i 

# Regional Haze Plan

for

Florida Class I Areas

Prepared by

Florida Department of Environment Protection

Division of Air Resource Management

March 17, 2010

(Amended August 31 , 2010)

(Amended September 17 , 2012)ii 

### Preface: This document contains summaries of the technical analyses used by Florida ’s Department of Environmental Protection (DEP), Division of Air Resource Management

, to
support Florida’s regional haze state implementation plan pursuant to §§107(d)(3)(D) and (E) of the Clean Air Act, as amended.iii TABLE OF CONTENTS

EXECUTIVE SUMMARY

................................ ................................ ................................ .......... vii

1.0 INTRODUCTION

................................ ................................ ................................ .................... 1
1.1 What is regi onal 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

NATURA L BACKGROUND CONDITIONS IN CLASS I AREAS

................................ ... 9
2.1 Estimating Natural Conditions for Florida Class I Areas

................................ ................... 10
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

...... 11
2.4 Pollutant Contributions to Visibility Impairment (2000

- 2004 Baseline Data)

................... 12

3.0 GLIDEPATHS TO NATURAL CONDITIONS IN 2064

................................ ...................... 17
3.1 Glidepaths for Class I Areas in Florida and Nearby States

................................ ................ 17

4.0 NATURE OF THE PROBLEM: CHIEF CAUSES OF VISIBILITY IMPAIRMENT IN

FLORIDA CLASS I AREAS

................................ ................................ ............................... 24
4.1 Baseline Emissions Inventory

................................ ................................ ............................. 24
4.1.1 Stationary Point Sources

................................ ................................ ............................ 25

4. 1.2 Stationary Area Sources

................................ ................................ ............................. 26

4.1.3 Off

- Road Mobile Sources

................................ ................................ .......................... 26
4.1.4 Highway Mobile Sources

................................ ................................ ........................... 27
4.1.5 Biogenic Emission Sources

................................ ................................ ........................ 27
4.1.6 Summary 2002 Base G2 (February 2007 version) Baseline Emissions Inventory for

FL

................................ ................................ ................................ ................................ ..... 27
4.1.7 Model Performance Improvements through Emissions Inventory Improvements

.... 28
4.2 Assessment of Relative Contributions from Specific Pollutants and Sources Categories

. 29

5.0 REGIONAL HAZE MODELING METHODS AND INPUTS

................................ ............. 30
5.1 Analysis Method

................................ ................................ ................................ ................. 30

5.2 Model Selection

................................ ................................ ................................ .................. 31

5.2.1 Select ion of Photochemical Grid Model

................................ ................................ .... 31
5.2.2 Selection of Meteorological Model

................................ ................................ ............ 32
5.2.3 Selection of Emissions Processing System

................................ ................................ 33iv 5.3 Select ion of the Modeling Year

................................ ................................ .......................... 34
5.4 Modeling Domains

................................ ................................ ................................ ............. 36
5.4.1 Horizontal Modeling Domain

................................ ................................ .................... 36
5.4.2 Vertical Modeling Domain

................................ ................................ ......................... 38

6.0 MODEL PERFORMANCE EVALUATION

................................ ................................ ......... 39
6.1 Modeling Performance Goals, and Criteria

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

6.2 VISTAS Domain

- Wide Performance

................................ ................................ ................. 41

6.3 Florida Class 1 Areas Performance

................................ ................................ .................... 44

7.0 LONG

- TERM STRATEGY FOR FLORIDA CLASS I AREAS

................................ .......... 50
7.1 Overview of the Long

- Term Strategy Development Process

................................ ............. 50

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?

................................ ................................ ................................ ................................ 50

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?

................................ ................................ ................................ ................................ 50

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

represent reasonable progress for those specific sources?

................................ ............... 50

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?

........... 50
7.2 Expected Visibil ity Results in 2018 for Florida Class I Areas under existing and planned emissions controls (G2 and, Best and Final Inventory (G4))

................................ ........... 51
7.2.1 Federal and State Control Requirements

................................ ................................ .... 51
7.2.2 Additional State programs to reduce emissions

................................ ......................... 53
7.2.3 Projected 2009 and 2018 G2 and Best and Final (G4) Emissions Inventories

.......... 54
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

................................ ............. 56
7.3 Relative Contribution from International Emissions to Visibility Impairment in 2018 at

VISTAS Class I areas

................................ ................................ ................................ ....... 75
7.4 Relative Contributions to Visibility Impairment: Pollutants, Source Categories, and Geographic Areas

................................ ................................ ................................ ............. 77
7.5 Relative Contributions to Visibility Impairment: Geographic Areas of Influence for

Florida Class I Areas

................................ ................................ ................................ ........ 81
7.5.1 Back Trajectory Analyses

................................ ................................ .......................... 81

7.5.2 Residence Time Plots

................................ ................................ ................................ . 82

7.5.3 SO 2 Areas of Influence

................................ ................................ ............................... 83

7.5.4 Emissions Sources within SO 2 Areas of Influence

................................ .................. 84v 7.5.5 Specific Source Types in the Areas of Influence for Florida and Nearby Class I

Areas

................................ ................................ ................................ ................................ 89
7.6 Reasonable Progress Requirement

................................ ................................ ...................... 93
7.7 Florida’s Reasonable Progress Analysis

................................ ................................ ............. 94

7.8 Best Available Retrofit Technology (BART) for Individual Sources

.............................. 111

7.8.1 BART

- Eligible Sources in Florida

................................ ................................ ......... 112

7.8.2 Florida’s BART Rule

................................ ................................ ............................... 114
7.8.3 Determination of Sources Subject to BART in Florida

................................ ........... 115
7.8.4 Determination of BART Requirements for Subject

- to

- BART Sources

................... 117

8.0 REASONABLE PROGRESS GOALS

................................ ................................ ................ 174

9.0 MONITORING STRATEGY

................................ ................................ ............................... 175

10.0 INTERSTATE CONSULTATION

................................ ................................ .................... 178

11.0 COMPREHENSIVE PERIODIC IMPLEMENTATION PLAN REVISIONS

................. 178

12.0 DETERMINATION OF ADEQUACY OF THE EXISTING PLAN

................................ 180vi LIST OF EXHIBITS

Exhibit 1: Rules Adopted by the State

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

, Reasonable Progress Determinations and Permits, and Verification of Shutdown s

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: Ai r Quality and Meteorological Preparations and Results

Appendix F 1 : 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 Planvii 

### EXECUTIVE SUMMARY

Pursuant to the Clean Air Act requirements contained in sections 169A and 169B, and the subsequent implementing regulations c ontained in 40 CFR 51.308, the S tate 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 affe cting 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. O ne staff person is directly connected to VISTAS

– the technical coordinator. All other personnel resources are within the state programs. Fun ding for VISTAS has been through grants from 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 cont racted resources, managed by the technical coordinator. To help direct the technical work, the state directors created three workgroups. To e ffect an open and inclusive direction of work, the workgroups consisted of federal, state, and private stakeholde r 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 industr y 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 S IP revision

.

Florida ’s proposed regional haze SIP consists of a set of rules, commitments, permit conditions

, and plans addressing the requirements of the federal regulations , as well as supporting

administrative and technical documentation. Specificall y , the materials proposed to be incorporated into the SIP include this document, Florida’s Regional Haze Plan for Class I

Areas

, Exhibit 1, Exhibit 2 (excluding those elements of the permits not specifically related to

the BART or reasonable progress emission limits) , and Exhibit 3. This document and portions of the exhibits have been amended twice (August 2010 and September 2012) since their original

submittal to EPA in March 2010.

The 2012 amendments were prompted by the court

- ordered termination o f EPA’s Clean Air Interstate Rule (CAIR) and EPA’s December 2011 proposal to disapprove those portions of Florida’s August 2010 proposed SIP revision that explicitly relied on CAIR. A t the time of EPA’s proposed December 2011 limited disapproval, EPA anti cipated that CAIR would be replaced by a successor rule, the Cross State Air Pollution Rule (CSAPR)

. But

, CSAPR was subsequently stayed and then vacated by the D.C. Circuit Court on August 21, 2012. The Courtviii further ordered EPA to continue implementing CAIR until such time as a valid replacement was promulgated. As a result, CAIR remains in effect as of September 2012 and emission reductions

from CAIR continu e to occur . However, those portions of Florida’s August 2010 proposed SIP

revision that had exp licitly relied on CAIR or its replacement are removed in the September

2012 amendments

.

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. (ame nded)

– These state rules implement the Best Available Retrofit Technology (BART) requirements. Rule 62

- 204.800, F.A.C., adopts the federal rule by reference, and Rule 62

- 296.340 adds specific state

- determined criteria as needed or required by the federal rule. 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 feder al regulations such as excluding ammonia and volatile organic compounds from BART review. At the time of rule adoption, EPA regulations allowed the state to presume that CAIR equals BART for sulfur dioxide (SO 2 ) and nitrogen oxides (NOx) from electric gen erating units. Given the court

- ordered termination

of CAIR , Florida is not requesting EPA approval of Rule 62

- 296.340, F.A.C., to the extent it allows sources to rely on CAIR to comply with BART.

Outside of these rules, and in response to the uncertaint y over CAIR ’s eventual replacement,

DEP has completed source

- specific BART determinations for the electric generating sources that had previously relied on CAIR.

DEP has evaluated 4 6 facilities having units that meet the BART applicability requirements. Of these, 33 meet the exemption criteria in the rule, 7 have been shut down, and 6 have completed

BART determinations.

2. Reasonable Progress Requirements

– The state is required to consider four factors (cost, time to comply, energy and non

- air impacts, and remaining useful life) in determining whether further reductions in visibility

- impairing pollutants would be reasonable for any sources in the state. To limit the scope of this requirement, and based on a VISTAS analys i s, DEP has focused its response to reasonable progress on sulfur dioxide (SO 2 ) emissions from large energy generating

and industrial sources. Based on cri teria to identify

, from among these sources, those that are most affecting visibility in Class I areas, 32 emissions units from 15 major facilities were targeted for four

- factor review.

Of the 32 units subject to reasonable progress review, nine hav e been shut down, and three unit s have taken federally enforceable permit condition s that limit SO 2 emissions such that they are no longer applicable to reasonable progress review

. For the remaining 20 units, DEP has completed

a four

- factor reasonable pro gress determination.

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 t he long

- range plan presented in this regional haze SIP.ix 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. 8 0

Saint Marks 14.37 26.31 5.39 11.67 12.80 2 2

. 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 stat e’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 rate s of progress for

each of the Florida Class I areas is:
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 0.1 8 19. 8 0

Saint Marks 0.24 22.89 0.24 2 2

. 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 d ependent 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 rules and programs that will result , or have resulted, in significant i mprovement of visibility in Class I areas. The most significant of these rules is 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 nati onal goal of natural conditions. The annualx sulfur dioxide and nitrogen oxide s emission reductions from CAIR that the state considered for the initial planning period have been fully realized as of 2010

. These CAIR

- based emission reductions will continue to be realized for the foreseeable future, while EPA determines its next steps in response to the vacatur of CSAPR.

. The long

- term strategy also includes new reductions of visibility precursor emissions resulting from the BART and reasonable progress requirements of the regional haze rule and the proposed new federal rule for Industrial, Commercial, and Institutional Boilers and Process Heaters (Boiler MACT rule)

. Initial

c ompliance with these rules will occur at various times throughout the planning period.

Through 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

impairi ng pollutants were included from all known source sectors and locations, including boundary conditions derived from a global model. Current visibility conditions were evaluated using data from public and private monitoring networks, and these and other as sociated data were used to validate model performance. Projected emissions were developed for 2018, considering growth and known or estimated emissions changes due to existing regulations. Substantial analysis was completed to determine visibility sensit ivity to specific pollutant reductions, and to parse

- out the source

- sector contributions.

The reasonable progress goals set forth in this plan , as amended in 2012, are largely based on

continued implementation of the CAIR

- driven emission reductions, along with other existing requirements. Regardless of CAIR’s current status, significant emission reductions forced by

CAIR have occurred as of 2010 . Thus, the reasonable progress goal s expressed above remain

valid for Florida. Moreover, as of September 2012

, CAIR remains in effect and its associated

emission reductions continue to occur. The following chart shows the actual Florida SO 2 emissions compared to the original VISTAS projections used to determine Florida’s reasonable progress goal s . Actual SO 2 em issions by 2010 (the most recent year of available data) are already less than the projected 2018 emissions. SO 2 is the dominant visibility

- impairing pollutant.

0 100,000 200,000 300,000 400,000 500,000 600,000 700,0002000200120022003200420052006200720082009201020112012201320142015201620172018tons 

## Florida SO 2 

## Annual Emissions
Compared with Regional Haze SIP Projections

Actual

RH SIPxi Importantly, the actual visibility measurements through 2010 at the three Florida Class I areas have responded consistent with the VISTAS predictions. The following charts show the

observed vs. predicted visibility improvement for the 20 percent worst days for each Class I area

.xiixiii The conclusion of this analysis is that the long

- term strategy for attaining the 2064 visibility goal can rely on recent CAIR

- driven emission reductions as well as existing and proposed new regulations to provide reas onable progress toward th is goal during the first ten

- year planning period ending in 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 ( 40 CFR 51.308(g)). The t ime 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. DEP will review the progress of the projected emissions chan ges 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.1 

### 1.0 INTRODUCTION

1.1 What is regional haze?

Regional haze is pollution from disparate sources that impairs visibility over a large region, including national parks, forests, and wilderness areas (federal “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 p articles

– 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 di oxides (SO 2 ) 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 re spiratory and cardiovascular systems. The United States Environmental Protection Agency ( EPA ) has set national ambient air quality standards for daily and annual levels of fine particles with diameter smaller than 2.5 **μ** m (PM2.5). The most important sour ces 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 windblown dust.

1.2 W hat 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 “preventio n 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 an d wilderness areas. On December 2, 1980, EPA promulgated regulations to address visibility impairment (45 FR 80084). The 1980 regulations were developed to address visibility impairment that is “reasonably attributable” to a single source or small group of sources. These regulations represented th e first phase in addressing visibility impairment and deferred action on regional haze that emanates from a variety of sources until monitoring, modeling and scientific knowledge about the relationships between pollutants and visibility impairment improved

.

In the 1990 Amendments to the CAA, Congress added section 169B and called on EPA to issue regional haze rules. The Regional Haze rule (RHR) that EPA promulgated on July 1, 1999 (64 FR 35713), revised the existing visibility regulations in order to in tegrate 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 EPA that set

out each state ’ s plan for complyi ng with the regional haze rule, including consultation and coordination with other states and with federal land managers. The timing of the SIP submittal is

tied to EPA ’s promulgation of designations for the National Ambient Air Quality Standard (NAAQS) f or fine particulate matter (PM2.5) at 40 CFR Part 81. States must submit a regional2 haze implementation plan to EPA within three years after the date of designation. Because EPA 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 h aze reduction efforts. EPA 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 a re 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.

F igure 1.2

- 1. Geographical Areas of Regional Planning Organizations

1.3 General overview of regional haze SIP requirements

The regional haze rule (RHR) at 40 CFR 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 s t ate and/or for each mandatory Class I Federal area located outside the s tate, which may be affected by emissions from sources within the s tate. States are to establish baseline visibility conditions for 2000

- 2004, natural background visibility conditions in 2064, and the rate3 of uniform progress between baseline and background conditions. The uniform rate of progress is also known as the “glidepath.”

The RHR then requires s tates 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

mos t impaired days over the period of the implementation plan, and ensure no degradation in visibility for the least impaired days over the same period ( 40 CFR 51.308(d)(1)).

In order to ensure that visibility goals are properly met and set, state implemen tation 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 t o calculate the degree of visibility impairment under natural visibility conditions for the most impaired and least impaired days ( 40 CFR 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 ( 40 CFR 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 expecte d under all federal and state regulations by 2009 and by 2018. During development of the long

term strategy, states are also required to consider specific factors such as the abovementioned ongoing control programs, measures to mitigate construction acti vities, source retirement and replacement schedules, smoke management programs for agriculture and forestry, and enforceability of specific measures ( 40 CFR 51.308(d)(3)).

In addition, a specific component of each state’s first long

- term strategy is dicta ted by the specific best available retrofit technology (BART) requirements in 40 CFR 51.308(e) of the

RHR. The RHR at 40 CFR 51.308(e) requires states to include a determination of BART for

each BART

- eligible source in the s tate that emits any air polluta nt, 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 wit hin 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 wit h 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 c onditions 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

in each SIP. The first interim review wou ld be due to EPA in December 2012. If emissions4 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 revi sed 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 o ther 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 Environment al Protection ( DEP ) is responsible for developing the Regional Haze SIP. This SIP establishes reasonable progress goals for visibility improvement at

each of the F lorida 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 areas5 In developing this SIP, we have also considered that emis sion 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 s tates and Federal Land Managers on the implementation of the visibility protection program, including development and review of implemen tation 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 s tate. 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

coor dination among s tates. 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 s tates. 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 visibili ty 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 a n important value. Figure 1.5

- 2 lists these Class I areas.6 Figure 1.5

- 1. Class I Areas in the VISTAS Region7 Mandatory Class I Federal Areas in the VISTAS Region Where Visibility is an Important Value

State Area Name Acreage Federal

Land Manager 40 CFR §81.401 Alabama Sipsey Wilderness 12,646 USDA

- FS
40 CFR §81.407 Florida Chassahowitzka Wilderness 23,360 USDI

FWS Everglades National Park 1,397,429 USDI

- NPS

St. Marks Wilderness 17,745 USDI

FWS 40 CFR §81.408 Georgia Cohotta Wilderness 33,776 USDA

- FS

Okefenokee Wilderness 343,850 USDI

FWS

Wolf Island Wilderness 5,126 USDI

FWS 40 CFR §81.411 Kentucky Mammoth Cave National Park 51,303 USDI

- NPS
40 CFR §81.422 North

Carolina Great Smoky 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 USDI

FWS
40 CFR §81.426 South

Carolina Cape Romain Wilderness 28,000 USDI

FWS
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 situa tion, calculation of initial8 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 contri butions to VISTAS mandatory Class I Federal areas are

also assessed. This document is included as Appendix F1.9 

### 2.0 ASSESSMENT OF BASELINE AND CURRENT CONDITIONS AND

ESTIMATE OF NATURAL BACKGROUND CONDITIONS IN CLASS I

AREAS

The goal of the Regional Haz e Rule is to restore natural visibility conditions to the 156 Class I

areas identified in the 1977 Clean Air Act Amendments. Sec tion 40 CFR 51.301(q) defines natural conditions: “Natural conditions include naturally occurring phenomena that reduce

visibi lity 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 imp airment, 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 regi onal 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

c hange 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) b aseline 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 facto rs. 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

a nd then calculating total light extinction. “Baseline” visibility is the starting point for the improvement of visibility conditions. It is the average of the Interagency Monitoring of

Protected Visual Environments (IMPROVE) monitoring data for 2000 thro ugh 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 states10 (51.308(d)(2)). “Current conditions” are assessed every five years as part of the SIP review

where actua l 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 EPA guidance, is based on annual average

conce ntrations 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 th e 20 percent worst days is estimated by assuming that fine particle concentrations for natural background are normally distributed and the 90 th percentile of the annual distribution represents natural background visibility on the 20 percent worst days.

In the 2003 guidance, EPA also provided that states may use a “refined approach” to estimate the values that characterize the natural visibility conditions of the Class I areas. The purpose of such a refinement would be to provide more accurate estimates wi th 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 e fficiency 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 g aseous 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 ammoni um sulfate and ammonium nitrate and for sea salt. The elemental carbon (light

- absorbing carbon), fine soil, and coarse mass terms do not change between the original and new IMPROVE equation.

The VISTAS states chose to use the new IMPROVE equation as the basis for the conceptual description because it takes into account the most recent review of the science and because it is recommended by the IMPROVE Steering Committee. For more detailed discussion of the two IMPROVE equations, see Appendi ces 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 average11 (200 0 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 EPA Guidance for Tracking Progress Under the Regional Haze Rule. When less than five years of data had been colle cted, 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. I MPROVE data record s 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 2 0 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 d ays is significantly better at Everglades (22.3 dv) than Chassahowitzka (25.7 dv) or St. Marks (26.3). On the other hand,

n atural 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.12 Table 2.3

- 1 Natural Background and Baseline Conditions for F lorida 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 asso ciated with high organic carbon levels due to fires.

Ammonium sulfate , (NH 4 ) 2 SO 4 , is the most important contributor to visibility impairment and fine particle mass on the 20 percent worst and 20 percent b est visibility days at all the Florida

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

account s for 6 0

- 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, su lfate 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

.13 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 300Swanquarter, NCCae Romain, SCOkefenokee, GAEverglades, FLChassahowitzka, FLSt. Marks, FLDolly Sods, WVShenandoah, VAJames River Face, VALinville Gorge, NCShining Rock, NCGreat SmokyMtns., TNCohutta, GASipsey, ALMammoth Cave, KYBrigantine, NJBreton, LAMingo, MOHercules Glade, MOUpper Buffalo, ARCaney Creek, ARExtinction (Mm-1)

Sea Salt

Coarse

Soil

EC

POM

NH4NO3 (NH4)2SO4 Rayleigh

VISTAS coastal VISTAS inland Neighboring non

- VISTAS 0 50 100 150 200 250 300Swanquarter, NCCae Romain, SCOkefenokee, GAEverglades, FLChassahowitzka, FLSt. Marks, FLDolly Sods, WVShenandoah, VAJames River Face, VALinville Gorge, NCShining Rock, NCGreat SmokyMtns., TNCohutta, GASipsey, ALMammoth Cave, KYBrigantine, NJBreton, LAMingo, MOHercules Glade, MOUpper Buffalo, ARCaney Creek, ARExtinction (Mm-1)

Sea Salt

Coarse

Soil

EC

POM

NH4NO3 (NH4)2SO4 Rayleigh

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

Sea Salt

CM

Soil

EC

POM

NH4NO3 (NH4)2SO4
Rayleigh

VISTAS coastal VISTAS inland Neighboring non

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

Sea Salt

CM

Soil

EC

POM

NH4NO3 (NH4)2SO4
Rayleigh

VISTAS coastal VISTAS inland Neighboring non

- VISTASSwanquarter, NCCae Romain, SCOkefenokee, GAEverglades, FLChassahowitzka, FLSt. Marks, FLDolly Sods, WVShenandoah, VAJames River Face, VALinville Gorge, NCShining Rock, NCGreat SmokyMtns., TNCohutta, GASipsey, ALMammoth Cave, KYBrigantine, NJBreton, LAMingo, MOHercules Glade, MOUpper Buffalo, ARCaney Creek, AR14 Sulfate particles are formed in the atmosphere from sulfur dioxide emissions. Sulfate particles occur as hydrogen sulfate, H 2 (SO 4 ), ammonium bisulfate HNH 4 SO 4 , and ammonium sulfate, (NH 4 ) 2 SO 4 , depending on the availability of ammonia, NH3, in the atmosphere.

Particulate Organic Matter (POM) is the second most important contributor to fine particle mass and light extinction on the 20 percent haziest and the 20 percent clearest days at the Flor ida 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 ar eas. 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 ni trate , NH 4 NO 3 , 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 impai rment. 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 l evels 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 h igher 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 v isibility 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.15 Sea salt , NaCl, is observed at the co astal 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. T he 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 diamet ers 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 compo nent 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 multipli er 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 diff erences 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 wi dely 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 meteorological16 conditio ns. For the central and northern Florida Class I areas poor visibility days were most 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 i s 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 relate d 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, sub

Appendix E.3.17 

### 3.0 GLIDEPATHS TO NATURAL CONDITIONS IN 2064

As stated in section 1.3 above, the RHR directs states to gr aphically show what would be a “uniform rate of progress” toward natural conditions for each mandatory Class I Federal area

within the s tate as well as for each mandatory Class I Federal area located outside the s tate, which may be affected by emissions fr om sources within the s tate. 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 impairm ent 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 EPA guidance and used data collected from the IMPROVE monitoring sites as described in section 2 above. The glidepath is one of the indicators u sed 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 Na tional 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 a cts 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 conditions in the entire Southeast, and has the least amount of needed improvement to achieve

natural conditions.18 Table 3.1.1

– Rank Order of Current Visibility for Southeastern Class I Areas

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 20.2919 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) Observation20 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) Observation21 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) Observation22 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

O kefenokee

- 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) Observation23 Figure 3.1

- 5. Uniform Rate of Progress Glidepath for 20 percent worst days at Breton

National Wild erness Area

. 

### 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) Observation24 

### 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 40 CFR 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 d eveloped 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 in ventory 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 sou rces, 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

statio nary point sources. Stationary area sources are those sources whose individual emissions are relatively small, but due to the large number of these sources, the collective emissions from the source category could be significant (i.e., dry cleaners, servic e 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 equipmen t, 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 countyw ide 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, w hich 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, ex cept 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 ai r 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.25 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.

The actual base year inventory for the EGU sources used 2002 continuous emissions monitoring (CEM) data reported to the EPA ’s Acid Rain program or 2002 hourly emissions data provided by stakeholders. These data provide hourly emissions profiles for SO 2 and NOx that can be used in air quality modeling. Emissions profiles are used to estimate emissions of other pollutants (volatile organic compounds, carbon monoxide, ammonia, fine particles, soil) based on measure d

emissions of SO 2 and NOx. Electric Generating Units

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 EPA ’s modeling guidance. To develop a typical year 2002 emissions inventory for EGU sources, each unit’s average CEM heat input for 2 000 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 mo deling 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 polici es 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 least

cost means of meeting electric generation and capacity requirements while complying with specified constraints including air pollution regulations, transmission bottlenecks, and plant

specific operational constraints. The versatility of IPM allo ws users to specify which constraints to exercise, and to populate IPM with their own datasets.

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 Other Industrial Point Sources26 Consolidated Emissions Reporting Rule (CERR) for the year 2002. These em issions are temporally allocated to month, day, and hour using source category code ( SCC )

- based allocation

factors.

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

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

specific growth factors and stakeholder input on growth assumptions.

4.1.2 Stationary Area Sources

Stationary area sources are sources whose individual emissions are relatively small, but due to the large number of these sources, the collective emissions could be significant (i.e., combustion of fuels for heating, structure fires, service stations, etc.). Emissions are estimated by multiplying an emission factor by some known indicator of collective activity, su ch as fuel usage, number of households, or population. Stationary area source emissions are estimated at the countywide level.

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 t he 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 s tate records to ratio the number of acres burned over a longer term peri od (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 EPA ’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 eq uipment that can move but do not use the roadways, such as construction equipment, aircraft, railroad locomotives, lawn and garden equipment, etc.

For t he majority of the non

- road mobile sources, the emissions for 2002 were estimated using the

EPA ’s NONRO AD2005c 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 EPA ’s NONROAD model, the model growth assumptions were used to create the 2009 and 2018 future year inventories. The NONROAD27 model takes into consideratio n 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, rail road 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 averag e 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.

The MOBILE model takes into consideration regulations that affect emis sions 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 yea r. 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 use d.

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 inclu de 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 count y land use data in the Biogenic Emissions Landcover Database version 3 (BELD3) provided by the EPA . A separate land classification scheme, based upon satellite (AVHRR, 1 km spatial resolution) and census information, aided in defining the forest, agricult ure 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.28 Table 4.1. 2002 Emissions Inventory Summary for FL in tons per year.

CO NH 3 NOx PM10 PM2.5 SO 2 VOC

EGU 57,113 234 257,677 21,387 15,643 453,631 2,524

NON

- EGU 81,933 1,423 45,156 35,857 30,504 65,090 38,471

ONROAD 4,493,820 17,922 460,503 11,148 7,779 20,687 520,757

NON

- ROAD 1,920,729 134 180,627 18,281 17,415 20,614 272,072

AREA 71,079 37,446 28,872 443,346 58,878 40,491 404,302

FIRES 790,620 3,102 15,942 85,263 75,717 4,057 42,724

BIOGENICS 0 0 36,320 0 0 0 1,522,031

TOTAL 7,415,293 60,261 1,025,098 615,281 205,936 604,570 2,802,880

4.1.7 Model Performance Improvements 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, an d 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 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 an d Wildlife Service provided projection s of increased prescribed burning in 2009 and 2018; these data were incorporated in the Base G inventory for all states except Florida.

**•** For non

- road engines, the updated EPA NONROAD2005 emissions model was used in

Base G.

**•** For commercial marine emissions i n shipping lanes in the Gulf of Mexico and Atlantic Oceans, ENVIRON created gridded emissions for the VISTAS modeling domain using inventory data newly developed for EPA by Corbett at 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.29 4.2 Assessment of Relative Contributions from Specific Pollutants and Sources Categories

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

reduction of SO 2 emissions would be the most effective means of reducing ammonium sulfate.

With the lower SO 2 emissions in South Florida, and its greater distance from the large

continental emissions of SO 2 , th e Everglades National Park is occasionally dominated by carbon. As illustrated in Figure 4.2

- 1, 96 percent of SO 2 emissions in the VISTAS states are attributable to electric generating facilities and industrial point sources. As shown in Table 4.1, 92 percent of

SO 2 emissions in Florida are attributable to electric generating facilities and industrial point

sources (electric generating facilities, non

- electric generating facilities, and area sources).

Figure 4.2

- 1. SO 2 emissions in 2002 in the VISTAS States. 

## VISTAS States SO2 Emissions

- 2002

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



## VISTAS States SO2 Emissions

- 2002

Utilities Industrial On road
Non road Other Utilities: 76% Industrial: 20% 4.8 Million tons30 

### 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 evalu ation 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 ) i s a nonhydrostatic, prognostic meteorological model routinely used for urban

- and regional

scale photochemical, fine particulate matter, and regional haze regulatory modeling

studies.

**•** Emissions Model: The Sparse Matrix Operator Kernel Emissions ( SMOKE ) mod eling 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: EPA ’s Models

- 3/ Community Multiscale Air Quality (CMAQ) modeling system is an ‘One

- Atmosphere’ photochemical grid model capable of addressing ozone, particulate matter (PM), visibility and acid deposition at regional scale for periods up to one year.

The EPA Modeling Guidance recom mends 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 mo del 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 us ed 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.31 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 model s 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 thei r 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 model ing system used for this modeling demonstration.

5.2.1 Selection of Photochemical Grid Model

For a photochemical grid model to qualify as a candidate for use in a regional haze SIP, a s tate needs to show that it meets the same several general cri teria as a model for an attainment demonstration for a national ambient air quality standard (NAAQS): Criteria

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 perf orm 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.

The photochemical model selected for this study was CMAQ version 4.5. For more than a decade, the EPA has been developing the Models

- 3 CMAQ modeling system with the overarching aim of producing a ‘One

- Atmosphere’ air quality modeling system capable of addressing ozone, fine particulate matter, visibility and acid deposition within a commo n platform. The original justification for the Models

- 3 development emerged from the challenges posed by the 1990 CAAA and the EPA ’s desire to develop an advanced modeling framework for ‘holistic’ environmental modeling utilizing state

- of

- science represen tations of atmospheric processes in a high performance computing environment. The EPA completed the initial stage of development with Models

- 3 and released the CMAQ model in mid

- 1999 as the initial operating science model under the Models

- 3 framework. Th e most recent rendition is CMAQ version 4.5, which was released in September 2005. Overview of CMAQ32 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 PM 2 .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

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 foll owing criteria were established for the meteorological model to be used in this study: Criteria

Non

- Hydrostatic Formulation

Reasonably current, peer reviewed formulation

Simulates Cloud Physics

Publicly available on no or low cost

Output available in I/O API forma t

Supports Four Dimensional Data Assimilation (FDDA)

Enhanced treatment of Planetary Boundary Layer heights for AQ modeling

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

mes oscale studies. Overview of MM5

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 h ydrostatically

- 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 using33 this coordinate. MM5 fields can be easily used in other regional air quality models with different coordinate systems by performing a vertical interpolation, followed by a mass

- conservation re

adjustment.

Distinct planetary boundary layer ( PBL ) parameterizations are avai lable 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 fo r unstable regimes. The other scheme uses a prognostic equation for the second

- order turbulent kinetic energy, while diagnosing the other key boundary layer terms.

Initial and lateral boundary conditions are specified for real

- data cases from mesoscale three

dimensional analyses performed at 12

- hour intervals on the outermost grid mesh selected by the user. Surface fields are analyzed at three

- hour intervals. A Cressman

- based technique is used to analyze standard surface and radiosonde observations, u sing 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 regulat ory 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 ) an d 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 q uality 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 (Append icies E and F2 ).

5.2.3 Selection of Emissions Processing System

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. Criteria

File System Compatibility with the I/O API

Fi le Portability

Ability to grid emissions on a Lambert Conformal projection

Report Capability

Graphical Analysis Capability34 MOBILE6 Mobile Source Emissions

Biogenic Emissions Inventory System version 3 (BEIS

- 3)

Ability to process emissions for the proposed domain in a reasonable amount of time.

Ability to process control strategies

No or low cost for acquisition and maintenance

Expandable to support other species and mechanisms

The SMOKE Emissions Processing System Prototype was originally developed at the Micro

computing 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’. Th is 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. Overview of SMOKE

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

h igh

- 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 an d 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 pro jection, 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 numb er 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 emi ssions 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 Modelin g 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 se veral reasons.35 The EPA ’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 to consider when selecting one or more episodes for use in demonstrating reasonable progress in attaining the regional haze air quality goals. The EPA recommends that episode selection derive from three principal criteria:

**•** Simu late 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; an d

**•** 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 fu ll, 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 EPA also lists several other considerations to bear in mind when choosing potential regional haze episodes including: (a) choose periods which have already been modeled, (b) choose periods which are drawn from the years upon which the current design values are based, (c) include weekend days among those chosen, and (d) choose modeling periods that meet as many episode selection criteria as possible in the maximum number of nonattainment or Class I areas as possible. Finally, the EPA explicitly recommended in i ts 2007 guidance to use 2002 as the baseline inventory year.

VISTAS adopted a logical, stepwise approach in implementing the EPA guidance in order to identify the most preferable, representative year for regional haze modeling. These steps include the fo llowing:

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

e pisodes.

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 visibil ity 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 f or 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. T his analysis is discussed in more detail in the project report in Appendix B.36 Data Availability: In parallel with the CART analysis, episode characterization analyses, collaborative investigations by VISTAS states (e.g., NCDAQ, Georgia DNR, FL DEP)

intens ively studied the availability of PM 2.5 , meteorological, and emissions data and representativeness of alternative Baseline modeling periods from a regulatory standpoint. Additionally, 2002 was the year that EPA was requiring states to provide emissions

in ventory 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 EPA criteria for regional haze episode selection were directly considered in this process together with many other considerations (e.g., timing of new emissions or aerometric data deliver ies by the

EPA or the states to the modeling teams).

5.4 Modeling Domains

5.4.1 Horizontal Modeling Domain

The EPA ’s modeling guidance recommends a 12

- km modeling grid resolution for PM2.5 modeling while a 36

- km grid is considered acceptable for regional haze. For the VISTAS modeling, a coarse 36

- km grid resolution was used for modeling the entire 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 appropriat e 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 boundari es 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 nonattainme nt 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.37 Figure 5.4.1

- 1. The MM5 horiz ontal 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.38 5.4.2 Vertical Modeling Domain

The CMAQ ve rtical 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

wa s 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 mo del performance metrics when both the 34 layer and a 19 layer CMAQ models were compared to ambient monitoring data.39 

### 6.0 MODEL PERFORMANCE EVALUATION

The initial modeling effort focused on evaluating previous regional air quality modeling applications a nd 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, whic h 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

- atmospher e 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 Characteriza tion (SEARCH), National Acid Deposition Program (NADP) and Clean Air Status and Trends Network (CASTNet) monitoring networks (Figure 6.0

- 1).

Figure 6.0

- 1: Monitoring Networks used for VISTAS 2002 model performance evaluation

and their location within the VISTAS 12km domain.40 6.1 Modeling Performance Goals, and Criteria

In 2004, VISTAS established model performance goals and criteria for compon ents of fine particle mass (Table 6.1

- 1) based on previous model performance for ozone and fine particles.

The EPA 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. Sc atter 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 plo ts
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 EPA ’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 ad ded 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).41 6.2 VISTAS Domain

- Wide Performance

Furthe r 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 monitorin g 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 particul ate matter (PM 2.5 ), 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 w ill 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 a ppear 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 resp ect 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.42 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 BiasMean Fractional Error Sulfate

Nitrate

Organics

Criteria

Goal

All Sites CMAQ 12km - 2002 Monthly

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

-75.0 -25.0 25.0 75.0

Fractional BiasMean Fractional Error Sulfate

Nitrate

Organics

Criteria

Goal43 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 ( **μ** g/m 3 )Mean Fractional Error Sulfate

Nitrate

Organics

Criteria

Goal44 Figure 6.2

- 5: Bugle plot of mean fraction error for particulate matter and its component

sp ecies 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

T he 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 adjac ent 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 = l ight 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 = lig ht 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 fro m 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 ( **μ** g/m 3 )Mean Fractional Bias Sulfate

Nitrate

Organics

(+) Criteria

(+) Goal

(-) Goal

(-) Criteria45 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 n ot 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 th e 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 pr ediction 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 nitra te under prediction is exaggerated during extremely low levels of nitrates (< 1.0 μg/m 3 ) 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, DEP 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 relat ive reduction factor

calculation were applied to correct the performance issues. This is discussed further in section

7.2.4. DEP further asserts the one atmosphere modeling performed by the VISTAS contractors is representative of conditions in the southe astern states and is applicable for use in attainment

demonstrations.46 6.3.1 Chassahowitzka, FL

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

0 10 20 30 40 50 60 70 80 90

38 68 71 104 146 161 164 170 173 176 182 191 203 224 227 239 242 248 266 320 344 347

_ _ _ Avg

Julian Day in Best 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

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

0 20 40 60 80 100 120 140 160 180 200

5 17 26 41 44 47 56 89 116 128 155 200 206 236 260 278 281 290 296 329 332 341 350

_ _ Avg

Julian Day in Worst 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO447 6.3.2 Everglades, FL

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 h and bar, with modeled composition from the 12km CMAQ represented by the right hand bar. Best 20% Obs (left) vs 2002gt2a (right) at EVER1

0 5 10 15 20 25 30

23 29 32 71 80 104 134 137 140 164 176 191 206 212 242 245 248 251 254 284 287 296 299 302

_ Avg

Julian Day in Best 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

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

0 20 40 60 80 100 120 140 160 180 200

5 35 44 56 62 74 95 98 113 125 128 152 158 218 236 257 281 290 305 311 332 341 350 362

_ Avg

Julian Day in Worst 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO448 6.3.3 Okefenokee, GA

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 le ft hand bar, with modeled composition from the 12km CMAQ represented by the right hand bar. Best 20% Obs (left) vs 2002gt2a (right) at OKEF1

0 10 20 30 40 50 60

11 20 38 59 62 104 170 173 176 179 191 239 242 257 266 275 284 335 344 347 356 359

_ _ _ Avg

Julian Day in Best 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

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

0 50 100 150 200 250 300 350

17 23 44 56 89 95 122 128 155 188 197 200 218 236 254 260 263 281 296 308 338 341 350

_ _ Avg

Julian Day in Worst 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO44 9 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% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

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

0 50 100 150 200 250 300

35 44 47 56 89 95 98 107 116 122 125 128 131 155 185 188 197 200 260 290 293 296 341

_ _ Avg

Julian Day in Worst 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO450 

### 7.0 LONG

- TERM STRATEGY FOR FLORIDA CLASS I AREAS

As stated in section 1.3 above, the regional haze rule requires s tates 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 measure s 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 modelin g 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 sou rces 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?51 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

Clean Air Interstate Rule ( CAIR )

. Although there is uncertainty over CAIR’s eventual replacement as a resul t of the vacatur of CAIR’s intended successor, the Cross State Air Pollution Rule (CSAPR), significant emission reductions forced by CAIR have occurred as of

2010 and are integral to Florida’s long

- term strategy

. Moreover, CAIR remains in effect as of Sep tember 2012 and its associated emission reductions continue to occur.

NOx SIP Call . Phase I of the NO x SIP Call applies to certain EGUs and large non

- EGUs, including large industrial boilers and turbines, and cement kilns. Those states affected by the NOx SIP call in the VISTAS region have developed rules for the control of NOx emissions that have been approved by the EPA . 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

level s for the 2009 and 2018 future year inventories.

North Carolina Clean Smokestacks Act ( 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 SO 2 emi ssions by 2013. This legislation establishes annual caps on both SO 2 and NOx emissions for the two primary utility companies in North Carolina, Duke Energy and Progress Energy. These reductions must be made in North Carolina, and allowances are not

salea ble.

**•** Under a settlement agreement, Tampa Electric has install ed permanent emissions

- control equipment to meet stringent pollution limits. Consent Agreements (TECO, VEPCO ) and Gulf Power Crist 7 Voluntary Agreement

.

**•** Virginia Electric and Power Co. (VEPCO) agreed to spend $1.2 billion by 2013 to
eliminate 237,000 tons of SO 2 and NOx emissions each year from eight coal

- fired electricity generating plants in Virginia and West Virginia.

**•** Under a 2002 voluntary agreement

, Gulf Power has upgrade d its operation to significantly cut NOx emission s at its Crist generating plant.

One

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

submitted to the EPA to demonstrate attainment of the one

- hour ozone National Ambient Air Quality Standard (NAAQS). These SIPs require NOx reductions from specific coal fired power plants and address transportation plans in these cities.

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)52 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 real ized 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). EPA set a PM emissions standard for new heavy

- dut y 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. T hese NOx and NMHC standards were phased in together between 2007 and 2010, for diesel engines. Sulfur in diesel fuel must be lowered to enable modern pollution

- control technology to be effective on these trucks and buses. EPA will require a 97 percent red uction in the sulfur content of highway diesel fuel from its current level of 500 parts per million (low sulfur diesel, or LSD) to 15 parts per million (ultra

- low sulfur diesel, or ULSD).

Tier 2 Tailpipe (Onroad vehicles)

. EPA mobile rules include the Ti er 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 NO

x emissions bel ow a specified value

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

Large Spark Ignition and Recreational Vehicle Rule

. EPA has adopted new standards for emissions of oxides of nitrogen (NOx), hydrocarbons (HC), and carbon monoxide (CO) f rom 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 m ore 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 vesse ls in 2012. (http://www.epa.gov/nonroad

diesel/)

Industrial Boiler/Process Heater/RICE MACTs . The EPA originally issued final rules to substantially reduce emissions of toxic air pollutants from industrial, commercial and institutional boilers, process h eaters and from stationary reciprocating internal combustion engines (RICE). These rules reduce d emissions of a number of toxic air pollutants, including hydrogen chloride, manganese, lead, arsenic and mercury by 2009. These rules also reduced

emissions of SO 2 and PM in conjunction with the toxic air pollutant reductions. The applied Maximum Achievable Control Technology (MACT) control efficiencies were 4 percent for SO 2 and 40 percent for PM10 and PM2.5. While the EPA ’s industrial boiler MACT rules were53 vacated on June 8, 2007 , t he VISTAS “best and final” modeling run includes these reductions. It

was the consensus of the VISTAS states that these reductions would eventually be reinstated in some form well prior to 2018. The proposed Boiler MACT affects numerous relatively large boilers in Florida, including one coal

- fired unit ( Rock Tenn in Fernandina Beach) that has been identified as a unit to be addressed within the reasonable progress provisions for SO 2 reductions. The acid gas emissions requirement of this proposed rule would have the significant co

- benefit of further reducing SO 2 emissions for many of the subject sources.

Combustion Turbine MACT . The projection inventories do not include the NO x co

- benefit effects of the MACT regulations for Gas Turbines or stationary Reciprocating Internal Combustion Engines, which EPA estimates to be small compared to the overall inventory.

VOC 2

- , 4

- , 7

- , and 10

- year MACT Standards . Various point source MACTs and associated emission reductions were implem ented. 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 40 CFR 51. 30 8 (d)(3)(v)(A), states are also required to consider the air quality benefits of measures to mitigate the impacts of construction activities [section 40 CFR 51. 308(d)(3)(v)(B)] and agricultural and forestry smoke management [section

40 CFR 51

. 308(d)(3)(v) (E)].

With regard to the impact of construction activities, R ule 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 provi des 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.

Wit h 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 Forest Service operates a burn authorization program that considers the potenti al for smoke from the burn impacting smoke sensitive receptors (e.g., airports, roads, hospitals, urban areas, etc.). The SMP provides alternative s 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

.54 7.2.3 Projected 2009 and 2018 G2 and Best and Final (G4) Emissions Inventories

The G2 and Best and Final ( G4 ) inventor y 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 G 2 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 20 18 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

. I t should be noted tha t very few of the BART and

Reaso nable 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 avai lable, 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 change s 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 s ta te 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 co nsistent with the data used

in EPA ’s CAIR analyses supplemented by s tate 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 f acilities and open burning estimates, as well as controls used to project emissions for

EPA ’s Heavy Duty Diesel rulemaking and for the CAIR rulemaking.

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

emission inspection, Stage II vapor recovery, anti

- tampering, etc.) with changes based on specific s tate input. Nonroad control data and projections for 1996, 2010, 2015, and 2020 were

obtained from the EPA 's CAIR Technical Support Document, and straight line pro jections were

used to estimate 2009 and 2018 levels.

**•** The following bar charts show expected decreases in emissions of SO 2 and NOx across the VISTAS states from 2002 through 2018. Note that for SO 2 emissions in particular, which are the largest contributo rs 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 remain55 the largest contributor to haz e, comprising more than half of remaining SO 2 emissions in

every state.

Figure 7.2.3

- 1. Base G2 Annual SO 2 emissions for 2002, 2009, and 2018 in the VISTAS

states. West Virginia Alabama Florida Kentucky Georgia Mississippi South Carolina North Carolina Tennessee Virginia 100 200 300 400 500 600 700Annual SO2 (Thousand Tons) Miscellaneous

Nonroad

Onroad

Industrial

Other Fuel Combustion

EGU 2002

2009

2018

West Virginia Alabama Florida Kentucky Georgia Mississippi South Carolina North Carolina Tennessee Virginia 100 200 300 400 500 600 700Annual SO2 (Thousand Tons) Miscellaneous

Nonroad

Onroad

Industrial

Other Fuel Combustion

EGU 2002

2009

2018

Alabama Florida Kentucky Georgia Mississippi South Carolina North Carolina Tennessee Virginia 100 200 300 400 500 600 700Annual 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 VirginiaAnnualNOx(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 VirginiaAnnualNOx(Thousand Tons) Miscellaneous Nonroad Onroad Industrial Other Fuel Combustion EGU Miscellaneous Nonroad Onroad Industrial Other Fuel Combustion EGU 2002

2009

201856 Figure 7.2.3

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

Sta tes. 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 NH 3 NOx PM10 PM2.5 SO 2 VOC

EGU 35,928 1,631 86,165 9,007 5,910 186,055 1,910

NONEGU 87,037 1,544 46,020 39,872 33,946 65,511 36,880

ONROAD 3,308,863 21,549 312,321 9,801 6,104 2,584 336,707

NONROAD 2,104,920 148 163,794 15,613 14,866 8,967 209,543

AREA 57,011 38,616 28,187 503,230 64,589 36,699 420,172

FIRES 923,310 3,157 19,791 98,470 88,756 4,129 51,527

BIOGENICS 0 0 36,320 0 0 0 1,522,031

TOTAL 6,517,070 66,645 692,599 675,993 214,170 303,943 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 NH 3 NOx PM10 PM2.5 SO 2 VOC

EGU 54,146 2,976 74,640 10,022 6,886 138,340 2,422

NONEGU 96,819 1,829 51,902 46,456 39,430 75,047 42,811

ONROAD 2,520,315 25,885 148,486 8,178 3,994 2,506 219,554

NONROAD 2,323,327 171 127,885 12,497 11,868 7,536 183,452

AREA 53,903 40,432 30,708 578,516 72,454 38,317 489,975

FIRES 923,310 3,157 19,791 98,470 88,756 4,129 51,527

BIOGENICS 0 0 36,320 0 0 0 1,522,031

TOTAL 5,971,821 74,450 489,733 754,139 223,388 265,875 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 f or 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.57 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 Are a (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 f igures 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 f actors 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 da ys 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, s ome limited corrections were made in developing the relati ve 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 ).58 Figure 7.2.4

- 1 a . CMAQ 2018 Best and Final results compared to Uniform Rate of Progress

at Everglades National Park.59 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)60 Figure 7.2.4

- 2 a . CMAQ 2018 Best and Final results compared to Uniform Rate of Progress

at Chassahowitzka Wilderness Area.61 Figure 7.2.4

- 2b. CMAQ 2018 Best and Final results compared to Uniform Rate of Progress at ChassahowitzkaWilderness Area. (Correc ted for NO3 and SO4 model performance)62 Figure 7.2.4

- 3 a . CMAQ 2018 Best and Final results compared to Uniform Rate of Progress

at St. Marks Wilderness Area.63 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)64 Figure 7.2.4

- 4 a . CMAQ 2018 Best and Final results compared to Uniform Rate of Progress

at Okefenokee Wilderness Area.65 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)66 Figure 7.2.4

- 5 a . CMAQ 2018 Best and Final results compared to Uniform Rate of Progress

at Breton Island Wildlife Refuge.67 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)68 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

YearHaziness Index (Deciviews)

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction69 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

YearHaziness Index (Deciviews)

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction70 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

YearHaziness Index (Deciviews)

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

YearHaziness Index (Deciviews)

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction72 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 incr ease 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

YearHaziness Index (Deciviews)

Baseline Natural Condition (20% Best Days) Observation Method 1 Prediction73 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 0.774 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

C lass I areas outside of Florida (Okefenokee and Breton) , for which Florida sources may impact,75 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 redu ctions 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 visibi lity 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 E missions 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 re lative 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 Cana da 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 emiss ions 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 repre sents 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. Concurr ently 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.

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

- km

simulat ions, both using 2018 Base F emissions and boundary conditions from GEOS

-76 Chem. 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 emissio ns 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 quali ty 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 sca le 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 impact s 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 a reas. 

### Transboundary Man-Made Impacts

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

GEOS_4x5 GEOS_1x1 PSAT CMAQ77 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 run s 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 b y 30 percent for each pollutant sensitivity run. The pollutant contributions that were evaluated were:

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

**•** SO 2 from non

- EGU point sources in each 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 f rom 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 , p lus 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 we re included in these graphics.78 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. 

## 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

TN

SC

NC

MS

KY

GA

FL

AL79 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.

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. 

### 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

TN

SC

NC

MS

KY

GA

FL

AL



## 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

TN

SC

NC

MS

KY

GA

FL

AL80 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.

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

SO 2 from EGUs. The Everglades is somewhat different than any of the ot her 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 SO 2 emission reductions from non

- utility, industrial point sources. The pattern of relative SO 2 contributions from non

- EGUs among the various VISTAS states is similar to the pattern of

relative SO 2 contribu tions 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 2 0 percent best days ammonium nitrate has a somewhat larger contribution to visibility impairment. R educing 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 

## 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

TN

SC

NC

MS

KY

GA

FL

AL81 sources of VOC emi ssions 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 . T his 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 sensitivit y 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 tha t ammonium nitrate may be important for sites where the 20 percent worst days occur in the winter. We conclude that reducing SO 2 emissions from EGU and non

- EGU point sources in the VISTAS states 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 SO 2 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 SO 2 point sources located in those geographic areas. An SO 2 Area of Influence (AoI) was defined for each Class I area to represent the geographic area containing sources that would likely have the greatest impact on visibility at that Class I area. All SO 2 p oint sources within these Areas of Influence were identified and ranked by their 20 02 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 analy ses 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 traje ctory 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 speci fied 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 2002.82 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

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 poin t sources of emissions eminate.83 Figure 7.5.2. Example residence time plot for 20 percent worst visibility days in 2000

- 2004

for Eve rglades . Based on trajectories with 100m start height.

7.5.3 SO 2 Areas of Influence

The next step was to develop sulfate extinction weighted residence time plots to define the geographic area with highest 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 i nfluenced by organic carbon from fires. Sulfate

weighted 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 Influen ce for sources of SO 2 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 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.84 Figure 7.5.3. Example SO 2 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 SO 2 Areas of Influence

Residence time plots were then combined with geographically

- gridded emission data based on

the 2002 baseline and 2018 BaseG emissions inve ntories. 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 t he trajectories, these data were weighted by 1/d, where d was calculated as the distance, in kilometers, between the center of the grid cell in which a source is located and the center of the grid cell in which the IMPROVE monitor is located. The distance

- weighted point85 source SO 2 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

s ets. The distance weighted (1/d) gridded point source SO 2 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 displa yed 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 map s help visualize where the emissions reductions will be occurring between 2002 and 2018. The change in SO 2 emissions between 2002 and 2018 can be seen by comparing emissions source strengths in the two plots.

Note the emissions from each source are norma lized by the total emissions in the domain.

Sources that reduce SO 2 emissions by 2018 will show a lower contribution to emissions in the domain. On the 2018 map the grid cells with these sources will show a lighter color gradient than on the 2002 map.

Figure 7.5.4

- 1. Everglades 2002 and 2018 SO 2 distance weighted emissions * SO4

extinction

- weighted residence time plots. 

## Comparison 2002 vs 2018 SO2 Emissions * Residence

Time Using 100 m Start Height



## – Everglades, FL

2018 SO2 emissions 2002 SO2 emissions

Green circles indicate 100

- km and 200

- km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Tim e > 10%. Orange line perimeter indicate Area of Influence with Residence Time > 5%. Max Value = 37%86 Figures 7.5.4

- 2 through 7.5.4.4 illustrate gridded SO 2 distance weighted emissions * sulfate extinction weighted residence time plo ts for 2002 and 2018 emissions for Chassahowitzka, St. Marks, Okefenokee, and Breton . These plots illustrate the relative importance of Florida sources of SO 2 compared to sources in neighboring states.

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

Figure 7.5.4

- 2. 2002

- 2018 SO 2 distance weighted emissions * SO4 extinction

- weighted residence time plot for Chassahowitzka

. 

### Comparison 2002 vs 2018 SO2 Emissions * Residence

Time Using 100 m Start Height



### – Chassahowitzka , FL

2018 SO2 emissions 2002 SO2 emissions

Green circles indicate 100

- km and 200

- km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Tim e > 10%. Orange line perimeter indicate Area of Influence with Residence Time > 5%. Max Value = 68%87 Figure 7.5.4

- 3. 2002

- 2018 SO 2 distance weighted emissions * SO 4 extinction

- weighted residence time plot for St. Marks

. 

### Comparison 2002 vs 2018 SO2 Emissions * Residence

Time Using 100 m Start Height



### – St. Marks, FL

2018 SO2 emissions 2002 SO2 emissions

Green circles indicate 100

- km and 200

- km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Tim e > 10%. Orange line perimeter indicate Area of Influence with Residence Time > 5%. Max Value = 22%88 Figure 7.5.4

- 4. 2002

- 2018 SO 2 distance weighted emissions * SO 4 extinction

- weighted residence time plot for Okefenokee, GA

. 

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

2002 SO2 emissions 2018 SO2 emissions

Green circles indicate 100

- km and 200

- km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Tim e > 10%. Orange line perimeter indicate Area of Influence with Residence Time > 5%. Max Value = 25%89 Figure 7.5.4

- 5. 2002

- 2018 SO 2 distance weighted emissions * SO 4 extinction

- weighted residence time plot for Breton, LA.

7.5.5 Specific Source Types in the Areas of 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 SO 2 from stationary point sources . The following tables quantify the

emissions from specific source categories that emit SO 2 within the area of influence, defined as within the area having a 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 SO 2 Areas of

Influence for the Florida Class I areas are listed in Tables 7.5.5

- 1 through 7.5.5

- 5. 

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

2002 SO2 emissions 2018 SO2 emissions

Green circles indicate 100

- km and 200

- km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Tim e > 10%. Orange line perimeter indicate Area of Influence with Residence Time > 5%. Max Value = 10%90 Annual 2018 G2 Emissions

- Everglades Area o f Influence
Category VOC NOX CO SO 2 PM

- 10 PM

- 2.5 NH 3

Fuel Comb. Elec. Util. 0% 15% 1% 34% 3% 5% 6%

Fuel Comb. Industrial 1% 8% 2% 11% 4% 11% 0%

Fuel Comb. Other 1% 4% 0% 9% 2% 4% 0% Chemical & Allied Product Mfg 0% 0% 0% 36% 0% 0% 3%

Metals Processing 0% 0% 0% 1% 0% 0% 0%

Petroleum & Related Industries 0% 0% 0% 0% 0% 0% 0%

Other Industrial Processes 3% 1% 0% 1% 2% 2% 0%

Solvent Utilization 40% 0% 0% 0% 0% 0% 0% Storage & Transport 10% 0% 0% 0% 0% 1% 0%
Waste Disposal & Recycling 1% 0% 1% 0% 2% 5% 1%
Highway Vehicles 21% 34% 41% 1% 2% 3% 37%

Off

- highway 20% 34% 45% 5% 3% 9% 0%

Miscellaneous 3% 3% 10% 2% 82% 60% 52%

Table 7.5.5

- 1. 2018 Emissions Contributions from Major Source Categories in the Area of Influence for Everglades

.

Annual 2018 G2 Emissions

- Chassahowitzka Area of Influence

Category VOC NOX CO SO 2 PM

10 PM

2.5 NH 3 Fuel Comb. Elec. Util. 0% 19% 1% 48% 3% 5% 3%

Fuel Comb. Industrial 0% 13% 1% 17% 2% 5% 0%

Fuel Comb. Other 2% 4% 1% 7% 1% 3% 0%

Chemical & Allied Product Mfg 1% 1% 0% 12% 0% 1% 1%
Metals Processing 0% 0% 0% 4% 0% 0% 0%

Petroleum & Related Industries 0% 0% 0% 5% 0% 0% 0%

Other Industrial Processes 5% 4% 1% 4% 7% 11% 3%

Solvent Utilization 36% 0% 0% 0% 0% 0% 0% Storage & Transport 9% 0% 0% 0% 0% 0% 0%
Waste Disposal & Recycling 4% 3% 7% 0% 9% 19% 0%
Highway Vehicles 19% 27% 37% 1% 1% 1% 18%

Off

- highway 17% 25% 31% 2% 2% 4% 0%

Miscellaneous 6% 5% 20% 2% 73% 51% 75%

Table 7.5.5

- 2. 2018 Emissions Contributions from Major Source Categories in the Area of Influence for

Chassahowitzka

.91 Annual 2018 G2 Emissions

- St. Marks Area of Influence

Category VOC NOX CO SO 2 PM

10 PM

2.5 NH 3 Fuel Comb. Elec. Util. 0% 20% 1% 46% 4% 7% 2%

Fuel Comb. Industrial 1% 17% 2% 20% 3% 5% 0%

Fuel Comb. Other 2% 4% 1% 8% 1% 3% 0% Chemical & Allied Product Mfg 1% 1% 0% 7% 0% 1% 1%
Metals Processing 0% 0% 0% 4% 0% 0% 0%

Petroleum & Related Industries 2% 0% 0% 5% 0% 0% 0%

Other Industrial Processes 7% 7% 1% 8% 8% 11% 4%

Solvent Utilization 36% 0% 0% 0% 0% 0% 0% Storage & Transport 8% 0% 0% 0% 1% 1% 0%
Waste Disposal & Recycling 4% 2% 7% 0% 8% 18% 0%
Highway Vehicles 18% 21% 36% 0% 1% 1% 18%

Off

- highway 14% 24% 28% 2% 2% 4% 0%

Miscellaneous 7% 4% 23% 1% 71% 50% 75%

Table 7.5.5

- 3. 2018 Emissions Contributions from Major Source Categories in the Area of Influence for St.

Marks

.

Annual 2018 G2 Emissions

- Okefenokee Area of Influence

Category VOC NOX CO SO 2 PM

10 PM

2.5 NH 3 Fuel Comb. Elec. Util. 0% 22% 1% 51% 7% 12% 1%

Fuel Comb. Industrial 1% 16% 2% 25% 3% 5% 0%

Fuel Comb. Other 3% 4% 1% 6% 2% 4% 0% Chemical & Allied Product Mfg 1% 1% 0% 4% 0% 1% 0%
Metals Processing 0% 0% 0% 4% 0% 0% 0%

Petroleum & Related Industries 0% 0% 0% 0% 0% 0% 0%

Other Industrial Processes 7% 6% 1% 7% 8% 11% 2%

Solvent Utilization 39% 0% 0% 0% 0% 0% 0%
Storage & Transport 7% 0% 0% 0% 0% 0% 0%
Waste Disposal & Recycling 4% 3% 7% 0% 8% 16% 0%
Highway Vehicles 18% 25% 38% 1% 1% 1% 10%

Off

- highway 14% 19% 29% 1% 2% 3% 0%

Miscellaneous 6% 4% 20% 1% 68% 46% 86%

Table 7.5.5

- 4. 2018 Emissions Contributions from Major Source Categories in the Area of Influence for Okefenokee, GA

.92 Annual 2018 G2 Emissions

- Breton Area of Influence

Category VOC NOX CO SO 2 PM

10 PM

2.5 NH 3 Fuel Comb. Elec. Util. 1% 19% 1% 33% 3% 6% 1%

Fuel Comb. Industrial 2% 31% 4% 18% 8% 14% 0%

Fuel Comb. Other 4% 3% 2% 5% 2% 4% 0% Chemical & Allied Product Mfg 4% 4% 1% 24% 2% 3% 6%
Metals Processing 1% 1% 1% 4% 3% 5% 0%

Petroleum & Related Industries 4% 1% 1% 8% 1% 1% 0%

Other Industrial Processes 7% 5% 2% 5% 11% 13% 16%

Solvent Utilization 33% 0% 0% 0% 0% 0% 0% Storage & Transport 8% 0% 0% 0% 1% 1% 0%
Waste Disposal & Recycling 3% 1% 5% 0% 5% 10% 1%
Highway Vehicles 12% 13% 33% 0% 1% 1% 7%

Off

- highway 14% 20% 29% 2% 2% 4% 0%

Miscellaneous 6% 2% 21% 1% 62% 39% 69%

Table 7.5.5

- 5. 2018 Emissions Contributions from Major Source Categories in the Area of Influence for Breton, LA

.

T he next step in the analysis was to determine which particular sources to target for potential further reasonable progress emission reductions. Lists of SO 2 point sources within the Areas of Influence for each Class I areas were developed using the most c urrent (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 SO 2 sources within each Area of Influence, the following attributes were defined for each individual

unit:

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

**•** SO 2 emissions for 2002 and 2018

**•** 2018 control efficiency

**•** Distance to Class I areas (defined by centroid of the Class I are a)

**•** 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 th e sources of

SO 2 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 t he emissions, the greater the chance of visibility impairment), d is the distance (the greater the distance the more dispers e the emissions and the less impact on visibility), and RTmax

– a measure of the probability that the local climatology

wi ll 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 cont ribution to the total Q/d*RTmax for a given Class I area. Some states used a value of 0.5% relative contribution. In93 most cases, other states, in developing these data used the projected 2018 emissions, which for

EGU’s was based on the IPM electric gener ation 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

SO 2 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 Prog r ess 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 po tentially 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 t he reasonable progress requirements have been developed by the 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 r easonable progress goal that provide s for “reasonable progress” to be made with in 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, state s 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 ju dging 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, “Gi ven 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

S tates 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, the reasonable progress goals set forth in this plan , as amended in 2012, are l argely based on continued implementation of the CAIR

driven emission reductions, along with other existing requirements. Although there is uncertainty over CAIR’s eventual replacement, CAIR remains in effect and its associated

emission reductions continue to occur. Significant emission reductions forced by CAIR have

occurred as of 2010 . Thus, the reasonable progress goal s as originally developed remain valid94 for Florida. The following chart shows the actual Florida SO 2 emissions compared to the original VISTAS projections used to determine Florida’s reasonable progress goal s . Actual SO 2 emissions by 2010 (the most recent year of available data) are already less than the projected

2018 emissions.

Because of the large amount of emissions reductions of SO 2 and NOx in the Southeastern U.S. from existing rules and programs, Class I areas in VISTAS states are expected to make considerable visibility improvement during the first regional haze planning period.

7.7 F lorida’s Reasonable Progress Analysis

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 compl ete 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 long

range 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 reducti ons. 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, co nsidering state

- specific situations, to develop its reasonable progress requirements.

Focus on Sulfur Dioxide 0 100,000 200,000 300,000 400,000 500,000 600,000 700,0002000200120022003200420052006200720082009201020112012201320142015201620172018tons 

## Florida SO 2 

## Annual Emissions
Compared with Regional Haze SIP Projections

Actual

RH SIP95 VISTAS, through its modeling contractors, completed a series of sensitivity runs that evaluated visibility improvements to pollutant reduction s. These runs showed reduction of sulfur dioxide (SO 2 ) 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 expec ted (projected) to occur as a result of the already implemented (on

the

- books) regulations by 2018 for each of the Class I areas. Because of the large decreases in

SO 2 , primarily from CAIR, most of the Class I areas easily show visibility improvement at a
faster rate than that of a straight line path to meeting the national goal. Class I areas located along the coast show visibility improvement near the straight line path, sometimes marginally

better or marginally worse. The Florida Class I areas are in this category (see Figures 7.2.4

- 1 through 7.2.4

- 5). Because considerable progress (visibility improvement) was expected by 2018

with on

- the

- books regulations, and because SO 2 reductions provide the best opportunity for additional improvement, the VISTAS states (including Florida) concluded that it was appropriate to consider only SO 2 reductions in any reasonable progress analysis.

An analysis of the source categories where SO 2 emissions are emitted, show that electric generating units and other industria l 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 SO 2 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 SO 2 emissions ; t he greater the emissions, the more likely a source may impact visibility. The second variable is distance to a Class I area ; t he 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 sp readsheet 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 SO 2 emissions in tons per year, d represents distan ce 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* RTmax96 There were ver y little data or modeling results to provide a definitive analysis identify ing 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 s urrogate was to pair

- down the list of all stationary sources of SO 2 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 provid ing the greatest potential benefit to visibility improvement. To limit the list, VISTAS 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 b e appropriate for identifying the largest contributors of visibility impairment.

For reasons of better public clarity and understanding, we chose to develop a reasonable progress source selection metric based on Q/d that would be essentially equivalent to the nominal VISTAS metric of Q/d *RTMax. 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 use d 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 SO 2 nearest the Class I areas are required to address reasonable progress , while smaller sources (not expected to provide significant, cost

- effective reductions) are excluded. Similarly, we provided some bounds for emissions (Q) and distance (d) to affect a reasonable process. First, we exclude small (< 25 0 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 us ed 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 pe rmit review requires that visibility specifically be addressed (EPA’s Regional Haze Regulations, July 1, 1999)

. Finally, we use the 2002 emissions for Q, where VISTAS used the projected 2018 emissions. This is important in Florida for two reasons. First, we question some of the IPM projections that convert all of our oil

- fired boilers to natural gas. If we accepted this premise, emissions of SO 2 are presumed to be zero in 2018 and these units

would not be considered for reasonable progress review . Secon d, 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 reasonable progress process.

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

, 15 th , 18 th , 19 th , 27 th , and 30 th largest SO 2 sources (2002) in the state. Of all the SO 2 sources considered in the VISTAS area of influence around each of the Class I areas, the Florida

criterion subjects 67.6 percent of the emissions to reasonable progress review. In comparison, use of the criteria used by VISTAS (1% contribution using Q/d*RTmax) would select 70.5 percent of the emissions.97 TPY Percent of

Total

2002 Total Florida SO 2 Emissions (within VISTAS AOI) 497,867

Selected w/1% criteria 351,167 70.5%

Selected w/ Q/d >50 criteria 336,595 67.6%

Consequently, we believe that the Florida criterion is consistent with the general approach taken by the VISTAS states. A complete comparison of the Florida selection criteria with the general

VISTAS criteria is contained in the tables 7.7.1

– 7.7.4

. The disposition of the selected sources is provided in table 7.7.5.9899100101 Table 7.7.5 Reasonable Progress

Sources

Plant ID Plant Name Point ID Type 2002 SO2
EMISSION (TON/YR) 2010 SO2
EMISSION (TON/YR) Existing and Projected SO2

Control Equipment Disposition

Currently Shutdown

570040 TECO GANNON 1 coal

- fired boiler 5,157 0 Shutdown (2003) Provide documentation

2 coal

- fired boiler 4,942 0 Shutdown (2003) Provide documentation

3 coal

- fired boiler 5,602 0 Shutdown (2003) Provide documentation

4 coal

- fired boiler 5,577 0 Shutdown (2003) Provide documentation

5 coal

- fired boiler 8,043 0 Shutdown (2003) Provide documentation

6 coal

- fired boiler 16,097 0 Shutdown (2003) Provide documentation

1030011 PE BARTOW 1 oil/gas

- fired

boiler 6,483 0 Shutdown (2009) Provide documentation

2 oil/gas

- fired

boiler 6,149 0 Shutdown (2009) Provide documentation

3 oil/gas

- fired

boiler 11,249 0 Shutdown (2009) Provide documentation

Proposed Shutdown

110036 FPL PORT EVER 3 oil/gas

- fired

boiler 6,409 1,798 To be Shutdown (2013) Provide documentation

4 oil/gas

- fired

boiler 6,205 2,003 To be Shutdown (2013) Provide documentation

Subject to BART

250003 FPL TURKEY POINT 1 oil/gas

- fired

boiler 4,610 1,595 Subj. to BART

- satisfies

RP

2 oil/gas

- fired

boiler 4,327 1,576 Subj. to BART

- satisfies

RP

170004 PE CRYSTAL RIVER 1 coal

- fired boiler 18,998 14,874 Subj. to BART

- satisfies

RP

2 coal

- fired boiler 20,728 18,176 Subj. to BART

- satisfies

RP

0310045

- B JEA NORTHSIDE 3 oil/gas

- fired

boiler 7,146 912 Subj. to BART

- satisfies

RP

330045 GULF POWER CRIST 7 coal

- fired boiler 21,546 2,239 Subj. to BART

- satisfies

RP

810010 FPL MANATEE 1 oil/gas

- fired

boiler 13,930 3,481 Subj. to BART

- satisfies

RP

2 oil/gas

- fired

boiler 15,073 3,998 Subj. to BART

- satisfies

RP

1010017 PE ANCLOTE 1 oil/gas

- fired

boiler 13,879 3,325 Subj. to BART

- satisfies

RP

2 oil/gas

- fired

boiler 13,225 3,740 Subj. to BART

- satisfies

RP

4

- Factor Analysis

10006 GRU DEERHAVEN 5 coal

- fired boiler 6,969 3,161 Dry Scrubber No longer applicable

170004 PE CRYSTAL RIVER 3 coal

- fired boiler 26,436 1,660 Wet Scrubber 4

- factor analysis

4 coal

- fired boiler 24,635 4,730 Wet Scrubber 4

- factor analysis

0310045

- A JEA ST JOHNS RIVER 16 coal

- fired boiler 11,076 4,786 Wet Scrubber 4

- factor analysis

17 coal

- fired boiler 10,185 4,616 Wet Scrubber 4

- factor analysis

0310045

- B JEA NORTHSIDE 27 coal

- fired boiler 5,090 1,584 Dry Scrubber No longer applicable

530021 FL CRUSHED STONE 18 coal

- fired boiler 2,906 2,197 Dry Scrubber No longer applicable

1050004 LAKELAND MCINTOSH 6 coal

- fired boiler 6,994 4,198 Wet Scrubber 4

- factor analysis

1070025 SEMINOLE ELECTRIC 1 coal

- fired boiler 10,912 8,483 Wet Scrubber 4

- factor analysis

2 coal

- fired boiler 12,775 8,477 Wet Scrubber 4

- factor analysis

Non

- EGU Boiler MACT

890003 Rock Tenn 15 coal

- fired boiler 3,242 3,344 4

- factor analysis

336,595 104,953102 Case

- by

- Case Reasonable Progress Analys e s

A case

- by

- case reasonable progress analysis is required for all sources meeting the Florida

criterion (Q/d > 50). In some cases, described below, sources have taken federally enforceable

permit limitations that limit the source ’ s Q/d to less than 50. These sources are thus no longer

subject to the reasonable progress requirements. In addition, a number of sou rces are subject to

both the reasonable progress requirements and the Best Available Retrofit Technology (BART)

requirements. For these sources, the BART determination will satisfy their reasonable progress

requirements and no further reasonable progress review is necessary. For the remaining sources

subject to the reasonable progress requirements, DEP has completed a review and determin ed

appropriate SO 2 emission controls considering the following four

- factors:

**•** 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.

Applicability of Florida Crushed Stone

, JEA Northside , and GRU Deerhaven

Three units have taken federally enforceable permit conditions that limit SO 2 emissions such that they are no longer applicable to reasonable progress review.

Central Power and Lime is a coal

- fired power boiler co

- located with the Fl orida Crushed Stone (now CEMEX) cement plant. This unit has received a construction permit to convert the coal

fired boiler to a biomass boiler, and is prohibited from burning coal once it makes the appropriate changes to the boiler to burn biomass. Ultr a low sulfur distillate fuel oil will be used

for start

- up, shutdown, and bed stabilization. The commitment s to execute these changes are contained in the federally enforceable construction permit, the essential parts of which are

included in Exhibit 2. T he result of these changes is that the source is no longer subject to the reasonable progress review. The maximum allowed SO 2 emissions are now set at 591.3 tons per year. The distance to the nearest Class I area (Chassahowitzka NWA) is 22.4 kilometers. This results in a Q/ d of 26.4, well below the applicability criterion of Q/ d = 50 or greater. Thus, no further analysis of this source is required. Florida Crushed Stone

- Central Power and Lime

Circulating Fluidized Bed Boiler 1 (emissions unit 27) is a coal, coal coated with latex,

p etroleum coke, and landfill gas fired circulating fluidized bed (CFB) boiler rated at 298 MW. Sulfur dioxide emissions are controlled by the injection of limestone into the CFB boiler beds.

Residual SO 2 emissions are further controlled by the use of an a dd

- on spray dryer absorber JEA Northside103 system. The federally enforceable SO 2 emission limitation is 0.2 lb/MMBtu, 24

- hour average, and 0.15 lb/MMBtu, 30

- day rolling average. Annual emissions are thus limited to 1,816 tons

SO 2 per year.

Since 2002, the year for whic h data is used to determine applicability for reasonable progress review, permit conditions have been imposed on this unit that limit the annual emissions of SO 2

. In 2009, JEA requested and received the permit limitations listed above. The new, federally enforceable limits result in the source no longer being subject to the reasonable progress review. The JEA Northside source is located 69.3 kilometers from the closest Class I area (Okefenokee NWA). With maximum allowable SO 2 emissions of 1,816 tons, th e applicability metric Q/ d equals 26.2, which is less than the Q/ d = 50 or greater criterion. Thus , no further analysis of this source is required.

The coal

- fired boiler number 2 (emissions unit 5) is a 251 MW (nominal) fossil fuel fired steam

generator . Sulfur dioxide emissions are controlled with the use of a circulating dry scrubber. The scrubber is designed to achieve a target outlet SO 2 emission rate of 0.12 lb/MMBtu. This

scrubber came on

- lin e in 2009, providing significant reductions in SO 2 . The basis for this unit to be subject to the reasonable progress four

- factor review is that its Q/d ratio in 2002 was greater than 50 (6,969 tons/112.2 km = 62.12). Because of the addition of the scrubb er, GRU has requested a federally enforceable permit condition that limits SO 2 emissions to 5,500 tons per year, resulting in a maximum Q/d value of 49.0. A copy of the essential parts of the construction permit incorporating the lower limit is included i n Exhibit 2. Thus, no further analysis of this source is required. GRU Deerhaven

Shutdowns of Reasonable Progress Sources

Nine units subject to reasonable progress review are shutdown. This includes six units at the Tampa Electric Gannon facility, shut down in 2003, and three units at the Progress Energy Bartow facility, shut down in 2009. The permits requiring the shutdown of these sources are

included in Exhibit 2.

Two additional units subject to the reasonable progress review will soon be shut down. Th e se

are units 3 and 4 at the Florida Power and Light Port Everglades facility (to be shut down in 2013). A copy of DEP ’s intent to issue a permit is included in Exhibit 2.

Four

- Factor Reasonable Progress Determinations

Progress Energy Florida (PEF) Crystal River Units 3 and 4

Emissions units 3 and 4 are fossil fuel

- fired electric utility steam generators, each rated at 760 MW. Sulfur dioxide (SO 2 ) emissions are controlled with wet flue gas desulfurization (FGD) systems, designed to reduce emission s by 97%. The federally enforceable permit condition is set104 at 0.27 lb/MMBtu, based on a 30

- day rolling average. The FGD systems came on line in 2009 (unit 4) and 2010 (unit 3).

Wet limestone FGD systems are considered by DEP to be the top level SO 2 e missions control system for coal

- fired boilers. The source considered the potential for additional SO 2 reductions through the use of lower sulfur western coal but found that it would not be cost

- effective

.

Costs of Compliance

– The source is already incu rring the cost of the new FGD systems

. W hile lower sulfur coal is potentially available from the Powder River Basin (PRB), PRB coal is sub

bituminous coal that has unique combustion characteristics that would require additional operational modifications t o ensure continued safe and reliable unit performance. Moreover, the transportation of this coal from Wyoming to Florida would not only be cost prohibitive but also produce considerable secondary environmental impacts from unit trains travelling such a

di stance. Installing additional add

- on controls for PRB firing would take, at a minimum, several years or longer due to PEF’s need to continue operating the units as base

- load to supply reliable electric power to its customers.

Time Necessary for Complian ce

– The top level controls are already installed and are operating. No additional time for compliance is necessary.

Energy and Non Air Quality Environmental Impacts of Compliance

– The top level controls are already installed, so no additional energy or non air quality environmental impacts would occur.

The use of lower sulfur PRB coal could potentially include increased water use, additional solid

wastes

, secondary emissions caused by fuel transportation

, and additional energy usage for

control.

Rema ining Useful Life

– The source anticipates that these units will continue to operate for another 28 years.

In summary, DEP has determined that the existing FGD SO 2 control systems meet the reasonable progress goals of the state’s regional haze plan for th is planning period

.

Seminole Electric Cooperative (SECI)

Seminole Generating Station (SGS) Units 1 and 2 are solid fuel, dry

- bottom, wall

- fired units each with a maximum heat input of 7,172 million British thermal units per hour (MMBtu/hr) generating 736 MW each. Units 1 and 2 are currently authorized to burn coal as the primary fuel but are a lso authorized to burn a blend of coal and petroleum coke up to a maximum of 30 percent by weight petroleum coke. The maximum sulfur content of petroleum coke may not exceed 7.0 percent by weight on a dry basis. This sulfur content limit for petroleum co ke is 2.3 times the design coal nominal sulfur content of 3.0 percent by weight. Units 1 and 2 are each equipped with S O 2 continuous emissions monitoring systems as required by the Acid Rain Program.

In addition to electrostatic precipitators for control of particulate matter (PM), low

- NOx burners, and selective catalytic reduction (SCR) for NOx control and an alkali injection system to control emissions of sulfuric acid mist, Units 1 and 2 are each equipped with wet FGD scrubbers to control105 S O 2 emissions . The original FGD controls (1984) were upgraded in 2010 to comply with CAIR and other air regulatory initiatives (e.g., the Clean Air Mercury Rule). Improvements to the FGD systems included scrubber module modifications, upgrades to the module's mist el iminator wash system, expansion of the oxidation air system, and upgrades to the existing sparger rings in the absorber recycle tanks. Following these upgrades, the allowable S O 2 emissions rate for Units 1 and 2 was

reduced from 1.2 to 0.67 1b/MMBtu.

Wet FGD technology provides the highest S O 2 removal efficiencies for coal

- fired boilers. The SGS Unit 1 and 2 FGD control systems currently achieve approximately 92

- percent S O 2 removal. SECI proposes to make additional changes to Units 1 and 2 to achieve a m inimum S O 2 removal efficiency of 95 percent or, alternatively, an equivalent S O 2 emissions rate of no more than 0.25 lb/MMBtu on a

30

- day rolling average basis for both units

.

SECI is presently evaluating available options to achieve the proposed S O 2 remo val efficiency or emissions limit including, but not limited to, further modifications to the internal components of the FGD scrubbers, increase in limestone recirculation rates, and increased used of dibasic acid. SECI

will complete its evaluation and pr ovide DEP with the details of the selected option by March 1, 2013. The amount of time required to implement the selected option and achieve the proposed S O 2 emissions limits will depend on the option's design and if construction is required. Within 1 to 3 years following option selection (i.e., no later than March 1, 2016), SECI will achieve either the proposed S O 2 emissions limit or the removal efficiency requirements.

In general, wet limestone FGD systems are the top level SO 2 control device for coal

- fired utility boilers. To assure that these FGD systems remain operating at high performance levels, the company has agreed to upgrade the systems and take a federally enforceable permit condition to achieve 95 percent SO 2 removal efficiency. This will result in an emissions rate of 0.25 lb/MMBtu or better. In addition, the company is removing the option to burn petroleum coke.

DEP has determined that the existing FGD controls, with the upgrades, meet the reasonable progress goals of the state’s region al haze plan.

Costs of Compliance

– SGS units 1 and 2 both have the top level control device for coal

- fired utility boilers. As such, no lower level control option was reviewed. However, certain upgrades are available to improve the FGD system to achie ve 95 percent removal efficiency. While not quantified, the company has agreed to incur these costs.

Time Necessary for Compliance

– The company has estimated that compliance with the 95 percent SO 2 removal efficiency will be achieved by March 2016.

Energy and Non Air Quality Environmental Impacts of Compliance

– There are no additional energy and non air quality environmental impacts since the FGD system is already in place and operating.

Remaining Useful Life

– These units are anticipated to contin ue operation indefinitely.

In summary, DEP has determined that the existing FGD SO 2 control systems, with the described upgrades, meet the reasonable progress goals of the state’s regional haze plan for this planning106 period

. The applicable sections of th e federally enforceable permit conditions are located in

Exhibit 2.

Lakeland McIntosh

Boiler Unit 3 ( E missions U nit 6) is a nominal 364 MW fossil fuel

- fired steam generator that fires coal, coal and up to 20% petroleum coke (petcoke), low sulfur fuel oil (< 0.5% sulfur by weight), high sulfur fuel oil (> 0.5% sulfur by weight), and natural gas or propane. Sulfur dioxide (SO 2 ) emissions are controlled by a flue gas desulfurization (FGD) system. The maximum heat input rate is 3,640 million British thermal units per hour (MMBtu/hr). The federally enforceable SO 2 emission limitation is 1.2 lb/MMBtu when burning only coal. When burning blends of petroleum coke the limit is 0.718 lb/MMBtu.

Unit 3 is currently equipped with a wet limestone FGD system to cont rol SO 2 emissions. Unit 3 is subject to NSPS Subpart D, which has no minimum SO 2 percent reduction requirements. However, the current Title V permit requires a 65 percent reduction in SO 2 when the emissions are less than 0.75 lb/MMBtu and a 90 percent red uction when emissions are greater than or equal

to 0.75 lb/MMBtu. Based on the actual SO 2 emissions reported in 2002, the FGD system

reduced SO 2 emissions by 81 percent. The scrubber is of a tray tower type consisting of two

absorber modules. Each module provides approximately 55 percent capacity of total unit output.

In general, wet limestone FGD systems are the top level control device for coal

- fired utility boilers. However, the source considered several changes or upgrades to the FGD system to additionally reduce SO 2 emissions. These included lower sulfur fuel, FGD modifications, and complete replacement of the FGD system.

Costs of Compliance

-- Among the authorized fuels for Unit 3, petcoke has the highest sulfur content, an average of 3.9 percent by weight. Bituminous coal, with an average of 1.8 percent sulfur, is the fuel with next highest sulfur content. Lakeland Electric is authorized to burn up to 20 percent petcoke by weight with bituminous coal and

, as a result, the average sulfur content of the combined fuel (coal and petcoke) can be as high as 2.22 percent (80 percent coal with 1.8 percent sulfur and 20 percent petcoke with 3.9 percent sulfur) due to the high sulfur content of petcoke. Since coal is the most used fuel for Unit 3 , petcoke (20 percent of coal) can contribute significantly to the total SO 2 emissions from the unit. Lakeland Electric believes that curtailing petcoke firing is the most cost effective solution to reduce the sulfur content of fuel burned in

Unit 3. It is estimated that 17 lb of SO 2 would be reduced for every ton of coal burned instead of combined coal and petcoke (difference between 2.22 percent sulfur and 1.8 percent sulfur in 1 ton of fuel). Costs were not provided by Lakeland Electric, but DEP assum es the cost of eliminating petcoke as an authorized fuel to be minimal.

The existing FGD system is a 30

- year old Babcock & Wilcox design and is not designed to achieve 95 to 98 percent SO 2 removal without significant major upgrades in the existing equipme nt. Based on very preliminary assessment, the removal efficiency of the FGD system may be increased to a maximum of 95 percent with equipment improvements involving the existing FGD absorbers, slurry systems, additive systems, reheat systems, and stack(s) , which may cost as much as $25 million (Lakeland Electric estimate). A detailed engineering study will107 be needed to properly assess the cost of such an improvement. For example, a new landfill might be required since the solid waste disposal rate will i ncrease with additional SO 2 absorption. If dibasic acid is used in the scrubber to achieve higher removal efficiency, the issue of specific waste water treatment procedure will need to be addressed since the waste water will

have dibasic acid in it. This unit emitted 6,994 tons of SO 2 in 200 2 . Assuming the existing FGD provided 81 % control, then an additional 14 % control would reduce SO 2 emissions by another 5,153 tons. This would result in a cost effectiveness of approximately $4,852 / ton (or greater) of

reduction. DEP does not consider this a reasonable cost and therefore not necessary for achieving the reasonable progress goals for this planning period

.

An additional/replacement wet

- FGD system designed to achieve 98 percent SO 2 remo val would achieve the highest level of SO 2 control while Unit 3 remains operating and available to provide electric power to its customers. To develop a cost of a replacement wet

- FGD system, information developed for the recent CSAPR was used. Capital, f ixed operation and maintenance (O&M), and variable O&M are available on a dollar per kilowatt basis as part of EPA’s Integrated Planning Model (IPM) Base Case v. 4.10 (September 1, 2010) for Transport Rule; Documentation; Chapter 5, Emission Control Techno logies. These costs models were developed by engineering contractors such as Sargent & Lundy for the wet

- FGD cost model as well as other control technologies. Additionally, cost factors and methodology from the EPA

Cost Control Manual were used to develo p annualized costs not included in the EPA IPM cost

model. The annualized cost was based on the amount of historical operation in baseline year of 2002 and is estimated to be about $36.3 million. The compliance target was an emission rate of

0.063 lb/MMB tu, which is equivalent to 98 percent SO 2 removal based on 2002 operation, the basis of the DEP baseline emissions. The cost effectiveness of about $5,804 per ton of SO 2 removed was estimated using a reduction from the 2002 baseline year and an emission ra te of

0.063 lb/MMBtu. DEP does not consider this a reasonable cost and therefore not necessary for achieving the reasonable progress goals for this planning period

.

Time Necessary for Compliance

– The FGD system is already operating for this unit and no additional time is necessary for its operation. The options for upgrading the existing FGD and for replacing the existing FGD would both take three years or longer to complete. The option of reducing the potential fuel sulfur content could be completed immediately.

Energy and Non Air Quality Environmental Impacts of Compliance

-- The energy and non

- air quality impacts associated with an additional/replacement wet

- F GD system include additional limestone usage, disposal of FGD byproducts, increased water use, and additional energy.

Wet

- FGD requires about 3 percent of the energy of the unit for auxiliary power and backpressure. This is about 1.09 MW per ton of SO 2 re moved. The additional energy required to meet 0.063 lb/MMBtu with the reduction estimated using the 2002 baseline emissions is 6,822

megawatt

- hours (MWh). For each ton of SO 2 removed, about 1.05 tons of limestone are required due to the reactivity and lo sses in the system. Approximately 2.34 tons of FGD byproducts are produced per ton of SO 2 removed. For the estimated SO 2 removed based on 2002 emissions, an additional 6,572 tons of limestone would be required and 14,646 tons of byproducts generated. Ad ditional process water would also be required and is estimated to be 50 gallons per ton of SO 2 removed. Based on the SO 2 removed from the 2002 emissions, about108 312,953 gallons of additional water would be required. These additional impacts do not support an additional/replacement wet

- FGD system.

Remaining Useful Life

– These units are anticipated to continue operation indefinitely.

In summary, DEP has determined that the existing FGD SO 2 control system with the elimination of petcoke as an authorized fu el, meet the reasonable progress goals of the state’s regional haze plan for this planning period . A copy of the federally enforceable permit that eliminates petcoke

as an authorized fuel is included in Exhibit 2.

JEA St. Johns River Power Park (SJRPP)

SJRPP B oilers 1 and 2 (Emissions U nits 16 and 17) are fossil fuel

- fired steam generators rated at

679 MW each. The maximum heat input rate is 6,144 million British thermal units per hour (MMBtu/hr) per boiler. The boilers are fired with pulverized coal, a coal blend with a maximum of 30 percent petroleum coke (petcoke) by weight , natural gas, new No. 2 distillate fuel oil (startup and low

- load operation), and “on specification” used oil. The maximum coal sulfur content shall not exceed 4.0 percent , by weight. The maximum sulfur content of the petcoke

coal blend shall not exceed 4.0 percent, by weight. The maximum sulfur content of the No. 2 fuel oil is 0.76 percent, by weight. The federally

- enforceable permit conditions limit SO 2 emissions to 1. 2 lb /MMBtu (max two

- hour average), and 0.76 lb/MMBtu (90% reduction of the potential combustion concentration ) ; 30

- day rolling average

, when burning coal.

Boilers 1 and 2 are currently equipped with wet limestone flue gas desulfurization ( FGD ) systems to control SO 2 emissions. The FGD systems are capable of up to 90 percent reduction in

SO 2 emissions with a maximum SO 2 emission rate of 0.76 lb/MMBtu using the worst

- case fuel. The scrubbers are of a tray tower type consisting of three absorber modules. Each module provides a 50

- percent capacity of total boiler o utput. Additional equipment/processes supporting the scrubber system include limestone storage and handling, limestone slurry preparation system, slurry storage and transfer system, FGD blowdown, and gypsum dewatering, transfer, and

storage.

In general, wet limestone FGD systems are the top level control device for coal

- fired utility boilers. However, the source considered several changes or upgrades to the FGD system to

additionall y reduce SO 2 emissions. These included lower sulfur fuel, FGD modifications, and complete replacement of the FGD system.

Costs of Compliance

-- Increasing the existing SO 2 removal efficiency of the FGD system is possible with equipment improvements invol ving the existing FGD absorbers, slurry systems, additive systems, reheat systems, and stack(s). The capital costs for the potential improvements are likely in the range of $10 million to $30 million per boiler

. In conjunction with the equipment improvem ents, operating costs for increased SO 2 removal will include fixed and variable operating costs from about $3 million per year per boiler to over $4.5 million per year

per boiler

. Depending upon the options selected, up to an additional 5 percent SO 2 remo val is possible. An engineering study has commenced that will include an evaluation of the sulfur109 content for the various range of fuels authorized for SJRPP and refine these very preliminary

cost estimates. While these improvements look promising, DEP h as determined that it is premature to rely on these improvements for reasonable progress

.

To achieve greater SO 2 reductions from the current equipment would require either add

- on SO 2 controls after the existing equipment or a replacement of the current FG D system with systems designed to achieve 95 to 98 percent or greater SO 2 removal. The existing FGD systems are not designed to achieve 95 to 98 percent SO 2 removal without significant major upgrades in the existing equipment (e.g., absorber redesign to i ncrease liquid to air ratio, construction of new wet stack, etc.). As a result, an additional/replacement wet

- FGD system designed to achieve a total removal of 98 percent SO 2 removal would achieve the highest level of SO 2 control while Boilers

1 and 2 rem ain operating and available to provide electric power to its customers.

Wet

- FGD has been a widely available control technology for SO 2 removal over the last 30 years. To develop a cost of a replacement wet

- FGD, information developed for the recent CSAPR w as used. Capital, fixed operation and maintenance (O&M), and variable O&M are available on a dollar per kilowatt basis as part of EPA’s Integrated Planning Model (IPM) Base Case v. 4.10 (September 1, 2010) for Transport Rule; Documentation; Chapter 5. Emi ssion Control Technologies. These costs models were developed by engineering contractors such as Sargent & Lundy for the wet

- FGD cost model as well as other control technologies. Additionally, cost

factors from the EPA Cost Control Manual were used to co sts not included in the EPA IPM cost model. EPA’s “Guidance for Setting Reasonable Progress Goals Under the Regional Haze Program” suggests that costs should be evaluated and cost estimates should be based on EPA’s

Air Pollution Control Cost Manual. Annua lized costs were developed using the methodology in

the EPA Cost Control Manual.

E stimated costs and cost effectiveness are presented for an additional/replacement wet

- FGD for

either SJRPP Boiler 1 or 2 to achieve an SO 2 removal of 98 percent, the highest level of control.

Boilers 1 and 2 are identically designed units in close proximity that have a similar influence on visibility to Class I Areas. The annualized cost was based on the amount of historical operation

in ba seline year of 2002 and is estimated to be about $59.7 million. The cost effectiveness was determined against one half of the 2002 SO 2 emissions since both Boilers 1 and 2 are identically designed. The compliance target was an emission rate of 0.053 lb/M MBtu, which is equivalent to 98 percent SO 2 removal based on 2002 operation, the basis of the DEP baseline emissions. The cost effectiveness of about $6,383 per ton of SO 2 removed was estimated using a reduction from the 2002 baseline year and an emission rate of 0.053 lb/MMBtu. To contrast the cost effectiveness from this baseline year, the cost effectiveness using the emissions for the latest year, 2011, was estimated. T he cost effectiveness from the SO 2 emissions in 2011 was estimated to be $11,921 per ton of SO 2 removed. It should be recognized that the estimated costs are neither specific to SJRPP nor the layout of Boiler 1 nor 2. It may not be possible to install add

- on SO 2 equipment given the spatial constraints at the site. Notwithstanding, these cost effectiveness numbers are extremely high and considered unreasonable for SJRPP Boilers 1 and 2, which already have wet

- FGD systems. DEP does not consider this a reasonable cost and therefore not necessary for achieving the reasonable progress goals f or this planning period

.110 Time Necessary for Compliance

– The existing FGD system s are already operating for these

boilers and no additional time is necessary for their operation. The option for replacing the existing FGD systems would take three years or longer to complete. The option of making improvements to the existing FGD system s , including reducing the potential fuel sulfur content, could be implemented in a shorter time frame after JEA completes their evaluation.

Energy and Non Air Quality Enviro nmental Impacts of Compliance

-- The energy and non

- air quality impacts associated with an additional/replacement wet

- FGD system includes additional limestone usage, disposal of FGD byproducts, increased water use, and additional energy. Wet

FGD requires about 3 percent of the energy of the unit for auxiliary power and backpressure. This is about 1.09 MW per ton of SO 2 removed. The additional energy required to meet 98 percent SO 2 removal from the 2002 baseline emissions is 10,189 MWh.

For each ton of S O 2 removed, about 1.05 tons of limestone is required due to the reactivity and losses in the system. Approximately 2.34 tons of FGD byproducts are produced per ton of SO 2 removed. For the estimated SO 2 removed based on 2002 emissions, an additional 9,815 tons of limestone would be required and 21,874 tons of byproducts generated. Additional process water would also be required and is estimated to be 50 gallons per ton of SO 2 removed. Based on the

SO 2 removed from the 2002 emissions, about 467,389 gallons of additional water would be required. These additional impacts do not support an additional/replacement wet

- FGD system.

Remaining Useful Life

– These units are anticipated to continue operation for at least another 20

years.

In summary, DEP has deter mined that the existing FGD SO 2 control systems meet the reasonable progress goals of the state’s regional haze plan. DEP recognizes that JEA is in the process of evaluating additional measures to reduce SO 2 emissions in response to other federal regulati ons, but these measures are not necessary for the purpose of meeting the reasonable progress goals during the first planning period.

Rock Tenn

The No. 7 Power Boiler (Emissions Unit 15) is regulated under NSPS

- 40 CFR 60, Subpart D,

Standards of Perfo rmance for Fossil

- Fuel

- Fired Steam Generators for Which Construction Is Commenced After August 17, 1971. This emissions unit is fired primarily with coal

, Nos. 2 and 6 Fuel Oil, and natural gas may be fired during startup, shutdown, and malfunction, and a t other

times as needed for flame stabilization and to maintain steam production. The operation rate shall not exceed 1,021 MMBtu/hr (based on a 24

- hour average). Sulfur d ioxide (SO 2 ) e missions

shall not exceed 1.2 lb / MMBTU of heat input, 1,225.20 lb/hr and 5,366.38 TPY, derived from

solid fossil fuel or solid fossil fuel and wood residue. SO 2 e missions shall not exceed 0.8

lb / MMBTU of heat input derived from liquid fossil fuel or liquid fossil fuel and wood residue.

DEP recognize s that this unit is currently subject to the Industrial Boiler MACT rule. Any source subject to MACT standards must meet a level of control that is as stringent as the best 12 percent of sources in the industry. While the MACT standard is for acid gases, rather than for

SO 2 , DEP concludes that the SO 2 emissions reductions expected to result from measures taken to111 comply with the MACT standards will meet the reasonable progress goals of the state’s regional haze plan

.

A streamlined control analysis was c onducted by reviewing the supporting documentation regarding the emissions controls projected necessary to comply with the MACT standard. The control options potentially available include Dry Sorbent Injection/Fabric Filter (DIFF), wet scrubbing, or conve rsion to natural gas to meet the MACT standard. The supporting technical

information document for the industrial boiler MACT standard concluded that the least cost option for this unit would be DIFF, and projected the need to install DIFF with a total cap ital control cost of $35,244,447, and a total annual control cost of $10,084,579. SO 2 emissions are projected to be reduced by 68.6 percent. A wet scrubber, which was not projected to be needed to meet the MACT standard for this unit, could reduce emissi ons by 95 percent, but at a significantly higher cost.

SO 2 emissions from this unit in 2002 ( the year for which Florida used to determine applicability to the reasonable progress review) was 3,242 tons. This leads to a Q/d of 50.2, fractionally over

t he applicability threshold of 50. Based on the expected reduction of 68.6 percent from this baseline, the unit would reduce actual emissions by 2,224 tons per year. The resulting estimated

cost effectiveness of DIFF for SO 2 is over $4,500 per ton of SO 2 removed. Further, installation of this control technology would bring the unit’s Q/d to well below the threshold of 50. While a wet scrubber would result in greater emissions reduction, its annual costs are anticipated to be substantially higher and les s cost effective.

In summary, DEP finds that further analysis would not identify an emissions control more stringent than what the MACT standard will require. DEP will review the status of Rock Tenn’s compliance with the MACT standard as part of the co ntinuing periodic review process of the Regional Haze Rule.

7.8 Best Available Retrofit Technology (BART) for Individual Sources

Section 169A of the CAA directs s tates 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 s tates to include BART in their regional haze SIPs. On July 6, 2005, EPA 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 visibility

impairing air pollutant, was put in place between August 7, 1962 and August 7, 1977, and whose operations fall within one or more of 26 specifically listed source categories. Under the CAA, BART is required for any BART

- el igible source that a s tate determines ‘‘emits any air pollutant112 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 s tates 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 rema ining 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

- eli gible sources in Florida

. DEP 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.113 FACILITY

ID OWNER COMPANY NAME SITE NAME

1 0010006 CITY OF GAINESVILLE, GRU DEERHAVEN GENERATING STATION

2 0050009 ROCKTENN ( STONE CONTAINER

CORPORATION ) PANAMA CITY MILL

3 0050014 GULF POWER COMPANY LANSING SMITH PLANT

4 0090006 FLORIDA POWER & LIGHT (PCC) CAPE CANAVERAL POWER PLANT

5 0090196 RELIANT ENERGY INDIAN RIVER, L.L.C. INDIAN RIVER PLANT

- RELIANT

6 0110036 FLORIDA POWER & LIGHT (PPE) PORT EVERGLADES POWER PLANT

7 0170004 FLORIDA POWER CORPORATION CRYSTAL RIVER POWER PLANT

8 0250003 FLORIDA POWER & LIGHT (PTF) TURKEY POINT POWER PLANT

9 0310045 JEA NORTHSIDE/SJRPP

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

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

21 0610029 CITY OF VERO BEACH CITY OF VERO BEACH MUNICIPAL

UTILITIES

22 0730003 CITY OF TALLAHASSEE ARVAH B.HOPKINS GENERATING STATION

23 0810010 FLORIDA POWER & LIGHT (PMT) MANATEE POWER PLANT

24 0850001 FLORIDA POWER & LIGHT (PMR) FPL / MARTIN POWER PLANT

25 0890003 ROCKTENN ( 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

31 0990061 U.S.SUGAR CORP. BRYANT MILL U.S. SUGAR CORP. BRYANT MILL

32 1010017 FLORIDA POWER CORP. ANCLOTE POWER PLANT

33 1030011 FLORIDA POWER CORPORATION FPC

- BARTOW PLANT

34 1050003 LAKELAND ELECTRIC CHARLES LARSEN MEMORIAL POWER

PLANT

35 1050004 LAKELAND ELECTRIC C.D. MCINTOSH, JR. POWER PLANT

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

42 1110003 FT PIERCE UTILITIES AUTHORITY H D KING PWR PLNT

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 STATI114 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, 19 77 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:

SO 2 , N Ox, and PM10.

The BART Guidelines recommend addressing these visibility

- impairing pollutants: SO 2 , NO

x , and partic ulate matter, and suggest that s tates use their best judgment in determining whether to

address VOC or ammonia emissions. Florida addressed SO 2 and NOx, and used particulate matter less than 10 microns in diameter (PM 10 ) 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 R ule 62

- 296.340, Florida Administrative Code. In this rule,

DEP has adopt ed by reference the core of the federal rule, and include d additional r ule language

to make certain decisions where the federal rule leaves options to the state. The rule also require s that BART

- eligible sources submit a state construction permit application. In this application, the source must complete the BART analysis a s 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 optiona l 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 de

mi nim i s levels below which BART would not apply for that pollutant, and provid ing specific criteria for exemption from BART. A copy of the Florida BART rule is included in Exhibit 1

.

S ince the development of the Florida BART rule, EPA promulgated the Cross State Air Pollution Rule (CSAPR) as a successor to the CAIR program. The applicability of Florida to CSAPR

would have changed relative to the CAIR rule. Under CSAPR, Florida would be subject only to the ozone season NOx emissions cap. The annual emissi on caps associated with SO 2 and NOx

would no longer apply in Florida. Thus, whereas CAIR can satisfy the BART requirements for these pollutant s as originally relied upon in this SIP , CSAPR cannot

. However, on August 21, 2012, the D.C. Circuit Court vacated CSAPR and ordered EPA to continue implementing CAIR until a valid replacement could be implemented. Given the uncertainty over CAIR’s eventual replacement , Florida is not requesting EPA approval of Rule 62

- 296.340, F.A.C., to the extent it115 allows sources to rely on CAIR to comply with BART. However, it should be noted that CAIR will continue to be implemented

– and its emission reductions realized

– for several years, as EPA develops a replacemen t.

Outside of the Florida BART rule, and in response to the uncertainty over CAIR being replaced,

DEP has completed source

- specific BART determinations for the electric generating sources that had previously relied on CAIR.

7.8. 3 Determination of Sourc es 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 m eeting a simple emissions criterion applicable within two distances (50 km and 100 km) from any Class I area. Two of the BART

- eligible sources m e et this exemption. The second criterion is base d on source

- specific analyses to determine which

sources caus e 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 Florida

specific review of appropriateness (see Appendix L), a contribution threshold of 0.5 deciviews was used for determining which sources were subject to BART. 24 of the BART

- eligible

sources met this modeling exemption considering all three vi sibility impairing pollutants

.

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 reviewed by DEP

, and were also provided to

the 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 re

- submit their demonstration modeling to compl y with the modeling protocol. Eight 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, CF Industries , OUC Indian River, and Progress Energy

Florida Anclote

. All of these changes have been incorporated into their permits and are federally

enforceable. Additionally, two BART

- eligible source s , Tampa Electric Company Big Be nd and

Gulf Power Crist , provided information showing that the existing controls at the subject units are the top level of control for each of the visibility pollutants, and, thus, full BART review is not

necessary

. Finally, documentation for the BART

- eli gible units that have shut down is included

in Exhibit 2.

Included in the table is the disposition of all the BART

- eligible sources.116 Table 7.8.3-1 BART-Eligible Sources 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)

10006 CITY OF GAINESVILLE, GRU DEERHAVEN GENERATING STATION x 0.205

50009 STONE CONTAINER CORPORATION PANAMA CITY MILL x 0.498

50014 GULF POWER COMPANY LANSING SMITH PLANT x

90006 FLORIDA POWER & LIGHT (PCC) CAPE CANAVERAL POWER PLANT x

90196 OUC INDIAN RIVER INDIAN RIVER PLANT x 0.167

110036 FLORIDA POWER & LIGHT (PPE) PORT EVERGLADES POWER PLANT x

170004 FLORIDA POWER CORPORATION CRYSTAL RIVER POWER PLANT x

250003 FLORIDA POWER & LIGHT (PTF) TURKEY POINT POWER PLANT x

310045 JEA NORTHSIDE/SJRPP x

310071 IFF CHEMICAL HOLDINGS, INC. IFF CHEMICAL HOLDINGS, INC. x 0.269

330040 SOLUTIA INC. SOLUTIA INC. x

330042 INTERNATIONAL PAPER COMPANY PENSACOLA MILL x 0.057

330045 GULF POWER COMPANY CRIST ELECTRIC GENERATING PLANT xxx 0.21

470002 WHITE SPRINGS AGRICULTURAL CHEMICALS,INC WHITE SPRS AG CHEM-SR/SC CMPLX x

510003 U.S. SUGAR CORP. CLEWISTON MILL U.S. SUGAR CLEWISTON MILL AND REFINERY x 0.265

530010 CEMEX CEMEX x

570005 CF INDUSTRIES, INC., PLANT CITY PHOS CF INDUSTRIES, INC., PLANT CITY PHOSP x 0.498

570008 MOSAIC RIVERVIEW x 0.494

570024 IMC PHOSPHATES COMPANY PORT SUTTON TERMINAL x

570039 TAMPA ELECTRIC COMPANY BIG BEND STATION xxx

610029 CITY OF VERO BEACH CITY OF VERO BEACH MUNICIPAL UTILITIES x 0.17

730003 CITY OF TALLAHASSEE ARVAH B.HOPKINS GENERATING STATION x 0.392

810010 FLORIDA POWER & LIGHT (PMT) MANATEE POWER PLANT x 0.458

850001 FLORIDA POWER & LIGHT (PMR) FPL / MARTIN POWER PLANT x 0.449

890003 SMURFIT STONE CORPORATION (US) JSC-FERNANDINA BEACH PAPERBOARD MILL x 0.495

990016 ATLANTIC SUGAR ASSOCIATION ATLANTIC SUGAR MILL x 0.49

990019 OSCEOLA FARMS OSCEOLA FARMS x 0.302

990026 SUGAR CANE GROWERS CO-OP SUGAR CANE GROWERS CO-OP x 0.467

990042 FLORIDA POWER & LIGHT (PRV) RIVIERA POWER PLANT x

990045 LAKE WORTH UTIL. TOM G. SMITH PLANT x 0.11

990061 U.S.SUGAR CORP. BRYANT MILL U.S. SUGAR CORP. BRYANT MILL x

1010017 FLORIDA POWER CORP. ANCLOTE POWER PLANT x 0.499

1030011 FLORIDA POWER CORPORATION FPC-BARTOW PLANT x

1050003 LAKELAND ELECTRIC CHARLES LARSEN MEMORIAL POWER PLANT x

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 x 0.496

1070005 GEORGIA-PACIFIC CORP. PULP/PAPER MILL GEORGIA-PACIFIC CORP. PULP/PAPER MILL x 0.48

1070014 FLORIDA POWER & LIGHT (PPN) PUTNAM POWER PLANT x 0.491

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 0.39

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

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

TOTALS 12 24 2 8

xxx = BART satisfied by top-level control for SO2, NOx,PM117 In summary, of the 4 6 sources that are BART

- eligible, 26 have been determined to be exempt from BART, and 8 others have been shut down. The remaining 12 sources were required to complete a BART determination for the applicable units or show that existing controls on the

units are the top

- level control . The following section discusses the results of the BART

de terminations.

7.8. 4 Determination of BART Requirements for Subject

- to

- BART Sources

Permit applications or BART analyses were received from each of the 12 subject

- to

- BART

sources.
F or each of the subject

- to

- BART sources, DEP has made a BART determinatio n considering the

five factors: (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) t he degree of improvement in visibility which may reasonably be anticipated to result from the use of such technology

.

The BART determinations made by DEP are summarized below. The permit s issued for these sources reflect the imposition of BART and are federally enforceable.

BART

- eligible Emissions Units 7.8.4.1 CEMEX

CEMEX operates an existing P ortland cement plant with two P ortland 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 U.S. 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 par t 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

Baghouse No. E

- 36 of Kiln No 1. Blending Silo No. 2

– Cement Products BART

- eligible

is

009 BART

- eligible

Cement Plant STG Silos Dust Unit (Baghouse H

- 3)

– Cement Products

011 Raw Material Storage Silos & Feed System (Baghouses C

- 11, C

- 11A)118 Affected Pollutants

T he affected visibility

- impairing pollutants include NO X , PM 10 and SO 2

.

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, DEP deemed the application complete. December 3, 2007 , the a pplicant waived the 90 day processing clock until December 1 7, 2007

. DEP

issued a draft air construction Permit No. 0 530010

- 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 bei ng 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 deter minations 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 Retrof it 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 emissio n limits are federally enforceable.

BART controls follows:

NO X Kiln Controls

a. To control emissions of nitrogen oxides (NOx), CEMEX shall operate the inst alled SNCR system as previously authorized by Permits 0530010

- 026

- AC and 0530010

- 34

- AC. : To control emissions of nitrogen oxides (NO X ), CEMEX shall implement one of the following strategies:

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 exist ing 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.

SO 2 Kiln Controls : The present SO 2 control system consisting of dry alkali and lime scrubbing in the kiln system an d limestone scrubbing in the raw mill constitutes BART for EU 003.119 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

sha ll also comply with time

- averaged limits and monitoring requirements discussed further below. [Rule 62

- 296.340 (BART), F.A.C.]

PM/PM 10 Kiln Standards
To control emissions of PM from Kiln No. 1 CEMEX shall continue to operate and maintain baghouse E

- 55 as required by Permit 0530010

- 003

- AC (PSD

- FL

- 233) and the current Title V permit. :

To control emissions of PM from Cooler No. 1 CEMEX shall continue to operate and maintain baghouse F

- 18 as required by Permit 0530010

- 003

- AC (PSD

- FL

- 233) and the current Titl e V permit.

Baghouse Controls

Visibility : The applicant pr ovided a Post

- BART analysis for PM (finish mills) and NO X reductions (kiln) with the addition of Indirect Firing and SNCR, permitted by DEP 12/22/06 (Permit No. 0530010

- 026). These reduced emission rates provided a visibility impact of 0.933dv (8 th highes t, 98 th percentile) and reduced the number of days above the visibility threshold by approximately 45%. : Each emissions unit identified in the Table above shall control

PM/PM 10 by a baghouse system. Bags/filters in each baghouse control system shall be selected based on a PM design outlet specification of 0.01 grain (gr) per dry standard cubic foot (dscf) and a PM 10 design outlet specification of 0.007 gr/dscf. Compliance shall be demonstrated by maintaining the appropriate records and by complying with the mass emission limits, opacity limits and test requirements as indicat ed 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.

Pre

- BART and Post

- BART Visibility Impacts

BART

- Eligible

Sources Actual Visibility Impact (dv)* Impact with Reductions (dv)* Change in Visibility Impa ct (dv)

2001 1.457 0.933 0.524

2002 1.341 0.841 0.500

2003 1.354 0.848 0.506

*98 th percentile

Review The BART determination summarizes a limit or a technology for different units as follows:

Pollutant NOx Emissions Standard (VE) for Kiln 1:

Emission Limit Averaging

Time Compliance

Method Basis

NO X 2.0 lb/ton clinker 30

- operating day rolling CEMS Applicant Request,

PSD Avoidance and BART120 Pollutant SO 2 Emissions Standard (VE) for Kiln 1:

Emission Limit Averaging

Time Compliance

Method Basis

SO 2 0.17 lb/ton clinker 24

- operating hour rolling CEMS BART 15 lb/hr @ 150 TPH of feed

16.5 lb/hr @ 165 TPH of feed

EU No. PM/PM 10 and Visible Emissions Standards (VE) for Kiln 1 and Cooler 1:

Description Emission Limit lb/hr at 150 dry preheater feed lb/hr at 165 TPH dry preheater feed VE

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

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

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

EU No. w :

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%121 7.8.4.2 FPL Turkey Point

BART

- eligible Emissions Units for the Turkey Point Power Plant This facility 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 suppor ting Units 1 and 2, a natural gas

- fueled 1,150 MW combined cycle unit (Unit 5), and associated equipment. The facility is located at 9700 SW 344 Street, in Homestead,

Miami

- Dade County

.

The facility operates the following emissions units subject to th e requirements of the federal Regional Haze BART regulations: Unit 1 (EU001) and Unit 2 (EU002). Units 1 and 2 are fossil fuel fired steam electric generators consisting of boiler/steam generators which drive single reheat turbine generators with multi

- cyclones, with fly ash reinjection, to control particulate

matter emissions.

Affected Pollutants The affected (i.e., visibility

- impairing) pollutants for this project are: sulfur dioxide (SO 2 ), nitrogen oxides (NO X ) and particulate matter (PM).

Project Description and Status In 2009, FPL received a BART determination for these BART eligible units, although only for PM, which imposed a 20% visible emissions (VE) limit, a 0.7% sulfur fuel oil restriction and upgrades to the multi cyclones to a 0.07 lb/MMBtu emission rate for par ticulate matter. In addition, a PM control device additive study was required and done in 2010 to determine if a 0.05 lb/MMBtu emission rate could be achieved. The study results showed that the lower limit was not achievable using a calcium

- based additiv e, although the results indicated a 15% emission reduction could be achieved using the additive at loads above 50% when using a 500 ppm additive injection rate.

Due to the limited use of oil on Unit 1, the nearly negligible visibility impacts from PM, and the little incremental benefit of a new dust collector, FPL proposes to eliminate the requirement to install the 3.7 million dollar new dust collector (ESP) on Unit 1 and will continue to use the existing dust collector (multi

- cyclone) to meet a limit of 0.07 lb/MMBtu as BART for this unit.

Su b sequent to the request to change the PM BART limitations, FPL submitted a new proposed

BART determination for DEP to consider that includes SO 2 and NOx. Such a determination

would eliminate all reliance on CAIR in meeting their BART obligations.

Units 1 and 2

– BART Determination122 The BART

- eligible units are:

ID No. Emission Unit Description

001 Unit 1 is a 4,000 MMBtu/hr fuel oil

- fired boiler (4,150 MMBtu/hr when firing natural gas).

002 Unit 2 is a 4,000 MMBtu/hr fuel oil

- fired boiler (4,150 MMBtu/hr when firing natural gas).

Baseline Emissions FPL evaluated the control options for PM, SO 2 and NO x considering the baseline period of 2001

- 2003 for emissions based on the maximum 24

- hour averages for each p ollutant during this period. The following uncontrolled emission rates were evaluated based on stack testing and CEMS data from Units 1 and 2 during this period:

Uncontrolled Emission Rates*

Pollutant Unit1 Unit 2

SO 2 3,488 lb/hr 3,757 lb/hr

NO x 2,586 lb/hr 2,198 lb/hr

PM Filterable 144 lb/hr 144 lb/hr *Emissions of all pollutants are higher for fuel oil firing; therefore, the emission rates above are for fuel oil firing and were the highest (24

- hour average) emission rates measured by CEMS (for gaseous pollutants) and stack testing (PM) during the basel ine period.

PM Control Technology Review Particulate matter is emitted from the stacks of Units 1 and 2 as a result of the firing of fuel oil and natural gas to generate electricity. Each unit is equipped with low NO x burners and

Universal Oil Products (UOP) Air Correction Division multi

- cyclones with ash reinjection. The

multi

- cyclones consist of two tubular mechanical dust collector modules with 695 tubes per

collector.

Step 1. Identify all available retrofit control technologies

. The available ret rofit 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 baghouses following the existing multi

- cyclones.

• R eplace the existing multi

- cyclones with new state

- of

- the

- art multi

- cyclones.

Step 2. Eliminate technically infeasible options. Baghouses and ESP are generally recognized as the top controls with removal efficiencies greater than 99%. In this case, wet s crubbers have not demonstrated equivalent levels of control for PM compared to the top controls. B aghouses are deemed not feasible based on tests conducted by FPL at the Sanford plant found that particles generated from the combustion of oil

based fuels c aused considerable plugging of bags in pilot scale tests.

Step 3. Evaluate control effectiveness of remaining control technologies.123 Based on information submitted by FPL , that includes proposals provided by air pollution control device vendors, the effectiveness of the potential PM control techniques and available options are as follows:

Control Technology

Options Continuously

Achievable

Emission Rate Emissions From

Both Units

tons/year Potential

Reduction tons/year Percent

Reduction*

Existing Multi

cyclones 0.1 lb/MMBtu** 1,795 * 0 0

New ESP 0.03 lb/MMBtu 539 1,257 70%

Multi

- cyclone with

low sulfur fuel oil/ 0.070 lb/MMBtu 1,257 539 30%

* Baseline / Historical Maximum **May meet lower limits in conjunction with 0.7% sulfur fuel oil

Step 4. Evaluate the impacts of the remaining technologies and document the results. Based on information submitted by the applicant (assuming a 20

- year useful life and 7% annual interest rate), the following is a summary of the expected costs associated with the proposed control options:

Control Options Unit 1 Unit 2

Annualized

Cost $/ton

Removed Annualized

Cost $/ton

Removed

New ESP (0.030 lb/MMBtu) $6,676,195 10,623 $6,676,195 10,623

Multi

- cyclone upgrade &

Reduced Fuel sulfur % $961,552 3,568 $961,552 3,568

It should be noted that the estimated cost does not include any changes in construction associated with the close proximity of the nuclear units (i.e., Turkey Point Units 3 and 4). According to the applicant:

• The location of the ESP construction for Units 1 and 2 would increase security requirements and potentially require approval from the Nuclear Regulatory Commission.

• The energy required to operate two ESPs would be about 4,370 MW

- hr per year for both

unit s, or about 0.13 percent of gross generation.

• Ash collected by the ESP would require landfilling if it could not be recycled.

• It is estimated that 1,257 tons of ash would be generated from the ESPs requiring about 50 truck trips per year to remove it f rom the site.

• FPL has plans to shut down Unit 2 in the near future. Also, Units 1 and 2 are typically operated as cycling units rather than base

- loaded units.

Step 5. Evaluate visibility impacts. (See Step 5 below )

BART Determination for PM124 Part of the BART analysis is to consider the remaining useful life of the BART eligible unit. FPL has proposed to permanently shut down Unit 2. DEP has establish ed a permit condition requir ing the permanent shut down of Unit 2. Based on the high cost fo r such a small improvement in visibility, the company claims it is cost prohibitive to add an ESP to Unit 1. Also, with the applicant proposing a restriction of oil firing to approximately 25% capacity factor on Unit 1, the emission reductions for a new E SP on Unit 1 are further diminished, resulting in an excessively high cost per ton of PM removed. As an alternative emission reduction strategy that involves PM emissions reductions, the company has proposed the use of low sulfur (0.7 percent) residual fu el oil (vs. 1% sulfur oil used during the baseline period) and a reduction in the PM limit from the current allowable emission rate of 0.1 lb/MMBtu down to 0.07 lb/MMBtu, which is achievable with the existing multi

- cyclones controls and the lower sulfur fu el oil. At a comparative cost of less than $3,600/ton of PM removed, this option is considered cost effective given the source’s relatively close proximity to the nearest Class I area, and will produce a significant visibility improvement of 0.6 dv (i.e., 29 percent reduction in visibility impacts from base case).

Therefore, DEP has determine d that FPL’s proposal to meet 0.07 lb/MMBtu for PM with a 25% capacity factor limit for firing low sulfur fuel oil (0.7% sulfur) using the existing multi

- cyclone

co ntrols on Unit 1 and shut down of Unit 2 is a reasonable PM control alternative that serves the overall intent of the BART regulations.

SO 2 and NO x Control Technology Review FPL evaluated BART options for SO 2 and NO x control. A 5

- factor BART analysis wa s done for each of these pollutant as follows.

Step 1. Identify all available retrofit control technologies

. The available retrofit SO 2 control technologies for these boilers include the following:

• Add wet or dry scrubbers for acid gas control.

• Low Sulfur Fuel Oil options at 0.7% and 0.3% sulfur content.

FPL evaluated the available control options for SO 2 control and reviewed the EPA RACT/BACT/LEAR Clearinghouse for applications of the above options. A flue gas desulfurization (FGD) system (wet scru bber) is technically feasible to control acid gases on these oil and gas fired units. Although FPL found no determinations for oil and gas fired units employing FGD in the clearinghouse and the determinations found by the applicant have all been

based on lower sulfur fuel oil options to reduce SO 2 emissions.

The potential control options for NO x include:

• Retrofit Selective Catalytic Reduction (SCR).

• Retrofit Selective Non

- catalytic Reduction (SNCR).

• Existing Low NO x Burners.

Step 2. Eliminate technically infeasible options

. Although feasible, wet or dry scrubbers for SO 2 controls have not been used for comparable units that fire gas and fuel oil as discussed in Step 1. A Dry scrubber combined with bag house125 controls does not appear to be feasi ble in this case 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. Compared to firing natural gas, fuel oil has significantly higher sulfur content and FPL has proposed to control SO 2 by limiting fuel oil firing on Unit 1 to no more than a 25% capacity factor, and limit the sulfur content, which has a large impact on the feasibility of add

- on controls.

T he limited capacity f actor for fuel oil, the higher NO x producing fuel, makes the use of add on

NO x controls infeasible . FPL currently employs Low NO x Burners on Unit 1 which minimize

NO x formation in the combustion zone. Unit 1 is currently required to meet the emission lim it

based on Rule 62

- 296.570 F.A.C. for NO x RACT, which is 0.40 lb/MMBtu on gas and 0.53 lb/MMBtu on fuel oil based on continuous emission monitoring (CEMS) and a 30

- day rolling

average.

FPL has proposed to permanently shut down Unit 2, so no control evalu ation was required.

Step 3. Evaluate control effectiveness of remaining control technologies

. FPL judged the use of lower sulfur fuel oil a technically feasible option to reduce SO x emissions

from Unit 1. From a baseline of 1% sulfur fuel oil that was u sed in the 2001

- 2003 time

- frame, FPL considered firing fuel oil on Unit 1 with a limited 25% capacity factor, and at fuel oil sulfur

levels of 0.7% and 0.3%.

Control Technology

Options Continuously

Achievable

Emission Rate Emissions From

Both Units (tons/year) Potential

Reduction (tons/year) Cost

Effectiveness* ($/ton)

Baseline 1% S fuel

oil 1.1 lb/MMBtu 13,281 *

- -

0.7% S fuel oil 0.77 lb/MMBtu 10,675 2,606 19,197

0.3% S fuel oil 0.33 lb/MMBtu 4,575 8,706 16,044

* Baseline / Historical Maximum level for both units operating at 6,825 hrs/yr.

The above table indicates a relatively high cost effectiveness value for either the 0.7% or the 0.3% sulfur fuel oil options. As part of an earlier PM control technology determination for BART, FPL currently has a permit limit for no higher than 0.7% sulf ur fuel oil for Unit 1; therefore, the 0.7% sulfur fuel oil option is feasible even at the relatively high cost.

Step 4. Evaluate BART for the remaining technologies and document the result.126 Although technically feasible to install, wet or dry scru bbers have not been cost effective for comparable units that fire gas and fuel oil. FPL cites capital cost estimates of between $40 and $100 million dollars for FGD controls. Therefore ,the use of add

- on SO 2 control options like FGD or dry scrubber (foll owed by a baghouse), are determined by DEP to be cost prohibitive, especially considering the restriction to 25% capacity factor for fuel oil use in Unit 1 as proposed by FPL.

FPL proposed that add on NO x controls (SCR or SNCR) are cost prohibitive due to the high capital cost (between $10 and $20 million) and the limited visibility benefit. Considering the Unit 1 limited capacity factor (25%) for fuel oil, which has higher NO x emissions, and the current price difference between natural gas and fuel oil that further minimizes the actual use of fuel oil, the high cost of add

- on controls for NO x and SO 2 cannot be justified.

Step 5. Evaluate visibility impacts

.
The initial (PM o nly) BART determination analysis predicted visibility improvement with the

addition of ESP’s to both Units 1 and 2. Initial emission rates were determined from stack test

data and AP

- 42 emission factors to reflect the maximum 24

- hour average normal operat ion. Emissions were speciated into six particulate species with regards to specific size categories and modeled. Results of this initial modeling predicted a visibility improvement of 0.1 dv.

The modeling results of visibility improvement were based on particulate matter and percent sulfur fuel content. Base case emission rates for this modeling analysis were based on an

emission rate of 0.1 lb/MMBtu with a fuel oil sulfur fuel content of 1% or the allowable emission

rates for Units 1 and 2. The bas e case was then compared to the proposed BART determination of 0.07 lb PM/MMBtu and a sulfur content of 0.7 percent. The results of these analyses are

shown in the table below.

Control Technology PM Emission

Rate Sulfur Fuel

Content 8th highest impairm ent (2001) 8th highest impairment (2002) 8th highest impairment (2003)

Existing Base Case 0.1

lb/MMBtu 1% 2.2 dv 1.8 dv 1.9 dv

Multi

- Cyclones 0.07

lb/MMBtu 0.7% 1.6 dv 1.3 dv 1.4 dv

The modeling results predict a 28 percent visibility benefit for the proposed sulfur reduction and PM emission rate of 0.07 lb/MMBtu. Visibility improvements will be extended beyond these modeling results with a limit on the amount of fuel oil used in a year, to no more than a 25% capacity factor for operation of Unit 1 on this fuel, and a permanent shut down of Unit 2.

To calculate the visibility improvement due to the lower sulfur content fuel, first the baseline visibility impacts were estimated based on the maximum 8th highest 24

- hour average visibility127 impacts, which is 5.96 dv. Since sulfate particles contributed to more than 90

- percent of the total visibility impact, instead of using just the sulfate contribution, the total impact (due to all pollutants) was used as baseline. Future or controlled visibility impacts w ere estimated based on modeling the reduced SO 2 emissions rates, which will result from the burning of lower sulfur oil. These rates were calculated by multiplying the SO 2 emissions rates used in the baseline impact analysis by the ratio of the specific su lfur content (0.7% or 0.3%) and the baseline sulfur content (estimated to be 1

- percent). Visibility improvements were determined by subtracting future dv impacts from the baseline dv impacts.

The visibility cost effectiveness numbers were calculated from the annual costs and the visibility improvement in dv. V isibility cost effectiveness for switching from the approximate 1

- percent sulfur currently used to 0.7

- percent sulfur is more than $33 million/dv for a total visibility improvement of 1.5 dv. Incremen tal visibility cost effectiveness for switching to 0.3

- percent sulfur fuel is $35 million/dv for an additional improvement of 2.5 dv. These visibility improvements are extremely small for a very large cost.

BART Determination for NO x and SO 2 The baseline m odeling showed that nitrates contributed less than 3 percent of the visibility degradation associated with the all emissions. Based on th is relatively small modeled impact of

NO x emissions on visibility and the excessively high cost of add

- on control devi ces, DEP has

determine d that additional NO x reductions are not required for this BART determination. Likewise, the high cost of add

- on controls for SO 2 reductions is also not justified. DEP has

determine d that the following satisfies BART for the emissions of NO x and SO 2 :

• Limiting the sulfur content of the fuel oil fired to 0.7%, by weight, and restricting the firing of fuel oil to an annual capacity factor of 25%, constitutes BART for emissions reductions of

SO 2 from Unit 1.

• The existing low NO x burners designed to meet the Rule 62

- 296.570, F.A.C., NO x RACT standards, which are 0.40 lb/MMBtu on gas and 0.53 lb/MMBtu on fuel oil, on a 30 day rolling average, constitutes BART for emissions of NO x from Unit 1

.

• The permanent shutdown of Unit 2.

C onclusion Considering the above analysis, DEP has determine d that the level of control already in place for the affected pollutants NO x and PM are consistent with the original BART determination for

Turke y Point Plant Unit 1. Additional control strategies are not cost

- effective and are not required for these pollutants. BART for Sulfur dioxide is deemed to be limited operation on low sulfur fuel oil for Unit 1. As required in current state permits, emis sions from Unit 1 at the FPL Turkey Point Plant will meet the BART requirements by restricted use of fuel oil to 8,760,000 MMBtu/year heat input, equivalent to a capacity factor of 25%, and by employing the following operational and emission limiting stand ards:

**•** SO 2 : As soon as practicable, but not later than December 31, 2013, the sulfur content of the fuel fired in Unit 1 shall not exceed 0.7%, by weight. Upon reduction of the sulfur content of the fuel oil, SO 2 emissions from Units 1 shall not exceed 0 .77 lb/MMBtu, on a

3

- hour rolling average. The sulfur dioxide emission limitation shall apply at all times including startup, shutdown, and load change. Compliance shall be demonstrated through the use of the existing continuous emissions monitoring syst ems (CEMS).128 **•** NO x : NO x emissions from Unit 1 shall not exceed the following limits using the existing low NO x burners and based on a 30

- day rolling average:

**•** Natural Gas Fuel Oil

lb/MMBtu 0.40 0.53

lb/hour 1,610 2,041

**•** PM: Emissions of P M are limited to 0.07 lb/MMBtu when firing fuel oil. Limit met by firing natural gas, co

- firing natural gas with fuel oil containing less than 0.7% sulfur, and through the use of multi

- cyclones (mechanical dust collectors) and fly ash reinjection. Compli ance demonstrated by stack tests when fuel oil is fired for more than 400 hours annually.

For Unit 2, permit No. 0250003

- 018

- AC requires the permanent shutdown as soon as practicable but no later than December 31, 2013.

7.8.4. 3 Progress Energy Crystal River

BART

- eligible Emissions Units for the Progress Energy Crystal River Power Plant Progress Energy Florida (PEF) 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,

Florid a.

The facility operates the following emissions units subject to the requirements of the federal Regional Haze BART regulations : Unit 1 (EU001) and Unit 2 (EU002). Units 1 and 2 are tangentially

- fired, dry

- bottom boilers. The units may burn bituminous coal or bituminous coal and bituminous coal briquette mixture. Distillate fuel oil may be burned as a startup fuel. Units

1 and 2 have a nominal generation capacity of 440.5 and 523.8 megawatts (MW), respectively.

Affected Pollutants The affected (i.e., visibility

- impairing) pollutants for this project are: sulfur dioxide (SO 2 ), nitrogen oxides (NO X ) and particulate matter (PM)

.

Project Description and Status PEF has provided two optional BART solutions. The first would install scrubbers and SCRs to these units by 2018 and would extend the life of these units. The second would shutdown these units by December 31, 2020, with no new controls being installed. Because PEF is in the process of ownership change, and decisions on how these units will be addressed in response to other federal regulations is uncertain, PEF has requested that it have until January 1, 2015 to state which option it will pursue.

Review for SO 2 Step 1 The options presented by PEF for SO 2 control inc luded (1) switch to lower sulfur coal

, (2) dry flue ga s desulfurization (DFGD) lime spray dryer absorber (SDA), and (3) wet flue gas . Identify all available retrofit control technologies.129 desulfurization (WFGD)

. All of the identified available retrofit SO 2 control technologies are considered technically feasible for Units 1 and 2. These units are subject to NSPS Subpart Da.

Low Sulfur Coal . Units 1 and 2 currently burn bituminous coal

, bituminous coal and bituminous coal briquette mixture, distil late (No. 2) or on

- specification used fuel oil. No. 2 fuel oil is only used during start

- up and flame stabilization. Unit 1 has maximum authorized heat input rate of 3,750 MMBtu/hr. The maximum authorized heat input rate for Unit 2 is 4,795 MMBtu/hr.

Swit ching to a lower

- sulfur fuel will reduce SO 2 e missions compared to baseline coal, with no

modifications to the emission units.

DFGD Lime SDA. In a DFGD system, boiler flue gas mixes in a spray dryer with a mist of atomized fresh lime slurry. The types of DFGD systems typically installed on coal fir ed boilers are those utilizing either a SDA or a circulating dry scrubber (CDS). PEF discusses SDA control equipment but indicates the installation of the technology is a concern with respect to adequate available space and conditions of the units. The i nstallation of dry scrubbers would also necessitate additional PM control to prevent significant emission increases

.

WFGD. A WFGD system removes SO 2 from the exhaust stream by contacting the flue gas with an alkaline reagent in an absorber to form sulfite and sulfate salts. Key WFGD process design variables include flue gas flow rate, liquid

- to

- gas ratio, recycle slurry pH, flue gas SO 2 concentration , and solid concentration and retention time. The WFGD systems are typically located downstream of any PM control system. Induced draft fans are typically used to pull the boiler flue gas through the WFGD spray tower absorbers before the flue gas exits t hrough the

stack.

When WFGD systems are installed as a retrofit technology a new stack may be required due to corrosion concerns. Adequate space must be available for the ancillary equipment associated with the WFGD system including reagent grinding and slurry preparation, dry reagent storage area and reagent slurry storage tanks, and gypsum dewatering, storage, and handling equipment.

Step 2

PEF concludes all identified available retrofit SO 2 control technologi es are considered technically feasible for Units 1 and 2. . Eliminate technically infeasible options.

Step 3

T he PEF analysis states that the effectiveness of SO 2 emiss ion control by the use of a WFGD system is between 56 and 98 per cent and a DFGD is between 70 and 96 percent. For the purpose of this analysis a 95% percent control is assumed. The effectiveness of SO 2 emissions control by the use of low sulfur coal depends on the sulfur content of the lower sulfur coal that is avail able. PEF has indicated coal with sulfur contents of 0.68% and 0.35% is commercially available. . Evaluate control effectiveness of remaining control technologies.

Step 4

. Evaluate impacts and evaluate the results. Cost of Compliance. Economic analyses were developed for fuel switch to lower sulfur coal and FDG options. The direct operating cost estimates were based on DFGD with a puff jet fabric filter along with an SCR system retrofit and conceptual design and cost estimate. The 0.6 8% sulfur fuel switch is130 estimated with an ESP upgrade. The 0.35% sulfur fuel switch cost factors include performance, coal handling performance, safety for 0.35% coal and the replacement of an ESP with baghouse. The tables below provide summaries of con trol costs related to Units 1 and 2.

TABLE A.

SO 2 Control Technology Capital Cost ($) O&M $ per year Annualized Cost $ per year Baseline SO 2 emissions TPY

Unit s 1 and 2

0.68% sulfur coal 155,250,000 11,610,000 97,477,223 38,250

0.35% sulfur coal 516,206,250 11,198,250 296,706,765 38,250

FGD /SCR 445,029,412 118,461,366 364,602,684 38,250

TABLE B.

SO 2 Control Technology Controlled SO 2 Emission R ates SO 2 Emissions Reduction Cost Effectiveness $/ton

TPY TPY Average Incremental

Unit s 1 and 2

0.68% sulfur coal 27,000 11,250 8,665 N/A

0.35% sulfur coal 18,000 20,250 14,652 22,137

FGD /SCR 1,913 36,338 10,034 N/A

Energy Impacts. The energy impacts associated with the use of lower sulfur coals, such as Powder River Basin (PRB) coal are due to the lower heating values compared to the current coal used in Units 1 and 2. The heating values of the coal currently used are approximate ly 12,000 British thermal units per pound (Btu/lb) compared to the heating value of 8,500 Btu/lb for PRB

coal.

The operation of the DFGD system imposes energy penalty due to the increased fan power required to compensate for the higher pressure drop of th e absorber vessel.

Nonair

- Quality Impacts. The DFGD technology would have non

- air impacts due to the increased water use and the generation of additional solids. The fuel switch to a lower sulfur coal would have negligible non

- air quality environmental impacts.

Remaining Useful Life. A PM BART permit was issued for Crystal River Power Plant Units’ 1 and 2 on February 25, 2009 (Permit No. 0170004

- 017

- AC), which imposed a revised allowable PM emission limit along with a cease operation date of December 3 1, 2020 for the coal fired units addressed in this BART analysis. For the low sulfur coal control options it is assumed that some level of capital improvement will be required for the current ESP upgrades to accommodate the 0.68% sulfur coal, and the repl acement of the ESP’s with baghouses and other equipment modifications would be required for the firing of PRB coal. For this analysis it is assumed that ESP upgrades or replacement and other equipment modifications would not be complete until 2018. A sim ilar determination for the installation of the DFGD controls for Units 1 and 2 was made. Since the proposed unit retirement date is the end of 2020 this would result in a useful control option equipment life of two years.

Step 5 To calculate the visibility improvement due to the lower sulfur coal and the DFGD control options, the baseline visibility impacts were estimated based on the maximum 8 th highest 24 hour average visibility impacts which is 7.93 dv. Since sulfate particles contribute to more than 90% . Evaluate visibility imp acts.131 of the total visibility impact, instead of using the sulfate contribution alone, the total impact from all pollutants was used as a baseline.

The improvements in visibility were estimated based on modeling the reduced SO 2 emission rates, which will result from the burning of lower sulfur coal and the installation of the FGD systems with 95% control efficiency. The emission rates were calculat ed by multiplying the SO 2 emission rates used in the baseline impact analysis by the ratio of: 1) the specific sulfur content (0.68% or 0.35%) and the baseline sulfur content (1.02%) of the fuel sulfur option and 2) by the

uncontrolled baseline and the est imated control efficiency of the add

- on control equipment for the FGD option.

The visibility cost effectiveness for switching from 1.02% sulfur coal to 0.68% is more than $40.4 million/dv for a total visibility improvement of 2.41 dv. The incremental visi bility cost effectiveness for switching to 0.35% sulfur coal is $145 million/dv for an additional improvement of 1.37 dv. The DFGD system installed on Units 1 and 2 would have a visibility cost effectiveness of $79.4 million/dv combined for an additional improvement of 4.59 dv.

Selection of BART PEF has determined that switching to a lower sulfur fuel or installing an FGD system is cost

prohibitive, based on a December 31, 2020 retirement date. Without this retirement date, PEF’s

SO 2 BART determination for the Crystal River Power Plant Units 1 and 2 is a SO 2 emissions rate of 0.15 lb/MMBtu on a 30 day rolling average, or 95 percent control efficiency, which can be achieved with the use of sulfur dioxide controls such as DFGD system.

Review for NO X Step 1 The options presented by PEF for NOx control inc luded (1) selective catalytic reduction (SCR) and (2) selective non

- catalytic reduction (SNCR). Each of these technologies are

con sidered technically feasible for units 1 and 2. . Identify all available retrofit control technologies.

SCR SCR is a process for controlling emissions of NOx from stationary sources. The basic principle

of SCR is the reduction of NOx to N 2 and H 2 O by the reaction of NOx and ammonia (NH 3 ) within a catalyst bed. The primary reactions occurring in SCR require oxygen and performance is best at oxygen levels above 2 to 3 percent. The SCR catalyst typically has a finite life, and

some NH 3 slips through without being reacted.

SNCR

SNCR is a post

- combustion NOx control technology that reduces NOx into nitrogen gas and

water vapor by reacting the flue gas with a reagent. SNCR systems can either use ammonia or

urea as reagents. The chemical reaction for this technology is driven by high temper atures, typically from 1,600 to 2,100°F, normally found in combustion sources. High temperature

ionizes the NH 3 or urea instead of using a catalyst or non

- thermal plasma. The temperature window for SNCR is very important because of the following considerat ions:

_ If the temperature is too high more NO x is generated than is being chemically reduced. Also, the reagent is oxidized and little or no reduction occurs.132 _ At lower temperatures the reduction reactions are too slow and more ammonia “slips”.

Ammonia slip has the potential to affect boiler operation as well as ammonium bisulfate formation on the downstream boiler components.

Although there are examples of SNCRs installed on coal

- fired boilers, it is more common for

smaller boilers in the 100 MW size range. Successful application of SNCR in large boilers may be hampered due to the following characteristics:

_ Limited access to appropriate temperature regions _ Greater variations in combustion temperatures _ Longer distances over which reagent must be delivered and mixed _ Increased ammonia slip due to inefficient use of reagent

Step 2 Both SCR and SNCR are technically feasible for Crystal River Units 1 & 2. However for large pulverized coal fired boilers, SCR is a demonstrated technology and SNCR is not. . Eliminate technically infeasible options.

Step 3

T he PEF analysis states that the effectiveness of NOx emiss ion control by SNCR technology is approximately 25 percent. Control effectiveness of SCR technology can be up to 90 percent. . Evaluate control effectiveness of remaining control technologies.

Step 4 Cost of Compliance . Evaluate impacts and evaluate the results.

To develop a cost of SCR for Units 1 and 2, information developed for the recent Cross

- State Air Pollution Rule (CSAPR) was used . Capital, fixed operation and maintenance (O&M) and variable O&M are available on a $/kW basis as part of EPA’s Integrated Planning Model (IPM) Base Case v. 4.10 (9

- 1

- 2010) for Transport Rule; Documentation; Chapter 5, Emission Control Technologies. These costs models were developed by engineering contractors such as Sargent & Lundy for the wet

- FGD cost model. Capital cost of $193/kW and fixed O&M cost of $0.7/kW presented for 500 MW boiler size and 11,000 Btu/kWh option were used in the cost calculation. Additionally, cost factors from the EPA Cost Control Manual were used to costs not included in the EPA IPM cost model. Annualized costs were developed using the methodology in the EPA

Cost Control Manual. T he estimated annualized cost for SCR is about $92 .6 million with a cost effectiveness of $8,244 per ton of NOx removed. The cost effectiveness was estimated based on

90 percent control of baseline emissions, which was determined from the maximum annual

actual emissions for Units 1 and 2 combined from the period 2001

- 2003.

Since a detailed engineering study and a vendor quote for SNCR systems for Crystal River Units 1&2 are not available, the EPA IPM cost information for 100 MW boiler was scaled in the cost analysis to understand the magnitude of the cost effectiveness in the event the SNCR system is deemed practical by an engineering study. Using the ratio of cost information in the EPA IPM document for SNCR systems for fluidized bed type boilers of 100 MW and 500 MW sizes, a capital cost of $19/kW and fi xed O&M cost of $0.2/kW were used in the cost analysis for SNCRs for Crystal River Units 1&2. Note that the cost analysis does not include costs for reagent tank, reagent distribution and control systems, CEMS for ammonia and NO x133 emissions, or water treatm ent system if urea is used. Indirect operating costs such as property

tax, insurance, and administrative costs were also excluded for the cost analysis. T he estimated annualized cost for SNCR is about $8.4 million with a cost effectiveness of $2,687 per t on of

NOx removed. The cost effectiveness was estimated based on 25 percent control of baseline emissions, which was determined from the maximum annual actual emissions for Units 1 and 2 combined from the period 2001

- 2003.

Energy Impacts. The installatio n of SCR will result in a power requirement of about 0.6 percent due to the backpressure of the SCR catalyst and auxiliaries. This is about 50,700 MWh per year of lost energy. If a SNCR is installed, additional electrical power will be required to operate the reagent handling system. A water treatment system will be required if urea is used as a reagent,

which will also need additional power.

Nonair

- Quality Impacts

Ammonia slip is an iss ue with both SCR and SNCR operation. Other issues associated with

ammonia slip are odor and ammonium salt formation. If urea is used, water used with urea will

have to be treated prior to releasing or re

- using. . There would be some non

- air quality impacts associated with the storage and handling of ammonia. If anhydrous or aqueous 29 percent ammonia were used a Risk Management Plan (RMP) under EPA rules to implement Section 112(r) of the Clean Air Act would be required. Using 19 percent ammonia or urea would not require an RMP.

Remaining Useful Life . Based on the current permit, the Units 1 and 2 will cease to be operated

as coal

- fired units by December 31, 2020. Therefore, assuming the SCR or the SNCR systems will be operational by early 2018, only approximately 3 years of useful life will be left for the

SCR/SNCR units and as a result, the cost analysis assumed only 3 years of remaining life

.

Step 5 To calculate the visibility improvement due to the SCR systems, first the baseline visibility impacts were estimated based on the maximum 8th h ighest 24

- hour average visibility impact contributed by nitrate particles and NO 2 (6.43 dv for 2003 with 29.5% contribution from nitrate

and NO 2 ). Instead of modeling the reduced NO x emission rate, future or controlled visibility impacts were estimated bas ed on the baseline visibility impact and the ratio of reduced hourly

NO x emissions rate and the baseline hourly NO x emission rate. Visibility improvements were determined by subtracting future dv impacts from the baseline dv impacts. . Evaluate visibility impacts.

The visibility cost e ffectiveness numbers for SCR were calculated from the annual costs and the visibility improvement in dv

. V isibility cost effectiveness for SCR is approximately $54.2 million/dv (visibility improvement 1.71 dv).

The visibility cost effectiveness for S N CR is approximately $17.7 million/dv (visibility improvement 0.47 dv).

Selection of BART PEF proposes the existing combustion process, LNBs, and use of good combustion practices as

BART for NO x emissions from Units 1 and 2. DEP concurs, that under the option to shut down these units by December 31, 2020, this is the appropriate BART.134 Review for PM The visibility modeling analysis showed that even replacing the existing control devices with new ones that are designed to meet a limit of 0.015 lb/MMBtu w ould not result in more than a 0.15 deciview (dv) change in v isibility at the nearest Class I 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 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 (avera ge for both units), and to permanently cease operating the units as coal

- fired boilers within seven years of the final complian ce date of the BART regulations (end of year 2020).

Review Step 1

. Table C lists the control options that were identified.

Step 2

. All contr ol options identified in Table C are considered technically feasible.

Step 3

. Table C identifies the control efficiency for each pollutant under each control optionidentified.

Step 4

. Table C 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 con trol equipment options but did consider remaining useful life an issue.

Step 5

. Table C identifies the estimated visibility change for e ach control option based on the applicant’s visibility modeling analysis using the CALPUFF model.

Based on the BART an alysis 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 alternat ive 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, PEF has proposed to spend around $50 million to up grade 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 until the prescribed shutdown date . While historical tests on these units at full load and steady

- state ope ration 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. Considering the age of the existing uni ts, 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, DEP agrees with PEF’s proposal for reducing par ticulate

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 units as coal

- fired boilers by Decem ber 31, 2020. However, DEP recognizes that under the option to continue operation, the installation of a DFGD system will necessitate additional PM control to avoid significant emission increases.135 Table C . Control Equipment Analysis

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

% Rank $/ton Rank $ Rank dv Change $/dv Change Rank

Bag House

Conversion 94% PM 10 1 30,582 2 71,775,596 2

- 0.150 95,122,400 1

Polishing Bag House (following

existing ESP) 88% PM 10 3 40,354 3 73,131,683 3

- 0.110 127,235,127 3

New ESP 90% PM 10 2 49,274 4 144,223,206 4

- 0.130 127,098,392 2

Rebuilt ES P 85% PM 10 4 12,987 1 28,760,610 \ 1

- 0.100 27,316,290 4

Notes:

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

2. Total capital costs for both units.

CONCLUSION Considering the above analysis, DEP concludes that a dditional control strategies for SO 2 and NO

x are not cost

- effective if the units shutdown by December 31, 2020. However, a PM limitation of

0.04 lb/MMBtu for the combined two units is deemed to be BART. Should PEF choose not to shutdown the Crystal River Power Plant Units 1 and 2 , PEF m ust install SO 2 and NOx control technology, meeting the limits listed below by January 1, 2018

. Additional PM control, beyond the limit listed below, is not considered necessary for BART given the very small visibility improvement that would occur for the cost. However, the permit authorizing PEF to construct the

SO 2 control, should that option be selected, assumes this control to be a DFGD, and DEP will limit PM in this permit to 0.015 lb/MMBtu at both units

. DEP has allowed PEF until January 1

, 2015, to choose the BART option it wishes to follow. DEP believes that either option meet s the BART regulations.

SO 2 : The emission limit for Unit 1 and 2 is 0.15 lb/MMBtu or 95 percent reduction.
[ Established in Permit No. 0170004

- 036

- AC]136 NO X : The emission limit for Unit 1 and 2 is 0.09 lb/MMBtu

. [Established in Permit No.

0170004

- 038

- AC]

PM: Combined e missions from Units 1 and 2 shall not exceed 0.04 lb/MMBtu. Compliance demonstrated by stack test. [40 CFR 60, Subpart Da]137 7.8.4. 4 PCS Whi te 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 78 th Street, Whi te 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

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.138 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 v enturi 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

re cover 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 pri marily with natural gas.

Affected Pollutants The affected visibility

- impairing pollutants include NO X , PM 10 and SO 2

. Project Description and Status

On February 1 , 2007, PCS Phosphate submitted an application to satisfy the BART requirements. With

the s ubsequent additional information received on September 4 , 2007, DEP deemed the application complete. DEP issued a draft air construction Permit No. 0 470002

- 055

- AC on November 13 , 200 7 with

BART determinations

. PCS Phosphate filed a petition for formal ad ministrative hearing on January 23, 2008, after their request for extension of time to file an administrative hearing was denied by DEP . PCS Phosphate has been in negotiations with DEP to resolve the outstanding issues related to the proposed

BART. PCS P hosphate resolved all the outstanding issues with DEP in letters dated December 16, 2008 and January 20, 2009 to DEP

. DEP 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 DEP

.

BART Determinations

SAP Plants : At PCS Phosphate molten elemental sulfur is combusted to produce the source of the gaseous SO 2 used to manufacture sulfuric acid. Thus SO 2 is a valuable important intermediate raw material for the process. The key emissions are SO 2 , sulfur trioxide (SO 3 ) and sulfuric acid mist (SAM or H 2 SO 4 ). All are key intermediate raw materials or products as well as pollutants. NO X and minor amounts o f PM from the sulfur source (typically combustion of molten sulfur) will also exit through the SAP stack.

MAP/DAP Plants

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. T he over

- sized and under

- sized materials are crushed and recirculated through the granulator. : At the PCS Phosphate’s MAP/DAP plants; phosphoric acid is reacted with ammonia to produce fertilizer. In the basic ammoniated phos phate 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 key pollutants from the DAP/MAP plants are fluoride (particulate and gaseous), other PM in the form of DAP and MAP, and also NH 3.139 DFP Plants : At PCS Phosphat e’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 discha rged 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)

The initial modeling analysis indicated maximum vi sibility impacts of 3.07 dv during the period

2001

- 2003. The 8 th 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 22 nd 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 SO 2 , NO X and

PM 10 emissions. : In thi s 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 scrubber s. 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.

BART determinations are conducted on certain emission units constru cted 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 (PS D) 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.

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 whi ch 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 s tate will take this into account during the review process an d may find that the level of controls already in place

are consistent with BART.140 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. Indus tries 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.

DEP finds that the levels of controls already in place are consistent with BA RT. The SAP

emissions limits for SO 2 can be further reduced by slight modifications to the converter as explained in the technical document accompanying the BART permit. The following emission limits comprise BART and were incorporated into the final BAR T permit:

SAP Plants

SO 2 Emission Standards:

Source Sulfur Dioxide (SO 2 ) Emission Limits

SAP C 1 3.5 lb SO 2 /ton of 100% H 2 SO 4 and 335.4 lb/hr, 24

- hour rolling average by

CEMS 2

SAP D 3.5 lb SO 2 /ton of 100% H 2 SO 4 and 335.4 lb/hr, 24

- hour rolling average by

CEMS 2

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

emission limit for SO 2

.

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

NO X Emission Standard

Source s :

Nitrogen Oxides (NO X as NO 2 ) Emission Limits

SAP C (EU 021) 0.14 lb NO X /ton of 100% H 2 SO 4 . Testing using EPA Method 7E shall be done upon request by DEP

. SAP D (EU 022) 0.14 lb NO X /ton of 100% H 2 SO 4 . Testing using EPA Method 7E shall be done upon request by DEP

.

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. PM/PM 10 - Opacity (VE) Standard

DAP/MAP Plants

PM/PM 10 Emissions Standard

Emissions of particulate matter (PM) and particulate matter with a mean diameter of 10 microns or less (PM 1 0 ) shall not exceed the following limits based on the average of three test runs as determined by EPA Methods 5. :141 Source Particulate (PM/PM 10 ) Emission Limits

DAP/MAP Y (EU 008) 9.2 lb/hr by annual 3

- hr stack test

DAP/MAP Z (EU 032) 9.2 lb/hr by annual 3

- hr stack test

DAP/MAP Y

– Storage Building (EU 010) 4.7 lb/hr by annual 3

- hr stack test

DAP/MAP

– Shipping & Screening (EU 015) 1.46 lb/hr by annual 3

- hr stack test

To control emissions of particulate matter (PM/PM 10 ), the permittee shall operate and maintain the existing process and pollution control 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 /PM 10 emission limits given above.

SO 2 and NO X Controls and Emission Standards

To control SO 2 and NO X emissions from DAP/MAP Plants Y and Z, the permittee shall continue to use natural gas in the dryers with 1.5 weight percent sulfur fuel oil as backup. No numerical

emission limit is established. :

DFP Plants

PM/PM 10 Emissions Standard

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

Source Particulate (PM/PM 10 ) Emission Limits

A DFP (EU 003) 14.1 lb/hr by annual 3

- hr stack test

B DFP (EU 038) 14.1 lb/hr by annual 3

- hr stack test

DFP Feed Prep (EU 042) 4.5 lb/hr by annual 3

- hr stack test

A and B DFP Coolers (EU 044) 17.5 lb/hr by annual 3

- hr stack test

To control emissions of particulate matter (PM/PM 10 ), the permittee shall operate and maintain the existing process and pollution control 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/PM 10 emission limits given above.

SO 2 and NO X Controls and Emission Standards

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

emission limit is established. :

X Train (Dical Process):

PM/PM 10 Emissions Standard :142 Emissions of particulate matter (PM) and particulate matter with a mean diameter of 10 microns or less (PM 10 ) shall not exceed the following limits based on the average of three test runs as

determined by EPA Methods 5.

Source Particulate (PM/PM 10 ) Emission Limits

X Train (EU 004) 18 lb/hr by annual 3

- hr stack test

SO 2 and NO X Controls and Emission Standards

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

established. :

Swift Creek Mine:

PM/PM 10 Emissions Standard

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

Source Particulate (PM/PM 10 ) Emission Limits

Swift Creek Mine Rock Dryer (EU 064) 17.3 lb/hr by annual 3

- hr stack test

Swift Creek Mine Silos (EU 065) 6.27 lb/hr by annual 3

- hr stack test

SO 2 and NO X Controls and Emission Standards

To control SO 2 and NO X emissions from the Swift Creek Mine Rock Dryer, the p ermittee 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

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, F lorida. 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 steam

electrical generator.

Affected Pollutants The affected visibility

- impairing pollutants include NO X , PM 10 and SO 2

.143 Project Description and Status On January 31 , 2007, the City of Tallahassee submitted an application to satisfy the BART requirements.

T he application was made complete with additional information provided on March 4, 2007

. DEP 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 recei ved 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

B ecause 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.

BART

- eligible Emissions Units 7.8.4. 6 Tampa Electric Big Bend

Tampa Electric Company (TEC) operates four solid fuel

- fired electrical generating units (EGUs) at its Big Bend Station locoated in North Ruskin, Hillsborough County, Florida. The Big Bend Station is a nominal 2,028 MW electric generation facility, with units 1, 2, 3, and 4 having max imum heat input rates of 4,037, 3,996, 4,115, and 4,330 million British thermal units per hour (MMBtu/hr), respectively. The primary fuel is coal. Units 1, 2, and 3, meet the requirements of a BART

- eligible unit.

Affected Pollutants

The affected visibility

- impairing pollutants include NO x , PM10 and SO 2

.
BART Determination Appendix Y to Part 51 provides guidelines for BART determinations under the regional haze

rule. It states that if a BART

- eligible source has controls already in place which ar e the most stringent controls available, then it is not necessary to comprehensively complete the BART analysis. Further, the preamble to the rule states if a BART

- eligible source was modified after August 7, 1977, and if such a modification was a major m odification that resulted in the installation of controls, “ the State will take that into account during the review process and may find that the level of controls already in place are consistent with BART.” Based on the review below, DEP conclude s that t he TEC BART

- eligible emission units 1, 2, and 3 all currently have the most stringent controls for SO 2 , NOx, and PM10. As such, the existing controls represent the BART determination. Exhibit 2 contains the source’s request for this determination, along with its analysis of existing controls.144 Review Big Bend Station Units 1, 2, and 3 are presently equipped with a comprehensive suite of

emission control systems to reduce emissions of PM 10 , S O 2 , and NOx. TEC entered into agreements with DEP and EPA to r educe emissions at the Big Bend Stations. These judicially

enforceable agreements (DEP’s Consent Final Judgment and EPA’s Consent Decree) required minimization and monitoring of PM emissions, upgrades to the FGD scrubbers, and reductions

in NOx emissions through the installation of SCR systems.

Electrostatic Precipitators

– PM10 Controls Units 1, 2, and 3 are each equipped with electrostatic precipitators (ESPs). Through the Consent

Decree and Consent Final Judgment , TEC was required to complete a Best A vailable Control Technology (BACT) analysis for upgrading each ESP. This analysis resulted in physical upgrades to the ESPs and development of Best Operational Practices (BOPs), resulting in a high

level of PM removal and low emission rates. The PM emiss ion limit is 0.03 lb/MMBtu.

Wet Flue Gas Desulfurization

– SO 2 Controls Units 1, 2, and 3 are each equipped with wet flue gas desulfurization (FGD) scrubbers to control

SO 2 emissions. The Consent Decree and Consent Final Judgment required upgrades to the scrubbers resulting in high SO 2 removal rates. Each unit is required to remove at least 95 percent

of the SO 2 entering the FGD scrubbers.

Selective Catalytic Reduction

– NOx Controls Units 1, 2, and 3 are each equipped with selective catalytic reduction (SCR) technology to

control NOx emissions. The Consent Decree and Consent Final Judgment required reductions in NOx emission rates. TEC installed SCR control systems on each unit, res ulting in high NOx

removal rates. The NOx emissions limit is 0.12 lb/MMBtu.

Conclusion

The DEP concludes that the existing controls and emissions limits imposed on the TEC Big Bend units 1, 2, and 3, as required by the Consent Decree and Consent Final J udgment, are the top level of control for this source. Further, these controls are BACT

- level and meet the presumptive BART levels contained in the regional haze rule.

BART

- eligible Emissions Units 7.8.4. 7 Gulf Power Crist

Gulf Power, a Southern Company operates the existing Crist Electric Generating Plant, which is located in Escambia County, on Pate Road, off of 10 Mile Road on Governors Bayou, North of Pensacola, Florida. The Crist Electric Generating Plant consists of four active fossil fuel fired

s team generators (boilers) and three fly ash silos. Boilers 4 and 5 were Acid Rain Phase I substitution units and boilers 6 and 7 were Acid Rain Phase I units. All four boilers are subject to the Acid Rain Phase II requirements and are also regulated unde r the Cross State Air Pollution Rule (CAIR). Pulverized coal is the primary fuel for boilers 4, 5, 6 and 7. Natural gas, fuel oil,

and on

- specification used oil are used as supplemental fuels in all four of the units.145 Boilers 6 and 7 have the potential t o emit 250 tons or more of a visibility impairing air pollutant, were placed into operation between August 7, 1962 and August 7, 1977, and are included within one of the 26 specifically listed source categories. Therefore, the facility operates the follow ing emissions units subject to the requirements of Rule 62

- 296.340(BART), F.A.C.: Unit 6 (EU006) and Unit 7 (EU007).

Affected Pollutants

The affected visibility

- impairing pollutants include NO x , PM10 and SO 2

.

Project Description On May 21, 2012, Gulf Power submitted an application to satisfy the Crist Electric Generating Plant, Units 6 and 7 BART requirements. Gulf Power asserts that the existing emission controls in use at Crist satisfy BART requirements for Units 6 and 7 as a r esult of utilizing top level controls and/or complying

with better than BART alternatives. DEP has reviewed Gulf Power’s proposal in making its

determination.

Gulf Power previously submitted BART exemption modeling in 2007 for PM which demonstrated Units 6 and 7 visibility impacts were well below the visibility impairment threshold of 0.5 deciviews. This PM modeling was conducted prior to the installation of the wet FGD system, the SCR system, and

reconstructed ESP for Unit 6.

BART Determination

Review for NO x : NO x emissions from Units 6 and 7 are controlled by Foster Wheeler Low

NOx Burners and by a Selective Catalytic Reduction (SCR) system designed to achieve no less

than an 85% reduction in NOx emissions as measured across the SCR unit inlet and out let. The SCR system consists of a plate type structure arranged in four layers fabricated to applying ceramic catalyst material to a perforated stainless steel mesh grid plate. The SCR for Unit 7 came on line in 2005, and for Unit 6 in 2012.

The current permit limit for emissions of nitrogen oxides ( NOx ) from the combined operation of Units 4, 5, 6, and 7 shall not exceed 0.2 lb/MMBtu heat input based on a 30

- day rolling average except for periods when Unit 7 is shutdown. DEP believes that SCR technology with combustion controls represents the top level control for NOx. As such, no further controls are deemed appropriate for BART.

Review for SO 2 : The permittee operates a wet FGD system to control SO 2 emissions from Units

4

- 7. Units 4

- 7 share a com mon stack under normal conditions with the wet FGD system in operation. The wet FGD system is based on the Model CT

- 121 wet FGD process licensed from Chiyoda Corporation. The system consists of a single scrubber reactor vessel and supporting subsystems f or transport and processing flue gas exhaust, limestone, gypsum, or other solids, and water. The wet FGD system is designed to remove 95% SO 2 emissions. Since the wet FGD was

installed on a common stack for Units 4

- 7 additional SO2 emission reductions oc cur.

DEP believes that wet FGD systems are the top level SO 2 control for electric utility boilers. The system installed by Gulf Power came on

- line in late 2009. As such, no further controls are deemed appropriate for BART.146 Review for PM: PM emissions from Unit 6 are controlled by a cold side electrostatic precipitator (Wheelabrator Model # HaRDE). PM emissions from Unit 7 are controlled by cold side electrostatic precipitators designed by Alstom Power Inc.

The permit limit for particulate matter emiss ions shall not exceed 0.1 pound per million Btu heat input, as measured by applicable compliance methods.

DEP believes that PM control using ESP’s is the top level control for electric utility boilers. As such, no further controls are deemed appropriate for BART.

Conclusion

The top level emissions control technology sited above are currently installed and operat ing on Units 6 and 7 at Crist, therefore no additional time is required for complying with BART. There are no additional energy and non

- sir quality environmental impacts associated with these two units as the aforementioned top controls are in place. DEP has determined that Gulf Power’s Crist Electric Generating Plant Units 6 and 7 are utilizing top level controls for the affected pollutants SO 2 , NOx and PM. Federally enforceable Title V Permit emission limits are currently

established

.

BART

- eligible Emissions Units for the Martin Power Plant 7.8.4.8 FPL Mar tin

This facility consists of two oil and natural gas fired conventional fossil fuel steam electric generating stations (Units 1 and 2), two oil and natural gas fired combined cycle units (Units 3

an d 4), four oil and natural gas fired combined

- cycle combustion turbines (Unit 8), and associated support equipment. The Martin Plant is located 7 miles North of Indiantown on State Road 710, Indiantown, in Martin County.

The facility operates the following emissions units subject to the requirements of the federal Regional Haze BART regulations: Unit 1 (EU001) and Unit 2 (EU002). Units 1 and 2 are fossil fuel fired steam electric generators consisting of boiler/steam gen erators which drive single reheat turbine generators, and are equipped with low nitrogen oxides (NO X ) dual fuel firing

burners to reduce emissions of NO X, and multi

- cyclones, with fly ash reinjection, to control particulate matter emissions. The maximum c apacity of each generator is 863.3 megawatts (MW).

Affected Pollutants The affected (i.e., visibility

- impairing) pollutants for this project are: sulfur dioxide (SO 2 ), nitrogen oxides (NO X ) and particulate matter (PM).

Project Description and Status FPL submitted a BART applicability determination to DEP to address the BART requirements.

Review for SO 2 Step 1 The options presented by FPL for SO 2 control included (1) use of low sulfur fuel, and (2) flue gas desulfurization (FGD), including dry sorbent injection. Two grades of low sulfur fuel oil are . Identify all available retrofit control technologies.147 discussed: 0.3% and 0.7%. These units are subject to the NSPS Subpart Da limit of 0.8 lb pound of SO 2 per million British thermal units (lb/MMBtu) when firing fuel oil. FPL fires blends of natural gas and/or fuel oil as needed in order to comply with this SO 2 limit.

Low Sulfur Fuel

Switching to a lower

- sulfur fuel oil can reduce SO 2 emissions; however, the cost of implementing the change depends on both the product price difference and the difference in delivery cost (if any). The refined oil products that are readily a vailable to the Martin plant without special order only include 0.3% and 0.7% sulfur grades. . Units 1 and 2 currently burn natural gas, distillate (No. 2) or residual (No. 6)

fuel oil. The sulfur content of r esidual oil available in the marketplace can range from 0.3 percent to more than 2 percent. The facility’s Title V permit contains the following requirements concerning fuels: The only fuels allowed to be burned are low sulfur fuel oil containing a

maxim um of 0.7% sulfur content, by weight; natural gas; or, a mixture of low sulfur fuel oil containing a maximum of 1.0% sulfur content, by weight, and natural gas in a ratio that shall not exceed the sulfur dioxide emission limiting standard of 0.80 lb/MMBtu heat input.

The use of a lower sulfur fuel (i.e., switch from 0.7% to 0.3% sulfur grade) is considered to be a technically feasible option to reduce SO 2 emissions.

Step 2

. Eliminate technically infeasible options.

Flue Gas Desulfurization . The BART analysis submitted by FPL (analysis) discussed various

post

- combustion control technologies that rely on chemical reactions within the control device to

reduce the concentrat ion of SO 2 in the flue gas. These included the calcium

- based wet lime/limestone FGD system and dry FGD systems (e.g., dry sorbent injection). The analysis points out that use of post

- combustion controls are typically applied to coal

- fired boilers and not

to oil

- fired units due to chemical reaction technology considerations and efficiencies. DEP agrees with this finding. Wet or dry FGD systems, typically used for coal

- fired boilers , are not a feasible option for oil/gas

- fired utility boilers

.

Step 3 The FPL analysis states that the effectiveness of SO 2 emissions control using lower sulfur fuel depends on product availability and economic feasibility. Switching from 0.7% sulfur fuel oil to
0.3% sulfur fuel oil is a control method that can result in lower emissions of SO 2 with no added capital investment. . Evaluate control effectiveness of remaining control technologies.

Step 4

. Evaluate impacts and evaluate the results.

The analysis indicates that the current cost of 0.7% sulfur fuel oil is $114.63 per barrel, and the current cost of 0.3% sulfur fuel oil is $124.63. No other cost components (e.g., equipment, operation, and maintenance) are expected to be affected if the switch to lower sulfur fuel is made. Using appr opriate heat input and hours of operation data results in a cost differential of $190,328,407 annually. Cost of Compliance

- Fuel Cost

**•**

Cost Effectiveness148 Based on company data, the “ pounds of SO 2 in a barrel of oil ” is as follows: (8.1 lb/gal *42 gal/bbl*2 lb SO 2 /lb S* fraction of S in oil); thus, at 0.7 percent sulfur oil = 4.763 lb SO 2 /bbl and at 0.3 percent sulfur oil = 2.041 lb SO 2 /bbl.

Given the company

- provided cost for oil of $114.63/bbl for 0.7 percent sulfur oil and $124.63/bbl for 0.3 percent sulfur oil, and noting that the cost of per ton of emissions can be determined as (change in $/bbl / change in lb SO 2 /bbl) * 2000 lb/ton, the cost per ton of emissions to change from 0.7 percent fuel oil to 0.3 percent is $7,348/ton.

Step 5 Using the CALP UFF air quality model indicates that the modeled baseline visibility impact using the current permit limit of 0.8 lb/MMBtu (which is assured by firing fuel oil containing 0.7% sulfur) is 2.3 deciview (dv). The analysis also indicates that the total modele d visibility improvement considering 0.3% sulfur fuel would be 1.07 dv, for a modeled improvement of 1.23 dv. The resulting average visibility improvement cost effectiveness is calculated at nearly $155 million/dv. FPL concludes that this visibility impr ovement is extremely small for a very large cost, therefore, switching to 0.3% sulfur fuel is cost prohibitive. . Evaluate visibility impacts.

Selection of BART Because the cost of improvement is extremely high for switching to the 0.3% sulfur fuel, this option is cost prohibitive. FP L proposed that the current operating practice of using 0.7% sulfur fuel oil burned alone, or co

- fired with the requisite amount on natural gas in order to comply

with the NSPS limit of 0.8 lb/MMBtu be considered as BART for SO 2 emissions for Units 1 and

2

. DEP concurs.

Review for NO X

Step 1 The Martin Plant units are currently equipped with multiple NO X emissions control methods including: Flue gas recirculation, overfire air systems, staged combustion and low

- NO X burners. The only available additional option identified in the FPL analysis was the addition of a selective catalytic reduction (SCR) syste m. . Identify all available retrofit control technologies.

Step 2 The FPL analysis did not identify any technically infeasible options. . Eliminate technically infeasible options.

Step 3 Using the direct and indirect capital costs and the operation and maintenance costs for a SCR system from a study conducted in 2006 for Martin Units 1 and 2, a BART cost effectiveness calculation was performed. A pollution control efficiency of 90 percent was used for the SCR

system. . Evaluate control effectiveness of remaining control technologies.

Step 4 Considering the minimal impact on visibility from the reduction of NO X , the further addition of

add

- on controls, it is not reasonable to expend resources on additional NO X reductions. To show . Evaluate impa cts and evaluate the results.149 how expensive additional NO X cont rol could be, the option of adding a selective catalytic reduction (SCR) system was evaluated. A selective catalytic reduction (SCR) system, will require a direct capital investment of approximately $100 million per unit. Using the direct and indirect cap ital costs and the operation and maintenance costs for a SCR system from a study

conducted in 2006 for Martin Units 1 and 2, a BART cost effectiveness of $5,323 per ton was

calculated.

Step 5

Based on the CALPUFF modeling re sults, only 6 to 7 percent of the total visibility impact on the

closest class 1 area is attributable to the NO X emissions from these units. The visibility improvement was estimated at 0.15 d eci v iews, resulting in the visibility cost effectiveness of

appr oximately $203 million per deciview

. T his is a very large cost for a very small reduction in visibility impact. FPL’s opinion was that a SCR is not cost

- effective BART for NO X emissions

for Units 1 and 2. . Evaluate visibility impacts.

Selection of BART FPL proposed that the existing NO X reduction practices in use, which consist of flue gas recirculation, overfire air systems, staged combustion and low

- NO X burners low NO X burners, and good combustion practices, be considered as BART for NO X emissions for Units 1 and 2.

DEP co ncurs.

Review for PM Step 1 FPL identified ESP as a possible retrofit control for these units. . Identify all available retrofit control technologies.

Step 2 The FPL analysis did not identify any technically in feasible options. . Eliminate technically infeasible options.

Step 3 ESP’s are common particulate controls on utility boiler s with control effectiveness 98 percent.

FPL currently has future plans to install electrostatic precipita tors (ESP) for the purpose of controlling PM. Capital cost of each unit is approximately $55.6 million. Installation dates are expected to be in 2014. . Evaluate control effectiveness of remaining control technologies.

Step 4 Records of actual reported annual emissions reveal that PM emissions from Unit 1 for 2010 were 311 tons, and from Unit 2 were 247 tons. Assuming a collection efficiency of 98% for an ESP, these emissions could be reduced by a total of 547 tons for the year, assuming the equipment is

installed. This resul ts in a ratio of $111.2 million/547 tons reduced = $203,000/ton reduced of PM for the first year. Assuming operation of the ESP equipment for 10 years drops the ratio to $20,300/ton per year. This result assumes the capital cost is amortized over the 10 years. This calculation also assumes no maintenance and operating costs which, of course, is unrealistic. Therefore, the addition of ESP’s to these units is not considered cost effective pursuant to the BART review process. . Evaluate impacts and evaluate the results.

Step 5 . Evaluate visibility i mpacts.150 The baseline CALPUFF visibility modeling showed that only 4 to 6 percent of the total visibility degradation from Martin is due to PM emissions from Martin. This translates into less than 0.1 deciview impact at any of the Class I areas. The FPL analysis concluded that c ontrol of PM emissions will not provide a meaningful reduction in visibility impacts due to units 1 and 2.

C onclusion Considering the above analysis, DEP has determine d that the levels of controls already in place for the affected pollutants SO 2 , NO X and PM are consistent with BART for the Martin Plant. Additional control strategies are not cost

- effective and are not required. Emissions units 1 and 2

at the FPL Martin Plant meet the BART requirements by continuing to comply with the following operational and emission limiting standards:

SO 2 : 0.80 lb/MMBtu when firing liquid fossil fuel, met by firing natural gas, co

- firing natural gas with fuel oil containing less than 1% sulfur, or firing fuel oil alone containing less than 0.7% sulfur. Compliance demonstrated by continuous emissions monitoring systems (CEMS). [40 CFR 60, Subpart D]

NO X : 0.2 lb/MMBtu when firing natural gas, 0.3 lb/MMBtu when firing fuel oil, pro

- rat ed based on heat input when co

- firing gas and oil. Limits are met through the use of flue gas recirculation, overfire air systems, staged combustion and low

- NO X burners. Compliance demonstrated by continuous emissions monitoring systems (CEMS). [40 CFR 60, Subpart
D]

PM: 0.1 lb/MMBtu when firing fuel oil. Limit met by firing natural gas, co

- firing natural gas with fuel oil containing less than 1% sulfur, or firing fuel oil alone containing less than 0.7% sulfur, and through the use of multi

- cyclones (me chanical dust collectors) and fly ash reinjection. Compliance demonstrated by stack tests when fuel oil is fired for more than 400 hours annually. [40 CFR 60, Subpart D]

BART

- eligible Emissions Units for the Manatee Power Plant 7.8.4.9 FPL Manatee

Thi s facility consists of two oil and natural gas fired conventional steam electric generating units (Units 1 and 2), “4 on 1” gas

- fired combined cycle unit (Unit 3A

- 3D) and miscellaneous insignificant emissions units

. The Manatee Plant is located at 19050 S tate Road 62, Parrish,

Florida , in M anatee County.

The facility operates the following emissions units subject to the requirements of the federal Regional Haze BART regulations : Unit 1 (EU001) and Unit 2 (EU002). Units 1 and 2 are fossil

fuel fired steam electric generators consisting of boiler/steam generators which drive single reheat turbine generators, and are equipped with electrostatic precipitators, flue gas recirculation system, reburn and staged combustion. Units 1 and 2 are each nominal 80 0 MW (900 MW gross capacity).

Affected Pollutants The affected (i.e., visibility

- impairing) pollutants for this project are: sulfur dioxide (SO 2 ), nitrogen oxides (NO X ) and particulate matter (PM).15 1 Project Description and Status

Review for SO 2 Step 1 The options presented by FPL for SO 2 control included (1) use of low sulfur fuel, and (2) flue gas desulfurization (FGD) , including dry sorbent injection. Two grades of low sulfur fuel oil are

discussed: 0.3% and 0.7%. These units are subject to the NSPS Subpart D limit of 0.8 lb pound

of SO 2 per million British thermal units (lb/MMBtu) when firing fuel oil. FPL fires blends of natural gas and/or fuel oil as needed in order to comply with thi s SO 2 limit. . Identify all available retrofit control technologies.

Low Sulfur Fuel

Switching to a lower

- sulfur fuel oil can reduce SO 2 emissions; however, the cost of implementing the change depends on both the product price difference and the difference in delivery cost (if any). The r efined oil products that are readily a vailable to the Manatee Pla nt include 0.3% and 0.7% sulfur grades. The total annual cost of switching Units 1 and 2 from the fuel currently used to 0.7% sulfur fuel oil is more than $85 million, and from the fuel oil currently used to 0.3% sulfur fuel oil is more than $240 million. . Units 1 and 2 currently burn natural gas, distillate (No. 2) or residual (No. 6) fuel oil. The sulfur content of residual oil available in the marketplace can range from 0.3 percent to more than 2 percent.

The use of a lower sulfur fuel (i.e., switch from 1.0 % to 0.7% or 0.3% sulfur grade) is considered to be a technically feasible option to reduce SO 2 emissions due to the zero capital recovery cost and no capital investment only differential fuel cost

.

Step 2

. Eliminate technically infeasible options.

Flue Gas Desulfurization . The BART analysis submitted by FPL discussed various post

combustion control technologies that rely on chemical reactions within the control device to

reduce the concentration of SO 2 in the flue gas. These included the calcium

- based wet lime/limestone FGD system and dry FGD systems (e.g., dry sorbent injection). The analysis points out that use of post

- combustion controls are typically applied to coal

- fired boilers and not

to oil

- fired units due to chemical reaction technology considerations and efficiencies. This fact, coupled with high capital costs (ranging between $40 and $100 million), led FPL to the conclusion that installation of a FGD system would be cost prohibitive. This option was therefore rejected in the BART analysis.

FPL provide d detailed cost information but cautioned for illustrative purposes generic wet

- FGD cost estimates have been developed. A nnualized cost s are based on cost information provided in

EPA’s Integrated Planning Model (IPM) Base Case v. 4.10 (9

- 1

- 2010) for Transport Rule; Documentation; Chapter 5, Emission Control Technologies, Table 5

- 4. T hese costs are extremely high without even considering additional retrofit costs that is expected for ad ding FGD systems at M a n atee

. I t may not even be possible or technically feasible to co nstruct wet

FGD system at Manatee without major demolition efforts that would affect the continued operation of these units.” DEP a grees that wet or dry FGD systems are typically used for coal

fired boilers and not oil/gas

- fired boilers

.

Step 3

. Evaluate control effectiveness of remaining control technologies.152 T he FPL analysis states that the effectiveness of SO 2 emissions control us ing lower sulfur fuel depends on product availability and economic feasibility. Switching from 1.0 % sulfur fuel oil to

0.7% or 0.3% sulfur fuel oil is a method that can result in lower emissions of SO 2 with no added capital investment.

Step 4

. Evaluat e impacts and evaluate the results.

Based on the information provided by FPL, the current 1.0% sulfur fuel cost is $109.05 per barrel (bbl) (42 gallons). The analysis indicates that the current cost of 0.7% sulfur fuel oil i s $114.63 bbl , and the current cost of 0.3% sulfur fuel oil is $124.63 bbl . No other cost components (e.g., equipment, operation, and maintenance) are expected to be affected if the switch to lower sulfur fuel is made. Using appropriate heat input and ho urs of operati on results in a cost differential of $ 87,181,773 for 0.7% sulfur fuel oil and $243,421,511 for 0.3% sulfur

fuel oil

. Cost of Compliance

- Fuel C ost

Cost Effectiveness Based on company data, the “ pounds of SO 2 in a barrel of oil ” is as follows: (8.1 lb/gal *42 gal/bbl*2 lb SO 2 /lb S* fraction of S in oil); thus, at 1.0 percent sulfur fuel oil =6.804 lb SO 2 /bbl, and at 0.7 percent sulfur oil = 4.763 lb SO 2 /bbl and at 0.3 percent sulfur oil = 2.041 lb SO 2 /bbl.

Given the company

- pro vided cost for oil of $109.05/bbl for 1.0 percent sulfur oil, $114.63/bbl for 0.7 percent sulfur oil, and $124.63/bbl for 0.3 percent sulfur oil, and noting that the cost of per ton of emissions can be determined as (change in $/bbl / change in lb SO 2 /bbl) * 2000 lb/ton, the cost per ton of emissions to change from 1 percent fuel oil to 0.7 percent is $5,468/ton, the cost per ton to change from 1 percent to 0.3 percent is $6,542/ton, and the cost per ton of emissions to change from 0.7 percent fuel oil to 0 .3 percent is $7,348/ton.

Step 5 Using the CALPUFF air quality model indicates that the modeled baseline visibility impact using the current permit limit (firing fuel oil containing 1.0 % sulfur ) is 4.07 deciview ( dv ) . The

a nalysis also indicates that the total modeled improvement in visibility considering 0.7 % sulfur

fuel would be 0.87 dv and 0.3% sulfur fuel w ould be 2.38 dv

. The resulting average visibility improvement cost effectiveness is calculated at nearly $ 100 mill ion/dv burning 0.7% sulfur fuel and $102 million/dv burning 0.3% sulfur fuel

. FPL concludes that this visibility improvement is extremely small for a very large cost , therefore, switching to 0.3% sulfur fuel is cost prohibitive. . Evaluate visibility impacts.

Selection of BART

B ecause the cost of improvement is extremely high for switching to the 0.3% sulfur fuel, this option is cost prohibitive. FPL proposed that the switch from the current 1.0% sulfur fuel to

0.7% sulfur fuel oil burned alone, or co

- fired with the requisite amount on natural gas in order to comply with the NSPS limit of 0.8 lb/MMBtu be considered as BART for SO 2 emissions for

Units 1 and 2. DEP concurs.

Review for NO X

Step 1

. Identify all available retrofit control technologies.153 T he M anatee Plant units are curr ently equipped with multiple NO X emissions control methods including: Flue gas recirculation, overfire air systems, staged combustion

, low

- NO X burners and reburn. The only available additional option identified in the FPL analysis was the addition of a

s elective catalytic reduction (SCR) system.

Step 2 The SCR system is technically feasible. . Eliminate technically infeasible options.

Step 3 Using the direct and indirect capital costs a nd the operation and maintenance costs for a SCR system from a study conducted in 2006 for Ma natee Units 1 and 2, a BART cost effectiveness calculation was performed. The annualized cost of the purchase and operation of an SCR system

on both units is calc ulated as $31M. A pollution control efficiency of 90 percent was used for the SCR system. With this efficiency, a NOx reduction of 8,229 tons per year would occur. This results in a cost effectiveness of $3,776/ton. . Evaluate control effectiveness of remaining control technologies.

Step 4 Based on the modeling results, only 6 to 17 percent of the total visibility impact on the closest

C lass I area is attributable to the NO X emissions from these units. Considering the minimal impact on visibility from the reduction of NO X , the further addition of add

- on controls, FPL believes it is not reasonable to expend resources on additional NO X reductions. To show how expensive additional NO X control could be, the option of adding a selective catalytic reduction (SCR) system was evalu ated

. . Evaluate impacts and evalua te the results.

Step 5 Based on a capital expenditure of approximately $100 million per unit and annual operating costs of roughly $6 million, t he analysis indicated that , “the visibility cost effectiveness is approximately $ 66 million per deciview ... this is a very large cost for an insignificant amount of reduction in visibility impact.” FPL’s concludes, a SCR is therefore not cost e ffective as BART

for NO X emissions for Units 1 and 2 . Evaluate visibility impacts.

Selection of BART FPL proposed that the existing controls of low NO

x burners, reburn, overfire air system, staged combustion, and flue gas recirculation along with good combustion practice s, be considered as

BART for NO X emissions for Units 1 and 2. DEP concurs.

Review for PM

Step 1 FPL has been permitted to remove the existing cyclones with ESPs for the purpose of controlling PM

emissions from Units 1 and 2. The in

- service dates for the ESPs for Units 1 and 2 are the third quarter of

2012 and fourth quarter of 2013, respectively. Permit No. 0810010

- 016

- AC also requires a stricter PM

emissions standard for Units 1 and 2. ESP’s are generally considered to be the top

- level control for PM emissions from electric utility boilers. As such, no add itional retrofit technologies were

considered. . Identify all available retrofit control technologies.154 Step 2 The FPL analysis did not identify any technically infeasible options. . Eliminate technically infeasible options.

Step 3

Because the top

- level control technology is being installed, no additional control strategies were

evaluated. . Evaluate control effectiveness of remaining control technologies.

Step 4 Because the top

- level control technology is being installed, no additional control strategies were

evaluat ed. . Evaluate impacts and evaluate the results.

Step 5

The analysis concluded that additional c ontrol of PM emissions will not provide a meaningful reduction in visibility impacts due to units 1 and 2. FPL proposes the ESP’s are the BART for

PM emissions from Units 1 and 2. DEP concurs. . Evaluate visibility impacts.

C onclusion Considering the above analysis, DEP has determine d that the levels of controls already in place

, or soon to be in place, for the affected pollutants NO X and PM are consistent with BART for the

FPL Manatee Plant . Additional control strategies are not cost

- effective and are not required.

DEP has also determined SO 2 emissions and visibility impacts can be further reduced by switching from 1.0% sulfur fuel oil to 0.7% sulfur fuel oil alone or co

- fired with natural gas. FPL has recently submitted a permit application to DEP asking for a natural gas capacity increase from 5,670 MMBtu/hr to 8,650 MMBtu/hr, which is equivalent to the oil

- firing permit capacity. This alone will reduce SO 2 , PM, and NO

x emissions from U nits 1 and 2. FPL Manatee Plant Units 1 and 2 meet the BART requirements by recent upgrades to the emission units and firing a 0.7% sulfur fuel oil. The following operational and emission limiting standards for FPL

Manatee Units 1 and 2 are:

SO 2 : Sulfur dioxide emissions rate from each fossil fuel fired steam electric generator Units 1 and 2 shall not exceed 0.80 lb/MMBtu during any rolling 12 months. [Application No. 0810010

- 017

- AC and Rule 62

- 210.200(PTE), F.A.C.]

NO X : Emissions shall not exceed 0.3 lb/MMBtu. Limits are met through the use of f lue gas recirculation, overfire air systems , reburn , staged combustion and low

- NO X burners

. Compliance demonstrated by continuous emissions monitoring systems (CEMS). [40 CFR 60, Subpart Da]

PM: Emissions sha ll not exceed 0. 03 lb/MMBtu during normal operation . Compliance demonstrated by stack test. [40 CFR 60, Subpart Da]

Unit 1 is a pre

- NSPS boiler with a nominal rating of 985 MMBtu/hr heat input (natural gas) and 90 MW and is fired by natural gas and fuel oil. It has no pollution controls. Unit 2 is a NSPS Subpart D boiler with a nominal rating of 1,185 MMBtu/hr heat in put (natural gas) and includes 7.8.4.10 Lakeland Electric McIntosh155 flue gas recirculation (FGR) for NO X controls and no add

- on PM controls. The C.D. McIntosh Jr. Power Plant is located at 3030 East Lake Parker Drive in Lakeland, Polk County.

Affected Pollutants The affected (i.e., visibility

- impairing) pollutants for this project are: sulfur dioxide (SO 2 ), nitrogen oxides (NO X ) and particulate matter (PM).

Project Description and Status

Lakeland Electric (LE) submitted a BART applicability determination to DEP to address the BAR T requirements. The following sections describe site specific conditions which are considered in making

DEP ’s BART determination. There are three PSD Class I areas located within 300 km of the McIntosh facility:

**•** Chassahowitzka National Wilderness Area (N WA)

– 91 km

**•** Everglades National Park (NP)

– 249 km

**•** Okefenokee NWA

– 277 km

The analysis was performed for only the Chassahowitzka NWA, which is the nearest and the only Class I area where the visibility impacts were predicted to be higher than 0.5 dv.

Review for SO 2 Step 1 The options presented by LE for SO 2 control included (1) use of low sulfur fuel, and (2) flue gas desulfurization (FGD), including dry sorbent injection. Unit 2 is currently li mited to 0.7% fuel

oil and LE staff evaluated the option of utilizing this low sulfur fuel oil in Unit 1. Unit 1 is subject to the Florida SIP limit of Rule 62

- 296.405(1)(c)1.a F.A.C. which limits emissions to 2.75 pound of SO 2 per million British therma l units (lb/MMBtu) when firing fuel oil. Also, the upcoming Utility MACT discussed in the previous section effectively limits the operation of Units 1 and 2 to approximately an 8% capacity factor on fuel oil (starting in 2015). . Identify all available retrofit control technologies.

Low Sulfur Fuel Option: Unit 1 currently burns natural gas and fuel oil. The sulfur content of residual fuel oil available in the marketplace can range from 1 percent to more than 2 percent. The facility’s Title V permit contains the following requirements concerning fuel oil: For Unit 1,

the sulfur content of the as

- fired No. 6 fuel oil shall not exceed 2.5 percent, by weight if the SO 2 continuous emissions monitor system is not used for compliance. Switching to a lower

- sulfur

fuel oil can reduce SO 2 emissions; however, the c ost of implementing the change depends on both the product price difference and the difference in delivery cost (if any). The refined oil products evaluated for the McIntosh plant Unit 1 was 0.7% sulfur grade fuel oil (which is the same grade of fuel used in the newer boiler, Unit 2). The use of a lower sulfur fuel (i.e., switch from 2.5 % to 0.7% sulfur grade) is considered to be a technically feasible option to reduce SO 2 emissions.

Step 2

. Eliminate technically infeasible options.

Flue Gas Desulfurization . The BART analysis submitted by LE discussed various post

combustion control technologies that rely on chemical reactions within the control device to

reduce the concentration of SO 2 in the flue gas. These included the calcium

- based wet156 l ime/limestone FGD system and dry FGD systems (e.g., dry sorbent injection). The analysis points out that use of post

- combustion controls are typically applied to coal

- fired boilers and not

to oil

- fired units due to chemical reaction technology considerati ons and efficiencies.

DEP agrees that add

- on controls like FGD in this case is not a viable option for Unit 1 or Unit 2 which will have a limited capacity factor (effectively 8%) for fuel oil. C omparable oil and gas

fired boilers do not employ add

- on su lfur controls for BACT, but rather utilize low sulfur fuel oil as a means of reducing emissions. Again, it may not even be possible or technically feasible to

construct wet

- FGD system at the existing McIntosh units without major demolition efforts that

wo uld affect the continued operation of these units. DEP agrees that wet or dry FGD systems are typically used for coal

- fired boilers

, and are not considered appropriate technology for oil/gas

fired boilers like McIntosh Units 1 and 2.

Step 3

The LE analysis states that the effectiveness of SO 2 emissions control using lower sulfur fuel depends on product availability and economic feasibility. Switching from 2.5 % sulfur fuel oil to

0.7% sulfur fuel oil on Unit 1 is a control method that can result in lower emissions of SO 2 with no added capital investment. Unit 2 currently is restricted by permit to 0.7% fuel oil. The

reduction in sulfur content from 2.5% to 0.7% for Unit 1 has a contr ol effectiveness that reduces emission by more than one half compared to the currently fired high sulfur fuel oil. . Evaluate control effectiveness of remaining control technologies.

Step 4

. Evaluate impacts and evaluate the results.

The analysis indicates that the current cost of 2.5 % sulf ur fuel oil is $19.65 per MMBtu compared to the current cost of 0.7% sulfur fuel oil at $21.87 per MMBtu of the fuel oil. No other cost components (e.g., equipment, operation, and maintenance) are expected to be affected

if the switch to lower sulfur fue l is made. Using appropriate heat input and hours of operation data results in a cost differential of $8,345,694 annually. Cost of Compliance

- Fuel Cost

Cost Effectiveness Based on company data, the “ pounds of SO 2 per MMBtu ” is as follows : (8.3 lb/gal / 0.15 MMBtu/gal *2 lb SO 2 /lb S* fraction of S in oil); thus, at 2.5 percent sulfur fuel oil =2.767 lb

SO 2 /MMBtu, and at 0.7 percent sulfur oil = 0.775 lb SO 2 /MMBtu.

Given the company

- provided cost for oil of $19.649/MMBtu for 2.5 percent sulfur oil, and $21.871/MMBtu for 0.7 percent su lfur oil, and noting that the cost of per ton of emissions can be determined as (change in $/MMBtu / change in lb SO2/MMBtu) * 2000 lb/ton, the cost per ton of emissions to change from 2.5 percent fuel oil to 0.7 percent is $2,231/ton.

Cost of Compliance

– Scrubber To develop a cost of wet

- FGD for Units 1 and 2, information developed for the recent CSAPR was used. Capital, fixed operation and maintenance (O&M), and variable O&M are available on a dollar per kilowatt basis as part of EPA’s Integrated Planni ng Model (IPM) Base Case v. 4.10 (September 1, 2010) for Transport Rule; Documentation; Chapter 5, Emission Control Technologies. These costs models were developed by engineering contractors such as Sargent & Lundy for the wet

- FGD cost model. Additionally , cost factors from the EPA Cost Control

Manual were used to costs not included in the EPA IPM cost model. Annualized costs were developed using the methodology in the EPA Cost Control Manual. This control alternative157 allows the use of high sulfur fuel oi l and assumes a 98

- percent removal for the maximum annual

SO 2 emissions for Units 1 and 2 over the period 2001 through 2003. The annualized cost is $36.2 million with an average cost effectiveness of about $13,200 per ton of SO 2 removed. It should be recognized that that the estimated costs are not specific to the McIntosh Plant nor the layout of Units 1 and 2. LE indicates that i t may not be possible to install add

- on SO 2 equipment given the spatial constraints at the site. Notwithstandi ng, LE believes the cost effectiveness is extremely high and unreasonable as BART.

Step 5 Using the CALPUFF air quality model indicates that the modeled baseline visibility impact using the current permit limit of 2.75 lb/MMB tu for Unit 1 (which is assured by firing fuel oil containing 2.5 % sulfur) and Unit 2 on low sulfur fuel oil is 1.62 deciview (dv). The analysis also indicates that the total modeled visibility improvement considering 0.7% sulfur fuel for

Unit 1 would b e a modeled improvement of 0.74 dv. The resulting average visibility improvement cost effectiveness is calculated at $11.3 million/dv. LE concludes that this visibility improvement is very small for a very large cost, therefore, switching to 0.7% sulfur fuel is cost prohibitive. LE has opted to utilize the low sulfur fuel oil in Unit 1 even at this high cost, to be consistent with the fuel used in Unit 1. . Evaluate visibility impacts.

LE proposed that new shipments of fuel oil for Unit 1 will be limited to 0.7% su lfur content, the same as in Unit 2 and proposed this low sulfur fuel oil control option for both units as BART.

DEP concurs and establishes that new shipments of fuel oil shall be limited to 0.7% sulfur as this

BART determination for Units 1 and 2. A fe derally enforceable permit condition assures this

determination. Selection of BART

Review for NO X

Step 1

The McIntosh Plant Unit 1has no controls for NO X emission other than best operating practices for good combusti on. As mentioned previously, Unit 2 has FGR controls for NO X and currently meets a relatively low permit limit for NO X of 0.2 lb/MMBtu. This is considered very good control for Unit 2. This analysis will therefore cover Unit 1 as the worst case for NO X controls.

LE evaluated control methods including: Selective Catalytic Reduction (SCR) and Selective

Non

- Catalytic Reduction (SNCR). The temperature window for SNCR is very important because outside of it, either more NH3 slips through the system, or mo re NO x is generated than is being chemically reduced. NH3 slip has the potential to affect boiler operation as well as ammonium bisulfate formation on the downstream boiler components. SNCR is selective in that the reagent reacts primarily with NO X

. . Identify all available retrofit control technologies.

Step 2 SNCR has been demonstrated as a feasible NOx control technology for oil

- fired boilers. However, an optimum temperature range is important for the SNCR reaction to occur and an engineering study must be conducted to learn the feasibility of SNCR systems for Units 1 and 2. Therefore the only available additional option identified in the LE analysis was the addition of a selective catalytic reduction (SCR) system. . Eliminate technically infeasible options.158 Step 3

. Evaluate control effectiveness of remaining control technologies. Control efficiency ac hieved by a SCR system is estimated to be 80 percent, on average. Control efficiency achieved by a SNCR system is estimated to be in the range 50 to 60 percent. SCR is assumed to be the top ranked add

- on NOx control technology for Units 1 and 2.

Step 4

. Evaluate impacts and evaluate the results. Using the direct and indirect capital costs and the operation and maintenance costs for a SCR system from the EPA Fact Sheet for NOx controls LE found that the capital costs and operational costs are relatively hi gh. The annualized cost was found to be $2.7 million and the cost effectiveness of SCR was estimated to be $5,241/ton with a pollution control efficiency of 80 percent for the system. The installation of SCR will result in a power requirement of about 0 .6 percent due to the backpressure of the SCR catalyst and auxiliaries. This is about 2,800 MWh per year of lost energy.

There would be some non

- air quality impacts associated with the storage and handling of NH3. If anhydrous or aqueous 29 percent NH3 we re used, a Risk Management Plan (RMP) under EPA rules to implement Section 112(r) of the CAA would be required. Using 19 percent NH3 or urea would not require an RMP. If 19 percent aqueous NH3 were used, the amount would be 994

TPY for the maximum NOx redu ction. This is about 238,000 gallons, requiring about 34 tanker loads per year.

There are no current plans for Unit 1 and 2’s retirement.

Step 5 . Evaluate visibility impacts. Based on the modeling results, only about 19% of the total visibility impact o n the closest class I

area is attributable to the NO X emissions from these units. Based on a capital expenditure and annual costs of approximately $2.7million/yr for Unit 1 or Unit 2 and operating and maintenance costs contributing to the annual cost of S CR controls, the analysis indicated that the visibility cost effectiveness is cost prohibitive and would exceed a cost effectiveness of $11 million per deciview for a visibility improvement of only 0.25 dv. This is a very large cost for a relatively

small amount of reduction in visibility impact.

Selection of BART

LE ’s opinion was that a SCR is not cost

- effective as BART for NO X emissions in this case. DEP agrees and establishes that BART in this case is the current limit of 0.2 lb/MMBtu for NO X for

Unit 2 and no add

- on NO X control for Unit 1, but further purchase of fuel oil for this unit will be limited to no more than 0.7 percent sulfur.

Review for PM These units don’t currently have add

- on PM controls. Step 1

. Identify all available retrofit con trol technologies.

LE evaluated add

- on controls including fabric filters, ESPs and wet scrubbers to control PM

emissions.159 Step 2

. Eliminate technically infeasible options.

The LE analysis identified fabric filters and wet scrubbers as technically infeasible options. The use of a fabric filter baghouse depends on site specific exhaust characteristics such as particulate loading, temperature, and moisture content. Condensation of moisture may cause crusty caking or plugging of the fabric or require special additives. There is also a potential fire hazard in a baghouse if a spark or flame is accidentally admitted. For large oil

- fired boilers like the LE units

the use of a fabric f ilter control device is not common. Also, the PM from firing fuel oil can be sticky which can cause problems with cleaning fabric filters. Likewise, wet scrubbers like Venturi scrubbers were evaluated although they are not commonly used for large oil fir ed units like Units 1 and 2. In this case the waste water disposal problem made this option infeasible for these units. Therefore the options of baghouse or wet scrubber are not found to be technically feasible in this case and the only feasible control option for PM was found to be an ESP.

Step 3

. Evaluate control effectiveness of remaining control technologies. An ESP control is considered to be the most common and technically feasible option for LE units. ESPs are also ranked very high in terms of con trol efficiency greater than 99 percent. Other technologies have not demonstrated equivalent levels of control for PM compared to an ESP in this application.

Step 4

. Evaluate impacts and evaluate the results. The capital and annualized costs for an ESP a re significant. The estimated costs were developed from information available from recent Golder projects and EPA’s cost control manual. The EPA cost control manual was also used where individual cost factors were required and to develop the

annualized co st. The estimated annualized cost for ESP is about $3 million with a cost effectiveness of $65,865 per ton of PM removed . Other factors like operating costs and remaining useful life are shown below.

The installation of ESP will result in a power usage of about 0.3 percent due to electric field current usage and backpressure. This is about 1,400 MWh per year of lost energy.

There would be some non

- air quality impacts associated with the disposal of a sh that would have to be disposed of at a Class I landfill.

As reported by LE , there are no current plans for Unit 1 or 2 retirement and the current authorizations/permits limit operation on fuel oil for these units beginning in 2015.

Installation of a n ESP would require about two years for construction based on experience from recent retrofit projects

. It is therefore apparent to DEP , based on the costs and the limited use of fuel oil for Unit 1 and 2, that the addition of an ESP is not considered cos t effective after evaluating these unit specific factors.

Step 5 . Evaluate visibility impacts.

LE modeling found that PM only contributes about 5% of the total visibility impact from Units 1and 2. To calculate the visibility improvement due to controllin g PM using an ESP, first the baseline visibility impacts were estimated based on the maximum 8th highest 24

- hour average visibility impact contributed160 by PM particles (1.617 dv for 2002 with 6.2% contribution from PM). Instead of modeling the reduced

PM em ission rate, future or controlled visibility impacts were estimated based on the baseline visibility impact and the ratio of reduced hourly PM emissions rate and the baseline hourly PM emission rate. The reduced PM emission rate was estimated based on a p otential emission rate of 0.03 lb/MMBtu and the heat input capacity of Unit 1and 2. Visibility improvements were determined by subtracting future dv impacts from the baseline dv impacts.

The visibility cost effectiveness numbers were calculated from the annual costs and the visibility improvement in dv. Visibility cost effectiveness for an ESP is more than $41.7 million/dv (visibility improvement 0.07 dv) This is a very large cost for an small amount of visibility improvement. As the pollutant and visib ility cost effectiveness values above indicate, the cost of adding an ESP to either unit is extremely high for a very small improvement in visibility. As a result, it is concluded that adding additional PM controls is cost prohibitive and LE proposes the existing PM emissions limits as BART limits for Unit 1 and 2. The existing PM emissions limits are 0.1 lb/MMBtu for normal operation and 0.3

lb/MMBtu for soot

- blowing operation on Unit 1 and Unit 2 has a limit of 0.1 lb/MMBtu at all times.

C onclusion

Con sidering the above analysis, DEP has determine d that limited use of 0.7% sulfur fuel oil and the levels of controls already in place for the affected pollutants SO 2 , NO X and PM are consistent

with BART for the McIntosh Plant Units 1 and 2. Additional cont rol strategies are not cost

effective and are not required. Emissions Unit 1 at the Lakeland Electric McIntosh Plant currently meets the BART requirement by continuing to comply with the following operational and SIP emission limiting standards:

SO 2 : 0.8 0 lb/MMBtu when firing fuel oil, met by firing natural gas, co

- firing natural gas with fuel oil, or firing fuel oil alone containing not more than 0.7 % sulfur. Compliance demonstrated by continuous emissions monitoring systems (CEMS).

NO X : 0.20 lb/MMBtu when firing natural gas or firing fuel oil for Unit 2 by use of the existing FGR controls. Compliance is demonstrated by continuous emissions monitoring systems (CEMS). Unit 1 is uncontrolled for NO X

. PM: 0.1 lb/MMBtu when firing fuel oil and 0.3 lb/MMB tu for sootblowing for Unit 1 and 0.1 lb/MMBtu for Unit 2 at all times. These limits are met by firing natural gas, co

- firing natural gas with fuel oil, or firing fuel oil alone containing less than 0.7% sulfur.

7.8.4.11 JEA Northside

BART

- eligible Emission Unit at the Northside Power Plant

The existing Boiler No. 3 is a pre

- NSPS boiler with a nominal rating of 564 MW and fired by natural gas, landfill gas, No. 6 residual fuel oil, and used oil. Emissions from the NGS Boiler

No. 3 are uncontrolled. The Northside Generating Station (NGS) Plant is located at 4377 Heckscher Drive, Jacksonville, in Duval County.

Affected Pollutants The affected (i.e., visibility

- impairing) pollutants for this project are: sulfur dioxide (SO 2 ), nitrogen oxides (NO X ) a nd particulate matter (PM). Project Description and Status161 JEA submitted a BART applicability determination to DEP to address the BART requirements. The following site specific conditions are considered in making DEP ’ s BART determination in this case:

N orthside Unit 3 is located at a JEA power plant that has two other units which were repowered and converted to circulating fluidized bed boilers. These two units (Units 1 and 2) fire mainly petroleum coke and coal (about 10%) fuel blends. As part of the repowering of NGS Units 1 and 2, JEA made a voluntary community commitment of reducing emissions of SO 2 , NO X and PM to 10 percent below the 1994 and 1995 baseline years used in the permitting of the repowering project. This effectively reduced operation of Unit 3 when the new repowered units became operational. Based on the operation of Unit 3 on oil, the limiting pollutant is NO X , which is capped or limited by permit to 3,600 tons/year for Units 1, 2 and 3 over a 12

- month rolling average. This limitation f or NGS Units 1

- 3 is also a federally enforceable permit limit and

contained in the Title V Permit No. 0310045

- 030

- AV as Condition G.11.b.

Based on the operation of Unit 3 in 2002, this annual limit for NO X limits the SO 2 emission from oil firing to about 9,000 tons/year if Units 1 and 2 are not operating. This is equivalent to a capacity factor of about 21 percent at the authorized emission rate. If Units 1 and 2 are fully operational (the usual case), then Unit 3 is limited to a maximum of 3,506 tons/yea r based on the operational profile in 2002. This is equivalent to a capacity factor of about 8 percent at the authorized emission rate. As a result, and by virtue of the community commitment, and federally enforceable permit limits on annual emissions, th e actual capacity factor of Unit 3 on oil is limited to approximately 8%.

Review for SO 2 Step 1

. Identify all available retrofit control technologies. JEA identified the following potential retrofit technologies for SO 2 control (1) use of low sulfur fuel, and (2) flue gas desulfurization (FGD), including dry sorbent injection. This unit is subject

to the Florida SIP limit of Rule 62

- 296.405(1)(c)1.a F.A.C. which limits emissions to 1.98 lb pound of SO 2 per million Brit ish thermal units (lb/MMBtu) when firing fuel oil.

Unit 3 currently burns natural gas, landfill gas, distillate (No. 2) or residual (No. 6) fuel oil or used oil. The sulfur content of residual fuel oil available in the marketplace can range from 1

perc ent to more than 2 percent. The facility’s Title V permit contains the following requirements concerning fuel oil: “For Boiler No. 3, the sulfur content of the as

- fired No. 6 fuel oil shall not exceed 1.8 percent, by weight, if the SO 2 continuous emissio ns monitor system is temporarily inoperative.” Switching to a lower

- sulfur fuel oil can reduce SO 2 emissions; however, the cost of implementing the change depends on both the product price difference and the difference in delivery cost (if any). The refin ed oil products evaluated for the Northside plant was 1% sulfur grade fuel oil.

The use of a lower sulfur fuel (i.e., switch from 1.8% to 1% sulfur grade) is considered to be a technically feasible option to reduce SO 2 emissions.

Step 2

. Eliminate technically infeasible options.

Flue Gas Desulfurization . The BART analysis submitted by JEA (analysis) discussed various

post

- combustion control technologies that rely on chemical reactions within the control device to162 reduce the concentration of SO 2 in the flue gas. These included the calcium

- based wet lime/limestone FGD system and dry FGD systems (e.g., dry sorbent injection). The analysis points out that use of post

- combustion controls are typically applied to coal

- fired boilers and not

to oil

- fired units due to chemical reaction technology considerations and efficiencies.

DEP agrees that add

- on controls like FGD in this case is not a viable option for Unit 3 which has a limited capacity factor (effectively 8%) for fuel oil. JEA listed the compara ble BACT

determinations for SO 2 controls on oil and gas fired boilers and showed that none of the comparable oil and gas fired boilers employed add

- on sulfur controls for BACT, but rather utilized low sulfur fuel oil as a means of reducing emissions. It m ay not even be possible or technically feasible to construct wet

- FGD system at Northside without major demolition efforts that would affect the continued operation of this unit. DEP agrees with this finding.

Step 3

. Evaluate control effectiveness of re maining control technologies. The JEA analysis states that the effectiveness of SO 2 emissions control using lower sulfur fuel depends on product availability and economic feasibility. Switching from 1.8% sulfur fuel oil to

1% sulfur fuel oil is a control method that can result in lower emissions of SO 2 with no added capital investment. Control effectiveness is proportional to the fuel sulfur content.

Step 4

. Evaluate impacts and evaluate the results. Cost of Compliance

- Fuel Cost The analysis indicate s that the current cost of 1.8% sulfur fuel oil is $ 2.067 per gallon compared to the current cost of 1% sulfur fuel oil at $2.54 per gallon. No other cost components (e.g., equipment, operation, and maintenance) are expected to be affected if the switch t o lower sulfur fuel is made. Using appropriate heat input and hours of operation data results in a cost differential of $33,681,924 annually.

Cost Effectiveness Based on company data, the “ pounds of SO 2 in a gallon of oil ” is as follows: (8.3 lb/gal *2 l b

SO 2 /lb S* fraction of S in oil); thus, at 1.8 percent sulfur fuel oil =0.2988 lb SO 2 /gal, and at 1.0 percent sulfur oil = 0.1660 lb SO 2 /gal.

Given the company

- provided cost for oil of $2.067/gal for 1.8 percent sulfur oil, and $2.544/gal for 1.0 percent sulfur oil, and noting that the cost of per ton of emissions can be determined as (change in $/gal / change in lb SO 2 /gal) * 2000 lb/ton, the cost per ton of emissions to change from 1.8 percent fuel oil to 1.0 percent is $7,184/ton.

Step 5 . Evaluate vis ibility impacts. Using the CALPUFF air quality model indicates that the modeled baseline visibility impact using the current permit limit of 1.98 lb/MMBtu (which is assured by firing fuel oil containing 1.8% sulfur) is 3.61 deciview (dv). The analysis als o indicates that the total modeled visibility improvement considering 1% sulfur fuel would be a modeled improvement of 1.08 dv. The resulting average visibility improvement cost effectiveness is calculated at $31.1 million/dv.

JEA concludes that this vis ibility improvement is extremely small for a very large cost, therefore, switching to 1% sulfur fuel is cost prohibitive.163 Selection of BART Because the cost of improvement is relatively high for switching to the 1% sulfur fuel, this option is deemed cost prohibitive. JEA proposed that the current operating practice of using no more than 1.8% sulfur fuel oil burned alone, or higher sulfur fuel oil co

- fired with the requisite amount on natural gas in order to comply with the SIP limit of 1.98 lb/MMBtu be co nsidered as

BART for SO 2 emissions for Unit 3 at Northside. DEP concurs.

Review for NO X Unit 3 is currently equipped with Low

- NO x Burners (LNB) NOx control technology.

Step 1

. Identify all available retrofit control technologies.

JEA evaluated control methods including: Selective Catalytic Reduction (SCR) and Selective

Non

- Catalytic Reduction (SNCR). The temperature window for SNCR is very important because outside of it, either more NH3 slips through the system, or more NOx is generated than

is bei ng chemically reduced. NH3 slip has the potential to affect boiler operation as well as ammonium bisulfate formation on the downstream boiler components. SNCR is selective in that the reagent reacts primarily with NO X

.

Step 2

. Eliminate technically infeas ible options. The use of SNCR was not found to be feasible after JEA conducted a feasibility study on this unit and found that the temperature window for the conversion reaction of SNCR was not available on this unit. The only available additional option identified in the JEA analysis was the addition of a selective catalytic reduction (SCR) system

Step 3

. Evaluate control effectiveness of remaining control technologies. SCR NOx control effectiveness is nominally reviewed at 80 percent, typical of these units. At the 80 percent control level, emission reductions of NOx from Unit 3 would be approximately 1,137 tons annually. This value is based on the base load operation of Units 1 and 2 at this facility since the three units have a total emissions cap of 3,600 tons per year.

Step 4

. Evaluate impacts and evaluate the results. Using the direct and indirect capital costs and the operation and maintenance costs for a SCR system from the EPA Fact Sheet for SO 2 controls JEA found that the capital costs woul d be as much as $30 million dollars for this unit that currently fires mainly natural gas, a fuel with lower

NO X emissions compared to fuel oil, and the unit currently meets a SIP limit of 0.3 lbs/MMBtu

for NO X . The cost effectiveness of SCR was estimated to exceed $4500 /ton with a pollution control efficiency of 80 percent for the system.

Step 5 . Evaluate visibility impacts. Improvement in visibility, based on air quality modeling, with the inclusion of an SCR control is approximately 0.26 deci views. Based on a capital expenditure of approximately $30 million for Unit 3 and operating and maintenance costs contributing to the annual cost of SCR controls, the analysis indicated that the visibility cost effectiveness is cost prohibitive and would exceed a cost effectiveness of $ 20 million per deciview. This is a large cost for the amount of improvement in visibility. JEA’s conclusion was that a SCR is therefore not cost

- effective as BART for NO X emissions in this case.164 Selection of BART

Consi dering the relatively small impact on visibility from the reduction of NO X compared to SO 2 in this case, JEA concluded that add

- on controls are cost prohibitive and it is not reasonable to expend resources on additional NO X reductions in this case. DEP ag rees that no add on NO X controls are justified in this case for Unit 3 that mainly fires natural gas and meets a SIP limit of

0.3 lbs/MMBtu for NO X

.

Review for PM

Step 1

. Identify all available retrofit control technologies.

JEA evaluated add

- on control s including fabric filters, ESPs and wet scrubbers to control PM

emissions.

Step 2

. Eliminate technically infeasible options. The JEA analysis identified fabric filters and wet scrubbers as technically infeasible options.

The use of a fabric filter baghouse depends on site specific exhaust characteristics such as particulate loading, temperature, and moisture content. Condensation of moisture may cause crusty caking or plugging of the fabric or require special additives. There is also a potential f ire hazard in a baghouse if a spark or flame is accidentally admitted. For large oil

- fired boilers like the NGS Unit 3, the use of a fabric filter control device is not common. Also, the PM from firing fuel oil can be sticky which can cause problems with cleaning fabric filters. Likewise, wet scrubbers like Venturi scrubbers were evaluated although they are not commonly used for large oil fired units like Unit 3. In this case the waste water disposal problem and the relatively high operating and mainte nance costs made this option infeasible for Unit 3. Therefore the options of baghouse or wet scrubber are not found to be technically feasible in this case.

Step 3

. Evaluate control effectiveness of remaining control technologies.

An ESP control is consi dered to be the most common and technically feasible option for NGS Unit 3. ESPs are also ranked very high in terms of control efficiency greater than 99 percent. Other technologies have not demonstrated equivalent levels of control for PM compared to an ESP in this application. The potential reduction in PM emissions is estimated to be 449 tons per

year.

Step 4

. Evaluate impacts and evaluate the results. The estimated costs were developed from information available from recent Golder projects and

EPA’s cost control manual. The EPA cost control manual was also used where individual cost factors were required and to develop the annualized cost. JEA estimated the total capital cost of an ESP at approximately $60 million. The estimated annualized cost for ESP is about $8.1

million with a cost effectiveness of $18,083 per ton of PM removed . However considering the limited use of fuel oil under the JEA community commitment (federally enforceable limit/cap on emissions), the cost effectiveness is actually abo ut $29,000 per ton of PM removed . Other factors like operating costs and remaining useful life are shown below.

The installation of ESP will result in a power usage of about 0.3 percent due to electric field current usage and backpressure. This is about 3,600 MWh per year of lost energy.165 There would be some non

- air quality impacts associated with the disposal of ash. From 148 to 63 tons of fly ash would have to be disposed of at a Class I landfill.

As reported by JEA, there are no current plans for U nit 3’s retirement and the current authorizations/permits limit operation on fuel oil for this 35 year old unit. Installation of an ESP would require about two years for construction based on experience from recent retrofit projects

Step 5 . Evaluate visib ility impacts. To calculate the visibility improvement due to controlling PM using an ESP, first the baseline visibility impacts were estimated based on the maximum 8th highest 24

- hour average visibility impact contributed by PM particles (3.611 dv for 200 3). The PM fraction accounted for 5 percent of the total or 0.18 dv. Instead of modeling the reduced PM emission rate, future or controlled visibility impacts were estimated based on the baseline visibility impact and the ratio of reduced hourly PM emiss ions rate and the baseline hourly PM emission rate. The reduced PM emission rate was estimated based on a potential emission rate of 0.03 lb/MMBtu and the heat input capacity of Unit 3. The visibility improvement associated with an ESP was determined by subtracting future dv impacts from the baseline dv impacts. The estimated improvement is 0.10 dv.

The visibility cost effectiveness numbers were calculated from the annual costs and the visibility improvement in dv. Visibility cost effectiveness for a n ESP is more than $78 million/dv JEA concludes that this is a very large cost for a small amount of visibility improvement.

Conclusion Considering the above analysis, DEP has determine d that limited use of fuel oil and the levels of controls already in place for the affected pollutants SO 2 , NO X and PM are consistent with BART for the Northside Plant Unit3. Additional control strategies are not cost

- effective and are not required. Emiss ions Unit 3 at the JEA Northside Plant will meet the BART requirement by continuing to comply with the following operational and SIP emission limiting standards:

SO 2 : 1.98 lb/MMBtu when firing fuel oil, met by firing natural gas, co

- firing natural gas with fuel oil, or firing fuel oil alone containing not more than 1.8% sulfur. Compliance demonstrated by continuous emissions monitoring systems (CEMS).

NO X : 0.30 lb/MMBtu when firing natural gas or firing fuel oil. Limits are met through the use of best ope rating practices for good combustion. Compliance is demonstrated by continuous emissions monitoring systems (CEMS).

PM: 0.1 lb/MMBtu when firing fuel oil and 0.3 lb/MMBtu for sootblowing. These limits are met by firing natural gas, co

- firing natural gas with fuel oil, or firing fuel oil alone containing

less than 1.8% sulfur.

7.8.4.12 Gulf Power Smith

BART

- eligible Emissions Units for the Gulf Power Lansing Smith Plant This facility consists of two coal

- fired steam generators (Units 1 and 2), two simple cycle peaking units, two combined cycle combustion turbines, and miscellaneous insignificant166 emissions units. The Lansing Smith Plant is located at 4300 County Road 2300, Southport, Florida, in Bay County

.

The facility operates the following emissions units subject to the requirements of the federal Regional Haze BART regulations: Unit 1 (EU001) and Unit 2 (EU002). Units 1 and 2 are tangentially

- fired, dry

- bottom boilers, and both are equipped with hot and cold side electrostatic precipitators (ESP) and selective non

- catalytic reduction (SNCR). Unit 1 is also equipped with

low NO

x burners (LNB) with high momentum injection ports, and Unit 2 has LNB with an overfire air control system.

DEP asked Gul f Power to provide a proposed BART determination for these units. We have reviewed Gulf Power’s proposal and have used it as we deemed appropriate in making DEP ’s

BART determination.

Affected Pollutants The affected (i.e., visibility

- impairing) pollutants for this project are: sulfur dioxide (SO 2 ), nitrogen oxides (NO X ) and particulate matter (PM).

Project Description and Status Gulf Power submitted a BART analysis to DEP to address the BART requirements.

Review for SO 2 Step 1

. Identify al l available retrofit control technologies. The options presented by Gulf Power for SO 2 control included (1) switch to lower sulfur coal, (2) dry sorbent injection (DSI) with use of lower sulfur coal, (3) dry flue gas desulfurization (DFGD) lime spray dryer absorber (SDA), and (4) wet flue gas desulfurization (WFGD). All of

the identified available retrofit SO 2 control technologies are considered technically feasible for

Units 1 and 2.

Low Sulfur Coal . Units 1 and 2 currently burn coal, distillate (No. 2) or on

- specification used fuel oil. No. 2 fuel oil is only used during start

- up and flame stabilization. Combustion of on

specification used oil is limited to no more than 50,000 gallons per calendar year per boiler. Unit

1 has maximum authorized heat input rates of 1,944.8 MMBtu/hr for coal, and 153 MMBtu/hr

for No. 2 fuel oil and on

- specification used oil. Maximum authorized heat input rates for Unit 2 are 2,246.2 MMBtu/hr for coal and 76 MMBtu/hr for No. 2 fuel oil and on

- specification used oil.

S witching to a lower

- sulfur fuel, such as coal will reduce SO 2 emissions compared to baseline coal, with no modifications to the emission units.

Dry Sorbent Injection with the use of Low Sulfur Coal . DSI is a DFGD technology that uses an alkaline reagent to absorb SO 2 . Gulf Power proposes the use of trona, which is a more expensive reagent but is highly reactive and can be used to achieve the SO 2 removal efficiency needed to meet BART depending on the injection rate and the boiler flue SO 2 concentration.

DSI control technology injects reagent (e.g., trona ) directly into the boiler flue gas in the ductwork between the air heater and the particulate collection device. The sulfite/sulfate salts reaction products are then removed by a downstream PM control de vice. Since a gas/sorbent167 contacting vessel is not required, the DSI capital costs are lower, less physical space is required, and exhaust duct modifications are simpler compared to a DFGD lime SDA system. The reagent costs are higher and SO 2 control eff iciencies are lower using the DSI/trona technology. Gulf Power noted lime was considered as a component of the MATS compliance approach, but for BART using trona instead of lime would achieve further reductions in SO 2 emissions.

DFGD Lime SDA. In a DFGD system, boiler flue gas mixes in a spray dryer with a mist of atomized fresh lime slurry. The types of DFGD systems typically installed on coal fired boilers are those utilizing either a SDA or a circulating dry scrubber (CDS). Gulf Power con sidered both types of control equipment but concludes that the SDA and the CDS technologies have similar concerns with respect to adequate available space upstream of the existing PM control device for the installation of new equipment and the need for a l arger capacity PM control

device.

WFGD. A WFGD system removes SO 2 from the exhaust stream by contacting the flue gas with an alkaline reagent in an absorber to form sulfite and sulfate salts. Key WFGD process design variables include flue gas flow rate, liquid

- to

- gas ratio, recycle slurry pH, flue gas SO 2 concentration, and solid concentration and retention time. The WFGD systems are typically located downstream of any PM control system. Induced draft fans are typically used to pull the boiler flue gas through the WFGD spray tower absorbers before the flue gas exits through the

stack.

When WFGD systems are installed as a retrofit technology a new stack may be required due to corrosion concerns. Adequate space must be available for the ancillary equipm ent associated with the WFGD system including reagent grinding and slurry preparation, dry reagent storage area and reagent slurry storage tanks, and gypsum dewatering, storage, and handling equipment.

Step 2

. Eliminate technically infeasible options.

Gul f Power concludes all identified available retrofit SO 2 control technologies are considered technically feasible for Units 1 and 2. However, DFGD lime SDA is considered an inferior technology compared to WFGD and was not evaluated further for the followin g reasons: (1) WFGD will achieve higher SO 2 removal, (2) DFGD lime SDA technology is difficult to apply as a retrofit to existing boilers due to space considerations, (3) with the increased PM loading a new PM control device will need to be installed, (4) with the inclusion of the cost of a baghouse for the DFGD lime SDA option, WFGD will achieve greater emissions reduction at a lower cost compared to the DFGD line SDA system.

Step 3

. Evaluate control effectiveness of remaining control technologies.

The Gulf Power analysis states that the effectiveness of SO 2 emissions control using lower sulfur

coal is a method that can result in lower emissions of SO 2 with no added capital investment. The removal efficiency for Units 1 and 2 by switching to lower sulf ur coal would give an estimated

25% SO 2 reduction. The use of DSI with trona injection combined with coal would have a n estimated SO 2 removal efficiency of 48%

, based upon testing completed by Southern Company

. The control effectiveness of the WFGD syste m is estimated at 90 to 95% SO 2 removal for Units

1 and 2. This is the expected range based on meeting a 0.15 lb/MMBtu limit that takes into account startup, shutdown, and malfunction

. T he use of 0.15 lb/MMBtu results in an168 approximately 89.5 percent red uction from the baseline emissions on an annual basis. On the

maximum actual short

- term (24

- hour) basis, used in the visibility assessment, the reduction of

SO 2 using the 0.15 lb/MMBtu factor results in a 93 and 91.5 percent reduction at U nits 1 and 2,

re spectively.

Step 4

. Evaluate impacts and evaluate the results. Cost of Compliance. Economic analyses were developed for the DSI plus low

- sulfur coal and WFDG options. No

additional costs are associated with the switch to low

- sulfur coal. The capital cost of the DSI and the operation and maintenance (O&M) costs associated with lime injection will be incurred as a possible MATS compliance plan, additional cost associated with the DSI option is the difference

in O&M costs for the use of trona instead of lime. However, for the purpose of developing the BART costs, DEP has estimated the total and annualized cost for DSI based on data from a recent analysis, “IPM Model

– Revisions to Cost and Performance for APC Technologies, Dry Sorbent Injection Cost Dev elopment Methodology, Final, August 2010” by Sargent and Lundy,

LLC. WFGD costs were estimated based on a 20 year control system life, 7% interest rate, and energy penalty of 4 MW. GP developed the WFGD costs based on the $/kW cost of the most

recent WFG D under construction in the Southern Company system. This cost of 649.7 $/kW was derived from the detailed budget estimate for this ~500 MW unit. To develop the WFGD costs for each Smith unit, the derived $/kW value was multiplied times the gross rating of each unit. The resulting wet scrubber costs for each of the Smith units is considered conservative since no economy of scale adjustments were made for the smaller Smith units. T he fixed O&M was assumed to be 2% of the direct capital costs. This fixed O&M assumption is generally consistent with the Electric Power Research Institute (EPRI) methodology to estimate fixed O&M. The O&M estimate represents annual maintenance cost for labor and materials. T he variable O&M cost components included were limestone r eagent and gypsum handling. Limestone reagent cost assumed for the coastal region is $75/ton delivered to the site. Gypsum handling costs were assumed at $3/ton. Parasitic power was not included in the VOM cost, but is treated separately as

an energy cost penalty. Fuel switch to lower sulfur coal has no additional costs, therefore no further economic analysis was conducted by Gulf Power. The tables below provide summaries

of control costs related to Units 1 and 2.

SO 2 Control Technology Capital Cost ($) O &M $ per

year Energy

Cost $ per year Annualized

Cost $ per year Baseline

SO 2 emissions

TPY Baseline heat input MMBtu/yr

Unit 1

DSI plus

low

- sulfur

coal 11,751,000 883,047 Minimal 1,992,319 8,711 12,225,282

WFGD 111,748,400 2,196,10

2 1,771,737 14,516,098 8,711 12,225,282

Unit 2

DSI plus

low

- sulfur

coal 10,672,148 929,241 Minimal 1,936,692 9,220 12,856,565169 WFGD 133,188,500 2,420,94

9 1,593,028 16,586,029 9,220 12,856,565

SO 2 Control Technology Controlled SO 2 Emission Rates SO 2 Emissions Reduction Cost Effectiveness $/ton

lb/MMBtu TPY TPY Average Incremental

Unit 1

DSI plus

low

- sulfur

coal 0.74 4,536 4,175 477 N/A

WFGD 0.15 917 7,794 1,862 3,461

Unit 2

DSI plus

low

- sulfur

coal 0.74 4,770 4,451 435 N/A

WFGD .015 964 8,256 2,009 3,850

Energy Impacts. The energy impacts associated with the DSI technology are minimal compared to WFGD, which will impose an energy penalty of 4 MW per unit. The energy penalty is due to the increased fan power required to compensate for the higher pressure drop of the absorber vessel. Other energy uses associated with WFGD operations include reagent preparation, recirculation pumps, dewatering of spent reagent, and material handling. The energy penalties result in costs due to the purch ase of replacement power and capital recovery for additional generation capacity.

Nonair

- Quality Impacts. The DSI technology will produce a dry solid waste comprised of a mixture of spent reagent and flyash. WFGD will generate a wet sludge of spent reag ent that will require disposal. WFGD will also require substantial amounts of water and will generate a wastewater stream that will require treatment.

Remaining Useful Life. No adjustments were made to the annualized costs based on the conservative prem ise that the useful lives of Unit 1 and 2 will be as long as the 20 year capital recovery period of the WFGD control equipment.

Step 5 . Evaluate visibility impacts. Modeling was conducted in accordance with the approved BART modeling protocol to

determine the improvement in visibility at the St. Marks NWR for the three BART SO 2 control technologies evaluated. To determine visibility impacts for each unit, modeling was conducted separately for Units 1 and 2 for each control technology evaluated. For each Unit 1 model run,

the modeled SO 2 emissions rate for the Unit 1 and 2 common stack was the sum of: (a) the Unit

1 controlled SO 2 emissions rate, and (b) the Unit 2 baseline SO 2 emissions rate. The same approach was used for Unit 2.

The improvements in visibility compared to baseline levels due to reductions in Unit 1 SO 2 emissions range from 0.37 dv for the switch to low

- sulfur coal to 0.67 dv for WFGD for the170 maximum 8 th highest and from 0.34 dv and 0.51 dv, respectively for the 22 nd highest over 3

yea rs. Unit 2 improvements in visibility compared to baseline levels due to reductions in SO 2 emissions range from 0.27 dv for the switch to low

- sulfur coal to 0.61 dv for WFGD for the

maximum 8 th highest and from 0.24 dv and 0.45 dv respectively for the 22 n d highest over 3 years. The use of WFGD instead of DSI plus low

- sulfur coal option results in an incremental improvement in visibility of only 0.19 dv for Unit 1 and 0.22 dv for Unit 2 for the maximum 8 th
highest and only 0.07 dv for Unit 1 and 0.09 dv for Unit 2 for the 22 nd highest over 3 years.

Selection of BART

Gulf Power’s SO 2 BART determination for the Lansing Smith Plant Units 1 and 2 is a SO 2 emissions rate of 0.74 lb/MMBtu on a 30 day rolling a verage which can be achieved with the use of DSI with trona as the alkaline reagent. The average cost effectiveness of the DSI plus low

sulfur coal option is approximately four times lower than the WFGD with no adverse energy and nonair quality impacts. The incremental cost effectiveness of WFGD compared to the DSI plus

low

- sulfur coal option exceeds $3, 400 per ton of SO 2 removed.

Gulf Power concluded the DSI (trona) plus low

- sulfur coal option is the best option due to its substantially lower cost, decr eased energy cost and nonair

- quality impacts and the minimal additional visibility improvements that will occur with WFGD. DEP concludes that while the cost of a FGD is reasonable, the visibility improvement obtained for this cost

, $21,665,818/de c iview fo r Unit 1 and $27,190,211/de c iview for Unit 2, is not cost effective

. Therefore, DEP concurs that the appropriate BART determination is the DSI (trona) plus low

sulfur coal option.

Review for NO X Gulf Power’s Lansing Smith Plant Units 1 and 2 are currentl y equipped with low NO

x burners (LNB) with high momentum injection ports. Unit 2 has LNB’s with an overfire air control system. In addition to LNB technology, both units have SNCR for additional NO

x control. The

NO

x combustion controls on Units 1 and 2 were installed in response to CAIR. Units 1 and 2 are currently equipped with NO

x combustion controls and SNCR, this suite of

controls serves as the baseline and the focus of the NO

x BART analysis was confined to the evaluation of selective catalytic reduction (SCR) technology. Step 1

. Identify all available retrofit control technologies. The Lansing Smith Plant units are currently equipped with multiple NO X emissions control

methods including: overfire air control system, low

- NO X burners, and SNCR. The only available additional option identified in Gulf Power’s analysis is the addition of a selective catalytic reduction (SCR) system.

Step 2

. Eliminate technically infeasible options

. Gulf Power’s analysis did not identify any technically infeasible options.

Step 3

. Evaluate control effectiveness of remaining control technologies. Using the direct and indirect capital costs and the operation and maintenance costs for a SCR

system, a BART cost effectiveness calculation was performed. A pollution control efficiency of 90 percent was used for the SCR system.171 Step 4

. Evaluate impacts and evaluate the results. Cost of Compliance.

SCR costs were estimated based on a 20

- year control system life, 7% interest rate, and energy penalty of 1 MW. The tables below provide summaries of control costs related to Units 1 and 2.

The Baseline NO

x control technology includes current combustion controls plus SNCR.

NO

x Control

SCR Capital Cost ($) O&M Cost $ per year Energy Cost $ per year Annualized Cost $ per year

Unit 1 65,950,000 1,277,953 442,934 7,945,908

Unit 2 74,940,000 1,427,537 398,257 8,899,381

NO

x Control

SCR Baseline Heat Input Controlled NO

x Emission Rates NO

x Emissions

Reduction Average Cost

Effectiveness MMBtu/yr lb/MMBtu TPY TPY $ per ton

Unit 1

Baseline 12,225,282 0.34 2,047 N/A N/A

SCR 12,225,282 0.07 428 1,619 4,907

Unit 2

Baseline 12,856,565 0.27 1,729 N/A N/A

SCR 12,856,565 0.07 450 1,279 6,957

Energy Impacts. The energy impacts associated with the SCR system is estimated at 1 MW for each unit. The SCR systems will require additional electricity to run pumps and extra fan horsepower to overcome the high pressure drop in the systems.

Nonair

- Quality Impacts. The current SNCR and potential SCR systems require storage and handling of ammonia. There is also a potential of excess ammonia injection which leads to ammonia slip and is emitted into the atmosphere.

Remaining Useful Life. No adjustments were made to the annualized costs based on the conservative premise that the useful lives of Unit 1 and 2 will be as long as the 20 year capital recovery period of the SCR control equipment.

Step 5 . Evaluate visibility impacts.

Gulf Power indicates the contribution of Units 1 and 2 NO

x emissions to visibility impairment are low. Baseline visibility impacts due to NO

x emissions are only 3.9% of the total baseline impact. The same modeling approach described for the SO 2 BART analysis was used for NO

x . The modeled resu lts indicate there will be little to no improvement in visibility compared to baseline levels due to the installation of SCR technology. For Unit 1 the highest improvement in baseline visibility is 0.01 dv for the maximum 8 th highest. For Unit 2 the model results for the

maximum 8 th highest impact show no improvement in the baseline visibility.

Selection of BART Gulf Power proposed that the existing controls such as, low NO

x burners, overfire air system, and SNCR along with good combustion practices, be considered as BART for NO X emissions for172 Units 1 and 2. This determination was based on the cost effectiveness of $5,000 per ton for Unit 1 and $7,000 per ton for Unit 2 with little to no visibility impact with the installation of an SCR

system. DEP concurs with Gulf Power.

Review for PM Lansing Smith Plant Units 1 and 2 are equipped with highly efficient hot and cold side ESP s that achieve very low PM emission rates. The current PM controls serve as a baseline in the BART analysis.

Step 1

. Identify all available retrofit control technologies. The PM BART analysis for Units 1 and 2 was conducted for only the most stringent control technology, a fabric filter technology suc h as a bag house compared to the existing ESP s.

Step 2

. Eliminate technically infeasible options. Gulf Power’s analysis did not identify any technically infeasible options

Step 3

. Evaluate control effectiveness of remaining control technologies.

The col lection efficiencies for the fabric filter technology are on the order of 99% for particulate

smaller than 2.5 microns.

Step 4

. Evaluate impacts and evaluate the results. Cost of Compliance. Fabric filter technology costs were based on a 20 year control s ystem life, 7% interest rate, and an energy penalty of 1MW. The table below provides a summary of the cost effectiveness for PM control technologies. Baseline controls for PM in the table below are based on the hot and cold side ESP currently installed o n Units 1 and 2.

PM Control Technology Capital Cost O&M Cost Energy Cost Annualized Cost

$ $ $ per year $ per year

Unit 1 35,776,000 957,056 442,934 4,776,887

Unit 2 42,640,000 1,182,527 398,257 5,605,574

PM Control Technology Annualized

Cost Baseline Heat Input Controlled PM

Emission Rates PM

Emission

Reduction Average Cost

Effectiveness

$ per year MMBtu/yr lb/MMBtu TPY TPY $ per ton

Unit 1

Baseline N/A 12,225,282 0.015 92 N/A N/A

Fabric filter 4,776,887 12,225,282 0.008 48 44 108,566

Unit 2

Baseline N/A 12,856,565 0.014 88 N/A N/A

Fabric filter 5,605,574 12,856,565 0.008 51 37 153,268173 Energy Impacts. The energy impacts associated with the fabric filter system is estimated at 1 MW for each unit. The fabric filters increase the pressure drop in the boiler exhaust system requiring extra fan horsepower needed to overcome the increased pressure drop.

No nair

- Quality Impacts. The higher PM removal efficiency will increase the amount of solid waste that will need to be disposed of at an onsite or off sight landfill.

Remaining Useful Life. No adjustments were made to the annualized costs based on the

cons ervative premise that the useful lives of Unit 1 and 2 will be as long as the 20 year capital recovery period of the SCR control equipment.

Step 5 . Evaluate visibility impacts. The analysis concluded further control of PM emissions will not provide a meaningful reduction in visibility impacts due to Units 1 and 2. The baseline visibility impacts due to PM emissions are only 1.3% of the total baseline impact. To determine the visibility impacts the same modeling approach was used for PM as used for SO 2 and NO

x . It was demonstrated no improvements in visibility for both the maximum 8 th highest and highest 22 nd impacts compared to baseline levels due to the use of fabric filter technology.

Selection of BART The average cost effectiveness of fabric filters is $108,566 per ton removed for Unit 1 and $153,268 per ton removed for Unit 2. Gulf Power proposes the EPS’s currently installed on

Units 1 and 2 are considered BART ; therefore

, no additional add

- on control technologies are required and will not result in any meaningful reduction in visibility impacts. DEP concurs.

C onclusion Considering the above analysis, DEP has determined that the levels of controls already in place for the affected pollutants NO X and PM are consistent with BART for the Gulf Power’s Lansing Smith Plant. Additional control strategies are not cost

- effective and are not required due to the lack of visibility impact. DEP has also determined SO 2 emissions and visibility impacts can be further reduced by switching to lower sulfur coal along with the installation of DSI using trona as a reagent. Gulf Power’s Lansing Smith Plant Units 1 and 2 meet the SO 2 BART requirements by installing DSI/trona system and switching to lower sulfur coal. In order to coordinate this strategy with o ther regulatory obli gations, Gulf Power is given a compliance date of March 31

, 2016. The BART operational and emission limiting standards for Lansing Smith Units 1 and 2

are:

SO 2 : The emission limit for Unit 1 is 0.74 lb/MMBtu and Unit 2 is 0.74 lb/MMBt u. NO X : The combined NO

x emissions from Units 1 and 2 shall not exceed 4,700 tons during any consecutive 12 month rolling total as determined by CEMS data reported to the EPA Acid Rain database. [40 CFR 60, Subpart Da]

PM: Emissions shall not exceed 0.1 lb/MMBtu. Compliance demonstrated by stack test. [40 CFR 60, Subpart Da]174 

### 8.0 REASONABLE PROGRESS GOALS

The Regional Haze Rule at 40 CFR 51.308(d)(1) requires s tates 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, EPA released guidance on June 7, 2007 to use in setting re asonable 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

asse ssment 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 Implementa tion 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 i mprovements 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. The se modeling results were used to set the reasonable progress goal s

. 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 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 2064.175 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

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 IMPR OVE network. The implementation plan must also provide for the following:176 (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 ar eas 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

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 s tate. 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

continu ous monitors (BAMs) and continuous mass monitors (TEOMs), filter based speciated

monitors. A map of th e various locations around the s tate is included in Figure 9.1. These177 PM2.5 measurements help DEP characterize air pollution levels in areas across the s tate, and therefore aid in the analysis of visibility improvement in and near the Class I areas.

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 s tate’s abilit y 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, eac h 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 meas urements at about the same pace and with the same attention to quality as it has shown in the recent past.178 

### 10.0 INTERSTATE CONSULTATION

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

c onclusions 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 SO 2 Areas of Influence analyses. The Air Directors agreed to look at reasonable contr ol measures for sources on the lists for the SO 2 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 t hat 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

.

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 kil ometers 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.). 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 51.308(f) requires DEP to revise its regional haze implementation plan and submit a plan revision to EPA by July 31, 2018

, and every ten years thereafter. In accordance with the179 requirements listed in Section 51.308(f) of the federal rule for regional haze, Florida 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 EPA every five years following the initial

submittal of the SI P. 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 out side 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. [ DEP recognizes that the 2018 projections of EGU controls from the IPM runs represent one solution to how the CAIR requirements will be met. B y the time of the first periodic report, 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. Assessmen t 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. Anal ysis of any significant changes in or out of the s tate 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 40 CFR 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 moni toring 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 avai lable, 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

qua lity 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 good180 overall. However sulfate deposition is frequently overestimated in the models, particularly in the summer months. At the coastal sites, whe n 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 undere stimated 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 consisten t 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.

Ad ditional 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 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 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 dec laration 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.