STATE OF FLORIDA

DEPARTMENT OF ENVIRONMENTAL PROTECTION

PROPOSED REVISION TO STATE IMPLEMENTATION PLAN

NUMBER 2010-01

Regional Haze Plan

Regional Haze Plan for Florida Class I Areas

March 19, 2010

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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 implementation plan pursuant to §§107(d)(3)(D) and 

(E) of the Clean Air Act, as amended.
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TABLE OF CONTENTS

EXECUTIVE SUMMARY ........................................................................................................... vi

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

1.1 What is regional haze?.......................................................................................................... 1

1.2 What are the requirements under the Clean Air Act for addressing regional haze?............. 1

1.3 General overview of regional haze SIP requirements .......................................................... 2

1.4 Class I areas in and near Florida........................................................................................... 4

1.5 State and Federal Land Manager (FLM) coordination ......................................................... 5

2.0 ASSESSMENT OF BASELINE AND CURRENT CONDITIONS AND ESTIMATE OF 

NATURAL BACKGROUND CONDITIONS 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 version) Baseline Emissions Inventory for 

FL..................................................................................................................................... 25

4.1.7 Model Performance Improvements through Emissions Inventory Improvements .... 25

4.2 Assessment of Relative Contributions from Specific Pollutants and Sources 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 ................................................................ 30
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5.3 Selection of the Modeling Year.......................................................................................... 31

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 visibility 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 realized between the baseline and 

2018? ................................................................................................................................ 48

d.What is the process for identifying additional emission controls or reductions that 

represent reasonable progress for 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 Florida 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) Inventory Compared to the 

Uniform Rate of Progress Glidepaths for FL Class I Areas............................................. 54

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

VISTAS Class I areas....................................................................................................... 73

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

Geographic Areas............................................................................................................. 75

7.5 Relative Contributions to Visibility Impairment: 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 SO2 Areas of Influence............................................................................................... 80

7.5.4 Emissions Sources within SO2 Areas of Influence .................................................. 81
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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 Reasonable Progress Control Technology Rule Development............................................... 92

7.8 Best Available Retrofit Technology (BART) 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 Subject to BART in Florida................................................. 103

7.8.4 Determination of BART Requirements 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 PROGRESS GOALS ................................................................................ 120

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

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

11.0 COMPREHENSIVE PERIODIC IMPLEMENTATION PLAN REVISIONS ................. 124

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

Exhibit 1: Rules Adopted by the State

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

Exhibit 3: Consultation Materials

LIST OF APPENDICES

Appendix A: VISTAS Memorandum 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 Meteorological 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 Sensitivity Analysis and Source Apportionment

Appendix I: Data Access

Appendix J: Analysis of Consent 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 Plan
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EXECUTIVE SUMMARY

Pursuant to the Clean Air Act requirements contained in sections 169A and 169B, and the 

subsequent implementing regulations 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 SIP revision represents commitments and actions 

taken by the state affecting reasonable progress during the ten-year period from 2008 to 2018

towards the goal of attaining natural visibility conditions by 2064. It also provides for the 

implementation of Best Available Retrofit Technology (BART).

To develop this proposed SIP revision, the state has relied heavily on the work of the Southeast 

regional planning group VISTAS (Visibility Improvement States and 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 Agency (EPA), Region 4 states plus Virginia and 

West Virginia. One staff person is directly connected to VISTAS – the technical coordinator. 

All other personnel resources are within the state programs. Funding for VISTAS was through 

grants from the U.S. EPA. 

The data and analysis necessary to meet the requirements of the federal regional haze regulations 

are considerable. The 10 states, through VISTAS, completed most of the technical 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 land managers (or their 

representatives) from the National Park Service, the Fish and Wildlife Service, and the Forest 

Service. Private stakeholders included industry representatives with sources within the 10-state 

planning region. These workgroups discussed and reviewed the work completed by the 

contractors used by VISTAS. These data and analyses 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 also contains the administrative and technical 

documentation supporting these rules, commitments, and plans. The documents to be 

incorporated into the SIP include 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 primary 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 the Best Available Retrofit Technology (BART) 

requirements. The state rule adopts the federal 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 application submission dates and BART compliance dates. The rule also 

makes certain decisions required of the federal regulations such as allowing BART to equal 
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CAIR (Clean Air Interstate Rule) for electric generating units and excluding ammonia and 

volatile organic compounds from BART review.

The department has evaluated 46 facilities having 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. Rule 62-296.341, F.A.C. – This rule supports the reasonable progress requirements of the 

federal regulations, incorporating a construction permitting process for applicable sources to 

complete the statutorial ―four-factor‖ analysis. In developing the applicability criteria for this 

rule, the department recognized that significant progress towards the goal of achieving natural 

visibility conditions in federal Class I areas is expected to be made with existing regulatory 

programs. In fact, analysis provided by VISTAS indicates that all three Florida Class I areas 

approximately follow the uniform rate of progress (i.e., the nominal rate of progress benchmark) 

by 2018. Nevertheless, the federal rule requires the state to evaluate the potential for additional 

reductions in visibility-affecting pollutants considering a set of four prescribed factors. Based on 

VISTAS analyses, the department has determined that focusing on potential sulfur dioxide (SO2) 

emissions reductions from large industrial sources provides the best opportunity for significant 

additional visibility improvement in Class I areas. The state rule requires that certain sources of 

SO2 emissions (32 emissions units at 15 major facilities) meet a case-by-case Reasonable 

Progress Control Technology (RPCT) emission limitation based on an evaluation of the four 

factors. These limitations would be required to be in effect by 2018.

3. Reasonable Progress Goals – The state is required to set reasonable progress goals in units of 

deciviews applicable for 2018. These goals represent 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 for the 20 percent worst days and one for the 20 

percent best days. 

Visibility (dv) Baseline (00-04) Natural 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 response to the expected emissions reductions of 

visibility-impairing pollutants using air quality models. As a result, 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:
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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 that 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 predicted 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 periods will be dependent on the actual progress 

made in the previous planning periods. 

5. Long-Term Strategy for Regional Haze – The state 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 existing rules results in visibility improvement in 

all the Class I areas equal to the nominal uniform rate of progress toward the 2064 goal of natural 

conditions. But the long-term strategy also includes new reductions of visibility precursor 

emissions resulting from the Best Available Retrofit Technology (BART) and reasonable 

progress (Florida‘s Reasonable Progress Control Technology (RPCT) rule) requirements of the 

regional haze rule. Initial compliance with these rules and programs will occur at various times 

throughout the planning period, but no later than the end of the planning period in 2018. 

Through the Southeast regional planning organization VISTAS, state-of-the-art air quality 

modeling was completed to analyze the regional, national, and global contributions 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 associated data were used to validate model performance. 

Projected emissions were developed for 2018, considering growth and known or estimated 

emissions changes due to existing regulations. Substantial analysis was completed to determine 
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visibility sensitivity to specific pollutant reductions, and to parse-out the source-sector 

contributions. 

The conclusion of this analysis was that the long-term strategy for providing for additional 

visibility improvement (beyond existing rules and programs) should focus on reducing SO2

emissions. As a result, Florida‘s RPCT rule addresses SO2 emissions from certain large 

stationary sources having significant visibility impact. The determination of any required 

reductions will be conducted using the four statutory factors listed in the regional haze rule. Any 

resulting federally enforceable emission limitations will be complied with no later than 2018. 

6. Commitments – The state is committing to complete a mid-point review of the long-range 

plan as required in the regional haze rule (§51.308(g)). The time of the review will be 

coordinated with the other VISTAS states to take advantage of common analyses, but will be no

later than five years after submission of the initial implementation plan. We will review the 

progress of the projected emissions changes to judge the necessity of making any revisions to the 

plan. The state is also committing to complete comprehensive periodic revisions of the 

implementation plan for regional haze. The first revision is due to EPA by July 31, 2018 and 

every ten years thereafter.
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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 ―Class I‖ areas). Regional haze is 

caused by sources and activities emitting fine particles and their precursors. Those emissions are 

often transported over large regions. 

Particles affect visibility through the scattering and absorption of light, and fine particles –

particles similar in size to the wavelength of light – 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 (SO2) and nitrogen oxides (NOx). 

Reducing fine particles in the atmosphere is generally considered to be an effective method of 

reducing regional haze, and thus improving visibility. Fine particles also adversely impact 

human health, especially respiratory and cardiovascular systems. The United States 

Environmental Protection Agency (USEPA) has set national ambient air quality standards for 

daily and annual levels of fine particles 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 contributors to PM2.5 and visibility impairment 

include mobile source emissions, area sources, fires, and wind blown dust.

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

In Section 169A of the 1977 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 air pollution.‖ Congress adopted the visibility 

provisions to protect visibility in 156 national 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 visibility impairment that is ―reasonably 

attributable‖ to a single source or small group of sources. These regulations represented the first 

phase in addressing visibility impairment and deferred action on regional haze that emanates 

from a variety of sources until monitoring, modeling and scientific knowledge about the 

relationships between pollutants and visibility impairment improved. 

In the 1990 Amendments to the CAA, Congress added section 169B and called on USEPA to 

issue regional haze rules. The Regional Haze rule (RHR) that USEPA promulgated on July 1, 

1999 (64 FR 35713), revised the existing visibility regulations in order to integrate provisions 

addressing regional haze impairment and establish a comprehensive visibility protection program 

for Class I Federal areas. States are required to submit state implementation plans (SIPs) to 

USEPA that set out each states‘ plan for complying with the regional haze rule, including 

consultation and coordination with other states and with federal land managers. The timing of 
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the SIP submittal is tied to USEPA‘s promulgation of designations for the National Ambient Air 

Quality Standard (NAAQS) for fine particulate matter (PM2.5) at 40 CFR Part 81. States must 

submit a regional haze implementation plan to USEPA within three years after the date of 

designation. Because USEPA promulgated designation 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 visibility effects of various pollution sources over 

a wide geographic region. This wide reaching pollution net meant that many states – even those 

without Class I Areas – would be required to participate in haze reduction efforts. USEPA 

designated five Regional Planning Organizations (RPO) to assist with the coordination and 

cooperation needed to address 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 natural 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 establish baseline visibility 
3

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

uniform progress between baseline and background conditions. The uniform rate of progress is 

also known as the ―glidepath.‖ 

The RHR then requires States to establish reasonable progress goals (RPGs), expressed in 

deciviews, for visibility improvement at each affected Class I area covering each 

(approximately) 10-year period until 2064. The goals must provide for reasonable progress 

towards achieving natural visibility conditions, provide for improvement in visibility for the 

most impaired days over the period of the implementation plan, and ensure no degradation in 

visibility for the least impaired days over the same period (see §51.308(d)(1)). 

In order to ensure that visibility goals are properly met and set, 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 impaired days. SIPs must also contain supporting 

documentation for all required analyses used to calculate the degree of visibility impairment 

under natural visibility conditions for the most impaired and least impaired days (see 

§51.308(d)(2)). In addition, states must include a monitoring strategy for measuring, 

characterizing, and reporting of regional haze visibility impairment that is representative of all 

mandatory Class I Federal areas within the state (see §51.308(d)(4)).

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 present until 2018. The long-term strategy 

includes enforceable emissions limitations, compliance 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 factors such as the abovementioned 

ongoing control programs, measures to mitigate construction activities, source retirement and 

replacement schedules, smoke management programs for agriculture and forestry, and 

enforceability of specific measures (see §51.308(d)(3)).

In addition, a specific component of each state‘s first long-term strategy is dictated by the 

specific best available retrofit technology (BART) requirements in 40 CFR 51.308(e) of the 

RHR. The RHR at §51.308(e) requires states to include a determination of BART for each 

BART-eligible source in the State that emits any air pollutant, which may reasonably be 

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

Federal area. Clean Air Act section 169A(b) defines BART-eligible 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 determine BART according to five factors set out in 

section 169A(g)(7) of the Clean Air Act. Emission limitations representing BART and schedules 

for compliance with BART for each source subject to BART must be included in the long-term 

strategy.

State Implementation Plans for the first review 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 reasonable progress goals every 

5 years to assure that installed emissions controls are on track with emissions reduction forecasts 
4

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, then 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 borders: Everglades National Park, Chassahowitzka 

Wilderness Area, St. Marks Wilderness Area, and 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 air pollution sources are 

within 300 kilometers of three other Class I areas located in other states, for which consideration 

of visibility impact on these out-of-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 Division of Air Resource Management (DARM) in 

the Florida Department of Environmental Protection (FLDEP) is responsible for developing the 

Regional Haze SIP. This SIP establishes reasonable progress goals for visibility improvement at 

each of the Florida Class I areas, 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 areas
5

In developing this SIP, we have also considered 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 VISTAS, the southeastern states 

have worked together to assess state-by-state contributions to visibility impairment in specific 

class I areas, including those in Florida and those affected by emissions from Florida. This 

technical work is discussed further 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 development and review of implementation plan 

revisions and 5-year progress reports, and on the implementation of other programs having the 

potential to contribute to impairment of visibility in any mandatory Class I Federal area within 

the State. DEP commits to ongoing consultation with the FLMs throughout the implementation 

process, including annual discussion of the implementation process and the most recent 

IMPROVE monitoring data and VIEWS data.

Successful implementation of a regional haze program will involve long-term regional 

coordination among States. VISTAS was formed in 2001 to address regional haze and visibility 

problems in the southeastern United States. Jurisdictions represented by VISTAS members 

include the Eastern Band of Cherokee Indians; the States of Alabama, Florida, Georgia, 

Kentucky, Mississippi, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia; 

and the local air pollution control programs located in these States. A copy of the VISTAS 

Memorandum of Agreement and Bylaws is enclosed as Appendix A.

The objectives of the VISTAS project are to establish natural background visibility conditions 

across the mandatory Class I Federal areas, identify current visibility impairment levels, analyze 

emission control levels that will achieve interim visibility goals, and provide adequate 

documentation to member agencies so that they can develop their regional haze State/Tribal 

Implementation Plans (SIP/TIP). Figure 1.5-1 shows the 18 mandatory Class I Federal areas in 

the VISTAS Region, where visibility is an important value. Figure 1.5-2 lists these Class I areas.
6

Figure 1.5-1. Class I Areas in the VISTAS Region
7

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-FS

40 CFR §81.407 Florida Chassahowitzka Wilderness 23,360 USDIFWS

Everglades National Park 1,397,429 USDI-NPS

St. Marks Wilderness 17,745 USDIFWS

40 CFR §81.408 Georgia Cohotta Wilderness 33,776 USDA-FS

Okefenokee Wilderness 343,850 USDIFWS

Wolf 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-FS

Swanquarter 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 characterize regional haze in the southeastern 

United States. The report includes a review of the science and situation, calculation of initial 
8

baseline visibility, review of monitoring data/data gaps, and recommendations for additional 

monitoring, initial emission inventory characterization and projections, and compliance with 

existing control programs. Source contributions to VISTAS mandatory 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 BACKGROUND 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 naturally occurring phenomena that reduce visibility as 

measured in terms of light extinction, visual range, contrast, or coloration.‖ The Regional Haze 

SIPs must contain measures that make ―reasonable progress‖ toward this goal by reducing 

anthropogenic emissions that cause haze. 

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

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

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

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

inverse megameters (Mm-1). Light extinction affects the clarity and color of objects being 

viewed.

The measure used by the regional haze rule is the deciview (dv). Deciviews are calculated 

directly from light extinction using a logarithmic scale. The deciview is a useful measure for 

tracking progress in improving visibility, because each deciview change is an equal incremental 

change in visibility perceived by the human eye. Most people can detect a change in visibility at 

one deciview.

For each Class I area, there are three metrics of visibility that are part of the determination of 

reasonable progress:

1) natural conditions, 

2) baseline conditions, and 

3) current conditions. 

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

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

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

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

―Natural‖ visibility is determined by estimating the natural concentrations of visibility pollutants 

and then calculating total light extinction. ―Baseline‖ visibility is the starting point for the 
9

improvement of visibility conditions. It is the average of the Interagency Monitoring of 

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

equivalent to ―current‖ visibility conditions for this initial review period. The comparison of 

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

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

for Class I areas within its borders in consultation 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 impairment 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). Natural background visibility for the 20 percent worst 

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

normally distributed and the 90th percentile of the annual distribution represents natural 

background visibility on the 20 percent worst days.

In the 2003 guidance, USEPA also provided that states may use a ―refined approach‖ to estimate 

the values that characterize the natural visibility conditions of the Class I areas. The purpose of 

such a refinement would be to provide more accurate estimates with changes to the extinction 

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

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

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

In 2005 the IMPROVE Steering Committee made recommendations for a refined equation that 

modifies the terms of the original equation to account for the most recent data. The choice 

between use of the old or the new equation for calculating the visibility metrics for each Class I 

area is made by the state in which the Class I area is located.

The new IMPROVE equation accounts for the effect of particle size distribution on light 

extinction efficiency of sulfate, nitrate, and organic carbon. The mass multiplier for organic 

carbon (particulate organic matter) is increased from 1.4 to 1.8. New terms are added to the 

equation to account for light extinction by sea salt and light absorption by gaseous nitrogen 

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

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

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

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

change between the original and new IMPROVE equation.

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

description because it takes into account the most recent review of the science and because it is 
10

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 sites at the three Class I areas in Florida. A five year average 

(2000 to 2004) was calculated for each of the 20 percent worst and 20 percent best visibility days 

in accordance with 40 CFR 51.308(d)(2) and 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 methods, 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 records complete. The light extinction and deciview 

visibility values for the 20 percent worst and 20 percent best visibility days at the Class I areas 

are based on data and calculations included in Appendix B of this SIP. 

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

Table 2.3-1 presents estimated natural background and baseline visibility metrics for Florida

Class I areas. Note that Florida is not considering international emissions to be a component of 

natural background. Baseline visibility on the 20 percent 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). Visibility in the northern Florida Class I areas 

is generally better than the other Southeastern Class 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 Visibility 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, (NH4)2SO4, is the most important contributor to visibility impairment and 

fine particle mass on the 20 percent worst and 20 percent best visibility days at all the Florida

Class I areas. Sulfate levels on the 20 percent worst days at Chassahowitzka and St. Marks

accounts for 60-70 percent of the visibility impairment. At the Everglades, sulfates account for 

30-60 percent of the impairment. Across the VISTAS region, sulfate levels are higher at the 

Southern Appalachian sites than at the coastal sites of which Florida is grouped (Figure 2.4-1). 

And the sulfate levels in the Everglades are significantly 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 relatively 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 areas 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 areas using New IMPROVE equation

VISTAS coastal VISTAS inland Neighboring non-VISTAS

0

50

100

150

200

250

300 Swanquarter, NC Cae Romain, SC Okefenokee, GA Everglades, FL Chassahowitzka, FL St. Marks, FL Dolly Sods, WV Shenandoah, VA James River Face, VA Linville Gorge, NC Shining Rock, NC Great Smoky Mtns., TN Cohutta, GA Sipsey, AL Mammoth Cave, KY Brigantine, NJ Breton, LA Mingo, MO Hercules Glade, MO Upper Buffalo, AR Caney Creek, AR Extinction (Mm 

-1

)

Sea Salt

Coarse

Soil

EC

POM

NH4NO3

(NH4)2SO4

Rayleigh

VISTAS coastal VISTAS inland Neighboring non-VISTAS

0

50

100

150

200

250

300 Swanquarter, NC Cae Romain, SC Okefenokee, GA Everglades, FL Chassahowitzka, FL St. Marks, FL Dolly Sods, WV Shenandoah, VA James River Face, VA Linville Gorge, NC Shining Rock, NC Great Smoky Mtns., TN Cohutta, GA Sipsey, AL Mammoth Cave, KY Brigantine, NJ Breton, LA Mingo, MO Hercules Glade, MO Upper Buffalo, AR Caney Creek, AR Extinction (Mm 

-1

)

Sea Salt

Coarse

Soil

EC

POM

NH4NO3

(NH4)2SO4

Rayleigh

0

10

20

30

40

50

60 Extinction (Mm-1

)

Sea Salt

CM

Soil

EC

POM

NH4NO3

(NH4)2SO4

Rayleigh

VISTAS coastal VISTAS inland Neighboring non-VISTAS Swanquarter, NC Cae Romain, SC Okefenokee, GA Everglades, FL Chassahowitzka, FL St. Marks, FL Dolly Sods, WV Shenandoah, VA James River Face, VA Linville Gorge, NC Shining Rock, NC Great Smoky Mtns., TN Cohutta, GA Sipsey, AL Mammoth Cave, KY Brigantine, NJ Breton, LA Mingo, MO Hercules Glade, MO Upper Buffalo, AR Caney Creek, AR

0

10

20

30

40

50

60 Extinction (Mm-1

)

Sea Salt

CM

Soil

EC

POM

NH4NO3

(NH4)2SO4

Rayleigh

VISTAS coastal VISTAS inland Neighboring non-VISTAS Swanquarter, NC Cae Romain, SC Okefenokee, GA Everglades, FL Chassahowitzka, FL St. Marks, FL Dolly Sods, WV Shenandoah, VA James River Face, VA Linville Gorge, NC Shining Rock, NC Great Smoky Mtns., TN Cohutta, GA Sipsey, AL Mammoth Cave, KY Brigantine, NJ Breton, LA Mingo, MO Hercules Glade, MO Upper Buffalo, AR Caney Creek, AR
13

Sulfate particles are formed in the atmosphere from sulfur dioxide emissions. Sulfate particles 

occur as hydrogen sulfate, H2(SO4), ammonium bisulfate HNH4SO4, and ammonium sulfate, 

(NH4)2SO4, depending on the availability of ammonia, NH3, in the atmosphere. 

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

and light extinction on the 20 percent haziest and the 20 percent clearest days at the Florida Class 

I areas. Elevated levels of POM and Elemental 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 associated 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 and other mountain Class I areas 

the majority of carbon is from either biogenic emissions from vegetation or from wood burning. 

Wood burning is more important in the fall, winter, and early spring months. Biogenic 

emissions, chiefly emissions of volatile organics (VOC) from vegetation, peak in spring and 

summer. Biogenic carbon emissions at Cape Romain, SC, a coastal site similar to Swanquarter, 

were lower than emissions at the forested mountain sites. Carbon from gasoline and diesel 

engines is a relatively small contribution at the rural sites. At Millbrook, carbon from fossil fuel 

combustion is a larger percentage contribution than at the rural sites, but still less than 50 percent 

of total carbon measured. These results suggest that controlling anthropogenic sources of carbon 

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

of carbon will likely be more effective to reduce levels of PM2.5 in urban areas. 

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

oxides. In the VISTAS region, nitrate formation 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 lower temperatures; at elevated temperatures nitric 

acid remains in gaseous form. For this reason, particle nitrate levels are very low in the summer 

and a minor contributor to visibility impairment. Particle nitrate concentrations are higher on 

winter days and are more important for the coastal sites where 20 percent worst days can occur 

on winter days. Nitrogen oxides are emitted by fossil fuel combustion by point, area, on road, 

and non road sources. Modeling data (see section 7) indicate that in the VISTAS region 

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

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

than controls of nitrogen oxides. 

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

include agriculture, prescribed, wildland, and wild 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 effective 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 fine soil can be attributed to Saharan dust episodes, particularly 

at Everglades. No control strategies are indicated for fine soil. 
14

Sea salt, NaCl, is observed at the coastal sites. Sea salt contributions to visibility impairment are 

most important on the 20 percent clearest days when sulfate and POM levels are low. Sea salt 

levels do not contribute significantly to visibility on the 20 percent 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 source of sodium ion that cannot be distinguished 

from sodium chloride. Our best understanding to date suggests that using Cl- may underestimate 

sea salt levels by as much as a factor of two. This uncertainty has greatest implications for 

calculating sea salt levels under natural visibility conditions. 

Coarse particle mass (particles with diameters between 2.5 and 10 microns) has a relatively 

small contribution to visibility impairment because the light extinction efficiency of coarse mass 

is very low compared to the extinction efficiency for sulfate, nitrate, and carbon. 

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

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

may be positive or negative and is attributable to water and/or the factors used to calculate 

molecular weights of the other components. 

 

The New IMPROVE equation generally results in higher calculated light extinction on days with 

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

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

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

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

particulate organic matter in the new IMPROVE equation increases light extinction for current 

conditions more than for natural conditions. The new algorithm does not change the conclusion 

that in the VISTAS region and in Florida, the most effective means to improve visibility is to 

reduce sulfate concentrations.

PM2.5 trends in urban and Class I areas: IMPROVE data were compared to monitoring data 

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

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

close proximity to the Class I areas in the southeastern US, including Atlanta, GA; Birmingham, 

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

sulfate concentrations are comparable between urban and nearby Class I areas, while organic 

carbon, elemental carbon, and nitrate concentration are generally higher in urban areas than the 

Class I areas. These results suggest that sulfate is widely distributed regionally while urban areas 

see an additional incremental pollutant loading from local emissions sources. 

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

Analyses were used to characterize the relationship between meteorological conditions and 

visibility conditions at the Class I areas. Days were assigned to one of five visibility classes 

ranging from poor to good visibility. Days were then assigned to bins based on meteorological 

conditions. For the central and northern Florida Class I areas poor visibility days were most 
15

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 visibility is usually a multi-day event 

with building pollution levels. Precipitation was not a good predictor of visibility condition. In 

South Florida (Everglades), poor visibility is not differentiated by high temperatures 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 Appendix 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 NATURAL 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 conditions for each mandatory Class I Federal area 

within the State as well as for each mandatory Class I Federal area located 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 straight graphical line drawn from 

the baseline level of visibility impairment for 2000-2004 to the level representing no manmade 

impairment in 2064.

Each state must set goals for each Class I area that provide for reasonable progress towards 

achieving natural visibility conditions by 2064. Section 51.308(d)(1) of the regional haze rule 

requires that reasonable progress 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 developed in accordance with USEPA guidance and used 

data collected from the IMPROVE monitoring sites as described in section 2 above. The 

glidepath is one of the indicators used in setting reasonable 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 Area, St. Marks National Wilderness Area, 

Okefenokee National Wilderness Area, and Breton National Wilderness Area, assuming uniform 

rate of progress toward regional haze goals. Wolf Island National Wilderness Area does not 

have an IMPROVE monitor and the Okefenokee site acts as its surrogate. Natural background 

visibility at all five sites 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 Southeast. Of these five areas, 

Okefenokee has the worst current visibility and has greatest improvement to make to achieve 

natural visibility conditions (Table 3.1.1). The Everglades has the best current visibility 
16

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 Current Visibility for Southeastern Class I Areas
17

Figure 3.1-1. Uniform Rate of Progress Glidepath 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

Year

Haziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation
18

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

Year

Haziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation
19

Figure 3.1-3. Uniform Rate of Progress Glidepath 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

Year

Haziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation
20

Figure 3.1-4. Uniform Rate of Progress Glidepath for 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

Year

Haziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation
21

Figure 3.1-5. Uniform Rate of Progress Glidepath 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) requires a statewide emissions inventory of 

pollutants that are reasonably anticipated to cause or contribute to visibility impairment in any 

mandatory Class I area. An inventory was developed for the baseline year 2002 and projected 

to 2009 and 2018. The pollutants inventoried include volatile organic compounds, nitrogen 

oxides, fine particulate (PM2.5), coarse particulate (PM10), ammonia and sulfur dioxide. The 

baseline emissions inventory for 2002 was developed for Florida and the other VISTAS states

following the methods described in Appendix D. 

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

sources, off-road and on-road mobile sources, and biogenic sources. Stationary point sources are 

those sources that emit greater than a specified tonnage per year, with data provided at the 

facility level. Electric generating utilities and industrial sources are the major categories for 

stationary point sources. Stationary area sources are those sources whose 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

Year

Haziness Index (Deciviews)

Glide Path Natural Condition (Worst Days) Observation
22

are relatively small, but due to the large number of these sources, the collective emissions from 

the source category could be significant (i.e., dry cleaners, service stations, agricultural sources, 

fire emissions, etc.). These types of emissions are estimated on a countywide level. Non-road 

(or off-road) mobile sources are equipment that can move but do not use the roadways, i.e., lawn 

mowers, construction equipment, railroad locomotives, aircraft, etc. The emissions from these 

sources, like stationary area sources, are estimated on a countywide level. On-road mobile 

sources are automobiles, trucks, and motorcycles that use the roadway system. The emissions 

from these sources are estimated by vehicle type and road type and are summed to the 

countywide level. Biogenic sources are the natural sources like trees, crops, grasses and natural 

decay of plants. The emissions from these sources are estimated on a countywide level.

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

inventories. The first is the actual base year inventory. This inventory is the base year emissions 

that correspond to the meteorological data used, which for this modeling effort is data from 

2002. These emissions are used for evaluating the air quality model performance. 

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

actual base year inventory, except that for sources whose emissions change 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 typical base year inventory provide results which are then used 

to calculate relative reduction factors for future years. These relative reduction factors for future 

years are then used to demonstrate reasonable progress toward visibility goals. 

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

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

4.1.1 Stationary Point Sources

Point source emissions are emissions from individual sources having a fixed location. Generally, 

these sources must have permits to operate, and their emissions are inventoried on a regular 

schedule. Large sources emitting at least 100 tons per year (tpy) of a criteria pollutant, 10 tpy of 

a single hazardous air pollutant (HAP), or 25 tpy 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 sources 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 SO2 and NOx that 

can be used in air quality modeling. Emissions profiles are used to estimate emissions of other 

pollutants (volatile organic compounds, carbon monoxide, ammonia, fine particles, soil) based 

on measured emissions of SO2 and NOx. 
23

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

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

on 2002 operations to represent future operations, a typical base year emissions inventory was 

developed for EGUs. This approach is consistent with the USEPA‘s modeling guidance. To 

develop a typical year 2002 emissions inventory for EGU sources, each unit‘s average CEM heat 

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

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

The heat inputs for the period 2000 through 2004 were used because the modeling 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 average of the years the unit was operational was 

used. If a unit was shut down in 2002, but not permanently shutdown, the emissions and heat 

inputs from 2001 (or 2000) were used in the normalizing calculations. 

As part of the VISTAS air quality modeling, VISTAS, in cooperation with the other eastern 

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

the electric generating sector of the contiguous United States using the Integrated Planning 

Model (IPM). IPM is a dynamic linear optimization model that can be used to examine air 

pollution control policies for various pollutants throughout the contiguous United States for the 

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

the power system over a specified future period. Optimization logic in IPM determines the leastcost means of meeting electric generation and capacity requirements while complying with 

specified constraints including air pollution regulations, transmission bottlenecks, and plantspecific operational constraints. The versatility of IPM allows users to specify which constraints 

to exercise, and to populate IPM with their own datasets. 

Other Industrial Point Sources

For the non-EGU sources, the same inventory is used for both the actual and typical base year 

emissions inventories. The non-EGU category uses annual emissions as reported under the 

Consolidated Emissions Reporting Rule (CERR) for the year 2002. These emissions are 

temporally allocated to month, day, and hour using source category code (SCC)-based allocation 

factors. 

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

control data consistent with USEPA‘s CAIR analyses. This data was supplemented with statespecific growth factors and stakeholder input on growth assumptions.

4.1.2 Stationary Area Sources

Stationary area sources are sources whose individual emissions are relatively small, but due to 

the large number of these sources, the collective emissions could be significant (i.e., combustion 

of fuels for heating, structure fires, service stations, etc.). Emissions are estimated by 

multiplying an emission factor by some known indicator of collective activity, such as fuel 

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

the countywide level.
24

The actual base year inventory will serve as the typical base year inventory for all area source 

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

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

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

base year and future years. 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 burned over a longer term period (three or more years, as available 

from state records) to 2002. Based on these ratios, the 2002 acreage was then scaled up or down 

to develop a typical year inventory. 

Future year emissions

The VISTAS contractor generated future year emissions inventories for 2009 and 2018 for the 

regional haze modeling. Growth factors, supplied either by states or taken from the CAIR 

emission projections, were applied to project the controlled emissions to 2018. If no growth 

factor was available from either a state or the CAIR growth factor files, then 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 sources 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 categories 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 year emissions inventories. 

For the source categories estimated using the USEPA‘s NONROAD model, the model growth 

assumptions were used to create the 2009 and 2018 future year inventories. The NONROAD 

model takes into consideration regulations affecting emissions from these source categories. For 

the four largest airports in North Carolina, the 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 data (both before and after controls) for 1996 and 

2010, obtained from the CAIR Technical Support Document. When available, state-specific 

growth factors were used. 

4.1.4 Highway Mobile Sources

For onroad vehicles, the newest version of the MOBILE model, MOBILE6.2, was used. Key 

inputs for MOBILE include information on the age of vehicles on the 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 regulations that affect emissions from this source 

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

onroad vehicles using input data reflective of 2002. The MOBILE model then is run for 2018 

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

speeds, vehicle age and vehicle mix data were obtained from the Florida Department of 

Transportation (FLDOT). For urban 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 the SMOKE-BEIS3 (Biogenic Emission Inventory 

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

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

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

are the same as the emission factors as in UAM-BEIS3. The basis for the gridded land use data 

used by BEIS3 is the county land use data in the Biogenic Emissions Landcover Database 

version 3 (BELD3) provided by the USEPA. A separate land classification scheme, based upon 

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

agriculture and urban portions of each county. 

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 emission 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 NH3 NOx PM10 PM2.5 SO2 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 through Emissions Inventory Improvements 

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

emissions inventory, which in turn improve model performance. These inventory improvements 

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

here: 

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

estimates of future year utility production and emissions. Continuous Emissions 

Monitoring data was used to define seasonal variability in production and emissions. For 
26

Base G2 emissions, states updated IPM model projections from 2005 with control data 

provided by utility companies in 2006 through winter 2007. 

For on-road vehicle emissions, states and 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 improve annual and monthly estimates.

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

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

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

In 2006, 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 developed for USEPA by Corbett at University of Delaware. These 

emissions were incorporated in the Base G modeling. 

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

as available.

4.2 Assessment of Relative Contributions from Specific Pollutants and Sources Categories 

Ammonium sulfate is the largest contributor to visibility impairment at Florida Class I areas, and 

reduction of SO2 emissions would be the most effective means of reducing ammonium sulfate. 

With the lower SO2 emissions in South Florida, and its greater distance from the large 

continental emissions of SO2, the Everglades National Park is occasionally dominated by carbon. 

As illustrated in Figure 4.2-1, 96 percent of SO2 emissions in the VISTAS states are attributable 

to electric generating facilities and industrial point sources. As shown in Table 4.1, 92 percent of 

SO2 emissions in Florida are attributable to electric generating facilities and industrial point 

sources (electric generating facilities, non-electric generating facilities, and area sources).

VISTAS States SO2 Emissions - 2002 

Utilities

Industrial On road Non road Other

Industrial: 20% Utilities: 76%

4.8 Million tons

VISTAS States SO2 Emissions - 2002 

Utilities

Industrial On road Non road Other

Industrial: 20% Utilities: 76%

4.8 Million tons
27

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

5.0 REGIONAL HAZE MODELING 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 evaluation that begins by selection of the modeling 

system. VISTAS decided to use the following modeling system:

Meteorological Model: The Pennsylvania State University/National Center for 

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

nonhydrostatic, prognostic meteorological model routinely used for urban- and regionalscale photochemical, fine particulate matter, and regional haze regulatory modeling 

studies.

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

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

inputs of mobile, nonroad mobile, area, point, fire and biogenic emission sources for 

photochemical grid models.

Air Quality Model: USEPA‘s Models-3/ Community Multiscale Air Quality (CMAQ) 

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

addressing ozone, particulate matter (PM), visibility and acid deposition at regional 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 inventories were also developed for 2002 and 

processed through the emissions model. These inputs were used in the air quality model to 

predict fine particle mass and visibility. The model results for 2002 were compared with 

observed meteorological and air quality data to evaluate model performance. Several 

configurations of the meteorological and air quality model were evaluated to select a 

configuration that gave the best overall performance for the VISTAS region. 

Once model performance was deemed adequate, the current and future year emissions were 

processed through the emissions model. The air quality modeling results are used to determine a 

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

The complete modeling protocol used for this analysis can be found in Appendix C.
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 scientifically appropriate for the intended application 

and be freely accessible to all stakeholders. Scientifically appropriate means that the models 

address important physical and chemical phenomena in sufficient detail, using peer-reviewed 

methods. Freely accessible means that model formulations and coding are freely available for 

review and that the models are available to stakeholders, and their consultants, for execution and 

verification at no or low cost.

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

and capable of meeting the study's goals. These criteria were used in selecting the modeling 

system used for this modeling demonstration.

5.2.1 Selection of Photochemical Grid Model

Criteria

For a photochemical grid model to qualify as a candidate for use in a regional haze SIP, a State 

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

demonstration for a national ambient 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 evaluations 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 particulate matter, visibility and acid deposition within a common 

platform. The original justification for the Models-3 development emerged from the challenges 

posed by the 1990 CAAA and the USEPA‘s desire to develop an advanced modeling framework 

for  ̳holistic‘ environmental modeling utilizing state-of-science representations of atmospheric 

processes in a high performance computing environment. The USEPA completed the initial 

stage of development with Models-3 and released the CMAQ model in mid-1999 as the initial 

operating science model under the Models-3 framework. The most recent rendition is CMAQ 

version 4.5, which was released in September 2005. 
29

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

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

demonstrations SIPs, as well as the regional haze SIP. A number of features in CMAQ‘s 

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

modeling.

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

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

5.2.2 Selection of Meteorological Model 

Criteria

Meteorological models, either through objective, diagnostic, or prognostic analysis, extend 

available information about the state of the atmosphere to the grid upon which photochemical 

grid modeling is to be carried out. The criteria for selecting a meteorological model are based on 

both the models ability to accurately replicate important meteorological phenomena in the region 

of study, and the model's ability to interface with the rest of the modeling systems -- particularly 

the photochemical grid model. With these issues in mind, the following criteria were established 

for the meteorological model to be used in this study:

Non-Hydrostatic Formulation

Reasonably current, peer reviewed formulation

Simulates Cloud Physics

Publicly available on no or low cost

Output available in I/O API format

Supports Four Dimensional Data Assimilation (FDDA)

Enhanced treatment of Planetary Boundary Layer heights for AQ modeling

Overview of MM5

The non-hydrostatic MM5 model is a three-dimensional, limited-area, primitive equation, 

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

model has been under continuous development, improvement, testing and open peer-review for 

more than 20 years and has been used worldwide by hundreds of scientists for a variety of 

mesoscale studies. 

MM5 uses a terrain-following non-dimensionalized pressure, or "sigma", vertical coordinate 

similar to that used in many operational and research models. In the non-hydrostatic MM5, the 

sigma levels are defined according to the initial hydrostatically-balanced reference state so that 

the sigma levels are also time-invariant. The gridded meteorological fields produced by MM5 

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

this coordinate. MM5 fields can be easily used in other regional air quality models with different 
30

coordinate systems by performing a vertical interpolation, followed by a mass-conservation readjustment. 

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

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

momentum. One scheme uses a first-order eddy diffusivity formulation for stable and neutral 

environments and a modified first-order scheme for unstable regimes. The other scheme uses a 

prognostic equation for the second-order turbulent kinetic energy, while diagnosing the other key 

boundary layer terms. 

Initial and lateral boundary conditions are specified for real-data cases from mesoscale threedimensional analyses performed at 12-hour intervals on the outermost grid mesh selected by the 

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

analyze standard surface and radiosonde observations, using the National Meteorological 

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

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

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

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

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

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

richer application history in regulatory modeling studies compared with RAMS or other models. 

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

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

somewhat better photochemical model inputs than alternative models. 

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

the MM5 model, can be found in the meteorological modeling protocol (Appendicies E and F2).

5.2.3 Selection of Emissions Processing System 

Criteria

The principal criterion for an emissions processing system is that it accurately prepares 

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

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

system.

File System Compatibility with the I/O API

File Portability

Ability to grid emissions on a Lambert Conformal projection

Report Capability

Graphical Analysis Capability

MOBILE6 Mobile Source Emissions
31

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 Prototype was originally developed at the Microcomputing Center of North Carolina. As with most  ̳emissions models‘, SMOKE is principally 

an emission processing system and not a true emissions modeling system in which emissions 

estimates are simulated from  ̳first principles‘. This means that, with the exception of mobile 

and biogenic sources, its purpose is to provide an efficient, modern tool for converting emissions 

inventory data into the formatted emission files required by an air quality simulation model. For 

mobile sources, SMOKE actually simulates emissions rates based on input mobile-source 

activity data, emission factors and outputs from transportation travel-demand models. 

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

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

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

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

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

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

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

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

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

processing.

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

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

processing systems and models. It supports both gridded and county total land use scheme for 

biogenic emissions modeling. SMOKE can accommodate emissions files from up to 10 

countries and any pollutant can be processed by 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 project changes in air quality in response 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 Goals for Ozone, PM2.5, and Regional Haze identifies specific goals 
32

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 meteorological conditions;

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

design value or visibility impairment; 

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

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

monitor violating the NAAQS is based on multiple days.

For regional haze modeling, the guidance goes further by suggesting that the preferred approach 

is to model a full, representative year. Moreover, the required 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 monitoring data from the 2000 – 2004 baseline period. 

The USEPA also lists several other considerations to bear in mind when choosing potential 

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

choose periods which are drawn from the years upon which the current design values are based, 

(c) include weekend days among those chosen, and (d) choose modeling periods that meet as 

many episode selection criteria as possible in the maximum number of nonattainment or Class I 

areas as possible. Finally, the USEPA explicitly 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, representative year for regional haze modeling. 

These steps include the following:

Representativeness of Meteorological Conditions: The VISTAS meteorological 

contractor (BAMS) identified important meteorological characteristics and data sets in 

the VISTAS region directly relevant to the evaluation of candidate annual modeling 

episodes.

Initial Episode Typing: At the time of selection in 2003, meteorological and air quality 

data were available for 2002 for model inputs and model performance evaluation. 

VISTAS used Classification and Regression Tree 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 meteorological and air quality monitoring data for 2000 

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

baseline period. This analysis confirmed that visbility and PM2.5 mass in 2002 were 

representative of the five-year baseline period for the VISTAS Class I areas. This 

analysis is discussed in more detail in the project report in Appendix B.
33

Data Availability: In parallel with the CART analysis, episode characterization analyses, 

collaborative investigations by VISTAS states (e.g., NCDAQ, Georgia DNR, FL DEP) 

intensively studied the availability of PM2.5, meteorological, and emissions data and 

representativeness of alternative Baseline modeling periods from a regulatory standpoint. 

Additionally, 2002 was the year that USEPA was requiring states to provide emissions 

inventory data for the Comprehensive Emissions Reporting Rule, it made sense to use 

2002 as the modeling year to take advantage of the 2002 inventory.

Years to be used by other RPOs: VISTAS also considered what years other RPO would 

be modeling, and several had already chosen calendar year 2002 as the modeling year.

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

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

representative, and pragmatic choice for VISTAS regional haze modeling. All of the USEPA 

criteria for regional haze episode selection were directly considered in this process together with 

many other considerations (e.g., timing of new emissions or 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 resolution was used for modeling the 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-way nesting is believed to be 

appropriate for the generally stagnant conditions 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 location 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 choosing a smaller 12-km grid, modeling domain.

The coarse 36-km horizontal grid domain covers the continental United States. This domain was 

used as the outer grid domain for MM5 modeling with the CMAQ domain nested within the 

MM5 domain. Figure 5.4.1-1 shows the MM5 horizontal domain as the outer most, blue grid 

with the CMAQ 36-km domain nested in the MM5 domain. 
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 VISTAS 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 domain for which the reasonable progress goals will 

be assessed.

Figure 5.4.1-2. A more detailed view of the 12-km grid over the VISTAS region.
35

5.4.2 Vertical Modeling Domain

The CMAQ vertical structure is primarily defined by the vertical grid used in the MM5 

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

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

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

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

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

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

ambient monitoring data.

6.0 MODEL PERFORMANCE EVALUATION

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

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

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

recommending the model configuration for the annual emissions and air quality modeling, which 

is included as part of the VISTAS Emissions and Air Quality Modeling Protocol. The evaluation 

of the meteorological modeling configuration can be found in Appendix F.1, with a summary of 

the final meteorological and air quality modeling 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 Air Quality System (AQS), IMPROVE, Speciated 

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

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

(CASTNet) monitoring networks (Figure 6.0-1). 
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 model performance for ozone and fine particles. 

The USEPA modeling guidance for fine particulate matter at the time noted that PM models 

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

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

mean fractional error were selected as the most appropriate metrics to summarize model 

performance; other metrics were also calculated and are included for IMPROVE monitors in the 

full model performance evaluation (Appendix F.2). 

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

fine particle mass.

Fractional Bias Mean Fractional 

Error

Comment

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

model performance, considered excellent performance 

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

performance 

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

average performance. Exceeding this level of 

performance indicates fundamental concerns with the 

modeling system and triggers diagnostic evaluation.

Several graphic displays of model performance were prepared including: 

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

monitoring network, and month

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

monitoring site, and month

3. Spatially 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 plots‖ are relatively new tools in model performance evaluations, 

and have recently been included as model performance 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 examine model performance with respect to set goals 

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

consider the concentration of the species. Analysis of ―bugle plots‖ generally suggests that 

greater emphasis should be placed on performance of those components with the greatest 

contribution to PM mass and visibility impairment (e.g. sulfate and organic carbon) and that 

greater bias and error could be accepted for components with smaller contributions to total PM 

mass (e.g. elemental carbon, nitrate, and soil). 
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. Focus 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 fine particulate matter (PM2.5), as good model performance of the 

component species will dictate good model performance 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 bias (error) is used in lieu of mean bias (error), to prevent low 

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

statistics is found in Appendix F.2. The soccer and bugle plots for the all of the VISTAS 

IMPROVE monitors are included here for summary purposes. Plots have been developed for the 

average monthly concentrations and the performance statistics for all of the most significant light 

scattering component species (Sulfate, Nitrate, and Organic Carbon) for the 20% best days and 

20% worst days. Plots for individual IMPROVE monitors associated with Florida Class I areas 

are included in Appendix F.2.

The soccer plots of monthly concentrations (Figures. 6.2-1 and 6.2-2) show that values for nitrate 

generally fall outside of criteria performance thresholds. 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 poorer nitrate performance in the summer (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 performance 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 modeling results to be representative and acceptable for use in 

modeling projection for ozone, particulate matter, and regional haze. 
39

Figure 6.2-1: Soccer plot depicting both the mean fractional error and fractional bias for 

component concentration for all VISTAS sites based on Base G2 results. Each point 

represents a monthly value as compared to the 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 concentration for all VISTAS sites based on Base G2 

results. Each point represents a monthly value 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 Bias

Mean 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 Bias

Mean Fractional Error 

Sulfate

Nitrate

Organics

Criteria

Goal
40

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 compared 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/m3) Mean Fractional Error 

Sulfate

Nitrate

Organics

Criteria

Goal
41

Figure 6.2-5: Bugle plot of mean fraction error for particulate matter 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 the 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% best days followed by the chart for the 20% worst 

days. Stacked bar charts have been developed 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 order for both the observed (left hand bar) and 

modeled bar (right hand bar), so it easy to identify days when the prediction light extinction for 

the component differs from the observed. The total height of the bar provides the total 

reconstructed particulate matter 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/m3) Mean Fractional Bias 

Sulfate

Nitrate

Organics

(+) Criteria

(+) Goal

(-) Goal

(-) Criteria
42

A cursory view of the stacked bars charts reiterates that sulfates are a large contributor to light 

extinction in many of the Florida Class I areas and Okefenokee during most of the 20% best and 

20% worst days. The bar charts also suggest organic carbon and nitrates are other significant 

components in light extinction on the 20% best days. Concerning the 20% worst days, nitrates 

are not as significant and sulfates and organic carbon dominate the light extinction in both 

modeling and observations. Overall, modeling performance tends to under predict most species 

of PM on average at all four sites. Model performance is better during the 20% best days, where 

under prediction primarily takes place in the nitrates. During the 20% worst days, sulfates are a 

large source of the light extinction under prediction. Modeling performance is best at St. Marks 

and Chassahowitzka, with performance in the Everglades proportionally worse for the 20% best 

days. Okefenokee has a large amount of under prediction, 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, particularly concerning sulfates (20% best days) and organic carbon 

(20% worst days). 

Nitrates tend to be under predicted at all sites during the 20% best and worst days, especially 

during the summer. The model performance evaluation prepared by Environ recognizes the 

consistent issues with nitrate under prediction and 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 extremely low levels of 

nitrates (< 1.0 μg/m3) and at coastal sites, such as the Everglades National Park. In the stacked 

bar charts for the Everglades, nitrates are completely 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 Everglades falls within the lower levels of 

acceptability. Due to the poor modeling of nitrates in the Everglades, modifications to the 

relative reduction factor calculation 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 representative of conditions in the 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% group bEXT (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4
44

Figure 6.3-1: Stacked bar chart for CHAS1 on the 20% best days (top) and 20% worst 

days (bottom). Observed composition is presented in the left hand bar, with modeled 

composition from the 12km CMAQ represented 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% group bEXT (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% group bEXT (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4
45

Figure 6.3.2: Stacked bar chart for EVER1 on the 20% best days (top) and 20% worst days 

(bottom). Observed composition is presented in the left hand bar, with modeled 

composition from the 12km CMAQ represented 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% group bEXT (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% group bEXT (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4
46

Figure 6.3.3: Stacked bar chart for OKEF1 on the 20% best days (top) and 20% worst days 

(bottom). Observed composition is presented in the left hand bar, with modeled 

composition from the 12km CMAQ represented 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% group bEXT (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4
47

6.3.4 Saint Marks, FL

Figure 6.3.4: Stacked bar chart for SAMA on the 20% best days (top) and 20% worst days 

(bottom). Observed composition is presented in the left hand bar, with modeled 

composition represented by the right hand bar.

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% group bEXT (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% group bEXT (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4
48

7.0 LONG-TERM STRATEGY FOR FLORIDA CLASS I AREAS 

As stated in section 1.3 above, the regional haze rule requires States to establish reasonable 

progress goals, expressed in deciviews, for visibility improvement at each affected Class I area 

covering each (approximately) 10-year period until 2064. This first set of reasonable progress 

goals must be met through measures contained in the state‘s long-term strategy covering the 

period from the baseline until 2018. This section discusses development 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 regional haze in the 2000-2004 baseline timeframe were 

large stationary sources of sulfur dioxide emissions. 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 visibility 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 realized between the baseline and 

2018?

c. How do we select any additional sources that would provide potentially significant 

visibility benefits in making 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 controls represent BART in Florida?

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?
49

7.2 Expected Visibility Results in 2018 for Florida Class I Areas under existing and 

planned emissions controls (G2 and, Best and Final Inventory (G4)) 

There are significant control programs being implemented between the baseline period and 2018. 

These programs are described in more detail below.

7.2.1 Federal and State Control Requirements

CAIR. CAIR will permanently cap emissions of SO2 and NOx from EGUs in the eastern United 

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

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

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

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

NOx SIP call in the VISTAS region have developed rules for the control of NOx emissions that 

have been approved by the USEPA. The NOx SIP Call has resulted in a 68 percent reduction in 

NOx emissions from large stationary combustion 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-fired power plants in North Carolina 

must achieve a 77-percent cut in NOx emissions by 2009 and a 73-percent cut in SO2 emissions 

by 2013. This legislation establishes annual caps on both SO2 and NOx emissions for the two 

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

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

Consent Agreements (TECO, VEPCO) 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 reduce emissions while the permanent controls 

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

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

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

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

electricity generating plants in Virginia and West Virginia.

A 2002 agreement calls for Gulf Power to upgrade its operation to cut NOx emission 

rates by 61 percent at its Crist generating plant by 2007, with major reductions beginning 

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

area.

One-hour ozone SIPs (Atlanta / Birmingham / Northern Kentucky). New SIPs have been 

submitted to the USEPA to demonstrate attainment of the one-hour ozone National Ambient 

Air Quality Standard (NAAQS). These SIPs require NOx reductions from specific coal fired 

power plants and address transportation plans in these cities.
50

NOx RACT in 8-hour Nonattainment Area SIPs. The NCDAQ‘s SIP for the Charlotte / 

Rock Hill / Gastonia nonattainment area includes Reasonable Achievable Control 

Technology (RACT) for NOx for two facilities 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 Standard (for onroad trucks and buses). USEPA set a PM 

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

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

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

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

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

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

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

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

became effective in the 2005 model year. 

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

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

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

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

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

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

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

variety of farm and construction applications. Nonroad recreational vehicles include 

snowmobiles, off-highway motorcycles, and all-terrain-vehicles. These rules were initially 

effective in 2004 and will be fully phased-in by 2012.

Nonroad Diesel Rule. This rule sets standards that will reduce emissions by more than 90 

percent from nonroad diesel equipment, and reduce 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 marine vessels in 2012. 

(http://www.epa.gov/nonroad-diesel/)

Industrial Boiler/Process Heater/RICE MACTs. The USEPA issued final rules to 

substantially reduce emissions of toxic air pollutants from industrial, commercial and 

institutional boilers, process heaters and from stationary reciprocating internal combustion 

engines (RICE). These rules reduce emissions of a number of toxic air pollutants, including 

hydrogen chloride, manganese, lead, arsenic and mercury by 2009. This rule also reduces 
51

emissions of SO2 and PM in conjunction with the toxic air pollutant reductions. The applied 

Maximum Achievable Control Technology (MACT) control efficiencies were 4 percent for 

SO2 and 40 percent for PM10 and PM2.5. While the USEPA‘s industrial boiler MACT rules 

were vacated on June 8, 2007, the VISTAS ―best and final‖ modeling run includes these 

reductions. It was the concensus of the VISTAS states that these reductions would 

eventually be reinstated in some form well prior to 2018. 

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

effects of the MACT regulations for Gas Turbines or stationary Reciprocating Internal 

Combustion Engines, which 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 reductions occurring before 2002.

7.2.2 Additional State programs to reduce emissions 

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

as required under the 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 management [section 308(d)(3)(v) (E)]. 

With regard to the impact of construction activities, rule 62-296.320, F.A.C., General Pollution 

Emission Limiting Standards addresses construction related activities. In particular, section 

(4)(c) Unconfined Emissions of Particulate 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 and the application of water or chemicals to control emissions

during construction.

With regard to smoke management, Florida has a certified Smoke Management Plan (SMP)

meeting the intent of EPA‘s Interim Air Quality 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 Division of Forestry operates a burn authorization 

program that considers the potential for smoke from the burn impacting smoke sensitive 

receptors (e.g., airports, roads, hospitals, urban areas, etc.). The SMP provides alternatives for 

burning and is considerate of minimizing 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 assuring that major new or modified sources of visibility-affecting 

pollutants are installed with the best available control technology. The 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 emissions and required controls for the sources.
52

7.2.3 Projected 2009 and 2018 G2 and Best and Final (G4) Emissions Inventories

The G2 and Best and Final (G4) inventory for 2018 account for post-2002 emission reductions 

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

1, 2004. In general, the G2 emissions inventories were developed for 2009 and 2018 using 

current control information in Florida, and growing the 2002 inventory using EGAS6, 

MOBILE6, and IPM for electric generating units (EGUs). In addition, the Best and Final (G4)

inventory for 2018 includes any known BART and 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 should 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 changes 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 Final inventory. The difference between the two 

inventories is limited to a relatively few stationary 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 qualitative results.

For EGUs, IPM results were adjusted based on State and local (S/L) agency knowledge of 

planned emission controls at specific EGUs (including 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 ―Documentation of the 2002 Base Year and 2009 and 

2018 Projection Year Emission Inventories for VISTAS‖ dated 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 State and local agency data and updated forecasts 

from the Department of Energy (DOE).

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

dispensing facilities and open burning estimates, as well as controls used to project emissions for 

USEPA‘s Heavy Duty Diesel rulemaking and for the CAIR rulemaking.

Mobile source controls included local controls underlying the 2002 baseline inventory (vehicle 

emission inspection, Stage II vapor recovery, anti-tampering, etc.) with changes based on 

specific State input. Nonroad control data and projections for 1996, 2010, 2015, and 2020 were 

obtained from the USEPA's Clean Air Interstate Rule (CAIR) Technical Support Document, and 

straight line projections were used to estimate 2009 and 2018 levels.

The following bar charts show expected decreases in emissions of SO2 and NOx across 

the VISTAS states from 2002 through 2018. Note that for SO2 emissions in particular, 

which are the largest contributors to haze, emissions 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, comprising more than half of remaining SO2 emissions in 

every state.
53

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

states.

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

States.

West Virginia

Alabama

Florida

Kentucky

Georgia

Mississippi

North Carolina

South Carolina

Tennessee

Virginia

100

200

300

400

500

600

700 Annual SO2 (Thousand Tons)

Miscellaneous

Nonroad

Onroad

Industrial

Other Fuel Combustion

EGU

2002

2009

2018

West Virginia

Alabama

Florida

Kentucky

Georgia

Mississippi

North Carolina

South Carolina

Tennessee

Virginia

100

200

300

400

500

600

700 Annual SO2 (Thousand Tons)

Miscellaneous

Nonroad

Onroad

Industrial

Other Fuel Combustion

EGU

2002

2009

2018

Alabama

Florida

Kentucky

Georgia

Mississippi

North Carolina

South Carolina

Tennessee

Virginia

100

200

300

400

500

600

700 Annual SO2 (Thousand Tons)

Miscellaneous

Nonroad

Onroad

Industrial

Other Fuel Combustion

EGU

Miscellaneous

Nonroad

Onroad

Industrial

Other Fuel Combustion

EGU

2002

2009

2018

0

100

200

300

400

500

600

700

800

900

1,000

Alabama

Florida

Georgia

Kentucky

Mississippi

North Carolina

South Carolina

Tennessee

Virginia

West Virginia Annual NOx (Thousand Tons)

Miscellaneous Nonroad Onroad

Industrial Other Fuel Combustion EGU

2002

2009

2018

0

100

200

300

400

500

600

700

800

900

1,000

Alabama

Florida

Georgia

Kentucky

Mississippi

North Carolina

South Carolina

Tennessee

Virginia

West Virginia Annual NOx (Thousand Tons)

Miscellaneous Nonroad Onroad

Industrial Other Fuel Combustion EGU

Miscellaneous Nonroad Onroad

Industrial Other Fuel Combustion EGU

2002

2009

2018
54

Summary of Final Base Emissions Inventories for 2009 and 2018

Table 7.2.3-1 is a summary of the 2009 Base G2 emission inventory. The complete inventory 

and discussion of the methodology is contained in Appendix D.

Table 7.2.3-1. 2009 Emissions Inventory Summary for FL.

CO NH3 NOx PM10 PM2.5 SO2 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 66,645 692,599 675,993 214,170 303,943 2,578,768

Table 7.2.3-2 is a summary of the 2018 G4 emission inventory. The complete inventory and 

discussion of the methodology is contained in Appendix D.

Table 7.2.3-2. 2018 Emissions Inventory Summary for FL.

CO NH3 NOx PM10 PM2.5 SO2 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 74,450 489,733 754,139 223,388 265,875 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 deciview values for the 20 percent best days 

in each year are averaged together, producing a single 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. 
55

Figures 7.2.4-1 through 7.2.4-10 illustrate the predicted visibility improvement by 2018 resulting 

from the Best and Final inventory that represents implementation of existing federal and state 

regulations, and limited BART and reasonable progress compared to the Uniform Rate of 

Progress glidepath for Everglades National Park (EVER), Chassahowitzka National Wilderness 

Area (CHAS), St. Marks National Wilderness Area (SAMA), Okefenokee National Wilderness 

Area (OKEF), and Breton Island National Wilderness Area (BRET). The green 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 resulting from a uniform rate of progress (uniform 

glide slope) between current and natural conditions 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 existing and planned emission 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 represent the projected glidepaths based on the 

CMAQ 12 km results using the new IMPROVE equation 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 replicating 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 factors 

developed from the model predictions would be adversely 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 performance into account. Thus, some limited 

corrections were made in developing the relative reduction factors to account for those days in 

which model performance did not meet certain expectations. The details of this correction are 

developed in the Technical Support Document, section 5.3.3 (Appendix F1). 
56

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. (Corrected 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 results at Everglades National Park.

Reasonable Progress Assessment 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

Year Haziness Index (Deciviews)

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction
67

 

Figure 7.2.4-7 CMAQ 2018 Best and Final results at Chassahowitzka Wilderness Area.

Reasonable Progress Assessment 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

Year Haziness Index (Deciviews)

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction
68

Figure 7.2.4-8 CMAQ 2018 Best and Final results at St. Marks Wilderness Area.

Reasonable Progress Assessment 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

Year Haziness Index (Deciviews)

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction
69

Figure 7.2.4-9 CMAQ 2018 Best and Final results at Okefenokee Wilderness Area.

Reasonable Progress Assessment 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

Year Haziness Index (Deciviews)

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction
70

Figure 7.2.4-10 CMAQ 2018 Best and Final results at Breton Island Wildlife Refuge.

Tables 7.2.4-1 and 7.2.4-2 are provided to show the pollutant-specific 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 Assessment 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

Year Haziness Index (Deciviews)

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction
71

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 0.7
72

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, visibility improvements on the 20 percent worst days are expected to 

be slightly better than the uniform rate of progress glidepath by 2018 for Everglades and 

Chassahowitzka, and slightly less than the uniform rate of progress for St. Marks based on 

reductions from existing and planned emissions controls. Visibility improvement in nearby
73

Class I areas outside of Florida (Okefenokee and Breton), for which Florida sources may impact, 

are all slightly worse and slightly better, respectively, 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 percent worst visibility days, states are also required 

to assure that visibility on the 20 percent best days at the Class I areas does 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 Emissions to Visibility Impairment in 2018 at 

VISTAS Class I areas

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

loadings and visibility impairment at Class I areas in the continental United States. To evaluate 

the relative contribution of international emissions to visibility at Class I areas in the 

southeastern United States, VISTAS used a combination of modeling results from the global 

three-dimensional chemical transport model (GEOS-Chem) and CMAQ. VISTAS used the 

GEOS-Chem global model to generate initial and boundary conditions for the CMAQ modeling 

domain. GEOS-Chem was run for the 2002 modeling year using a 4 x 5 degree horizontal grid 

resolution and a 3-hour temporal resolution. Because emissions were based on monthly 

averages, the model does not capture the episodic variability in emissions. The GEOS-Chem 

outputs were used to calculate initial and boundary conditions for the national CMAQ modeling 

domain. The national CMAQ domain included portions of Canada and Mexico, so emissions for 

these countries were included within the national CMAQ modeling domain or as part of the 

boundary conditions outside the national modeling domain, as appropriate.

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

Class I areas. Because all international fires are treated as natural emissions in these runs, both 

cases underestimate the contributions from international fires that are due to anthropogenic 

burning. 

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

In the first run United States anthropogenic emissions were removed, and in the second 

run both United States and international anthropogenic emissions were removed. The 

difference represents international anthropogenic emissions, in the absence of United 

States anthropogenic emissions (e.g. compared to 2064 levels). Harvard University 

provided GEOS-Chem results to VISTAS for 2002 international contribution on 4 x 5 

degree grid. Concurrently Harvard modeling for the Electric Power Research Institute 

(EPRI) provided GEOS-Chem results for 2001 international contribution 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 emissions and boundary conditions from GEOSChem. In the first CMAQ run, all global natural and anthropogenic emissions in 2018 

are active. In the second CMAQ run, only global (United States and international) 

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

against 2018 conditions rather than natural background conditions. 

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

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

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

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

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

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

international contribution is higher using CMAQ and PSAT than in the GEOS-Chem runs 

because the grid scale is finer (more accurate dispersion of emissions) and because the 

background atmosphere includes loadings from current United States anthropogenic 

emissions (greater photochemical activity). Similar charts for nitrate and organic carbon 

mass, for impacts on 20 percent worst visibility days, and for impacts of international 

emissions on calculated light extinction are included in Appendix H. 

Figure 7.3-1. Estimated international 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

1.2

BRIG

HEGL

CACR

MING

UPBU

BRETEVER

CHAS

SAMA

COHU

OKEF

ROMA

SIPS

SHRO

GRSM

LIGO

MACASWAN

JARI

DOSO

SHEN

(NH4)2SO4 Conc. ( g/m

3

)

GEOS_4x5 GEOS_1x1 PSAT CMAQ
75

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

Geographic Areas

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

pollutants contributing to visibility impairment at each Class I area. To understand the relative 

benefit of further reducing emissions from different pollutants, source sectors, and geographic 

areas, VISTAS engaged the Georgia Institute of Technology to perform emission sensitivity 

model runs using CMAQ. Emissions sensitivities were initially performed for three episodes 

representing winter and summer conditions: Jan 2002, July 2001, and July 2002. These runs 

used the initial 2018 projections inventory and considered 30 percent reductions from specific 

pollutants, source categories, and geographic areas. Emissions sensitivities were repeated using 

the 2009 Base D projection inventory and two month-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:

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

and Boundary Conditions from outside the 12 km domain.

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

Conditions

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

and other RPOs

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

NH3 from all sources in VISTAS and other RPOs

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

modeling domain

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

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

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

Results are shown in Figures 7.4-1 through 7.4-4 below for the average of the 20 percent worst 

visibility days for each of three Florida Class I areas, plus Okefenokee. Responses for 20 percent 

worst days were calculated by averaging the responses of the 20 percent worst days that were 

modeled in the two episodes. For the Florida sites, 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 visibility 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 categories and geographic areas. 

Figure 7.4-2. CMAQ projections of visibility 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_EGU

SO2_nonEGU

NOx_Ground

NOx_Point

NH3

VOCs

PC_Ground

PC_Point

PC_Fires

Bext (Mm-1

)

Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

T N

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

SO2_EGU

SO2_nonEGU

NOx_Ground

NOx_Point

NH3

VOCs

PC_Ground

PC_Point

PC_Fires

Bext (Mm-1

)

Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

T N

SC

NC

MS

KY

GA

FL

AL
77

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 inventory for visibility-reducing 

pollutants in different source categories 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 categories 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

SO2_EGU

SO2_nonEGU

NOx_Ground

NOx_Point

NH3

VOCs

PC_Ground

PC_Point

PC_Fires

Bext (Mm-1

)

Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

T N

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_Ground

NOx_Point

NH3

VOCs

PC_Ground

PC_Point

PC_Fires

Bext (Mm-1

)

Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

T N

SC

NC

MS

KY

GA

FL

AL
78

As Figures 7.4-1 through 7.4-4 illustrate, the greatest visibility benefits on the 20 percent worst 

days for the Florida Class I areas and Okefenokee are projected to result from further reducing 

SO2 from EGUs. The Everglades is somewhat different than any of the other Class I areas with a 

greater relative influence from carbon (VOC) and boundary conditions. Contributions from 

other RPOs are comparatively small and the greatest benefits would likely be from further EGU 

reductions within Florida, Georgia, and Alabama. Additional benefits are projected from SO2

emission reductions from non-utility, industrial point sources. The pattern of relative SO2

contributions from non-EGUs among the various VISTAS states is similar to the pattern of 

relative SO2 contributions from EGUs.

Because ammonium nitrate is a relatively small 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. For the 20 percent best days ammonium nitrate has 

a somewhat larger contribution to visibility impairment. Reducing ammonia emissions would be 

more beneficial for reducing ammonium nitrate contributions to visibility impairment than 

further reducing nitrogen oxide emissions from 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 vegetative emissions. Controlling anthropogenic 

sources of VOC emissions has little if any visibility benefit at the Class I areas. Reducing 

primary carbon from point sources, ground level sources or fires have a relatively small visibility 

benefit on average, although fires can have a large impact 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 sensitivity runs are consistent with the conclusions 

drawn from the 2000-2004 baseline monitoring data (see section 2.4 above). The results indicate 

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

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

occur in the winter. We conclude that reducing SO2 emissions from EGU and non-EGU point 

sources in the VISTAS states would have the greatest visibility benefits for the Florida and 

nearby Class I areas. 

7.5 Relative Contributions to Visibility Impairment: Geographic Areas of Influence for 

Florida Class I Areas

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

the VISTAS states would be the most effective sources to control to improve visibility at the 

Florida and nearby Class I areas, the next step was to identify the specific geographic areas that 

most likely influence visibility in each Class I area, and then to identify the major SO2 point 

sources located in those geographic areas. An SO2 Area of Influence (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 SO2 point sources within these Areas of 
79

Influence were identified and ranked by their 2002 emissions. The following sections contain a 

broad overview of the steps in the Area of Influence 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 back trajectories for IMPROVE monitoring sites in 

Florida and neighboring Class I areas for the 2000-2004 baseline period. Back trajectory 

analyses use interpolated measured or modeled meteorological fields to estimate the most likely 

central path of air masses that arrive at a receptor at a given time. The method essentially 

follows a parcel of air backward in hourly steps for a specified length of time. Figure 7.5.1 is an 

example of a back trajectory analysis for Everglades 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 trajectories for 20 percent 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 years of back trajectories 

for the 20 percent worst visibility days in 2000-2004. Residence time is the frequency that winds 
80

pass over a specific geographic area on the path to a Class I area. Separate residence time plots 

were generated using trajectories with 100m and 500m start heights. As illustrated in Figure 

7.5.2, winds influencing Everglades on the 20 percent worst days come from directions that 

move over either highly populated areas or areas where large point sources of emissions eminate.

Figure 7.5.2. Example residence time plot for 20 percent worst visibility days in 2000-2004 

for Everglades. Based on trajectories with 100m start height.

7.5.3 SO2 Areas of Influence

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

geographic area with highest probability 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 focus on the 20 percent worst days that are influenced 

by sulfate and place less importance on days influenced by organic carbon from fires. Sulfateweighted back trajectories for the 20 percent worst days were combined for 5 years of data. The 

resulting sulfate extinction-weighted residence time plots were used to define the geographic 

Area of Influence for sources of SO2 emissions. In Figure 7.5.3 the area representing 10 percent 

or greater residence time is outlined in red and the area representing 5 percent or greater 
81

residence time is outlined in gray. The VISTAS states focused their analyses on the Area of 

Influence defined by 5 percent or greater sulfate extinction-weighted residence time. 

Figure 7.5.3. Example SO2 Area of Influence plot for sulfate 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 SO2 Areas of Influence 

Residence time plots were then combined with geographically-gridded emission data based on 

the 2002 baseline and 2018 BaseG emissions inventories. 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 chemical transformation of point source emissions along 

the path of the trajectories, these data were weighted by 1/d, where d was calculated as the 
82

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

center of the grid cell in which the IMPROVE monitor is located. The distance-weighted point 

source SO2 emissions are then combined with the gridded extinction-weighted back-trajectory 

residence times at a spatial resolution of 36-km. 

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

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

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

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

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

Figures 7.5.4-1 illustrates 2002 and 2018 distance weighted gridded emissions * sulfate 

extinction weighted residence time plots for Everglades. These maps help visualize where the 

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

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

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

Sources that reduce SO2 emissions by 2018 will show a lower contribution to emissions in the 

domain. On the 2018 map the grid cells with these sources will show a lighter color gradient 

than on the 2002 map. 

Figure 7.5.4-1. Everglades 2002 and 2018 SO2 distance weighted emissions * SO4 

extinction-weighted residence time plots.

Comparison 2002 vs 2018 SO2 Emissions * Residence 

Time Using 100 m Start Height – Everglades, FL

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 = 37%
83

Figures 7.5.4-2 through 7.5.4.4 illustrate gridded SO2 distance weighted emissions * sulfate 

extinction weighted residence time plots for 2002 and 2018 emissions for Chassahowitzka, St. 

Marks, Okefenokee, and Breton. These plots illustrate the relative importance of Florida sources 

of SO2 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 SO2 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

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 = 68%
84

Figure 7.5.4-3. 2002-2018 SO2 distance weighted emissions * SO4 extinction-weighted 

residence time plot for St. Marks.

Comparison 2002 vs 2018 SO2 Emissions * Residence 

Time Using 100 m Start Height – St. Marks, FL

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 = 22%
85

Figure 7.5.4-4. 2002-2018 SO2 distance weighted emissions * SO4 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 SO2 distance weighted emissions * SO4 extinction-weighted 

residence time plot for Breton, LA.

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

As previously concluded in section 7.4, the greatest opportunity to affect visibility improvement 

is to reduce emissions of SO2 from stationary point sources. The following tables quantify the 

emissions from specific source categories that emit SO2 within the area of influence, defined as 

within the area having a residence time of 5% or greater surrounding each Class I area. The 

contributions from major source categories to the 2018 Base G2 inventory for the SO2 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 SO2 PM-10 PM-2.5 NH3

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 Major Source Categories in the Area of Influence for 

Everglades.

Annual 2018 G2 Emissions - Chassahowitzka Area of Influence

Category VOC NOX CO SO2

PM10PM2.5 NH3

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 Major Source Categories in the Area of Influence for 

Chassahowitzka.
88

Annual 2018 G2 Emissions - St. Marks Area of Influence

Category VOC NOX CO SO2

PM10PM2.5 NH3

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 Major Source Categories in the Area of Influence for St. 

Marks. 

Annual 2018 G2 Emissions - Okefenokee Area of Influence

Category VOC NOX CO SO2

PM10PM2.5 NH3

Fuel Comb. Elec. Util. 0% 22% 1% 51% 7% 12% 1%

Fuel Comb. Industrial 1% 16% 2% 25% 3% 5% 0%

Fuel Comb. Other 3% 4% 1% 6% 2% 4% 0%

Chemical & Allied Product Mfg 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 Major Source Categories in the Area of Influence for 

Okefenokee, GA.
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Annual 2018 G2 Emissions - Breton Area of Influence

Category VOC NOX CO SO2

PM10PM2.5 NH3

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 Major Source Categories in the 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 emission reductions. Lists of SO2 point sources within the Areas of 

Influence for each Class I areas were developed using the most current (BaseG) VISTAS 2002 

base year and 2018 future year emissions. For this purpose the Area of Influence was defined as 

the counties with maximum sulfate extinction weighted residence time greater than 5. For SO2

sources within each Area of Influence, the following attributes were defined for each individual 

unit: 

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

SO2 emissions for 2002 and 2018

2018 control efficiency

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

Emissions divided by distance (Q/d), a metric that accounts for the dispersion of 

emissions over distance 

Maximum sulfate extinction weighted residence time (RTmax)

Based on this information, we could establish an objective criteria for identifying the sources of 

SO2 having the greatest potential for visibility impairment in each Class I area. The metric that 

most states have used to best summarize the relative 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 measure of the probability that 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 with each unit, most states paired-down the list 

of visibility-impairing sources to those having at least a 1% relative contribution to the total 

Q/d*RTmax for a given Class I area. Some states used a value of 0.5% relative contribution. In 
90

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 these units will convert to natural gas by 2018, thus producing essentially no 

SO2 emissions. Thus, for the purpose of identifying sources for potential reasonable progress 

review, we used the 2002 emissions. These data were used to develop the Florida-specific 

criteria to determine sources for reasonable progress four-factor review. 

7.6 Reasonable Progress Requirement

The Clean Air Act (Sec. 169A) requires that states provide for reasonable progress toward 

attaining the goal of preventing any future, and remedying any existing impairment of visibility 

in mandatory class I Federal areas. In determining reasonable progress, states shall consider the 

four factors listed below.

the costs of compliance

the time necessary for compliance

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

the remaining useful life of any potentially affected sources.

Regulations implementing this requirement have been promulgated in 40 CFR Part 51, Subpart P 

– Protection of Visibility. Section 51.308 specifically 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 shall 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 visibility conditions by 2064. Further, in establishing 

whether reasonable progress is being made, states must consider the straight line path from

baseline (2000-2004) to natural conditions in 2064 (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 visibility improvement, the state first considers all 

current regulations and programs, and then determines the amount of additional improvement 

that may be necessary to represent 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, including the ozone and PM2.5 NAAQS, for many 

States this [determining the visibility improvement from these reductions] will be an important 

step in determining your RPG, and it may be all that is necessary to achieve reasonable progress 

in the first planning period for some States.‖ In fact, a strong argument could be made that the 

sum of all the existing regulations, including CAIR, 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 SO2 and NOx in the Southeastern U.S. 

from existing rules and programs, Class I areas in VISTAS states are expected to make 

considerable visibility improvement during the first regional haze planning period. 

Most of the VISTAS states at this point used the information developed in the previous sections 

to determine whether further reductions in emissions could be required based on the reasonable 

progress requirement of the regional haze rule. To that end, additional information was 

developed to address the four statutory factors to be used to justify any additional reductions. 

Through VISTAS, estimated costs of additional control for SO2 were developed through the 

EPA-developed AirControlNet database. States could then use these data along with knowledge 

of the amount of progress already anticipated to occur from existing requirements, and make

judgments on the degree of additional SO2 reduction to be required. States could then either 

compel sources to meet the determined reduction, under the authority of the Clean Air Act, or 

more likely request that the selected sources perform their own four-factor analysis and submit

its conclusions to the state for final action, again under the authority of the Clean Air Act.

Florida chose a modified process to addressing the reasonable progress four factor requirements

because of some state-specific issues. This process included developing a state rule that requires 

certain large sources of SO2 to submit an application proposing Reasonable Progress Control 

Technology (RPCT) for each applicable unit. The RPCT would consider the four factors and 

demonstrate how these factors were taken into consideration in selecting RPCT for each unit.

We chose to proceed this way because Florida has very strict statutory limitations on imposing 

emissions limitations outside of legislative direction or a construction permit application. In 

addition, we believe that the source-specific information (cost of controls, expected life of the 

source, etc.) needed for completing the four-factor analysis can best be evaluated by the source 

operators. For example, the generic cost information contained in AirControlNet cannot account 

for the unique site and configuration of each source. We also believe that had we pursued a 

state-determined evaluation (either through the authority of the Clean Air Act or state rule), it

would have lead to time-consuming and resource intensive litigation from the industries being 

regulated. Consequently, we decided to implement the reasonable progress provisions through 

the requirement that select sources submit a construction permit application in which a fourfactor evaluation is made by the source. The source would then provide a recommendation to 

the agency of appropriate Reasonable Progress Control Technology (RPCT) for each subject 

unit. The agency would then evaluate the recommendation, make a final determination, and 

issue a permit making the changes federally enforceable. This approach is analogous to the 

procedures the state took in implementing the Best Available Retrofit Technology (BART) 

requirements of the regional haze rule. A copy of the state rule 62.296.341, Florida 

Administrative Code, entitled Regional Haze - Reasonable Progress Control Technology, is 

included in Appendix M. 

Because of the time it took to develop the rule, and the implementation schedule contained in the 

rule, any reductions in emissions that may result from this process cannot be determined prior to 

the submission of the SIP. Thus, the state rule represents a commitment to achieve any 

reductions associated with the reasonable progress requirements by 2018, consistent with the 

requirements of the federal regional haze rule. While the regional haze rule requires that the 

reasonable progress goals take into consideration the reasonable progress four-factor analysis, 
92

this requirement is simply administrative because it does not change the actual compliance 

requirements. Therefore, while the state cannot quantify the exact visibility improvement

associated with the reasonable progress reductions at the time of SIP submission, all of these 

reductions will be completed before the reasonable progress goal date of 2018.

To elaborate on the unique aspect of the Florida approach, especially the timing of the reasonable 

progress reductions contained in the rule, an important consideration was Governor Crist‘s 

greenhouse gas reduction Orders in July of 2007. These Orders directly affect the electric utility 

sector and will require each company to evaluate its future generation makeup. The electric 

utilities were very concerned about making relatively quick and costly pollution control 

decisions related to the reasonable progress requirements when the greenhouse gas reductions 

may result in a complete or significant redirection of the generation makeup. We agreed with 

their concern. One way to allow the industry time to fully evaluate their options was to allow 

more time to decide the reasonable progress reductions. We believe in the long run, the 

decisions regarding greenhouse gas reductions will provide a much greater improvement to 

visibility (i.e., a movement away from fossil-fuel use). 

7.7 Reasonable Progress Control Technology Rule Development

As stated earlier, Florida participated in regional planning through the Visibility Improvement 

State and Tribal Association of the Southeast (VISTAS) regional planning organization. 

VISTAS was established to complete common technical 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 determination of reasonable progress toward the longrange visibility goal. This information helped states decide which pollutants and which sources 

of these pollutants best provided for reasonable progress. Further, limited information was 

gathered to help states address the cost of potential pollutant reductions. While VISTAS 

provided a common dataset for states to use in their reasonable progress requirement, the states 

did not attempt to establish a common procedure that all states would use. Instead, each state 

developed its own analysis of these data, considering state-specific situations, to develop its 

reasonable progress requirements. 

Focus on Sulfur Dioxide

VISTAS, through its modeling contractors, completed a series of sensitivity runs that evaluated 

visibility improvements to pollutant reductions. These runs showed reduction of sulfur dioxide 

(SO2) provided the greatest benefit to visibility improvement throughout the VISTAS domain. 

(See Figures 7.4.1 through 7.4.4) The VISTAS modeling contractors also 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 SO2, 

primarily from CAIR, most of the Class I areas easily show visibility improvement at a faster 

rate than that of a straight line path to meeting the 2064 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 areas are in this category (see Figures 7.2.4-1 through 
93

7.2.4-5). Because considerable progress (visibility improvement) was expected by 2018 with onthe-books regulations, and because SO2 reductions provide the best opportunity for additional 

improvement, the VISTAS states (including Florida) concluded that it was appropriate to 

consider only SO2 reductions in any reasonable progress analysis.

An analysis of the source categories where SO2 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 Florida 

Beginning with the limitation to consider only SO2 from electric generation and other industrial 

sources, Florida considered how to best apply the reasonable progress requirements. Through 

VISTAS, a common analysis was completed to identify the area 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 impact any source may have on a particular Class I area. The first 

variable is the amount of SO2 emissions; the greater the emissions, the more likely a source may 

impact visibility. The second variable is distance 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 states 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 identify a list of sources that 

would be considered for a reasonable progress four-factor analysis.

The VISTAS states considered a number of different combinations of these variables as a 

surrogate for visibility impact. Using the notation that Q represents SO2 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 visibility impact. The purpose for formulating such a surrogate was to 

pair-down the list of all stationary sources of SO2 to a shorter list representing 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, while providing the greatest potential benefit to visibility 

improvement. To limit the list, most states considered a unit-specific threshold of 1 percent of 

the Q/d*RTmax relative contribution considering all sources within the area of influence of each 

Class I area to be appropriate for identifying the largest contributors of visibility impairment. 
94

Developing the Florida Reasonable Progress Control Technology (RPCT) Rule

The agency proceeded to develop state rule 62-296.341, Florida Administrative Code, (provided 

in Appendix M) that would require certain sources to recommend RPCT. One of the critical 

elements of the rule was the determination of which source units would be subject. The starting 

point for making this selection was the area of influence data provided by VISTAS using the 1 

percent contribution criteria of the Q/d*RTmax metric. However, incorporating the RTmax 

parameter into a state rule proved to be problematic due to its underlying complexity (i.e., all the 

definitions and calculation procedures have to be rolled into the rule). To avoid this complexity, 

we decided to use a Q/d metric that would have the effect of selecting a set of source units 

similar to that selected using the Q/d*RTmax, 1% criteria. 

The Q/d metric was, in fact, used previously by VISTAS states in providing a cutpoint for when 

the VISTAS 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 near the 

nominal straight line path 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 

SO2 nearest the Class I areas are required to determine RPCT, while smaller sources (not 

expected to provide significant, cost-effective reductions) are excluded. Similarly, we provided 

some bounds in the rule for emissions (Q) and distance (d) to affect a reasonable process. First, 

we exempt 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 area were 

considered. Third, we only consider sources which commenced construction or submitted 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). Finally, we 

note that the Florida RPCT rule uses 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 SO2 are presumed to be zero in 2018 and these units would not be 

considered for RPCT. 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 sources are addressed through the RPCT process.

The Florida criterion (Q/d>=50) captures for RPCT determination the 1

st through 9th, 15th, 18th

, 

19th, 27th, and 30th largest SO2 sources (2002) in the state. Of all the SO2 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 RPCT review. In comparison, use of the criteria used by a number of 

other VISTAS states (1% contribution using Q/d*RTmax) would select 70.5 percent of the 

emissions.
95

TPY

Percent of 

Total

2002 Total Florida SO2 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 selection criteria with the general 

VISTAS criteria is contained in the tables 7.7.1 – 7.7.4. 
96
97
98

Current Status of RPCT Sources

Many of the emission units subject to the RPCT rule either have already reduced SO2 emissions 

or will be reducing SO2 emissions soon. These reductions have come about from company 

decisions to shut-down or re-power certain units, or to install new control equipment (scrubbers) 

in response to the CAIR regulations. In the case where sources are responding to CAIR, the 

department is engaging these sources to commit to the continuous operation of these control 

equipment, where CAIR would not have this requirement. Table 7.7.5 shows source units 

subject to the RPCT rule and their current status for reducing SO2 emissions. With a number of 

the sources subject to the RPCT rule already committed to significant SO2 emission reductions, 

the effect of the RPCT rule would be to address the oil-fired utility boilers, the single non-utility 

coal-fired boiler (Smurfit-Stone), and to ensure that all the emission reductions are required for 

year round operation.
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100

7.8 Best Available Retrofit Technology (BART) for Individual Sources 

Section 169A of the CAA directs States to assess certain large, older emission sources for 

additional controls in order to address visibility impacts. States are directed to conduct BART 

determinations for such sources in specific source categories, and which contribute to visibility 

impairment in Class I areas. The 1999 regional haze rule includes the BART requirement, and 

directs States to include BART in their regional haze SIPs. On July 6, 2005, USEPA published a 

revised final rule, including Appendix Y to 40 CFR Part 51, the Guidelines for BART 

Determinations Under the Regional Haze Rule (hereinafter referred to as the ―BART 

Guidelines‖) that provides 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 potential to emit 250 tons or more of a visibilityimpairing air pollutant, was put in place between August 7, 1962 and August 7, 1977, and whose 

operations fall within one or more of 26 specifically 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 contribute to any impairment of visibility in any 

such area.‘‘ 

For those sources subject to BART, Section 169A(g)(7) of the CAA requires that States must 

consider the following factors in making BART determinations: (1) the costs of compliance, (2) 

the energy and non-air quality environmental impacts of compliance, (3) any existing pollution 

control technology in use at the source, (4) the remaining useful life of the source, and (5) the 

degree of improvement in visibility which may reasonably be anticipated to result from the use 

of such technology. 

7.8.1 BART-Eligible Sources in Florida

The following is a list of facilities with BART-eligible sources in Florida. The department 

determined the list using information from the ARMS database, and modified through surveys 

sent to potential eligible sources and through public workshops 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 CORPORATION 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, INC. 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. CLEWISTON MILL CLEWISTON MILL AND REFINERY

16 0530010 CEMEX CEMEX

17 0570005 CF INDUSTRIES, INC., PLANT CITY PHOS PLANT CITY PHOSP

18 0570008 MOSAIC CARGILL--RIVERVIEW FACILITY

19 0570024 IMC PHOSPHATES COMPANY 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 ARVAH 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 ASSOCIATION 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 Y
102

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 emission units identified in the previous two 

bullets were greater than 250 tons per year of any of these visibility-impairing pollutants: 

SO2, NOx, and PM10.

The BART Guidelines recommend addressing these visibility-impairing pollutants: SO2, NOx, 

and particulate matter, and suggest that States use their best judgment in determining whether to 

address VOC or ammonia emissions. Florida addressed SO2 and NOx, and used particulate 

matter less than 10 microns in diameter (PM10) as an indicator for particulate matter to identify 

BART-eligible units, as the BART Guidelines recommend. As discussed in detail in Appendix 

L, VISTAS modeling demonstrated that VOCs and ammonia from point sources are not 

significant for visibility impairment. For this reason Florida did not evaluate 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 Available Retrofit Technology 

requirement, Florida developed state rule 62-296.340, Florida Administrative Code. In this rule, 

we adopt by reference the core of the federal rule, 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 state 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 exclusion of volatile organic 

compounds (VOC) and ammonia from BART review, accepting BART equal to CAIR for 

electric generating units (thereby limiting BART 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 the Florida BART rule is included as Appendix L.

The USEPA has determined that, as a whole, the CAIR cap-and-trade program improves 

visibility more than implementing BART for individual sources in states affected by CAIR. A 

State that opts to participate in the CAIR program under 40 CFR 96.201-.224 (Subpart AAA 

through EEE) need not require affected BART-eligible EGU to install, operate, and maintain 

BART for SO2 or NOx emissions. All (or nearly all) coal-fired EGU‘s in Florida are expected to 

control for SO2 and NOx in response to CAIR, and since Florida is participating in CAIR and 

accepts the USEPA‘s overall finding that CAIR ―substitutes‖ for BART for NOx and SO2, 

Florida‘s EGUs were allowed to submit BART exemption modeling demonstrations for PM 
103

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 Available Retrofit Technology Determinations (Appendix 

Y to Part 51, BART Guidelines), Florida may exempt sources from BART if they do not cause 

or contribute to visibility impairment in a Class I area. In accordance with the Florida BART 

rule exemptions, and the federal BART Guidelines, 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 simple emissions criterion applicable within two 

distances (50 km and 100 km) from any Class I area. Four of the BART-eligible sources met 

this exemption. The second criterion is base on source-specific analyses to determine which 

sources cause or contribute to visibility impairment 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 appropriateness (see Appendix L), a contribution 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 using 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 with the modeling protocol. Six BART-eligible 

sources made changes to their operation in order to meet the exemption criteria. These sources 

are Georgia Pacific-Palatka, Smurfit-Stone- Fernandina 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 criteria
105

In summary, of the 46 sources that are BART-eligible, 39 were determined to be exempt from 

BART, 2 others have been shut down. The remaining 5 sources were required to submit a permit 

application containing a BART determination for the applicable units. The following section 

discusses the results of the BART determinations. 

7.8.4 Determination of BART Requirements 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 compliance, (2) the energy and nonair quality environmental impacts of compliance, (3) any existing pollution control technology in 

use at the source, (4) the remaining useful life of the source, and (5) the degree of improvement 

in visibility which may reasonably be anticipated to result from the use of such technology.

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 with two portland cement lines (Lines 1 and 2). 

These include: two Polysius GEPOL preheater kilns (Kilns 1 and 2); two clinker 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 following 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 NOX, PM10 and SO2.
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 the 90 day processing clock until December 17, 2007. 

The Department issued a draft air construction 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 Available Control Technology determination (BACT) 

review since 1977 one or more times and the permitted values stand up very well compared to 

recent determinations made throughout the country even for Greenfield sites. We took those 

factors into consideration and determined it was not necessary to submit a full five factor 

analysis and determined that the controls in place constituted Best Available Retrofit Technology 

(BART).

Overall, the controls consist of highly effective sulfur dioxide scrubbing in the calciner, low raw 

material sulfur, fabric filter baghouses for particulate matter and selective 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 subjected to enforcement actions leading to 

installation of present controls.

All controls including emission limits are federally enforceable.

BART controls follows:

NOX Kiln Controls: To control emissions of nitrogen oxides (NOX), 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 authorized by Permits 0530010-026-AC and 0530010-34-

AC. 

b. In lieu of a. above, CEMEX is authorized to install a selective catalytic 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 interim 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.

SO2 Kiln Controls: The present SO2 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 maintain 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 permittee 
107

shall also comply with time-averaged limits and monitoring requirements discussed further 

below. [Rule 62-296.340 (BART), F.A.C.]

PM/PM10 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/PM10 by a baghouse system. Bags/filters in each baghouse control system shall be 

selected based on a PM design outlet specification of 0.01 grain (gr) per dry standard cubic 

foot (dscf) and a PM10 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 requirements as indicated in BART Permit 0530010-

30-AC. Records may include, but are not limited 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 NOX

reductions (kiln) with the addition of Indirect Firing and SNCR, permitted by the Department 

12/22/06 (Permit No. 0530010-026). These reduced emission rates provided a visibility impact 

of 0.933dv (8th highest, 98th percentile) and reduced the number 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

*98th percentile 
108

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

NOX 2.0 lb/ton clinker 30-operating

day rolling CEMS Applicant Request, PSD Avoidance and BART

SO2 Emissions Standard (VE) for Kiln 1:

Pollutant Emission Limit Averaging 

Time

Compliance 

Method Basis

SO2

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/PM10 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/PM10 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 (Units 1 and 2), five fuel oil-fired black start 2.75 MW diesel peaking generators 

supporting Units 1 and 2, a natural gas-fueled 1,150 MW combined cycle 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 units 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 air burners and Universal Oil Products (UOP) Air 

Correction Division multi-cyclones with reinjection. The multi-cyclones consist of two tubular 

mechanical dust collectors with 695 tubes per collector.

Affected Pollutants

The affected visibility-impairing pollutants include NOX, PM10 and SO2. For electric utilities 

subject to CAIR, only particulate matter 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 received on May 7, 2007, July 6, 2007, September 13, 2007, and 

January 31, 2008, the Department deemed the application 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 make changes. A revised draft permit was issued on 

October 17, 2008. Additional information was received on November 14, 2008 and February 13, 2009. 

Another revised draft permit was issued on March 2, 2009. This version of the draft 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. Bag houses and ESP are generally recognized as the 

top controls with removal efficiencies greater than 99%. Although feasible, wet scrubbers have 

not demonstrated equivalent levels of control for PM. Bag houses do not appear to be a good 

choice either, since tests conducted by FPL at the Sanford plant found that particles generated 

from the combustion of oil-based fuels caused considerable plugging of bags in pilot scale tests. 

The initial visibility modeling analysis showed that replacing the existing multi-cyclones with 

new electrostatic precipitators (ESP) 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 visibility 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 high. The applicant considered the age of the 

units, the low annual capacity factor (less than 30%), the very high cost of replacing the PM 

control devices (>$10,000 per ton of PM removed) and the inability to secure a guarantee from 

the vendor of a retrofitted control device emission limit of less than 0.03 lb/MMBtu. In order to 

provide a greater potential improvement in visibility at the Class 1 area with a much lower 

capital investment requirement, the applicant proposed instead to: upgrade the existing multicyclones, reduce the allowable PM limit from 0.1 lb/MMBtu to 0.07 lb/MMBtu (along with a 
110

study of different fuel additives aimed at possibly further reducing the PM limit to 0.05 

lb/MMBtu), reduce the allowable sulfur content of the fuel from 1% to 0.7% (0.77 lb 

SO2/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%. Although feasible, wet scrubbers have not demonstrated equivalent levels 

of control for PM. Also, bag houses do not appear to be a good choice either, since tests 

conducted by FPL at the Sanford plant found that particles generated from the combustion of 

oil-based fuels caused considerable plugging 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 any adverse non-air quality environmental impacts from the 

control equipment options and did not consider 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 analysis using the CALPUFF model.

Based on the BART analysis for the Turkey Point project, meeting an emissions standard of 

0.015 lb/MMBtu may technically be achieved by the installation of a new ESP on each of these 

units; however, the vendor will not provide a guarantee 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 greater than $10,000 per ton of PM10 removed to achieve a 

0.1dv reduction in visibility, replacing the existing multi-clones with a new ESP is not 

considered to be cost effective. As an alternative, 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 results 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% PM10 1 10,623 2 94,000,000 2 -0.10 134,000,000 2

Multi-cyclone upgrade

and reduced fuel sulfur

30% PM10

30% SO2

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 generating (FFSG) units and associated equipment. The 

facility is located in Citrus County on Power Line Road, West of U.S. Highway 19, in Crystal River, 

Florida. The facility operates the following emissions units 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 by electrostatic precipitators (ESP).

Affected Pollutants

The affected visibility-impairing pollutants include NOX, PM10 and SO2. For electric utilities 

subject to CAIR, only particulate matter 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 application 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 determinations was issued on December 18, 

2008. The applicant published the Public 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. The initial visibility modeling analysis showed that 

even replacing the existing control devices with new 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 applicant also expressed concern that the cost of 

substantially upgrading the 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 replacing 
112

the PM control devices, the applicant proposed instead to upgrade the existing ESP for Unit 2, 

reduce their allowable PM limit from 0.1 lb/MMBtu to 0.04 lb/MMBtu (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 any adverse non-air quality environmental impacts from the 

control equipment options but did consider 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 analysis using the CALPUFF model.

Based on the BART analysis for the Crystal River project, meeting an emissions standard of 

0.015 lb/MMBtu may be achieved by all proposed options; however, it is not cost effective to do 

so recognizing that these units are nearing the end of their useful life. As an alternative to 

potentially spending around $150 million to replace the particulate matter 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 operate 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 cost effective. Considering the age of the existing units, it is not 

reasonable to require the large capital expenditure needed to bring emissions down to levels 

achievable by new units and control devices given the very limited remaining useful life. 

Therefore, the Department accepts Progress Energy 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 reported on the last five year‘s stack 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% PM10 1 30,582 2 71,775,596 2 -0.150 95,122,400 1

Polishing Bag House 

(following existing 

ESP)

88% PM10 3 40,354 3 73,131,683 3 -0.110 127,235,127 3

New ESP 90% PM10 2 49,274 4 144,223,206 4 -0.130 127,098,392 2

Rebuilt ESP 85% PM10 4 12,987 1 28,760,610 1 -0.100 27,316,290 4

Notes:

1. Average annual cost per ton removed for both 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 Chemicals, Inc., d/b/a PCS Phosphate, operates a 

phosphate fertilizer manufacturing complex in White Springs, Florida. The facility is located at 

15843 SE 78th Street, White Springs, Hamilton County. This 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 (MAP)/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 Prep
114

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 process to control sulfur dioxide emissions and 

demisters to control sulfuric acid mist emissions. 

All of the listed DAP/MAP plants include medium 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 using fresh water for final cleanup. Emissions from 

the dryers, coolers, mills and screens are controlled by cyclones, wet scrubbers with phosphoric 

acid or pond water as the scrubbing medium, and 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 medium 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 Train (EU 004) limestone handling, the DFP Feed 

Prep area (EU 042), DFP Product Silos (EU 062) include fabric filter baghouses designed to 

recover process or product raw materials and to control particulate matter emissions.

Swift Creek Mine Rock Dryer (EU 064) and Swift Creek Mine Silos (EU 065) include wet 

cyclonic scrubber to control particulate matter emissions. The Rock Dryer is fired primarily 

with natural gas. 

Affected Pollutants

The affected visibility-impairing pollutants include NOX, PM10 and SO2.

Project Description and Status

On February 1, 2007, PCS Phosphate submitted an application 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 Phosphate filed a petition for formal administrative 

hearing on January 23, 2008, after their request for extension of time to file an administrative hearing was 

denied by the Department. PCS Phosphate has been in negotiations with the Department to resolve the 

outstanding issues related to the proposed BART. PCS Phosphate resolved all the outstanding issues with 

the Department in letters dated December 16, 2008 and January 20, 2009 to the Department. The 

Department will issue a revised draft BART construction 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 Determinations
115

SAP Plants: At PCS Phosphate molten elemental sulfur is combusted to produce the source of 

the gaseous SO2 used to manufacture sulfuric acid. Thus SO2 is a valuable important 

intermediate raw material for the process. The key emissions are SO2, sulfur trioxide (SO3) and 

sulfuric acid mist (SAM or H2SO4). All are key intermediate raw materials or products as well 

as pollutants. NOX and minor amounts of PM from the sulfur 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 ammoniated phosphate process, anhydrous ammonia 

is reacted with phosphoric acid. The slurry produced by the ammoniation is then sprayed onto a 

bed of solids in the granulator and additional ammonia (if required) is added to complete the acid 

neutralization and produced the final product grade. 

The resulting slurry/solids mixture is dried in a fossil fuel fired direct contact rotary dryer. The 

dried solids are then screened to remove on size product. The product size material is cooled and 

then conveyed to storage. The over-sized and under-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 NH3.

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 fired, fluidized bed reactor. As 

the feed is introduced into the reactor 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 screened 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 venturi and cyclonic scrubbers. The 

dried solids are then screened to remove on-size product. The product size material is conveyed 

to storage. The over-sized and under-sized materials are crushed and recirculated to the pug 

mill. 

The initial modeling analysis indicated maximum visibility impacts of 3.07 dv during the period 

2001-2003. The 8th highest visibility impairment value for each of the three years is over the 

comparison threshold of 0.5 dv (range 1.63-1.77). The 22nd highest impairment is 1.72 dv. In 

addition, there are 87, 93 and 91 days (2001-2003) predicted to have visibility impairment of 

over 0.5 dv. The primary focus of the determination was on the effects of lower SO2, NOX and 

PM10 emissions. 

BART determinations are conducted on certain emission units constructed between 1962 and 

1977, during which time top/down case-by-case determinations for best available control 

technology (BACT) were not conducted. BACT is a requirement for projects subject to the rules 

for the prevention of significant deterioration (PSD) promulgated by EPA in January 1975. 

However since 1977 and most recently since the reference period 2001-2003 Line 1 has been 

subjected to a number of reviews that resulted in the installation of control equipment or 

inclusion of federally enforceable emission 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 effectiveness of remaining control technologies;

Step 4 - Evaluate impacts and 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. Furthermore, 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, 1977 such that controls were installed, the State will take this 

into account during the review process and may find 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 emissions units at other facilities, like C.F. Industries SAPs B, C 

(2004-2007). The details of the previous reviews 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 SO2 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 incorporated into the final BART permit:

SAP Plants

SO2 Emission Standards: 

Source Sulfur Dioxide (SO2) Emission Limits

SAP C13.5 lb SO2/ton of 100% H2SO4 and 335.4 lb/hr, 24-hour rolling average by 

CEMS2

SAP D 3.5 lb SO2/ton of 100% H2SO4 and 335.4 lb/hr, 24-hour rolling average by 

CEMS2

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

emission limit for SO2. 

2 This BART determination requires an SO2 CEMS to be used to demonstrate continuous 

compliance with the SO2 emission standards and limitations specified above.

NOX Emission Standards: 

Source Nitrogen Oxides (NOX as NO2) Emission Limits

SAP C (EU 0.14 lb NOX/ton of 100% H2SO4. Testing using EPA Method 7E shall 
117

021) be done upon request by the Department. 

SAP D (EU 

022)

0.14 lb NOX/ton of 100% H2SO4. Testing using EPA Method 7E shall 

be done upon request by the Department. 

PM/PM10 - Opacity (VE) Standard

As determined by EPA Method 9, visible emissions from the two SAP shall not exceed the 

current 10% opacity limits based on a 6-minute average.

DAP/MAP Plants

PM/PM10 Emissions Standard: 

Emissions of particulate matter (PM) and particulate matter with a mean diameter of 10 microns 

or less (PM10) shall not exceed the following limits based on the average of three test runs as 

determined by EPA Methods 5. 

Source Particulate (PM/PM10) 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/PM10), the permittee shall operate and maintain 

the existing process and pollution control technology for each of the DAP/MAP plants. The 

applicant is authorized to make improvements to the process and pollution control equipment for 

the purposes of reliably meeting the new BART PM/PM10 emission limits given above.

SO2 and NOX Controls and Emission Standards: 

To control SO2 and NOX 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 fuel oil as backup. No numerical 

emission limit is established.

DFP Plants

PM/PM10 Emissions Standard: 

Emissions of particulate matter (PM) and particulate matter with a mean diameter of 10 microns 

or less (PM10) shall not exceed the following limits based on the average of three test runs as 

determined by EPA Methods 5. 

Source Particulate (PM/PM10) 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 test
118

Source Particulate (PM/PM10) 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/PM10), the permittee shall operate and maintain 

the existing process and pollution control technology for each of the DAP/MAP plants. The 

applicant is authorized to make improvements to the process and pollution control equipment for 

the purposes of reliably meeting the new BART PM/PM10 emission limits given above.

SO2 and NOX Controls and Emission Standards: 

To control SO2 and NOX 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/PM10 Emissions Standard: 

Emissions of particulate matter (PM) and particulate matter with a mean diameter of 10 microns 

or less (PM10) shall not exceed the following limits based on the average of three test runs as 

determined by EPA Methods 5. 

Source Particulate (PM/PM10) Emission Limits

X Train (EU 004) 18 lb/hr by annual 3-hr stack test

SO2 and NOX Controls and Emission Standards: 

To control SO2 and NOX emissions from X Train, 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.

Swift Creek Mine:

PM/PM10 Emissions Standard: 

Emissions of particulate matter (PM) and particulate matter with a mean diameter of 10 microns 

or less (PM10) shall not exceed the following limits based on the average of three test runs as 

determined by EPA Methods 5. 

Source Particulate (PM/PM10) 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

SO2 and NOX Controls and Emission Standards: 

To control SO2 and NOX 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 Units
119

Particulate Matter Emissions Limit: A Visible Emissions Standard of 5% Opacity for each 

baghouse.

7.8.4.5 City of Tallahassee Purdom

BART-eligible Emissions Units

The City of Tallahassee operates the existing Sam O. Purdom Generating Station, which is an existing 

power plant located in Wakulla County at 667 Port Leon Drive in St. Marks, Florida. Unit 7 at this 

facility is a BART-eligible unit that is fired primarily with fuel oil 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-impairing pollutants include NOX, PM10 and SO2. EPA determined that 

CAIR satisfied the BART requirements for NOX and SO2.

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 additional 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 final permit was issued on September 11, 2007 requiring 

permanent shutdown of Unit 7.

BART Determination

The following permit condition represents the BART 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 state (expressed in deciviews) that provide for 

reasonable progress towards achieving natural visibility. In addition, 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 degradation in visibility for 

the least impaired days over the State Implementation Plan (SIP) period. The state must also 

provide an assessment of the number of years it would take to attain natural visibility conditions 

if improvement continues at the rate represented by the reasonable progress goal.

In accordance with the requirements of 40 CFR §51.308(d)(1), this Regional Haze 

Implementation Plan establishes reasonable progress 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 deciviews) that would need to be maintained 

during each implementation period in order to attain natural visibility conditions by 2064. 

Through VISTAS, we modeled the expected visibility improvements under existing federal and 

state regulations, BART determinations in Florida and neighboring states, and any additional 

control measures found to be reasonable 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 reasonable measures (including those from the Florida 

RPCT rule) 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) Natural 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
121

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 0.17 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 accompanied by a strategy for monitoring regional haze 

visibility impairment. Specifically, the Regional Haze Rule states at 40 CFR 51.308(d)(4):

―(4) Monitoring strategy and other implementation plan requirements. The State must 

submit with the implementation plan a monitoring strategy for measuring, characterizing, 

and reporting of regional haze visibility impairment that is representative of all 

mandatory Class I Federal areas within the State. This monitoring strategy must be 

coordinated with the monitoring strategy required in §51.305 for reasonably attributable 

visibility impairment. Compliance with this requirement may be met through 

participation in the IMPROVE network. The implementation plan must also provide for 

the following:

(i) The establishment of any additional monitoring sites or equipment needed to 

assess whether reasonable progress goals to address regional haze for all 

mandatory Class I Federal areas within the State are being achieved.

(ii)-(vi) [Other implementation plan requirements that pertain to reporting and use 

of monitoring data and an emission inventory.]‖

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

1. Track the expected visibility improvements resulting from emissions reductions 

identified in this SIP. 

2. Better understand the atmospheric processes of importance to haze

3. Identify chemical species in the ambient particulate matter and relate them to 

emissions from sources
122

4. Evaluate regional air quality models for haze and construct relative response factors 

(RRFs) for using those models

The primary monitoring network for regional haze, both nationwide and in Florida, is the 

IMPROVE network. Given that IMPROVE monitoring data from 2000-2004 serve as the 

baseline for the regional haze program, the future regional haze monitoring strategy must 

necessarily be based on, or directly comparable to, IMPROVE. The IMPROVE measurements 

provide the only long-term record available for tracking visibility improvement or degradation 

and therefore Florida intends to rely on the IMPROVE network for complying with the regional 

haze monitoring requirement in the Regional Haze Rule. 

There are currently 3 IMPROVE sites 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 IMPROVE monitors that represent conditions at 

each of them.

In addition, DEP and the local air agencies in the state operate a fairly comprehensive PM2.5 

network of the filter based Federal reference method monitors, Federal equivalent method 

continuous monitors (BAMs) and continuous mass monitors (TEOMs), filter based speciated 

monitors. A map of the various locations around the State is included 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 Florida‘s regional haze monitoring strategy, and it is 

difficult to visualize how the objectives listed 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 reasonable progress toward visibility improvement 

in some of its Class I areas. 

Data produced by the IMPROVE monitoring network will be used nearly continuously for 

preparing the 5-year progress reports and the 10-year SIP revisions, each of which relies on 

analysis of the preceding five years of data. Consequently, the monitoring data from the 

IMPROVE sites needs to be readily accessible and to be kept up to date. Presumably, IMPROVE 

will continue to process information from its own measurements at about the same pace and with 

the same attention to quality as it has shown 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 rate 
124

of progress, SO2 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 held their first formal consultation meeting to 

review the Base G modeling results and the SO2 Areas of Influence analyses. The Air Directors 

agreed to look at reasonable control measures for sources on the lists for the SO2 Areas of 

Influence. Each state would consider sources within their state and would identify sources in 

neighboring states that they would like to have that neighboring state consider. States 

acknowledged that the review process would differ among states since some Class I areas are 

projected to see visibility improvements near the uniform rate of progress while most Class I 

areas are projected to have greater improvements than uniform rate of progress. 

In April 2007, representatives of all the Regional Planning Organizations (RPO), including 

VISTAS, met with the federal land managers (FLM) 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 Directors met for their second formal interstate 

consultation. States shared their lists of sources in their state and neighboring states for each 

Class I area. They also shared their criteria for listing sources and their plans for further 

interstate consultation. 

In June 2007, the VISTAS states met with the 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 sources 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.). Nevertheless, DEP has passed a reasonable 

progress rule that would require potential additional reductions of SO2 (the primary visibility 

impairing pollutant) from specified large sources located in proximity of Class I areas. See 

section 7.6. The rule would require these sources to determine Reasonable Progress Control 

Technology (RPCT) to be implemented no later than December 31, 2017. 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 visibility in these areas. Consultation

reports and letters may be found in Exhibit 3.

11.0 COMPREHENSIVE PERIODIC 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 and every ten years thereafter. In accordance 

with the requirements listed in Section 51.308(f) of the federal rule for regional haze, Florida
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commits to revising and submitting this regional haze implementation plan by July 31, 2018 and 

every ten years thereafter.

In addition, Section 51.308(g) requires periodic reports evaluating progress towards the 

reasonable progress goals established for each mandatory Class I area. In accordance with the 

requirements listed in Section 51.308(g) of the federal rule for regional 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 the reasonable progress goal 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 status 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 recognizes that the 2018 projections of EGU 

controls from the IPM runs represent one solution to how the CAIR requirements will be 

met. By the time of the first periodic report, the DEP anticipates that the actual 

compliance strategy for the various utility companies will be much more defined. An 

assessment of those actual compliance plans will be done for the first periodic report.]

3. Assessment of changes in visibility conditions at each class I area (current vs. baseline), 

expressed as 5-year averages of annual values for 20 percent best and worst days;

4. Analysis of emission changes over the 5-year period, identified by source or activity;

5. Analysis of any siginificant changes in or out of the State 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 addressed in the SIP revision for reasonable 

progress.

DEP also commits to ongoing consultation with the FLMs throughout the implementation 

process, including annual discussion of the implementation process and the most recent 

IMPROVE monitoring data and VIEWS data.

There are several technical improvements that are recommended in the emissions inventory and 

air quality models that are used to support regulatory decisions for regional haze. These 

improvements recommended, as funding is available, to support the next long term strategy. The 

following is an overall summary; Appendix K contains a fuller discussion of possible technical 

improvements.

First and foremost, continued improvements are needed in the integrated one-atmosphere air 

quality models that are used to project air quality responses to emissions reductions. As our 

understanding of partitioning between gaseous and aerosol phases improves, this understanding 

needs to be reflected in the models. Improvements can also be made in how the models handle 

individual pollutants. Sulfate performance for the CMAQ regional air quality model is good 

overall. However sulfate deposition is frequently overestimated in the models, particularly in the 
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summer months. At the coastal sites, when winds are blowing from the Gulf of Mexico or 

Atlantic Ocean, CMAQ underestimates measured sulfate at the monitors. CMAQ‘s processes 

also should be reviewed for sulfate formation over water. Nitrate is overestimated by the model 

in the winter and underestimated in the summer, although summer monitored values of nitrate 

are very low. Additional improvements in seasonal allocation of ammonia emissions would 

improve model estimates of ammonium nitrate formation. Organic carbon is generally 

underestimated in the summer months. Improvements are needed in the characterization of both 

primary carbon emissions and formation of secondary organic carbon. 

Other improvements needed include better tools for organic carbon source apportionment, and 

more consistent measurement techniques between rural and urban monitoring networks. To 

improve our understanding of the contribution of fire from natural forest fires, prescribed 

burning, land clearing, and agricultural burning, states need improved record keeping. 

Additional improvements to international emissions inventory are also needed, to improve our 

understanding of boundary conditions for our modeling domain and of the contributions from 

international emissions to pollutant concentrations at the VISTAS Class I areas. 

12.0 DETERMINATION OF ADEQUACY OF THE EXISTING PLAN

Depending on the findings of the 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 the Administrator a negative declaration that 

further revision of the SIP is not needed at this time.

2) DEP determines that the existing SIP may be inadequate to ensure reasonable progress 

due to emissions from other states which participated in the regional planning process. 

DEP provides notification to the Administrator and the states that participated in regional 

planning. DEP collaborates with states through the regional planning process to address 

the SIP‘s deficiencies.

3) DEP determines that the current SIP may be inadequate to ensure reasonable progress 

due to emissions from another country. DEP provides notification, along with available 

information, to the Administrator.

4) DEP determines that the existing SIP is inadequate to ensure reasonable progress due to 

emissions within the state. DEP will revise its SIP to address the plan‘s deficiencies 

within one year.