §51.308(e) requi res states to include a dete rmination of BART for each BART-eligible source in the State that emits any air pollutant, wh ich may reasonably be anticipated to cause or contribu te to any impairment of visibi lity in any mandatory Class I Federal area. Clean Air Act s ection 169A(b) defines BART-eligib le sources as sources in 26 specific source categories, in operation within a 15-year period prior to enactment of the 1977 Clean Air Act Amendments. States must determ ine BART according to fi ve factors set out in section 169A(g)(7) of the Clean Air Act. Emis sion limitations representing BART and schedules for compliance with BART for each source subject to BART must be included in the long-term strategy.

State Implementation Plans for the first revi ew period are due December 17, 2007. These plans will cover long-term strategies for visibility improvement between baseline conditions in 2000- 2004 and 2018. States are required to evaluate progress toward r easonable progress goals every 5 years to assure that installed emissions controls are on track w ith emissions reduction forecasts4 in each SIP. The first interim review would be due to USEPA in December 2012. If emissions controls are not on track to meet SIP forecasts, th en states would need to take action to assure emissions controls by 2018 will be consistent with the SIP or to revise the SIP to be consistent with the revised emissions forecast.

1.4 Class I areas in and near Florida

Florida has four Class I areas within its borde rs: Everglades National Park, Chassahowitzka Wilderness Area, St. Marks Wilderness Area, an d Bradwell Bay Wilderness Area. Bradwell Bay is one of only two Class I areas in the country for which visibility is not considered an important value. As such, the RHR does not apply to Bradwell Bay. Florida ai r pollution sources are within 300 kilometers of three ot her Class I areas located in othe r states, for which consideration of visibility impact on these out-o f-state Class I areas must be addressed in the SIP. These are Okefenokee Wilderness Area (Georgia), Wolf Island Wilderness Area (Georgia), and Breton Wilderness Area (Louisiana). The Florida Divi sion of Air Resource Management (DARM) in the Florida Department of Environmental Protec tion (FLDEP) is responsible for developing the Regional Haze SIP. This SIP establishes reasonab le progress goals for visibility improvement at each of the Florida Class I area s, and a long-term strategy that will achieve those reasonable progress goals within the first regional haze planning period.

Figure 1.4-1. Florida and Nearby Class I areas5 In developing this SIP, we have also consider ed that emission sources outside of Florida may affect visibility at these Florida Class I areas, and that emission sources within Florida may affect visibility at the Class I areas in neighboring states. Through VI STAS, the southeastern states have worked together to assess state-by-state contributi ons to visibility impairment in specific class I areas, including those in Florida and t hose affected by emissions from Florida. This technical work is discussed furthe r in chapters 5, 6, and 7 below. Consultations to date between Florida and other states are summarized in chapter 10; consultations are ongoing.

1.5 State and Federal Land Manager (FLM) coordination

As required by 40 CFR §51.308(i), the regional haze SIP must include procedures for continuing consultation between the States and Federal Land Managers on the implementation of the visibility protection program, including deve lopment and review of implementation plan revisions and 5-year progress reports, and on th e implementation of other programs having the potential to contribute to impairme nt of visibility in any mandat ory Class I Federal area within the State. DEP commits to ongoing consultati on with the FLMs throughout the implementation process, including annual discus sion of the implementation process and the most recent IMPROVE monitoring data and VIEWS data.

Successful implementation of a regional h aze program will involve long-term regional coordination among States. VISTAS was formed in 2001 to address regional haze and visibility problems in the southeastern United States. Jurisdictions represented by VISTAS members include the Eastern Band of Ch erokee Indians; the States of Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolin a, South Carolina, Tennessee, Virginia, and West Virginia; and the local air pollution cont rol programs located in these St ates. A copy of the VISTAS Memorandum of Agreement and Bylaws is enclosed as Appendix A.

The objectives of the VISTAS project are to es tablish natural background visibility conditions across the mandatory Class I Federal areas, identify current visibility impairment levels, analyze emission control levels that will achieve inte rim visibility goals, and provide adequate documentation to member agencies so that they can develop their regi onal haze State/Tribal Implementation Plans (SIP/TIP). Figure 1.5-1 sh ows the 18 mandatory Class I Federal areas in the VISTAS Region, where visibility is an important value. Figure 1.5-2 list s these Class I areas.6 Figure 1.5-1. Class I Areas in the VISTAS Region7 Mandatory Class I Federal Areas in the VISTAS Region Where Visibility is an Important Value

State Area Name Acreage Federal Land Manager 40 CFR §81.401 Alabama Sipsey Wilderness 12,646 USDA-FS40 CFR §81.407 Florida Chassahowitzka Wilderness 23,360 USDIFWS Everglades National Park 1,397,429 USDI-NPSSt. Marks Wilderness 17,745 USDIFWS 40 CFR §81.408 Georgia Cohotta Wilderness 33,776 USDA-FSOkefenokee Wilderness 343,850 USDIFWSWolf Island Wilderness 5,126 USDIFWS 40 CFR §81.411 Kentucky Mammoth Cave National Park 51,303 USDI-NPS 40 CFR §81.422 North Carolina Great Smoky Mountains National Park 273,551 USDI-NPS

Joyce Kilmer-Slickrock Wilderness 10,201 USDA-FS Linville Gorge Wilderness 7,575 USDA-FS Shining Rock Wilderness 13,350 USDA-FSSwanquarter Wilderness 9,000 USDIFWS 40 CFR §81.426 South Carolina Cape Romain Wilderness 28,000 USDIFWS 40 CFR §81.428 Tennessee Great Smoky Mountains National Park 241,207 USDI-NPS

Joyce Kilmer-Slickrock Wilderness 3,832 USDA-FS 40 CFR §81.433 Virginia James River Face Wilderness 8,703 USDA-FS Shenandoah National Park 190,535 USDI-NPS 40 CFR §81.435 West Virginia Dolly Sods Wilderness 10,215 USDA-FS

Otter Creek Wilderness 20,000 USDA-FS

Figure 1.5-2. Mandatory Class I Federal Areas in the VISTAS Region Where Visibility is an Important Value

A technical support document for the regional haze state implementation plans has been prepared cooperatively by VISTAS staff and states to ch aracterize regional haze in the southeastern United States. The report includes a review of the science and situati on, calculation of initial8 baseline visibility, review of monitoring data/data gaps, and recommendations for additional monitoring, initial emission inve ntory characterization and proj ections, and compliance with existing control programs. Source contributions to VISTAS mandatory Class I Federal areas are also assessed. This document is included as Appendix F1.



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

The goal of the Regional Haze Rule is to restore natural visibility conditions to the 156 Class I areas identified in the 1977 Clean Air Act Amendments. Sec. 51.301(q) defines natural conditions: “Natural conditions include naturall y occurring phenomena that reduce visibility as measured in terms of light extinction, visual ra nge, contrast, or colorati on.” The Regional Haze SIPs must contain measures that make “rea sonable progress” toward this goal by reducing anthropogenic emissions that cause haze.

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

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

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

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

Each of the three metrics includes the concentrati on data of the visibility pollutants as different terms in the light extinction algorithm, with respective extinction coefficients and relative humidity factors. Total light extinction when c onverted to deciviews (dv) is calculated for the average of the 20 percent best and 20 percent worst visibility days.

“Natural” visibility is determined by estimating th e natural concentrations of visibility pollutants and then calculating total light extinction. “Baseline” visibility is the starting point for the9 improvement of visibility conditions. It is the average of the Inte ragency Monitoring of Protected Visual Environments (IMPROVE) monitoring data for 2000 through 2004 and is equivalent to “current” visibility conditions for this initial review peri od. The comparison of initial baseline conditions to natural visibility conditions indicates the amount of improvement necessary to attain natural visi bility by 2064. Each state must estimate natural visibility levels for Class I areas within its borders in consulta tion with Federal Land Managers and other states (51.308(d)(2)). “Current conditions ” are assessed every five years as part of the SIP review where actual progress in reducing visibility impa irment is compared to the reductions committed to in the SIP.

2.1 Estimating Natural Conditions for Florida Class I Areas

Natural background visibility, as defined in 2003 USEPA guidance, is based on annual average concentrations of fine particle components. The same annual average natural background visibility is assumed for all Class I areas in the eastern United States (separate values are estimated for the western United States). Natura l background visibility for the 20 percent worst days is estimated by assuming that fine partic le concentrations for natural background are normally distributed and the 90 th percentile of the annual di stribution represents natural background visibility on the 20 percent worst days.

In the 2003 guidance, USEPA also provided that st ates may use a “refined approach” to estimate the values that characterize the na tural visibility conditions of th e Class I areas. The purpose of such a refinement would be to provide more accu rate estimates with changes to the extinction algorithm that may include the concentration va lues, factors to calculate extinction from a measured particular species and particle size, the extinction coefficien ts for certain compounds, geographical variation (by al titude) of a fixed value, and the addition of visibi lity pollutants.

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

The new IMPROVE equation accounts for the e ffect of particle size distribution on light extinction efficiency of sulfate, nitrate, and organic carbon. The mass multiplier for organic carbon (particulate organic matter) is increased from 1.4 to 1.8. New terms are added to the equation to account for light extinction by sea salt and light absorpti on by gaseous nitrogen dioxide. Site-specific values are used for Rayl eigh scattering to account for the site-specific effects of elevation and temperat ure. Separate relative humidity enhancement factors are used for small and large size distribut ions of ammonium sulfate and ammonium nitrate and for sea salt. The elemental carbon (light-absorbing carb on), fine soil, and co arse mass terms do not change between the original and new IMPROVE equation.

The VISTAS states chose to use the new IMPR OVE equation as the basis for the conceptual description because it takes into account the most recent review of the science and because it is10 recommended by the IMPROVE Steering Committee. For more detailed discussion of the two IMPROVE equations, see Appendicies B and L.

2.2 Estimating Baseline Conditions for Florida Class I Areas

Baseline visibility conditions at each Florida Class I area are estimated using sampling data collected at IMPROVE monitoring s ites at the three Class I areas in Florida. A five year average (2000 to 2004) was calculated for each of the 20 pe rcent worst and 20 percent best visibility days in accordance with 40 CFR 51.308(d)(2) and the USEPA Guidance for Tracking Progress Under the Regional Haze Rule. When less than five years of data had been collected, an acceptable average was calculated if at least three complete years were available. When less than three years of data were available data substitution met hods, as descibed in Appendix B, were used to provide sufficient data. IMPROVE data records for the Everglades had four years of complete data and no substitution of data was made. However, Chassahowitzka and St. Marks both required data substitution to make their record s complete. The light extinction and deciview visibility values for the 20 percent worst and 20 pe rcent best visibility da ys at the Class I areas are based on data and calculations in cluded in Appendix B of this SIP.

2.3 Summary of Natural Background and Base line Conditions for Florida Class I Areas

Table 2.3-1 presents estimated natural backgrou nd and baseline visibility metrics for Florida Class I areas. Note that Florida is not consideri ng international emissions to be a component of natural background. Baseline visibility on the 20 per cent worst days is significantly better at Everglades (22.3 dv) than Chassahowitzka (25.7 dv) or St. Marks (26.3). On the other hand, natural background visibility is slightly worse for Everglades (12.1 dv) than either Chassahowitzka (11.0 dv) or St. Marks (11.7 dv). Vi sibility in the northern Florida Class I areas is generally better than the other Southeastern Cl ass I areas, but the Everglades is significantly better than any of the Southeastern areas.11 Table 2.3-1 Natural Background and Baseline Conditions for Florida Class I Areas

Natural Background Conditions Class 1 area Average for 20 percent Worst Days (deciviews) Average for 20 percent Best Days (deciviews) Average for 20 percent Worst Days Bext (Mm-1) Average for 20 percent Best Days Bext (Mm-1) Everglades N.P. 12.1 5.2 42.7 16.9 Chassahowitzka W.A. 11.0 5.9 30.5 18.1 St. Marks W.A. 11.7 5.4 33.1 17.2

Baseline Visibility Conditions 2000-2004 Class 1 area Average for 20 percent Worst Days (deciviews) Average for 20 percent Best Days (deciviews) Bext (Mm-1) Average for 20 percent Worst Days Bext (Mm-1) Average for 20 percent Best Days Everglades N.P. 22.3 11.7 116.4 32.3 Chassahowitzka W.A. 25.7 15.5 135.0 47.7 St. Marks W.A. 26.3 14.4 142.1 42.7

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

The 20 percent best and worst visibility days at the Florida sites occur throughout the year. The worst days are associated with high sulfate levels, with many of the extremely bad visibility days often associated with high organic carbon levels due to fires.

Ammonium sulfate , (NH 4 ) 2 SO 4 , 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 the Florida Class I areas. Sulfate levels on the 20 percen t worst days at Chassahowitzka and St. Marks accounts for 60-70 percent of the visibility impair ment. At the Everglades, sulfates account for 30-60 percent of the impairment. Across the VIST AS region, sulfate levels are higher at the Southern Appalachian sites than at the coastal s ites of which Florida is grouped (Figure 2.4-1). And the sulfate levels in the Everglades are si gnificantly lower than even Chassahowitzka or St. Marks. On the 20 percent clearest days, sulfate levels are a little more uniform across the region (Figure 2.4-2), however, it still remains a rela tively large fraction of the total visibility impairment.12 Figure 2.4-1. Average light extinction for the 20 percent Haziest Days in 2000-2004 at VISTAS and neighboring Class I area s using New IMPROVE equation

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

VISTAS coastal VISTAS inland Neighboring non-VISTAS 0 50 100 150 200 250 300Swanquarter, NCCae Romain, SCOkefenokee, GAEverglades, FLChassahowitzka, FLSt. Marks, FLDolly Sods, WVShenandoah, VAJames River Face, VALinville Gorge, NCShining Rock, NCGreat Smoky Mtns., TNCohutta, GASipsey, ALMammoth Cave, KYBrigantine, NJBreton, LAMingo, MOHercules Glade, MOUpper Buffalo, ARCaney Creek, ARExtinction (Mm-1) Sea Salt Coarse Soil EC POM NH4NO3 (NH4)2SO4 Rayleigh

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

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

VISTAS coastal VISTAS inland Neighboring non-VISTASSwanquarter, NCCae Romain, SCOkefenokee, GAEverglades, FLChassahowitzka, FLSt. Marks, FLDolly Sods, WVShenandoah, VAJames River Face, VALinville Gorge, NCShining Rock, NCGreat Smoky Mtns., TNCohutta, GASipsey, ALMammoth Cave, KYBrigantine, NJBreton, LAMingo, MOHercules Glade, MOUpper Buffalo, ARCaney Creek, AR 0 10 20 30 40 50 60Extinction (Mm-1)

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

VISTAS coastal VISTAS inland Neighboring non-VISTASSwanquarter, NCCae Romain, SCOkefenokee, GAEverglades, FLChassahowitzka, FLSt. Marks, FLDolly Sods, WVShenandoah, VAJames River Face, VALinville Gorge, NCShining Rock, NCGreat Smoky Mtns., TNCohutta, GASipsey, ALMammoth Cave, KYBrigantine, NJBreton, LAMingo, MOHercules Glade, MOUpper Buffalo, ARCaney Creek, AR13 Sulfate particles are formed in the atmosphere fr om sulfur dioxide emissions. Sulfate particles occur as hydrogen sulfate, H 2 (SO 4 ), ammonium bisulfate HNH 4 SO 4 , and ammonium sulfate, (NH 4 ) 2 SO 4 , depending on the availability of ammonia, NH3, in the atmosphere.

Particulate Organic Matter (POM) is the second most important c ontributor to fine particle mass and light extinction on the 20 percent haziest and th e 20 percent clearest days at the Florida Class I areas. Elevated levels of POM and Elementa l Carbon, EC, indicate impact from wildfires or prescribed fire. Significant fire impacts are frequent enough to affect the Class I areas in Florida. Fires tend to be episodic and a ssociated with dry conditions so year to year impacts can vary considerably. VISTAS collected additional samples of carbon at five sites, including Great Smoky Mountains and Millbrook, (Raleigh, NC), to better understand sources contributing to carbon in rural and urban areas. At Great Smoky Mountains a nd other mountain Class I areas the majority of carbon is from either biogenic emissions from vegetation or from wood burning. Wood burning is more important in the fall, winter, and early spring months. Biogenic emissions, chiefly emissions of volatile organi cs (VOC) from vegetation, peak in spring and summer. Biogenic carbon emissions at Cape Roma in, SC, a coastal site similar to Swanquarter, were lower than emissions at the forested m ountain sites. Carbon from gasoline and diesel engines is a relatively small cont ribution at the rural sites. At Millbrook, carbon from fossil fuel combustion is a larger percentage contribution than at the rural sites, but still less than 50 percent of total carbon meas ured. These results suggest that cont rolling anthropogenic sources of carbon will have little benefit in improving visibility in Class I area s. Controlling anthropogenic sources of carbon will likely be more effective to re duce levels of PM2.5 in urban areas.

Ammonium nitrate , NH 4 NO 3 , is formed in the atmosphere by reaction of ammonia and nitrogen oxides. In the VISTAS region, nitrate formati on is limited by availability of ammonia and by temperature. Ammonia preferentially reacts with sulfur dioxide and sulfate before reacting with nitrogen oxides. Particle nitrate is formed at lo wer temperatures; at elevated temperatures nitric acid remains in gaseous form. For this reason, par ticle nitrate levels are very low in the summer and a minor contributor to visibi lity impairment. Particle nitr ate concentrations are higher on winter days and are more important for the coas tal sites where 20 percent worst days can occur on winter days. Nitrogen oxides are emitted by fossil fuel combustion by point, area, on road, and non road sources. Modeling data (see sect ion 7) indicate that in the VISTAS region ammonium nitrate formation is limited by ammoni a concentrations and s uggest that for winter days, controls of ammonia sources would be more effective in reducing a mmonium nitrate levels than controls of nitrogen oxides.

Elemental Carbon, EC, is a comparatively minor contributor to visibility impairment. Sources include agriculture, prescribed, wildland, and wi ld fires and incomplete combustion of fossil fuels. EC levels are higher at urban monitors than at the Class I areas and suggest controls of fossil fuel combustion sources would be more ef fective to reduce PM2.5 in urban areas than to improve visibility in Class I areas.

Soil fine particles are minor contributors to visibility impairment at most sites on most days. Occasional episodes of elevated fi ne soil can be attributed to Saharan dust episodes, particularly at Everglades. No control strategi es are indicated for fine soil.14 Sea salt , NaCl, is observed at the coastal sites. Sea sa lt contributions to visi bility impairment are most important on the 20 percent clearest days wh en sulfate and POM levels are low. Sea salt levels do not contribute significantly to visibility on the 20 percen t worst visibility days. The new IMPROVE equation uses Chloride ion, Cl - , from routine IMPROVE measurements to calculate sea salt levels. Cl - may react with nitrate and volatize off the filter, so that Cl - may underestimate sea salt levels. VISTAS funded sodium ion, Na + , analyses of the IMPROVE filters from Oct 2003 to Dec 2004 for nine sites. Sodium ion measures may overestimate sea salt levels because sodium nitrate is an additional so urce of sodium ion that cannot be distinguished from sodium chloride. Our best unders tanding to date sugg ests that using Cl - may underestimate sea salt levels by as much as a factor of two. This uncertainty has greatest implications for calculating sea salt levels under natural visibility conditions.

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

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

The New IMPROVE equation generally results in higher calculat ed light extinction on days with higher mass and lower light extinction on days with lower mass. This tends to increase calculated light extinction for cu rrent conditions and to decrease calculated light extinction for natural visibility conditions. Adding sea salt to the new IM PROVE equation increases light extinction for both current and natural visibility conditions. Increasing the mass multiplier for particulate organic matter in the new IMPROVE equation increases light extinction for current conditions more than for natural conditions. The new algorithm does not change the conclusion that in the VISTAS region and in Florida, the mo st effective means to improve visibility is to reduce sulfate concentrations.

PM2.5 trends in urban and Class I areas: IMPROVE data were compared to monitoring data from the Speciated Trends Network (STN) in ne arby urban areas to understand the similarities and differences in composition of fine particle mass. Several PM2.5 nonattainment areas are in close proximity to the Class I areas in the sout heastern US, including Atlanta, GA; Birmingham, AL; Charleston, WV; Chattanooga, TN; Louisv ille, KY; and Knoxville, TN. Ammonium sulfate concentrations are comp arable between urban and nearby Class I areas, while organic carbon, elemental carbon, and nitrate concentration are generally higher in urban areas than the Class I areas. These results suggest that sulfate is widely distribut ed regionally while urban areas see an additional incremental pollutant lo ading from local emissions sources.

Role of meteorology in determ ining visibility conditions: Classification and Regression Tree Analyses were used to char acterize the relationship between meteorological conditions and visibility conditions at the Class I areas. Days were assigned to one of five visibility classes ranging from poor to good visibility. Days were then assigned to bins based on meteorological conditions. For the central and northern Florida Class I areas poor visibility days were most15 likely to occur on days with high temperatures, high relative humidity, low wind speeds, where previous days were also with poorer visibility. That is poor visibi lity is usually a multi-day event with building pollution levels. Precipitation was not a good predictor of vi sibility condition. In South Florida (Everglades), poor vi sibility is not diffe rentiated by high temper atures and relative humidity and is more related to precipitation and number of rain events. Weights were assigned to days based on frequency of occurrence of days with similar meteorological conditions.

The above analyses are further discussed in Appe ndix B. Also, the pollutant-specific extinction for each of the best and worst 20 percent days are provided graphically in Appendix E, subAppendix E.3.



### 3.0 GLIDEPATHS TO NAT URAL CONDITIONS IN 2064

As stated in section 1.3 above, the RHR directs states to graphically show what would be a “uniform rate of progress” toward natural c onditions for each mandatory Class I Federal area within the State as well as for each mandatory Class I Federal area locat ed outside the State, which may be affected by emissions from sources within the State. The uniform rate of progress is also known as the “glidepath.” The glidepath is simply a st raight graphical line drawn from the baseline level of visibility impairment for 2000-2004 to th e level representing no manmade impairment in 2064.

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

Uniform rate of progress graphs (glidepaths), were developed for each Class I area in the VISTAS region. The glidepaths were develope d in accordance with USEPA guidance and used data collected from the IMPROVE monitoring s ites as described in section 2 above. The glidepath is one of the indicators used in setting reasona ble progress goals.

3.1 Glidepaths for Class I Areas in Florida and Nearby States

The following are glidepaths for the 20 percent most impaired days for Everglades National Park, Chassahowitzka National Wilderness Ar ea, St. Marks National Wilderness Area, Okefenokee National Wilderness Area, and Bret on National Wilderness Area, assuming uniform rate of progress toward regiona l haze goals. Wolf Island National Wilderness Area does not have an IMPROVE monitor and the Okefenokee s ite acts as its surroga te. Natural background visibility at all five site s is calculated to be about 11 to 12 dv. In general, these five Class I areas have better visibility conditions than in other areas of the Sout heast. Of these five areas, Okefenokee has the worst current visibility and has greatest improvement to make to achieve natural visibility conditions (T able 3.1.1). The Everglades ha s the best current visibility16 conditions in the entire Southeast, and has the least amount of needed improvement to achieve natural conditions.

Class I Area State Baseline - Avg. of 20% Worst Days (2000-2004) (dv) Natural Background

Goal (dv) Necessary Improvement in Visibility to Attain Goal (dv) Everglades FL 22.30 12.09 10.21 Swanquarter NC 24.71 11.55 13.17 Chassahowitzka FL 25.75 11.03 14.71 Breton LA 26.00 11.93 14.07 Upper Buffalo AR 26.27 11.49 14.78 Saint Marks FL 26.31 11.67 14.64 Caney Creek AR 26.36 11.57 14.79 Cape Romain SC 26.48 12.10 14.38 Hercules - Glade MO 26.75 11.24 15.50 Okefenokee GA 27.13 11.21 15.93 Mingo MO 28.05 11.62 16.43 Shining Rock NC 28.66 11.47 17.18 Linville Gorge NC 28.77 11.19 17.59 Brigantine NJ 29.01 12.06 16.95 Sipsey AL 29.03 10.90 18.12 Dolly Sods WV 29.04 10.36 18.68 James River Face VA 29.12 11.09 18.02 Shenandoah VA 29.31 11.37 17.94 Cadiz KY 29.39 10.82 18.58 Cohutta GA 30.25 10.78 19.48 Great Smoky Mountains TN 30.28 11.05 19.22 Mammoth Cave KY 31.37 11.08 20.29

Table 3.1.1 – Rank Order of Cu rrent Visibility for Sout heastern Class I Areas17 Figure 3.1-1. Uniform Rate of Progress Glidep ath for 20 percent worst days at Everglades National Park. 

### Uniform Rate of Progress Glide Path Everglades - W20% Data Days

22.30 21.45 19.92 18.22 16.52 14.82 13.11 12.09

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 Yea r Haziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation18 Figure 3.1-2. Uniform Rate of Progress Glidepath for 20 percent worst days Chassahowitzka National Wilderness Area. 

### Uniform Rate of Progress Glide Path

Chassahowitzka - W20% Data Days

25.75 24.52
22.31

19.86

17.41

14.96

12.50 11.03

0 5 10 15 20 25 30 35

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

Glide Path Natural Condition (Worst Days) Observation19 Figure 3.1-3. Uniform Rate of Progress Glidep ath for 20 percent worst days at St. Marks National Wilderness Area. 

### Uniform Rate of Progress Glide Path Saint Marks - W20% Data Days

26.31 25.09
22.89

20.45

18.01

15.57

13.13 11.67

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 Yea r Haziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation20 Figure 3.1-4. Uniform Rate of Progress Glidepath fo r 20 percent worst days at Okefenokee National Wilderness Area. 

### Uniform Rate of Progress Glide Path Okefenokee - W20% Data Days

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 Yea r Haziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation21 Figure 3.1-5. Uniform Rate of Progress Glidep ath for 20 percent worst days at Breton National Wilderness Area.



### 4.0 NATURE OF THE PROBLEM: CHIEF CAUSES OF VISIBILITY
IMPAIRMENT IN FLORIDA CLASS I AREAS

4.1 Baseline Emissions Inventory

The Regional Haze Rule at 51.308(d) (4) (v) re quires a statewide emissions inventory of pollutants that are reasonably anticipated to cause or contribute to visibility impairment in any mandatory Class I area. An inventory was de veloped for the baseline year 2002 and projected to 2009 and 2018. The pollutants inventoried include volatile organic compounds, nitrogen oxides, fine particulate (PM2.5) , coarse particulate (PM10), a mmonia and sulfur dioxide. The baseline emissions inventory for 2002 was devel oped for Florida and the other VISTAS states following the methods described in Appendix D.

There are five different emissi on inventory source classificati ons: stationary point and area sources, off-road and on-road mobile sources, and biogenic sources. Stati onary point sources are those sources that emit greater than a specified tonnage per year, with data provided at the facility level. Electric genera ting utilities and industrial sour ces are the major categories for stationary point sources. Stationary area sources are those sources whos e individual emissions 

### Uniform Rate of Progress Glide Path Breton - W20% Data Days

26.00 24.82
22.71
20.37
18.02
15.68
13.34 11.93

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 Yea r Haziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation22 are relatively small, but due to the large number of these sources, the collective emissions from the source category could be signi ficant (i.e., dry cleaners, servi ce stations, agricultural sources, fire emissions, etc.). These types of emissions are estimated on a countywide level. Non-road (or off-road) mobile sources are equipment that can move but do not use the roadways, i.e., lawn mowers, construction equipment, railroad locomotiv es, aircraft, etc. The emissions from these sources, like stationary area sources, are estimat ed on a countywide level. On-road mobile sources are automobiles, trucks, and motorcycle s that use the roadway system. The emissions from these sources are estimated by vehicle type and road type and are summed to the countywide level. Biogenic sour ces are the natural sources like tr ees, crops, grasses and natural decay of plants. The emissions from these sources are estimated on a countywide level.

In addition to the various source classificati ons, there are also various types of emission inventories. The first is the actua l base year inventory. This inve ntory is the base year emissions that correspond to the meteorological data used, which for this modeling effort is data from 2002. These emissions are used for evaluati ng the air quality model performance.

The second type of inventory is th e typical base year inventory. Th is inventory is similar to the actual base year inventory, except that for sour ces whose emissions change signficantly from year to year, a more typical emission value is used. In this modeling effort, typical emissions were developed for the electric generating units (EGUs) and the wildland fire emissions. The air quality modeling runs using the t ypical base year inve ntory provide results which are then used to calculate relative reduction factors for future y ears. These relative reduction factors for future years are then used to dem onstrate reasonable progress to ward visibili ty goals.

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

4.1.1 Stationary Point Sources

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

Electric Generating Units
The actual base year inventory for the EGU s ources used 2002 continuous emissions monitoring (CEM) data reported to the USEPA’s Acid Rain program or 2002 hourly emissions data provided by stakeholders. These data provide hourly emissions profiles for SO 2 and NOx that can be used in air quality mode ling. Emissions profiles are used to estimate emissions of other pollutants (volatile organic co mpounds, carbon monoxide, ammonia, fine particles, soil) based on measured emissions of SO 2 and NOx.23 Emissions from EGU vary daily and seasonally as a function of variabil ity in energy demand and utilization and outage schedules. To avoid anomalies in future y ear emissions created by relying on 2002 operations to represent future operations, a typical base year em issions inventory was developed for EGUs. This approach is consis tent with the USEPA’s modeling guidance. To develop a typical year 2002 emi ssions inventory for EGU sources, each unit’s average CEM heat input for 2000 through 2004 was divided by the 2002 act ual heat input to gene rate a unit specific normalizing factor. This normalizing factor was then multiplied by the 2002 actual emissions. The heat inputs for the period 2000 through 2004 we re used because the modeling current design values use monitored data from this same 5-year period. If a unit was shut down for an entire year during the 2000 through 2004 period, the averag e of the years the unit was operational was used. If a unit was shut down in 2002, but not permanently shutdown, the emissions and heat inputs from 2001 (or 2000) were used in the normalizing calculations.

As part of the VISTAS air quality modeling, VI STAS, in cooperation with the other eastern RPOs, contracted with ICF Resources, L.L.C., to generate future year emission inventories for the electric generating sector of the contiguous United States using the Integrated Planning Model (IPM). IPM is a dynamic linear optimiza tion model that can be used to examine air pollution control policies for vari ous pollutants throughout the con tiguous United States for the entire electric power system. Th e dynamic nature of IPM enables projection of the behavior of the power system over a specified future period. Optimization logic in IP M determines the leastcost means of meeting electric generation and capacity requirements while complying with specified constraints including air pollution regulations, transm ission bottlenecks, and plantspecific operational constraints. The versatility of IPM allows users to specify which constraints to exercise, and to populate IP M with their own datasets.

Other Industrial Point Sources
For the non-EGU sources, the same inventory is us ed for both the actual and typical base year emissions inventories. The non-EGU category uses annual emissions as reported under the Consolidated Emissions Reporting Rule (CE RR) for the year 2002. These emissions are temporally allocated to month, day, and hour us ing source category code (SCC)-based allocation factors.

The general approach for assembling future year data was to use recently updated growth and control data consistent with USEPA’s CAIR anal yses. This data was supplemented with statespecific growth factors and stakeh older input on growth assumptions.

4.1.2 Stationary Area Sources
Stationary area sources are sources whose individua l emissions are relatively small, but due to the large number of these sources , the collective emissions could be significant (i.e., combustion of fuels for heating, structure fires, service stations, etc.). Emissions are estimated by multiplying an emission factor by some known i ndicator of collective activity, such as fuel usage, number of households, or population. Stat ionary area source emissions are estimated at the countywide level.24 The actual base year inventory will serve as the t ypical base year inventor y for all area source categories except for wildland fires. For wildland fires, a typical year inventory was used to avoid anomalies in wildfire activ ity in 2002 compared to longer te rm averages. Development of a typical year fire inventory provided the capabil ity of using a comparable data set for both the base year and future years. Thus, fire emissions remain the same for air quality modeling in both the base and any future years. The VISTAS Fire Special Interest Work Group used State records to ratio the number of acres burne d over a longer term period (thr ee or more years, as available from state records) to 2002. Based on these rati os, the 2002 acreage was then scaled up or down to develop a typical year inventory.

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

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

For the source categories estimated using the USEPA’s NONROAD model, the model growth assumptions were used to create the 2009 and 2018 future year inventories. The NONROAD model takes into consideration re gulations affecting emissions from these source categories. For the four largest airports in North Carolina, th e Federal Aviation Adminstration’s Terminal Area Forecast was used to project growth in aircraft emissions. For the commercial marine, railroad locomotives and the remaining airport emissions, the VISTAS contractor calculated the future growth in emissions using detailed inventory da ta (both before and af ter controls) for 1996 and 2010, obtained from the CAIR Technical Support Do cument. When available, state-specific growth factors were used.

4.1.4 Highway Mobile Sources
For onroad vehicles, the newest version of the MOBILE model, MOBILE6.2, was used. Key inputs for MOBILE include information on the age of vehicles on the roads, the average speeds on the roads, the mix of vehicles on the roads, any programs in place in an area to reduce emissions for motor vehicles (e.g., emissions inspection programs), and temperature.25 The MOBILE model takes into consideration regulati ons that affect emissions from this source sector. The same MOBILE run is used to repres ent the actual and typical year emissions for onroad vehicles using input da ta reflective of 2002. The MOBI LE model then is run for 2018 inventory using input data refl ective of that year. The 2002 ve hicle miles traveled (VMT), speeds, vehicle age and vehicle mix data were obtained from the Florida Department of Transportation (FLDOT). For ur ban areas in Florida that run travel demand models (TDMs), VMT and speed data from TDMs were used.

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

4.1.6 Summary 2002 Base G2 (February 2007 version) Baseline Emissions Inventory for FL
Table 4.1 is a summary of the 2002 baseline emi ssion inventory for Florida. The complete inventory and discussion of the methodology is contained in Appendix D. The emissions summaries for other VISTAS states can also be found in Appendix D.

Table 4.1. 2002 Emissions Inventory Summary for FL in tons per year.
CO NH 3 NOx PM10 PM2.5 SO 2 VOC EGU 57,113 234 257,677 21,387 15,643 453,631 2,524 NON-EGU 81,933 1,423 45,156 35,857 30,504 65,090 38,471 ONROAD 4,493,820 17,922 460, 503 11,148 7,779 20,687 520,757 NON-ROAD 1,920,729 134 180, 627 18,281 17,415 20,614 272,072 AREA 71,079 37,446 28,872 443,346 58,878 40,491 404,302 FIRES 790,620 3,102 15,942 85,263 75,717 4,057 42,724 BIOGENICS 0 0 36,320 0 0 0 1,522,031 TOTAL 7,415,293 60,261 1,025,098 615,281 205,936 604,570 2,802,880

4.1.7 Model Performance Improvements thro ugh Emissions Inventory Improvements
Since the initial model performa nce evaluation, VISTAS has made several improvements to the emissions inventory, which in turn improve mode l performance. These inventory improvements are detailed in the VISTAS emissions invent ory report and Appendix D, and are summarized here:

 For electric generating utilities, the Integrat ed Planning Model (IPM) was used to provide estimates of future year utility producti on and emissions. Continuous Emissions Monitoring data was used to define seasonal variability in production and emissions. For26 Base G2 emissions, states updated IPM mode l projections from 2005 with control data provided by utility companies in 2006 through winter 2007.

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

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

 For fires, the VISTAS states provided fire act ivity data for 2002 for wildfires, prescribed fire, land clearing and agricultural burn ing and MACTEC developed a 2002 fire inventory. Where data allowed, Alpine Geophysics modeled fire ev ents as point sources. In 2006, United States Forest Service and Fish and Wildlife Service provided projections of increased prescribed burning in 2009 and 2018; these data were incorporated in the Base G inventory for all states except Florida.

 For non-road engines, the updated USEPA NONROAD2005 emissions model was used in Base G.

 For commercial marine emissions in shipping lanes in the Gulf of Mexico and Atlantic Oceans, ENVIRON created gridded emissions for the VISTAS modeling domain using inventory data newly develope d for USEPA by Corbett at Univ ersity of Delaware. These emissions were incorporated in the Base G modeling.

 Updated inventories from the neighboring RP Os, Mexico, and Canada were incorporated as available.

4.2 Assessment of Relative Contributions from Specific Pollutants and Sources Categories

Ammonium sulfate is the largest contributor to vi sibility impairment at Florida Class I areas, and reduction of SO 2 emissions would be the most effective means of reducing ammonium sulfate. With the lower SO 2 emissions in South Florida, and its greater distance from the large continental emissions of SO 2 , the Everglades National Park is occasionally dominated by carbon. As illustrated in Figure 4.2-1, 96 percent of SO 2 emissions in the VISTAS states are attributable to electric generating facilities and industrial point sources. As shown in Table 4.1, 92 percent of SO 2 emissions in Florida are attributable to el ectric generating facili ties and industrial point sources (electric generating faci lities, non-electric generating f acilities, and area sources).



## VISTAS States SO2 Emissions - 2002

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



## VISTAS States SO2 Emissions - 2002

Utilities Industrial On road Non road Other Utilities: 76% Industrial: 20% 4.8 Million tons27 Figure 4.2-1. SO 2 emissions in 2002 in the VISTAS States.



### 5.0 REGIONAL HAZE MODELI NG METHODS AND INPUTS

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

5.1 Analysis Method

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

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

 Emissions Model: The Sparse Matrix Oper ator Kernel Emissions (SMOKE) modeling system is an emissions modeling system that generates hourly gridded speciated emission inputs of mobile, nonroad mobile, area, poi nt, fire and biogenic emission sources for photochemical grid models.

 Air Quality Model: USEPA’s Models-3/ Co mmunity Multiscale Air Quality (CMAQ) modeling system is an ‘One-Atmosphere’ photochemical grid model capable of addressing ozone, particulate ma tter (PM), visibility and acid deposition at regional scale for periods up to one year.

The USEPA Modeling Guidance recommends modeling an entire year or at a minimum several days in each quarter of a year to adequately represent the range of meteorological conditions that contribute to elevated levels of fine particulate matter. The year 2002 was selected by VISTAS as the modeling year for this demonstration. Meteorological inputs were developed for 2002 using the meteorological model. Emission i nventories were also developed for 2002 and processed through the emissions model. These i nputs were used in the air quality model to predict fine particle mass a nd visibility. The model results for 2002 were compared with observed meteorological and air quality data to evaluate m odel performance. Several configurations of the meteor ological and air quality model were evaluated to select a configuration that gave the best overa ll performance for the VISTAS region.

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

The complete modeling protocol used for th is analysis can be found in Appendix C.28 5.2 Model Selection

To ensure that a modeling study is defensible, care must be taken in the selection of the models to be used. The models selected must be scient ifically appropriate for the intended application and be freely accessible to all stakeholders. Scie ntifically appropriate means that the models address important physical and chemical phenomen a in sufficient detail, using peer-reviewed methods. Freely accessible means that model form ulations and coding are freely available for review and that the models are available to stak eholders, and their consul tants, for execution and verification at no or low cost.

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

5.2.1 Selection of Photochemical Grid Model

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

The model has received a scientific peer review The model can be demonstrated applicable to the problem on a theoretical basis Data bases needed to perform the analysis are available and adequate Available past appropriate performance evalua tions have shown the model is not biased toward underestimates or overestimates A protocol on methods and procedures to be followed has been established The developer of the model must be willing to make the source code available to users for free or for a reasonable cost, and the model cannot otherwise be proprietary.

Overview of CMAQ
The photochemical model selected for this study was CMAQ version 4.5. For more than a decade, the USEPA has been developing the Models-3 CMAQ modeling system with the overarching aim of producing a ‘One-Atmosphere ’ air quality modeling system capable of addressing ozone, fine particul ate matter, visibility and ac id deposition within a common platform. The original justification for the M odels-3 development emerged from the challenges posed by the 1990 CAAA and the USEPA’s desire to develop an advanced modeling framework for ‘holistic’ environmental modeling utilizing st ate-of-science representations of atmospheric processes in a high performance computing envi ronment. The USEPA completed the initial stage of development with Models-3 and rel eased the CMAQ model in mid-1999 as the initial operating science model under the Models-3 fr amework. The most recent rendition is CMAQ version 4.5, which was released in September 2005.29 An advantage of choosing CMAQ as the atmosphe ric model is the ability to do one-atmospheric modeling. The same model configuration is being applied for the ozone and PM 2.5 attainment demonstrations SIPs, as well as the regional haze SIP. A number of features in CMAQ’s theoretical formulation and tec hnical implementation make the model well suited for annual PM modeling.

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

5.2.2 Selection of Meteorological Model

Criteria
Meteorological models, either through objectiv e, diagnostic, or pr ognostic analysis, extend available information about the state of the atmosphere to the grid upon which photochemical grid modeling is to be carried out. The criteria for selecting a meteorological model are based on both the models ability to accura tely replicate important meteorological phenomena in the region of study, and the model's ability to interface with the rest of the modeling systems -- particularly the photochemical grid model. With these issues in mind, the following criteria were established for the meteorological model to be used in this study: Non-Hydrostatic Formulation Reasonably current, peer reviewed formulation Simulates Cloud Physics Publicly available on no or low cost Output available in I/O API format Supports Four Dimensional Data Assimilation (FDDA) Enhanced treatment of Planetary Bo undary Layer heights for AQ modeling

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

MM5 uses a terrain-following non-dimensionalized pressure, or "sigma", vertical coordinate similar to that used in many ope rational and research models. In the non-hydrostatic MM5, the sigma levels are defined according to the initial hydrostatically-balanced reference state so that the sigma levels are also time-invariant. Th e gridded meteorological fields produced by MM5 are directly compatible with the input requirements of ‘one atmo sphere’ air-quality models using this coordinate. MM5 fields can be easily used in other regional air quality models with different30 coordinate systems by performing a vertical in terpolation, followed by a mass-conservation readjustment.

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

Initial and lateral boundary conditions are specified for r eal-data cases from mesoscale threedimensional analyses performed at 12-hour interv als on the outermost grid mesh selected by the user. Surface fields are analyzed at three-hour in tervals. A Cressman-based technique is used to analyze standard surface and radiosonde observations, usi ng the National Meteorological Center's spectral analysis, as a first guess. The lateral boundary data are introduced using a relaxation technique applied in the outermost five rows and columns of the coarsest grid domain.

MM5 modeling system in regulatory air quality app lication studies have b een widely reported in the literature (e.g., Emery et al., 1999; Tesche et al., 2000, 20 03) and many have involved comparisons with other prognostic models such as the Regional Atmospheric Modeling System (RAMS) and the Systems Application Internati onal Mesoscale Model. The MM5 enjoys a far richer application history in re gulatory modeling studies compared with RAMS or other models. Furthermore, in evaluations of these models in over 60 recent regional scale air quality application studies since 1995, it has generally be en found that the MM5 model tends to produce somewhat better photochemical model i nputs than alternative models.

The configuration used for this modeling demonstra tion, as well as a more detailed description of the MM5 model, can be found in the meteorologi cal modeling protocol (Appendicies E and F2).

5.2.3 Selection of Emissions Processing System

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

File System Compatibility with the I/O API File Portability Ability to grid emissions on a Lambert Conformal projection Report Capability Graphical Analysis Capability

MOBILE6 Mobile Source Emissions31 Biogenic Emissions Inventory System version 3 (BEIS-3) Ability to process emissions for the proposed domain in a reasonable amount of time. Ability to process control strategies No or low cost for acquisition and maintenance Expandable to support other species and mechanisms

Overview of SMOKE
The SMOKE Emissions Processing System Protot ype was originally developed at the Microcomputing Center of North Carolina. As with most ‘emissions models’, SMOKE is principally

an emission processing system and not a true emissions modeling system in which emissions estimates are simulated from ‘first principles’. This means that, with the exception of mobile and biogenic sources, its purpose is to provide an efficient, modern tool for converting emissions inventory data into the formatted emission files required by an air quality simulation model. For mobile sources, SMOKE actually simulates em issions rates based on input mobile-source activity data, emission factors and outputs from transportatio n travel-demand models.

SMOKE was originally designed to allow emissions data processing methods to utilize emergent high-performance-computing as applied to sparse-matrix algor ithms. Indeed, SMOKE is the fastest emissions processing tool currently available to the air quality modeling community. The sparse matrix approach utilized throughout SM OKE permits both rapid a nd flexible processing of emissions data. The processing is rapid because SMOKE utilizes a series of matrix calculations instead of less effici ent algorithms used in previous systems. The processing is flexible because the processing steps of tem poral projection, controls, chemical speciation, temporal allocation, and spatial allocation have been separate d into independent operations wherever possible. The results from these st eps are merged together at a final stage of processing.

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

5.3 Selection of the Modeling Year

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

The USEPA’s April 2007 Guidance on the use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goal s for Ozone, PM2.5, and Regional Haze identifies specific goals32 to consider when selecting one or more episodes for use in demonstrating reasonable progress in attaining the regional haze air quality goals. The USEPA recommends that episode selection derive from three principal criteria:

 Simulate a variety of me teorological conditions;

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

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

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

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

The USEPA also lists several ot her considerations to bear in mind when choosing potential regional haze episodes including: (a) choose pe riods which have already been modeled, (b) choose periods which are drawn from the years upon which the current design values are based, (c) include weekend days among those chosen, a nd (d) choose modeling periods that meet as many episode selection criteria as possible in the maximum number of nonattainment or Class I areas as possible. Finally, the USEPA explici tly recommended in its 2007 guidance to use 2002 as the baseline inventory year.

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

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

Initial Episode Typing: At the time of selection in 2003, meteorological and air quality data were available for 2002 for model i nputs and model performance evaluation. VISTAS used Classification and Regression Tree Analyses to evaluate visibility conditions for 2000, 2001, and 2002, the candidate modeling years. The year 2002 was found to be representative of conditions in the other two years. Subsequently, these analyses were repeated with the meteorologi cal and air quality monitoring data for 2000 to 2004 to evaluate how well the 2002 m odeling year represented the full 2000-2004 baseline period. This analysis confirme d that visbility and PM2.5 mass in 2002 were representative of the five-y ear baseline period for the VI STAS Class I areas. This analysis is discussed in more detail in the project report in Appendix B.33 Data Availability: In parallel with the CART analysis , episode characterization analyses, collaborative investigations by VISTAS st ates (e.g., NCDAQ, Ge orgia DNR, FL DEP) intensively studied the availability of PM 2.5, meteorological, and emissions data and representativeness of alternative Baseline m odeling periods from a regulatory standpoint. Additionally, 2002 was the year that USEPA was requiring stat es to provide emissions inventory data for the Comprehensive Emissi ons Reporting Rule, it made sense to use 2002 as the modeling year to take advantage of the 2002 inventory. Years to be used by other RPOs: VISTAS also considered what years other RPO would be modeling, and several had already chosen calendar year 2002 as the modeling year.

After a lengthy process of integrated studies, th e episode selection process culminated in the selection of calendar year 2002 (1 January through 31 December) as the most current, representative, and pragmatic choice for VIST AS regional haze modeling. All of the USEPA criteria for regional haze episode selection were di rectly considered in this process together with many other considerations (e.g., timing of new em issions or aerometric data deliveries by the USEPA or the states to the modeling teams).

5.4 Modeling Domains

5.4.1 Horizontal Modeling Domain
The USEPA’s modeling guidance recommends a 12-km modeling grid resolution for PM2.5 modeling while a 36-km grid is considered acceptable for regional haze. For the VISTAS modeling, a coarse 36-km grid re solution was used for modeling th e entire United States and a finer 12-km grid was used to model the eastern United States.

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

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

The coarse 36-km horizontal grid domain covers th e continental United States. This domain was used as the outer grid domain for MM5 mode ling with the CMAQ domain nested within the MM5 domain. Figure 5.4.1-1 shows the MM5 horizo ntal domain as the outer most, blue grid with the CMAQ 36-km domain nested in the MM5 domain.34 Figure 5.4.1-1. The MM5 horizontal domain is the outer most, blue grid, with the CMAQ 36-km domain nested in the MM5 domain.

To achieve finer spatial resolution in the VI STAS states, a one-way nested high resolution (12-km grid resolution) was used. Figure 5.4.1- 2 shows the 12-km grid, modeling domain for the VISTAS region. This is the modeling domai n for which the reasonable progress goals will be assessed.

Figure 5.4.1-2. A more detailed view of th e 12-km grid over the VISTAS region.35 5.4.2 Vertical Modeling Domain
The CMAQ vertical structure is primarily defined by the vert ical grid used in the MM5 modeling. The MM5 model employed a terrain following coordinate system defined by pressure, using 34 layers that extend from the surface to the 100 mb. A layer-averaging scheme was used to generate 19 verti cal layers for CMAQ to reduce th e computational cost of the CMAQ simulations. The effects of layer averag ing were evaluated in conjunction with the VISTAS modeling effort and was found to ha ve a relatively minor effect on the model performance metrics when both the 34 layer a nd a 19 layer CMAQ models were compared to ambient monitoring data.



### 6.0 MODEL PERFORMA NCE EVALUATION

The initial modeling effort focused on evalua ting previous regional air quality modeling applications and testing candidate model conf igurations for the SMOKE emissions and CMAQ model for the VISTAS 36-km and 12-km modeling do mains. This effort resulted in a report recommending the model configuration for the an nual emissions and air quality modeling, which is included as part of the VISTAS Emissions a nd Air Quality Modeling Protocol. The evaluation of the meteorological modeling configuration can be found in Appendix F.1, with a summary of the final meteorological and air quality mode ling configuration in the modeling protocol contained in Appendix E and Appendix C, respectively.

Air quality model performance for the 2002 modeling year was initially tested in 2004 using an early version of the VISTAS emissions inventory. In keeping with the one-atmosphere objective of the CMAQ modeling platform , model performance was evaluated based on measured ozone, fine particles, and acid deposition in the Ai r Quality System (AQS), IMPROVE, Speciated Trends Network (STN), Southeastern Aeroso l Research and Charac terization (SEARCH), National Acid Deposition Program (NADP) a nd Clean Air Status and Trends Network (CASTNet) monitoring networ ks (Figure 6.0-1).36 Figure 6.0-1: Monitoring Networks used for VISTAS 2002 model performance evaluation and their location within the VISTAS 12km domain.37 6.1 Modeling Performance Goals, and Criteria

In 2004, VISTAS established model performance goals and criteria for components of fine particle mass (Table 6.1-1) based on previous mode l performance for ozone and fine particles. The USEPA modeling guidance for fine particulat e matter at the time noted that PM models might not be able to achieve the same leve l of performance as ozone models. VISTAS’ evaluation considered several sta tistical performance measures a nd displays. Fractional bias and mean fractional error were selected as the most appropriate metrics to summarize model performance; other metrics were also calculated and are included for IMPROVE monitors in the full model performance evaluation (Appendix F.2).

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

Fractional Bias Mean Fractional Error Comment

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

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

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

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

The “soccer plots” and “bugle pl ots” are relatively new tools in model performance evaluations, and have recently been included as model pe rformance evaluation displays in the USEPA’s modeling guidance for Ozone, PM2.5, and Regional Haze (2007). Both “soccer plots” and “bugle plots” allow for convenient way to examin e model performance with respect to set goals and criteria. The bugle plots ha ve the added benefit of adjus ting the goals and criteria to consider the concentration of th e species. Analysis of “bugle plots” generally suggests that greater emphasis should be placed on performa nce of those components with the greatest contribution to PM mass and visibility impair ment (e.g. sulfate and or ganic carbon) and that greater bias and error could be accepted for comp onents with smaller contributions to total PM mass (e.g. elemental carbon, nitrate, and soil).38 6.2 VISTAS Domain-Wide Performance

Further discussion of model performance in this document will focus on the comparison of observational data from the IMPROVE monitors and model output data from the 2002 VISTAS BaseG2-Actual annual air quality modeling. Fo cus is limited to the IMPROVE monitoring network as these sites are the locations used in projecting attainment visibility improvement goals in the Class I areas.

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

In our analyses, mean fractional bi as (error) is used in lieu of mean bias (error), to prevent low observations and model predictions from skewing the metrics. A full list of model performance statistics is found in Appendix F.2. The soccer and bugle plots for th e all of the VISTAS IMPROVE monitors are included here for summary purposes. Plots have been developed for the average monthly concentrations and the performan ce statistics for all of the most significant light scattering component species (Sulfate, Nitrate, and Organic Carbon) for the 20% best days and 20% worst days. Plots for indi vidual IMPROVE monitors associat ed with Florida Class I areas are included in Appendix F.2.

The soccer plots of monthly concentrations (Figur es. 6.2-1 and 6.2-2) show that values for nitrate generally fall outside of criteria performance th resholds. Sulfates and organic carbon generally fall within goal thresholds, with a couple of months falling just outside the goal thresholds but well within the criteria thresholds. Figure 6.2-3 contains separate soccer plots for each season. The seasonal plots emphasize poore r nitrate performance in the su mmer (does not even appear on the plots provided because performance is off scale with other constituents), when observed nitrate is quite low and predicted nitrate is even lower. When concentration is factored into performance criteria, nitrate pe rformance improves with respect to MFB and MFE (Figures 6.2-4 and 6.2-5)

Additionally, performance assed at the “one atmosphere” level was also deemed acceptable for ozone and particulate matter at various monitoring sites (STN, FRM, CASTNet, etc.). Overall, VISTAS found the Base G2 mode ling results to be representa tive and acceptable for use in modeling projection for ozone, particul ate matter, and regional haze.39 Figure 6.2-1: Soccer plot depicting both the mean fractional error and fractional bias for component concentration for a ll VISTAS sites based on Base G2 results. Each point represents a monthly value as compared to th e model performance criteria (red box) and modeling performance goals (green box).

Figure 6.2-2: A zoomed view of the soccer plot depicting both the mean fractional error and fractional bias for component concentrat ion for all VISTAS sites based on Base G2 results. Each point represents a monthly val ue as compared to the model performance criteria (red box) and modeling performance goals (green box). All Sites CMAQ 12km - 2002 Monthly

0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0 180.0 200.0

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

Nitrate

Organics

Criteria

Goal

All Sites CMAQ 12km - 2002 Monthly

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

-75.0 -25.0 25.0 75.0 Fractional BiasMean Fractional Error Sulfate

Nitrate

Organics

Criteria

Goal40 Figure 6.2-3: Seasonal soccer plots based on Base G1 results for all VISTAS IMPROVE monitors.

Figure 6.2-4: Bugle plot of the mean fraction bias for particulate matter and its component concentrations for all VISTAS sites based on Base G2 results. Each point represents a monthly mean fraction bias value as compa red to the model performance criteria (red lines) and modeling performance goals (green lines). All Sites CMAQ 12km - 2002 Monthly

0.0 50.0 100.0 150.0 200.0

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

Nitrate

Organics

Criteria

Goal41 Figure 6.2-5: Bugle plot of me an fraction error for particulate matte r and its component species for all VISTAS sites based on Base G2 results. Each point represents a monthly mean fraction bias value as compared to th e model performance criteria (red lines) and modeling performance goals (green lines).

6.3 Florida Class 1 Areas Performance

The following section provides stack bar charts comparing observed fine particulate matter composition and modeled fine particulate matter composition. The charts have been split into two charts, with the first displaying the 20% be st days followed by the chart for the 20% worst days. Stacked bar charts have been develope d for each of the four IMPROVE monitoring sites located in Florida: Chassahowitzka Wilderness (CHAS), Everglades National Park (EVER), and St. Marks Wilderness (SAMA), as well as the adjacent Class I area in Georgia: Okefenokee (OKEF) (See Figure 6.3.1-6.3.4).

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

 Yellow = light extinction due to sulfates (bextSO4)

 Red = light extinction due to nitrates (bextNO3)

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

 Black = light extinction due to elemental (bextEC)

 Brown = light extinction due to soil (bextSoil)

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

The components are presented in the same or der for both the observed (left hand bar) and modeled bar (right hand bar), so it easy to identify days when th e prediction light extinction for the component differs from the observed. The total height of the bar provides the total reconstructed particulate matte r light extinction value. All Sites CMAQ 12km - 2002 Monthly

-200.0 -150.0 -100.0

-50.0

0.0 50.0 100.0 150.0 200.0

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

Nitrate

Organics

(+) Criteria

(+) Goal

(-) Goal

(-) Criteria42 A cursory view of the stacked bars charts reiterates that sulfates are a large cont ributor to light extinction in many of th e Florida Class I areas and Okefenok ee during most of the 20% best and 20% worst days. The bar charts also suggest or ganic carbon and nitrates are other significant components in light extinction on the 20% best da ys. Concerning the 20% worst days, nitrates are not as significant and sulf ates and organic carbon dominat e the light extinction in both modeling and observations. Over all, modeling performance tends to under predict most species of PM on average at all four sites. Model perf ormance is better during the 20% best days, where under prediction primarily takes place in the nitrat es. During the 20% worst days, sulfates are a large source of the light extincti on under prediction. Modeling performance is best at St. Marks and Chassahowitzka, with performance in the Ev erglades proportionally worse for the 20% best days. Okefenokee has a large amount of under pr ediction, primarily concerning sulfates, during the 20% worst days. There are several days at each Florida Class I area where a large over or under prediction occurs, pa rticularly concerning sulfates (2 0% best days) and organic carbon (20% worst days).

Nitrates tend to be under predicted at all site s during the 20% best and worst days, especially during the summer. The model performance eval uation prepared by Environ recognizes the consistent issues with nitrate under prediction an d attribute it to several causes, including errors with convective rainfall in the meteorological model and uncertainty in sea salt and ammonia concentrations. The nitrate under prediction is exaggerated during extr emely low levels of nitrates (< 1.0 μ g/m 3 ) and at coastal sites, such as the Ev erglades National Park. In the stacked bar charts for the Everglades, nitrates are comp letely missing in the modeled contributions in both the 20% best and 20% worst days.

Overall, the FLDEP found model performance to fall within acceptable limits for model performance, although the performance for the Ev erglades falls within the lower levels of acceptability. Due to the poor modeling of nitrat es in the Everglades, modifications to the relative reduction factor calculati on were applied to correct the performance issues. This is discussed further in section 7.2.4. The FLDEP further asserts the one atmosphere modeling performed by the VISTAS contractors is representa tive of conditions in th e southeastern states and is applicable for use in attainment demonstrations.43 6.3.1 Chassahowitzka, FL

Best 20% Obs (left) vs 2002gt2a (right) at CHAS1

0 10 20 30 40 50 60 70 80 90

38 68 71 104 146 161 164 170 173 176 182 191 203 224 227 239 242 248 266 320 344 347 _ _ _ Avg Julian Day in Best 20% groupbEXT (1/Mm) bCM
bSOIL
bEC
bOC
bNO3
bSO444 Figure 6.3-1: Stacked bar char t for CHAS1 on the 20% best days (top) and 20% worst

days (bottom). Observed composition is p resented in the left hand bar, with modeled composition from the 12km CMAQ rep resented by the right hand bar. 6.3.2 Everglades, FL Worst 20% Obs (left) vs 2002gt2a (right) at CHAS1

0 20 40 60 80 100 120 140 160 180 200

5 17 26 41 44 47 56 89 116 128 155 200 206 236 260 278 281 290 296 329 332 341 350 _ _ Avg Julian Day in Worst 20% groupbEXT (1/Mm) bCM
bSOIL
bEC
bOC
bNO3
bSO4

Best 20% Obs (left) vs 2002gt2a (right) at EVER1

0 5 10 15 20 25 30

23 29 32 71 80 104 134 137 140 164 176 191 206 212 242 245 248 251 254 284 287 296 299 302 _ Avg Julian Day in Best 20% groupbEXT (1/Mm) bCM
bSOIL
bEC
bOC
bNO3
bSO445 Figure 6.3.2: Stacked bar chart fo r EVER1 on the 20% best days (top) and 20% worst days (bottom). Observed composition is presen ted in the left hand bar, with modeled composition from the 12km CMAQ rep resented by the right hand bar.

6.3.3 Okefenokee, GA Worst 20% Obs (left) vs 2002gt2a (right) at EVER1

0 20 40 60 80 100 120 140 160 180 200

5 35 44 56 62 74 95 98 113 125 128 152 158 218 236 257 281 290 305 311 332 341 350 362 _ Avg Julian Day in Worst 20% groupbEXT (1/Mm) bCM
bSOIL
bEC
bOC
bNO3
bSO4

Best 20% Obs (left) vs 2002gt2a (right) at OKEF1

0 10 20 30 40 50 60

11 20 38 59 62 104 170 173 176 179 191 239 242 257 266 275 284 335 344 347 356 359 _ _ _ Avg Julian Day in Best 20% groupbEXT (1/Mm) bCM
bSOIL
bEC
bOC
bNO3
bSO446 Figure 6.3.3: Stacked bar chart fo r OKEF1 on the 20% best days (top) and 20% worst days (bottom). Observed composition is presen ted in the left hand bar, with modeled composition from the 12km CMAQ rep resented by the right hand bar. Worst 20% Obs (left) vs 2002gt2a (right) at OKEF1

0 50 100 150 200 250 300 350

17 23 44 56 89 95 122 128 155 188 197 200 218 236 254 260 263 281 296 308 338 341 350 _ _ Avg Julian Day in Worst 20% groupbEXT (1/Mm) bCM
bSOIL
bEC
bOC
bNO3
bSO447 6.3.4 Saint Marks, FL

Figure 6.3.4: Stacked bar chart fo r SAMA on the 20% best days (top) and 20% worst days (bottom). Observed composition is presen ted in the left hand bar, with modeled composition represented by the right hand bar. Best 20% Obs (left) vs 2002gt2a (right) at SAMA1

0 10 20 30 40 50 60 70 80 90 100

11 14 20 32 38 68 80 158 161 173 176 179 194 206 209 218 224 239 266 302 344 356 _ _ _ Avg Julian Day in Best 20% groupbEXT (1/Mm) bCM
bSOIL
bEC
bOC
bNO3
bSO4

Worst 20% Obs (left) vs 2002gt2a (right) at SAMA1

0 50 100 150 200 250 300

35 44 47 56 89 95 98 107 116 122 125 128 131 155 185 188 197 200 260 290 293 296 341 _ _ Avg Julian Day in Worst 20% groupbEXT (1/Mm) bCM
bSOIL
bEC
bOC
bNO3
bSO448 

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

As stated in section 1.3 above, the regional haze rule requires St ates to establish reasonable progress goals, expressed in deciviews, for visibi lity improvement at each affected Class I area covering each (approximately) 10-ye ar 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 section discusses devel opment of Florida’s long-term strategy.

7.1 Overview of the Long-Term Strategy Development Process

The monitored data and modeling analyses cited in sections 2 and 5 above establish that for the VISTAS region, the key contributors to regiona l haze in the 2000-2004 baseline timeframe were large stationary sources of sulfur dioxide emissi ons. Keeping that key conclusion in mind, this section addresses the following questions:

a. Assuming implementation of existing federal and state air regulatory requirements in Florida and the VISTAS region, how much vi sibility improvement, compared to the glidepath, would we expect to see at Class I areas in or near Florida between now and 2018?

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

c. How do we select any additional sources th at would provide potentially significant visibility benefits in ma king reasonable progress?

d. What is the process for identifying additional emission controls or reductions that represent reasonable progress for those specific sources?

e. What additional emission contro ls represent BART in Florida?

f. Given the additional emission reductions expected from BART and reasonable progress improvements, how much additiona l visibility improvement, compared to the glidepath, will we expect to see at Class I areas in Florida between the baseline and 2018?49 7.2 Expected Visibility Results in 2018 fo r Florida Class I Areas under existing and planned emissions controls (G2 and, Best and Final Inventory (G4))

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

7.2.1 Federal and State Control Requirements

CAIR

. CAIR will permanently cap emissions of SO 2 and NOx from EGUs in the eastern United States by 2015. When fully implemented, CAIR will reduce SO 2 emissions from EGUs in these states by more than 70 percent, and NOx emissions by more than 60 percent, from 2003 levels. The CAIR rule was remanded in December 2008, to correct for deficiencies, but remains in effect until a replacement rule is finalized. The proposed replacement (Transport Rule) was published on August 2, 2010 (75 FR 45210) and is expected to be final by mid 2011.

NOx SIP Call . Phase I of the NO x SIP call applies to certain EGUs and large non-EGUs, including large industrial boilers and turbines, and cement kilns. Those states affected by the NOx SIP call in the VISTAS region have develope d rules for the control of NOx emissions that have been approved by the USEPA. The NOx SI P Call has resulted in a 68 percent reduction in NOx emissions from large stationary combusti on sources. For this analysis, we capped the emissions for NOx SIP call-affected sources at 2007 levels , and carried forward the capped levels for the 2009 and 2018 future year inventories.

North Carolina CSA . Under the act, enacted in 2002, coal-f ired power plants in North Carolina must achieve a 77-percent cut in NOx em issions by 2009 and a 73-percent cut in SO 2 emissions by 2013. This legislation esta blishes annual caps on both SO 2 and NOx emissions for the two primary utility companies in North Carolina, Duke Energy and Progress Energy. These reductions must be made in North Caro lina, and allowances are not saleable.

Consent Agreements (TECO, VEPCO) and Gulf Power Crist 7 Voluntary Agreement.

 Under a settlement agreement, by 2008, Tampa Electric will install permanent emissions-control equipment to meet stringent pollution limits; implement a series of interim pollution-reduction measures to reduc e emissions while the permanent controls are designed and installed; and retire pollu tion emission allowances that Tampa Electric or others could use, or sell to others, to emit additional NOx, SO 2 , and PM.

 Virginia Electric and Power Co. (VEPCO) agreed to spend $1.2 billion by 2013 to eliminate 237,000 tons of SO 2 and NOx emissions each year from eight coal-fired electricity generating plants in Virginia and West Virginia.

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

area.

One-hour ozone SIPs (Atlanta / Birmingham / Northern Kentucky). New SIPs have been submitted to the USEPA to demonstrate attainme nt of the one-hour ozone National Ambient Air50 Quality Standard (NAAQS). These SIPs require NOx reductions from specific coal fired power plants and address transporta tion plans in these cities.

NOx RACT in 8-hour Nonattainment Area SIPs . The NCDAQ’s SIP for the Charlotte / Rock Hill / Gastonia nonattainment area includes Reas onable Achievable Control Technology (RACT) for NOx for two facilitie s located in the nonattainment area: Philip Morris USA and Norandal USA. These controls were also modeled in 2018. Additional RACT controls may be realized as other companies subject to RACT complete the determination, but RACT-level controls were assumed for just these two sources.

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

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

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

Large Spark Ignition and Recreational Vehicle Rule . USEPA has adopted new standards for emissions of oxides of nitrogen (NOx), hydroc arbons (HC), and carbon monoxide (CO) from several groups of previously unr egulated nonroad engines. Include d in these are large industrial spark-ignition engines and recr eational vehicles. Nonroad sp ark-ignition engines are those powered by gasoline, liquid propane gas, or comp ressed natural gas rated over 19 kilowatts (kW) (25 horsepower). These engines are used in co mmercial and industrial applications, including forklifts, electric generators, airport baggage transport vehicl es, and a variety of farm and construction applications. Nonr oad recreational vehicles incl ude snowmobiles, off-highway motorcycles, and all-terrain-vehicles. These ru les were initially eff ective in 2004 and will be fully phased-in by 2012.

Nonroad Diesel Rule . This rule sets standards that will reduce emissions by more than 90 percent from nonroad diesel equipment, and re duce sulfur levels by 99 percent from current levels in nonroad diesel fuel starting in 2007. This step will apply to most nonroad diesel fuel in 2010 and to fuel used in locomotives and ma rine vessels in 2012. (http://www.epa.gov/nonroaddiesel/)

Industrial Boiler/Proces s Heater/RICE MACTs . The USEPA originally issued final rules to substantially reduce emissions of toxic air polluta nts from industrial, commercial and institutional boilers, process h eaters and from stationary reci procating internal combustion51 engines (RICE). These rules reduced emissions of a number of toxic ai r pollutants, including hydrogen chloride, manganese, lead, arsenic and mercury by 2009. These rules also reduced emissions of SO 2 and PM in conjunction with the toxi c air pollutant reductions. The applied Maximum Achievable Control Technology (MACT) control efficiencies were 4 percent for SO 2 and 40 percent for PM10 and PM2.5. While the USEPA’s indus trial boiler MACT rules were vacated on June 8, 2007, the VISTAS “best and final” modeling run includes these reductions. It was the consensus of the VISTAS states that thes e reductions would eventua lly be reinstated in some form well prior to 2018.

On June 4, 2010, the USEPA published the proposed Industrial, Commercial, and Institutional Boiler and Process Heater Rule (Boiler MACT , (75 FR 32006)). The proposed Boiler MACT affects numerous relatively large boilers in Florida, includin g one coal-fired unit (Smurfit-Stone in Fernandina Beach) that has been identified as a unit to be addressed within the reasonable progress provisions for SO 2 reductions. The acid gas emissi ons requirement of this proposed rule would have the significant co-benefit of fu rther reducing SO 2 emissions for many of the subject sources.

Combustion Turbine MACT . The projection inventor ies do not include the NO x co-benefit effects of the MACT regulations for Gas Turb ines or stationary Reciprocating Internal Combustion Engines, which USEPA estimates to be small compared to the overall inventory.

VOC 2-, 4-, 7-, and 10-year MACT Standards . Various point source MACTs and associated emission reductions were implemented. We did not count reducti ons occurring before 2002.

7.2.2 Additional State program s to reduce emissions
In addition to accounting for specific emission re ductions due to ongoing air pollution programs as required under the RHR section 308 (d)(3)(v)(A), states are also required to consider the air quality benefits of measures to mitigate the impacts of construction activities [section 308(d)(3)(v)(B)] and agricultural and forestry smoke mana gement [section 308(d)(3)(v) (E)].

With regard to the impact of construction ac tivities, rule 62-296.320, F.A.C., General Pollution Emission Limiting Standards addresses construction related activities. In particular, section (4)(c) Unconfined Emissions of Pa rticulate Matter provides that reasonable precautions be taken to prevent or eliminate emissions. For example, the rule addresses paving and maintenance of roads, parking areas, and yards a nd the application of water or chemicals to control emissions during construction.

With regard to smoke management, Florida ha s a certified Smoke Management Plan (SMP) meeting the intent of EPA’s Interim Air Qualit y Policy on Wildland and Prescribed Fires. The Environmental Protection Agency, Region 4, acknowledged receipt of this SMP and its certification in February 2002.

The SMP follows the requirements for such a plan contained in the Interim Air Quality Policy on Wildland and Prescribed Fires. The Florida Di vision of Forestry operates a burn authorization program that considers the potential for sm oke from the burn impacting smoke sensitive52 receptors (e.g., airports, roads, hospitals, urban areas, etc.). The SMP provides alternatives for burning and is considerate of mi nimizing air pollutants. The SMP is located in Appendix N.

The Prevention of Significant Deterioration ( PSD) new source review program also affects visibility in Class I area by assu ring that major new or modified sources of visibility-affecting pollutants are installed with the best availabl e control technology. Th e air quality review required for PSD sources includes an analysis of visibility impacts. The results of this analysis weighs-in on the determination of the allowed em issions and required controls for the sources.

7.2.3 Projected 2009 and 2018 G2 and Best a nd Final (G4) Emi ssions Inventories
The G2 and Best and Final (G4) inventory for 2018 account for post-2002 emission reductions from promulgated and proposed federal, state, loca l, and site-specific control programs as of July 1, 2004. In general, the G2 emissions inve ntories were developed for 2009 and 2018 using current control information in Florida, and growing the 2002 inve ntory using EGAS6, MOBILE6, and IPM for electric generating units (E GUs). In addition, the Best and Final (G4) inventory for 2018 includes any known BART a nd Reasonable Progress emissions changes, and changes to specific oil-fired EGUs in Florida to adjust for the IPM solution which assumed complete conversion to natural gas. It shoul d be noted that very few of the BART and Reasonable Progress changes were available at the time of the last modeling run. For Florida, the only the Georgia Pacific Palatka BART chan ges were included. Many of the projections contained in this document are based on the G2 inventory, however, when available, some figures and results are based on the Best and Fi nal inventory. The diffe rence between the two inventories is limited to a relatively few stationa ry point source changes that were updated for the purpose of the final modeling run. These changes mostly affect sulfur dioxide emissions (52,540 tons addition) with the Best and Final emissions having greater values, however on a state-wide basis, either inventory provides similar qua litative results.

For EGUs, IPM results were adjusted based on State and local (S/L ) agency knowledge of planned emission controls at specific EGUs (inc luding the changes to the oil-fired units in Florida mentioned in the previous paragraph) . These updates for G4 are documented in the MACTEC emissions inventory report “Documen tation of the 2002 Base Year and 2009 and 2018 Projection Year Emission Inventories for VISTAS” da ted July 2007. (Appendix D)
For non-EGUs, we used recently updated growth and control data consistent with the data used in USEPA’s CAIR analyses supplemented by Stat e and local agency data and updated forecasts from the Department of Energy (DOE).

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

Mobile source controls included local controls underl ying the 2002 baseline inventory (vehicle emission inspection, Stage II vapor recovery, anti-tampering, etc.) with changes based on specific State input. Nonroad control data and projections for 1996, 2010, 2015, and 2020 were53 obtained from the USEPA's Clean Air Interstate Rule (CAIR) Technical Support Document, and straight line projections were us ed to estimate 2009 and 2018 levels.

 The following bar charts show expected decreases in emissions of SO 2 and NOx across the VISTAS states from 2002 through 2018. Note that for SO 2 emissions in particular, which are the largest contributors to haze, em issions from electric generating facilities are expected to decrease dramatically (57 percent in Florida) between 2002 and 2018. However, even after implementation of CAIR , EGU emissions are projected to remain the largest contributor to haze, compri sing more than half of remaining SO 2 emissions in every state.

Figure 7.2.3-1. Base G2 Annual SO 2 emissions for 2002, 2009, and 2018 in the VISTAS states. W e s t V

i r g i n i a
A

l a b

a m

a F l o r i d

a

K e n t u cky G

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

S o u t h

C

a r o l i n a

N

o r t h

C

a r o l i n a

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

2 0 1 8

W e s t V

i r g i n i a
A

l a b

a m

a F l o r i d

a

K e n t u cky G

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

S o u t h

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a r o l i n a

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o r t h

C

a r o l i n a

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

2 0 1 8

A

l a b

a m

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a

K e n t u cky G

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

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a r o l i n a

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o r t h

C

a r o l i n a

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

2 0 1 854 Figure 7.2.3-2. Base G2 Annual NOx emis sions in 2002, 2009, and 2018 in the VISTAS States.

Summary of Final Base Emissi ons Inventories for 2009 and 2018

Table 7.2.3-1 is a summary of the 2009 Base G2 emission inventory. The complete inventory and discussion of the methodology is contained in Appendix D.

Table 7.2.3-1. 2009 Emissions Inventory Summary for FL.
CO NH 3 NOx PM10 PM2.5 SO 2 VOC EGU 35,928 1,631 86,165 9,007 5,910 186,055 1,910 NONEGU 87,037 1,544 46, 020 39,872 33,946 65,511 36,880 ONROAD 3,308,863 21,549 312, 321 9,801 6,104 2,584 336,707 NONROAD 2,104,920 148 163, 794 15,613 14,866 8,967 209,543 AREA 57,011 38,616 28,187 503,230 64,589 36,699 420,172 FIRES 923,310 3,157 19,791 98,470 88,756 4,129 51,527 BIOGENICS 0 0 36,320 0 0 0 1,522,031
TOTAL 6,517,070 66,645 692,599 675,99 3 214,170 303,943 2,578,768

Table 7.2.3-2 is a summary of the 2018 G4 emi ssion inventory. The complete inventory and discussion of the methodology is contained in Appendix D. 0 100 200 300 400 500 600 700 800 900 1,000

Ala b

a m

a Flor i da Ge o r g ia Kentucky Mississippi

N

ort

h Carolina South Carolina

T

e n n e ssee

V i rgin i a

W e st

V

i r g i ni

aAnnual NOx (Thousand Tons) Miscellaneous Nonroad Onroad Industrial Other Fuel Combustion EGU 2 0 0 2
2 0 0 9

2 0

1 8

0 100 200 300 400 500 600 700 800 900 1,000

Ala b

a m

a Flor i da Ge o r g ia Kentucky Mississippi

N

ort

h Carolina South Carolina

T

e n n e ssee

V i rgin i a

W e st

V

i r g i ni

aAnnual NOx (Thousand Tons) Miscellaneous Nonroad Onroad Industrial Other Fuel Combustion EGU Miscellaneous Nonroad Onroad Industrial Other Fuel Combustion EGU 2 0 0 2
2 0 0 9

2 0

1 855 Table 7.2.3-2. 2018 Emissions Inventory Summary for FL.
CO NH 3 NOx PM10 PM2.5 SO 2 VOC EGU 54,146 2,976 74,640 10,022 6,886 138,340 2,422 NONEGU 96,819 1,829 51, 902 46,456 39,430 75,047 42,811 ONROAD 2,520,315 25,885 148, 486 8,178 3,994 2,506 219,554 NONROAD 2,323,327 171 127, 885 12,497 11,868 7,536 183,452 AREA 53,903 40,432 30,708 578,516 72,454 38,317 489,975 FIRES 923,310 3,157 19,791 98,470 88,756 4,129 51,527 BIOGENICS 0 0 36,320 0 0 0 1,522,031
TOTAL 5,971,821 74,450 489,733 754,13 9 223,388 265,875 2,511,772

7.2.4 Model Results for the 2018 Base Best and Final (G4) Inventory Compared to the Uniform Rate of Progress Glidepaths for FL Class I Areas
Using 2000 - 2004 IMPROVE monitoring data, the deci view values for the 20 percent best days in each year are averaged together, producing a si ngle average deciview value for the best days. Similarly, the deciview values for the 20 percent worst days in each year are averaged together, producing a single average deciview value for the worst days.

Figures 7.2.4-1 through 7.2.4-10 illust rate the predicted visibility improvement by 2018 resulting from the Best and Final inventory that represen ts implementation of ex isting federal and state regulations, and limited BART and reasonable pr ogress compared to the Uniform Rate of Progress glidepath for Everglades National Pa rk (EVER), Chassahowitzka National Wilderness Area (CHAS), St. Marks National Wilderness Area (SAMA), Okefenokee National Wilderness Area (OKEF), and Breton Island National Wilderne ss Area (BRET). The gr een lines show the target natural condition in 2064 for the 20 percent worst days or 20 percent best days. The red line shows incremental deciview changes resulti ng from a uniform rate of progress (uniform glide slope) between current and natural cond itions in 2064, for average 20 percent worst visibility days or 20 percent best visibility days in the 2000-2004 baseline period. The purple lines show the improvement expected from ex isting and planned emissi on controls during the period of the first long term strategy. The legend for the purple line refers to Method 1—this is the EPA method of averaging all 20% worst days in 2002 without consideration of model performance.

The “a” version of figures 7.2.4-1 to 7.2.4-5 repres ent the projected glidepaths based on the CMAQ 12 km results using the new IMPROVE e quation without any corrections for model performance. The “b” version of figures 7.2.4-1 to 7.2.4-5 make corrections to the calculation of the relative reduction factors to account for poor model performance in re plicating NO3 and SO4 concentrations. It was found that NO3 levels were greatly under-predicted by the model compared to the actual measurements in some areas, with the Everglades being affected the most. Also, but to a lesser extent, there were some days in which SO4 model performance was poor. The consequence of this poor model performance is that the relative reduction factors56 developed from the model predictions would be adve rsely affected if these days are included in the calculation. It was our best judgment that the reliability of the relative reduction factor would be improved if the calculation took model pe rformance into account. Thus, some limited corrections were made in developing the relative reduction factors to acc ount for those days in which model performance did not meet certain exp ectations. The details of this correction are developed in the Technical Support Do cument, section 5.3.3 (Appendix F1).

Figure 7.2.4-1a. CMAQ 2018 Best and Final results compared to Uniform Rate of Progress at Everglades National Park.57 Figure 7.2.4-1b. CMAQ 2018 Best and Final results compared to Uniform Rate of Progress at Everglades National Park. (Corrected for NO3 and SO4 model performance)58 Figure 7.2.4-2a. CMAQ 2018 Best and Final results compared to Uniform Rate of Progress at ChassahowitzkaWilderness Area.59 Figure 7.2.4-2b. CMAQ 2018 Best and Final results compared to Uniform Rate of Progress at ChassahowitzkaWilderness Area. (Correct ed for NO3 and SO4 model performance)60 Figure 7.2.4-3a. CMAQ 2018 Best and Final results compared to Uniform Rate of Progress at St. Marks Wilderness Area.61 Figure 7.2.4-3b. CMAQ 2018 Best and Final results compared to Uniform Rate of Progress at St. Marks Wilderness Area. (Corrected for NO3 and SO4 model performance)62 Figure 7.2.4-4a. CMAQ 2018 Best and Final results compared to Uniform Rate of Progress at Okefenokee Wilderness Area.63 Figure 7.2.4-4b. CMAQ 2018 Best and Final results compared to Uniform Rate of Progress at Okefenokee Wilderness Area. (Corrected for NO3 and SO4 model performance)64 Figure 7.2.4-5a. CMAQ 2018 Best and Final results compared to Uniform Rate of Progress at Breton Island Wildlife Refuge.65 Figure 7.2.4-5b. CMAQ 2018 Best and Final results compared to Uniform Rate of Progress at Breton Island Wildlife Refuge. (Corrected for NO3 and SO4 model performance)66 Figure 7.2.4-6 CMAQ 2018 Best and Final res ults at Everglades National Park. 

### Reasonable Progress Assessm ent Base G2 inventory, new IMPROVE Algorithm Everglades - Best 20% Days

11.69 11.69 11.69
11.35 11.46

5.21

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)

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction67 Figure 7.2.4-7 CMAQ 2018 Best and Final resul ts at Chassahowitzka Wilderness Area. 

### Reasonable Progress Assessm ent Base G2 inventory, new IMPROVE Algorithm Chassahowitzka - Best 20% Days

15.51 15.51 15.51

13.83 13.91

5.91

0 5 10 15 20 25 30 35

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

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction68 Figure 7.2.4-8 CMAQ 2018 Best and Final res ults at St. Marks Wilderness Area. 

### Reasonable Progress Assessm ent Base G2 inventory, new IMPROVE Algorithm Saint Marks - Best 20% Days

14.37 14.37 14.37

13.15 12.80

5.39

0 5 10 15 20 25 30 35

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

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction69 Figure 7.2.4-9 CMAQ 2018 Best and Final res ults at Okefenokee Wilderness Area. 

### Reasonable Progress Assessm ent Base G2 inventory, new IMPROVE Algorithm Okefenokee - Best 20% Days

15.23 15.23 15.23

14.25 13.92

5.31

0 5 10 15 20 25 30 35

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

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction70 Figure 7.2.4-10 CMAQ 2018 Best and Final resul ts at Breton Island Wildlife Refuge.

Tables 7.2.4-1 and 7.2.4-2 are prov ided to show the pollutant-s pecific extinction without and with the model performance correction, respectively. The data shows the unusually large percentage increase in NO3 extinction when the poor performance data is included. 

### Reasonable Progress Assessm ent Base G2 inventory, new IMPROVE Algorithm

Breton - Best 20% Days

14.04 14.04 14.04
13.75 13.50

5.33

0 5 10 15 20 25 30 35

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

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction71 Table 7.2.4-1

Class I Area Name State IMPROVE Site # Days in 2002 2000

‐ 2004 Baseline dV Natural Background dV 2018 dV Reduction Target Modeled 2018 dV Reduction Difference from dV Reduction Target Chassahowitzka FL CHAS1 23 25.7 11.3 3.4 3.5 ‐ 0.1 Everglades FL EVER1 24 22.3 12.3 2.3 1.5 0.8
Saint Marks FL SAMA1 23 26.3 11.6 3.4 3.3 0.1
Okefenokee GA OKEF1 23 27.1 11.5 3.6 3.3 0.3
Breton LA BRET1 24 26.0 12.3 3.2 2.8 0.4

Percent Change in Average Extinction (Mm-1) for Worst 20% Days

bEXT (w/ Ray) bSO4 bNO3 bOC bEC bSOIL bCM bSS
Chassahowitzka FL CHAS1 ‐ 30%

‐ 43%

‐ 2%

‐ 6%

‐ 29% 22% 28% 0% Everglades FL EVER1 ‐ 12%

‐ 39% 131%

‐ 6%

‐ 22% 2% 7% 0%
Saint Marks FL SAMA1 ‐ 28%

‐ 42% 17%

‐ 4%

‐ 12% 12% 6% 0%
Okefenokee GA OKEF1 ‐ 28%

‐ 44% 37%

‐ 6%

‐ 14% 16% 8% 0%
Breton LA BRET1 ‐ 24%

‐ 41% 68% 4%

‐ 9% 14% 0% 0%

Absolute Change in Average Extinction (Mm-1) for Worst 20% Days

bEXT (w/ Ray) bSO4 bNO3 bOC bEC bSOIL bCM bSS
Chassahowitzka FL CHAS1 ‐ 40.3

‐ 37.9

‐ 0.1

‐ 1.3

‐ 1.9 0.2 0.8 0.0 Everglades FL EVER1 ‐ 14.0

‐ 16.5 6.3

‐ 2.8

‐ 1.3 0.0 0.2 0.0
Saint Marks FL SAMA1 ‐ 39.9

‐ 39.3 0.8

‐ 0.9

‐ 0.7 0.1 0.1 0.0
Okefenokee GA OKEF1 ‐ 45.4

‐ 45.0 1.8

‐ 1.8

‐ 0.9 0.1 0.2 0.0
Breton LA BRET1 ‐ 34.0

‐ 39.9 5.8 0.4

‐ 0.5 0.1 0.0 0.0

2000-2004 Baseline Average Extinction (Mm-1) for Worst 20% Days

bEXT (w/ Ray) bSO4 bNO3 bOC bEC bSOIL bCM bSS
Chassahowitzka FL CHAS1 135.0 87.4 5.1 20.6 6.7 0.9 3.0 0.2 Everglades FL EVER1 116.4 42.5 4.8 47.1 5.9 0.9 3.0 1.2
Saint Marks FL SAMA1 142.1 93.1 4.5 24.2 5.6 0.8 2.6 0.2
Okefenokee GA OKEF1 159.4 102.9 4.9 30.3 6.2 0.8 3.1 0.2
Breton LA BRET1 139.0 97.0 8.5 11.7 5.2 1.0 3.8 0.772 Table 7.2.4-2

Class I Area Name State IMPROVE Site # Days in 2002 2000

‐ 2004 Baseline dV Natural Background dV 2018 dV Reduction Target Modeled 2018 dV Reduction Difference from dV Reduction Target Chassahowitzka FL CHAS1 23 25.7 11.3 3.4 3.5 ‐ 0.1 Everglades FL EVER1 24 22.3 12.3 2.3 2.5 ‐ 0.2
Saint Marks FL SAMA1 23 26.3 11.6 3.4 3.4 0.0
Okefenokee GA OKEF1 23 27.1 11.5 3.6 3.4 0.3
Breton LA BRET1 24 26.0 12.3 3.2 3.8 ‐ 0.6

Percent Change in Average Extinction (Mm-1) for Worst 20% Days

bEXT (w/ Ray) bSO4 bNO3 bOC bEC bSOIL bCM bSS
Chassahowitzka FL CHAS1 ‐ 30%

‐ 43%

‐ 2%

‐ 6%

‐ 29% 22% 28% 0% Everglades FL EVER1 ‐ 18%

‐ 40% 0%

‐ 6%

‐ 23% 2% 7% 0%
Saint Marks FL SAMA1 ‐ 29%

‐ 43% 17%

‐ 4%

‐ 12% 12% 7% 0%
Okefenokee GA OKEF1 ‐ 29%

‐ 44% 37%

‐ 6%

‐ 14% 16% 9% 0%
Breton LA BRET1 ‐ 32%

‐ 46% 0% 4%

‐ 11% 16% 0% 0%

Absolute Change in Average Extinction (Mm-1) for Worst 20% Days

bEXT (w/ Ray) bSO4 bNO3 bOC bEC bSOIL bCM bSS
Chassahowitzka FL CHAS1 ‐ 40.3

‐ 37.9

‐ 0.1

‐ 1.3

‐ 1.9 0.2 0.8 0.0 Everglades FL EVER1 ‐ 21.1

‐ 17.1 0.0

‐ 2.9

‐ 1.3 0.0 0.2 0.0
Saint Marks FL SAMA1 ‐ 40.8

‐ 40.2 0.8

‐ 0.9

‐ 0.7 0.1 0.2 0.0
Okefenokee GA OKEF1 ‐ 46.1

‐ 45.7 1.8

‐ 1.8

‐ 0.9 0.1 0.3 0.0
Breton LA BRET1 ‐ 44.3

‐ 44.3 0.0 0.5

‐ 0.6 0.2 0.0 0.0

Absolute Change in Average Extinction (Mm-1) for Worst 20% Days

bEXT (w/ Ray) bSO4 bNO3 bOC bEC bSOIL bCM bSS
Chassahowitzka FL CHAS1 ‐ 40.3

‐ 37.9

‐ 0.1

‐ 1.3

‐ 1.9 0.2 0.8 0.0 Everglades FL EVER1 ‐ 21.1

‐ 17.1 0.0

‐ 2.9

‐ 1.3 0.0 0.2 0.0
Saint Marks FL SAMA1 ‐ 40.8

‐ 40.2 0.8

‐ 0.9

‐ 0.7 0.1 0.2 0.0
Okefenokee GA OKEF1 ‐ 46.1

‐ 45.7 1.8

‐ 1.8

‐ 0.9 0.1 0.3 0.0
Breton LA BRET1 ‐ 44.3

‐ 44.3 0.0 0.5

‐ 0.6 0.2 0.0 0.0

In Class I areas in Florida, vi sibility improvements on the 20 per cent worst days are expected to be slightly better than the uni form rate of progress glidep ath by 2018 for Everglades and Chassahowitzka, and slightly less than the uni form rate of progress for St. Marks based on reductions from existing and pla nned emissions controls. Visibility improvement in nearby73 Class I areas outside of Florida (Okefenokee and Breton), for which Florida sources may impact, are all slightly worse and slightly better, respectiv ely, than the uniform rate of progress. In all cases, the rate of visibility improvement is hovering near the uniform rate of progress. Additional emissions reductions from application of BART or reasonable progress requirements, which have not been taken in to account in the final modeling run, would provide additional visibility improvement.

In addition to improving visibility on the 20 percen t worst visibility days, states are also required to assure that visibility on the 20 percent best da ys at the Class I areas do es not degrade. Figures 7.2.4-6 to 7.2.4-10 show that visibility on the best days improves.

7.3 Relative Contribution from International Em issions to Visibility Impairment in 2018 at
VISTAS Class I areas

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

Two complementary methods were used to calcul ate the impact of inte rnational emissions at Class I areas. Because all inte rnational fires are treated as natu ral emissions in these runs, both cases underestimate the contributi ons from international fires th at are due to anthropogenic burning.

1) International emissions are represented by th e difference between two GEOS-Chem runs. In the first run United States anthropogenic emissions were removed, and in the second run both United States and international an thropogenic emissions were removed. The difference represents intern ational anthropogenic emissions , in the absence of United States anthropogenic emissions (e.g. compared to 2064 levels). Harvard University provided GEOS-Chem results to VISTAS fo r 2002 international c ontribution on 4 x 5 degree grid. Concurrently Harvard modeling for the Electric Power Research Institute (EPRI) provided GEOS-Chem results for 2001 international cont ribution on a 1 x 1 degree grid scale.74 2) International emissions are represented by the difference between two CMAQ 36-km simulations, both using 2018 Base F emi ssions and boundary conditions from GEOSChem. In the first CMAQ run, all global na tural and anthropogeni c emissions in 2018 are active. In the second CMAQ run, only global (United States and international) natural emissions are active. Here the impacts of internat ional emissions are compared against 2018 conditions rather th an natural background conditions.

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

As illustrated in Figure 7.3-1 for annual averag e contributions to sulfate at VISTAS and neighboring Class I areas, the estim ated international c ontributions are higher at Class I areas near the Canadian and Mexican borders and along the eastern coast. The estimated international contribution is higher using CMAQ and PSAT than in the GEOS-Chem runs because the grid scale is finer (more accura te dispersion of emissions) and because the background atmosphere includes loadings fr om current United States anthropogenic emissions (greater photochemical activity). Si milar charts for nitrate and organic carbon mass, for impacts on 20 percent worst visibility days, and for impacts of international emissions on calculated light extincti on are included in Appendix H.

Figure 7.3-1. Estimated internatio nal emissions contributions to sulfate at VISTAS and neighboring Class I areas. 

### Transboundary Man-Made Impacts

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

GEOS_4x5 GEOS_1x1 PSAT CMAQ75 7.4 Relative Contributions to Visibility Impa irment: Pollutants, Source Categories, and Geographic Areas

An important step toward identi fying further reasonable progress measures is to identify the key pollutants contributing to visibility impairment at each Class I area. To understand the relative benefit of further reducing emissions from differe nt pollutants, source sectors, and geographic areas, VISTAS engaged the Georgia Institute of Technology to perform emission sensitivity model runs using CMAQ. Emissions sensitivities were initially performed for three episodes representing winter and summer conditions: Jan 2002, July 2001, and July 2002. These runs used the initial 2018 projections inventory and co nsidered 30 percent reductions from specific pollutants, source categories, and geographic areas. Emissions sens itivities were repeated using the 2009 Base D projection inventory and two mo nth-long episodes from 2002: Jun 1-Jul 10 and Nov 19 – Dec 19. Emissions in 2009 were reduced by 30 percent for each pollutant sensitivity run. The pollutant contributions that were evaluated were:

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

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

 NOx from ground level (on-road plus non-road plus area) sources in each VISTAS state and other RPOs

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

 NH3 from all sources in VISTAS and other RPOs

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

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

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

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

Results are shown in Figures 7.4-1 through 7.4-4 below for the av erage of the 20 percent worst visibility days for each of thr ee Florida Class I areas, plus Okefenokee. Responses for 20 percent worst days were calculated by averaging the res ponses of the 20 percent worst days that were modeled in the two episodes. For the Florida site s, responses on four to five of the 20 percent worst visibility days were included in these graphics.76 Figure 7.4-1. CMAQ projections of visibili ty responses on 20 percent worst days at Everglades to 30 percent reductions from the 2009 Base D inventory for visibility-reducing pollutants in different source cate gories and geographic areas.

Figure 7.4-2. CMAQ projections of visibili ty responses on 20 percent worst days at Chassahowitzka to 30 percent reductions from the 2009 Base D inventory for visibility-reducing pollutants in different source categories and geographic areas. 

## Everglades, FL (Worst)

-4.50 -4.00 -3.50 -3.00 -2.50 -2.00 -1.50
-1.00 -0.50
0.00 0.50

SO2_E GU SO2_nonEGU NOx_Grou n d NOx_Point N H

3 VOCs PC_Ground

P C_

Po

i n t PC_FiresBext(Mm-1) Bio. Antro. BCs MRPO M-VU CEN VISTAS
WV
VA
TN SC NC MS
KY GA
FL
AL



### Chassahowitzka, FL (Worst)

-8.00
-7.00
-6.00 -5.00
-4.00
-3.00
-2.00
-1.00
0.00
1.00

S

O

2_EGU

S

O

2_nonE G

U
NO

x_

Ground

N

O

x_Po i nt N H

3 VOCs
PC

_G

r o und PC_Point

P C _Fi

r esBext(Mm-1) Bio. Antro. BCs MRPO M-VU CEN VISTAS
WV
VA
TN SC NC MS
KY GA
FL
AL77 Figure 7.4-3. CMAQ projections of visibility responses on 20 percent worst days at St. Marks to 30 percent reductions from the 2009 Base D invent ory for visibility-reducing pollutants in different source cate gories and geographic areas.

Figure 7.4-4. CMAQ projections of visibility responses on 20 percent worst days at Okefenokee to 30 percent reductions from the 2009 Base D inventory for visibility-reducing pollutants in different source ca tegories and geographic areas. 

## St._Marks, FL (Worst)

-16.00
-14.00
-12.00
-10.00

-8.00
-6.00
-4.00
-2.00
0.00
2.00

SO

2_

EG

U

SO

2 _

n onEG U NOx_Grou n d NOx_Point N H

3 VOCs
PC _ G

ro

und PC _ Po

i nt

PC _ F i r esBext(Mm-1) Bio. Antro. BCs MRPO M-VU CEN VISTAS
WV
VA TN SC NC MS
KY GA
FL
AL



## Okefenokee, GA (Worst)

-10.00

-8.00

-6.00

-4.00

-2.00

0.00

2.00

SO2 _ EGU SO2_nonEGU NOx_Gr ou

nd NO x _Point NH3 VOCs PC_G r ound

P C

_Po i nt

PC _ Fi

r esBext(Mm-1) Bio. Antro. BCs MRPO M-VU CEN VISTAS
WV VA TN SC NC MS
KY GA FL AL78 As Figures 7.4-1 through 7.4-4 illu strate, the greatest visibility benefits on the 20 percent worst days for the Florida Class I areas and Okefenok ee are projected to result from further reducing SO 2 from EGUs. The Everglades is somewhat differe nt than any of the othe r Class I areas with a greater relative influence from carbon (VOC) a nd boundary conditions. Contributions from other RPOs are comparatively small and the greate st benefits would likel y be from further EGU reductions within Florida, Georgia, and Alabam a. Additional benefits are projected from SO 2 emission reductions from non-ut ility, industrial point sources. The pattern of relative SO 2 contributions from non-EGUs among the various VI STAS states is similar to the pattern of relative SO 2 contributions from EGUs.

Because ammonium nitrate is a relatively sm all contributor to PM2.5 mass and visibility impairment on the 20 percent worst days at all the Class I areas, the benefits of reducing NOx and NH3 emissions at these sites are small. Fo r the 20 percent best days ammonium nitrate has a somewhat larger contribution to visibility im pairment. Reducing ammonia emissions would be more beneficial for reducing ammonium nitrate contributions to visibility impairment than further reducing nitrogen oxide emissions fr om either ground or point sources.

VOCs do contribute to visibility impairment, but as shown in the charts above, this contribution is largely from biogenic sources such as ve getative emissions. Controlling anthropogenic sources of VOC emissions has little if any vi sibility benefit at th e Class I areas. Reducing primary carbon from point sources, ground level sources or fires have a relati vely small visibility benefit on average, although fires can have a large im pact on individual days. This is consistent with the monitoring data that shows that most of measured organic carbon is secondary in origin and primary carbon is only a small fraction of the total measured carbon (Appendix B).

Note that these results from the emission sensitiv ity runs are consistent with the conclusions drawn from the 2000-2004 baseline monitoring data (see section 2.4 ab ove). The results indicate that sulfate is the dominant contributor to visibi lity impairment on the 20 percent worst days at all sites, and that ammonium nitrate may be im portant for sites where th e 20 percent worst days occur in the winter. We conclude that reducing SO 2 emissions from EGU and non-EGU point sources in the VISTAS states w ould have the greatest visibility benefits for the Florida and nearby Class I areas.

7.5 Relative Contributions to Visibility Im pairment: Geographic A reas of Influence for
Florida Class I Areas

Once it was determined that SO 2 emission reductions from E GU and non-EGU point sources in the VISTAS states would be the most effective s ources to control to impr ove visibility at the Florida and nearby Class I areas, the next step was to identify th e specific geographic areas that most likely influence visibility in each Cla ss I area, and then to identify the major SO 2 point sources located in those geographic areas. An SO 2 Area of Influence (AoI) was defined for each Class I area to represent the geographic area containing sources that would likely have the greatest impact on visibility at that Class I area. All SO 2 point sources within these Areas of79 Influence were identified and ra nked by their 2002 emissions. Th e following sections contain a broad overview of the steps in the Area of Infl uence analyses. See Appendix F1 for a more detailed discussion of these analyses and plots for additional Class I areas.

7.5.1 Back Trajectory Analyses

The first step was to generate meteorological ba ck trajectories for IMPROVE monitoring sites in Florida and neighboring Cla ss I areas for the 2000-2004 base line period. Back trajectory analyses use interpolated measured or modeled meteorological fields to estimate the most likely central path of air masses that arrive at a receptor at a given time. The method essentially follows a parcel of air backward in hourly step s for a specified length of time. Figure 7.5.1 is an example of a back trajectory analysis for Evergl ades National Park for the 20 percent worst days in 2002.

Trajectories were started at 100 meters and 500 meters above the surface and run backward from the site for 72-hours. These individual back tr ajectories for 20 percen t worst days in 2002 were also useful in evaluating model performance for individual days at the Class I areas.

7.5.2 Residence Time Plots
The next step was to plot residence time for each Class I area using five year s of back trajectories for the 20 percent worst vi sibility days in 2000-2004. Residenc e time is the frequency that winds80 pass over a specific geographic area on the path to a Class I area. Separate residence time plots were generated using trajectorie s with 100m and 500m start height s. As illustrated in Figure 7.5.2, winds influencing Everglades on the 20 per cent worst days come from directions that move over either highly populated ar eas or areas where large point sources of emissions eminate.

Figure 7.5.2. Example residence time plot fo r 20 percent worst visibility days in 2000-2004 for Everglades. Based on trajec tories with 100m start height.

7.5.3 SO 2 Areas of Influence
The next step was to develop sulfate extinction weighted residence time plots to define the geographic area with highest proba bility of influencing the receptor on the 20 percent worst days in 2000-2004 that were dominated by sulfate. Each back trajectory was weighted by sulfate extinction for that day. This allows us to focu s on the 20 percent worst days that are influenced by sulfate and place less importance on days influe nced by organic carbon from fires. Sulfateweighted back trajectories for th e 20 percent worst days were combined for 5 years of data. The resulting sulfate extinction-wei ghted residence time plots were used to define the geographic Area of Influence for sources of SO 2 emissions. In Figure 7.5.3 th e area representi ng 10 percent or greater residence time is outlined in red and the area representing 5 percent or greater81 residence time is outlined in gray. The VISTAS states focused their analyses on the Area of Influence defined by 5 percent or greater sulf ate extinction-weighted residence time.

Figure 7.5.3. Example SO 2 Area of Influence plot for sulf ate extinction weighted residence time for 20 percent worst visibility days in 2000-2004 for Everglades. Based on trajectories with 100m start height.

7.5.4 Emissions Sources within SO 2 Areas of Influence

Residence time plots were then combined w ith geographically-gridded emission data based on the 2002 baseline and 2018 BaseG emissions inventorie s. Plots were generated for the Areas of Influence defined by trajectories with 100 m and 500 m start heights. As a way of incorporating the effects of transport, deposition, and chemi cal transformation of point source emissions along the path of the trajectories, these data were weighted by 1/d, where d was calculated as the82 distance, in kilometers, between the center of the grid cell in wh ich a source is located and the center of the grid cell in which the IMPROVE m onitor is located. The distance-weighted point source SO 2 emissions are then combined with the gr idded extinction-weight ed back-trajectory residence times at a spatia l resolution of 36-km.

The final step was to combine the residence times and gridded emissions data in plots and data sets. The distance weighted (1/d) gridded point source SO 2 emissions were multiplied by the total extinction-weighted back-tra jectory residence times on a grid cell by grid cell basis. These results were then normalized by the domain-wid e total and displayed as a percentage. The analysis was done using both the 2002 a nd 2018 base year inventories.

Figures 7.5.4-1 illustrates 2002 and 2018 distance weighted gridded emissions * sulfate extinction weighted residence time plots for Ever glades. These maps help visualize where the emissions reductions will be occurring between 2002 and 2018. The change in SO 2 emissions between 2002 and 2018 can be seen by comparing em issions source strengths in the two plots. Note the emissions from each source are norma lized by the total emissions in the domain. Sources that reduce SO 2 emissions by 2018 will show a lower contribution to emissions in the domain. On the 2018 map the grid cells with th ese sources will show a lighter color gradient than on the 2002 map.

Figure 7.5.4-1. Everglades 2002 and 2018 SO 2 distance weighted emissions * SO4 extinction-weighted residence time plots. 

## Comparison 2002 vs 2018 SO2 Emissions * Residence Time Using 100 m Start Height 

## – Everglades, FL

2018 SO2 emissions 2002 SO2 emissions

Green circles indicate 100-km and 200-km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Time > 10%. Orange line perimeter indicate Area of Influence with Residence Time > 5%. Max Value = 37%83 Figures 7.5.4-2 through 7.5.4.4 illustrate gridded SO 2 distance weighted emissions * sulfate extinction weighted residence time plots fo r 2002 and 2018 emissions for Chassahowitzka, St. Marks, Okefenokee, and Breton. These plots illust rate the relative importa nce of Florida sources of SO 2 compared to sources in neighboring states.

These analyses are serving as the basis for consultation among the VISTAS states.

Figure 7.5.4-2. 2002-2018 SO 2 distance weighted emissions * SO4 extinction-weighted residence time plot for Chassahowitzka. 

## Comparison 2002 vs 2018 SO2 Emissions * Residence Time Using 100 m Start Height 

## – Chassahowitzka, FL

2018 SO2 emissions 2002 SO2 emissions

Green circles indicate 100-km and 200-km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Time > 10%. Orange line perimeter indicate Area of Influence with Residence Time > 5%. Max Value = 68%84 Figure 7.5.4-3. 2002-2018 SO 2 distance weighted emissions * SO 4 extinction-weighted residence time plot for St. Marks. 

### Comparison 2002 vs 2018 SO2 Emissions * Residence Time Using 100 m Start Height 

### – St. Marks, FL

2018 SO2 emissions 2002 SO2 emissions

Green circles indicate 100-km and 200-km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Time > 10%. Orange line perimeter indicate Area of Influence with Residence Time > 5%. Max Value = 22%85 Figure 7.5.4-4. 2002-2018 SO 2 distance weighted emissions * SO 4 extinction-weighted residence time plot for Okefenokee, GA. 

## 2002 vs 2018 SO2 Emissions weighted by Residence Time Okefenokee, GA

2002 SO2 emissions 2018 SO2 emissions

Green circles indicate 100-km and 200-km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Time > 10%. Orange line perimeter indicate Area of Influence with Residence Time > 5%. Max Value = 25%86 Figure 7.5.4-5. 2002-2018 SO 2 distance weighted emissions * SO 4 extinction-weighted residence time plot for Breton, LA.

7.5.5 Specific Source Types in the Areas of Infl uence for Florida and Nearby Class I Areas

As previously concluded in section 7.4, the greate st opportunity to affect visibility improvement is to reduce emissions of SO 2 from stationary point sources. The following tables quantify the emissions from specific source categories that emit SO 2 within the area of influence, defined as within the area having a reside nce time of 5% or greater su rrounding each Class I area. The contributions from major source categories to the 2018 Base G2 inventory for the SO 2 Areas of Influence for the Florida Class I areas are listed in Tables 7.5.5-1 through 7.5.5-5. 

### 2002 vs 2018 SO2 Emissions weighted by Residence Time Breton, LA

2002 SO2 emissions 2018 SO2 emissions

Green circles indicate 100-km and 200-km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Time > 10%. Orange line perimeter indicate Area of Influence with Residence Time > 5%. Max Value = 10%87 Annual 2018 G2 Emissions - Everglades Area of Influence Category VOC NOX CO SO 2 PM-10 PM-2.5 NH 3 Fuel Comb. Elec. Util. 0% 15% 1% 34% 3% 5% 6% Fuel Comb. Industrial 1% 8% 2% 11% 4% 11% 0% Fuel Comb. Other 1% 4% 0% 9% 2% 4% 0% Chemical & Allied Product Mfg 0% 0% 0% 36% 0% 0% 3% Metals Processing 0% 0% 0% 1% 0% 0% 0% Petroleum & Related Industries 0% 0% 0% 0% 0% 0% 0% Other Industrial Processes 3% 1% 0% 1% 2% 2% 0% Solvent Utilization 40% 0% 0% 0% 0% 0% 0% Storage & Transport 10% 0% 0% 0% 0% 1% 0% Waste Disposal & Recycling 1% 0% 1% 0% 2% 5% 1% Highway Vehicles 21% 34% 41% 1% 2% 3% 37% Off-highway 20% 34% 45% 5% 3% 9% 0% Miscellaneous 3% 3% 10% 2% 82% 60% 52%

Table 7.5.5-1. 2018 Emissions Contributions from Ma jor Source Categories in th e Area of Influence for Everglades.

Annual 2018 G2 Emissions - Chassahowitzka Area of Influence

Category VOC NOX CO SO 2 PM10 PM2.5 NH 3 Fuel Comb. Elec. Util. 0% 19% 1% 48% 3% 5% 3% Fuel Comb. Industrial 0% 13% 1% 17% 2% 5% 0% Fuel Comb. Other 2% 4% 1% 7% 1% 3% 0% Chemical & Allied Product Mfg 1% 1% 0% 12% 0% 1% 1% Metals Processing 0% 0% 0% 4% 0% 0% 0% Petroleum & Related Industries 0% 0% 0% 5% 0% 0% 0% Other Industrial Processes 5% 4% 1% 4% 7% 11% 3% Solvent Utilization 36% 0% 0% 0% 0% 0% 0% Storage & Transport 9% 0% 0% 0% 0% 0% 0% Waste Disposal & Recycling 4% 3% 7% 0% 9% 19% 0% Highway Vehicles 19% 27% 37% 1% 1% 1% 18% Off-highway 17% 25% 31% 2% 2% 4% 0% Miscellaneous 6% 5% 20% 2% 73% 51% 75%

Table 7.5.5-2. 2018 Emissions Contributions from Ma jor Source Categories in th e Area of Influence for Chassahowitzka.88 Annual 2018 G2 Emissions - St. Marks Area of Influence

Category VOC NOX CO SO 2 PM10 PM2.5 NH 3 Fuel Comb. Elec. Util. 0% 20% 1% 46% 4% 7% 2% Fuel Comb. Industrial 1% 17% 2% 20% 3% 5% 0% Fuel Comb. Other 2% 4% 1% 8% 1% 3% 0% Chemical & Allied Product Mfg 1% 1% 0% 7% 0% 1% 1% Metals Processing 0% 0% 0% 4% 0% 0% 0% Petroleum & Related Industries 2% 0% 0% 5% 0% 0% 0% Other Industrial Processes 7% 7% 1% 8% 8% 11% 4% Solvent Utilization 36% 0% 0% 0% 0% 0% 0% Storage & Transport 8% 0% 0% 0% 1% 1% 0% Waste Disposal & Recycling 4% 2% 7% 0% 8% 18% 0% Highway Vehicles 18% 21% 36% 0% 1% 1% 18% Off-highway 14% 24% 28% 2% 2% 4% 0% Miscellaneous 7% 4% 23% 1% 71% 50% 75%
Table 7.5.5-3. 2018 Emissions Contributions from Majo r Source Categories in the Area of Influence for St. Marks.

Annual 2018 G2 Emissions - Okefenokee Area of Influence

Category VOC NOX CO SO 2 PM10 PM2.5 NH 3 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 1% 1% 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%

Table 7.5.5-4. 2018 Emissions Contributions from Ma jor Source Categories in th e Area of Influence for Okefenokee, GA.89 Annual 2018 G2 Emissions - Breton Area of Influence

Category VOC NOX CO SO 2 PM10 PM2.5 NH 3 Fuel Comb. Elec. Util. 1% 19% 1% 33% 3% 6% 1% Fuel Comb. Industrial 2% 31% 4% 18% 8% 14% 0% Fuel Comb. Other 4% 3% 2% 5% 2% 4% 0% Chemical & Allied Product Mfg 4% 4% 1% 24% 2% 3% 6% Metals Processing 1% 1% 1% 4% 3% 5% 0% Petroleum & Related Industries 4% 1% 1% 8% 1% 1% 0% Other Industrial Processes 7% 5% 2% 5% 11% 13% 16% Solvent Utilization 33% 0% 0% 0% 0% 0% 0% Storage & Transport 8% 0% 0% 0% 1% 1% 0% Waste Disposal & Recycling 3% 1% 5% 0% 5% 10% 1% Highway Vehicles 12% 13% 33% 0% 1% 1% 7% Off-highway 14% 20% 29% 2% 2% 4% 0% Miscellaneous 6% 2% 21% 1% 62% 39% 69%

Table 7.5.5-5. 2018 Emissions Contributions from Ma jor Source Categories in th e Area of Influence for Breton, LA.

The next step in the analysis was to determine which particular sources to target for potential further reasonable progress em ission reductions. Lists of SO 2 point sources within the Areas of Influence for each Class I areas were develope d using the most current (BaseG) VISTAS 2002 base year and 2018 future year emissions. For th is purpose the Area of Influence was defined as the counties with maximum sulfat e extinction weighted residence time greater than 5. For SO 2 sources within each Area of Influence, the follo wing attributes were defined for each individual unit:

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

 SO 2 emissions for 2002 and 2018

 2018 control efficiency

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

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

 Maximum sulfate extinction weighted residence time (RTmax)

Based on this information, we could establish an objective criteria for identifying the sources of SO 2 having the greatest potential fo r visibility impairment in each Class I area. The metric that most states have used to best summarize the re lative visibility impact is Q/d * RTmax, where Q represents the amount of emissions (the greater the emissions, the greater the chance of visibility impairment), d is the distance (the greater the distance the more disperse the emissions and the less impact on visibility), and RTmax – a measur e of the probability th at the local climatology will direct winds from the source to the Class I area (larger RTmax leads to greater visibility impairment). Based on this product, associated w ith each unit, most stat es paired-down the list of visibility-impairing sources to those having at least a 1% rela tive contribution to the total Q/d*RTmax for a given Class I area. Some states used a value of 0.5% relative contribution. In90 most cases, other states, in developing these data used the projected 2018 emissions, which for EGU’s was based on the IPM electric generation demand model. The IPM model produced what we believe to likely be inaccurate projections for oil-fired EGU’s in Florida. Namely, IPM projects that all of th ese units will convert to natural gas by 2018, thus producing essentially no SO 2 emissions. Thus, for the purpose of iden tifying sources for potenti al reasonable progress review, we used the 2002 emissions. These data were used to devel op the Florida-specific criteria to determine sources for reason able progress four-factor review.

7.6 Reasonable Progress Requirement

The Clean Air Act (Sec. 169A) requires that st ates provide for reas onable progress toward attaining the goal of preventing any future, and remedying any existing impairment of visibility in mandatory class I Federal areas. In determin ing reasonable progress, st ates shall consider the four factors listed below.

 the costs of compliance

 the time necessary for compliance

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

 the remaining useful life of any potentially affected sources.

Regulations implementing this requirement have been promulgated in 40 CFR Part 51, Subpart P – Protection of Visibility. Section 51.308 speci fically addresses the regional haze program requirements. Guidance on implementing the reasonable progress requirements have been developed by the U.S. EPA, “Guidance for Setting Reasonable Progress Goals Under the Regional Haze Program” revised June 1, 2007.

It is the stated objective of the regional haze rule that states sh all establish a reasonable progress goal that provides for “reasonable progress” to be made within the planning period for each Class I area toward the goal of natural visibil ity conditions by 2064. Fu rther, in establishing whether reasonable progress is be ing made, states must consider the straight line path from baseline (2000-2004) to natural conditions in 206 4 (i.e., the uniform rate of progress). In establishing the reasonable progress goal for a Class I area, states must consider the four factors listed above. In judging the adequacy of visibil ity improvement, the stat e first considers all current regulations and programs, and then determines the amount of additional improvement that may be necessary to represen t reasonable progress. As stated in EPA’s guidance, “Given the significant emissions reductions that we anticipate to result from BART, the CAIR, and the implementation of other CAA programs, in cluding the ozone and PM2.5 NAAQS, for many States this [determining the visibility improveme nt from these reductions] will be an important step in determining your RPG, and it may be all th at is necessary to achieve reasonable progress in the first planning period for some States.” In f act, we assert this is true for Florida, that the sum of all the existing regulations, including CAIR and the proposed Boiler MACT will result in “reasonable progress” of visibility improvement in all of the Florida Class I areas.91 Because of the large amount of emissions reductions of SO 2 and NOx in the Southeastern U.S. from existing rules and programs, Class I area s in VISTAS states are expected to make considerable visibility improvement during the first regional haze planning period.



### 7.7 Florida’s Reasonable Progress Analysis

As stated earlier, Florida participated in re gional planning through the Visibility Improvement State and Tribal Association of the Southeas t (VISTAS) regional planning organization. VISTAS was established to complete common tec hnical requirements of the regional haze SIP, such as air quality modeling and data analysis. Through this process, VISTAS provided states data and information related to the state’s dete rmination of reasonable pr ogress toward the longrange visibility goal. This information helped states decide which pollu tants and which sources of these pollutants best provided for reasonable progress. Further, limited information was gathered to help states addr ess the cost of potential pollu tant reductions. While VISTAS provided a common dataset for stat es to use in their reasonable progress requirement, the states did not attempt to establish a co mmon procedure that all states w ould use. Instead, each state developed its own analysis of th ese data, considering state-specif ic situations, to develop its reasonable progress requirements.

Focus on Sulfur Dioxide

VISTAS, through its modeling contractors, complete d a series of sensitivity runs that evaluated visibility improvements to pollutant reductions. These runs showed reduction of sulfur dioxide (SO 2 ) provided the greatest benefit to visibili ty improvement throughout the VISTAS domain. (See Figures 7.4.1 through 7.4.4) The VISTAS modeling contractors al so evaluated the visibility improvement expected (projected) to occur as a result of the already implemented (on-the-books) regulations by 2018 for each of the Class I areas . Because of the large decreases in SO 2 , primarily from CAIR, most of the Class I areas eas ily show visibility improvement at a faster rate than that of a straight lin e path to meeting the national goal . Class I areas located along the coast show visibility improvement near the straight line path, sometimes marginally better or marginally worse. The Florida Class I area s are in this category (see Figures 7.2.4-1 through 7.2.4-5). Because considerable progress (visibil ity improvement) was expected by 2018 with onthe-books regulations, and because SO 2 reductions provide the best opportunity for additional improvement, the VISTAS states (including Flor ida) concluded that it was appropriate to consider only SO 2 reductions in any reasonable progress analysis.

An analysis of the source categories where SO 2 emissions are emitted, show that electric generating units and other industrial boilers that burn coal and oil represent approximately 95 percent of the emissions. As a result, VISTAS states, including Florida, decided to limit the reasonable progress analysis to these source categories.

Implementation of Reasonable Progress in Florida92 Beginning with the limitation to consider only SO 2 from electric generation and other industrial sources, Florida considered how to best appl y the reasonable progress requirements. Through VISTAS, a common analysis was completed to identify the ar ea of influence (AOI) surrounding each Class I area (see section 7.5). This analysis considered three variables that each play a strong role in determining the imp act any source may have on a part icular Class I area. The first variable is the amount of SO 2 emissions; the greater the emissi ons, the more likely a source may impact visibility. The second variable is dist ance to a Class I area; the farther you get from a source, the less the impact (acknowledging that chemistry builds in a delay). And the third variable is frequency of winds (residence time ) in the direction of the Class I area from the source (trajectory analysis). This is a surrogate for the probability that winds will blow toward a Class I area. VISTAS contractors provided stat es a spreadsheet containing emissions, distance, and residence time values for each source-Class I area combination. The states then engaged in considerable discussion on how to best use this information to id entify a list of sources that would be considered for a reasonab le progress four-factor analysis.

The VISTAS states considered a number of di fferent combinations of these variables as a surrogate for visibility impact. Usin g the notation that Q represents SO 2 emissions in tons per year, d represents distance in kilometers, and RTmax represents the maximum residence time for a source-Class I area, states considered the following metrics, all of which logically relate to potential visibility impairment:

Q/d*RTmax, Q*RTmax, Q/d, Q/d**2* RTmax

There were very little data or modeling results to provide a definitive analysis identifying the best performing surrogate. Most VISTAS states made a judgment that Q/d*RTmax represented a reasonable surrogate for visibi lity impact. The purpose for formul ating such a surrogate was to pair-down the list of all stationary sources of SO 2 to a shorter list repr esenting the sources of greatest potential impact to Class I areas. This list would limit the number of sources having to undergo a four-factor analysis, wh ile providing the greatest pote ntial benefit to visibility improvement. To limit the list, VISTAS considered a unit-specific threshol d of 1 percent of the Q/d*RTmax relative contribution co nsidering all sources within the area of influence of each Class I area to be appropriate for identifying the la rgest contributors of visibility impairment.

For reasons of better public clar ity and understanding, we chose to develop a reasonable progress source selection metric based on Q/d that woul d be essentially equivalent to the nominal VISTAS metric of Q/d*RTMax. The Q/d metric wa s, in fact, used previously by VISTAS states in providing a cutpoint for when the VIST AS contractors would perform CALPUFF BART exemption modeling for relatively small sources. The cutpoint used was Q/d = 10, a value that a limited amount of evaluation modeling showed a reasonable probability the visibility impacts would not be significant (i.e., < 0.5 dv). Since visibility in Class I areas in or near Florida is expected to improve at very ne ar the nominal straight line pa th to the 2064 goal with current rules, we chose a cutpoint of Q/d = 50 (five times the nominal significance criteria). This assures us that many of the largest sources of SO 2 nearest the Class I areas are required to address93 reasonable progress, while smaller sources (not expected to provide si gnificant, cost-effective reductions) are excluded. Similarly, we provi ded some bounds for emissions (Q) and distance (d) to affect a reasonable pro cess. First, we exclude small (< 250 tons per year) units, the rationale being that any reductions would be very small and likely not very cost effective. Second, we do not consider any sources outside of 300 kilometers from a Class I area. This limitation is consistent with the bounds used in the BART exemption analysis where only sources within this distance from a Class I ar ea were considered. Third, we only consider sources which commenced construction or submitte d a complete application prior to August 30, 1999, a date after which permit review requires that visibility specifically be addressed (EPA’s Regional Haze Regulations, July 1, 1999). Fina lly, we use the 2002 emissions for Q, where VISTAS used the projected 2018 emissions. This is important in Florida for two reasons. First, we question some of the IPM projections that convert all of our oil-fired boilers to natural gas. If we accepted this premise, emissions of SO 2 are presumed to be zero in 2018 and these units would not be considered for reasonable progress review. Second, as a general strategy, we don’t want to base our selection of sources for four-factor review on IPM’s guess at how the CAIR market-based reductions will occur. We believe it is best to start with the known largest sources having the potential to impair visibility and make sure that these sour ces are addressed through the reasonable progress process.

The Florida criterion (Q/d>=50) captures for reasonable progress analysis the 1 st through 9 th

, 15 th , 18 th , 19 th , 27 th , and 30 th largest SO 2 sources (2002) in the state. Of all the SO 2 sources considered in the VISTAS area of influence around each of the Class I areas, the Florida criterion subjects 67.6 percent of the emissions to reasonable progress review. In comparison, use of the criteria used by VISTAS (1% c ontribution using Q/d*RTmax) would select 70.5 percent of the emissions.

TPY Percent of Total 2002 Total Florida SO 2 Emissions (within VISTAS AOI) 497,867 Selected w/1% criteria 351,167 70.5% Selected w/ Q/d >50 criteria 336,595 67.6%

Consequently, we believe that the Florida criterion is consistent with the general approach taken by the VISTAS states. A complete comparison of the Florida selec tion criteria with the general VISTAS criteria is contained in the tables 7.7.1 – 7.7.4.949596 CAIR and Proposed Boiler MACT as Representing Reasonable Progress

Many of the emission units subject to reasonable pr ogress analysis, as defined above, either have already reduced SO 2 emissions or will be reducing SO 2 emissions soon. These reductions have come about from company decisions to shut-down or re-power certain unit s, or to install new control equipment (scrubbers) in response to the CAIR regulations. In addition, on April 30, 2010, USEPA proposed revised boiler MACT standards. This rule will re quire reduction in acid gases which will have the co-benefit of reducing SO 2 emissions either through the use of scrubbers or fuel switching. The Smurfit-Stone f acility in Fernandina Beach (formerly Jefferson Smurfit), one of the listed reas onable progress sources subject to the four-factor analysis, is subject to the Boiler MACT ru le. In the case where sources are responding to CAIR, the department is engaging these sources to comm it to the continuous opera tion of these control equipment, where CAIR would not have this requirement. Table 7.7.5 shows source units subject to reasonable progress review a nd their current status for reducing SO 2 emissions. As can be seen, SO 2 reductions are well underway to mee ting, and probably exceeding, the amount needed to reach the 2018 projection. While SO 2 reductions due to the original Boiler MACT rule are included in the 2018 projec tion, the revised Boiler MACT rule is expected to result in even greater reductions. USEPA estimates that 340,000 tons of SO 2 would be reduced nationwide. Table 7.7.6 lists the boi lers and process heaters in Fl orida subject to the proposed federal rule.

All sources identified as subject to reasonable progress review are al so subject to either CAIR or the proposed Boiler MACT. Both CAIR and the proposed Boiler MACT rule will result in significant SO 2 reductions in Florida. In sum, thes e reductions are estimated to exceed the reductions already evaluated as part of the 2018 projections presented in this plan.97 CAIR and Boiler MACT Meeting the Four-Factor Analysis Requirement

For EGUs subject to CAIR, the EPA evaluated a number of factors, in cluding the cost of compliance and the time necessary for compliance. In the CAIR rule, EPA determined “that the earliest reasonable deadline for compliance with the final highly cost effective control levels for reducing emissions was 2015 . . .” (70 FR 25197-25198, May 12, 2005). Florida believes that the cost of compliance and the time necessary for compliance are the dominant factors for determining if additional reductions would be re asonable from CAIR sources. Based on detailed analysis in the May 12, 2005, CAIR rule, Florid a concludes that CAIR controls satisfy reasonable progress for SO 2 for the first planning period ending in 2018.

Similarly, for sources subject to the boiler MACT rule, the EP A evaluated the reduction in emissions of air pollutants taking into consid eration the cost of achieving such emission reductions and any non-air quality and energy requirements. In addition, the time for compliance is to be within three years of the final rule, wh ich is projected to be the end of 2013. While the boiler MACT analysis considered acid gases and toxic emissions like mercury, the control of acid gases is associated with the control of SO 2 and thus provides complementary reductions. In fact, EPA states in its proposal

“Finally, we considered the fact that sett ing conventional MACT standards for HCl as well as PM (as a surrogate for metals incl uding manganese) would result in significant reductions in emissions of other pollutants, most notably SO 2 , non-condensable PM, and other non-HAP acid gases (e.g., hydrogen bromid e) and would likely also result in additional reductions in emissions of mercur y and other HAP metals (e.g., selenium). The additional reductions of SO 2 alone attributable to the proposed MACT standard for HCl are estimated to be 340,000 tons per year in the third year following promulgation of the proposed HCl standard.”

It should also be noted that the Guidelines for BART Determinations Under the Regional Haze Rule suggests that MACT standards will likely be more stringent than BART determinations. Since many of the BART and reasonable progre ss factors are the same, it follows that MACT can be applied for reasonable progress assessments.

Given that both the CAIR and the Boiler MACT emission reduction requirements were developed using processes similar to the regional haze four-factor approach, it is reasonable to accept that sources that must comply with either rule satisfy the reasonable progress requirements. Further, since the visibility improve ment in all of the Class I areas in and adjacent to Florida are consistent with the nominal straig ht-line path to achieving the national visibility goal by 2064, Florida asserts that reasonable progress is met for the subject sources with the combination of CAIR and Boiler MACT requirements.9899 Table 7.7.6 - Florida Units Subject to the Industrial, Commercial, and Institutional Boilers and Process Heaters Rule (Boiler M ACT)

FacilityID UnitID Classification Design Capacity (mmBtu/hr) MACT Fuel Category FLACHFood BOILER NO. 1 Boiler 100 Liquid
FLACHFood BOILER NO. 3 Boiler 100 Gas 1 FLACHFood DV3 HEATEC Process Heater <10 Gas 1 FLCFRC252 Boiler #3 (McBurney) Boiler 85 Biomass
FLCFRC252 Boilers #4 & #5 (Hurst) Boiler 29.9 Biomass FLFLPowerandLight Aux Boiler Boiler 15.032 Gas 1 FLFPLLauderdale Aux Boiler Boiler 15.5 Gas 1
FLFPLMartin PMG Aux Boiler Boiler 16.3 Gas 1 FLGPHawthorne EU 001 Wood Fired Boiler Boiler 224 Biomass FLGPHosfordOSB 011

‐ THERMAL OIL HEATER #1 Process Heater 40 Biomass FLGPHosfordOSB 011

‐ THERMAL OIL HEATER #2 Process Heater 40 Biomass FLGPPalatka EU15 Boiler 540 Gas 1 FLGPPalatka EU16 Boiler 512.7 Biomass FLGPPalatka EU44 Boiler 244.6 Gas 1 FLHighPerformance Heater Process Heater <10 Gas 1 FLIPPensacola Power Boiler 3 / EU ID 33 Boiler 347 Coal
FLIPPensacola Power Boiler 4 / EU ID 37 Boiler 666 Biomass FLIPPensacola Power Boiler 5 / EU ID 02 Boiler 195 Gas 1 FLIPPensacola Power Boiler 6 / EU ID 03 Boiler 535 Gas 1 FLMOSAIC263 Auxiliary Process Steam Boiler (EU 055) Boiler 93.2 Gas 1 FLMOSAIC263 Package Watertube Boiler (EU 051) Boiler 64 Gas 1 FLMosaicFertilizerRiverview264 Auxiliary Steam Boiler (EU 043) Boiler 130 Gas 1 FLNavalAirStationJacksonville EC015 Boiler 1.69 Liquid FLNavalAirStationJacksonville EC102 Boiler 6.3 Liquid
FLNavalAirStationJacksonville EU071 Boiler 13 Gas 1 FLNavalAirStationJacksonville EU072 Boiler 13 Gas 1 FLNavalAirStationJacksonville EU076 Boiler 13 Gas 1 FLNavalAirStationJacksonville EU079 Boiler <10 Gas 1 FLNavalAirStationJacksonville EU087 Boiler <10 Gas 1 FLNavalAirStationJacksonville EU088

‐ 1 Boiler 25.11 Gas 1 FLNavalAirStationJacksonville EU088

‐ 2 Boiler 25.11 Gas 1 FLNavalAirStationJacksonville EU088

‐ 3 Boiler 10.1 Gas 1 FLNavalAirStationJacksonville EU089

‐ 1 Boiler 14.65 Gas 1 FLNavalAirStationJacksonville EU089

‐ 2 Boiler 14.65 Gas 1 FLNavalAirStationJacksonville EU090 Boiler 14.65 Gas 1 FLNavalAirStationJacksonville EU87

‐ 1 Boiler 31.4 Gas 1 FLNavalAirStationJacksonville EU87

‐ 2 Boiler 31.4 Gas 1 FLNavalAirStationJacksonville EU87

‐ 3 Boiler 31.4 Gas 1 FLNorthFloridaLumber 3

‐ 3900

‐ 150 Boiler 23 Biomass FLNorthFloridaLumber 3

‐ 5700

‐ 200 Boiler 2.4 Biomass
FLNorthFloridaLumber 4

‐ 5700

‐ 01 Boiler 42.9 Biomass FLNorthFloridaLumber HYB3948

‐ 150

‐ 7 Boiler 1.8 Biomass FLOkeelanta268 Boiler No. 16 Boiler 211 Liquid
FLOsceolaFarms Boiler No. 2 Boiler 280 Biomass FLOsceolaFarms Boiler No. 3 Boiler 292 Biomass FLOsceolaFarms Boiler No. 4 Boiler 280 Biomass
FLOsceolaFarms Boiler No. 5 Boiler 330 Biomass FLOsceolaFarms Boiler No. 6 Boiler 379 Biomass FLPepperidgeFarm HOT WAT GAS FIRED Process Heater <10 Gas 1 FLPepperidgeFarm SML Gas Fired Boiler <10 Gas 1 FLPLBartowPowerPlantStPeters b EU004 Boiler 15.5 Gas 1 FLRayonierPerformance PB06 Boiler 525 Biomass FLSACBranford Raw Mill Air Heater Process Heater 32 Gas 1 FLSmurfit

‐ Stone 5PB Boiler 805 Biomass FLSmurfit

‐ Stone 7PB Boiler 1021 Coal FLSolutiaIncPensacola B

‐ 4 Boiler 241 Liquid
FLSolutiaIncPensacola B

‐ 5 Boiler 241 Liquid FLSolutiaIncPensacola B

‐ 6 Boiler 241 Liquid FLSolutiaIncPensacola B

‐ 7 Boiler 388 Gas 1 FLSolutiaIncPensacola B

‐ 8 Boiler 388 Gas 1 FLSolutiaIncPensacola B

‐ 9 Boiler 344 Gas 1 FLSolutiaIncPensacola H2 Plant Process Heater 180 Gas 1 FLSolutiaIncPensacola V

‐ 1 Process Heater 10.5 Gas 1 FLSolutiaIncPensacola V

‐ 2 Process Heater 10.5 Gas 1 FLSolutiaIncPensacola V

‐ 3 Process Heater 10.5 Gas 1 FLSolutiaIncPensacola V

‐ 4 Process Heater 16.9 Gas 1 FLSolutiaIncPensacola V

‐ 5 Process Heater 16.9 Gas 1 FLSolutiaIncPensacola V

‐ 6 Process Heater 16.9 Gas 1 FLSolutiaIncPensacola V

‐ 7 Process Heater 26 Gas 1 FLSolutiaIncPensacola V

‐ 8 Process Heater 28 Gas 1 FLStRegis 040

‐ A Process Heater 56.7 Gas 2 FLStRegis 040

‐ B Process Heater 56.7 Gas 2 FLStRegis 040

‐ C Process Heater 56.7 Gas 2 FLStRegis 041

‐ A Process Heater 28.4 Gas 2 FLStRegis 041

‐ B Process Heater 28.4 Gas 2 FLStRegis 041

‐ C Process Heater 28.4 Gas 2 FLStRegis 042 Process Heater 55.65 Gas 2 FLSugarCaneGrowersCoop Boiler No. 1 Boiler 266.7 Biomass FLSugarCaneGrowersCoop Boiler No. 2 Boiler 269 Biomass FLSugarCaneGrowersCoop Boiler No. 3 Boiler 229 Biomass FLSugarCaneGrowersCoop Boiler No. 4 Boiler 572 Biomass FLSugarCaneGrowersCoop Boiler No. 5 Boiler 439.1 Biomass FLSugarCaneGrowersCoop Boiler No. 8 Boiler 504 Biomass FLUSSugarCorp Boiler No. 1 Boiler 495 Biomass FLUSSugarCorp Boiler No. 2 Boiler 447 Biomass FLUSSugarCorp Boiler No. 4 Boiler 633 Biomass FLUSSugarCorp Boiler No. 7 Boiler 812 Biomass FLUSSugarCorp Boiler No. 8 Boiler 1185 Biomass100 

### 7.8 Best Available Retrofit Technology (BART) for Individual Sources

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

A BART-eligible source is one which has the poten tial to emit 250 tons or more of a visibilityimpairing air pollutant, was put in place be tween August 7, 1962 and August 7, 1977, and whose operations fall within one or more of 26 specifi cally listed source categories. Under the CAA, BART is required for any BART-eligible source that a State determines ‘‘emits any air pollutant which may reasonably be anticipated to cause or c ontribute to any impairment of visibility in any such area.’’

For those sources subject to BART, Section 169A( g)(7) of the CAA requires that States must consider the following factors in making BART de terminations: (1) the costs of compliance, (2) the energy and non-air quality environmental imp acts of compliance, (3 ) any existing pollution control technology in use at the source, (4) the remaining usef ul life of the source, and (5) the degree of improvement in visibility which may r easonably be anticipated to result from the use of such technology.

7.8.1 BART-Eligible Sources in Florida

The following is a list of facilities with BART-eligible sources in Florida. The department determined the list using information from th e ARMS database, and modified through surveys sent to potential eligib le sources and through public workshop s held to develop the state BART rule.101 FACILITY ID OWNER COMPANY NAME SITE NAME CAIR
1 0010006 CITY OF GAINESVILLE, GRU DEERHAVEN GENERATING STATION Y 2 0050009 STONE CONTAINER CORPORATION PANAMA CITY MILL 3 0050014 GULF POWER COMPANY LANSING SMITH PLANT Y 4 0090006 FLORIDA POWER & LIGHT (PCC) CAPE CANAVERAL POWER PLANT Y 5 0090196 RELIANT ENERGY INDIAN RIVER, L.L.C. INDIAN RIVER PLANT - RELIANT Y 6 0110036 FLORIDA POWER & LIGHT (PPE) PORT EVERGLADES POWER PLANT Y
7 0170004 FLORIDA POWER CORPORATIO N CRYSTAL RIVER POWER PLANT Y 8 0250003 FLORIDA POWER & LIGHT (PTF) TURKEY POINT POWER PLANT Y 9 0310045 JEA NORTHSIDE/SJRPP Y 10 0310071 IFF CHEMICAL HOLDINGS, IN C. IFF CHEMICAL HOLDINGS, INC. 11 0330040 SOLUTIA INC. SOLUTIA INC. 12 0330042 INTERNATIONAL PAPER COMPANY PENSACOLA MILL 13 0330045 GULF POWER COMPANY CRIST ELECTRIC GENERATING PLANT Y

14 0470002 WHITE SPRINGS AGRICULTURAL CHEMICALS,INC WHITE SPRS AG CHEM-SR/SC CMPLX

15 0510003 U.S. SUGAR CORP. CLEWISTO N MILL CLEWISTON MILL AND REFINERY 16 0530010 CEMEX CEMEX 17 0570005 CF INDUSTRIES, INC., PL ANT CITY PHOS PL ANT CITY PHOSP 18 0570008 MOSAIC CARGILL--RIVERVIEW FACILITY 19 0570024 IMC PHOSPHATES CO MPANY PORT SUTTON TERMINAL 20 0570039 TAMPA ELECTRIC COMPANY BIG BEND STATION Y 21 0610029 CITY OF VERO BEACH CITY OF VERO BEACH MUNICIPAL UTILITIES Y 22 0730003 CITY OF TALLAHASSEE AR VAH B.HOPKINS GENERATING STATION Y 23 0810010 FLORIDA POWER & LIGHT (PMT) MANATEE POWER PLANT Y 24 0850001 FLORIDA POWER & LIGHT (PMR) FPL / MARTIN POWER PLANT Y
25 0890003 SMURFIT STONE CORPORATION (US) JSC-FERNANDINA BEACH PAPERBOARD MILL 26 0990016 ATLANTIC SUGAR ASSO CIATION ATLANTIC SUGAR MILL 27 0990019 OSCEOLA FARMS OSCEOLA FARMS 28 0990026 SUGAR CANE GROWERS CO-OP SUGAR CANE GROWERS CO-OP 29 0990042 FLORIDA POWER & LIGHT (PRV) RIVIERA POWER PLANT
30 0990045 LAKE WORTH UTIL. TOM G. SMITH PLANT Y 31 0990061 U.S.SUGAR CORP. BRYANT MILL U.S. SUGAR CORP. BRYANT MILL 32 1010017 FLORIDA POWER CORP. ANCLOTE POWER PLANT Y 33 1030011 FLORIDA POWER CORPORATION FPC-BARTOW PLANT Y

34 1050003 LAKELAND ELECTRIC CHARLES LARSEN MEMORIAL POWER PLANT Y
35 1050004 LAKELAND ELECTRIC C.D. MCINTOSH, JR. POWER PLANT Y 36 1050046 MOSAIC - BARTOW BARTOW 37 1050053 MOSAIC - GREEN BAY GREEN BAY PLANT 38 1050055 IMC PHOSPHATES COMPANY SOUTH PIERCE 39 1050059 MOSAIC - NEW WALES NEW WALES

40 1070005 GEORGIA-PACIFIC CORP. PULP/PAPER MILL PALATKA
41 1070014 FLORIDA POWER & LIGHT (PPN) PUTNAM POWER PLANT Y
42 1110003 FT PIERCE UTILITIES AUTHORITY H D KING PWR PLNT Y 43 1130003 STERLING FIBERS, INC. STERLING FIBERS, INC.

44 1130005 EXXONMOBIL PRODUCTION COMPANY ST REGIS TREATING FAC AND JAY GAS PLANT 45 1230001 BUCKEYE FLORIDA, LIMITED PARTNERSHIP PERRY

46 1290001 TALLAHASSEE CITY PURDOM GENERATING STA. PURDOM GENERATING STATI Y102 The BART-eligible sources were identified using the methodology in the BART Guidelines.

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

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

 The limited potential emissions from all emissi on units identified in the previous two bullets were greater than 250 t ons per year of any of these visibility-impairing pollutants: SO 2 , NOx, and PM10.

The BART Guidelines recommend addressing these visibility-impairing pollutants: SO 2 , NO x , and particulate matter, and suggest that States us e their best judgment in determining whether to address VOC or ammonia emi ssions. Florida addressed SO 2 and NOx, and used particulate matter less than 10 microns in diameter (PM 10 ) as an indicator for part iculate matter to identify BART-eligible units, as the BART Guidelines re commend. As discussed in detail in Appendix L, VISTAS modeling demonstrated that VOC s and ammonia from point sources are not significant for visibility impairment. For this re ason Florida did not evalua te emissions of VOCs and ammonia in BART determinations.

7.8.2 Florida’s BART Rule

In support of the federal regional haze rule for the Best Availabl e Retrofit Technology requirement, Florida developed st ate rule 62-296.340, Florida Administ rative Code. In this rule, we adopt by reference the core of the federal ru le, and include additional rule language to make certain decisions where the federal rule leaves options to the state. We also provide for a requirement that BART-eligible sources submit a st ate construction permit application. In this application, the source must complete the BART analysis required in the federal rule and recommend the BART controls or emission limitations consistent with that process. The state then makes the final BART determination and issues a construction permit making any changes federally enforceable.

The optional components or decisions in the state rule include the exclus ion of volatile organic compounds (VOC) and ammonia from BART re view, accepting BART equal to CAIR for electric generating units (thereby limiting BA RT for EGU’s to PM only), providing for deminimus levels below which BART would not apply for that pollutant, and provides specific criteria for exemption from BART. A copy of th e Florida BART rule is included as Appendix L.

The USEPA has determined that, as a whol e, the CAIR cap-and-trade program improves visibility more than implementing BART for indi vidual sources in states affected by CAIR. A State that opts to partic ipate in the CAIR program under 40 CFR 96.201-.224 (Subpart AAA through EEE) need not require affected BART-elig ible EGU to install, operate, and maintain BART for SO 2 or NOx emissions. All (or nearly all) co al-fired EGU’s in Flor ida are expected to control for SO 2 and NOx in response to CAIR, and since Florida is participating in CAIR and accepts the USEPA’s overall finding that CA IR “substitutes” for BART for NOx and SO 2 , Florida’s EGUs were allowed to submit BART exemption modeling demonstrations for PM103 emissions only. All but two EGUs demonstrated that they do not contribute to visibility impairment in any Class I area. Each has submitted a BART determination for PM.

7.8.3 Determination of Sources Subject to BART in Florida

Under the federal Guidelines for Best Availa ble Retrofit Technology De terminations (Appendix Y to Part 51, BART Guidelines), Florida may ex empt sources from BART if they do not cause or contribute to visibility impairment in a Cla ss I area. In accordance with the Florida BART rule exemptions, and the federal BART Guide lines, two exemption criteria were used to determine which BART-eligible sources could be exempted from the BART determination process. The first is based on meeting a simp le emissions criterion applicable within two distances (50 km and 100 km) from any Class I ar ea. Four of the BA RT-eligible sources met this exemption. The second criterion is base on source-specific analys es to determine which sources cause or contribute to visibility impa irment using the CALPUFF air quality model. A CALPUFF modeling protocol developed by VISTAS was used to assure consistent and adequate analysis and is included in Appendix L. In accordance with the Guidelines, and a Floridaspecific review of appropriate ness (see Appendix L), a contribu tion threshold of 0.5 deciviews was used for determining which sources were subject to BART. 36 of the BART-eligible sources met this modeling exemption.

Table 7.8.3-1 lists the facilities that were able to demonstrate exemption from BART based on CALPUFF modeling conducted usin g the VISTAS modeling protocol and the new IMPROVE equation. These modeling demonstrations were carefully reviewed by the department, and were provided to the U.S. EPA Region 4 office and to the federal land managers for their review and comment. Based on these reviews, a number of BART-eligible sources had to correct and resubmit their demonstration modeling to comply w ith the modeling protocol. Six BART-eligible sources made changes to their ope ration in order to meet the exemption criteria. These sources are Georgia Pacific-Palatka, Smurfit-Stone- Fe rnandina Beach, Smurfit- Stone – Panama City, Mosaic New Wales, Mosaic Riverview, and CF Industries. All of these changes have been incorporated into their permits and are federally enforceable. .

Also included in the table is the disposition of all the BART-eligible sources.104 Table 7.8.3-1 BART Eligible Sources in Florida and Their Disposition

FACILITY ID OWNER COMPANY NAME SITE NAME Full BART Meets Modeling Exemption Meets Model Rule

Exempt. Shut down Max Visib Impact

(dv)
0010006 CITY OF GAINESVILLE, GRU DEERHAVEN GENERATING STATION x 0.018

0050009 STONE CONTAINER CORPORATION PANAMA CITY MILL xx 0.498

0050014 GULF POWER COMPANY LANSING SMITH PLANT x 0.060 0090006 FLORIDA POWER & LIGHT (PCC) CAPE CANAVERAL POWER PLANT x 0.206

0090196 RELIANT ENERGY INDIAN RIVER, L.L.C. INDIAN RIVER PLANT - RELIANT x 0.178 0110036 FLORIDA POWER & LIGHT (PPE) PORT EVERGLADES POWER PLANT x 0.462

0170004 FLORIDA POWER CORPORATION CRYSTAL RIVER POWER PLANT x 0250003 FLORIDA POWER & LIGHT (PTF) TURKEY POINT POWER PLANT x

0310045 JEA NORTHSIDE/SJRPP x 0.402
0310071 IFF CHEMICAL HOLDINGS, INC. IFF CHEMICAL HOLDINGS, INC. x 0.269

0330040 SOLUTIA INC. SOLUTIA INC. x

0330042 INTERNATIONAL PAPER COMPANY PENSACOLA MILL x 0.057

0330045 GULF POWER COMPANY CRIST ELECTRIC GENERATING PLANT x 0.210

0470002 WHITE SPRINGS AGRICULTURAL CHEMICALS,INC WHITE SPRS AG CHEM-SR/SC CMPLX x

0510003 U.S. SUGAR CORP. CLEWISTON MILL U.S. SUGAR CLEWISTON MILL AND REFINERY x 0.265

0530010 CEMEX CEMEX x

0570005 CF INDUSTRIES, INC., PLANT CITY PHOS CF INDUSTRIES, INC., PLANT CITY PHOSP xx 0.498

0570008 MOSAIC RIVERVIEW xx 0.494

0570024 IMC PHOSPHATES COMPANY PORT SUTTON TERMINAL x

0570039 TAMPA ELECTRIC COMPANY BIG BEND STATION x 0.466

0610029 CITY OF VERO BEACH CITY OF VERO BEACH MUNICIPAL UTILITIES x 0.052

0730003 CITY OF TALLAHASSEE ARVAH B.HOPKINS GENERATING STATION x 0.444 0810010 FLORIDA POWER & LIGHT (PMT) MANATEE POWER PLANT x 0.458

0850001 FLORIDA POWER & LIGHT (PMR) FPL / MARTIN POWER PLANT x 0.449

0890003 SMURFIT STONE CORPORATION (US) JSC-FERNANDINA BEACH PAPERBOARD MILL xx 0.495

0990016 ATLANTIC SUGAR ASSOCIATION ATLANTIC SUGAR MILL x 0.490

0990019 OSCEOLA FARMS OSCEOLA FARMS x 0.302

0990026 SUGAR CANE GROWERS CO -OP SUGAR CANE GROWERS CO-OP x 0.467 0990042 FLORIDA POWER & LIGHT (PRV) RIVIERA POWER PLANT x 0.194

0990045 LAKE WORTH UTIL. TOM G. SMITH PLANT x

0990061 U.S.SUGAR CORP. BRYANT MILL U.S. SUGAR CORP. BRYANT MILL x

1010017 FLORIDA POWER CORP. ANCLOTE POWER PLANT x 0.488

1030011 FLORIDA POWER CORPORATION FPC-BARTOW PLANT x 0.182

1050003 LAKELAND ELECTRIC CHARLES LARSEN MEMORIAL POWER PLANT x 0.074
1050004 LAKELAND ELECTRIC C.D. MCINTOSH, JR. POWER PLANT x 0.215

1050046 MOSAIC - BARTOW BARTOW x 0.144

1050053 MOSAIC - GREEN BAY GREEN BAY PLANT x 0.219

1050055 IMC PHOSPHATES COMPANY SOUTH PIERCE x 0.494

1050059 MOSAIC - NEW WALES NEW WALES xx 0.496

1070005 GEORGIA-PACIFIC CORP. PULP/PAPER MILL GEORGIA-PACIFIC CORP. PULP/PAPER MILL xx 0.480 1070014 FLORIDA POWER & LIGHT (PPN) PUTNAM POWER PLANT x 0.099

1110003 FT PIERCE UTILITIES AUTHORITY FT PIERCE UTIL/H D KING PWR PLNT x
1130003 STERLING FIBERS, INC. STERLING FIBERS, INC. x

1130005 EXXONMOBIL PRODUCTION COMPANY ST REGIS TREATING FAC AND JAY GAS PLANT x 0.390

1230001 BUCKEYE FLORIDA, LIMITED PARTNERSHIP BUCKEYE FLORIDA, LIMITED PARTNERSHIP x 0.284

1290001 TALLAHASSEE CITY PURDOM GENERATING STA. TALLAHASSEE CITY PURDOM GENERATING STATI x

5 35 4 2 xx = federally enforceable permit changes made to meet exemption criteria105 In summary, of the 46 sources that are BART-elig ible, 39 were determined to be exempt from BART, 2 others have been shut down. The remain ing 5 sources were required to submit a permit application containing a BART determination for the applicable units. The following section discusses the results of th e BART determinations.

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

Permit applications were received from each of the subject-to-BART sources. Below is a summary of the disposition for each of the BART-eligible sources subject to a BART determination considering the five factors: (1 ) the costs of complian ce, (2) the energy and nonair quality environmental impacts of compliance, (3) any existing pollutio n control technology in use at the source, (4) the remaining useful life of the source, and (5) the degree of improvement in visibility which may reasonably be anticipa ted to result from the use of such technology.

The permits issued for these sources reflect the imposition of BART and are federally enforceable.

7.8.4.1 CEMEX
BART-eligible Emissions Units CEMEX, operates an existing portland cement plant w ith two portland cement lin es (Lines 1 and 2). These include: two Polysius GEPOL preheater kilns (Kilns 1 and 2); two clinke r coolers and associated raw mills; finish mills; cement and clinker handling equipment; coal handling equipment; silos; and air pollution control devices. The nominal capacity of each kiln is 780,000 tons per year (TPY) of clinker. The facility is located at the existing plant which is located on highway 98, northwest of Brooksville, in Hernando County, Florida. The facility operates the fo llowing emissions units subject to the requirements of Rule 62-296.340(BART), F.A.C. The BART emissions units are part of Line 1: EU No. Emission Unit Description

002 No.1 Kiln Feed System (Baghouse D-31) – Pyroprocessing/Raw Mill System
003 Cement Kiln No. 1 (Baghouse E-55) – Pyroprocessing/Raw Mill System
004 Cement Plant Clinker Cooler No. 1 (Baghouse F-18) – Clinker Handling System
005 Finish Mills No. 1 and No. 2 with two dust collectors (Baghouse G-23) – Finish Mill System
006 Clinker Storage Silo Nos. 1 & 2 (Baghouse F-31) – Clinker Handling System

008 Baghouse No. F-17 of Kiln No.1 Blending Silo No. 1 – Cement Products is not BART-eligible
Baghouse No. E-36 of Kiln No 1. Blending Silo No. 2 – Cement Products is BART-eligible
009 Cement Plant STG Silos Dust Unit (Baghouse H-3) – Cement Products
011 Raw Material Storage Silos & Feed System (Baghouses C-11, C-11A)

Affected Pollutants The affected visibility-impairing pollutants include NO X , PM 10 and SO 2 .106 Project Description and Status On February 1, 2007, CEMEX submitted an application to satisfy the BART requirements. With the subsequent additional information received on September 5, 2007, the Department deemed the application complete. December 3, 2007, the applicant waived th e 90 day processing clock until December 17, 2007. The Department issued a draft air construc tion Permit No. 0530010-030-AC on December 17, 2008 with BART determinations.

BART Determinations The BART determination for different units at this facility are basically the top control technology currently being employed. The kiln was subjected to Prevention of Significant Deterioration (PSD) review and Best Availa ble Control Technology de termination (BACT) review since 1977 one or more times and the permitted values stand up very well compared to recent determinations made throughout the countr y even for Greenfield sites. We took those factors into consideration and determined it wa s not necessary to submit a full five factor analysis and determined that the controls in pl ace constituted Best Ava ilable Retrofit Technology (BART).

Overall, the controls consist of highly effective sulfur dioxide scrubbing in the calciner, low raw material sulfur, fabric filter baghouses for part iculate matter and selec tive non-catalytic reduction (SNCR) for nitrogen oxides control.

An identical kiln was built alongside Kiln 1 that underwent BACT in the early 80’s. Both kilns have since undergone BACT and have been s ubjected to enforcement actions leading to installation of present controls.

All controls including emission limits are federally enforceable. BART controls follows:

NO X Kiln Controls : To control emissions of nitrogen oxides (NO X ), CEMEX shall implement one of the following strategies:

a. To control emissions of nitrogen oxides (NOx), CEMEX shall operate the installed SNCR system as previously author ized by Permits 0530010-026-AC and 0530010-34- AC.

b. In lieu of a. above, CEMEX is authorized to install a selective cat alytic reduction (SCR) system between the preheater and the raw mill to augment or replace the existing SNCR system. In the event the applicant chooses to install the SCR, installation and compliance with limit of 1.2 lb/ton of kiln preheater feed must be completed by October 31, 2013. The SNCR system shall be used in the interi m to meet the compliance dates and limits in Permits 0530010-026 and 0530010-34-AC. The injectors may be rearranged as needed to optimize the final configuration.

SO 2 Kiln Controls : The present SO 2 control system consisting of dry alkali and lime scrubbing in the kiln system and limestone scrubbing in the raw mill constitutes BART for EU 003. CEMEX shall continue to operate and mainta in Kiln No. 1 as indicated in Permit 0530010-003- AC (PSD-FL-233) and the current Title V permit. No later than January 1, 2014, the permittee107 shall also comply with time-averaged limits and monitoring requirements discussed further below. [Rule 62-296.340 (BART), F.A.C.]

PM/PM 10 Kiln Standards : To control emissions of PM from Kiln No. 1 CEMEX shall continue to operate and maintain baghouse E-55 as required by Permit 0530010-003-AC (PSD-FL-233) and the current Title V permit.
To control emissions of PM from Cooler No. 1 CEMEX shall continue to operate and maintain baghouse F-18 as required by Permit 0530010-003-AC (PSD-FL-233) and the current Title V permit. Baghouse Controls : Each emissions unit identified in the Table above shall control PM/PM 10 by a baghouse system. Bags/filters in each baghouse control system shall be selected based on a PM design outlet specifica tion of 0.01 grain (gr) pe r dry standard cubic foot (dscf) and a PM 10 design outlet specification of 0.007 gr/dscf. Compliance shall be demonstrated by maintaining the appropriate records and by complying with the mass emission limits, opacity limits and test re quirements as indicated in BART Permit 0530010- 30-AC. Records may include, but are not lim ited to, Specifications stated in Purchase Orders, Specifications provided by Suppliers at the time of Delivery, and Records required by the CEMEX O&M Plan. Visibility : The applicant provided a Post-BART analysis for PM (finish mills) and NO X reductions (kiln) with the addition of Indirect Firing and SNCR, permitted by the Department 12/22/06 (Permit No. 0530010-026). These reduced em ission rates provided a visibility impact of 0.933dv (8 th highest, 98 th percentile) and reduced the numbe r of days above the visibility threshold by approximately 45%. Pre-BART and Post-BART Visibility Impacts

BART-Eligible Sources Actual Visibility Impact (dv)* Impact with Reductions (dv)* Change in Visibility Impact (dv)

2001 1.457 0.933 0.524

2002 1.341 0.841 0.500

2003 1.354 0.848 0.506

*98 th percentile108 Review The BART determination summarizes a limit or a technology for different units as follows: NOx Emissions Standard (VE) for Kiln 1:

Pollutant Emission Limit Averaging Time Compliance Method Basis

NO X 2.0 lb/ton clinker 30-operating day rolling CEMS Applicant Request, PSD Avoidance and BART SO 2 Emissions Standard (VE) for Kiln 1:

Pollutant Emission Limit Averaging Time Compliance Method Basis

SO 2 0.17 lb/ton clinker 24-operating hour rolling CEMS BART 15 lb/hr @ 150 TPH of feed 16.5 lb/hr @ 165 TPH of feed
PM/PM 10 and Visible Emissions Standards (VE) for Kiln 1 and Cooler 1:

EU No. Description Emission Limit lb/hr at 150 dry preheater feed lb/hr at 165 TPH dry preheater feed VE

003 Kiln No. 1 0.31 lb/ton clinker 27.0 29.7 10

004 Cooler No. 1 0.15 lb/ton clinker 13.6 14.9 10

PM/PM 10 and Visible Emissions Standards (VE) for the Emissions units listed below :

EU No. Description BART limit lb/hr (3-hr) BART Opacity

002 No.1 Kiln Feed System (Baghouse D-31) Pyroprocessing / Raw Mill System 1.02 5%

005 Finish Mill No. 1 and No. 2 with two bag collectors (Baghouse G-23) – Finish Mill System 9.0 (for each mill) 5%

006 Clinker Storage Silo Nos. 1 & 2 (Baghouse F-31) Clinker Handling System 1.45 5%

008 Kiln No 1. Blending Silos [Baghouse No. (E-36)(silo 2)] Cement Products 1.02 5%

009 Cement Plant Storage Silos Dust Unit [Baghouse No. (H-3)(silos 1-5)] – Cement Products 5.0 5%

011 Raw Material Storage Silos (Baghouse C-11) – Raw Material Handling 1.29 5%

Transfer Belt (Baghouse C-11A) – Raw Material Handling 0.86 5%109 7.8.4.2 FPL Turkey Point

BART-eligible Emissions Units Florida Power & Light Company (FPL) operates an existing power plant known as the Turkey Point Power Plant, which consists of two residual fuel oil and natural gas-fueled 440 MW fossil fuel steam electrical generators (Un its 1 and 2), five fuel oil-fired black start 2.75 MW diesel peaking generators supporting Units 1 and 2, a natural ga s-fueled 1,150 MW combined cycl e unit (Unit 5), and associated equipment. The facility is located in Miami-Dade County at 9700 SW 344 Street, in Homestead, Florida. The facility operates the following emissions un its subject to the requirements of Rule 62- 296.340(BART), F.A.C.: Boiler 1 (EU-001) and Boiler 2 (EU-002). Particulate matter emissions are currently controlled from each unit by low excess ai r burners and Universal Oil Products (UOP) Air Correction Division multi-cyclones with reinjection. The multi-cyc lones consist of two tubular mechanical dust collectors with 695 tubes per collector.

Affected Pollutants The affected visibility-impairing pollutants include NOX, PM10 and SO 2 . For electric utilities subject to CAIR, only particulate ma tter is subject to BART review. Project Description and Status On January 31, 2007, FPL submitted an application to satisfy the BART requirements. With the subsequent additional information receive d on May 7, 2007, July 6, 2007, September 13, 2007, and January 31, 2008, the Department deemed the applicati on complete. The Department issued draft air construction permit No. 0250003-008-AC on May 23, 2008. The applicant commented on the draft and entered into negotiations with the Department to ma ke changes. A revised draft permit was issued on October 17, 2008. Additional information was recei ved on November 14, 2008 and February 13, 2009. Another revised draft permit was issued on March 2, 2009. This version of the dr aft was published in the Miami Herald on March 13, 2009 and the final permit was issued on April 14, 2009.

BART Determinations Boilers 1 and 2 fire oil as the primary fuel. Ba g houses and ESP are generally recognized as the top controls with removal efficiencies greater than 99%. Although feasible, wet scrubbers have not demonstrated equivalent le vels of control for PM. Bag houses do not appear to be a good choice either, since tests conducted by FPL at th e Sanford plant found that particles generated from the combustion of oil-based fuels caused consid erable plugging of bags in pilot scale tests. The initial visibility modeling analysis showed that replacing the existing multi-cyclones with new electrostatic precipitators (E SP) that are designed to meet the current BACT limit of 0.015 lb/MMBtu would only result in a 0.1 deciview (dv) change in visi bility at the nearest class 1 area. The applicant also expressed concern that the cost of replacing the existing multi-cyclones with new control devices would be unreasonably hi gh. The applicant considered the age of the units, the low annual capacity fa ctor (less than 30%), the very high cost of replacing the PM control devices (>$10,000 per ton of PM removed) and the inability to s ecure a guarantee from the vendor of a retrofitted contro l device emission limit of less than 0.03 lb/MMBtu. In order to provide a greater potential improve ment in visibility at the Class 1 area with a much lower capital investment requirement, the applicant pr oposed instead to: upgrade the existing multicyclones, reduce the allowable PM limit from 0.1 lb/MMBtu to 0.07 lb/MMBtu (along with a110 study of different fuel additives aimed at possibly further reducing the PM limit to 0.05 lb/MMBtu), reduce the allowabl e sulfur content of the fuel from 1% to 0.7% (0.77 lb SO 2 /MMbtu) and to reduce the opacity from 40% to 20% (12-month rolling average).

Review Step 1 . Identify all available retrofit control technologies. The available retrofit control technologies for these boilers include the following:

 Add wet scrubbers following the existing multi-cyclones.

 Add electrostatic precipitators (ESP) following the existing multi-cyclones.

 Add bag houses following the existing multi-cyclones.

 Replace the existing multi-cyclones with new state-of-the-art multi-cyclones. Step 2 . Eliminate technically infeasible options. Bag houses and ESP are generally recognized as the top controls with removal efficiencies greater than 99%. Alth ough feasible, wet scrubbers have not demonstrated equivalent levels of control for PM. Also, bag houses do not a ppear to be a good choi ce either, since tests conducted by FPL at the Sanford plant found that particles generated from the combustion of oil-based fuels caused considerable pl ugging of bags in pilot scale tests. All control options identified in Table A are considered technically feasible. Step 3 . Table A identifies the control efficiency for each pollutant under each control option identified. Step 4 . Table A identifies the rank for total capital costs as well as the rank for overall control effectiveness ($/ton). Energy impacts were considered in the cost analysis. The applicant did not identify a ny adverse non-air quality environmental impacts from the control equipment options and did not cons ider remaining useful life an issue. Step 5 . Table A identifies the estimated visibility change for each control option based on the applicant’s visibility modeling an alysis using the CALPUFF model. Based on the BART analysis for the Turkey Poin t project, meeting an emissions standard of 0.015 lb/MMBtu may technically be achieved by th e installation of a new ESP on each of these units; however, the vendor will not provide a gu arantee of being able to meet less than 0.3 lb/MMBtu because of issues related to installing a retrofit ESP on these old emissions units. At a $94,000,000 capital investment and great er than $10,000 per ton of PM 10 removed to achieve a 0.1dv reduction in visibility, replacing the ex isting multi-clones with a new ESP is not considered to be cost effective. As an a lternative, the Department accepts Florida Power Company’s proposal for reducing particulate matter and sulfur dioxide emissions from the current allowable emissions limits of 0.1 lb/MMBtu and 1.0% sulfur fuel, respectively, down to 0.07 lb/MMBtu and 0.7% sulfur fuel. This option re sults in a modeled improvement in visibility of 0.6 dv at an investment of approximately $7,300,000.111 Table A. Control Equipment Analysis

Control Option Control Efficiency Cost Effectiveness 2 Capital Costs 3 Visibility Impacts

Percent 1 Rank $/ton Rank $ Rank dv Change $/dv Change Rank

New ESP 70% PM 10 1 10,623 2 94,000,000 2 -0.10 134,000,000 2 Multi-cyclone upgrade and reduced fuel sulfur 30% PM 10 30% SO 2 2 3,568 1 7,281,340 1 -0.60 3,200,000 1

Notes:

1. Percent reduction from current permitted limits.

2. Average annual cost per ton removed.

3. Total capital costs for both units.

7.8.4.3 Progress Energy Crystal River

BART-eligible Emissions Units Progress Energy Florida operates an existing power plant known as the Crystal River Power Plant, which consists of four coal-fired fossil fuel steam gene rating (FFSG) units and asso ciated equipment. The facility is located in C itrus County on Power Line Road, West of U.S. Highway 19, in Crystal River, Florida. The facility operates the following emi ssions units subject to th e requirements of Rule 62- 296.340(BART), F.A.C.: Boiler 1 (EU-001) and Boiler 2 (EU-002). Particulate matter emissions are currently controlled by electro static precipitators (ESP). Affected Pollutants The affected visibility-imp airing pollutants include NO X , PM 10 and SO 2 . For electric utilities subject to CAIR, only particulate ma tter is subject to BART review. Project Description and Status On January 31, 2007, Progress Energy Florida submitted an application to satisfy the BART requirements. With the subsequent additional information received on August 29, 2007, the Department deemed the application complete. The Department issued draft air construction permit No. 0170004-017-AC on November 21, 2007. The applicant submitted a new applic ation and a request to withdraw the draft permit on December 24, 2007. Additional information was submitted on October 22, 2008, which made the new application complete. A revised draft permit with BART determina tions was issued on December 18, 2008. The applicant published the P ublic Notice of Intent to Issue Permit on January 24, 2009 and submitted comments on January 27, 2009. The final permit with a BART determination was issued on February 26, 2009.

BART Determinations Boilers 1 and 2 fire coal as the primary fuel. Th e initial visibility modeling analysis showed that even replacing the existing control devices with ne w ones that are designed to meet the current BACT limit of 0.015 lb/MMBtu would not result in more than a 0.15 deciview (dv) change in visibility at the nearest class 1 area. The app licant also expressed conc ern that the cost of substantially upgrading th e existing PM controls or replacing them with new control devices would be extremely costly. Considering the age of the units and the very high cost of replacing112 the PM control devices, the applicant proposed instead to upgrade the existing ESP for Unit 2, reduce their allowable PM limit fr om 0.1 lb/MMBtu to 0.04 lb/MMB tu (average for both units), and to permanently cease operating the units as coal -fired boilers within seven years of the final compliance date of the BART regulations.

Review Step 1 . Table A lists the control options that were identified. Step 2 . All control options identified in Table A are considered technically feasible. Step 3 . Table A identifies the control efficiency for each pollutant under each control option identified. Step 4 . Table A identifies the rank for total capital costs as well as the rank for overall control effectiveness ($/ton). Energy impacts were considered in the cost analysis. The applicant did not identify a ny adverse non-air quality environmental impacts from the control equipment options but did consider remaini ng useful life an issue. Step 5 . Table A identifies the estimated visibility change for each control option based on the applicant’s visibility modeling an alysis using the CALPUFF model. Based on the BART analysis for the Crystal Rive r project, meeting an emissions standard of 0.015 lb/MMBtu may be achieved by all proposed opti ons; however, it is not co st effective to do so recognizing that these units ar e nearing the end of th eir useful life. As an alternative to potentially spending around $150 million to repla ce the particulate matte r control devices on these old units in order to continue to operate them for many years beyond the BART implementation date, Progress Energy Florida, Inc. has proposed to spend around $50 million to upgrade the existing ESP for Unit 2 in order to meet a proposed combined emissions limit from Units 1 and 2 of 0.04 lb/MMBtu in order to op erate them for only seven years beyond the final BART implementation date. While historical tests on these units at full load and steady-state operation have shown them capable of emitting PM at levels less than 0.04 lb/MMBtu, normal operation tends to vary at levels less than full load, which results in emissions close to the permitted limit of 0.1 lb/MMBtu. If the Units were newer and were going to remain in operation for at least 20 years, a clear argument could be made that replacing the existing ESPs with new, state-of-the-art, ESPs would be co st effective. Considering the ag e of the existing units, it is not reasonable to require the large capital expenditu re needed to bring emissions down to levels achievable by new units and cont rol devices given the very lim ited remaining useful life. Therefore, the Department accepts Progress En ergy Florida, Inc.’s proposal for reducing particulate matter emissions from the current allowable emissions limit of 0.1 lb/MMBtu down to levels near what has been re ported on the last five year’s st ack tests (0.04 lb/MMBtu) with a commitment cease operating these old units as coal-fired boilers by December 31, 2020. The units may be repowered with a fuel other than coal. Such repowering will require an air construction permit application prior to any changes.113 Table A. Control Equipment Analysis

Control Option Control Efficiency Cost Effectiveness 1 Capital Costs 2 Visibility Impacts

Percent Rank $/ton Rank $ Rank dv Change $/dv Change Rank

Bag House Conversion 94% PM 10 1 30,582 2 71,775,596 2 -0.150 95,122,400 1 Polishing Bag House (following existing ESP) 88% PM 10 3 40,354 3 73,131,683 3 -0.110 127,235,127 3

New ESP 90% PM 10 2 49,274 4 144,223,206 4 -0.130 127,098,392 2

Rebuilt ESP 85% PM 10 4 12,987 1 28,760,610 1 -0.100 27,316,290 4

Notes:

1. Average annual cost per ton removed for bot h units assuming 7 year remaining life.

2. Total capital costs for both units.

7.8.4.4 PCS White Springs

BART-eligible Emissions Units The applicant, White Springs Agricultural Chem icals, Inc., d/b/a PCS Phosphate, operates a phosphate fertilizer manufacturing co mplex in White Springs, Florida. The facility is located at 15843 SE 78 th Street, White Springs, Hamilton County. Th is site is located about 25 kilometers from the Okefenokee National Wilderness Area, a Class I Area. The facility operates the following listed emissions units that are subject to the requirements of Best Available Retrofit Technology (BART) as listed in Rule 62-296.340(BART), F.A.C.: BART Eligible Emissions Units at the PCS Phosphate Facility

EU No. Emissions Unit Description

001 #2 Phoshate Rock Grinder 003 A Defluorinated Phosphate Plant (DFP) 004 X Train (Dical Process) 008 Y Train - #1 Diammonium Phosphate (DAP) Plant 010 #1 Monoammonium Phosphate (M AP)/DAP – Storage Building 015 MAP/DAP Shipping & Screening Facility 021 C Sulfuric Acid Plant (SAP)

022 D SAP
032 Z Train - #2 DAP Plant

038 B DFP 042 DFP Feed Prep114 044 A & B DFP Coolers 054 Molten Sulfur System
062 DFP Silos 064 Swift Creek Mine Rock Dryer
065 Swift Creek Mine Silos
SAP C and D plants use the double absorption pro cess to control sulfur dioxide emissions and demisters to control sulfuric acid mist emissions. All of the listed DAP/MAP plants include medi um to high efficiency wet scrubbers that use phosphoric acid and then pond water to reduce F and PM from the reactor and granulators. They are also equipped with abatement scrubbers usin g fresh water for final cleanup. Emissions from the dryers, coolers, mills and screens are cont rolled by cyclones, wet scrubbers with phosphoric acid or pond water as the scrubbing medium, an d by abatement scrubbers using fresh water. A and B DFP Coolers (EU 044) and Swift Creek Mine Silos (EU 065) use wet cyclonic scrubbers with pond water as the scrubbing medi um to control particulate matter emissions. A and B DFP Plants (EU 003 and 038, respectively) include cross-flow packed wet scrubber with pond water as the scrubbing medium to control particulate matter emissions. X Train Dical Process (EU 004) rotary dryer include series of wet venturi and cyclonic scrubbers to control particulate matter emissions. The #2 Phosphate Rock Grinder (EU 001), X Trai n (EU 004) limestone handling, the DFP Feed Prep area (EU 042), DFP Product Silos (EU 062) in clude fabric filter ba ghouses designed to recover process or product raw materials a nd to control particul ate matter emissions. Swift Creek Mine Rock Dryer (EU 064) and Sw ift Creek Mine Silos (EU 065) include wet cyclonic scrubber to control particulate matter em issions. The Rock Dryer is fired primarily with natural gas.

Affected Pollutants The affected visibility-imp airing pollutants include NO X , PM 10 and SO 2
. Project Description and Status On February 1, 2007, PCS Phosphate submitted an applic ation to satisfy the BART requirements. With the subsequent additional information received on September 4, 2007, the Department deemed the application complete. The Department issued a draft air construction Permit No. 0470002-055-AC on November 13, 2007 with BART determinations. PCS P hosphate filed a petition fo r formal administrative hearing on January 23, 2008, after their request for extens ion of time to file an administrative hearing was denied by the Department. PCS Phosphate has been in negotiations with the De partment to resolve the outstanding issues related to the pr oposed BART. PCS Phosphate resolv ed all the outstanding issues with the Department in letters date d December 16, 2008 and January 20, 2009 to the Department. The Department will issue a revised draft BART construc tion permit which will incorporate the resolution of the outstanding issues as listed in PCS Phosphate’s December 16, 2008 and January 20, 2009 letters to the Department.

BART Determinations115 SAP Plants : At PCS Phosphate molten elemental sulfur is combusted to pr oduce the source of the gaseous SO 2 used to manufacture su lfuric acid. Thus SO 2 is a valuable important intermediate raw material for the process. The key emissions are SO 2 , sulfur trioxide (SO 3 ) and sulfuric acid mist (SAM or H 2 SO 4 ). All are key intermediate raw materials or products as well as pollutants. NO X and minor amounts of PM from the su lfur source (typically combustion of molten sulfur) will also exit through the SAP stack. MAP/DAP Plants : At the PCS Phosphate’s MAP/DAP plants; phosphoric acid is reacted with ammonia to produce fertilizer. In the basic am moniated phosphate pro cess, anhydrous ammonia is reacted with phosphoric acid. The slurry produ ced by the ammoniation is then sprayed onto a bed of solids in the granulator and additional amm onia (if required) is added to complete the acid neutralization and produced the final product grade. The resulting slurry/solids mixture is dried in a fo ssil fuel fired direct cont act rotary dryer. The dried solids are then scre ened to remove on size product. The product size material is cooled and then conveyed to storage. The over-sized and und er-sized materials are crushed and recirculated through the granulator. The key pollutants from the DAP/MAP plants are fluoride (particulate and gaseous), other PM in the form of DAP and MAP, and also NH 3. DFP Plants : At PCS Phosphate’s DFP Plants; phosphate rock is reacted with soda ash slurry and limestone. The resulting mixture is introduced into a natural gas fi red, fluidized bed reactor. As the feed is introduced into the r eactor a proportional amount of finished feed is discharged into a fluidized air cooler. In the cooler, material from the reactor and cyclone are cooled and transported to a holding bin. The product is screen ed and oversize material is sent to grinding roll mills for sizing. X Train (Dical Process) : In this process, limestone reacts with phosphoric acid in a pug mill. The resulting slurry is then dried in a direct contact rotary dryer fired with either natural gas or fuel oil. The rotary dryer is controlled by a system of wet vent uri and cyclonic scrubbers. The dried solids are then scre ened to remove on-size product. Th e product size material is conveyed to storage. The over-sized a nd under-sized materials are crushe d and recirculated to the pug mill. The initial modeling analysis indicated maximum visibility impacts of 3.07 dv during the period 2001-2003. The 8 th highest visibility impairment value fo r each of the three years is over the comparison threshold of 0.5 dv (range 1.63-1.77). The 22 nd highest impairment is 1.72 dv. In addition, there are 87, 93 and 91 days (2001-2003) pr edicted to have visibility impairment of over 0.5 dv. The primary focus of the dete rmination was on the effects of lower SO 2 , NO X and PM 10 emissions.

BART determinations are conducted on certain emission units constructed between 1962 and 1977, during which time top/down case-by-case de terminations for best available control technology (BACT) were not conducted. BACT is a requirement for projects subject to the rules for the prevention of significant deteriorat ion (PSD) promulgated by EPA in January 1975. However since 1977 and most recently since th e reference period 2001-2003 Line 1 has been subjected to a number of review s that resulted in the instal lation of control equipment or inclusion of federally enforceable em ission limits characteristic of BACT.116 The BART process is described in 40 Code of Federal Regulation (CFR) Part 51, Appendix Y as follows: Step 1 - Identify all available retrofit control technologies; Step 2 - Eliminate technically infeasible options; Step 3 - Evaluate control effectiven ess of remaining control technologies; Step 4 - Evaluate impacts a nd document the results; and Step 5 - Evaluate visibility impacts. According to 40 CFR 51, Appendix Y, paragraph IV.D.9, there are circumstances under which it is not necessary to comprehensively complete each step of the BART analysis. Under such circumstances, it is allowable to skip the remaining analyses in this section, including the visibility analysis in Step 5. Furthermor e, according to 40 CFR 51, Appendix Y, paragraph II.A.2, for an emissions unit which began operation within the 1962–1977 time window, but was modified (major) after August 7, 197 7 such that controls were inst alled, the State will take this into account during the review process and may fi nd that the level of controls already in place are consistent with BART. The applicant submitted the application on the basis of the original design, and the subsequent PSD/BACT review of similar emissi ons units at other facilities, like C.F. Industries SAPs B, C (2004-2007). The details of the previous review s and specific additional enforceable conditions are given in the technical document accompanying the BART permit. The Department finds that the levels of controls already in place are consistent with BART. The SAP emissions limits for SO 2 can be further reduced by slight modifications to the converter as explained in the technical document accompanying the BART permit. The following emission limits comprise BART and were incor porated into the final BART permit:

SAP Plants

SO 2 Emission Standards:

Source Sulfur Dioxide (SO 2 ) Emission Limits

SAP C 1 3.5 lb SO 2 /ton of 100% H 2 SO 4 and 335.4 lb/hr, 24-hour rolling average by CEMS 2

SAP D 3.5 lb SO 2 /ton of 100% H 2 SO 4 and 335.4 lb/hr, 24-hour rolling average by CEMS 2

1 The applicant is authorized to modify the c onverter tower to comply with the new BART

emission limit for SO 2
. 2 This BART determination requires an SO 2 CEMS to be used to demonstrate continuous compliance with the SO 2 emission standards and limitations specified above.

NO X Emission Standards : Source Nitrogen Oxides (NO X as NO 2 ) Emission Limits SAP C (EU 0.14 lb NO X /ton of 100% H 2 SO 4 . Testing using EPA Method 7E shall117 021) be done upon request by the Department. SAP D (EU 022) 0.14 lb NO X /ton of 100% H 2 SO 4 . Testing using EPA Method 7E shall be done upon request by the Department.

PM/PM 10 - Opacity (VE) Standard As determined by EPA Method 9, visible emissi ons from the two SAP shall not exceed the current 10% opacity limits based on a 6-minute average.

DAP/MAP Plants

PM/PM 10 Emissions Standard : Emissions of particulate matter (PM) and particul ate matter with a mean diameter of 10 microns or less (PM 10 ) shall not exceed the following limits base d on the average of three test runs as determined by EPA Methods 5.

Source Particulate (PM/PM 10 ) Emission Limits DAP/MAP Y (EU 008) 9.2 lb/hr by annual 3-hr stack test DAP/MAP Z (EU 032) 9.2 lb/hr by annual 3-hr stack test DAP/MAP Y – Storage Building (EU 010) 4.7 lb/hr by annual 3-hr stack test DAP/MAP – Shipping & Screening (EU 015) 1.46 lb/hr by annual 3-hr stack test

To control emissions of particulate matter (PM/PM 10 ), the permittee shall operate and maintain the existing process and pollution control tec hnology for each of the DAP/MAP plants. The applicant is authorized to make improvements to the process and pollutio n control equipment for the purposes of reliably meeting the new BART PM/PM 10 emission limits given above.

SO 2 and NO X Controls and Emission Standards :
To control SO 2 and NO X emissions from DAP/MAP Plants Y and Z, the permittee shall continue to use natural gas in the dryers with 1.5 weight percent sulfur fu el oil as backup. No numerical emission limit is established.

DFP Plants

PM/PM 10 Emissions Standard : Emissions of particulate matter (PM) and particul ate matter with a mean diameter of 10 microns or less (PM 10 ) shall not exceed the following limits base d on the average of three test runs as determined by EPA Methods 5.

Source Particulate (PM/PM 10 ) Emission Limits A DFP (EU 003) 14.1 lb/hr by annual 3-hr stack test B DFP (EU 038) 14.1 lb/hr by annual 3-hr stack test DFP Feed Prep (EU 042) 4.5 lb/hr by annual 3-hr stack test118 Source Particulate (PM/PM 10 ) Emission Limits A and B DFP Coolers (EU 044) 17.5 lb /hr by annual 3-hr stack test

To control emissions of particulate matter (PM/PM 10 ), the permittee shall operate and maintain the existing process and pollution control tec hnology for each of the DAP/MAP plants. The applicant is authorized to make improvements to the process and pollutio n control equipment for the purposes of reliably meeting the new BART PM/PM 10 emission limits given above.

SO 2 and NO X Controls and Emission Standards :
To control SO 2 and NO X emissions from A and B DFP Plants, the permittee shall continue to use natural gas in the dryers with 1.5 weight percent sulfur fuel oil as backup. No numerical emission limit is established.

X Train (Dical Process):

PM/PM 10 Emissions Standard : Emissions of particulate matter (PM) and particul ate matter with a mean diameter of 10 microns or less (PM 10 ) shall not exceed the following limits base d on the average of three test runs as determined by EPA Methods 5.

Source Particulate (PM/PM 10 ) Emission Limits X Train (EU 004) 18 lb/hr by annual 3-hr stack test

SO 2 and NO X Controls and Emission Standards :
To control SO 2 and NO X emissions from X Train, the permittee shall continue to use natural gas in the dryers with 1.5 weight per cent sulfur fuel oil as backup. No numerical emission limit is established.

Swift Creek Mine:

PM/PM 10 Emissions Standard : Emissions of particulate matter (PM) and particul ate matter with a mean diameter of 10 microns or less (PM 10 ) shall not exceed the following limits base d on the average of three test runs as determined by EPA Methods 5.

Source Particulate (PM/PM 10 ) Emission Limits Swift Creek Mine Rock Dryer (EU 064) 17.3 lb/hr by annual 3-hr stack test Swift Creek Mine Silos (EU 065) 6.27 lb/hr by annual 3-hr stack test

SO 2 and NO X Controls and Emission Standards :
To control SO 2 and NO X emissions from the Swift Creek Mine Rock Dryer, the permittee shall continue to use natural gas in the dryers with 1.5 weight percent sulfur fuel oil as backup. No numerical emission limit is established.

Shipping Units119 Particulate Matter Emissions Limit : A Visible Emissions Standa rd of 5% Opacity for each baghouse.

7.8.4.5 City of Tallahassee Purdom

BART-eligible Emissions Units The City of Tallahassee operates th e existing Sam O. Purdom Generatin g Station, which is an existing power plant located in Wakulla Coun ty at 667 Port Leon Drive in St. Ma rks, Florida. Unit 7 at this facility is a BART-eligible unit that is fired primarily with fuel oi l and natural gas. The unit began operation in 1966 and is a 621 MMBtu per hour steam generator paired with a nominal 44 MW steamelectrical generator.

Affected Pollutants The affected visibility-imp airing pollutants include NO X , PM 10 and SO 2 . EPA determined that CAIR satisfied the BART requirements for NO X and SO 2. Project Description and Status On January 31, 2007, the City of Tallahassee submitted an application to satisfy the BART requirements. The application was made complete with addi tional information provided on March 4, 2007. The Department issued a draft air construction Permit No. 120001-008-AC on May 24, 2007 requiring that Unit 7 permanently cease operation no later than December 31, 2013 to satisfy BART. No comments were received on the draft permit and a fina l permit was issued on September 11, 2007 requiring permanent shutdown of Unit 7.

BART Determination The following permit condition represents the BA RT determination for Unit 7 at the Purdom Generating Station:

Review Because the applicant requested a complete and permanent shut-down of the unit thus eliminating all emissions from the unit, no further BART analysis was required.120 

### 8.0 REASONABLE PROGRESS GOALS

The Regional Haze Rule at 40 CFR section 51.308(d )(1) requires States to establish reasonable progress goals for each Class I area within the st ate (expressed in deciviews) that provide for reasonable progress towards achievi ng natural visibility. In a ddition, USEPA 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 degrad ation in visibility for the least impaired days over the State Implemen tation Plan (SIP) period. The state must also provide an assessment of the number of years it woul d take to attain natural visibility conditions if improvement continues at the rate represen ted by the reasonab le progress goal.

In accordance with the requirements of 40 CFR §51.308(d)(1), this Regional Haze Implementation Plan establishes reasonable progre ss goals for each Class I area in Florida. To calculate the rate of progress represented by each reasonable progress goal, DEP compared baseline visibility conditions to natural visibility conditions in each class I area and determined the uniform rate of visibility improvement (in d eciviews) that would need to be maintained during each implementation period in order to attain natural visibil ity conditions by 2064. Through VISTAS, we modeled the expected visi bility improvements under existing federal and state regulations, BART determin ations in Florida and neighbor ing states, and any additional control measures found to be re asonable to implement in this review period. All of these elements are part of the long-term strategy. These modeling results were used to set the reasonable progress goals. Since not all expected reductions were included in the final modeling runs (due to timing of the runs to be complete in time for SIP submittals), reductions will likely be greater when all BART reductions and reas onable measures are taken into account.

The reasonable progress goals shown in Table 8.0 -1 reflect the results of the final VISTAS modeling analysis for each of the Florida Class I areas. Visibility improvement is expected to follow the nominal uniform rate of progress for achieving the long-term goal by 2064.

Table 8.0-1. Florida Reasonable Progress Goals

Visibility (dv) Baseline (00-04) Natura l Conditions 2018 Reasonable Progress Goals Class I Area Best 20% Worst 20% Best 20% Worst 20% Best 20% Worst 20%

Chassahowitzka 15.51 25.75 5.91 11.03 13.91 22.27 Everglades 11.69 22. 30 5.21 12.09 11.46 19.90 Saint Marks 14.37 26.31 5.39 11.67 12.80 23.01

Worst 20 Percent Days121 Class I Area Uniform Rate of Progress (dv/yr) 2018 Uniform Rate of Progress (dv) 2018 Predicted Rate of Progress (dv/yr) 2018 Predicted Visibility (dv)

Chassahowitzka 0.25 22.31 0.25 22.27 Everglades 0.17 19.92 0.17 19.90 Saint Marks 0.24 22.89 0.24 23.01

Best 20 Percent Days Class I Area Uniform Rate of Progress (dv/yr) 2018 Uniform Rate of Progress (dv) 2018 Predicted Rate of Progress (dv/yr) 2018 Predicted Visibility (dv)

Chassahowitzka 0.00 15.51 0.11 13.91 Everglades 0.00 11.69 0.02 11.46 Saint Marks 0.00 14.37 0.11 12.80



### 9.0 MONITORING STRATEGY

The State Implementation Plan is to be accomp anied by a strategy for monitoring regional haze visibility impairment. Specifically, the Regi onal Haze Rule states at 40 CFR 51.308(d)(4):

“(4) Monitoring strategy and other im plementation plan requirements. The State must submit with the implementation plan a mon itoring strategy for measuring, characterizing, and reporting of regional haze visibility impa irment that is representative of all mandatory Class I Federal areas within the State. This monitoring strategy must be coordinated with the monito ring strategy required in §51.305 for reasonably attributable visibility impairment. Compliance with this requirement may be met through participation in the IMPROVE network. The im plementation plan must also provide for the following: (i) The establishment of any additional m onitoring sites or equipment needed to assess whether reasonable progress goal s to address regional haze for all mandatory Class I Federal areas within the State are being achieved. (ii)-(vi) [Other implementation plan requi rements that pertain to reporting and use of monitoring data and an emission inventory.]”

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

1. Track the expected visibility improvement s resulting from emissions reductions identified in this SIP. 2. Better understand the atmospheric pr ocesses of importance to haze 3. Identify chemical species in the ambien t particulate matter and relate them to emissions from sources 4. Evaluate regional air quality models for haze and construct relative response factors (RRFs) for using those models122 The primary monitoring network for regional haze , both nationwide and in Florida, is the IMPROVE network. Given that IMPROVE monitoring data from 2000-2004 serve as the baseline for the regional haze program, the fu ture regional haze monitoring strategy must necessarily be based on, or di rectly comparable to, IMPROV E. The IMPROVE measurements provide the only long-term record available for tracking visibility improvement or degradation and therefore Florida intends to rely on the IMPROVE network fo r complying with the regional haze monitoring requirement in the Regional Haze Rule.

There are currently 3 IMPROVE site s in the State. In addition, as Table 1 shows, an IMPROVE site just across the border in Georgia serves as the monitoring site for both the Okefenokee and Wolf Island Class 1 areas, both of which lie in Georgia.

Class I Area IMPROVE Site Designation Everglades National Park (FL) EVER Chassahowitzka Wilderness Area (FL) CHAS St. Marks Wilderness Area(FL) SAMA Okefenokee Wilderness Area (GA) OKEF Wolf Island Wildlife Refuge (GA) OKEF

Table 1. Class I areas in Florida and IMPROV E monitors that represent conditions at each of them.

In addition, DEP and the local ai r agencies in the state operate a fairly comprehensive PM2.5 network of the filter based Federal referen ce method monitors, Federal equivalent method continuous monitors (BAMs) and continuous ma ss monitors (TEOMs), filter based speciated monitors. A map of the various locations ar ound the State is incl uded in Figure 9.1. These PM2.5 measurements help the DEP characterize air pollution levels in areas across the state, and therefore aid in the analysis of visibility improvement in and near the Class I areas.123 Figure 9. 1 PM2.5 Monitoring Network in Florida

The IMPROVE measurements are central to Florid a’s regional haze monito ring strategy, and it is difficult to visualize how the objectives liste d above could be met without the monitoring provided by IMPROVE. Any reduction in the scope of the IMPROVE network in Florida would jeopardize the State’s ability to demonstrate reas onable progress toward visibility improvement in some of its Class I areas.

Data produced by the IMPROVE monitoring networ k will be used nearly continuously for preparing the 5-year progress reports and the 10- year SIP revisions, each of which relies on analysis of the preceding five years of data . Consequently, the monitoring data from the IMPROVE sites needs to be read ily accessible and to be kept up to date. Presumably, IMPROVE will continue to process information from its ow n measurements at about the same pace and with the same attention to quality as it has shown in the recent past.



### 10.0 INTERSTATE CONSULTATION

The VISTAS states have jointly developed the technical analyses to define the visibility improvement by 2018 under existing federal and state regulations compared to the uniform rate124 of progress, SO 2 Areas of Influence for each Class I area, and methods to prioritize contributions from individual sources within the Areas of Influence. The states collectively accept the conclusions of these analyses.

In December 2006 the VISTAS State Air Directors he ld their first formal consultation meeting to review the Base G mode ling results and the SO 2 Areas of Influence analyses. The Air Directors agreed to look at reasonable control m easures for sources on the lists for the SO 2 Areas of Influence. Each state would c onsider sources within their state and would identify sources in neighboring states that they w ould like to have that neighbori ng state consider. States acknowledged that the review pr ocess would differ among states since some Class I areas are projected to see visibility improvements near th e uniform rate of progress while most Class I areas are projected to have greater improve ments than uniform rate of progress.

In April 2007, representatives of all the Regional Planning Or ganizations (RPO), including VISTAS, met with the federal land managers (F LM) in Lakewood, Colorado to brief them on the technical work completed for the regional haze SIPs. The FLM’s and the RPO’s consulted on what needed to be in the regional haze SIPs. A summary of the meeting is located in Exhibit 3.

In May 2007, the VISTAS State Air Director s met for their second formal interstate consultation. States shared their lists of sour ces in their state and neighboring states for each Class I area. They also shared their criteria for listing sources and their plans for further interstate co nsultation.

In June 2007, the VISTAS states met with th e Federal Land Managers specifically for the purpose of consulting on the state’ s visibility plans. A summary of this meeting is included in Exhibit 3.

The DEP has evaluated the impact of Florida so urces on Class I areas in neighboring states. Both Georgia and Louisiana have class 1 areas within 300 kilometers of Florida sources. Considerable progress in improving visibility is expected in each of these Class I areas from existing regulatory programs (e.g. CAIR, etc.). The DEP has consulted with the responsible states regarding its impacts on these Class I areas and the strategy we have for further reducing Florida source’s impact on visibili ty in these areas. Consultation reports and letters may be found in Exhibit 3.



### 11.0 COMPREHENSIVE PERIOD IC IMPLEMENTATION PLAN
REVISIONS

40 CFR section 51.308(f) requires the DEP to revise its regional haze implementation plan and submit a plan revision to USEPA by July 31, 2018, a nd every ten years therea fter. In accordance with the requirements listed in Section 51.308(f) of the federal rule fo r regional haze, Florida commits to revising and submitting this regiona l haze implementation plan by July 31, 2018 and every ten years thereafter.125 In addition, Section 51.308(g) re quires periodic reports ev aluating progress towards the reasonable progress goals established for each mandatory Class I area. In accordance with the requirements listed in Section 51.308 (g) of the federal rule for re gional haze, the DEP commits to submitting a report on reasonable progress to USEPA every five years following the initial submittal of the SIP. The report will be in the form of a SIP revision. The reasonable progress report will evaluate the progress made towards th e reasonable progress go al for each mandatory Class I area located within Florida and in each mandatory Class I area located outside Florida which may be affected by emissions from within Florida

.
The requirements listed in 51.308( g) include the following: 1. Description of the stat us of implementation; 2. Summary of emission reductions achieved thus far, including especially the status of implementation of the CAIR compliance plans for EGUs compared to the control assumed in the modeling. [The DEP rec ognizes that the 2018 projections of EGU controls from the IPM runs represent one so lution to how the CAIR requirements will be met. By the time of the first periodic re port, the DEP anticipates that the actual compliance strategy for the various utility co mpanies will be much more defined. An assessment of those actual compliance plans wi ll be done for the first periodic report.] 3. Assessment of changes in visibility conditions at each class I area (cur rent vs. baseline), expressed as 5-year averages of annual va lues for 20 percent best and worst days; 4. Analysis of emission changes over the 5-year period, identified by s ource or activity; 5. Analysis of any siginificant changes in or out of the Stat e which have impeded progress; 6. Assessment of the sufficiency of the implementation plan to meet RPGs; 7. Review and any modifications to our visibility monitoring plan. All requirements listed in 51.308(g) shall be ad dressed in the SIP revision for reasonable
progress.

DEP also commits to ongoing consultation w ith the FLMs throughout the implementation process, including annual discus sion of the implementation process and the most recent IMPROVE monitoring data and VIEWS data.

There are several technical improvements that ar e recommended in the emissions inventory and air quality models that are used to support re gulatory decisions for regional haze. These improvements recommended, as funding is available, to support the next long term strategy. The following is an overall summary; Appendix K contai ns a fuller discussion of possible technical improvements.

First and foremost, continued improvements are n eeded in the integrated one-atmosphere air quality models that are used to project air qua lity responses to emissi ons reductions. As our understanding of partitioning between gaseous a nd aerosol phases improves, this understanding needs to be reflected in the models. Improvement s can also be made in how the models handle individual pollutants. Sulfate performance for the CMAQ regional air quality model is good overall. However sulfate deposition is frequently overestimated in the models, particularly in the summer months. At the coastal sites, when wi nds are blowing from the Gulf of Mexico or Atlantic Ocean, CMAQ underestimates measured sulfate at the monitors. CMAQ’s processes also should be reviewed for sulfate formation over water. Nitrate is over estimated by the model126 in the winter and underestimated in the summ er, although summer monitored values of nitrate are very low. Additional improvements in se asonal allocation of ammonia emissions would improve model estimates of ammonium nitrat e formation. Organic carbon is generally underestimated in the summer months. Improvement s are needed in the ch aracterization of both primary carbon emissions and formati on of secondary organic carbon.

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



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

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

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

1) DEP determines that the existing SIP requires no further substantive revision in order to achieve established goals. DEP provides to th e Administrator a negative declaration that further revision of the SIP is not needed at this time. 2) DEP determines that the exis ting SIP may be inadequate to ensure reasonable progress due to emissions from other states which pa rticipated in the regi onal planning process. DEP provides notification to the Administrator and the states th at participated in regional planning. DEP collaborates with states through the regional planning process to address the SIP’s deficiencies. 3) DEP determines that the curre nt SIP may be inadequate to ensure reasonable progress due to emissions from another country. DE P provides notification, along with available information, to the Administrator. 4) DEP determines that the existing SIP is in adequate to ensure reasonable progress due to emissions within the state. DEP will revise its SIP to address the plan’s deficiencies within one year.