# Regional Haze

State Implementation Plan

for

South Carolina Class I Federal Areas

Prepared by
South Carolina Department of Heal th and Environmental Control
Bureau of Air Quality

December 21, 2007

### Preface: This document contains the technical support for South Carolina’s Bureau of Air Quality to submit to the United States Environm ental Protection Agency (EPA) a regional haze State Implementation Plan pursuant to §§107(d)(3)(D ) and (E) of the Clean Air Act, as amended.2 

## EXECUTIVE SUMMARY

Introduction

Haze is defined as visibility impairment cau sed by absorption and scattering of light by fine particles. Visibility impairment is considered the “best understood and most easily measured effect of air polluti on.” Regional haze is caused by sources and activities that emit these fine particles and their precurs ors over a large regi on, including areas of concern, such as national park s, forests, and wilderness ar eas (“Class I” Federal areas). The Clean Air Act mandates requirements to protect visibility, especially in Class I areas.

In 1999, the U.S. Environmental Protection Ag ency (EPA) finalized the Regional Haze Rule. The rule calls for state, tribal, and fe deral agencies to work together to improve visibility in 156 national parks and wilderne ss areas, including the 21 Class I Wilderness areas. The States are required to develop and implement air quality protection plans (State Implementation Plans) to reduce the po llution that causes visi bility impairment. These plans should establish goals and emissi on reduction strategies based on trends from various sources including area source emissi ons, mobile source emissions (both on-road and non-road emissions), commercial marine em issions, biogenic emissions, and wildfire and agriculture emissions. Under the Regiona l Haze Rule, states are required to develop SIPs to reduce visibility impairment with the express intent that by 2064, the visibility in the Class I areas will be re turned to natural conditions.

Five multi-state regional planning organizations (RPOs) currently are working together to develop the technical basis fo r these State plans. The pr oducts of the regional planning organizations will be used by States to es tablish monitoring strategies for evaluating visibility conditions, baselines, and trends, and to develop long-term (10 to 15 years) strategies for making "reasonabl e progress" toward elimina ting all manmade visibility impairment from mandatory Class I areas.

With the help of the Visibility Improvement - State and Tribal Association of the Southeast ( VISTAS) RPO, South Carolina has deve loped a SIP to address visibility impairment in the State’s Class I Federa l Area--the Cape Romain Wilderness Area-- located on the eastern coast of South Carolina.

In developing the SIP, South Carolina has pr epared a long-term strategy and examined the possible application of BART in order to establish reason able progress goals for Cape Romain. As will be discussed further, for the 20 percent worst days, South Carolina has adopted a reasonable progress goal of a 3.8 deciview (dv) reduction in visibility impairment by 2018, which is consistent with the uniform rate of progress needed to achieve a natural background condition of 12.1 dv by 2064. Likewise, South Carolina has also adopted a reasonable progress goal fo r the 20 percent best days that would result in a 1.4 dv reduction in visibility impairment . The aforementioned predicted reductions in visibility impairment are expected to result from implementation of existing and3 planned emission controls that will be discussed in further detail throughout the remaining portion of this SIP document.4 

## ACKNOWLEDGEMENTS

SC DHEC would like to acknowledge the numb er of consultants and contractors who were instrumental in preparing the South Carolina Regional Haze SIP.

Air Resource Specialists: Monitoring Data Analysis

Alpine Geophysics: Emissions and Air Quality Modeli ng, Technical Advisor for Emissions Inventory

Atmospheric Research and Analysis, Inc.: Operation of continuous monitors at Millbrook, NC

Baron Applied Meteorology: Meteorological Modeling

Desert Research Institute: Carbon Source At tribution including samp le filter preparation, sample analysis using Gas Chromatography and Mass Spectrometry, source attribution using Chemical Mass Balance analyses and Positive Matrix Factorization

Earth Tech, Inc: BART Modeling, CALPUFF Training

ENVIRON: Emissions a nd Air Quality Modeling

Georgia Institute of Technology: Emissi ons and Air Quality modeling sensitivities

Harvard University: GEOS-Chem global model

ICF Consulting: Integrated Planning Model for future electric utility generation

MACTEC: 2002, 2009, and 2018 Emissions I nventory and Projections, CALPUFF training

E.H. Pechan and Associates: 2002 Mobile Inventory

Research Triangle Institute: Chemi cal Analysis of Monitoring Samples

System Applications International: Meteorological Characterization

Tennessee Valley Authority: Operation of continuous monitors at Great Smoky Mountain National Park and preparation of fi nal project report a nd draft manuscript

Ivar Tombach, Environmental Consultant

TRC, Inc: BART Modeling5 University of California, Riverside: Emissions and Air Quality Modeling

VISTAS Technical Coordinator, Pat Brewer

Woods Hole: Analysis of Carbon 14 isotope in carbon samples as part of carbon source attribution project6 TABLE OF CONTENTS

U LIST OF ACRONYMS/ABBREVIATIONS

U ............................................................. 11

U 1.0

U U INTRODUCTION

U ................................................................................................. 13

U 1.1 What Is Regional Haze?

U ........................................................................................ 13

U 1.2 What Are The Requirements Under The Clean Air Act For Addressing Regional Haze?

U ........................................................................................................................... 13

U 1.3 General Overview Of Re gional Haze SIP Requirements

U ..................................... 15

U 1.4 Class I Federal Area In South Carolina

U ................................................................ 16

U 1.5 State And Federal Land Ma nager (FLM) Coordination

U ....................................... 17

U 2.0

U U ASSESSMENT OF BASELINE AND CURRENT CONDITIONS AND ESTIMATE OF NATURAL BACKGROUND CONDITIONS IN CLASS I AREAS

U .............................. 19

U 2.1 Estimating Natural Conditions For S outh Carolina Class I Federal Area

U ............ 21

U 2.2 Estimating Baseline Conditions For S outh Carolina Class I Federal Area

U .......... 21

U 2.3 Summary of Natural Background And Ba seline Conditions For South Carolina Class I Federal Area

U .................................................................................................... 22

U 2.4 Pollutant Contributions To Visibility Impairment (2000-2004 Baseline Data)

U .... 22

U 3.0

U U GLIDEPATHS TO NATURAL CONDITIONS IN 2064

U ........................................ 27

U 3.1 Glidepath For Class I Fede ral Area In South Carolina

U ......................................... 27

U 4.0

U U NATURE OF THE PROBLEM: CHIEF CAUSES OF VISIBILITY IMPAIRMENT IN SOUTH CAROLINA CLASS I FEDERAL AREAS

U ....................................................... 29

U 4.1 Baseline Emissions Inventory

U ............................................................................... 29

U 4.1.1 Stationary Point Sources

U .................................................................................. 30

U 4.1.2 Stationary Area Sources

U ................................................................................... 31

U 4.1.3 Off-Road Mobile Sources

U ................................................................................ 32

U 4.1.4 Highway Mobile Sources

U ................................................................................. 32

U 4.1.5 Biogenic Emission Sources

U .............................................................................. 32

U 4.1.6 Summary 2002 (Base G2) Baseline Emissions Inventory for SC

U ................... 33

U 4.1.7 Model Performance Improvements through Emissions Inventory Improvements

U ........................................................................................................... 33

U 4.2 Assessment of Relative Contributions from Specific Pollutants and Sources Categories

U ................................................................................................................... 34

U 5.0

U U REGIONAL HAZE MODELING METHODS AND INPUTS

U ............................... 35

U 5.1 Analysis Method

U ................................................................................................... 35

U 5.2 Model Selection

U .................................................................................................... 36

U 5.2.1 Selection of Photochemical Grid Model

U .......................................................... 36

U 5.2.2 Selection of Meteorological Model

U ................................................................. 37

U 5.2.3 Selection of Emissions Processing System

U ...................................................... 38

U 5.3 Model Year Selection

U ......................................................................................... 40

U 5.4 Modeling Domains

U ................................................................................................ 41

U 5.4.1 Horizontal Modeling Domain

U .......................................................................... 41

U 5.4.2 Vertical Modeling Domain

U .............................................................................. 43

U 6.0

U U MODEL PERFORMANCE EVALUATION

U .......................................................... 44

U 6.1 Modeling Performance Goals, Criteria, and Results

U ............................................ 45

U 6.2 VISTAS Domain-Wide Performance

U ................................................................... 46

U 6.3 South Carolina Class I Area Performance

U ............................................................ 507 U 7.0

U U LONG TERM STRATEGY FOR SOUTH CAROLINA’S CLASS I FEDERAL AREA

U 53

U 7.1 Overview of the Long Term Strategy Development Process

U ............................... 53

U 7.2 Expected Visibility Results in 2018 fo r the Cape Romain Class I Federal Area under existing and planned emission controls (Base G2 Inventory)

U .......................... 54

U 7.2.1 Federal and State Control Requirements

U ......................................................... 54

U 7.2.2 Additional State programs to reduce emissions

U ............................................... 57

U 7.2.3 Projected 2009 and 2018 Base G2 Emissions Inventories

U ............................... 57

U 7.2.4 Model Results for the 2018 Base G2 Inve ntory Compared to the Uniform Rate of Progress Glidepath for SC Class I Federal Area

U .................................................. 60

U 7.3 Relative Contributions from International Emissions to Visibility Impairment in 2018 at VISTAS Class I areas

U .................................................................................... 63

U 7.4 Relative Contributions to Visibility Im pairment: Pollutants, Source Categories, and Geographic Areas.

U ................................................................................................ 67

U 7.5 Relative Contributions to Visibility Impairment: Geographic Locations of the Largest Emission Sources Contributing to Visi bility Impairment in SC Class I Federal Area

U ............................................................................................................................. 70

U 7.5.1 Back Trajectory Analyses

U ................................................................................ 70

U 7.5.2 Residence Time Plots

U ....................................................................................... 70

U 7.5.3 SO 2 Areas of Influence

U .................................................................................... 72

U 7.5.4 Emission Sources Within the SO 2 AOI

U ........................................................... 73

U 7.5.5 Specific Source Types in the Areas of Influence for SC Class I Federal Area

U 75

U 7.6 Which Control Measures for Individua l Sources Achieve Reasonable Progress?

U 78

U 7.7 Which Control Measures for Individua l Sources Achieve Reasonable Progress?

U 79

U 7.8 BART Determinations in South Carolina

U ............................................................. 82

U 7.8.1 BART-Eligible Sources in South Carolina

U ...................................................... 82

U 7.8.2 Determination of Sources Subj ect to BART in South Carolina

U ...................... 84

U 7.8.3 Evaluation of Sources Subjec t to BART in South Carolina

U ............................ 86

U 7.9 Post-BART Visibility Improvement Expected at Cape Romain by 2018, Compared to the Glidepath

U ......................................................................................... 95

U 8.0

U U REASONABLE PROGRESS GOALS

U .................................................................... 98

U 8.1 Four Factor Analyses for Sources affecting Cape Romain

U ................................... 98

U 8.1.1 Four Factor Analysis of Sources Effecting Visibility at Cape Romain

U ......... 102

U 9.0

U U MONITORING STRATEGIES

U ............................................................................ 109

U 10.0

U U INTERSTATE CONSULTATION ON SC CONTRIBUTION TO VISIBILITY IMPAIRMENT IN CLASS I FEDERAL AREAS IN NEIGHBORING STATES

U ............... 112

U 11.0

U U COMPREHENSIVE PERIODIC IMPLE MENTATION PLAN REVISIONS

U ...... 116

U 12.0

U U DETERMINATION OF THE ADEQUACY OF THE EXISTING PLAN

U ............ 1198 LIST OF APPENDICES

Appendix A: VISTAS Memorandu m of Agreement and Bylaws

Appendix B: Conceptual Description of IMPROVE equations

Appendix C: Modeling Analysis Protocol

Appendix D: Emissions Preparation and Results

Appendix E: Air Quality and Meteor ological Preparation and Results

Appendix F: Model Performance Evaluation

Appendix G: Modeling Results and Supplemental Analysis

Appendix H: Reasonable Progress Evaluation & Long Term Strategy

Appendix I: Data Access

Appendix J: Documentation of C onsultation Among States and RPOs

Appendix K: Recommended Improvements for Mid-Course Review

Appendix L: WINHAZE Images fo r the Class I Federal Areas

Appendix M: CAIR/CAMR Implementation

Appendix N: Four Factor Analyses

Appendix O: Supplemental New IMPROVE Algorithm Analysis

Appendix P: Notice of General Public Interest

Appendix Q: Legal Authority9 LIST OF TABLES AND FIGURES

U F IGURE 1.2-1 G EOGRAPHICAL A REA OF R EGIONAL P LANNING O RGANIZATIONS

U .............. 14

U F IGURE 1.4

- 1 S OUTH C AROLINA C LASS I F EDERAL A REA

U ................................................ 16

U F IGURE 1.5

- 1 C LASS I F EDERAL A REAS IN THE VISTAS R EGION

U .................................... 18

U F IGURE 2.0

- 1 IMPROVE M ONITORING S ITES IN THE VISTAS R EGION

U .......................... 20

U T ABLE 2.3

- 1 N ATURAL B ACKGROUND AND B ASELINE C ONDITIONS FOR C APE R OMAIN

U . 22

U F IGURE 2.4

- 1 A VERAGE L IGHT E XTINCTION FOR THE 20% W ORST D AYS IN 2000-2004 (B ASED ON N EW IMPROVE E QUATION )

U ................................................................... 23

U F IGURE 2.4

- 2 A VERAGE L IGHT E XTINCTION FOR THE 20% B EST D AYS IN 2000-2004 (B ASED ON N EW IMPROVE E QUATION )

U ................................................................... 24

U F IGURE 2.4

- 3 D ISTRIBUTION OF P OLLUTANTS AT N ATURAL C ONDITIONS

U ........................ 25

U F IGURE 3.1

- 1 U NIFORM R ATE OF P ROGRESS G LIDEPATH FOR 20% W ORST D AYS AT C APE R OMAIN , SC

U .............................................................................................................. 28

U T ABLE 4.1 2002 E MISSION I NVENTORY S UMMARY FOR SC

U ............................................... 33

U F IGURE 4.2

- 1 SO 2 E MISSIONS IN 2002 FOR THE VISTAS STATES

U ..................................... 34

U F IGURE 5.4.1

- 1 T HE MM5 H ORIZONTAL D OMAIN IS THE OUTERMOST BLUE GRID , WITH

THE CMAQ 36- KM D OMAIN NESTED IN THE MM5 D OMAIN

U ...................................... 42

U F IGURE 5.4.1

- 2 DETAILED VIEW OF THE 12- KM GRID OVER THE VISTAS REGION

U ............ 43

U F IGURE 6.0

- 1 M ONITORING N ETWORKS USED FOR VISTAS 2002 M ODEL P ERFORMANCE E VALUATION

U ............................................................................................................. 45

U T ABLE 6.1 M ODEL P ERFORMANCE G OALS & C RITERIA FOR C OMPONENTS OF F INE P ARTICLE M ASS

U ......................................................................................................... 45

U F IGURE 6.3

- 1 S TACKED B AR C HARTS FOR C APE R OMAIN ON THE 20% B EST ( TOP ) AND 20% W ORST ( BOTTOM ) D AYS

.

U ................................................................................... 52

U F IGURE 7.2.3

- 1 B ASE G2 A NNUAL SO 2 E MISSIONS FOR 2002, 2009, AND 2018 IN THE VISTAS STATES

U ........................................................................................................ 58

U F IGURE 7.2.3

- 2 B ASE G2 A NNUAL NO X E MISSIONS IN 2002, 2009, AND 2018 IN THE VISTAS STATES

U ........................................................................................................ 59

U T ABLE 7.2.3

- 1 2009 E MISSION I NVENTORY S UMMARY FOR SC

U ....................................... 59

U T ABLE 7.2.3

- 2 2018 E MISSIONS I NVENTORY S UMMARY FOR SC

U ...................................... 60

U F IGURE 7.2.4

- 1 CMAQ 2018 B ASE G2 R ESULTS C OMPARED TO THE U NIFORM R ATE OF P ROGRESS G LIDEPATH AT C APE R OMAIN

U .................................................................. 61

U F IGURE 7.2.4

- 2 P ROJECTED V ISIBILITY I MPROVEMENT ON 20% W ORST V ISIBILITY D AYS

AT VISTAS AND N EIGHBORING C LASS I F EDERAL A REAS FOR THE 2018 B ASE G2 A R

UN

U ........................................................................................................................... 62

U F IGURE 7.2.4

- 3 P ROJECTED V ISIBILITY I MPROVEMENTS ON 20% B EST D AYS AT VISTAS

AND N EIGHBORING C LASS I F EDERAL A REAS FOR THE 2018 B ASE G2 CMAQ R UN

U . 63

U F IGURE 7.3

- 1 E STIMATED I NTERNATIONAL E MISSIONS C ONTRIBUTIONS TO S ULFATE AT VISTAS AND N EIGHBORING C LASS I F EDERAL A REAS

.

U ............................................ 65

U F IGURE 7.3

- 2 I NTERNATIONAL T RANSPORT E FFECT ON U NIFORM R ATE OF P ROGRESS AT C APE R OMAIN

U ............................................................................................................ 66

U F IGURE 7.3

- 3 M EASURED PM C OMPONENT C ONCENTRATIONS ON 20% W ORST V ISIBILITY D AYS IN 2002 AT C APE R OMAIN

U ................................................................................ 67

U F IGURE 7.4

- 1 CMAQ P ROJECTIONS OF V ISIBILITY R ESPONSES ON 20% W ORST D AYS AT C APE R OMAIN TO 30% R EDUCTIONS FROM THE 2009 B ASE D I NVENTORY FOR10 V ISIBILITY -R EDUCING P OLLUTANTS IN D IFFERENT S OURCE C ATEGORIES AND G EOGRAPHIC A REAS

U .................................................................................................. 68

U F IGURE 7.5.1 E XAMPLE B ACK T RAJECTORIES FOR 20% W ORST D AYS IN 2002 FOR C APE R OMAIN , SC

U .............................................................................................................. 71

U F IGURE 7.5.2 E XAMPLE R ESIDENCE T IME P LOTS FOR 20% W ORST D AYS IN 2000-2004 FOR C APE R OMAIN (B ASED ON T RAJECTORIES WITH 100 M S TART H EIGHT )

U .................... 72

U F IGURE 7.5.3 E XAMPLE SO 2 AOI P LOT FOR S ULFATE E XTINCTION W EIGHTED R ESIDENCE T IME FOR 20% W ORST D AYS IN 2000-2004 FOR C APE R OMAIN (B ASED ON T RAJECTORIES WITH 100 M S TART H EIGHT )

U ............................................................. 73

U F IGURE 7.5.4

- 1 C APE R OMAIN 2002 AND 2018 SO 2 D ISTANCE W EIGHTED E MISSIONS * SO 4 E XTINCTION -W EIGHTED R ESIDENCE T IME P LOTS

U .............................................. 74

U F IGURE 7.5.4

- 2 C APE R OMAIN , SC 2002 SO 2 E MISSIONS * SO 4 E XTINCTION W EIGHTED R ESIDENCE T IME P LOTS

U ............................................................................................. 75

U F IGURE 7.5. 5

- 1 2018 E MISSIONS C ONTRIBUTIONS FROM M AJOR S OURCE C ATEGORIES IN

THE AOI FOR C APE R OMAIN

U ...................................................................................... 76

U F IGURE 7.5.5

- 2 2018 E MISSION C ONTRIBUTION FROM M AJOR S OURCE C ATEGORIES IN THE AOI FOR C APE R OMAIN

U ............................................................................................. 77

U T ABLE 7.8.2

- 1 BART M ODELING R ESULTS

U ..................................................................... 84

U T ABLE 7.8.2

- 2 N EW IMPROVE S EA S ALT E STIMATION M ETHODS & BART V ISIBILITY I MPACTS

U ..................................................................................................................... 86

U T ABLE 7.8.3

- 1 M ODEL P LANT E XEMPT S OURCES S UBJECT TO BART IN SC

U ................... 95

U 7.9

- 1 P REDICTED V ISIBILITY FOR THE 20% W ORST D AYS IN 2018 AT C APE R OMAIN U SING

THE N EW IMPROVE ALGORITHM

.

U ............................................................................ 95

U 7.9

- 2 P REDICTED V ISIBILITY FOR THE 20% B EST D AYS IN 2018 AT C APE R OMAIN U SING

THE N EW IMPROVE ALGORITHM

.

U ............................................................................ 96

U F IGURE 7.9

- 3 V ISIBILITY I MPROVEMENTS ON 20% WORST DAYS AT C APE R OMAIN

BETWEEN 2002

– 2004 B ASELINE C ONDITIONS ( LEFT ) AND 2018 B ASE G2 A

PROJECTED VISIBILITY ( RIGHT ).

U ................................................................................. 97

U F IGURE 7.9

– 4 P ROJECTED V ISIBILITY ON 20% W ORST D AYS FOR N ATURAL B ACKGROUND V ISIBILITY C ONDITIONS AT C APE R OMAIN

.

U ............................................................... 97

U T ABLE 8.0

- 1 R EASONABLE P ROGRESS G OALS FOR SC

U ..................................................... 98

U T ABLE 8.0

- 2 N ON -EGU S OURCES S UBJECT TO F OUR F ACTORS AS R ANKED ON R ELATIVE C OST OF C OMPLIANCE

.

U ............................................................................................ 101

U T ABLE 10.0

– 1 S OUTH C AROLINA S OURCES A FFECTING C LASS I F EDERAL A REAS IN N ORTH C AROLINA

U ................................................................................................... 114

U T ABLE 10.0

- 2 S OUTH C AROLINA S OURCES A FFECTING C LASS I F EDERAL A REAS IN G EORGIA

U .................................................................................................................. 11511 1 2 B LIST OF ACRONYMS/ABBREVIATIONS

AOI - Area of Influence BART - Best Available Retrofit Technology BRET1 - Breton Wilderness Area, LA BRIG - Brigantine Wilderness Area CAA - Clean Air Act CACR1 - Caney Creek Wilderness Area, AR CAIR - Clean Air Interstate Rule CAMR - Clean Air Mercury Rule CEM - Continuous Emissions Monitoring CERR - Consolidated Emissions Reporting Rule CHAS1 - Chassohowitzka Wilderness Area, FL CMAQ - Community Multiscale Air Quality COHU1 - Cohutta Wilderness Area, GA DOE - Department of Energy DOSO1 - Dolly Sods Wilderness Area, WV DV - Deciview EAC - Early Action Compacts EGU - Electric Generating Unit EPA - Environmental Protection Agency EVER1 - Everglades National Park, FL FAA - Federal Aviation Administration FDDA - Four Dimensional Data Assimilation FLM - Federal Land Manager GCVTC - Grand Canyon Visibil ity Transport Commission GRSM1 - Great Smoky Mountains National Park, TN HAP - Hazardous Air Pollutants HEGL1 - Hercules Glade Wilderness Area, MO IMPROVE - Interagency Monitoring of Protected Visual Environments IPM - Integrated Planning Model JARI1 - James River Face Wilderness Area, VA LIGO1 - Linville Gorge Wilderness Area, NC LSD - Low Sulfur Diesel LTS - Long-Term Strategy MACA1 - Mammoth Cave National Park, KY MING1 - Mingo National Wildlife Refuge, MO MM5 - Mesoscale Meteorological Model NAAQS - National Ambient Air Quality Standard NOX - Nitrogen Oxides OKEF1 - Okefenokee Wilderness Area, GA OTB/OTW - On the Books/On the Way PBL - Planetary Boundary Layer PM - Particulate Matter
PM 2.5 - Particulate Matter with a diameter of 2.5 micrometers or less (fine particulate)
PM 10 - Particulate Matter with a diameter of 10 micrometers or less (coarse particulate)12 POM - Particulate Organic Matter PSAT - PM Source Apportionment Technology PSU/NCAR - Pennsylvania State University/National Center for Atmospheric Research RAMS - Regional Atmospheric Modeling System RHR - Regional Haze Rule ROMA1 - Cape Romain Wilderness Area, SC RPG - Reasonable Progress Goals RPO - Regional Planning Organization RRF – Relative Response Factor SAMA1 - St. Marks National Wildlife Refuge, FL SAMI - Southern Appalach ian Mountains Initiative SCC - Source Category Code SCFC - South Carolina Forestry Commission SC DHEC - South Carolina Department of Health and Environmental Control SHEN1 - Shenandoah National Park, VA SHRO1 - Shining Rock Wilderness Area, NC SIP - State Implementation Plan SIPS1 - Sipsey Wilderness Area, AL SMOKE - The Sparse Matrix Operator Kernel Emissions (model) SMP - Smoke Management Plan SOA - Secondary Organic Aerosols SO 2 - Sulfur Dioxide STN - Speciated Trends Network SWAN1 - Swanquarter National Wildlife Area, NC TDM - Travel Demand Models UAM - Urban Airshed Model UPBU - Upper Buffalo Wilderness Area VISTAS - Visibility Improvement - State and Tribal Association of the Southeast VOC - Volatile Organic Compounds VMT - Vehicle Miles Traveled13 0 B 

### 1.0 INTRODUCTION

1 3 B 1.1 What Is Regional Haze?

Haze is defined as visibility impairment cau sed by absorption and scattering of light by fine particles. Fine particles--particles sim ilar in size to the wavelength of light--are most efficient, per unit of mass, at reducing visibil ity. Visibility impairment is considered the “best understood and most easily measured e ffect of air pollution.” Regional haze is caused by sources and activities that emit thes e fine particles and their precursors over a large region, including areas of concern, such as national parks, forests, and wilderness areas (“Class I” Federal areas).

Haze-causing fine particles may either be emitted directly to the air or be formed downwind when certain gaseous emissions se rve as precursors to the formation of particles. Of these precu rsors, sulfur dioxides (SO 2 ) and nitrogen oxides (NO X ) significantly contribute to regional haze. As a result, reducing particulate matter (“PM”), especially PM 2.5 (particles with diameter smaller than 2.5 μ m), in the atmosphere is generally considered to be an effective method of reduci ng those precursors of regional haze and thus improving visibility. Sour ces with the greatest emissions of PM 2.5 and its precursors are coal-fired power plants, industrial boilers an d other combustion sources. Other significant contributors to visibility impairment include mobile source emissions, area sources, wildfires, agricultural fires, and wind-blown dust.

1 4 B 1.2 What Are The Requirements Under The Clean Air Act For Addressing Regional

Haze?

In Section 169A of the 1977 Amendments to the Clean Air Act (CAA), Congress set forth a program for protecting visibility in Class I Federal areas, which calls for the “prevention of any future, and the remedying of any existing, im pairment of visibility in mandatory Class I Federal areas which impair ment results from manmade air pollution.” Congress adopted the visibility provisions to protect visi bility in 156 Class I Federal areas consisting of national parks (over 6,000 acres), wilder ness areas (over 5,000 acres), national memorial parks (over 5,000 acres), and international parks that were in existence as of August 1977. On December 2, 1980, th e United States (U.S .) Environmental Protection Agency (EPA) promulgated regulations to address visibility impairment. The 1980 regulations were developed to address vi sibility impairment that is “reasonably attributable” to a single source or small group of sources. These regulations represented the first phase in addressing visibility im pairment and deferred action on regional haze that emanates from a variety of sources until monitoring, mode ling, and scientific knowledge about the relationships between po llutants and visibility impairment had improved.

In the 1990 Amendments to the CAA, Congress added Section 169B and called on the EPA to issue regional haze rules. The Regional Haze rule (RHR) that the EPA14 promulgated on July 1, 1999 (64 FR 35713) revise d the existing visibil ity regulations in order to integrate provisions addressing regional haze impairment and establish a comprehensive visibility protection program for Cl ass I Federal areas. States are required to submit state implementation plans (SIPs) to the EPA that set out a state’s plan for complying with the RHR, including consulta tion and coordination with other states and with Federal Land Managers (FLM) from th e U.S. Fish and Wildlife Services, U.S. National Park Service, and U.S. Forest Servi ce. The timing of the SIP submittal is tied to the EPA’s promulgation of designations for the National Ambient Air Quality Standard (NAAQS) for fine particulate matter pursuant to 40 CFR Part 81. States must submit a regional haze implementation plan to the EPA within three years after the date of designation. Because the EPA promulga ted designation on December 17, 2004, regional haze SIPs must be submitted by December 17, 2007.

The RHR addressed the combined visibility e ffects of various pollution sources over a wide geographic region. This wide-reachi ng pollution net meant that many states--even those without Class I Areas--w ould be required to participat e in haze reduction efforts. The EPA designated five Regi onal Planning Organizations (R PO) to assist with the coordination and cooperation needed to address the visibility issue. Those states that make up the southeastern portion of the co ntiguous U.S. are known as Visibility Improvement - State and Tribal Association of the Southeas t (VISTAS) and include the following states: Alabama, Florida, Geor gia, Kentucky, Mississipp i, North Carolina, South Carolina, Tennessee, Vi rginia, and West Virginia.

Figure 1.2-1 Geographical Area of Regional Planning Organizations15 1 5 B 1.3 General Overview Of Region al Haze SIP Requirements

Section 51.308(d) of the RHR requires states to demonstrate reasona ble progress toward meeting the national goal of a re turn to natural visibility conditions by 2064. As a guide for reasonable progress, the RHR directs stat es to graphically show what would be a “uniform rate of progress” toward natural conditions for each mandatory Class I Federal area within the State. The uniform rate of progress is also known as the “glidepath.”

The RHR then requires States to establish re asonable progress goals (RPGs), expressed in deciviews (dv), for visibility improvement at each affected Class I Federal area covering each (approximately) ten-year period until 2064 . The goals must provide for reasonable progress towards achieving natura l visibility conditi ons, i.e., improvement in visibility for the most impaired days over the period of the implementation pl an, and ensure no degradation in visibility for the least im paired days over the same period [40 CFR 51.308(d)(1)].

In order to ensure that visi bility goals are properly met and set, a SIP must include determinations of the baseline visibilityconditions (expressed in dv) for the most impaired and least impaired days for each Cla ss I area in that State. SIPs must also contain supporting documentation for all required analyses used to calculate the degree of visibility impairment under natu ral visibility conditions for the most impaired and least impaired days [40 CFR 51.308(d)(2)]. In ad dition, states must include a monitoring strategy for measuring, characterizing, an d reporting of regional haze visibility impairment that is representative of all manda tory Class I Federal areas within the state [40 CFR 51.308(d)(4)].

Initial RPGs have been established in the State’s long-term stra tegy (LTS) covering the period from the present until 2018. The LTS includes enforceable emissions limitations, compliance schedules, and other measures as necessary to achieve the reasonable progress goals, including all controls required or expect ed under all fede ral and state regulations by 2009 and by 2018. During devel opment of the LTS, states are also required to consider specific factors su ch as the abovementioned ongoing control programs, measures to mitigate construction act ivities, source retirement and replacement schedules, smoke management programs for ag riculture and forestry, and enforceability of specific measures [40 CFR 51.308(d)(3)].

In addition, each state’s SIP consists of m easures necessary to establishing reasonable progress goals, including the LTS and specifi c best available retr ofit technology (BART) requirements in 40 CFR 51.308(e) of the RHR. Section 51.308(e) of the RHR requires states to include a determination of BART for each BART-eligible source in the State that emits any air pollutant that may reasonably be anticipated to cause or contribute to any impairment of visibility in any manda tory Class I Federal area. Clean Air Act Section 169A(b) defines BART-eligible sources as sources in 26 specific source categories that were placed in operation during a 15-year period prior to enactment of the 1977 Clean Air Act Amendments. In determini ng BART, States must consider the five (5) factors set out in Secti on 169A (g)(7) of the Clean Air Act. Emission limitations16 representing BART and schedules for complia nce with BART for each source subject to BART must be included in the LTS.

1 6 B 1.4 Class I Federal Area In South Carolina

South Carolina has one Class I Federal area within its borde rs: Cape Romain. The SC Department of Health and Environmental C ontrol (SC DHEC) Bureau of Air Quality is responsible for developing the South Carolina’ s Regional Haze SIP. This SIP establishes RPGs for visibility improvement at this Cl ass I Federal area and determines the BART requirements and long-term strategy necessary to achieve those RPGs within the first regional haze planning period.

Figure 1.4 - 1 South Carolina Class I Federal Area

In developing this SIP, SC DHEC has also considered that emission sources outside of South Carolina may affect visibility at its Cl ass I Federal area, and that emission sources within South Carolina may affect visibility at Class I Federa l areas in neighboring states. Through VISTAS, the southeastern states have worked together to a ssess state-by-state contributions to visibility impairment in sp ecific Class I Federal areas, including those in South Carolina and those affected by emissi ons from South Carolina. This technical work is discussed further in Chapters five, six, and seven below. A full discussion of the process and outcome of consultations betw een South Carolina and other states is contained in Chapter 10.17 Prior to the formation of VISTAS, the southern states cooperated in a voluntary regional partnership “to identify and recommend r easonable measures to remedy existing and prevent future adverse effects from human-i nduced air pollution on th e air quality related values of the Southern Appalachian Mountains .” States cooperate d with the FLMs, the EPA, industry, environmental organizations , and academia to complete a technical assessment of the impacts of acid depositi on, ozone, and fine particles on sensitive resources in the Southern Appalachians. Th e Southern Appalachian Mountains Initiative (SAMI) Final Report was delivered in Augus t 2002. The SAMI assessment concluded that ammonium sulfate is the major contributor to visibility impairment in the Southern Appalachian Mountains, and to improve visi bility, it is most important to reduce SO 2 emissions. SAMI also concluded that reduc ing ammonia emissions would be helpful to reduce ammonium nitrate contributions to visi bility impairment. Emission controls for organic carbon, elemental carbon, and soil were expected to be less important for improving visibility. SAMI modeling found that on the haziest days, much of the benefit of emission reductions would occur in the st ate where emission reductions were made. Emissions in surrounding SAMI states and stat es outside the SAMI re gion also contribute to air quality in the SAMI Class I Federa l areas. The SAMI st ates supported strong national multi-pollutant legislation to accomplish its mission. Emissions reductions to meet national health standards for ozone and fi ne particles were expected to also improve air quality in the Southern Appalachian Moun tains. SAMI states committed to consider air quality benefits in the S outhern Appalachians as they developed SIPs for health standards.

In 2002 the North Carolina legislature passed the North Carolina Clean Smokestacks Act (NC CSA) that requires NC utilities to redu ce emissions of sulfur dioxide and nitrogen dioxides. The SAMI analyses and recomm endations supported development of the NC CSA. While not directly aff ecting coal-fired power plants in South Carolina, the NC CSA, by reducing emissions that may be tr ansferred across state boundaries, will reduce visibility impairment in South Carolina and is therefore mentioned throughout the SIP as an effective inter-state measure to cont rol emissions. In 2005, EPA promulgated the Clean Air Interstate Rule (CAIR) to require emissions reductions for SO 2 and NO X from electric generating units (EGUs) in 25 easte rn states. South Carolina is a CAIR participating state. The CAIR rule allows for interstate trading of emissions to help EGUs find cost-effective reductions. These re ductions will also improve visibility in South Carolina’s Class I Federal area.

1 7 B 1.5 State And Federal Land Manager (FLM) Coordination

As required by 40 CFR Section 51.308(i), the re gional haze SIP must include procedures for continuing consultation between the stat es and the FLMs on the implementation of the visibility protection progr am (including development and review of implementation plan revisions and five-year progress re ports) and on the implementation of other programs having the potential to contribute to impairment of visibili ty in any mandatory Class I Federal area within the State.18 Successful implementation of a regional haze program will involve long-term regional coordination among States. VISTAS was esta blished in 2001 to init iate and coordinate activities associated with the management of regional haze, visibility and other air quality issues in the Southeastern United States. Jurisdictions represen ted by VISTAS members include the Eastern Band of Cherokee Indians; the States of Alabama, Florida, Georgia, Kentucky, Mississippi, North Ca rolina, South Carolina, Tennessee, Virginia, and West Virginia; and the local air pollution control programs located in these States. A copy of the VISTAS Memorandum of Agreement and Bylaws is enclosed as Appendix A.

The objectives of the VISTAS project are to establish natural background visibility conditions across the mandatory Class I Fede ral areas, identify current visibility impairment levels, analyze emission control levels that will achieve interim visibility goals, and provide adequate documentation to me mber agencies so that they can develop their regional haze State/Trib al Implementation Plans (SIP s/TIPs). Figure 1.5-1 shows the 18 mandatory Class I Federal areas in the VISTAS Region.

Figure 1.5 - 1 Class I Federal Areas in the VISTAS Region
A technical support document for the regional haze SIPs was prepared cooperatively by VISTAS staff and states to characterize regiona l haze in the southeastern U.S. The report includes a review of the science, calculation of initial baseline visibility, review of monitoring data/data gaps and recommenda tions for additional monitoring, initial emission inventory characterization and proj ections, and emission reduction requirements of other CAA programs. Source contributions to visibility impairment at VISTAS mandatory Class I Federal areas are also assessed.19 1 B 

### 2.0 ASSESSMENT OF BASELINE AND CURRENT CONDITIONS

AND ESTIMATE OF NATURAL BACKGROUND CONDITIONS IN

CLASS I AREAS

The goal of the CAA is to restore natural vi sibility conditions to the 156 Class I Federal areas identified in the 1977 Clean Air Ac t Amendments. The RHR Section 51.301(q) defines natural conditions: “N atural conditions includes na turally occurring phenomena that reduce visibility as meas ured in terms of light extinc tion, visual range, contrast, or coloration.” The Regional H aze SIPs must contain meas ures that make “reasonable progress” toward this goal by reducing anthropogenic emissions that cause haze.

An easily understood measure of vi sibility to most people is vi sual range. Vi sual range is the greatest distance, in kilometers or miles, at which a dark object can be viewed against the sky. For evaluating the re lative contributions of polluta nts to visibility impairment, however, the most useful measure of visibil ity 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 RHR is the deciview (dv). Deciviews are calculated directly from light extinction using a logarithmic scal e. The dv is a useful measure for tracking progress in improving visibility, because each dv change is an equal incremental change in visibility perceived by the human eye. Most people can easily detect a one dv change in visibility.

For each Class I Federal area, th ere are three metrics of visibi lity that are part of the determination of reasonable progress:

1) Natural conditions, 2) Baseline conditions, and 3) Current conditions.

Each of the three aforementioned metrics includes the concentration data of the visibility pollutants as different terms in the light extinction algorithm, with respective extinction coefficients and relative humidity factors. To tal light extinction, when converted to dv, is calculated for the average of the 20 percent be st and 20 percent worst visibility days.

“Natural” visibility is determined by estimatin g the natural concentrations of visibility pollutants and then calculating total light exti nction with the light ex tinction algorithm. “Baseline” visibility is the starting poin t for the improvement of visibility.

To assist states in conductin g visibility-related research a nd in creating Federal and State implementation plans for the protection of visibility in Class I Federal areas, the Interagency Monitoring of Protected Vi sual Environments (IMPROVE) program was20 established in 1985. IMPROVE is a cooperative measurement effort among representatives from Federal and regional- state organizations. IMPROVE’s objectives are, among other things, to establish current vi sibility conditions in Class I Federal areas, to track changes in visibili ty, to identify emission sources responsible for man-made visibility impairments, and to provide regional haze moni toring data for all Class I Federal areas where practical. IMPROVE’s mo nitoring network consists of aerosol and optical samplers. The network began in 1988 with 20 monitoring sites in Class I Federal areas and quickly grew to 30 sites by 1999. Fi nally, with the enactment of the RHR, the network has been expanded to 80 more sites (for a total of 110 sites) with additional sites for special projects. Figure 2.0-1 depicts the location of IMPROVE monitoring sites in the VISTAS region.



### 18 Class I areas in VISTAS st ates and monitor locations

STN PM2.5 (selected) SEARCH

Figure 2.0 - 1 IMPROVE Monitoring Sites in the VISTAS Region

The average of the IMPROVE monitoring data for 2000 through 2004 define “current” visibility conditions for this initial period. The differen ce between baseline conditions and natural visibility conditions is the amount of improvement necessary to attain natural visibility by 2064. Each state must estimate natural visibility levels for Class I Federal areas within its borders in consultation with FLM and othe r states [40 CFR 51.308(d)(2)]. “Current conditions” are assessed every five years as part of the SIP review where actual progress in reducing visibility impairment is compared to the reasonable progress goals of this SIP.21 1 8 B 2.1 Estimating Natural Conditions For South Carolina Class I Federal Area

Natural background visibility, as defined in Guidance for Estimating Natural Visibility Condition Under the Regional Haze Program , EPA-454/B-03-005, September 2003, is based on annual average concentrations of fi ne particle components. The same annual average natural background visibility is assu med for all Class I Federal areas in the eastern U.S. (separate values are estimated for the western U.S.). Natural background visibility for the 20 percent worst days is estimated by assuming that fine particle concentrations for natural background ar e normally distributed and that the 90 th percentile of the annual distribution repr esents natural background visib ility on the 20 percent worst days.

In the 2003 guidance, the EPA also provided that states may use a “refined approach” to estimate the values that characterize the natura l visibility conditions of the Class I areas. The purpose of such a refinement would be to provide more accurate estimates with changes to the extinction algorithm that may include the concentration values, factors to calculate extinction from a measured particular species and particle size, the extinction coefficients for certain compounds, geographical variation (by altitude ) of a fixed value, and the addition of visibility pollutants.

In 2005, the IMPROVE Steering Committee made recommendations for a refined equation that modifies the terms of the orig inal 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 Federal area is made by the state in which the Class I Federal area is located.

The new IMPROVE equation accounts for the e ffect of particle size distribution on light extinction efficiency of sulfate, nitrate, a nd organic carbon. New terms are added to the equation to account for light extinction by s ea salt and light ab sorption by gaseous nitrogen dioxide. Site-specific values are used for Rayleigh scatte ring to account for the site-specific effects of elevation (and temperature). Separate relative humidity enhancement factors are used for small size distribution sulfate or nitrate, large size distribution sulfate or nitrate, and sea sa lt. The elemental carbon (light-absorbing carbon), fine soil, and coarse mass terms do not change between the original and new IMPROVE equations.

The VISTAS states chose to use the ne w IMPROVE equation as the basis for the conceptual description because it takes into account the most recent review of the science and because it is recommended by the IMPROVE Steering Committee. For more detailed discussion of the two IMPROVE equations, see Appendix B.2.

1 9 B 2.2 Estimating Baseline Conditions For South Carolina Class I Federal Area

SC DHEC estimated baseline visibility c onditions at Cape Romain based on sampling data collected at its IMPROVE monitoring site. A five-y ear average (2000 to 2004) was calculated for each value (both best and worst) in accordance with 40 CFR 51.308(d)(2)22 and the EPA Guidance for Tracking Progress Under the Regional Haze Rule . The light extinction and dv visibility values for these worst and best days are based on data and calculations included in Appendix B.4 of this SIP. Appendix B includes a demonstration of the appropriateness of these values for each Class I area.

2 0 B 2.3 Summary of Natural Background And Ba seline Conditions For South Carolina

Class I Federal Area

Table 2.3-1 presents estimated natural bac kground and baseline visibility metrics for Cape Romain. Baseline visibi lity on the 20 percent worst days at Cape Romain is generally between 25 an d 30 dv. Natural background visibilit y is predicted to be between 12 and 13 dv. Appendix F, Chapter 4 clarifies how each term was derived to predict the following estimates.

Table 2.3 - 1 Natural Background and Baseline Conditions for Cape Romain

2 1 B 2.4 Pollutant Contributions To Visibilit y Impairment (2000-2004 Baseline Data)

Ammonium sulfate [(NH 4 ) 2 SO 4 ] contributes the most to visi bility impairment and fine particle mass on the 20 percent worst and 20 percent best vi sibility days at Cape Romain. Sulfate levels on the 20 percent worst days account for 60-70 percent of the visibility impairment. However, across the VISTAS region, sulfate levels during the 20 percent worst days are lower at the coastal sites than at the inland sites (s ee Figure 2.4-1). On the 20 percent clearest days, sulfate levels are more uniform across the region (Figure 2.4-2). Baseline Visibility Conditions 2000-2004 Class I area Average for 20% Worst Days (deciviews) Average for 20% Best Days (deciviews) Average for 20% Worst Days B ext (Mm-1) Average for 20% Best Days B ext (Mm-1)

Cape Romain 26.5 14.3 147.3 42.4 Natural Background Conditions
Class I area Average for 20% Worst Days (deciviews) Average for 20% Best Days (deciviews) B ext (Mm-1) Average for 20% Worst Days B ext (Mm-1) Average for 20% Best Days

Cape Romain 12.1 5.9 34.2 18.123 Figure 2.4 - 1 Average Light Extinction for the 20% Worst Days in 2000-2004 (Based on New IMPROVE Equation)24 Figure 2.4 - 2 Average Light Extinction fo r the 20% Best Days in 2000-2004 (Based on New IMPROVE Equation)

Particulate Organic Matter (POM) is the second highest contribu tor to fine particle mass and light extinction on the 20 percent haziest and the 20 percent clearest days at Cape Romain. Elevated levels of POM and Elem ental Carbon (EC) indicate impact from wildfires or prescribed fire (defined in Appendix D.2b). VISTAS collected additional samples of carbon at five sites, including Cape Romain, to better understand sources contributing to carbon in rural and urban areas. Samples were analyzed to define the amount of carbon-14 isotope as an indicato r of the amount of carbon from modern sources (vegetative emissions, fires) a nd the amount of carbon from fossil sources (gasoline, diesel, oil). For most samples, the ratio of modern car bon to fossil carbon was greater than 0.60 throughout the year. At Great Smoky Mountains and other mountain Class I areas, the majority of carbon is from biogenic emissions either from vegetation or from wood burning. Biogenic carbon emissions at Cape Romain and other coastal sites are lower than emissions at the forested mountain sites. Carbon from gasoline and diesel engines is a relatively small contribution at th e rural sites. At Cape Romain, carbon from fossil fuel combustion is a larger percentage contribution than at the rural sites, but still is less than 50 percent of total carbon measured. These results suggest that controlling anthropogenic sources of carbon will have little benefit in improving vi sibility in Class I areas since the majority of PO M comes from natural (i.e., bi ogenic sources). Controlling anthropogenic sources of carbon will likely be more effective to reduce levels of PM 2.5 in urban areas.

Ammonium nitrate , NH 4 NO 3 , is formed in the atmosphere by reaction of ammonia and nitrogen oxides. In the VISTAS region, nitr ate formation is limited by availability of ammonia and by temperature. Amm onia preferentially reacts with SO 2 and sulfate before reacting with nitrogen oxides. Nitrate particul ates are formed at lower temperatures; at elevated temperatures nitrates remain in gase ous form. For this reason, nitrate particulate levels are very low in the summer and a minor contributor to visibility impairment. Nitrate particulate concentrati ons are higher on winter days and are more significant 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 indicates that in the VISTAS region ammonium nitrate formation is limited by ammonia concentrations and sugges ts that for winter days, controls of ammonia sources would be more effective in reducing ammonium nitrate levels than controls of nitrogen oxides. This is di scussed in more detail in Appendix B.3.

Elemental Carbon, EC, is a comparatively minor contribu tion to visibility impairment. However, EC did account for the third largest contributor to visibili ty impairment on the 20 percent haziest days at the Cape Romain Class I Federal area. Sources include agriculture, prescribed, wildla nd, wild fires and incomplete combustion of fossil fuels. EC levels are higher at urban monitors than at the Class I areas and suggest controls of fossil fuel combustion sources would be more effective to reduce PM 2.5 in urban areas than to improve visibility in Class I areas.25 Sea salt (NaCl) is observed at the coasta l sites. As Figure 2.4-2 indicates , NaCl contributions to visibility impairment are hi ghest on the 20 percent clearest days when sulfate and POM levels are low. NaCl levels do not contribute signifi cantly to visibility on the 20 percent worst visibility days. Fi gure 2.4-3 compares distribution of pollutants at default natural conditions after applying the new IMPROVE algorithm. As indicated, NaCl does account for a portion of visibility impairment under natural conditions.

Natural Conditions -- New IMPROVE Algorithm -

0 10 20 30 40 50

Great Sm o ky

Mtn

s

Sh e nand oa

h

Ca p e Romain Oke fe

noke e Ever g lades Breton * Left = B20%; Center = Average; Right = W20%

*Breton based on 2001Q2-2002Q1 & 2002Q4-2003Q3bext (Mm-1) Sea Salt

CM

Soil

EC

POM

NH4NO3

(NH4)2SO4

Rayleigh

Figure 2.4 - 3 Distribution of Pollutants at Natural Conditions

Coarse particle mass (particles with diameters betw een 2.5 and 10 microns) makes a relatively small contribution to visibility impairment because the light extinction efficiency of coarse mass is very low compar ed to the extinction efficiency for sulfate, nitrate, and carbon.

An unidentified component is reported by IMPROVE as the difference between the total
PM 2.5 mass measured on the filter and the su m of the measured components. This unidentified mass may be positive or negative a nd is attributable to water and/or the factors used to calculate molecular weights of the other components.

PM 2.5 trends in urban an d Class I areas: IMPROVE data were compared to monitoring data from the Speciated Trends Network (S TN) in nearby urban areas to understand the similarities and differences in compositi on of fine particle mass. Several PM 2.5 nonattainment areas are in close proximity to the Class I areas in th e southeastern U.S., including Atlanta, GA; Birmingham, AL; Ch arleston, WV; Chattanoog a, TN; Louisville, KY; and Knoxville, TN. Ammonium sulfate concentrations are comparable between urban and nearby Class I areas, while or ganic carbon, elemental carbon, and nitrate concentrations are generally highe r in urban areas than in the Class I areas. These results26 suggest that sulfate is widely distributed re gionally while urban areas see an additional incremental pollutant loading fr om local emissions sources.

Soil fine particles are minor contri butors to visibility impairme nt at most sites on most days. Occasional episodes of elevated fine soil can be attributed to Saharan dust episodes, particularly at Everglades, FL, but ra rely are seen at Cape Romain. No control strategies are indicated for fine soil.

Role of meteorology in determ ining visibility conditions: Classification and Regression Tree Analyses were used to characterize the relationship between meteorological conditions and visibility conditi ons at the Class I Federal area . Days were assigned to one of five visibility classes, ranging from poor to good visibility. Days were then assigned to bins based on mete orological conditions. For Ca pe Romain, poor visibility days were most likely to occur on days w ith high temperatures, high relative humidity, low wind speeds, and elevated PM 2.5 mass at upwind urban areas. Precipitation was not a good predictor of visibility conditions. Weights were as signed to days based on frequency of occurrence of days with similar meteorological conditions.

The above analyses are further discussed in Appendix B.3.27 2 B 

### 3.0 GLIDEPATHS TO NATURAL CONDITIONS IN 2064

As stated in section 1.3 above, the RHR di rects each State to show what would be a “uniform rate of progress” toward natural conditions for each mandatory Class I Federal area within the. The uniform rate of progr ess 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 repr esenting no manmade im pairment in 2064.

Each state must set goals for each Class I Fe deral area in that State that provide for reasonable progress towards ach ieving natural visibility conditions by 2064. Section 51.308(d)(1) of the RHR re quires that reasonable progress goals must:

(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 (glidepat hs) were developed under the auspices of VISTAS for each Class I Fede ral area in the VISTAS region. The glidepaths were developed in accordance with EPA guidance (a s described in section 3 above) and using data collected from the IMPROVE monitoring sites. Glidepaths were developed using relative response factors (RRFs). RRFs are expr essed as the ratio of the modeling results for the future-year to the results of the ba se year (2018/2002) and ar e Class I area and PM species specific. The steps used to project future-year visibility for the worst 20 percent and best 20 percent visibility days are discu ssed further in Appendix F (Chapter 4). The glidepath is meant to be used as a gu ide in setting reasonable progress goals.

2 B 3.1 Glidepath For Class I Federal Area In South Carolina

The following is a glidepath for Cape Romain. Natural background visibility is predicted to be between 12 and 13 dv.28 

### Uniform Rate of Reasona ble Progress Glide Path Cape Romain - 20% Data Days
New IMPROVE Equation, 12 km grid

26.48 25.53

23.15

20.77

18.39

16.01

13.64 12.21 22.72

0 5 10 15 20 25 30 35

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

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

Figure 3.1 - 1 Uniform Rate of Progress G lidepath for 20% Worst Days at Cape Romain, SC29 3 B 

### 4.0 NATURE OF THE PROBLE M: CHIEF CAUSES OF

VISIBILITY IMPAIRMENT IN SOUTH CAROLINA CLASS I

FEDERAL AREAS

2 3 B 4.1 Baseline Emissions Inventory

Section 51.308(d)(4)(v) of the RHR require s a statewide emissions inventory of pollutants that are reasonably anticipated to cause or contribute to visibility impairment in any mandatory Class I Federal area. An inve ntory was developed for the baseline year 2002 and estimated for 2009 and 2018. The po llutants inventoried include VOCs, nitrogen oxides (NO X ), fine particulate (PM 2.5 ), coarse particulate (PM 10 ), ammonia, and SO 2 . In general, the baseline emissions i nventory for 2002 was developed using control information for South Carolina. A complete detailed discussion of the baseline emissions inventory is contai ned in Appendix D.3.

There are five different emission inventory source classifications, stationary point and area sources, off-road and on -road mobile sources, and bioge nic sources. Stationary point sources are those sources that emit greater than a specified tonnage per year and the data is provided at the facility level. Stationary area sources are those sources whose emissions are relatively small but due to the large number of these sources, the collective emissions could be significant (e.g., dry cleaner s, service stations, etc.) These types of emissions are estimated on the county level. Off-road mobile sources are equipment that can move but do not use the roadways (e.g., lawn mowers, construction equipment, railroad locomotives, aircraft, etc.). The em issions from these sour ces, like stationary area sources, are estimated on the county 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 t ype and are summed to the county level. Biogenic sources are natural sources like tree s, crops, grasses, a nd natural decay of plants. The emissions from these so urces are estimated on a county level.

In addition to the various sour ce classifications, there are al so various types of emission inventories. The first is the actual base year inventory. This invent ory is the base year emissions that correspond to the meteorological data; for this modeling effort it is 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; howeve r, for sources that may have significant changes from year to year, a more typical emission value is use d. In this modeling effort, typical emissions were developed for the EGUs and the wildla nd fire emissions. The air quality modeling results using these emissions are used in calcu lating the relative reduc tion factors used in the attainment demonstration test.30 In the sections that follow, a synopsis of the inventories used for each source classification are discussed. The detailed discussions of the emissions inventory development can be found in Appendices D.1 and D.4.c.

4 1 B 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. Major sources having emissions of 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 at least every three years. Minor sources have been inventoried less frequently. The point source emissions da ta can be grouped into the EGU sources and the other point sources (i.e., non-EGUs).

6 7 B U 4.1.1.1 Electric Generating Units

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. Th is data provides hourly emissions profiles that can be used in the modeli ng of these large sources of NO X and helps to provide more accurate modeling of these sources.

Since the NO X emissions from EGU sources are a significant part of the emissions inventory, a typical base year emissions i nventory was developed for these sources to avoid anomalies in emissions due to vari ability in meteorology and in economic and outage factors in 2002. This approach is cons istent with the EPA’s modeling guidance. To develop a typical year 2002 emissions in ventory for EGU sources, the average CEM heat input for 2000 through 2004 for each unit wa s 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 since the modeling current desi gn values use monitoring data from this same five-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 shutdow n, the emissions and heat inputs for 2001 (or 2000) were used in the normalizing calculations.

As part of the VISTAS modeling, VIST AS and the Midwest Regional Planning Organization contracted with ICF Resources, L.L.C. to generate a future year emission inventory for the electric generating sector of the contiguous U.S. using the Integrated Planning Model (IPM) version 2.1.9 updated with state-specific information. IPM is a dynamic linear optimization model that can be used to examine air pollution control policies for various pollutants from the en tire electric power system throughout the contiguous U.S. The dynamic nature of IPM en ables the projection of the behavior of the power system over a specified future period. The optimization logic determines the leastcost means of meeting electric generation and capacity requirements while complying with specified constraints, including air pollution regulations, tr ansmission bottlenecks,31 and plant-specific operational constraints. The versatility of IPM allows users to specify which constraints to exercise and po pulate IPM with their own datasets.

6 8 B U 4.1.1.2 Other Point Sources

For the non-EGU sources, the same inventory was used for both the actual and typical base year emissions inventories. The non-EGU category used annual emissions as reported for the Consolidated Emissions Re porting Rule (CERR) for the year 2002. These emissions were temporally allocated to month, day, and hour using source category code (SCC) based allocation factors.

The general approach for assembling future y ear data was to use recently updated growth and control data consistent wi th the EPA’s CAIR analyses. This data was supplemented with state-specific growth factors and stakeholder input on growth assumptions.

4 2 B 4.1.2 Stationary Area Sources

Stationary area sources include sources whos e individual emissions are relatively small but, due to the large number of these sources, the collective emissions could be significant (e.g., combustion of fuels for heating, structure fires, servic e stations, etc.). Emissions are estimated by multiplying an em ission factor by some known indicator of collective activity, such as fu el usage, number in household, or population. Stationary area source emissions are estimated on the county level.

South Carolina used the 2002 CERR data as the basis for the stationary area source inventory. In addition, the VISTAS cont ractor calculated emissions for animal husbandry, wildland fires, and particulate matte r from paved and unpaved roads. For the remainder of the states within the VISTAS modeling domain, the stat e-supplied data or the CERR data for 2002 was used.

The actual base year inventory will serve as th e typical base year inventory for all area source categories except for wildland fires. Fo r wildland fires, a typical year inventory was used to avoid anomalies in wildfire activity in 2002 compar ed to longer term averages. Development of a typical year fire inventory provided the capability of using a comparable data set for both the base year and future years. Thus, fire emissions remain the same for air quality mode ling in both the base and any future years. The VISTAS Fire Special Interest Work Group used state records to ratio the num ber of acres burned over a longer term period (three or more years, as available from st ate records) to 2002. Based on these ratios, the 2002 acreage was then scaled up or down to develop a typical year inventory.

The VISTAS contractor generated future year area emissions inventories for 2009 and 2018 for the regional haze modeling. Growth fact ors, supplied either by states or taken from the CAIR emission projections, were a pplied to project the controlled emissions to 2018. If no growth factor was available from either a state or the CAIR growth factor32 files, then the US EPA's Economic Growth and Analysis System Version 5 growth factors were used.

4 3 B 4.1.3 Off-Road Mobile Sources

Off-road, or “non-road,” mobile sources are equipment that can move but do not use the roadways, such as construction equipment, aircraft, railroad locomotives, lawn and garden equipment, etc. For the majority of the non-road mobile sources, the emissions were estimated using EPA’s NONROAD2005c m odel. For the three source categories not included in the NONROAD model (i.e., airc raft engines, railroad locomotives, and commercial marine vessels), more traditiona l methods of estimating the emissions were used. The same inventory will be used for both the actual and typical base year emission inventories.

For the source categories estimated using EPA’s NONROAD model, the model growth assumptions were used to create the 2009 and 2018 future year inventories. The NONROAD model takes into consideration rules that are in effect that could impact the emissions from these source categories. Fo r the largest airports in SC, the FAA’s Terminal Area Forecast was used to project growth in aircraft emissions. For the commercial marine, railroad locomotive, and 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 CAIR Technical Support Document. When available, state- specific growth factors were used.

4 B 4.1.4 Highway Mobile Sources

For on-road vehicles, the newest version of the MOBILE model, MOBILE6.2, was used. Key inputs for MOBILE6.2 include information on the age of vehicles on the roads, the average speed on the roads, the mix of vehicl es on the roads, any c ontrol technologies in place in an area to reduce emissions for motor vehicles (e.g., emissions inspection programs), and temperature.

The MOBILE6 model takes into consideration ru les that are in effect that impact the emissions from this source sector. The same MOBILE run is used to represent the actual and typical year emissions for on-road vehicles us ing input data refl ective of 2002. The same model was then run for the future y ear emissions inventor y using input data reflective of those years. For South Carolin a, the speed, vehicle age, and vehicle mix data are all MOBILE6.2 defaults. The 2002 ve hicle miles traveled (VMT) were obtained from the South Carolina Department of Transportation (SCDOT).

4 5 B 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 essentially the Urban Airshed Model (UAM)-BEIS3 model but also includes modifications to use MM5 data,33 gridded land use data, and an important sc ience update. The emi ssion factors that are used in SMOKE-BEIS3 are the same as the emission factors in UAM-BEIS3.

The basis for the gridded land use data used by BEIS3 is the county land use data in the Biogenic Emissions Landcover Database ve rsion 3 (BELD3) provided by EPA. A separate land classificati on scheme, based upon satellite (AVHRR, 1-km spatial resolution) and census information, aided in de fining the forested, agricultural, and urban portions of each county.

4 6 B 4.1.6 Summary 2002 (Base G2) Baseline Emissions Inventory for SC

Table 4.1 is a summary of the 2002 baseline emission inventory for SC. 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.4.b.

Table 4.1 2002 Emission Inventory Summary for SC

VOC NO X PM 2.5 PM 10 NH 3 SO 2 Point 38,928 130,681 27,766 36,779 1,552 263,790 Area 175,666 24,602 63,802 287,162 29,074 14,087 On-Road Mobile 114,861 138,941 2,473 6,505 4,646 5,909 Off-Road Mobile 55,016 50,249 3,945 4,152 33 4,866 Biogenics 901,638 8,770 (NO) 0 0 0 0 TOTAL 128,6109 353,243 97,986 334,598 35,305 288,652

*Most emissions values were taken from Documentation of the Revised 2002 Base Year, Revised 2018, and Initial 2009 Emission Inventories for VISTAS v.11 prepared by the VISTAS contractor, MACTEC. The exception is Biogenic emissions, which were developed by VISTAS using SMOKE output s to calculate the annual estimates.

4 7 B 4.1.7 Model Performance Improvements through Emissions Inventory Improvements

Since the initial model perf ormance evaluation, VISTAS ha s made several improvements to the emissions inventory, which, in tu rn, improves model performance. These inventory improvements detailed in both the VI STAS emissions inventory report and in Appendix D.5, are summarized here:

• For electric generating utilities, the Inte grated Planning Model (IPM) was used to provide estimates of future year utili ty production and emissions. Continuous Emissions Monitoring data was used to de fine seasonal variab ility in production and emissions. For Base G2 emissions , states updated IPM model projections from 2005 with control data provided by utility comp anies in 2006 through winter 2007.

• For on-road vehicle emissions, states and local agencies provided updated MOBILE model input and vehi cle-miles-traveled data.

• For ammonia emissions from agricultural sources, the Carnegie Mellon University ammonia model was used to improve annual and monthly estimates.34 • For fires, the VISTAS states provided fire activity data for 2002 for wildfires, prescribed fires, and land clearing a nd agricultural burning all from which MACTEC developed a 2002 fi re inventory. Where data allowed, Alpine Geophysics modeled fire events as point sources. In 2006, the U.S. Forest Service and U.S. Fish and Wildlife Serv ice provided projecti ons of increased prescribed burning in 2009 and 2018; these data were incorporated in the Base G inventory for all states except Florida.

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

• For commercial marine emissions in shi pping lanes in the Gulf of Mexico and Atlantic Oceans, ENVIRON created gri dded emissions for the VISTAS modeling domain using inventory data newly developed for USEPA by Corbett at University of Delaware. These emissi ons were incorporated in the Base G modeling.

• Updated inventories from the neighbori ng RPOs, Mexico, and Canada were incorporated as available.

2 4 B 4.2 Assessment of Relative Contributions from Specific Pollutants and Sources Categories

Ammonium sulfate is the largest contributor to visibility impairment at Cape Romain. Reduction of sulfur dioxide emissions woul d be the most effective means to reduce ammonium sulfate. As illustrated in Figure 4.2-1, 96 percent of the 2002 SO 2 emissions in the VISTAS states are attributable to el ectric generating facili ties and industrial point sources. In South Carolina, 91 percent of the 2002 SO 2 emissions are attributable to electric utilities and indust rial point sources (Table 4.1)



## VISTAS States SO2 Emissions - 2002

Utilities Industrial On road Non road Other Utilities: 76% Industrial: 20%

96% of SO2 Emissions in the VISTAS States in 2002 from Utility and Industrial Sources 4.8 Million tons

Figure 4.2 - 1 SO 2 Emissions in 2002 fo r the VISTAS states35 4 B 

### 5.0 REGIONAL HAZE MODE LING METHODS AND INPUTS

Modeling for regional haze was performed by VISTAS for ten (10) Southeast states, including South Carolina. The sections below outline the methods and inputs used by VISTAS for the regional modeling. Additiona l details are provided in Appendix C.

2 5 B 5.1 Analysis Method

The modeling analysis is a complex technical evaluation that begins by selection of the modeling system. VISTAS chose to use the following modeling system:

• Meteorological Model: Th e Pennsylvania State Univer sity/National Center for Atmospheric Research (PSU/NCAR) Meso scale Meteorological Model (MM5) is a nonhydrostatic, prognostic meteorological model routinely used for urban- and regional-scale photochemical, fine particul ate matter, and regional haze regulatory modeling studies.

• Emissions Model: The Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system is an emissions modeli ng system that generates hourly gridded speciated emission inputs of mobile, non -road 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 a “One-A tmosphere” photochemical grid model capable of addressing ozone, particul ate matter (PM), visibility, and acid deposition at a regional scale for periods up to one year.

EPA Modeling Guidance recommends modeling an entire year or, at a minimum, several days in each quarter to ade quately represent the range of meteorological conditions that contribute to elevated levels of fine PM. The year 2002 was selected by VISTAS as the modeling year for this demonstration. Me teorological inputs were developed for 2002 and processed through the emissions model. These inputs were used in the air quality model to predict fine particle mass and vi sibility. The model results for 2002 were compared with observed mete orological and air quality data to evaluate model performance. Several configurations of th e meteorological and air quality model were evaluated to select a configuration that gave the best overall performance for the VISTAS region.

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

The complete modeling protocol used for this analysis can be found in Appendix C.36 2 6 B 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. The models should also apply scientifically-appropr iate means to address important physical and chemical phenomena in sufficient detail, using peer-r eviewed methods. “Freely accessible” means that model formulations and coding are freel y available for review and that the models are available to stakeholders and their consul tants 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 goa ls. These criteria we re used in selecting the modeling system used for this modeling attainment demonstration.

4 8 B 5.2.1 Selection of Photochemical Grid Model

6 9 B U 5.2.1.1 Criteria

For a photochemical grid model to qualify as a candidate for use in a regional haze SIP, a State needs to show that this model meets th e same several general criteria as a model for an attainment demonstration for a NAAQS:

• The model must have received a scientific peer review,

• The model can be demonstrated applicable to the problem on a theoretical basis,

• Databases needed to perform the analys is must be available and adequate,

• Available past appropriate performance ev aluations must show the model is not biased towards underestimates or overestimates,

• A protocol on methods and procedures to be followed must have been established, and

• 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.

7 0 B U 5.2.1.2 Overview of CMAQ

The photochemical model selected for this st udy was CMAQ version 4.4. For more than a decade, the EPA has been developing the Models-3 CMAQ modeling system with the overarching aim of producing a ‘one-atmosphe re’ air quality modeling system capable of addressing ozone, fine particulate matter, vi sibility, and acid deposition within a common platform. The original justification for the Models-3 development emerged from the challenges posed by the 1990 CAAA and EPA’s de sire to develop an advanced modeling framework for ‘holistic’ environmenta l modeling utilizing state-of-science37 representations of atmospheri c processes in a high-performance computing environment. EPA completed the initial stage of developm ent with Models-3 and released the CMAQ model in mid 1999 as the initial operating sc ience model under the Models-3 framework. The most recent rendition is CMAQ version 4.5, which was released in September 2005.

An advantage of choosing CMAQ as the atmospheric model is the ability to do oneatmospheric modeling. Since South Carolina will be using the same modeling exercise for the ozone and PM 2.5 attainment demonstrations SIPs, as well as the regional haze SIP, having a model that can handle both ozone and particulate matter is essential. A number of features in CMAQ’s theoretical formula tion and technical implementation make the model well suited for annual PM modeling.

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

4 9 B 5.2.2 Selection of Meteorological Model

7 1 B U 5.2.2.1 Criteria
Meteorological models, either through objec tive, diagnostic, or prognostic analysis, extend available information about the state of the atmosphere to the grid upon which photochemical grid modeling is to be ca rried out. The crite ria for selecting a meteorological model are based on both the model’s ability to accurately replicate important meteorological phenomena in the re gion of study, and the model's ability to interface with the rest of th e modeling systems--particularly the photochemical grid model. With these issues in mind, the fo llowing criteria were established for the meteorological model to be used in this study:

• Non-Hydrostatic Formulation,

• Reasonably current, peer reviewed formulation,

• Simulates Cloud Physics,

• Publicly available at no or low cost,

• Output available in I/O API format,

• Supports Four Dimensional Data Assimilation (FDDA), and

• Enhanced treatment of Planetary Bo undary Layer heights for AQ modeling.

7 2 B U 5.2.2.2 Overview of MM5

The non-hydrostatic MM5 model is a three-di mensional, limited-area, primitive equation, prognostic model that has been us ed widely in regional air quality model applications. The basic model has been under continuous development, improvement, testing, and38 open peer-review for more than 20 years an d has been used worldwide by hundreds of scientists for a variety of mesoscale studies.

MM5 uses a terrain-following non-dimensiona lized pressure, or “sigma,” vertical coordinate similar to that used in many operational and research models. In the nonhydrostatic MM5, the sigma levels are define d according to the initial hydrostaticallybalanced reference state so that the sigma le vels are also time-invariant. The gridded meteorological fields produced by MM5 ar e directly compatible with the input requirements of ‘one atmosphere’ air-quality models using this coordinate. MM5 fields can be easily used in other re gional air quality mode ls with different coordinate systems by performing a vertical inte rpolation, followed by a mass-conservation re-adjustment.

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

Initial and lateral boundary condi tions 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 Cressmanbased technique is used to analyze standard surface and radiosonde observations, using the National Meteorological Center's spectral analysis, as a first guess. The lateral boundary data are introduced us ing a relaxation technique a pplied in the outermost five rows and columns of the coarsest grid domain.

MM5 modeling system in regulatory air quali ty 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 pr ognostic models, such as the Regional Atmospheric Modeling System (RAMS) a nd the Systems Applic ation International Mesoscale Model. The MM5 enjoys a far richer application hi story in regulatory modeling studies compared with RAMS and ot her models. Furthermore, in evaluations of these models in over 60 recent regional scal e air quality applica tion 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 de monstration, as well as a more detailed description of the MM5 model, can be f ound in the Modeling Pr otocol (Appendix C).

5 0 B 5.2.3 Selection of Emissions Processing System

7 3 B U 5.2.3.1 Criteria
The principal criterion for an emissions proces sing system is that it accurately prepares emissions files in a format suitable for the photochemical grid model being used. The39 following list includes clarification of this cr iterion and additional desirable criteria for effective use of the system.

• File System Compatibility with the I/O API,

• File Portability,

• Ability to grid emissions on a Lambert Conformal projection,

• Report Capability,

• Graphical Analysis Capability,

• MOBILE6 Mobile Source Emissions,

• Biogenic Emissions Inventory System version 2 (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, and

• Expandable to support other species and mechanisms.

7 4 B U 5.2.3.2 Overview of SMOKE

The SMOKE Emissions Processing System Pr ototype 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 “f irst principles.” This means that, with the exception of mobile and biogenic sources , its purpose is to provide an efficient, modern tool for converting emissions invent ory data into the formatted emission files required by an air quality simulation model. For mobile sources, SMOKE actually simulates emissions rates based on input m obile-source activity data, emission factors and outputs from transportation travel-demand models.

SMOKE was originally designed to allow emi ssions data processing methods to utilize emergent high-performance-computing as app lied to sparse-matrix algorithms. Indeed, SMOKE is the fastest emissions processing to ol currently available to the air quality modeling community. The sparse matrix a pproach utilized throughout SMOKE permits both rapid and flexible proces sing of emissions data. The processing is rapid because SMOKE utilizes a series of matr ix calculations instead of less efficient algorithms used in previous systems. The processing is flexib le because the processing steps of temporal projection, controls, chemical speciation, temporal allocation , and spatial allocation have been separated into independent operations wh erever possible. The results from these steps are merged together at a final stage of processing.

SMOKE contains a number of ma jor features that make it an attractive component of the modeling system. The model supports a variet y of input formats from other emissions40 processing systems and models. It supports both gridded and county total land use schemes for biogenic emissions modeling. SMOKE can accommodate emissions files from up to 10 countries and any pollutant can be processed by the system.

For additional information about the SMOKE model please refer to the Modeling Protocol (Appendix C).

5 1 B 5.3 Model Year Selection

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 ch anges in air quality in response to changes in emissions. The year 2002 was selected as the base year for several reasons.

The EPA’s April 2007 Guidance on the use of Models and Other Analyses for Demonstrating Attainment of Air Qua lity 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 attaini ng the regional haze air quality goals. The EPA recommends that episode selection derive from three principal criteria:

• Simulate a variety of me teorological conditions;

• Model time periods in which observed concen trations are close to the appropriate baseline design value or visibility impairment;

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

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

For regional haze modeling, the guidance goes fu rther by suggesting that the preferred approach is to model a full, representative year. Moreover, the RRF values should be based on model results averaged over the 20 pe rcent worst and 20 percent best visibility days determined for each Class I area based on monitoring data from the 2000 – 2004 baseline period.

The EPA also lists several other consideratio ns to bear in mind wh en choosing potential regional haze episodes including: (a) choose periods that have al ready been modeled, (b) choose periods that are drawn from th e 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 selecti on criteria as possible in the maximum number of nonattainment or Class I ar eas as possible. Finally, th e EPA explicitly recommended in its 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 pr eferable, representative year for regional haze modeling. These steps include the following:41 U Representativeness of Meteorological Conditions:

U The VISTAS meteorological contractor (BAMS) identified important meteorological characteristics and data sets in the VISTAS region directly releva nt to the evaluation of candidate annual modeling episodes.

U Initial Episode Typing:

U 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 Classificati on and Regression Tree Analyses to evaluate visibility conditions fo r 2000, 2001, and 2002, the candidate modeling years. The year 2002 was found to be repr esentative of conditions in the other two years. Subsequently, these analyses were repeated with the meteorological and air quality monitoring data for 2000 to 2004 to evaluate how well the 2002 modeling year represented the full 2000-2004 baseline period. This analysis confirmed that visibility and PM 2.5 mass in 2002 were representative of the fiveyear baseline period for the VISTAS Class I areas. This analysis is discussed in more detail in the project report in Appendix C.

U Data Availability:

U In parallel with the CART an alysis, episode characterization analyses, collaborative inve stigations by VISTAS states intensively studied the availability of PM 2.5 , meteorological, and emissions data and representativeness of alternative Baseline modeling pe riods from a regulatory standpoint. Additionally, 2002 was the year that the EPA was requi ring states to provide emissions inventory data for the Comp rehensive Emissions Reporting Rule, it made sense to use 2002 as the modeling year to take advantage of the 2002 inventory.

U Years to be used by other RPOs:

U VISTAS also considered what years other RPO would be modeling, and several had alr eady 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 Janua ry through 31 December) as the most current, representative, and pragmatic choice for VI STAS regional haze modeling. All of the EPA criteria for regional haze episode selection were directly considered in this process together with many other considerations (e.g., timing of new emissions or aerometric data deliveries by the EPA or the states to the modeling teams).

2 7 B 5.4 Modeling Domains

5 2 B 5.4.1 Horizontal Modeling Domain

The CMAQ model was run in one-way nested gr id mode. This allowed the larger outer domains to feed concentration data to the inner nested domain. One-way nesting is believed to be appropriate fo r the generally stagnant cond itions experienced during South42 Carolina ozone episodes. Two-way nesting was not considered due to numerical and computational uncertainty asso ciated with the technique.

The horizontal coarse grid modeling domain boundaries were determined through a national effort to develop a common grid projection and b oundary. Since this national modeling domain was used in the VISTAS regional haze modeling, it was used for the attainment demonstration as well. A smalle r 12-km grid modeling domain was selected in an attempt to balance location of areas of interest, such as ozone and fine particulate matter nonattainment areas, as well as Class I Federal 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 cove rs the continental U.S. This domain was used as the outer grid domain for MM5 m odeling with the CMAQ domain nested within the MM5 domain. Figure 2.4.1-1 shows the MM5 horizontal domain as the outer most, blue grid with the CMAQ 36-km doma in nested in the MM5 domain.

Figure 5.4.1 - 1 The MM5 Horizontal Domain is the outermost blue grid, with the CMAQ 36-km Domain nested in the MM5 Domain

To achieve finer spatial resolution in th e 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. Th is is the modeling domain upon which the reasonable progress goals will be assessed.43 Figure 5.4.1 - 2 detailed view of the 12-km grid over the VISTAS region

5 3 B 5.4.2 Vertical Modeling Domain

The CMAQ vertical structure is primarily defined by the vert ical grid used in the MM5 modeling. The MM5 model employed a terrain following coordinate system defined by pressure, using 34 layers that extend from th e surface to the 100 mb. A layer-averaging scheme was used to generate 19 vertical layers for CMAQ. The effects of layer averaging were evaluated in conjunction w ith the VISTAS modeling effort and were found to have a relatively mi nor effect on the model perf ormance metrics when both the 34 layer and a 19 layer CMAQ models were compared to ambient monitoring data.44 5 B 

### 6.0 MODEL PERFORMANCE EVALUATION

The initial modeling effort focused on evalua ting previous regional air quality modeling applications and testing candidate model c onfigurations for the SMOKE emissions and CMAQ model for the VISTAS’ 36-km and 12- km modeling domains. This effort resulted in a report recommending the model configuration for the annual emissions and air quality modeling, which is included as pa rt of the VISTAS Emi ssions and Air Quality Modeling Protocol. The evaluation of the me teorological modeling configuration can be found in Appendix F (Chapter1), with a summ ary of the final meteorological and air quality modeling configurati on in the modeling protocol contained in Appendix D.4.c and Appendix C, respectively.

Air quality model performance for the 2002 mo deling year was initially tested in 2004 using an early version of the VISTAS emi ssions inventory. In keeping with the oneatmosphere objective of the CMAQ mode ling platform, model performance was evaluated compared to measured ozone, fine particles, and acid deposition in the Air Quality System (AQS), IMPROVE, Speciated Trends Network (STN), Southeastern Aerosol Research and Characterization (SEARCH), National Acid Deposition Program (NADP) and Clean Air Status and Trends Network (CASTNet) monitoring networks (Figure 6.0-1). A detailed examination of the results was published in 2005 in the Journal of Air and Waste Management (see A ppendix B.3) as well as being summarized in Appendix B.1.45 Figure 6.0 - 1 Monitoring Networks used for VISTAS 2002 Model Performance Evaluation

2 8 B 6.1 Modeling Performance Goals, Criteria, and Results

In 2004 VISTAS established model performan ce goals and criteria for components of fine particle mass (Table 6.1) based on prev ious model performance for ozone and fine particles. EPA modeling guidance for fine pa rticulate matter at the time noted that PM models may not be able to achieve the sa me level of performance as ozone models. VISTAS’ evaluation considered several statis tical performance measures and displays. Fractional bias and fractional gr oss error were selected as the most appropriate metrics to summarize model performance; other metrics we re also calculated and included in the full model performance evaluation (Appendix F, Chapter 3).

Table 6.1 Model Performance Goals & Criteri a for Components of Fine Particle Mass

Fractional Bias Fractional Error Comment46 U <

U 15%

U <

U 35% Goal for PM model performance based on ozone model performance, considered excellent performance

U <

U 30%

U <

U 50% Goal for PM model performance, considered good performance

U <

U 60%

U <

U 75% Criteria for PM model performance, considered average performance. Exceeding this level of performance indicates fundamental concerns with the modeling system and triggered diagnostic evaluation.

Several graphic displays of model perf ormance were prepared including:

1. Scatter plots of predicted and observed c oncentrations and deposition by species, monitoring network, and month; 2. Time series plots of predicted and obs erved concentrations and deposition by species, monitoring site, and month; 3. Spatially averaged time series plots; 4. Time series plots of monthly fractional bias and error for a species, region, and network;
5. Performance goal plots (“soccer plots” ) that summarize mode l performance by species, region, season; and 6. Concentration performance plots (“bugle pl ots”) 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 relative ly new tools in model performance evaluations, and have recently been included as model performance evaluation displays in the EPA’s modeling guidance for Ozone, PM 2.5 , and Regional Haze (2007). Both “soccer plots” and “bugle plots” allo w for convenient way to examine model performance with respect to se t goals and criteria. The bugle plots have the added benefit of adjusting the goals and criteria to consider the concentration of the species. Analysis of “bugle plots” generall y suggests that greater emphasis should be placed on performance of those components with th e 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 sma ller contributions to total PM mass (e.g., elemental carbon, nitrate, and soil).

2 9 B 6.2 VISTAS Domain-Wide Performance

Further discussion of model performance in this document will focus on the comparison of observational data from the IMPROVE moni tors and model output data from the 2002 VISTAS BaseG2-Actual annual air quality m odeling. Focus is limited to the IMPROVE monitoring network as these site s are the locations used in projecting attainment visibility improvement goals in the Class I areas.47 The evaluation will primarily focus on the air quality model’s performance with respect to individual components of fine particulate matter (PM 2.5 ), as good model performance of the component species will dictate good m odel performance of to tal or reconstituted fine particulate matter. Model performance of the total fine particulate matter and the resulting total light extinction will also be provided as a means to discuss the overall model performance for this Implementation Plan.

In this analysis, mean fractional bias (error) is used in lieu of mean bias (error) to prevent low observations and model predictions from sk ewing the metrics. A full list of model performance statistics is found in Appendix F (Chapter 3). 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 mo st significant light scattering component species (Sulfate, Nitrate, and Organic Carbon) for the 20 percen t best days and 20 percent worst days. Plots for individual IMPROVE mo nitors associated with Cape Romain are included in Appendix F (Chapter 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, wi th 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 pl ots emphasize poorer nitrate performance in the summer (does not even appear on the pl ots 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 in to performance criteria , nitrate performance improves with respect to MFB and MFE (Figures 6.2-4 and 6.2-5)

Additionally, performance asse ssed 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 acce ptable for use in modeling projec tion for ozone, particulate matter, and regional haze.48 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

Figure 6.2-1: Soccer plot depicting both th e mean fractional error and fractional bias for component concentration for all VI STAS 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 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

Goal

Figure 6.2-2: A zoomed-in 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).49 Figure 6.2-3: Seasonal soccer plots based on Base G1 results for all VISTAS IMPROVE monitors.

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

Goal

Figure 6.2-4: Bugle plot of the mean fracti on bias for particulate matter and its component concentrations fo r 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).50 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

(-) Criteria

Figure 6.2-5: Bugle plot of mean fracti on error for partic ulate matter and its component species for all VISTAS sites ba sed on Base G2 results. Each point represents a monthly mean fraction bi as value as compared to the model performance criteria (red lines) and modeling performance goals (green lines).

3 0 B 6.3 South Carolina Class I Area Performance

The following section provides stacked bar ch arts comparing observed fine particulate matter composition and modeled fine particul ate matter composition. The charts display both the 20 percent best days a nd the 20 percent worst days.

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

• Yellow = light extinction due to sulfates (b ext SO4)

• Red = light extinction due to nitrates (b ext NO3)

• Green = light extinction due to organic carbon (b ext OC)

• Black = light extinction due to elemental (b ext EC)

• Brown = light extinction due to soil (b ext Soil)

• Grey = light extinction due to coarse mass (b ext CM)

The components are presented in the same or der for both the observe d (left hand bar) and modeled bar (right hand bar), so it easy to identify days when the prediction light extinction for the component differs from th e observed. The total height of the bar provides the total reconstructed partic ulate matter light extinction value.

A cursory view of the stacked bar charts reiter ates that sulfates are a large contributor to light extinction at Cape Romain on both the 20 percent best days and the 20 percent worst days.51 The bar charts also suggest that organic carbon and nitrates are important on the 20 percent best days at Cape Romain. The bar charts for the 20 percent best reiterate the general under prediction seen in previous sections. The unde r prediction of sulfate on the 20 percent best days appears to be the crux of the under prediction, with the under prediction of nitrate factoring in heavily on some days.

Comparing the 20 percent best day chart to the 20 percent worst days chart, illustrates that the various components of particle pollution play a more prominent role in the 20 percent best days than with the 20 percent worst days. Also, the species make-up on the 20 percent best days varies more widely co mpared to the 20 percent worst days. This suggests accurately modeling each species is especially important on the 20 percent best days.

With the bar chart for the 20 percent wors t days, you can see model performance does improve across the sites. Light extinction due to sulfate prediction is better, but it still falls short on some days. Light extincti on due to organic carbon also becomes more important to total light extinction. Much lik e the sulfate component, the organic carbon component accuracy has improved performan ce over the 20 percent best day series, though some days are still under predicted.

Overall, SC DHEC found model performance to fall within acceptable limits for model performance. SC DHEC further asserts th e one atmosphere modeling performed by the VISTAS contractors is represen tative of conditions in the southeastern states and is applicable for use in attainment demonstrations.

Best 20% Observed vs 2002gt2a at Cape Romain Observation (left) vs Modeled Base G2a (right)

0 10 20 30 40 50 60 70

14 23 62 65 68 92 104 107 125 161 170 173 176 191 227 242 245 248 263 266 269 284 353 _ _ Avg Julian Day in Best 20% groupbEXT (1/Mm) bCM
bSOIL
bEC
bOC
bNO3
bSO452 Worst 20% Observed vs. 2002gt2a at Cape Romain Observations (left) vs Modeled Base G2a (right)

0 50 100 150 200 250 300

17 29 44 47 53 110 122 155 158 182 185 188 197 200 206 212 236 254 257 260 293 296 299 341 _ Avg
Julian Day in Worst 20% groupbEXT (1/Mm) bCM

bSOIL

bEC

bOC

bNO3

bSO4

Figure 6.3 - 1 Stacked Bar Charts for Cape Romain on the 20% Best (top) and 20% Worst (bottom) Days.53 6 B 

### 7.0 LONG TERM STRATEGY FOR SOUTH CAROLINA’S CLASS I

FEDERAL AREA

As stated in section 1.3 a bove, the RHR requires a State’s SIP to establish reasonable progress goals (RPGs) for each Class I Federal ar ea in the State. This first set of RPGs must be met by emission reductions requi red by other CAA programs, by any BART requirements, and by measures in each St ate’s long term strategy covering the period from the present until 2018. Th is section discusses devel opment of South Carolina’s long-term strategy.

3 1 B 7.1 Overview of the Long Term Strategy Development Process

The monitored data and modeling analyses cite d in Sections 2 and 5 above establish that, for the VISTAS region, the key contributors to regional haze in the 2002-2004 baseline timeframe were large stationary sources of sulfur dioxide emissions. Keeping that key conclusion in mind, this section a ddresses the following questions:

a. Assuming only existing and planned emi ssion reductions from other CAA will take place in South Carolina and the VISTAS region, how much visibility improvement, compared to the glidepat h, would we expect to see at Cape Romain between now and 2018?

b. If additional emission reductions were needed, from what pollutants and source categories would the greatest visi bility benefits be realized at Cape Romain between now and 2018, and what visibility improvement will those emission controls achie ve at Cape Romain?

c. After accounting for emission reductions from other CAA programs and from BART, from what pollutant source categories could further reductions achieve the most visibility improvement at Cape Romain?

d. What types of emission sources do we find in Cape Romain’s AOI?

e. Which specific individual sources in those geographic locations have the greatest visibility impacts at Cape Romain?

f. What additional emission controls ach ieve reasonable progress for those specific sources?

g. Would additional emission controls for specific sources identified as contributing to visibility impairment at Cape Romain result in further reasonable progress?54 h. Given the additional emission reductions expected from reasonable progress and BART, how much additional visibi lity improvement, compared to the glidepath, will we expect to see at Cape Romain between now and 2018?

3 2 B 7.2 Expected Visibility Results in 2018 for the Cape Romain Class I Federal Area under existing and planned emission controls (Base G2 Inventory)

Significant CAA emission control programs a nd consent decrees are being implemented, or will be implemented between the ba seline period and 2018. These programs and decrees are described in more detail below and are projected to result in substantial visibility improvement at Cape Romain.

5 4 B 7.2.1 Federal and State Control Requirements

U Clean Air Interstate Rule (CAIR) + Clean Air Mercury Rule (CAMR).

U CAIR will permanently cap emissions of sulfur dioxide (SO 2 ) and nitrogen oxides (NO X ) in the eastern U.S. When fully implemented, CAIR will reduce SO 2 emissions in these states by over 70 percent and NO X emissions by over 60 percent from 2003 levels. Appendix M illustrates what sources within South Ca rolina’s AOI are subject to CAIR and those identified source’s planned emission reduc tions. The Clean Air Mercury Rule will reduce utility emissions of mercury from 48 tons a year to 15 tons, a reduction of nearly 70 percent. While mercury is not a visibi lity-impairing pollutant , CAMR establishes standards of performance that limit mercury emissions from new and existing coal-fired power plants and creates a market-based cap-and-trade program that will reduce nationwide utility emissions of mercury whil e simultaneously reduci ng sulfur dioxide SO 2 and nitrogen oxides NO X emissions.

U NO X SIP Call

U . Phase I of the NO X SIP call applies to certain EGUs and large non-EGUs, including large industrial boilers /turbines and cement kilns. States in the VISTAS region affected by the NO X SIP call have developed rules for the control of NO X emissions that have been approved by the EPA. This rule results in a 68 percent reduction in NO X emissions from large stationary combusti on sources by 2006. For this analysis, we capped the emissions for NO X SIP call-affected sources at 2007 levels and carried forward the capped levels for the 200 9/2018 future year inventories.

U NC Clean Smokestacks Act

U . Under the act, which was en acted in 2002, coal-fired power plants in North Carolina must achieve a 77-percent cut in nitrogen oxide (NO X ) emissions by 2009 and a 73-percent cut in sulfur dioxide (SO 2 ) emissions by 2013.

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

U .

• A 2004 EPA consent decree calls for Santee Cooper to install and commence operation of continuous emission control equipment for PM/SO 2 /NO X emissions; comply with system-wide annual PM/SO 2 /NO X emissions limits; agree not to buy, sell, or trade PM/SO 2 /NO X allowances allocated to Santee Cooper System as55 a result of said agreement; and to comply with emission unit limits of said agreement.

• Under a settlement agreement, Tampa El ectric will install permanent emissionscontrol equipment to meet stringent pollution limits; implement a series of interim pollution-reduction measures to reduce emissions while the permanent controls are designed and in stalled; and retire polluti on emission allowances that Tampa Electric or others could use/sell to others to emit additional pollution into the environment.

• Virginia Electric and Power Co. (VEPCO) agreed to eliminate 237,000 tons of sulfur dioxide (SO 2 ) and nitrogen oxides (NO X ) emissions each year from eight coal-fired electricity generating plants in Virginia and West Virginia between now and 2013.

• A 2002 agreement calls for Gulf Power to upgrade its op eration to cut NO X emission rates by 61 percent at its Cr ist generating plant by 2007, with major reductions beginning in early 2005. The Cr ist plant is a significant source of NO X emissions in the Pensacola area.

• A July 2, 2007, consent decree between the EPA and East Kentucky Power Cooperative (EKPC) requires the utility to reduce its NO X and SO 2 emissions by installing/continuously operating selec tive catalytic reduction (SCR) technology and flue gas desulfurization equipment (FGD). According to the EPA, the plant’s total emissions will drop from between 50 and 75 percent from 2005 levels. As with all federal consent decrees, EKPC is precluded from using reductions required under other programs, su ch as the Clean Air Interstate Rule.

U One-hour ozone SIPs (Atlanta / Bi rmingham / Northern Kentucky).

U The purpose of these respective SIPs is to grant final approval to the request for redesignation of the 1-hour ozone NAAQS nonattainment area to attainment. New SIPs have been submitted to the EPA for public comment and approval. These SIPs require NO X reductions from specific coal-fired power and address transporta tion plans in these cities.

U NO x RACT in 8-hour Nonattainment Area SIPs

U . NC DENR applied NO X RACT to two facilities located in the Charlotte nonattainment area: Philip Morris USA and Norandal USA.

U Heavy Duty Diesel (2007) Engine Standard

U . A PM emissions standard for new heavy-duty engines of 0.01 grams per brake- horsepower-hour (g/bhp-hr) is to take full effect for 2007 model year diesel engines. This standard also requires reductions in NO X and non-methane hydrocarbons (NMHC) of 0.20 g/bhp-hr and 0.14 g/ bhp-hr, respectively. These NO X and NMHC standards for diesel engines will be phased in together between 2007 and 2010. Also, sulfur in diesel fuel must be lowered to enable modern pollution-control technology to be effective on these diesel-powered trucks and buses. The EPA will require a 97 percent reduction in the sulfur content of highway diesel fuel from its current level of 500 parts per million (low sulfur diesel, or LSD) to 15 parts per million (ult ra-low sulfur diesel, or ULSD).56 U Tier 2 Tailpipe

U . Tier 2 is a fleet averaging pr ogram, modeled after the California LEV II standards. Manufacturers can produ ce vehicles with emissions ranging from relatively dirty to zero, but the mix of vehicles a manufact urer sells each year must have average NO X emissions below a specified value

.

U Large Spark Ignition and Recreational Vehicle Rule

U . EPA has adopted new standards for emission of oxides of nitrogen (NO X ), hydrocarbons (HC), and carbon monoxide (CO) from several groups of previously unregulated non-road engines. Included in these sources are the Large Industrial Spark-Ignition Engines and Recreational Vehicles. Spark-ignition non-road engine s 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 trans port vehicles, and a variety of farm and construction applic ations. Recreational Vehicles include snowmobiles, off-highway motorcycles, and al l-terrain-vehicles. Rules were initiated in 2004 and are fully phased in by 2012.

U Non-road Diesel Rule

U . This rule sets standards that will dramatically reduce emissions by over 90 percent from non-road diesel equipment and dramatically reduce sulfur levels by 99 percent from current levels in non-road diesel fuel starting in 2007. This rule will apply to most non-road diesel fuel in 2010 and to fuel in locomotives and marine vessels in 2012.

U Industrial Boiler/Process Heater MACT standards

U . The applied MACT control efficiencies were four percent for SO 2 and 40 percent for PM 10 and PM 2.5 . However, as of June 08, 2007, the Industrial Boiler/ Process Heater MACT standard was vacated. A mandate without a stay was i ssued July 30, 2007. As a result, sources that were subject to the rule may need to file applications for permits containing MACT limits derived on a case-by-case basi s within a time specified by the EPA or SC DHEC.

U Combustion Turbine MACT

U . The projection inventor ies do not include the NO X cobenefit effects of the MACT regulations for Gas Turbines or sta tionary Reciprocating Internal Combustion Engines, which the EPA estimates to be small compared to the overall inventory.

U VOC 2-, 4-, 7-, and 10-year MACT Standards

U . Various point source MACTs and associated emission reductions were im plemented. We did not count reductions occurring before 2002.

U Smoke Management Plan (SMP)

U . The Grand Canyon Visibility Transport Commission (GCVTC) Report notes that “emissions from fire (wildfire and prescribed fire) are an important episodic c ontributor to visibility-impairing aerosols.” As such, Section 51.308(d)(3)(v) of the RHR requires that each state consider and address the effects of smoke on visibility. The SC Fo restry Commission (“SCFC”) has developed (and recently revised as of August 2006) the Smoke Management57 Guideline for Vegetative Debris Burning Operations , which serves to regulate vegetative debris burning for forestry, agri culture, and wildlife purposes in the State of SC. SC DHEC’s Bureau of Air Quality has developed a state air pollution control regulation (R. 61-62.2, Prohibition of Open Burning ) that prohibits: open burning of any/all household garbage, open burning for th e purpose of land clearing or right of way maintenance in areas other than pre dominantly residential, and open burning of residential construction wa ste from building and cons truction operations unless specific conditions are met.

When weighed together, these documents address all sources of fire used for land management purposes within South Carolin a and effectively minimize visibility impacts while recognizing the important ecological role that fires can and do play. Appendix H.1 includes a memorandum of understanding between the SCFC and SC DHEC and represents the State’s collective commitment to develop a comprehensive approach to establish and mainta in a smoke management plan.

U Early Action Compacts (EAC).

U Forty-five of 46 counties in South Carolina along with the Department and EPA Region IV signed EACs in December 2002. By signing the EAC, EPA agreed to defer th e effective date of the nonattainment designation for participating ar eas. The Department has b een working with the EPA, state and local governments, industry, envi ronmental groups, a nd other interested parties to develop a strategy to reduce th e pollution that creates ground-level ozone. In March 2004, participating EAC areas in South Carolina, submitted local Early Action Plans to include emi ssion reduction strategies.

5 B 7.2.2 Additional State programs to reduce emissions

In addition to considering emission reductio ns due to ongoing air pollution programs as required under the RHR Section 3 08(d)(3)(v)(A), states are also required to consider the measures to mitigate the impacts of cons truction activities [40 CFR 51.308(d)(3)(v)(B)] and agricultural and forestry smoke mana gement [40 CFR 51.308(d)(3)(v)(E)]. These measures are discussed in more detail in Appendix H.

5 6 B 7.2.3 Projected 2009 and 2018 Base G2 Emissions Inventories

The Base G inventories for 2009 and 2018 acc ount for (post-2002) emission reductions from promulgated and proposed federal, state, local, and site-specifi c control programs as of July 1, 2004. In general, emissions inventories were developed for 2009 and 2018 using current control information in South Carolina: the 2002 inventory was grown using EGAS6, MOBILE6, and IPM for EGUs.

For EGUs, IPM results were adjusted base d on State and local (S /L) agency knowledge of planned emission controls at specific E GUs. For non-EGUs, we used recently updated growth and control data consistent with the data used in EPA’s CAIR analyses supplemented by S/L agency data and updated fo recasts from the Department of Energy (DOE).58 Area source controls were estimated using know n state-level Stage I controls on gasoline dispensing facilities and open burning estimates , as well as controls used to project emissions for EPA’s Heavy Duty Diesel rulemaking and for the CAIR rulemaking.

Mobile source controls included local c ontrols underlying the 2002 baseline inventory (vehicle emission inspection, Stage II vapor recovery, anti-tamperi ng, etc.) with changes based on specific State input. Non-road c ontrol data and proj ections for 1996, 2010, 2015, and 2020 were obtained from the EPA' s CAIR Technical Support Document, and straight line projections were us ed to estimate 2009 and 2018 levels.

The following bar charts show expected decreases in emissions of SO 2 and NO X across the VISTAS states from 2002 through 2018. (Sim ilar charts for other visibility impairing pollutants are contained in Appendix H.5). Note that for SO 2 in particular, which is the largest contributor to haze in the VISTAS region, emissions are expected to decrease dramatically between 2002 and 2018. However, EGU emissions will remain the largest contributor, 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 states59 Figure 7.2.3 - 2 Base G2 Annual NOx Emissions in 2002, 2009, and 2018 in the VISTAS states

8 1 B U Summary Final Base G2 Emissi ons Inventories for 2009 and 2018

Table 7.2.3-1 is a summary of the 2009 Base G2emission inventory. The complete inventory and discussion of the methodology is contained in Appendi x D.2 (a, b, and c).

Table 7.2.3 - 1 2009 Emission Inventory Summary for SC
VOC tpy NO X tpy PM 2.5 tpy PM 10 tpy NH 3 tpy SO 2 tpy Point 36,325 86,934 25,432 36,007 1,921 172,933 Area 162,152 25,259 67,568 307,452 31,594 14,410 On-Road Mobile 71,781 91,471 1,834 5,510 5,466 713 Off-Road Mobile 43,061 43,235 3,294 3,471 36 1,701 Biogenics 901,638 8,770 (NO) 0 0 0 0 TOTAL 1,214,957 255,669 98,128 352,440 39,017 189,757

*Most emissions values were taken from

U Documentation of the Revised 2002 Base Year, Revised 2018, and Initial 2009 Emission Inventories for VISTAS v.11,

U prepared by the VISTAS contractor, MACTEC. The exception is Biogenic emissions, which were developed by EPA for the 2002 NEI. Biogenic emissions were not grown for future years and remained at the 2002 levels.

Table 7.2.3-2 is a summary of the 2018 Base G2 emission inventory. The complete inventory and discussion of the methodology is contained in Appendi x D. 2 (a, b, and c).60 Table 7.2.3 - 2 2018 Emissions Inventory Summary for SC

VOC tpy NO X tpy PM 2.5 tpy PM 10 tpy NH 3 tpy SO 2 tpy Point 44,562 95,477 36,118 53,054 2,396 146,851 Area 177,273 26,491 70,274 333,404 34,535 14,816 On-Road Mobile 41,866 39,348 988 3,994 5,878 584 Off-Road Mobile 36,131 31,758 2,474 2,617 41 1,198 Biogenics 901,638 8,770 (NO) 0 0 0 0 TOTAL 1,201,470 201,844 109,854 393,069 42,850 163,449

*Most emissions values were taken from

U Documentation of the Revised 2002 Base Year, Revised 2018, and Initial 2009 Emission Inventories for VISTAS v.11

U prepared by the VISTAS contractor, MACTEC. The exception is Biogenic emissions which were developed by EPA for the 2002 NEI. Biogenic emissions were not grown for future years and remained at the 2002 levels.

5 7 B 7.2.4 Model Results for the 2018 Base G2 Inventory Compared to the Uniform Rate of Progress Glidepath for SC Class I Federal Area

Using 2000 - 2004 IMPROVE monitoring data, the dv values for the 20 percent best days in each year are averaged together, producing a single average dv value for the best days. Similarly, the dv values for the 20 percent worst days in each year are averaged together, producing a single average dv va lue for the worst days.

Figure 7.2.4-1 illustrates the predicted visi bility improvement by 2018 resulting from the Base G2 inventory that represents impl ementation of existing federal and state regulations, compared to the uniform rate of progress glidepath for Cape Romain. The red lines show the target natural condition in 2064 for the 20 percent worst days, and the incremental dv changes resulting from a unifo rm rate of progress between current and natural conditions in 2064, for average 20 percent worst visibility days in the 2000-2004 baseline period. The purple lines show the improvement expected from existing and planned emission controls during the peri od of the first long term strategy.61 

### Uniform Rate of Reasona ble Progress Glide Path Cape Romain - 20% Data Days

26.48 25.52

23.12

20.73

18.33

15.94

13.54 12.10 22.72

0 5 10 15 20 25 30 35

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

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

Figure 7.2.4 - 1 CMAQ 2018 Base G2 Results Compared to the Uniform Rate of Progress Glidepath at Cape Romain

Note that for the Class I Federal area in SC, vi sibility is expected to improve to levels better than the glidepath by 2018 based solely on reductions from existing and planned emission controls from other CAA programs.

Figure 7.2.4-2 illustrates that visibility impr ovements at all the VI STAS mountain Class I Federal areas and most of the coastal Class I Federal areas are projected to be better than the area’s respective uniform rates of progre ss. In Figure 7.2.4-2, the percentage of the target reduction achieved for Cape Roma in using the new IMPROVE algorithm is approximately 110 percent. This means that th e rate of improvement expected from other CAA programs is at least 10 percent greater th an the visibility improvement that would be achieved in 2018 with Cape Roma in’s uniform rate of progress.62 Figure 7.2.4 - 2 Projected Visibility Improve ment on 20% Worst Visibility Days at VISTAS and Neighboring Class I Fed eral Areas for the 2018 Base G2a Run

In addition to improving visibility on the 20 pe rcent worst visibility days, states are also required to protect visibility on the 20 percent best days at th e Class I Federal areas. As illustrated in Figure 7.2.4-3, visibility on the 20 pe rcent best days is projected to improve in 2018 at all VISTAS Class I Federal areas as a result of the 2018 Base G2 emissions reductions. In Figure 7.2.4-3, the percentage of the target achieved for Cape Romain is about -10 percent. Zero percent change woul d mean no change in visibility; while -10 percent means that a 10 percent improveme nt in visibility has occurred. 2018 Base G2a CMAQ Visibility Project ion for 20% Worst Days – 36 kmPercent of target reduction

VISTA nonVISTAS

0 20 40 60 80 100 120 140 160 180 200COHUDOSOGRSMJARILIGOMACASHENSHROSIPSCHASEVEROKEFROMASAMASWANBRETBRIGCACRHEGLMINGUPBU CMAQ New IMPROVE Algorithm
CMAQ Old IMPROVE Algorithm63 2018 Base G2a CMAQ Visibility Projection for 20% Best Days

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%COHUDOSOGRSMJARILIGOMACASHENSHROSIPSCHASEVEROKEFROMASAMASWANBRETBRIGCACRHEGLMINGUPBUPercent of target reduction achieved

VISTAS non-VISTAS CMAQ New IMPROVE Algorithm

CMAQ Old IMPROVE Algorithm

Figure 7.2.4 - 3 Projected Visibility Impr ovements on 20% Best Days at VISTAS and Neighboring Class I Federal A reas for the 2018 Base G2 CMAQ Run

3 B 7.3 Relative Contributions from Internationa l Emissions to Visibi lity Impairment in

2018 at VISTAS Class I areas

Emissions from Mexico, Canada, Central Amer ica, Asia, and Africa contribute to PM 2.5 loadings and visibility impairment in Class I federal areas in the continental U.S. To evaluate the relative contribution of internationa l emissions to visibility at Class I areas in the southeastern US, VISTAS used a combin ation of modeling results from the GEOSChem global model 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 gr id resolution and a three-hour temporal resolution. Emissions we re based on monthly averages, so the model does not capture the episodic variability in emissions. The GEOS-Chem outputs were used to calculate in itial and boundary portions of Canada and Mexico, so emissions for these countries were included within the na tional CMAQ modeling domain or as part of the boundary conditions outside the national modeling domain, as appropriate. The reliability of these data sets is discussed in Appendix F (Appendix G).

Two complementary methods were used to calculate the impact of international emissions at Class I areas. Because the inte rnational emissions inventory used in GEOSCHEM did not distinguish betw een wildfires and anthropogeni c fires, all international64 fire emissions were assumed to be wildfi re, and the contributions attributable to international anthropogenic fires are underestimated in these projections.

1)

U International emissions as the di fference between two GEOS-Chem runs

U - In the first run, U.S. anthropogenic emissions were removed, and in the second run, U.S. and international anthropogenic emi ssions were removed. The difference is attributable to internat ional anthropogenic emissions , in the absence of US anthropogenic emission (e.g., compared to 2064 levels). Harvard University provided GEOS-Chem results to VISTAS for 2002 international contribution on a 4 x 5 degree grid. Concurrently, Ha rvard modeling for EPRI provided GEOSChem results for 2001 international contribu tion on a 1 x 1 degree grid scale.

2)

U International emissions as the difference between two CMAQ 36-km simulations, both using 2018 Base F em issions and boundary conditions from GEOS-Chem

U - In the first CMAQ run, all global natural and anthropogenic emissions in 2018 are active. In th e second CMAQ run, only global (U.S. and international) natural emissions are active. Here, the impacts of international emissions are compared against 2018 c onditions rather than natural background conditions.

VISTAS has compared its results to resu lts from other RPOs on the impact of international emissions and boundary condition s at Class I Federal areas in 2002; the results were similar.

As illustrated in Figure 7.3-1, for annual averag e contribution to sulfate at VISTAS and neighboring Class I Federal areas, the estimated international contributions are higher at Class I Federal areas near the Canadian and Mexican borders and along the eastern coast. The estimated international contribution is higher using CMAQ than in the GEOS-Chem runs because the grid scale is finer (more accurate dispersion of emissions) and because the background atmosphere includes loadings from current U.S. anthropogenic emissions (greater photochemical activity). Similar charts for n itrate and organic carbon mass, for impacts of 20 percent worst visibility days, and for impacts of inte rnational emissions on calculated light extinction, are includ ed in Appendix F (Chapter 5).65 Figure 7.3 - 1 Estimated International Em issions Contributions to Sulfate at VISTAS and Neighboring Class I Federal Areas.

Figure 7.3-2 illustrates the effect of reduc ing the incremental contribution due to international emissions when considering the visibility improvement in 2018 compared to the uniform rate of progress at Cape Romai n. The open triangle represents the estimate with all emissions and the diamond represen ts estimates when contributions from international emissions are removed.66 

### Uniform Rate of Reasona ble Progress Glide Path Cape Romain - 20% Data Days

26.48 25.52

23.12

20.73

18.33

15.94

13.54 12.10 22.72 21.41

0 5 10 15 20 25 30 35

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

Glide Path Natural Condition (Worst Days) Observation Method 1 Prediction Method 1 Prediction w/o Intl. Trans.

Figure 7.3 - 2 International Transport Effect on Uniform Rate of Progress at Cape Romain

For Cape Romain, accounting for the internat ional contribution does not change the conclusion that reductions of U.S. anthropogeni c emissions will result in greater than the uniform rate of progress by 2018. However, as for other Class I Federal areas such as Breton, LA, and St. Marks, FL, accounting for the incremental contribution from international emissions at Cape Romain does ch ange the rate of improvement projected in 2018 from slightly less than the uniform rate of progress to slightly better than the uniform rate of progress. In the future, as the atmosphere becomes closer to natural background conditions, the incremental contri bution from international emissions will become more important. The information is included in this SIP documentation to provide reference for future asse ssments of reasonable progress. In short, in this initial LTS for Cape Romain, the amount of vi sibility improvement projected by 2018 is independent of any contribution to visibility improvement by in ternational emissions.

In Figure 7.3-3, CMAQ projections of contributions from inte rnational emissions to PM mass on 20 percent worst visibility days in 2002 at Cape Romain, are compared to measured PM components at the site on thos e days. The stacked bar chart separates domestic and international attributes of mass on the 20 percent worst days. Attribution of international contribution at Cape Romain ranges between 0 and 18 percent on the worst days.67 ROMA1 Domestic and International Aerosol Mass by Species 20% Worst Days (2002)

0 5 10 15 20 25 30 35 40DomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternationalDomesticInternational

1/17 1/29 2/13 2/16 2/22 4/20 5/2 6/4 6/7 7/1 7/4 7/7 7/16 7/19 7/25 7/31 8/24 9/11 9/14 9/17 10/20 10/23 10/26 12/7Mass (μg/m3)

Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt

Figure 7.3 - 3 Measured PM Component Con centrations on 20% Worst Visibility Days in 2002 at Cape Romain

3 4 B 7.4 Relative Contributions to Visibility Impairment: Pollutants, Source Categories, and Geographic Areas.

An important step towards identifying future reasonable progress measures is to identify the key pollutants contributing to visibility impairment at Cape Romain. To understand the relative benefit of further reducing emissions from differen t pollutants, source sectors, and geographic areas, VISTAS engaged the Ge orgia Institute of Technology to perform emission sensitivity model runs using CMAQ. Emission sensitivities were initially performed for three episodes representing wi nter and summer conditions: January 2002, July 2001, and July 2002. Thes e runs used the initial 20 18 projections inventory and considered 30 percent reductions from spec ific pollutants, sour ce categories, and geographic areas. Emission sens itivities were repeated us ing the 2009 Base D emissions inventory and two month-long episodes from 2002: June 1 - July 10, and November 19 - December 19. Emissions in 2009 were reduced by 30 percent for each pollutant sensitivity run. The pollutant contributions that were evaluated were:

• SO 2 from EGU sources in each VISTAS stat e, other RPOs in the VISTAS 12-km grid, and Boundary Conditions from outside the 12-km domain.68 • SO 2 from non-EGU point sources in each VISTAS state, other RPOs, and Boundary Conditions.

• NO X from ground level (on-road plus non -road area) sources in each VISTAS state and other RPOs.

• NO X from point (EGU and non-EGU) sources in each VISTAS state and other RPOs.

• NH 3 from all sources in the VISTAS and other RPOs.

• VOCs from anthropogenic and biogenic s ources in the 12-km modeling domain.

• Primary Carbon from all ground level sour ces in each VISTAS state and other RPOs.

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

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

Results are shown below for Cape Romain for the average of the 20 percent worst visibility days.



### Cape Romain, SC (20% Worst Days)

-8.00
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S O2_ E GU

S O2_ n on

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NOx _ G

round NOx _Point NH3 VO C

s

P C _Gro und

P C _P

oi

n t PC _Fi

resΔBext(Mm-1) Bio.

Antro.
BCs

MRPO
M-VU
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VISTAS

WV

VA

TN
SC
NC
MS

KY
GA

FL

AL

Figure 7.4 - 1 CMAQ Projections of Visibi lity Responses on 20% Worst Days at Cape Romain to 30% Reductions from th e 2009 Base D Inventory for VisibilityReducing Pollutants in Different So urce Categories and Geographic Areas69 As Figure 7.4-1 shows, the greatest benefi ts on the 20 percent worst days for Cape Romain are projected to resu lt from further reducing SO 2 from EGUs. Reductions from EGUs in SC would have the greatest benefits ; the contribution from other VISTAS states is relatively small. Additional, smalle r benefits would come from additional SO 2 emission reductions from non-utility, industrial point sources. While determined not to be necessary at this time, SC DHEC will re-examine the need for additional non-EGU controls during the state’s fi ve-year progress report.

As shown in Figure 7.4-1, ammonia (NH 3 ) is a large contributor to visibility impairment on the 20 percent worst days in Cape Romain. As such, some of the worst days at Cape Romain and other coastal sites in VISTAS st ates occur in the winter when ammonium nitrate has a somewhat larger contribution to visibility impairment. Therefore, reducing ammonia emissions would be more bene ficial for reducing ammonium nitrate contributions to visibility impairment in wintertime than further reducing NO X emissions from either ground or point sources.

VOCs in South Carolina originate primarily from biogenic sources (such as vegetative emissions) and also contribute to visibili ty impairment. Controlling anthropogenic sources of VOCs has little, if any, visibility benefit at Cape Romain. Reducing primary carbon from point sources, ground level sources, and fires is projected to have almost no visibility benefit at Cape Romain.

As Figure 7.4-1 also shows, Cape Romain experiences a high degree of impact from boundary conditions coming from the east. This is primarily due to atmospheric conditions that cause SO 2 originating from the eastern coas t of the U.S. to be blown out over the Atlantic Ocean and then re-enter the model domain. Rather than classify these emissions as unique to South Carolina, th e model attributes these emissions as a boundary condition.

Note that these results from emission sensitivity runs are consistent with the conclusions drawn from the 2000-2004 baseline monitoring data. Again, sulfate was found to be the dominant contributor to visibility impair ment on peak summertime days. Wintertime ammonia is an important contributor for site s near the coast. SC DHEC concludes that reducing SO 2 emissions from EGU and non-EGU point sources in the VISTAS states would have the greatest visibi lity benefits for Cape Romai n. Contributions from outside the VISTAS 12-km modeling domain are pa rticularly important to determining contributions to coastal Class I Federal ar eas like Cape Romain. These results are consistent with CMAQ model results, in that contributions from international emissions to visibility at VISTAS Class I Federal areas are closer to the boundaries of the modeling domain. See Appendix F (Chapter 5) for further discussion.70 3 5 B 7.5 Relative Contributions to Visibility Impairment: Geographic Locations of the Largest Emission Sources Contributing to Visibility Impairment in SC Class I

Federal Area

Once the key pollutants and source categories contributing to visibility impairment at each Class I Federal area have been identifi ed, it is necessary to focus on where the greatest contributing sources are located. SO 2 emissions from EGU and non-EGU point sources have already been established as the greatest contributors to visibility impairment. To identify the specific point sources of SO 2 that are likely to be major contributors to visibility impairment at Cape Romain, the next step was to identify the specific geographic areas that most likely infl uence visibility in each Class I Federal area, and then identify the major SO 2 point sources located in t hose geographic areas. An SO 2 AOI was defined for Cape Romain to repres ent the geographic area containing sources that would likely have the greatest impact on visibility and ra nked by their 2018 Base G emissions. This analysis was based on existing data (from 2002) that was then used to determine which sources within the AOI rema in significant in 2018. Since the objective of this analysis is to understand which sources are most important in terms of SO 2 emissions, the worst days were selected based on SO 4 loading. The following sections contain a broad overview of the analytic st eps in the AOI analysis. See Appendix H (Chapter 4) for a more deta iled discussion of each step.

5 8 B 7.5.1 Back Trajectory Analyses

The first step was to generate meteorological back trajectories for IMPROVE monitoring sites in SC and neighboring Class I Federal areas in the baseline timeframe (2000-2004). Back trajectory analyses use interpolated m easured or modeled meteorological fields to estimate the most likely central pa th of air masses that arrive at a receptor at a given time. The method essentially follows a parcel of ai r backwards in hourly steps for a specified length of time. Figure 7.5.1 is an example of a back trajectory analysis for Cape Romain for the 20 percent worst days in 2002.

Trajectories were started at 100 meters above the surface and run backward from the site for 72 hours. These individual back trajecto ries for 20 percent worst days in 2002 were also useful in evaluating model performan ce for individual days at the Class I Federal

area.

5 9 B 7.5.2 Residence Time Plots

The next step was to develop residence time pl ots using five years of back trajectories for the 20 percent worst visibility days do minating the 2000-2004 timeframe. Residence time is the frequency that winds pass over a specific geographic area on the path to a Class I Federal area. As illustrated in Fi gure 7.5.2, winds influenc ing the Cape Romain Class I Federal area on the 20 percent wors t days indicate a north -south gradient of influence along the eastern U.S. coast.71Figure 7.5.1 Example Back Trajectories for 20% Worst Days in 2002 for Cape Romain, SC72 Residence Time for 20% Worst Days in 2000-2004
Cape Romain, SC

Figure 7.5.2 Example Residence Time Plot s for 20% Worst D ays in 2000-2004 for Cape Romain (Based on Traject ories with 100 m Start Height)

6 0 B 7.5.3 SO 2 Areas of Influence

The next step was to develop sulfate extinction-weighted re sidence 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 dominate d by sulfate. Each back trajectory was weighted by sulfate extinction for that da y, allowing for focus on the 20 percent worst days that are influenced by sulfate and pl acing less importance on days influenced by organic carbon or fires. Sulf ate-weighted back trajectories for the 20 percent worst days were combined for five years of data. The resulting sulfat e extinction-weighted residence time plots were used to define the geographic AOI for source of SO 2 emissions. In Figure 7.5.3, the area representing 10 percent or greater reside nce time is outlin ed in red, whereas the gray area represents 5 percent or greater residence time. The VISTAS states focused their analysis on the AOI defined by 5 percent or greater sulfate extinctionweighted residence times.73 

## SO2 Area of Influence for Cape Romain, SC

Green circles indicate 100-km and 200-km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Time > 10% Orange line perimeter indicate Area of Influence with Residence Time > 5%.

Figure 7.5.3 Example SO 2 AOI Plot for Sulfate Extincti on Weighted Residence Time for 20% Worst Days in 2000-2004 for Cape Romain (Based on Trajectories with 100 m Start Height)

6 1 B 7.5.4 Emission Sources Within the SO 2 AOI

Residence time plots were then combined with geographically-gri dded emissions data based on the 2002 baseline and 2018 Base G emissi ons inventories. Pl ots were generated for the AOI defined by trajectories with 100 m and 500 m start heights. As a way of incorporating the effects of transport, depos ition, and chemical transformation of point source emissions along the path of the trajecto ry, this data was we ighted by 1/d, where d was calculated as the distance between grid cell centers in kilometers. The distanceweighted point source SO 2 emissions were then combined with the gridded extinctionweighted back trajectory residence times at a spatial resolution of 36 km.

The final step was to combine the residen ce time plots and gridded emissions data in plots and data sets. Th e 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 are th en normalized by the domain-wide total and74 displayed as a percentage. The analysis is carried out for both the 2002 and 2018 base year inventories.

Figure 7.5.4-1 illustrates the result of this final analysis step for the 2002 and 2018 distance-weighted, gridded emissions, sulfat e extinction-weighted, residence time plots for Cape Romain. These maps help visual ize where the emission reductions will be occurring between 2002 and 2018. The change in SO 2 emissions between 2002 and 2018 can be seen by comparing emission source stre ngths in the two plots. Note that the emissions from each source are normalized by th e total emissions in the domain. Sources that reduce SO 2 emission by 2018 will show a lower contribution to emission in the domain. On the 2018 map, the grid cells with these sources will show a lighter color gradient than on the 2002 map. Because the to tal emissions in the domain are smaller in 2018, a source that does not change emissi ons between 2002 and 2018 may actually appear to increase in importanc e in 2018 compared to 2002.

Figure 7.5.4 - 1 Cape Romain 2002 and 2018 SO 2 Distance Weight ed Emissions * SO 4 Extinction-Weighted Residence Time Plots

Figure 7.5.4-2 illustrates gridded SO 2 distance-weighted emission, sulfate extinctionweighted residence time plots for 2018 emissions for Cape Romain. This plot illustrates



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

### – Cape Romain, SC

2018 SO2 emissions

Green circles indicate 100-km and 200-km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Time

U >

U 10%. Orange line perimeter indicate Area of Influence with Residence Time

U >

U 5%. Max Value = 56%75 the relative importance of SC sources of SO 2 compared to sources in neighboring states. Additional analysis, including 2002 and 2018 dist ance-weighted emissions residence time plots for the Cape Romain and neighboring stat es, is contained in Appendix H and serves as the basis for consultation among the VISTAS states.



## 2018 SO2 Emissions weight ed by Residence Time Cape Romain, SC

Green circles indicate 100-km and 200-km radii from Class I area. Red line perimeter indicate Area of Influence with Residence Time > 10%. Orange line perimeter indicate Area of Influence with Residence Time > 5%.

Figure 7.5.4 - 2 Cape Romain, SC 2002 SO 2 Emissions * SO 4 Extinction Weighted Residence Time Plots

6 2 B 7.5.5 Specific Source Types in the Areas of Influence for SC Class I Federal Area

The next step in the analysis was to review the emissions inventories to determine the source categories, as well as specific sour ces, found to have the greatest impact on visibility in Cape Romain.

Lists of SO 2 point sources within the AOI for Ca pe Romain were developed using the most current (Base G) VISTAS 2002 base year and 2018 future year emissions. For this purpose, the AOI was defined as the count ies with a maximum sulfate extinctionweighted residence time greater than 5. For SO 2 sources within each AOI, the following attributes were defined for each individual unit:

• State, county, source (plant), and industry identification codes;76 • SO 2 emissions for 2002 and 2018;

• 2018 control efficiency;

• Distance to Class I Federal areas (defin ed by centroid of the Class I area);

• Emissions divided by distance (Q/d), a me tric that accounts for the dispersion of emissions over distance; and

• Maximum sulfate extinction-weight ed residence time (RTMax).

The review was conducted in a top-down fashi on, starting with an analysis of the major source categories in Cape Romain’s SO 2 AOI to determine which major categories had the highest residual contributio n to the area in 2018. It wa s also importan t to identify reductions that are projected to occur between 2 002 and 2018 within each category or at specific units. This allowed VISTAS states to determine if certain source categories or units that had yet to be cont rolled under the future year ba se case had the potential for reduction. Once the highest source types were identified, subcategories within these sources types were reviewed. The contributi ons from major source categories to the 2018 Base GS inventory for the SO 2 AOI for Cape Romain are listed in Table 7.5.5-1.

84% 40% 65% 1% 14% 3% 4% Miscellaneous 0% 4% 2% 1% 32% 19% 14% Off-highway 11% 1% 1% 1% 41% 25% 19% Highway Vehicles 0% 15% 7% 1% 7% 3% 4% Waste Disposal & Recycling 0% 1% 0% 0% 0% 0% 7% Storage & Transport 0% 0% 0% 0% 0% 0% 40% Solvent Utilization 2% 9% 8% 6% 1% 6% 6% Other Industrial Processes 0% 0% 0% 0% 0% 0% 0% Petroleum & Related Industries 0% 0% 0% 1% 0% 0% 0% Metals Processing 1% 1% 1% 4% 0% 1% 1% Chemical & Allied Product Mfg 1% 7% 4% 6% 2% 5% 4% Fuel Comb. Other 0% 5% 3% 34% 2% 17% 1% Fuel Comb. Industrial 1% 16% 8% 46% 1% 20% 0% Fuel Comb. Elec. Util. NH3 PM-2.5 PM-10 SO2 CO NOX VOC Tier 

### Annual 2018 BaseG2 Emissions (%) Within Area of Influence Cape Romain, SC

Figure 7.5. 5 - 1 2018 Emissions Contributions from Major Source Categories in the AOI for Cape Romain

In general, it was found that fuel combusti on from electric utilities and industrial processes produces the majority of the remaining SO 2 emissions, even after77 implementation of CAIR. Together, these sour ce categories contribute 80 percent of the 2018 SO 2 emissions for the AOI of Cape Romain.

This table can also be used to evaluate the major source categories contributing to emissions of NO X , NH 3 , and PM emissions in 2018. For instance, highway vehicles and off-road vehicles are major sources of NO X emissions, in addition to electric utilities and industrial boilers. The source category “mis cellaneous” (which includes agricultural sources and fires) is the major contributor to NH 3 and PM.

The contributions to SO 2 emissions in 2018 from the two highest source categories, Electric Utilities and Industrial Boilers, have been further broken down into subcategories. Table 7.5.5-2 indicates subc ategories for the AOI for Cape Romain. Within Electric Utilities, almost all of the SO 2 emissions are attributable to coal-fired power plants. Within Industria l Boilers, most emissions are attributable, again, to coalfired boilers.

4% Chemical and Allied Product Manufacturing 3% Other Industrial Processes-Mineral Products 0% Other Industrial Processes-Rubber & Miscellaneous Plastic Products 3% Other Industrial-Wood, Pulp & Paper, & Publishing Products 0% Other Industrial Processes-Textiles, Leather, & Apparel Products 0% Other Industrial Processes-Agriculture, Food, & Kindred Products 0% Fuel Comb. Industrial-Internal Combustion 2% Fuel Comb. Industrial-Other 2% Fuel Comb. Industrial-Gas 9% Fuel Comb. Industrial-Oil 21% Fuel Comb. Industrial-Coal 1% Fuel Comb. Elec. Util.-Internal Combustion 2% Fuel Comb. Elec. Util.-Other 0% Fuel Comb. Elec. Util.-Gas 0% Fuel Comb. Elec. Util.-Oil 44% Fuel Comb. Elec. Util.-Coal SO2 Tier Annual 2018 SO2 Emissions (%) Within Area of Influence Cape Romain, SC

Figure 7.5.5 - 2 2018 Emission Contribution from Major Source Categories in the AOI for Cape Romain

These analyses indicated that South Caro lina should consider whether any additional control measures for certain coal-fired util ity and industrial boile rs could achieve any reasonable progress. The lists of individual sources were also used to determine if78 individual sources in other source ca tegories are major contributors to SO 2 emissions in the AOI.

3 6 B 7.6 Which Control Measures for Individual Sources Achieve Reasonable Progress?

The next step was to identify emission reduction s that have already occurred or will occur by 2018 within each source category or at specif ic units. Unit-level tables of emission comparisons from 2002 to 2018 were develo ped, allowing VISTAS States to review emission reductions from other CAA programs. These tables assigned suggested future year control technology from: IPM forecas ting and State modification for EGUs, and control efficiency tables for Non-EGU point sources.

Once emission control profiles for specific un its are understood, the next step is to determine what, if any, additional controls woul d be technically feasibly, and to estimate costs of those control measures.

For EGUs, the 2018 IPM file used by VIST AS for EGU sources was obtained and matched to the 2018 base case inventory of EGU sources. This step was conducted to ensure that incremental controls consider ed for these source types did not duplicate existing base case assumptions.

For non-EGUs, VISTAS used EPA’s AirContro lNET database, modified for the VISTAS emission inventories. The core of AirCont rolNET is a relational database system in which control technologies are linked to sour ces within EPA emissions inventories. The system contains a database of control m easure applicability, efficiency, and cost information for reducing emissions. The cont rol measure data file in AirControlNET includes not only the technology's control effi ciency and calculated emission reductions for that source, but also estimates the cost s (annual and capital) for application of the control measure.

Using the modified inventories identifi ed above, VISTAS ran every available SO 2 control strategy in AirControlNET agai nst the EGU and non-EGU point source inventories to develop a master list of incrementalcontrol strategies for the entire VISTAS 36-km domain.

For the sources within the AOI for Cape Romain, the master list of incremental control measures was sorted to determine the lowest to highest cost of incremental control measures. These data were combined in a ma ster spreadsheet with the distance from the emission release point to Cape Romain or IMPROVE monitor (in km), the 2018 residual emissions and distance (Q/d) or squared distance (Q/d^2), and the normalized 2018 SO 2 point source emissions times distance-weight ed residence time (RTMax) values for the county in which the emission release point wa s located. South Carolina used these to evaluate control measures and costs as part of its statutory f actor analysis for reasonable further progress. Table 8.0-2 lists these data in terms of the State’s four factor analysis.79 3 7 B 7.7 Which Control Measures for Individual Sources Achieve Reasonable Progress?

U SC DHEC Process on Reasonable Progress Assessment

U :

Step 1: Determine pollutants of concern.

VISTAS evaluated the species contribution on the 20 percent worst days and concluded that sulfate accounted for more than 70 per cent of the visibility -impairing pollution. VISTAS States concluded that controlling SO 2 emissions was the appropriate step in addressing the reasonable progress assessment for 2018.

Step 2: Determine which source categories should be evaluated for reasonable progress.

Since SO 2 point source emissions in 2018 represen t more than 95 percent of the total SO 2 emissions inventory, VISTAS States conclu ded that the focus should be on EGU and non-EGU SO 2 emissions.

Step 3: Determine if CAIR is sufficient for reasonable progress.

“In determining the emissions and visi bility improvement achieved during each implementation period, States should include all air quality improvements that will be achieved by other programs and activitie s under the CAA and any State air pollution control requirements” (64 FR 35733). In keeping with this recommendation, South Carolina, as part of its l ong-term reasonable progress anal ysis to consider potential sources contributing to visibi lity impairment, examined other CAA requirements such as CAIR. SC DHEC evaluated the amount of SO 2 reduction from EGU’s due to CAIR. Since the EGUs will be reducing their SO 2 emissions by approximately 78 percent through these programs, SC DHEC concluded that additional EGU control during this time period (between 2007 and 2018) was not reasonable.

Under the RHR, each “state must identify th e uniform rate of progress over the 60 year period that would be needed to attain na tural background conditions by the year 2064” (64 FR 35732). To calculate the rate of progre ss represented by each reasonable progress goal, South Carolina compared baseline visi bility conditions to natural visibility conditions in its Class I Fede ral area and determined the uni form rate of visibility improvement (measured in dv) that woul d need to be maintained during each implementation period in order to attain natural visibility conditions by 2064. SO 2 emission reductions that are pr edicted by the IPM to meet th e CAIR requirements are not certain due to the current rule’s provisions for regional emissions trading and the use of banked Title IV allowances. However, “SIP approvability criteria [under CAIR] are intended to provide as much cer tainty as possible that, whic hever sources a State chooses to control, the controls will result in the required amount of emissions reductions” (91 FR 25256). Furthermore, for EGUs subject to CA IR, the EPA evaluated a number of factors including the cost of compliance and the time necessary for compliance. Within the CAIR rule, EPA determined “that the earli est reasonable deadline for compliance with the final highly-cost effective control leve ls for reducing emissions was 2015...” (70 FR80 25197-25198). Based on this analysis, SC DHEC has determined th at CAIR controls satisfy reasonable progress for the initial planning period.

These criteria along with the success of NO X SIP call reductions and SC DHEC’s ongoing review of Prevention of Significant De terioration/New Source Review permits serve to address this uncertainty and pr omote confidence in projected reasonable progress. Continued collabo ration between SC DHEC and the FLM will ensure that trends in growth are addressed. SC DH EC also intends to re-evaluate the IPM predictions of SO 2 reductions for CAIR at the time of the next periodic report in 2012 to ensure that the individual unit’s reductions currently predicted by IPM for CAIR are in fact taking place. Based on the visi bility improvements expected from SO 2 emission reductions predicted by IPM and required by CA IR, SC DHEC concludes that additional SO 2 control measures for South Carolina’s E GUs would not achieve reasonable progress during this first assessment period (between baseline and 2018).

Step 4: Determine which emission units would be evaluated based on impact.

SC DHEC calculated the frac tional contribution from all emission units within the SO 2 AOI for Cape Romain and identified those emission units with a contribution of 0.01 or above (or greater than or equal to one percent contri bution) to the visibility impairment at Cape Romain.

SC DHEC chose to adopt one percent as the contribution threshold used to consider a source for four factor analysis of reasonable further controls based on precedence established in other Federal programs.

• The BART rule specified that a maxi mum impact of 0.5 dv is an acceptable threshold for establishing significance. This threshold is representative of a five percent change in visible perception.

• The NO X SIP call established a significance le vel for 126 petitions (the level at which a State’s contribution to another state’s ozone was considered significant) at 4 parts per million. This threshold is representative of approximately 3.75 percent of the 1-hour ozone standard (which was in place at the time).

• The CAIR rule established an PM contribution threshold of 0.2 μg/m 3 as significant. This threshol d is representative of 1.3 percent of the annual PM 2.5 standard of 15 μg/m 3

.

• The NAAQS assign significant impact levels for human health standards based on a source’s contribution to air quality. These significance levels represent a percentage of the NAAQS. Those facilities able to demonstrate that a new or modified source’s contribution is less th an a particular signi ficance level does not have to complete further modeling demonstration. SC DHEC examined all the averaging periods for the criteria pollutants and determined that the 0.5 percent threshold contribution chosen for reasona ble further progress was more stringent than any significant impact level (the most restrictive being NO X at 1 μg/m 3 or 1 percent). While SC DHEC recognizes the important di fference between significant impact levels of human health standards and visibility st andards, the Department contends that its81 approach to establishing a visibility contribu tion threshold based in part on impact levels related to the precedence established by human health standards is appropriate and serves to further protect visibility.

Step 5: Evaluate the four factors.

Each emission unit identified in Step 4 ab ove was evaluated usi ng the statutory and regulatory factors of: (1) cost of compliance, (2) time necessary for compliance, (3) the energy and non-air quality environmental imp acts of compliance, and (4) the remaining useful life of the emissions unit. For the limited purpose of evaluating the cost for the reasonable progress assessment in this first regional haze SIP, SC DHEC believes it is not equitable to require non-EGUs to bear a grea ter economic burden than EGUs for a given control strategy.

In determining reasonable costs of contro ls for the EGU sector, SC DHEC determined that the cost of S0 2 controls (the primary contributor to visibility impairment in the state) beyond those required by CSA and CAIR are not reasonable. The capital and operating costs to implement CSA were converted to the same 15-year time period as the costs from AirControlNet. The unit-by-unit costs ranged from about $912 to $1,922 dollars per ton of SO 2 removed; with an average cost per system ranging from $1,231 to $1,375 dollars per ton of SO 2 removed (Appendix H.2.a).

Therefore, $2,000.00 per ton was selected as the threshold for costs for non-EGU sources. This limit acts as an inherent barrier to non- EGUs considering additional controls. The cost of controls is significant--not just in te rms of capital cost, but also the cost of labor needed to efficiently and e ffectively maintain the equipment. “The availability of skilled labor--specifically, boilermakers--is an important constraint for the installation of significant amount s of emission controls. Boilermakers are skilled steel workers who are specially trained to install both NO X controls, such as SCR, and SO 2 controls, such as scrubbers” (69 FR 4617).

The Mid-Atlantic/Northeast Visibility Unit (MANE-VU) RPO contracted with MACTEC, Inc. to evaluate control options a nd four statutory factors for assessing RPGs for regional haze in the MANE-VU region. The project report summ arizes the control measures available for SO 2 emissions from electric utiliti es and industrial boilers (the report also covers kilns, residential heating oil and wood combustion). In brief, sources can switch to a fuel source with lower sulfur content or install pos t-combustion controls. For coal-fired and oil-fired boilers, lower su lfur fuel may be a cost-effective option. Sources have to consider that changing from a bituminous coal to a sub-bituminous coal may affect boiler efficiency, and fuels may need to be blended rather than substituted to maintain boiler performance. Boiler modifi cation may also be needed. Post-combustion controls rely on flue gas desulfurization, scrubbers either dry or wet using either limestone or lime. There are significant capital costs to construct an absorber tower and operational costs of the sorbent and waste handling facilities. While generic costs assumptions can be made as a function of bo iler capacity, site-speci fic factors--such as availability of materials a nd construction crews, transp ortation access for sorbent,82 physical space at the facility for tower and waste handling, and potential for reuse of waste as gypsum--all affect actual costs.

3 8 B 7.8 BART Determinations in South Carolina

Section 169A of the CAA directs states to a ssess certain emission sources for additional controls in order to address visibility imp acts. States are directed to conduct BART determinations for such sources in specific source categories and for sources that contribute to visibility impai rment in Class I Federal area s. The 1999 RHR includes the BART requirement and directs states to incl ude BART in their regional haze SIPs. On July 6, 2005, the EPA published a revised fi nal rule, including Appendix Y to 40 CFR Part 51, the Guidelines for BART Determinati ons Under the Regional Haze Rule (herein referred to as Bart Guidance), that provides guidance to states on determining which of its sources should be subject to BART a nd how BART should be determined for each

source.

A BART-eligible source is one that has the potential to emit 250 tons or more per year of a visibility-impairing pollutant; that wa s put in place between August 7, 1962, and August 7, 1977; and (3) whose operations fall w ithin one or more of the 26 specificallylisted sources categories. Under the CAA, a BART determination is required for any BART-eligible source that a state determines “emits any air pollutant which may reasonable be anticipated to cau se or contribute to any impair ment of visibility in any such area.”

In November 1980, EPA released guidance to use in determining which sources are BART-eligible and therefore subject to BART c ontrols. Procedures outlined in Part I of this guidance allow states to consider loca l conditions and circumstances in their BART decision-making. This fact rec ognizes that states have the most complete knowledge of local factors that may affect a BART decision.

For those sources subject to BART, Section 169A(g)(7) of th e CAA requires that states must consider the following four factors in making BART determinations: the cost of compliance, the energy and non-air quality environmental impacts of compliance, any existing pollution control technology in use at the source, and the remaining useful life of the source. In addition, states should also consider the degree of improvement in visibility that may reasonably be anticipated to result from the use of such technology.

6 3 B 7.8.1 BART-Eligible Sources in South Carolina

The following is a list of facilities with BART -eligible sources in South Carolina. TRC Environmental Corporation was retained by VISTAS to conduct BART exemption modeling for several sources in South Carolina. A list of these ten facilities is included in Appendix H.6. Three of the te n facilities (Shaw Industries: Anderson; Solutia, Inc.; and Honeywell: Clemson) were later determin ed to be exempt fo rm BART exemption modeling due to boiler size.83 Appendix H.7 contains a detail ed description of each BART -eligible emission unit at the following facilities identified as having BA RT-eligible sources in South Carolina.

U FACILITY NAME

ALBEMARLE, CORP. BOWATER INC. PAPER/PULP BP AMOCO CHEMICAL-COOPER RIVER PLANT DAK AMERICA EASTMAN CHEMICAL INTERNATIONAL PAPER GEORGETOWN MILL INVISTA MAY PLANT INVISTA SARL SPARTANBURG (FORMERLY KOSA:ARTEVA) ISG GEORGETOWN MEADWESTVACO KRAFT MILL MILLIKEN CHEMICAL: DEWEY PLANT OWENS CORNING: ANDERSON RHODIA CHARLESTON SANTEE COOPER GRAINGER SANTEE COOPER JEFFERIES SANTEE COOPER WINYAH SCE&G CANADYS SCE&G WATEREE SCE&G WILLIAMS STONE CONTAINER FLORENCE WELLMAN INC PALMETTO PLANT

These BART-eligible sources were identified using th e following methodology in the BART Guidelines:

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

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

• The potential emissions, considering fede rally enforceable permit limits, from all emission units identified in the previous tw o bullets were greater than 250 tons or more per year of any of these vi sibility-impairing pollutants: SO 2 , NO X , and
PM 10

.
The BART Guidelines recommend addressing these visibility-impairing pollutants (SO 2 , NO X , and PM) and suggest that states use thei r best judgment in determining whether to address VOC and/or ammonia (NH 3 ) emissions. As the BART Guidelines recommend, South Carolina addressed SO 2 and NO X , and used PM 10 as an indicator for PM to identify BART-eligible units. As discussed in detail in Appendix H.5, VISTAS modeling demonstrates that neither VOCs nor NH 3 form point sources in the Southeast can reasonably be anticipated to c ontribute to visibility impair ment in any Class I Federal84 area. For this reason, South Carolina di d not evaluate emissions of VOC and NH 3 in its BART determinations.

6 4 B 7.8.2 Determination of Sources Subject to BART in South Carolina

Under the BART Guidelines, states should exem pt sources from BART if it is found that they do not contribute to visibility impairmen t in a Class I Federal area. In accordance with the BART guidelines, South Carolina c hose to perform source-specific analysis to determine which sources cause or contribute to visibility impairment using the CALPUFF model. The CALPUFF modeling protocol used for determining which facilities are subject to BART is included in Appendix H. In accordance with the BART Guidelines and as justified in Appendix H (Section 7.4), a contribution threshold of 0.5 dv was used to determine which sources in South Carolina were subject to BART.

Nineteen of 21 sources were ab le to demonstrate exemption. Facilities found to be subject to BART were asked to complete a BART analysis. Results of these demonstrations are summarized below in Ta ble 7.8.2-1. Additional details are available in Appendix H.7. Appendix H.4: South Carolina’s Evaluation of Source Emissions for BART Eligibility includes additional information related to each facility’s emission source units, dates of operation, and emission unit’s pollutants’ potential/act ual emissions.
Table 7.8.2 - 1 BART Modeling Results
Facility Class I Area* Impact (Change in dv) Albemarle VOC Exempt BP Amoco Chemical - Cooper River Plant VOC Exempt

Rhodia - Charleston VOC Exempt Eastman Chemical VOC Exempt INVISTA - Spartanburg VOC Exempt Stone Container - Florence Cape Romain 0.375** ISG Georgetown Cape Romain 0.279 Owens Corning - Anderson VOC Exempt Milliken Chemical - Dewey VOC Exempt DAK Americas*** Cape Romain Wolf Island 0.471

MeadWestvaco Corp. Cape Romain Okefenokee Wolf Island 0.438

SCE&G - Canadys Cape Romain Okefenokee Wolf Island 0.341

International Paper - Georgetown Cape Romain Wolf Island 0.456

Wellman - Palmetto Cape Romain Linville Gorge Shining Rock 0.22485 INVISTA - Camden Plant Cape Romain Great Smokies Linville Gorge Shining Rock 0.244

Bowater Cape Romain Great Smokies Joyce Kilmer Linville Gorge Shining Rock 0.381

Santee Cooper - Jefferies Cape Romain Wolf Island 0.318

SCE&G - Williams Cape Romain Okefenokee Wolf Island 1.83

Santee Cooper - Winyah Cape Romain Wolf Island 0.476

Santee Cooper - Grainger Cape Romain Swanquarter 0.235**

SCE&G - Wateree Cape Romain Great Smokies Linville Gorge Shining Rock Wolf Island 0.95

* Class I Federal areas w ithin 300 km of the source ** Screening modeling max impact

As approved by EPA (Appendix O.1), SC DHEC is also using the chloride derived IMPROVE default sea salt estimate as the de fault value for use in the BART assessment for this first planning period. The Regi onal Haze rule, however, gives the state significant flexibility in maki ng decisions concerning the BART analysis as part of the regional haze process.

VISTAS along with other consu ltants have examined the effe cts of sea salt and proposed a hierarchy of methods for sea salt estimatio n based on a consideration of different factors impinging on how technicall y reliable each method is for estimating sea salt in the atmosphere. After taking into account the various method variable s as presented in Appendix O.3 (including chlorine depletion and blank problems) SC DHEC is convinced that the use of the VISTAS s odium ion data is justified an d provides the most realistic, technically sound, yet reasonably conservative, estimate of s ea salt in the Cape Romain Class I Federal area. This September 2002 through December 2004 sodium ion dataset results in a 0.77 ug/m 3 sea salt estimate for the Cape Romain Class I Federal area.

As a result, SC DHEC has chosen to accept additional information on a case-by-case basis for several BART facilities that have requested a more refined approach to their BART exemption modeling. While SC DHEC be lieves that the use of the sodium ion derived alternative sea salt estimate would be ju stified for any facility modeling visibility86 impairment at Cape Romain, that refinement is not necessary if a facility exempts with a less refined method.

Table 7.8.2-2 presents the facili ties who requested an altern ative to the default sea salt estimation. Note that some facilities presente d data calculated with sea salt estimated by elemental sodium and that data is included for those facilities.

Table 7.8.2 - 2 New IMPROVE Sea Salt Es timation Methods & BART Visibility Impacts

Visibility Impacts (ug/m3) by BART-eligible facility Hierarchy Period of data Sea salt concentration (ug/m3) DAK Cooper River MeadWestvaco IP Georgetown* Santee Winyah Santee Jefferies

Na + 9/2002- 12/2004 0.77 0.494 0.455 0.477 0.478 0.273

Cl - 2000-2004 0.02 0.571 0.538 0.551 0.565 0.318

Cl Not analyzed (NA) NA NA NA NA NA

Na 9/1994- 11/2001 1.1 0.458 0.418 Not presented 0.439 0.252

Six of South Carolina’s BART-eligible sources are EGUs. EPA has determined that, as a whole, the CAIR cap-and-trade program will imp rove visibility more than implementing BART for individual sources in states which participate in the federal CAIR program. A state that opts to participate in the CAIRprogram need not require affected BARTeligible EGUs to install BART for SO 2 or NO X emissions. Given that South Carolina is participating in CAIR and accepts EPA’s ove rall finding that CAIR “substitutes” for BART for SO 2 and NO X , SC’s EGUs were allowed to submit BART exemption modeling demonstrations for PM emissions only. Four of the six EGUs demonstrated that they do not contribute to visibility impairment in South Carolina’s Class I Federal area. These facilities were SCE&G – Canadys plant a nd Santee Cooper’s – Jefferies, Winyah, and Grainger facilities.

In total, 19 of SC’s 21 BART-eligible sources were able to demonstr ate that they did not cause or contribute to visibi lity impairment in any Class I Federal area within 300 km of the sources. At least 10 sources used the new IMPROVE equation in efforts to demonstrate exemption, including: all three SCE&G facilities (Canadys, Williams, and Wateree); all three Santee Cooper facilities (Winyah, Jefferies, and Grainger); DAK; IP Georgetown; MeadWestvaco; and INVISTA.

6 5 B 7.8.3 Evaluation of Sources Subject to BART in South Carolina

7 5 B U 7.8.3.1 Screened and Refined Evaluations of S ources Subject to BART in South Carolina

SCE&G Wateree: SCE&G Wateree Station is located in Eastover, South Carolina. The station consists of two identical pulveri zed coal-fired, wet bottom boilers. The two boilers produce superheated steam, which is us ed in the two dedicated turbine generators.87Units 1 and 2 are equipped with fabric filter baghouses to control PM emissions, and lowNO X burners and SCR to control NO X emissions. Although Units 1 and 2 commenced commercial operation in the earl y ‘70s, there is no near-term limitation on the useful life of this unit.

SCE&G also plans to install two we t limestone scrubbers to control SO 2 emissions in the summer (July/August) of 2009. Wateree Station Units 1 and 2 will be retrofitted with a flue gas desulfurization (FGD) system usi ng a limestone slurry in a spray tower to remove SO 2 from the gas stream. Although designed to control SO 2 emissions, the FGD system will provide the added benefit of removing sulfates, a principal constituent of condensable PM 10 . Installing a FGD system will result in added improvements in visibility impairment attri butable to Units 1 and 2.

The installation of the FGD systems will resu lt in a visibility improvement of about 59 percent, in terms of delta dv relative to natural conditions, on the worst-case visibility day. Furthermore, as shown in Table 7.8.2-1 above, the 98 th percentile delta dv resulting from the installation of the FGD systems is well below the 0.5 delta dv threshold, used to determine whether a source contributes to visi bility impairment as stated in the BART Guidelines. In addition, an analysis of annualized costs associated with additional PM 10 control options determined that any additiona l control measure is not cost effective in terms of visibility improvements.

SCE&G Williams: South Carolina Electric & Gas (S CE&G) Williams Station is located in Goose Creek, South Carolina. The station consists of a single pulverized coal-fired, dry bottom boiler. The boiler produce superheated steam, wh ich is used in a turbine generator. Although Unit 1 commenced commer cial operation in 1973, there is no nearterm limitation on the useful life of this unit.

Unit 1 is currently equipped with low-NO X burners and SCR to control NO X emissions and an electrostatic precip itator (ESP) to control PM 10 emissions, the later of which has been demonstrated to achieve performance leve ls comparable to those being specified as BACT for new coal-fired boilers. The existing control device, therefore, is considered representative of BART for PM 10

.
In October 2009, SCE&G plans to retrofit W illiams Station Unit 1 with a FGD system using a limestone slurry in a spray tower to remove SO 2 from the gas stream. Although designed to control SO 2 emissions, the FGD system will provide the added benefit of removing sulfates, a principal constituent of condensable PM 10 . PM 10 emissions will be reduced from 925 tpy to 464 tpy following the installation of the FGD system. This 50 percent reduction is at tributable to the rem oval of condensable PM 10 , principally sulfates, in the FGD system.

The installation of the FGD system will result in a visibility improvement of about 35 percent, in terms of delta dv relative to natural conditions, on the worst-case visibility day. Furthermore, the 98 th percentile delta dv resulting from the installation of the FGD88 system is only slightly above th e 1.0 delta dv threshol d (2 occurrences in 3 years), used to determine whether a source could causes visib ility impairment as stated in the BART Guidelines. The installation of any of the PM 10 control options will only result in an additional visibility improvement of less than 1 percent on the worst- case visibility day. Furthermore, the annual costs associated with the PM 10 control options are not cost effective in terms of visibility improvements.

SCE&G Canadys: Canadys Station is located in Colleton County, South Carolina, and is owned and operated by SCE&G. There are two BART eligible units at the Canadys Station. Unit 2 is a pulverized coal-fired we t bottom generating unit with a cold side ESP for PM 10 control. Unit 3 is a pulverized coal-fired dry bottom unit with a fabric filter for
PM 10 control. Unit 2 and 3 were cons tructed in 1964 and 1967 respectively.

Canadys Station is located within 300 km of three Class I areas, including: Cape Romain, Okefenokee Wilderness Area, and Wolf Isla nd National Wildlife Refuge (NWR).

As shown in Table 7.8.2-1 above, the results of the refined CALPUFF modeling analysis, using the VISTAS 4 km domain, demonstrates that PM 10 emissions from Canadys Station’s Units 2 and 3 do not cause or contri bute to regional haze in any Class I area within 300 km of the plant. The eighth-highest delta dv visibi lity impact a ttributable to Canadys Station’s Units 2 and 3 is only 0.341 delta dv which is well below the exemption threshold of 0.5 delta dv. Thus, Canadys Station’s Units 2 and 3 are not “subject to BART” and are exempt from furthe r analysis under the BART rule.

Santee Cooper: Santee Cooper reviewed the criteria for BART -eligibility and determined that two EGUs at each of the three generating stations li sted below are BART-eligible sources. These units are pulverized coal-fired, dry-bottom boile rs. Each of the six BART-eligible boilers employs an electrostatic precipitator (ESP) to control particulate emissions, and the two BART-eligible boilers at the Winyah Generating Station also use flue gas desulfurization (FGD) systems to reduce sulfur emissions. The potential annual emissions from the BART-eligible units at each of the three facilities exceed 250 tpy of each of NO X , SO 2 , and total PM 10 (condensable and filterab le). In addition, all six BART-eligible sources were in existence on August 7, 1977, and began operation after August 7, 1962. Therefore, all three of these Santee Cooper facili ties are BART-eligible.

U Winyah:

U The modeled peak, 24-hour average visibility impacts at Cape Romain attributable to Santee Cooper’s Winyah Ge nerating Station (using the default annual average natural background for representing natural background conditions) indicate that the refined analyses was requ ired since the peak visibil ity impact estimated (1.566 delta dv) using the default screening approach we re greater than the 0.5 delta dv contribution threshold. It is worth noting that this was not the case for visibility impacts at Wolf Island NWR which were less than the 0.5 delta dv contribution threshold. Thus, Wolf Island NWR was screened out a nd only visibility impacts at Cape Romain were evaluated using refined modeling.89 Refined modeling analyses indicated that for the 98th percentile impact (represented as the maximum 8th-high 24-hour average impact ) among the three years of analysis, the Winyah Generating Station is not subject to further BART analysis. Using the revised IMPROVE light extinction algor ithm (using all three estimates of the annual average sea salt concentration; 0.77 μ g/m 3 as estimated by the VISTAS Technical Contractor; 1.1

μ g/m 3 as estimated by Santee Cooper; and 0.2 μ g/m 3 as the default IMPROVE value), the visibility impact of the Winyah Generating Stat ion at Cape Romain was determined to be 0.476, 0.435, and 0.561 delta dv respectively. Thes e modeling results demonstrate that the Winyah Generating Station doe s not cause or contribute to visibility impairment and is not subject to BART when the more repr esentative sea salt c oncentrations of 0.77

μ g/m 3 as estimated by the VISTAS Technical Contractor and 1.1 μ g/m 3 as estimated by Santee Cooper are utilized because PM 10 emissions from the facility do not contribute to visibility impairment at Cape Romain as estimated using refined modeling techniques.

U Jefferies:

U The modeled peak, 24-hour average visibility impacts at Cape Romain attributable to Santee Cooper’s Jefferies Generating Station (usi ng the default annual average natural background for representing natural background conditions) indicate that the refined analyses were required since the peak visibility impact estimated (0.774 delta dv) using the default screening approach wa s greater than the 0.5 delta dv contribution threshold. Visibility impairment at Wolf Island NWR using the default screening approach was predicted to be less than the 0.5 delta dv cont ribution threshold. Thus, Wolf Island NWR was screened out a nd only visibility impacts at Cape Romain were evaluated using refined modeling.

Refined modeling analyses indicated that for the 98th percentile impact (represented as the maximum 8th-high 24-hour average impact ) among the three years of analysis, the Jefferies Generating Station is not subject to further BART analysis. Using the revised IMPROVE light extinction algor ithm (using all three estimates of the annual average sea salt concentration; 0.77 μ g/m 3 as estimated by the VISTAS Technical Contractor; 1.1

μ g/m 3 as estimated by Santee Cooper; and 0.2 μ g/m 3 as the default IMPROVE value), Jefferies’ contribution to visibility impairment was determined to be 0.273 μ g/m 3 , 0.252

μ g/m 3 , and 0.318 μ g/m 3 respectively - less than the 0.5 delta dv contribu tion threshold. Thus, PM 10 emissions from the facility do not contri bute to visibility impairment at Cape Romain as estimated using refined modeli ng techniques and the Jefferies Generating Station is exempt from furt her BART analysis.

U Grainger:

U The modeled peak, 24-hour average vi sibility impacts at Cape Romain attributable to PM 10 emissions from Santee Cooper’s Gr ainger Generating Station (using the default annual average natural back ground for representing natural background conditions) indicate that the refined analyses were not required since the 0.235 delta dv peak visibility impact estimated using the de fault screening approach is less than the 0.5 delta dv contribution threshold at both Cape Romain and Swanquarter National Wildlife Area.

DAK: DAK Americas, LLC (DAK) operates a facility (Cooper River Facility) in Moncks Corner, South Carolina, just north of Charleston. DAK reviewed the criteria for90 BART-eligibility and determined that ther e are two emission units considered BARTeligible at the Cooper River Facility. Poten tial emissions from these units exceed 250 tpy of NO X , SO 2 , and PM 10 , each of which is considered a visibility-affecting pollutant.

However, DAK’s Cooper River Facility is no t subject to BART because the plant does not contribute to visibility impairment at either Wolf Island NWR or Cape Romain as estimated using refined modeling techniques.

The results of analys es indicate DAK’s Cooper River Fa cility should not be considered subject to BART because its BART-eligible sources do not contribute to visibility impairment at the Wolf Island NWR as dem onstrated by a refine d modeling (using all three estimates of the annual av erage sea salt concentration; 0.77 μ g/m3 as estimated by the VISTAS Technical Contractor; 1.1 μ g/m3 as estimated by Santee Cooper; and 0.2

μ g/m3 as the default IMPROVE value) analysis that quantified the 98 th percentile 24- hour average visibility impact as 0.471, 0.437, and 0.544 delta dv, respectively. These modeling results demonstrate that the DAK C ooper River Facility does not cause or contribute to visibility impairment and is not subject to BART when the more representative sea salt concentrations of 0.77 μ g/m3 as estimated by the VISTAS Technical Contractor and 1.1 μ g/m3 as estimated by Santee Cooper are utilized because emissions from the DAK BART-eligible units do not contribute to visibility impairment at Cape Romain based on the revised IMPROVE algorithm and refined analyses of background sea salt concentrations.

In addition, DAK has opted to accept emissi on limitations in its Part 70 Air Quality Operating Permit so as to demonstrate exemption from further BART analysis.

Bowater: The Bowater Mill is located in Catawb a, South Carolina. Linville Gorge, Shining Rock, and Joyce Kilmer-Slickrock Wilderness Areas are located approximately 140 km, 180 km, and 280 km west-northwest of the mill, respectively. Great Smoky Mountains National Park is located approxima tely 216 km west-northwest of the mill at its nearest boundary. Cape Romain is locat ed approximately 230 km southeast of the mill.

Bowater reviewed the criteria for BART-eligib ility and determined that there are five emission units considered BART-eligible sources at the Bowater Mill. Each of these emission units has potential emissions that exceed 250 tpy of NO X , SO 2 , and PM 10 , which are considered visibility-affecting pollutants.

Modeling results indicate that the Bowater BART eligible units do not cause or contribute to any visibility impairment in an y of the nearby Class I areas. When 98th percentile refined modeling was conducted, the greatest visibility impact was predicted at Linville Gorge, with a value of 0.381 delta dv for the highest 98 th percentile 24-hour average visibility impact modeled over the three year period using the revised IMPROVE algorithm. Thus, since the predicted impact at all Class I areas is less than the contribution threshold 0.5 delta dv, Bowater has successfully demonstrated that it is exempt from the BART requirements.91 Wellman: The Wellman facility is located in Florence, SC. The potential emissions of NO X , SO X , and PM from all BART-eligible sources at the facility ar e equal to 517 tpy, 4,188 tpy, and 209 tpy, respectively. Only those units that were constructed and began operation within the BART time period ar e subject to the BART regulations.

Three Class I areas are between 100 and 300 k ilometers of Wellman. These include the Cape Romain Class I area in South Carolina and the Shining Rock and Linville Gorge Class I areas in North Carolina. For each Cl ass I area, a refined modeling analysis using the VISTAS 4 km modeling domain was pe rformed, with receptor locations and elevations were based on the inpu t files received from VISTAS.

The refined modeling results represent the 98 th percentile (8 th highest) values over each year at all receptors. The largest degree of visibility impairment resulting from the emissions of BART eligible sources from th e Wellman facility was predicted at Cape Romain for the 2002 modeled year, with an 8 th highest value of 0.175 delta dv using the revised IMPROVE algorithm. Based on th e dispersion modeling analysis using CALPUFF, the impacts from the BART elig ible sources are less than the guideline threshold and therefore exempt fr om further BART evaluation.

INVISTA (SARL): INVISTA SARL (INVISTA) ope rates a nylon fiber and flake manufacturing facility (the Camden Plant) in Lugoff, Kershaw County, South Carolina. INVISTA’s Camden Plant has determined that four emission units comprise the BART eligible sources at the INVISTA Camden fa cility. These units were in existence on August 7, 1977, and began operatio n after August 7, 1962. This collection of emission units has potential emissions of 1,167 tpy NO X , 251 tpy PM 10 , and 4,598 tpy SO 2

.

There are four Class I areas within 300 km of the INVISTA Camden Plant. These include Cape Romain (167 km), Great Sm okey Mountains (267 km), Linville Gorge (208), and Shining Rock (232 km).

Modeled peak, 24-hour average vi sibility impacts at Cape Romain attributable to the INVISTA Camden Plant indicate that the peak visibility impact estimated (0.723 delta dv) using the default screening approach we re greater than the 0.5 delta dv contribution threshold at all four Class I areas, thus indicating a refined m odeling approach was

necessary.

Refined modeling results representing natura l background conditions (i.e., annual average and 20 percent best days) indicate that for the 98 th percentile impact, represented as the highest, 8th-high 24-hour average impact among the three years of analysis, INVISTA’s contribution to visibility impairment at Ca pe Romain was determined to be 0.244 delta dv. Refined modeling analyses determined th at INVISTA’s contribu tion to visibility impairment at Great Smoky Mountains Nationa l Park, and Linville Gorge, and Shining Rock Wilderness Areas was also less than the 0.5 threshold. Thus, refined modeling analyses indicated that the INVISTA Camd en Plant is exempt from BART and not subject to further BART analysis.92 IP Georgetown: The International Paper Georgetown, South Carolina Pulp and Paper Mill is located in Georgetown, South Carolin a. There are two (2) Class I areas within 300 kilometers of the Georgetown Mill: Ca pe Romain and Wolf Island NWR. Cape Romain is located approximately 29.5 kilomete rs south of the mill, and Wolf Island is located approximately 290 kilome ters southwest of the mill.

IP Georgetown reviewed the criteria for BA RT-eligibility and determined that nine emission units are BART-eligible sources at th e facility because potential emissions for each exceed 250 tpy for of NO X , SO 2 , and PM 10 , which are considered visibility-affecting pollutants.

IP Georgetown’s refined methodology used the CALPUFF model (with the refined sea salt concentrations of 0.77 μ g/m3 as estimated by the VISTAS Technical Contractor) with three years of meteorol ogical data developed by VI STAS along with the revised IMPROVE algorithm. URS evaluated the 98 th percentile impact s since the 4 km modeling domain was used for the analysis.

The maximum change in visibility values resu lting from the emissions of BART eligible sources from the IP Georgetown facility is 0.477 delta dv over a three year period at Cape Romain. Therefore, the visibi lity impacts were less than the threshold of 0.5 delta dv using refined estimates of light extinctio n based on the revised IMPROVE algorithm and refined analysis of background sea salt concentrations. Based on this analysis, IP Georgetown Mill is exempt from any further requirements under the BART rule since the BART eligible emission units do not cause or contribute to visibility impairment within any Federal Class I area.

MeadWestvaco: MeadWestvaco Corporation operates an unbleached paper mill in North Charleston, South Carolina. There are th ree (3) Class I areas within 300 kilometers of MeadWestvaco: Cape Romain (29 km), Okefenokee Wilderness Area (297 km), and Wolf Island NWR (212 km). MeadWestvaco re viewed the criteria for BART-eligibility and determined that there are fourteen emissi on units that are BART-eligible sources. This collection of units has poten tial emissions of 630 tpy of NO X , 592 tpy of PM 10 , 2143 tpy of SO 2

.
Modeling was performed using the VISTAS de fault screening approach that indicates visibility impairment at Okefenokee and Wolf Island is less than the 0.5 delta dv contribution threshold. Thus, Okefenokee and Wolf Island were sc reened out and only visibility impacts at Cape Romain we re evaluated using refined modeling.

The results of this refined modeling analys es using the VISTAS 4-km modeling domain indicate that MeadWestvaco is not subject to BART. The maximum change in visibility impairment quantified by the 98 th percentile 24-hour average visibility impact at Cape Romain resulting from the emissions of BART e ligible sources from th e facility is 0.408 delta dv. Thus, predicted visibi lity is less than the 0.5 delta dv c ontribution threshold93 using refined estimates of light extinctio n based on the revised IMPROVE algorithm and refined analysis of background sea salt concentrations.

ISG Georgetown: The ISG Georgetown Inc. Steel mill in Georgetown, South Carolina (ISG Georgetown) has been identified as a BART eligible source; due to the fact that the mill contains units that were placed in service between 1962 and 1977 and that potential emissions of one pollutant, NO X , exceed 250 tpy.

There are two Class I areas within 300 km of the ISG mill: Cape Romain and Wolf Island NWR. The distance to Cape Romain is about 30 km, while the distance to Wolf Island is about 290 km.

Modeling was performed using the VISTAS de fault screening approach that indicates visibility impairment at Wolf Island is le ss than the 0.5 delta dv contribution threshold. Thus, Wolf Island was screened out and only visibility impacts at Cape Romain were evaluated using refined modeling.

A refined modeling analysis was performed using the VISTAS 4 km modeling domain. The tabulated values indicate that among the three years modeled, the highest 98th percentile predicted impact is 0.279 delta dv at Cape Romain. Th e maximum predicted visibility impact at the Wolf Island Class I area was predicte d to be much lower at 0.034 delta dv. These results demonstrate that IS G Georgetown’s peak daily emissions are well below the threshold that would cause or contribute to visibili ty impairment. Therefore, the source is not subject to BART.

7 6 B U 7.8.3.2 VOC Evaluations of Sources Subj ect to BART in South Carolina

Several sources preliminarily identified as BART-eligible emission units have potential emissions equal to or greater than 250 tpy for volatile organic compounds (VOC) only, which VISTAS and SC DHEC have determined are not visibility-aff ecting pollutants. Section 4.1.3 of the VISTAS BART Modeling Protocol (Appendix C) describes the regional modeling analyses showing that cumu lative VOC emissions do not contribute to visibility impairment within the VISTAS region:

“Emissions sensitivity simulations run for VISTAS by Georgia Institute of Technology using VISTAS’ 12 x 12 km grid and CMAQ v 4.3 for episodes in July 2001 and January 2002 demonstrated very low to no response of organic carbon levels and light extinction at Class I ar eas to changing VOC emissions from all anthropogenic sources in the VISTAS 12- km modeling domain (eastern US)... VOC emissions from all anthropogenic point sources in every VISTAS State were reduced by 100% (i.e., eliminated). The maximum 24-hr impact of all VOC emissions from all point sources th roughout the VISTAS domain was thus determined to be less than 0.5 dv (compared to annual average natural background) at every Class I area in th e VISTAS domain and in adjacent States. It follows that the impact of any one BART-eligible source would be much less than 0.5 dv. Based on these analyses, the VI STAS States have c oncluded that VOC94 emissions from BART sources do not cause or contribute to visibility impairment and do not need to be included in BART analyses.”

Of the original 21 sources considered by SC DHEC as part of its BART analysis, five were given exemption status solely for this reason. Using the BART Guidelines, SCDHEC determined that the following sour ces do not contribu te to visibility impairment in the VISTAS Class I Federal areas:

• Albemarle Corp.

• BP Amoco Chemical – Cooper River Plant

• Rhodia – Charleston

• Eastman Chemical

• INVISTA – Spartanburg

The results of CMAQ modeling for the aforementioned sources indicate that VOC has a negligible effect on visibility for any Class I Federal area within the AOI. The modeling examined all sources in the Southeastern Region and determined that the maximum impact from VOC on any Class I Federal area within 300 km of South Carolina was less than 0.2 deciviews.

While the aforementioned sources may also emit NO X , SO 2 , and PM; discussions with EPA confirmed that for BART-eligible sources that have a potential to emit equal to or greater than 250 tpy of VOCs, but determin ed not to have an impact on visibility impairment (and whose emissions of other pollu tants are less than the 250 tpy threshold), BART exemption status may be granted.

7 B U 7.8.3.3 Model Plant Exemption Evaluation for Sources Subject to BART in South

Carolina

In the final BART guidelines, EPA described a modified approach, using model plants based on representative sources sharing certain characteristics, which states could use to simplify the BART determination process. Based on EPA’s model plant analysis, it was determined that “a State could reasonably choose to exempt sources that emit less than 500 tons per year of NO X or SO 2 (or combined NO X and SO 2 ), as long as they are located more than 50 kilometers from any Class I ar ea; and sources that em it less than 1000 tons per year of NO X and SO 2 (or combined NO X and SO 2 ) that are locate d more than 100 kilometers from any Class I area” (51 FR 39119). Table 7.8.3-1 illustrates the sources within South Carolina that qualify as exempt us ing this rationale. The distances used in this analysis were derived from the great ci rcle equation that accounts for the differences in spherical and Euclidean geometry. In spherical geometry the distance between two points on a sphere is calculated by measur ing the shortest dist ance between any two points using a path of an arc. To determine these distances, the Department measures the distance from each source’s stack to the IMPROVE monitor at Cape Romain.95 Table 7.8.3 - 1 Model Plant Exempt Sources Subject to BART in SC

Source PM Emissions (tpy) SO2 Emissions (tpy) NO X Emissions (tpy) NO X + SO2 + PM Emissions (tpy) Distance to closest Class I Area (in km)
Owens Corning 285 276 309 870 101.1 (Shining Rock) Milliken Chemical 21 343 65 429 75.2 (Shining Rock)

3 9 B 7.9 Post-BART Visibility Improvement Expected at Cape Romain by 2018, Compared to the Glidepath

As discussed earlier in Chapte r 7, visibility on the 20 percen t worst visibility days is expected to improve to le vels better than the glidep ath by 2018 based solely on reductions from existing and planned emi ssion controls. Figure 7.9-1 reveals the predicted visibility (dv) in 2018 on the worst days at Cape Romain.



### Uniform Rate of Reasona ble Progress Glide Path Cape Romain - 20% Data Days
New IMPROVE Equation, 12 km grid

26.48 25.53

23.15

20.77

18.39

16.01

13.64 12.21 22.72

0 5 10 15 20 25 30 35

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

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

7.9 - 1 Predicted Visibility for the 20% Wor st Days in 2018 at Cape Romain Using the New IMPROVE algorithm.
In addition to improvement in visibility on th e 20 percent worst visibility days, visibility on the 20 percent best days is also projected to improve in 2018 at Cape Romain. Figure 7.9-2 reveals the predicted visibi lity (dv) in 2018 on the best days at Cape Romain.96 7.9 - 2 Predicted Visibility for the 20% Best Days in 2018 at Cape Romain Using the New IMPROVE algorithm.
The expected change in visibility at Cape Romain between 2000-2004 baseline conditions on the 20 percent worst days and 2018 projections for Base G2a is illustrated in Figure 7.9-3. In contrast, natural background visibility conditions for the 20 percent worst days are illustrated in Figure 7.9- 4. These images were generated using WINHAZE for each of the Class I Federal ar eas and are included in Appendix L. WINHAZE is a photographic imaging tool that accounts for the effect of concentrations of the fine particle compone nts and relative humidity on visibility. These images illustrate that notable improvements in visibility are expected by 2018 and that significantly greater improvements are needed to reach natural background conditions. Year

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

### Reasonable Progress Assessment Base G2 inventory, new IMPROVE Algorithm Cape Romain - Best 20% Days

14.2 14.2 14.2

12.7

0 5 1 1 2 2 3 3

2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064Haziness Index (Deciviews)97 Figure 7.9 - 3 Visibility Improvements on 20% worst days at Cape Romain between 2002 – 2004 Baseline Conditions (left) and 2018 Base G2a projected visibility (right).

Figure 7.9 – 4 Projected Visibility on 20% Worst Da ys for Natural Background Visibility Conditions at Cape Romain. 

### 2000-04 Baseline 147.3 Mm -1 

### 2018 Projected 100.4 Mm -1



### 2064 Natural Conditions 34.3 Mm -198 7 B 

### 8.0 REASONABLE PROGRESS GOALS

The RHR at 40 CFR Section 51.308 (d)(1) requires States to es tablish RPGs (in dv) for each Class I Federal area within the state that provide for reasonable progress towards achieving natural visibility. In addition, EPA released guidance on June 7, 2007, to use in setting RPGs. The goals must provide impr ovement in visibility for the most impaired days and ensure no degradati on in visibility for the leas t impaired days over the SIP period. The state must also provide an assessm ent of the number of years it would take to attain natural visibility conditions if improve ment continues at the rate represented by the RPGs.

In accordance with the requirements of 40 CF R Section 51.308(d)(1), this Regional Haze SIP establishes RPGs (expresse d in dv) for each Class I Federal area. To determine the rate of progress represented by each RPG, So uth Carolina compared baseline visibility conditions to natural visibility conditions in each Class I area and determined the uniform rate of visibility improvement (measured in dv) that would need to be maintained during each implementation period in order to attain natural visibility conditions by 2064. These RPGs provide for an improvement in visibility for the most impaired days over the period of the implementation plan and ensure no degradation in visibility for the least impaired days over the same period for South Carolina.

South Carolina hereby adopts the following r easonable progress goals for Cape Romain.

Table 8.0 - 1 Reasonable Progress Goals for SC

Class I Area Baseline Visibility, 20 % Worst Days Reasonable Progress Goal (Deciview Improvement Expected by

2018, 20% Worst Days) Baseline Visibility, 20% Best Days Reasonable Progress Goal (Expected Visibility Degradation, 20% Best Days)

Cape Romain 26.5 22.7 (3.8) 14.2 12.7 (1.5)

4 0 B 8.1 Four Factor Analyses for Sources affecting Cape Romain

Section 51.308(d)(1) of the final RHR requires that a State establish “reasonable progress goals” for each Class I Federal area within th e state. In establishing reasonable progress goals, the CAA provided analytical requirements for setting these goals (know as the four factors) which include: the cost of complia nce, the time necessary for compliance, the energy and non-air quality environmental im pacts of compliance, and the remaining useful life of any existing source su bject to such requirements.99 In developing South Carolina’s reasonabl e progress goals, VISTAS, in November 2006, contracted with Alpine Geophysics to develop a cost control analysis that showed various control options for sources with in an area of influence for Cape Romain and other Class I

areas.

Since sulfate is the predominant cause of vi sibility impairment in the VISTAS region, South Carolina and the other VISTAS states decided to concentrate on reduction of SO 2 , the precursor to sulfate formation, during this first planning period. As such, the cost control list was sorted for the SO 2 emissions (Q) of each source divided by the distance (d) in kilometers to each class one area, or Q/d, and maximum residence time, RTmax. All sources with a Q/d greater than or equal to five and an RTmax greater than or equal to 10 percent were added to the list for further consideration because it has been determined that these sources may contribute to visibi lity impairment on the 20 percent worst days.

Sources meeting these criteria include:

• Santee Cooper – Jefferies, South Carolina

• Santee Cooper – Cross, South Carolina

• SCE&G – Canadys, South Carolina

• International Paper – Georgetown, South Carolina

• Giant Cement, South Carolina

• Holcim Holly Hill, South Carolina

• MeadWestvaco, South Carolina

• DAK Americas, South Carolina

• International Pa Savannah Electric – McIntosh, Georgia

• International Paper – Savannah, Georgia

A cost threshold of $2,000.00 per ton for controls was then applied. Of the remaining facilities, all EGUs subject to CAIR were not considered as part of South Carolina’s control strategy because CAIR makes substantial SO 2 emission reductions and SO 2 is the leading pollutant that cont ributes to visibility impa irment at Cape Romain.

As Table 7.8.2 - 3 illustrates, only two BART-eligible EGU sources in South Carolina emit any air pollutant which may reasonably be anticipated to cause or contribute to any impairment of visibility in any Class I ar ea. The two facilities, SCE&G Williams and SCE&G Wateree, were unable to demonstrate less than a 0.5 dv impact at Cape Romain. However, as stated earlier in the narrativ e of this appendix, these two BART-eligible EGU sources are subject to CAIR, and South Carolina has determined that implementation of CAIR is equivalent to BART. As such no formal BART determinations were made for either of these two South Carolina sources.

SC DHEC multiplied the Q/d factors and th e RTmax factors for all sources within a sulfate AOI for a given Class I area, then calculated a total and divided each product by that total to estimate the fracti onal contribution of each unit. Only the units with fractions100 of 0.01 or above (or greater th an or equal to one percen t contribution) were then evaluated using the four factor analysis.

Table 8.0-2 illustrates South Carolina’s appr oach to establishing reasonable progress goals by ranking the non-EGU sources that were evaluated using the four factors. This list of sources was generated by evaluating each facility’s relative cost of compliance. The cost of compliance (cost per ton) was calculated by dividing the total cost of the control strategy application by the amount of tons of SO 2 reduced by the application of the control strategy.

Under South Carolina’s approach, facili ties whose costs fa ll below the $2,000.00 threshold amount established were asked to conduct a four factor analysis. These facilities include:

• MeadWestvaco, SC

• International Paper, Georgetown, SC

• Giant Cement, SC

• International Paper, Savannah, GA

• Holcim: Holly Hill, SC

• DAK Americas, SC

The final four-factor analysis for the rema ining non-EGU sources was performed by the facilities and reviewed by the Department.

The reasonable progress goals established by SC DHEC for this first planning period take into account the controls identified as r easonable within the four factor analyses.101Table 8.0 - 2 Non-EGU Sources Subject to Four Factors as Rankedon Relative Cost of Compliance.2,0022,018Control Strategy ApplicationAOI and Associated MetricsState Plant Name ID Measure Name SO2 (TPY) SO2 (TPY) CE Tons Reduced CE Total Cost Cost Per Ton Distance (km) Q/d RTMax Q/d*Rtmax Rel ContributionSCMEADWESTVACO006Low Sulfur Oil (0.9%)2,3152,706083931$1,522,570$1,81529.5891.4772.766655.35720.063978822SCINTERNATIONAL PAPER:GEORGETOWN001FGD3,1523,65543,27490$14,556,791$4,44656.0565.2169.844554.26640.04378076SCINTERNATIONAL PAPER:GEORGETOWN001Low Sulfur Oil (0.9%)3,1523,65541,13331$2,056,395$1,81556.0565.2169.844554.26640.04378076SCGIANT CEMENT CO005FGD2,8253,89301,94750$19,317,345$9,92479.8048.7972.763549.96040.034126235SCGIANT CEMENT CO005Low Sulfur Oil (0.9%)2,8253,89301,20731$2,190,705$1,81579.8048.7972.763549.96040.034126235SCHOLCIM:HOLLY HILL002Low Sulfur Oil (0.9%)2,9574,07401,26331$2,292,345$1,81582.5649.3554.082668.8480.025655986GAINTERNATIONAL PAPER - SAVANNAH MILLPB13FGD7,6438,57847,68590$22,704,952$2,955165.9851.6831.041604.14720.015420877SCHOLCIM:HOLLY HILL001Low Sulfur Oil (0.9%)1,3361,841057131$1,036,365$1,81582.5622.2954.081205.44320.011588084SCDAK AMERICAS002Low Sulfur Oil (0.9%)496492415331$277,695$1,81532.5715.1172.761099.40360.010568711SCBP AMOCO CHEMICAL-COOPER RIVER PLANT002Low Sulfur Oil (0.9%)488392012131$220,502$1,81535.9510.9072.76793.0840.007624021SCBP AMOCO CHEMICAL-COOPER RIVER PLANT001Low Sulfur Oil (0.9%)486390012131$219,680$1,81535.9510.8672.76790.17360.007596042SCBAYER POLYMERS GOOSE CREEK001Low Sulfur Oil (0.9%)29924107531$135,318$1,81535.956.6972.76486.76440.00467933SCSTONE CONTAINER FLORENCE016FGD Wet Scrubber2,6603,10902,79890$10,882,328$3,889138.6022.4320.83467.21690.004491417SCSTONE CONTAINER FLORENCE016Coal Washing2,6603,10901,08835$523,594$481138.6022.4320.83467.21690.004491417SCINTERNATIONAL PAPER:EASTOVER007FGD3,0433,45343,09490$26,841,804$8,677139.3524.7813.20327.0960.003144417FLJEFFERSON SMURFIT CORPORATION (US)15FGD3,2423,63943,26090$25,163,913$7,720303.7911.9815.18181.85640.001748209NCINVISTA, S.A.R.L.G-247Low Sulfur Oil (0.9%)3,2492,611081031$1,469,295$1,815218.0111.9812.57150.58860.001447628NCINVISTA, S.A.R.L.G-247FGD3,2492,61102,35090$3,754,002$1,597218.0111.9812.57150.58860.001447628SCSTONE CONTAINER FLORENCE015Low Sulfur Oil (0.9%)622727420431$370,864$1,815138.605.2420.83109.14920.001049266GAINTERNATIONAL PAPER - AUGUSTA MILLPB2AFGD1,5741,76741,58390$15,181,164$9,593218.318.0912.3399.74970.000958907SCGALEY&LORD:SOCIETY HILL001FGD1,1211,112499690$1,079,058$1,083175.466.3413.9488.37960.000849605FLGEORGIA-PACIFIC CORP. PULP/PAPER MILL15FGD3,7034,32903,89690$17,425,139$4,473410.9210.537.1274.97360.000720731FLGEORGIA-PACIFIC CORP. PULP/PAPER MILL15Low Sulfur Oil (0.9%)3,7034,32901,34231$2,435,476$1,815410.9210.537.1274.97360.000720731NCSOUTHERN STATES CHEMICALG-21Increase % Conversion9311,112094685$427,461$452216.645.1312.5764.48410.000619894VASTONE CONTAINER CORP2FGD3,5624,00643,58990$17,362,178$4,838573.686.988.5859.88840.000575715TNBOWATER NEWSPRINT & DIRECTORY015FGD5,2325,87145,26090$23,758,976$4,517538.1610.915.1055.6410.000534884TNINTERTRADE HOLDINGS, INC.001Increase % Conversion1,8372,53042,39895$4,141,525$1,727492.735.135.1026.1630.000251508102 6 B 8.1.1 Four Factor Analysis of Sources Effecting Visibility at Cape Romain

7 8 B U 8.1.1.1 Effectiveness of Addition Control Technology Options

In general the following control technologies are typical options considered by facilities whose emissions include visibili ty impacting pollutants.

Fabric Filters: In a fabric filter, flue gas passes through a ti ghtly woven or felted fa bric, causing particles in the gas to be collected on the fabric by sieving, electrostatic attraction, and other mechanisms. Extra compartments for maintena nce or offline cleaning generally increase the reliability, but also the expense of capital costs. However, fabric filter baghouses in general provide high collection e fficiencies for both coarse a nd fine particulates and are relatively insensitive to fluctua tions in gas stream conditions.

ESPs: ESPs are generally considered the most wide ly applied particulat e removal device for EGUs. In fact ESPs are in use by about 80 percent of the U.S. coal power plants (Monroe, 2003). These devices us e electrical forces to move particles entrained within an exhaust stream onto collector plates.

Several major hurdles exist with EGU use of a wet ESP. First, without the presence of a wet FGD system, the wet ESP adds additional cost, increases water demand on the plant, generates a visible moisture plume at the stack outlet, and introduces a wastewater stream that is generated by the wet ESP. In addition to these issues, the capital cost of a wet ESP is high as compared to other technologies due to the higher cost of the alloy materials required. In addition, a higher grade of mate rial is required to withstand the highly corrosive conditions presented by the wet and acidic flue gas stream. Consequently, wet ESPs have seen limited application to large-sc ale, coal-fired utility boilers in the past.

Flue Gas Desulphurization (FGD): The most common method to remove SO 2 from coal-fired power plant flue gas is a wet scrubber. Most FGD processes use wet lim estone, forced-oxidation scrubbing. Another process for SO 2 control, used widely for low sulfur Western coals, is a lime-based spray dryer followed by a bag house that collects bo th the reacted lime along with all of the coal ash. However, this approach is onl y effective for those sources using eastern bituminous coal and unfortunately is not widely applicable to all bituminous coal applications. This technology is only effective for SO 2 control for low sulfur coals, is more expensive than the alternatives, and cr eates a large waste stream that has to be carefully handled for disposal (Monroe, 2003).

For those non-EGUs the cost of FGD may be substantially higher than for EGUs. The total costs include the capital costs associ ated with constructing a FGD system, the operation and maintenance, and waste handli ng costs. The cost per ton will vary significantly based on boiler si ze and type, with a total cost ranging between $3,000 and103 $20,000 dollars per ton. In addition, FGD ha s been found to slightly reduce energy production. The process itself requires that more fuel is burned pe r unit energy produced. In addition, FGD systems also experience an increase in byproducts including wastewater and increased carbon emissions.

Compact Hybrid Particulate Collector (COHPACTM): Activated Carbon Injection is another control technology used for part iculate control is a high A/C ratio fabric filter installed after an existing particulate control device (typically a cold side ESP). Commonly referred to as a Compact Hybrid Particulate Collector (COHPACTM), this technology was develope d and trademarked by the Electric Power Research Institute (EPRI).

The design of activated carbon injection typically relies upon the existing equipment (ESPs) used to remove fly ash from the flue gas to also remove the added activated carbon. A large disadvantage is that the carbon will contaminate the fly ash making it unusable for recycling and may threaten the pe rformance of the electrostatic precipitator for its intended use of removing fly ash. In jection of activated car bon in a bag house will not need as much activated carbon as an electrostatic precipita tor, but will also contaminate the fly ash. Another more signifi cant drawback to this technology is that the domestic supply of activated carbon is currently not suffi cient (Monroe, 2003).

The Max-9 Electrostatic Fabric Filter is a dust collection technology that combines discharge electrodes and fabric filters. The Max-9 ESFF is fundamentally an electrostatic precipitator/pulse-jet baghouse hybrid, employi ng high voltage discharge electrodes to charge particulate, but with fa bric filters instead of collect ing plates in the casing. The technology was developed through a partnership of the Environmental Protection Agency (EPA) and Southern Research Institute (SRI) to address fine particulate and the need for a compact footprint. However, to date, the GE Max-9 has not been applied to any largescale, coal-fired boilers and is not considered commercially available for application.

Coal Washing: Mechanical cleaning of coal entails separating out non-coal material in a liquid medium (commonly called “washing”). The washing medium is an aqueous chemical solution prepared to enhance wettability and dissociation of the coal and non-coal materials or to produce specific gravities ca librated higher than water alone. Wet or “hydraulic” cleaning techniques may also include part icle agitation by aeration. To meet environmental regulations, technically advanced wash plants can remove as much as 40 percent of the inorganic sulf ur in coal. Appendix H.3 fu rther examines the issue of variations of varieties of sulfur content in American coal that in turn affect the results of separation methods. Furthermore, while the costs associated with coal washing are relatively low (approximately $500/ton), the non-air quality environmental impacts are significant in that it creates a by-product in the form of po lluted waste streams. In 2002, about 25 percent of the raw coal processed through preparati on plants was discharged to waste ponds as “refuse,” composed of shale, clay, coal, low-grad e shaley coal, and preparation chemicals (R. Bonskowski, W. Watson, and F. Freme, 2006).104 7 9 B U 8.1.1.2 Existing Source Specific Controls

MeadWestvaco: MeadWestvaco Corporation operates an unbleached paper mill in North Charleston, South Carolina. MeadWestvaco’s primary source of SO 2 emissions are from its two recovery boilers: Recovery Boiler No. 1 and Rec over Boiler No. 9. The units were installed in 1984 and 1966 respectively. Emissions from Recovery Boiler No. 1 are controlled by an ESP installed in 1984. Emissions from Recovery Boiler No. 9 are also controlled by an ESP, installed in 1977. The No.1 and No.9 recovery boilers are permitted to burn No.6 fuel oil, black liquor so lids, spec oil and pitch as fuel. However, on August 25, 2006, MeadWestvaco submitted a Title V permit renewal application that included a 0.5 percent sulfur limit for fuel oil burned in the No. 9 Recovery Boiler. The No. 1 Recovery Boiler has a SO 2 PSD limit that requires the boiler to limit the amount of high sulfur No. 6 fuel oil burned.

International Paper, Georgetown: The International Pa per Georgetown, South Carolina Pulp and Paper Mill is located in Ge orgetown, South Carolina. The Mill began operations in 1937. Primary operations at the mill include multiple fuel-fired boilers, chemical recovery operations, wood pul ping operations, bleached papermaking, and additional operations and equipment necessary to support these opera tions. Unit ID 01 includes equipment that supports the Power and Recovery Operations at the Mill and consists of: No. 1 Power Boiler, No. 2 Powe r Boiler, No. 1 Recovery Boiler, and No. 2 Recovery Boiler.

• No. 1 Power Boiler: The No. 1 Power Boiler is capable of burning bark, coal/synfuel, No. 6 fuel oil, natural gas, and propane. In addition, the boilers are capable of burning the following alternativ e fuels: wood-waste (including pulping rejects and peat), used oil, cleanup medi a materials from virgin No. 6 fuel oil spills/leaks, wastewater sludge, containerb oard clippings/pellets, paper waste, and tire-derived fuel (TDF) and other rubber products. Emissions from this unit are controlled by a cyclone and electrostatic precipitator.

• No. 2 Power Boiler: The No. 2 Powe r Boiler is capable of burning bark, coal/synfuel, No. 6 fuel oil, natural gas, and propane. In addition, the boilers are capable of burning the following alternativ e fuels: wood-waste (including pulping rejects and peat), used oil, cleanup medi a materials from virgin No. 6 fuel oil spills/leaks, wastewater sludge, containerb oard clippings/pellets, paper waste, and tire-derived fuel (TDF) and other rubber products. Emissions from this unit are controlled by a cyclone and el ectrostatic precipitator.

• No. 1 Recovery Boiler: This recovery boiler fires black liquor solids and No. 6 fuel oil, natural gas, and/or propane as auxiliary fuels. The No. 1 Recovery Boiler nominal throughput is 3.2 million pounds of black liquor solids per day, 494 MMBtu/hr from firing fuel oil, 24,000 scf/hr of natural gas, and 60 MMBtu/hr of propane. The unit is controlled by an electrostatic precipitator.

• No. 2 Recovery Boiler: This recovery boiler fires black liquor solids and No. 6 fuel oil, natural gas, and/or propane as auxiliary fuels. The No. 2 Recovery Boiler nominal throughput is 4.1 million pounds of black liquor solids per day, 494 MMBtu/hr from firing fuel oil, 24,000 scf/hr of natural gas, and 60 MMBtu/hr of105 propane. The unit is controll ed by an electrostatic pr ecipitator followed by a wet scrubber.

International Paper, Savannah: The Savannah Mill is located in Chatham County, Georgia, across the Savannah River from South Carolina. It occupies a 450-acre site, and produces unbleached kraft linerboard, corrugati ng medium, and saturating kraft paper. The Savannah Mill’s primary source of proce ss steam used in its pulp and papermaking operations is the No. 13 Power Boiler unit with a maximum firing rate of up to 1,280 MMBtu/hour. The boiler is equipped with an electrostatic precipitator (ESP) for controlling particulate emissions. The service li fe of the No. 13 Boiler is estimated to be at least 40 years from installation, or until approximately 2022.

Giant Cement: Giant Cement Company owns and operates a Portland cement manufacturing facility located in Harleyville, South Carolina . Giant’s operations at this facility include limestone (marl) quarry ing, stone crushing, cement manufacturing, packaging, and shipping. The facility operate s one dry process cement kiln system to produce cement clinker. The cement kiln system is equipped with fabr ic filters to control PM emissions. In 2005, Giant completed the modernization of its cement manufacturing facility at this site, which replaced four wet-process cement kilns with the newer single dry-process kiln. In order to generate e nough heat necessary to produce clinker, the facility is permitted (Part 70 Title V/PSD Pe rmit issued in 2003) to combust a multitude of fuels consisting of more conventional fuels (e.g., oil, coal, natural gas) as well as waste derived fuels (e.g., liquid and solid hazardous wa ste-derived fuels). Giant’s PSD permit application utilized an emi ssion factor of 3.5 pounds of SO 2 per ton of clinker produced, substantially lowering emissions reported from its previous wet-process kiln system.

Holcim: Holcim, Inc. operates a cement plant near Holly Hill, SC. This process consists of two wet process kilns and associated proc ess equipment that we re replaced in 2003 by a new preheater/precalciner kiln with associated process equipment. The kiln system consists of a preheater/precalciner kiln with an in-line raw mill. The kiln progressively heats the raw materials to over 2600 degrees Fahrenheit, producing clinker from the raw materials. The kiln is permitted to burn coal, carbon based materials such as coke, No. 6 fuel oil, natural gas, hazardous waste derived fuel (HWDF), tire derived fuel (TDF), and non-hazardous waste derived fuel. Emissions from the kiln are controlled by an ESP and fabric filters.

DAK Americas: DAK operates a facility (known as the Cooper River Facility) in Moncks Corner, South Carolina, just north of Charleston. The Cooper River Facility (formerly operated by Dupont) has three main process areas consisting of: 1) a continuous polymerization area for polyethylene terephthala te (PET) production, 2) a PET finishing area to produ ce fiber products, and 3) a PET finishing are to produce bottle resin products. The Cooper Rive r Facility’s primary source of SO 2 emissions are from its coal-fired boiler (Boiler No. 2). Boiler No. 2 is a 206 MMBtu/hr b ituminous coal-fired boiler. Boiler No. 2 is equipped with a ba ghouse to control PM emissions and a one percent sulfur limit on the coal used in Boiler No. 2 controls sulfur emissions. Compliance with this limit is demonstrat ed through calculations based on 12-month106 rolling sum averages from supplier certifications as stated in the faci lity’s Part 70 Title V Permit. The service life of Boiler No. 2 is estimated to be at least 50 years from installation, or at least 31 additional years.

8 0 B U 8.1.1.3 Source Specific Control Technology Options

MeadWestvaco: Since Recovery Boiler No. 9 currently opera tes using low sulfur No. 6 fuel oil, MeadWestvaco conducted an abbreviated four factor analysis focused on evaluating the feasibility of burning alternative low-sulfur fuels for Recovery Boiler No. 1. This analysis is summarized in Appendix N. The cost altern atives to switching from high sulfur (2.1 percent sulf ur content) No. 6 fuel oil to low sulfur No. 6 fuel oil (0.5 percent sulfur content) for Recovery Boiler No. 1 range from $3,359.00 per ton to $7,463.00 per ton of SO 2 removed; above the $2,000.00 cost per ton threshold. Switching to lower sulfur fuel oil (e.g., No. 2 Fuel oil) and na tural gas would also require equipment and operating modifications that w ould further increase costs an d require significant time to implement. Switching to lower sulfur fuels would also result in non -air quality impacts related to additional environmental and safety issues associated with increased truck traffic and additional manpower and costs for monitoring and maintaining systems.

IP Georgetown: In conducting a four factor analysis, IP Georgetown focused its evaluation on the feasibility of burning alternative low-sulfur fuels (the costs of which were identifies as below the cost threshold of $2,000.00 per ton). This analysis is summarized in Appendix N. The cost alternatives associated with switching to lower sulfur fuel range from $6,417.00 per ton to $ 10,012.00 per ton of SO 2 removed. In addition, the changes required to modify the fuel tanks, piping sy stems, delivery systems, and burners would also require considerable time to implement. The additional non-air quality impacts of switching to lower sulfur fuels would also limit the IP Georgeto wn Mill’s ability to engage in burning tire-derived fuels, create additional environmental and safety issues associated with increased truck traffic, a nd require secondary containment associated with increased tank storage.

IP Savannah: In conducting a four factor analysis, IP Sava nnah focused its evaluation on the feasibility of several SO 2 control technologies, including: 1) a wet caustic packed scrubber (FGD), 2) a wet limestone spray tower (FGD), 3) a semi-dry lime spray dryer, 4) dry sorbent injection (DSI), and 5) lower sulfur fuels. These technologies were chosen because they have been demonstrated and are commercially av ailable for coal-fired utility boilers. All of the control options have been demonstr ated and are consider ed technologically feasible. The cost associated with each of these alterna tives was calculated using the Coal Utility Environmental Cost (CUECost) so ftware tool and determined to be above the $2,000.00 cost per ton threshold (w ith costs ranging from $2,950.00 to $34,927.00 per ton of SO 2 removed). These alternatives would take from 2010 to 2012 to implement and result in significant non-air quality impact s to include: substantial increased use of fresh water, the generation of industrial wast ewater, potentially subs tantial incr eases in107 solid waste generated, additional environmen tal and safety issues associated with increased truck traffic, and require additional ma npower and costs for monitoring and maintaining systems.

Giant: In conducting a four factor an alysis, Giant focused its evaluation on the feasibility of burning lower sulfur fuels (the costs of whic h were identified by the Department as below the cost threshold of $2,000.00 per ton). This analysis is summari zed in Appendix N. Giant determined that its costs associated wi th switching to lower sulfur fuel would range from $7,801.00 per ton to $11,152.00 per ton of SO 2 removed. The time required to install fuel tanks, piping systems, and deliver y systems capable of being used with lower sulfur fuel oils would also require considerable time to implement. The modernized cement kiln system (completed in 2005) provid es Giant the option to operate with up to 100 percent substitution rates of conventio nal fuels. The addi tional non-air quality impacts of switching to lower su lfur fuels would also limit the facility’s ability to engage in burning liquid and solid waste-derived fuel s, effectively adding to the non-air quality impacts of finding alternate means of disposi ng and storing this waste.

Holcim: In conducting a four factor analysis, Holcim also focused its evaluation on the feasibility of burning lower sulfur fuels (the costs of which were identified by the Department as below the cost threshold of $2,000.00 per ton). This analysis is summarized in Appendix N. Holcim’s analysis determined that the cost to switch to fuel oil with a sulfur content of 0.9 percent would cost $41,039.00 per ton of SO 2 removed. Moreover, the additional non-air quality impacts of switchi ng to lower sulfur fuels w ould also limit the facility’s ability to engage in burning hazardous waste and tire derived fuel, thereby potentially increasing the non-air quality impacts associated with disposing and storing this waste. Holcim also contends th at up to 95 percent of SO 2 emission produced at its Holly Hill plant can be absorbed in the process. This is because the raw materials used to produce “kiln feed” at the Holly Hill plant consists primarily of limestone (or other forms of calcium carbonate) and its new kiln system uses a highly alkaline feed material. Holcim’s analysis indicates that the new process could result in potential SO 2 reductions of 160 tons per year.

DAK Americas: In conducting a four factor analysis, DAK Americas focused its evaluation on the feasibility of several SO 2 control technologies, in cluding: (1) low sulfur coal, (2) wet flue gas desulfurization (FGD), (3 ) spray dryer absorber (SDA), (4) fluidized-bed combustion (FBC) with limestone, and (5) dry sorbent in jection (DSI). These technologies were chosen because they have been demonstrat ed and are commercially available for coalfired utility boilers. Of th e aforementioned control techno logies all but the FBC were deemed to be technologically feasible. Of the remaining four options, all were determined to be above the cost threshol d of $2,000.00 per ton (see Appendix N). In addition each of the control technologies would take from one to three years to implement and (with the exception of fuel switching) result in significant non-air quality108 impacts to include: substantial increased use of fresh water, the generation of industrial wastewater, and potentially substantial in creases in solid waste generated.

The results of the four factor analyses (contained in Appendi x N) indicate that no additional controls are reasona ble at this time. However, further future reductions by way of additional controls have been disc ussed and will be evaluated during the next planning stage.109 8 B 

### 9.0 MONITORING STRATEGIES

The State Implementation Plan is to be accompanied by a strategy for monitoring regional haze visibility impairment. Specifi cally, the Regional Haze Rule states at 40 CFR 51.308(d)(4):

“(4) Monitoring strategy and other im plementation plan requirements. The State must submit with the implementation plan a 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 attributab le visibility impairment . Compliance with this requirement may be met through particip ation in the IMPROVE network. The implementation plan must also provide for the following: (i) The establishment of any additional monitoring sites or equipment needed to assess whether reasonable progress goals to address regional haze for all mandatory Class I Federa l areas within the State are being achieved. (ii)-(vi) [Other implementation plan re quirements that pertain to reporting and use of monitoring data and an emission inventory.]”

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

1. Track the expected visibility impr ovements resulting from emissions reductions identified in this SIP. 2. Better understand the atmospheric processes of importance to haze. 3. Identify chemical species in the ambien t particulate matter and relate them to emissions from sources. 4. Evaluate regional air quality models for haze and construct relative response factors (RRFs) for using those models.

The primary monitoring network for regiona l haze, both nationwide and in South Carolina, 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 base d on, or directly comparable to, IMPROVE. The IMPROVE measurements provide the only long-term record available for tracking visibility improvement or degradation and ther efore South Carolina intends to rely on the IMPROVE network for complying with the re gional haze monitoring requirement in the Regional Haze Rule.

There is currently one IMPROVE site in the South Carolina, representing the Cape Romain National Wildlife Refuge Class I Federal area (ROMA1). The State supplements the IMPROVE sampling by operating additional co -located monitoring. Monitoring at Cape Romain includes:110 • A Tapered Element Oscillating Micr obalance (TEOM) for continuously measuring PM 2.5 mass concentration;

• An Aethalometer for continuous ly measuring Black Carbon;

• An integrating nephelometer, supported by VISTAS, for continuously measuring light scattering; and

• Continuous monitoring of NO2 and SO2 precursor gasses.

The Optec NGN-2 Nephalometer has been tran sferred to South Carolina. The operation and data reporting is supported by VISTAS through 2008. Funding will be sought to continue operation but is not assured.

Additional haze-related measurem ents were made in South Carolina in 2002-2005 as part of special monitoring studi es by the VISTAS regional pl anning organization. These studies were intended to better u nderstand source contributions to PM 2.5 mass and visibility. They included con tinuous monitoring of sulfate, nitrate, and carbon to better understand daily trends in PM 2.5 , detailed analyses of car bon collected on high volume filters to identify source contributions to carbon, and additional analyses of sodium and ammonium on IMPROVE filter samples. Fu nding does not exist to continue these special studies; however, the equipment has been transferred to SC DHEC.

South Carolina has acquired several continuous sulfate monitors a nd expects to operate them at urban and rural sites to further the understanding of both PM 2.5 and visibility formation and trends in South Carolina. SC DHEC will operate the units discussed above as long as funds allow.

In addition, SC DHEC operates a fairly comprehensive PM 2.5 network of the filter based Federal Reference Method samplers, continuou s mass monitors (TEOMs), STN protocol filter based speciated samplers and the continuous speciated particulate monitors described above. These PM 2.5 measurements help SC DHEC characterize air pollution levels in areas across the stat e, and therefore aid in the anal ysis of visibility improvement in and near the Class I areas.

The IMPROVE measurements are central to South Carolina’s regional haze monitoring strategy, and it is difficult to visualize how the objectives listed above could be met without the monitoring provided by IMPR OVE. Any reduction in the scope of the IMPROVE network in South Carolina and neighboring Class I Federal areas would jeopardize the State’s ability to demonstr ate reasonable progress toward visibility improvement in its Class I areas and plan for appropriate future prog ress. In particular, South Carolina’s regional haze strategy relies on emission reductions that will result from the CAIR and reductions in ne ighboring States, which occur on different time scales and will most likely not be spatially uniform. Monitoring at representative Class I area is important to document the different air quali ty responses to the emissions reductions.

Because the current IMPROVE monitor at Cape Romain represents a unique Class I Federal area, and a significant component of the contributions are re gional, the reduction of the IMPROVE network by shutting down the existing IMPROVE site or any the closer111 IMPROVE monitoring sites impedes tracking the progress or planning improvements at Cape Romain. In the event the Cape Romain monitoring is scheduled for elimination, South Carolina, in consultation with the US EPA and relevant FLMs, will develop an alternative approach for meeting the track ing goal, perhaps by seeking contingency funding to carry out limited monitoring or by relying on data from nearby urban monitoring sites to demonstrate trends in speciated PM 2.5 mass.

Data produced by the IMPROVE monitoring netw ork will be used ne arly continuously for preparing the five-year progress reports a nd the 10-year SIP revisions, each of which relies on analysis of the preceding five years of data. Consequentl y, the monitoring data from the IMPROVE sites needs to be readily accessible and to be kept up to date. Presumably, IMPROVE will continue to proce ss information from its own measurements at about the same pace and with the same atte ntion to quality as it has shown in the recent past. The VIEWS web site has been main tained by VISTAS and the other Regional Planning Organizations to provide ready access to the IMPROVE data and data analysis tools. South Carolina is encouraging VISTAS and the other RPOs to maintain VIEWS or a similar data management system to facilita te availability analysis of the IMPROVE and visibility related data.112 9 B 

### 10.0 INTERSTATE CONSULTATION ON SC CONTRIBUTION TO

VISIBILITY IMPAIRMENT IN CLASS I FEDERAL AREAS IN

NEIGHBORING STATES

As required by 40 CFR Section 51.308(i), each state’s regional haze SIP must include interstate consultation between the states on the implementation of the visibility protection program.

The VISTAS states have jointly developed th e technical analysis necessary to define visibility improvement by 2018 under existing fe deral and state regulations compared to the uniform rate of progress, SO 2 AOIs for each Class I Federal area, and the methods to prioritize contributi ons from individual so urces within the AOI. The VISTAS states collectively accept the conclusi ons of this analysis.

In December 2006, the VISTAS State Air Direct ors held their first formal consultation meeting to review the Base G modeling results and the SO 2 AOI analysis. The Air Directors agreed to look at reasonable co ntrol measures for sources on the lists for SO 2 AOIs. This meant that each state would cons ider sources within their state as well as identify sources in neighboring states that th ey would like neighboring states to consider. States acknowledged that the review process would necessarily differ among states since some Class I Federal areas are projected to have greater improvements than the uniform rate of progress.

In May 2007, the VISTAS State Air Director s met for their second formal interstate consultation. At this meeting, states shared the aforementioned lists of sources in their states and those sources being considered in neighboring states . States also shared their criteria for listing sources as well as their plans for further interstate consultation.

South Carolina has evaluated the impacts of both South Carolina sources and sources in neighboring states and has determined that no additional reasonable control measures are necessary in order to mitigate impacts on eith er South Carolina’s or neighboring states’ Class I Federal areas. South Carolina ha s also consulted with neighboring states regarding this decision not to require any additional cont rols. Analysis of impacts from South Carolina sources is discussed in greater detail in Appendix J.

Likewise, neighboring states ha ve shared suggestions for c ontrols to be adopted for sources within the South Carolina Class I Federal area AOI. After consulting with neighboring states and evaluating each source identified as contributing to visibility impairment at Cape Romain (via the sources’ four factor analyses), South Carolina has not asked any neighboring state to adopt any measures beyond what has been identified as reasonable in its respective SIP.113 South Carolina received requests for informati on and/or invitations to consult with the states of New Jersey (NJ) and New Hampsh ire (NH). The MANE-VU states of NJ and NH sent letters to South Carolina in the spring of 2007 stating that, based on 2002 emissions, South Carolina was found to contribute to visibility impairment in each of its corresponding Class I Federal areas. As such, these MANE-VU states asked South Carolina to participate in further consu ltation with MANE-VU during the summer of 2007. SC DHEC sent response letters to NJ an d NH and expressed its intent to consult with them through VISTAS representatives. SC DHEC’s responses to NJ and NH, along with coordination between the VISTAS and MANE-VU RPOs, are referenced in Appendix J.

Georgia (GA) and North Carolina (NC) also submitted letters to South Carolina requesting that SC DHEC consider adding se veral in-state sources to the SC DHEC’s final “Four-Factor Analysis” list of facilities so as to account for those facilities that GA believes are likely to contribute more than 0.5 percent, and NC be lieve are likely to contribute more than one percent to the total visibility impairment at one or more Class I Federal areas in GA and NC respectively. Th ese sources have been identified in Tables 10.0-1 and 10.0-2. In its response to this re quest, SC DHEC provided GA and NC with a list of sources identified as likely to contri bute by one percent or more to visibility impairment in South Carolina as well as a jus tification as to why or why not each facility would be included in South Carolina’s final “F our-Factor Analysis” list of facilities. SC DHEC’s responses are included as Appendix J.

In addition, SC DHEC also analyzed whic h sources within the State are likely to contribute to visibility impairment in Class I Federal areas in other states. This analysis includes the distance to each Class I Federal ar ea as well as the magnitude of the source’s impact. This analysis is included in Appendix J.114Table 10.0 – 1 South Carolina Sources Affecting Class I Federal Areas in North CarolinaPast/ProjectedEmissions (tpy)South Carolina Facility Sources RelativeContribution2002 2018Distance (km) to Class 1AreasExistingControls0.0327451392,386 4,347 SHRO – 93Duke Energy – Lee 0010.0118113622,386 4,347 JOKI - 165ESP0.0311951972,643 4,140 SHRO - 93Duke Energy – Lee 0020.0112525362,643 4,140 JOKI - 165ESP0.0548090084,384 7,274 SHRO - 93Duke Energy – Lee 0030.0197690564,384 7,274 JOKI - 165ESPSantee Cooper – Jefferies 003 0.01140849 11,315 6,761 SWAN - 425 ESPs (1969/75)Santee Cooper – Jefferies 004 0.011150031 11,789 6,609 SWAN - 425 ESPs (1970/75)115Table 10.0 - 2 South Carolina Sources Affecting Class I Federal Areas in GeorgiaPast/ProjectedEmissions (tpy)South Carolina Facility Sources RelativeContribution2002 2018Distance (km) to GA Class 1AreasExistingControlsSantee Cooper – Jefferies 003 0.0107831745 11,315 6,761 WOLF - 245 ESPs (1969/75)Santee Cooper – Jefferies 004 0.0105411703 11,789 6,609 WOLF - 242 ESPs (1970/75)Santee Cooper – Cross 002 0.005396825410,260 3,380 WOLF – 244 ESP (1984)SCR (1995)FGD (1995)Santee Cooper – Cross 03 0.0061226033 0 3,835 WOLF – 244Giant Cement 005 0.0068471727 2,825 3,893 WOLF – 223 Fabric FiltersHolcim – Holly Hill 002 0.0055876516 496 492 WOLF - 235 ESPFabric Filters2,315 2,706 OKEF – 297MeadWestvaco 0060.0081280804 2,315 2,706 WOLF - 213ESP(1984 + 1977)0.0066036666 6,214 5,203 OKEF – 295SCE&G Canadys 0010.0165538159 6,214 5,203 WOLF – 201ESP0.0065278568 6,585 5,144 OKEF – 295SCE&G Canadys 0020.0163651554 6,585 5,144 WOLF - 201ESP116 1 0 B 

### 11.0 COMPREHENSIVE PERIODI C IMPLEMENTATION PLAN

REVISIONS

40 CFR Section 51.308(f) requires South Carolin a to revise its regi onal haze SIP and submit a plan revision to EPA by July 31, 2018, and every ten years thereafter. In accordance with the requirements listed in Section 51.308(f) of the RHR, South Carolina commits to revising and submitting this regional haze SIP by July 31, 2018, and every ten years thereafter.

In addition, Section 51.308(g) requires period ic reports evaluating progress towards the RPGs established for each mandatory Class I area. “The EPA believes that a requirement for regular SIP revisions will result in a mo re effective program over time and provide a focus for demonstrating ongoing progress and ma king mid-course corrections in emission strategies” (62 FR 41151).

In accordance with the requirements listed in Section 51.308(g) of the RHR, South Carolina commits to submitting a report on reasonable progress to EPA every five years following the initial submittal of the SIP. The report will be in the form of a SIP revision. The reasonable progress report will evaluate the progress made towards the RPG for each mandatory Class I Federal area located with in South Carolina and in each mandatory Class I Federal area located outside South Carolina that may be affected by emissions from South Carolina sources

.
The requirements listed in 51.308( g) include the following:

1. Description of the stat us of implementation; 2. Summary of emission reductions achieved thus far, including especially the status of implementation of the CAIR complia nce plans for EGUs compared to the control assumed in the modeling. [SC DHEC recognizes that the 2018 projections of EGU controls from the IP M runs represent one solution to how the CAIR requirements will be met. By th e time of the first periodic report, SC DHEC anticipates that the actual comp liance strategy for the various utility companies will be much more defined. An assessment of those actual compliance plans will be done for the first periodic report.] 3. Assessment of changes in visibility conditions at each class I area (current vs. baseline), expressed as 5-year averages of annual values for 20 percent best and worst days; 4. Analysis of emission changes over the 5- year period, identif ied by source or activity;
5. Analysis of any significant changes in or out of th e State which have impeded

progress; 6. Assessment of the sufficiency of the implementation plan to meet RPGs;
7. Review and any modifications to our visibility monitoring plan.117 All requirements listed in 51.308(g) shall be addressed in the SIP revision for reasonable

progress.

More specifically, the in itial 5-year Progress Report (due in 2013) will examine the effect of emission reductions as well as seek to evaluate the effectiveness of emission management measures implemented. Therefore this Report will provide for a comparison of emission inventories, ultimately expressing the change in visibility for the most impaired and least impaired days over th e past five years. Appendix K references recommendations to consider when perf orming mid-course reviews.

Moreover, due to the uncerta inty of some measures, th is Progress Re port will also provide SC DHEC the opportunity to evalua te the overall effectiveness of proposed measures to reduce visibility impairment to include the effect of state and federal measures such as CAIR.

In keeping with the EPA’s recommendations related to consultation, each five-year review will also enlist the support of approp riate state, local, and tribal air pollution control agencies as well as the corres ponding FLMs. SC DHE C also commits to continued consultation among the states and FLMs as it relates to any SIP revisions and/or the implementation of other program s having the potential to contribute to visibility impairment. The State anticipates th at this will occur in much the same fashion as did the pre-hearing meetings, comment s, and responses, required by 40 CFR 51.308(i)(3) and included in Appendix J.

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 f unding is available, to support the next long term strategy. The following is an ove rall summary; Appendix K contains a fuller discussion of possible technical improvements.

Continued improvements are needed in the in tegrated one-atmosphere air quality models that are used to project air quality respons es to emissions reductions. As 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 performan ce for the CMAQ regional air quality model is overall good. However, sulfate deposition is frequently overestimated in the models, particularly in the summer months. At the coastal sites, when winds are blowing from the Gulf of Mexico or Atlantic Ocean, CM AQ underestimates measured sulfate at the monitors. CMAQ’s processes also should be re viewed for sulfate formation over water. Nitrate is overestimated by the model in the winter and underestimated in the summer, although summer monitored values of nitrate ar e very low. Additional improvements in seasonal allocation of ammonia emissions w ould improve model estimates of ammonium nitrate formation. Organic carbon is genera lly underestimated in the summer months. Improvements are needed in the characteri zation of both primary carbon emissions and formation of secondary organic carbon.118 Other improvements needed include be tter tools for organic carbon source apportionment, and more consistent measur ement techniques between rural and urban monitoring networks. To impr ove understanding of the contri bution of fire from natural forest fires, prescribed burning, land clea ring, and agricultural burning, states need improved record keeping. Addi tional improvements to intern ational emissions inventory are also needed, to impr ove understanding of boundary conditions for our modeling domain and of the contributions from internat ional emissions to pollutant concentrations at the VISTAS Class I areas.119 1 B 

### 12.0 DETERMINATION OF THE ADEQUACY OF THE EXISTING

PLAN

Depending on the findings of the five-year pr ogress report, South Carolina commits to taking one of the actions listed in 40 CFR Section 51.308(h). The findings of the fiveyear progress report will determine wh ich action is appropriate and necessary.

U List of Possible Actions - 40 CFR Section 51.308(h)

1) If South Carolina determines that the ex isting SIP requires no further substantive revision in order to achieve established goals, it will provide to the Administrator a declaration that further revi sion of the SIP are not needed. 2) If South Carolina determines that the ex isting SIP may be inadequate to ensure reasonable progress due to emissions from other states that participated in the regional planning process, it will provide notification to the Administrator and collaborate with the states that particip ated in regional planning to address the SIP’s deficiencies. 3) If South Carolina determines that the current SIP may be inadequate to ensure reasonable progress due to emissions fr om another country, it will provide notification of such, along with available information making such a demonstration, to the Administrator. 4) If South Carolina determines that the existing SIP is inadequate to ensure reasonable progress due to emissions within the state, it will revise its SIP to address the plan’s deficiencies within one year after submitting such notification to the Administrator.