Regional Haze 

State Implementation Plan 

for 

South Carolina Class I Federal Areas 

Prepared by 

South Carolina Department of Health 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 Environmental 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 caused by absorption and scattering of light by 

fine particles. Visibility impairment is considered the “best understood and most easily 

measured effect of air pollution.” Regional haze is caused by sources and activities that 

emit these 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). 

The Clean Air Act mandates requirements to protect visibility, especially in Class I areas. 

In 1999, the U.S. Environmental Protection Agency (EPA) finalized the Regional Haze 

Rule. The rule calls for state, tribal, and federal agencies to work together to improve 

visibility in 156 national parks and wilderness 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 pollution that causes visibility impairment. 

These plans should establish goals and emission reduction strategies based on trends from 

various sources including area source emissions, mobile source emissions (both on-road 

and non-road emissions), commercial marine emissions, biogenic emissions, and wildfire 

and agriculture emissions. Under the Regional 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 returned to natural conditions. 

Five multi-state regional planning organizations (RPOs) currently are working together to 

develop the technical basis for these State plans. The products of the regional planning 

organizations will be used by States to establish monitoring strategies for evaluating 

visibility conditions, baselines, and trends, and to develop long-term (10 to 15 years) 

strategies for making "reasonable progress" toward eliminating 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 developed a SIP to address visibility 

impairment in the State’s Class I Federal Area--the Cape Romain Wilderness Area--

located on the eastern coast of South Carolina. 

In developing the SIP, South Carolina has prepared a long-term strategy and examined 

the possible application of BART in order to establish reasonable 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 for 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 and 
3 

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 number 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 Modeling, 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 Attribution including sample 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 and Air Quality Modeling 

Georgia Institute of Technology: Emissions 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 Inventory and Projections, CALPUFF 

training 

E.H. Pechan and Associates: 2002 Mobile Inventory 

Research Triangle Institute: Chemical 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 final project report and draft manuscript 

Ivar Tombach, Environmental Consultant 

TRC, Inc: BART Modeling 
5 

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 project 
6 

TABLE OF CONTENTS 

ULIST OF ACRONYMS/ABBREVIATIONSU............................................................. 11 

U1.0U UINTRODUCTIONU ................................................................................................. 13

U1.1 What Is Regional Haze?U........................................................................................ 13 

U1.2 What Are The Requirements Under The Clean Air Act For Addressing Regional 

Haze?U........................................................................................................................... 13 

U1.3 General Overview Of Regional Haze SIP RequirementsU ..................................... 15 

U1.4 Class I Federal Area In South CarolinaU ................................................................ 16 

U1.5 State And Federal Land Manager (FLM) CoordinationU ....................................... 17 

U2.0U UASSESSMENT OF BASELINE AND CURRENT CONDITIONS AND ESTIMATE 

OF NATURAL BACKGROUND CONDITIONS IN CLASS I AREASU.............................. 19

U2.1 Estimating Natural Conditions For South Carolina Class I Federal AreaU ............ 21 

U2.2 Estimating Baseline Conditions For South Carolina Class I Federal AreaU .......... 21 

U2.3 Summary of Natural Background And Baseline Conditions For South Carolina 

Class I Federal AreaU.................................................................................................... 22 

U2.4 Pollutant Contributions To Visibility Impairment (2000-2004 Baseline Data)U.... 22 

U3.0U UGLIDEPATHS TO NATURAL CONDITIONS IN 2064U........................................ 27

U3.1 Glidepath For Class I Federal Area In South CarolinaU......................................... 27 

U4.0U UNATURE OF THE PROBLEM: CHIEF CAUSES OF VISIBILITY IMPAIRMENT 

IN SOUTH CAROLINA CLASS I FEDERAL AREASU....................................................... 29

U4.1 Baseline Emissions InventoryU............................................................................... 29 

U4.1.1 Stationary Point SourcesU.................................................................................. 30 

U4.1.2 Stationary Area SourcesU................................................................................... 31 

U4.1.3 Off-Road Mobile SourcesU................................................................................ 32 

U4.1.4 Highway Mobile SourcesU................................................................................. 32 

U4.1.5 Biogenic Emission SourcesU.............................................................................. 32 

U4.1.6 Summary 2002 (Base G2) Baseline Emissions Inventory for SCU ................... 33 

U4.1.7 Model Performance Improvements through Emissions Inventory 

ImprovementsU ........................................................................................................... 33 

U4.2 Assessment of Relative Contributions from Specific Pollutants and Sources 

CategoriesU ................................................................................................................... 34 

U5.0U UREGIONAL HAZE MODELING METHODS AND INPUTSU ............................... 35

U5.1 Analysis MethodU ................................................................................................... 35 

U5.2 Model SelectionU .................................................................................................... 36 

U5.2.1 Selection of Photochemical Grid ModelU.......................................................... 36 

U5.2.2 Selection of Meteorological ModelU ................................................................. 37 

U5.2.3 Selection of Emissions Processing SystemU...................................................... 38 

U5.3 Model Year SelectionU ......................................................................................... 40 

U5.4 Modeling DomainsU................................................................................................ 41 

U5.4.1 Horizontal Modeling DomainU.......................................................................... 41 

U5.4.2 Vertical Modeling DomainU .............................................................................. 43 

U6.0U UMODEL PERFORMANCE EVALUATIONU .......................................................... 44

U6.1 Modeling Performance Goals, Criteria, and ResultsU ............................................ 45 

U6.2 VISTAS Domain-Wide PerformanceU ................................................................... 46 

U6.3 South Carolina Class I Area PerformanceU ............................................................ 50 
7 

U7.0U ULONG TERM STRATEGY FOR SOUTH CAROLINA’S CLASS I FEDERAL AREAU

 53

U7.1 Overview of the Long Term Strategy Development ProcessU ............................... 53 

U7.2 Expected Visibility Results in 2018 for the Cape Romain Class I Federal Area 

under existing and planned emission controls (Base G2 Inventory)U .......................... 54 

U7.2.1 Federal and State Control RequirementsU ......................................................... 54 

U7.2.2 Additional State programs to reduce emissionsU............................................... 57 

U7.2.3 Projected 2009 and 2018 Base G2 Emissions InventoriesU............................... 57 

U7.2.4 Model Results for the 2018 Base G2 Inventory Compared to the Uniform Rate 

of Progress Glidepath for SC Class I Federal AreaU .................................................. 60 

U7.3 Relative Contributions from International Emissions to Visibility Impairment in 

2018 at VISTAS Class I areasU .................................................................................... 63 

U7.4 Relative Contributions to Visibility Impairment: Pollutants, Source Categories, 

and Geographic Areas.U................................................................................................ 67 

U7.5 Relative Contributions to Visibility Impairment: Geographic Locations of the 

Largest Emission Sources Contributing to Visibility Impairment in SC Class I Federal 

AreaU............................................................................................................................. 70

U7.5.1 Back Trajectory AnalysesU................................................................................ 70 

U7.5.2 Residence Time PlotsU....................................................................................... 70 

U7.5.3 SO2 Areas of InfluenceU .................................................................................... 72 

U7.5.4 Emission Sources Within the SO2 AOIU ........................................................... 73 

U7.5.5 Specific Source Types in the Areas of Influence for SC Class I Federal AreaU 75 

U7.6 Which Control Measures for Individual Sources Achieve Reasonable Progress?U 78 

U7.7 Which Control Measures for Individual Sources Achieve Reasonable Progress?U 79 

U7.8 BART Determinations in South CarolinaU............................................................. 82 

U7.8.1 BART-Eligible Sources in South CarolinaU...................................................... 82 

U7.8.2 Determination of Sources Subject to BART in South CarolinaU ...................... 84 

U7.8.3 Evaluation of Sources Subject to BART in South CarolinaU ............................ 86 

U7.9 Post-BART Visibility Improvement Expected at Cape Romain by 2018, 

Compared to the GlidepathU ......................................................................................... 95 

U8.0U UREASONABLE PROGRESS GOALSU .................................................................... 98

U8.1 Four Factor Analyses for Sources affecting Cape RomainU................................... 98 

U8.1.1 Four Factor Analysis of Sources Effecting Visibility at Cape RomainU......... 102 

U9.0U UMONITORING STRATEGIESU ............................................................................ 109

U10.0U UINTERSTATE CONSULTATION ON SC CONTRIBUTION TO VISIBILITY 

IMPAIRMENT IN CLASS I FEDERAL AREAS IN NEIGHBORING STATESU............... 112

U11.0U UCOMPREHENSIVE PERIODIC IMPLEMENTATION PLAN REVISIONSU ...... 116

U12.0U UDETERMINATION OF THE ADEQUACY OF THE EXISTING PLANU ............ 119
8 

LIST OF APPENDICES 

Appendix A: VISTAS Memorandum 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 Meteorological 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 Consultation Among States and RPOs 

Appendix K: Recommended Improvements for Mid-Course Review 

Appendix L: WINHAZE Images for 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 Authority 
9 

LIST OF TABLES AND FIGURES 

UFIGURE 1.2-1 GEOGRAPHICAL AREA OF REGIONAL PLANNING ORGANIZATIONSU .............. 14 

UFIGURE 1.4 - 1 SOUTH CAROLINA CLASS I FEDERAL AREAU................................................ 16 

UFIGURE 1.5 - 1 CLASS I FEDERAL AREAS IN THE VISTAS REGIONU .................................... 18 

UFIGURE 2.0 - 1 IMPROVE MONITORING SITES IN THE VISTAS REGIONU .......................... 20 

UTABLE 2.3 - 1 NATURAL BACKGROUND AND BASELINE CONDITIONS FOR CAPE ROMAINU . 22 

UFIGURE 2.4 - 1 AVERAGE LIGHT EXTINCTION FOR THE 20% WORST DAYS IN 2000-2004

(BASED ON NEW IMPROVE EQUATION)U................................................................... 23 

UFIGURE 2.4 - 2 AVERAGE LIGHT EXTINCTION FOR THE 20% BEST DAYS IN 2000-2004

(BASED ON NEW IMPROVE EQUATION)U................................................................... 24 

UFIGURE 2.4 - 3 DISTRIBUTION OF POLLUTANTS AT NATURAL CONDITIONSU........................ 25 

UFIGURE 3.1 - 1 UNIFORM RATE OF PROGRESS GLIDEPATH FOR 20% WORST DAYS AT CAPE 

ROMAIN, SCU .............................................................................................................. 28 

UTABLE 4.1 2002 EMISSION INVENTORY SUMMARY FOR SCU ............................................... 33 

UFIGURE 4.2 - 1 SO2 EMISSIONS IN 2002 FOR THE VISTAS STATESU..................................... 34 

UFIGURE 5.4.1 - 1 THE MM5 HORIZONTAL DOMAIN IS THE OUTERMOST BLUE GRID, WITH 

THE CMAQ 36-KM DOMAIN NESTED IN THE MM5 DOMAINU...................................... 42 

UFIGURE 5.4.1 - 2 DETAILED VIEW OF THE 12-KM GRID OVER THE VISTAS REGIONU............ 43 

UFIGURE 6.0 - 1 MONITORING NETWORKS USED FOR VISTAS 2002 MODEL PERFORMANCE 

EVALUATIONU ............................................................................................................. 45 

UTABLE 6.1 MODEL PERFORMANCE GOALS & CRITERIA FOR COMPONENTS OF FINE 

PARTICLE MASSU......................................................................................................... 45 

UFIGURE 6.3 - 1 STACKED BAR CHARTS FOR CAPE ROMAIN ON THE 20% BEST (TOP) AND 

20% WORST (BOTTOM) DAYS.U................................................................................... 52 

UFIGURE 7.2.3 - 1 BASE G2 ANNUAL SO2 EMISSIONS FOR 2002, 2009, AND 2018 IN THE 

VISTAS STATESU ........................................................................................................ 58 

UFIGURE 7.2.3 - 2 BASE G2 ANNUAL NOX EMISSIONS IN 2002, 2009, AND 2018 IN THE 

VISTAS STATESU ........................................................................................................ 59 

UTABLE 7.2.3 - 1 2009 EMISSION INVENTORY SUMMARY FOR SCU ....................................... 59 

UTABLE 7.2.3 - 2 2018 EMISSIONS INVENTORY SUMMARY FOR SCU...................................... 60 

UFIGURE 7.2.4 - 1 CMAQ 2018 BASE G2 RESULTS COMPARED TO THE UNIFORM RATE OF 

PROGRESS GLIDEPATH AT CAPE ROMAINU .................................................................. 61 

UFIGURE 7.2.4 - 2 PROJECTED VISIBILITY IMPROVEMENT ON 20% WORST VISIBILITY DAYS 

AT VISTAS AND NEIGHBORING CLASS I FEDERAL AREAS FOR THE 2018 BASE G2A 

RUNU ........................................................................................................................... 62 

UFIGURE 7.2.4 - 3 PROJECTED VISIBILITY IMPROVEMENTS ON 20% BEST DAYS AT VISTAS

AND NEIGHBORING CLASS I FEDERAL AREAS FOR THE 2018 BASE G2 CMAQ RUNU . 63 

UFIGURE 7.3 - 1 ESTIMATED INTERNATIONAL EMISSIONS CONTRIBUTIONS TO SULFATE AT 

VISTAS AND NEIGHBORING CLASS I FEDERAL AREAS.U............................................ 65 

UFIGURE 7.3 - 2 INTERNATIONAL TRANSPORT EFFECT ON UNIFORM RATE OF PROGRESS AT 

CAPE ROMAINU............................................................................................................ 66 

UFIGURE 7.3 - 3 MEASURED PM COMPONENT CONCENTRATIONS ON 20% WORST VISIBILITY 

DAYS IN 2002 AT CAPE ROMAINU................................................................................ 67 

UFIGURE 7.4 - 1 CMAQ PROJECTIONS OF VISIBILITY RESPONSES ON 20% WORST DAYS AT 

CAPE ROMAIN TO 30% REDUCTIONS FROM THE 2009 BASE D INVENTORY FOR 
10 

VISIBILITY-REDUCING POLLUTANTS IN DIFFERENT SOURCE CATEGORIES AND 

GEOGRAPHIC AREASU.................................................................................................. 68 

UFIGURE 7.5.1 EXAMPLE BACK TRAJECTORIES FOR 20% WORST DAYS IN 2002 FOR CAPE 

ROMAIN, SCU .............................................................................................................. 71 

UFIGURE 7.5.2 EXAMPLE RESIDENCE TIME PLOTS FOR 20% WORST DAYS IN 2000-2004 FOR 

CAPE ROMAIN (BASED ON TRAJECTORIES WITH 100 M START HEIGHT)U.................... 72 

UFIGURE 7.5.3 EXAMPLE SO2 AOI PLOT FOR SULFATE EXTINCTION WEIGHTED RESIDENCE 

TIME FOR 20% WORST DAYS IN 2000-2004 FOR CAPE ROMAIN (BASED ON 

TRAJECTORIES WITH 100 M START HEIGHT)U ............................................................. 73 

UFIGURE 7.5.4 - 1 CAPE ROMAIN 2002 AND 2018 SO2 DISTANCE WEIGHTED EMISSIONS *

SO4 EXTINCTION-WEIGHTED RESIDENCE TIME PLOTSU.............................................. 74 

UFIGURE 7.5.4 - 2 CAPE ROMAIN, SC 2002 SO2 EMISSIONS * SO4 EXTINCTION WEIGHTED 

RESIDENCE TIME PLOTSU............................................................................................. 75 

UFIGURE 7.5. 5 - 1 2018 EMISSIONS CONTRIBUTIONS FROM MAJOR SOURCE CATEGORIES IN 

THE AOI FOR CAPE ROMAINU...................................................................................... 76 

UFIGURE 7.5.5 - 2 2018 EMISSION CONTRIBUTION FROM MAJOR SOURCE CATEGORIES IN THE 

AOI FOR CAPE ROMAINU............................................................................................. 77 

UTABLE 7.8.2 - 1 BART MODELING RESULTSU ..................................................................... 84 

UTABLE 7.8.2 - 2 NEW IMPROVE SEA SALT ESTIMATION METHODS & BART VISIBILITY 

IMPACTSU..................................................................................................................... 86 

UTABLE 7.8.3 - 1 MODEL PLANT EXEMPT SOURCES SUBJECT TO BART IN SCU ................... 95 

U7.9 - 1 PREDICTED VISIBILITY FOR THE 20% WORST DAYS IN 2018 AT CAPE ROMAIN USING 

THE NEW IMPROVE ALGORITHM.U ............................................................................ 95 

U7.9 - 2 PREDICTED VISIBILITY FOR THE 20% BEST DAYS IN 2018 AT CAPE ROMAIN USING 

THE NEW IMPROVE ALGORITHM.U ............................................................................ 96 

UFIGURE 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).U ................................................................................. 97 

UFIGURE 7.9 – 4 PROJECTED VISIBILITY ON 20% WORST DAYS FOR NATURAL BACKGROUND 

VISIBILITY CONDITIONS AT CAPE ROMAIN.U ............................................................... 97 

UTABLE 8.0 - 1 REASONABLE PROGRESS GOALS FOR SCU..................................................... 98 

UTABLE 8.0 - 2 NON-EGU SOURCES SUBJECT TO FOUR FACTORS AS RANKED ON RELATIVE 

COST OF COMPLIANCE.U ............................................................................................ 101 

UTABLE 10.0 – 1 SOUTH CAROLINA SOURCES AFFECTING CLASS I FEDERAL AREAS IN 

NORTH CAROLINAU ................................................................................................... 114 

UTABLE 10.0 - 2 SOUTH CAROLINA SOURCES AFFECTING CLASS I FEDERAL AREAS IN 

GEORGIAU.................................................................................................................. 115 
11 

12BLIST 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 Visibility 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 

PM2.5 - Particulate Matter with a diameter of 2.5 micrometers or less (fine particulate) 

PM10 - 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 Appalachian 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 

SO2 - 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 Traveled 

 
13 

0B1.0 INTRODUCTION 

13B1.1 What Is Regional Haze? 

Haze is defined as visibility impairment caused by absorption and scattering of light by 

fine particles. Fine particles--particles similar in size to the wavelength of light--are most 

efficient, per unit of mass, at reducing visibility. Visibility impairment is considered the 

“best understood and most easily measured effect of air pollution.” Regional haze is 

caused by sources and activities that emit these 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 serve as precursors to the formation of 

particles. Of these precursors, sulfur dioxides (SO2) and nitrogen oxides (NOX) 

significantly contribute to regional haze. As a result, reducing particulate matter (“PM”), 

especially PM2.5 (particles with diameter smaller than 2.5 μm), in the atmosphere is 

generally considered to be an effective method of reducing those precursors of regional 

haze and thus improving visibility. Sources with the greatest emissions of PM2.5 and its 

precursors are coal-fired power plants, industrial boilers and other combustion sources. 

Other significant contributors to visibility impairment include mobile source emissions, 

area sources, wildfires, agricultural fires, and wind-blown dust. 

14B1.2 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, impairment of visibility in 

mandatory Class I Federal areas which impairment results from manmade air pollution.” 

Congress adopted the visibility provisions to protect visibility in 156 Class I Federal 

areas consisting of national parks (over 6,000 acres), wilderness 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, the United States (U.S.) Environmental 

Protection Agency (EPA) promulgated regulations to address visibility impairment. The 

1980 regulations were developed to address visibility impairment that is “reasonably 

attributable” to a single source or small group of sources. These regulations represented 

the first phase in addressing visibility impairment and deferred action on regional haze 

that emanates from a variety of sources until monitoring, modeling, and scientific 

knowledge about the relationships between pollutants and visibility impairment 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 EPA 
14 

promulgated on July 1, 1999 (64 FR 35713) revised the existing visibility regulations in 

order to integrate provisions addressing regional haze impairment and establish a 

comprehensive visibility protection program for Class I Federal areas. States are required 

to submit state implementation plans (SIPs) to the EPA that set out a state’s plan for 

complying with the RHR, including consultation and coordination with other states and 

with Federal Land Managers (FLM) from the U.S. Fish and Wildlife Services, U.S. 

National Park Service, and U.S. Forest Service. 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 promulgated designation on December 17, 2004, regional 

haze SIPs must be submitted by December 17, 2007. 

The RHR addressed the combined visibility effects of various pollution sources over a 

wide geographic region. This wide-reaching pollution net meant that many states--even 

those without Class I Areas--would be required to participate in haze reduction efforts. 

The EPA designated five Regional Planning Organizations (RPO) to assist with the 

coordination and cooperation needed to address the visibility issue. Those states that 

make up the southeastern portion of the contiguous U.S. are known as Visibility 

Improvement - State and Tribal Association of the Southeast (VISTAS) and include the 

following states: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, 

South Carolina, Tennessee, Virginia, and West Virginia. 

Figure 1.2-1 Geographical Area of Regional Planning Organizations 
15 

15B1.3 General Overview Of Regional Haze SIP Requirements 

Section 51.308(d) of the RHR requires states to demonstrate reasonable progress toward 

meeting the national goal of a return to natural visibility conditions by 2064. As a guide 

for reasonable progress, the RHR directs states to graphically show what would be a 

“uniform rate of progress” toward natural conditions for each mandatory Class I Federal 

area within the State. The uniform rate of progress is also known as the “glidepath.” 

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

deciviews (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 natural visibility conditions, i.e., 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 [40 CFR 

51.308(d)(1)]. 

In order to ensure that visibility goals are properly met and set, a SIP must include 

determinations of the baseline visibility conditions (expressed in dv) for the most 

impaired and least impaired days for each Class I area in that State. SIPs must also 

contain supporting documentation for all required analyses used to calculate the degree of 

visibility impairment under natural visibility conditions for the most impaired and least 

impaired days [40 CFR 51.308(d)(2)]. In addition, states must include a monitoring 

strategy for measuring, characterizing, and reporting of regional haze visibility 

impairment that is representative of all mandatory Class I Federal areas within the state 

[40 CFR 51.308(d)(4)].

Initial RPGs have been established in the State’s long-term strategy (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 expected under all federal and state 

regulations by 2009 and by 2018. During development of the LTS, states are also 

required to consider specific factors such as the abovementioned ongoing control 

programs, measures to mitigate construction activities, source retirement and replacement 

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

of specific measures [40 CFR 51.308(d)(3)]. 

In addition, each state’s SIP consists of measures necessary to establishing reasonable 

progress goals, including the LTS and specific best available retrofit 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 mandatory 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 determining BART, States must consider the five 

(5) factors set out in Section 169A (g)(7) of the Clean Air Act. Emission limitations 
16 

representing BART and schedules for compliance with BART for each source subject to 

BART must be included in the LTS. 

16B1.4 Class I Federal Area In South Carolina 

South Carolina has one Class I Federal area within its borders: Cape Romain. The SC 

Department of Health and Environmental Control (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 Class 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 Class I Federal area, and that emission sources 

within South Carolina may affect visibility at Class I Federal areas in neighboring states. 

Through VISTAS, the southeastern states have worked together to assess state-by-state 

contributions to visibility impairment in specific Class I Federal areas, including those in 

South Carolina and those affected by emissions 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 between 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 reasonable measures to remedy existing and 

prevent future adverse effects from human-induced air pollution on the air quality related 

values of the Southern Appalachian Mountains.” States cooperated with the FLMs, the 

EPA, industry, environmental organizations, and academia to complete a technical 

assessment of the impacts of acid deposition, ozone, and fine particles on sensitive 

resources in the Southern Appalachians. The Southern Appalachian Mountains Initiative 

(SAMI) Final Report was delivered in August 2002. The SAMI assessment concluded 

that ammonium sulfate is the major contributor to visibility impairment in the Southern 

Appalachian Mountains, and to improve visibility, it is most important to reduce SO2

emissions. SAMI also concluded that reducing ammonia emissions would be helpful to 

reduce ammonium nitrate contributions to visibility 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 state where emission reductions were made. 

Emissions in surrounding SAMI states and states outside the SAMI region also contribute 

to air quality in the SAMI Class I Federal areas. The SAMI states supported strong 

national multi-pollutant legislation to accomplish its mission. Emissions reductions to 

meet national health standards for ozone and fine particles were expected to also improve 

air quality in the Southern Appalachian Mountains. SAMI states committed to consider 

air quality benefits in the Southern 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 reduce emissions of sulfur dioxide and nitrogen 

dioxides. The SAMI analyses and recommendations supported development of the NC 

CSA. While not directly affecting coal-fired power plants in South Carolina, the NC 

CSA, by reducing emissions that may be transferred across state boundaries, will reduce 

visibility impairment in South Carolina and is therefore mentioned throughout the SIP as 

an effective inter-state measure to control emissions. In 2005, EPA promulgated the 

Clean Air Interstate Rule (CAIR) to require emissions reductions for SO2 and NOX from 

electric generating units (EGUs) in 25 eastern 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 reductions will also improve visibility in 

South Carolina’s Class I Federal area. 

17B1.5 State And Federal Land Manager (FLM) Coordination 

As required by 40 CFR Section 51.308(i), the regional haze SIP must include procedures 

for continuing consultation between the states and the FLMs on the implementation of 

the visibility protection program (including development and review of implementation 

plan revisions and five-year progress reports) and on the implementation of other 

programs having the potential to contribute to impairment of visibility in any mandatory 

Class I Federal area within the State. 
18 

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

coordination among States. VISTAS was established in 2001 to initiate and coordinate 

activities associated with the management of regional haze, visibility and other air quality 

issues in the Southeastern United States. Jurisdictions represented by VISTAS members 

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

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

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

the VISTAS Memorandum of Agreement and Bylaws is enclosed as Appendix A. 

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

conditions across the mandatory Class I Federal areas, identify current visibility 

impairment levels, analyze emission control levels that will achieve interim visibility 

goals, and provide adequate documentation to member agencies so that they can develop 

their regional haze State/Tribal Implementation Plans (SIPs/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 regional 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 recommendations for additional monitoring, initial 

emission inventory characterization and projections, and emission reduction requirements 

of other CAA programs. Source contributions to visibility impairment at VISTAS 

mandatory Class I Federal areas are also assessed. 
19 

1B2.0 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 visibility conditions to the 156 Class I Federal 

areas identified in the 1977 Clean Air Act Amendments. The RHR Section 51.301(q) 

defines natural conditions: “Natural conditions includes naturally occurring phenomena 

that reduce visibility as measured in terms of light extinction, visual range, contrast, or 

coloration.” The Regional Haze SIPs must contain measures that make “reasonable 

progress” toward this goal by reducing anthropogenic emissions that cause haze. 

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

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

the sky. For evaluating the relative contributions of pollutants to visibility impairment, 

however, the most useful measure of visibility impairment is light extinction, which is 

usually expressed in units of inverse megameters (Mm-1). Light extinction affects the 

clarity and color of objects being viewed. 

The measure used by the RHR is the deciview (dv). Deciviews are calculated directly 

from light extinction using a logarithmic scale. 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, there are three metrics of visibility that are part of the 

determination of reasonable progress: 

1) Natural conditions, 

2) Baseline conditions, and 

3) Current conditions. 

Each of the three 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. Total light extinction, when converted to dv, is 

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

“Natural” visibility is determined by estimating the natural concentrations of visibility 

pollutants and then calculating total light extinction with the light extinction algorithm. 

“Baseline” visibility is the starting point for the improvement of visibility. 

To assist states in conducting visibility-related research and in creating Federal and State 

implementation plans for the protection of visibility in Class I Federal areas, the 

Interagency Monitoring of Protected Visual Environments (IMPROVE) program was 
20 

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 visibility conditions in Class I Federal areas, 

to track changes in visibility, to identify emission sources responsible for man-made 

visibility impairments, and to provide regional haze monitoring data for all Class I 

Federal areas where practical. IMPROVE’s monitoring 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. Finally, 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 states 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 difference 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 other 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 

18B2.1 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 fine particle components. The same annual 

average natural background visibility is assumed 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 are normally distributed and that the 90th percentile 

of the annual distribution represents natural background visibility 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 natural visibility conditions of the Class I areas. 

The purpose of such a refinement would be to provide more accurate estimates with 

changes to the extinction algorithm that may include the concentration values, factors to 

calculate extinction from a measured particular species and particle size, the extinction 

coefficients for certain compounds, geographical variation (by altitude) of a fixed value, 

and the addition of visibility pollutants. 

In 2005, the IMPROVE Steering Committee made recommendations for a refined 

equation that modifies the terms of the original equation to account for the most recent 

data. The choice between use of the old or the new equation for calculating the visibility 

metrics for each Class I Federal area is made by the state in which the Class I Federal 

area is located. 

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

extinction efficiency of sulfate, nitrate, and organic carbon. New terms are added to the 

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

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

site-specific effects of elevation (and temperature). Separate relative humidity 

enhancement factors are used for small size distribution sulfate or nitrate, large size 

distribution sulfate or nitrate, and sea salt. 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 new IMPROVE equation as the basis for the 

conceptual description because it takes into account the most recent review of the science 

and because it is recommended by the IMPROVE Steering Committee. For more 

detailed discussion of the two IMPROVE equations, see Appendix B.2. 

19B2.2 Estimating Baseline Conditions For South Carolina Class I Federal Area 

SC DHEC estimated baseline visibility conditions at Cape Romain based on sampling 

data collected at its IMPROVE monitoring site. A five-year 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. 

20B2.3 Summary of Natural Background And Baseline Conditions For South Carolina 

Class I Federal Area 

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

Cape Romain. Baseline visibility on the 20 percent worst days at Cape Romain is 

generally between 25 and 30 dv. Natural background visibility 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

21B2.4 Pollutant Contributions To Visibility Impairment (2000-2004 Baseline Data) 

Ammonium sulfate [(NH4)2SO4] contributes the most to visibility impairment and fine 

particle mass on the 20 percent worst and 20 percent best visibility 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 (see 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 Bext

(Mm-1) 

Average for 

20% Best Days

Bext (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)

Bext (Mm-1) 

Average for 

20% Worst 

Days 

Bext (Mm-1) 

Average for 

20% Best Days

Cape Romain 12.1 5.9 34.2 18.1 
23 

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 for the 20% Best Days in 2000-2004 (Based 

on New IMPROVE Equation) 

Particulate Organic Matter (POM) is the second highest contributor 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 Elemental 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 indicator of the amount of carbon from modern 

sources (vegetative emissions, fires) and the amount of carbon from fossil sources 

(gasoline, diesel, oil). For most samples, the ratio of modern carbon 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 the 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 visibility in Class I 

areas since the majority of POM comes from natural (i.e., biogenic sources). Controlling 

anthropogenic sources of carbon will likely be more effective to reduce levels of PM2.5 in 

urban areas. 

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

nitrogen oxides. In the VISTAS region, nitrate formation is limited by availability of 

ammonia and by temperature. Ammonia preferentially reacts with SO2 and sulfate before 

reacting with nitrogen oxides. Nitrate particulates are formed at lower temperatures; at 

elevated temperatures nitrates remain in gaseous form. For this reason, nitrate particulate 

levels are very low in the summer and a minor contributor to visibility impairment. 

Nitrate particulate concentrations 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 suggests that for winter days, controls of 

ammonia sources would be more effective in reducing ammonium nitrate levels than 

controls of nitrogen oxides. This is discussed in more detail in Appendix B.3. 

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

However, EC did account for the third largest contributor to visibility impairment on the 

20 percent haziest days at the Cape Romain Class I Federal area. Sources include 

agriculture, prescribed, wildland, wild fires and incomplete combustion of fossil fuels. 

EC levels are higher at urban monitors than at the Class I areas and suggest controls of 

fossil fuel combustion sources would be more effective to reduce PM2.5 in urban areas 

than to improve visibility in Class I areas. 
25 

Sea salt (NaCl) is observed at the coastal sites. As Figure 2.4-2 indicates, NaCl 

contributions to visibility impairment are highest on the 20 percent clearest days when 

sulfate and POM levels are low. NaCl levels do not contribute significantly to visibility 

on the 20 percent worst visibility days. Figure 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 Smoky Mtns

Shenandoah

Cape Romain

Okefenokee

Everglades

Breton *

Left = B20%; Center = Average; Right = W20%

*Breton based on 2001Q2-2002Q1 & 2002Q4-2003Q3 

bext (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 between 2.5 and 10 microns) makes a 

relatively small contribution to visibility impairment because the light extinction 

efficiency of coarse mass is very low compared to the extinction efficiency for sulfate, 

nitrate, and carbon. 

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

PM2.5 mass measured on the filter and the sum of the measured components. This 

unidentified mass may be positive or negative and is attributable to water and/or the 

factors used to calculate molecular weights of the other components. 

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

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

similarities and differences in composition of fine particle mass. Several PM2.5

nonattainment areas are in close proximity to the Class I areas in the southeastern U.S., 

including Atlanta, GA; Birmingham, AL; Charleston, WV; Chattanooga, TN; Louisville, 

KY; and Knoxville, TN. Ammonium sulfate concentrations are comparable between 

urban and nearby Class I areas, while organic carbon, elemental carbon, and nitrate 

concentrations are generally higher in urban areas than in the Class I areas. These results 
26 

suggest that sulfate is widely distributed regionally while urban areas see an additional 

incremental pollutant loading from local emissions sources. 

Soil fine particles are minor contributors to visibility impairment at most sites on most 

days. Occasional episodes of elevated fine soil can be attributed to Saharan dust 

episodes, particularly at Everglades, FL, but rarely are seen at Cape Romain. No control 

strategies are indicated for fine soil. 

Role of meteorology in determining visibility conditions: Classification and Regression 

Tree Analyses were used to characterize the relationship between meteorological 

conditions and visibility conditions at the Class I 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 meteorological conditions. For Cape Romain, poor visibility 

days were most likely to occur on days with high temperatures, high relative humidity, 

low wind speeds, and elevated PM2.5 mass at upwind urban areas. Precipitation was not a 

good predictor of visibility conditions. Weights were assigned to days based on 

frequency of occurrence of days with similar meteorological conditions. 

The above analyses are further discussed in Appendix B.3. 
27 

2B3.0 GLIDEPATHS TO NATURAL CONDITIONS IN 2064 

As stated in section 1.3 above, the RHR directs 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 progress is also known as the “glidepath.” The 

glidepath is simply a straight graphical line drawn from the baseline level of visibility 

impairment for 2000-2004 to the level representing no manmade impairment in 2064. 

Each state must set goals for each Class I Federal area in that State that provide for 

reasonable progress towards achieving natural visibility conditions by 2064. Section 

51.308(d)(1) of the RHR requires 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 (glidepaths) were developed under the auspices of 

VISTAS for each Class I Federal area in the VISTAS region. The glidepaths were 

developed in accordance with EPA guidance (as described in section 3 above) and using 

data collected from the IMPROVE monitoring sites. Glidepaths were developed using 

relative response factors (RRFs). RRFs are expressed as the ratio of the modeling results 

for the future-year to the results of the base year (2018/2002) and are 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 discussed further in Appendix F (Chapter 4). The 

glidepath is meant to be used as a guide in setting reasonable progress goals. 

2B3.1 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 Reasonable 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

Year

Haziness Index (Deciviews)

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

Figure 3.1 - 1 Uniform Rate of Progress Glidepath for 20% Worst Days at Cape 

Romain, SC 

 
29 

3B4.0 NATURE OF THE PROBLEM: CHIEF CAUSES OF 

VISIBILITY IMPAIRMENT IN SOUTH CAROLINA CLASS I 

FEDERAL AREAS 

23B4.1 Baseline Emissions Inventory 

Section 51.308(d)(4)(v) of the RHR requires 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 inventory was developed for the baseline year 

2002 and estimated for 2009 and 2018. The pollutants inventoried include VOCs, 

nitrogen oxides (NOX), fine particulate (PM2.5), coarse particulate (PM10), ammonia, and 

SO2. In general, the baseline emissions inventory for 2002 was developed using control 

information for South Carolina. A complete detailed discussion of the baseline emissions 

inventory is contained 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 biogenic 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 cleaners, 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 emissions from these sources, 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 type and are summed to the county level. 

Biogenic sources are natural sources like trees, crops, grasses, and natural decay of 

plants. The emissions from these sources are estimated on a county level. 

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

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

emissions that correspond to the meteorological data; 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; however, for sources that may have significant changes from year 

to year, a more typical emission value is used. In this modeling effort, typical emissions 

were developed for the EGUs and the wildland fire emissions. The air quality modeling 

results using these emissions are used in calculating the relative reduction 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. 

41B4.1.1 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 data can be grouped into the 

EGU sources and the other point sources (i.e., non-EGUs). 

67BU4.1.1.1 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. This data provides hourly emissions profiles 

that can be used in the modeling of these large sources of NOX and helps to provide more 

accurate modeling of these sources. 

Since the NOX emissions from EGU sources are a significant part of the emissions 

inventory, a typical base year emissions inventory was developed for these sources to 

avoid anomalies in emissions due to variability in meteorology and in economic and 

outage factors in 2002. This approach is consistent with the EPA’s modeling guidance. 

To develop a typical year 2002 emissions inventory for EGU sources, the average CEM 

heat input for 2000 through 2004 for each unit was divided by the 2002 actual heat input 

to generate a unit specific normalizing factor. This normalizing factor was then 

multiplied by the 2002 actual emissions. The heat inputs for the period 2000 through 

2004 were used since the modeling current design 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 shutdown, the emissions and heat inputs for 2001 

(or 2000) were used in the normalizing calculations. 

As part of the VISTAS modeling, VISTAS 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 entire electric power system throughout the 

contiguous U.S. The dynamic nature of IPM enables 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, transmission bottlenecks, 
31 

and plant-specific operational constraints. The versatility of IPM allows users to specify 

which constraints to exercise and populate IPM with their own datasets. 

68BU4.1.1.2 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 Reporting 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 year data was to use recently updated growth 

and control data consistent with the EPA’s CAIR analyses. This data was supplemented 

with state-specific growth factors and stakeholder input on growth assumptions. 

42B4.1.2 Stationary Area Sources 

Stationary area sources include sources whose 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, service stations, etc.). 

Emissions are estimated by multiplying an emission factor by some known indicator of 

collective activity, such as fuel 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 contractor calculated emissions for animal 

husbandry, wildland fires, and particulate matter from paved and unpaved roads. For the 

remainder of the states within the VISTAS modeling domain, the state-supplied data or 

the CERR data for 2002 was used. 

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

source categories except for wildland fires. For wildland fires, a typical year inventory 

was used to avoid anomalies in wildfire activity in 2002 compared to longer term 

averages. Development of a typical year fire inventory provided the capability of using a 

comparable data set for both the base year and future years. Thus, fire emissions remain 

the same for air quality modeling in both the base and any future years. The VISTAS 

Fire Special Interest Work Group used state records to ratio the number of acres burned 

over a longer term period (three or more years, as available from state records) to 2002. 

Based on these ratios, the 2002 acreage was then scaled up or down to develop a typical 

year inventory. 

The VISTAS contractor generated future year area emissions inventories for 2009 and 

2018 for the regional haze modeling. Growth factors, supplied either by states or taken 

from the CAIR emission projections, were applied to project the controlled emissions to 

2018. If no growth factor was available from either a state or the CAIR growth factor 
32 

files, then the US EPA's Economic Growth and Analysis System Version 5 growth 

factors were used. 

43B4.1.3 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 model. For the three source categories 

not included in the NONROAD model (i.e., aircraft engines, railroad locomotives, and 

commercial marine vessels), more traditional 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. For 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. 

4B4.1.4 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 vehicles on the roads, any control 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 rules 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 using input data reflective of 2002. The 

same model was then run for the future year emissions inventory using input data 

reflective of those years. For South Carolina, the speed, vehicle age, and vehicle mix 

data are all MOBILE6.2 defaults. The 2002 vehicle miles traveled (VMT) were obtained 

from the South Carolina Department of Transportation (SCDOT). 

45B4.1.5 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 science update. The emission 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 version 3 (BELD3) provided by EPA. A 

separate land classification scheme, based upon satellite (AVHRR, 1-km spatial 

resolution) and census information, aided in defining the forested, agricultural, and urban 

portions of each county. 

46B4.1.6 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 NOX PM2.5 PM10 NH3 SO2

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 outputs to calculate the annual estimates. 

47B4.1.7 Model Performance Improvements through Emissions Inventory 

Improvements 

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

to the emissions inventory, which, in turn, improves model performance. These 

inventory improvements detailed in both the VISTAS emissions inventory report and in 

Appendix D.5, are summarized here: 

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

provide estimates of future year utility production and emissions. Continuous 

Emissions Monitoring data was used to define seasonal variability in production 

and emissions. For Base G2 emissions, states updated IPM model projections 

from 2005 with control data provided by utility companies in 2006 through winter 

2007. 

• For on-road vehicle emissions, states and local agencies provided updated 

MOBILE model input and vehicle-miles-traveled data. 

• For ammonia emissions from agricultural sources, the Carnegie Mellon 

University ammonia model was used to improve annual and monthly estimates. 
34 

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

prescribed fires, and land clearing and agricultural burning all from which 

MACTEC developed a 2002 fire 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 Service provided projections of increased 

prescribed burning in 2009 and 2018; these data were incorporated in the Base G 

inventory for all states except Florida. 

• For non-road engines, the updated EPA NONROAD2005 emissions model was 

used in Base G. 

• For commercial marine emissions in shipping lanes in the Gulf of Mexico and 

Atlantic Oceans, ENVIRON created gridded emissions for the VISTAS modeling 

domain using inventory data newly developed for USEPA by Corbett at 

University of Delaware. These emissions were incorporated in the Base G 

modeling. 

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

incorporated as available. 

24B4.2 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 would be the most effective means to reduce 

ammonium sulfate. As illustrated in Figure 4.2-1, 96 percent of the 2002 SO2 emissions 

in the VISTAS states are attributable to electric generating facilities and industrial point 

sources. In South Carolina, 91 percent of the 2002 SO2 emissions are attributable to 

electric utilities and industrial point sources (Table 4.1)

VISTAS States SO2 Emissions - 2002 

Utilities

Industrial

On road

Non road

Other

Industrial: 20% Utilities: 76%

96% of SO2 Emissions in the VISTAS States in 2002 

from Utility and Industrial Sources

4.8 Million tons

Figure 4.2 - 1 SO2 Emissions in 2002 for the VISTAS states
35 

4B5.0 REGIONAL HAZE MODELING 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. Additional details are provided in Appendix C. 

25B5.1 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: The Pennsylvania State University/National Center for 

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

a nonhydrostatic, prognostic meteorological model routinely used for urban- and 

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

modeling studies. 

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

modeling system is an emissions modeling system that generates hourly gridded 

speciated emission inputs of mobile, 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-Atmosphere” photochemical grid model 

capable of addressing ozone, particulate 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 adequately 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. Meteorological 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 visibility. The model results for 2002 were 

compared with observed meteorological and air quality data to evaluate model 

performance. Several configurations of the meteorological and air quality model were 

evaluated to select a configuration that gave the best overall performance for the VISTAS 

region. 

Once model performance 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 visibility impairment, which is used to determine 

reasonable progress. 

The complete modeling protocol used for this analysis can be found in Appendix C.
36 

26B5.2 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-appropriate means to address important physical and chemical 

phenomena in sufficient detail, using peer-reviewed methods. “Freely accessible” means 

that model formulations and coding are freely available for review and that the models 

are available to stakeholders and their consultants for execution and verification at no or 

low cost. 

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

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

the modeling system used for this modeling attainment demonstration. 

48B5.2.1 Selection of Photochemical Grid Model 

69BU5.2.1.1 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 the 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 analysis must be available and adequate, 

• Available past appropriate performance evaluations 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. 

70BU5.2.1.2 Overview of CMAQ

The photochemical model selected for this study 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-atmosphere’ air quality modeling system capable of 

addressing ozone, fine particulate matter, visibility, and acid deposition within a common 

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

challenges posed by the 1990 CAAA and EPA’s desire to develop an advanced modeling 

framework for ‘holistic’ environmental modeling utilizing state-of-science 
37 

representations of atmospheric processes in a high-performance computing environment. 

EPA completed the initial stage of development with Models-3 and released the CMAQ 

model in mid 1999 as the initial operating science model under the Models-3 framework. 

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 PM2.5 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 formulation and technical implementation make the 

model well suited for annual PM modeling. 

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

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

49B5.2.2 Selection of Meteorological Model 

71BU5.2.2.1 Criteria

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

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

photochemical grid modeling is to be carried out. The criteria for selecting a 

meteorological model are based on both the model’s ability to accurately replicate 

important meteorological phenomena in the region of study, and the model's ability to 

interface with the rest of the modeling systems--particularly the photochemical grid 

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

meteorological model to be used in this study: 

• Non-Hydrostatic Formulation, 

• Reasonably current, peer reviewed formulation, 

• Simulates Cloud Physics, 

• Publicly available at no or low cost, 

• Output available in I/O API format, 

• Supports Four Dimensional Data Assimilation (FDDA), and 

• Enhanced treatment of Planetary Boundary Layer heights for AQ modeling. 

72BU5.2.2.2 Overview of MM5

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

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

The basic model has been under continuous development, improvement, testing, and 
38 

open peer-review for more than 20 years and has been used worldwide by hundreds of 

scientists for a variety of mesoscale studies. 

MM5 uses a terrain-following non-dimensionalized pressure, or “sigma,” vertical 

coordinate similar to that used in many operational and research models. In the nonhydrostatic MM5, the sigma levels are defined according to the initial hydrostaticallybalanced reference state so that the sigma levels are also time-invariant. The gridded 

meteorological fields produced by MM5 are directly compatible with the input 

requirements of ‘one atmosphere’ air-quality models using this coordinate. MM5 fields 

can be easily used in other regional air quality models with different coordinate systems 

by performing a vertical interpolation, followed by a mass-conservation re-adjustment. 

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

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

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

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

scheme uses a prognostic equation for the second-order turbulent kinetic energy, while 

diagnosing the other key boundary layer terms. 

Initial and lateral boundary conditions are specified for real-data cases from mesoscale 

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 using a relaxation technique applied in the outermost five 

rows and columns of the coarsest grid domain. 

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

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

have involved comparisons with other prognostic models, such as the Regional 

Atmospheric Modeling System (RAMS) and the Systems Application International 

Mesoscale Model. The MM5 enjoys a far richer application history in regulatory 

modeling studies compared with RAMS and other models. Furthermore, in evaluations 

of these models in over 60 recent regional scale air quality application studies since 1995, 

it has generally been found that the MM5 model tends to produce somewhat better 

photochemical model inputs than alternative models. 

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

description of the MM5 model, can be found in the Modeling Protocol (Appendix C). 

50B5.2.3 Selection of Emissions Processing System 

73BU5.2.3.1 Criteria

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

emissions files in a format suitable for the photochemical grid model being used. The 
39 

following list includes clarification of this criterion and additional desirable criteria for 

effective use of the system. 

• File System Compatibility with the I/O API, 

• File Portability, 

• Ability to grid emissions on a Lambert Conformal projection, 

• Report Capability, 

• Graphical Analysis Capability, 

• MOBILE6 Mobile Source Emissions, 

• 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. 

74BU5.2.3.2 Overview of SMOKE

The SMOKE Emissions Processing System Prototype was originally developed at the 

Micro-computing Center of North Carolina. As with most “emissions models,” SMOKE 

is principally an emission processing system and not a true emissions modeling system in 

which emissions estimates are simulated from “first principles.” This means that, with 

the exception of mobile and biogenic sources, its purpose is to provide an efficient, 

modern tool for converting emissions inventory data into the formatted emission files 

required by an air quality simulation model. For mobile sources, SMOKE actually 

simulates emissions rates based on input mobile-source activity data, emission factors 

and outputs from transportation travel-demand models. 

 

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

emergent high-performance-computing as applied to sparse-matrix algorithms. Indeed, 

SMOKE is the fastest emissions processing tool currently available to the air quality 

modeling community. The sparse matrix approach utilized throughout SMOKE permits 

both rapid and flexible processing of emissions data. The processing is rapid because 

SMOKE utilizes a series of matrix calculations instead of less efficient algorithms used in 

previous systems. The processing is flexible because the processing steps of temporal 

projection, controls, chemical speciation, temporal allocation, and spatial allocation have 

been separated into independent operations wherever possible. The results from these 

steps are merged together at a final stage of processing. 

 

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

modeling system. The model supports a variety of input formats from other emissions 
40 

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). 

51B5.3 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 changes 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 Quality Goals for Ozone, PM2.5, and Regional Haze

identifies specific goals to consider when selecting one or more episodes for use in 

demonstrating reasonable progress in attaining the regional haze air quality goals. The 

EPA recommends that episode selection derive from three principal criteria: 

• Simulate a variety of meteorological conditions; 

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

baseline design value or visibility impairment; 

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

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

monitor violating the NAAQS is based on multiple days. 

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

approach is to model a full, representative year. Moreover, the RRF values should be 

based on model results averaged over the 20 percent 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 considerations to bear in mind when choosing potential 

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

(b) choose periods that are drawn from the years upon which the current design values 

are based, (c) include weekend days among those chosen, and (d) choose modeling 

periods that meet as many episode selection criteria as possible in the maximum number 

of nonattainment or Class I areas as possible. Finally, the EPA explicitly recommended 

in 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 preferable, representative year for regional haze modeling. 

These steps include the following: 
41 

URepresentativeness of Meteorological Conditions:U The VISTAS meteorological 

contractor (BAMS) identified important meteorological characteristics and data 

sets in the VISTAS region directly relevant to the evaluation of candidate annual 

modeling episodes. 

UInitial 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 Classification and Regression Tree Analyses to 

evaluate visibility conditions for 2000, 2001, and 2002, the candidate modeling 

years. The year 2002 was found to be representative of conditions in the other 

two years. Subsequently, these analyses were repeated with the meteorological 

and air quality monitoring data for 2000 to 2004 to evaluate how well the 2002 

modeling year represented the full 2000-2004 baseline period. This analysis 

confirmed that visibility and PM2.5 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. 

UData Availability:U In parallel with the CART analysis, episode characterization 

analyses, collaborative investigations by VISTAS states intensively studied the 

availability of PM2.5, meteorological, and emissions data and representativeness 

of alternative Baseline modeling periods from a regulatory standpoint. 

Additionally, 2002 was the year that the EPA was requiring states to provide 

emissions inventory data for the Comprehensive Emissions Reporting Rule, it 

made sense to use 2002 as the modeling year to take advantage of the 2002 

inventory. 

UYears to be used by other RPOs:U VISTAS also considered what years other RPO 

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

modeling year. 

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

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

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

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

together with many other considerations (e.g., timing of new emissions or aerometric data 

deliveries by the EPA or the states to the modeling teams). 

27B5.4 Modeling Domains 

52B5.4.1 Horizontal Modeling Domain 

The CMAQ model was run in one-way nested grid mode. This allowed the larger outer 

domains to feed concentration data to the inner nested domain. One-way nesting is 

believed to be appropriate for the generally stagnant conditions experienced during South 
42 

Carolina ozone episodes. Two-way nesting was not considered due to numerical and 

computational uncertainty associated with the technique. 

The horizontal coarse grid modeling domain boundaries were determined through a 

national effort to develop a common grid projection and boundary. Since this national 

modeling domain was used in the VISTAS regional haze modeling, it was used for the 

attainment demonstration as well. A smaller 12-km grid modeling domain was selected 

in an attempt to balance location of areas of interest, such as ozone and fine particulate 

matter nonattainment areas, as well as Class 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 covers the continental U.S. This domain was 

used as the outer grid domain for MM5 modeling 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 domain 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 the VISTAS states, a one-way nested high 

resolution (12-km grid resolution) was used. Figure 5.4.1-2 shows the 12-km grid, 

modeling domain for the VISTAS region. This is the modeling domain 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 

53B5.4.2 Vertical Modeling Domain 

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

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

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

scheme was used to generate 19 vertical layers for CMAQ. The effects of layer 

averaging were evaluated in conjunction with the VISTAS modeling effort and were 

found to have a relatively minor effect on the model performance metrics when both the 

34 layer and a 19 layer CMAQ models were compared to ambient monitoring data. 
44 

5B6.0 MODEL PERFORMANCE EVALUATION

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

applications and testing candidate model configurations for the SMOKE emissions and 

CMAQ model for the VISTAS’ 36-km and 12-km modeling domains. This effort 

resulted in a report recommending the model configuration for the annual emissions and 

air quality modeling, which is included as part of the VISTAS Emissions and Air Quality 

Modeling Protocol. The evaluation of the meteorological modeling configuration can be 

found in Appendix F (Chapter1), with a summary of the final meteorological and air 

quality modeling configuration in the modeling protocol contained in Appendix D.4.c 

and Appendix C, respectively. 

Air quality model performance for the 2002 modeling year was initially tested in 2004 

using an early version of the VISTAS emissions inventory. In keeping with the oneatmosphere objective of the CMAQ modeling 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 Appendix 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 

28B6.1 Modeling Performance Goals, Criteria, and Results 

In 2004 VISTAS established model performance goals and criteria for components of 

fine particle mass (Table 6.1) based on previous model performance for ozone and fine 

particles. EPA modeling guidance for fine particulate matter at the time noted that PM 

models may not be able to achieve the same level of performance as ozone models. 

VISTAS’ evaluation considered several statistical performance measures and displays. 

Fractional bias and fractional gross error were selected as the most appropriate metrics to 

summarize model performance; other metrics were also calculated and included in the 

full model performance evaluation (Appendix F, Chapter 3). 

Table 6.1 Model Performance Goals & Criteria for Components of Fine Particle 

Mass 

Fractional Bias Fractional Error Comment 
46 

U<U15% U<U35% Goal for PM model performance based on 

ozone model performance, considered excellent 

performance 

U<U30% U<U50% Goal for PM model performance, considered 

good performance 

U<U60% U<U75% 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 performance were prepared including: 

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

monitoring network, and month; 

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

species, monitoring site, and month; 

3. Spatially 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 model performance by 

species, region, season; and 

6. Concentration performance plots (“bugle plots”) that display fractional bias or 

error as a function of concentration by species, region, monitoring network, and 

month. 

The “soccer plots” and “bugle plots” are relatively new tools in model performance 

evaluations, and have recently been included as model performance evaluation displays 

in the EPA’s modeling guidance for Ozone, PM2.5, and Regional Haze (2007). Both 

“soccer plots” and “bugle plots” allow for convenient way to examine model 

performance with respect to set goals and criteria. The bugle plots have the added benefit 

of adjusting the goals and criteria to consider the concentration of the species. Analysis 

of “bugle plots” generally suggests that greater emphasis should be placed on 

performance of those components with the greatest contribution to PM mass and 

visibility impairment (e.g., sulfate and organic carbon) and that greater bias and error 

could be accepted for components with smaller contributions to total PM mass (e.g., 

elemental carbon, nitrate, and soil). 

29B6.2 VISTAS Domain-Wide Performance 

Further discussion of model performance in this document will focus on the comparison 

of observational data from the IMPROVE monitors and model output data from the 2002 

VISTAS BaseG2-Actual annual air quality modeling. Focus is limited to the IMPROVE 

monitoring network as these sites are the locations used in projecting attainment visibility 

improvement goals in the Class I areas. 
47 

The evaluation will primarily focus on the air quality model’s performance with respect 

to individual components of fine particulate matter (PM2.5), as good model performance 

of the component species will dictate good model performance of total or reconstituted 

fine particulate matter. Model performance of the total fine particulate matter and the 

resulting total light extinction will also be provided as a means to discuss the overall 

model performance for this Implementation Plan. 

In this analysis, mean fractional bias (error) is used in lieu of mean bias (error) to prevent 

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

performance statistics is found in Appendix F (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 most significant light scattering component species (Sulfate, 

Nitrate, and Organic Carbon) for the 20 percent best days and 20 percent worst days. 

Plots for individual IMPROVE monitors 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, with a couple of months falling just outside 

the goal thresholds but well within the criteria thresholds. Figure 6.2-3 contains separate 

soccer plots for each season. The seasonal plots emphasize poorer nitrate performance in 

the summer (does not even appear on the plots provided because performance is off scale 

with other constituents), when observed nitrate is quite low and predicted nitrate is even 

lower. When concentration is factored into performance criteria, nitrate performance 

improves with respect to MFB and MFE (Figures 6.2-4 and 6.2-5) 

Additionally, performance assessed at the “one atmosphere” level was also deemed 

acceptable for ozone and particulate matter at various monitoring sites (STN, FRM, 

CASTNet, etc.). Overall, VISTAS found the Base G2 modeling results to be 

representative and acceptable for use in modeling projection for ozone, particulate matter, 

and regional haze. 
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 Bias

Mean Fractional Error 

Sulfate

Nitrate

Organics

Criteria

Goal

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

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

Each point represents a monthly value as compared to the model performance 

criteria (red box) and modeling performance goals (green box). 

All Sites CMAQ 12km - 2002 Monthly

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

-75.0 -25.0 25.0 75.0

Fractional Bias

Mean Fractional Error 

Sulfate

Nitrate

Organics

Criteria

Goal

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 (μg/m3)

Mean Fractional Error 

Sulfate

Nitrate

Organics

Criteria

Goal

Figure 6.2-4: Bugle plot of the mean fraction bias for particulate matter and its 

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

point represents a monthly mean fraction bias value as compared to the model 

performance criteria (red lines) and modeling performance goals (green lines). 
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 (μg/m3)

Mean Fractional Bias 

Sulfate

Nitrate

Organics

(+) Criteria

(+) Goal

(-) Goal

(-) Criteria

Figure 6.2-5: Bugle plot of mean fraction error for particulate matter and its 

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

represents a monthly mean fraction bias value as compared to the model 

performance criteria (red lines) and modeling performance goals (green lines). 

30B6.3 South Carolina Class I Area Performance 

The following section provides stacked bar charts comparing observed fine particulate 

matter composition and modeled fine particulate matter composition. The charts display 

both the 20 percent best days and 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 (bextSO4) 

• Red = light extinction due to nitrates (bextNO3) 

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

• Black = light extinction due to elemental (bextEC) 

• Brown = light extinction due to soil (bextSoil) 

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

The components are presented in the same order for both the observed (left hand bar) and 

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

extinction for the component differs from the observed. The total height of the bar 

provides the total reconstructed particulate matter light extinction value. 

A cursory view of the stacked bar charts reiterates 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 under 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 compared 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 worst 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 extinction due to organic carbon also becomes more 

important to total light extinction. Much like the sulfate component, the organic carbon 

component accuracy has improved performance 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 the one atmosphere modeling performed by the 

VISTAS contractors is representative of conditions in the southeastern states and is 

applicable for use in attainment demonstrations. 

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% group

bEXT (1/Mm)

bCM

bSOIL

bEC

bOC

bNO3

bSO4
52 

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% group

bEXT (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 

6B7.0 LONG TERM STRATEGY FOR SOUTH CAROLINA’S CLASS I 

FEDERAL AREA 

As stated in section 1.3 above, the RHR requires a State’s SIP to establish reasonable 

progress goals (RPGs) for each Class I Federal area in the State. This first set of RPGs 

must be met by emission reductions required by other CAA programs, by any BART 

requirements, and by measures in each State’s long term strategy covering the period 

from the present until 2018. This section discusses development of South Carolina’s 

long-term strategy. 

31B7.1 Overview of the Long Term Strategy Development Process 

The monitored data and modeling analyses cited in Sections 2 and 5 above establish that, 

for the VISTAS region, the key contributors to regional haze in the 2002-2004 baseline 

timeframe were large stationary sources of sulfur dioxide emissions. Keeping that key 

conclusion in mind, this section addresses the following questions: 

a. Assuming only existing and planned emission reductions from other CAA will 

take place in South Carolina and the VISTAS region, how much visibility 

improvement, compared to the glidepath, 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 visibility benefits be realized at Cape 

Romain between now and 2018, and what visibility improvement will those 

emission controls achieve 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 achieve 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 visibility improvement, compared to the 

glidepath, will we expect to see at Cape Romain between now and 2018? 

32B7.2 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 and consent decrees are being implemented, 

or will be implemented between the baseline 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. 

54B7.2.1 Federal and State Control Requirements 

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

permanently cap emissions of sulfur dioxide (SO2) and nitrogen oxides (NOX) in the 

eastern U.S. When fully implemented, CAIR will reduce SO2 emissions in these states 

by over 70 percent and NOX emissions by over 60 percent from 2003 levels. Appendix 

M illustrates what sources within South Carolina’s AOI are subject to CAIR and those 

identified source’s planned emission reductions. 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 visibility-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 while simultaneously reducing sulfur dioxide 

SO2 and nitrogen oxides NOX emissions. 

 

UNOX SIP CallU. Phase I of the NOX 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 NOX SIP call have developed rules for the control of NOX emissions that 

have been approved by the EPA. This rule results in a 68 percent reduction in NOX

emissions from large stationary combustion sources by 2006. For this analysis, we 

capped the emissions for NOX SIP call-affected sources at 2007 levels and carried 

forward the capped levels for the 2009/2018 future year inventories. 

UNC Clean Smokestacks ActU. Under the act, which was enacted in 2002, coal-fired power 

plants in North Carolina must achieve a 77-percent cut in nitrogen oxide (NOX) 

emissions by 2009 and a 73-percent cut in sulfur dioxide (SO2) emissions by 2013. 

UConsent 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/SO2/NOX emissions; 

comply with system-wide annual PM/SO2/NOX emissions limits; agree not to 

buy, sell, or trade PM/SO2/NOX allowances allocated to Santee Cooper System as 
55 

a result of said agreement; and to comply with emission unit limits of said 

agreement. 

• Under a settlement agreement, Tampa Electric 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 installed; and retire pollution 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 (SO2) and nitrogen oxides (NOX) 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 operation to cut NOX 

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

reductions beginning in early 2005. The Crist plant is a significant source of 

NOX emissions in the Pensacola area. 

• A July 2, 2007, consent decree between the EPA and East Kentucky Power 

Cooperative (EKPC) requires the utility to reduce its NOX and SO2 emissions by 

installing/continuously operating selective 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, such as the Clean Air Interstate Rule. 

UOne-hour ozone SIPs (Atlanta / Birmingham / 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 NOX 

reductions from specific coal-fired power and address transportation plans in these 

cities. 

UNOx RACT in 8-hour Nonattainment Area SIPsU. NC DENR applied NOX RACT to 

two facilities located in the Charlotte nonattainment area: Philip Morris USA and 

Norandal USA. 

UHeavy Duty Diesel (2007) Engine StandardU. 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 NOX and non-methane hydrocarbons (NMHC) of 0.20 g/bhp-hr and 0.14 g/ bhp-hr, 

respectively. These NOX 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 (ultra-low sulfur diesel, or ULSD). 
56 

UTier 2 TailpipeU. Tier 2 is a fleet averaging program, modeled after the California 

LEV II standards. Manufacturers can produce vehicles with emissions ranging from 

relatively dirty to zero, but the mix of vehicles a manufacturer sells each year must 

have average NOX emissions below a specified value.

ULarge Spark Ignition and Recreational Vehicle RuleU. EPA has adopted new standards 

for emission of oxides of nitrogen (NOX), 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 engines are those powered by gasoline, liquid 

propane gas, or compressed natural gas rated over 19 kilowatts (kW) (25 

horsepower). These engines are used in commercial and industrial applications, 

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

variety of farm and construction applications. Recreational Vehicles include 

snowmobiles, off-highway motorcycles, and all-terrain-vehicles. Rules were initiated 

in 2004 and are fully phased in by 2012. 

UNon-road Diesel RuleU. 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.

UIndustrial Boiler/Process Heater MACT standardsU. The applied MACT control 

efficiencies were four percent for SO2 and 40 percent for PM10 and PM2.5. However, 

as of June 08, 2007, the Industrial Boiler/Process Heater MACT standard was 

vacated. A mandate without a stay was issued 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 basis within a time specified by the EPA or 

SC DHEC. 

UCombustion Turbine MACTU. The projection inventories do not include the NOX cobenefit effects of the MACT regulations for Gas Turbines or stationary Reciprocating 

Internal Combustion Engines, which the EPA estimates to be small compared to the 

overall inventory.

UVOC 2-, 4-, 7-, and 10-year MACT StandardsU. Various point source MACTs and 

associated emission reductions were implemented. We did not count reductions 

occurring before 2002. 

USmoke 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 contributor 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 Forestry Commission (“SCFC”) 

has developed (and recently revised as of August 2006) the Smoke Management 
57 

Guideline for Vegetative Debris Burning Operations, which serves to regulate 

vegetative debris burning for forestry, agriculture, 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 the purpose of land clearing or right of 

way maintenance in areas other than predominantly residential, and open burning of 

residential construction waste from building and construction operations unless 

specific conditions are met. 

When weighed together, these documents address all sources of fire used for land 

management purposes within South Carolina 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 maintain a smoke management plan. 

UEarly 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 the effective date of the nonattainment 

designation for participating areas. The Department has been working with the EPA, 

state and local governments, industry, environmental groups, and other interested 

parties to develop a strategy to reduce the pollution that creates ground-level ozone. 

In March 2004, participating EAC areas in South Carolina, submitted local Early 

Action Plans to include emission reduction strategies. 

5B7.2.2 Additional State programs to reduce emissions 

In addition to considering emission reductions due to ongoing air pollution programs as 

required under the RHR Section 308(d)(3)(v)(A), states are also required to consider the 

measures to mitigate the impacts of construction activities [40 CFR 51.308(d)(3)(v)(B)] 

and agricultural and forestry smoke management [40 CFR 51.308(d)(3)(v)(E)]. These 

measures are discussed in more detail in Appendix H. 

56B7.2.3 Projected 2009 and 2018 Base G2 Emissions Inventories 

The Base G inventories for 2009 and 2018 account for (post-2002) emission reductions 

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

of July 1, 2004. In general, 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 based on State and local (S/L) agency knowledge 

of planned emission controls at specific EGUs. For non-EGUs, we used recently updated 

growth and control data consistent with the data used in EPA’s CAIR analyses 

supplemented by S/L agency data and updated forecasts from the Department of Energy 

(DOE). 
58 

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

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

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

Mobile source controls included local controls underlying the 2002 baseline inventory 

(vehicle emission inspection, Stage II vapor recovery, anti-tampering, etc.) with changes 

based on specific State input. Non-road control data and projections for 1996, 2010, 

2015, and 2020 were obtained from the EPA's CAIR Technical Support Document, and 

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

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

the VISTAS states from 2002 through 2018. (Similar charts for other visibility impairing 

pollutants are contained in Appendix H.5). Note that for SO2 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 SO2 emissions in every state. 

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

VISTAS states 
59 

Figure 7.2.3 - 2 Base G2 Annual NOx Emissions in 2002, 2009, and 2018 in the 

VISTAS states 

81BUSummary Final Base G2 Emissions 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 Appendix D.2 (a, b, and c). 

Table 7.2.3 - 1 2009 Emission Inventory Summary for SC 

 VOC tpy NOX tpy PM2.5 tpy PM10 tpy NH3 tpy SO2 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 UDocumentation 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 Appendix D. 2 (a, b, and c). 
60 

Table 7.2.3 - 2 2018 Emissions Inventory Summary for SC 

 VOC tpy NOX tpy PM2.5 tpy PM10 tpy NH3 tpy SO2 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 UDocumentation of the Revised 2002 Base Year, Revised 2018, and Initial 

2009 Emission Inventories for VISTAS v.11U 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. 

57B7.2.4 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 value for the worst days. 

Figure 7.2.4-1 illustrates the predicted visibility improvement by 2018 resulting from the 

Base G2 inventory that represents implementation 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 uniform 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 period of the first long term strategy. 
61 

Uniform Rate of Reasonable 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

Year

Haziness 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, visibility 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 improvements at all the VISTAS 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 progress. In Figure 7.2.4-2, the percentage of the 

target reduction achieved for Cape Romain using the new IMPROVE algorithm is 

approximately 110 percent. This means that the rate of improvement expected from other 

CAA programs is at least 10 percent greater than the visibility improvement that would 

be achieved in 2018 with Cape Romain’s uniform rate of progress. 
62 

Figure 7.2.4 - 2 Projected Visibility Improvement on 20% Worst Visibility Days at 

VISTAS and Neighboring Class I Federal Areas for the 2018 Base G2a Run 

In addition to improving visibility on the 20 percent worst visibility days, states are also 

required to protect visibility on the 20 percent best days at the Class I Federal areas. As 

illustrated in Figure 7.2.4-3, visibility on the 20 percent 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 would mean no change in visibility; while -10 

percent means that a 10 percent improvement in visibility has occurred. 

2018 Base G2a CMAQ Visibility Projection for 20% Worst Days – 36 km

Percent of target reduction 

VISTA nonVISTAS

0

20

40

60

80

100

120

140

160

180

200

COHU

DOSO

GRSM

JARI

LIGO

MACA

SHEN

SHRO

SIPS

CHAS

EVER

OKEF

ROMA

SAMA

SWAN

BRET

BRIG

CACR

HEGL

MING

UPBU

CMAQ New IMPROVE Algorithm

CMAQ Old IMPROVE Algorithm
63 

2018 Base G2a CMAQ Visibility Projection for 20% Best Days

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

COHU

DOSO

GRSM

JARI

LIGO

MACA

SHEN

SHRO

SIPS

CHAS

EVER

OKEF

ROMA

SAMA

SWAN

BRET

BRIG

CACR

HEGL

MING

UPBU

Percent of target reduction achieved

VISTAS non-VISTAS

CMAQ New IMPROVE Algorithm

CMAQ Old IMPROVE Algorithm

Figure 7.2.4 - 3 Projected Visibility Improvements on 20% Best Days at VISTAS 

and Neighboring Class I Federal Areas for the 2018 Base G2 CMAQ Run 

3B7.3 Relative Contributions from International Emissions to Visibility Impairment in 

2018 at VISTAS Class I areas 

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

loadings and visibility impairment in Class I federal areas in the continental U.S. To 

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

the southeastern US, VISTAS used a combination 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 grid resolution and a 

three-hour temporal resolution. Emissions were based on monthly averages, so the model 

does not capture the episodic variability in emissions. The GEOS-Chem outputs were 

used to calculate initial and boundary portions of Canada and Mexico, so emissions for 

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

the boundary conditions outside the national modeling domain, as appropriate. 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 international emissions inventory used in GEOSCHEM did not distinguish between wildfires and anthropogenic fires, all international 
64 

fire emissions were assumed to be wildfire, and the contributions attributable to 

international anthropogenic fires are underestimated in these projections.

 

1) UInternational emissions as the difference between two GEOS-Chem runsU - In 

the first run, U.S. anthropogenic emissions were removed, and in the second run, 

U.S. and international anthropogenic emissions were removed. The difference is 

attributable to international 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, Harvard modeling for EPRI provided GEOSChem results for 2001 international contribution on a 1 x 1 degree grid scale. 

2) UInternational emissions as the difference between two CMAQ 36-km 

simulations, both using 2018 Base F emissions and boundary conditions from 

GEOS-ChemU - In the first CMAQ run, all global natural and anthropogenic 

emissions in 2018 are active. In the second CMAQ run, only global (U.S. and 

international) natural emissions are active. Here, the impacts of international 

emissions are compared against 2018 conditions rather than natural background 

conditions. 

VISTAS has compared its results to results from other RPOs on the impact of 

international emissions and boundary conditions at Class I Federal areas in 2002; the 

results were similar. 

As illustrated in Figure 7.3-1, for annual average 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 nitrate and organic carbon mass, for 

impacts of 20 percent worst visibility days, and for impacts of international emissions on 

calculated light extinction, are included in Appendix F (Chapter 5). 
65 

Figure 7.3 - 1 Estimated International Emissions Contributions to Sulfate at 

VISTAS and Neighboring Class I Federal Areas.

Figure 7.3-2 illustrates the effect of reducing the incremental contribution due to 

international emissions when considering the visibility improvement in 2018 compared to 

the uniform rate of progress at Cape Romain. The open triangle represents the estimate 

with all emissions and the diamond represents estimates when contributions from 

international emissions are removed. 
66 

 

Uniform Rate of Reasonable 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

Year

Haziness 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 international contribution does not change the 

conclusion that reductions of U.S. anthropogenic 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 change 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 contribution from international emissions will 

become more important. The information is included in this SIP documentation to 

provide reference for future assessments of reasonable progress. In short, in this initial 

LTS for Cape Romain, the amount of visibility improvement projected by 2018 is 

independent of any contribution to visibility improvement by international emissions. 

In Figure 7.3-3, CMAQ projections of contributions from international 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 those 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

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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 12/7

Mass (μg/m3

)

Amm SO4 Amm NO3 POM LAC Soil CM Sea Salt

Figure 7.3 - 3 Measured PM Component Concentrations on 20% Worst Visibility 

Days in 2002 at Cape Romain 

34B7.4 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 different pollutants, source sectors, 

and geographic areas, VISTAS engaged the Georgia Institute of Technology to perform 

emission sensitivity model runs using CMAQ. Emission sensitivities were initially 

performed for three episodes representing winter and summer conditions: January 2002, 

July 2001, and July 2002. These runs used the initial 2018 projections inventory and 

considered 30 percent reductions from specific pollutants, source categories, and 

geographic areas. Emission sensitivities were repeated using 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: 

 

• SO2 from EGU sources in each VISTAS state, other RPOs in the VISTAS 12-km 

grid, and Boundary Conditions from outside the 12-km domain. 
68 

• SO2 from non-EGU point sources in each VISTAS state, other RPOs, and 

Boundary Conditions. 

• NOX from ground level (on-road plus non-road area) sources in each VISTAS 

state and other RPOs. 

• NOX from point (EGU and non-EGU) sources in each VISTAS state and other 

RPOs. 

• NH3 from all sources in the VISTAS and other RPOs. 

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

• Primary Carbon from all ground level sources in each VISTAS state and other 

RPOs. 

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

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

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

visibility days. 

Cape Romain, SC (20% Worst Days)

-8.00

-7.00

-6.00

-5.00

-4.00

-3.00

-2.00

-1.00

0.00

1.00

SO2_EGU

SO2_nonEGU

NOx_Ground

NOx_Point

NH3

VOCs

PC_Ground

PC_Point

PC_Fires

Δ

Bext (Mm-1)

Bio.

Antro.

BCs

MRPO

M-VU

CEN

VISTAS

WV

VA

TN

SC

NC

MS

KY

GA

FL

AL

Figure 7.4 - 1 CMAQ Projections of Visibility Responses on 20% Worst Days at 

Cape Romain to 30% Reductions from the 2009 Base D Inventory for VisibilityReducing Pollutants in Different Source Categories and Geographic Areas 
69 

As Figure 7.4-1 shows, the greatest benefits on the 20 percent worst days for Cape 

Romain are projected to result from further reducing SO2 from EGUs. Reductions from 

EGUs in SC would have the greatest benefits; the contribution from other VISTAS states 

is relatively small. Additional, smaller benefits would come from additional SO2

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 five-year progress report. 

As shown in Figure 7.4-1, ammonia (NH3) 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 states occur in the winter when ammonium 

nitrate has a somewhat larger contribution to visibility impairment. Therefore, reducing 

ammonia emissions would be more beneficial for reducing ammonium nitrate 

contributions to visibility impairment in wintertime than further reducing NOX emissions 

from either ground or point sources. 

VOCs in South Carolina originate primarily from biogenic sources (such as vegetative 

emissions) and also contribute to visibility 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 SO2 originating from the eastern coast 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, the 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 impairment on peak summertime days. Wintertime 

ammonia is an important contributor for sites near the coast. SC DHEC concludes that 

reducing SO2 emissions from EGU and non-EGU point sources in the VISTAS states 

would have the greatest visibility benefits for Cape Romain. Contributions from outside 

the VISTAS 12-km modeling domain are particularly important to determining 

contributions to coastal Class I Federal areas 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 

35B7.5 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 identified, it is necessary to focus on where the 

greatest contributing sources are located. SO2 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 SO2 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 influence visibility in each Class I Federal area, 

and then identify the major SO2 point sources located in those geographic areas. An SO2

AOI was defined for Cape Romain to represent the geographic area containing sources 

that would likely have the greatest impact on visibility and ranked 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 remain significant in 2018. Since the objective 

of this analysis is to understand which sources are most important in terms of SO2

emissions, the worst days were selected based on SO4 loading. The following sections 

contain a broad overview of the analytic steps in the AOI analysis. See Appendix H 

(Chapter 4) for a more detailed discussion of each step. 

58B7.5.1 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 measured or modeled meteorological fields to 

estimate the most likely central path of air masses that arrive at a receptor at a given time. 

The method essentially follows a parcel of air 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 trajectories for 20 percent worst days in 2002 were 

also useful in evaluating model performance for individual days at the Class I Federal 

area. 

59B7.5.2 Residence Time Plots 

The next step was to develop residence time plots using five years of back trajectories for 

the 20 percent worst visibility days dominating 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 Figure 7.5.2, winds influencing the Cape Romain 

Class I Federal area on the 20 percent worst days indicate a north-south gradient of 

influence along the eastern U.S. coast. 
71 

Figure 7.5.1 Example Back Trajectories for 20% Worst Days in 2002 for Cape Romain, 
72 

Residence Time for 20% Worst Days in 2000-2004 

Cape Romain, SC

Figure 7.5.2 Example Residence Time Plots for 20% Worst Days in 2000-2004 for 

Cape Romain (Based on Trajectories with 100 m Start Height) 

60B7.5.3 SO2 Areas of Influence 

The next step was to develop sulfate extinction-weighted residence time plots to define 

the geographic area with highest probability of influencing the receptor on the 20 percent 

worst days in 2000-2004 that were dominated by sulfate. Each back trajectory was 

weighted by sulfate extinction for that day, allowing for focus on the 20 percent worst 

days that are influenced by sulfate and placing less importance on days influenced by 

organic carbon or fires. Sulfate-weighted back trajectories for the 20 percent worst days 

were combined for five years of data. The resulting sulfate extinction-weighted residence 

time plots were used to define the geographic AOI for source of SO2 emissions. In 

Figure 7.5.3, the area representing 10 percent or greater residence time is outlined 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 SO2 AOI Plot for Sulfate Extinction Weighted Residence Time 

for 20% Worst Days in 2000-2004 for Cape Romain (Based on Trajectories with 

100 m Start Height) 

61B7.5.4 Emission Sources Within the SO2 AOI 

Residence time plots were then combined with geographically-gridded emissions data 

based on the 2002 baseline and 2018 Base G emissions inventories. Plots 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, deposition, and chemical transformation of point 

source emissions along the path of the trajectory, this data was weighted by 1/d, where d 

was calculated as the distance between grid cell centers in kilometers. The distanceweighted point source SO2 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 residence time plots and gridded emissions data in 

plots and data sets. The distance-weighted (1/d) gridded point source SO2 emissions 

were multiplied by the total extinction-weighted back trajectory residence times on a grid 

cell by grid cell basis. These results are then normalized by the domain-wide total and 
74 

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, sulfate extinction-weighted, residence time plots 

for Cape Romain. These maps help visualize where the emission reductions will be 

occurring between 2002 and 2018. The change in SO2 emissions between 2002 and 2018 

can be seen by comparing emission source strengths in the two plots. Note that the 

emissions from each source are normalized by the total emissions in the domain. Sources 

that reduce SO2 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 total emissions in the domain are smaller in 

2018, a source that does not change emissions between 2002 and 2018 may actually 

appear to increase in importance in 2018 compared to 2002. 

Figure 7.5.4 - 1 Cape Romain 2002 and 2018 SO2 Distance Weighted Emissions * 

SO4 Extinction-Weighted Residence Time Plots 

Figure 7.5.4-2 illustrates gridded SO2 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 SO2 compared to sources in neighboring states. 

Additional analysis, including 2002 and 2018 distance-weighted emissions residence time 

plots for the Cape Romain and neighboring states, is contained in Appendix H and serves 

as the basis for consultation among the VISTAS states. 

2018 SO2 Emissions weighted 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 SO2 Emissions * SO4 Extinction Weighted 

Residence Time Plots 

62B7.5.5 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 sources, found to have the greatest impact on 

visibility in Cape Romain. 

Lists of SO2 point sources within the AOI for Cape 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 counties with a maximum sulfate extinctionweighted residence time greater than 5. For SO2 sources within each AOI, the following 

attributes were defined for each individual unit: 

• State, county, source (plant), and industry identification codes; 
76 

• SO2 emissions for 2002 and 2018; 

• 2018 control efficiency; 

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

• Emissions divided by distance (Q/d), a metric that accounts for the dispersion of 

emissions over distance; and 

• Maximum sulfate extinction-weighted residence time (RTMax). 

The review was conducted in a top-down fashion, starting with an analysis of the major 

source categories in Cape Romain’s SO2 AOI to determine which major categories had 

the highest residual contribution to the area in 2018. It was also important to identify 

reductions that are projected to occur between 2002 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 controlled under the future year base case had the potential for 

reduction. Once the highest source types were identified, subcategories within these 

sources types were reviewed. The contributions from major source categories to the 2018 

Base GS inventory for the SO2 AOI for Cape Romain are listed in Table 7.5.5-1. 

Miscellaneous 4% 3% 14% 1% 65% 40% 84%

Off-highway 14% 19% 32% 1% 2% 4% 0%

Highway Vehicles 19% 25% 41% 1% 1% 1% 11%

Waste Disposal & Recycling 4% 3% 7% 1% 7% 15% 0%

Storage & Transport 7% 0% 0% 0% 0% 1% 0%

Solvent Utilization 40% 0% 0% 0% 0% 0% 0%

Other Industrial Processes 6% 6% 1% 6% 8% 9% 2%

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

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

Chemical & Allied Product Mfg 1% 1% 0% 4% 1% 1% 1%

Fuel Comb. Other 4% 5% 2% 6% 4% 7% 1%

Fuel Comb. Industrial 1% 17% 2% 34% 3% 5% 0%

Fuel Comb. Elec. Util. 0% 20% 1% 46% 8% 16% 1%

Tier VOC NOX CO SO2 PM-10 PM-2.5 NH3

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 combustion from electric utilities and industrial 

processes produces the majority of the remaining SO2 emissions, even after 
77 

implementation of CAIR. Together, these source categories contribute 80 percent of the 

2018 SO2 emissions for the AOI of Cape Romain. 

This table can also be used to evaluate the major source categories contributing to 

emissions of NOX, NH3, and PM emissions in 2018. For instance, highway vehicles and 

off-road vehicles are major sources of NOX emissions, in addition to electric utilities and 

industrial boilers. The source category “miscellaneous” (which includes agricultural 

sources and fires) is the major contributor to NH3 and PM. 

The contributions to SO2 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 subcategories for the AOI for Cape Romain. 

Within Electric Utilities, almost all of the SO2 emissions are attributable to coal-fired 

power plants. Within Industrial Boilers, most emissions are attributable, again, to coalfired boilers. 

Chemical and Allied Product Manufacturing 4%

Other Industrial Processes-Mineral Products 3%

Other Industrial Processes-Rubber & Miscellaneous Plastic Products 0%

Other Industrial-Wood, Pulp & Paper, & Publishing Products 3%

Other Industrial Processes-Textiles, Leather, & Apparel Products 0%

Other Industrial Processes-Agriculture, Food, & Kindred Products 0%

Fuel Comb. Industrial-Internal Combustion 0%

Fuel Comb. Industrial-Other 2%

Fuel Comb. Industrial-Gas 2%

Fuel Comb. Industrial-Oil 9%

Fuel Comb. Industrial-Coal 21%

Fuel Comb. Elec. Util.-Internal Combustion 1%

Fuel Comb. Elec. Util.-Other 2%

Fuel Comb. Elec. Util.-Gas 0%

Fuel Comb. Elec. Util.-Oil 0%

Fuel Comb. Elec. Util.-Coal 44%

Tier SO2

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 Carolina should consider whether any additional 

control measures for certain coal-fired utility and industrial boilers could achieve any 

reasonable progress. The lists of individual sources were also used to determine if 
78 

individual sources in other source categories are major contributors to SO2 emissions in 

the AOI. 

36B7.6 Which Control Measures for Individual Sources Achieve Reasonable Progress? 

The next step was to identify emission reductions that have already occurred or will occur 

by 2018 within each source category or at specific units. Unit-level tables of emission 

comparisons from 2002 to 2018 were developed, allowing VISTAS States to review 

emission reductions from other CAA programs. These tables assigned suggested future 

year control technology from: IPM forecasting and State modification for EGUs, and 

control efficiency tables for Non-EGU point sources. 

Once emission control profiles for specific units are understood, the next step is to 

determine what, if any, additional controls would be technically feasibly, and to estimate 

costs of those control measures. 

For EGUs, the 2018 IPM file used by VISTAS for EGU sources was obtained and 

matched to the 2018 base case inventory of EGU sources. This step was conducted to 

ensure that incremental controls considered for these source types did not duplicate 

existing base case assumptions. 

For non-EGUs, VISTAS used EPA’s AirControlNET database, modified for the VISTAS 

emission inventories. The core of AirControlNET is a relational database system in 

which control technologies are linked to sources within EPA emissions inventories. The 

system contains a database of control measure applicability, efficiency, and cost 

information for reducing emissions. The control measure data file in AirControlNET 

includes not only the technology's control efficiency and calculated emission reductions 

for that source, but also estimates the costs (annual and capital) for application of the 

control measure. 

Using the modified inventories identified above, VISTAS ran every available SO2 control 

strategy in AirControlNET against the EGU and non-EGU point source inventories to 

develop a master list of incremental control 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 master 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 SO2

point source emissions times distance-weighted residence time (RTMax) values for the 

county in which the emission release point was located. South Carolina used these to 

evaluate control measures and costs as part of its statutory factor analysis for reasonable 

further progress. Table 8.0-2 lists these data in terms of the State’s four factor analysis. 
79 

37B7.7 Which Control Measures for Individual Sources Achieve Reasonable Progress? 

USC DHEC Process on Reasonable Progress AssessmentU: 

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 percent of the visibility-impairing pollution. 

VISTAS States concluded that controlling SO2 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 SO2 point source emissions in 2018 represent more than 95 percent of the total SO2

emissions inventory, VISTAS States concluded that the focus should be on EGU and 

non-EGU SO2 emissions. 

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

“In determining the emissions and visibility improvement achieved during each 

implementation period, States should include all air quality improvements that will be 

achieved by other programs and activities under the CAA and any State air pollution 

control requirements” (64 FR 35733). In keeping with this recommendation, South 

Carolina, as part of its long-term reasonable progress analysis to consider potential 

sources contributing to visibility impairment, examined other CAA requirements such as 

CAIR. SC DHEC evaluated the amount of SO2 reduction from EGU’s due to CAIR. 

Since the EGUs will be reducing their SO2 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 the uniform rate of progress over the 60 year 

period that would be needed to attain natural background conditions by the year 2064” 

(64 FR 35732). To calculate the rate of progress represented by each reasonable progress 

goal, South Carolina compared baseline visibility conditions to natural visibility 

conditions in its Class I Federal 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. SO2

emission reductions that are predicted by the IPM to meet the 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 certainty as possible that, whichever 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 CAIR, 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 earliest reasonable deadline for compliance with 

the final highly-cost effective control levels for reducing emissions was 2015...” (70 FR 
80 

25197-25198). Based on this analysis, SC DHEC has determined that CAIR controls 

satisfy reasonable progress for the initial planning period. 

These criteria along with the success of NOX SIP call reductions and SC DHEC’s 

ongoing review of Prevention of Significant Deterioration/New Source Review permits 

serve to address this uncertainty and promote confidence in projected reasonable 

progress. Continued collaboration between SC DHEC and the FLM will ensure that 

trends in growth are addressed. SC DHEC also intends to re-evaluate the IPM 

predictions of SO2 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 visibility improvements expected from SO2 emission 

reductions predicted by IPM and required by CAIR, SC DHEC concludes that additional 

SO2 control measures for South Carolina’s EGUs 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 fractional contribution from all emission units within the SO2

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 contribution) 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 maximum 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 NOX SIP call established a significance level 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/m3 as 

significant. This threshold is representative of 1.3 percent of the annual PM2.5 

standard of 15 μg/m3. 

• 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 than a particular significance 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 reasonable further progress was more stringent 

than any significant impact level (the most restrictive being NOX at 1 μg/m3

 or 1 

percent). 

While SC DHEC recognizes the important difference between significant impact levels 

of human health standards and visibility standards, the Department contends that its 
81 

approach to establishing a visibility contribution 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 above was evaluated using the statutory and 

regulatory factors of: (1) cost of compliance, (2) time necessary for compliance, (3) the 

energy and non-air quality environmental impacts 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 greater economic burden than EGUs for a given 

control strategy. 

In determining reasonable costs of controls for the EGU sector, SC DHEC determined 

that the cost of S02 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 SO2 removed; with an average cost per system ranging from $1,231 to $1,375 

dollars per ton of SO2 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 terms of capital cost, but also the 

cost of labor needed to efficiently and effectively maintain the equipment. “The 

availability of skilled labor--specifically, boilermakers--is an important constraint for the 

installation of significant amounts of emission controls. Boilermakers are skilled steel 

workers who are specially trained to install both NOX controls, such as SCR, and SO2

controls, such as scrubbers” (69 FR 4617). 

The Mid-Atlantic/Northeast Visibility Unit (MANE-VU) RPO contracted with 

MACTEC, Inc. to evaluate control options and four statutory factors for assessing RPGs 

for regional haze in the MANE-VU region. The project report summarizes the control 

measures available for SO2 emissions from electric utilities 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 post-combustion controls. 

For coal-fired and oil-fired boilers, lower sulfur 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 modification 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 boiler capacity, site-specific factors--such as 

availability of materials and construction crews, transportation 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. 

38B7.8 BART Determinations in South Carolina 

Section 169A of the CAA directs states to assess certain emission sources for additional 

controls in order to address visibility impacts. States are directed to conduct BART 

determinations for such sources in specific source categories and for sources that 

contribute to visibility impairment in Class I Federal areas. The 1999 RHR includes the 

BART requirement and directs states to include BART in their regional haze SIPs. On 

July 6, 2005, the EPA published a revised final rule, including Appendix Y to 40 CFR 

Part 51, the Guidelines for BART Determinations 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 and 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 was put in place between August 7, 1962, and 

August 7, 1977; and (3) whose operations fall within 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 cause or contribute to any impairment 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 controls. Procedures outlined in Part I of 

this guidance allow states to consider local conditions and circumstances in their BART 

decision-making. This fact recognizes 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 the 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. 

63B7.8.1 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 ten facilities (Shaw Industries: Anderson; Solutia, Inc.; and 

Honeywell: Clemson) were later determined to be exempt form BART exemption 

modeling due to boiler size. 
83 

Appendix H.7 contains a detailed description of each BART-eligible emission unit at the 

following facilities identified as having BART-eligible sources in South Carolina. 

UFACILITY 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 the 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 existence on August 7, 1977, and began operation at 

some point on or after August 7, 1962; and 

• The potential emissions, considering federally enforceable permit limits, from all 

emission units identified in the previous two bullets were greater than 250 tons or 

more per year of any of these visibility-impairing pollutants: SO2, NOX, and 

PM10. 

The BART Guidelines recommend addressing these visibility-impairing pollutants (SO2, 

NOX, and PM) and suggest that states use their best judgment in determining whether to 

address VOC and/or ammonia (NH3) emissions. As the BART Guidelines recommend, 

South Carolina addressed SO2 and NOX, and used PM10 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 NH3 form point sources in the Southeast can 

reasonably be anticipated to contribute to visibility impairment in any Class I Federal 
84 

area. For this reason, South Carolina did not evaluate emissions of VOC and NH3 in its 

BART determinations. 

64B7.8.2 Determination of Sources Subject to BART in South Carolina 

Under the BART Guidelines, states should exempt sources from BART if it is found that 

they do not contribute to visibility impairment in a Class I Federal area. In accordance 

with the BART guidelines, South Carolina chose 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 able 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 Table 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/actual 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 

0.224 
85 

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 within 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 default value for use in the BART assessment 

for this first planning period. The Regional Haze rule, however, gives the state 

significant flexibility in making decisions concerning the BART analysis as part of the 

regional haze process. 

VISTAS along with other consultants have examined the effects of sea salt and proposed 

a hierarchy of methods for sea salt estimation based on a consideration of different 

factors impinging on how technically reliable each method is for estimating sea salt in the 

atmosphere. After taking into account the various method variables as presented in 

Appendix O.3 (including chlorine depletion and blank problems) SC DHEC is convinced 

that the use of the VISTAS sodium ion data is justified and provides the most realistic, 

technically sound, yet reasonably conservative, estimate of sea salt in the Cape Romain 

Class I Federal area. This September 2002 through December 2004 sodium ion dataset 

results in a 0.77 ug/m3

 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 believes that the use of the sodium ion 

derived alternative sea salt estimate would be justified for any facility modeling visibility 
86 

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 facilities who requested an alternative to the default sea salt 

estimation. Note that some facilities presented 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 Estimation 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 improve 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 CAIR program need not require affected BARTeligible EGUs to install BART for SO2 or NOX emissions. Given that South Carolina is 

participating in CAIR and accepts EPA’s overall finding that CAIR “substitutes” for 

BART for SO2 and NOX, 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 and Santee Cooper’s – Jefferies, Winyah, and 

Grainger facilities. 

In total, 19 of SC’s 21 BART-eligible sources were able to demonstrate that they did not 

cause or contribute to visibility 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. 

65B7.8.3 Evaluation of Sources Subject to BART in South Carolina 

75BU7.8.3.1 Screened and Refined Evaluations of Sources 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 pulverized coal-fired, wet bottom boilers. The two 

boilers produce superheated steam, which is used in the two dedicated turbine generators. 
87 

Units 1 and 2 are equipped with fabric filter baghouses to control PM emissions, and lowNOX burners and SCR to control NOX emissions. Although Units 1 and 2 commenced 

commercial operation in the early ‘70s, there is no near-term limitation on the useful life 

of this unit. 

SCE&G also plans to install two wet limestone scrubbers to control SO2 emissions in the 

summer (July/August) of 2009. Wateree Station Units 1 and 2 will be retrofitted with a 

flue gas desulfurization (FGD) system using a limestone slurry in a spray tower to 

remove SO2 from the gas stream. Although designed to control SO2 emissions, the FGD 

system will provide the added benefit of removing sulfates, a principal constituent of 

condensable PM10. Installing a FGD system will result in added improvements in 

visibility impairment attributable to Units 1 and 2. 

The installation of the FGD systems will result 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 98th 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 visibility impairment as stated in the BART 

Guidelines. In addition, an analysis of annualized costs associated with additional PM10 

control options determined that any additional control measure is not cost effective in 

terms of visibility improvements. 

SCE&G Williams: South Carolina Electric & Gas (SCE&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, which is used in a turbine 

generator. Although Unit 1 commenced commercial operation in 1973, there is no nearterm limitation on the useful life of this unit. 

Unit 1 is currently equipped with low-NOX burners and SCR to control NOX emissions 

and an electrostatic precipitator (ESP) to control PM10 emissions, the later of which has 

been demonstrated to achieve performance levels comparable to those being specified as 

BACT for new coal-fired boilers. The existing control device, therefore, is considered 

representative of BART for PM10 . 

In October 2009, SCE&G plans to retrofit Williams Station Unit 1 with a FGD system 

using a limestone slurry in a spray tower to remove SO2 from the gas stream. Although 

designed to control SO2 emissions, the FGD system will provide the added benefit of 

removing sulfates, a principal constituent of condensable PM10. PM10 emissions will be 

reduced from 925 tpy to 464 tpy following the installation of the FGD system. This 50 

percent reduction is attributable to the removal of condensable PM10, 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 98th percentile delta dv resulting from the installation of the FGD 
88 

system is only slightly above the 1.0 delta dv threshold (2 occurrences in 3 years), used to 

determine whether a source could causes visibility impairment as stated in the BART 

Guidelines. The installation of any of the PM10 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 PM10 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 wet bottom generating unit with a cold side ESP 

for PM10 control. Unit 3 is a pulverized coal-fired dry bottom unit with a fabric filter for 

PM10 control. Unit 2 and 3 were constructed 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 Island 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 PM10 emissions from Canadys 

Station’s Units 2 and 3 do not cause or contribute to regional haze in any Class I area 

within 300 km of the plant. The eighth-highest delta dv visibility impact attributable 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 further 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 listed below are BART-eligible sources. These 

units are pulverized coal-fired, dry-bottom boilers. 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 NOX, SO2, 

and total PM10 (condensable and filterable). 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 facilities are BART-eligible. 

UWinyah:U The modeled peak, 24-hour average visibility impacts at Cape Romain 

attributable to Santee Cooper’s Winyah Generating Station (using the default annual 

average natural background for representing natural background conditions) indicate that 

the refined analyses was required since the peak visibility impact estimated (1.566 delta 

dv) using the default screening approach were 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 and 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 algorithm (using all three estimates of the annual average sea 

salt concentration; 0.77 μg/m3

 as estimated by the VISTAS Technical Contractor; 1.1 

μg/m3 as estimated by Santee Cooper; and 0.2 μg/m3 as the default IMPROVE value), the 

visibility impact of the Winyah Generating Station at Cape Romain was determined to be 

0.476, 0.435, and 0.561 delta dv respectively. These modeling results demonstrate that 

the Winyah Generating Station does not cause or contribute to visibility impairment and 

is not subject to BART when the more representative sea salt concentrations of 0.77 

μg/m3 as estimated by the VISTAS Technical Contractor and 1.1 μg/m3 as estimated by 

Santee Cooper are utilized because PM10 emissions from the facility do not contribute to 

visibility impairment at Cape Romain as estimated using refined modeling techniques. 

UJefferies:U The modeled peak, 24-hour average visibility impacts at Cape Romain 

attributable to Santee Cooper’s Jefferies Generating Station (using 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 was 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 contribution threshold. Thus, Wolf 

Island NWR was screened out and 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 algorithm (using all three estimates of the annual average sea 

salt concentration; 0.77 μg/m3

 as estimated by the VISTAS Technical Contractor; 1.1 

μg/m3 as estimated by Santee Cooper; and 0.2 μg/m3 as the default IMPROVE value), 

Jefferies’ contribution to visibility impairment was determined to be 0.273 μg/m3

, 0.252 

μg/m3

, and 0.318 μg/m3 respectively - less than the 0.5 delta dv contribution threshold. 

Thus, PM10 emissions from the facility do not contribute to visibility impairment at Cape 

Romain as estimated using refined modeling techniques and the Jefferies Generating 

Station is exempt from further BART analysis. 

 

UGrainger:U The modeled peak, 24-hour average visibility impacts at Cape Romain 

attributable to PM10 emissions from Santee Cooper’s Grainger Generating Station (using 

the default annual average natural background 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 default 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 for 
90 

BART-eligibility and determined that there are two emission units considered BARTeligible at the Cooper River Facility. Potential emissions from these units exceed 250 tpy 

of NOX, SO2, and PM10, each of which is considered a visibility-affecting pollutant. 

However, DAK’s Cooper River Facility is not 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 analyses indicate DAK’s Cooper River Facility should not be considered 

subject to BART because its BART-eligible sources do not contribute to visibility 

impairment at the Wolf Island NWR as demonstrated by a refined modeling (using all 

three estimates of the annual average sea salt concentration; 0.77 μg/m3 as estimated by 

the VISTAS Technical Contractor; 1.1 μg/m3 as estimated by Santee Cooper; and 0.2 

μg/m3 as the default IMPROVE value) analysis that quantified the 98th percentile 24-

hour average visibility impact as 0.471, 0.437, and 0.544 delta dv, respectively. These 

modeling results demonstrate that the DAK Cooper 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 μg/m3 as estimated by the VISTAS 

Technical Contractor and 1.1 μg/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 emission 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 Catawba, 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 approximately 216 km west-northwest of the mill at 

its nearest boundary. Cape Romain is located approximately 230 km southeast of the 

mill. 

Bowater reviewed the criteria for BART-eligibility 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 NOX, SO2, and PM10, 

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 any 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 98th 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 

NOX, SOX, and PM from all BART-eligible sources at the facility are 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 are subject to the BART regulations. 

Three Class I areas are between 100 and 300 kilometers 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 Class I area, a refined modeling analysis using 

the VISTAS 4 km modeling domain was performed, with receptor locations and 

elevations were based on the input files received from VISTAS. 

The refined modeling results represent the 98th percentile (8th highest) values over each 

year at all receptors. The largest degree of visibility impairment resulting from the 

emissions of BART eligible sources from the Wellman facility was predicted at Cape 

Romain for the 2002 modeled year, with an 8th highest value of 0.175 delta dv using the 

revised IMPROVE algorithm. Based on the dispersion modeling analysis using 

CALPUFF, the impacts from the BART eligible sources are less than the guideline 

threshold and therefore exempt from further BART evaluation. 

INVISTA (SARL): INVISTA SARL (INVISTA) operates 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 facility. These units were in existence on 

August 7, 1977, and began operation after August 7, 1962. This collection of emission 

units has potential emissions of 1,167 tpy NOX, 251 tpy PM10, and 4,598 tpy SO2. 

There are four Class I areas within 300 km of the INVISTA Camden Plant. These 

include Cape Romain (167 km), Great Smokey Mountains (267 km), Linville Gorge 

(208), and Shining Rock (232 km). 

Modeled peak, 24-hour average visibility 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 were greater than the 0.5 delta dv contribution 

threshold at all four Class I areas, thus indicating a refined modeling approach was 

necessary. 

Refined modeling results representing natural background conditions (i.e., annual average 

and 20 percent best days) indicate that for the 98th 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 Cape Romain was determined to be 0.244 delta 

dv. Refined modeling analyses determined that INVISTA’s contribution to visibility 

impairment at Great Smoky Mountains National 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 Camden 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 Carolina. There are two (2) Class I areas within 

300 kilometers of the Georgetown Mill: Cape Romain and Wolf Island NWR. Cape 

Romain is located approximately 29.5 kilometers south of the mill, and Wolf Island is 

located approximately 290 kilometers southwest of the mill.

IP Georgetown reviewed the criteria for BART-eligibility and determined that nine 

emission units are BART-eligible sources at the facility because potential emissions for 

each exceed 250 tpy for of NOX, SO2, and PM10, which are considered visibility-affecting 

pollutants. 

IP Georgetown’s refined methodology used the CALPUFF model (with the refined sea 

salt concentrations of 0.77 μg/m3 as estimated by the VISTAS Technical Contractor) 

with three years of meteorological data developed by VISTAS along with the revised 

IMPROVE algorithm. URS evaluated the 98th percentile impacts since the 4 km 

modeling domain was used for the analysis. 

The maximum change in visibility values resulting 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 visibility impacts were less than the threshold of 0.5 delta dv 

using refined estimates of light extinction 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 three (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 reviewed the criteria for BART-eligibility 

and determined that there are fourteen emission units that are BART-eligible sources. 

This collection of units has potential emissions of 630 tpy of NOX, 592 tpy of PM10, 2143 

tpy of SO2. 

Modeling was performed using the VISTAS default 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 screened out and only 

visibility impacts at Cape Romain were evaluated using refined modeling. 

The results of this refined modeling analyses using the VISTAS 4-km modeling domain 

indicate that MeadWestvaco is not subject to BART. The maximum change in visibility 

impairment quantified by the 98th percentile 24-hour average visibility impact at Cape 

Romain resulting from the emissions of BART eligible sources from the facility is 0.408 

delta dv. Thus, predicted visibility is less than the 0.5 delta dv contribution threshold 
93 

using refined estimates of light extinction 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, NOX, 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 default screening approach that indicates 

visibility impairment at Wolf Island is less 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. The maximum predicted 

visibility impact at the Wolf Island Class I area was predicted to be much lower at 0.034 

delta dv. These results demonstrate that ISG Georgetown’s peak daily emissions are well 

below the threshold that would cause or contribute to visibility impairment. Therefore, 

the source is not subject to BART. 

76BU7.8.3.2 VOC Evaluations of Sources Subject 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-affecting pollutants. 

Section 4.1.3 of the VISTAS BART Modeling Protocol (Appendix C) describes the 

regional modeling analyses showing that cumulative 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 areas 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 throughout 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 the 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 VISTAS States have concluded that VOC 
94 

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 sources do not contribute 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 NOX, SO2, 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 determined not to have an impact on visibility 

impairment (and whose emissions of other pollutants are less than the 250 tpy threshold), 

BART exemption status may be granted. 

7BU7.8.3.3 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 NOX or SO2 (or combined NOX and SO2), as long as they are located 

more than 50 kilometers from any Class I area; and sources that emit less than 1000 tons 

per year of NOX and SO2 (or combined NOX and SO2) that are located 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 using this rationale. The distances used in 

this analysis were derived from the great circle 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 measuring the shortest distance 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) 

NOX 

Emissions 

(tpy) 

NOX + 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) 

39B7.9 Post-BART Visibility Improvement Expected at Cape Romain by 2018, 

Compared to the Glidepath 

As discussed earlier in Chapter 7, visibility on the 20 percent worst visibility days is 

expected to improve to levels better than the glidepath by 2018 based solely on 

reductions from existing and planned emission controls. Figure 7.9-1 reveals the 

predicted visibility (dv) in 2018 on the worst days at Cape Romain. 

Uniform Rate of Reasonable 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

Year

Haziness Index (Deciviews)

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

7.9 - 1 Predicted Visibility for the 20% Worst Days in 2018 at Cape Romain Using 

the New IMPROVE algorithm. 

In addition to improvement in visibility on the 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 visibility (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 areas and are included in Appendix L. 

WINHAZE is a photographic imaging tool that accounts for the effect of concentrations 

of the fine particle components 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 2064

Haziness 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 Days 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-1
98 

7B8.0 REASONABLE PROGRESS GOALS 

The RHR at 40 CFR Section 51.308(d)(1) requires States to establish 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 improvement in visibility for the most impaired 

days and ensure no degradation in visibility for the least impaired days over the SIP 

period. The state must also provide an assessment of the number of years it would take to 

attain natural visibility conditions if improvement continues at the rate represented by the 

RPGs. 

In accordance with the requirements of 40 CFR Section 51.308(d)(1), this Regional Haze 

SIP establishes RPGs (expressed in dv) for each Class I Federal area. To determine the 

rate of progress represented by each RPG, South 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 reasonable 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) 

 

40B8.1 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 the 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 compliance, the time necessary for compliance, the 

energy and non-air quality environmental impacts of compliance, and the remaining 

useful life of any existing source subject to such requirements. 
99 

In developing South Carolina’s reasonable progress goals, VISTAS, in November 2006, 

contracted with Alpine Geophysics to develop a cost control analysis that showed various 

control options for sources within an area of influence for Cape Romain and other Class I 

areas. 

Since sulfate is the predominant cause of visibility impairment in the VISTAS region, 

South Carolina and the other VISTAS states decided to concentrate on reduction of SO2, 

the precursor to sulfate formation, during this first planning period. As such, the cost 

control list was sorted for the SO2 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 visibility 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 SO2 emission reductions and SO2 is the 

leading pollutant that contributes to visibility impairment 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 area. 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 narrative 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 the 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 fractional contribution of each unit. Only the units with fractions 
100 

of 0.01 or above (or greater than or equal to one percent contribution) were then 

evaluated using the four factor analysis. 

Table 8.0-2 illustrates South Carolina’s approach 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 SO2 reduced by the application of 

the control strategy. 

Under South Carolina’s approach, facilities whose costs fall 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 remaining 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 reasonable within the four factor analyses. 
101 

Table 8.0 - 2 Non-EGU Sources Subject to Four Factors as Ranked on Relative Cost of Compliance. 

2,002 2,018 Control Strategy Application AOI and 

State Plant Name ID Measure Name 

SO2 

(TPY) 

SO2 (TPY) 

CE 

Tons 

Reduced 

CE 

Total Cost 

Cost Per 

Ton 

Distance 

(km) 

SC MEADWESTVACO 006 Low Sulfur Oil (0.9%) 2,315 2,706 0 839 31 $1,522,570 $1,815 29.58 SC INTERNATIONAL PAPER:GEORGETOWN 001 FGD 3,152 3,655 4 3,274 90 $14,556,791 $4,446 56.05 SC INTERNATIONAL PAPER:GEORGETOWN 001 Low Sulfur Oil (0.9%) 3,152 3,655 4 1,133 31 $2,056,395 $1,815 56.05 SC GIANT CEMENT CO 005 FGD 2,825 3,893 0 1,947 50 $19,317,345 $9,924 79.80 SC GIANT CEMENT CO 005 Low Sulfur Oil (0.9%) 2,825 3,893 0 1,207 31 $2,190,705 $1,815 79.80 SC HOLCIM:HOLLY HILL 002 Low Sulfur Oil (0.9%) 2,957 4,074 0 1,263 31 $2,292,345 $1,815 82.56 GA INTERNATIONAL PAPER - SAVANNAH MILL PB13 FGD 7,643 8,578 4 7,685 90 $22,704,952 $2,955 165.98 SC HOLCIM:HOLLY HILL 001 Low Sulfur Oil (0.9%) 1,336 1,841 0 571 31 $1,036,365 $1,815 82.56 SC DAK AMERICAS 002 Low Sulfur Oil (0.9%) 496 492 4 153 31 $277,695 $1,815 32.57 SC BP AMOCO CHEMICAL-COOPER RIVER PLANT 002 Low Sulfur Oil (0.9%) 488 392 0 121 31 $220,502 $1,815 35.95 SC BP AMOCO CHEMICAL-COOPER RIVER PLANT 001 Low Sulfur Oil (0.9%) 486 390 0 121 31 $219,680 $1,815 35.95 SC BAYER POLYMERS GOOSE CREEK 001 Low Sulfur Oil (0.9%) 299 241 0 75 31 $135,318 $1,815 35.95 SC STONE CONTAINER FLORENCE 016 FGD Wet Scrubber 2,660 3,109 0 2,798 90 $10,882,328 $3,889 138.60 SC STONE CONTAINER FLORENCE 016 Coal Washing 2,660 3,109 0 1,088 35 $523,594 $481 138.60 SC INTERNATIONAL PAPER:EASTOVER 007 FGD 3,043 3,453 4 3,094 90 $26,841,804 $8,677 139.35 FL JEFFERSON SMURFIT CORPORATION (US) 15 FGD 3,242 3,639 4 3,260 90 $25,163,913 $7,720 303.79 NC INVISTA, S.A.R.L. G-247 Low Sulfur Oil (0.9%) 3,249 2,611 0 810 31 $1,469,295 $1,815 218.01 NC INVISTA, S.A.R.L. G-247 FGD 3,249 2,611 0 2,350 90 $3,754,002 $1,597 218.01 SC STONE CONTAINER FLORENCE 015 Low Sulfur Oil (0.9%) 622 727 4 204 31 $370,864 $1,815 138.60 GA INTERNATIONAL PAPER - AUGUSTA MILL PB2A FGD 1,574 1,767 4 1,583 90 $15,181,164 $9,593 218.31 SC GALEY&LORD:SOCIETY HILL 001 FGD 1,121 1,112 4 996 90 $1,079,058 $1,083 175.46 FL GEORGIA-PACIFIC CORP. PULP/PAPER MILL 15 FGD 3,703 4,329 0 3,896 90 $17,425,139 $4,473 410.92 FL GEORGIA-PACIFIC CORP. PULP/PAPER MILL 15 Low Sulfur Oil (0.9%) 3,703 4,329 0 1,342 31 $2,435,476 $1,815 410.92 NC SOUTHERN STATES CHEMICAL G-21 Increase % Conversion 931 1,112 0 946 85 $427,461 $452 216.64 VA STONE CONTAINER CORP 2 FGD 3,562 4,006 4 3,589 90 $17,362,178 $4,838 573.68 TN BOWATER NEWSPRINT & DIRECTORY 015 FGD 5,232 5,871 4 5,260 90 $23,758,976 $4,517 538.16 TN INTERTRADE HOLDINGS, INC. 001 Increase % Conversion 1,837 2,530 4 2,398 95 $4,141,525 $1,727 492.73 
102 

6B8.1.1 Four Factor Analysis of Sources Effecting Visibility at Cape Romain 

78BU8.1.1.1 Effectiveness of Addition Control Technology Options 

In general the following control technologies are typical options considered by facilities 

whose emissions include visibility impacting pollutants. 

Fabric Filters: 

In a fabric filter, flue gas passes through a tightly woven or felted fabric, causing particles 

in the gas to be collected on the fabric by sieving, electrostatic attraction, and other 

mechanisms. Extra compartments for maintenance or offline cleaning generally increase 

the reliability, but also the expense of capital costs. However, fabric filter baghouses in 

general provide high collection efficiencies for both coarse and fine particulates and are 

relatively insensitive to fluctuations in gas stream conditions. 

ESPs: 

ESPs are generally considered the most widely applied particulate 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 use 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 material 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-scale, coal-fired utility boilers in the past. 

Flue Gas Desulphurization (FGD): 

The most common method to remove SO2 from coal-fired power plant flue gas is a wet 

scrubber. Most FGD processes use wet limestone, forced-oxidation scrubbing. Another 

process for SO2 control, used widely for low sulfur Western coals, is a lime-based spray 

dryer followed by a bag house that collects both the reacted lime along with all of the 

coal ash. However, this approach is only 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 SO2 control for low sulfur coals, is 

more expensive than the alternatives, and creates 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 associated with constructing a FGD system, the 

operation and maintenance, and waste handling costs. The cost per ton will vary 

significantly based on boiler size and type, with a total cost ranging between $3,000 and 
103 

$20,000 dollars per ton. In addition, FGD has been found to slightly reduce energy 

production. The process itself requires that more fuel is burned per 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 particulate 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 developed 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 performance of the electrostatic precipitator 

for its intended use of removing fly ash. Injection of activated carbon in a bag house will 

not need as much activated carbon as an electrostatic precipitator, but will also 

contaminate the fly ash. Another more significant drawback to this technology is that the 

domestic supply of activated carbon is currently not sufficient (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, employing high voltage discharge electrodes to 

charge particulate, but with fabric filters instead of collecting 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 calibrated higher than water alone. Wet or “hydraulic” 

cleaning techniques may also include particle agitation by aeration. To meet 

environmental regulations, technically advanced wash plants can remove as much as 40 

percent of the inorganic sulfur in coal. Appendix H.3 further 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 polluted waste streams. In 2002, 

about 25 percent of the raw coal processed through preparation plants was discharged to 

waste ponds as “refuse,” composed of shale, clay, coal, low-grade shaley coal, and 

preparation chemicals (R. Bonskowski, W. Watson, and F. Freme, 2006). 
104 

79BU8.1.1.2 Existing Source Specific Controls

MeadWestvaco: MeadWestvaco Corporation operates an unbleached paper mill in 

North Charleston, South Carolina. MeadWestvaco’s primary source of SO2 emissions 

are from its two recovery boilers: Recovery Boiler No. 1 and Recover 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 solids, 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 SO2 PSD limit that requires the boiler to limit the amount of 

high sulfur No. 6 fuel oil burned. 

International Paper, Georgetown: The International Paper Georgetown, South 

Carolina Pulp and Paper Mill is located in Georgetown, South Carolina. The Mill began 

operations in 1937. Primary operations at the mill include multiple fuel-fired boilers, 

chemical recovery operations, wood pulping operations, bleached papermaking, and 

additional operations and equipment necessary to support these operations. Unit ID 01 

includes equipment that supports the Power and Recovery Operations at the Mill and 

consists of: No. 1 Power Boiler, No. 2 Power 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 alternative fuels: wood-waste (including pulping 

rejects and peat), used oil, cleanup media materials from virgin No. 6 fuel oil 

spills/leaks, wastewater sludge, containerboard 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 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 alternative fuels: wood-waste (including pulping 

rejects and peat), used oil, cleanup media materials from virgin No. 6 fuel oil 

spills/leaks, wastewater sludge, containerboard 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. 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 of 
105 

propane. The unit is controlled by an electrostatic precipitator 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, corrugating medium, and saturating kraft paper. 

The Savannah Mill’s primary source of process 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 life 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) quarrying, stone crushing, cement manufacturing, 

packaging, and shipping. The facility operates one dry process cement kiln system to 

produce cement clinker. The cement kiln system is equipped with fabric 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 enough heat necessary to produce clinker, the 

facility is permitted (Part 70 Title V/PSD Permit 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 waste-derived fuels). Giant’s PSD permit 

application utilized an emission factor of 3.5 pounds of SO2 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 process equipment that were 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 terephthalate (PET) production, 2) a 

PET finishing area to produce fiber products, and 3) a PET finishing are to produce bottle 

resin products. The Cooper River Facility’s primary source of SO2 emissions are from its 

coal-fired boiler (Boiler No. 2). Boiler No. 2 is a 206 MMBtu/hr bituminous coal-fired 

boiler. Boiler No. 2 is equipped with a baghouse 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 demonstrated through calculations based on 12-month 
106 

rolling sum averages from supplier certifications as stated in the facility’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. 

80BU8.1.1.3 Source Specific Control Technology Options

MeadWestvaco: 

Since Recovery Boiler No. 9 currently operates 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 alternatives to switching from high 

sulfur (2.1 percent sulfur 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 SO2 removed; above the $2,000.00 cost per ton threshold. Switching to lower 

sulfur fuel oil (e.g., No. 2 Fuel oil) and natural gas would also require equipment and 

operating modifications that would further increase costs and 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 SO2 removed. In addition, the changes 

required to modify the fuel tanks, piping systems, 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 Georgetown Mill’s ability to 

engage in burning tire-derived fuels, create additional environmental and safety issues 

associated with increased truck traffic, and require secondary containment associated 

with increased tank storage. 

IP Savannah: 

In conducting a four factor analysis, IP Savannah focused its evaluation on the feasibility 

of several SO2 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 available for coal-fired utility boilers. All 

of the control options have been demonstrated and are considered technologically 

feasible. The cost associated with each of these alternatives was calculated using the 

Coal Utility Environmental Cost (CUECost) software tool and determined to be above 

the $2,000.00 cost per ton threshold (with costs ranging from $2,950.00 to $34,927.00 

per ton of SO2 removed). These alternatives would take from 2010 to 2012 to implement 

and result in significant non-air quality impacts to include: substantial increased use of 

fresh water, the generation of industrial wastewater, potentially substantial increases in 
107 

solid waste generated, additional environmental and safety issues associated with 

increased truck traffic, and require additional manpower and costs for monitoring and 

maintaining systems. 

 

Giant: 

In conducting a four factor analysis, Giant 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. 

Giant determined that its costs associated with switching to lower sulfur fuel would range 

from $7,801.00 per ton to $11,152.00 per ton of SO2 removed. The time required to 

install fuel tanks, piping systems, and delivery 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) provides Giant the option to operate with up to 

100 percent substitution rates of conventional fuels. The additional non-air quality 

impacts of switching to lower sulfur fuels would also limit the facility’s ability to engage 

in burning liquid and solid waste-derived fuels, effectively adding to the non-air quality 

impacts of finding alternate means of disposing 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 SO2 removed. Moreover, the additional 

non-air quality impacts of switching to lower sulfur fuels would 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 that up to 95 percent of SO2 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 SO2 reductions 

of 160 tons per year. 

DAK Americas: 

In conducting a four factor analysis, DAK Americas focused its evaluation on the 

feasibility of several SO2 control technologies, including: (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 injection (DSI). These technologies were 

chosen because they have been demonstrated and are commercially available for coalfired utility boilers. Of the aforementioned control technologies all but the FBC were 

deemed to be technologically feasible. Of the remaining four options, all were 

determined to be above the cost threshold 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 quality 
108 

impacts to include: substantial increased use of fresh water, the generation of industrial 

wastewater, and potentially substantial increases in solid waste generated. 

The results of the four factor analyses (contained in Appendix N) indicate that no 

additional controls are reasonable at this time. However, further future reductions by 

way of additional controls have been discussed and will be evaluated during the next 

planning stage. 
109 

8B9.0 MONITORING STRATEGIES 

The State Implementation Plan is to be accompanied by a strategy for monitoring 

regional haze visibility impairment. Specifically, the Regional Haze Rule states at 40 

CFR 51.308(d)(4): 

“(4) Monitoring strategy and other implementation plan requirements. The State 

must submit with the implementation plan a monitoring strategy for measuring, 

characterizing, and reporting of regional haze visibility impairment that is 

representative of all mandatory Class I Federal areas within the State. This 

monitoring strategy must be coordinated with the monitoring strategy required in 

§51.305 for reasonably attributable visibility impairment. Compliance with this 

requirement may be met through participation in the IMPROVE network. The 

implementation plan must also provide for the following: 

(i) The establishment of any additional monitoring sites or equipment 

needed to assess whether reasonable progress goals to address regional 

haze for all mandatory Class I Federal areas within the State are being 

achieved. 

(ii)-(vi) [Other implementation plan requirements that pertain to reporting 

and use of monitoring data and an emission inventory.]” 

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

1. Track the expected visibility improvements resulting from emissions 

reductions identified in this SIP. 

2. Better understand the atmospheric processes of importance to haze. 

3. Identify chemical species in the ambient particulate matter and relate them to 

emissions from sources. 

4. Evaluate regional air quality models for haze and construct relative response 

factors (RRFs) for using those models. 

The primary monitoring network for regional haze, both nationwide and in 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 based on, or directly comparable to, IMPROVE. 

The IMPROVE measurements provide the only long-term record available for tracking 

visibility improvement or degradation and therefore South Carolina intends to rely on the 

IMPROVE network for complying with the regional 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 Microbalance (TEOM) for continuously 

measuring PM2.5 mass concentration; 

• An Aethalometer for continuously 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 transferred 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 measurements were made in South Carolina in 2002-2005 as part 

of special monitoring studies by the VISTAS regional planning organization. These 

studies were intended to better understand source contributions to PM2.5 mass and 

visibility. They included continuous monitoring of sulfate, nitrate, and carbon to better 

understand daily trends in PM2.5, detailed analyses of carbon collected on high volume 

filters to identify source contributions to carbon, and additional analyses of sodium and 

ammonium on IMPROVE filter samples. Funding 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 and expects to operate 

them at urban and rural sites to further the understanding of both PM2.5 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 PM2.5 network of the filter based 

Federal Reference Method samplers, continuous mass monitors (TEOMs), STN protocol 

filter based speciated samplers and the continuous speciated particulate monitors 

described above. These PM2.5 measurements help SC DHEC characterize air pollution 

levels in areas across the state, and therefore aid in the analysis of visibility improvement 

in and near the Class I areas. 

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 IMPROVE. 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 demonstrate reasonable progress toward visibility 

improvement in its Class I areas and plan for appropriate future progress. In particular, 

South Carolina’s regional haze strategy relies on emission reductions that will result from 

the CAIR and reductions in neighboring 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 quality 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 regional, the reduction 

of the IMPROVE network by shutting down the existing IMPROVE site or any the closer 
111 

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 tracking 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 PM2.5 mass. 

Data produced by the IMPROVE monitoring network will be used nearly continuously 

for preparing the five-year progress reports and the 10-year SIP revisions, each of which 

relies on analysis of the preceding five years of data. Consequently, the monitoring data 

from the IMPROVE sites needs to be readily accessible and to be kept up to date. 

Presumably, IMPROVE will continue to process information from its own measurements 

at about the same pace and with the same attention to quality as it has shown in the recent 

past. The VIEWS web site has been maintained 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 facilitate availability analysis of the IMPROVE and 

visibility related data. 
112 

9B10.0 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 the technical analysis necessary to define 

visibility improvement by 2018 under existing federal and state regulations compared to 

the uniform rate of progress, SO2 AOIs for each Class I Federal area, and the methods to 

prioritize contributions from individual sources within the AOI. The VISTAS states 

collectively accept the conclusions of this analysis. 

In December 2006, the VISTAS State Air Directors held their first formal consultation 

meeting to review the Base G modeling results and the SO2 AOI analysis. The Air 

Directors agreed to look at reasonable control measures for sources on the lists for SO2

AOIs. This meant that each state would consider sources within their state as well as 

identify sources in neighboring states that they 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 Directors 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 either South Carolina’s or neighboring states’ 

Class I Federal areas. South Carolina has also consulted with neighboring states 

regarding this decision not to require any additional controls. Analysis of impacts from 

South Carolina sources is discussed in greater detail in Appendix J. 

Likewise, neighboring states have shared suggestions for controls 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 information and/or invitations to consult with the 

states of New Jersey (NJ) and New Hampshire (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 consultation with MANE-VU during the summer of 

2007. SC DHEC sent response letters to NJ and 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 several 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 believe 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. These sources have been identified in Tables 

10.0-1 and 10.0-2. In its response to this request, SC DHEC provided GA and NC with a 

list of sources identified as likely to contribute by one percent or more to visibility 

impairment in South Carolina as well as a justification as to why or why not each facility 

would be included in South Carolina’s final “Four-Factor Analysis” list of facilities. SC 

DHEC’s responses are included as Appendix J. 

In addition, SC DHEC also analyzed which 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 area as well as the magnitude of the source’s 

impact. This analysis is included in Appendix J. 
114 

Table 10.0 – 1 South Carolina Sources Affecting Class I Federal Areas in North Carolina 

Past/Projected Emissions (tpy) 

South Carolina Facility Sources Relative 

Contribution 

2002 2018 

Distance (km) to Class 1 Areas 

ExistiContr

Duke Energy – Lee 001 0.032745139 2,386 4,347 SHRO – 93 

0.011811362 2,386 4,347 JOKI - 165 

ESP 

Duke Energy – Lee 002 0.031195197 2,643 4,140 SHRO - 93 

0.011252536 2,643 4,140 JOKI - 165 

ESP 

Duke Energy – Lee 003 0.054809008 4,384 7,274 SHRO - 93 

0.019769056 4,384 7,274 JOKI - 165 

ESP 

Santee Cooper – Jefferies 003 0.01140849 11,315 6,761 SWAN - 425 ESPs Santee Cooper – Jefferies 004 0.011150031 11,789 6,609 SWAN - 425 ESPs 
115 

Table 10.0 - 2 South Carolina Sources Affecting Class I Federal Areas in Georgia 

Past/Projected Emissions (tpy) 

South Carolina Facility Sources Relative 

Contribution 

2002 2018 

Distance (km) to GA Class 1 Areas 

ExisCon

Santee Cooper – Jefferies 003 0.0107831745 11,315 6,761 WOLF - 245 ESPSantee Cooper – Jefferies 004 0.0105411703 11,789 6,609 WOLF - 242 ESPSantee Cooper – Cross 002 0.0053968254 10,260 3,380 WOLF – 244 ESP 

SCR FGD 

Santee Cooper – Cross 03 0.0061226033 0 3,835 WOLF – 244 Giant Cement 005 0.0068471727 2,825 3,893 WOLF – 223 FabrHolcim – Holly Hill 002 0.0055876516 496 492 WOLF - 235 ESP 

Fabr

MeadWestvaco 006 2,315 2,706 OKEF – 297 

0.0081280804 2,315 2,706 WOLF - 213 

ESP (198

SCE&G Canadys 001 0.0066036666 6,214 5,203 OKEF – 295 

0.0165538159 6,214 5,203 WOLF – 201 

ESP 

SCE&G Canadys 002 0.0065278568 6,585 5,144 OKEF – 295 

0.0163651554 6,585 5,144 WOLF - 201 

ESP 
116 

10B11.0 COMPREHENSIVE PERIODIC IMPLEMENTATION PLAN 

REVISIONS

40 CFR Section 51.308(f) requires South Carolina to revise its regional 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 periodic 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 more effective program over time and provide a 

focus for demonstrating ongoing progress and making 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 within 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 status of implementation; 

2. Summary of emission reductions achieved thus far, including especially the status 

of implementation of the CAIR compliance plans for EGUs compared to the 

control assumed in the modeling. [SC DHEC recognizes that the 2018 

projections of EGU controls from the IPM runs represent one solution to how the 

CAIR requirements will be met. By the time of the first periodic report, SC 

DHEC anticipates that the actual compliance strategy for the various utility 

companies will be much more defined. An assessment of those actual compliance 

plans will be done for the first periodic report.] 

3. Assessment of changes in visibility conditions at each class I area (current vs. 

baseline), expressed as 5-year averages of annual values for 20 percent best and 

worst days; 

4. Analysis of emission changes over the 5-year period, identified by source or 

activity; 

5. Analysis of any significant changes in or out of the State which have impeded 

progress; 

6. Assessment of the sufficiency of the implementation plan to meet RPGs; 

7. Review and any modifications to our visibility monitoring plan. 
117 

All requirements listed in 51.308(g) shall be addressed in the SIP revision for reasonable 

progress. 

More specifically, the initial 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 the past five years. Appendix K references 

recommendations to consider when performing mid-course reviews. 

Moreover, due to the uncertainty of some measures, this Progress Report will also 

provide SC DHEC the opportunity to evaluate 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 appropriate state, local, and tribal air pollution 

control agencies as well as the corresponding FLMs. SC DHEC also commits to 

continued consultation among the states and FLMs as it relates to any SIP revisions 

and/or the implementation of other programs having the potential to contribute to 

visibility impairment. The State anticipates that this will occur in much the same fashion 

as did the pre-hearing meetings, comments, 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 funding is available, to support the next 

long term strategy. The following is an overall summary; Appendix K contains a fuller 

discussion of possible technical improvements. 

Continued improvements are needed in the integrated one-atmosphere air quality models 

that are used to project air quality responses 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 performance 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, CMAQ underestimates measured sulfate at the 

monitors. CMAQ’s processes also should be reviewed for sulfate formation over water. 

Nitrate is overestimated by the model in the winter and underestimated in the summer, 

although summer monitored values of nitrate are very low. Additional improvements in 

seasonal allocation of ammonia emissions would improve model estimates of ammonium 

nitrate formation. Organic carbon is generally underestimated in the summer months. 

Improvements are needed in the characterization of both primary carbon emissions and 

formation of secondary organic carbon. 
118 

Other improvements needed include better tools for organic carbon source 

apportionment, and more consistent measurement techniques between rural and urban 

monitoring networks. To improve understanding of the contribution of fire from natural 

forest fires, prescribed burning, land clearing, and agricultural burning, states need 

improved record keeping. Additional improvements to international emissions inventory 

are also needed, to improve understanding of boundary conditions for our modeling 

domain and of the contributions from international emissions to pollutant concentrations 

at the VISTAS Class I areas. 
119 

1B12.0 DETERMINATION OF THE ADEQUACY OF THE EXISTING 

PLAN 

Depending on the findings of the five-year progress 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 which action is appropriate and necessary. 

UList of Possible Actions - 40 CFR Section 51.308(h)

1) If South Carolina determines that the existing SIP requires no further substantive 

revision in order to achieve established goals, it will provide to the Administrator 

a declaration that further revision of the SIP are not needed. 

2) If South Carolina determines that the existing 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 participated 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 from 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.